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7940bb16af575a6587ba7d772122e0356212e8b6 | 2,989 | py | Python | lab2/text_recognizer/networks/line_cnn_sliding_window.py | raminzahedi/fsdl-text-recognizer-project | b683173e5e19fbbdb3c6ab42990406e2a0c73e81 | [
"MIT"
] | null | null | null | lab2/text_recognizer/networks/line_cnn_sliding_window.py | raminzahedi/fsdl-text-recognizer-project | b683173e5e19fbbdb3c6ab42990406e2a0c73e81 | [
"MIT"
] | null | null | null | lab2/text_recognizer/networks/line_cnn_sliding_window.py | raminzahedi/fsdl-text-recognizer-project | b683173e5e19fbbdb3c6ab42990406e2a0c73e81 | [
"MIT"
] | null | null | null | import pathlib
from typing import Tuple
from boltons.cacheutils import cachedproperty
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Activation, Conv2D, Dense, Dropout, Flatten, Input, MaxPooling2D, Permute, Reshape, TimeDistributed, Lambda, ZeroPadding2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import Model as KerasModel
from text_recognizer.models.line_model import LineModel
from text_recognizer.networks.lenet import lenet
from text_recognizer.networks.misc import slide_window
def line_cnn_sliding_window(
input_shape: Tuple[int, ...],
output_shape: Tuple[int, ...],
window_width: float=16,
window_stride: float=10) -> KerasModel:
"""
Input is an image with shape (image_height, image_width)
Output is of shape (output_length, num_classes)
"""
image_height, image_width = input_shape
output_length, num_classes = output_shape
image_input = Input(shape=input_shape)
# (image_height, image_width)
image_reshaped = Reshape((image_height, image_width, 1))(image_input)
# (image_height, image_width, 1)
image_patches = Lambda(
slide_window,
arguments={'window_width': window_width, 'window_stride': window_stride}
)(image_reshaped)
# (num_windows, image_height, window_width, 1)
# Make a LeNet and get rid of the last two layers (softmax and dropout)
convnet = lenet((image_height, window_width, 1), (num_classes,))
convnet = KerasModel(inputs=convnet.inputs, outputs=convnet.layers[-2].output)
convnet_outputs = TimeDistributed(convnet)(image_patches)
# (num_windows, 128)
# Now we have to get to (output_length, num_classes) shape. One way to do it is to do another sliding window with
# width = floor(num_windows / output_length)
# Note that this will likely produce too many items in the output sequence, so take only output_length,
# and watch out that width is at least 2 (else we will only be able to predict on the first half of the line)
##### Your code below (Lab 2)
convnet_outputs_extra_dim = Lambda(lambda x: tf.expand_dims(x, -1))(convnet_outputs)
# (num_windows, 128, 1)
num_windows = int((image_width - window_width) / window_stride) + 1
width = int(num_windows / output_length)
conved_convnet_outputs = Conv2D(num_classes, (width, 128), (width, 1), activation='softmax')(convnet_outputs_extra_dim)
# (image_width / width, 1, num_classes)
squeezed_conved_convnet_outputs = Lambda(lambda x: tf.squeeze(x, 2))(conved_convnet_outputs)
# (max_length, num_classes)
# Since we floor'd the calculation of width, we might have too many items in the sequence. Take only output_length.
softmax_output = Lambda(lambda x: x[:, :output_length, :])(squeezed_conved_convnet_outputs)
##### Your code above (Lab 2)
model = KerasModel(inputs=image_input, outputs=softmax_output)
model.summary()
return model
| 40.391892 | 158 | 0.733021 |
7940bb9d022b8becf1b3bc325752a96df4b493c3 | 904 | py | Python | pm/view/landingpage.py | fvclaus/photomap | 4147658193f75b0decd8586ca4ff27619bfc70e1 | [
"BSD-3-Clause"
] | null | null | null | pm/view/landingpage.py | fvclaus/photomap | 4147658193f75b0decd8586ca4ff27619bfc70e1 | [
"BSD-3-Clause"
] | 10 | 2019-12-11T17:21:51.000Z | 2022-03-02T06:09:41.000Z | pm/view/landingpage.py | fvclaus/photomap | 4147658193f75b0decd8586ca4ff27619bfc70e1 | [
"BSD-3-Clause"
] | null | null | null | import datetime
from django.http import HttpResponseBadRequest
from django.shortcuts import render
from django.views.decorators.csrf import csrf_protect, ensure_csrf_cookie
from pm.form.registration import RegistrationForm
@ensure_csrf_cookie
@csrf_protect
def view(request):
if request.method == "GET":
today = datetime.date.today().strftime("%w")
next = request.GET.get("next")
registration_form = RegistrationForm()
return render(request, "index.html", {"day": today, "next": next, "registration_form": registration_form})
else:
return HttpResponseBadRequest()
def view_album_login(request):
""" Renders the album login for guests. """
if request.method == "GET":
today = datetime.date.today()
return render(request, "album-share-login.html", {"day": today.strftime("%w")})
else:
return HttpResponseBadRequest()
| 32.285714 | 114 | 0.702434 |
7940bca8c2206024000ad8ebb5ff2b3cdec2a2d2 | 353 | py | Python | app/lcars.py | elijaheac/rpi_lcars | 47dcd84eb7f631cde8572ffc280247cc89c5a42d | [
"MIT"
] | 2 | 2017-11-24T21:54:56.000Z | 2020-06-30T01:08:10.000Z | app/lcars.py | hepteract/rpi_lcars | 47dcd84eb7f631cde8572ffc280247cc89c5a42d | [
"MIT"
] | null | null | null | app/lcars.py | hepteract/rpi_lcars | 47dcd84eb7f631cde8572ffc280247cc89c5a42d | [
"MIT"
] | null | null | null | from screens.authorize import ScreenAuthorize
from ui.ui import UserInterface
# global config
UI_PLACEMENT_MODE = True
RESOLUTION = (800, 480)
FPS = 60
DEV_MODE = False
if __name__ == "__main__":
firstScreen = ScreenAuthorize()
ui = UserInterface(firstScreen, RESOLUTION, UI_PLACEMENT_MODE, FPS, DEV_MODE)
while (True):
ui.tick()
| 22.0625 | 81 | 0.730878 |
7940bcfd934907f8719c126d82f34970c1a7c515 | 4,716 | py | Python | assets/src/ba_data/python/ba/_enums.py | Dliwk/ballistica | eaff316b3c6203b2465c768c88c473c1478b492a | [
"MIT"
] | 6 | 2021-04-16T14:25:25.000Z | 2021-11-18T17:20:19.000Z | assets/src/ba_data/python/ba/_enums.py | Dliwk/ballistica | eaff316b3c6203b2465c768c88c473c1478b492a | [
"MIT"
] | 1 | 2021-08-30T10:09:06.000Z | 2021-09-21T10:44:15.000Z | assets/src/ba_data/python/ba/_enums.py | Dliwk/ballistica | eaff316b3c6203b2465c768c88c473c1478b492a | [
"MIT"
] | 2 | 2021-04-20T15:39:27.000Z | 2021-07-18T08:45:56.000Z | # Released under the MIT License. See LICENSE for details.
"""Enums generated by tools/update_python_enums_module in ba-internal."""
from enum import Enum
class InputType(Enum):
"""Types of input a controller can send to the game.
Category: Enums
"""
UP_DOWN = 2
LEFT_RIGHT = 3
JUMP_PRESS = 4
JUMP_RELEASE = 5
PUNCH_PRESS = 6
PUNCH_RELEASE = 7
BOMB_PRESS = 8
BOMB_RELEASE = 9
PICK_UP_PRESS = 10
PICK_UP_RELEASE = 11
RUN = 12
FLY_PRESS = 13
FLY_RELEASE = 14
START_PRESS = 15
START_RELEASE = 16
HOLD_POSITION_PRESS = 17
HOLD_POSITION_RELEASE = 18
LEFT_PRESS = 19
LEFT_RELEASE = 20
RIGHT_PRESS = 21
RIGHT_RELEASE = 22
UP_PRESS = 23
UP_RELEASE = 24
DOWN_PRESS = 25
DOWN_RELEASE = 26
class UIScale(Enum):
"""The overall scale the UI is being rendered for. Note that this is
independent of pixel resolution. For example, a phone and a desktop PC
might render the game at similar pixel resolutions but the size they
display content at will vary significantly.
Category: Enums
'large' is used for devices such as desktop PCs where fine details can
be clearly seen. UI elements are generally smaller on the screen
and more content can be seen at once.
'medium' is used for devices such as tablets, TVs, or VR headsets.
This mode strikes a balance between clean readability and amount of
content visible.
'small' is used primarily for phones or other small devices where
content needs to be presented as large and clear in order to remain
readable from an average distance.
"""
LARGE = 0
MEDIUM = 1
SMALL = 2
class TimeType(Enum):
"""Specifies the type of time for various operations to target/use.
Category: Enums
'sim' time is the local simulation time for an activity or session.
It can proceed at different rates depending on game speed, stops
for pauses, etc.
'base' is the baseline time for an activity or session. It proceeds
consistently regardless of game speed or pausing, but may stop during
occurrences such as network outages.
'real' time is mostly based on clock time, with a few exceptions. It may
not advance while the app is backgrounded for instance. (the engine
attempts to prevent single large time jumps from occurring)
"""
SIM = 0
BASE = 1
REAL = 2
class TimeFormat(Enum):
"""Specifies the format time values are provided in.
Category: Enums
"""
SECONDS = 0
MILLISECONDS = 1
class Permission(Enum):
"""Permissions that can be requested from the OS.
Category: Enums
"""
STORAGE = 0
class SpecialChar(Enum):
"""Special characters the game can print.
Category: Enums
"""
DOWN_ARROW = 0
UP_ARROW = 1
LEFT_ARROW = 2
RIGHT_ARROW = 3
TOP_BUTTON = 4
LEFT_BUTTON = 5
RIGHT_BUTTON = 6
BOTTOM_BUTTON = 7
DELETE = 8
SHIFT = 9
BACK = 10
LOGO_FLAT = 11
REWIND_BUTTON = 12
PLAY_PAUSE_BUTTON = 13
FAST_FORWARD_BUTTON = 14
DPAD_CENTER_BUTTON = 15
OUYA_BUTTON_O = 16
OUYA_BUTTON_U = 17
OUYA_BUTTON_Y = 18
OUYA_BUTTON_A = 19
OUYA_LOGO = 20
LOGO = 21
TICKET = 22
GOOGLE_PLAY_GAMES_LOGO = 23
GAME_CENTER_LOGO = 24
DICE_BUTTON1 = 25
DICE_BUTTON2 = 26
DICE_BUTTON3 = 27
DICE_BUTTON4 = 28
GAME_CIRCLE_LOGO = 29
PARTY_ICON = 30
TEST_ACCOUNT = 31
TICKET_BACKING = 32
TROPHY1 = 33
TROPHY2 = 34
TROPHY3 = 35
TROPHY0A = 36
TROPHY0B = 37
TROPHY4 = 38
LOCAL_ACCOUNT = 39
ALIBABA_LOGO = 40
FLAG_UNITED_STATES = 41
FLAG_MEXICO = 42
FLAG_GERMANY = 43
FLAG_BRAZIL = 44
FLAG_RUSSIA = 45
FLAG_CHINA = 46
FLAG_UNITED_KINGDOM = 47
FLAG_CANADA = 48
FLAG_INDIA = 49
FLAG_JAPAN = 50
FLAG_FRANCE = 51
FLAG_INDONESIA = 52
FLAG_ITALY = 53
FLAG_SOUTH_KOREA = 54
FLAG_NETHERLANDS = 55
FEDORA = 56
HAL = 57
CROWN = 58
YIN_YANG = 59
EYE_BALL = 60
SKULL = 61
HEART = 62
DRAGON = 63
HELMET = 64
MUSHROOM = 65
NINJA_STAR = 66
VIKING_HELMET = 67
MOON = 68
SPIDER = 69
FIREBALL = 70
FLAG_UNITED_ARAB_EMIRATES = 71
FLAG_QATAR = 72
FLAG_EGYPT = 73
FLAG_KUWAIT = 74
FLAG_ALGERIA = 75
FLAG_SAUDI_ARABIA = 76
FLAG_MALAYSIA = 77
FLAG_CZECH_REPUBLIC = 78
FLAG_AUSTRALIA = 79
FLAG_SINGAPORE = 80
OCULUS_LOGO = 81
STEAM_LOGO = 82
NVIDIA_LOGO = 83
FLAG_IRAN = 84
FLAG_POLAND = 85
FLAG_ARGENTINA = 86
FLAG_PHILIPPINES = 87
FLAG_CHILE = 88
MIKIROG = 89
| 23.698492 | 77 | 0.653308 |
7940bd0212e34e3649a65560faf2d75fabbefec2 | 429 | py | Python | tests/utils/test_aggregate.py | trumanw/ScaffoldGraph | a594e5c5effe6c5e45c0061a235ccbeb64e416f9 | [
"MIT"
] | null | null | null | tests/utils/test_aggregate.py | trumanw/ScaffoldGraph | a594e5c5effe6c5e45c0061a235ccbeb64e416f9 | [
"MIT"
] | null | null | null | tests/utils/test_aggregate.py | trumanw/ScaffoldGraph | a594e5c5effe6c5e45c0061a235ccbeb64e416f9 | [
"MIT"
] | 1 | 2021-03-12T15:55:02.000Z | 2021-03-12T15:55:02.000Z | """
scaffoldgraph tests.utils.test_aggregate
"""
import scaffoldgraph as sg
from scaffoldgraph.utils import aggregate
from .. import mock_sdf, mock_sdf_2
def test_aggregate(sdf_file, sdf_file_2):
net_1 = sg.ScaffoldNetwork.from_sdf(sdf_file)
net_2 = sg.ScaffoldNetwork.from_sdf(sdf_file_2)
network = aggregate([net_1, net_2])
assert network.num_scaffold_nodes == 14
assert network.num_molecule_nodes == 4
| 25.235294 | 51 | 0.7669 |
7940bdbebd9e1470feb3d25ef1bbd7a5b7059e75 | 4,344 | py | Python | ingestion/src/metadata/ingestion/source/snowflake_usage.py | rongfengliang/OpenMetadata | f91bcc03f63cd193d40a21ce25a398cddb389fa4 | [
"Apache-2.0"
] | null | null | null | ingestion/src/metadata/ingestion/source/snowflake_usage.py | rongfengliang/OpenMetadata | f91bcc03f63cd193d40a21ce25a398cddb389fa4 | [
"Apache-2.0"
] | null | null | null | ingestion/src/metadata/ingestion/source/snowflake_usage.py | rongfengliang/OpenMetadata | f91bcc03f63cd193d40a21ce25a398cddb389fa4 | [
"Apache-2.0"
] | null | null | null | # Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, Iterable, Iterator, Union
from metadata.ingestion.api.source import Source, SourceStatus
# This import verifies that the dependencies are available.
from metadata.ingestion.models.table_queries import TableQuery
from metadata.ingestion.ometa.openmetadata_rest import MetadataServerConfig
from metadata.ingestion.source.snowflake import SnowflakeConfig
from metadata.ingestion.source.sql_alchemy_helper import (
SQLAlchemyHelper,
SQLSourceStatus,
)
from metadata.utils.helpers import get_start_and_end
class SnowflakeUsageSource(Source):
# SELECT statement from mysql information_schema to extract table and column metadata
SQL_STATEMENT = """
select query_type,query_text,user_name,database_name,
schema_name,start_time,end_time
from table(information_schema.query_history(
end_time_range_start=>to_timestamp_ltz('{start_date}'),
end_time_range_end=>to_timestamp_ltz('{end_date}')));
"""
# CONFIG KEYS
WHERE_CLAUSE_SUFFIX_KEY = "where_clause"
CLUSTER_SOURCE = "cluster_source"
CLUSTER_KEY = "cluster_key"
USE_CATALOG_AS_CLUSTER_NAME = "use_catalog_as_cluster_name"
DATABASE_KEY = "database_key"
SERVICE_TYPE = "Snowflake"
DEFAULT_CLUSTER_SOURCE = "CURRENT_DATABASE()"
def __init__(self, config, metadata_config, ctx):
super().__init__(ctx)
start, end = get_start_and_end(config.duration)
self.analysis_date = start
print(start)
print(end)
self.sql_stmt = SnowflakeUsageSource.SQL_STATEMENT.format(
start_date=start, end_date=end
)
self.alchemy_helper = SQLAlchemyHelper(
config, metadata_config, ctx, "Snowflake", self.sql_stmt
)
self._extract_iter: Union[None, Iterator] = None
self._database = "Snowflake"
self.report = SQLSourceStatus()
@classmethod
def create(cls, config_dict, metadata_config_dict, ctx):
config = SnowflakeConfig.parse_obj(config_dict)
metadata_config = MetadataServerConfig.parse_obj(metadata_config_dict)
return cls(config, metadata_config, ctx)
def prepare(self):
pass
def _get_raw_extract_iter(self) -> Iterable[Dict[str, Any]]:
"""
Provides iterator of result row from SQLAlchemy helper
:return:
"""
rows = self.alchemy_helper.execute_query()
for row in rows:
yield row
def next_record(self) -> Iterable[TableQuery]:
"""
Using itertools.groupby and raw level iterator, it groups to table and yields TableMetadata
:return:
"""
for row in self._get_raw_extract_iter():
tq = TableQuery(
query=row["query_type"],
user_name=row["user_name"],
starttime=str(row["start_time"]),
endtime=str(row["end_time"]),
analysis_date=self.analysis_date,
aborted=True if "1969" in str(row["end_time"]) else False,
database=row["database_name"],
sql=row["query_text"],
)
if row["schema_name"] is not None:
self.report.scanned(f"{row['database_name']}.{row['schema_name']}")
else:
self.report.scanned(f"{row['database_name']}")
yield tq
def get_report(self):
return self.report
def close(self):
self.alchemy_helper.close()
def get_status(self) -> SourceStatus:
return self.report
| 38.105263 | 99 | 0.679788 |
7940be595c376bce28bf8957e1d2b3d9c62bb993 | 941 | py | Python | merge_states.py | abr-98/COVID-19_regression_analysis | f902f4771f665ee27c33a9cb7e3e4c83be54493d | [
"MIT"
] | null | null | null | merge_states.py | abr-98/COVID-19_regression_analysis | f902f4771f665ee27c33a9cb7e3e4c83be54493d | [
"MIT"
] | null | null | null | merge_states.py | abr-98/COVID-19_regression_analysis | f902f4771f665ee27c33a9cb7e3e4c83be54493d | [
"MIT"
] | 1 | 2020-06-07T08:50:29.000Z | 2020-06-07T08:50:29.000Z | import pandas as pd
import numpy as np
import os
import json
def merge():
df=pd.read_csv('covid_19_india.csv', index_col=0)
s_names=df['State/UnionTerritory'].unique()
df_2=pd.read_csv('total_country_mod.csv')
df3=df_2
df3.to_csv('total_state_data.csv',index=False)
with open('date_rec_mod.json', 'r') as ip:
data = json.load(ip)
for name in s_names:
print(name)
df3=pd.read_csv('total_state_data.csv')
i=0
confirmed=[]
death=[]
rec=[]
while i<len(df3):
date=df3.iloc[i]['Date']
print(date)
if data[date].get(name) is None:
confirmed.append(0)
death.append(0)
rec.append(0)
else:
confirmed.append(data[date][name]['confirmed'])
death.append(data[date][name]['death'])
rec.append(data[date][name]['recovered'])
i+=1
df3[name+'_con']=confirmed
df3[name+'_death']=death
df3[name+'_rec']=rec
df3.to_csv('total_state_data.csv',index=False)
merge()
| 16.224138 | 51 | 0.659936 |
7940bef0e0c1dfcd9e484ef4afa583ad339018c9 | 112 | py | Python | python_in_out_context_manager/__init__.py | samesense/python_in_out_context_manager | b8263ac7f974f76cc2e91b67a9cf37b1960b98f5 | [
"MIT"
] | null | null | null | python_in_out_context_manager/__init__.py | samesense/python_in_out_context_manager | b8263ac7f974f76cc2e91b67a9cf37b1960b98f5 | [
"MIT"
] | null | null | null | python_in_out_context_manager/__init__.py | samesense/python_in_out_context_manager | b8263ac7f974f76cc2e91b67a9cf37b1960b98f5 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
__author__ = """Perry Evans"""
__email__ = '[email protected]'
__version__ = '0.1.0'
| 18.666667 | 33 | 0.625 |
7940c06bb984814d728645fb9d6c6a7fb9bc4744 | 12,923 | py | Python | Tools/Tools/Scripts/webkitpy/tool/multicommandtool.py | VincentWei/mdolphin-core | 48ffdcf587a48a7bb4345ae469a45c5b64ffad0e | [
"Apache-2.0"
] | 6 | 2017-05-31T01:46:45.000Z | 2018-06-12T10:53:30.000Z | Tools/Tools/Scripts/webkitpy/tool/multicommandtool.py | FMSoftCN/mdolphin-core | 48ffdcf587a48a7bb4345ae469a45c5b64ffad0e | [
"Apache-2.0"
] | null | null | null | Tools/Tools/Scripts/webkitpy/tool/multicommandtool.py | FMSoftCN/mdolphin-core | 48ffdcf587a48a7bb4345ae469a45c5b64ffad0e | [
"Apache-2.0"
] | 2 | 2017-07-17T06:02:42.000Z | 2018-09-19T10:08:38.000Z | # Copyright (c) 2009 Google Inc. All rights reserved.
# Copyright (c) 2009 Apple Inc. All rights reserved.
#
# 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
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# MultiCommandTool provides a framework for writing svn-like/git-like tools
# which are called with the following format:
# tool-name [global options] command-name [command options]
import sys
from optparse import OptionParser, IndentedHelpFormatter, SUPPRESS_USAGE, make_option
from webkitpy.tool.grammar import pluralize
from webkitpy.common.system.deprecated_logging import log
class Command(object):
name = None
show_in_main_help = False
def __init__(self, help_text, argument_names=None, options=None, long_help=None, requires_local_commits=False):
self.help_text = help_text
self.long_help = long_help
self.argument_names = argument_names
self.required_arguments = self._parse_required_arguments(argument_names)
self.options = options
self.requires_local_commits = requires_local_commits
self.tool = None
# option_parser can be overriden by the tool using set_option_parser
# This default parser will be used for standalone_help printing.
self.option_parser = HelpPrintingOptionParser(usage=SUPPRESS_USAGE, add_help_option=False, option_list=self.options)
# This design is slightly awkward, but we need the
# the tool to be able to create and modify the option_parser
# before it knows what Command to run.
def set_option_parser(self, option_parser):
self.option_parser = option_parser
self._add_options_to_parser()
def _add_options_to_parser(self):
options = self.options or []
for option in options:
self.option_parser.add_option(option)
# The tool calls bind_to_tool on each Command after adding it to its list.
def bind_to_tool(self, tool):
# Command instances can only be bound to one tool at a time.
if self.tool and tool != self.tool:
raise Exception("Command already bound to tool!")
self.tool = tool
@staticmethod
def _parse_required_arguments(argument_names):
required_args = []
if not argument_names:
return required_args
split_args = argument_names.split(" ")
for argument in split_args:
if argument[0] == '[':
# For now our parser is rather dumb. Do some minimal validation that
# we haven't confused it.
if argument[-1] != ']':
raise Exception("Failure to parse argument string %s. Argument %s is missing ending ]" % (argument_names, argument))
else:
required_args.append(argument)
return required_args
def name_with_arguments(self):
usage_string = self.name
if self.options:
usage_string += " [options]"
if self.argument_names:
usage_string += " " + self.argument_names
return usage_string
def parse_args(self, args):
return self.option_parser.parse_args(args)
def check_arguments_and_execute(self, options, args, tool=None):
if len(args) < len(self.required_arguments):
log("%s required, %s provided. Provided: %s Required: %s\nSee '%s help %s' for usage." % (
pluralize("argument", len(self.required_arguments)),
pluralize("argument", len(args)),
"'%s'" % " ".join(args),
" ".join(self.required_arguments),
tool.name(),
self.name))
return 1
return self.execute(options, args, tool) or 0
def standalone_help(self):
help_text = self.name_with_arguments().ljust(len(self.name_with_arguments()) + 3) + self.help_text + "\n\n"
if self.long_help:
help_text += "%s\n\n" % self.long_help
help_text += self.option_parser.format_option_help(IndentedHelpFormatter())
return help_text
def execute(self, options, args, tool):
raise NotImplementedError, "subclasses must implement"
# main() exists so that Commands can be turned into stand-alone scripts.
# Other parts of the code will likely require modification to work stand-alone.
def main(self, args=sys.argv):
(options, args) = self.parse_args(args)
# Some commands might require a dummy tool
return self.check_arguments_and_execute(options, args)
# FIXME: This should just be rolled into Command. help_text and argument_names do not need to be instance variables.
class AbstractDeclarativeCommand(Command):
help_text = None
argument_names = None
long_help = None
def __init__(self, options=None, **kwargs):
Command.__init__(self, self.help_text, self.argument_names, options=options, long_help=self.long_help, **kwargs)
class HelpPrintingOptionParser(OptionParser):
def __init__(self, epilog_method=None, *args, **kwargs):
self.epilog_method = epilog_method
OptionParser.__init__(self, *args, **kwargs)
def error(self, msg):
self.print_usage(sys.stderr)
error_message = "%s: error: %s\n" % (self.get_prog_name(), msg)
# This method is overriden to add this one line to the output:
error_message += "\nType \"%s --help\" to see usage.\n" % self.get_prog_name()
self.exit(1, error_message)
# We override format_epilog to avoid the default formatting which would paragraph-wrap the epilog
# and also to allow us to compute the epilog lazily instead of in the constructor (allowing it to be context sensitive).
def format_epilog(self, epilog):
if self.epilog_method:
return "\n%s\n" % self.epilog_method()
return ""
class HelpCommand(AbstractDeclarativeCommand):
name = "help"
help_text = "Display information about this program or its subcommands"
argument_names = "[COMMAND]"
def __init__(self):
options = [
make_option("-a", "--all-commands", action="store_true", dest="show_all_commands", help="Print all available commands"),
]
AbstractDeclarativeCommand.__init__(self, options)
self.show_all_commands = False # A hack used to pass --all-commands to _help_epilog even though it's called by the OptionParser.
def _help_epilog(self):
# Only show commands which are relevant to this checkout's SCM system. Might this be confusing to some users?
if self.show_all_commands:
epilog = "All %prog commands:\n"
relevant_commands = self.tool.commands[:]
else:
epilog = "Common %prog commands:\n"
relevant_commands = filter(self.tool.should_show_in_main_help, self.tool.commands)
longest_name_length = max(map(lambda command: len(command.name), relevant_commands))
relevant_commands.sort(lambda a, b: cmp(a.name, b.name))
command_help_texts = map(lambda command: " %s %s\n" % (command.name.ljust(longest_name_length), command.help_text), relevant_commands)
epilog += "%s\n" % "".join(command_help_texts)
epilog += "See '%prog help --all-commands' to list all commands.\n"
epilog += "See '%prog help COMMAND' for more information on a specific command.\n"
return epilog.replace("%prog", self.tool.name()) # Use of %prog here mimics OptionParser.expand_prog_name().
# FIXME: This is a hack so that we don't show --all-commands as a global option:
def _remove_help_options(self):
for option in self.options:
self.option_parser.remove_option(option.get_opt_string())
def execute(self, options, args, tool):
if args:
command = self.tool.command_by_name(args[0])
if command:
print command.standalone_help()
return 0
self.show_all_commands = options.show_all_commands
self._remove_help_options()
self.option_parser.print_help()
return 0
class MultiCommandTool(object):
global_options = None
def __init__(self, name=None, commands=None):
self._name = name or OptionParser(prog=name).get_prog_name() # OptionParser has nice logic for fetching the name.
# Allow the unit tests to disable command auto-discovery.
self.commands = commands or [cls() for cls in self._find_all_commands() if cls.name]
self.help_command = self.command_by_name(HelpCommand.name)
# Require a help command, even if the manual test list doesn't include one.
if not self.help_command:
self.help_command = HelpCommand()
self.commands.append(self.help_command)
for command in self.commands:
command.bind_to_tool(self)
@classmethod
def _add_all_subclasses(cls, class_to_crawl, seen_classes):
for subclass in class_to_crawl.__subclasses__():
if subclass not in seen_classes:
seen_classes.add(subclass)
cls._add_all_subclasses(subclass, seen_classes)
@classmethod
def _find_all_commands(cls):
commands = set()
cls._add_all_subclasses(Command, commands)
return sorted(commands)
def name(self):
return self._name
def _create_option_parser(self):
usage = "Usage: %prog [options] COMMAND [ARGS]"
return HelpPrintingOptionParser(epilog_method=self.help_command._help_epilog, prog=self.name(), usage=usage)
@staticmethod
def _split_command_name_from_args(args):
# Assume the first argument which doesn't start with "-" is the command name.
command_index = 0
for arg in args:
if arg[0] != "-":
break
command_index += 1
else:
return (None, args[:])
command = args[command_index]
return (command, args[:command_index] + args[command_index + 1:])
def command_by_name(self, command_name):
for command in self.commands:
if command_name == command.name:
return command
return None
def path(self):
raise NotImplementedError, "subclasses must implement"
def command_completed(self):
pass
def should_show_in_main_help(self, command):
return command.show_in_main_help
def should_execute_command(self, command):
return True
def _add_global_options(self, option_parser):
global_options = self.global_options or []
for option in global_options:
option_parser.add_option(option)
def handle_global_options(self, options):
pass
def main(self, argv=sys.argv):
(command_name, args) = self._split_command_name_from_args(argv[1:])
option_parser = self._create_option_parser()
self._add_global_options(option_parser)
command = self.command_by_name(command_name) or self.help_command
if not command:
option_parser.error("%s is not a recognized command" % command_name)
command.set_option_parser(option_parser)
(options, args) = command.parse_args(args)
self.handle_global_options(options)
(should_execute, failure_reason) = self.should_execute_command(command)
if not should_execute:
log(failure_reason)
return 0 # FIXME: Should this really be 0?
result = command.check_arguments_and_execute(options, args, self)
self.command_completed()
return result
| 42.370492 | 146 | 0.67879 |
7940c08048a94a74620253246228043355e2f74f | 1,972 | py | Python | browser_fetcher/logger.py | PrVrSs/browser-fetcher | 8540ca138037077dc2ac45df65111e06f8b2c0f4 | [
"Apache-2.0"
] | null | null | null | browser_fetcher/logger.py | PrVrSs/browser-fetcher | 8540ca138037077dc2ac45df65111e06f8b2c0f4 | [
"Apache-2.0"
] | null | null | null | browser_fetcher/logger.py | PrVrSs/browser-fetcher | 8540ca138037077dc2ac45df65111e06f8b2c0f4 | [
"Apache-2.0"
] | null | null | null | import logging
from logging.config import dictConfig as logging_dict_config
import click
TRACE_LOG_LEVEL = 5
LOG_LEVELS = {
'critical': logging.CRITICAL,
'error': logging.ERROR,
'warning': logging.WARNING,
'info': logging.INFO,
'debug': logging.DEBUG,
'trace': TRACE_LOG_LEVEL,
}
LOGGING_CONFIG = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'default': {
'()': 'browser_fetcher.logger.BrowserFetcherFormatter',
'fmt': '%(asctime)s %(levelname)s %(message)s',
'datefmt': '%H:%M:%S',
},
},
'handlers': {
'default': {
'formatter': 'default',
'class': 'logging.StreamHandler',
'stream': 'ext://sys.stderr',
},
},
'loggers': {
'browser_fetcher': {'handlers': ['default'], 'level': 'ERROR'},
},
}
class BrowserFetcherFormatter(logging.Formatter):
level_name_colors = {
TRACE_LOG_LEVEL: lambda message: click.style(message, fg='blue'),
logging.DEBUG: lambda message: click.style(message, fg='cyan'),
logging.INFO: lambda message: click.style(message, fg='green'),
logging.WARNING: lambda message: click.style(message, fg='yellow'),
logging.ERROR: lambda message: click.style(message, fg='red'),
logging.CRITICAL: lambda message: click.style(message, fg='bright_red'),
}
def formatMessage(self, record):
colored = self.level_name_colors[record.levelno]
record.__dict__['message'] = colored(record.msg % record.args)
record.__dict__['asctime'] = colored(f'[+] {record.asctime}')
record.__dict__['levelname'] = colored(f'{record.levelname}')
return super().formatMessage(record)
def configure_logging(log_level):
logging.addLevelName(TRACE_LOG_LEVEL, 'TRACE')
logging_dict_config(LOGGING_CONFIG)
logging.getLogger('browser_fetcher').setLevel(LOG_LEVELS[log_level])
| 29 | 80 | 0.630832 |
7940c0e04664f68c39955eb72e112128b8730c55 | 2,440 | py | Python | configs/learning_gem5/part2/run_simple.py | taomiao/gem5 | 4effe34f94b599add133357473e1b120b54719ab | [
"BSD-3-Clause"
] | 135 | 2016-10-21T03:31:49.000Z | 2022-03-25T01:22:20.000Z | configs/learning_gem5/part2/run_simple.py | taomiao/gem5 | 4effe34f94b599add133357473e1b120b54719ab | [
"BSD-3-Clause"
] | 35 | 2017-03-10T17:57:46.000Z | 2022-02-18T17:34:16.000Z | configs/learning_gem5/part2/run_simple.py | taomiao/gem5 | 4effe34f94b599add133357473e1b120b54719ab | [
"BSD-3-Clause"
] | 48 | 2016-12-08T12:03:13.000Z | 2022-02-16T09:16:13.000Z | # -*- coding: utf-8 -*-
# Copyright (c) 2017 Jason Lowe-Power
# All rights reserved.
#
# 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
# notice, this list of conditions and the following disclaimer;
# redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution;
# neither the name of the copyright holders nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Authors: Jason Lowe-Power
""" Simple config/run script for the HelloObject
This is probably the simplest gem5 config file you can possibly create.
It creates a Root object and one *very* simple SimObject and simulates the
system. Since there are no events, this "simulation" should finish immediately
"""
from __future__ import print_function
from __future__ import absolute_import
# import the m5 (gem5) library created when gem5 is built
import m5
# import all of the SimObjects
from m5.objects import *
# set up the root SimObject and start the simulation
root = Root(full_system = False)
# Create an instantiation of the simobject you created
root.hello = SimpleObject()
# instantiate all of the objects we've created above
m5.instantiate()
print("Beginning simulation!")
exit_event = m5.simulate()
print('Exiting @ tick %i because %s' % (m5.curTick(), exit_event.getCause()))
| 42.068966 | 78 | 0.783197 |
7940c2e056b552818502f531d6f2924479c05986 | 42 | py | Python | models/__init__.py | gmshashank/Deep_Flow_Prediction | 9b4c388b70a458cddac20258242a6a36965524bc | [
"MIT"
] | null | null | null | models/__init__.py | gmshashank/Deep_Flow_Prediction | 9b4c388b70a458cddac20258242a6a36965524bc | [
"MIT"
] | null | null | null | models/__init__.py | gmshashank/Deep_Flow_Prediction | 9b4c388b70a458cddac20258242a6a36965524bc | [
"MIT"
] | null | null | null | from .unet import Generator, weights_init
| 21 | 41 | 0.833333 |
7940c302f2268937e491a1f7c49a26a12b36486d | 6,896 | py | Python | copct-master/baxter_experiments.py | jhomble/electron435 | 2a94a901679a1ebbdeea01bb9e888d365d536bec | [
"MIT"
] | null | null | null | copct-master/baxter_experiments.py | jhomble/electron435 | 2a94a901679a1ebbdeea01bb9e888d365d536bec | [
"MIT"
] | null | null | null | copct-master/baxter_experiments.py | jhomble/electron435 | 2a94a901679a1ebbdeea01bb9e888d365d536bec | [
"MIT"
] | null | null | null | #!/usr/bin/env python
import time
import copct
M = 3
def causes(v):
"""
Causal relation for the robotic imitation learning domain.
v is a sequence of intentions or actions.
Each element v[i] is of the form (state, task name, parameter values).
Returns the set of all possible causes of v.
"""
g = set() # set of possible causes
arm_ids = ("left","right")
clear_ids = ("discard-bin")
states, tasks, args = zip(*v)
if len(v) == 1:
if tasks == ("move arm and grasp",):
arm, object_id = args[0]
dest_id = arm_ids[int(arm)-1]
asm_type = dict(states[0])[object_id]
if asm_type not in ("DockCase","DockDrawer"):
g.add((states[0], "move unobstructed object",(object_id, dest_id, (), ())))
if tasks == ("put down grasped object",):
arm, dest_id, dM, dt = args[0]
object_id = dict(states[0])["gripping"][int(arm)-1]
g.add((states[0], "move unobstructed object", (object_id, dest_id, dM, dt)))
if tasks == ("move unobstructed object",):
object_id, dest_id, dM, dt = args[0]
if dest_id in arm_ids:
g.add((states[0], "move object", args[0]))
else:
asm_type = dict(states[0])[dest_id]
if (asm_type=="DockCase") or (dest_id in clear_ids):
g.add((states[0],"move unobstructed object to free spot", (object_id, dest_id)))
g.add((states[0],"move object", args[0]))
if tasks == ("move object",):
object_id, dest_id, dM, dt = args[0]
if dest_id not in arm_ids:
if (dest_id=="dock-case_6") or (dest_id in clear_ids):
g.add((states[0],"move object to free spot", (object_id, dest_id)))
if tasks == ("move object to free spot",):
object_id, dest_id = args[0]
if dest_id=="discard-bin":
g.add((states[0],"discard object",(object_id,)))
if len(v)==2:
if tasks == ("move grasped object","release"):
arm, dest_id, dM, dt = args[0]
object_id = dict(states[0])["gripping"][int(arm)-1]
asm_type = dict(states[0])[object_id]
if asm_type not in ("DockCase","DockDrawer"):
g.add((states[0], "put down grasped object", args[0]))
if tasks == ("move arm and grasp","put down grasped object"):
arm_0, object_id = args[0]
arm_1, dest_id, dM, dt = args[1]
asm_type = dict(states[0])[object_id]
if (arm_0==arm_1) and not (asm_type=="DockDrawer"):
g.add((states[0],"move unobstructed object",(object_id, dest_id, dM, dt)))
if len(v)==3:
if tasks == ("move arm and grasp","move grasped object","release"):
arm_0, object_id = args[0]
arm_1, _, _, dt = args[1]
arm_2, = args[2]
asm_type = dict(states[0])[object_id]
if (arm_0==arm_1) and (arm_1==arm_2) and (asm_type=="DockDrawer"):
distance = sum([x**2 for (x,) in dt])**0.5
if distance > 1:
g.add((states[0],"open dock drawer",(object_id, states[2])))
else:
g.add((states[0],"close dock drawer",(object_id,)))
return g
def run_experiments(check_irr=True):
results = {}
# Dock maintenance demos
demos = ["demo_%s_%d"%(skill, di) for di in [1,2] for skill in ["remove_red_drive","replace_red_with_green","replace_red_with_spare","swap_red_with_green"]]
# Block stacking demos
demos += ["demo_il", "demo_ai", "demo_um"]
# Cover demos
print("Covering demos...")
for demo_name in demos:
print(demo_name)
# import demo and ground truth
exec_str = "from baxter_corpus.%s import demo"%demo_name
exec(exec_str, globals())
exec_str = "from baxter_corpus.%s_ground_truth import ground_truth"%demo_name
exec(exec_str, globals())
# Cover and prune by each parsimony criterion
results[demo_name] = {}
start_time = time.clock()
status, tlcovs, g = copct.explain(causes, demo, M=M)
results[demo_name]["run_time"] = time.clock()-start_time
results[demo_name]["tlcovs"], results[demo_name]["g"] = tlcovs, g
results[demo_name]["tlcovs_mc"] = [u for (u,_,_,_,_) in copct.minCardinalityTLCovers(tlcovs)[0]]
results[demo_name]["tlcovs_md"] = [u for (u,_,_,_,_) in copct.maxDepthTLCovers(tlcovs)[0]]
results[demo_name]["tlcovs_xd"] = [u for (u,_,_,_,_) in copct.minimaxDepthTLCovers(tlcovs)[0]]
results[demo_name]["tlcovs_mp"] = [u for (u,_,_,_,_) in copct.minParametersTLCovers(tlcovs)[0]]
results[demo_name]["tlcovs_fsn"] = [u for (u,_,_,_,_) in copct.minForestSizeTLCovers(tlcovs)[0]]
results[demo_name]["tlcovs_fsx"] = [u for (u,_,_,_,_) in copct.maxForestSizeTLCovers(tlcovs)[0]]
start_time = time.clock()
if check_irr:
status, tlcovs_irr = copct.irredundantTLCovers(tlcovs, timeout=1000)
if status == False: print("IRR timeout")
else:
tlcovs_irr = tlcovs
results[demo_name]["run_time_irr"] = time.clock()-start_time
results[demo_name]["tlcovs_irr"] = [u for (u,_,_,_,_) in tlcovs_irr]
results[demo_name]["u in tlcovs"] = ground_truth in [u for (u,_,_,_,_) in tlcovs]
results[demo_name]["u in tlcovs_mc"] = ground_truth in results[demo_name]["tlcovs_mc"]
results[demo_name]["u in tlcovs_md"] = ground_truth in results[demo_name]["tlcovs_md"]
results[demo_name]["u in tlcovs_xd"] = ground_truth in results[demo_name]["tlcovs_xd"]
results[demo_name]["u in tlcovs_mp"] = ground_truth in results[demo_name]["tlcovs_mp"]
results[demo_name]["u in tlcovs_fsn"] = ground_truth in results[demo_name]["tlcovs_fsn"]
results[demo_name]["u in tlcovs_fsx"] = ground_truth in results[demo_name]["tlcovs_fsx"]
results[demo_name]["u in tlcovs_irr"] = ground_truth in results[demo_name]["tlcovs_irr"]
# display results
criteria = ["_mc", "_irr", "_md", "_xd", "_mp", "_fsn", "_fsx"]
print("Accuracy:")
for crit in criteria:
correct_demos = [d for d in results if results[d]["u in tlcovs%s"%crit]]
print('%s: %f%%'%(crit, 1.0*len(correct_demos)/len(demos)))
print("# of covers found:")
print(["Demo","Runtime (explain)", "Runtime (irr)"]+criteria)
for demo_name in demos:
num_tlcovs = [len(results[demo_name]["tlcovs%s"%crit]) for crit in criteria]
print([demo_name, results[demo_name]["run_time"], results[demo_name]["run_time_irr"]]+num_tlcovs)
return results
if __name__ == "__main__":
check_irr = raw_input("Run irredundancy checks? May take several minutes. [y/n]")
results = run_experiments(check_irr == "y")
| 51.462687 | 160 | 0.595708 |
7940c341c5192dea1298b72340b3872a94e194e8 | 544 | py | Python | client_service/python/src/client_service_python/scripts/add_two_ints_server.py | vlantonov/ros_samples | f49ca64e00f4d4b6461ba1512c02c9045c5b631a | [
"MIT"
] | null | null | null | client_service/python/src/client_service_python/scripts/add_two_ints_server.py | vlantonov/ros_samples | f49ca64e00f4d4b6461ba1512c02c9045c5b631a | [
"MIT"
] | null | null | null | client_service/python/src/client_service_python/scripts/add_two_ints_server.py | vlantonov/ros_samples | f49ca64e00f4d4b6461ba1512c02c9045c5b631a | [
"MIT"
] | null | null | null | #!/usr/bin/env python
from __future__ import print_function
from client_service_python.srv import AddTwoInts, AddTwoIntsResponse
import rospy
def handle_add_two_ints(req):
print("Returning [%s + %s = %s]" % (req.a, req.b, (req.a + req.b)))
return AddTwoIntsResponse(req.a + req.b)
def add_two_ints_server():
rospy.init_node("add_two_ints_server")
s = rospy.Service("add_two_ints", AddTwoInts, handle_add_two_ints)
print("Ready to add two ints.")
rospy.spin()
if __name__ == "__main__":
add_two_ints_server()
| 23.652174 | 71 | 0.715074 |
7940c3878706863d1d5f750165bbd70a99b3eab3 | 2,005 | py | Python | src/transformers/commands/transformers_cli.py | ashokei/transformers | 5e637e6c690e45d13ebf7296e1ea9dcc188d0f07 | [
"Apache-2.0"
] | 10 | 2021-05-31T07:18:08.000Z | 2022-03-19T09:20:11.000Z | src/transformers/commands/transformers_cli.py | tutussss/transformers | 67ff1c314a61a2d5949b3bb48fa3ec7e9b697d7e | [
"Apache-2.0"
] | 1 | 2021-08-03T12:23:01.000Z | 2021-08-10T08:35:22.000Z | src/transformers/commands/transformers_cli.py | tutussss/transformers | 67ff1c314a61a2d5949b3bb48fa3ec7e9b697d7e | [
"Apache-2.0"
] | 3 | 2021-09-19T08:20:42.000Z | 2022-02-19T16:32:40.000Z | #!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from transformers.commands.add_new_model import AddNewModelCommand
from transformers.commands.convert import ConvertCommand
from transformers.commands.download import DownloadCommand
from transformers.commands.env import EnvironmentCommand
from transformers.commands.lfs import LfsCommands
from transformers.commands.run import RunCommand
from transformers.commands.serving import ServeCommand
from transformers.commands.user import UserCommands
def main():
parser = ArgumentParser("Transformers CLI tool", usage="transformers-cli <command> [<args>]")
commands_parser = parser.add_subparsers(help="transformers-cli command helpers")
# Register commands
ConvertCommand.register_subcommand(commands_parser)
DownloadCommand.register_subcommand(commands_parser)
EnvironmentCommand.register_subcommand(commands_parser)
RunCommand.register_subcommand(commands_parser)
ServeCommand.register_subcommand(commands_parser)
UserCommands.register_subcommand(commands_parser)
AddNewModelCommand.register_subcommand(commands_parser)
LfsCommands.register_subcommand(commands_parser)
# Let's go
args = parser.parse_args()
if not hasattr(args, "func"):
parser.print_help()
exit(1)
# Run
service = args.func(args)
service.run()
if __name__ == "__main__":
main()
| 35.803571 | 97 | 0.784539 |
7940c48bd8c5f655a27522835e4de9652852c4c2 | 3,408 | py | Python | utils.py | Louiealbp/ContrastiveLearningGoalReaching | 4ef3e22cb8276a8c2f4f313e2b27138b9cd361b3 | [
"MIT"
] | null | null | null | utils.py | Louiealbp/ContrastiveLearningGoalReaching | 4ef3e22cb8276a8c2f4f313e2b27138b9cd361b3 | [
"MIT"
] | null | null | null | utils.py | Louiealbp/ContrastiveLearningGoalReaching | 4ef3e22cb8276a8c2f4f313e2b27138b9cd361b3 | [
"MIT"
] | null | null | null | import numpy as np
import torch
from collections import deque
import os
def make_dir(dir_path):
try:
os.mkdir(dir_path)
except OSError:
pass
return dir_path
def preprocess_obs(obs, bits=5):
"""Preprocessing image, see https://arxiv.org/abs/1807.03039."""
bins = 2**bits
assert obs.dtype == torch.float32
if bits < 8:
obs = torch.floor(obs / 2**(8 - bits))
obs = obs / bins
obs = obs + torch.rand_like(obs) / bins
obs = obs - 0.5
return obs
def center_translate(imgs, size):
n, c, h, w = imgs.shape
assert size >= h and size >= w
outs = np.zeros((n, c, size, size), dtype=imgs.dtype)
h1 = (size - h) // 2
w1 = (size - w) // 2
outs[:, :, h1:h1 + h, w1:w1 + w] = imgs
return outs
def random_translate(imgs, size, return_random_idxs=False, h1s=None, w1s=None):
n, c, h, w = imgs.shape
assert size >= h and size >= w
outs = np.zeros((n, c, size, size), dtype=imgs.dtype)
h1s = np.random.randint(0, size - h + 1, n) if h1s is None else h1s
w1s = np.random.randint(0, size - w + 1, n) if w1s is None else w1s
for out, img, h1, w1 in zip(outs, imgs, h1s, w1s):
out[:, h1:h1 + h, w1:w1 + w] = img
if return_random_idxs: # So can do the same to another set of imgs.
return outs, dict(h1s=h1s, w1s=w1s)
return outs
def calculate_reward_func(agent):
def reward_func(ag_next, g, third_argument):
if agent.args.encoder_type == 'pixel':
ag_next = torch.as_tensor(center_translate(ag_next, agent.args.image_size)).float()
g = torch.as_tensor(center_translate(g, agent.args.image_size)).float()
if agent.args.cuda:
ag_next.cuda()
g.cuda()
if not agent.args.load_reward_curl:
ag_next_enc = agent.contrastive_learner.encode(ag_next, ema = True)
g_enc = agent.contrastive_learner.encode(g, ema = True)
else:
ag_next_enc = agent.reward_contrastive.encode(ag_next, ema = True)
g_enc = agent.reward_contrastive.encode(g, ema = True)
if agent.args.cosine_similarity:
distances = (ag_next_enc * g_enc).sum(dim = 1) / (torch.norm(ag_next_enc, dim = 1) * torch.norm(g_enc, dim = 1))
if agent.args.not_sparse_reward:
rewards = distances.cpu().numpy()
else:
rewards = (distances > agent.args.cosine_cutoff).cpu().numpy()
else:
distances = torch.sqrt(((ag_next_enc - g_enc) **2).sum(dim = 1))
if agent.args.not_sparse_reward:
rewards = torch.exp(-distances).cpu().numpy()
else:
rewards = (distances < 1).cpu().numpy()
if agent.args.zero_one_reward:
return rewards
else:
return rewards - 1
else:
ag_next = torch.as_tensor(ag_next).float()
g = torch.as_tensor(g).float()
if agent.args.cuda:
ag_next.cuda()
g.cuda()
distances = torch.sqrt(((ag_next - g) **2).sum(dim = 1))
rewards = (distances < 0.03) .cpu().numpy()
if agent.args.zero_one_reward:
return rewards
else:
return rewards - 1
return reward_func
| 38.727273 | 128 | 0.559272 |
7940c4b7eaa5f7f64c506fc96442c25d4c200f31 | 205 | py | Python | citypay/api/__init__.py | citypay/citypay-pos-python-client | df21205504c5b5bd75b5ac5e2a34fb9430f7e4db | [
"MIT"
] | null | null | null | citypay/api/__init__.py | citypay/citypay-pos-python-client | df21205504c5b5bd75b5ac5e2a34fb9430f7e4db | [
"MIT"
] | null | null | null | citypay/api/__init__.py | citypay/citypay-pos-python-client | df21205504c5b5bd75b5ac5e2a34fb9430f7e4db | [
"MIT"
] | null | null | null | from __future__ import absolute_import
# flake8: noqa
# import apis into api package
from citypay.api.device_module_api import DeviceModuleApi
from citypay.api.payment_module_api import PaymentModuleApi
| 25.625 | 59 | 0.853659 |
7940c585e1dd7533dfc57d5dad917df0a2c635b0 | 8,984 | py | Python | fedml_api/distributed/fedopt/FedOptAggregator.py | xuwanwei/FedML | c049a30d9839c4554e7e14b0c18275e96fea8130 | [
"Apache-2.0"
] | 1,120 | 2020-07-22T02:30:52.000Z | 2022-03-31T08:10:44.000Z | fedml_api/distributed/fedopt/FedOptAggregator.py | xuwanwei/FedML | c049a30d9839c4554e7e14b0c18275e96fea8130 | [
"Apache-2.0"
] | 113 | 2020-07-27T03:48:09.000Z | 2022-03-30T03:25:56.000Z | fedml_api/distributed/fedopt/FedOptAggregator.py | xuwanwei/FedML | c049a30d9839c4554e7e14b0c18275e96fea8130 | [
"Apache-2.0"
] | 381 | 2020-07-22T06:12:57.000Z | 2022-03-30T18:38:35.000Z | import copy
import logging
import random
import time
import numpy as np
import torch
import wandb
from .optrepo import OptRepo
from .utils import transform_list_to_tensor
class FedOptAggregator(object):
def __init__(self, train_global, test_global, all_train_data_num,
train_data_local_dict, test_data_local_dict, train_data_local_num_dict, worker_num, device,
args, model_trainer):
self.trainer = model_trainer
self.args = args
self.train_global = train_global
self.test_global = test_global
self.val_global = self._generate_validation_set()
self.all_train_data_num = all_train_data_num
self.train_data_local_dict = train_data_local_dict
self.test_data_local_dict = test_data_local_dict
self.train_data_local_num_dict = train_data_local_num_dict
self.worker_num = worker_num
self.device = device
self.model_dict = dict()
self.sample_num_dict = dict()
self.flag_client_model_uploaded_dict = dict()
self.opt = self._instantiate_opt()
for idx in range(self.worker_num):
self.flag_client_model_uploaded_dict[idx] = False
def _instantiate_opt(self):
return OptRepo.name2cls(self.args.server_optimizer)(
filter(lambda p: p.requires_grad, self.get_model_params()), lr=self.args.server_lr, momentum=self.args.server_momentum,
)
def get_model_params(self):
# return model parameters in type of generator
return self.trainer.model.parameters()
def get_global_model_params(self):
# return model parameters in type of ordered_dict
return self.trainer.get_model_params()
def set_global_model_params(self, model_parameters):
self.trainer.set_model_params(model_parameters)
def add_local_trained_result(self, index, model_params, sample_num):
logging.info("add_model. index = %d" % index)
self.model_dict[index] = model_params
self.sample_num_dict[index] = sample_num
self.flag_client_model_uploaded_dict[index] = True
def check_whether_all_receive(self):
for idx in range(self.worker_num):
if not self.flag_client_model_uploaded_dict[idx]:
return False
for idx in range(self.worker_num):
self.flag_client_model_uploaded_dict[idx] = False
return True
def aggregate(self):
start_time = time.time()
model_list = []
training_num = 0
for idx in range(self.worker_num):
if self.args.is_mobile == 1:
self.model_dict[idx] = transform_list_to_tensor(self.model_dict[idx])
model_list.append((self.sample_num_dict[idx], self.model_dict[idx]))
training_num += self.sample_num_dict[idx]
logging.info("len of self.model_dict[idx] = " + str(len(self.model_dict)))
# logging.info("################aggregate: %d" % len(model_list))
(num0, averaged_params) = model_list[0]
for k in averaged_params.keys():
for i in range(0, len(model_list)):
local_sample_number, local_model_params = model_list[i]
w = local_sample_number / training_num
if i == 0:
averaged_params[k] = local_model_params[k] * w
else:
averaged_params[k] += local_model_params[k] * w
# server optimizer
# save optimizer state
self.opt.zero_grad()
opt_state = self.opt.state_dict()
# set new aggregated grad
self.set_model_global_grads(averaged_params)
self.opt = self._instantiate_opt()
# load optimizer state
self.opt.load_state_dict(opt_state)
self.opt.step()
end_time = time.time()
logging.info("aggregate time cost: %d" % (end_time - start_time))
return self.get_global_model_params()
def set_model_global_grads(self, new_state):
new_model = copy.deepcopy(self.trainer.model)
new_model.load_state_dict(new_state)
with torch.no_grad():
for parameter, new_parameter in zip(
self.trainer.model.parameters(), new_model.parameters()
):
parameter.grad = parameter.data - new_parameter.data
# because we go to the opposite direction of the gradient
model_state_dict = self.trainer.model.state_dict()
new_model_state_dict = new_model.state_dict()
for k in dict(self.trainer.model.named_parameters()).keys():
new_model_state_dict[k] = model_state_dict[k]
# self.trainer.model.load_state_dict(new_model_state_dict)
self.set_global_model_params(new_model_state_dict)
def client_sampling(self, round_idx, client_num_in_total, client_num_per_round):
if client_num_in_total == client_num_per_round:
client_indexes = [client_index for client_index in range(client_num_in_total)]
else:
num_clients = min(client_num_per_round, client_num_in_total)
np.random.seed(round_idx) # make sure for each comparison, we are selecting the same clients each round
client_indexes = np.random.choice(range(client_num_in_total), num_clients, replace=False)
logging.info("client_indexes = %s" % str(client_indexes))
return client_indexes
def _generate_validation_set(self, num_samples=10000):
if self.args.dataset.startswith("stackoverflow"):
test_data_num = len(self.test_global.dataset)
sample_indices = random.sample(range(test_data_num), min(num_samples, test_data_num))
subset = torch.utils.data.Subset(self.test_global.dataset, sample_indices)
sample_testset = torch.utils.data.DataLoader(subset, batch_size=self.args.batch_size)
return sample_testset
else:
return self.test_global
def test_on_server_for_all_clients(self, round_idx):
if self.trainer.test_on_the_server(self.train_data_local_dict, self.test_data_local_dict, self.device, self.args):
return
if round_idx % self.args.frequency_of_the_test == 0 or round_idx == self.args.comm_round - 1:
logging.info("################local_test_on_all_clients : {}".format(round_idx))
train_num_samples = []
train_tot_corrects = []
train_losses = []
test_num_samples = []
test_tot_corrects = []
test_losses = []
for client_idx in range(self.args.client_num_in_total):
# train data
metrics = self.trainer.test(self.train_data_local_dict[client_idx], self.device, self.args)
train_tot_correct, train_num_sample, train_loss = metrics['test_correct'], metrics['test_total'], metrics['test_loss']
train_tot_corrects.append(copy.deepcopy(train_tot_correct))
train_num_samples.append(copy.deepcopy(train_num_sample))
train_losses.append(copy.deepcopy(train_loss))
"""
Note: CI environment is CPU-based computing.
The training speed for RNN training is to slow in this setting, so we only test a client to make sure there is no programming error.
"""
if self.args.ci == 1:
break
# test on training dataset
train_acc = sum(train_tot_corrects) / sum(train_num_samples)
train_loss = sum(train_losses) / sum(train_num_samples)
wandb.log({"Train/Acc": train_acc, "round": round_idx})
wandb.log({"Train/Loss": train_loss, "round": round_idx})
stats = {'training_acc': train_acc, 'training_loss': train_loss}
logging.info(stats)
# test data
test_num_samples = []
test_tot_corrects = []
test_losses = []
if round_idx == self.args.comm_round - 1:
metrics = self.trainer.test(self.test_global, self.device, self.args)
else:
metrics = self.trainer.test(self.val_global, self.device, self.args)
test_tot_correct, test_num_sample, test_loss = metrics['test_correct'], metrics['test_total'], metrics[
'test_loss']
test_tot_corrects.append(copy.deepcopy(test_tot_correct))
test_num_samples.append(copy.deepcopy(test_num_sample))
test_losses.append(copy.deepcopy(test_loss))
# test on test dataset
test_acc = sum(test_tot_corrects) / sum(test_num_samples)
test_loss = sum(test_losses) / sum(test_num_samples)
wandb.log({"Test/Acc": test_acc, "round": round_idx})
wandb.log({"Test/Loss": test_loss, "round": round_idx})
stats = {'test_acc': test_acc, 'test_loss': test_loss}
logging.info(stats)
| 44.256158 | 148 | 0.645147 |
7940c5d443f6a7946a217e06d2a4f26f23236d76 | 315 | py | Python | main.py | arthursgonzaga/GoogleNewsScraping | f07ba1185f24e5ccc1c090604b15a63c2ed0ce49 | [
"MIT"
] | null | null | null | main.py | arthursgonzaga/GoogleNewsScraping | f07ba1185f24e5ccc1c090604b15a63c2ed0ce49 | [
"MIT"
] | null | null | null | main.py | arthursgonzaga/GoogleNewsScraping | f07ba1185f24e5ccc1c090604b15a63c2ed0ce49 | [
"MIT"
] | null | null | null | import pandas as pd
from GoogleNews import GoogleNews
SEARCHING = 'Dados'
googlenews = GoogleNews()
googlenews.set_lang('pt')
googlenews.search(SEARCHING)
print("Searching for... " + SEARCHING)
results = googlenews.result()
df = pd.DataFrame(results)
df.to_csv('exported_results.csv', index=False)
print("Done!")
| 22.5 | 46 | 0.761905 |
7940c7eb238e5dffd2bde01cada8fcef5dafbbf4 | 9,681 | py | Python | jobs/migrations/0011_auto__add_field_job_company_name.py | shipci/pythondotorg | eab6421261174c5f9040a4b50654e54e2ce90c9c | [
"Apache-2.0"
] | null | null | null | jobs/migrations/0011_auto__add_field_job_company_name.py | shipci/pythondotorg | eab6421261174c5f9040a4b50654e54e2ce90c9c | [
"Apache-2.0"
] | null | null | null | jobs/migrations/0011_auto__add_field_job_company_name.py | shipci/pythondotorg | eab6421261174c5f9040a4b50654e54e2ce90c9c | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding field 'Job.company_name'
db.add_column('jobs_job', 'company_name',
self.gf('django.db.models.fields.CharField')(max_length=100, null=True, blank=True),
keep_default=False)
def backwards(self, orm):
# Deleting field 'Job.company_name'
db.delete_column('jobs_job', 'company_name')
models = {
'auth.group': {
'Meta': {'object_name': 'Group'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': "orm['auth.Permission']", 'blank': 'True'})
},
'auth.permission': {
'Meta': {'unique_together': "(('content_type', 'codename'),)", 'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
'companies.company': {
'Meta': {'ordering': "('name',)", 'object_name': 'Company'},
'_about_rendered': ('django.db.models.fields.TextField', [], {}),
'about': ('markupfield.fields.MarkupField', [], {'rendered_field': 'True', 'blank': 'True'}),
'about_markup_type': ('django.db.models.fields.CharField', [], {'default': "'restructuredtext'", 'max_length': '30', 'blank': 'True'}),
'contact': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'logo': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'}),
'url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'})
},
'contenttypes.contenttype': {
'Meta': {'unique_together': "(('app_label', 'model'),)", 'ordering': "('name',)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
'jobs.job': {
'Meta': {'ordering': "('-created',)", 'object_name': 'Job'},
'_description_rendered': ('django.db.models.fields.TextField', [], {}),
'_requirements_rendered': ('django.db.models.fields.TextField', [], {}),
'agencies': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'category': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'jobs'", 'to': "orm['jobs.JobCategory']"}),
'city': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'company': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'jobs'", 'to': "orm['companies.Company']", 'null': 'True', 'blank': 'True'}),
'company_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'contact': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'country': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '100'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now', 'db_index': 'True', 'blank': 'True'}),
'creator': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'jobs_job_creator'", 'to': "orm['users.User']", 'null': 'True', 'blank': 'True'}),
'description': ('markupfield.fields.MarkupField', [], {'rendered_field': 'True', 'blank': 'True'}),
'description_markup_type': ('django.db.models.fields.CharField', [], {'default': "'restructuredtext'", 'max_length': '30', 'blank': 'True'}),
'dt_end': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'dt_start': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_featured': ('django.db.models.fields.BooleanField', [], {'default': 'False', 'db_index': 'True'}),
'job_types': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "'jobs'", 'to': "orm['jobs.JobType']", 'blank': 'True'}),
'last_modified_by': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'jobs_job_modified'", 'to': "orm['users.User']", 'null': 'True', 'blank': 'True'}),
'location_slug': ('django.db.models.fields.SlugField', [], {'max_length': '350'}),
'region': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'requirements': ('markupfield.fields.MarkupField', [], {'rendered_field': 'True', 'blank': 'True'}),
'requirements_markup_type': ('django.db.models.fields.CharField', [], {'default': "'restructuredtext'", 'max_length': '30', 'blank': 'True'}),
'status': ('django.db.models.fields.CharField', [], {'default': "'draft'", 'db_index': 'True', 'max_length': '20'}),
'telecommuting': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'updated': ('django.db.models.fields.DateTimeField', [], {'blank': 'True'}),
'url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'})
},
'jobs.jobcategory': {
'Meta': {'ordering': "('name',)", 'object_name': 'JobCategory'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'})
},
'jobs.jobtype': {
'Meta': {'ordering': "('name',)", 'object_name': 'JobType'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'})
},
'users.user': {
'Meta': {'object_name': 'User'},
'_bio_rendered': ('django.db.models.fields.TextField', [], {}),
'bio': ('markupfield.fields.MarkupField', [], {'rendered_field': 'True', 'blank': 'True'}),
'bio_markup_type': ('django.db.models.fields.CharField', [], {'default': "'markdown'", 'max_length': '30', 'blank': 'True'}),
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}),
'email_privacy': ('django.db.models.fields.IntegerField', [], {'default': '2'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': "orm['auth.Group']", 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'search_visibility': ('django.db.models.fields.IntegerField', [], {'default': '1'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': "orm['auth.Permission']", 'blank': 'True'}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'})
}
}
complete_apps = ['jobs'] | 79.352459 | 184 | 0.563062 |
7940c823b9107fad04627ad8f4fb5bc42f9d9b55 | 2,557 | py | Python | yabmp/channel/factory.py | zlpqingmei/yabmp | c91284e2701c9887a42804179eae1874d48082fe | [
"Apache-2.0"
] | 36 | 2015-07-27T01:05:50.000Z | 2021-12-05T05:09:15.000Z | yabmp/channel/factory.py | zlpqingmei/yabmp | c91284e2701c9887a42804179eae1874d48082fe | [
"Apache-2.0"
] | 9 | 2015-05-20T05:52:21.000Z | 2022-02-11T03:39:55.000Z | yabmp/channel/factory.py | zlpqingmei/yabmp | c91284e2701c9887a42804179eae1874d48082fe | [
"Apache-2.0"
] | 20 | 2015-05-15T01:56:18.000Z | 2021-09-29T07:15:46.000Z | # Copyright 2015-2016 Cisco Systems, Inc.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""Twisted message queue factory
reference from https://github.com/pika/pika/blob/master/examples/twisted_service.py
"""
import logging
import pika
from twisted.internet import protocol
from twisted.internet import reactor
from .protocol import PikaProtocol
LOG = logging.getLogger(__name__)
class PikaFactory(protocol.ReconnectingClientFactory):
def __init__(self, url, routing_key):
self.parameters = pika.URLParameters(url)
self.client = None
self.queued_messages = []
self.routing_key = routing_key
def startedConnecting(self, connector):
LOG.info('Started to connect to AMQP')
def buildProtocol(self, addr):
self.resetDelay()
LOG.info('Connected AMQP')
self.client = PikaProtocol(self.parameters)
self.client.factory = self
self.client.ready.addCallback(self.client.connected)
return self.client
def clientConnectionLost(self, connector, reason):
LOG.info('Lost connection. Reason: %s', reason.getErrorMessage())
protocol.ReconnectingClientFactory.clientConnectionLost(self, connector, reason.getErrorMessage())
def clientConnectionFailed(self, connector, reason):
LOG.info('Connection failed. Reason: %s', reason.getErrorMessage())
protocol.ReconnectingClientFactory.clientConnectionFailed(self, connector, reason.getErrorMessage())
def send_message(self, exchange=None, routing_key=None, message=None):
if not routing_key:
routing_key = self.routing_key
self.queued_messages.append((exchange, routing_key, message))
if self.client is not None:
self.client.send()
def connect(self):
try:
reactor.connectTCP(
host=self.parameters.host,
port=self.parameters.port,
factory=self)
except Exception as e:
LOG.error(e)
| 34.554054 | 108 | 0.695346 |
7940c87da98a967e3f8281f08d5adaa607616b8f | 395 | py | Python | causal_curve/__init__.py | piaodangdang/causal-curve | 954af307c3fd77b9aaff280b59556b1f90a3659a | [
"MIT"
] | 1 | 2020-12-23T02:26:54.000Z | 2020-12-23T02:26:54.000Z | causal_curve/__init__.py | piaodangdang/causal-curve | 954af307c3fd77b9aaff280b59556b1f90a3659a | [
"MIT"
] | null | null | null | causal_curve/__init__.py | piaodangdang/causal-curve | 954af307c3fd77b9aaff280b59556b1f90a3659a | [
"MIT"
] | null | null | null | """causal_curve module"""
import warnings
from statsmodels.genmod.generalized_linear_model import DomainWarning
from causal_curve.gps import GPS
from causal_curve.tmle import TMLE
from causal_curve.mediation import Mediation
# Suppress statsmodel warning for gamma family GLM
warnings.filterwarnings("ignore", category=DomainWarning)
warnings.filterwarnings("ignore", category=UserWarning)
| 26.333333 | 69 | 0.840506 |
7940c9399f802ed2b58ce90ab958d351585bbc27 | 5,804 | py | Python | runway_model.py | genekogan/deeplab-pytorch | c58f66878a1c5012c68a24eabdd15090b9becf4c | [
"MIT"
] | 5 | 2019-05-20T23:15:42.000Z | 2021-12-03T19:21:15.000Z | runway_model.py | Erinqi/deeplab-pytorch | c58f66878a1c5012c68a24eabdd15090b9becf4c | [
"MIT"
] | null | null | null | runway_model.py | Erinqi/deeplab-pytorch | c58f66878a1c5012c68a24eabdd15090b9becf4c | [
"MIT"
] | 5 | 2019-05-18T15:23:24.000Z | 2022-01-11T10:52:56.000Z | from __future__ import absolute_import, division, print_function
import pickle
import numpy as np
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
import yaml
from addict import Dict
from libs.models import *
from libs.utils import DenseCRF
from demo import *
import runway
classes = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'street sign', 12: 'stop sign', 13: 'parking meter', 14: 'bench', 15: 'bird', 16: 'cat', 17: 'dog', 18: 'horse', 19: 'sheep', 20: 'cow', 21: 'elephant', 22: 'bear', 23: 'zebra', 24: 'giraffe', 25: 'hat', 26: 'backpack', 27: 'umbrella', 28: 'shoe', 29: 'eye glasses', 30: 'handbag', 31: 'tie', 32: 'suitcase', 33: 'frisbee', 34: 'skis', 35: 'snowboard', 36: 'sports ball', 37: 'kite', 38: 'baseball bat', 39: 'baseball glove', 40: 'skateboard', 41: 'surfboard', 42: 'tennis racket', 43: 'bottle', 44: 'plate', 45: 'wine glass', 46: 'cup', 47: 'fork', 48: 'knife', 49: 'spoon', 50: 'bowl', 51: 'banana', 52: 'apple', 53: 'sandwich', 54: 'orange', 55: 'broccoli', 56: 'carrot', 57: 'hot dog', 58: 'pizza', 59: 'donut', 60: 'cake', 61: 'chair', 62: 'couch', 63: 'potted plant', 64: 'bed', 65: 'mirror', 66: 'dining table', 67: 'window', 68: 'desk', 69: 'toilet', 70: 'door', 71: 'tv', 72: 'laptop', 73: 'mouse', 74: 'remote', 75: 'keyboard', 76: 'cell phone', 77: 'microwave', 78: 'oven', 79: 'toaster', 80: 'sink', 81: 'refrigerator', 82: 'blender', 83: 'book', 84: 'clock', 85: 'vase', 86: 'scissors', 87: 'teddy bear', 88: 'hair drier', 89: 'toothbrush', 90: 'hair brush', 91: 'banner', 92: 'blanket', 93: 'branch', 94: 'bridge', 95: 'building-other', 96: 'bush', 97: 'cabinet', 98: 'cage', 99: 'cardboard', 100: 'carpet', 101: 'ceiling-other', 102: 'ceiling-tile', 103: 'cloth', 104: 'clothes', 105: 'clouds', 106: 'counter', 107: 'cupboard', 108: 'curtain', 109: 'desk-stuff', 110: 'dirt', 111: 'door-stuff', 112: 'fence', 113: 'floor-marble', 114: 'floor-other', 115: 'floor-stone', 116: 'floor-tile', 117: 'floor-wood', 118: 'flower', 119: 'fog', 120: 'food-other', 121: 'fruit', 122: 'furniture-other', 123: 'grass', 124: 'gravel', 125: 'ground-other', 126: 'hill', 127: 'house', 128: 'leaves', 129: 'light', 130: 'mat', 131: 'metal', 132: 'mirror-stuff', 133: 'moss', 134: 'mountain', 135: 'mud', 136: 'napkin', 137: 'net', 138: 'paper', 139: 'pavement', 140: 'pillow', 141: 'plant-other', 142: 'plastic', 143: 'platform', 144: 'playingfield', 145: 'railing', 146: 'railroad', 147: 'river', 148: 'road', 149: 'rock', 150: 'roof', 151: 'rug', 152: 'salad', 153: 'sand', 154: 'sea', 155: 'shelf', 156: 'sky-other', 157: 'skyscraper', 158: 'snow', 159: 'solid-other', 160: 'stairs', 161: 'stone', 162: 'straw', 163: 'structural-other', 164: 'table', 165: 'tent', 166: 'textile-other', 167: 'towel', 168: 'tree', 169: 'vegetable', 170: 'wall-brick', 171: 'wall-concrete', 172: 'wall-other', 173: 'wall-panel', 174: 'wall-stone', 175: 'wall-tile', 176: 'wall-wood', 177: 'water-other', 178: 'waterdrops', 179: 'window-blind', 180: 'window-other', 181: 'wood', 182: 'unlabeled'}
label_to_id = {v: k for k, v in classes.items()}
classes_list = [c for c in classes.values()]
def inference2(model, image, raw_image=None, postprocessor=None):
_, _, H, W = image.shape
# Image -> Probability map
logits = model(image)
logits = F.interpolate(logits, size=(H, W), mode="bilinear", align_corners=False)
probs = F.softmax(logits, dim=1)[0]
probs = probs.detach().cpu().numpy()
# Refine the prob map with CRF
if postprocessor and raw_image is not None:
probs = postprocessor(raw_image, probs)
labelmap = np.argmax(probs, axis=0)
return labelmap
def run_model(model, inputs):
image = np.array(inputs['image'])
image, raw_image = preprocessing(image, model['device'], model['config'])
labelmap = inference2(model['model'], image, raw_image, model['postprocessor'])
return labelmap
@runway.setup(options={'checkpoint': runway.file(extension='.pth')})
def setup(opts):
config_path = 'configs/cocostuff164k.yaml'
model_path = opts['checkpoint']
cuda = torch.cuda.is_available()
crf = False
with open(config_path, 'r') as f:
CONFIG = Dict(yaml.load(f))
device = get_device(cuda)
torch.set_grad_enabled(False)
#classes = get_classtable(CONFIG)
postprocessor = setup_postprocessor(CONFIG) if crf else None
model = eval(CONFIG.MODEL.NAME)(n_classes=CONFIG.DATASET.N_CLASSES)
state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)
model.load_state_dict(state_dict)
model.eval()
model.to(device)
print("Model:", CONFIG.MODEL.NAME)
return Dict({'model': model, 'device': device, 'config': CONFIG, 'postprocessor':postprocessor})
@runway.command('mask_all', inputs={'image': runway.image}, outputs={'image': runway.segmentation(label_to_id=label_to_id)})
def mask_all(model, inputs):
labelmap = run_model(model, inputs).astype(np.uint8)
return {'image': labelmap }
@runway.command('mask_one', inputs={'image': runway.image, 'class': runway.category(choices=classes_list)}, outputs={'image': runway.image})
def mask_one(model, inputs):
labelmap = run_model(model, inputs)
labelmap = 255.0 * np.array(labelmap==classes_list.index(inputs['class']))
image_out = np.dstack([labelmap] * 3).astype(np.uint8)
return {'image': image_out }
@runway.command('detect', inputs={'image': runway.image}, outputs={'classes': runway.array(runway.text)})
def detect(model, inputs):
labelmap = run_model(model, inputs)
labels = [classes_list[l] for l in np.unique(labelmap)]
return {'classes': labels }
if __name__ == '__main__':
runway.run()
| 56.901961 | 2,846 | 0.64938 |
7940c9790dd7ff87be87b15ac35e2712cc9097c1 | 40,396 | py | Python | tensor2tensor/data_generators/text_encoder.py | Mozen/Transorformer-tensor2tensor | e29b7dded31c6e909a4bd91fd2523517a15d93b3 | [
"Apache-2.0"
] | null | null | null | tensor2tensor/data_generators/text_encoder.py | Mozen/Transorformer-tensor2tensor | e29b7dded31c6e909a4bd91fd2523517a15d93b3 | [
"Apache-2.0"
] | null | null | null | tensor2tensor/data_generators/text_encoder.py | Mozen/Transorformer-tensor2tensor | e29b7dded31c6e909a4bd91fd2523517a15d93b3 | [
"Apache-2.0"
] | null | null | null | # coding=utf-8
# Copyright 2018 The Tensor2Tensor Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Encoders for text data.
* TextEncoder: base class
* ByteTextEncoder: for ascii text
* TokenTextEncoder: with user-supplied vocabulary file
* SubwordTextEncoder: invertible
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from itertools import chain
import math
import re
import tempfile
import time
import numpy as np
import six
from six.moves import range # pylint: disable=redefined-builtin
from tensor2tensor.data_generators import tokenizer
import tensorflow as tf
# Reserved tokens for things like padding and EOS symbols.
PAD = "<pad>"
EOS = "<EOS>"
RESERVED_TOKENS = [PAD, EOS]
NUM_RESERVED_TOKENS = len(RESERVED_TOKENS)
PAD_ID = RESERVED_TOKENS.index(PAD) # Normally 0
EOS_ID = RESERVED_TOKENS.index(EOS) # Normally 1
if six.PY2:
RESERVED_TOKENS_BYTES = RESERVED_TOKENS
else:
RESERVED_TOKENS_BYTES = [bytes(PAD, "ascii"), bytes(EOS, "ascii")]
# Regular expression for unescaping token strings.
# '\u' is converted to '_'
# '\\' is converted to '\'
# '\213;' is converted to unichr(213)
_UNESCAPE_REGEX = re.compile(r"\\u|\\\\|\\([0-9]+);")
_ESCAPE_CHARS = set(u"\\_u;0123456789")
# Unicode utility functions that work with Python 2 and 3
def native_to_unicode(s):
return s if is_unicode(s) else to_unicode(s)
def unicode_to_native(s):
if six.PY2:
return s.encode("utf-8") if is_unicode(s) else s
else:
return s
def is_unicode(s):
if six.PY2:
if isinstance(s, unicode):
return True
else:
if isinstance(s, str):
return True
return False
def to_unicode(s, ignore_errors=False):
if is_unicode(s):
return s
error_mode = "ignore" if ignore_errors else "strict"
return s.decode("utf-8", errors=error_mode)
def to_unicode_ignore_errors(s):
return to_unicode(s, ignore_errors=True)
def strip_ids(ids, ids_to_strip):
"""Strip ids_to_strip from the end ids."""
ids = list(ids)
while ids and ids[-1] in ids_to_strip:
ids.pop()
return ids
class TextEncoder(object):
"""Base class for converting from ints to/from human readable strings."""
def __init__(self, num_reserved_ids=NUM_RESERVED_TOKENS):
self._num_reserved_ids = num_reserved_ids
@property
def num_reserved_ids(self):
return self._num_reserved_ids
def encode(self, s):
"""Transform a human-readable string into a sequence of int ids.
The ids should be in the range [num_reserved_ids, vocab_size). Ids [0,
num_reserved_ids) are reserved.
EOS is not appended.
Args:
s: human-readable string to be converted.
Returns:
ids: list of integers
"""
return [int(w) + self._num_reserved_ids for w in s.split()]
def decode(self, ids, strip_extraneous=False):
"""Transform a sequence of int ids into a human-readable string.
EOS is not expected in ids.
Args:
ids: list of integers to be converted.
strip_extraneous: bool, whether to strip off extraneous tokens
(EOS and PAD).
Returns:
s: human-readable string.
"""
if strip_extraneous:
ids = strip_ids(ids, list(range(self._num_reserved_ids or 0)))
return " ".join(self.decode_list(ids))
def decode_list(self, ids):
"""Transform a sequence of int ids into a their string versions.
This method supports transforming individual input/output ids to their
string versions so that sequence to/from text conversions can be visualized
in a human readable format.
Args:
ids: list of integers to be converted.
Returns:
strs: list of human-readable string.
"""
decoded_ids = []
for id_ in ids:
if 0 <= id_ < self._num_reserved_ids:
decoded_ids.append(RESERVED_TOKENS[int(id_)])
else:
decoded_ids.append(id_ - self._num_reserved_ids)
return [str(d) for d in decoded_ids]
@property
def vocab_size(self):
raise NotImplementedError()
class ByteTextEncoder(TextEncoder):
"""Encodes each byte to an id. For 8-bit strings only."""
def encode(self, s):
numres = self._num_reserved_ids
if six.PY2:
if isinstance(s, unicode):
s = s.encode("utf-8")
return [ord(c) + numres for c in s]
# Python3: explicitly convert to UTF-8
return [c + numres for c in s.encode("utf-8")]
def decode(self, ids, strip_extraneous=False):
if strip_extraneous:
ids = strip_ids(ids, list(range(self._num_reserved_ids or 0)))
numres = self._num_reserved_ids
decoded_ids = []
int2byte = six.int2byte
for id_ in ids:
if 0 <= id_ < numres:
decoded_ids.append(RESERVED_TOKENS_BYTES[int(id_)])
else:
decoded_ids.append(int2byte(id_ - numres))
if six.PY2:
return "".join(decoded_ids)
# Python3: join byte arrays and then decode string
return b"".join(decoded_ids).decode("utf-8", "replace")
def decode_list(self, ids):
numres = self._num_reserved_ids
decoded_ids = []
int2byte = six.int2byte
for id_ in ids:
if 0 <= id_ < numres:
decoded_ids.append(RESERVED_TOKENS_BYTES[int(id_)])
else:
decoded_ids.append(int2byte(id_ - numres))
# Python3: join byte arrays and then decode string
return decoded_ids
@property
def vocab_size(self):
return 2 ** 8 + self._num_reserved_ids
class ClassLabelEncoder(TextEncoder):
"""Encoder for class labels."""
def __init__(self, class_labels=None, class_labels_fname=None):
super(ClassLabelEncoder, self).__init__(num_reserved_ids=0)
if class_labels_fname:
with tf.gfile.Open(class_labels_fname) as f:
class_labels = [label.strip() for label in f.readlines()]
assert class_labels
self._class_labels = class_labels
def encode(self, s):
label_str = s
return self._class_labels.index(label_str)
def decode(self, ids, strip_extraneous=False):
del strip_extraneous
label_id = ids
if isinstance(label_id, list):
assert len(label_id) == 1
label_id, = label_id
if isinstance(label_id, np.ndarray):
label_id = np.squeeze(label_id)
return self._class_labels[label_id]
def decode_list(self, ids):
return [self._class_labels[i] for i in ids]
@property
def vocab_size(self):
return len(self._class_labels)
class OneHotClassLabelEncoder(ClassLabelEncoder):
"""One-hot encoder for class labels."""
def encode(self, label_str, on_value=1, off_value=0): # pylint: disable=arguments-differ
e = np.full(self.vocab_size, off_value, dtype=np.int32)
e[self._class_labels.index(label_str)] = on_value
return e.tolist()
def decode(self, ids, strip_extraneous=False):
del strip_extraneous
label_id = ids
if isinstance(label_id, np.ndarray):
label_id = np.squeeze(label_id).astype(np.int8).tolist()
assert isinstance(label_id, list)
assert len(label_id) == self.vocab_size
return self._class_labels[label_id.index(1)]
@property
def vocab_size(self):
return len(self._class_labels)
class TokenTextEncoder(TextEncoder):
"""Encoder based on a user-supplied vocabulary (file or list)."""
def __init__(self,
vocab_filename,
reverse=False,
vocab_list=None,
replace_oov=None,
num_reserved_ids=NUM_RESERVED_TOKENS):
"""Initialize from a file or list, one token per line.
Handling of reserved tokens works as follows:
- When initializing from a list, we add reserved tokens to the vocab.
- When initializing from a file, we do not add reserved tokens to the vocab.
- When saving vocab files, we save reserved tokens to the file.
Args:
vocab_filename: If not None, the full filename to read vocab from. If this
is not None, then vocab_list should be None.
reverse: Boolean indicating if tokens should be reversed during encoding
and decoding.
vocab_list: If not None, a list of elements of the vocabulary. If this is
not None, then vocab_filename should be None.
replace_oov: If not None, every out-of-vocabulary token seen when
encoding will be replaced by this string (which must be in vocab).
num_reserved_ids: Number of IDs to save for reserved tokens like <EOS>.
"""
super(TokenTextEncoder, self).__init__(num_reserved_ids=num_reserved_ids)
self._reverse = reverse
self._replace_oov = replace_oov
if vocab_filename:
self._init_vocab_from_file(vocab_filename)
else:
assert vocab_list is not None
self._init_vocab_from_list(vocab_list)
def encode(self, s):
"""Converts a space-separated string of tokens to a list of ids."""
sentence = s
tokens = sentence.strip().split()
if self._replace_oov is not None:
tokens = [t if t in self._token_to_id else self._replace_oov
for t in tokens]
ret = [self._token_to_id[tok] for tok in tokens]
return ret[::-1] if self._reverse else ret
def decode(self, ids, strip_extraneous=False):
return " ".join(self.decode_list(ids))
def decode_list(self, ids):
seq = reversed(ids) if self._reverse else ids
return [self._safe_id_to_token(i) for i in seq]
@property
def vocab_size(self):
return len(self._id_to_token)
def _safe_id_to_token(self, idx):
return self._id_to_token.get(idx, "ID_%d" % idx)
def _init_vocab_from_file(self, filename):
"""Load vocab from a file.
Args:
filename: The file to load vocabulary from.
"""
with tf.gfile.Open(filename) as f:
tokens = [token.strip() for token in f.readlines()]
def token_gen():
for token in tokens:
yield token
self._init_vocab(token_gen(), add_reserved_tokens=False)
def _init_vocab_from_list(self, vocab_list):
"""Initialize tokens from a list of tokens.
It is ok if reserved tokens appear in the vocab list. They will be
removed. The set of tokens in vocab_list should be unique.
Args:
vocab_list: A list of tokens.
"""
def token_gen():
for token in vocab_list:
if token not in RESERVED_TOKENS:
yield token
self._init_vocab(token_gen())
def _init_vocab(self, token_generator, add_reserved_tokens=True):
"""Initialize vocabulary with tokens from token_generator."""
self._id_to_token = {}
non_reserved_start_index = 0
if add_reserved_tokens:
self._id_to_token.update(enumerate(RESERVED_TOKENS))
non_reserved_start_index = len(RESERVED_TOKENS)
self._id_to_token.update(
enumerate(token_generator, start=non_reserved_start_index))
# _token_to_id is the reverse of _id_to_token
self._token_to_id = dict((v, k)
for k, v in six.iteritems(self._id_to_token))
def store_to_file(self, filename):
"""Write vocab file to disk.
Vocab files have one token per line. The file ends in a newline. Reserved
tokens are written to the vocab file as well.
Args:
filename: Full path of the file to store the vocab to.
"""
with tf.gfile.Open(filename, "w") as f:
for i in range(len(self._id_to_token)):
f.write(self._id_to_token[i] + "\n")
def _escape_token(token, alphabet):
"""Escape away underscores and OOV characters and append '_'.
This allows the token to be expressed as the concatenation of a list
of subtokens from the vocabulary. The underscore acts as a sentinel
which allows us to invertibly concatenate multiple such lists.
Args:
token: A unicode string to be escaped.
alphabet: A set of all characters in the vocabulary's alphabet.
Returns:
escaped_token: An escaped unicode string.
Raises:
ValueError: If the provided token is not unicode.
"""
if not isinstance(token, six.text_type):
raise ValueError("Expected string type for token, got %s" % type(token))
token = token.replace(u"\\", u"\\\\").replace(u"_", u"\\u")
# 没见过的词用 ord()ASCII值 代替?
ret = [c if c in alphabet and c != u"\n" else r"\%d;" % ord(c) for c in token]
return u"".join(ret) + "_"
def _unescape_token(escaped_token):
"""Inverse of _escape_token().
Args:
escaped_token: a unicode string
Returns:
token: a unicode string
"""
def match(m):
if m.group(1) is None:
return u"_" if m.group(0) == u"\\u" else u"\\"
try:
return six.unichr(int(m.group(1)))
except (ValueError, OverflowError) as _:
return u"\u3013" # Unicode for undefined character.
trimmed = escaped_token[:-1] if escaped_token.endswith("_") else escaped_token
return _UNESCAPE_REGEX.sub(match, trimmed)
class SubwordTextEncoder(TextEncoder):
"""Class for invertibly encoding text using a limited vocabulary.
Invertibly encodes a native string as a sequence of subtokens from a limited
vocabulary.
A SubwordTextEncoder is built from a corpus (so it is tailored to the text in
the corpus), and stored to a file. See text_encoder_build_subword.py.
It can then be loaded and used to encode/decode any text.
Encoding has four phases:
1. Tokenize into a list of tokens. Each token is a unicode string of either
all alphanumeric characters or all non-alphanumeric characters. We drop
tokens consisting of a single space that are between two alphanumeric
tokens.
2. Escape each token. This escapes away special and out-of-vocabulary
characters, and makes sure that each token ends with an underscore, and
has no other underscores.
3. Represent each escaped token as a the concatenation of a list of subtokens
from the limited vocabulary. Subtoken selection is done greedily from
beginning to end. That is, we construct the list in order, always picking
the longest subtoken in our vocabulary that matches a prefix of the
remaining portion of the encoded token.
4. Concatenate these lists. This concatenation is invertible due to the
fact that the trailing underscores indicate when one list is finished.
"""
def __init__(self, filename=None):
"""Initialize and read from a file, if provided.
Args:
filename: filename from which to read vocab. If None, do not load a
vocab
"""
self._alphabet = set()
self.filename = filename
if filename is not None:
self._load_from_file(filename)
super(SubwordTextEncoder, self).__init__()
def encode(self, s):
"""Converts a native string to a list of subtoken ids.
Args:
s: a native string.
Returns:
a list of integers in the range [0, vocab_size)
"""
return self._tokens_to_subtoken_ids(
tokenizer.encode(native_to_unicode(s)))
def encode_without_tokenizing(self, token_text):
"""Converts string to list of subtoken ids without calling tokenizer.
This treats `token_text` as a single token and directly converts it
to subtoken ids. This may be useful when the default tokenizer doesn't
do what we want (e.g., when encoding text with tokens composed of lots of
nonalphanumeric characters). It is then up to the caller to make sure that
raw text is consistently converted into tokens. Only use this if you are
sure that `encode` doesn't suit your needs.
Args:
token_text: A native string representation of a single token.
Returns:
A list of subword token ids; i.e., integers in the range [0, vocab_size).
"""
return self._tokens_to_subtoken_ids([native_to_unicode(token_text)])
def decode(self, ids, strip_extraneous=False):
"""Converts a sequence of subtoken ids to a native string.
Args:
ids: a list of integers in the range [0, vocab_size)
strip_extraneous: bool, whether to strip off extraneous tokens
(EOS and PAD).
Returns:
a native string
"""
if strip_extraneous:
ids = strip_ids(ids, list(range(self._num_reserved_ids or 0)))
return unicode_to_native(
tokenizer.decode(self._subtoken_ids_to_tokens(ids)))
def decode_list(self, ids):
return [self._subtoken_id_to_subtoken_string(s) for s in ids]
@property
def vocab_size(self):
"""The subtoken vocabulary size."""
return len(self._all_subtoken_strings)
def _tokens_to_subtoken_ids(self, tokens):
"""Converts a list of tokens to a list of subtoken ids.
Args:
tokens: a list of strings.
Returns:
a list of integers in the range [0, vocab_size)
"""
ret = []
for token in tokens:
ret.extend(self._token_to_subtoken_ids(token))
return ret
def _token_to_subtoken_ids(self, token):
"""Converts token to a list of subtoken ids.
Args:
token: a string.
Returns:
a list of integers in the range [0, vocab_size)
"""
cache_location = hash(token) % self._cache_size
cache_key, cache_value = self._cache[cache_location]
if cache_key == token:
return cache_value
ret = self._escaped_token_to_subtoken_ids(
_escape_token(token, self._alphabet))
self._cache[cache_location] = (token, ret)
return ret
def _subtoken_ids_to_tokens(self, subtokens):
"""Converts a list of subtoken ids to a list of tokens.
Args:
subtokens: a list of integers in the range [0, vocab_size)
Returns:
a list of strings.
"""
concatenated = "".join(
[self._subtoken_id_to_subtoken_string(s) for s in subtokens])
split = concatenated.split("_")
ret = []
for t in split:
if t:
unescaped = _unescape_token(t + "_")
if unescaped:
ret.append(unescaped)
return ret
def _subtoken_id_to_subtoken_string(self, subtoken):
"""Converts a subtoken integer ID to a subtoken string."""
if 0 <= subtoken < self.vocab_size:
return self._all_subtoken_strings[subtoken]
return u""
def _escaped_token_to_subtoken_strings(self, escaped_token):
"""Converts an escaped token string to a list of subtoken strings.
Args:
escaped_token: An escaped token as a unicode string.
Returns:
A list of subtokens as unicode strings.
"""
# NOTE: This algorithm is greedy; it won't necessarily produce the "best"
# list of subtokens.
ret = []
start = 0
token_len = len(escaped_token)
while start < token_len:
for end in range(
min(token_len, start + self._max_subtoken_len), start, -1):
# 应该是说先按取整个字符串，如果当前的字符串满足要求就使用，不满足要求居缩小
subtoken = escaped_token[start:end]
if subtoken in self._subtoken_string_to_id:
ret.append(subtoken)
start = end
break
else: # Did not break
# If there is no possible encoding of the escaped token then one of the
# characters in the token is not in the alphabet. This should be
# impossible and would be indicative of a bug.
assert False, "Token substring not found in subtoken vocabulary."
return ret
def _escaped_token_to_subtoken_ids(self, escaped_token):
"""Converts an escaped token string to a list of subtoken IDs.
Args:
escaped_token: An escaped token as a unicode string.
Returns:
A list of subtoken IDs as integers.
"""
return [
self._subtoken_string_to_id[subtoken]
for subtoken in self._escaped_token_to_subtoken_strings(escaped_token)
]
@classmethod
def build_from_generator(cls,
generator,
target_size,
max_subtoken_length=None,
reserved_tokens=None):
"""Builds a SubwordTextEncoder from the generated text.
Args:
generator: yields text.
target_size: int, approximate vocabulary size to create.
max_subtoken_length: Maximum length of a subtoken. If this is not set,
then the runtime and memory use of creating the vocab is quadratic in
the length of the longest token. If this is set, then it is instead
O(max_subtoken_length * length of longest token).
reserved_tokens: List of reserved tokens. The global variable
`RESERVED_TOKENS` must be a prefix of `reserved_tokens`. If this
argument is `None`, it will use `RESERVED_TOKENS`.
Returns:
SubwordTextEncoder with `vocab_size` approximately `target_size`.
"""
# 计算 token 的一个字典，key 是 token value 是 token 的数量
token_counts = collections.defaultdict(int)
for item in generator:
for tok in tokenizer.encode(native_to_unicode(item)):
token_counts[tok] += 1
encoder = cls.build_to_target_size(
target_size, token_counts, 1, 1e3,
max_subtoken_length=max_subtoken_length,
reserved_tokens=reserved_tokens)
return encoder
@classmethod
def build_to_target_size(cls,
target_size,
token_counts,
min_val,
max_val,
max_subtoken_length=None,
reserved_tokens=None,
num_iterations=4):
"""
# 理解这句话： `vocab_size` near `target_size`
使得生成的 vocab 数据接近 target size
Builds a SubwordTextEncoder that has `vocab_size` near `target_size`.
使用 二分法递归搜索
Uses simple recursive binary search to find a minimum token count that most
closely matches the `target_size`.
Args:
target_size: Desired vocab_size to approximate.
token_counts: A dictionary of token counts, mapping string to int.
min_val: An integer; lower bound for the minimum token count.
max_val: An integer; upper bound for the minimum token count.
max_subtoken_length: Maximum length of a subtoken. If this is not set,
then the runtime and memory use of creating the vocab is quadratic in
the length of the longest token. If this is set, then it is instead
O(max_subtoken_length * length of longest token).
reserved_tokens: List of reserved tokens. The global variable
`RESERVED_TOKENS` must be a prefix of `reserved_tokens`. If this
argument is `None`, it will use `RESERVED_TOKENS`.
num_iterations: An integer; how many iterations of refinement.
Returns:
A SubwordTextEncoder instance.
Raises:
ValueError: If `min_val` is greater than `max_val`.
"""
if min_val > max_val:
raise ValueError("Lower bound for the minimum token count "
"is greater than the upper bound.")
if target_size < 1:
raise ValueError("Target size must be positive.")
if reserved_tokens is None:
reserved_tokens = RESERVED_TOKENS
def bisect(min_val, max_val):
""" Bisection to find the right size."""
present_count = (max_val + min_val) // 2
tf.logging.info("Trying min_count %d" % present_count)
subtokenizer = cls()
subtokenizer.build_from_token_counts(
token_counts, present_count, num_iterations,
max_subtoken_length=max_subtoken_length,
reserved_tokens=reserved_tokens)
# Being within 1% of the target size is ok.
is_ok = abs(subtokenizer.vocab_size - target_size) * 100 < target_size
# If min_val == max_val, we can't do any better than this.
if is_ok or min_val >= max_val or present_count < 2:
return subtokenizer
if subtokenizer.vocab_size > target_size:
other_subtokenizer = bisect(present_count + 1, max_val)
else:
other_subtokenizer = bisect(min_val, present_count - 1)
if other_subtokenizer is None:
return subtokenizer
if (abs(other_subtokenizer.vocab_size - target_size) <
abs(subtokenizer.vocab_size - target_size)):
return other_subtokenizer
return subtokenizer
return bisect(min_val, max_val)
def build_from_token_counts(self,
token_counts,
min_count,
num_iterations=4,
reserved_tokens=None,
max_subtoken_length=None):
"""Train a SubwordTextEncoder based on a dictionary of word counts.
Args:
token_counts: a dictionary of Unicode strings to int.
min_count: an integer - discard subtokens with lower counts.
num_iterations: an integer. how many iterations of refinement.
reserved_tokens: List of reserved tokens. The global variable
`RESERVED_TOKENS` must be a prefix of `reserved_tokens`. If this
argument is `None`, it will use `RESERVED_TOKENS`.
max_subtoken_length: Maximum length of a subtoken. If this is not set,
then the runtime and memory use of creating the vocab is quadratic in
the length of the longest token. If this is set, then it is instead
O(max_subtoken_length * length of longest token).
Raises:
ValueError: if reserved is not 0 or len(RESERVED_TOKENS). In this case, it
is not clear what the space is being reserved for, or when it will be
filled in.
"""
if reserved_tokens is None:
reserved_tokens = RESERVED_TOKENS
else:
# There is not complete freedom in replacing RESERVED_TOKENS.
for default, proposed in zip(RESERVED_TOKENS, reserved_tokens):
if default != proposed:
raise ValueError("RESERVED_TOKENS must be a prefix of "
"reserved_tokens.")
# Initialize the alphabet. Note, this must include reserved tokens or it can
# result in encoding failures.
# 这个只是提取所有的 key?
alphabet_tokens = chain(six.iterkeys(token_counts),
[native_to_unicode(t) for t in reserved_tokens])
# 按照 key 进行字母排序?
# 生成所有的字母 +
self._init_alphabet_from_tokens(alphabet_tokens)
# Bootstrap the initial list of subtokens with the characters from the
# alphabet plus the escaping characters.
# 这里没有看懂? 觉得这个 _alphabet 是一个 character 的 list, 不是 subtoken
self._init_subtokens_from_list(list(self._alphabet),
reserved_tokens=reserved_tokens)
# We build iteratively. On each iteration, we segment all the words,
# then count the resulting potential subtokens, keeping the ones
# with high enough counts for our new vocabulary.
if min_count < 1:
min_count = 1
for i in range(num_iterations):
tf.logging.info("Iteration {0}".format(i))
# Collect all substrings of the encoded token that break along current
# subtoken boundaries.
subtoken_counts = collections.defaultdict(int)
for token, count in six.iteritems(token_counts):
iter_start_time = time.time()
# token 这里表示的是一个词, _alphabet 表示的是一个字母表
escaped_token = _escape_token(token, self._alphabet)
# 这里面的一些思路没有看明白... 主要是 self._subtoken_string_to_id, 这里明明就是 character 的集合
# 那么导出来的都是 character?
subtokens = self._escaped_token_to_subtoken_strings(escaped_token)
# 虽然看懂了这部分逻辑，但是还是不懂在做啥
start = 0
for subtoken in subtokens:
# last_position: 当前遍历的最后一个点
last_position = len(escaped_token) + 1
# 如果设定了某个 token 的最大长度，则 last_position 则是 当前位置加上最大长度 max_subtoken_length
if max_subtoken_length is not None:
last_position = min(last_position, start + max_subtoken_length)
#
for end in range(start + 1, last_position):
new_subtoken = escaped_token[start:end]
subtoken_counts[new_subtoken] += count
start += len(subtoken)
iter_time_secs = time.time() - iter_start_time
if iter_time_secs > 0.1:
tf.logging.info(u"Processing token [{0}] took {1} seconds, consider "
"setting Text2TextProblem.max_subtoken_length to a "
"smaller value.".format(token, iter_time_secs))
# Array of sets of candidate subtoken strings, by length.
len_to_subtoken_strings = []
for subtoken_string, count in six.iteritems(subtoken_counts):
lsub = len(subtoken_string)
if count >= min_count:
while len(len_to_subtoken_strings) <= lsub:
len_to_subtoken_strings.append(set())
len_to_subtoken_strings[lsub].add(subtoken_string)
# Consider the candidates longest to shortest, so that if we accept
# a longer subtoken string, we can decrement the counts of its prefixes.
new_subtoken_strings = []
for lsub in range(len(len_to_subtoken_strings) - 1, 0, -1):
subtoken_strings = len_to_subtoken_strings[lsub]
for subtoken_string in subtoken_strings:
count = subtoken_counts[subtoken_string]
if count >= min_count:
# Exclude alphabet tokens here, as they must be included later,
# explicitly, regardless of count.
if subtoken_string not in self._alphabet:
new_subtoken_strings.append((count, subtoken_string))
for l in range(1, lsub):
subtoken_counts[subtoken_string[:l]] -= count
# Include the alphabet explicitly to guarantee all strings are encodable.
new_subtoken_strings.extend((subtoken_counts.get(a, 0), a)
for a in self._alphabet)
new_subtoken_strings.sort(reverse=True)
# Reinitialize to the candidate vocabulary.
new_subtoken_strings = [subtoken for _, subtoken in new_subtoken_strings]
if reserved_tokens:
escaped_reserved_tokens = [
_escape_token(native_to_unicode(t), self._alphabet)
for t in reserved_tokens
]
new_subtoken_strings = escaped_reserved_tokens + new_subtoken_strings
self._init_subtokens_from_list(new_subtoken_strings)
tf.logging.info("vocab_size = %d" % self.vocab_size)
@property
def all_subtoken_strings(self):
return tuple(self._all_subtoken_strings)
def dump(self):
"""Debugging dump of the current subtoken vocabulary."""
subtoken_strings = [(i, s)
for s, i in six.iteritems(self._subtoken_string_to_id)]
print(u", ".join(u"{0} : '{1}'".format(i, s)
for i, s in sorted(subtoken_strings)))
def _init_subtokens_from_list(self, subtoken_strings, reserved_tokens=None):
""" Initialize token information from a list of subtoken strings.
Args:
subtoken_strings: a list of subtokens # character? but not?
reserved_tokens: List of reserved tokens. We must have `reserved_tokens`
as None or the empty list, or else the global variable `RESERVED_TOKENS`
must be a prefix of `reserved_tokens`.
Raises:
ValueError: if reserved is not 0 or len(RESERVED_TOKENS). In this case, it
is not clear what the space is being reserved for, or when it will be
filled in.
"""
if reserved_tokens is None:
reserved_tokens = []
if reserved_tokens:
self._all_subtoken_strings = reserved_tokens + subtoken_strings
else:
self._all_subtoken_strings = subtoken_strings
# we remember the maximum length of any subtoken to avoid having to
# check arbitrarily long strings.
self._max_subtoken_len = max([len(s) for s in subtoken_strings])
self._subtoken_string_to_id = {
s: i + len(reserved_tokens)
for i, s in enumerate(subtoken_strings) if s
}
# Initialize the cache to empty.
self._cache_size = 2 ** 20
self._cache = [(None, None)] * self._cache_size
def _init_alphabet_from_tokens(self, tokens):
"""Initialize alphabet from an iterable of token or subtoken strings."""
# Include all characters from all tokens in the alphabet to guarantee that
# any token can be encoded. Additionally, include all escaping characters.
self._alphabet = {c for token in tokens for c in token}
self._alphabet |= _ESCAPE_CHARS
def _load_from_file_object(self, f):
"""Load from a file object.
Args:
f: File object to load vocabulary from
"""
subtoken_strings = []
for line in f:
s = line.strip()
# Some vocab files wrap words in single quotes, but others don't
if ((s.startswith("'") and s.endswith("'")) or
(s.startswith("\"") and s.endswith("\""))):
s = s[1:-1]
subtoken_strings.append(native_to_unicode(s))
self._init_subtokens_from_list(subtoken_strings)
self._init_alphabet_from_tokens(subtoken_strings)
def _load_from_file(self, filename):
"""Load from a vocab file."""
if not tf.gfile.Exists(filename):
raise ValueError("File %s not found" % filename)
with tf.gfile.Open(filename) as f:
self._load_from_file_object(f)
def store_to_file(self, filename, add_single_quotes=True):
with tf.gfile.Open(filename, "w") as f:
for subtoken_string in self._all_subtoken_strings:
if add_single_quotes:
f.write("'" + unicode_to_native(subtoken_string) + "'\n")
else:
f.write(unicode_to_native(subtoken_string) + "\n")
class ImageEncoder(object):
"""Encoder class for saving and loading images."""
def __init__(self, num_reserved_ids=0, height=None, width=None, channels=3):
assert num_reserved_ids == 0
self._height = height
self._width = width
self._channels = channels
@property
def num_reserved_ids(self):
return 0
def encode(self, s):
"""Transform a string with a filename into a list of RGB integers.
Args:
s: path to the file with an image.
Returns:
ids: list of integers
"""
try:
import matplotlib.image as im # pylint: disable=g-import-not-at-top
except ImportError as e:
tf.logging.warning(
"Reading an image requires matplotlib to be installed: %s", e)
raise NotImplementedError("Image reading not implemented.")
return im.imread(s)
def decode(self, ids, strip_extraneous=False):
"""Transform a sequence of int ids into an image file.
Args:
ids: list of integers to be converted.
strip_extraneous: unused
Returns:
Path to the temporary file where the image was saved.
Raises:
ValueError: if the ids are not of the appropriate size.
"""
del strip_extraneous
_, tmp_file_path = tempfile.mkstemp("_decode.png")
if self._height is None or self._width is None:
size = int(math.sqrt(len(ids) / self._channels))
length = size * size * self._channels
else:
size = None
length = self._height * self._width * self._channels
if len(ids) != length:
raise ValueError("Length of ids (%d) must be height (%d) x width (%d) x "
"channels (%d); %d != %d.\n Ids: %s"
% (len(ids), self._height, self._width, self._channels,
len(ids), length, " ".join([str(i) for i in ids])))
with tf.Graph().as_default():
raw = tf.constant(ids, dtype=tf.uint8)
if size is None:
img = tf.reshape(raw, [self._height, self._width, self._channels])
else:
img = tf.reshape(raw, [size, size, self._channels])
png = tf.image.encode_png(img)
op = tf.write_file(tmp_file_path, png)
with tf.Session() as sess:
sess.run(op)
return tmp_file_path
def decode_list(self, ids):
"""Transform a sequence of int ids into an image file.
Args:
ids: list of integers to be converted.
Returns:
Singleton list: path to the temporary file where the image was saved.
"""
return [self.decode(ids)]
@property
def vocab_size(self):
return 256
class RealEncoder(object):
"""Encoder class for saving and loading float values."""
def encode(self, s):
"""Transform a string (space separated float values) into a float array.
Args:
s: space separated float values.
Returns:
Array of float values.
"""
return [float(w) for w in s.split()]
def decode(self, ids, strip_extraneous=False):
"""Transform sequence of float values into string (float values).
Args:
ids: array of floats to be converted.
strip_extraneous: unused
Returns:
String having space separated float values.
Raises:
ValueError: if the ids are not of the appropriate size.
"""
del strip_extraneous
return " ".join([str(i) for i in ids])
| 38.037665 | 93 | 0.614863 |
7940c9b8c0fc7a3e1e67b6fb4618a0a6ee7bc85d | 123 | py | Python | codes_auto/1359.circular-permutation-in-binary-representation.py | smartmark-pro/leetcode_record | 6504b733d892a705571eb4eac836fb10e94e56db | [
"MIT"
] | null | null | null | codes_auto/1359.circular-permutation-in-binary-representation.py | smartmark-pro/leetcode_record | 6504b733d892a705571eb4eac836fb10e94e56db | [
"MIT"
] | null | null | null | codes_auto/1359.circular-permutation-in-binary-representation.py | smartmark-pro/leetcode_record | 6504b733d892a705571eb4eac836fb10e94e56db | [
"MIT"
] | null | null | null | #
# @lc app=leetcode.cn id=1359 lang=python3
#
# [1359] circular-permutation-in-binary-representation
#
None
# @lc code=end | 17.571429 | 54 | 0.731707 |
7940ca1830e4fc51366def626a59bb08921f880b | 81 | py | Python | src/checkin/utils.py | FSU-ACM/Programming-Contest-Suite | 459b03992aa3df3d8bc5a04b0b6ee24c3055f51f | [
"MIT"
] | 1 | 2021-12-14T16:30:11.000Z | 2021-12-14T16:30:11.000Z | src/checkin/utils.py | FSU-ACM/Programming-Contest-Suite | 459b03992aa3df3d8bc5a04b0b6ee24c3055f51f | [
"MIT"
] | 18 | 2021-12-19T01:20:59.000Z | 2022-03-22T00:27:15.000Z | src/checkin/utils.py | FSU-ACM/Programming-Contest-Suite | 459b03992aa3df3d8bc5a04b0b6ee24c3055f51f | [
"MIT"
] | 1 | 2022-03-04T04:19:51.000Z | 2022-03-04T04:19:51.000Z | def checkin_auth(user):
return user.profile.is_volunteer() or user.is_superuser
| 27 | 56 | 0.814815 |
7940cab69bdced061ff816afbf40c8ef43820264 | 4,668 | py | Python | test.py | MattFerraro/radon | 795b74fd7d12e7c4a191646e6bdf9e0386c8e95e | [
"Apache-2.0"
] | 2 | 2018-11-08T03:30:55.000Z | 2021-09-19T01:40:14.000Z | test.py | MattFerraro/radon | 795b74fd7d12e7c4a191646e6bdf9e0386c8e95e | [
"Apache-2.0"
] | null | null | null | test.py | MattFerraro/radon | 795b74fd7d12e7c4a191646e6bdf9e0386c8e95e | [
"Apache-2.0"
] | 1 | 2018-12-30T04:07:17.000Z | 2018-12-30T04:07:17.000Z | # import numpy as np
# import matplotlib.pyplot as plt
# # Number of samplepoints
# N = 600
# # sample spacing
# T = 1.0 / 800.0
# x = np.linspace(0.0, N*T, N)
# y = np.sin(50.0 * 2.0 * np.pi * x) + 0.5 * np.sin(80.0 * 2.0 * np.pi * x)
# # yf = numpy
# # xf = np.linspace(0.0, 1.0/(2.0*T), N/2)
# ft = np.fft.rfft(y)
# print ft
# plt.plot(ft)
# # fig, ax = plt.subplots()
# # ax.plot(xf, 2.0/N * np.abs(yf[:N/2]))
# plt.show()
import pylab
import numpy
import math
# see: http://glowingpython.blogspot.com/2011/08/how-to-plot-frequency-spectrum-with.html
# and
# http://docs.scipy.org/doc/numpy/reference/routines.fft.html
# Since our input data is real, the negative frequency components
# don't include any new information, and are not interesting to us.
# The rfft routines understand this, and rfft takes n real points and
# returns n/2+1 complex output points. The corresponding inverse
# knows this, and acts accordingly.
#
# these are the routines we want for real valued data
#
# note that the scipy version of rfft returns that data differently
#
# M. Zingale (2013-03-03)
def singleFreqSine(npts):
# a pure sine with no phase shift will result in pure imaginary
# signal
f_0 = 0.2
xmax = 10.0/f_0
xx = numpy.linspace(0.0, xmax, npts, endpoint=False)
# input frequency
f_0 = 0.2
f = numpy.sin(2.0*math.pi*f_0*xx)
return xx, f
def singleFreqSinePlusShift(npts):
# a pure sine with no phase shift will result in pure imaginary
# signal
f_0 = 0.2
xmax = 10.0/f_0
xx = numpy.linspace(0.0, xmax, npts, endpoint=False)
# input frequency
f_0 = 0.2
f = numpy.sin(2.0*math.pi*f_0*xx + math.pi/4)
return xx, f
def twoFreqSine(npts):
# a pure sine with no phase shift will result in pure imaginary
# signal
f_0 = 0.2
f_1 = 0.5
xmax = 10.0/f_0
xx = numpy.linspace(0.0, xmax, npts, endpoint=False)
# input frequency
f_0 = 0.2
f = 0.5*(numpy.sin(2.0*math.pi*f_0*xx) + numpy.sin(2.0*math.pi*f_1*xx))
return xx, f
def singleFreqCosine(npts):
# a pure cosine with no phase shift will result in pure real
# signal
f_0 = 0.2
xmax = 10.0/f_0
xx = numpy.linspace(0.0, xmax, npts, endpoint=False)
# input frequency
f_0 = 0.2
f = numpy.cos(2.0*math.pi*f_0*xx)
return xx, f
def plotFFT(xx, f, outfile):
pylab.clf()
pylab.rc("font", size=9)
npts = len(xx)
# Forward transform: f(x) -> F(k)
fk = numpy.fft.rfft(f)
# Normalization -- the '2' here comes from the fact that we are
# neglecting the negative portion of the frequency space, since
# the FFT of a real function contains redundant information, so
# we are only dealing with 1/2 of the frequency space.
norm = 2.0/npts
fk = fk*norm
# element 0 of fk is the DC component -- we don't want to plot that
fk_r = fk.real
fk_i = fk.imag
# the fftfreq returns the postive and negative (and 0) frequencies
# the newer versions of numpy (>=1.8) have an rfftfreq() function
# that really does what we want.
k = numpy.fft.fftfreq(len(xx))[range(0, npts/2+1)]
# the last element is negative, because of the symmetry, but should
# be positive (see
# http://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.fft.rfftfreq.html)
k[-1] *= -1
kfreq = k*npts/max(xx)
# Inverse transform: F(k) -> f(x) -- without the normalization
fkinv = numpy.fft.irfft(fk/norm)
pylab.subplot(411)
pylab.plot(xx, f)
pylab.xlabel("x")
pylab.ylabel("f(x)")
pylab.subplot(412)
pylab.plot(kfreq, fk_r, label=r"Re($\mathcal{F}$)")
pylab.plot(kfreq, fk_i, ls=":", label=r"Im($\mathcal{F}$)")
pylab.xlabel(r"$\nu_k$")
pylab.ylabel("F(k)")
pylab.legend(fontsize="small", frameon=False)
pylab.subplot(413)
pylab.plot(kfreq, numpy.abs(fk))
pylab.xlabel(r"$\nu_k$")
pylab.ylabel(r"|F(k)|")
pylab.subplot(414)
pylab.plot(xx, fkinv.real)
pylab.xlabel(r"$\nu_k$")
pylab.ylabel(r"inverse F(k)")
pylab.tight_layout()
pylab.savefig(outfile)
#-----------------------------------------------------------------------------
def main():
npts = 256
# FFT of sine
xx, f = singleFreqSine(npts)
plotFFT(xx, f, "fft-sine.png")
# FFT of cosine
xx, f = singleFreqCosine(npts)
plotFFT(xx, f, "fft-cosine.png")
# FFT of sine with pi/4 phase
xx, f = singleFreqSinePlusShift(npts)
plotFFT(xx, f, "fft-sine-phase.png")
# FFT of two sines
xx, f = twoFreqSine(npts)
plotFFT(xx, f, "fft-two-sines.png")
if __name__ == '__main__':
main()
| 22.123223 | 89 | 0.618895 |
7940cc446fa241c1fc0c5af5ee43d9c714d9f37b | 3,095 | py | Python | setup.py | wfarah/psrdada-python | d74aa784a49975c329b983c82dcfd1918dfd7736 | [
"Apache-2.0"
] | 4 | 2019-06-26T03:51:30.000Z | 2020-09-22T23:26:08.000Z | setup.py | wfarah/psrdada-python | d74aa784a49975c329b983c82dcfd1918dfd7736 | [
"Apache-2.0"
] | 2 | 2020-07-31T22:23:12.000Z | 2020-10-06T18:45:02.000Z | setup.py | TRASAL/psrdada-python | 977cb6c6501998082d6f836f8103c0a8fcf35485 | [
"Apache-2.0"
] | 3 | 2020-01-13T19:36:06.000Z | 2020-09-22T23:37:28.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Setup script for the PSRDada python bindings.
Build and install the package using distutils.
"""
# pylint: disable=all
from Cython.Build import cythonize
from setuptools import setup
from distutils.extension import Extension
from os import environ, path
with open('README.md') as readme_file:
README = readme_file.read()
with open(path.join('psrdada', '__version__.py')) as version_file:
version = {}
exec(version_file.read(), version)
PROJECT_VERSION = version['__version__']
# Get the header locations from the environment
INCLUDE_DIRS = []
if "CPATH" in environ:
flags = environ["CPATH"].split(':')
for flag in flags:
# when usingn spack, there is no -I prefix
INCLUDE_DIRS.append(flag)
if "CFLAGS" in environ:
flags = environ["CFLAGS"].split(' ')
for flag in flags:
if flag[0:2] == '-I':
# when usingn spack, there is no -I prefix
INCLUDE_DIRS.append(flag[2:-1])
# keep the original order
INCLUDE_DIRS.reverse()
# Get the header locations from the environment
LIBRARY_DIRS = []
if "LD_LIBRARY_PATH" in environ:
flags = environ["LD_LIBRARY_PATH"].split(':')
for flag in flags:
# when usingn spack, there is no -I prefix
LIBRARY_DIRS.append(flag)
# keep the original order
LIBRARY_DIRS.reverse()
EXTENSIONS = [
Extension(
"psrdada.ringbuffer",
["psrdada/ringbuffer.pyx"],
libraries=["psrdada"],
library_dirs=LIBRARY_DIRS,
include_dirs=INCLUDE_DIRS
),
Extension(
"psrdada.reader",
["psrdada/reader.pyx"],
libraries=["psrdada"],
library_dirs=LIBRARY_DIRS,
include_dirs=INCLUDE_DIRS
),
Extension(
"psrdada.writer",
["psrdada/writer.pyx"],
libraries=["psrdada"],
library_dirs=LIBRARY_DIRS,
include_dirs=INCLUDE_DIRS
),
Extension(
"psrdada.viewer",
["psrdada/viewer.pyx"],
libraries=["psrdada"],
library_dirs=LIBRARY_DIRS,
include_dirs=INCLUDE_DIRS
),
]
setup(
name='psrdada',
version=PROJECT_VERSION,
description="Python3 bindings to the ringbuffer implementation in PSRDada",
long_description=README + '\n\n',
author="Jisk Attema",
author_email='[email protected]',
url='https://github.com/NLeSC/psrdada-python',
packages=['psrdada',],
package_dir={'psrdada': 'psrdada'},
include_package_data=True,
license="Apache Software License 2.0",
zip_safe=False,
keywords='psrdada',
classifiers=[
'Development Status :: 2 - Pre-Alpha',
'Intended Audience :: Developers',
'License :: OSI Approved :: Apache Software License',
'Natural Language :: English',
"Programming Language :: Python :: 2",
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.5',
],
test_suite='tests',
ext_modules=cythonize(EXTENSIONS),
)
| 28.136364 | 79 | 0.634249 |
7940cc77c0eab60d114bf6b0fc6027661ca2dfff | 1,254 | py | Python | pipelines/ner_demo_replace/scripts/create_config.py | dogatekin/projects | 4fa445a165de30c7e7877e17dc7fe8bea12b8198 | [
"MIT"
] | null | null | null | pipelines/ner_demo_replace/scripts/create_config.py | dogatekin/projects | 4fa445a165de30c7e7877e17dc7fe8bea12b8198 | [
"MIT"
] | null | null | null | pipelines/ner_demo_replace/scripts/create_config.py | dogatekin/projects | 4fa445a165de30c7e7877e17dc7fe8bea12b8198 | [
"MIT"
] | null | null | null | import typer
from pathlib import Path
import spacy
def create_config(model_name: str, component_to_replace: str, output_path: Path):
nlp = spacy.load(model_name)
# replace the component with a new default component
nlp.remove_pipe(component_to_replace)
nlp.add_pipe(component_to_replace)
# create a new config as a copy of the loaded pipeline's config
config = nlp.config.copy()
# revert most training settings to the current defaults
default_config = spacy.blank(nlp.lang).config
config["corpora"] = default_config["corpora"]
config["training"] = default_config["training"]
# set the vectors if the loaded pipeline has vectors
if len(nlp.vocab.vectors) > 0:
config["paths"]["vectors"] = model_name
# source all components from the loaded pipeline and freeze all except the
# component to replace
config["training"]["frozen_components"] = []
for pipe_name in nlp.component_names:
config["components"][pipe_name] = {"source": model_name}
if pipe_name != component_to_replace:
config["training"]["frozen_components"].append(pipe_name)
# save the config
config.to_disk(output_path)
if __name__ == "__main__":
typer.run(create_config)
| 31.35 | 81 | 0.708134 |
7940cca3a75b3f0d970913bb741f0006a02e36fe | 1,232 | py | Python | test/board_test.py | hr23232323/gomoku-ML-AI | 9766427b4885ffb171072fc4e4106b7b34faff8c | [
"MIT"
] | 1 | 2018-03-22T05:22:58.000Z | 2018-03-22T05:22:58.000Z | test/board_test.py | hr23232323/gomoku-ML-AI | 9766427b4885ffb171072fc4e4106b7b34faff8c | [
"MIT"
] | null | null | null | test/board_test.py | hr23232323/gomoku-ML-AI | 9766427b4885ffb171072fc4e4106b7b34faff8c | [
"MIT"
] | null | null | null | import pytest
from board import Board, Stone
from .helpers import run_move_seq
def won_games():
col_win = [(3, 3), (3, 4),
(4, 3), (4, 4),
(5, 3), (5, 4),
(6, 3), (6, 4),
(7, 3)]
row_win = [(3, 3), (4, 3),
(3, 4), (4, 4),
(3, 5), (4, 5),
(3, 6), (4, 6),
(3, 7)]
diag_win = [(0, 0), (14, 14),
(1, 1), (10, 10),
(2, 2), (9, 5),
(3, 3), (8, 7),
(4, 4)]
anti_diag_win = [(1, 6), (14, 14),
(2, 5), (13, 12),
(3, 4), (13, 11),
(4, 3), (10, 10),
(5, 2)]
yield run_move_seq(col_win)
yield run_move_seq(row_win)
yield run_move_seq(diag_win)
yield run_move_seq(anti_diag_win)
@pytest.mark.parametrize("game", won_games())
def test_winners(game):
print(game)
assert game.winner == Stone.black
def test_take_move():
b = run_move_seq([(1, 1), (1, 1)], 3, 3)
assert b.grid[1][1] == -1
def test_take_move_twice_fail():
with pytest.raises(ValueError):
run_move_seq([(1, 1), (1, 1), (1, 1)])
| 24.64 | 46 | 0.413961 |
7940cce4bdd21c7d181288b692e6fc210fcbdac2 | 17,906 | py | Python | geo_png_tiler/mercantile/__init__.py | sasakiassociates/qgis-geo-png-db | bb71daa68e3721074482944d12f6323ce5136fed | [
"MIT"
] | 1 | 2021-10-01T11:44:59.000Z | 2021-10-01T11:44:59.000Z | geo_png_tiler/mercantile/__init__.py | sasakiassociates/qgis-geo-png-db | bb71daa68e3721074482944d12f6323ce5136fed | [
"MIT"
] | null | null | null | geo_png_tiler/mercantile/__init__.py | sasakiassociates/qgis-geo-png-db | bb71daa68e3721074482944d12f6323ce5136fed | [
"MIT"
] | null | null | null | """Web mercator XYZ tile utilities"""
from collections import namedtuple
import math
import sys
import warnings
if sys.version_info < (3,):
from collections import Sequence
else:
from collections.abc import Sequence
__version__ = "1.1.2"
__all__ = [
"Bbox",
"LngLat",
"LngLatBbox",
"Tile",
"bounding_tile",
"bounds",
"children",
"feature",
"lnglat",
"parent",
"quadkey",
"quadkey_to_tile",
"simplify",
"tile",
"tiles",
"ul",
"xy_bounds",
]
Tile = namedtuple("Tile", ["x", "y", "z"])
"""An XYZ web mercator tile
Attributes
----------
x, y, z : int
x and y indexes of the tile and zoom level z.
"""
LngLat = namedtuple("LngLat", ["lng", "lat"])
"""A longitude and latitude pair
Attributes
----------
lng, lat : float
Longitude and latitude in decimal degrees east or north.
"""
LngLatBbox = namedtuple("LngLatBbox", ["west", "south", "east", "north"])
"""A geographic bounding box
Attributes
----------
west, south, east, north : float
Bounding values in decimal degrees.
"""
Bbox = namedtuple("Bbox", ["left", "bottom", "right", "top"])
"""A web mercator bounding box
Attributes
----------
left, bottom, right, top : float
Bounding values in meters.
"""
class MercantileError(Exception):
"""Base exception"""
class InvalidLatitudeError(MercantileError):
"""Raised when math errors occur beyond ~85 degrees N or S"""
class InvalidZoomError(MercantileError):
"""Raised when a zoom level is invalid"""
class ParentTileError(MercantileError):
"""Raised when a parent tile cannot be determined"""
class QuadKeyError(MercantileError):
"""Raised when errors occur in computing or parsing quad keys"""
class TileArgParsingError(MercantileError):
"""Raised when errors occur in parsing a function's tile arg(s)"""
def _parse_tile_arg(*args):
"""parse the *tile arg of module functions
Parameters
----------
tile : Tile or sequence of int
May be be either an instance of Tile or 3 ints, X, Y, Z.
Returns
-------
Tile
Raises
------
TileArgParsingError
"""
if len(args) == 1:
args = args[0]
if len(args) == 3:
return Tile(*args)
else:
raise TileArgParsingError(
"the tile argument may have 1 or 3 values. Note that zoom is a keyword-only argument"
)
def ul(*tile):
"""Returns the upper left longitude and latitude of a tile
Parameters
----------
tile : Tile or sequence of int
May be be either an instance of Tile or 3 ints, X, Y, Z.
Returns
-------
LngLat
Examples
--------
>>> ul(Tile(x=0, y=0, z=1))
LngLat(lng=-180.0, lat=85.0511287798066)
>>> mercantile.ul(1, 1, 1)
LngLat(lng=0.0, lat=0.0)
"""
tile = _parse_tile_arg(*tile)
xtile, ytile, zoom = tile
n = 2.0 ** zoom
lon_deg = xtile / n * 360.0 - 180.0
lat_rad = math.atan(math.sinh(math.pi * (1 - 2 * ytile / n)))
lat_deg = math.degrees(lat_rad)
return LngLat(lon_deg, lat_deg)
def bounds(*tile):
"""Returns the bounding box of a tile
Parameters
----------
tile : Tile or sequence of int
May be be either an instance of Tile or 3 ints, X, Y, Z.
Returns
-------
LngLatBBox
"""
tile = _parse_tile_arg(*tile)
xtile, ytile, zoom = tile
a = ul(xtile, ytile, zoom)
b = ul(xtile + 1, ytile + 1, zoom)
return LngLatBbox(a[0], b[1], b[0], a[1])
def truncate_lnglat(lng, lat):
if lng > 180.0:
lng = 180.0
elif lng < -180.0:
lng = -180.0
if lat > 90.0:
lat = 90.0
elif lat < -90.0:
lat = -90.0
return lng, lat
def xy(lng, lat, truncate=False):
"""Convert longitude and latitude to web mercator x, y
Parameters
----------
lng, lat : float
Longitude and latitude in decimal degrees.
truncate : bool, optional
Whether to truncate or clip inputs to web mercator limits.
Returns
-------
x, y : float
y will be inf at the North Pole (lat >= 90) and -inf at the
South Pole (lat <= -90).
"""
if truncate:
lng, lat = truncate_lnglat(lng, lat)
x = 6378137.0 * math.radians(lng)
if lat <= -90:
y = float("-inf")
elif lat >= 90:
y = float("inf")
else:
y = 6378137.0 * math.log(math.tan((math.pi * 0.25) + (0.5 * math.radians(lat))))
return x, y
def lnglat(x, y, truncate=False):
"""Convert web mercator x, y to longitude and latitude
Parameters
----------
x, y : float
web mercator coordinates in meters.
truncate : bool, optional
Whether to truncate or clip inputs to web mercator limits.
Returns
-------
LngLat
"""
R2D = 180 / math.pi
A = 6378137.0
lng, lat = (
x * R2D / A,
((math.pi * 0.5) - 2.0 * math.atan(math.exp(-y / A))) * R2D,
)
if truncate:
lng, lat = truncate_lnglat(lng, lat)
return LngLat(lng, lat)
def xy_bounds(*tile):
"""Get the web mercator bounding box of a tile
Parameters
----------
tile : Tile or sequence of int
May be be either an instance of Tile or 3 ints, X, Y, Z.
Returns
-------
Bbox
"""
tile = _parse_tile_arg(*tile)
xtile, ytile, zoom = tile
left, top = xy(*ul(xtile, ytile, zoom))
right, bottom = xy(*ul(xtile + 1, ytile + 1, zoom))
return Bbox(left, bottom, right, top)
def _tile(lng, lat, zoom, truncate=False):
if truncate:
lng, lat = truncate_lnglat(lng, lat)
lat = math.radians(lat)
n = 2.0 ** zoom
xtile = (lng + 180.0) / 360.0 * n
try:
ytile = (
(1.0 - math.log(math.tan(lat) + (1.0 / math.cos(lat))) / math.pi) / 2.0 * n
)
except ValueError:
raise InvalidLatitudeError(
"Y can not be computed for latitude {} radians".format(lat)
)
else:
return xtile, ytile, zoom
def tile(lng, lat, zoom, truncate=False):
"""Get the tile containing a longitude and latitude
Parameters
----------
lng, lat : float
A longitude and latitude pair in decimal degrees.
zoom : int
The web mercator zoom level.
truncate : bool, optional
Whether or not to truncate inputs to limits of web mercator.
Returns
-------
Tile
"""
xtile, ytile, zoom = _tile(lng, lat, zoom, truncate=truncate)
xtile = int(math.floor(xtile))
ytile = int(math.floor(ytile))
return Tile(xtile, ytile, zoom)
def quadkey(*tile):
"""Get the quadkey of a tile
Parameters
----------
tile : Tile or sequence of int
May be be either an instance of Tile or 3 ints, X, Y, Z.
Returns
-------
str
"""
tile = _parse_tile_arg(*tile)
xtile, ytile, zoom = tile
qk = []
for z in range(zoom, 0, -1):
digit = 0
mask = 1 << (z - 1)
if xtile & mask:
digit += 1
if ytile & mask:
digit += 2
qk.append(str(digit))
return "".join(qk)
def quadkey_to_tile(qk):
"""Get the tile corresponding to a quadkey
Parameters
----------
qk : str
A quadkey string.
Returns
-------
Tile
"""
if len(qk) == 0:
return Tile(0, 0, 0)
xtile, ytile = 0, 0
for i, digit in enumerate(reversed(qk)):
mask = 1 << i
if digit == "1":
xtile = xtile | mask
elif digit == "2":
ytile = ytile | mask
elif digit == "3":
xtile = xtile | mask
ytile = ytile | mask
elif digit != "0":
warnings.warn(
"QuadKeyError will not derive from ValueError in mercantile 2.0.",
DeprecationWarning,
)
raise QuadKeyError("Unexpected quadkey digit: %r", digit)
return Tile(xtile, ytile, i + 1)
def tiles(west, south, east, north, zooms, truncate=False):
"""Get the tiles overlapped by a geographic bounding box
Parameters
----------
west, south, east, north : sequence of float
Bounding values in decimal degrees.
zooms : int or sequence of int
One or more zoom levels.
truncate : bool, optional
Whether or not to truncate inputs to web mercator limits.
Yields
------
Tile
Notes
-----
A small epsilon is used on the south and east parameters so that this
function yields exactly one tile when given the bounds of that same tile.
"""
if truncate:
west, south = truncate_lnglat(west, south)
east, north = truncate_lnglat(east, north)
if west > east:
bbox_west = (-180.0, south, east, north)
bbox_east = (west, south, 180.0, north)
bboxes = [bbox_west, bbox_east]
else:
bboxes = [(west, south, east, north)]
for w, s, e, n in bboxes:
# Clamp bounding values.
w = max(-180.0, w)
s = max(-85.051129, s)
e = min(180.0, e)
n = min(85.051129, n)
if not isinstance(zooms, Sequence):
zooms = [zooms]
epsilon = 1.0e-9
for z in zooms:
llx, lly, llz = _tile(w, s, z)
if lly % 1 < epsilon / 10:
lly = lly - epsilon
urx, ury, urz = _tile(e, n, z)
if urx % 1 < epsilon / 10:
urx = urx - epsilon
# Clamp left x and top y at 0.
llx = 0 if llx < 0 else llx
ury = 0 if ury < 0 else ury
llx, urx, lly, ury = map(lambda x: int(math.floor(x)), [llx, urx, lly, ury])
for i in range(llx, min(urx + 1, 2 ** z)):
for j in range(ury, min(lly + 1, 2 ** z)):
yield Tile(i, j, z)
def parent(*tile, **kwargs):
"""Get the parent of a tile
The parent is the tile of one zoom level lower that contains the
given "child" tile.
Parameters
----------
tile : Tile or sequence of int
May be be either an instance of Tile or 3 ints, X, Y, Z.
zoom : int, optional
Determines the *zoom* level of the returned parent tile.
This defaults to one lower than the tile (the immediate parent).
Returns
-------
Tile
Examples
--------
>>> parent(Tile(0, 0, 2))
Tile(x=0, y=0, z=1)
>>> parent(Tile(0, 0, 2), zoom=0)
Tile(x=0, y=0, z=0)
"""
tile = _parse_tile_arg(*tile)
# zoom is a keyword-only argument.
zoom = kwargs.get("zoom", None)
if zoom is not None and (tile[2] < zoom or zoom != int(zoom)):
raise InvalidZoomError(
"zoom must be an integer and less than that of the input tile"
)
x, y, z = tile
if x != int(x) or y != int(y) or z != int(z):
raise ParentTileError("the parent of a non-integer tile is undefined")
target_zoom = z - 1 if zoom is None else zoom
# Algorithm heavily inspired by https://github.com/mapbox/tilebelt.
return_tile = tile
while return_tile[2] > target_zoom:
xtile, ytile, ztile = return_tile
if xtile % 2 == 0 and ytile % 2 == 0:
return_tile = Tile(xtile // 2, ytile // 2, ztile - 1)
elif xtile % 2 == 0:
return_tile = Tile(xtile // 2, (ytile - 1) // 2, ztile - 1)
elif not xtile % 2 == 0 and ytile % 2 == 0:
return_tile = Tile((xtile - 1) // 2, ytile // 2, ztile - 1)
else:
return_tile = Tile((xtile - 1) // 2, (ytile - 1) // 2, ztile - 1)
return return_tile
def children(*tile, **kwargs):
"""Get the children of a tile
The children are ordered: top-left, top-right, bottom-right, bottom-left.
Parameters
----------
tile : Tile or sequence of int
May be be either an instance of Tile or 3 ints, X, Y, Z.
zoom : int, optional
Returns all children at zoom *zoom*, in depth-first clockwise winding order.
If unspecified, returns the immediate (i.e. zoom + 1) children of the tile.
Returns
-------
list
Examples
--------
>>> children(Tile(0, 0, 0))
[Tile(x=0, y=0, z=1), Tile(x=0, y=1, z=1), Tile(x=1, y=0, z=1), Tile(x=1, y=1, z=1)]
>>> children(Tile(0, 0, 0), zoom=2)
[Tile(x=0, y=0, z=2), Tile(x=0, y=1, z=2), Tile(x=0, y=2, z=2), Tile(x=0, y=3, z=2), ...]
"""
tile = _parse_tile_arg(*tile)
# zoom is a keyword-only argument.
zoom = kwargs.get("zoom", None)
xtile, ytile, ztile = tile
if zoom is not None and (ztile > zoom or zoom != int(zoom)):
raise InvalidZoomError(
"zoom must be an integer and greater than that of the input tile"
)
target_zoom = zoom if zoom is not None else ztile + 1
tiles = [tile]
while tiles[0][2] < target_zoom:
xtile, ytile, ztile = tiles.pop(0)
tiles += [
Tile(xtile * 2, ytile * 2, ztile + 1),
Tile(xtile * 2 + 1, ytile * 2, ztile + 1),
Tile(xtile * 2 + 1, ytile * 2 + 1, ztile + 1),
Tile(xtile * 2, ytile * 2 + 1, ztile + 1),
]
return tiles
def simplify(tiles):
"""Reduces the size of the tileset as much as possible by merging leaves into parents.
Parameters
----------
tiles : Sequence of tiles to merge.
Returns
-------
list
"""
def merge(merge_set):
"""Checks to see if there are 4 tiles in merge_set which can be merged.
If there are, this merges them.
This returns a list of tiles, as well as a boolean indicating if any were merged.
By repeatedly applying merge, a tileset can be simplified.
"""
upwards_merge = {}
for tile in merge_set:
tile_parent = parent(tile)
if tile_parent not in upwards_merge:
upwards_merge[tile_parent] = set()
upwards_merge[tile_parent] |= {tile}
current_tileset = []
changed = False
for supertile, children in upwards_merge.items():
if len(children) == 4:
current_tileset += [supertile]
changed = True
else:
current_tileset += list(children)
return current_tileset, changed
# Check to see if a tile and its parent both already exist.
# If so, discard the child (it's covered in the parent)
root_set = set()
for tile in tiles:
x, y, z = tile
supers = [parent(tile, zoom=i) for i in range(z + 1)]
for supertile in supers:
if supertile in root_set:
continue
root_set |= {tile}
# Repeatedly run merge until no further simplification is possible.
is_merging = True
while is_merging:
root_set, is_merging = merge(root_set)
return root_set
def rshift(val, n):
return (val % 0x100000000) >> n
def bounding_tile(*bbox, **kwds):
"""Get the smallest tile containing a geographic bounding box
NB: when the bbox spans lines of lng 0 or lat 0, the bounding tile
will be Tile(x=0, y=0, z=0).
Parameters
----------
bbox : sequence of float
west, south, east, north bounding values in decimal degrees.
Returns
-------
Tile
"""
if len(bbox) == 2:
bbox += bbox
w, s, e, n = bbox
truncate = bool(kwds.get("truncate"))
if truncate:
w, s = truncate_lnglat(w, s)
e, n = truncate_lnglat(e, n)
# Algorithm ported directly from https://github.com/mapbox/tilebelt.
try:
tmin = tile(w, s, 32, truncate=truncate)
tmax = tile(e, n, 32, truncate=truncate)
except InvalidLatitudeError:
return Tile(0, 0, 0)
cell = tmin[:2] + tmax[:2]
z = _getBboxZoom(*cell)
if z == 0:
return Tile(0, 0, 0)
x = rshift(cell[0], (32 - z))
y = rshift(cell[1], (32 - z))
return Tile(x, y, z)
def _getBboxZoom(*bbox):
MAX_ZOOM = 28
for z in range(0, MAX_ZOOM):
mask = 1 << (32 - (z + 1))
if (bbox[0] & mask) != (bbox[2] & mask) or (bbox[1] & mask) != (bbox[3] & mask):
return z
return MAX_ZOOM
def feature(
tile, fid=None, props=None, projected="geographic", buffer=None, precision=None
):
"""Get the GeoJSON feature corresponding to a tile
Parameters
----------
tile : Tile or sequence of int
May be be either an instance of Tile or 3 ints, X, Y, Z.
fid : str, optional
A feature id.
props : dict, optional
Optional extra feature properties.
projected : str, optional
Non-standard web mercator GeoJSON can be created by passing
'mercator'.
buffer : float, optional
Optional buffer distance for the GeoJSON polygon.
precision : int, optional
GeoJSON coordinates will be truncated to this number of decimal
places.
Returns
-------
dict
"""
west, south, east, north = bounds(tile)
if projected == "mercator":
west, south = xy(west, south, truncate=False)
east, north = xy(east, north, truncate=False)
if buffer:
west -= buffer
south -= buffer
east += buffer
north += buffer
if precision and precision >= 0:
west, south, east, north = (
round(v, precision) for v in (west, south, east, north)
)
bbox = [min(west, east), min(south, north), max(west, east), max(south, north)]
geom = {
"type": "Polygon",
"coordinates": [
[[west, south], [west, north], [east, north], [east, south], [west, south]]
],
}
xyz = str(tile)
feat = {
"type": "Feature",
"bbox": bbox,
"id": xyz,
"geometry": geom,
"properties": {"title": "XYZ tile %s" % xyz},
}
if props:
feat["properties"].update(props)
if fid is not None:
feat["id"] = fid
return feat
| 25.113604 | 97 | 0.558584 |
7940cd0f1d5cff31f5484717798bb05d88006c56 | 7,916 | py | Python | dataporten/tests/test_parsers.py | frafra/django-dataporten | 4236017611e08d08bd810be0beae1b994cb5fc67 | [
"MIT"
] | 4 | 2019-01-06T17:56:07.000Z | 2021-03-21T19:16:35.000Z | dataporten/tests/test_parsers.py | frafra/django-dataporten | 4236017611e08d08bd810be0beae1b994cb5fc67 | [
"MIT"
] | 9 | 2019-10-21T17:23:53.000Z | 2021-06-10T21:06:25.000Z | dataporten/tests/test_parsers.py | frafra/django-dataporten | 4236017611e08d08bd810be0beae1b994cb5fc67 | [
"MIT"
] | 2 | 2019-04-29T11:48:59.000Z | 2020-01-06T09:54:55.000Z | from datetime import datetime
from django.test import TestCase
from freezegun import freeze_time
import pytest
from ..parsers import (
Course,
Group,
group_factory,
MainProfile,
Membership,
OrganisationUnit,
Semester,
StudyProgram,
datetime_from,
)
class TestDatetimeFrom(TestCase):
def test_basic_correctness(self):
dt = datetime_from('2017-08-14T22:00:01Z')
self.assertEqual(
[dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second],
[2017, 8, 14, 22, 0, 1],
)
class TestGroupFactory:
def test_study_program_factory(self, study_program_json):
study_program = next(group_factory(study_program_json))
assert type(study_program) is StudyProgram
def test_course_factory(self, course_json):
course = next(group_factory(course_json))
assert type(course) is Course
def test_main_profile_factory(self, main_profile_json):
main_profile = next(group_factory(main_profile_json))
assert type(main_profile) is MainProfile
def test_group_factory_given_iterable_argument(
self,
study_program_json,
course_json,
):
groups = group_factory(study_program_json, course_json)
assert len(list(groups)) == 2
class TestGroup:
def test_properties_present(self, study_program_json):
group_example = Group(study_program_json)
assert group_example.name == 'Fysikk og matematikk - masterstudium (5-årig)'
assert group_example.url == 'http://www.ntnu.no/studier/mtfyma'
assert group_example.group_type == 'prg'
def test_active_membership(self, study_program_json):
group_example = Group(study_program_json)
assert group_example.membership
class TestMembership():
def test_perpetual_membership(self):
unending_membership = Membership(
{
"basic": "member",
"displayName": "Student",
"active": True,
"fsroles": [
"STUDENT"
]
}
)
assert bool(unending_membership) is True
def test_inactive_membership(self):
inactive_membership = Membership(
{
"basic": "member",
"displayName": "Student",
"active": False,
"fsroles": [
"STUDENT"
]
}
)
assert bool(inactive_membership) is False
@freeze_time('2017-08-13')
def test_limited_membership(self):
limited_membership = Membership(
{
"notAfter": "2017-08-14T22:00:00Z",
"active": True,
"subjectRelations": "undervisning",
"basic": "member",
"fsroles": [
"STUDENT"
],
"displayName": "Student"
}
)
assert bool(limited_membership) is True
@freeze_time('2017-08-15')
def test_expired_membership(self):
limited_membership = Membership(
{
"notAfter": "2017-08-14T22:00:00Z",
"active": True,
"subjectRelations": "undervisning",
"basic": "member",
"fsroles": [
"STUDENT"
],
"displayName": "Student"
}
)
assert bool(limited_membership) is False
def test_retrieval_of_json_from_membership_object(self, membership_json):
"""Original JSON should be stored on the membership object."""
membership = Membership(membership_json)
assert membership.json == membership_json
def test_string_representation_of_membership(self, membership_json):
"""The displayName property should be used for str representation."""
# With a displayName attribute
membership = Membership(membership_json)
assert str(membership) == 'Ansatt'
# Without a displayName attribute
del membership_json['displayName']
membership = Membership(membership_json)
assert str(membership) == 'Ukjent'
def test_primary_affiliation_property(self, membership_json):
"""Membership affiliations should be retrievable."""
membership = Membership(membership_json)
assert membership.primary_affiliation == 'employee'
assert membership.affiliations == [
'employee',
'member',
'affiliate',
'student',
]
@freeze_time('2017-01-01')
class TestCourse:
def test_course_code(self, finished_course):
assert finished_course.code == 'EXPH0004'
def test_finished_course(self, finished_course):
assert not finished_course.membership
assert finished_course.semester.year == 2014
def test_ongoing_course_with_end_time(self, ongoing_course):
assert ongoing_course.membership
assert ongoing_course.semester.year == 2017
@pytest.mark.skip(reason='Flaky for some versions. TODO.')
def test_ongoing_course_without_end_time(self, non_finished_course):
assert non_finished_course.membership
assert non_finished_course.semester.year == 2019
def test_split_on_membership(self, finished_course, non_finished_course, ongoing_course):
courses = [
finished_course,
non_finished_course,
ongoing_course,
]
active, inactive = Course.split_on_membership(courses)
assert finished_course.code in inactive.keys()
assert finished_course in inactive.values()
assert non_finished_course.code in active.keys()
assert non_finished_course in active.values()
assert ongoing_course.code in active.keys()
assert ongoing_course in active.values()
class TestStudyProgram:
def test_study_program_basic_properties(self, study_program_json):
study_program = StudyProgram(study_program_json)
assert study_program.code == 'MTFYMA'
class TestMainProfile:
def test_main_profile_basic_properties(self, main_profile_json):
main_profile = MainProfile(main_profile_json)
assert main_profile.code == 'MTFYMA-IM'
class TestOrganisationUnit:
def test_organisation_unit_basic_properties(self, organisation_unit_json):
organisation_unit = OrganisationUnit(organisation_unit_json)
assert organisation_unit.uid == 'fc:org:ntnu.no:unit:167500'
@freeze_time('2017-08-27')
class TestSemester(TestCase):
def setUp(self):
autumn_semester_date = datetime_from('2016-09-14T22:00:00Z')
spring_semester_date = datetime_from('2016-04-04T22:00:00Z')
self.autumn_semester = Semester(autumn_semester_date)
self.spring_semester = Semester(spring_semester_date)
self.present_semester = Semester.now()
def test_year_of_semester(self):
self.assertEqual(self.autumn_semester.year, 2016)
self.assertEqual(self.present_semester.year, 2017)
def test_semester_season(self):
self.assertEqual(self.autumn_semester.season, Semester.AUTUMN)
self.assertEqual(self.spring_semester.season, Semester.SPRING)
def test_subtracting_semesters(self):
same_semester_diff = self.present_semester - self.present_semester
same_season_diff = self.present_semester - self.autumn_semester
negative_diff = self.autumn_semester - self.present_semester
different_season_diff = self.autumn_semester - self.spring_semester
different_season_negative_diff = self.spring_semester - self.present_semester
self.assertEqual(
[same_semester_diff, same_season_diff, negative_diff, different_season_diff, different_season_negative_diff],
[0, 2, -2, 1, -3],
)
| 33.542373 | 121 | 0.646412 |
7940cdfb351f0f45d4bd3a973ea64f8c27cd749e | 5,970 | py | Python | app/ext/orm/orm_base.py | jonatasoli/fastapi-design-api-example | 16e620123d77506e6b4d6cc3947749f46bcd08be | [
"MIT"
] | 6 | 2021-06-21T19:38:07.000Z | 2022-01-27T14:53:32.000Z | app/ext/orm/orm_base.py | jonatasoli/fastapi-design-api-example | 16e620123d77506e6b4d6cc3947749f46bcd08be | [
"MIT"
] | null | null | null | app/ext/orm/orm_base.py | jonatasoli/fastapi-design-api-example | 16e620123d77506e6b4d6cc3947749f46bcd08be | [
"MIT"
] | 2 | 2021-06-27T13:22:07.000Z | 2022-02-26T12:21:13.000Z | from abc import ABCMeta
from typing import Any, Generic, Type, TypeVar, List
from fastapi.encoders import jsonable_encoder
from loguru import logger
from pydantic import BaseModel, parse_obj_as
from sqlalchemy.exc import DataError, DatabaseError, DisconnectionError, IntegrityError
from sqlalchemy.sql.expression import select, text
from ext.db.base_class import BaseModel
ModelType = TypeVar("ModelType", bound=BaseModel)
CreateSchemaType = TypeVar("CreateSchemaType", bound=BaseModel)
UpdateSchemaType = TypeVar("UpdateSchemaType", bound=BaseModel)
class CRUDBase(
Generic[ModelType, CreateSchemaType, UpdateSchemaType], metaclass=ABCMeta
):
def __init__(self, model: Type[ModelType]):
"""
CRUD object with default methods to Create, Read, Update (CRU).
**Parameters**
* `model`: A SQLAlchemy model class
* `schema`: A Pydantic model (schema) class
"""
self.model = model
def obj_in_to_db_obj(self, obj_in: Any):
obj_in_data = jsonable_encoder(obj_in)
return self.model(**obj_in_data)
def obj_in_to_db_obj_attrs(self, obj_in: Any, db_obj: Any):
obj_data = jsonable_encoder(db_obj)
if isinstance(obj_in, dict):
update_data = obj_in
else:
update_data = obj_in.dict(exclude_unset=True)
for field in obj_data:
if field in update_data:
setattr(db_obj, field, update_data[field])
return db_obj
async def list(self, query: Any = None, order_by: Any = None):
try:
async with self.Meta.session() as db:
full_query = select(self.model)
if query is not None:
if not isinstance(query, list):
query = [query]
full_query = full_query.filter(*query)
if order_by is not None:
full_query = full_query.order_by(text(order_by))
smtm = await db.execute(full_query)
items = smtm.scalars().all()
return parse_obj_as(List[self.Meta.response_list_type], items)
except (DataError, DatabaseError, DisconnectionError, IntegrityError) as err:
logger.error(f"SQLAlchemy error {err}")
except Exception as e:
logger.error(f"Error in dao {e}")
raise e
async def _get_query(self, query: Any):
try:
async with self.Meta.session() as db:
if not isinstance(query, list):
query = [query]
smtm = await db.execute(select(self.model).filter(*query))
return smtm.scalars().first()
except (DataError, DatabaseError, DisconnectionError, IntegrityError) as err:
logger.error(f"SQLAlchemy error {err}")
except Exception as e:
logger.error(f"Error in dao {e}")
raise e
async def _get(self, obj_id: Any):
try:
query = self.model.id == obj_id
return await self._get_query(query)
except (DataError, DatabaseError, DisconnectionError, IntegrityError) as err:
logger.error(f"SQLAlchemy error {err}")
except Exception as e:
logger.error(f"Error in dao {e}")
raise e
async def get(self, obj_id: Any):
try:
db_obj = await self._get(obj_id)
response = None
if db_obj:
response = self.Meta.response_get_type.from_orm(db_obj)
return response
except (DataError, DatabaseError, DisconnectionError, IntegrityError) as err:
logger.error(f"SQLAlchemy error {err}")
except Exception as e:
logger.error(f"Error in dao {e}")
raise e
async def get_query(self, query: Any):
try:
db_obj = await self._get_query(query)
response = None
if db_obj:
response = self.Meta.response_get_type.from_orm(db_obj)
return response
except (DataError, DatabaseError, DisconnectionError, IntegrityError) as err:
logger.error(f"SQLAlchemy error {err}")
except Exception as e:
logger.error(f"Error in dao {e}")
raise e
async def update_or_create(self, query: Any, obj_in: CreateSchemaType) -> ModelType:
result = await self.get_query(query)
if not result:
result = await self.create(obj_in)
created = True
else:
result = await self.update(result.id, obj_in)
created = False
return result, created
async def create(self, obj_in: CreateSchemaType) -> ModelType:
try:
data_db = self.obj_in_to_db_obj(obj_in=obj_in)
async with self.Meta.session() as db:
db.add(data_db)
await db.commit()
response = self.Meta.response_create_type.from_orm(data_db)
return response
except (DataError, DatabaseError, DisconnectionError, IntegrityError) as err:
logger.error(f"SQLAlchemy error {err}")
except Exception as e:
logger.error(f"Error in dao {e}")
raise e
async def update(self, obj_id: Any, obj_in: UpdateSchemaType) -> ModelType:
try:
db_obj = await self._get(obj_id)
response = None
if db_obj:
db_obj = self.obj_in_to_db_obj_attrs(obj_in, db_obj)
async with self.Meta.session() as db:
db.add(db_obj)
await db.commit()
response = self.Meta.response_update_type.from_orm(db_obj)
return response
except (DataError, DatabaseError, DisconnectionError, IntegrityError) as err:
logger.error(f"SQLAlchemy error {err}")
except Exception as e:
logger.error(f"Error in dao {e}")
raise e
| 35.963855 | 88 | 0.598827 |
7940ceaa61885a7365f2470309102778635822ad | 369 | py | Python | Python/Euler1.py | poc1673/Project-Euler-Exercises | 80044be236f56dd29d5db41296e0e3d683085a03 | [
"MIT"
] | null | null | null | Python/Euler1.py | poc1673/Project-Euler-Exercises | 80044be236f56dd29d5db41296e0e3d683085a03 | [
"MIT"
] | null | null | null | Python/Euler1.py | poc1673/Project-Euler-Exercises | 80044be236f56dd29d5db41296e0e3d683085a03 | [
"MIT"
] | null | null | null | def check_mod(number, mod_val):
if ((number % mod_val)==0) :
return number
else:
return 0
def get_mod_sum(max_val,mod_val):
return_val = 0
for i in range(1,max_val):
return_val = return_val + check_mod( i, mod_val )
return return_val
euler_1_results = get_mod_sum(1000,3)+ get_mod_sum(1000,5) - get_mod_sum(1000,15)
print(euler_1_results)
| 24.6 | 81 | 0.701897 |
7940cf9fc374d7add1579145f2b30e5bfe418350 | 425 | py | Python | src/veem/models/address.py | veeminc/Veem-python-sdk | 2f7527af0139a3f12e544fe2b51b3021df404f3c | [
"MIT"
] | 1 | 2021-07-05T22:52:46.000Z | 2021-07-05T22:52:46.000Z | src/veem/models/address.py | veeminc/Veem-python-sdk | 2f7527af0139a3f12e544fe2b51b3021df404f3c | [
"MIT"
] | 1 | 2020-09-15T16:25:39.000Z | 2020-09-15T16:25:39.000Z | src/veem/models/address.py | veeminc/Veem-python-sdk | 2f7527af0139a3f12e544fe2b51b3021df404f3c | [
"MIT"
] | 2 | 2021-08-11T18:05:08.000Z | 2022-02-06T08:20:49.000Z |
from veem.models.base import Base
class Address(Base):
def __init__(self,
line1=None,
line2=None,
city=None,
stateProvince=None,
postalCode=None,
**kwargs):
self.line1 = line1
self.line2 = line2
self.city = city
self.stateProvince = stateProvince
self.postalCode = postalCode
| 23.611111 | 42 | 0.508235 |
7940d074b28f983c9788f27ee07ef62b4efa333c | 2,034 | py | Python | eval2.py | myutman/deep-voice-conversion | d707d0ea54d73d2a2df53f2f73b6d23f4afc5231 | [
"MIT"
] | null | null | null | eval2.py | myutman/deep-voice-conversion | d707d0ea54d73d2a2df53f2f73b6d23f4afc5231 | [
"MIT"
] | null | null | null | eval2.py | myutman/deep-voice-conversion | d707d0ea54d73d2a2df53f2f73b6d23f4afc5231 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# /usr/bin/python2
from __future__ import print_function
import tensorflow as tf
from models import Net2
import argparse
from hparam import hparam as hp
from tensorpack.predict.base import OfflinePredictor
from tensorpack.predict.config import PredictConfig
from tensorpack.tfutils.sessinit import SaverRestore
from tensorpack.tfutils.sessinit import ChainInit
from data_load import Net2DataFlow
def get_eval_input_names():
#return ['x_mfccs', 'y_spec']
return ['x_mfccs', 'y_spec', 'y_mel']
def get_eval_output_names():
return ['net2/eval/summ_loss']
def eval(logdir1, logdir2):
# Load graph
model = Net2()
# dataflow
df = Net2DataFlow(hp.test2.data_path, hp.test2.batch_size)
ckpt1 = tf.train.latest_checkpoint(logdir1)
ckpt2 = tf.train.latest_checkpoint(logdir2)
session_inits = []
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
if ckpt1:
session_inits.append(SaverRestore(ckpt1, ignore=['global_step']))
pred_conf = PredictConfig(
model=model,
input_names=get_eval_input_names(),
output_names=get_eval_output_names(),
session_init=ChainInit(session_inits))
predictor = OfflinePredictor(pred_conf)
x_mfccs, y_spec, y_mel = next(df().get_data())
summ_loss, = predictor(x_mfccs, y_spec, y_mel)
writer = tf.summary.FileWriter(logdir2)
writer.add_summary(summ_loss)
writer.close()
def get_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('case1', type=str, help='experiment case name of train1')
parser.add_argument('case2', type=str, help='experiment case name of train2')
arguments = parser.parse_args()
return arguments
if __name__ == '__main__':
args = get_arguments()
hp.set_hparam_yaml(args.case2)
logdir_train1 = '{}/{}/train1'.format(hp.logdir_path, args.case1)
logdir_train2 = '{}/{}/train2'.format(hp.logdir_path, args.case2)
eval(logdir1=logdir_train1, logdir2=logdir_train2)
print("Done")
| 28.25 | 81 | 0.717797 |
7940d0919da910b05d8f8b2568532d402bbece43 | 3,714 | py | Python | src/python/pants/backend/codegen/protobuf/lint/buf/rules.py | bastianwegge/pants | 43f0b90d41622bee0ed22249dbaffb3ff4ad2eb2 | [
"Apache-2.0"
] | null | null | null | src/python/pants/backend/codegen/protobuf/lint/buf/rules.py | bastianwegge/pants | 43f0b90d41622bee0ed22249dbaffb3ff4ad2eb2 | [
"Apache-2.0"
] | 14 | 2020-09-26T02:01:56.000Z | 2022-03-30T10:19:28.000Z | src/python/pants/backend/codegen/protobuf/lint/buf/rules.py | bastianwegge/pants | 43f0b90d41622bee0ed22249dbaffb3ff4ad2eb2 | [
"Apache-2.0"
] | null | null | null | # Copyright 2022 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from dataclasses import dataclass
from pants.backend.codegen.protobuf.lint.buf.skip_field import SkipBufField
from pants.backend.codegen.protobuf.lint.buf.subsystem import BufSubsystem
from pants.backend.codegen.protobuf.target_types import (
ProtobufDependenciesField,
ProtobufSourceField,
)
from pants.core.goals.lint import LintResult, LintResults, LintTargetsRequest
from pants.core.util_rules.external_tool import DownloadedExternalTool, ExternalToolRequest
from pants.core.util_rules.source_files import SourceFilesRequest
from pants.core.util_rules.stripped_source_files import StrippedSourceFiles
from pants.engine.fs import Digest, MergeDigests
from pants.engine.platform import Platform
from pants.engine.process import FallibleProcessResult, Process
from pants.engine.rules import Get, MultiGet, collect_rules, rule
from pants.engine.target import FieldSet, Target, TransitiveTargets, TransitiveTargetsRequest
from pants.engine.unions import UnionRule
from pants.util.logging import LogLevel
from pants.util.strutil import pluralize
@dataclass(frozen=True)
class BufFieldSet(FieldSet):
required_fields = (ProtobufSourceField,)
sources: ProtobufSourceField
dependencies: ProtobufDependenciesField
@classmethod
def opt_out(cls, tgt: Target) -> bool:
return tgt.get(SkipBufField).value
class BufRequest(LintTargetsRequest):
field_set_type = BufFieldSet
name = BufSubsystem.options_scope
@rule(desc="Lint with Buf", level=LogLevel.DEBUG)
async def run_buf(request: BufRequest, buf: BufSubsystem) -> LintResults:
if buf.skip:
return LintResults([], linter_name=request.name)
transitive_targets = await Get(
TransitiveTargets,
TransitiveTargetsRequest((field_set.address for field_set in request.field_sets)),
)
all_stripped_sources_request = Get(
StrippedSourceFiles,
SourceFilesRequest(
tgt[ProtobufSourceField]
for tgt in transitive_targets.closure
if tgt.has_field(ProtobufSourceField)
),
)
target_stripped_sources_request = Get(
StrippedSourceFiles,
SourceFilesRequest(
(field_set.sources for field_set in request.field_sets),
for_sources_types=(ProtobufSourceField,),
enable_codegen=True,
),
)
download_buf_get = Get(
DownloadedExternalTool, ExternalToolRequest, buf.get_request(Platform.current)
)
target_sources_stripped, all_sources_stripped, downloaded_buf = await MultiGet(
target_stripped_sources_request, all_stripped_sources_request, download_buf_get
)
input_digest = await Get(
Digest,
MergeDigests(
(
target_sources_stripped.snapshot.digest,
all_sources_stripped.snapshot.digest,
downloaded_buf.digest,
)
),
)
process_result = await Get(
FallibleProcessResult,
Process(
argv=[
downloaded_buf.exe,
"lint",
*buf.args,
"--path",
",".join(target_sources_stripped.snapshot.files),
],
input_digest=input_digest,
description=f"Run Buf on {pluralize(len(request.field_sets), 'file')}.",
level=LogLevel.DEBUG,
),
)
result = LintResult.from_fallible_process_result(process_result)
return LintResults([result], linter_name=request.name)
def rules():
return [*collect_rules(), UnionRule(LintTargetsRequest, BufRequest)]
| 33.763636 | 93 | 0.710285 |
7940d1244c0eac7ed2ab14c8fd461940d0f0ea00 | 26,256 | py | Python | test/integration/smoke/test_routers.py | K0zka/cloudstack | ed099c3f964e4b18a3c431b59cdb63533ec91d81 | [
"Apache-2.0"
] | null | null | null | test/integration/smoke/test_routers.py | K0zka/cloudstack | ed099c3f964e4b18a3c431b59cdb63533ec91d81 | [
"Apache-2.0"
] | 6 | 2020-11-16T20:46:02.000Z | 2022-02-01T01:06:41.000Z | test/integration/smoke/test_routers.py | pkoistin/czo | 43cf1da865c1e4ed6523fb5b2ba315a547fac79f | [
"Apache-2.0"
] | null | null | null | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
""" BVT tests for routers
"""
# Import Local Modules
from marvin.codes import FAILED
from marvin.cloudstackTestCase import cloudstackTestCase
from marvin.cloudstackAPI import (stopRouter,
restartNetwork,
startRouter,
rebootRouter)
from marvin.lib.utils import (cleanup_resources,
get_process_status,
get_host_credentials)
from marvin.lib.base import (Account,
ServiceOffering,
VirtualMachine)
from marvin.lib.common import (get_domain,
get_zone,
get_template,
list_hosts,
list_routers,
list_networks,
list_zones,
list_vlan_ipranges)
from nose.plugins.attrib import attr
# Import System modules
import time
_multiprocess_shared_ = True
class TestRouterServices(cloudstackTestCase):
@classmethod
def setUpClass(cls):
testClient = super(TestRouterServices, cls).getClsTestClient()
cls.apiclient = testClient.getApiClient()
cls.services = testClient.getParsedTestDataConfig()
# Get Zone, Domain and templates
cls.domain = get_domain(cls.apiclient)
cls.zone = get_zone(cls.apiclient, testClient.getZoneForTests())
cls.services['mode'] = cls.zone.networktype
template = get_template(
cls.apiclient,
cls.zone.id,
cls.services["ostype"]
)
if template == FAILED:
cls.fail(
"get_template() failed to return template\
with description %s" %
cls.services["ostype"])
cls.services["virtual_machine"]["zoneid"] = cls.zone.id
# Create an account, network, VM and IP addresses
cls.account = Account.create(
cls.apiclient,
cls.services["account"],
domainid=cls.domain.id
)
cls.service_offering = ServiceOffering.create(
cls.apiclient,
cls.services["service_offerings"]
)
cls.vm_1 = VirtualMachine.create(
cls.apiclient,
cls.services["virtual_machine"],
templateid=template.id,
accountid=cls.account.name,
domainid=cls.account.domainid,
serviceofferingid=cls.service_offering.id
)
cls.cleanup = [
cls.account,
cls.service_offering
]
return
@classmethod
def tearDownClass(cls):
try:
cls.apiclient = super(
TestRouterServices,
cls
).getClsTestClient().getApiClient()
# Clean up, terminate the created templates
cleanup_resources(cls.apiclient, cls.cleanup)
except Exception as e:
raise Exception("Warning: Exception during cleanup : %s" % e)
return
def setUp(self):
self.apiclient = self.testClient.getApiClient()
self.hypervisor = self.testClient.getHypervisorInfo()
return
@attr(tags=["advanced", "basic", "sg", "smoke"], required_hardware="true")
def test_01_router_internal_basic(self):
"""Test router internal basic zone
"""
# Validate the following
# 1. Router only does dhcp
# 2. Verify that ports 67 (DHCP) and 53 (DNS) are open on UDP
# by checking status of dnsmasq process
# Find router associated with user account
if self.zone.networktype == "Basic":
list_router_response = list_routers(
self.apiclient,
listall="true"
)
else:
list_router_response = list_routers(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(list_router_response, list),
True,
"Check list response returns a valid list"
)
router = list_router_response[0]
hosts = list_hosts(
self.apiclient,
zoneid=router.zoneid,
type='Routing',
state='Up',
id=router.hostid
)
self.assertEqual(
isinstance(hosts, list),
True,
"Check list host returns a valid list"
)
host = hosts[0]
self.debug("Router ID: %s, state: %s" % (router.id, router.state))
self.assertEqual(
router.state,
'Running',
"Check list router response for router state"
)
if self.hypervisor.lower() in ('vmware', 'hyperv'):
result = get_process_status(
self.apiclient.connection.mgtSvr,
22,
self.apiclient.connection.user,
self.apiclient.connection.passwd,
router.linklocalip,
"service dnsmasq status",
hypervisor=self.hypervisor
)
else:
try:
host.user, host.passwd = get_host_credentials(
self.config, host.ipaddress)
result = get_process_status(
host.ipaddress,
22,
host.user,
host.passwd,
router.linklocalip,
"service dnsmasq status"
)
except KeyError:
self.skipTest(
"Marvin configuration has no host credentials to\
check router services")
res = str(result)
self.debug("Dnsmasq process status: %s" % res)
self.assertEqual(
res.count("running"),
1,
"Check dnsmasq service is running or not"
)
return
@attr(tags=["advanced", "advancedns"], required_hardware="false")
def test_02_router_internal_adv(self):
"""Test router internal advanced zone
"""
# Validate the following
# 1. Router does dhcp, dns, gateway, LB, PF, FW
# 2. verify that dhcp, dns ports are open on UDP
# 3. dnsmasq, haproxy processes should be running
# Find router associated with user account
list_router_response = list_routers(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(list_router_response, list),
True,
"Check list response returns a valid list"
)
router = list_router_response[0]
hosts = list_hosts(
self.apiclient,
zoneid=router.zoneid,
type='Routing',
state='Up',
id=router.hostid
)
self.assertEqual(
isinstance(hosts, list),
True,
"Check list response returns a valid list"
)
host = hosts[0]
self.debug("Router ID: %s, state: %s" % (router.id, router.state))
self.assertEqual(
router.state,
'Running',
"Check list router response for router state"
)
if self.hypervisor.lower() in ('vmware', 'hyperv'):
result = get_process_status(
self.apiclient.connection.mgtSvr,
22,
self.apiclient.connection.user,
self.apiclient.connection.passwd,
router.linklocalip,
"service dnsmasq status",
hypervisor=self.hypervisor
)
else:
try:
host.user, host.passwd = get_host_credentials(
self.config, host.ipaddress)
result = get_process_status(
host.ipaddress,
22,
host.user,
host.passwd,
router.linklocalip,
"service dnsmasq status"
)
except KeyError:
self.skipTest(
"Marvin configuration has no host credentials\
to check router services")
res = str(result)
self.debug("Dnsmasq process status: %s" % res)
self.assertEqual(
res.count("running"),
1,
"Check dnsmasq service is running or not"
)
if self.hypervisor.lower() in ('vmware', 'hyperv'):
result = get_process_status(
self.apiclient.connection.mgtSvr,
22,
self.apiclient.connection.user,
self.apiclient.connection.passwd,
router.linklocalip,
"service haproxy status",
hypervisor=self.hypervisor
)
else:
try:
host.user, host.passwd = get_host_credentials(
self.config, host.ipaddress)
result = get_process_status(
host.ipaddress,
22,
host.user,
host.passwd,
router.linklocalip,
"service haproxy status"
)
except KeyError:
self.skipTest(
"Marvin configuration has no host credentials\
to check router services")
res = str(result)
self.assertEqual(
res.count("running"),
1,
"Check haproxy service is running or not"
)
self.debug("Haproxy process status: %s" % res)
return
@attr(tags=["advanced", "advancedns", "smoke"], required_hardware="false")
def test_03_restart_network_cleanup(self):
"""Test restart network
"""
# Validate the following
# 1. When cleanup = true, router is destroyed and a new one created
# 2. New router should have the same public IP
# Find router associated with user account
list_router_response = list_routers(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(list_router_response, list),
True,
"Check list response returns a valid list"
)
router = list_router_response[0]
# Store old values before restart
old_publicip = router.publicip
timeout = 10
# Network should be in Implemented or Setup stage before restart
while True:
networks = list_networks(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(networks, list),
True,
"Check list response returns a valid list"
)
network = networks[0]
if network.state in ["Implemented", "Setup"]:
break
elif timeout == 0:
break
else:
time.sleep(self.services["sleep"])
timeout = timeout - 1
self.debug(
"Restarting network with ID: %s, Network state: %s" % (
network.id,
network.state
))
cmd = restartNetwork.restartNetworkCmd()
cmd.id = network.id
cmd.cleanup = True
self.apiclient.restartNetwork(cmd)
# Get router details after restart
list_router_response = list_routers(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(list_router_response, list),
True,
"Check list response returns a valid list"
)
router = list_router_response[0]
self.assertEqual(
router.publicip,
old_publicip,
"Public IP of the router should remain same after network restart"
)
return
@attr(tags=["advanced", "advancedns", "smoke"], required_hardware="true")
def test_04_restart_network_wo_cleanup(self):
"""Test restart network without cleanup
"""
# Validate the following
# 1. When cleanup = false, router is restarted and
# all services inside the router are restarted
# 2. check 'uptime' to see if the actual restart happened
timeout = 10
# Network should be in Implemented or Setup stage before restart
while True:
networks = list_networks(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(networks, list),
True,
"Check list response returns a valid list"
)
network = networks[0]
if network.state in ["Implemented", "Setup"]:
break
elif timeout == 0:
break
else:
time.sleep(self.services["sleep"])
timeout = timeout - 1
self.debug(
"Restarting network with ID: %s, Network state: %s" % (
network.id,
network.state
))
cmd = restartNetwork.restartNetworkCmd()
cmd.id = network.id
cmd.cleanup = False
self.apiclient.restartNetwork(cmd)
# Get router details after restart
list_router_response = list_routers(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(list_router_response, list),
True,
"Check list response returns a valid list"
)
router = list_router_response[0]
hosts = list_hosts(
self.apiclient,
zoneid=router.zoneid,
type='Routing',
state='Up',
id=router.hostid
)
self.assertEqual(
isinstance(hosts, list),
True,
"Check list response returns a valid list"
)
host = hosts[0]
if self.hypervisor.lower() in ('vmware', 'hyperv'):
res = get_process_status(
self.apiclient.connection.mgtSvr,
22,
self.apiclient.connection.user,
self.apiclient.connection.passwd,
router.linklocalip,
"uptime",
hypervisor=self.hypervisor
)
else:
try:
host.user, host.passwd = get_host_credentials(
self.config, host.ipaddress)
res = get_process_status(
host.ipaddress,
22,
host.user,
host.passwd,
router.linklocalip,
"uptime"
)
except KeyError:
self.skipTest(
"Marvin configuration has no host credentials\
to check router services")
# res = 12:37:14 up 1 min, 0 users, load average: 0.61, 0.22, 0.08
# Split result to check the uptime
result = res[0].split()
self.debug("Router Uptime: %s" % result)
self.assertEqual(
str(result[1]),
'up',
"Check router is running or not"
)
if str(result[3]) == "min,":
self.assertEqual(
(int(result[2]) < 3),
True,
"Check uptime is less than 3 mins or not"
)
else:
self.assertEqual(
str(result[3]),
'sec,',
"Check uptime is in seconds"
)
return
@attr(tags=["advanced", "advancedns", "smoke"], required_hardware="false")
def test_05_router_basic(self):
"""Test router basic setup
"""
# Validate the following:
# 1. verify that listRouters returned a 'Running' router
# 2. router will have dns same as that seen in listZones
# 3. router will have a guestIP and a linkLocalIp"
list_router_response = list_routers(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(list_router_response, list),
True,
"Check list response returns a valid list"
)
self.assertNotEqual(
len(list_router_response),
0,
"Check list router response"
)
for router in list_router_response:
self.assertEqual(
router.state,
'Running',
"Check list router response for router state"
)
zones = list_zones(
self.apiclient,
id=router.zoneid
)
self.assertEqual(
isinstance(zones, list),
True,
"Check list response returns a valid list"
)
zone = zones[0]
self.assertEqual(
router.dns1,
zone.dns1,
"Compare DNS1 of router and zone"
)
self.assertEqual(
router.dns2,
zone.dns2,
"Compare DNS2 of router and zone"
)
self.assertEqual(
hasattr(router, 'guestipaddress'),
True,
"Check whether router has guest IP field"
)
self.assertEqual(
hasattr(router, 'linklocalip'),
True,
"Check whether router has link local IP field"
)
return
@attr(tags=["advanced", "advancedns", "smoke"], required_hardware="false")
def test_06_router_advanced(self):
"""Test router advanced setup
"""
# Validate the following
# 1. verify that listRouters returned a 'Running' router
# 2. router will have dns and gateway as in listZones, listVlanIpRanges
# 3. router will have guest,public and linklocal IPs
list_router_response = list_routers(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(list_router_response, list),
True,
"Check list response returns a valid list"
)
self.assertNotEqual(
len(list_router_response),
0,
"Check list router response"
)
for router in list_router_response:
self.assertEqual(
router.state,
'Running',
"Check list router response for router state"
)
zones = list_zones(
self.apiclient,
id=router.zoneid
)
self.assertEqual(
isinstance(zones, list),
True,
"Check list response returns a valid list"
)
zone = zones[0]
self.assertEqual(
router.dns1,
zone.dns1,
"Compare DNS1 of router and zone"
)
self.assertEqual(
router.dns2,
zone.dns2,
"Compare DNS2 of router and zone"
)
self.assertEqual(
hasattr(router, 'guestipaddress'),
True,
"Check whether router has guest IP field"
)
self.assertEqual(
hasattr(router, 'linklocalip'),
True,
"Check whether router has link local IP field"
)
# Fetch corresponding ip ranges information from listVlanIpRanges
ipranges_response = list_vlan_ipranges(
self.apiclient,
zoneid=router.zoneid
)
self.assertEqual(
isinstance(ipranges_response, list),
True,
"Check list response returns a valid list"
)
iprange = ipranges_response[0]
self.assertEqual(
router.gateway,
iprange.gateway,
"Check gateway with that of corresponding IP range"
)
return
@attr(tags=["advanced", "advancedns", "smoke"], required_hardware="false")
def test_07_stop_router(self):
"""Test stop router
"""
# Validate the following
# 1. listRouter should report the router for the account as stopped
list_router_response = list_routers(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(list_router_response, list),
True,
"Check list response returns a valid list"
)
router = list_router_response[0]
self.debug("Stopping the router with ID: %s" % router.id)
# Stop the router
cmd = stopRouter.stopRouterCmd()
cmd.id = router.id
self.apiclient.stopRouter(cmd)
# List routers to check state of router
router_response = list_routers(
self.apiclient,
id=router.id
)
self.assertEqual(
isinstance(router_response, list),
True,
"Check list response returns a valid list"
)
# List router should have router in stopped state
self.assertEqual(
router_response[0].state,
'Stopped',
"Check list router response for router state"
)
return
@attr(tags=["advanced", "advancedns", "smoke"], required_hardware="false")
def test_08_start_router(self):
"""Test start router
"""
# Validate the following
# 1. listRouter should report the router for the account as stopped
list_router_response = list_routers(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(list_router_response, list),
True,
"Check list response returns a valid list"
)
router = list_router_response[0]
self.debug("Starting the router with ID: %s" % router.id)
# Start the router
cmd = startRouter.startRouterCmd()
cmd.id = router.id
self.apiclient.startRouter(cmd)
# List routers to check state of router
router_response = list_routers(
self.apiclient,
id=router.id
)
self.assertEqual(
isinstance(router_response, list),
True,
"Check list response returns a valid list"
)
# List router should have router in running state
self.assertEqual(
router_response[0].state,
'Running',
"Check list router response for router state"
)
return
def verifyRouterResponse(self, router_response, ip):
if (router_response) and (isinstance(router_response, list)) and \
(router_response[0].state == "Running") and \
(router_response[0].publicip == ip):
return True
return False
@attr(tags=["advanced", "advancedns", "smoke"], required_hardware="false")
def test_09_reboot_router(self):
"""Test reboot router
"""
# Validate the following
# 1. listRouter should report the router for the account as stopped
list_router_response = list_routers(
self.apiclient,
account=self.account.name,
domainid=self.account.domainid
)
self.assertEqual(
isinstance(list_router_response, list),
True,
"Check list response returns a valid list"
)
router = list_router_response[0]
public_ip = router.publicip
self.debug("Rebooting the router with ID: %s" % router.id)
# Reboot the router
cmd = rebootRouter.rebootRouterCmd()
cmd.id = router.id
self.apiclient.rebootRouter(cmd)
# List routers to check state of router
retries_cnt = 6
while retries_cnt >= 0:
router_response = list_routers(
self.apiclient,
id=router.id
)
if self.verifyRouterResponse(router_response, public_ip):
self.debug("Router is running successfully after reboot")
return
time.sleep(10)
retries_cnt = retries_cnt - 1
self.fail(
"Router response after reboot is either is invalid\
or in stopped state")
return
| 32.861076 | 79 | 0.527994 |
7940d16244ac84df22dfab08da253baf0342a122 | 3,447 | py | Python | userbot/modules/blacklist.py | LetterIce/OUBnew | 6b059ed80976448f0611d73a77e648668518f131 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | 37 | 2020-02-22T15:37:16.000Z | 2022-03-03T13:54:04.000Z | userbot/modules/blacklist.py | LetterIce/OUBnew | 6b059ed80976448f0611d73a77e648668518f131 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | 26 | 2020-03-30T23:03:16.000Z | 2021-08-30T10:09:08.000Z | userbot/modules/blacklist.py | LetterIce/OUBnew | 6b059ed80976448f0611d73a77e648668518f131 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | 279 | 2020-02-22T06:38:42.000Z | 2022-03-03T13:58:10.000Z | # Copyright (C) 2019 The Raphielscape Company LLC.
#
# Licensed under the Raphielscape Public License, Version 1.d (the "License");
# you may not use this file except in compliance with the License.
# port to userbot from uniborg by @keselekpermen69
import asyncio
import io
import re
import userbot.modules.sql_helper.blacklist_sql as sql
from telethon import events, utils
from telethon.tl import types, functions
from userbot import CMD_HELP, bot
from userbot.events import register
@register(incoming=True, disable_edited=True, disable_errors=True)
async def on_new_message(event):
# TODO: exempt admins from locks
name = event.raw_text
snips = sql.get_chat_blacklist(event.chat_id)
for snip in snips:
pattern = r"( |^|[^\w])" + re.escape(snip) + r"( |$|[^\w])"
if re.search(pattern, name, flags=re.IGNORECASE):
try:
await event.delete()
except Exception as e:
await event.reply("I do not have DELETE permission in this chat")
await sleep(1)
await reply.delete()
sql.rm_from_blacklist(event.chat_id, snip.lower())
break
@register(outgoing=True, pattern="^.addbl(?: |$)(.*)")
async def on_add_black_list(addbl):
text = addbl.pattern_match.group(1)
to_blacklist = list(
{trigger.strip() for trigger in text.split("\n") if trigger.strip()}
)
for trigger in to_blacklist:
sql.add_to_blacklist(addbl.chat_id, trigger.lower())
await addbl.edit(
"`Added` **{}** `to the blacklist in the current chat`".format(text)
)
@register(outgoing=True, pattern="^.listbl(?: |$)(.*)")
async def on_view_blacklist(listbl):
all_blacklisted = sql.get_chat_blacklist(listbl.chat_id)
OUT_STR = "Blacklists in the Current Chat:\n"
if len(all_blacklisted) > 0:
for trigger in all_blacklisted:
OUT_STR += f"`{trigger}`\n"
else:
OUT_STR = "`There are no blacklist in current chat.`"
if len(OUT_STR) > 4096:
with io.BytesIO(str.encode(OUT_STR)) as out_file:
out_file.name = "blacklist.text"
await listbl.client.send_file(
listbl.chat_id,
out_file,
force_document=True,
allow_cache=False,
caption="BlackLists in the Current Chat",
reply_to=listbl,
)
await listbl.delete()
else:
await listbl.edit(OUT_STR)
@register(outgoing=True, pattern="^.rmbl(?: |$)(.*)")
async def on_delete_blacklist(rmbl):
text = rmbl.pattern_match.group(1)
to_unblacklist = list(
{trigger.strip() for trigger in text.split("\n") if trigger.strip()}
)
successful = 0
for trigger in to_unblacklist:
if sql.rm_from_blacklist(rmbl.chat_id, trigger.lower()):
successful += 1
if not successful:
await rmbl.edit("`Blacklist` **{}** `doesn't exist.`".format(text))
else:
await rmbl.edit("`Blacklist` **{}** `was deleted successfully`".format(text))
CMD_HELP.update(
{
"blacklist": ".listbl\
\nUsage: Lists all active userbot blacklist in a chat.\
\n\n.addbl <keyword>\
\nUsage: Saves the message to the 'blacklist keyword'.\
\nThe bot will delete to the message whenever 'blacklist keyword' is mentioned.\
\n\n.rmbl <keyword>\
\nUsage: Stops the specified blacklist."
}
)
| 33.144231 | 85 | 0.631274 |
7940d215729bb1d0d44578f29e286b297a252b4f | 3,737 | py | Python | autobot/handler/response/replymarkupbuilder.py | andreacioni/AutoBot | 6cfcaf3a36adf6ba15c93f517fbc08ac9f93b389 | [
"BSD-3-Clause"
] | 1 | 2019-05-16T10:08:35.000Z | 2019-05-16T10:08:35.000Z | autobot/handler/response/replymarkupbuilder.py | andreacioni/AutoBot | 6cfcaf3a36adf6ba15c93f517fbc08ac9f93b389 | [
"BSD-3-Clause"
] | null | null | null | autobot/handler/response/replymarkupbuilder.py | andreacioni/AutoBot | 6cfcaf3a36adf6ba15c93f517fbc08ac9f93b389 | [
"BSD-3-Clause"
] | 2 | 2018-12-24T23:51:28.000Z | 2019-05-16T15:53:56.000Z | import logging
from telegram import ReplyKeyboardMarkup, ReplyKeyboardRemove, KeyboardButton
from autobot import AutoBotConstants
from autobot.configuration import AutoBotConfig
class ReplyMarkupBuilder(object):
_logger = logging.getLogger(__name__)
@classmethod
def build_keyboard(cls, config, entry):
"""Build the keyboard according to the entry's specs.
This method returns:
- None: keyboard need to be left on the screen (if already displayed)
- Instance of ReplyKeyboardMarkup that holds the new keyboard to be displayed
- Instance of ReplyKeyboardRemove means that a currently displayed keyboard need to be remove"""
button_list = cls._build_keyboard_markup(config, entry)
if button_list is not None:
if button_list: # If button_list is a not empty array we proceed to build the keyboard
keyboard_markup = ReplyKeyboardMarkup(\
button_list, \
entry[AutoBotConfig.RESIZE_KEYBOARD]\
if AutoBotConfig.RESIZE_KEYBOARD in entry else False, \
entry[AutoBotConfig.ONE_TIME_KEYBOARD]\
if AutoBotConfig.ONE_TIME_KEYBOARD in entry else False)
else:
keyboard_markup = None # Leave the previous keyboard. This is the default behaviour
else:
keyboard_markup = ReplyKeyboardRemove() # Remove the keyboard if KEYBOARD_OPTIONS = None (null)
return keyboard_markup
@classmethod
def _build_keyboard_markup(cls, config, entry):
"""This method returns:
- None: remove previous defined keyboard, if exists
- []: leaving the previous defined keyboard, if exists
- [[...],...]: new keyboard"""
reply_keyboard = []
# Checking type of KEYBOARD_OPTIONS parameter
if entry[AutoBotConfig.KEYBOARD_OPTIONS] is None:
reply_keyboard = None
elif isinstance(entry[AutoBotConfig.KEYBOARD_OPTIONS], list) or \
isinstance(entry[AutoBotConfig.KEYBOARD_OPTIONS], str):
# If "keyboard_options" field is a string we search by "id"
# for a keyboard previously defined in another entry
if not isinstance(entry[AutoBotConfig.KEYBOARD_OPTIONS], list):
ref_id = entry[AutoBotConfig.KEYBOARD_OPTIONS]
entry[AutoBotConfig.KEYBOARD_OPTIONS] = config[ref_id][AutoBotConfig.KEYBOARD_OPTIONS]
# If KEYBOARD_OPTIONS is a not empty list we proceed to keyboard button building,
# otherwise we return an empty array (that, in this context, means leaving a previously
# defined keyboard)
if entry[AutoBotConfig.KEYBOARD_OPTIONS]:
# Then we create the appropriate KeyboardButton istance.
# Now we add the '/' to every command found in keyboard
for row in entry[AutoBotConfig.KEYBOARD_OPTIONS]:
button_list = []
for response_id in row:
if config[response_id][AutoBotConfig.HANDLER_TYPE] == 'COMMAND':
button_list.append(KeyboardButton('/' + config[response_id][AutoBotConfig.ON]))
else:
button_list.append(KeyboardButton(config[response_id][AutoBotConfig.ON]))
reply_keyboard.append(button_list)
else:
cls._logger.warning('Invalid KEYBOARD_OPTIONS parameter in entry: %s. Removing previously defined keyboard', entry[AutoBotConfig.ID])
return reply_keyboard | 49.171053 | 145 | 0.630185 |
7940d3022948e6b52d6395c21a6e86f12c270f0a | 431 | py | Python | Python/Algorithms/50.py | DimitrisJim/leetcode_solutions | 765ea578748f8c9b21243dec9dc8a16163e85c0c | [
"Unlicense"
] | 2 | 2021-01-15T17:22:54.000Z | 2021-05-16T19:58:02.000Z | Python/Algorithms/50.py | DimitrisJim/leetcode_solutions | 765ea578748f8c9b21243dec9dc8a16163e85c0c | [
"Unlicense"
] | null | null | null | Python/Algorithms/50.py | DimitrisJim/leetcode_solutions | 765ea578748f8c9b21243dec9dc8a16163e85c0c | [
"Unlicense"
] | null | null | null | class Solution:
def myPow(self, x: float, n: int) -> float:
if x == 0:
return 0
# flip sign of exponent.
pos_exp, n = (True, n) if n > 0 else (False, -n)
result = 1
while n > 0:
if n & 1:
result *= x
x *= x
n //= 2
# a ** (-b) => 1 / a ** b
if pos_exp:
return result
return 1 / result
| 22.684211 | 56 | 0.38051 |
7940d3399168414cb842b828d084456674329ec3 | 1,922 | py | Python | bin/thresholder.py | a7mad3akef/ProjectBlog | 6cabd7d6d025af6a51fca49915b75987fc0284c5 | [
"MIT"
] | 1 | 2018-02-04T22:52:32.000Z | 2018-02-04T22:52:32.000Z | bin/thresholder.py | a7mad3akef/ProjectBlog | 6cabd7d6d025af6a51fca49915b75987fc0284c5 | [
"MIT"
] | null | null | null | bin/thresholder.py | a7mad3akef/ProjectBlog | 6cabd7d6d025af6a51fca49915b75987fc0284c5 | [
"MIT"
] | null | null | null | #!/home/k0f4/Desktop/Blog-API-with-Django-Rest-Framework/bin/python2
#
# The Python Imaging Library
# $Id$
#
# this demo script illustrates how a 1-bit BitmapImage can be used
# as a dynamically updated overlay
#
try:
from tkinter import *
except ImportError:
from Tkinter import *
from PIL import Image, ImageTk
import sys
#
# an image viewer
class UI(Frame):
def __init__(self, master, im, value=128):
Frame.__init__(self, master)
self.image = im
self.value = value
self.canvas = Canvas(self, width=im.size[0], height=im.size[1])
self.backdrop = ImageTk.PhotoImage(im)
self.canvas.create_image(0, 0, image=self.backdrop, anchor=NW)
self.canvas.pack()
scale = Scale(self, orient=HORIZONTAL, from_=0, to=255,
resolution=1, command=self.update_scale, length=256)
scale.set(value)
scale.bind("<ButtonRelease-1>", self.redraw)
scale.pack()
# uncomment the following line for instant feedback (might
# be too slow on some platforms)
# self.redraw()
def update_scale(self, value):
self.value = float(value)
self.redraw()
def redraw(self, event=None):
# create overlay (note the explicit conversion to mode "1")
im = self.image.point(lambda v, t=self.value: v >= t, "1")
self.overlay = ImageTk.BitmapImage(im, foreground="green")
# update canvas
self.canvas.delete("overlay")
self.canvas.create_image(0, 0, image=self.overlay, anchor=NW,
tags="overlay")
# --------------------------------------------------------------------
# main
if len(sys.argv) != 2:
print("Usage: thresholder file")
sys.exit(1)
root = Tk()
im = Image.open(sys.argv[1])
if im.mode != "L":
im = im.convert("L")
# im.thumbnail((320,200))
UI(root, im).pack()
root.mainloop()
| 24.329114 | 74 | 0.596254 |
7940d3f5e5385c7f8f43c465f9dbda144ca51f8d | 2,059 | py | Python | utils/models/vgg16.py | pksvision/Video-Dehazing-Pytorch | f318114d27c9b4e36c605027f351b6035fad9140 | [
"MIT"
] | null | null | null | utils/models/vgg16.py | pksvision/Video-Dehazing-Pytorch | f318114d27c9b4e36c605027f351b6035fad9140 | [
"MIT"
] | null | null | null | utils/models/vgg16.py | pksvision/Video-Dehazing-Pytorch | f318114d27c9b4e36c605027f351b6035fad9140 | [
"MIT"
] | null | null | null | import torch
import torch.nn as nn
import torch.nn.functional as F
class Vgg16(torch.nn.Module):
def __init__(self):
super(Vgg16, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
def forward(self, X):
h = F.relu(self.conv1_1(X))
h = F.relu(self.conv1_2(h))
relu1_2 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv2_1(h))
h = F.relu(self.conv2_2(h))
relu2_2 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv3_1(h))
h = F.relu(self.conv3_2(h))
h = F.relu(self.conv3_3(h))
relu3_3 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv4_1(h))
h = F.relu(self.conv4_2(h))
h = F.relu(self.conv4_3(h))
relu4_3 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv5_1(h))
h = F.relu(self.conv5_2(h))
h = F.relu(self.conv5_3(h))
relu5_3 = h
return [relu1_2, relu2_2, relu3_3, relu4_3, relu5_3] | 36.767857 | 78 | 0.603205 |
7940d5426e0a4603cd00c882e25f42ffbb5de7a2 | 402,128 | py | Python | results/v200_cuda70_k40c.py | supreethms1809/magma-2.2.0 | b713d0b0a7a4724847e3a4050987c5ea9e7a7279 | [
"BSD-3-Clause"
] | 31 | 2015-06-19T14:41:12.000Z | 2021-02-15T12:47:57.000Z | results/v200_cuda70_k40c.py | supreethms1809/magma-2.2.0 | b713d0b0a7a4724847e3a4050987c5ea9e7a7279 | [
"BSD-3-Clause"
] | 3 | 2020-01-02T05:21:16.000Z | 2020-01-07T20:04:05.000Z | results/v200_cuda70_k40c.py | supreethms1809/magma-2.2.0 | b713d0b0a7a4724847e3a4050987c5ea9e7a7279 | [
"BSD-3-Clause"
] | 17 | 2015-04-01T14:26:48.000Z | 2021-12-27T06:12:15.000Z | import numpy
from numpy import array, nan, inf
version = '2.0.0'
cuda = '7.0'
device = 'Kepler K40c'
cpu = '2x8 core Sandy Bridge E5-2670'
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/cgeqrf.txt
# numactl --interleave=all ../testing/testing_cgeqrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
cgeqrf = array([
[ 10, 10, nan, nan, 0.03, 0.00, nan ],
[ 20, 20, nan, nan, 0.21, 0.00, nan ],
[ 30, 30, nan, nan, 0.63, 0.00, nan ],
[ 40, 40, nan, nan, 1.38, 0.00, nan ],
[ 50, 50, nan, nan, 2.33, 0.00, nan ],
[ 60, 60, nan, nan, 3.39, 0.00, nan ],
[ 70, 70, nan, nan, 1.95, 0.00, nan ],
[ 80, 80, nan, nan, 2.80, 0.00, nan ],
[ 90, 90, nan, nan, 3.22, 0.00, nan ],
[ 100, 100, nan, nan, 4.96, 0.00, nan ],
[ 200, 200, nan, nan, 17.26, 0.00, nan ],
[ 300, 300, nan, nan, 39.33, 0.00, nan ],
[ 400, 400, nan, nan, 62.66, 0.01, nan ],
[ 500, 500, nan, nan, 92.62, 0.01, nan ],
[ 600, 600, nan, nan, 120.91, 0.01, nan ],
[ 700, 700, nan, nan, 154.35, 0.01, nan ],
[ 800, 800, nan, nan, 183.25, 0.01, nan ],
[ 900, 900, nan, nan, 216.61, 0.02, nan ],
[ 1000, 1000, nan, nan, 251.76, 0.02, nan ],
[ 2000, 2000, nan, nan, 637.35, 0.07, nan ],
[ 3000, 3000, nan, nan, 835.71, 0.17, nan ],
[ 4000, 4000, nan, nan, 1139.10, 0.30, nan ],
[ 5000, 5000, nan, nan, 1614.96, 0.41, nan ],
[ 6000, 6000, nan, nan, 1849.25, 0.62, nan ],
[ 7000, 7000, nan, nan, 2001.77, 0.91, nan ],
[ 8000, 8000, nan, nan, 2121.95, 1.29, nan ],
[ 9000, 9000, nan, nan, 2194.95, 1.77, nan ],
[ 10000, 10000, nan, nan, 2253.10, 2.37, nan ],
[ 12000, 12000, nan, nan, 2335.09, 3.95, nan ],
[ 14000, 14000, nan, nan, 2384.30, 6.14, nan ],
[ 16000, 16000, nan, nan, 2407.06, 9.08, nan ],
[ 18000, 18000, nan, nan, 2422.32, 12.84, nan ],
[ 20000, 20000, nan, nan, 2459.15, 17.35, nan ],
])
# numactl --interleave=all ../testing/testing_cgeqrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
cgeqrf_gpu = array([
[ 10, 10, nan, nan, 0.00, 0.00, nan ],
[ 20, 20, nan, nan, 0.04, 0.00, nan ],
[ 30, 30, nan, nan, 0.12, 0.00, nan ],
[ 40, 40, nan, nan, 0.27, 0.00, nan ],
[ 50, 50, nan, nan, 0.50, 0.00, nan ],
[ 60, 60, nan, nan, 0.85, 0.00, nan ],
[ 70, 70, nan, nan, 1.06, 0.00, nan ],
[ 80, 80, nan, nan, 1.55, 0.00, nan ],
[ 90, 90, nan, nan, 2.09, 0.00, nan ],
[ 100, 100, nan, nan, 5.61, 0.00, nan ],
[ 200, 200, nan, nan, 12.84, 0.00, nan ],
[ 300, 300, nan, nan, 31.01, 0.00, nan ],
[ 400, 400, nan, nan, 51.59, 0.01, nan ],
[ 500, 500, nan, nan, 77.84, 0.01, nan ],
[ 600, 600, nan, nan, 105.33, 0.01, nan ],
[ 700, 700, nan, nan, 137.06, 0.01, nan ],
[ 800, 800, nan, nan, 167.70, 0.02, nan ],
[ 900, 900, nan, nan, 196.97, 0.02, nan ],
[ 1000, 1000, nan, nan, 234.10, 0.02, nan ],
[ 2000, 2000, nan, nan, 604.80, 0.07, nan ],
[ 3000, 3000, nan, nan, 1014.24, 0.14, nan ],
[ 4000, 4000, nan, nan, 1392.96, 0.25, nan ],
[ 5000, 5000, nan, nan, 1410.27, 0.47, nan ],
[ 6000, 6000, nan, nan, 1696.41, 0.68, nan ],
[ 7000, 7000, nan, nan, 1928.78, 0.95, nan ],
[ 8000, 8000, nan, nan, 2048.87, 1.33, nan ],
[ 9000, 9000, nan, nan, 2133.31, 1.82, nan ],
[ 10000, 10000, nan, nan, 2208.32, 2.42, nan ],
[ 12000, 12000, nan, nan, 2320.47, 3.97, nan ],
[ 14000, 14000, nan, nan, 2378.70, 6.15, nan ],
[ 16000, 16000, nan, nan, 2411.01, 9.06, nan ],
[ 18000, 18000, nan, nan, 2430.66, 12.80, nan ],
[ 20000, 20000, nan, nan, 2474.69, 17.24, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/cgesvd.txt
# numactl --interleave=all ../testing/testing_cgesvd -UN -VN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
cgesvd_UN = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.00, nan ],
[ nan, 80, 80, nan, 0.00, nan ],
[ nan, 90, 90, nan, 0.00, nan ],
[ nan, 100, 100, nan, 0.00, nan ],
[ nan, 200, 200, nan, 0.01, nan ],
[ nan, 300, 300, nan, 0.03, nan ],
[ nan, 400, 400, nan, 0.05, nan ],
[ nan, 500, 500, nan, 0.08, nan ],
[ nan, 600, 600, nan, 0.11, nan ],
[ nan, 700, 700, nan, 0.15, nan ],
[ nan, 800, 800, nan, 0.19, nan ],
[ nan, 900, 900, nan, 0.24, nan ],
[ nan, 1000, 1000, nan, 0.29, nan ],
[ nan, 2000, 2000, nan, 1.14, nan ],
[ nan, 3000, 3000, nan, 2.94, nan ],
[ nan, 4000, 4000, nan, 5.85, nan ],
[ nan, 5000, 5000, nan, 10.14, nan ],
[ nan, 6000, 6000, nan, 16.06, nan ],
[ nan, 7000, 7000, nan, 23.87, nan ],
[ nan, 8000, 8000, nan, 34.14, nan ],
[ nan, 9000, 9000, nan, 46.82, nan ],
[ nan, 10000, 10000, nan, 62.64, nan ],
[ nan, 300, 100, nan, 0.00, nan ],
[ nan, 600, 200, nan, 0.02, nan ],
[ nan, 900, 300, nan, 0.04, nan ],
[ nan, 1200, 400, nan, 0.07, nan ],
[ nan, 1500, 500, nan, 0.10, nan ],
[ nan, 1800, 600, nan, 0.14, nan ],
[ nan, 2100, 700, nan, 0.19, nan ],
[ nan, 2400, 800, nan, 0.25, nan ],
[ nan, 2700, 900, nan, 0.33, nan ],
[ nan, 3000, 1000, nan, 0.40, nan ],
[ nan, 6000, 2000, nan, 1.74, nan ],
[ nan, 9000, 3000, nan, 4.66, nan ],
[ nan, 12000, 4000, nan, 9.57, nan ],
[ nan, 15000, 5000, nan, 17.05, nan ],
[ nan, 18000, 6000, nan, 27.53, nan ],
[ nan, 21000, 7000, nan, 41.54, nan ],
[ nan, 24000, 8000, nan, 60.43, nan ],
[ nan, 27000, 9000, nan, 83.63, nan ],
[ nan, 100, 300, nan, 0.00, nan ],
[ nan, 200, 600, nan, 0.02, nan ],
[ nan, 300, 900, nan, 0.04, nan ],
[ nan, 400, 1200, nan, 0.07, nan ],
[ nan, 500, 1500, nan, 0.11, nan ],
[ nan, 600, 1800, nan, 0.16, nan ],
[ nan, 700, 2100, nan, 0.21, nan ],
[ nan, 800, 2400, nan, 0.27, nan ],
[ nan, 900, 2700, nan, 0.33, nan ],
[ nan, 1000, 3000, nan, 0.41, nan ],
[ nan, 2000, 6000, nan, 1.76, nan ],
[ nan, 3000, 9000, nan, 4.72, nan ],
[ nan, 4000, 12000, nan, 9.72, nan ],
[ nan, 5000, 15000, nan, 17.39, nan ],
[ nan, 6000, 18000, nan, 28.11, nan ],
[ nan, 7000, 21000, nan, 42.57, nan ],
[ nan, 8000, 24000, nan, 61.30, nan ],
[ nan, 9000, 27000, nan, 85.19, nan ],
[ nan, 10000, 100, nan, 0.03, nan ],
[ nan, 20000, 200, nan, 0.08, nan ],
[ nan, 30000, 300, nan, 0.27, nan ],
[ nan, 40000, 400, nan, 0.51, nan ],
[ nan, 50000, 500, nan, 1.11, nan ],
[ nan, 60000, 600, nan, 1.19, nan ],
[ nan, 70000, 700, nan, 1.62, nan ],
[ nan, 80000, 800, nan, 2.20, nan ],
[ nan, 90000, 900, nan, 3.32, nan ],
[ nan, 100000, 1000, nan, 4.15, nan ],
[ nan, 200000, 2000, nan, 24.47, nan ],
[ nan, 100, 10000, nan, 0.02, nan ],
[ nan, 200, 20000, nan, 0.09, nan ],
[ nan, 300, 30000, nan, 0.23, nan ],
[ nan, 400, 40000, nan, 0.45, nan ],
[ nan, 500, 50000, nan, 0.78, nan ],
[ nan, 600, 60000, nan, 1.26, nan ],
[ nan, 700, 70000, nan, 1.90, nan ],
[ nan, 800, 80000, nan, 2.82, nan ],
[ nan, 900, 90000, nan, 3.48, nan ],
[ nan, 1000, 100000, nan, 4.47, nan ],
[ nan, 2000, 200000, nan, 28.57, nan ],
])
# numactl --interleave=all ../testing/testing_cgesvd -US -VS -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
cgesvd_US = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.01, nan ],
[ nan, 70, 70, nan, 0.01, nan ],
[ nan, 80, 80, nan, 0.01, nan ],
[ nan, 90, 90, nan, 0.01, nan ],
[ nan, 100, 100, nan, 0.02, nan ],
[ nan, 200, 200, nan, 0.02, nan ],
[ nan, 300, 300, nan, 0.05, nan ],
[ nan, 400, 400, nan, 0.09, nan ],
[ nan, 500, 500, nan, 0.15, nan ],
[ nan, 600, 600, nan, 0.21, nan ],
[ nan, 700, 700, nan, 0.29, nan ],
[ nan, 800, 800, nan, 0.38, nan ],
[ nan, 900, 900, nan, 0.49, nan ],
[ nan, 1000, 1000, nan, 0.62, nan ],
[ nan, 2000, 2000, nan, 3.06, nan ],
[ nan, 3000, 3000, nan, 9.19, nan ],
[ nan, 4000, 4000, nan, 19.73, nan ],
[ nan, 5000, 5000, nan, 38.32, nan ],
[ nan, 6000, 6000, nan, 64.95, nan ],
[ nan, 7000, 7000, nan, 104.35, nan ],
[ nan, 8000, 8000, nan, 152.36, nan ],
[ nan, 9000, 9000, nan, 228.85, nan ],
[ nan, 10000, 10000, nan, 318.21, nan ],
[ nan, 300, 100, nan, 0.03, nan ],
[ nan, 600, 200, nan, 0.04, nan ],
[ nan, 900, 300, nan, 0.08, nan ],
[ nan, 1200, 400, nan, 0.15, nan ],
[ nan, 1500, 500, nan, 0.23, nan ],
[ nan, 1800, 600, nan, 0.35, nan ],
[ nan, 2100, 700, nan, 0.49, nan ],
[ nan, 2400, 800, nan, 0.65, nan ],
[ nan, 2700, 900, nan, 0.85, nan ],
[ nan, 3000, 1000, nan, 1.16, nan ],
[ nan, 6000, 2000, nan, 6.55, nan ],
[ nan, 9000, 3000, nan, 18.25, nan ],
[ nan, 12000, 4000, nan, 32.23, nan ],
[ nan, 15000, 5000, nan, 54.41, nan ],
[ nan, 18000, 6000, nan, 91.60, nan ],
[ nan, 21000, 7000, nan, 142.99, nan ],
[ nan, 24000, 8000, nan, 199.30, nan ],
[ nan, 27000, 9000, nan, 291.64, nan ],
[ nan, 100, 300, nan, 0.02, nan ],
[ nan, 200, 600, nan, 0.07, nan ],
[ nan, 300, 900, nan, 0.20, nan ],
[ nan, 400, 1200, nan, 0.42, nan ],
[ nan, 500, 1500, nan, 0.76, nan ],
[ nan, 600, 1800, nan, 1.26, nan ],
[ nan, 700, 2100, nan, 2.00, nan ],
[ nan, 800, 2400, nan, 2.84, nan ],
[ nan, 900, 2700, nan, 3.92, nan ],
[ nan, 1000, 3000, nan, 5.81, nan ],
[ nan, 2000, 6000, nan, 45.92, nan ],
[ nan, 3000, 9000, nan, 150.94, nan ],
[ nan, 4000, 12000, nan, 344.05, nan ],
[ nan, 5000, 15000, nan, 662.96, nan ],
[ nan, 6000, 18000, nan, 1134.65, nan ],
[ nan, 7000, 21000, nan, 1763.91, nan ],
[ nan, 8000, 24000, nan, 2454.96, nan ],
[ nan, 9000, 27000, nan, 3673.78, nan ],
[ nan, 10000, 100, nan, 0.07, nan ],
[ nan, 20000, 200, nan, 0.20, nan ],
[ nan, 30000, 300, nan, 0.53, nan ],
[ nan, 40000, 400, nan, 1.18, nan ],
[ nan, 50000, 500, nan, 1.98, nan ],
[ nan, 60000, 600, nan, 3.21, nan ],
[ nan, 70000, 700, nan, 4.66, nan ],
[ nan, 80000, 800, nan, 6.69, nan ],
[ nan, 90000, 900, nan, 9.56, nan ],
[ nan, 100000, 1000, nan, 12.59, nan ],
[ nan, 200000, 2000, nan, 88.63, nan ],
[ nan, 100, 10000, nan, 0.23, nan ],
[ nan, 200, 20000, nan, 1.97, nan ],
[ nan, 300, 30000, nan, 6.28, nan ],
[ nan, 400, 40000, nan, 14.25, nan ],
[ nan, 500, 50000, nan, 25.16, nan ],
[ nan, 600, 60000, nan, 44.44, nan ],
[ nan, 700, 70000, nan, 67.91, nan ],
[ nan, 800, 80000, nan, 104.45, nan ],
[ nan, 900, 90000, nan, 144.65, nan ],
[ nan, 1000, 100000, nan, 191.75, nan ],
[ nan, 2000, 200000, nan, 1503.65, nan ],
])
# numactl --interleave=all ../testing/testing_cgesdd -UN -VN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
cgesdd_UN = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.00, nan ],
[ nan, 80, 80, nan, 0.00, nan ],
[ nan, 90, 90, nan, 0.00, nan ],
[ nan, 100, 100, nan, 0.00, nan ],
[ nan, 200, 200, nan, 0.01, nan ],
[ nan, 300, 300, nan, 0.03, nan ],
[ nan, 400, 400, nan, 0.05, nan ],
[ nan, 500, 500, nan, 0.08, nan ],
[ nan, 600, 600, nan, 0.11, nan ],
[ nan, 700, 700, nan, 0.15, nan ],
[ nan, 800, 800, nan, 0.19, nan ],
[ nan, 900, 900, nan, 0.24, nan ],
[ nan, 1000, 1000, nan, 0.29, nan ],
[ nan, 2000, 2000, nan, 1.13, nan ],
[ nan, 3000, 3000, nan, 2.92, nan ],
[ nan, 4000, 4000, nan, 5.83, nan ],
[ nan, 5000, 5000, nan, 10.13, nan ],
[ nan, 6000, 6000, nan, 16.08, nan ],
[ nan, 7000, 7000, nan, 23.89, nan ],
[ nan, 8000, 8000, nan, 34.17, nan ],
[ nan, 9000, 9000, nan, 46.88, nan ],
[ nan, 10000, 10000, nan, 62.73, nan ],
[ nan, 300, 100, nan, 0.00, nan ],
[ nan, 600, 200, nan, 0.02, nan ],
[ nan, 900, 300, nan, 0.04, nan ],
[ nan, 1200, 400, nan, 0.07, nan ],
[ nan, 1500, 500, nan, 0.10, nan ],
[ nan, 1800, 600, nan, 0.14, nan ],
[ nan, 2100, 700, nan, 0.19, nan ],
[ nan, 2400, 800, nan, 0.24, nan ],
[ nan, 2700, 900, nan, 0.32, nan ],
[ nan, 3000, 1000, nan, 0.39, nan ],
[ nan, 6000, 2000, nan, 1.73, nan ],
[ nan, 9000, 3000, nan, 4.65, nan ],
[ nan, 12000, 4000, nan, 9.58, nan ],
[ nan, 15000, 5000, nan, 17.05, nan ],
[ nan, 18000, 6000, nan, 27.46, nan ],
[ nan, 21000, 7000, nan, 41.66, nan ],
[ nan, 24000, 8000, nan, 74.53, nan ],
[ nan, 27000, 9000, nan, 103.25, nan ],
[ nan, 100, 300, nan, 0.00, nan ],
[ nan, 200, 600, nan, 0.02, nan ],
[ nan, 300, 900, nan, 0.04, nan ],
[ nan, 400, 1200, nan, 0.08, nan ],
[ nan, 500, 1500, nan, 0.12, nan ],
[ nan, 600, 1800, nan, 0.17, nan ],
[ nan, 700, 2100, nan, 0.22, nan ],
[ nan, 800, 2400, nan, 0.29, nan ],
[ nan, 900, 2700, nan, 0.37, nan ],
[ nan, 1000, 3000, nan, 0.45, nan ],
[ nan, 2000, 6000, nan, 2.10, nan ],
[ nan, 3000, 9000, nan, 5.90, nan ],
[ nan, 4000, 12000, nan, 12.55, nan ],
[ nan, 5000, 15000, nan, 22.87, nan ],
[ nan, 6000, 18000, nan, 37.76, nan ],
[ nan, 7000, 21000, nan, 57.64, nan ],
[ nan, 8000, 24000, nan, 82.74, nan ],
[ nan, 9000, 27000, nan, 85.03, nan ],
[ nan, 10000, 100, nan, 0.02, nan ],
[ nan, 20000, 200, nan, 0.08, nan ],
[ nan, 30000, 300, nan, 0.19, nan ],
[ nan, 40000, 400, nan, 0.51, nan ],
[ nan, 50000, 500, nan, 0.79, nan ],
[ nan, 60000, 600, nan, 1.16, nan ],
[ nan, 70000, 700, nan, 1.62, nan ],
[ nan, 80000, 800, nan, 2.21, nan ],
[ nan, 90000, 900, nan, 3.36, nan ],
[ nan, 100000, 1000, nan, 4.19, nan ],
[ nan, 200000, 2000, nan, 24.51, nan ],
[ nan, 100, 10000, nan, 0.02, nan ],
[ nan, 200, 20000, nan, 0.09, nan ],
[ nan, 300, 30000, nan, 0.23, nan ],
[ nan, 400, 40000, nan, 0.44, nan ],
[ nan, 500, 50000, nan, 0.74, nan ],
[ nan, 600, 60000, nan, 1.20, nan ],
[ nan, 700, 70000, nan, 1.86, nan ],
[ nan, 800, 80000, nan, 2.77, nan ],
[ nan, 900, 90000, nan, 3.33, nan ],
[ nan, 1000, 100000, nan, 4.32, nan ],
[ nan, 2000, 200000, nan, 28.52, nan ],
])
# numactl --interleave=all ../testing/testing_cgesdd -US -VS -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
cgesdd_US = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.00, nan ],
[ nan, 80, 80, nan, 0.00, nan ],
[ nan, 90, 90, nan, 0.00, nan ],
[ nan, 100, 100, nan, 0.00, nan ],
[ nan, 200, 200, nan, 0.02, nan ],
[ nan, 300, 300, nan, 0.05, nan ],
[ nan, 400, 400, nan, 0.08, nan ],
[ nan, 500, 500, nan, 0.11, nan ],
[ nan, 600, 600, nan, 0.16, nan ],
[ nan, 700, 700, nan, 0.21, nan ],
[ nan, 800, 800, nan, 0.27, nan ],
[ nan, 900, 900, nan, 0.34, nan ],
[ nan, 1000, 1000, nan, 0.43, nan ],
[ nan, 2000, 2000, nan, 1.69, nan ],
[ nan, 3000, 3000, nan, 4.31, nan ],
[ nan, 4000, 4000, nan, 8.28, nan ],
[ nan, 5000, 5000, nan, 13.79, nan ],
[ nan, 6000, 6000, nan, 21.55, nan ],
[ nan, 7000, 7000, nan, 31.66, nan ],
[ nan, 8000, 8000, nan, 44.57, nan ],
[ nan, 9000, 9000, nan, 60.54, nan ],
[ nan, 10000, 10000, nan, 80.15, nan ],
[ nan, 300, 100, nan, 0.01, nan ],
[ nan, 600, 200, nan, 0.03, nan ],
[ nan, 900, 300, nan, 0.06, nan ],
[ nan, 1200, 400, nan, 0.10, nan ],
[ nan, 1500, 500, nan, 0.15, nan ],
[ nan, 1800, 600, nan, 0.21, nan ],
[ nan, 2100, 700, nan, 0.29, nan ],
[ nan, 2400, 800, nan, 0.37, nan ],
[ nan, 2700, 900, nan, 0.50, nan ],
[ nan, 3000, 1000, nan, 0.62, nan ],
[ nan, 6000, 2000, nan, 2.97, nan ],
[ nan, 9000, 3000, nan, 8.21, nan ],
[ nan, 12000, 4000, nan, 17.17, nan ],
[ nan, 15000, 5000, nan, 30.84, nan ],
[ nan, 18000, 6000, nan, 50.12, nan ],
[ nan, 21000, 7000, nan, 76.50, nan ],
[ nan, 24000, 8000, nan, 141.47, nan ],
[ nan, 27000, 9000, nan, 153.96, nan ],
[ nan, 100, 300, nan, 0.01, nan ],
[ nan, 200, 600, nan, 0.03, nan ],
[ nan, 300, 900, nan, 0.06, nan ],
[ nan, 400, 1200, nan, 0.11, nan ],
[ nan, 500, 1500, nan, 0.16, nan ],
[ nan, 600, 1800, nan, 0.23, nan ],
[ nan, 700, 2100, nan, 0.32, nan ],
[ nan, 800, 2400, nan, 0.41, nan ],
[ nan, 900, 2700, nan, 0.53, nan ],
[ nan, 1000, 3000, nan, 0.65, nan ],
[ nan, 2000, 6000, nan, 3.04, nan ],
[ nan, 3000, 9000, nan, 8.42, nan ],
[ nan, 4000, 12000, nan, 17.51, nan ],
[ nan, 5000, 15000, nan, 31.34, nan ],
[ nan, 6000, 18000, nan, 68.91, nan ],
[ nan, 7000, 21000, nan, 77.48, nan ],
[ nan, 8000, 24000, nan, 112.14, nan ],
[ nan, 9000, 27000, nan, 155.68, nan ],
[ nan, 10000, 100, nan, 0.04, nan ],
[ nan, 20000, 200, nan, 0.18, nan ],
[ nan, 30000, 300, nan, 0.46, nan ],
[ nan, 40000, 400, nan, 1.07, nan ],
[ nan, 50000, 500, nan, 1.78, nan ],
[ nan, 60000, 600, nan, 2.94, nan ],
[ nan, 70000, 700, nan, 4.08, nan ],
[ nan, 80000, 800, nan, 5.89, nan ],
[ nan, 90000, 900, nan, 8.13, nan ],
[ nan, 100000, 1000, nan, 10.32, nan ],
[ nan, 200000, 2000, nan, 69.11, nan ],
[ nan, 100, 10000, nan, 0.06, nan ],
[ nan, 200, 20000, nan, 0.24, nan ],
[ nan, 300, 30000, nan, 0.65, nan ],
[ nan, 400, 40000, nan, 1.29, nan ],
[ nan, 500, 50000, nan, 2.28, nan ],
[ nan, 600, 60000, nan, 3.92, nan ],
[ nan, 700, 70000, nan, 5.86, nan ],
[ nan, 800, 80000, nan, 7.78, nan ],
[ nan, 900, 90000, nan, 7.62, nan ],
[ nan, 1000, 100000, nan, 9.68, nan ],
[ nan, 2000, 200000, nan, 57.84, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/cgetrf.txt
# numactl --interleave=all ../testing/testing_cgetrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
cgetrf = array([
[ 10, 10, nan, nan, 0.24, 0.00, nan ],
[ 20, 20, nan, nan, 0.75, 0.00, nan ],
[ 30, 30, nan, nan, 1.79, 0.00, nan ],
[ 40, 40, nan, nan, 3.53, 0.00, nan ],
[ 50, 50, nan, nan, 4.87, 0.00, nan ],
[ 60, 60, nan, nan, 5.56, 0.00, nan ],
[ 70, 70, nan, nan, 1.04, 0.00, nan ],
[ 80, 80, nan, nan, 1.50, 0.00, nan ],
[ 90, 90, nan, nan, 2.07, 0.00, nan ],
[ 100, 100, nan, nan, 2.68, 0.00, nan ],
[ 200, 200, nan, nan, 12.27, 0.00, nan ],
[ 300, 300, nan, nan, 28.70, 0.00, nan ],
[ 400, 400, nan, nan, 47.89, 0.00, nan ],
[ 500, 500, nan, nan, 71.91, 0.00, nan ],
[ 600, 600, nan, nan, 96.13, 0.01, nan ],
[ 700, 700, nan, nan, 123.22, 0.01, nan ],
[ 800, 800, nan, nan, 151.84, 0.01, nan ],
[ 900, 900, nan, nan, 180.86, 0.01, nan ],
[ 1000, 1000, nan, nan, 211.73, 0.01, nan ],
[ 2000, 2000, nan, nan, 528.57, 0.04, nan ],
[ 3000, 3000, nan, nan, 882.00, 0.08, nan ],
[ 4000, 4000, nan, nan, 1134.12, 0.15, nan ],
[ 5000, 5000, nan, nan, 1295.05, 0.26, nan ],
[ 6000, 6000, nan, nan, 1537.40, 0.37, nan ],
[ 7000, 7000, nan, nan, 1710.51, 0.53, nan ],
[ 8000, 8000, nan, nan, 1861.76, 0.73, nan ],
[ 9000, 9000, nan, nan, 1918.72, 1.01, nan ],
[ 10000, 10000, nan, nan, 2032.62, 1.31, nan ],
[ 12000, 12000, nan, nan, 2204.18, 2.09, nan ],
[ 14000, 14000, nan, nan, 2327.20, 3.14, nan ],
[ 16000, 16000, nan, nan, 2409.66, 4.53, nan ],
[ 18000, 18000, nan, nan, 2464.08, 6.31, nan ],
[ 20000, 20000, nan, nan, 2521.36, 8.46, nan ],
])
# numactl --interleave=all ../testing/testing_cgetrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
cgetrf_gpu = array([
[ 10, 10, nan, nan, 0.01, 0.00, nan ],
[ 20, 20, nan, nan, 0.06, 0.00, nan ],
[ 30, 30, nan, nan, 0.20, 0.00, nan ],
[ 40, 40, nan, nan, 0.45, 0.00, nan ],
[ 50, 50, nan, nan, 0.79, 0.00, nan ],
[ 60, 60, nan, nan, 1.24, 0.00, nan ],
[ 70, 70, nan, nan, 0.45, 0.00, nan ],
[ 80, 80, nan, nan, 0.67, 0.00, nan ],
[ 90, 90, nan, nan, 0.92, 0.00, nan ],
[ 100, 100, nan, nan, 1.22, 0.00, nan ],
[ 200, 200, nan, nan, 6.40, 0.00, nan ],
[ 300, 300, nan, nan, 16.85, 0.00, nan ],
[ 400, 400, nan, nan, 31.71, 0.01, nan ],
[ 500, 500, nan, nan, 49.86, 0.01, nan ],
[ 600, 600, nan, nan, 70.11, 0.01, nan ],
[ 700, 700, nan, nan, 92.57, 0.01, nan ],
[ 800, 800, nan, nan, 118.35, 0.01, nan ],
[ 900, 900, nan, nan, 143.45, 0.01, nan ],
[ 1000, 1000, nan, nan, 176.99, 0.02, nan ],
[ 2000, 2000, nan, nan, 474.03, 0.04, nan ],
[ 3000, 3000, nan, nan, 821.59, 0.09, nan ],
[ 4000, 4000, nan, nan, 1153.80, 0.15, nan ],
[ 5000, 5000, nan, nan, 1450.97, 0.23, nan ],
[ 6000, 6000, nan, nan, 1739.32, 0.33, nan ],
[ 7000, 7000, nan, nan, 1932.86, 0.47, nan ],
[ 8000, 8000, nan, nan, 2097.77, 0.65, nan ],
[ 9000, 9000, nan, nan, 2053.40, 0.95, nan ],
[ 10000, 10000, nan, nan, 2184.81, 1.22, nan ],
[ 12000, 12000, nan, nan, 2374.47, 1.94, nan ],
[ 14000, 14000, nan, nan, 2499.31, 2.93, nan ],
[ 16000, 16000, nan, nan, 2582.38, 4.23, nan ],
[ 18000, 18000, nan, nan, 2627.16, 5.92, nan ],
[ 20000, 20000, nan, nan, 2676.83, 7.97, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/cheevd.txt
# numactl --interleave=all ../testing/testing_cheevd -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_cheevd -JN -N 123 -N 1234 --range 12000:20000:2000
cheevd_JN = array([
[ 10, nan, 0.0000, nan, nan, nan, nan ],
[ 20, nan, 0.0001, nan, nan, nan, nan ],
[ 30, nan, 0.0001, nan, nan, nan, nan ],
[ 40, nan, 0.0001, nan, nan, nan, nan ],
[ 50, nan, 0.0002, nan, nan, nan, nan ],
[ 60, nan, 0.0003, nan, nan, nan, nan ],
[ 70, nan, 0.0005, nan, nan, nan, nan ],
[ 80, nan, 0.0007, nan, nan, nan, nan ],
[ 90, nan, 0.0009, nan, nan, nan, nan ],
[ 100, nan, 0.0012, nan, nan, nan, nan ],
[ 200, nan, 0.0048, nan, nan, nan, nan ],
[ 300, nan, 0.0094, nan, nan, nan, nan ],
[ 400, nan, 0.0156, nan, nan, nan, nan ],
[ 500, nan, 0.0229, nan, nan, nan, nan ],
[ 600, nan, 0.0324, nan, nan, nan, nan ],
[ 700, nan, 0.0442, nan, nan, nan, nan ],
[ 800, nan, 0.0572, nan, nan, nan, nan ],
[ 900, nan, 0.0739, nan, nan, nan, nan ],
[ 1000, nan, 0.0906, nan, nan, nan, nan ],
[ 2000, nan, 0.4168, nan, nan, nan, nan ],
[ 3000, nan, 1.2620, nan, nan, nan, nan ],
[ 4000, nan, 2.2903, nan, nan, nan, nan ],
[ 5000, nan, 3.7342, nan, nan, nan, nan ],
[ 6000, nan, 5.5810, nan, nan, nan, nan ],
[ 7000, nan, 8.0046, nan, nan, nan, nan ],
[ 8000, nan, 10.9571, nan, nan, nan, nan ],
[ 9000, nan, 14.7192, nan, nan, nan, nan ],
[ 10000, nan, 19.1044, nan, nan, nan, nan ],
[ 12000, nan, 30.6692, nan, nan, nan, nan ],
[ 14000, nan, 45.5317, nan, nan, nan, nan ],
[ 16000, nan, 65.0317, nan, nan, nan, nan ],
[ 18000, nan, 90.1935, nan, nan, nan, nan ],
[ 20000, nan, 118.9253, nan, nan, nan, nan ],
])
# numactl --interleave=all ../testing/testing_cheevd -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_cheevd -JV -N 123 -N 1234 --range 12000:20000:2000
cheevd_JV = array([
[ 10, nan, 0.0001, nan, nan, nan, nan ],
[ 20, nan, 0.0002, nan, nan, nan, nan ],
[ 30, nan, 0.0002, nan, nan, nan, nan ],
[ 40, nan, 0.0004, nan, nan, nan, nan ],
[ 50, nan, 0.0005, nan, nan, nan, nan ],
[ 60, nan, 0.0006, nan, nan, nan, nan ],
[ 70, nan, 0.0009, nan, nan, nan, nan ],
[ 80, nan, 0.0011, nan, nan, nan, nan ],
[ 90, nan, 0.0014, nan, nan, nan, nan ],
[ 100, nan, 0.0017, nan, nan, nan, nan ],
[ 200, nan, 0.0092, nan, nan, nan, nan ],
[ 300, nan, 0.0148, nan, nan, nan, nan ],
[ 400, nan, 0.0242, nan, nan, nan, nan ],
[ 500, nan, 0.0347, nan, nan, nan, nan ],
[ 600, nan, 0.0421, nan, nan, nan, nan ],
[ 700, nan, 0.0556, nan, nan, nan, nan ],
[ 800, nan, 0.0705, nan, nan, nan, nan ],
[ 900, nan, 0.0886, nan, nan, nan, nan ],
[ 1000, nan, 0.1098, nan, nan, nan, nan ],
[ 2000, nan, 0.4374, nan, nan, nan, nan ],
[ 3000, nan, 1.4212, nan, nan, nan, nan ],
[ 4000, nan, 2.6029, nan, nan, nan, nan ],
[ 5000, nan, 4.2259, nan, nan, nan, nan ],
[ 6000, nan, 6.4208, nan, nan, nan, nan ],
[ 7000, nan, 9.2599, nan, nan, nan, nan ],
[ 8000, nan, 12.7538, nan, nan, nan, nan ],
[ 9000, nan, 17.2134, nan, nan, nan, nan ],
[ 10000, nan, 22.4110, nan, nan, nan, nan ],
[ 12000, nan, 36.7797, nan, nan, nan, nan ],
[ 14000, nan, 54.8882, nan, nan, nan, nan ],
[ 16000, nan, 78.0173, nan, nan, nan, nan ],
[ 18000, nan, 108.8207, nan, nan, nan, nan ],
[ 20000, nan, 143.8242, nan, nan, nan, nan ],
])
# numactl --interleave=all ../testing/testing_cheevd_gpu -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_cheevd_gpu -JN -N 123 -N 1234 --range 12000:20000:2000
cheevd_gpu_JN = array([
[ 10, nan, 0.0002, nan, nan, nan, nan ],
[ 20, nan, 0.0002, nan, nan, nan, nan ],
[ 30, nan, 0.0002, nan, nan, nan, nan ],
[ 40, nan, 0.0003, nan, nan, nan, nan ],
[ 50, nan, 0.0003, nan, nan, nan, nan ],
[ 60, nan, 0.0004, nan, nan, nan, nan ],
[ 70, nan, 0.0007, nan, nan, nan, nan ],
[ 80, nan, 0.0009, nan, nan, nan, nan ],
[ 90, nan, 0.0011, nan, nan, nan, nan ],
[ 100, nan, 0.0014, nan, nan, nan, nan ],
[ 200, nan, 0.0049, nan, nan, nan, nan ],
[ 300, nan, 0.0095, nan, nan, nan, nan ],
[ 400, nan, 0.0159, nan, nan, nan, nan ],
[ 500, nan, 0.0230, nan, nan, nan, nan ],
[ 600, nan, 0.0317, nan, nan, nan, nan ],
[ 700, nan, 0.0431, nan, nan, nan, nan ],
[ 800, nan, 0.0546, nan, nan, nan, nan ],
[ 900, nan, 0.0697, nan, nan, nan, nan ],
[ 1000, nan, 0.0843, nan, nan, nan, nan ],
[ 2000, nan, 0.3551, nan, nan, nan, nan ],
[ 3000, nan, 1.2662, nan, nan, nan, nan ],
[ 4000, nan, 2.2914, nan, nan, nan, nan ],
[ 5000, nan, 3.7343, nan, nan, nan, nan ],
[ 6000, nan, 5.5809, nan, nan, nan, nan ],
[ 7000, nan, 7.9963, nan, nan, nan, nan ],
[ 8000, nan, 10.9362, nan, nan, nan, nan ],
[ 9000, nan, 14.7066, nan, nan, nan, nan ],
[ 10000, nan, 19.0569, nan, nan, nan, nan ],
[ 12000, nan, 30.6401, nan, nan, nan, nan ],
[ 14000, nan, 45.3730, nan, nan, nan, nan ],
[ 16000, nan, 65.0345, nan, nan, nan, nan ],
[ 18000, nan, 89.8408, nan, nan, nan, nan ],
[ 20000, nan, 118.9498, nan, nan, nan, nan ],
])
# numactl --interleave=all ../testing/testing_cheevd_gpu -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_cheevd_gpu -JV -N 123 -N 1234 --range 12000:20000:2000
cheevd_gpu_JV = array([
[ 10, nan, 0.0004, nan, nan, nan, nan ],
[ 20, nan, 0.0004, nan, nan, nan, nan ],
[ 30, nan, 0.0005, nan, nan, nan, nan ],
[ 40, nan, 0.0007, nan, nan, nan, nan ],
[ 50, nan, 0.0008, nan, nan, nan, nan ],
[ 60, nan, 0.0010, nan, nan, nan, nan ],
[ 70, nan, 0.0013, nan, nan, nan, nan ],
[ 80, nan, 0.0015, nan, nan, nan, nan ],
[ 90, nan, 0.0019, nan, nan, nan, nan ],
[ 100, nan, 0.0023, nan, nan, nan, nan ],
[ 200, nan, 0.0104, nan, nan, nan, nan ],
[ 300, nan, 0.0174, nan, nan, nan, nan ],
[ 400, nan, 0.0289, nan, nan, nan, nan ],
[ 500, nan, 0.0409, nan, nan, nan, nan ],
[ 600, nan, 0.0516, nan, nan, nan, nan ],
[ 700, nan, 0.0696, nan, nan, nan, nan ],
[ 800, nan, 0.0879, nan, nan, nan, nan ],
[ 900, nan, 0.1108, nan, nan, nan, nan ],
[ 1000, nan, 0.1354, nan, nan, nan, nan ],
[ 2000, nan, 0.5875, nan, nan, nan, nan ],
[ 3000, nan, 1.4799, nan, nan, nan, nan ],
[ 4000, nan, 2.6911, nan, nan, nan, nan ],
[ 5000, nan, 4.3855, nan, nan, nan, nan ],
[ 6000, nan, 6.6081, nan, nan, nan, nan ],
[ 7000, nan, 9.4027, nan, nan, nan, nan ],
[ 8000, nan, 12.9168, nan, nan, nan, nan ],
[ 9000, nan, 17.3143, nan, nan, nan, nan ],
[ 10000, nan, 22.7453, nan, nan, nan, nan ],
[ 12000, nan, 36.8039, nan, nan, nan, nan ],
[ 14000, nan, 55.2819, nan, nan, nan, nan ],
[ 16000, nan, 79.4427, nan, nan, nan, nan ],
[ 18000, nan, 110.5094, nan, nan, nan, nan ],
[ 20000, nan, 148.1804, nan, nan, nan, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/cheevd_2stage.txt
# numactl --interleave=all ../testing/testing_cheevdx_2stage -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
cheevdx_2stage_JN = array([
[ 10, 10, 0.00 ],
[ 20, 20, 0.00 ],
[ 30, 30, 0.00 ],
[ 40, 40, 0.00 ],
[ 50, 50, 0.00 ],
[ 60, 60, 0.00 ],
[ 70, 70, 0.00 ],
[ 80, 80, 0.00 ],
[ 90, 90, 0.00 ],
[ 100, 100, 0.00 ],
[ 200, 200, 0.00 ],
[ 300, 300, 0.02 ],
[ 400, 400, 0.04 ],
[ 500, 500, 0.06 ],
[ 600, 600, 0.09 ],
[ 700, 700, 0.11 ],
[ 800, 800, 0.13 ],
[ 900, 900, 0.16 ],
[ 1000, 1000, 0.17 ],
[ 2000, 2000, 0.45 ],
[ 3000, 3000, 0.81 ],
[ 4000, 4000, 1.26 ],
[ 5000, 5000, 1.91 ],
[ 6000, 6000, 2.46 ],
[ 7000, 7000, 3.21 ],
[ 8000, 8000, 4.15 ],
[ 9000, 9000, 5.31 ],
[ 10000, 10000, 6.75 ],
[ 12000, 12000, 9.83 ],
[ 14000, 14000, 13.76 ],
[ 16000, 16000, 18.61 ],
[ 18000, 18000, 24.65 ],
[ 20000, 20000, 32.06 ],
])
# numactl --interleave=all ../testing/testing_cheevdx_2stage -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
cheevdx_2stage_JV = array([
[ 10, 10, 0.00 ],
[ 20, 20, 0.00 ],
[ 30, 30, 0.00 ],
[ 40, 40, 0.00 ],
[ 50, 50, 0.00 ],
[ 60, 60, 0.00 ],
[ 70, 70, 0.00 ],
[ 80, 80, 0.00 ],
[ 90, 90, 0.00 ],
[ 100, 100, 0.00 ],
[ 200, 200, 0.01 ],
[ 300, 300, 0.03 ],
[ 400, 400, 0.05 ],
[ 500, 500, 0.08 ],
[ 600, 600, 0.10 ],
[ 700, 700, 0.12 ],
[ 800, 800, 0.15 ],
[ 900, 900, 0.18 ],
[ 1000, 1000, 0.21 ],
[ 2000, 2000, 0.60 ],
[ 3000, 3000, 1.16 ],
[ 4000, 4000, 2.03 ],
[ 5000, 5000, 3.25 ],
[ 6000, 6000, 4.99 ],
[ 7000, 7000, 7.15 ],
[ 8000, 8000, 9.77 ],
[ 9000, 9000, 13.08 ],
[ 10000, 10000, 17.55 ],
[ 12000, 12000, 28.13 ],
[ 14000, 14000, 42.38 ],
[ 16000, 16000, 61.25 ],
[ 18000, 18000, 86.19 ],
[ 20000, 20000, 116.90 ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/chemv.txt
# numactl --interleave=all ../testing/testing_chemv -L -N 123 -N 1234 --range 10:90:1 --range 100:900:10 --range 1000:9000:100 --range 10000:20000:2000
chemv_L = array([
[ 10, 0.03, 0.03, 0.03, 0.03, 0.04, 0.02, 0.48, 0.00, 2.13e-07, 2.13e-07, 2.70e-07, nan ],
[ 11, 0.04, 0.03, 0.04, 0.03, 0.05, 0.02, 0.58, 0.00, 1.94e-07, 1.85e-07, 1.23e-07, nan ],
[ 12, 0.04, 0.03, 0.04, 0.03, 0.06, 0.02, 0.68, 0.00, 1.12e-07, 1.12e-07, 1.12e-07, nan ],
[ 13, 0.05, 0.03, 0.05, 0.03, 0.07, 0.02, 0.79, 0.00, 1.64e-07, 2.07e-07, 1.64e-07, nan ],
[ 14, 0.06, 0.03, 0.06, 0.03, 0.08, 0.02, 0.91, 0.00, 2.81e-07, 2.46e-07, 2.81e-07, nan ],
[ 15, 0.06, 0.03, 0.07, 0.03, 0.09, 0.02, 0.64, 0.00, 1.42e-07, 1.80e-07, 2.84e-07, nan ],
[ 16, 0.07, 0.03, 0.08, 0.03, 0.11, 0.02, 1.17, 0.00, 1.69e-07, 1.69e-07, 1.69e-07, nan ],
[ 17, 0.07, 0.04, 0.08, 0.03, 0.11, 0.02, 1.17, 0.00, 1.59e-07, 2.51e-07, 1.59e-07, nan ],
[ 18, 0.08, 0.03, 0.09, 0.03, 0.12, 0.02, 1.47, 0.00, 2.14e-07, 2.18e-07, 2.18e-07, nan ],
[ 19, 0.09, 0.03, 0.10, 0.03, 0.13, 0.02, 1.09, 0.00, 2.04e-07, 2.01e-07, 1.25e-07, nan ],
[ 20, 0.10, 0.03, 0.11, 0.03, 0.14, 0.02, 1.80, 0.00, 1.97e-07, 2.13e-07, 1.97e-07, nan ],
[ 21, 0.11, 0.03, 0.12, 0.03, 0.16, 0.02, 1.22, 0.00, 2.03e-07, 2.57e-07, 2.57e-07, nan ],
[ 22, 0.12, 0.03, 0.13, 0.03, 0.18, 0.02, 1.33, 0.00, 1.94e-07, 2.33e-07, 1.94e-07, nan ],
[ 23, 0.13, 0.03, 0.14, 0.03, 0.19, 0.02, 1.58, 0.00, 1.71e-07, 1.67e-07, 2.49e-07, nan ],
[ 24, 0.14, 0.03, 0.15, 0.03, 0.21, 0.02, 2.57, 0.00, 1.64e-07, 2.44e-07, 1.87e-07, nan ],
[ 25, 0.15, 0.04, 0.17, 0.03, 0.22, 0.02, 1.71, 0.00, 2.56e-07, 2.29e-07, 2.16e-07, nan ],
[ 26, 0.18, 0.03, 0.18, 0.03, 0.23, 0.03, 1.85, 0.00, 1.64e-07, 1.55e-07, 1.64e-07, nan ],
[ 27, 0.18, 0.03, 0.19, 0.03, 0.26, 0.02, 1.99, 0.00, 2.15e-07, 1.58e-07, 2.23e-07, nan ],
[ 28, 0.21, 0.03, 0.21, 0.03, 0.30, 0.02, 2.13, 0.00, 2.15e-07, 2.46e-07, 1.52e-07, nan ],
[ 29, 0.22, 0.03, 0.23, 0.03, 0.31, 0.02, 2.28, 0.00, 2.94e-07, 2.71e-07, 2.21e-07, nan ],
[ 30, 0.24, 0.03, 0.24, 0.03, 0.34, 0.02, 2.64, 0.00, 2.29e-07, 2.29e-07, 2.62e-07, nan ],
[ 31, 0.26, 0.03, 0.26, 0.03, 0.35, 0.02, 1.99, 0.00, 1.54e-07, 2.54e-07, 1.54e-07, nan ],
[ 32, 0.27, 0.03, 0.29, 0.03, 0.39, 0.02, 2.77, 0.00, 1.33e-07, 2.46e-07, 1.49e-07, nan ],
[ 33, 0.27, 0.03, 0.24, 0.04, 0.37, 0.02, 2.25, 0.00, 2.55e-07, 2.33e-07, 2.08e-07, nan ],
[ 34, 0.30, 0.03, 0.26, 0.04, 0.40, 0.02, 3.38, 0.00, 2.80e-07, 2.31e-07, 3.17e-07, nan ],
[ 35, 0.33, 0.03, 0.28, 0.04, 0.42, 0.02, 2.52, 0.00, 2.25e-07, 2.44e-07, 2.44e-07, nan ],
[ 36, 0.33, 0.03, 0.28, 0.04, 0.43, 0.03, 2.66, 0.00, 3.35e-07, 2.37e-07, 2.37e-07, nan ],
[ 37, 0.36, 0.03, 0.31, 0.04, 0.44, 0.03, 2.81, 0.00, 2.31e-07, 3.26e-07, 2.13e-07, nan ],
[ 38, 0.36, 0.03, 0.33, 0.04, 0.48, 0.03, 2.96, 0.00, 3.62e-07, 3.17e-07, 3.05e-07, nan ],
[ 39, 0.40, 0.03, 0.34, 0.04, 0.49, 0.03, 2.52, 0.01, 2.19e-07, 2.19e-07, 1.96e-07, nan ],
[ 40, 0.40, 0.03, 0.36, 0.04, 0.54, 0.02, 4.28, 0.00, 2.90e-07, 3.02e-07, 2.90e-07, nan ],
[ 41, 0.42, 0.03, 0.38, 0.04, 0.56, 0.03, 3.65, 0.00, 3.12e-07, 2.83e-07, 2.83e-07, nan ],
[ 42, 0.44, 0.03, 0.41, 0.04, 0.56, 0.03, 2.92, 0.01, 2.76e-07, 2.72e-07, 1.87e-07, nan ],
[ 43, 0.44, 0.04, 0.41, 0.04, 0.59, 0.03, 3.78, 0.00, 2.81e-07, 2.81e-07, 2.88e-07, nan ],
[ 44, 0.44, 0.04, 0.43, 0.04, 0.65, 0.02, 3.95, 0.00, 2.82e-07, 2.68e-07, 1.94e-07, nan ],
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[ 1600, 81.34, 0.25, 125.89, 0.16, 42.54, 0.48, 22.21, 0.92, 1.84e-06, 1.98e-06, 1.99e-06, nan ],
[ 1700, 86.35, 0.27, 132.23, 0.17, 44.08, 0.52, 19.70, 1.17, 1.90e-06, 2.09e-06, 1.96e-06, nan ],
[ 1800, 91.05, 0.28, 138.08, 0.19, 46.66, 0.56, 17.99, 1.44, 2.27e-06, 2.32e-06, 2.11e-06, nan ],
[ 1900, 96.29, 0.30, 147.48, 0.20, 49.50, 0.58, 17.34, 1.67, 2.27e-06, 2.08e-06, 1.94e-06, nan ],
[ 2000, 100.69, 0.32, 149.74, 0.21, 51.56, 0.62, 17.13, 1.87, 2.41e-06, 2.20e-06, 2.13e-06, nan ],
[ 2100, 103.26, 0.34, 155.55, 0.23, 38.66, 0.91, 17.13, 2.06, 2.33e-06, 2.47e-06, 2.34e-06, nan ],
[ 2200, 110.70, 0.35, 164.32, 0.24, 40.87, 0.95, 16.86, 2.30, 2.41e-06, 2.62e-06, 2.43e-06, nan ],
[ 2300, 114.45, 0.37, 170.13, 0.25, 42.47, 1.00, 17.55, 2.41, 2.65e-06, 2.50e-06, 2.51e-06, nan ],
[ 2400, 120.34, 0.38, 179.40, 0.26, 44.12, 1.04, 17.28, 2.67, 2.51e-06, 2.74e-06, 2.62e-06, nan ],
[ 2500, 124.76, 0.40, 182.63, 0.27, 45.90, 1.09, 17.29, 2.89, 2.87e-06, 2.60e-06, 2.58e-06, nan ],
[ 2600, 130.06, 0.42, 191.85, 0.28, 47.76, 1.13, 17.24, 3.14, 2.64e-06, 2.72e-06, 2.90e-06, nan ],
[ 2700, 134.18, 0.43, 195.80, 0.30, 49.24, 1.19, 17.55, 3.32, 2.82e-06, 2.94e-06, 2.77e-06, nan ],
[ 2800, 138.53, 0.45, 199.85, 0.31, 50.32, 1.25, 17.70, 3.55, 2.73e-06, 2.55e-06, 2.66e-06, nan ],
[ 2900, 142.31, 0.47, 208.37, 0.32, 52.30, 1.29, 18.28, 3.68, 2.62e-06, 2.53e-06, 2.53e-06, nan ],
[ 3000, 145.82, 0.49, 215.66, 0.33, 53.84, 1.34, 17.49, 4.12, 3.28e-06, 2.94e-06, 3.06e-06, nan ],
[ 3100, 152.03, 0.51, 222.95, 0.34, 43.46, 1.77, 17.07, 4.51, 2.84e-06, 2.84e-06, 2.76e-06, nan ],
[ 3200, 155.83, 0.53, 232.90, 0.35, 45.13, 1.82, 17.71, 4.63, 2.50e-06, 2.83e-06, 2.70e-06, nan ],
[ 3300, 161.12, 0.54, 228.77, 0.38, 46.44, 1.88, 17.74, 4.91, 3.05e-06, 2.75e-06, 2.67e-06, nan ],
[ 3400, 164.29, 0.56, 241.48, 0.38, 47.64, 1.94, 17.81, 5.20, 3.29e-06, 3.45e-06, 2.87e-06, nan ],
[ 3500, 167.64, 0.58, 247.57, 0.40, 48.85, 2.01, 17.77, 5.52, 2.75e-06, 2.74e-06, 2.73e-06, nan ],
[ 3600, 172.30, 0.60, 246.35, 0.42, 50.13, 2.07, 16.44, 6.31, 2.92e-06, 3.01e-06, 2.92e-06, nan ],
[ 3700, 174.47, 0.63, 255.45, 0.43, 51.23, 2.14, 17.86, 6.13, 2.64e-06, 2.66e-06, 2.71e-06, nan ],
[ 3800, 180.53, 0.64, 255.65, 0.45, 52.46, 2.20, 17.72, 6.52, 2.87e-06, 3.09e-06, 2.99e-06, nan ],
[ 3900, 177.22, 0.69, 252.50, 0.48, 53.72, 2.27, 17.93, 6.79, 3.52e-06, 3.90e-06, 3.58e-06, nan ],
[ 4000, 175.40, 0.73, 262.37, 0.49, 54.72, 2.34, 17.59, 7.28, 3.26e-06, 3.19e-06, 3.56e-06, nan ],
[ 4100, 179.59, 0.75, 265.28, 0.51, 46.14, 2.92, 17.97, 7.49, 3.35e-06, 3.85e-06, 3.38e-06, nan ],
[ 4200, 176.70, 0.80, 259.02, 0.55, 47.23, 2.99, 17.82, 7.92, 3.97e-06, 3.47e-06, 3.72e-06, nan ],
[ 4300, 180.89, 0.82, 258.71, 0.57, 48.43, 3.06, 17.94, 8.25, 3.72e-06, 4.09e-06, 4.02e-06, nan ],
[ 4400, 184.19, 0.84, 255.64, 0.61, 49.39, 3.14, 18.05, 8.58, 3.89e-06, 4.00e-06, 4.11e-06, nan ],
[ 4500, 184.15, 0.88, 254.38, 0.64, 50.42, 3.21, 17.92, 9.04, 3.50e-06, 3.78e-06, 3.92e-06, nan ],
[ 4600, 186.46, 0.91, 254.29, 0.67, 51.61, 3.28, 16.77, 10.10, 3.67e-06, 4.01e-06, 3.83e-06, nan ],
[ 4700, 186.86, 0.95, 257.27, 0.69, 52.44, 3.37, 17.87, 9.89, 3.25e-06, 3.96e-06, 3.58e-06, nan ],
[ 4800, 189.13, 0.97, 260.82, 0.71, 53.61, 3.44, 16.83, 10.96, 3.40e-06, 3.51e-06, 3.21e-06, nan ],
[ 4900, 187.07, 1.03, 259.63, 0.74, 54.29, 3.54, 17.88, 10.75, 3.81e-06, 3.63e-06, 3.46e-06, nan ],
[ 5000, 188.40, 1.06, 260.84, 0.77, 54.99, 3.64, 17.64, 11.34, 3.82e-06, 3.93e-06, 3.68e-06, nan ],
[ 5100, 191.66, 1.09, 257.90, 0.81, 55.91, 3.72, 17.68, 11.78, 4.54e-06, 4.33e-06, 4.23e-06, nan ],
[ 5200, 191.47, 1.13, 258.20, 0.84, 48.62, 4.45, 17.26, 12.54, 4.56e-06, 3.60e-06, 3.79e-06, nan ],
[ 5300, 191.94, 1.17, 262.04, 0.86, 49.76, 4.52, 17.82, 12.62, 4.35e-06, 4.20e-06, 3.78e-06, nan ],
[ 5400, 194.27, 1.20, 259.81, 0.90, 50.70, 4.60, 17.99, 12.97, 4.37e-06, 4.19e-06, 4.00e-06, nan ],
[ 5500, 194.28, 1.25, 260.00, 0.93, 51.46, 4.70, 17.94, 13.49, 3.96e-06, 4.21e-06, 3.79e-06, nan ],
[ 5600, 198.22, 1.27, 268.37, 0.94, 52.12, 4.82, 17.86, 14.05, 4.30e-06, 4.23e-06, 4.11e-06, nan ],
[ 5700, 200.45, 1.30, 262.64, 0.99, 52.79, 4.93, 17.82, 14.59, 4.82e-06, 4.57e-06, 4.38e-06, nan ],
[ 5800, 199.09, 1.35, 267.61, 1.01, 53.51, 5.03, 17.96, 14.99, 4.72e-06, 4.16e-06, 4.47e-06, nan ],
[ 5900, 203.15, 1.37, 266.56, 1.04, 54.17, 5.14, 18.07, 15.42, 4.44e-06, 4.57e-06, 4.30e-06, nan ],
[ 6000, 202.69, 1.42, 269.76, 1.07, 54.76, 5.26, 18.14, 15.88, 3.96e-06, 4.19e-06, 4.37e-06, nan ],
[ 6100, 205.20, 1.45, 266.80, 1.12, 56.28, 5.29, 17.88, 16.65, 4.42e-06, 4.36e-06, 4.26e-06, nan ],
[ 6200, 207.12, 1.49, 270.98, 1.14, 50.06, 6.14, 17.99, 17.09, 4.30e-06, 3.72e-06, 3.63e-06, nan ],
[ 6300, 208.37, 1.52, 271.91, 1.17, 50.13, 6.34, 17.94, 17.71, 4.10e-06, 3.97e-06, 3.92e-06, nan ],
[ 6400, 209.85, 1.56, 271.57, 1.21, 51.56, 6.36, 19.63, 16.70, 4.27e-06, 4.06e-06, 4.05e-06, nan ],
[ 6500, 212.24, 1.59, 276.25, 1.22, 51.80, 6.53, 18.00, 18.78, 4.34e-06, 4.60e-06, 4.33e-06, nan ],
[ 6600, 213.18, 1.64, 274.91, 1.27, 51.85, 6.72, 18.11, 19.25, 4.47e-06, 4.21e-06, 4.24e-06, nan ],
[ 6700, 214.71, 1.67, 273.98, 1.31, 52.51, 6.84, 18.11, 19.84, 4.60e-06, 3.92e-06, 4.02e-06, nan ],
[ 6800, 212.88, 1.74, 275.89, 1.34, 53.18, 6.96, 18.16, 20.37, 5.02e-06, 4.63e-06, 4.52e-06, nan ],
[ 6900, 216.07, 1.76, 277.31, 1.37, 53.74, 7.09, 18.00, 21.17, 4.32e-06, 4.04e-06, 4.02e-06, nan ],
[ 7000, 218.19, 1.80, 278.26, 1.41, 54.96, 7.13, 18.11, 21.65, 3.98e-06, 4.41e-06, 4.03e-06, nan ],
[ 7100, 216.76, 1.86, 283.29, 1.42, 55.11, 7.32, 18.08, 22.31, 3.96e-06, 4.29e-06, 3.91e-06, nan ],
[ 7200, 219.23, 1.89, 282.76, 1.47, 50.10, 8.28, 17.71, 23.42, 4.42e-06, 4.70e-06, 4.28e-06, nan ],
[ 7300, 223.14, 1.91, 284.65, 1.50, 50.74, 8.40, 17.67, 24.14, 4.03e-06, 3.88e-06, 4.05e-06, nan ],
[ 7400, 222.20, 1.97, 284.49, 1.54, 51.15, 8.57, 17.91, 24.46, 4.23e-06, 4.36e-06, 4.13e-06, nan ],
[ 7500, 224.39, 2.01, 282.23, 1.59, 51.94, 8.67, 17.97, 25.05, 5.06e-06, 4.69e-06, 4.76e-06, nan ],
[ 7600, 228.22, 2.03, 281.63, 1.64, 52.31, 8.84, 17.87, 25.87, 4.94e-06, 5.63e-06, 4.68e-06, nan ],
[ 7700, 225.17, 2.11, 280.73, 1.69, 53.66, 8.84, 16.44, 28.86, 4.60e-06, 4.57e-06, 4.48e-06, nan ],
[ 7800, 224.26, 2.17, 283.87, 1.71, 53.24, 9.14, 17.82, 27.32, 4.75e-06, 4.47e-06, 4.67e-06, nan ],
[ 7900, 224.83, 2.22, 282.47, 1.77, 54.19, 9.22, 17.83, 28.00, 5.08e-06, 4.45e-06, 4.40e-06, nan ],
[ 8000, 226.50, 2.26, 282.77, 1.81, 54.30, 9.43, 17.73, 28.88, 5.85e-06, 5.49e-06, 5.44e-06, nan ],
[ 8100, 226.77, 2.32, 284.08, 1.85, 55.60, 9.44, 17.26, 30.41, 5.83e-06, 5.85e-06, 5.67e-06, nan ],
[ 8200, 224.83, 2.39, 286.92, 1.88, 49.90, 10.78, 16.65, 32.32, 4.92e-06, 4.59e-06, 5.16e-06, nan ],
[ 8300, 227.40, 2.42, 285.61, 1.93, 50.76, 10.86, 17.03, 32.37, 5.43e-06, 5.51e-06, 5.73e-06, nan ],
[ 8400, 225.12, 2.51, 286.44, 1.97, 51.50, 10.96, 16.65, 33.90, 4.96e-06, 5.60e-06, 4.89e-06, nan ],
[ 8500, 229.40, 2.52, 284.63, 2.03, 52.00, 11.12, 17.78, 32.51, 5.41e-06, 5.41e-06, 5.64e-06, nan ],
[ 8600, 227.86, 2.60, 286.32, 2.07, 52.61, 11.25, 17.80, 33.25, 5.15e-06, 4.98e-06, 5.36e-06, nan ],
[ 8700, 226.74, 2.67, 289.05, 2.10, 52.73, 11.49, 18.19, 33.30, 5.43e-06, 5.18e-06, 5.32e-06, nan ],
[ 8800, 224.33, 2.76, 285.81, 2.17, 53.56, 11.57, 17.92, 34.58, 5.13e-06, 5.60e-06, 5.29e-06, nan ],
[ 8900, 227.91, 2.78, 287.04, 2.21, 54.03, 11.73, 17.85, 35.50, 5.01e-06, 4.99e-06, 5.17e-06, nan ],
[ 9000, 226.85, 2.86, 283.02, 2.29, 54.48, 11.90, 17.94, 36.13, 5.66e-06, 5.96e-06, 6.03e-06, nan ],
[ 10000, 234.22, 3.42, 285.95, 2.80, 54.54, 14.67, 17.67, 45.27, 5.57e-06, 5.37e-06, 5.49e-06, nan ],
[ 12000, 238.44, 4.83, 291.02, 3.96, 54.29, 21.22, 17.40, 66.22, 7.29e-06, 6.05e-06, 6.06e-06, nan ],
[ 14000, 248.44, 6.31, 293.55, 5.34, 54.85, 28.59, 16.85, 93.08, 6.63e-06, 6.07e-06, 7.14e-06, nan ],
[ 16000, 253.09, 8.09, 291.10, 7.04, 54.93, 37.29, 15.65, 130.87, 6.96e-06, 8.01e-06, 7.32e-06, nan ],
[ 18000, 252.46, 10.27, 296.09, 8.75, 54.98, 47.15, 16.55, 156.62, 8.14e-06, 8.25e-06, 7.92e-06, nan ],
[ 20000, 255.45, 12.53, 294.49, 10.87, 54.14, 59.11, 17.20, 186.02, 8.79e-06, 8.79e-06, 9.47e-06, nan ],
])
# numactl --interleave=all ../testing/testing_chemv -U -N 123 -N 1234 --range 10:90:1 --range 100:900:10 --range 1000:9000:100 --range 10000:20000:2000
chemv_U = array([
[ 10, 0.03, 0.03, 0.03, 0.03, 0.04, 0.02, 0.48, 0.00, 2.13e-07, 1.07e-07, 2.86e-07, nan ],
[ 11, 0.04, 0.03, 0.04, 0.03, 0.05, 0.02, 0.58, 0.00, 1.94e-07, 1.23e-07, 1.73e-07, nan ],
[ 12, 0.04, 0.03, 0.04, 0.03, 0.06, 0.02, 1.36, 0.00, 1.78e-07, 1.59e-07, 1.78e-07, nan ],
[ 13, 0.05, 0.03, 0.05, 0.03, 0.07, 0.02, 0.70, 0.00, 2.07e-07, 1.49e-07, 1.51e-07, nan ],
[ 14, 0.05, 0.03, 0.06, 0.03, 0.09, 0.02, 0.91, 0.00, 1.52e-07, 1.52e-07, 2.04e-07, nan ],
[ 15, 0.07, 0.03, 0.07, 0.03, 0.10, 0.02, 1.04, 0.00, 1.95e-07, 1.42e-07, 1.80e-07, nan ],
[ 16, 0.07, 0.03, 0.08, 0.03, 0.12, 0.02, 1.17, 0.00, 1.33e-07, 2.40e-07, 2.38e-07, nan ],
[ 17, 0.08, 0.03, 0.09, 0.03, 0.11, 0.02, 1.17, 0.00, 1.59e-07, 1.62e-07, 1.59e-07, nan ],
[ 18, 0.09, 0.03, 0.10, 0.03, 0.13, 0.02, 0.98, 0.00, 2.37e-07, 2.12e-07, 2.37e-07, nan ],
[ 19, 0.10, 0.03, 0.11, 0.03, 0.13, 0.02, 0.45, 0.01, 3.17e-07, 2.24e-07, 2.24e-07, nan ],
[ 20, 0.12, 0.03, 0.12, 0.03, 0.16, 0.02, 1.80, 0.00, 1.97e-07, 1.55e-07, 2.13e-07, nan ],
[ 21, 0.12, 0.03, 0.13, 0.03, 0.16, 0.02, 1.76, 0.00, 2.84e-07, 1.83e-07, 2.75e-07, nan ],
[ 22, 0.13, 0.03, 0.14, 0.03, 0.18, 0.02, 1.93, 0.00, 2.60e-07, 3.57e-07, 1.94e-07, nan ],
[ 23, 0.15, 0.03, 0.15, 0.03, 0.20, 0.02, 2.36, 0.00, 2.99e-07, 1.85e-07, 2.52e-07, nan ],
[ 24, 0.15, 0.03, 0.16, 0.03, 0.21, 0.02, 2.28, 0.00, 2.41e-07, 2.38e-07, 2.40e-07, nan ],
[ 25, 0.16, 0.03, 0.17, 0.03, 0.22, 0.02, 2.47, 0.00, 2.36e-07, 2.41e-07, 2.41e-07, nan ],
[ 26, 0.17, 0.03, 0.19, 0.03, 0.25, 0.02, 2.67, 0.00, 1.85e-07, 1.64e-07, 1.64e-07, nan ],
[ 27, 0.18, 0.03, 0.20, 0.03, 0.26, 0.02, 3.23, 0.00, 2.23e-07, 2.13e-07, 2.55e-07, nan ],
[ 28, 0.20, 0.03, 0.21, 0.03, 0.28, 0.02, 2.13, 0.00, 2.46e-07, 2.28e-07, 2.75e-07, nan ],
[ 29, 0.22, 0.03, 0.22, 0.03, 0.28, 0.03, 2.47, 0.00, 1.97e-07, 2.71e-07, 1.47e-07, nan ],
[ 30, 0.24, 0.03, 0.24, 0.03, 0.33, 0.02, 2.44, 0.00, 2.01e-07, 1.42e-07, 2.29e-07, nan ],
[ 31, 0.26, 0.03, 0.26, 0.03, 0.32, 0.03, 2.60, 0.00, 1.95e-07, 2.54e-07, 2.63e-07, nan ],
[ 32, 0.26, 0.03, 0.24, 0.04, 0.36, 0.02, 2.77, 0.00, 2.15e-07, 2.46e-07, 1.81e-07, nan ],
[ 33, 0.27, 0.03, 0.23, 0.04, 0.35, 0.03, 2.94, 0.00, 2.33e-07, 1.83e-07, 2.38e-07, nan ],
[ 34, 0.31, 0.03, 0.24, 0.04, 0.39, 0.03, 2.38, 0.00, 3.37e-07, 2.31e-07, 3.41e-07, nan ],
[ 35, 0.32, 0.03, 0.26, 0.04, 0.41, 0.03, 2.52, 0.00, 3.31e-07, 3.45e-07, 3.31e-07, nan ],
[ 36, 0.34, 0.03, 0.28, 0.04, 0.44, 0.02, 3.77, 0.00, 3.35e-07, 3.18e-07, 2.37e-07, nan ],
[ 37, 0.36, 0.03, 0.29, 0.04, 0.47, 0.02, 2.81, 0.00, 2.31e-07, 3.46e-07, 2.92e-07, nan ],
[ 38, 0.35, 0.03, 0.31, 0.04, 0.50, 0.02, 3.15, 0.00, 3.05e-07, 2.37e-07, 3.62e-07, nan ],
[ 39, 0.37, 0.03, 0.32, 0.04, 0.50, 0.03, 3.12, 0.00, 3.09e-07, 3.09e-07, 1.97e-07, nan ],
[ 40, 0.41, 0.03, 0.35, 0.04, 0.53, 0.03, 4.28, 0.00, 2.13e-07, 2.13e-07, 2.38e-07, nan ],
[ 41, 0.41, 0.03, 0.36, 0.04, 0.56, 0.02, 3.65, 0.00, 3.35e-07, 2.83e-07, 2.33e-07, nan ],
[ 42, 0.43, 0.03, 0.36, 0.04, 0.58, 0.03, 3.61, 0.00, 3.05e-07, 2.76e-07, 2.81e-07, nan ],
[ 43, 0.48, 0.03, 0.40, 0.04, 0.61, 0.03, 3.78, 0.00, 4.70e-07, 3.66e-07, 3.75e-07, nan ],
[ 44, 0.50, 0.03, 0.43, 0.04, 0.64, 0.03, 3.95, 0.00, 2.76e-07, 2.68e-07, 3.48e-07, nan ],
[ 45, 0.54, 0.03, 0.45, 0.04, 0.67, 0.03, 3.34, 0.01, 2.68e-07, 3.39e-07, 3.06e-07, nan ],
[ 46, 0.55, 0.03, 0.46, 0.04, 0.73, 0.02, 4.58, 0.00, 2.78e-07, 2.62e-07, 4.15e-07, nan ],
[ 47, 0.55, 0.03, 0.48, 0.04, 0.76, 0.02, 3.64, 0.01, 3.42e-07, 4.08e-07, 2.57e-07, nan ],
[ 48, 0.61, 0.03, 0.51, 0.04, 0.80, 0.02, 3.99, 0.00, 3.28e-07, 3.20e-07, 2.51e-07, nan ],
[ 49, 0.62, 0.03, 0.54, 0.04, 0.79, 0.03, 3.95, 0.01, 3.90e-07, 3.48e-07, 3.40e-07, nan ],
[ 50, 0.63, 0.03, 0.50, 0.04, 0.79, 0.03, 4.11, 0.01, 3.08e-07, 3.15e-07, 3.24e-07, nan ],
[ 51, 0.65, 0.03, 0.56, 0.04, 0.82, 0.03, 4.28, 0.01, 2.92e-07, 3.08e-07, 3.08e-07, nan ],
[ 52, 0.70, 0.03, 0.60, 0.04, 0.86, 0.03, 5.83, 0.00, 3.10e-07, 3.69e-07, 2.94e-07, nan ],
[ 53, 0.70, 0.03, 0.63, 0.04, 0.92, 0.03, 4.62, 0.01, 3.86e-07, 3.60e-07, 3.60e-07, nan ],
[ 54, 0.72, 0.03, 0.63, 0.04, 0.92, 0.03, 4.02, 0.01, 3.60e-07, 2.55e-07, 2.30e-07, nan ],
[ 55, 0.76, 0.03, 0.64, 0.04, 0.96, 0.03, 4.17, 0.01, 2.94e-07, 2.77e-07, 4.04e-07, nan ],
[ 56, 0.74, 0.04, 0.66, 0.04, 0.96, 0.03, 4.32, 0.01, 3.47e-07, 2.81e-07, 3.47e-07, nan ],
[ 57, 0.79, 0.03, 0.69, 0.04, 0.99, 0.03, 5.33, 0.01, 2.76e-07, 2.68e-07, 2.76e-07, nan ],
[ 58, 0.75, 0.04, 0.71, 0.04, 1.02, 0.03, 5.51, 0.01, 2.94e-07, 2.63e-07, 2.37e-07, nan ],
[ 59, 0.79, 0.04, 0.73, 0.04, 1.02, 0.03, 4.79, 0.01, 2.76e-07, 2.67e-07, 2.04e-07, nan ],
[ 60, 0.80, 0.04, 0.74, 0.04, 0.95, 0.03, 4.76, 0.01, 2.70e-07, 3.18e-07, 2.29e-07, nan ],
[ 61, 0.90, 0.03, 0.76, 0.04, 1.12, 0.03, 5.12, 0.01, 3.26e-07, 3.13e-07, 3.15e-07, nan ],
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[ 3200, 156.68, 0.52, 202.81, 0.40, 44.30, 1.85, 17.07, 4.80, 3.16e-06, 3.15e-06, 2.85e-06, nan ],
[ 3300, 162.62, 0.54, 184.73, 0.47, 46.24, 1.88, 17.00, 5.13, 3.85e-06, 3.85e-06, 4.00e-06, nan ],
[ 3400, 164.64, 0.56, 184.70, 0.50, 47.52, 1.95, 16.97, 5.45, 2.96e-06, 2.87e-06, 2.95e-06, nan ],
[ 3500, 170.84, 0.57, 186.75, 0.52, 48.68, 2.01, 17.10, 5.73, 2.93e-06, 2.66e-06, 2.86e-06, nan ],
[ 3600, 169.74, 0.61, 190.31, 0.55, 49.91, 2.08, 16.32, 6.36, 3.42e-06, 3.34e-06, 3.80e-06, nan ],
[ 3700, 177.29, 0.62, 193.98, 0.56, 51.39, 2.13, 17.12, 6.40, 2.87e-06, 2.78e-06, 3.11e-06, nan ],
[ 3800, 179.19, 0.64, 197.52, 0.59, 52.65, 2.19, 17.13, 6.75, 2.70e-06, 2.64e-06, 2.83e-06, nan ],
[ 3900, 186.13, 0.65, 196.60, 0.62, 53.84, 2.26, 17.18, 7.08, 3.01e-06, 3.01e-06, 3.20e-06, nan ],
[ 4000, 185.84, 0.69, 217.79, 0.59, 54.91, 2.33, 17.54, 7.30, 3.19e-06, 3.50e-06, 3.35e-06, nan ],
[ 4100, 185.79, 0.72, 190.82, 0.71, 47.69, 2.82, 17.54, 7.67, 3.16e-06, 3.20e-06, 3.25e-06, nan ],
[ 4200, 190.51, 0.74, 196.91, 0.72, 46.90, 3.01, 17.25, 8.19, 2.94e-06, 3.14e-06, 3.05e-06, nan ],
[ 4300, 193.16, 0.77, 202.69, 0.73, 48.01, 3.08, 17.38, 8.51, 4.10e-06, 3.99e-06, 3.66e-06, nan ],
[ 4400, 200.44, 0.77, 197.34, 0.79, 48.81, 3.17, 17.31, 8.95, 3.93e-06, 4.10e-06, 4.22e-06, nan ],
[ 4500, 203.32, 0.80, 203.87, 0.79, 50.34, 3.22, 17.41, 9.31, 3.64e-06, 3.49e-06, 3.36e-06, nan ],
[ 4600, 211.19, 0.80, 204.27, 0.83, 51.20, 3.31, 15.79, 10.73, 3.72e-06, 4.14e-06, 3.93e-06, nan ],
[ 4700, 208.98, 0.85, 207.46, 0.85, 52.02, 3.40, 17.36, 10.18, 3.53e-06, 3.32e-06, 3.45e-06, nan ],
[ 4800, 213.39, 0.86, 231.68, 0.80, 53.27, 3.46, 17.28, 10.67, 3.75e-06, 3.49e-06, 4.00e-06, nan ],
[ 4900, 217.34, 0.88, 211.35, 0.91, 54.31, 3.54, 17.43, 11.02, 4.77e-06, 4.12e-06, 4.09e-06, nan ],
[ 5000, 217.89, 0.92, 212.16, 0.94, 55.40, 3.61, 17.54, 11.41, 3.91e-06, 4.21e-06, 4.11e-06, nan ],
[ 5100, 224.54, 0.93, 214.13, 0.97, 55.88, 3.73, 17.49, 11.90, 3.84e-06, 4.02e-06, 4.12e-06, nan ],
[ 5200, 225.88, 0.96, 216.40, 1.00, 48.07, 4.50, 17.31, 12.50, 3.98e-06, 3.85e-06, 3.95e-06, nan ],
[ 5300, 225.23, 1.00, 215.94, 1.04, 49.22, 4.57, 17.42, 12.90, 4.05e-06, 4.15e-06, 3.98e-06, nan ],
[ 5400, 229.69, 1.02, 220.13, 1.06, 50.30, 4.64, 17.49, 13.34, 4.03e-06, 4.35e-06, 4.22e-06, nan ],
[ 5500, 226.23, 1.07, 218.30, 1.11, 50.83, 4.76, 17.49, 13.84, 4.02e-06, 3.92e-06, 3.92e-06, nan ],
[ 5600, 235.21, 1.07, 245.52, 1.02, 51.96, 4.83, 17.57, 14.28, 4.29e-06, 4.24e-06, 4.10e-06, nan ],
[ 5700, 236.80, 1.10, 227.04, 1.15, 52.89, 4.92, 17.69, 14.70, 4.77e-06, 5.23e-06, 4.91e-06, nan ],
[ 5800, 241.20, 1.12, 220.65, 1.22, 53.98, 4.99, 17.51, 15.37, 4.00e-06, 3.68e-06, 3.88e-06, nan ],
[ 5900, 244.98, 1.14, 223.90, 1.24, 54.50, 5.11, 17.87, 15.59, 3.98e-06, 4.41e-06, 4.33e-06, nan ],
[ 6000, 248.77, 1.16, 226.31, 1.27, 55.54, 5.19, 17.71, 16.27, 4.32e-06, 4.43e-06, 4.41e-06, nan ],
[ 6100, 245.26, 1.21, 225.22, 1.32, 55.91, 5.33, 17.79, 16.74, 4.43e-06, 4.31e-06, 4.45e-06, nan ],
[ 6200, 251.93, 1.22, 228.87, 1.34, 49.58, 6.20, 17.61, 17.46, 4.39e-06, 4.14e-06, 4.38e-06, nan ],
[ 6300, 255.29, 1.24, 223.06, 1.42, 49.94, 6.36, 17.99, 17.66, 4.73e-06, 4.70e-06, 4.44e-06, nan ],
[ 6400, 254.91, 1.29, 251.92, 1.30, 50.96, 6.43, 19.16, 17.10, 4.45e-06, 4.46e-06, 4.65e-06, nan ],
[ 6500, 256.51, 1.32, 223.59, 1.51, 51.72, 6.54, 17.76, 19.03, 4.66e-06, 4.47e-06, 4.30e-06, nan ],
[ 6600, 262.24, 1.33, 229.47, 1.52, 52.71, 6.61, 17.72, 19.67, 4.92e-06, 4.68e-06, 4.92e-06, nan ],
[ 6700, 268.03, 1.34, 226.32, 1.59, 53.08, 6.77, 17.92, 20.05, 4.93e-06, 4.88e-06, 4.53e-06, nan ],
[ 6800, 264.83, 1.40, 222.46, 1.66, 53.98, 6.86, 17.62, 21.00, 4.60e-06, 4.45e-06, 4.74e-06, nan ],
[ 6900, 273.66, 1.39, 228.40, 1.67, 54.07, 7.05, 17.91, 21.28, 4.67e-06, 4.89e-06, 4.82e-06, nan ],
[ 7000, 270.04, 1.45, 231.85, 1.69, 55.39, 7.08, 17.64, 22.23, 4.26e-06, 4.55e-06, 4.46e-06, nan ],
[ 7100, 274.60, 1.47, 228.16, 1.77, 56.22, 7.17, 17.91, 22.53, 4.55e-06, 4.41e-06, 4.72e-06, nan ],
[ 7200, 278.24, 1.49, 262.18, 1.58, 49.93, 8.31, 17.81, 23.29, 4.22e-06, 4.27e-06, 4.23e-06, nan ],
[ 7300, 276.34, 1.54, 233.27, 1.83, 50.93, 8.37, 17.48, 24.39, 5.24e-06, 5.12e-06, 5.05e-06, nan ],
[ 7400, 278.54, 1.57, 233.55, 1.88, 51.69, 8.48, 17.52, 25.02, 4.60e-06, 4.37e-06, 4.52e-06, nan ],
[ 7500, 277.82, 1.62, 236.78, 1.90, 52.17, 8.63, 17.72, 25.39, 5.21e-06, 4.73e-06, 4.96e-06, nan ],
[ 7600, 287.78, 1.61, 236.63, 1.95, 52.87, 8.74, 17.58, 26.28, 4.72e-06, 4.69e-06, 5.04e-06, nan ],
[ 7700, 286.97, 1.65, 233.36, 2.03, 53.61, 8.85, 16.69, 28.43, 4.32e-06, 4.09e-06, 4.32e-06, nan ],
[ 7800, 289.09, 1.68, 235.05, 2.07, 54.32, 8.96, 16.92, 28.77, 4.46e-06, 4.44e-06, 4.60e-06, nan ],
[ 7900, 284.54, 1.75, 237.69, 2.10, 54.72, 9.13, 17.60, 28.38, 4.46e-06, 4.52e-06, 4.59e-06, nan ],
[ 8000, 285.28, 1.80, 263.84, 1.94, 55.41, 9.24, 17.69, 28.95, 5.27e-06, 4.86e-06, 4.44e-06, nan ],
[ 8100, 287.83, 1.82, 240.83, 2.18, 56.06, 9.37, 17.41, 30.16, 4.34e-06, 4.73e-06, 4.32e-06, nan ],
[ 8200, 281.69, 1.91, 236.72, 2.27, 51.54, 10.44, 16.13, 33.37, 4.80e-06, 4.50e-06, 4.70e-06, nan ],
[ 8300, 283.71, 1.94, 235.15, 2.34, 50.99, 10.81, 16.31, 33.80, 5.24e-06, 4.67e-06, 4.83e-06, nan ],
[ 8400, 275.83, 2.05, 238.62, 2.37, 51.84, 10.89, 17.53, 32.21, 5.19e-06, 4.73e-06, 5.06e-06, nan ],
[ 8500, 284.49, 2.03, 241.77, 2.39, 52.51, 11.01, 17.66, 32.73, 5.19e-06, 5.32e-06, 5.76e-06, nan ],
[ 8600, 277.18, 2.14, 240.07, 2.47, 53.13, 11.14, 17.56, 33.71, 5.26e-06, 5.40e-06, 5.18e-06, nan ],
[ 8700, 282.21, 2.15, 238.54, 2.54, 53.54, 11.31, 17.64, 34.34, 5.61e-06, 5.89e-06, 5.39e-06, nan ],
[ 8800, 271.06, 2.29, 271.17, 2.29, 54.14, 11.45, 17.79, 34.83, 5.56e-06, 6.12e-06, 5.77e-06, nan ],
[ 8900, 273.99, 2.31, 243.10, 2.61, 54.83, 11.56, 17.77, 35.67, 5.21e-06, 5.30e-06, 5.21e-06, nan ],
[ 9000, 275.19, 2.36, 239.25, 2.71, 55.31, 11.72, 17.52, 36.99, 5.86e-06, 5.36e-06, 5.32e-06, nan ],
[ 10000, 273.06, 2.93, 246.20, 3.25, 54.78, 14.61, 17.44, 45.89, 6.36e-06, 6.46e-06, 6.26e-06, nan ],
[ 12000, 272.96, 4.22, 281.82, 4.09, 55.21, 20.87, 17.47, 65.96, 6.27e-06, 6.43e-06, 6.34e-06, nan ],
[ 14000, 292.80, 5.36, 251.79, 6.23, 55.43, 28.29, 16.95, 92.53, 6.79e-06, 6.49e-06, 6.86e-06, nan ],
[ 16000, 295.56, 6.93, 286.86, 7.14, 55.23, 37.08, 15.47, 132.36, 8.87e-06, 8.64e-06, 9.27e-06, nan ],
[ 18000, 286.28, 9.05, 260.26, 9.96, 54.47, 47.59, 16.31, 158.92, 7.91e-06, 8.04e-06, 8.33e-06, nan ],
[ 20000, 290.03, 11.03, 292.17, 10.95, 53.57, 59.74, 17.22, 185.87, 9.58e-06, 1.02e-05, 9.49e-06, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/cpotrf.txt
# numactl --interleave=all ../testing/testing_cpotrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
cpotrf = array([
[ 10, nan, nan, 0.33, 0.00, nan ],
[ 20, nan, nan, 1.19, 0.00, nan ],
[ 30, nan, nan, 2.58, 0.00, nan ],
[ 40, nan, nan, 4.56, 0.00, nan ],
[ 50, nan, nan, 3.01, 0.00, nan ],
[ 60, nan, nan, 5.43, 0.00, nan ],
[ 70, nan, nan, 6.45, 0.00, nan ],
[ 80, nan, nan, 6.88, 0.00, nan ],
[ 90, nan, nan, 8.10, 0.00, nan ],
[ 100, nan, nan, 8.69, 0.00, nan ],
[ 200, nan, nan, 41.02, 0.00, nan ],
[ 300, nan, nan, 17.59, 0.00, nan ],
[ 400, nan, nan, 33.80, 0.00, nan ],
[ 500, nan, nan, 58.03, 0.00, nan ],
[ 600, nan, nan, 70.90, 0.00, nan ],
[ 700, nan, nan, 99.63, 0.00, nan ],
[ 800, nan, nan, 111.37, 0.01, nan ],
[ 900, nan, nan, 145.55, 0.01, nan ],
[ 1000, nan, nan, 187.16, 0.01, nan ],
[ 2000, nan, nan, 573.72, 0.02, nan ],
[ 3000, nan, nan, 1011.85, 0.04, nan ],
[ 4000, nan, nan, 1318.35, 0.06, nan ],
[ 5000, nan, nan, 1545.89, 0.11, nan ],
[ 6000, nan, nan, 1759.68, 0.16, nan ],
[ 7000, nan, nan, 1899.25, 0.24, nan ],
[ 8000, nan, nan, 2032.99, 0.34, nan ],
[ 9000, nan, nan, 2145.05, 0.45, nan ],
[ 10000, nan, nan, 2229.54, 0.60, nan ],
[ 12000, nan, nan, 2387.09, 0.97, nan ],
[ 14000, nan, nan, 2504.14, 1.46, nan ],
[ 16000, nan, nan, 2598.13, 2.10, nan ],
[ 18000, nan, nan, 2649.71, 2.94, nan ],
[ 20000, nan, nan, 2710.26, 3.94, nan ],
])
# numactl --interleave=all ../testing/testing_cpotrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
cpotrf_gpu = array([
[ 10, nan, nan, 0.00, 0.00, nan ],
[ 20, nan, nan, 0.01, 0.00, nan ],
[ 30, nan, nan, 0.03, 0.00, nan ],
[ 40, nan, nan, 0.08, 0.00, nan ],
[ 50, nan, nan, 0.14, 0.00, nan ],
[ 60, nan, nan, 0.24, 0.00, nan ],
[ 70, nan, nan, 0.37, 0.00, nan ],
[ 80, nan, nan, 0.51, 0.00, nan ],
[ 90, nan, nan, 0.75, 0.00, nan ],
[ 100, nan, nan, 0.99, 0.00, nan ],
[ 200, nan, nan, 17.26, 0.00, nan ],
[ 300, nan, nan, 12.99, 0.00, nan ],
[ 400, nan, nan, 26.48, 0.00, nan ],
[ 500, nan, nan, 45.74, 0.00, nan ],
[ 600, nan, nan, 61.09, 0.00, nan ],
[ 700, nan, nan, 89.28, 0.01, nan ],
[ 800, nan, nan, 105.37, 0.01, nan ],
[ 900, nan, nan, 137.82, 0.01, nan ],
[ 1000, nan, nan, 174.87, 0.01, nan ],
[ 2000, nan, nan, 631.87, 0.02, nan ],
[ 3000, nan, nan, 1150.55, 0.03, nan ],
[ 4000, nan, nan, 1507.42, 0.06, nan ],
[ 5000, nan, nan, 1770.97, 0.09, nan ],
[ 6000, nan, nan, 1989.97, 0.14, nan ],
[ 7000, nan, nan, 2145.11, 0.21, nan ],
[ 8000, nan, nan, 2273.88, 0.30, nan ],
[ 9000, nan, nan, 2363.62, 0.41, nan ],
[ 10000, nan, nan, 2431.79, 0.55, nan ],
[ 12000, nan, nan, 2566.33, 0.90, nan ],
[ 14000, nan, nan, 2682.30, 1.36, nan ],
[ 16000, nan, nan, 2764.15, 1.98, nan ],
[ 18000, nan, nan, 2796.48, 2.78, nan ],
[ 20000, nan, nan, 2846.62, 3.75, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/dgeqrf.txt
# numactl --interleave=all ../testing/testing_dgeqrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
dgeqrf = array([
[ 10, 10, nan, nan, 0.01, 0.00, nan ],
[ 20, 20, nan, nan, 0.05, 0.00, nan ],
[ 30, 30, nan, nan, 0.16, 0.00, nan ],
[ 40, 40, nan, nan, 0.36, 0.00, nan ],
[ 50, 50, nan, nan, 0.64, 0.00, nan ],
[ 60, 60, nan, nan, 0.99, 0.00, nan ],
[ 70, 70, nan, nan, 0.53, 0.00, nan ],
[ 80, 80, nan, nan, 0.78, 0.00, nan ],
[ 90, 90, nan, nan, 1.04, 0.00, nan ],
[ 100, 100, nan, nan, 1.33, 0.00, nan ],
[ 200, 200, nan, nan, 4.94, 0.00, nan ],
[ 300, 300, nan, nan, 11.41, 0.00, nan ],
[ 400, 400, nan, nan, 18.41, 0.00, nan ],
[ 500, 500, nan, nan, 27.28, 0.01, nan ],
[ 600, 600, nan, nan, 36.17, 0.01, nan ],
[ 700, 700, nan, nan, 46.54, 0.01, nan ],
[ 800, 800, nan, nan, 56.05, 0.01, nan ],
[ 900, 900, nan, nan, 65.84, 0.01, nan ],
[ 1000, 1000, nan, nan, 78.03, 0.02, nan ],
[ 2000, 2000, nan, nan, 200.11, 0.05, nan ],
[ 3000, 3000, nan, nan, 318.52, 0.11, nan ],
[ 4000, 4000, nan, nan, 416.39, 0.21, nan ],
[ 5000, 5000, nan, nan, 532.87, 0.31, nan ],
[ 6000, 6000, nan, nan, 637.28, 0.45, nan ],
[ 7000, 7000, nan, nan, 707.45, 0.65, nan ],
[ 8000, 8000, nan, nan, 761.67, 0.90, nan ],
[ 9000, 9000, nan, nan, 805.55, 1.21, nan ],
[ 10000, 10000, nan, nan, 840.49, 1.59, nan ],
[ 12000, 12000, nan, nan, 918.36, 2.51, nan ],
[ 14000, 14000, nan, nan, 958.36, 3.82, nan ],
[ 16000, 16000, nan, nan, 986.38, 5.54, nan ],
[ 18000, 18000, nan, nan, 1006.20, 7.73, nan ],
[ 20000, 20000, nan, nan, 1026.70, 10.39, nan ],
])
# numactl --interleave=all ../testing/testing_dgeqrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
dgeqrf_gpu = array([
[ 10, 10, nan, nan, 0.00, 0.00, nan ],
[ 20, 20, nan, nan, 0.01, 0.00, nan ],
[ 30, 30, nan, nan, 0.03, 0.00, nan ],
[ 40, 40, nan, nan, 0.07, 0.00, nan ],
[ 50, 50, nan, nan, 0.13, 0.00, nan ],
[ 60, 60, nan, nan, 0.23, 0.00, nan ],
[ 70, 70, nan, nan, 0.28, 0.00, nan ],
[ 80, 80, nan, nan, 0.41, 0.00, nan ],
[ 90, 90, nan, nan, 0.57, 0.00, nan ],
[ 100, 100, nan, nan, 1.55, 0.00, nan ],
[ 200, 200, nan, nan, 3.55, 0.00, nan ],
[ 300, 300, nan, nan, 8.66, 0.00, nan ],
[ 400, 400, nan, nan, 15.07, 0.01, nan ],
[ 500, 500, nan, nan, 23.08, 0.01, nan ],
[ 600, 600, nan, nan, 31.25, 0.01, nan ],
[ 700, 700, nan, nan, 40.84, 0.01, nan ],
[ 800, 800, nan, nan, 51.02, 0.01, nan ],
[ 900, 900, nan, nan, 59.29, 0.02, nan ],
[ 1000, 1000, nan, nan, 71.68, 0.02, nan ],
[ 2000, 2000, nan, nan, 191.68, 0.06, nan ],
[ 3000, 3000, nan, nan, 315.89, 0.11, nan ],
[ 4000, 4000, nan, nan, 401.12, 0.21, nan ],
[ 5000, 5000, nan, nan, 522.65, 0.32, nan ],
[ 6000, 6000, nan, nan, 629.81, 0.46, nan ],
[ 7000, 7000, nan, nan, 700.79, 0.65, nan ],
[ 8000, 8000, nan, nan, 763.90, 0.89, nan ],
[ 9000, 9000, nan, nan, 808.15, 1.20, nan ],
[ 10000, 10000, nan, nan, 844.01, 1.58, nan ],
[ 12000, 12000, nan, nan, 902.83, 2.55, nan ],
[ 14000, 14000, nan, nan, 952.47, 3.84, nan ],
[ 16000, 16000, nan, nan, 984.75, 5.55, nan ],
[ 18000, 18000, nan, nan, 1006.21, 7.73, nan ],
[ 20000, 20000, nan, nan, 1031.65, 10.34, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/dgesvd.txt
# numactl --interleave=all ../testing/testing_dgesvd -UN -VN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
dgesvd_UN = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.00, nan ],
[ nan, 80, 80, nan, 0.00, nan ],
[ nan, 90, 90, nan, 0.00, nan ],
[ nan, 100, 100, nan, 0.00, nan ],
[ nan, 200, 200, nan, 0.01, nan ],
[ nan, 300, 300, nan, 0.03, nan ],
[ nan, 400, 400, nan, 0.05, nan ],
[ nan, 500, 500, nan, 0.07, nan ],
[ nan, 600, 600, nan, 0.09, nan ],
[ nan, 700, 700, nan, 0.13, nan ],
[ nan, 800, 800, nan, 0.15, nan ],
[ nan, 900, 900, nan, 0.19, nan ],
[ nan, 1000, 1000, nan, 0.23, nan ],
[ nan, 2000, 2000, nan, 0.89, nan ],
[ nan, 3000, 3000, nan, 2.29, nan ],
[ nan, 4000, 4000, nan, 4.62, nan ],
[ nan, 5000, 5000, nan, 8.25, nan ],
[ nan, 6000, 6000, nan, 13.23, nan ],
[ nan, 7000, 7000, nan, 19.88, nan ],
[ nan, 8000, 8000, nan, 28.43, nan ],
[ nan, 9000, 9000, nan, 39.52, nan ],
[ nan, 10000, 10000, nan, 52.77, nan ],
[ nan, 300, 100, nan, 0.00, nan ],
[ nan, 600, 200, nan, 0.02, nan ],
[ nan, 900, 300, nan, 0.03, nan ],
[ nan, 1200, 400, nan, 0.06, nan ],
[ nan, 1500, 500, nan, 0.08, nan ],
[ nan, 1800, 600, nan, 0.11, nan ],
[ nan, 2100, 700, nan, 0.15, nan ],
[ nan, 2400, 800, nan, 0.18, nan ],
[ nan, 2700, 900, nan, 0.24, nan ],
[ nan, 3000, 1000, nan, 0.29, nan ],
[ nan, 6000, 2000, nan, 1.24, nan ],
[ nan, 9000, 3000, nan, 3.30, nan ],
[ nan, 12000, 4000, nan, 6.74, nan ],
[ nan, 15000, 5000, nan, 12.11, nan ],
[ nan, 18000, 6000, nan, 19.56, nan ],
[ nan, 21000, 7000, nan, 29.55, nan ],
[ nan, 24000, 8000, nan, 43.21, nan ],
[ nan, 27000, 9000, nan, 59.53, nan ],
[ nan, 100, 300, nan, 0.00, nan ],
[ nan, 200, 600, nan, 0.02, nan ],
[ nan, 300, 900, nan, 0.04, nan ],
[ nan, 400, 1200, nan, 0.06, nan ],
[ nan, 500, 1500, nan, 0.09, nan ],
[ nan, 600, 1800, nan, 0.12, nan ],
[ nan, 700, 2100, nan, 0.17, nan ],
[ nan, 800, 2400, nan, 0.20, nan ],
[ nan, 900, 2700, nan, 0.26, nan ],
[ nan, 1000, 3000, nan, 0.31, nan ],
[ nan, 2000, 6000, nan, 1.31, nan ],
[ nan, 3000, 9000, nan, 3.46, nan ],
[ nan, 4000, 12000, nan, 7.03, nan ],
[ nan, 5000, 15000, nan, 12.58, nan ],
[ nan, 6000, 18000, nan, 20.59, nan ],
[ nan, 7000, 21000, nan, 31.25, nan ],
[ nan, 8000, 24000, nan, 44.75, nan ],
[ nan, 9000, 27000, nan, 62.03, nan ],
[ nan, 10000, 100, nan, 0.01, nan ],
[ nan, 20000, 200, nan, 0.06, nan ],
[ nan, 30000, 300, nan, 0.15, nan ],
[ nan, 40000, 400, nan, 0.38, nan ],
[ nan, 50000, 500, nan, 0.62, nan ],
[ nan, 60000, 600, nan, 0.93, nan ],
[ nan, 70000, 700, nan, 1.30, nan ],
[ nan, 80000, 800, nan, 1.74, nan ],
[ nan, 90000, 900, nan, 2.46, nan ],
[ nan, 100000, 1000, nan, 3.10, nan ],
[ nan, 200000, 2000, nan, 18.29, nan ],
[ nan, 100, 10000, nan, 0.01, nan ],
[ nan, 200, 20000, nan, 0.05, nan ],
[ nan, 300, 30000, nan, 0.16, nan ],
[ nan, 400, 40000, nan, 0.32, nan ],
[ nan, 500, 50000, nan, 0.58, nan ],
[ nan, 600, 60000, nan, 0.89, nan ],
[ nan, 700, 70000, nan, 1.40, nan ],
[ nan, 800, 80000, nan, 1.84, nan ],
[ nan, 900, 90000, nan, 2.20, nan ],
[ nan, 1000, 100000, nan, 2.85, nan ],
[ nan, 2000, 200000, nan, 19.86, nan ],
])
# numactl --interleave=all ../testing/testing_dgesvd -US -VS -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
dgesvd_US = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.01, nan ],
[ nan, 60, 60, nan, 0.01, nan ],
[ nan, 70, 70, nan, 0.01, nan ],
[ nan, 80, 80, nan, 0.01, nan ],
[ nan, 90, 90, nan, 0.02, nan ],
[ nan, 100, 100, nan, 0.02, nan ],
[ nan, 200, 200, nan, 0.08, nan ],
[ nan, 300, 300, nan, 0.06, nan ],
[ nan, 400, 400, nan, 0.10, nan ],
[ nan, 500, 500, nan, 0.15, nan ],
[ nan, 600, 600, nan, 0.23, nan ],
[ nan, 700, 700, nan, 0.31, nan ],
[ nan, 800, 800, nan, 0.40, nan ],
[ nan, 900, 900, nan, 0.51, nan ],
[ nan, 1000, 1000, nan, 0.64, nan ],
[ nan, 2000, 2000, nan, 3.17, nan ],
[ nan, 3000, 3000, nan, 8.43, nan ],
[ nan, 4000, 4000, nan, 17.26, nan ],
[ nan, 5000, 5000, nan, 30.03, nan ],
[ nan, 6000, 6000, nan, 48.01, nan ],
[ nan, 7000, 7000, nan, 71.13, nan ],
[ nan, 8000, 8000, nan, 100.03, nan ],
[ nan, 9000, 9000, nan, 138.28, nan ],
[ nan, 10000, 10000, nan, 184.97, nan ],
[ nan, 300, 100, nan, 0.02, nan ],
[ nan, 600, 200, nan, 0.10, nan ],
[ nan, 900, 300, nan, 0.08, nan ],
[ nan, 1200, 400, nan, 0.14, nan ],
[ nan, 1500, 500, nan, 0.23, nan ],
[ nan, 1800, 600, nan, 0.35, nan ],
[ nan, 2100, 700, nan, 0.47, nan ],
[ nan, 2400, 800, nan, 0.63, nan ],
[ nan, 2700, 900, nan, 0.81, nan ],
[ nan, 3000, 1000, nan, 1.07, nan ],
[ nan, 6000, 2000, nan, 5.81, nan ],
[ nan, 9000, 3000, nan, 16.38, nan ],
[ nan, 12000, 4000, nan, 33.27, nan ],
[ nan, 15000, 5000, nan, 48.81, nan ],
[ nan, 18000, 6000, nan, 75.32, nan ],
[ nan, 21000, 7000, nan, 112.07, nan ],
[ nan, 24000, 8000, nan, 156.84, nan ],
[ nan, 27000, 9000, nan, 222.43, nan ],
[ nan, 100, 300, nan, 0.02, nan ],
[ nan, 200, 600, nan, 0.08, nan ],
[ nan, 300, 900, nan, 0.09, nan ],
[ nan, 400, 1200, nan, 0.16, nan ],
[ nan, 500, 1500, nan, 0.26, nan ],
[ nan, 600, 1800, nan, 0.40, nan ],
[ nan, 700, 2100, nan, 0.55, nan ],
[ nan, 800, 2400, nan, 0.70, nan ],
[ nan, 900, 2700, nan, 0.90, nan ],
[ nan, 1000, 3000, nan, 1.13, nan ],
[ nan, 2000, 6000, nan, 6.18, nan ],
[ nan, 3000, 9000, nan, 15.90, nan ],
[ nan, 4000, 12000, nan, 31.34, nan ],
[ nan, 5000, 15000, nan, 53.58, nan ],
[ nan, 6000, 18000, nan, 88.90, nan ],
[ nan, 7000, 21000, nan, 132.87, nan ],
[ nan, 8000, 24000, nan, 187.01, nan ],
[ nan, 9000, 27000, nan, 254.09, nan ],
[ nan, 10000, 100, nan, 0.06, nan ],
[ nan, 20000, 200, nan, 0.28, nan ],
[ nan, 30000, 300, nan, 0.48, nan ],
[ nan, 40000, 400, nan, 1.01, nan ],
[ nan, 50000, 500, nan, 1.84, nan ],
[ nan, 60000, 600, nan, 2.61, nan ],
[ nan, 70000, 700, nan, 4.08, nan ],
[ nan, 80000, 800, nan, 5.43, nan ],
[ nan, 90000, 900, nan, 7.95, nan ],
[ nan, 100000, 1000, nan, 10.22, nan ],
[ nan, 200000, 2000, nan, 70.82, nan ],
[ nan, 100, 10000, nan, 0.06, nan ],
[ nan, 200, 20000, nan, 0.39, nan ],
[ nan, 300, 30000, nan, 0.62, nan ],
[ nan, 400, 40000, nan, 1.20, nan ],
[ nan, 500, 50000, nan, 3.25, nan ],
[ nan, 600, 60000, nan, 4.00, nan ],
[ nan, 700, 70000, nan, 5.34, nan ],
[ nan, 800, 80000, nan, 7.31, nan ],
[ nan, 900, 90000, nan, 8.49, nan ],
[ nan, 1000, 100000, nan, 14.48, nan ],
[ nan, 2000, 200000, nan, 83.46, nan ],
])
# numactl --interleave=all ../testing/testing_dgesdd -UN -VN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
dgesdd_UN = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.00, nan ],
[ nan, 80, 80, nan, 0.00, nan ],
[ nan, 90, 90, nan, 0.00, nan ],
[ nan, 100, 100, nan, 0.00, nan ],
[ nan, 200, 200, nan, 0.01, nan ],
[ nan, 300, 300, nan, 0.03, nan ],
[ nan, 400, 400, nan, 0.05, nan ],
[ nan, 500, 500, nan, 0.07, nan ],
[ nan, 600, 600, nan, 0.09, nan ],
[ nan, 700, 700, nan, 0.12, nan ],
[ nan, 800, 800, nan, 0.15, nan ],
[ nan, 900, 900, nan, 0.19, nan ],
[ nan, 1000, 1000, nan, 0.23, nan ],
[ nan, 2000, 2000, nan, 0.89, nan ],
[ nan, 3000, 3000, nan, 2.30, nan ],
[ nan, 4000, 4000, nan, 4.65, nan ],
[ nan, 5000, 5000, nan, 8.29, nan ],
[ nan, 6000, 6000, nan, 13.32, nan ],
[ nan, 7000, 7000, nan, 20.01, nan ],
[ nan, 8000, 8000, nan, 28.62, nan ],
[ nan, 9000, 9000, nan, 39.78, nan ],
[ nan, 10000, 10000, nan, 53.14, nan ],
[ nan, 300, 100, nan, 0.00, nan ],
[ nan, 600, 200, nan, 0.02, nan ],
[ nan, 900, 300, nan, 0.03, nan ],
[ nan, 1200, 400, nan, 0.06, nan ],
[ nan, 1500, 500, nan, 0.08, nan ],
[ nan, 1800, 600, nan, 0.12, nan ],
[ nan, 2100, 700, nan, 0.16, nan ],
[ nan, 2400, 800, nan, 0.20, nan ],
[ nan, 2700, 900, nan, 0.26, nan ],
[ nan, 3000, 1000, nan, 0.29, nan ],
[ nan, 6000, 2000, nan, 1.35, nan ],
[ nan, 9000, 3000, nan, 3.65, nan ],
[ nan, 12000, 4000, nan, 7.62, nan ],
[ nan, 15000, 5000, nan, 13.86, nan ],
[ nan, 18000, 6000, nan, 22.81, nan ],
[ nan, 21000, 7000, nan, 34.07, nan ],
[ nan, 24000, 8000, nan, 49.03, nan ],
[ nan, 27000, 9000, nan, 59.73, nan ],
[ nan, 100, 300, nan, 0.00, nan ],
[ nan, 200, 600, nan, 0.02, nan ],
[ nan, 300, 900, nan, 0.04, nan ],
[ nan, 400, 1200, nan, 0.06, nan ],
[ nan, 500, 1500, nan, 0.09, nan ],
[ nan, 600, 1800, nan, 0.12, nan ],
[ nan, 700, 2100, nan, 0.17, nan ],
[ nan, 800, 2400, nan, 0.20, nan ],
[ nan, 900, 2700, nan, 0.26, nan ],
[ nan, 1000, 3000, nan, 0.31, nan ],
[ nan, 2000, 6000, nan, 1.30, nan ],
[ nan, 3000, 9000, nan, 3.45, nan ],
[ nan, 4000, 12000, nan, 7.02, nan ],
[ nan, 5000, 15000, nan, 12.59, nan ],
[ nan, 6000, 18000, nan, 20.60, nan ],
[ nan, 7000, 21000, nan, 31.25, nan ],
[ nan, 8000, 24000, nan, 44.72, nan ],
[ nan, 9000, 27000, nan, 61.98, nan ],
[ nan, 10000, 100, nan, 0.01, nan ],
[ nan, 20000, 200, nan, 0.06, nan ],
[ nan, 30000, 300, nan, 0.15, nan ],
[ nan, 40000, 400, nan, 0.38, nan ],
[ nan, 50000, 500, nan, 0.61, nan ],
[ nan, 60000, 600, nan, 0.93, nan ],
[ nan, 70000, 700, nan, 1.33, nan ],
[ nan, 80000, 800, nan, 1.78, nan ],
[ nan, 90000, 900, nan, 2.42, nan ],
[ nan, 100000, 1000, nan, 3.13, nan ],
[ nan, 200000, 2000, nan, 18.41, nan ],
[ nan, 100, 10000, nan, 0.01, nan ],
[ nan, 200, 20000, nan, 0.05, nan ],
[ nan, 300, 30000, nan, 0.16, nan ],
[ nan, 400, 40000, nan, 0.32, nan ],
[ nan, 500, 50000, nan, 0.58, nan ],
[ nan, 600, 60000, nan, 0.90, nan ],
[ nan, 700, 70000, nan, 1.41, nan ],
[ nan, 800, 80000, nan, 1.86, nan ],
[ nan, 900, 90000, nan, 2.20, nan ],
[ nan, 1000, 100000, nan, 2.85, nan ],
[ nan, 2000, 200000, nan, 19.71, nan ],
])
# numactl --interleave=all ../testing/testing_dgesdd -US -VS -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
dgesdd_US = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.00, nan ],
[ nan, 80, 80, nan, 0.00, nan ],
[ nan, 90, 90, nan, 0.00, nan ],
[ nan, 100, 100, nan, 0.01, nan ],
[ nan, 200, 200, nan, 0.03, nan ],
[ nan, 300, 300, nan, 0.05, nan ],
[ nan, 400, 400, nan, 0.09, nan ],
[ nan, 500, 500, nan, 0.13, nan ],
[ nan, 600, 600, nan, 0.18, nan ],
[ nan, 700, 700, nan, 0.24, nan ],
[ nan, 800, 800, nan, 0.31, nan ],
[ nan, 900, 900, nan, 0.38, nan ],
[ nan, 1000, 1000, nan, 0.47, nan ],
[ nan, 2000, 2000, nan, 1.88, nan ],
[ nan, 3000, 3000, nan, 5.33, nan ],
[ nan, 4000, 4000, nan, 9.18, nan ],
[ nan, 5000, 5000, nan, 15.73, nan ],
[ nan, 6000, 6000, nan, 23.32, nan ],
[ nan, 7000, 7000, nan, 33.88, nan ],
[ nan, 8000, 8000, nan, 47.62, nan ],
[ nan, 9000, 9000, nan, 64.52, nan ],
[ nan, 10000, 10000, nan, 84.79, nan ],
[ nan, 300, 100, nan, 0.01, nan ],
[ nan, 600, 200, nan, 0.03, nan ],
[ nan, 900, 300, nan, 0.06, nan ],
[ nan, 1200, 400, nan, 0.10, nan ],
[ nan, 1500, 500, nan, 0.16, nan ],
[ nan, 1800, 600, nan, 0.22, nan ],
[ nan, 2100, 700, nan, 0.30, nan ],
[ nan, 2400, 800, nan, 0.37, nan ],
[ nan, 2700, 900, nan, 0.48, nan ],
[ nan, 3000, 1000, nan, 0.62, nan ],
[ nan, 6000, 2000, nan, 2.68, nan ],
[ nan, 9000, 3000, nan, 6.89, nan ],
[ nan, 12000, 4000, nan, 13.83, nan ],
[ nan, 15000, 5000, nan, 24.11, nan ],
[ nan, 18000, 6000, nan, 39.47, nan ],
[ nan, 21000, 7000, nan, 58.52, nan ],
[ nan, 24000, 8000, nan, 83.76, nan ],
[ nan, 27000, 9000, nan, 114.69, nan ],
[ nan, 100, 300, nan, 0.01, nan ],
[ nan, 200, 600, nan, 0.03, nan ],
[ nan, 300, 900, nan, 0.07, nan ],
[ nan, 400, 1200, nan, 0.11, nan ],
[ nan, 500, 1500, nan, 0.17, nan ],
[ nan, 600, 1800, nan, 0.23, nan ],
[ nan, 700, 2100, nan, 0.33, nan ],
[ nan, 800, 2400, nan, 0.39, nan ],
[ nan, 900, 2700, nan, 0.50, nan ],
[ nan, 1000, 3000, nan, 0.65, nan ],
[ nan, 2000, 6000, nan, 2.76, nan ],
[ nan, 3000, 9000, nan, 7.10, nan ],
[ nan, 4000, 12000, nan, 14.07, nan ],
[ nan, 5000, 15000, nan, 24.53, nan ],
[ nan, 6000, 18000, nan, 40.57, nan ],
[ nan, 7000, 21000, nan, 60.37, nan ],
[ nan, 8000, 24000, nan, 85.93, nan ],
[ nan, 9000, 27000, nan, 117.67, nan ],
[ nan, 10000, 100, nan, 0.03, nan ],
[ nan, 20000, 200, nan, 0.18, nan ],
[ nan, 30000, 300, nan, 0.32, nan ],
[ nan, 40000, 400, nan, 0.65, nan ],
[ nan, 50000, 500, nan, 1.56, nan ],
[ nan, 60000, 600, nan, 1.98, nan ],
[ nan, 70000, 700, nan, 2.51, nan ],
[ nan, 80000, 800, nan, 3.18, nan ],
[ nan, 90000, 900, nan, 4.16, nan ],
[ nan, 100000, 1000, nan, 6.71, nan ],
[ nan, 200000, 2000, nan, 36.14, nan ],
[ nan, 100, 10000, nan, 0.04, nan ],
[ nan, 200, 20000, nan, 0.23, nan ],
[ nan, 300, 30000, nan, 0.42, nan ],
[ nan, 400, 40000, nan, 0.67, nan ],
[ nan, 500, 50000, nan, 2.56, nan ],
[ nan, 600, 60000, nan, 2.99, nan ],
[ nan, 700, 70000, nan, 3.50, nan ],
[ nan, 800, 80000, nan, 3.78, nan ],
[ nan, 900, 90000, nan, 4.42, nan ],
[ nan, 1000, 100000, nan, 9.12, nan ],
[ nan, 2000, 200000, nan, 41.81, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/dgetrf.txt
# numactl --interleave=all ../testing/testing_dgetrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
dgetrf = array([
[ 10, 10, nan, nan, 0.03, 0.00, nan ],
[ 20, 20, nan, nan, 0.18, 0.00, nan ],
[ 30, 30, nan, nan, 0.36, 0.00, nan ],
[ 40, 40, nan, nan, 0.91, 0.00, nan ],
[ 50, 50, nan, nan, 1.42, 0.00, nan ],
[ 60, 60, nan, nan, 2.00, 0.00, nan ],
[ 70, 70, nan, nan, 1.75, 0.00, nan ],
[ 80, 80, nan, nan, 2.43, 0.00, nan ],
[ 90, 90, nan, nan, 3.05, 0.00, nan ],
[ 100, 100, nan, nan, 3.70, 0.00, nan ],
[ 200, 200, nan, nan, 3.47, 0.00, nan ],
[ 300, 300, nan, nan, 8.27, 0.00, nan ],
[ 400, 400, nan, nan, 14.01, 0.00, nan ],
[ 500, 500, nan, nan, 21.65, 0.00, nan ],
[ 600, 600, nan, nan, 28.58, 0.01, nan ],
[ 700, 700, nan, nan, 37.02, 0.01, nan ],
[ 800, 800, nan, nan, 45.15, 0.01, nan ],
[ 900, 900, nan, nan, 54.03, 0.01, nan ],
[ 1000, 1000, nan, nan, 62.55, 0.01, nan ],
[ 2000, 2000, nan, nan, 153.45, 0.03, nan ],
[ 3000, 3000, nan, nan, 260.01, 0.07, nan ],
[ 4000, 4000, nan, nan, 347.38, 0.12, nan ],
[ 5000, 5000, nan, nan, 444.73, 0.19, nan ],
[ 6000, 6000, nan, nan, 529.58, 0.27, nan ],
[ 7000, 7000, nan, nan, 594.63, 0.38, nan ],
[ 8000, 8000, nan, nan, 652.53, 0.52, nan ],
[ 9000, 9000, nan, nan, 694.22, 0.70, nan ],
[ 10000, 10000, nan, nan, 737.16, 0.90, nan ],
[ 12000, 12000, nan, nan, 811.39, 1.42, nan ],
[ 14000, 14000, nan, nan, 854.75, 2.14, nan ],
[ 16000, 16000, nan, nan, 891.71, 3.06, nan ],
[ 18000, 18000, nan, nan, 915.62, 4.25, nan ],
[ 20000, 20000, nan, nan, 942.41, 5.66, nan ],
])
# numactl --interleave=all ../testing/testing_dgetrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
dgetrf_gpu = array([
[ 10, 10, nan, nan, 0.00, 0.00, nan ],
[ 20, 20, nan, nan, 0.02, 0.00, nan ],
[ 30, 30, nan, nan, 0.07, 0.00, nan ],
[ 40, 40, nan, nan, 0.16, 0.00, nan ],
[ 50, 50, nan, nan, 0.27, 0.00, nan ],
[ 60, 60, nan, nan, 0.47, 0.00, nan ],
[ 70, 70, nan, nan, 0.59, 0.00, nan ],
[ 80, 80, nan, nan, 0.86, 0.00, nan ],
[ 90, 90, nan, nan, 1.10, 0.00, nan ],
[ 100, 100, nan, nan, 1.43, 0.00, nan ],
[ 200, 200, nan, nan, 2.26, 0.00, nan ],
[ 300, 300, nan, nan, 5.95, 0.00, nan ],
[ 400, 400, nan, nan, 11.38, 0.00, nan ],
[ 500, 500, nan, nan, 18.16, 0.00, nan ],
[ 600, 600, nan, nan, 26.82, 0.01, nan ],
[ 700, 700, nan, nan, 35.30, 0.01, nan ],
[ 800, 800, nan, nan, 44.51, 0.01, nan ],
[ 900, 900, nan, nan, 54.18, 0.01, nan ],
[ 1000, 1000, nan, nan, 63.98, 0.01, nan ],
[ 2000, 2000, nan, nan, 178.98, 0.03, nan ],
[ 3000, 3000, nan, nan, 315.08, 0.06, nan ],
[ 4000, 4000, nan, nan, 407.04, 0.10, nan ],
[ 5000, 5000, nan, nan, 526.51, 0.16, nan ],
[ 6000, 6000, nan, nan, 640.55, 0.22, nan ],
[ 7000, 7000, nan, nan, 718.58, 0.32, nan ],
[ 8000, 8000, nan, nan, 779.09, 0.44, nan ],
[ 9000, 9000, nan, nan, 815.55, 0.60, nan ],
[ 10000, 10000, nan, nan, 861.60, 0.77, nan ],
[ 12000, 12000, nan, nan, 932.31, 1.24, nan ],
[ 14000, 14000, nan, nan, 972.53, 1.88, nan ],
[ 16000, 16000, nan, nan, 1005.85, 2.71, nan ],
[ 18000, 18000, nan, nan, 1021.26, 3.81, nan ],
[ 20000, 20000, nan, nan, 1041.20, 5.12, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/dpotrf.txt
# numactl --interleave=all ../testing/testing_dpotrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
dpotrf = array([
[ 10, nan, nan, 0.08, 0.00, nan ],
[ 20, nan, nan, 0.33, 0.00, nan ],
[ 30, nan, nan, 0.67, 0.00, nan ],
[ 40, nan, nan, 0.82, 0.00, nan ],
[ 50, nan, nan, 1.86, 0.00, nan ],
[ 60, nan, nan, 2.18, 0.00, nan ],
[ 70, nan, nan, 1.82, 0.00, nan ],
[ 80, nan, nan, 2.56, 0.00, nan ],
[ 90, nan, nan, 2.50, 0.00, nan ],
[ 100, nan, nan, 2.66, 0.00, nan ],
[ 200, nan, nan, 13.10, 0.00, nan ],
[ 300, nan, nan, 5.00, 0.00, nan ],
[ 400, nan, nan, 9.81, 0.00, nan ],
[ 500, nan, nan, 16.93, 0.00, nan ],
[ 600, nan, nan, 20.47, 0.00, nan ],
[ 700, nan, nan, 28.69, 0.00, nan ],
[ 800, nan, nan, 33.21, 0.01, nan ],
[ 900, nan, nan, 42.32, 0.01, nan ],
[ 1000, nan, nan, 55.57, 0.01, nan ],
[ 2000, nan, nan, 169.70, 0.02, nan ],
[ 3000, nan, nan, 296.46, 0.03, nan ],
[ 4000, nan, nan, 476.04, 0.04, nan ],
[ 5000, nan, nan, 567.42, 0.07, nan ],
[ 6000, nan, nan, 661.73, 0.11, nan ],
[ 7000, nan, nan, 719.67, 0.16, nan ],
[ 8000, nan, nan, 786.40, 0.22, nan ],
[ 9000, nan, nan, 827.97, 0.29, nan ],
[ 10000, nan, nan, 864.94, 0.39, nan ],
[ 12000, nan, nan, 931.65, 0.62, nan ],
[ 14000, nan, nan, 982.09, 0.93, nan ],
[ 16000, nan, nan, 1024.56, 1.33, nan ],
[ 18000, nan, nan, 1045.70, 1.86, nan ],
[ 20000, nan, nan, 1072.76, 2.49, nan ],
])
# numactl --interleave=all ../testing/testing_dpotrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
dpotrf_gpu = array([
[ 10, nan, nan, 0.00, 0.00, nan ],
[ 20, nan, nan, 0.00, 0.00, nan ],
[ 30, nan, nan, 0.01, 0.00, nan ],
[ 40, nan, nan, 0.02, 0.00, nan ],
[ 50, nan, nan, 0.04, 0.00, nan ],
[ 60, nan, nan, 0.06, 0.00, nan ],
[ 70, nan, nan, 0.09, 0.00, nan ],
[ 80, nan, nan, 0.14, 0.00, nan ],
[ 90, nan, nan, 0.19, 0.00, nan ],
[ 100, nan, nan, 0.25, 0.00, nan ],
[ 200, nan, nan, 5.02, 0.00, nan ],
[ 300, nan, nan, 3.66, 0.00, nan ],
[ 400, nan, nan, 7.40, 0.00, nan ],
[ 500, nan, nan, 13.39, 0.00, nan ],
[ 600, nan, nan, 17.87, 0.00, nan ],
[ 700, nan, nan, 26.15, 0.00, nan ],
[ 800, nan, nan, 31.48, 0.01, nan ],
[ 900, nan, nan, 41.97, 0.01, nan ],
[ 1000, nan, nan, 54.41, 0.01, nan ],
[ 2000, nan, nan, 200.03, 0.01, nan ],
[ 3000, nan, nan, 358.55, 0.03, nan ],
[ 4000, nan, nan, 585.91, 0.04, nan ],
[ 5000, nan, nan, 693.11, 0.06, nan ],
[ 6000, nan, nan, 803.28, 0.09, nan ],
[ 7000, nan, nan, 864.01, 0.13, nan ],
[ 8000, nan, nan, 935.87, 0.18, nan ],
[ 9000, nan, nan, 969.98, 0.25, nan ],
[ 10000, nan, nan, 998.77, 0.33, nan ],
[ 12000, nan, nan, 1053.51, 0.55, nan ],
[ 14000, nan, nan, 1095.34, 0.84, nan ],
[ 16000, nan, nan, 1128.53, 1.21, nan ],
[ 18000, nan, nan, 1139.54, 1.71, nan ],
[ 20000, nan, nan, 1159.31, 2.30, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/dsyevd.txt
# numactl --interleave=all ../testing/testing_dsyevd -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_dsyevd -JN -N 123 -N 1234 --range 12000:20000:2000
dsyevd_JN = array([
[ 10, nan, 0.0000, nan, nan, nan, nan ],
[ 20, nan, 0.0001, nan, nan, nan, nan ],
[ 30, nan, 0.0001, nan, nan, nan, nan ],
[ 40, nan, 0.0001, nan, nan, nan, nan ],
[ 50, nan, 0.0002, nan, nan, nan, nan ],
[ 60, nan, 0.0003, nan, nan, nan, nan ],
[ 70, nan, 0.0004, nan, nan, nan, nan ],
[ 80, nan, 0.0005, nan, nan, nan, nan ],
[ 90, nan, 0.0007, nan, nan, nan, nan ],
[ 100, nan, 0.0010, nan, nan, nan, nan ],
[ 200, nan, 0.0049, nan, nan, nan, nan ],
[ 300, nan, 0.0097, nan, nan, nan, nan ],
[ 400, nan, 0.0162, nan, nan, nan, nan ],
[ 500, nan, 0.0247, nan, nan, nan, nan ],
[ 600, nan, 0.0335, nan, nan, nan, nan ],
[ 700, nan, 0.0448, nan, nan, nan, nan ],
[ 800, nan, 0.0585, nan, nan, nan, nan ],
[ 900, nan, 0.0740, nan, nan, nan, nan ],
[ 1000, nan, 0.0918, nan, nan, nan, nan ],
[ 2000, nan, 0.3935, nan, nan, nan, nan ],
[ 3000, nan, 1.3113, nan, nan, nan, nan ],
[ 4000, nan, 2.3401, nan, nan, nan, nan ],
[ 5000, nan, 3.7926, nan, nan, nan, nan ],
[ 6000, nan, 5.7090, nan, nan, nan, nan ],
[ 7000, nan, 8.1075, nan, nan, nan, nan ],
[ 8000, nan, 11.0877, nan, nan, nan, nan ],
[ 9000, nan, 14.7262, nan, nan, nan, nan ],
[ 10000, nan, 19.0244, nan, nan, nan, nan ],
[ 12000, nan, 30.4198, nan, nan, nan, nan ],
[ 14000, nan, 44.7864, nan, nan, nan, nan ],
[ 16000, nan, 63.6517, nan, nan, nan, nan ],
[ 18000, nan, 87.5615, nan, nan, nan, nan ],
[ 20000, nan, 114.6263, nan, nan, nan, nan ],
])
# numactl --interleave=all ../testing/testing_dsyevd -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_dsyevd -JV -N 123 -N 1234 --range 12000:20000:2000
dsyevd_JV = array([
[ 10, nan, 0.0001, nan, nan, nan, nan ],
[ 20, nan, 0.0001, nan, nan, nan, nan ],
[ 30, nan, 0.0003, nan, nan, nan, nan ],
[ 40, nan, 0.0004, nan, nan, nan, nan ],
[ 50, nan, 0.0005, nan, nan, nan, nan ],
[ 60, nan, 0.0007, nan, nan, nan, nan ],
[ 70, nan, 0.0009, nan, nan, nan, nan ],
[ 80, nan, 0.0011, nan, nan, nan, nan ],
[ 90, nan, 0.0016, nan, nan, nan, nan ],
[ 100, nan, 0.0024, nan, nan, nan, nan ],
[ 200, nan, 0.0095, nan, nan, nan, nan ],
[ 300, nan, 0.0155, nan, nan, nan, nan ],
[ 400, nan, 0.0241, nan, nan, nan, nan ],
[ 500, nan, 0.0349, nan, nan, nan, nan ],
[ 600, nan, 0.0413, nan, nan, nan, nan ],
[ 700, nan, 0.0547, nan, nan, nan, nan ],
[ 800, nan, 0.0690, nan, nan, nan, nan ],
[ 900, nan, 0.0897, nan, nan, nan, nan ],
[ 1000, nan, 0.1082, nan, nan, nan, nan ],
[ 2000, nan, 0.4302, nan, nan, nan, nan ],
[ 3000, nan, 1.4400, nan, nan, nan, nan ],
[ 4000, nan, 2.4758, nan, nan, nan, nan ],
[ 5000, nan, 3.9899, nan, nan, nan, nan ],
[ 6000, nan, 6.0522, nan, nan, nan, nan ],
[ 7000, nan, 8.7300, nan, nan, nan, nan ],
[ 8000, nan, 12.2179, nan, nan, nan, nan ],
[ 9000, nan, 16.2830, nan, nan, nan, nan ],
[ 10000, nan, 21.0444, nan, nan, nan, nan ],
[ 12000, nan, 33.7725, nan, nan, nan, nan ],
[ 14000, nan, 48.9610, nan, nan, nan, nan ],
[ 16000, nan, 70.3212, nan, nan, nan, nan ],
[ 18000, nan, 97.6670, nan, nan, nan, nan ],
[ 20000, nan, 129.6026, nan, nan, nan, nan ],
])
# numactl --interleave=all ../testing/testing_dsyevd_gpu -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_dsyevd_gpu -JN -N 123 -N 1234 --range 12000:20000:2000
dsyevd_gpu_JN = array([
[ 10, nan, 0.0003, nan, nan, nan, nan ],
[ 20, nan, 0.0003, nan, nan, nan, nan ],
[ 30, nan, 0.0003, nan, nan, nan, nan ],
[ 40, nan, 0.0004, nan, nan, nan, nan ],
[ 50, nan, 0.0005, nan, nan, nan, nan ],
[ 60, nan, 0.0006, nan, nan, nan, nan ],
[ 70, nan, 0.0007, nan, nan, nan, nan ],
[ 80, nan, 0.0008, nan, nan, nan, nan ],
[ 90, nan, 0.0011, nan, nan, nan, nan ],
[ 100, nan, 0.0014, nan, nan, nan, nan ],
[ 200, nan, 0.0053, nan, nan, nan, nan ],
[ 300, nan, 0.0109, nan, nan, nan, nan ],
[ 400, nan, 0.0183, nan, nan, nan, nan ],
[ 500, nan, 0.0275, nan, nan, nan, nan ],
[ 600, nan, 0.0384, nan, nan, nan, nan ],
[ 700, nan, 0.0511, nan, nan, nan, nan ],
[ 800, nan, 0.0662, nan, nan, nan, nan ],
[ 900, nan, 0.0836, nan, nan, nan, nan ],
[ 1000, nan, 0.1031, nan, nan, nan, nan ],
[ 2000, nan, 0.4390, nan, nan, nan, nan ],
[ 3000, nan, 1.3023, nan, nan, nan, nan ],
[ 4000, nan, 2.3194, nan, nan, nan, nan ],
[ 5000, nan, 3.7612, nan, nan, nan, nan ],
[ 6000, nan, 5.6569, nan, nan, nan, nan ],
[ 7000, nan, 7.9992, nan, nan, nan, nan ],
[ 8000, nan, 10.9571, nan, nan, nan, nan ],
[ 9000, nan, 14.5415, nan, nan, nan, nan ],
[ 10000, nan, 18.8142, nan, nan, nan, nan ],
[ 12000, nan, 30.2485, nan, nan, nan, nan ],
[ 14000, nan, 44.3241, nan, nan, nan, nan ],
[ 16000, nan, 63.1912, nan, nan, nan, nan ],
[ 18000, nan, 86.6927, nan, nan, nan, nan ],
[ 20000, nan, 114.4557, nan, nan, nan, nan ],
])
# numactl --interleave=all ../testing/testing_dsyevd_gpu -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_dsyevd_gpu -JV -N 123 -N 1234 --range 12000:20000:2000
dsyevd_gpu_JV = array([
[ 10, nan, 0.0003, nan, nan, nan, nan ],
[ 20, nan, 0.0003, nan, nan, nan, nan ],
[ 30, nan, 0.0004, nan, nan, nan, nan ],
[ 40, nan, 0.0006, nan, nan, nan, nan ],
[ 50, nan, 0.0007, nan, nan, nan, nan ],
[ 60, nan, 0.0009, nan, nan, nan, nan ],
[ 70, nan, 0.0011, nan, nan, nan, nan ],
[ 80, nan, 0.0013, nan, nan, nan, nan ],
[ 90, nan, 0.0018, nan, nan, nan, nan ],
[ 100, nan, 0.0022, nan, nan, nan, nan ],
[ 200, nan, 0.0089, nan, nan, nan, nan ],
[ 300, nan, 0.0147, nan, nan, nan, nan ],
[ 400, nan, 0.0233, nan, nan, nan, nan ],
[ 500, nan, 0.0343, nan, nan, nan, nan ],
[ 600, nan, 0.0407, nan, nan, nan, nan ],
[ 700, nan, 0.0541, nan, nan, nan, nan ],
[ 800, nan, 0.0702, nan, nan, nan, nan ],
[ 900, nan, 0.1030, nan, nan, nan, nan ],
[ 1000, nan, 0.1247, nan, nan, nan, nan ],
[ 2000, nan, 0.4084, nan, nan, nan, nan ],
[ 3000, nan, 1.3218, nan, nan, nan, nan ],
[ 4000, nan, 2.4399, nan, nan, nan, nan ],
[ 5000, nan, 3.9356, nan, nan, nan, nan ],
[ 6000, nan, 6.0690, nan, nan, nan, nan ],
[ 7000, nan, 8.7427, nan, nan, nan, nan ],
[ 8000, nan, 12.2263, nan, nan, nan, nan ],
[ 9000, nan, 16.3056, nan, nan, nan, nan ],
[ 10000, nan, 21.5106, nan, nan, nan, nan ],
[ 12000, nan, 34.9720, nan, nan, nan, nan ],
[ 14000, nan, 52.3683, nan, nan, nan, nan ],
[ 16000, nan, 77.9785, nan, nan, nan, nan ],
[ 18000, nan, 105.1761, nan, nan, nan, nan ],
[ 20000, nan, 141.3858, nan, nan, nan, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/dsyevd_2stage.txt
# numactl --interleave=all ../testing/testing_dsyevdx_2stage -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
dsyevdx_2stage_JN = array([
[ 10, 10, 0.00 ],
[ 20, 20, 0.00 ],
[ 30, 30, 0.00 ],
[ 40, 40, 0.00 ],
[ 50, 50, 0.00 ],
[ 60, 60, 0.00 ],
[ 70, 70, 0.00 ],
[ 80, 80, 0.00 ],
[ 90, 90, 0.00 ],
[ 100, 100, 0.00 ],
[ 200, 200, 0.00 ],
[ 300, 300, 0.02 ],
[ 400, 400, 0.03 ],
[ 500, 500, 0.05 ],
[ 600, 600, 0.07 ],
[ 700, 700, 0.09 ],
[ 800, 800, 0.11 ],
[ 900, 900, 0.12 ],
[ 1000, 1000, 0.14 ],
[ 2000, 2000, 0.48 ],
[ 3000, 3000, 0.86 ],
[ 4000, 4000, 1.27 ],
[ 5000, 5000, 1.86 ],
[ 6000, 6000, 2.67 ],
[ 7000, 7000, 3.50 ],
[ 8000, 8000, 4.55 ],
[ 9000, 9000, 5.45 ],
[ 10000, 10000, 6.73 ],
[ 12000, 12000, 9.83 ],
[ 14000, 14000, 13.61 ],
[ 16000, 16000, 18.00 ],
[ 18000, 18000, 23.60 ],
[ 20000, 20000, 30.02 ],
])
# numactl --interleave=all ../testing/testing_dsyevdx_2stage -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
dsyevdx_2stage_JV = array([
[ 10, 10, 0.00 ],
[ 20, 20, 0.00 ],
[ 30, 30, 0.00 ],
[ 40, 40, 0.00 ],
[ 50, 50, 0.00 ],
[ 60, 60, 0.00 ],
[ 70, 70, 0.00 ],
[ 80, 80, 0.00 ],
[ 90, 90, 0.00 ],
[ 100, 100, 0.00 ],
[ 200, 200, 0.01 ],
[ 300, 300, 0.03 ],
[ 400, 400, 0.05 ],
[ 500, 500, 0.06 ],
[ 600, 600, 0.08 ],
[ 700, 700, 0.10 ],
[ 800, 800, 0.13 ],
[ 900, 900, 0.15 ],
[ 1000, 1000, 0.18 ],
[ 2000, 2000, 0.52 ],
[ 3000, 3000, 1.03 ],
[ 4000, 4000, 1.78 ],
[ 5000, 5000, 2.76 ],
[ 6000, 6000, 4.09 ],
[ 7000, 7000, 5.84 ],
[ 8000, 8000, 8.14 ],
[ 9000, 9000, 10.56 ],
[ 10000, 10000, 13.82 ],
[ 12000, 12000, 22.20 ],
[ 14000, 14000, 33.41 ],
[ 16000, 16000, 47.43 ],
[ 18000, 18000, 66.80 ],
[ 20000, 20000, 90.45 ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/dsymv.txt
# numactl --interleave=all ../testing/testing_dsymv -L -N 123 -N 1234 --range 10:90:1 --range 100:900:10 --range 1000:9000:100 --range 10000:20000:2000
dsymv_L = array([
[ 10, 0.01, 0.04, 0.01, 0.03, 0.01, 0.02, 0.08, 0.00, 1.78e-16, 8.88e-17, 8.88e-17, nan ],
[ 11, 0.01, 0.03, 0.01, 0.03, 0.01, 0.02, 0.14, 0.00, 4.04e-17, 8.07e-17, 8.07e-17, nan ],
[ 12, 0.01, 0.03, 0.01, 0.03, 0.02, 0.02, 0.10, 0.00, 1.11e-16, 3.70e-17, 3.70e-17, nan ],
[ 13, 0.01, 0.03, 0.01, 0.03, 0.02, 0.02, 0.12, 0.00, 6.83e-17, 6.83e-17, 6.83e-17, nan ],
[ 14, 0.01, 0.03, 0.01, 0.03, 0.02, 0.02, 0.22, 0.00, 6.34e-17, 6.34e-17, 6.34e-17, nan ],
[ 15, 0.02, 0.03, 0.02, 0.03, 0.02, 0.02, 0.15, 0.00, 1.18e-16, 1.18e-16, 1.18e-16, nan ],
[ 16, 0.02, 0.03, 0.02, 0.03, 0.03, 0.02, 0.29, 0.00, 1.11e-16, 1.11e-16, 1.11e-16, nan ],
[ 17, 0.02, 0.03, 0.02, 0.03, 0.03, 0.02, 0.29, 0.00, 1.04e-16, 1.04e-16, 1.57e-16, nan ],
[ 18, 0.02, 0.03, 0.02, 0.03, 0.03, 0.02, 0.24, 0.00, 9.87e-17, 1.48e-16, 9.87e-17, nan ],
[ 19, 0.02, 0.04, 0.02, 0.03, 0.03, 0.02, 0.35, 0.00, 4.67e-17, 4.67e-17, 4.67e-17, nan ],
[ 20, 0.02, 0.03, 0.03, 0.03, 0.04, 0.02, 0.44, 0.00, 8.88e-17, 8.88e-17, 8.88e-17, nan ],
[ 21, 0.03, 0.03, 0.03, 0.03, 0.04, 0.02, 0.30, 0.00, 8.46e-17, 1.69e-16, 8.46e-17, nan ],
[ 22, 0.03, 0.03, 0.03, 0.03, 0.05, 0.02, 0.33, 0.00, 1.61e-16, 1.61e-16, 1.61e-16, nan ],
[ 23, 0.03, 0.03, 0.03, 0.03, 0.05, 0.02, 0.36, 0.00, 1.54e-16, 1.54e-16, 1.54e-16, nan ],
[ 24, 0.04, 0.03, 0.04, 0.03, 0.05, 0.02, 0.42, 0.00, 1.48e-16, 1.48e-16, 1.48e-16, nan ],
[ 25, 0.04, 0.03, 0.04, 0.03, 0.05, 0.02, 0.42, 0.00, 7.11e-17, 7.11e-17, 7.11e-17, nan ],
[ 26, 0.04, 0.04, 0.04, 0.03, 0.06, 0.02, 0.49, 0.00, 1.37e-16, 1.37e-16, 1.37e-16, nan ],
[ 27, 0.04, 0.03, 0.04, 0.03, 0.07, 0.02, 0.37, 0.00, 1.32e-16, 1.32e-16, 1.32e-16, nan ],
[ 28, 0.05, 0.04, 0.05, 0.03, 0.07, 0.02, 0.57, 0.00, 1.90e-16, 1.90e-16, 1.27e-16, nan ],
[ 29, 0.05, 0.03, 0.04, 0.04, 0.07, 0.02, 0.56, 0.00, 1.23e-16, 1.23e-16, 1.23e-16, nan ],
[ 30, 0.06, 0.03, 0.06, 0.03, 0.08, 0.02, 0.65, 0.00, 1.18e-16, 1.18e-16, 1.18e-16, nan ],
[ 31, 0.06, 0.03, 0.06, 0.03, 0.09, 0.02, 0.49, 0.00, 1.15e-16, 1.15e-16, 1.15e-16, nan ],
[ 32, 0.07, 0.03, 0.06, 0.03, 0.09, 0.02, 0.68, 0.00, 1.11e-16, 1.11e-16, 1.11e-16, nan ],
[ 33, 0.06, 0.04, 0.05, 0.04, 0.09, 0.03, 0.78, 0.00, 1.61e-16, 1.08e-16, 1.08e-16, nan ],
[ 34, 0.07, 0.04, 0.06, 0.04, 0.10, 0.02, 0.62, 0.00, 2.09e-16, 2.09e-16, 1.57e-16, nan ],
[ 35, 0.08, 0.03, 0.06, 0.04, 0.10, 0.02, 0.88, 0.00, 1.02e-16, 1.52e-16, 1.02e-16, nan ],
[ 36, 0.09, 0.03, 0.07, 0.04, 0.12, 0.02, 0.70, 0.00, 1.48e-16, 1.48e-16, 1.48e-16, nan ],
[ 37, 0.09, 0.03, 0.07, 0.04, 0.12, 0.02, 0.98, 0.00, 1.44e-16, 9.60e-17, 1.44e-16, nan ],
[ 38, 0.09, 0.03, 0.07, 0.04, 0.14, 0.02, 0.73, 0.00, 1.40e-16, 9.35e-17, 1.40e-16, nan ],
[ 39, 0.10, 0.03, 0.08, 0.04, 0.11, 0.03, 0.77, 0.00, 1.37e-16, 9.11e-17, 9.11e-17, nan ],
[ 40, 0.10, 0.03, 0.08, 0.04, 0.14, 0.02, 1.06, 0.00, 8.88e-17, 1.33e-16, 8.88e-17, nan ],
[ 41, 0.11, 0.03, 0.09, 0.04, 0.14, 0.02, 0.85, 0.00, 1.73e-16, 1.73e-16, 1.73e-16, nan ],
[ 42, 0.12, 0.03, 0.09, 0.04, 0.15, 0.02, 0.89, 0.00, 2.11e-16, 1.27e-16, 8.46e-17, nan ],
[ 43, 0.12, 0.03, 0.10, 0.04, 0.16, 0.02, 0.93, 0.00, 1.65e-16, 1.65e-16, 1.65e-16, nan ],
[ 44, 0.12, 0.03, 0.10, 0.04, 0.16, 0.02, 1.38, 0.00, 1.21e-16, 1.61e-16, 1.61e-16, nan ],
[ 45, 0.13, 0.03, 0.11, 0.04, 0.17, 0.02, 1.02, 0.00, 1.18e-16, 1.58e-16, 1.18e-16, nan ],
[ 46, 0.12, 0.04, 0.11, 0.04, 0.18, 0.02, 1.13, 0.00, 1.54e-16, 1.54e-16, 1.54e-16, nan ],
[ 47, 0.14, 0.03, 0.11, 0.04, 0.17, 0.03, 0.90, 0.01, 1.51e-16, 1.51e-16, 1.51e-16, nan ],
[ 48, 0.13, 0.04, 0.12, 0.04, 0.20, 0.02, 1.16, 0.00, 2.22e-16, 2.22e-16, 2.22e-16, nan ],
[ 49, 0.14, 0.04, 0.12, 0.04, 0.20, 0.03, 1.21, 0.00, 1.45e-16, 2.18e-16, 1.45e-16, nan ],
[ 50, 0.16, 0.03, 0.12, 0.04, 0.20, 0.03, 1.02, 0.01, 2.13e-16, 2.13e-16, 1.42e-16, nan ],
[ 51, 0.16, 0.03, 0.13, 0.04, 0.20, 0.03, 1.31, 0.00, 2.09e-16, 1.39e-16, 1.39e-16, nan ],
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[ 2200, 29.18, 0.33, 37.27, 0.26, 16.09, 0.60, 10.38, 0.93, 1.65e-15, 1.86e-15, 1.71e-15, nan ],
[ 2300, 30.77, 0.34, 38.64, 0.27, 16.70, 0.63, 10.37, 1.02, 1.58e-15, 1.43e-15, 1.43e-15, nan ],
[ 2400, 32.01, 0.36, 40.28, 0.29, 17.54, 0.66, 10.52, 1.10, 1.47e-15, 1.37e-15, 1.47e-15, nan ],
[ 2500, 33.26, 0.38, 41.40, 0.30, 18.28, 0.68, 10.55, 1.19, 1.64e-15, 1.59e-15, 1.59e-15, nan ],
[ 2600, 33.39, 0.41, 42.94, 0.31, 18.81, 0.72, 10.74, 1.26, 1.62e-15, 1.62e-15, 1.49e-15, nan ],
[ 2700, 35.48, 0.41, 44.62, 0.33, 19.66, 0.74, 10.76, 1.36, 1.39e-15, 1.35e-15, 1.52e-15, nan ],
[ 2800, 36.39, 0.43, 42.17, 0.37, 20.03, 0.78, 11.81, 1.33, 1.54e-15, 1.54e-15, 1.62e-15, nan ],
[ 2900, 37.56, 0.45, 47.02, 0.36, 20.98, 0.80, 10.81, 1.56, 1.72e-15, 1.72e-15, 1.65e-15, nan ],
[ 3000, 38.89, 0.46, 48.13, 0.37, 21.53, 0.84, 11.14, 1.62, 1.67e-15, 1.59e-15, 1.59e-15, nan ],
[ 3100, 39.55, 0.49, 49.66, 0.39, 17.43, 1.10, 10.67, 1.80, 2.05e-15, 2.05e-15, 2.13e-15, nan ],
[ 3200, 40.98, 0.50, 50.96, 0.40, 18.08, 1.13, 11.10, 1.85, 1.85e-15, 1.99e-15, 1.71e-15, nan ],
[ 3300, 43.06, 0.51, 52.10, 0.42, 18.56, 1.17, 11.19, 1.95, 2.00e-15, 1.86e-15, 1.86e-15, nan ],
[ 3400, 43.56, 0.53, 52.69, 0.44, 19.05, 1.21, 11.67, 1.98, 1.94e-15, 2.14e-15, 2.01e-15, nan ],
[ 3500, 44.48, 0.55, 54.44, 0.45, 19.59, 1.25, 10.86, 2.26, 2.01e-15, 2.08e-15, 2.21e-15, nan ],
[ 3600, 45.65, 0.57, 55.03, 0.47, 20.18, 1.29, 11.16, 2.32, 2.21e-15, 2.15e-15, 2.15e-15, nan ],
[ 3700, 46.26, 0.59, 57.18, 0.48, 20.68, 1.32, 11.55, 2.37, 2.09e-15, 2.09e-15, 2.03e-15, nan ],
[ 3800, 47.83, 0.60, 58.59, 0.49, 21.15, 1.37, 11.90, 2.43, 2.21e-15, 1.97e-15, 2.09e-15, nan ],
[ 3900, 47.83, 0.64, 58.30, 0.52, 21.66, 1.41, 11.34, 2.68, 2.16e-15, 2.16e-15, 2.10e-15, nan ],
[ 4000, 47.27, 0.68, 58.96, 0.54, 22.23, 1.44, 11.73, 2.73, 2.22e-15, 2.22e-15, 2.16e-15, nan ],
[ 4100, 47.16, 0.71, 61.03, 0.55, 18.17, 1.85, 11.03, 3.05, 1.94e-15, 2.05e-15, 2.05e-15, nan ],
[ 4200, 47.68, 0.74, 61.36, 0.58, 18.89, 1.87, 11.27, 3.13, 1.84e-15, 2.11e-15, 2.11e-15, nan ],
[ 4300, 47.78, 0.77, 63.22, 0.59, 19.49, 1.90, 11.43, 3.24, 2.54e-15, 2.59e-15, 2.54e-15, nan ],
[ 4400, 48.96, 0.79, 63.70, 0.61, 19.77, 1.96, 11.45, 3.38, 2.22e-15, 2.17e-15, 2.17e-15, nan ],
[ 4500, 48.41, 0.84, 63.09, 0.64, 20.29, 2.00, 11.57, 3.50, 2.37e-15, 2.27e-15, 2.27e-15, nan ],
[ 4600, 50.03, 0.85, 63.93, 0.66, 20.77, 2.04, 12.17, 3.48, 2.22e-15, 2.13e-15, 2.13e-15, nan ],
[ 4700, 49.54, 0.89, 64.87, 0.68, 21.11, 2.09, 11.82, 3.74, 2.90e-15, 2.47e-15, 2.47e-15, nan ],
[ 4800, 50.15, 0.92, 65.49, 0.70, 21.45, 2.15, 11.96, 3.85, 2.27e-15, 2.37e-15, 2.46e-15, nan ],
[ 4900, 50.04, 0.96, 65.26, 0.74, 21.90, 2.19, 11.67, 4.12, 2.27e-15, 2.23e-15, 2.32e-15, nan ],
[ 5000, 50.21, 1.00, 65.18, 0.77, 22.34, 2.24, 11.81, 4.24, 2.32e-15, 2.27e-15, 2.27e-15, nan ],
[ 5100, 51.47, 1.01, 65.95, 0.79, 22.68, 2.29, 12.06, 4.31, 2.14e-15, 2.14e-15, 2.10e-15, nan ],
[ 5200, 51.42, 1.05, 66.38, 0.81, 19.73, 2.74, 11.80, 4.59, 2.54e-15, 2.54e-15, 2.45e-15, nan ],
[ 5300, 50.59, 1.11, 66.50, 0.84, 20.08, 2.80, 11.78, 4.77, 2.15e-15, 1.93e-15, 2.19e-15, nan ],
[ 5400, 51.71, 1.13, 66.88, 0.87, 20.29, 2.88, 11.55, 5.05, 2.11e-15, 1.98e-15, 1.94e-15, nan ],
[ 5500, 51.90, 1.17, 66.63, 0.91, 20.77, 2.91, 11.85, 5.11, 1.98e-15, 2.23e-15, 2.03e-15, nan ],
[ 5600, 53.21, 1.18, 66.80, 0.94, 21.23, 2.95, 11.92, 5.26, 2.19e-15, 2.44e-15, 2.44e-15, nan ],
[ 5700, 53.62, 1.21, 67.62, 0.96, 21.44, 3.03, 11.51, 5.65, 2.55e-15, 2.39e-15, 2.47e-15, nan ],
[ 5800, 52.81, 1.27, 66.61, 1.01, 21.83, 3.08, 11.57, 5.82, 2.43e-15, 2.35e-15, 2.20e-15, nan ],
[ 5900, 53.60, 1.30, 66.89, 1.04, 21.42, 3.25, 11.56, 6.02, 2.54e-15, 2.31e-15, 2.31e-15, nan ],
[ 6000, 53.86, 1.34, 67.43, 1.07, 22.57, 3.19, 12.20, 5.90, 2.80e-15, 2.50e-15, 2.58e-15, nan ],
[ 6100, 54.13, 1.37, 67.66, 1.10, 22.77, 3.27, 11.63, 6.40, 2.53e-15, 2.16e-15, 2.16e-15, nan ],
[ 6200, 55.08, 1.40, 67.27, 1.14, 20.19, 3.81, 11.64, 6.60, 2.49e-15, 2.57e-15, 2.49e-15, nan ],
[ 6300, 55.33, 1.43, 67.39, 1.18, 20.63, 3.85, 11.58, 6.86, 2.53e-15, 2.45e-15, 2.67e-15, nan ],
[ 6400, 55.14, 1.49, 67.21, 1.22, 20.59, 3.98, 11.68, 7.01, 2.98e-15, 2.70e-15, 2.49e-15, nan ],
[ 6500, 56.27, 1.50, 67.28, 1.26, 21.03, 4.02, 11.59, 7.29, 2.59e-15, 2.59e-15, 2.52e-15, nan ],
[ 6600, 56.51, 1.54, 68.35, 1.27, 21.40, 4.07, 11.78, 7.40, 2.82e-15, 3.03e-15, 3.17e-15, nan ],
[ 6700, 56.93, 1.58, 68.39, 1.31, 21.55, 4.17, 11.48, 7.82, 3.67e-15, 3.46e-15, 3.60e-15, nan ],
[ 6800, 56.85, 1.63, 68.36, 1.35, 21.91, 4.22, 11.71, 7.90, 3.21e-15, 3.21e-15, 3.34e-15, nan ],
[ 6900, 57.23, 1.66, 68.61, 1.39, 22.17, 4.30, 11.62, 8.20, 3.23e-15, 2.83e-15, 3.03e-15, nan ],
[ 7000, 58.03, 1.69, 68.59, 1.43, 22.56, 4.34, 11.81, 8.30, 2.99e-15, 2.79e-15, 2.73e-15, nan ],
[ 7100, 57.98, 1.74, 68.87, 1.46, 22.63, 4.46, 11.57, 8.71, 2.82e-15, 3.07e-15, 2.82e-15, nan ],
[ 7200, 58.68, 1.77, 68.68, 1.51, 20.52, 5.05, 11.49, 9.02, 3.16e-15, 2.91e-15, 2.97e-15, nan ],
[ 7300, 58.92, 1.81, 69.04, 1.54, 20.59, 5.18, 11.55, 9.23, 3.05e-15, 2.87e-15, 2.68e-15, nan ],
[ 7400, 59.08, 1.85, 69.41, 1.58, 21.08, 5.20, 11.69, 9.37, 3.13e-15, 3.13e-15, 3.01e-15, nan ],
[ 7500, 59.16, 1.90, 68.77, 1.64, 21.31, 5.28, 11.61, 9.69, 2.97e-15, 3.03e-15, 3.27e-15, nan ],
[ 7600, 60.42, 1.91, 69.81, 1.66, 21.58, 5.35, 11.72, 9.86, 3.23e-15, 3.35e-15, 3.29e-15, nan ],
[ 7700, 60.08, 1.97, 69.68, 1.70, 21.82, 5.44, 11.45, 10.35, 2.78e-15, 2.60e-15, 2.66e-15, nan ],
[ 7800, 60.07, 2.03, 69.58, 1.75, 22.09, 5.51, 11.65, 10.45, 2.62e-15, 2.80e-15, 3.09e-15, nan ],
[ 7900, 59.19, 2.11, 69.74, 1.79, 21.89, 5.70, 11.58, 10.78, 2.88e-15, 3.28e-15, 3.22e-15, nan ],
[ 8000, 59.26, 2.16, 69.09, 1.85, 22.44, 5.70, 11.55, 11.09, 2.90e-15, 3.01e-15, 3.30e-15, nan ],
[ 8100, 60.01, 2.19, 70.52, 1.86, 22.68, 5.79, 11.43, 11.48, 3.26e-15, 2.92e-15, 2.92e-15, nan ],
[ 8200, 59.56, 2.26, 71.13, 1.89, 20.60, 6.53, 11.45, 11.74, 3.33e-15, 3.11e-15, 3.33e-15, nan ],
[ 8300, 60.30, 2.29, 70.92, 1.94, 21.03, 6.55, 11.49, 11.99, 3.51e-15, 3.51e-15, 3.34e-15, nan ],
[ 8400, 59.26, 2.38, 70.74, 2.00, 21.17, 6.67, 11.67, 12.09, 2.92e-15, 3.03e-15, 2.87e-15, nan ],
[ 8500, 60.85, 2.38, 72.08, 2.00, 21.31, 6.78, 11.53, 12.54, 3.69e-15, 3.42e-15, 3.37e-15, nan ],
[ 8600, 60.36, 2.45, 72.34, 2.04, 21.70, 6.82, 11.63, 12.72, 2.86e-15, 3.23e-15, 3.07e-15, nan ],
[ 8700, 60.10, 2.52, 73.17, 2.07, 21.82, 6.94, 11.75, 12.89, 3.55e-15, 3.76e-15, 3.66e-15, nan ],
[ 8800, 59.12, 2.62, 71.88, 2.16, 21.35, 7.26, 11.65, 13.30, 3.36e-15, 3.36e-15, 3.57e-15, nan ],
[ 8900, 60.15, 2.63, 73.73, 2.15, 22.31, 7.10, 11.68, 13.57, 3.37e-15, 3.58e-15, 3.27e-15, nan ],
[ 9000, 59.98, 2.70, 71.82, 2.26, 22.37, 7.24, 11.75, 13.79, 4.19e-15, 4.60e-15, 4.50e-15, nan ],
[ 10000, 61.97, 3.23, 72.50, 2.76, 22.36, 8.94, 11.82, 16.92, 3.41e-15, 3.59e-15, 3.46e-15, nan ],
[ 12000, 63.02, 4.57, 72.94, 3.95, 22.08, 13.04, 12.07, 23.86, 3.87e-15, 3.79e-15, 4.24e-15, nan ],
[ 14000, 66.04, 5.94, 74.04, 5.30, 21.91, 17.90, 12.00, 32.66, 4.09e-15, 3.90e-15, 3.90e-15, nan ],
[ 16000, 66.91, 7.65, 73.62, 6.96, 22.53, 22.72, 11.50, 44.52, 4.72e-15, 4.89e-15, 4.77e-15, nan ],
[ 18000, 66.61, 9.73, 74.82, 8.66, 21.98, 29.49, 11.98, 54.08, 5.41e-15, 5.10e-15, 5.15e-15, nan ],
[ 20000, 68.25, 11.72, 74.67, 10.71, 21.32, 37.53, 12.31, 64.99, 5.32e-15, 5.73e-15, 5.87e-15, nan ],
])
# numactl --interleave=all ../testing/testing_dsymv -U -N 123 -N 1234 --range 10:90:1 --range 100:900:10 --range 1000:9000:100 --range 10000:20000:2000
dsymv_U = array([
[ 10, 0.01, 0.03, 0.01, 0.03, 0.01, 0.02, 0.12, 0.00, 8.88e-17, 4.44e-17, 8.88e-17, nan ],
[ 11, 0.01, 0.03, 0.01, 0.03, 0.01, 0.02, 0.14, 0.00, 4.04e-17, 8.07e-17, 8.07e-17, nan ],
[ 12, 0.01, 0.03, 0.01, 0.03, 0.02, 0.02, 0.16, 0.00, 7.40e-17, 3.70e-17, 3.70e-17, nan ],
[ 13, 0.01, 0.03, 0.01, 0.03, 0.02, 0.02, 0.17, 0.00, 6.83e-17, 6.83e-17, 6.83e-17, nan ],
[ 14, 0.01, 0.03, 0.02, 0.03, 0.02, 0.02, 0.20, 0.00, 6.34e-17, 6.34e-17, 6.34e-17, nan ],
[ 15, 0.02, 0.03, 0.02, 0.03, 0.02, 0.02, 0.15, 0.00, 5.92e-17, 5.92e-17, 1.18e-16, nan ],
[ 16, 0.02, 0.03, 0.02, 0.03, 0.03, 0.02, 0.29, 0.00, 1.11e-16, 1.11e-16, 5.55e-17, nan ],
[ 17, 0.02, 0.03, 0.02, 0.03, 0.03, 0.02, 0.32, 0.00, 1.57e-16, 2.09e-16, 1.04e-16, nan ],
[ 18, 0.02, 0.03, 0.02, 0.03, 0.04, 0.02, 0.32, 0.00, 1.48e-16, 9.87e-17, 9.87e-17, nan ],
[ 19, 0.02, 0.03, 0.03, 0.03, 0.04, 0.02, 0.40, 0.00, 9.35e-17, 9.35e-17, 9.35e-17, nan ],
[ 20, 0.03, 0.03, 0.03, 0.03, 0.04, 0.02, 0.29, 0.00, 8.88e-17, 8.88e-17, 8.88e-17, nan ],
[ 21, 0.03, 0.03, 0.03, 0.03, 0.05, 0.02, 0.48, 0.00, 1.69e-16, 8.46e-17, 8.46e-17, nan ],
[ 22, 0.03, 0.03, 0.03, 0.03, 0.05, 0.02, 0.33, 0.00, 1.61e-16, 1.61e-16, 1.61e-16, nan ],
[ 23, 0.04, 0.03, 0.04, 0.03, 0.06, 0.02, 0.51, 0.00, 1.54e-16, 7.72e-17, 7.72e-17, nan ],
[ 24, 0.04, 0.03, 0.04, 0.03, 0.06, 0.02, 0.56, 0.00, 1.48e-16, 2.22e-16, 1.48e-16, nan ],
[ 25, 0.04, 0.03, 0.04, 0.03, 0.06, 0.02, 0.68, 0.00, 1.42e-16, 1.42e-16, 1.42e-16, nan ],
[ 26, 0.04, 0.03, 0.05, 0.03, 0.07, 0.02, 0.45, 0.00, 2.05e-16, 1.37e-16, 2.05e-16, nan ],
[ 27, 0.05, 0.03, 0.05, 0.03, 0.08, 0.02, 0.49, 0.00, 1.32e-16, 1.32e-16, 1.97e-16, nan ],
[ 28, 0.05, 0.03, 0.05, 0.03, 0.08, 0.02, 0.52, 0.00, 1.27e-16, 1.90e-16, 1.27e-16, nan ],
[ 29, 0.06, 0.03, 0.06, 0.03, 0.08, 0.02, 0.91, 0.00, 1.23e-16, 1.84e-16, 1.23e-16, nan ],
[ 30, 0.06, 0.03, 0.06, 0.03, 0.09, 0.02, 0.65, 0.00, 1.78e-16, 1.18e-16, 1.78e-16, nan ],
[ 31, 0.06, 0.03, 0.06, 0.03, 0.09, 0.02, 0.64, 0.00, 1.15e-16, 2.29e-16, 1.72e-16, nan ],
[ 32, 0.07, 0.03, 0.07, 0.03, 0.11, 0.02, 0.74, 0.00, 1.67e-16, 1.67e-16, 1.67e-16, nan ],
[ 33, 0.07, 0.03, 0.06, 0.04, 0.11, 0.02, 0.72, 0.00, 1.61e-16, 1.08e-16, 1.08e-16, nan ],
[ 34, 0.07, 0.03, 0.06, 0.04, 0.11, 0.02, 0.77, 0.00, 2.09e-16, 2.61e-16, 1.57e-16, nan ],
[ 35, 0.08, 0.03, 0.06, 0.04, 0.12, 0.02, 0.81, 0.00, 1.52e-16, 1.52e-16, 1.02e-16, nan ],
[ 36, 0.09, 0.03, 0.07, 0.04, 0.13, 0.02, 0.86, 0.00, 1.48e-16, 1.97e-16, 9.87e-17, nan ],
[ 37, 0.10, 0.03, 0.08, 0.04, 0.13, 0.02, 0.91, 0.00, 1.92e-16, 1.44e-16, 1.92e-16, nan ],
[ 38, 0.10, 0.03, 0.08, 0.04, 0.15, 0.02, 1.04, 0.00, 1.87e-16, 1.40e-16, 1.40e-16, nan ],
[ 39, 0.11, 0.03, 0.08, 0.04, 0.15, 0.02, 1.01, 0.00, 9.11e-17, 9.11e-17, 1.37e-16, nan ],
[ 40, 0.12, 0.03, 0.09, 0.04, 0.16, 0.02, 1.15, 0.00, 1.33e-16, 1.78e-16, 1.78e-16, nan ],
[ 41, 0.12, 0.03, 0.09, 0.04, 0.16, 0.02, 1.20, 0.00, 2.60e-16, 1.73e-16, 1.73e-16, nan ],
[ 42, 0.13, 0.03, 0.10, 0.04, 0.17, 0.02, 0.89, 0.00, 1.69e-16, 1.27e-16, 1.27e-16, nan ],
[ 43, 0.13, 0.03, 0.10, 0.04, 0.18, 0.02, 0.93, 0.00, 1.65e-16, 2.48e-16, 1.65e-16, nan ],
[ 44, 0.14, 0.03, 0.11, 0.04, 0.20, 0.02, 1.28, 0.00, 1.61e-16, 1.61e-16, 2.42e-16, nan ],
[ 45, 0.14, 0.03, 0.11, 0.04, 0.20, 0.02, 1.34, 0.00, 1.58e-16, 1.58e-16, 1.18e-16, nan ],
[ 46, 0.15, 0.03, 0.11, 0.04, 0.21, 0.02, 1.40, 0.00, 1.54e-16, 1.54e-16, 1.54e-16, nan ],
[ 47, 0.16, 0.03, 0.12, 0.04, 0.23, 0.02, 1.11, 0.00, 1.51e-16, 1.51e-16, 1.51e-16, nan ],
[ 48, 0.17, 0.03, 0.13, 0.04, 0.22, 0.02, 1.16, 0.00, 1.48e-16, 1.48e-16, 1.48e-16, nan ],
[ 49, 0.17, 0.03, 0.13, 0.04, 0.23, 0.02, 1.58, 0.00, 2.18e-16, 2.90e-16, 2.18e-16, nan ],
[ 50, 0.17, 0.03, 0.13, 0.04, 0.22, 0.02, 1.34, 0.00, 2.13e-16, 2.13e-16, 2.13e-16, nan ],
[ 51, 0.18, 0.03, 0.14, 0.04, 0.24, 0.02, 1.31, 0.00, 2.09e-16, 2.09e-16, 2.09e-16, nan ],
[ 52, 0.17, 0.03, 0.14, 0.04, 0.25, 0.02, 1.36, 0.00, 2.73e-16, 2.05e-16, 2.05e-16, nan ],
[ 53, 0.18, 0.03, 0.15, 0.04, 0.26, 0.02, 1.50, 0.00, 2.01e-16, 2.68e-16, 2.01e-16, nan ],
[ 54, 0.19, 0.03, 0.15, 0.04, 0.27, 0.02, 1.56, 0.00, 1.97e-16, 2.63e-16, 1.97e-16, nan ],
[ 55, 0.20, 0.03, 0.16, 0.04, 0.28, 0.02, 1.61, 0.00, 1.94e-16, 1.94e-16, 1.29e-16, nan ],
[ 56, 0.22, 0.03, 0.17, 0.04, 0.29, 0.02, 1.58, 0.00, 2.54e-16, 2.54e-16, 2.54e-16, nan ],
[ 57, 0.23, 0.03, 0.17, 0.04, 0.29, 0.02, 1.63, 0.00, 1.87e-16, 1.87e-16, 1.87e-16, nan ],
[ 58, 0.24, 0.03, 0.18, 0.04, 0.31, 0.02, 1.69, 0.00, 2.45e-16, 1.84e-16, 1.84e-16, nan ],
[ 59, 0.25, 0.03, 0.19, 0.04, 0.31, 0.02, 1.41, 0.01, 2.41e-16, 2.41e-16, 2.41e-16, nan ],
[ 60, 0.25, 0.03, 0.19, 0.04, 0.32, 0.02, 1.81, 0.00, 2.96e-16, 2.96e-16, 2.96e-16, nan ],
[ 61, 0.25, 0.03, 0.19, 0.04, 0.33, 0.02, 1.98, 0.00, 1.75e-16, 1.75e-16, 1.75e-16, nan ],
[ 62, 0.27, 0.03, 0.20, 0.04, 0.34, 0.02, 1.93, 0.00, 2.29e-16, 1.72e-16, 1.72e-16, nan ],
[ 63, 0.28, 0.03, 0.20, 0.04, 0.35, 0.02, 1.61, 0.01, 2.26e-16, 2.26e-16, 2.26e-16, nan ],
[ 64, 0.29, 0.03, 0.23, 0.04, 0.36, 0.02, 1.66, 0.01, 2.78e-16, 2.22e-16, 2.22e-16, nan ],
[ 65, 0.23, 0.04, 0.21, 0.04, 0.36, 0.02, 1.71, 0.01, 2.73e-16, 2.19e-16, 3.28e-16, nan ],
[ 66, 0.25, 0.04, 0.21, 0.04, 0.37, 0.02, 1.77, 0.01, 2.15e-16, 1.61e-16, 2.15e-16, nan ],
[ 67, 0.25, 0.04, 0.22, 0.04, 0.38, 0.02, 1.82, 0.01, 1.59e-16, 1.59e-16, 1.59e-16, nan ],
[ 68, 0.26, 0.04, 0.23, 0.04, 0.36, 0.03, 1.87, 0.01, 2.09e-16, 2.09e-16, 1.57e-16, nan ],
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[ 3800, 46.75, 0.62, 50.95, 0.57, 21.27, 1.36, 12.79, 2.26, 2.33e-15, 2.45e-15, 2.57e-15, nan ],
[ 3900, 48.99, 0.62, 51.21, 0.59, 21.76, 1.40, 12.26, 2.48, 2.22e-15, 2.16e-15, 2.22e-15, nan ],
[ 4000, 48.50, 0.66, 49.61, 0.65, 22.21, 1.44, 12.73, 2.51, 2.56e-15, 2.67e-15, 2.50e-15, nan ],
[ 4100, 48.65, 0.69, 50.65, 0.66, 19.35, 1.74, 12.59, 2.67, 2.88e-15, 2.83e-15, 2.77e-15, nan ],
[ 4200, 49.70, 0.71, 52.90, 0.67, 19.05, 1.85, 12.52, 2.82, 2.76e-15, 2.65e-15, 2.92e-15, nan ],
[ 4300, 50.32, 0.74, 52.70, 0.70, 19.62, 1.88, 12.76, 2.90, 2.27e-15, 2.27e-15, 2.43e-15, nan ],
[ 4400, 52.27, 0.74, 52.98, 0.73, 19.88, 1.95, 12.85, 3.01, 2.38e-15, 2.43e-15, 2.58e-15, nan ],
[ 4500, 52.88, 0.77, 55.18, 0.73, 20.39, 1.99, 12.84, 3.16, 2.83e-15, 3.03e-15, 2.78e-15, nan ],
[ 4600, 54.63, 0.77, 55.26, 0.77, 20.80, 2.04, 12.82, 3.30, 2.52e-15, 2.52e-15, 2.77e-15, nan ],
[ 4700, 55.24, 0.80, 54.69, 0.81, 21.24, 2.08, 12.69, 3.48, 2.52e-15, 2.66e-15, 2.71e-15, nan ],
[ 4800, 54.87, 0.84, 53.79, 0.86, 21.62, 2.13, 12.95, 3.56, 2.51e-15, 2.42e-15, 2.46e-15, nan ],
[ 4900, 55.53, 0.86, 56.64, 0.85, 22.02, 2.18, 12.76, 3.76, 2.51e-15, 2.55e-15, 2.64e-15, nan ],
[ 5000, 56.37, 0.89, 56.95, 0.88, 22.48, 2.22, 12.75, 3.92, 2.46e-15, 2.32e-15, 2.64e-15, nan ],
[ 5100, 58.46, 0.89, 57.05, 0.91, 22.78, 2.28, 12.70, 4.10, 2.41e-15, 2.36e-15, 2.41e-15, nan ],
[ 5200, 57.67, 0.94, 57.92, 0.93, 19.94, 2.71, 12.81, 4.22, 2.62e-15, 2.54e-15, 2.62e-15, nan ],
[ 5300, 58.05, 0.97, 59.53, 0.94, 20.26, 2.77, 13.15, 4.27, 2.57e-15, 2.66e-15, 2.62e-15, nan ],
[ 5400, 59.53, 0.98, 60.26, 0.97, 20.57, 2.84, 12.97, 4.50, 2.69e-15, 2.44e-15, 2.53e-15, nan ],
[ 5500, 57.56, 1.05, 60.46, 1.00, 20.95, 2.89, 12.99, 4.66, 2.40e-15, 2.40e-15, 2.40e-15, nan ],
[ 5600, 59.85, 1.05, 55.46, 1.13, 21.25, 2.95, 13.21, 4.75, 2.52e-15, 2.68e-15, 2.60e-15, nan ],
[ 5700, 60.81, 1.07, 61.42, 1.06, 21.50, 3.02, 13.28, 4.89, 2.63e-15, 2.63e-15, 2.63e-15, nan ],
[ 5800, 61.62, 1.09, 60.57, 1.11, 21.84, 3.08, 13.21, 5.09, 3.14e-15, 2.98e-15, 3.06e-15, nan ],
[ 5900, 62.11, 1.12, 60.34, 1.15, 22.25, 3.13, 13.21, 5.27, 3.08e-15, 3.01e-15, 3.01e-15, nan ],
[ 6000, 62.73, 1.15, 60.36, 1.19, 22.62, 3.18, 13.42, 5.37, 3.41e-15, 3.26e-15, 3.41e-15, nan ],
[ 6100, 63.24, 1.18, 62.70, 1.19, 22.88, 3.25, 13.60, 5.47, 3.28e-15, 2.83e-15, 3.06e-15, nan ],
[ 6200, 64.35, 1.19, 62.83, 1.22, 20.34, 3.78, 13.30, 5.78, 3.01e-15, 3.08e-15, 2.86e-15, nan ],
[ 6300, 65.18, 1.22, 61.21, 1.30, 20.71, 3.83, 13.54, 5.86, 3.68e-15, 3.75e-15, 3.46e-15, nan ],
[ 6400, 65.59, 1.25, 59.16, 1.38, 20.89, 3.92, 13.50, 6.07, 3.98e-15, 3.77e-15, 4.05e-15, nan ],
[ 6500, 65.41, 1.29, 62.61, 1.35, 21.09, 4.01, 13.53, 6.25, 3.08e-15, 3.29e-15, 3.50e-15, nan ],
[ 6600, 66.11, 1.32, 62.96, 1.38, 21.55, 4.04, 13.66, 6.38, 3.72e-15, 3.58e-15, 3.58e-15, nan ],
[ 6700, 68.50, 1.31, 62.19, 1.44, 21.67, 4.14, 13.41, 6.70, 4.00e-15, 4.14e-15, 3.87e-15, nan ],
[ 6800, 67.12, 1.38, 62.37, 1.48, 22.04, 4.20, 13.66, 6.77, 3.81e-15, 3.68e-15, 3.61e-15, nan ],
[ 6900, 68.81, 1.38, 63.45, 1.50, 22.18, 4.29, 13.62, 6.99, 2.97e-15, 3.10e-15, 3.23e-15, nan ],
[ 7000, 68.40, 1.43, 62.55, 1.57, 22.31, 4.39, 13.62, 7.20, 3.51e-15, 4.03e-15, 3.83e-15, nan ],
[ 7100, 69.54, 1.45, 62.54, 1.61, 22.80, 4.42, 13.64, 7.39, 3.20e-15, 3.27e-15, 3.07e-15, nan ],
[ 7200, 69.41, 1.49, 60.26, 1.72, 20.71, 5.01, 13.62, 7.61, 3.03e-15, 3.28e-15, 3.16e-15, nan ],
[ 7300, 70.08, 1.52, 63.34, 1.68, 20.68, 5.15, 13.71, 7.77, 3.55e-15, 3.61e-15, 3.43e-15, nan ],
[ 7400, 71.68, 1.53, 62.92, 1.74, 20.87, 5.25, 13.64, 8.03, 3.38e-15, 3.81e-15, 3.56e-15, nan ],
[ 7500, 71.12, 1.58, 64.26, 1.75, 21.21, 5.31, 13.76, 8.18, 3.70e-15, 3.58e-15, 3.76e-15, nan ],
[ 7600, 72.80, 1.59, 63.72, 1.81, 21.46, 5.38, 14.00, 8.26, 3.47e-15, 3.47e-15, 3.29e-15, nan ],
[ 7700, 72.09, 1.65, 65.16, 1.82, 21.67, 5.47, 13.79, 8.60, 3.72e-15, 3.72e-15, 3.54e-15, nan ],
[ 7800, 72.82, 1.67, 64.01, 1.90, 22.18, 5.49, 13.49, 9.02, 3.26e-15, 3.44e-15, 3.61e-15, nan ],
[ 7900, 71.86, 1.74, 63.66, 1.96, 22.46, 5.56, 13.73, 9.09, 3.45e-15, 3.63e-15, 3.51e-15, nan ],
[ 8000, 71.55, 1.79, 60.71, 2.11, 22.27, 5.75, 13.72, 9.33, 3.30e-15, 3.30e-15, 3.41e-15, nan ],
[ 8100, 72.31, 1.81, 66.29, 1.98, 22.92, 5.73, 13.65, 9.62, 3.82e-15, 3.87e-15, 3.65e-15, nan ],
[ 8200, 70.82, 1.90, 66.22, 2.03, 21.31, 6.31, 13.79, 9.76, 3.49e-15, 3.44e-15, 3.49e-15, nan ],
[ 8300, 71.92, 1.92, 66.03, 2.09, 21.07, 6.54, 13.53, 10.18, 3.45e-15, 3.45e-15, 3.62e-15, nan ],
[ 8400, 69.83, 2.02, 65.13, 2.17, 21.03, 6.71, 13.98, 10.10, 3.63e-15, 3.57e-15, 3.46e-15, nan ],
[ 8500, 71.22, 2.03, 67.03, 2.16, 21.58, 6.70, 13.69, 10.55, 4.01e-15, 4.01e-15, 3.91e-15, nan ],
[ 8600, 70.28, 2.10, 66.43, 2.23, 21.30, 6.95, 13.80, 10.72, 3.75e-15, 3.44e-15, 3.49e-15, nan ],
[ 8700, 70.97, 2.13, 66.90, 2.26, 21.91, 6.91, 13.83, 10.95, 3.35e-15, 3.45e-15, 3.66e-15, nan ],
[ 8800, 69.03, 2.24, 64.27, 2.41, 21.63, 7.16, 14.02, 11.05, 4.19e-15, 4.29e-15, 4.55e-15, nan ],
[ 8900, 69.58, 2.28, 67.62, 2.34, 21.89, 7.24, 14.08, 11.26, 3.83e-15, 3.68e-15, 3.93e-15, nan ],
[ 9000, 69.48, 2.33, 66.65, 2.43, 22.53, 7.19, 14.07, 11.52, 3.33e-15, 3.44e-15, 3.39e-15, nan ],
[ 10000, 69.04, 2.90, 68.06, 2.94, 22.26, 8.99, 14.24, 14.04, 3.87e-15, 3.68e-15, 3.82e-15, nan ],
[ 12000, 69.45, 4.15, 65.73, 4.38, 21.81, 13.20, 14.40, 20.00, 5.15e-15, 5.08e-15, 5.15e-15, nan ],
[ 14000, 73.33, 5.35, 71.39, 5.49, 22.58, 17.36, 14.79, 26.50, 5.07e-15, 5.26e-15, 4.94e-15, nan ],
[ 16000, 74.65, 6.86, 67.95, 7.54, 21.96, 23.31, 14.22, 36.00, 5.12e-15, 4.89e-15, 5.06e-15, nan ],
[ 18000, 72.16, 8.98, 73.26, 8.85, 21.83, 29.68, 14.77, 43.87, 6.11e-15, 6.27e-15, 6.72e-15, nan ],
[ 20000, 72.64, 11.01, 69.95, 11.44, 20.89, 38.30, 15.25, 52.47, 5.96e-15, 5.68e-15, 5.78e-15, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/log.txt
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/setup.txt
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/sgeqrf.txt
# numactl --interleave=all ../testing/testing_sgeqrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
sgeqrf = array([
[ 10, 10, nan, nan, 0.00, 0.00, nan ],
[ 20, 20, nan, nan, 0.02, 0.00, nan ],
[ 30, 30, nan, nan, 0.06, 0.00, nan ],
[ 40, 40, nan, nan, 0.25, 0.00, nan ],
[ 50, 50, nan, nan, 0.42, 0.00, nan ],
[ 60, 60, nan, nan, 0.69, 0.00, nan ],
[ 70, 70, nan, nan, 0.88, 0.00, nan ],
[ 80, 80, nan, nan, 0.84, 0.00, nan ],
[ 90, 90, nan, nan, 0.99, 0.00, nan ],
[ 100, 100, nan, nan, 0.97, 0.00, nan ],
[ 200, 200, nan, nan, 4.62, 0.00, nan ],
[ 300, 300, nan, nan, 10.57, 0.00, nan ],
[ 400, 400, nan, nan, 19.32, 0.00, nan ],
[ 500, 500, nan, nan, 29.73, 0.01, nan ],
[ 600, 600, nan, nan, 41.45, 0.01, nan ],
[ 700, 700, nan, nan, 55.58, 0.01, nan ],
[ 800, 800, nan, nan, 67.12, 0.01, nan ],
[ 900, 900, nan, nan, 91.96, 0.01, nan ],
[ 1000, 1000, nan, nan, 119.32, 0.01, nan ],
[ 2000, 2000, nan, nan, 330.72, 0.03, nan ],
[ 3000, 3000, nan, nan, 570.95, 0.06, nan ],
[ 4000, 4000, nan, nan, 725.82, 0.12, nan ],
[ 5000, 5000, nan, nan, 897.94, 0.19, nan ],
[ 6000, 6000, nan, nan, 1049.26, 0.27, nan ],
[ 7000, 7000, nan, nan, 1111.54, 0.41, nan ],
[ 8000, 8000, nan, nan, 1373.64, 0.50, nan ],
[ 9000, 9000, nan, nan, 1485.26, 0.65, nan ],
[ 10000, 10000, nan, nan, 1581.15, 0.84, nan ],
[ 12000, 12000, nan, nan, 1719.35, 1.34, nan ],
[ 14000, 14000, nan, nan, 1792.15, 2.04, nan ],
[ 16000, 16000, nan, nan, 1886.77, 2.89, nan ],
[ 18000, 18000, nan, nan, 1909.49, 4.07, nan ],
[ 20000, 20000, nan, nan, 2045.64, 5.21, nan ],
])
# numactl --interleave=all ../testing/testing_sgeqrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
sgeqrf_gpu = array([
[ 10, 10, nan, nan, 0.00, 0.00, nan ],
[ 20, 20, nan, nan, 0.01, 0.00, nan ],
[ 30, 30, nan, nan, 0.03, 0.00, nan ],
[ 40, 40, nan, nan, 0.07, 0.00, nan ],
[ 50, 50, nan, nan, 0.13, 0.00, nan ],
[ 60, 60, nan, nan, 0.21, 0.00, nan ],
[ 70, 70, nan, nan, 0.33, 0.00, nan ],
[ 80, 80, nan, nan, 0.49, 0.00, nan ],
[ 90, 90, nan, nan, 0.65, 0.00, nan ],
[ 100, 100, nan, nan, 0.72, 0.00, nan ],
[ 200, 200, nan, nan, 6.29, 0.00, nan ],
[ 300, 300, nan, nan, 13.58, 0.00, nan ],
[ 400, 400, nan, nan, 23.77, 0.00, nan ],
[ 500, 500, nan, nan, 27.62, 0.01, nan ],
[ 600, 600, nan, nan, 40.84, 0.01, nan ],
[ 700, 700, nan, nan, 51.96, 0.01, nan ],
[ 800, 800, nan, nan, 69.73, 0.01, nan ],
[ 900, 900, nan, nan, 82.84, 0.01, nan ],
[ 1000, 1000, nan, nan, 99.61, 0.01, nan ],
[ 2000, 2000, nan, nan, 296.04, 0.04, nan ],
[ 3000, 3000, nan, nan, 489.48, 0.07, nan ],
[ 4000, 4000, nan, nan, 705.17, 0.12, nan ],
[ 5000, 5000, nan, nan, 888.12, 0.19, nan ],
[ 6000, 6000, nan, nan, 1021.01, 0.28, nan ],
[ 7000, 7000, nan, nan, 1096.15, 0.42, nan ],
[ 8000, 8000, nan, nan, 1309.73, 0.52, nan ],
[ 9000, 9000, nan, nan, 1423.20, 0.68, nan ],
[ 10000, 10000, nan, nan, 1526.76, 0.87, nan ],
[ 12000, 12000, nan, nan, 1679.62, 1.37, nan ],
[ 14000, 14000, nan, nan, 1768.33, 2.07, nan ],
[ 16000, 16000, nan, nan, 1872.85, 2.92, nan ],
[ 18000, 18000, nan, nan, 1911.42, 4.07, nan ],
[ 20000, 20000, nan, nan, 1996.32, 5.34, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/sgesvd.txt
# numactl --interleave=all ../testing/testing_sgesvd -UN -VN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
sgesvd_UN = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.00, nan ],
[ nan, 80, 80, nan, 0.00, nan ],
[ nan, 90, 90, nan, 0.00, nan ],
[ nan, 100, 100, nan, 0.00, nan ],
[ nan, 200, 200, nan, 0.01, nan ],
[ nan, 300, 300, nan, 0.03, nan ],
[ nan, 400, 400, nan, 0.04, nan ],
[ nan, 500, 500, nan, 0.06, nan ],
[ nan, 600, 600, nan, 0.08, nan ],
[ nan, 700, 700, nan, 0.11, nan ],
[ nan, 800, 800, nan, 0.14, nan ],
[ nan, 900, 900, nan, 0.19, nan ],
[ nan, 1000, 1000, nan, 0.22, nan ],
[ nan, 2000, 2000, nan, 0.74, nan ],
[ nan, 3000, 3000, nan, 1.75, nan ],
[ nan, 4000, 4000, nan, 3.30, nan ],
[ nan, 5000, 5000, nan, 5.96, nan ],
[ nan, 6000, 6000, nan, 9.46, nan ],
[ nan, 7000, 7000, nan, 14.05, nan ],
[ nan, 8000, 8000, nan, 19.90, nan ],
[ nan, 9000, 9000, nan, 27.30, nan ],
[ nan, 10000, 10000, nan, 36.58, nan ],
[ nan, 300, 100, nan, 0.00, nan ],
[ nan, 600, 200, nan, 0.02, nan ],
[ nan, 900, 300, nan, 0.03, nan ],
[ nan, 1200, 400, nan, 0.05, nan ],
[ nan, 1500, 500, nan, 0.07, nan ],
[ nan, 1800, 600, nan, 0.10, nan ],
[ nan, 2100, 700, nan, 0.13, nan ],
[ nan, 2400, 800, nan, 0.17, nan ],
[ nan, 2700, 900, nan, 0.22, nan ],
[ nan, 3000, 1000, nan, 0.26, nan ],
[ nan, 6000, 2000, nan, 1.06, nan ],
[ nan, 9000, 3000, nan, 2.61, nan ],
[ nan, 12000, 4000, nan, 5.07, nan ],
[ nan, 15000, 5000, nan, 8.24, nan ],
[ nan, 18000, 6000, nan, 13.18, nan ],
[ nan, 21000, 7000, nan, 19.68, nan ],
[ nan, 24000, 8000, nan, 28.20, nan ],
[ nan, 27000, 9000, nan, 38.52, nan ],
[ nan, 100, 300, nan, 0.00, nan ],
[ nan, 200, 600, nan, 0.02, nan ],
[ nan, 300, 900, nan, 0.04, nan ],
[ nan, 400, 1200, nan, 0.06, nan ],
[ nan, 500, 1500, nan, 0.08, nan ],
[ nan, 600, 1800, nan, 0.11, nan ],
[ nan, 700, 2100, nan, 0.15, nan ],
[ nan, 800, 2400, nan, 0.18, nan ],
[ nan, 900, 2700, nan, 0.23, nan ],
[ nan, 1000, 3000, nan, 0.28, nan ],
[ nan, 2000, 6000, nan, 1.06, nan ],
[ nan, 3000, 9000, nan, 2.64, nan ],
[ nan, 4000, 12000, nan, 4.99, nan ],
[ nan, 5000, 15000, nan, 9.06, nan ],
[ nan, 6000, 18000, nan, 14.39, nan ],
[ nan, 7000, 21000, nan, 21.74, nan ],
[ nan, 8000, 24000, nan, 30.43, nan ],
[ nan, 9000, 27000, nan, 41.90, nan ],
[ nan, 10000, 100, nan, 0.01, nan ],
[ nan, 20000, 200, nan, 0.04, nan ],
[ nan, 30000, 300, nan, 0.09, nan ],
[ nan, 40000, 400, nan, 0.21, nan ],
[ nan, 50000, 500, nan, 0.33, nan ],
[ nan, 60000, 600, nan, 0.49, nan ],
[ nan, 70000, 700, nan, 0.69, nan ],
[ nan, 80000, 800, nan, 0.94, nan ],
[ nan, 90000, 900, nan, 1.32, nan ],
[ nan, 100000, 1000, nan, 1.65, nan ],
[ nan, 200000, 2000, nan, 9.03, nan ],
[ nan, 100, 10000, nan, 0.01, nan ],
[ nan, 200, 20000, nan, 0.05, nan ],
[ nan, 300, 30000, nan, 0.13, nan ],
[ nan, 400, 40000, nan, 0.25, nan ],
[ nan, 500, 50000, nan, 0.45, nan ],
[ nan, 600, 60000, nan, 0.68, nan ],
[ nan, 700, 70000, nan, 1.06, nan ],
[ nan, 800, 80000, nan, 1.29, nan ],
[ nan, 900, 90000, nan, 1.60, nan ],
[ nan, 1000, 100000, nan, 2.00, nan ],
[ nan, 2000, 200000, nan, 12.29, nan ],
])
# numactl --interleave=all ../testing/testing_sgesvd -US -VS -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
sgesvd_US = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.01, nan ],
[ nan, 70, 70, nan, 0.01, nan ],
[ nan, 80, 80, nan, 0.01, nan ],
[ nan, 90, 90, nan, 0.02, nan ],
[ nan, 100, 100, nan, 0.02, nan ],
[ nan, 200, 200, nan, 0.08, nan ],
[ nan, 300, 300, nan, 0.05, nan ],
[ nan, 400, 400, nan, 0.08, nan ],
[ nan, 500, 500, nan, 0.13, nan ],
[ nan, 600, 600, nan, 0.18, nan ],
[ nan, 700, 700, nan, 0.23, nan ],
[ nan, 800, 800, nan, 0.31, nan ],
[ nan, 900, 900, nan, 0.41, nan ],
[ nan, 1000, 1000, nan, 0.49, nan ],
[ nan, 2000, 2000, nan, 2.46, nan ],
[ nan, 3000, 3000, nan, 7.44, nan ],
[ nan, 4000, 4000, nan, 15.79, nan ],
[ nan, 5000, 5000, nan, 29.23, nan ],
[ nan, 6000, 6000, nan, 52.14, nan ],
[ nan, 7000, 7000, nan, 64.01, nan ],
[ nan, 8000, 8000, nan, 91.31, nan ],
[ nan, 9000, 9000, nan, 128.34, nan ],
[ nan, 10000, 10000, nan, 179.45, nan ],
[ nan, 300, 100, nan, 0.03, nan ],
[ nan, 600, 200, nan, 0.08, nan ],
[ nan, 900, 300, nan, 0.06, nan ],
[ nan, 1200, 400, nan, 0.10, nan ],
[ nan, 1500, 500, nan, 0.17, nan ],
[ nan, 1800, 600, nan, 0.25, nan ],
[ nan, 2100, 700, nan, 0.34, nan ],
[ nan, 2400, 800, nan, 0.40, nan ],
[ nan, 2700, 900, nan, 0.54, nan ],
[ nan, 3000, 1000, nan, 0.74, nan ],
[ nan, 6000, 2000, nan, 3.26, nan ],
[ nan, 9000, 3000, nan, 8.68, nan ],
[ nan, 12000, 4000, nan, 17.44, nan ],
[ nan, 15000, 5000, nan, 31.24, nan ],
[ nan, 18000, 6000, nan, 53.73, nan ],
[ nan, 21000, 7000, nan, 82.38, nan ],
[ nan, 24000, 8000, nan, 110.54, nan ],
[ nan, 27000, 9000, nan, 165.19, nan ],
[ nan, 100, 300, nan, 0.02, nan ],
[ nan, 200, 600, nan, 0.07, nan ],
[ nan, 300, 900, nan, 0.08, nan ],
[ nan, 400, 1200, nan, 0.13, nan ],
[ nan, 500, 1500, nan, 0.20, nan ],
[ nan, 600, 1800, nan, 0.27, nan ],
[ nan, 700, 2100, nan, 0.39, nan ],
[ nan, 800, 2400, nan, 0.50, nan ],
[ nan, 900, 2700, nan, 0.63, nan ],
[ nan, 1000, 3000, nan, 0.78, nan ],
[ nan, 2000, 6000, nan, 3.81, nan ],
[ nan, 3000, 9000, nan, 9.76, nan ],
[ nan, 4000, 12000, nan, 20.42, nan ],
[ nan, 5000, 15000, nan, 37.34, nan ],
[ nan, 6000, 18000, nan, 62.36, nan ],
[ nan, 7000, 21000, nan, 97.75, nan ],
[ nan, 8000, 24000, nan, 144.67, nan ],
[ nan, 9000, 27000, nan, 202.69, nan ],
[ nan, 10000, 100, nan, 0.05, nan ],
[ nan, 20000, 200, nan, 0.19, nan ],
[ nan, 30000, 300, nan, 0.29, nan ],
[ nan, 40000, 400, nan, 0.58, nan ],
[ nan, 50000, 500, nan, 1.03, nan ],
[ nan, 60000, 600, nan, 1.59, nan ],
[ nan, 70000, 700, nan, 2.15, nan ],
[ nan, 80000, 800, nan, 3.09, nan ],
[ nan, 90000, 900, nan, 4.30, nan ],
[ nan, 100000, 1000, nan, 5.67, nan ],
[ nan, 200000, 2000, nan, 38.28, nan ],
[ nan, 100, 10000, nan, 0.07, nan ],
[ nan, 200, 20000, nan, 0.35, nan ],
[ nan, 300, 30000, nan, 0.56, nan ],
[ nan, 400, 40000, nan, 0.89, nan ],
[ nan, 500, 50000, nan, 2.49, nan ],
[ nan, 600, 60000, nan, 3.38, nan ],
[ nan, 700, 70000, nan, 4.27, nan ],
[ nan, 800, 80000, nan, 5.83, nan ],
[ nan, 900, 90000, nan, 7.41, nan ],
[ nan, 1000, 100000, nan, 15.30, nan ],
[ nan, 2000, 200000, nan, 107.41, nan ],
])
# numactl --interleave=all ../testing/testing_sgesdd -UN -VN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
sgesdd_UN = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.00, nan ],
[ nan, 80, 80, nan, 0.00, nan ],
[ nan, 90, 90, nan, 0.00, nan ],
[ nan, 100, 100, nan, 0.00, nan ],
[ nan, 200, 200, nan, 0.01, nan ],
[ nan, 300, 300, nan, 0.03, nan ],
[ nan, 400, 400, nan, 0.04, nan ],
[ nan, 500, 500, nan, 0.06, nan ],
[ nan, 600, 600, nan, 0.08, nan ],
[ nan, 700, 700, nan, 0.10, nan ],
[ nan, 800, 800, nan, 0.13, nan ],
[ nan, 900, 900, nan, 0.17, nan ],
[ nan, 1000, 1000, nan, 0.20, nan ],
[ nan, 2000, 2000, nan, 0.75, nan ],
[ nan, 3000, 3000, nan, 1.75, nan ],
[ nan, 4000, 4000, nan, 3.32, nan ],
[ nan, 5000, 5000, nan, 5.98, nan ],
[ nan, 6000, 6000, nan, 9.49, nan ],
[ nan, 7000, 7000, nan, 14.10, nan ],
[ nan, 8000, 8000, nan, 19.96, nan ],
[ nan, 9000, 9000, nan, 27.30, nan ],
[ nan, 10000, 10000, nan, 36.36, nan ],
[ nan, 300, 100, nan, 0.00, nan ],
[ nan, 600, 200, nan, 0.01, nan ],
[ nan, 900, 300, nan, 0.03, nan ],
[ nan, 1200, 400, nan, 0.05, nan ],
[ nan, 1500, 500, nan, 0.07, nan ],
[ nan, 1800, 600, nan, 0.09, nan ],
[ nan, 2100, 700, nan, 0.13, nan ],
[ nan, 2400, 800, nan, 0.16, nan ],
[ nan, 2700, 900, nan, 0.21, nan ],
[ nan, 3000, 1000, nan, 0.24, nan ],
[ nan, 6000, 2000, nan, 1.02, nan ],
[ nan, 9000, 3000, nan, 2.58, nan ],
[ nan, 12000, 4000, nan, 5.11, nan ],
[ nan, 15000, 5000, nan, 9.16, nan ],
[ nan, 18000, 6000, nan, 13.59, nan ],
[ nan, 21000, 7000, nan, 21.94, nan ],
[ nan, 24000, 8000, nan, 31.62, nan ],
[ nan, 27000, 9000, nan, 43.79, nan ],
[ nan, 100, 300, nan, 0.00, nan ],
[ nan, 200, 600, nan, 0.02, nan ],
[ nan, 300, 900, nan, 0.04, nan ],
[ nan, 400, 1200, nan, 0.06, nan ],
[ nan, 500, 1500, nan, 0.09, nan ],
[ nan, 600, 1800, nan, 0.12, nan ],
[ nan, 700, 2100, nan, 0.16, nan ],
[ nan, 800, 2400, nan, 0.19, nan ],
[ nan, 900, 2700, nan, 0.25, nan ],
[ nan, 1000, 3000, nan, 0.30, nan ],
[ nan, 2000, 6000, nan, 1.21, nan ],
[ nan, 3000, 9000, nan, 3.10, nan ],
[ nan, 4000, 12000, nan, 5.95, nan ],
[ nan, 5000, 15000, nan, 10.80, nan ],
[ nan, 6000, 18000, nan, 17.28, nan ],
[ nan, 7000, 21000, nan, 26.20, nan ],
[ nan, 8000, 24000, nan, 35.64, nan ],
[ nan, 9000, 27000, nan, 49.67, nan ],
[ nan, 10000, 100, nan, 0.01, nan ],
[ nan, 20000, 200, nan, 0.04, nan ],
[ nan, 30000, 300, nan, 0.10, nan ],
[ nan, 40000, 400, nan, 0.21, nan ],
[ nan, 50000, 500, nan, 0.33, nan ],
[ nan, 60000, 600, nan, 0.48, nan ],
[ nan, 70000, 700, nan, 0.67, nan ],
[ nan, 80000, 800, nan, 0.92, nan ],
[ nan, 90000, 900, nan, 1.29, nan ],
[ nan, 100000, 1000, nan, 1.61, nan ],
[ nan, 200000, 2000, nan, 10.96, nan ],
[ nan, 100, 10000, nan, 0.01, nan ],
[ nan, 200, 20000, nan, 0.06, nan ],
[ nan, 300, 30000, nan, 0.17, nan ],
[ nan, 400, 40000, nan, 0.33, nan ],
[ nan, 500, 50000, nan, 0.61, nan ],
[ nan, 600, 60000, nan, 0.93, nan ],
[ nan, 700, 70000, nan, 1.46, nan ],
[ nan, 800, 80000, nan, 1.75, nan ],
[ nan, 900, 90000, nan, 2.36, nan ],
[ nan, 1000, 100000, nan, 2.97, nan ],
[ nan, 2000, 200000, nan, 18.77, nan ],
])
# numactl --interleave=all ../testing/testing_sgesdd -US -VS -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
sgesdd_US = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.00, nan ],
[ nan, 80, 80, nan, 0.00, nan ],
[ nan, 90, 90, nan, 0.00, nan ],
[ nan, 100, 100, nan, 0.00, nan ],
[ nan, 200, 200, nan, 0.02, nan ],
[ nan, 300, 300, nan, 0.04, nan ],
[ nan, 400, 400, nan, 0.06, nan ],
[ nan, 500, 500, nan, 0.09, nan ],
[ nan, 600, 600, nan, 0.12, nan ],
[ nan, 700, 700, nan, 0.16, nan ],
[ nan, 800, 800, nan, 0.21, nan ],
[ nan, 900, 900, nan, 0.27, nan ],
[ nan, 1000, 1000, nan, 0.34, nan ],
[ nan, 2000, 2000, nan, 1.25, nan ],
[ nan, 3000, 3000, nan, 2.95, nan ],
[ nan, 4000, 4000, nan, 5.38, nan ],
[ nan, 5000, 5000, nan, 9.73, nan ],
[ nan, 6000, 6000, nan, 14.35, nan ],
[ nan, 7000, 7000, nan, 20.93, nan ],
[ nan, 8000, 8000, nan, 28.99, nan ],
[ nan, 9000, 9000, nan, 38.69, nan ],
[ nan, 10000, 10000, nan, 50.61, nan ],
[ nan, 300, 100, nan, 0.01, nan ],
[ nan, 600, 200, nan, 0.02, nan ],
[ nan, 900, 300, nan, 0.04, nan ],
[ nan, 1200, 400, nan, 0.07, nan ],
[ nan, 1500, 500, nan, 0.10, nan ],
[ nan, 1800, 600, nan, 0.14, nan ],
[ nan, 2100, 700, nan, 0.20, nan ],
[ nan, 2400, 800, nan, 0.25, nan ],
[ nan, 2700, 900, nan, 0.32, nan ],
[ nan, 3000, 1000, nan, 0.40, nan ],
[ nan, 6000, 2000, nan, 1.73, nan ],
[ nan, 9000, 3000, nan, 4.16, nan ],
[ nan, 12000, 4000, nan, 8.09, nan ],
[ nan, 15000, 5000, nan, 14.18, nan ],
[ nan, 18000, 6000, nan, 23.02, nan ],
[ nan, 21000, 7000, nan, 33.96, nan ],
[ nan, 24000, 8000, nan, 47.74, nan ],
[ nan, 27000, 9000, nan, 64.54, nan ],
[ nan, 100, 300, nan, 0.01, nan ],
[ nan, 200, 600, nan, 0.03, nan ],
[ nan, 300, 900, nan, 0.05, nan ],
[ nan, 400, 1200, nan, 0.08, nan ],
[ nan, 500, 1500, nan, 0.12, nan ],
[ nan, 600, 1800, nan, 0.17, nan ],
[ nan, 700, 2100, nan, 0.23, nan ],
[ nan, 800, 2400, nan, 0.28, nan ],
[ nan, 900, 2700, nan, 0.36, nan ],
[ nan, 1000, 3000, nan, 0.46, nan ],
[ nan, 2000, 6000, nan, 1.76, nan ],
[ nan, 3000, 9000, nan, 4.37, nan ],
[ nan, 4000, 12000, nan, 8.32, nan ],
[ nan, 5000, 15000, nan, 14.78, nan ],
[ nan, 6000, 18000, nan, 23.99, nan ],
[ nan, 7000, 21000, nan, 35.67, nan ],
[ nan, 8000, 24000, nan, 49.53, nan ],
[ nan, 9000, 27000, nan, 67.04, nan ],
[ nan, 10000, 100, nan, 0.02, nan ],
[ nan, 20000, 200, nan, 0.10, nan ],
[ nan, 30000, 300, nan, 0.18, nan ],
[ nan, 40000, 400, nan, 0.35, nan ],
[ nan, 50000, 500, nan, 0.75, nan ],
[ nan, 60000, 600, nan, 0.98, nan ],
[ nan, 70000, 700, nan, 1.28, nan ],
[ nan, 80000, 800, nan, 1.61, nan ],
[ nan, 90000, 900, nan, 2.16, nan ],
[ nan, 100000, 1000, nan, 3.45, nan ],
[ nan, 200000, 2000, nan, 17.92, nan ],
[ nan, 100, 10000, nan, 0.04, nan ],
[ nan, 200, 20000, nan, 0.18, nan ],
[ nan, 300, 30000, nan, 0.37, nan ],
[ nan, 400, 40000, nan, 0.54, nan ],
[ nan, 500, 50000, nan, 1.84, nan ],
[ nan, 600, 60000, nan, 2.13, nan ],
[ nan, 700, 70000, nan, 2.53, nan ],
[ nan, 800, 80000, nan, 2.65, nan ],
[ nan, 900, 90000, nan, 3.06, nan ],
[ nan, 1000, 100000, nan, 6.52, nan ],
[ nan, 2000, 200000, nan, 26.37, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/sgetrf.txt
# numactl --interleave=all ../testing/testing_sgetrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
sgetrf = array([
[ 10, 10, nan, nan, 0.03, 0.00, nan ],
[ 20, 20, nan, nan, 0.08, 0.00, nan ],
[ 30, 30, nan, nan, 0.46, 0.00, nan ],
[ 40, 40, nan, nan, 0.69, 0.00, nan ],
[ 50, 50, nan, nan, 1.55, 0.00, nan ],
[ 60, 60, nan, nan, 2.41, 0.00, nan ],
[ 70, 70, nan, nan, 1.89, 0.00, nan ],
[ 80, 80, nan, nan, 3.22, 0.00, nan ],
[ 90, 90, nan, nan, 3.83, 0.00, nan ],
[ 100, 100, nan, nan, 4.70, 0.00, nan ],
[ 200, 200, nan, nan, 17.09, 0.00, nan ],
[ 300, 300, nan, nan, 10.21, 0.00, nan ],
[ 400, 400, nan, nan, 20.55, 0.00, nan ],
[ 500, 500, nan, nan, 30.92, 0.00, nan ],
[ 600, 600, nan, nan, 41.22, 0.00, nan ],
[ 700, 700, nan, nan, 53.89, 0.00, nan ],
[ 800, 800, nan, nan, 68.44, 0.00, nan ],
[ 900, 900, nan, nan, 82.98, 0.01, nan ],
[ 1000, 1000, nan, nan, 98.87, 0.01, nan ],
[ 2000, 2000, nan, nan, 259.86, 0.02, nan ],
[ 3000, 3000, nan, nan, 434.36, 0.04, nan ],
[ 4000, 4000, nan, nan, 611.21, 0.07, nan ],
[ 5000, 5000, nan, nan, 747.73, 0.11, nan ],
[ 6000, 6000, nan, nan, 918.61, 0.16, nan ],
[ 7000, 7000, nan, nan, 1058.44, 0.22, nan ],
[ 8000, 8000, nan, nan, 1189.63, 0.29, nan ],
[ 9000, 9000, nan, nan, 1290.44, 0.38, nan ],
[ 10000, 10000, nan, nan, 1374.99, 0.48, nan ],
[ 12000, 12000, nan, nan, 1515.55, 0.76, nan ],
[ 14000, 14000, nan, nan, 1622.48, 1.13, nan ],
[ 16000, 16000, nan, nan, 1701.52, 1.60, nan ],
[ 18000, 18000, nan, nan, 1772.35, 2.19, nan ],
[ 20000, 20000, nan, nan, 1955.54, 2.73, nan ],
])
# numactl --interleave=all ../testing/testing_sgetrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
sgetrf_gpu = array([
[ 10, 10, nan, nan, 0.00, 0.00, nan ],
[ 20, 20, nan, nan, 0.02, 0.00, nan ],
[ 30, 30, nan, nan, 0.07, 0.00, nan ],
[ 40, 40, nan, nan, 0.15, 0.00, nan ],
[ 50, 50, nan, nan, 0.30, 0.00, nan ],
[ 60, 60, nan, nan, 0.49, 0.00, nan ],
[ 70, 70, nan, nan, 0.62, 0.00, nan ],
[ 80, 80, nan, nan, 1.02, 0.00, nan ],
[ 90, 90, nan, nan, 1.28, 0.00, nan ],
[ 100, 100, nan, nan, 1.63, 0.00, nan ],
[ 200, 200, nan, nan, 7.12, 0.00, nan ],
[ 300, 300, nan, nan, 7.40, 0.00, nan ],
[ 400, 400, nan, nan, 13.95, 0.00, nan ],
[ 500, 500, nan, nan, 23.01, 0.00, nan ],
[ 600, 600, nan, nan, 33.22, 0.00, nan ],
[ 700, 700, nan, nan, 45.55, 0.01, nan ],
[ 800, 800, nan, nan, 57.58, 0.01, nan ],
[ 900, 900, nan, nan, 72.55, 0.01, nan ],
[ 1000, 1000, nan, nan, 86.70, 0.01, nan ],
[ 2000, 2000, nan, nan, 259.14, 0.02, nan ],
[ 3000, 3000, nan, nan, 458.04, 0.04, nan ],
[ 4000, 4000, nan, nan, 662.44, 0.06, nan ],
[ 5000, 5000, nan, nan, 782.17, 0.11, nan ],
[ 6000, 6000, nan, nan, 986.56, 0.15, nan ],
[ 7000, 7000, nan, nan, 1158.16, 0.20, nan ],
[ 8000, 8000, nan, nan, 1310.63, 0.26, nan ],
[ 9000, 9000, nan, nan, 1423.20, 0.34, nan ],
[ 10000, 10000, nan, nan, 1528.95, 0.44, nan ],
[ 12000, 12000, nan, nan, 1678.63, 0.69, nan ],
[ 14000, 14000, nan, nan, 1792.04, 1.02, nan ],
[ 16000, 16000, nan, nan, 1871.16, 1.46, nan ],
[ 18000, 18000, nan, nan, 1942.72, 2.00, nan ],
[ 20000, 20000, nan, nan, 2063.41, 2.58, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/spotrf.txt
# numactl --interleave=all ../testing/testing_spotrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
spotrf = array([
[ 10, nan, nan, 0.08, 0.00, nan ],
[ 20, nan, nan, 0.48, 0.00, nan ],
[ 30, nan, nan, 0.97, 0.00, nan ],
[ 40, nan, nan, 1.47, 0.00, nan ],
[ 50, nan, nan, 2.05, 0.00, nan ],
[ 60, nan, nan, 2.62, 0.00, nan ],
[ 70, nan, nan, 3.24, 0.00, nan ],
[ 80, nan, nan, 3.70, 0.00, nan ],
[ 90, nan, nan, 4.11, 0.00, nan ],
[ 100, nan, nan, 4.62, 0.00, nan ],
[ 200, nan, nan, 14.21, 0.00, nan ],
[ 300, nan, nan, 5.86, 0.00, nan ],
[ 400, nan, nan, 13.02, 0.00, nan ],
[ 500, nan, nan, 23.00, 0.00, nan ],
[ 600, nan, nan, 25.45, 0.00, nan ],
[ 700, nan, nan, 37.27, 0.00, nan ],
[ 800, nan, nan, 41.51, 0.00, nan ],
[ 900, nan, nan, 56.13, 0.00, nan ],
[ 1000, nan, nan, 73.14, 0.00, nan ],
[ 2000, nan, nan, 285.91, 0.01, nan ],
[ 3000, nan, nan, 534.96, 0.02, nan ],
[ 4000, nan, nan, 801.25, 0.03, nan ],
[ 5000, nan, nan, 1001.93, 0.04, nan ],
[ 6000, nan, nan, 1189.53, 0.06, nan ],
[ 7000, nan, nan, 1338.30, 0.09, nan ],
[ 8000, nan, nan, 1485.15, 0.11, nan ],
[ 9000, nan, nan, 1590.04, 0.15, nan ],
[ 10000, nan, nan, 1685.62, 0.20, nan ],
[ 12000, nan, nan, 1844.96, 0.31, nan ],
[ 14000, nan, nan, 1985.70, 0.46, nan ],
[ 16000, nan, nan, 2093.90, 0.65, nan ],
[ 18000, nan, nan, 2175.25, 0.89, nan ],
[ 20000, nan, nan, 2261.66, 1.18, nan ],
])
# numactl --interleave=all ../testing/testing_spotrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
spotrf_gpu = array([
[ 10, nan, nan, 0.00, 0.00, nan ],
[ 20, nan, nan, 0.00, 0.00, nan ],
[ 30, nan, nan, 0.01, 0.00, nan ],
[ 40, nan, nan, 0.02, 0.00, nan ],
[ 50, nan, nan, 0.03, 0.00, nan ],
[ 60, nan, nan, 0.06, 0.00, nan ],
[ 70, nan, nan, 0.09, 0.00, nan ],
[ 80, nan, nan, 0.14, 0.00, nan ],
[ 90, nan, nan, 0.19, 0.00, nan ],
[ 100, nan, nan, 0.26, 0.00, nan ],
[ 200, nan, nan, 5.24, 0.00, nan ],
[ 300, nan, nan, 4.10, 0.00, nan ],
[ 400, nan, nan, 9.09, 0.00, nan ],
[ 500, nan, nan, 16.56, 0.00, nan ],
[ 600, nan, nan, 20.83, 0.00, nan ],
[ 700, nan, nan, 31.40, 0.00, nan ],
[ 800, nan, nan, 36.79, 0.00, nan ],
[ 900, nan, nan, 50.27, 0.00, nan ],
[ 1000, nan, nan, 66.30, 0.01, nan ],
[ 2000, nan, nan, 300.63, 0.01, nan ],
[ 3000, nan, nan, 627.97, 0.01, nan ],
[ 4000, nan, nan, 952.86, 0.02, nan ],
[ 5000, nan, nan, 1170.66, 0.04, nan ],
[ 6000, nan, nan, 1407.59, 0.05, nan ],
[ 7000, nan, nan, 1564.87, 0.07, nan ],
[ 8000, nan, nan, 1748.87, 0.10, nan ],
[ 9000, nan, nan, 1858.51, 0.13, nan ],
[ 10000, nan, nan, 1957.65, 0.17, nan ],
[ 12000, nan, nan, 2109.81, 0.27, nan ],
[ 14000, nan, nan, 2253.65, 0.41, nan ],
[ 16000, nan, nan, 2340.03, 0.58, nan ],
[ 18000, nan, nan, 2402.42, 0.81, nan ],
[ 20000, nan, nan, 2477.84, 1.08, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/ssyevd.txt
# numactl --interleave=all ../testing/testing_ssyevd -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_ssyevd -JN -N 123 -N 1234 --range 12000:20000:2000
ssyevd_JN = array([
[ 10, nan, 0.0000, nan, nan, nan, nan ],
[ 20, nan, 0.0000, nan, nan, nan, nan ],
[ 30, nan, 0.0001, nan, nan, nan, nan ],
[ 40, nan, 0.0001, nan, nan, nan, nan ],
[ 50, nan, 0.0001, nan, nan, nan, nan ],
[ 60, nan, 0.0002, nan, nan, nan, nan ],
[ 70, nan, 0.0003, nan, nan, nan, nan ],
[ 80, nan, 0.0003, nan, nan, nan, nan ],
[ 90, nan, 0.0004, nan, nan, nan, nan ],
[ 100, nan, 0.0005, nan, nan, nan, nan ],
[ 200, nan, 0.0036, nan, nan, nan, nan ],
[ 300, nan, 0.0066, nan, nan, nan, nan ],
[ 400, nan, 0.0109, nan, nan, nan, nan ],
[ 500, nan, 0.0166, nan, nan, nan, nan ],
[ 600, nan, 0.0221, nan, nan, nan, nan ],
[ 700, nan, 0.0282, nan, nan, nan, nan ],
[ 800, nan, 0.0361, nan, nan, nan, nan ],
[ 900, nan, 0.0448, nan, nan, nan, nan ],
[ 1000, nan, 0.0547, nan, nan, nan, nan ],
[ 2000, nan, 0.2286, nan, nan, nan, nan ],
[ 3000, nan, 0.8753, nan, nan, nan, nan ],
[ 4000, nan, 1.5114, nan, nan, nan, nan ],
[ 5000, nan, 2.4695, nan, nan, nan, nan ],
[ 6000, nan, 3.6914, nan, nan, nan, nan ],
[ 7000, nan, 5.2513, nan, nan, nan, nan ],
[ 8000, nan, 7.1277, nan, nan, nan, nan ],
[ 9000, nan, 9.4860, nan, nan, nan, nan ],
[ 10000, nan, 12.0984, nan, nan, nan, nan ],
[ 12000, nan, 19.1925, nan, nan, nan, nan ],
[ 14000, nan, 28.1555, nan, nan, nan, nan ],
[ 16000, nan, 39.5701, nan, nan, nan, nan ],
[ 18000, nan, 54.4460, nan, nan, nan, nan ],
[ 20000, nan, 71.2115, nan, nan, nan, nan ],
])
# numactl --interleave=all ../testing/testing_ssyevd -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_ssyevd -JV -N 123 -N 1234 --range 12000:20000:2000
ssyevd_JV = array([
[ 10, nan, 0.0001, nan, nan, nan, nan ],
[ 20, nan, 0.0001, nan, nan, nan, nan ],
[ 30, nan, 0.0002, nan, nan, nan, nan ],
[ 40, nan, 0.0003, nan, nan, nan, nan ],
[ 50, nan, 0.0004, nan, nan, nan, nan ],
[ 60, nan, 0.0004, nan, nan, nan, nan ],
[ 70, nan, 0.0006, nan, nan, nan, nan ],
[ 80, nan, 0.0007, nan, nan, nan, nan ],
[ 90, nan, 0.0008, nan, nan, nan, nan ],
[ 100, nan, 0.0010, nan, nan, nan, nan ],
[ 200, nan, 0.0075, nan, nan, nan, nan ],
[ 300, nan, 0.0113, nan, nan, nan, nan ],
[ 400, nan, 0.0181, nan, nan, nan, nan ],
[ 500, nan, 0.0253, nan, nan, nan, nan ],
[ 600, nan, 0.0292, nan, nan, nan, nan ],
[ 700, nan, 0.0371, nan, nan, nan, nan ],
[ 800, nan, 0.0449, nan, nan, nan, nan ],
[ 900, nan, 0.0563, nan, nan, nan, nan ],
[ 1000, nan, 0.0668, nan, nan, nan, nan ],
[ 2000, nan, 0.2178, nan, nan, nan, nan ],
[ 3000, nan, 0.8985, nan, nan, nan, nan ],
[ 4000, nan, 1.5710, nan, nan, nan, nan ],
[ 5000, nan, 2.5299, nan, nan, nan, nan ],
[ 6000, nan, 3.8416, nan, nan, nan, nan ],
[ 7000, nan, 5.4748, nan, nan, nan, nan ],
[ 8000, nan, 7.3099, nan, nan, nan, nan ],
[ 9000, nan, 9.6490, nan, nan, nan, nan ],
[ 10000, nan, 12.4513, nan, nan, nan, nan ],
[ 12000, nan, 20.1533, nan, nan, nan, nan ],
[ 14000, nan, 29.5625, nan, nan, nan, nan ],
[ 16000, nan, 41.5526, nan, nan, nan, nan ],
[ 18000, nan, 57.5692, nan, nan, nan, nan ],
[ 20000, nan, 74.9994, nan, nan, nan, nan ],
])
# numactl --interleave=all ../testing/testing_ssyevd_gpu -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_ssyevd_gpu -JN -N 123 -N 1234 --range 12000:20000:2000
ssyevd_gpu_JN = array([
[ 10, nan, 0.0002, nan, nan, nan, nan ],
[ 20, nan, 0.0002, nan, nan, nan, nan ],
[ 30, nan, 0.0002, nan, nan, nan, nan ],
[ 40, nan, 0.0003, nan, nan, nan, nan ],
[ 50, nan, 0.0003, nan, nan, nan, nan ],
[ 60, nan, 0.0004, nan, nan, nan, nan ],
[ 70, nan, 0.0004, nan, nan, nan, nan ],
[ 80, nan, 0.0005, nan, nan, nan, nan ],
[ 90, nan, 0.0006, nan, nan, nan, nan ],
[ 100, nan, 0.0007, nan, nan, nan, nan ],
[ 200, nan, 0.0035, nan, nan, nan, nan ],
[ 300, nan, 0.0068, nan, nan, nan, nan ],
[ 400, nan, 0.0114, nan, nan, nan, nan ],
[ 500, nan, 0.0167, nan, nan, nan, nan ],
[ 600, nan, 0.0229, nan, nan, nan, nan ],
[ 700, nan, 0.0300, nan, nan, nan, nan ],
[ 800, nan, 0.0380, nan, nan, nan, nan ],
[ 900, nan, 0.0462, nan, nan, nan, nan ],
[ 1000, nan, 0.0571, nan, nan, nan, nan ],
[ 2000, nan, 0.2297, nan, nan, nan, nan ],
[ 3000, nan, 0.8690, nan, nan, nan, nan ],
[ 4000, nan, 1.5042, nan, nan, nan, nan ],
[ 5000, nan, 2.4603, nan, nan, nan, nan ],
[ 6000, nan, 3.6922, nan, nan, nan, nan ],
[ 7000, nan, 5.2239, nan, nan, nan, nan ],
[ 8000, nan, 7.1195, nan, nan, nan, nan ],
[ 9000, nan, 9.4281, nan, nan, nan, nan ],
[ 10000, nan, 12.0485, nan, nan, nan, nan ],
[ 12000, nan, 19.1882, nan, nan, nan, nan ],
[ 14000, nan, 28.0731, nan, nan, nan, nan ],
[ 16000, nan, 39.2928, nan, nan, nan, nan ],
[ 18000, nan, 54.2887, nan, nan, nan, nan ],
[ 20000, nan, 70.8357, nan, nan, nan, nan ],
])
# numactl --interleave=all ../testing/testing_ssyevd_gpu -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_ssyevd_gpu -JV -N 123 -N 1234 --range 12000:20000:2000
ssyevd_gpu_JV = array([
[ 10, nan, 0.0003, nan, nan, nan, nan ],
[ 20, nan, 0.0003, nan, nan, nan, nan ],
[ 30, nan, 0.0004, nan, nan, nan, nan ],
[ 40, nan, 0.0005, nan, nan, nan, nan ],
[ 50, nan, 0.0005, nan, nan, nan, nan ],
[ 60, nan, 0.0006, nan, nan, nan, nan ],
[ 70, nan, 0.0008, nan, nan, nan, nan ],
[ 80, nan, 0.0009, nan, nan, nan, nan ],
[ 90, nan, 0.0011, nan, nan, nan, nan ],
[ 100, nan, 0.0012, nan, nan, nan, nan ],
[ 200, nan, 0.0067, nan, nan, nan, nan ],
[ 300, nan, 0.0110, nan, nan, nan, nan ],
[ 400, nan, 0.0178, nan, nan, nan, nan ],
[ 500, nan, 0.0250, nan, nan, nan, nan ],
[ 600, nan, 0.0289, nan, nan, nan, nan ],
[ 700, nan, 0.0370, nan, nan, nan, nan ],
[ 800, nan, 0.0454, nan, nan, nan, nan ],
[ 900, nan, 0.0577, nan, nan, nan, nan ],
[ 1000, nan, 0.0684, nan, nan, nan, nan ],
[ 2000, nan, 0.2455, nan, nan, nan, nan ],
[ 3000, nan, 0.9087, nan, nan, nan, nan ],
[ 4000, nan, 1.5867, nan, nan, nan, nan ],
[ 5000, nan, 2.5830, nan, nan, nan, nan ],
[ 6000, nan, 3.9431, nan, nan, nan, nan ],
[ 7000, nan, 5.6448, nan, nan, nan, nan ],
[ 8000, nan, 7.7442, nan, nan, nan, nan ],
[ 9000, nan, 10.3376, nan, nan, nan, nan ],
[ 10000, nan, 13.3178, nan, nan, nan, nan ],
[ 12000, nan, 21.3168, nan, nan, nan, nan ],
[ 14000, nan, 31.3873, nan, nan, nan, nan ],
[ 16000, nan, 44.8046, nan, nan, nan, nan ],
[ 18000, nan, 62.8077, nan, nan, nan, nan ],
[ 20000, nan, 82.5465, nan, nan, nan, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/ssyevd_2stage.txt
# numactl --interleave=all ../testing/testing_ssyevdx_2stage -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
ssyevdx_2stage_JN = array([
[ 10, 10, 0.00 ],
[ 20, 20, 0.00 ],
[ 30, 30, 0.00 ],
[ 40, 40, 0.00 ],
[ 50, 50, 0.00 ],
[ 60, 60, 0.00 ],
[ 70, 70, 0.00 ],
[ 80, 80, 0.00 ],
[ 90, 90, 0.00 ],
[ 100, 100, 0.00 ],
[ 200, 200, 0.00 ],
[ 300, 300, 0.02 ],
[ 400, 400, 0.03 ],
[ 500, 500, 0.04 ],
[ 600, 600, 0.05 ],
[ 700, 700, 0.06 ],
[ 800, 800, 0.08 ],
[ 900, 900, 0.09 ],
[ 1000, 1000, 0.10 ],
[ 2000, 2000, 0.29 ],
[ 3000, 3000, 0.58 ],
[ 4000, 4000, 0.89 ],
[ 5000, 5000, 1.27 ],
[ 6000, 6000, 1.61 ],
[ 7000, 7000, 2.22 ],
[ 8000, 8000, 2.73 ],
[ 9000, 9000, 3.57 ],
[ 10000, 10000, 4.32 ],
[ 12000, 12000, 6.14 ],
[ 14000, 14000, 8.60 ],
[ 16000, 16000, 11.57 ],
[ 18000, 18000, 15.06 ],
[ 20000, 20000, 19.16 ],
])
# numactl --interleave=all ../testing/testing_ssyevdx_2stage -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
ssyevdx_2stage_JV = array([
[ 10, 10, 0.00 ],
[ 20, 20, 0.00 ],
[ 30, 30, 0.00 ],
[ 40, 40, 0.00 ],
[ 50, 50, 0.00 ],
[ 60, 60, 0.00 ],
[ 70, 70, 0.00 ],
[ 80, 80, 0.00 ],
[ 90, 90, 0.00 ],
[ 100, 100, 0.00 ],
[ 200, 200, 0.00 ],
[ 300, 300, 0.02 ],
[ 400, 400, 0.04 ],
[ 500, 500, 0.05 ],
[ 600, 600, 0.06 ],
[ 700, 700, 0.08 ],
[ 800, 800, 0.10 ],
[ 900, 900, 0.11 ],
[ 1000, 1000, 0.13 ],
[ 2000, 2000, 0.38 ],
[ 3000, 3000, 0.75 ],
[ 4000, 4000, 1.30 ],
[ 5000, 5000, 1.99 ],
[ 6000, 6000, 2.72 ],
[ 7000, 7000, 3.83 ],
[ 8000, 8000, 5.36 ],
[ 9000, 9000, 6.90 ],
[ 10000, 10000, 8.89 ],
[ 12000, 12000, 14.34 ],
[ 14000, 14000, 20.46 ],
[ 16000, 16000, 29.42 ],
[ 18000, 18000, 40.68 ],
[ 20000, 20000, 54.31 ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/ssymv.txt
# numactl --interleave=all ../testing/testing_ssymv -L -N 123 -N 1234 --range 10:90:1 --range 100:900:10 --range 1000:9000:100 --range 10000:20000:2000
ssymv_L = array([
[ 10, 0.01, 0.03, 0.01, 0.03, 0.01, 0.02, 0.12, 0.00, 4.77e-08, 4.77e-08, 4.77e-08, nan ],
[ 11, 0.01, 0.03, 0.01, 0.03, 0.02, 0.02, 0.12, 0.00, 4.33e-08, 8.67e-08, 4.33e-08, nan ],
[ 12, 0.01, 0.03, 0.01, 0.03, 0.02, 0.02, 0.15, 0.00, 3.97e-08, 1.99e-08, 3.97e-08, nan ],
[ 13, 0.01, 0.03, 0.01, 0.03, 0.02, 0.02, 0.17, 0.00, 3.67e-08, 3.67e-08, 7.34e-08, nan ],
[ 14, 0.02, 0.03, 0.01, 0.03, 0.02, 0.02, 0.22, 0.00, 6.81e-08, 3.41e-08, 6.81e-08, nan ],
[ 15, 0.02, 0.03, 0.02, 0.03, 0.03, 0.02, 0.22, 0.00, 6.36e-08, 6.36e-08, 6.36e-08, nan ],
[ 16, 0.02, 0.03, 0.02, 0.03, 0.03, 0.02, 0.29, 0.00, 5.96e-08, 5.96e-08, 5.96e-08, nan ],
[ 17, 0.02, 0.03, 0.02, 0.03, 0.03, 0.02, 0.20, 0.00, 1.12e-07, 5.61e-08, 5.61e-08, nan ],
[ 18, 0.02, 0.03, 0.02, 0.03, 0.03, 0.02, 0.24, 0.00, 5.30e-08, 5.30e-08, 5.30e-08, nan ],
[ 19, 0.03, 0.03, 0.02, 0.03, 0.04, 0.02, 0.40, 0.00, 5.02e-08, 5.02e-08, 5.02e-08, nan ],
[ 20, 0.03, 0.03, 0.03, 0.03, 0.04, 0.02, 0.27, 0.00, 7.15e-08, 4.77e-08, 7.15e-08, nan ],
[ 21, 0.03, 0.03, 0.03, 0.03, 0.05, 0.02, 0.30, 0.00, 9.08e-08, 1.36e-07, 6.81e-08, nan ],
[ 22, 0.04, 0.03, 0.03, 0.03, 0.05, 0.02, 0.53, 0.00, 4.33e-08, 8.67e-08, 4.33e-08, nan ],
[ 23, 0.04, 0.03, 0.03, 0.03, 0.06, 0.02, 0.36, 0.00, 8.29e-08, 8.29e-08, 8.29e-08, nan ],
[ 24, 0.04, 0.03, 0.04, 0.03, 0.06, 0.02, 0.63, 0.00, 7.95e-08, 7.95e-08, 7.95e-08, nan ],
[ 25, 0.04, 0.03, 0.04, 0.03, 0.06, 0.02, 0.68, 0.00, 7.63e-08, 7.63e-08, 3.81e-08, nan ],
[ 26, 0.05, 0.03, 0.04, 0.03, 0.07, 0.02, 0.45, 0.00, 7.34e-08, 7.34e-08, 3.67e-08, nan ],
[ 27, 0.05, 0.03, 0.05, 0.03, 0.07, 0.02, 0.53, 0.00, 7.06e-08, 7.06e-08, 7.06e-08, nan ],
[ 28, 0.06, 0.03, 0.05, 0.03, 0.08, 0.02, 0.76, 0.00, 3.41e-08, 6.81e-08, 6.81e-08, nan ],
[ 29, 0.06, 0.03, 0.05, 0.03, 0.08, 0.02, 0.56, 0.00, 9.87e-08, 1.32e-07, 6.58e-08, nan ],
[ 30, 0.07, 0.03, 0.06, 0.03, 0.09, 0.02, 0.46, 0.00, 6.36e-08, 6.36e-08, 6.36e-08, nan ],
[ 31, 0.07, 0.03, 0.06, 0.03, 0.09, 0.02, 0.69, 0.00, 6.15e-08, 9.23e-08, 9.23e-08, nan ],
[ 32, 0.07, 0.03, 0.06, 0.03, 0.11, 0.02, 0.74, 0.00, 5.96e-08, 5.96e-08, 5.96e-08, nan ],
[ 33, 0.08, 0.03, 0.07, 0.03, 0.11, 0.02, 0.72, 0.00, 5.78e-08, 5.78e-08, 5.78e-08, nan ],
[ 34, 0.09, 0.03, 0.07, 0.03, 0.11, 0.02, 0.83, 0.00, 1.12e-07, 1.12e-07, 1.12e-07, nan ],
[ 35, 0.10, 0.03, 0.08, 0.03, 0.11, 0.02, 0.88, 0.00, 5.45e-08, 5.45e-08, 8.17e-08, nan ],
[ 36, 0.10, 0.03, 0.08, 0.03, 0.13, 0.02, 0.66, 0.00, 7.95e-08, 5.30e-08, 7.95e-08, nan ],
[ 37, 0.11, 0.03, 0.08, 0.03, 0.13, 0.02, 0.69, 0.00, 7.73e-08, 5.15e-08, 7.73e-08, nan ],
[ 38, 0.11, 0.03, 0.09, 0.03, 0.14, 0.02, 0.73, 0.00, 7.53e-08, 1.00e-07, 1.00e-07, nan ],
[ 39, 0.11, 0.03, 0.09, 0.03, 0.15, 0.02, 0.77, 0.00, 4.89e-08, 4.89e-08, 4.89e-08, nan ],
[ 40, 0.12, 0.03, 0.10, 0.03, 0.14, 0.02, 0.81, 0.00, 9.54e-08, 9.54e-08, 9.54e-08, nan ],
[ 41, 0.13, 0.03, 0.10, 0.04, 0.15, 0.02, 1.11, 0.00, 6.98e-08, 9.30e-08, 6.98e-08, nan ],
[ 42, 0.12, 0.03, 0.11, 0.03, 0.16, 0.02, 0.95, 0.00, 6.81e-08, 4.54e-08, 6.81e-08, nan ],
[ 43, 0.14, 0.03, 0.11, 0.03, 0.17, 0.02, 0.93, 0.00, 8.87e-08, 8.87e-08, 8.87e-08, nan ],
[ 44, 0.15, 0.03, 0.11, 0.04, 0.18, 0.02, 0.98, 0.00, 1.08e-07, 1.08e-07, 6.50e-08, nan ],
[ 45, 0.14, 0.03, 0.12, 0.03, 0.18, 0.02, 1.09, 0.00, 8.48e-08, 6.36e-08, 8.48e-08, nan ],
[ 46, 0.16, 0.03, 0.13, 0.03, 0.19, 0.02, 1.07, 0.00, 8.29e-08, 8.29e-08, 8.29e-08, nan ],
[ 47, 0.16, 0.03, 0.13, 0.03, 0.21, 0.02, 1.11, 0.00, 1.01e-07, 1.22e-07, 8.12e-08, nan ],
[ 48, 0.16, 0.03, 0.14, 0.03, 0.20, 0.02, 1.16, 0.00, 7.95e-08, 7.95e-08, 7.95e-08, nan ],
[ 49, 0.17, 0.03, 0.14, 0.03, 0.20, 0.02, 1.21, 0.00, 7.79e-08, 7.79e-08, 7.79e-08, nan ],
[ 50, 0.18, 0.03, 0.15, 0.04, 0.21, 0.02, 1.26, 0.00, 1.14e-07, 7.63e-08, 7.63e-08, nan ],
[ 51, 0.20, 0.03, 0.16, 0.03, 0.22, 0.02, 1.31, 0.00, 7.48e-08, 1.12e-07, 7.48e-08, nan ],
[ 52, 0.19, 0.03, 0.16, 0.03, 0.24, 0.02, 1.10, 0.01, 1.10e-07, 1.10e-07, 1.47e-07, nan ],
[ 53, 0.20, 0.03, 0.17, 0.03, 0.23, 0.03, 1.14, 0.01, 1.08e-07, 1.08e-07, 7.20e-08, nan ],
[ 54, 0.20, 0.03, 0.17, 0.03, 0.26, 0.02, 1.19, 0.01, 7.06e-08, 1.06e-07, 7.06e-08, nan ],
[ 55, 0.22, 0.03, 0.18, 0.03, 0.26, 0.02, 1.23, 0.01, 6.94e-08, 6.94e-08, 6.94e-08, nan ],
[ 56, 0.23, 0.03, 0.19, 0.03, 0.27, 0.02, 1.58, 0.00, 6.81e-08, 6.81e-08, 6.81e-08, nan ],
[ 57, 0.23, 0.03, 0.19, 0.03, 0.28, 0.02, 1.32, 0.01, 6.69e-08, 6.69e-08, 6.69e-08, nan ],
[ 58, 0.23, 0.03, 0.20, 0.04, 0.27, 0.03, 1.37, 0.01, 6.58e-08, 6.58e-08, 6.58e-08, nan ],
[ 59, 0.27, 0.03, 0.22, 0.03, 0.29, 0.02, 1.75, 0.00, 6.47e-08, 9.70e-08, 9.70e-08, nan ],
[ 60, 0.28, 0.03, 0.21, 0.03, 0.32, 0.02, 1.46, 0.01, 9.54e-08, 9.54e-08, 9.54e-08, nan ],
[ 61, 0.29, 0.03, 0.22, 0.03, 0.32, 0.02, 1.22, 0.01, 9.38e-08, 9.38e-08, 1.25e-07, nan ],
[ 62, 0.29, 0.03, 0.23, 0.03, 0.32, 0.02, 1.64, 0.00, 9.23e-08, 9.23e-08, 9.23e-08, nan ],
[ 63, 0.31, 0.03, 0.24, 0.03, 0.32, 0.03, 1.35, 0.01, 9.08e-08, 9.08e-08, 9.08e-08, nan ],
[ 64, 0.33, 0.03, 0.25, 0.03, 0.35, 0.02, 2.05, 0.00, 8.94e-08, 8.94e-08, 8.94e-08, nan ],
[ 65, 0.28, 0.03, 0.21, 0.04, 0.34, 0.03, 1.71, 0.01, 1.47e-07, 1.47e-07, 1.47e-07, nan ],
[ 66, 0.29, 0.03, 0.22, 0.04, 0.33, 0.03, 2.18, 0.00, 1.44e-07, 1.73e-07, 1.44e-07, nan ],
[ 67, 0.29, 0.03, 0.23, 0.04, 0.34, 0.03, 1.82, 0.01, 1.42e-07, 8.54e-08, 8.54e-08, nan ],
[ 68, 0.27, 0.04, 0.21, 0.05, 0.31, 0.03, 1.87, 0.01, 1.12e-07, 8.41e-08, 1.40e-07, nan ],
[ 69, 0.28, 0.03, 0.24, 0.04, 0.31, 0.03, 1.93, 0.01, 8.29e-08, 8.29e-08, 1.11e-07, nan ],
[ 70, 0.32, 0.03, 0.25, 0.04, 0.37, 0.03, 1.67, 0.01, 1.09e-07, 8.17e-08, 8.17e-08, nan ],
[ 71, 0.34, 0.03, 0.26, 0.04, 0.38, 0.03, 2.52, 0.00, 1.61e-07, 1.07e-07, 1.07e-07, nan ],
[ 72, 0.32, 0.03, 0.26, 0.04, 0.40, 0.03, 2.10, 0.01, 1.06e-07, 1.06e-07, 1.06e-07, nan ],
[ 73, 0.35, 0.03, 0.26, 0.04, 0.42, 0.03, 2.16, 0.01, 1.57e-07, 1.05e-07, 1.05e-07, nan ],
[ 74, 0.34, 0.03, 0.28, 0.04, 0.41, 0.03, 2.22, 0.01, 7.73e-08, 5.15e-08, 7.73e-08, nan ],
[ 75, 0.34, 0.03, 0.28, 0.04, 0.42, 0.03, 2.28, 0.01, 1.02e-07, 7.63e-08, 1.02e-07, nan ],
[ 76, 0.38, 0.03, 0.29, 0.04, 0.45, 0.03, 1.96, 0.01, 1.00e-07, 1.00e-07, 7.53e-08, nan ],
[ 77, 0.38, 0.03, 0.30, 0.04, 0.45, 0.03, 2.02, 0.01, 7.43e-08, 7.43e-08, 9.91e-08, nan ],
[ 78, 0.37, 0.03, 0.30, 0.04, 0.47, 0.03, 2.07, 0.01, 9.78e-08, 9.78e-08, 9.78e-08, nan ],
[ 79, 0.41, 0.03, 0.32, 0.04, 0.47, 0.03, 1.77, 0.01, 1.45e-07, 9.66e-08, 9.66e-08, nan ],
[ 80, 0.39, 0.03, 0.32, 0.04, 0.48, 0.03, 2.59, 0.01, 9.54e-08, 9.54e-08, 9.54e-08, nan ],
[ 81, 0.43, 0.03, 0.33, 0.04, 0.43, 0.03, 2.65, 0.01, 9.42e-08, 9.42e-08, 1.41e-07, nan ],
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[ 5100, 81.80, 0.64, 116.70, 0.45, 39.63, 1.31, 24.99, 2.08, 1.20e-06, 1.32e-06, 1.24e-06, nan ],
[ 5200, 81.70, 0.66, 116.05, 0.47, 40.07, 1.35, 24.96, 2.17, 1.29e-06, 1.46e-06, 1.34e-06, nan ],
[ 5300, 81.07, 0.69, 115.87, 0.48, 40.95, 1.37, 25.50, 2.20, 1.20e-06, 1.27e-06, 1.15e-06, nan ],
[ 5400, 81.93, 0.71, 121.72, 0.48, 40.62, 1.44, 25.02, 2.33, 1.54e-06, 1.60e-06, 1.58e-06, nan ],
[ 5500, 82.32, 0.74, 111.02, 0.55, 42.41, 1.43, 25.21, 2.40, 1.40e-06, 1.46e-06, 1.35e-06, nan ],
[ 5600, 83.08, 0.76, 121.81, 0.51, 42.96, 1.46, 25.48, 2.46, 1.53e-06, 1.31e-06, 1.40e-06, nan ],
[ 5700, 83.85, 0.78, 119.93, 0.54, 43.39, 1.50, 25.26, 2.57, 1.46e-06, 1.54e-06, 1.50e-06, nan ],
[ 5800, 82.77, 0.81, 121.03, 0.56, 44.25, 1.52, 24.64, 2.73, 1.47e-06, 1.43e-06, 1.35e-06, nan ],
[ 5900, 83.78, 0.83, 120.88, 0.58, 44.84, 1.55, 25.27, 2.76, 1.28e-06, 1.24e-06, 1.20e-06, nan ],
[ 6000, 84.32, 0.85, 121.64, 0.59, 45.67, 1.58, 25.36, 2.84, 1.30e-06, 1.34e-06, 1.34e-06, nan ],
[ 6100, 84.77, 0.88, 124.88, 0.60, 45.95, 1.62, 25.21, 2.95, 1.52e-06, 1.44e-06, 1.44e-06, nan ],
[ 6200, 86.79, 0.89, 125.44, 0.61, 36.08, 2.13, 24.87, 3.09, 1.42e-06, 1.30e-06, 1.38e-06, nan ],
[ 6300, 87.15, 0.91, 125.61, 0.63, 37.59, 2.11, 24.37, 3.26, 1.98e-06, 1.98e-06, 1.98e-06, nan ],
[ 6400, 87.27, 0.94, 125.28, 0.65, 38.29, 2.14, 24.79, 3.30, 1.49e-06, 1.37e-06, 1.41e-06, nan ],
[ 6500, 88.13, 0.96, 126.10, 0.67, 38.66, 2.19, 25.06, 3.37, 1.65e-06, 1.50e-06, 1.50e-06, nan ],
[ 6600, 88.38, 0.99, 126.63, 0.69, 39.37, 2.21, 25.13, 3.47, 1.66e-06, 1.70e-06, 1.66e-06, nan ],
[ 6700, 88.80, 1.01, 126.64, 0.71, 39.44, 2.28, 25.04, 3.59, 1.49e-06, 1.57e-06, 1.60e-06, nan ],
[ 6800, 88.61, 1.04, 128.08, 0.72, 40.16, 2.30, 25.38, 3.64, 1.65e-06, 1.69e-06, 1.62e-06, nan ],
[ 6900, 88.08, 1.08, 128.69, 0.74, 40.89, 2.33, 25.12, 3.79, 1.59e-06, 1.63e-06, 1.56e-06, nan ],
[ 7000, 89.51, 1.10, 131.72, 0.74, 41.53, 2.36, 25.32, 3.87, 1.36e-06, 1.40e-06, 1.33e-06, nan ],
[ 7100, 90.20, 1.12, 131.67, 0.77, 41.99, 2.40, 25.03, 4.03, 1.65e-06, 1.55e-06, 1.48e-06, nan ],
[ 7200, 91.52, 1.13, 132.28, 0.78, 42.57, 2.44, 25.35, 4.09, 2.14e-06, 2.17e-06, 2.17e-06, nan ],
[ 7300, 91.56, 1.16, 132.24, 0.81, 43.05, 2.48, 25.36, 4.20, 1.64e-06, 1.54e-06, 1.47e-06, nan ],
[ 7400, 91.05, 1.20, 131.64, 0.83, 43.67, 2.51, 25.72, 4.26, 1.68e-06, 1.68e-06, 1.65e-06, nan ],
[ 7500, 92.46, 1.22, 131.60, 0.85, 44.32, 2.54, 25.40, 4.43, 1.73e-06, 1.66e-06, 1.76e-06, nan ],
[ 7600, 93.93, 1.23, 130.72, 0.88, 43.45, 2.66, 25.05, 4.61, 1.73e-06, 1.61e-06, 1.57e-06, nan ],
[ 7700, 93.01, 1.28, 132.65, 0.89, 44.99, 2.64, 25.44, 4.66, 1.68e-06, 1.68e-06, 1.78e-06, nan ],
[ 7800, 94.93, 1.28, 131.72, 0.92, 45.44, 2.68, 25.36, 4.80, 1.50e-06, 1.53e-06, 1.57e-06, nan ],
[ 7900, 94.43, 1.32, 132.52, 0.94, 46.06, 2.71, 25.39, 4.92, 1.51e-06, 1.48e-06, 1.45e-06, nan ],
[ 8000, 94.07, 1.36, 134.03, 0.96, 46.77, 2.74, 25.81, 4.96, 1.71e-06, 1.86e-06, 1.89e-06, nan ],
[ 8100, 95.93, 1.37, 135.74, 0.97, 46.55, 2.82, 24.99, 5.25, 1.48e-06, 1.45e-06, 1.39e-06, nan ],
[ 8200, 96.76, 1.39, 134.89, 1.00, 38.45, 3.50, 24.65, 5.46, 1.58e-06, 1.46e-06, 1.55e-06, nan ],
[ 8300, 97.66, 1.41, 133.79, 1.03, 39.84, 3.46, 25.37, 5.43, 1.68e-06, 1.65e-06, 1.68e-06, nan ],
[ 8400, 97.73, 1.44, 133.90, 1.05, 39.19, 3.60, 25.43, 5.55, 1.71e-06, 1.71e-06, 1.80e-06, nan ],
[ 8500, 98.11, 1.47, 134.31, 1.08, 40.18, 3.60, 25.12, 5.75, 1.67e-06, 1.58e-06, 1.58e-06, nan ],
[ 8600, 98.82, 1.50, 134.98, 1.10, 41.25, 3.59, 24.53, 6.03, 1.65e-06, 1.67e-06, 1.65e-06, nan ],
[ 8700, 98.18, 1.54, 132.35, 1.14, 41.45, 3.65, 25.11, 6.03, 1.99e-06, 1.91e-06, 1.85e-06, nan ],
[ 8800, 97.61, 1.59, 133.98, 1.16, 41.94, 3.69, 24.86, 6.23, 2.03e-06, 1.89e-06, 1.75e-06, nan ],
[ 8900, 99.10, 1.60, 134.71, 1.18, 41.20, 3.85, 24.50, 6.47, 1.84e-06, 1.73e-06, 1.76e-06, nan ],
[ 9000, 100.32, 1.62, 133.35, 1.21, 42.00, 3.86, 24.85, 6.52, 1.65e-06, 1.60e-06, 1.55e-06, nan ],
[ 10000, 103.15, 1.94, 134.51, 1.49, 46.59, 4.29, 24.76, 8.08, 1.71e-06, 1.95e-06, 1.76e-06, nan ],
[ 12000, 103.38, 2.79, 139.21, 2.07, 45.93, 6.27, 24.42, 11.79, 2.08e-06, 1.87e-06, 1.95e-06, nan ],
[ 14000, 111.09, 3.53, 140.81, 2.78, 46.68, 8.40, 24.32, 16.12, 2.58e-06, 2.27e-06, 2.55e-06, nan ],
[ 16000, 112.26, 4.56, 139.66, 3.67, 47.13, 10.86, 23.76, 21.55, 2.62e-06, 2.59e-06, 2.59e-06, nan ],
[ 18000, 113.15, 5.73, 143.27, 4.52, 46.33, 13.99, 24.10, 26.88, 2.50e-06, 2.50e-06, 2.41e-06, nan ],
[ 20000, 116.15, 6.89, 143.68, 5.57, 46.93, 17.05, 24.47, 32.69, 2.69e-06, 2.64e-06, 2.64e-06, nan ],
])
# numactl --interleave=all ../testing/testing_ssymv -U -N 123 -N 1234 --range 10:90:1 --range 100:900:10 --range 1000:9000:100 --range 10000:20000:2000
ssymv_U = array([
[ 10, 0.01, 0.03, 0.01, 0.03, 0.01, 0.02, 0.10, 0.00, 9.54e-08, 9.54e-08, 4.77e-08, nan ],
[ 11, 0.01, 0.03, 0.01, 0.03, 0.01, 0.02, 0.09, 0.00, 4.33e-08, 4.33e-08, 4.33e-08, nan ],
[ 12, 0.01, 0.03, 0.01, 0.03, 0.02, 0.02, 0.15, 0.00, 1.99e-08, 1.99e-08, 3.97e-08, nan ],
[ 13, 0.01, 0.03, 0.01, 0.03, 0.02, 0.02, 0.17, 0.00, 3.67e-08, 3.67e-08, 7.34e-08, nan ],
[ 14, 0.02, 0.03, 0.01, 0.03, 0.02, 0.02, 0.22, 0.00, 6.81e-08, 6.81e-08, 6.81e-08, nan ],
[ 15, 0.02, 0.03, 0.01, 0.03, 0.03, 0.02, 0.25, 0.00, 9.54e-08, 6.36e-08, 9.54e-08, nan ],
[ 16, 0.02, 0.03, 0.02, 0.03, 0.03, 0.02, 0.18, 0.00, 2.98e-08, 2.98e-08, 5.96e-08, nan ],
[ 17, 0.02, 0.03, 0.02, 0.04, 0.03, 0.02, 0.29, 0.00, 1.12e-07, 1.12e-07, 1.12e-07, nan ],
[ 18, 0.03, 0.03, 0.02, 0.03, 0.03, 0.02, 0.24, 0.00, 5.30e-08, 7.95e-08, 7.95e-08, nan ],
[ 19, 0.03, 0.03, 0.03, 0.03, 0.04, 0.02, 0.40, 0.00, 2.51e-08, 5.02e-08, 5.02e-08, nan ],
[ 20, 0.03, 0.03, 0.03, 0.03, 0.04, 0.02, 0.44, 0.00, 7.15e-08, 7.15e-08, 7.15e-08, nan ],
[ 21, 0.04, 0.03, 0.03, 0.03, 0.05, 0.02, 0.32, 0.00, 9.08e-08, 9.08e-08, 4.54e-08, nan ],
[ 22, 0.04, 0.03, 0.04, 0.03, 0.05, 0.02, 0.53, 0.00, 4.33e-08, 8.67e-08, 8.67e-08, nan ],
[ 23, 0.04, 0.03, 0.04, 0.03, 0.06, 0.02, 0.36, 0.00, 4.15e-08, 4.15e-08, 8.29e-08, nan ],
[ 24, 0.04, 0.03, 0.04, 0.03, 0.07, 0.02, 0.42, 0.00, 7.95e-08, 7.95e-08, 7.95e-08, nan ],
[ 25, 0.05, 0.03, 0.05, 0.03, 0.07, 0.02, 0.68, 0.00, 7.63e-08, 7.63e-08, 7.63e-08, nan ],
[ 26, 0.05, 0.03, 0.05, 0.03, 0.07, 0.02, 0.49, 0.00, 1.10e-07, 1.10e-07, 1.10e-07, nan ],
[ 27, 0.06, 0.03, 0.05, 0.03, 0.07, 0.02, 0.79, 0.00, 1.06e-07, 7.06e-08, 1.06e-07, nan ],
[ 28, 0.06, 0.03, 0.05, 0.03, 0.08, 0.02, 0.85, 0.00, 6.81e-08, 6.81e-08, 1.02e-07, nan ],
[ 29, 0.06, 0.03, 0.06, 0.03, 0.09, 0.02, 0.56, 0.00, 9.87e-08, 1.32e-07, 6.58e-08, nan ],
[ 30, 0.07, 0.03, 0.06, 0.03, 0.09, 0.02, 0.87, 0.00, 9.54e-08, 9.54e-08, 9.54e-08, nan ],
[ 31, 0.07, 0.03, 0.07, 0.03, 0.10, 0.02, 1.04, 0.00, 9.23e-08, 9.23e-08, 6.15e-08, nan ],
[ 32, 0.08, 0.03, 0.07, 0.03, 0.11, 0.02, 0.68, 0.00, 5.96e-08, 5.96e-08, 8.94e-08, nan ],
[ 33, 0.09, 0.03, 0.07, 0.03, 0.10, 0.02, 0.72, 0.00, 8.67e-08, 8.67e-08, 8.67e-08, nan ],
[ 34, 0.09, 0.03, 0.08, 0.03, 0.10, 0.02, 0.77, 0.00, 8.41e-08, 1.12e-07, 1.12e-07, nan ],
[ 35, 0.09, 0.03, 0.08, 0.03, 0.11, 0.02, 0.88, 0.00, 8.17e-08, 8.17e-08, 5.45e-08, nan ],
[ 36, 0.09, 0.03, 0.09, 0.03, 0.12, 0.02, 1.24, 0.00, 7.95e-08, 1.06e-07, 7.95e-08, nan ],
[ 37, 0.11, 0.03, 0.09, 0.03, 0.13, 0.02, 0.91, 0.00, 7.73e-08, 7.73e-08, 7.73e-08, nan ],
[ 38, 0.10, 0.03, 0.10, 0.03, 0.14, 0.02, 0.73, 0.00, 1.00e-07, 1.00e-07, 1.00e-07, nan ],
[ 39, 0.11, 0.03, 0.10, 0.03, 0.14, 0.02, 1.09, 0.00, 7.34e-08, 4.89e-08, 4.89e-08, nan ],
[ 40, 0.11, 0.03, 0.11, 0.03, 0.14, 0.02, 1.06, 0.00, 9.54e-08, 7.15e-08, 9.54e-08, nan ],
[ 41, 0.13, 0.03, 0.11, 0.03, 0.16, 0.02, 0.85, 0.00, 6.98e-08, 9.30e-08, 9.30e-08, nan ],
[ 42, 0.13, 0.03, 0.12, 0.03, 0.16, 0.02, 1.17, 0.00, 9.08e-08, 9.08e-08, 9.08e-08, nan ],
[ 43, 0.13, 0.03, 0.12, 0.03, 0.17, 0.02, 0.99, 0.00, 8.87e-08, 8.87e-08, 8.87e-08, nan ],
[ 44, 0.14, 0.03, 0.13, 0.03, 0.18, 0.02, 1.28, 0.00, 1.30e-07, 1.30e-07, 1.08e-07, nan ],
[ 45, 0.15, 0.03, 0.13, 0.03, 0.19, 0.02, 1.45, 0.00, 1.06e-07, 8.48e-08, 8.48e-08, nan ],
[ 46, 0.17, 0.03, 0.14, 0.03, 0.19, 0.02, 1.40, 0.00, 8.29e-08, 8.29e-08, 8.29e-08, nan ],
[ 47, 0.18, 0.02, 0.15, 0.03, 0.20, 0.02, 1.11, 0.00, 1.22e-07, 1.22e-07, 1.22e-07, nan ],
[ 48, 0.18, 0.03, 0.16, 0.03, 0.21, 0.02, 1.16, 0.00, 7.95e-08, 1.19e-07, 1.19e-07, nan ],
[ 49, 0.19, 0.03, 0.16, 0.03, 0.21, 0.02, 1.21, 0.00, 1.56e-07, 1.56e-07, 1.56e-07, nan ],
[ 50, 0.20, 0.03, 0.16, 0.03, 0.21, 0.02, 1.26, 0.00, 1.14e-07, 7.63e-08, 1.14e-07, nan ],
[ 51, 0.20, 0.03, 0.16, 0.03, 0.23, 0.02, 1.71, 0.00, 1.12e-07, 1.12e-07, 1.12e-07, nan ],
[ 52, 0.22, 0.03, 0.18, 0.03, 0.25, 0.02, 1.36, 0.00, 1.10e-07, 7.34e-08, 7.34e-08, nan ],
[ 53, 0.21, 0.03, 0.18, 0.03, 0.26, 0.02, 1.50, 0.00, 1.44e-07, 1.44e-07, 1.44e-07, nan ],
[ 54, 0.22, 0.03, 0.18, 0.03, 0.26, 0.02, 1.56, 0.00, 1.06e-07, 1.06e-07, 1.06e-07, nan ],
[ 55, 0.23, 0.03, 0.19, 0.03, 0.25, 0.03, 1.52, 0.00, 1.39e-07, 1.04e-07, 1.39e-07, nan ],
[ 56, 0.23, 0.03, 0.19, 0.03, 0.27, 0.02, 1.58, 0.00, 1.36e-07, 1.36e-07, 1.02e-07, nan ],
[ 57, 0.23, 0.03, 0.20, 0.03, 0.28, 0.02, 1.63, 0.00, 6.69e-08, 1.00e-07, 1.00e-07, nan ],
[ 58, 0.24, 0.03, 0.21, 0.03, 0.27, 0.03, 1.69, 0.00, 1.32e-07, 9.87e-08, 1.32e-07, nan ],
[ 59, 0.26, 0.03, 0.21, 0.03, 0.28, 0.03, 1.86, 0.00, 1.29e-07, 1.29e-07, 9.70e-08, nan ],
[ 60, 0.25, 0.03, 0.21, 0.03, 0.30, 0.02, 1.81, 0.00, 1.27e-07, 1.27e-07, 9.54e-08, nan ],
[ 61, 0.29, 0.03, 0.22, 0.03, 0.30, 0.03, 1.87, 0.00, 9.38e-08, 9.38e-08, 9.38e-08, nan ],
[ 62, 0.30, 0.03, 0.22, 0.04, 0.32, 0.02, 1.93, 0.00, 1.23e-07, 1.23e-07, 1.23e-07, nan ],
[ 63, 0.28, 0.03, 0.24, 0.03, 0.31, 0.03, 1.99, 0.00, 1.51e-07, 9.08e-08, 1.21e-07, nan ],
[ 64, 0.26, 0.03, 0.25, 0.03, 0.33, 0.03, 2.05, 0.00, 8.94e-08, 8.94e-08, 8.94e-08, nan ],
[ 65, 0.27, 0.03, 0.20, 0.04, 0.34, 0.03, 1.71, 0.01, 1.17e-07, 8.80e-08, 1.17e-07, nan ],
[ 66, 0.29, 0.03, 0.21, 0.04, 0.34, 0.03, 1.77, 0.01, 1.73e-07, 1.44e-07, 1.16e-07, nan ],
[ 67, 0.29, 0.03, 0.22, 0.04, 0.35, 0.03, 2.25, 0.00, 1.14e-07, 1.14e-07, 1.14e-07, nan ],
[ 68, 0.30, 0.03, 0.23, 0.04, 0.37, 0.03, 2.32, 0.00, 1.40e-07, 1.12e-07, 1.12e-07, nan ],
[ 69, 0.28, 0.03, 0.24, 0.04, 0.37, 0.03, 2.03, 0.00, 1.38e-07, 1.11e-07, 1.11e-07, nan ],
[ 70, 0.32, 0.03, 0.24, 0.04, 0.40, 0.03, 2.61, 0.00, 1.09e-07, 1.09e-07, 1.09e-07, nan ],
[ 71, 0.32, 0.03, 0.25, 0.04, 0.39, 0.03, 2.04, 0.01, 1.34e-07, 1.61e-07, 1.34e-07, nan ],
[ 72, 0.32, 0.03, 0.26, 0.04, 0.40, 0.03, 2.59, 0.00, 1.59e-07, 1.32e-07, 1.32e-07, nan ],
[ 73, 0.33, 0.03, 0.26, 0.04, 0.43, 0.03, 2.67, 0.00, 1.57e-07, 1.05e-07, 1.31e-07, nan ],
[ 74, 0.36, 0.03, 0.26, 0.04, 0.41, 0.03, 2.22, 0.01, 1.80e-07, 1.03e-07, 1.29e-07, nan ],
[ 75, 0.38, 0.03, 0.28, 0.04, 0.47, 0.02, 2.28, 0.01, 1.53e-07, 1.02e-07, 1.02e-07, nan ],
[ 76, 0.39, 0.03, 0.29, 0.04, 0.49, 0.02, 2.34, 0.01, 1.00e-07, 1.00e-07, 1.51e-07, nan ],
[ 77, 0.40, 0.03, 0.31, 0.04, 0.50, 0.02, 2.40, 0.01, 1.49e-07, 1.49e-07, 1.49e-07, nan ],
[ 78, 0.41, 0.03, 0.32, 0.04, 0.50, 0.02, 2.46, 0.01, 1.22e-07, 9.78e-08, 9.78e-08, nan ],
[ 79, 0.42, 0.03, 0.31, 0.04, 0.50, 0.03, 2.52, 0.01, 1.45e-07, 1.45e-07, 1.45e-07, nan ],
[ 80, 0.42, 0.03, 0.32, 0.04, 0.54, 0.02, 2.59, 0.01, 1.43e-07, 1.43e-07, 1.43e-07, nan ],
[ 81, 0.43, 0.03, 0.33, 0.04, 0.53, 0.03, 2.65, 0.01, 1.41e-07, 9.42e-08, 1.18e-07, nan ],
[ 82, 0.44, 0.03, 0.34, 0.04, 0.54, 0.03, 2.28, 0.01, 1.63e-07, 1.40e-07, 9.30e-08, nan ],
[ 83, 0.46, 0.03, 0.34, 0.04, 0.58, 0.02, 2.34, 0.01, 1.38e-07, 1.38e-07, 1.15e-07, nan ],
[ 84, 0.48, 0.03, 0.36, 0.04, 0.55, 0.03, 2.85, 0.01, 1.82e-07, 1.82e-07, 1.82e-07, nan ],
[ 85, 0.46, 0.03, 0.36, 0.04, 0.58, 0.03, 2.92, 0.01, 1.35e-07, 1.57e-07, 1.35e-07, nan ],
[ 86, 0.47, 0.03, 0.36, 0.04, 0.60, 0.03, 2.99, 0.01, 1.33e-07, 1.33e-07, 1.33e-07, nan ],
[ 87, 0.48, 0.03, 0.37, 0.04, 0.59, 0.03, 3.06, 0.01, 1.75e-07, 1.32e-07, 1.32e-07, nan ],
[ 88, 0.49, 0.03, 0.38, 0.04, 0.58, 0.03, 2.63, 0.01, 1.30e-07, 1.30e-07, 1.73e-07, nan ],
[ 89, 0.50, 0.03, 0.40, 0.04, 0.62, 0.03, 2.69, 0.01, 1.71e-07, 1.71e-07, 1.71e-07, nan ],
[ 90, 0.51, 0.03, 0.39, 0.04, 0.63, 0.03, 3.27, 0.01, 1.27e-07, 1.27e-07, 1.27e-07, nan ],
[ 100, 0.65, 0.03, 0.49, 0.04, 0.69, 0.03, 3.26, 0.01, 1.14e-07, 1.14e-07, 1.14e-07, nan ],
[ 110, 0.74, 0.03, 0.60, 0.04, 0.85, 0.03, 3.41, 0.01, 1.73e-07, 2.08e-07, 1.73e-07, nan ],
[ 120, 0.85, 0.03, 0.71, 0.04, 0.97, 0.03, 3.58, 0.01, 1.91e-07, 1.59e-07, 1.91e-07, nan ],
[ 130, 0.90, 0.04, 0.79, 0.04, 1.07, 0.03, 4.20, 0.01, 2.35e-07, 1.76e-07, 1.76e-07, nan ],
[ 140, 0.99, 0.04, 0.92, 0.04, 1.19, 0.03, 4.48, 0.01, 1.63e-07, 1.91e-07, 1.91e-07, nan ],
[ 150, 1.22, 0.04, 1.06, 0.04, 1.37, 0.03, 5.76, 0.01, 1.78e-07, 2.03e-07, 1.78e-07, nan ],
[ 160, 1.39, 0.04, 1.17, 0.04, 1.61, 0.03, 5.69, 0.01, 1.91e-07, 1.91e-07, 2.15e-07, nan ],
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[ 6700, 98.80, 0.91, 122.32, 0.73, 40.10, 2.24, 25.99, 3.45, 1.71e-06, 1.60e-06, 1.64e-06, nan ],
[ 6800, 98.49, 0.94, 121.58, 0.76, 39.37, 2.35, 26.90, 3.44, 1.72e-06, 1.72e-06, 1.76e-06, nan ],
[ 6900, 101.43, 0.94, 122.26, 0.78, 40.98, 2.32, 26.12, 3.65, 1.56e-06, 1.63e-06, 1.63e-06, nan ],
[ 7000, 100.54, 0.97, 123.01, 0.80, 42.17, 2.32, 26.21, 3.74, 1.74e-06, 1.74e-06, 1.67e-06, nan ],
[ 7100, 103.94, 0.97, 122.55, 0.82, 42.63, 2.37, 25.74, 3.92, 1.82e-06, 1.79e-06, 1.65e-06, nan ],
[ 7200, 103.70, 1.00, 125.66, 0.83, 42.65, 2.43, 25.51, 4.06, 1.86e-06, 1.86e-06, 1.83e-06, nan ],
[ 7300, 105.65, 1.01, 124.82, 0.85, 43.17, 2.47, 26.50, 4.02, 1.74e-06, 1.81e-06, 1.91e-06, nan ],
[ 7400, 104.11, 1.05, 126.18, 0.87, 44.10, 2.48, 26.01, 4.21, 1.78e-06, 1.81e-06, 1.85e-06, nan ],
[ 7500, 107.47, 1.05, 126.42, 0.89, 43.80, 2.57, 26.59, 4.23, 1.86e-06, 1.92e-06, 1.82e-06, nan ],
[ 7600, 108.39, 1.07, 127.79, 0.90, 45.15, 2.56, 26.66, 4.33, 1.61e-06, 1.57e-06, 1.61e-06, nan ],
[ 7700, 107.23, 1.11, 127.68, 0.93, 45.18, 2.62, 26.72, 4.44, 2.03e-06, 1.97e-06, 2.03e-06, nan ],
[ 7800, 109.23, 1.11, 125.07, 0.97, 45.84, 2.66, 26.85, 4.53, 1.82e-06, 1.82e-06, 1.88e-06, nan ],
[ 7900, 106.97, 1.17, 127.64, 0.98, 46.55, 2.68, 26.89, 4.64, 1.82e-06, 1.92e-06, 1.82e-06, nan ],
[ 8000, 107.41, 1.19, 124.06, 1.03, 47.13, 2.72, 26.94, 4.75, 1.95e-06, 1.92e-06, 1.95e-06, nan ],
[ 8100, 107.32, 1.22, 129.15, 1.02, 47.45, 2.77, 26.56, 4.94, 1.87e-06, 1.90e-06, 1.96e-06, nan ],
[ 8200, 107.08, 1.26, 129.21, 1.04, 41.00, 3.28, 26.50, 5.07, 1.79e-06, 1.79e-06, 1.73e-06, nan ],
[ 8300, 106.65, 1.29, 127.81, 1.08, 40.33, 3.42, 25.98, 5.30, 2.35e-06, 2.32e-06, 2.26e-06, nan ],
[ 8400, 104.77, 1.35, 128.08, 1.10, 40.74, 3.46, 26.65, 5.30, 1.83e-06, 1.80e-06, 1.80e-06, nan ],
[ 8500, 106.42, 1.36, 128.56, 1.12, 41.24, 3.50, 26.93, 5.37, 2.01e-06, 2.13e-06, 2.07e-06, nan ],
[ 8600, 104.55, 1.42, 130.22, 1.14, 41.71, 3.55, 27.36, 5.41, 1.73e-06, 1.70e-06, 1.73e-06, nan ],
[ 8700, 104.70, 1.45, 129.83, 1.17, 42.03, 3.60, 27.42, 5.52, 1.96e-06, 1.99e-06, 1.91e-06, nan ],
[ 8800, 103.13, 1.50, 129.94, 1.19, 41.57, 3.73, 27.05, 5.73, 1.97e-06, 1.91e-06, 1.97e-06, nan ],
[ 8900, 103.49, 1.53, 129.56, 1.22, 42.71, 3.71, 27.50, 5.76, 1.92e-06, 1.89e-06, 1.89e-06, nan ],
[ 9000, 104.74, 1.55, 129.71, 1.25, 42.53, 3.81, 27.66, 5.86, 2.12e-06, 1.95e-06, 1.98e-06, nan ],
[ 10000, 103.25, 1.94, 131.85, 1.52, 47.52, 4.21, 27.63, 7.24, 2.32e-06, 2.34e-06, 2.37e-06, nan ],
[ 12000, 104.28, 2.76, 138.14, 2.08, 46.86, 6.15, 28.20, 10.21, 2.20e-06, 2.36e-06, 2.24e-06, nan ],
[ 14000, 110.00, 3.56, 140.47, 2.79, 47.26, 8.30, 28.60, 13.71, 2.69e-06, 2.86e-06, 2.93e-06, nan ],
[ 16000, 111.32, 4.60, 140.06, 3.66, 46.57, 10.99, 28.70, 17.84, 2.72e-06, 2.69e-06, 2.66e-06, nan ],
[ 18000, 108.82, 5.95, 144.85, 4.47, 47.66, 13.60, 29.10, 22.27, 2.63e-06, 2.77e-06, 2.63e-06, nan ],
[ 20000, 110.59, 7.23, 146.55, 5.46, 48.07, 16.64, 29.69, 26.94, 2.93e-06, 2.95e-06, 3.03e-06, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/zgeqrf.txt
# numactl --interleave=all ../testing/testing_zgeqrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
zgeqrf = array([
[ 10, 10, nan, nan, 0.03, 0.00, nan ],
[ 20, 20, nan, nan, 0.20, 0.00, nan ],
[ 30, 30, nan, nan, 0.60, 0.00, nan ],
[ 40, 40, nan, nan, 0.67, 0.00, nan ],
[ 50, 50, nan, nan, 1.19, 0.00, nan ],
[ 60, 60, nan, nan, 1.82, 0.00, nan ],
[ 70, 70, nan, nan, 1.67, 0.00, nan ],
[ 80, 80, nan, nan, 2.33, 0.00, nan ],
[ 90, 90, nan, nan, 3.05, 0.00, nan ],
[ 100, 100, nan, nan, 4.02, 0.00, nan ],
[ 200, 200, nan, nan, 13.25, 0.00, nan ],
[ 300, 300, nan, nan, 28.80, 0.01, nan ],
[ 400, 400, nan, nan, 45.63, 0.01, nan ],
[ 500, 500, nan, nan, 64.89, 0.01, nan ],
[ 600, 600, nan, nan, 83.71, 0.01, nan ],
[ 700, 700, nan, nan, 104.84, 0.02, nan ],
[ 800, 800, nan, nan, 124.62, 0.02, nan ],
[ 900, 900, nan, nan, 142.89, 0.03, nan ],
[ 1000, 1000, nan, nan, 166.61, 0.03, nan ],
[ 2000, 2000, nan, nan, 392.27, 0.11, nan ],
[ 3000, 3000, nan, nan, 617.21, 0.23, nan ],
[ 4000, 4000, nan, nan, 752.86, 0.45, nan ],
[ 5000, 5000, nan, nan, 843.50, 0.79, nan ],
[ 6000, 6000, nan, nan, 919.89, 1.25, nan ],
[ 7000, 7000, nan, nan, 963.62, 1.90, nan ],
[ 8000, 8000, nan, nan, 1001.74, 2.73, nan ],
[ 9000, 9000, nan, nan, 1013.66, 3.84, nan ],
[ 10000, 10000, nan, nan, 1025.90, 5.20, nan ],
[ 12000, 12000, nan, nan, 1060.31, 8.69, nan ],
[ 14000, 14000, nan, nan, 1065.46, 13.74, nan ],
[ 16000, 16000, nan, nan, 1086.57, 20.11, nan ],
[ 18000, 18000, nan, nan, 1052.68, 29.55, nan ],
[ 20000, 20000, nan, nan, 1086.37, 39.28, nan ],
])
# numactl --interleave=all ../testing/testing_zgeqrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
zgeqrf_gpu = array([
[ 10, 10, nan, nan, 0.00, 0.00, nan ],
[ 20, 20, nan, nan, 0.04, 0.00, nan ],
[ 30, 30, nan, nan, 0.12, 0.00, nan ],
[ 40, 40, nan, nan, 0.27, 0.00, nan ],
[ 50, 50, nan, nan, 0.50, 0.00, nan ],
[ 60, 60, nan, nan, 0.81, 0.00, nan ],
[ 70, 70, nan, nan, 1.87, 0.00, nan ],
[ 80, 80, nan, nan, 2.68, 0.00, nan ],
[ 90, 90, nan, nan, 3.38, 0.00, nan ],
[ 100, 100, nan, nan, 2.44, 0.00, nan ],
[ 200, 200, nan, nan, 10.65, 0.00, nan ],
[ 300, 300, nan, nan, 24.62, 0.01, nan ],
[ 400, 400, nan, nan, 40.02, 0.01, nan ],
[ 500, 500, nan, nan, 58.90, 0.01, nan ],
[ 600, 600, nan, nan, 76.82, 0.02, nan ],
[ 700, 700, nan, nan, 96.76, 0.02, nan ],
[ 800, 800, nan, nan, 116.19, 0.02, nan ],
[ 900, 900, nan, nan, 133.96, 0.03, nan ],
[ 1000, 1000, nan, nan, 156.90, 0.03, nan ],
[ 2000, 2000, nan, nan, 380.77, 0.11, nan ],
[ 3000, 3000, nan, nan, 619.00, 0.23, nan ],
[ 4000, 4000, nan, nan, 753.39, 0.45, nan ],
[ 5000, 5000, nan, nan, 811.99, 0.82, nan ],
[ 6000, 6000, nan, nan, 901.27, 1.28, nan ],
[ 7000, 7000, nan, nan, 954.28, 1.92, nan ],
[ 8000, 8000, nan, nan, 997.31, 2.74, nan ],
[ 9000, 9000, nan, nan, 1012.29, 3.84, nan ],
[ 10000, 10000, nan, nan, 1028.54, 5.19, nan ],
[ 12000, 12000, nan, nan, 1066.20, 8.65, nan ],
[ 14000, 14000, nan, nan, 1076.92, 13.59, nan ],
[ 16000, 16000, nan, nan, 1099.34, 19.87, nan ],
[ 18000, 18000, nan, nan, 1059.24, 29.37, nan ],
[ 20000, 20000, nan, nan, 1092.57, 39.06, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/zgesvd.txt
# numactl --interleave=all ../testing/testing_zgesvd -UN -VN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
zgesvd_UN = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.00, nan ],
[ nan, 80, 80, nan, 0.00, nan ],
[ nan, 90, 90, nan, 0.00, nan ],
[ nan, 100, 100, nan, 0.00, nan ],
[ nan, 200, 200, nan, 0.02, nan ],
[ nan, 300, 300, nan, 0.04, nan ],
[ nan, 400, 400, nan, 0.07, nan ],
[ nan, 500, 500, nan, 0.10, nan ],
[ nan, 600, 600, nan, 0.14, nan ],
[ nan, 700, 700, nan, 0.19, nan ],
[ nan, 800, 800, nan, 0.24, nan ],
[ nan, 900, 900, nan, 0.31, nan ],
[ nan, 1000, 1000, nan, 0.38, nan ],
[ nan, 2000, 2000, nan, 1.65, nan ],
[ nan, 3000, 3000, nan, 4.43, nan ],
[ nan, 4000, 4000, nan, 9.16, nan ],
[ nan, 5000, 5000, nan, 16.45, nan ],
[ nan, 6000, 6000, nan, 26.95, nan ],
[ nan, 7000, 7000, nan, 40.59, nan ],
[ nan, 8000, 8000, nan, 59.34, nan ],
[ nan, 9000, 9000, nan, 81.91, nan ],
[ nan, 10000, 10000, nan, 113.08, nan ],
[ nan, 300, 100, nan, 0.00, nan ],
[ nan, 600, 200, nan, 0.02, nan ],
[ nan, 900, 300, nan, 0.05, nan ],
[ nan, 1200, 400, nan, 0.09, nan ],
[ nan, 1500, 500, nan, 0.14, nan ],
[ nan, 1800, 600, nan, 0.19, nan ],
[ nan, 2100, 700, nan, 0.26, nan ],
[ nan, 2400, 800, nan, 0.33, nan ],
[ nan, 2700, 900, nan, 0.45, nan ],
[ nan, 3000, 1000, nan, 0.55, nan ],
[ nan, 6000, 2000, nan, 2.72, nan ],
[ nan, 9000, 3000, nan, 7.58, nan ],
[ nan, 12000, 4000, nan, 16.04, nan ],
[ nan, 15000, 5000, nan, 29.34, nan ],
[ nan, 18000, 6000, nan, 48.41, nan ],
[ nan, 21000, 7000, nan, 74.00, nan ],
[ nan, 24000, 8000, nan, 109.63, nan ],
[ nan, 27000, 9000, nan, 152.19, nan ],
[ nan, 100, 300, nan, 0.00, nan ],
[ nan, 200, 600, nan, 0.03, nan ],
[ nan, 300, 900, nan, 0.06, nan ],
[ nan, 400, 1200, nan, 0.09, nan ],
[ nan, 500, 1500, nan, 0.14, nan ],
[ nan, 600, 1800, nan, 0.20, nan ],
[ nan, 700, 2100, nan, 0.27, nan ],
[ nan, 800, 2400, nan, 0.35, nan ],
[ nan, 900, 2700, nan, 0.44, nan ],
[ nan, 1000, 3000, nan, 0.54, nan ],
[ nan, 2000, 6000, nan, 2.65, nan ],
[ nan, 3000, 9000, nan, 7.45, nan ],
[ nan, 4000, 12000, nan, 15.92, nan ],
[ nan, 5000, 15000, nan, 29.27, nan ],
[ nan, 6000, 18000, nan, 48.93, nan ],
[ nan, 7000, 21000, nan, 75.28, nan ],
[ nan, 8000, 24000, nan, 111.25, nan ],
[ nan, 9000, 27000, nan, 154.86, nan ],
[ nan, 10000, 100, nan, 0.04, nan ],
[ nan, 20000, 200, nan, 0.14, nan ],
[ nan, 30000, 300, nan, 0.34, nan ],
[ nan, 40000, 400, nan, 0.92, nan ],
[ nan, 50000, 500, nan, 1.51, nan ],
[ nan, 60000, 600, nan, 2.24, nan ],
[ nan, 70000, 700, nan, 3.17, nan ],
[ nan, 80000, 800, nan, 4.32, nan ],
[ nan, 90000, 900, nan, 6.39, nan ],
[ nan, 100000, 1000, nan, 8.07, nan ],
[ nan, 200000, 2000, nan, 46.25, nan ],
[ nan, 100, 10000, nan, 0.03, nan ],
[ nan, 200, 20000, nan, 0.14, nan ],
[ nan, 300, 30000, nan, 0.37, nan ],
[ nan, 400, 40000, nan, 0.76, nan ],
[ nan, 500, 50000, nan, 1.42, nan ],
[ nan, 600, 60000, nan, 2.40, nan ],
[ nan, 700, 70000, nan, 3.65, nan ],
[ nan, 800, 80000, nan, 5.71, nan ],
[ nan, 900, 90000, nan, 6.29, nan ],
[ nan, 1000, 100000, nan, 8.23, nan ],
[ nan, 2000, 200000, nan, 52.39, nan ],
])
# numactl --interleave=all ../testing/testing_zgesvd -US -VS -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
zgesvd_US = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.01, nan ],
[ nan, 60, 60, nan, 0.01, nan ],
[ nan, 70, 70, nan, 0.01, nan ],
[ nan, 80, 80, nan, 0.01, nan ],
[ nan, 90, 90, nan, 0.02, nan ],
[ nan, 100, 100, nan, 0.02, nan ],
[ nan, 200, 200, nan, 0.03, nan ],
[ nan, 300, 300, nan, 0.08, nan ],
[ nan, 400, 400, nan, 0.13, nan ],
[ nan, 500, 500, nan, 0.21, nan ],
[ nan, 600, 600, nan, 0.30, nan ],
[ nan, 700, 700, nan, 0.44, nan ],
[ nan, 800, 800, nan, 0.56, nan ],
[ nan, 900, 900, nan, 0.73, nan ],
[ nan, 1000, 1000, nan, 0.91, nan ],
[ nan, 2000, 2000, nan, 4.42, nan ],
[ nan, 3000, 3000, nan, 11.70, nan ],
[ nan, 4000, 4000, nan, 23.44, nan ],
[ nan, 5000, 5000, nan, 42.13, nan ],
[ nan, 6000, 6000, nan, 67.29, nan ],
[ nan, 7000, 7000, nan, 102.72, nan ],
[ nan, 8000, 8000, nan, 144.77, nan ],
[ nan, 9000, 9000, nan, 204.11, nan ],
[ nan, 10000, 10000, nan, 269.33, nan ],
[ nan, 300, 100, nan, 0.03, nan ],
[ nan, 600, 200, nan, 0.05, nan ],
[ nan, 900, 300, nan, 0.11, nan ],
[ nan, 1200, 400, nan, 0.20, nan ],
[ nan, 1500, 500, nan, 0.32, nan ],
[ nan, 1800, 600, nan, 0.47, nan ],
[ nan, 2100, 700, nan, 0.69, nan ],
[ nan, 2400, 800, nan, 0.92, nan ],
[ nan, 2700, 900, nan, 1.21, nan ],
[ nan, 3000, 1000, nan, 1.56, nan ],
[ nan, 6000, 2000, nan, 8.36, nan ],
[ nan, 9000, 3000, nan, 24.47, nan ],
[ nan, 12000, 4000, nan, 50.80, nan ],
[ nan, 15000, 5000, nan, 95.80, nan ],
[ nan, 18000, 6000, nan, 155.11, nan ],
[ nan, 21000, 7000, nan, 247.36, nan ],
[ nan, 24000, 8000, nan, 349.44, nan ],
[ nan, 27000, 9000, nan, 505.15, nan ],
[ nan, 100, 300, nan, 0.02, nan ],
[ nan, 200, 600, nan, 0.09, nan ],
[ nan, 300, 900, nan, 0.26, nan ],
[ nan, 400, 1200, nan, 0.55, nan ],
[ nan, 500, 1500, nan, 1.01, nan ],
[ nan, 600, 1800, nan, 1.68, nan ],
[ nan, 700, 2100, nan, 2.80, nan ],
[ nan, 800, 2400, nan, 4.25, nan ],
[ nan, 900, 2700, nan, 6.30, nan ],
[ nan, 1000, 3000, nan, 8.68, nan ],
[ nan, 2000, 6000, nan, 67.62, nan ],
[ nan, 3000, 9000, nan, 218.64, nan ],
[ nan, 4000, 12000, nan, 522.39, nan ],
[ nan, 5000, 15000, nan, 978.86, nan ],
[ nan, 6000, 18000, nan, 1689.01, nan ],
[ nan, 7000, 21000, nan, 2645.05, nan ],
[ nan, 8000, 24000, nan, 3888.23, nan ],
[ nan, 9000, 27000, nan, 5567.58, nan ],
[ nan, 10000, 100, nan, 0.12, nan ],
[ nan, 20000, 200, nan, 0.37, nan ],
[ nan, 30000, 300, nan, 0.93, nan ],
[ nan, 40000, 400, nan, 2.19, nan ],
[ nan, 50000, 500, nan, 3.85, nan ],
[ nan, 60000, 600, nan, 6.07, nan ],
[ nan, 70000, 700, nan, 9.08, nan ],
[ nan, 80000, 800, nan, 12.51, nan ],
[ nan, 90000, 900, nan, 18.42, nan ],
[ nan, 100000, 1000, nan, 23.63, nan ],
[ nan, 200000, 2000, nan, 162.04, nan ],
[ nan, 100, 10000, nan, 0.30, nan ],
[ nan, 200, 20000, nan, 3.03, nan ],
[ nan, 300, 30000, nan, 9.53, nan ],
[ nan, 400, 40000, nan, 20.84, nan ],
[ nan, 500, 50000, nan, 41.41, nan ],
[ nan, 600, 60000, nan, 67.98, nan ],
[ nan, 700, 70000, nan, 103.39, nan ],
[ nan, 800, 80000, nan, 160.38, nan ],
[ nan, 900, 90000, nan, 226.14, nan ],
[ nan, 1000, 100000, nan, 298.09, nan ],
[ nan, 2000, 200000, nan, 2272.09, nan ],
])
# numactl --interleave=all ../testing/testing_zgesdd -UN -VN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
zgesdd_UN = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.00, nan ],
[ nan, 80, 80, nan, 0.00, nan ],
[ nan, 90, 90, nan, 0.00, nan ],
[ nan, 100, 100, nan, 0.00, nan ],
[ nan, 200, 200, nan, 0.02, nan ],
[ nan, 300, 300, nan, 0.04, nan ],
[ nan, 400, 400, nan, 0.07, nan ],
[ nan, 500, 500, nan, 0.11, nan ],
[ nan, 600, 600, nan, 0.15, nan ],
[ nan, 700, 700, nan, 0.20, nan ],
[ nan, 800, 800, nan, 0.25, nan ],
[ nan, 900, 900, nan, 0.32, nan ],
[ nan, 1000, 1000, nan, 0.39, nan ],
[ nan, 2000, 2000, nan, 1.68, nan ],
[ nan, 3000, 3000, nan, 4.49, nan ],
[ nan, 4000, 4000, nan, 9.27, nan ],
[ nan, 5000, 5000, nan, 16.59, nan ],
[ nan, 6000, 6000, nan, 27.14, nan ],
[ nan, 7000, 7000, nan, 40.88, nan ],
[ nan, 8000, 8000, nan, 59.86, nan ],
[ nan, 9000, 9000, nan, 82.61, nan ],
[ nan, 10000, 10000, nan, 113.91, nan ],
[ nan, 300, 100, nan, 0.00, nan ],
[ nan, 600, 200, nan, 0.03, nan ],
[ nan, 900, 300, nan, 0.05, nan ],
[ nan, 1200, 400, nan, 0.10, nan ],
[ nan, 1500, 500, nan, 0.14, nan ],
[ nan, 1800, 600, nan, 0.21, nan ],
[ nan, 2100, 700, nan, 0.29, nan ],
[ nan, 2400, 800, nan, 0.38, nan ],
[ nan, 2700, 900, nan, 0.51, nan ],
[ nan, 3000, 1000, nan, 0.65, nan ],
[ nan, 6000, 2000, nan, 3.16, nan ],
[ nan, 9000, 3000, nan, 9.35, nan ],
[ nan, 12000, 4000, nan, 19.92, nan ],
[ nan, 15000, 5000, nan, 36.61, nan ],
[ nan, 18000, 6000, nan, 59.62, nan ],
[ nan, 21000, 7000, nan, 92.49, nan ],
[ nan, 24000, 8000, nan, 117.98, nan ],
[ nan, 27000, 9000, nan, 187.83, nan ],
[ nan, 100, 300, nan, 0.01, nan ],
[ nan, 200, 600, nan, 0.03, nan ],
[ nan, 300, 900, nan, 0.06, nan ],
[ nan, 400, 1200, nan, 0.10, nan ],
[ nan, 500, 1500, nan, 0.15, nan ],
[ nan, 600, 1800, nan, 0.22, nan ],
[ nan, 700, 2100, nan, 0.30, nan ],
[ nan, 800, 2400, nan, 0.40, nan ],
[ nan, 900, 2700, nan, 0.52, nan ],
[ nan, 1000, 3000, nan, 0.65, nan ],
[ nan, 2000, 6000, nan, 3.49, nan ],
[ nan, 3000, 9000, nan, 10.22, nan ],
[ nan, 4000, 12000, nan, 22.45, nan ],
[ nan, 5000, 15000, nan, 42.14, nan ],
[ nan, 6000, 18000, nan, 70.81, nan ],
[ nan, 7000, 21000, nan, 110.01, nan ],
[ nan, 8000, 24000, nan, 162.02, nan ],
[ nan, 9000, 27000, nan, 159.23, nan ],
[ nan, 10000, 100, nan, 0.04, nan ],
[ nan, 20000, 200, nan, 0.14, nan ],
[ nan, 30000, 300, nan, 0.33, nan ],
[ nan, 40000, 400, nan, 0.93, nan ],
[ nan, 50000, 500, nan, 1.50, nan ],
[ nan, 60000, 600, nan, 2.25, nan ],
[ nan, 70000, 700, nan, 3.18, nan ],
[ nan, 80000, 800, nan, 4.28, nan ],
[ nan, 90000, 900, nan, 6.37, nan ],
[ nan, 100000, 1000, nan, 8.06, nan ],
[ nan, 200000, 2000, nan, 46.19, nan ],
[ nan, 100, 10000, nan, 0.03, nan ],
[ nan, 200, 20000, nan, 0.14, nan ],
[ nan, 300, 30000, nan, 0.37, nan ],
[ nan, 400, 40000, nan, 0.77, nan ],
[ nan, 500, 50000, nan, 1.42, nan ],
[ nan, 600, 60000, nan, 2.36, nan ],
[ nan, 700, 70000, nan, 3.63, nan ],
[ nan, 800, 80000, nan, 5.75, nan ],
[ nan, 900, 90000, nan, 6.28, nan ],
[ nan, 1000, 100000, nan, 8.15, nan ],
[ nan, 2000, 200000, nan, 52.04, nan ],
])
# numactl --interleave=all ../testing/testing_zgesdd -US -VS -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000 -N 300,100 -N 600,200 -N 900,300 -N 1200,400 -N 1500,500 -N 1800,600 -N 2100,700 -N 2400,800 -N 2700,900 -N 3000,1000 -N 6000,2000 -N 9000,3000 -N 12000,4000 -N 15000,5000 -N 18000,6000 -N 21000,7000 -N 24000,8000 -N 27000,9000 -N 100,300 -N 200,600 -N 300,900 -N 400,1200 -N 500,1500 -N 600,1800 -N 700,2100 -N 800,2400 -N 900,2700 -N 1000,3000 -N 2000,6000 -N 3000,9000 -N 4000,12000 -N 5000,15000 -N 6000,18000 -N 7000,21000 -N 8000,24000 -N 9000,27000 -N 10000,100 -N 20000,200 -N 30000,300 -N 40000,400 -N 50000,500 -N 60000,600 -N 70000,700 -N 80000,800 -N 90000,900 -N 100000,1000 -N 200000,2000 -N 100,10000 -N 200,20000 -N 300,30000 -N 400,40000 -N 500,50000 -N 600,60000 -N 700,70000 -N 800,80000 -N 900,90000 -N 1000,100000 -N 2000,200000
zgesdd_US = array([
[ nan, 10, 10, nan, 0.00, nan ],
[ nan, 20, 20, nan, 0.00, nan ],
[ nan, 30, 30, nan, 0.00, nan ],
[ nan, 40, 40, nan, 0.00, nan ],
[ nan, 50, 50, nan, 0.00, nan ],
[ nan, 60, 60, nan, 0.00, nan ],
[ nan, 70, 70, nan, 0.01, nan ],
[ nan, 80, 80, nan, 0.01, nan ],
[ nan, 90, 90, nan, 0.01, nan ],
[ nan, 100, 100, nan, 0.01, nan ],
[ nan, 200, 200, nan, 0.04, nan ],
[ nan, 300, 300, nan, 0.08, nan ],
[ nan, 400, 400, nan, 0.13, nan ],
[ nan, 500, 500, nan, 0.19, nan ],
[ nan, 600, 600, nan, 0.27, nan ],
[ nan, 700, 700, nan, 0.35, nan ],
[ nan, 800, 800, nan, 0.42, nan ],
[ nan, 900, 900, nan, 0.52, nan ],
[ nan, 1000, 1000, nan, 0.65, nan ],
[ nan, 2000, 2000, nan, 2.84, nan ],
[ nan, 3000, 3000, nan, 7.86, nan ],
[ nan, 4000, 4000, nan, 14.50, nan ],
[ nan, 5000, 5000, nan, 25.34, nan ],
[ nan, 6000, 6000, nan, 40.82, nan ],
[ nan, 7000, 7000, nan, 60.47, nan ],
[ nan, 8000, 8000, nan, 87.49, nan ],
[ nan, 9000, 9000, nan, 114.55, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/zgetrf.txt
# numactl --interleave=all ../testing/testing_zgetrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
zgetrf = array([
[ 10, 10, nan, nan, 0.24, 0.00, nan ],
[ 20, 20, nan, nan, 0.70, 0.00, nan ],
[ 30, 30, nan, nan, 1.65, 0.00, nan ],
[ 40, 40, nan, nan, 2.92, 0.00, nan ],
[ 50, 50, nan, nan, 2.67, 0.00, nan ],
[ 60, 60, nan, nan, 4.04, 0.00, nan ],
[ 70, 70, nan, nan, 0.99, 0.00, nan ],
[ 80, 80, nan, nan, 1.38, 0.00, nan ],
[ 90, 90, nan, nan, 1.86, 0.00, nan ],
[ 100, 100, nan, nan, 2.37, 0.00, nan ],
[ 200, 200, nan, nan, 10.08, 0.00, nan ],
[ 300, 300, nan, nan, 22.19, 0.00, nan ],
[ 400, 400, nan, nan, 35.50, 0.00, nan ],
[ 500, 500, nan, nan, 51.16, 0.01, nan ],
[ 600, 600, nan, nan, 67.19, 0.01, nan ],
[ 700, 700, nan, nan, 85.26, 0.01, nan ],
[ 800, 800, nan, nan, 104.65, 0.01, nan ],
[ 900, 900, nan, nan, 121.40, 0.02, nan ],
[ 1000, 1000, nan, nan, 140.36, 0.02, nan ],
[ 2000, 2000, nan, nan, 337.28, 0.06, nan ],
[ 3000, 3000, nan, nan, 518.22, 0.14, nan ],
[ 4000, 4000, nan, nan, 628.86, 0.27, nan ],
[ 5000, 5000, nan, nan, 679.77, 0.49, nan ],
[ 6000, 6000, nan, nan, 773.23, 0.74, nan ],
[ 7000, 7000, nan, nan, 830.05, 1.10, nan ],
[ 8000, 8000, nan, nan, 883.49, 1.55, nan ],
[ 9000, 9000, nan, nan, 895.76, 2.17, nan ],
[ 10000, 10000, nan, nan, 934.98, 2.85, nan ],
[ 12000, 12000, nan, nan, 988.22, 4.66, nan ],
[ 14000, 14000, nan, nan, 1023.69, 7.15, nan ],
[ 16000, 16000, nan, nan, 1051.55, 10.39, nan ],
[ 18000, 18000, nan, nan, 1060.84, 14.66, nan ],
[ 20000, 20000, nan, nan, 1077.25, 19.80, nan ],
])
# numactl --interleave=all ../testing/testing_zgetrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
zgetrf_gpu = array([
[ 10, 10, nan, nan, 0.01, 0.00, nan ],
[ 20, 20, nan, nan, 0.08, 0.00, nan ],
[ 30, 30, nan, nan, 0.26, 0.00, nan ],
[ 40, 40, nan, nan, 0.58, 0.00, nan ],
[ 50, 50, nan, nan, 0.89, 0.00, nan ],
[ 60, 60, nan, nan, 1.43, 0.00, nan ],
[ 70, 70, nan, nan, 0.51, 0.00, nan ],
[ 80, 80, nan, nan, 0.82, 0.00, nan ],
[ 90, 90, nan, nan, 1.11, 0.00, nan ],
[ 100, 100, nan, nan, 1.45, 0.00, nan ],
[ 200, 200, nan, nan, 7.00, 0.00, nan ],
[ 300, 300, nan, nan, 17.59, 0.00, nan ],
[ 400, 400, nan, nan, 31.04, 0.01, nan ],
[ 500, 500, nan, nan, 49.68, 0.01, nan ],
[ 600, 600, nan, nan, 67.35, 0.01, nan ],
[ 700, 700, nan, nan, 88.73, 0.01, nan ],
[ 800, 800, nan, nan, 110.91, 0.01, nan ],
[ 900, 900, nan, nan, 134.44, 0.01, nan ],
[ 1000, 1000, nan, nan, 163.87, 0.02, nan ],
[ 2000, 2000, nan, nan, 412.29, 0.05, nan ],
[ 3000, 3000, nan, nan, 635.10, 0.11, nan ],
[ 4000, 4000, nan, nan, 755.79, 0.23, nan ],
[ 5000, 5000, nan, nan, 796.58, 0.42, nan ],
[ 6000, 6000, nan, nan, 888.50, 0.65, nan ],
[ 7000, 7000, nan, nan, 942.64, 0.97, nan ],
[ 8000, 8000, nan, nan, 995.14, 1.37, nan ],
[ 9000, 9000, nan, nan, 1005.68, 1.93, nan ],
[ 10000, 10000, nan, nan, 1040.82, 2.56, nan ],
[ 12000, 12000, nan, nan, 1082.99, 4.25, nan ],
[ 14000, 14000, nan, nan, 1109.47, 6.60, nan ],
[ 16000, 16000, nan, nan, 1130.70, 9.66, nan ],
[ 18000, 18000, nan, nan, 1130.42, 13.76, nan ],
[ 20000, 20000, nan, nan, 1141.78, 18.68, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/zheevd.txt
# numactl --interleave=all ../testing/testing_zheevd -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_zheevd -JN -N 123 -N 1234 --range 12000:20000:2000
zheevd_JN = array([
[ 10, nan, 0.0000, nan, nan, nan, nan ],
[ 20, nan, 0.0001, nan, nan, nan, nan ],
[ 30, nan, 0.0001, nan, nan, nan, nan ],
[ 40, nan, 0.0002, nan, nan, nan, nan ],
[ 50, nan, 0.0003, nan, nan, nan, nan ],
[ 60, nan, 0.0004, nan, nan, nan, nan ],
[ 70, nan, 0.0006, nan, nan, nan, nan ],
[ 80, nan, 0.0009, nan, nan, nan, nan ],
[ 90, nan, 0.0012, nan, nan, nan, nan ],
[ 100, nan, 0.0016, nan, nan, nan, nan ],
[ 200, nan, 0.0066, nan, nan, nan, nan ],
[ 300, nan, 0.0135, nan, nan, nan, nan ],
[ 400, nan, 0.0236, nan, nan, nan, nan ],
[ 500, nan, 0.0363, nan, nan, nan, nan ],
[ 600, nan, 0.0522, nan, nan, nan, nan ],
[ 700, nan, 0.0729, nan, nan, nan, nan ],
[ 800, nan, 0.0957, nan, nan, nan, nan ],
[ 900, nan, 0.1250, nan, nan, nan, nan ],
[ 1000, nan, 0.1551, nan, nan, nan, nan ],
[ 2000, nan, 0.7983, nan, nan, nan, nan ],
[ 3000, nan, 2.0276, nan, nan, nan, nan ],
[ 4000, nan, 3.9053, nan, nan, nan, nan ],
[ 5000, nan, 6.5959, nan, nan, nan, nan ],
[ 6000, nan, 10.3139, nan, nan, nan, nan ],
[ 7000, nan, 15.0515, nan, nan, nan, nan ],
[ 8000, nan, 21.1750, nan, nan, nan, nan ],
[ 9000, nan, 28.9088, nan, nan, nan, nan ],
[ 10000, nan, 38.1991, nan, nan, nan, nan ],
[ 12000, nan, 60.9986, nan, nan, nan, nan ],
[ 14000, nan, 92.8384, nan, nan, nan, nan ],
[ 16000, nan, 134.8674, nan, nan, nan, nan ],
[ 18000, nan, 189.3054, nan, nan, nan, nan ],
[ 20000, nan, 257.6523, nan, nan, nan, nan ],
])
# numactl --interleave=all ../testing/testing_zheevd -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_zheevd -JV -N 123 -N 1234 --range 12000:20000:2000
zheevd_JV = array([
[ 10, nan, 0.0002, nan, nan, nan, nan ],
[ 20, nan, 0.0002, nan, nan, nan, nan ],
[ 30, nan, 0.0004, nan, nan, nan, nan ],
[ 40, nan, 0.0006, nan, nan, nan, nan ],
[ 50, nan, 0.0008, nan, nan, nan, nan ],
[ 60, nan, 0.0010, nan, nan, nan, nan ],
[ 70, nan, 0.0015, nan, nan, nan, nan ],
[ 80, nan, 0.0020, nan, nan, nan, nan ],
[ 90, nan, 0.0025, nan, nan, nan, nan ],
[ 100, nan, 0.0031, nan, nan, nan, nan ],
[ 200, nan, 0.0151, nan, nan, nan, nan ],
[ 300, nan, 0.0254, nan, nan, nan, nan ],
[ 400, nan, 0.0427, nan, nan, nan, nan ],
[ 500, nan, 0.0637, nan, nan, nan, nan ],
[ 600, nan, 0.0841, nan, nan, nan, nan ],
[ 700, nan, 0.1146, nan, nan, nan, nan ],
[ 800, nan, 0.1469, nan, nan, nan, nan ],
[ 900, nan, 0.1908, nan, nan, nan, nan ],
[ 1000, nan, 0.2328, nan, nan, nan, nan ],
[ 2000, nan, 1.0885, nan, nan, nan, nan ],
[ 3000, nan, 2.4379, nan, nan, nan, nan ],
[ 4000, nan, 4.6361, nan, nan, nan, nan ],
[ 5000, nan, 7.7226, nan, nan, nan, nan ],
[ 6000, nan, 12.1252, nan, nan, nan, nan ],
[ 7000, nan, 17.9808, nan, nan, nan, nan ],
[ 8000, nan, 25.4648, nan, nan, nan, nan ],
[ 9000, nan, 34.8593, nan, nan, nan, nan ],
[ 10000, nan, 46.1960, nan, nan, nan, nan ],
[ 12000, nan, 74.9908, nan, nan, nan, nan ],
[ 14000, nan, 114.6719, nan, nan, nan, nan ],
[ 16000, nan, 167.3222, nan, nan, nan, nan ],
[ 18000, nan, 240.0484, nan, nan, nan, nan ],
[ 20000, nan, 325.0192, nan, nan, nan, nan ],
])
# numactl --interleave=all ../testing/testing_zheevd_gpu -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_zheevd_gpu -JN -N 123 -N 1234 --range 12000:20000:2000
zheevd_gpu_JN = array([
[ 10, nan, 0.0002, nan, nan, nan, nan ],
[ 20, nan, 0.0002, nan, nan, nan, nan ],
[ 30, nan, 0.0003, nan, nan, nan, nan ],
[ 40, nan, 0.0003, nan, nan, nan, nan ],
[ 50, nan, 0.0005, nan, nan, nan, nan ],
[ 60, nan, 0.0006, nan, nan, nan, nan ],
[ 70, nan, 0.0009, nan, nan, nan, nan ],
[ 80, nan, 0.0012, nan, nan, nan, nan ],
[ 90, nan, 0.0015, nan, nan, nan, nan ],
[ 100, nan, 0.0019, nan, nan, nan, nan ],
[ 200, nan, 0.0073, nan, nan, nan, nan ],
[ 300, nan, 0.0147, nan, nan, nan, nan ],
[ 400, nan, 0.0257, nan, nan, nan, nan ],
[ 500, nan, 0.0396, nan, nan, nan, nan ],
[ 600, nan, 0.0567, nan, nan, nan, nan ],
[ 700, nan, 0.0788, nan, nan, nan, nan ],
[ 800, nan, 0.1032, nan, nan, nan, nan ],
[ 900, nan, 0.1339, nan, nan, nan, nan ],
[ 1000, nan, 0.1665, nan, nan, nan, nan ],
[ 2000, nan, 0.8288, nan, nan, nan, nan ],
[ 3000, nan, 2.0168, nan, nan, nan, nan ],
[ 4000, nan, 3.8777, nan, nan, nan, nan ],
[ 5000, nan, 6.5738, nan, nan, nan, nan ],
[ 6000, nan, 10.2345, nan, nan, nan, nan ],
[ 7000, nan, 15.0260, nan, nan, nan, nan ],
[ 8000, nan, 21.0258, nan, nan, nan, nan ],
[ 9000, nan, 28.6735, nan, nan, nan, nan ],
[ 10000, nan, 37.7639, nan, nan, nan, nan ],
[ 12000, nan, 60.8922, nan, nan, nan, nan ],
[ 14000, nan, 92.3358, nan, nan, nan, nan ],
[ 16000, nan, 134.6948, nan, nan, nan, nan ],
[ 18000, nan, 188.2464, nan, nan, nan, nan ],
[ 20000, nan, nan, nan, nan, nan, nan ], # failed to run
])
# numactl --interleave=all ../testing/testing_zheevd_gpu -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:10000:1000
# numactl --interleave=all ../testing/testing_zheevd_gpu -JV -N 123 -N 1234 --range 12000:20000:2000
zheevd_gpu_JV = array([
[ 10, nan, 0.0004, nan, nan, nan, nan ],
[ 20, nan, 0.0005, nan, nan, nan, nan ],
[ 30, nan, 0.0006, nan, nan, nan, nan ],
[ 40, nan, 0.0008, nan, nan, nan, nan ],
[ 50, nan, 0.0010, nan, nan, nan, nan ],
[ 60, nan, 0.0013, nan, nan, nan, nan ],
[ 70, nan, 0.0017, nan, nan, nan, nan ],
[ 80, nan, 0.0022, nan, nan, nan, nan ],
[ 90, nan, 0.0026, nan, nan, nan, nan ],
[ 100, nan, 0.0032, nan, nan, nan, nan ],
[ 200, nan, 0.0139, nan, nan, nan, nan ],
[ 300, nan, 0.0217, nan, nan, nan, nan ],
[ 400, nan, 0.0347, nan, nan, nan, nan ],
[ 500, nan, 0.0518, nan, nan, nan, nan ],
[ 600, nan, 0.0695, nan, nan, nan, nan ],
[ 700, nan, 0.0948, nan, nan, nan, nan ],
[ 800, nan, 0.1221, nan, nan, nan, nan ],
[ 900, nan, 0.1565, nan, nan, nan, nan ],
[ 1000, nan, 0.1968, nan, nan, nan, nan ],
[ 2000, nan, 0.9374, nan, nan, nan, nan ],
[ 3000, nan, 2.3136, nan, nan, nan, nan ],
[ 4000, nan, 4.4228, nan, nan, nan, nan ],
[ 5000, nan, 7.6197, nan, nan, nan, nan ],
[ 6000, nan, 11.9714, nan, nan, nan, nan ],
[ 7000, nan, 17.6985, nan, nan, nan, nan ],
[ 8000, nan, 25.1101, nan, nan, nan, nan ],
[ 9000, nan, 34.8214, nan, nan, nan, nan ],
[ 10000, nan, 45.9189, nan, nan, nan, nan ],
[ 12000, nan, 76.1595, nan, nan, nan, nan ],
[ 14000, nan, 117.6711, nan, nan, nan, nan ],
[ 16000, nan, 169.1880, nan, nan, nan, nan ],
[ 18000, nan, 238.5689, nan, nan, nan, nan ],
[ 20000, nan, nan, nan, nan, nan, nan ], # failed to run
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/zheevd_2stage.txt
# numactl --interleave=all ../testing/testing_zheevdx_2stage -JN -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
zheevdx_2stage_JN = array([
[ 10, 10, 0.00 ],
[ 20, 20, 0.00 ],
[ 30, 30, 0.00 ],
[ 40, 40, 0.00 ],
[ 50, 50, 0.00 ],
[ 60, 60, 0.00 ],
[ 70, 70, 0.00 ],
[ 80, 80, 0.00 ],
[ 90, 90, 0.00 ],
[ 100, 100, 0.00 ],
[ 200, 200, 0.01 ],
[ 300, 300, 0.04 ],
[ 400, 400, 0.07 ],
[ 500, 500, 0.10 ],
[ 600, 600, 0.14 ],
[ 700, 700, 0.18 ],
[ 800, 800, 0.22 ],
[ 900, 900, 0.28 ],
[ 1000, 1000, 0.31 ],
[ 2000, 2000, 0.73 ],
[ 3000, 3000, 1.38 ],
[ 4000, 4000, 2.21 ],
[ 5000, 5000, 3.24 ],
[ 6000, 6000, 4.56 ],
[ 7000, 7000, 6.06 ],
[ 8000, 8000, 7.71 ],
[ 9000, 9000, 10.22 ],
[ 10000, 10000, 12.83 ],
[ 12000, 12000, 19.36 ],
[ 14000, 14000, 27.85 ],
[ 16000, 16000, 39.63 ],
[ 18000, 18000, 54.54 ],
[ 20000, 20000, 72.02 ],
])
# numactl --interleave=all ../testing/testing_zheevdx_2stage -JV -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
zheevdx_2stage_JV = array([
[ 10, 10, 0.00 ],
[ 20, 20, 0.00 ],
[ 30, 30, 0.00 ],
[ 40, 40, 0.00 ],
[ 50, 50, 0.00 ],
[ 60, 60, 0.00 ],
[ 70, 70, 0.00 ],
[ 80, 80, 0.00 ],
[ 90, 90, 0.00 ],
[ 100, 100, 0.00 ],
[ 200, 200, 0.01 ],
[ 300, 300, 0.05 ],
[ 400, 400, 0.08 ],
[ 500, 500, 0.13 ],
[ 600, 600, 0.16 ],
[ 700, 700, 0.21 ],
[ 800, 800, 0.25 ],
[ 900, 900, 0.31 ],
[ 1000, 1000, 0.36 ],
[ 2000, 2000, 0.99 ],
[ 3000, 3000, 2.05 ],
[ 4000, 4000, 3.70 ],
[ 5000, 5000, 5.96 ],
[ 6000, 6000, 9.13 ],
[ 7000, 7000, 12.63 ],
[ 8000, 8000, 17.80 ],
[ 9000, 9000, 24.84 ],
[ 10000, 10000, 32.58 ],
[ 12000, 12000, 53.73 ],
[ 14000, 14000, 85.04 ],
[ 16000, 16000, 122.13 ],
[ 18000, 18000, 179.24 ],
[ 20000, 20000, 185.08 ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/zhemv.txt
# numactl --interleave=all ../testing/testing_zhemv -L -N 123 -N 1234 --range 10:90:1 --range 100:900:10 --range 1000:9000:100 --range 10000:20000:2000
zhemv_L = array([
[ 10, 0.02, 0.04, 0.03, 0.03, 0.04, 0.03, 0.30, 0.00, 7.12e-16, 3.55e-16, 7.12e-16, nan ],
[ 11, 0.03, 0.04, 0.04, 0.03, 0.05, 0.02, 0.51, 0.00, 2.28e-16, 2.28e-16, 2.42e-16, nan ],
[ 12, 0.04, 0.03, 0.04, 0.03, 0.06, 0.02, 0.60, 0.00, 3.99e-16, 4.50e-16, 3.05e-16, nan ],
[ 13, 0.04, 0.04, 0.05, 0.03, 0.07, 0.02, 0.53, 0.00, 3.06e-16, 5.47e-16, 2.82e-16, nan ],
[ 14, 0.05, 0.04, 0.06, 0.03, 0.08, 0.02, 0.81, 0.00, 2.84e-16, 3.59e-16, 2.84e-16, nan ],
[ 15, 0.06, 0.03, 0.06, 0.03, 0.09, 0.02, 0.64, 0.00, 2.65e-16, 2.96e-16, 2.96e-16, nan ],
[ 16, 0.07, 0.03, 0.07, 0.03, 0.10, 0.02, 0.72, 0.00, 2.48e-16, 4.46e-16, 2.48e-16, nan ],
[ 17, 0.07, 0.03, 0.08, 0.03, 0.11, 0.02, 1.32, 0.00, 4.67e-16, 7.01e-16, 4.31e-16, nan ],
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[ 690, 23.54, 0.16, 27.09, 0.14, 31.58, 0.12, 10.52, 0.36, 2.38e-15, 2.38e-15, 2.22e-15, nan ],
[ 700, 23.67, 0.17, 27.46, 0.14, 32.50, 0.12, 10.71, 0.37, 2.36e-15, 2.44e-15, 2.28e-15, nan ],
[ 710, 24.49, 0.16, 26.95, 0.15, 33.11, 0.12, 10.55, 0.38, 1.96e-15, 2.41e-15, 2.12e-15, nan ],
[ 720, 24.45, 0.17, 28.30, 0.15, 33.26, 0.12, 10.65, 0.39, 2.08e-15, 2.38e-15, 2.44e-15, nan ],
[ 730, 25.27, 0.17, 28.85, 0.15, 32.88, 0.13, 10.68, 0.40, 2.34e-15, 2.32e-15, 2.19e-15, nan ],
[ 740, 24.95, 0.18, 28.50, 0.15, 33.00, 0.13, 6.52, 0.67, 2.35e-15, 2.62e-15, 2.48e-15, nan ],
[ 750, 26.05, 0.17, 29.46, 0.15, 33.89, 0.13, 10.61, 0.43, 2.29e-15, 2.06e-15, 2.02e-15, nan ],
[ 760, 26.64, 0.17, 30.06, 0.15, 34.25, 0.14, 10.31, 0.45, 2.72e-15, 2.85e-15, 2.69e-15, nan ],
[ 770, 26.86, 0.18, 30.25, 0.16, 34.67, 0.14, 10.59, 0.45, 2.25e-15, 2.58e-15, 1.92e-15, nan ],
[ 780, 26.53, 0.18, 31.08, 0.16, 35.08, 0.14, 10.74, 0.45, 2.17e-15, 2.15e-15, 2.10e-15, nan ],
[ 790, 26.46, 0.19, 30.90, 0.16, 34.97, 0.14, 10.69, 0.47, 2.55e-15, 2.65e-15, 2.51e-15, nan ],
[ 800, 27.58, 0.19, 31.69, 0.16, 35.33, 0.15, 9.79, 0.52, 2.42e-15, 2.03e-15, 2.11e-15, nan ],
[ 810, 28.13, 0.19, 32.68, 0.16, 37.01, 0.14, 10.62, 0.49, 2.39e-15, 2.26e-15, 2.27e-15, nan ],
[ 820, 28.68, 0.19, 33.09, 0.16, 37.42, 0.14, 10.76, 0.50, 2.92e-15, 2.91e-15, 2.77e-15, nan ],
[ 830, 28.91, 0.19, 33.66, 0.16, 38.09, 0.14, 6.63, 0.83, 1.94e-15, 2.20e-15, 2.20e-15, nan ],
[ 840, 29.14, 0.19, 33.45, 0.17, 37.71, 0.15, 6.52, 0.87, 2.50e-15, 2.34e-15, 2.40e-15, nan ],
[ 850, 30.02, 0.19, 34.84, 0.17, 38.86, 0.15, 10.64, 0.54, 2.43e-15, 2.21e-15, 2.04e-15, nan ],
[ 860, 30.58, 0.19, 35.26, 0.17, 38.97, 0.15, 8.38, 0.71, 2.66e-15, 2.60e-15, 2.60e-15, nan ],
[ 870, 30.80, 0.20, 29.90, 0.20, 38.14, 0.16, 10.57, 0.57, 2.61e-15, 2.62e-15, 2.36e-15, nan ],
[ 880, 30.88, 0.20, 36.05, 0.17, 39.02, 0.16, 10.56, 0.59, 2.15e-15, 2.25e-15, 2.10e-15, nan ],
[ 890, 31.14, 0.20, 35.64, 0.18, 38.47, 0.16, 6.48, 0.98, 2.23e-15, 2.30e-15, 2.39e-15, nan ],
[ 900, 31.36, 0.21, 36.44, 0.18, 39.86, 0.16, 10.52, 0.62, 2.49e-15, 2.58e-15, 2.54e-15, nan ],
[ 1000, 34.54, 0.23, 40.25, 0.20, 43.53, 0.18, 7.94, 1.01, 2.91e-15, 2.52e-15, 2.56e-15, nan ],
[ 1100, 37.58, 0.26, 44.24, 0.22, 27.85, 0.35, 9.69, 1.00, 3.65e-15, 2.94e-15, 2.90e-15, nan ],
[ 1200, 40.89, 0.28, 49.07, 0.24, 30.37, 0.38, 9.19, 1.26, 3.29e-15, 3.26e-15, 3.42e-15, nan ],
[ 1300, 43.94, 0.31, 54.12, 0.25, 31.86, 0.42, 8.49, 1.59, 2.97e-15, 2.80e-15, 2.83e-15, nan ],
[ 1400, 48.88, 0.32, 58.37, 0.27, 31.59, 0.50, 8.30, 1.89, 3.26e-15, 3.47e-15, 3.08e-15, nan ],
[ 1500, 52.37, 0.34, 62.30, 0.29, 36.69, 0.49, 8.18, 2.20, 2.93e-15, 3.07e-15, 3.05e-15, nan ],
[ 1600, 55.54, 0.37, 68.13, 0.30, 38.75, 0.53, 6.83, 3.00, 3.13e-15, 3.02e-15, 3.13e-15, nan ],
[ 1700, 59.04, 0.39, 71.84, 0.32, 42.31, 0.55, 8.33, 2.78, 3.36e-15, 3.41e-15, 3.21e-15, nan ],
[ 1800, 61.61, 0.42, 73.92, 0.35, 42.52, 0.61, 5.49, 4.72, 4.38e-15, 4.54e-15, 4.69e-15, nan ],
[ 1900, 65.81, 0.44, 75.86, 0.38, 43.67, 0.66, 8.38, 3.45, 3.59e-15, 3.83e-15, 4.08e-15, nan ],
[ 2000, 64.05, 0.50, 75.89, 0.42, 45.49, 0.70, 7.75, 4.13, 3.65e-15, 3.67e-15, 3.89e-15, nan ],
[ 2100, 65.00, 0.54, 77.25, 0.46, 34.75, 1.02, 8.39, 4.21, 6.07e-15, 5.02e-15, 6.11e-15, nan ],
[ 2200, 66.22, 0.59, 77.65, 0.50, 36.94, 1.05, 7.73, 5.01, 3.96e-15, 3.89e-15, 3.69e-15, nan ],
[ 2300, 66.90, 0.63, 77.43, 0.55, 37.81, 1.12, 5.59, 7.58, 4.81e-15, 4.26e-15, 4.36e-15, nan ],
[ 2400, 68.92, 0.67, 78.42, 0.59, 39.96, 1.15, 7.60, 6.07, 4.94e-15, 6.12e-15, 5.35e-15, nan ],
[ 2500, 70.75, 0.71, 78.04, 0.64, 41.18, 1.21, 7.93, 6.31, 4.82e-15, 4.92e-15, 5.28e-15, nan ],
[ 2600, 71.48, 0.76, 78.32, 0.69, 40.66, 1.33, 8.25, 6.56, 4.04e-15, 4.36e-15, 4.12e-15, nan ],
[ 2700, 72.95, 0.80, 78.34, 0.74, 44.07, 1.32, 8.38, 6.96, 5.41e-15, 4.79e-15, 5.22e-15, nan ],
[ 2800, 75.35, 0.83, 80.05, 0.78, 39.39, 1.59, 8.08, 7.76, 5.05e-15, 5.05e-15, 5.24e-15, nan ],
[ 2900, 74.46, 0.90, 80.71, 0.83, 46.52, 1.45, 8.46, 7.96, 5.33e-15, 5.35e-15, 6.32e-15, nan ],
[ 3000, 77.47, 0.93, 81.59, 0.88, 47.36, 1.52, 8.35, 8.63, 4.80e-15, 4.72e-15, 4.70e-15, nan ],
[ 3100, 78.48, 0.98, 82.81, 0.93, 39.22, 1.96, 8.36, 9.20, 5.19e-15, 5.50e-15, 5.79e-15, nan ],
[ 3200, 79.65, 1.03, 83.56, 0.98, 40.98, 2.00, 5.82, 14.09, 5.40e-15, 5.92e-15, 5.41e-15, nan ],
[ 3300, 81.60, 1.07, 84.45, 1.03, 41.60, 2.10, 5.63, 15.47, 4.76e-15, 5.11e-15, 5.52e-15, nan ],
[ 3400, 81.24, 1.14, 85.99, 1.08, 42.24, 2.19, 5.59, 16.56, 4.99e-15, 4.91e-15, 5.40e-15, nan ],
[ 3500, 83.51, 1.17, 86.68, 1.13, 43.52, 2.25, 5.58, 17.56, 5.52e-15, 5.19e-15, 5.35e-15, nan ],
[ 3600, 86.15, 1.20, 86.96, 1.19, 43.86, 2.37, 7.94, 13.06, 5.32e-15, 4.66e-15, 4.84e-15, nan ],
[ 3700, 86.89, 1.26, 87.30, 1.26, 44.48, 2.46, 8.40, 13.05, 5.90e-15, 5.96e-15, 5.50e-15, nan ],
[ 3800, 88.55, 1.31, 88.15, 1.31, 45.73, 2.53, 8.32, 13.90, 6.32e-15, 6.32e-15, 5.75e-15, nan ],
[ 3900, 87.07, 1.40, 88.01, 1.38, 47.38, 2.57, 8.35, 14.58, 5.69e-15, 5.87e-15, 5.85e-15, nan ],
[ 4000, 87.23, 1.47, 88.36, 1.45, 46.94, 2.73, 7.58, 16.90, 6.07e-15, 6.24e-15, 6.19e-15, nan ],
[ 4100, 87.52, 1.54, 88.33, 1.52, 37.67, 3.57, 7.85, 17.13, 7.58e-15, 6.75e-15, 6.77e-15, nan ],
[ 4200, 88.07, 1.60, 89.23, 1.58, 41.55, 3.40, 8.25, 17.12, 5.38e-15, 5.44e-15, 5.83e-15, nan ],
[ 4300, 89.30, 1.66, 89.20, 1.66, 42.70, 3.47, 8.40, 17.62, 7.09e-15, 7.48e-15, 6.85e-15, nan ],
[ 4400, 90.71, 1.71, 89.30, 1.73, 42.42, 3.65, 8.04, 19.26, 8.27e-15, 8.48e-15, 7.80e-15, nan ],
[ 4500, 90.28, 1.80, 89.33, 1.81, 45.28, 3.58, 8.43, 19.22, 6.07e-15, 6.87e-15, 6.12e-15, nan ],
[ 4600, 91.19, 1.86, 89.41, 1.89, 45.53, 3.72, 8.33, 20.34, 6.07e-15, 5.73e-15, 6.42e-15, nan ],
[ 4700, 92.80, 1.90, 90.05, 1.96, 45.66, 3.87, 8.54, 20.70, 7.42e-15, 9.41e-15, 8.83e-15, nan ],
[ 4800, 92.74, 1.99, 89.29, 2.06, 45.55, 4.05, 6.95, 26.52, 6.90e-15, 7.21e-15, 7.61e-15, nan ],
[ 4900, 91.98, 2.09, 89.49, 2.15, 47.22, 4.07, 8.53, 22.52, 6.72e-15, 7.17e-15, 6.81e-15, nan ],
[ 5000, 92.62, 2.16, 90.20, 2.22, 47.63, 4.20, 8.46, 23.66, 7.08e-15, 6.39e-15, 6.63e-15, nan ],
[ 5100, 93.76, 2.22, 90.73, 2.29, 47.86, 4.35, 8.48, 24.54, 6.77e-15, 7.20e-15, 7.69e-15, nan ],
[ 5200, 94.54, 2.29, 91.77, 2.36, 43.47, 4.98, 8.18, 26.44, 7.67e-15, 7.32e-15, 7.63e-15, nan ],
[ 5300, 94.45, 2.38, 91.60, 2.45, 44.28, 5.08, 8.57, 26.22, 8.51e-15, 8.42e-15, 7.62e-15, nan ],
[ 5400, 95.78, 2.44, 91.94, 2.54, 43.81, 5.33, 8.37, 27.88, 6.74e-15, 7.04e-15, 6.80e-15, nan ],
[ 5500, 95.67, 2.53, 91.52, 2.65, 44.36, 5.46, 8.55, 28.33, 7.61e-15, 7.40e-15, 6.95e-15, nan ],
[ 5600, 97.12, 2.58, 91.29, 2.75, 46.51, 5.40, 7.70, 32.58, 8.96e-15, 8.30e-15, 8.46e-15, nan ],
[ 5700, 98.86, 2.63, 91.67, 2.84, 46.12, 5.64, 8.56, 30.37, 7.18e-15, 8.18e-15, 6.99e-15, nan ],
[ 5800, 97.15, 2.77, 92.31, 2.92, 47.35, 5.69, 8.60, 31.31, 8.33e-15, 7.19e-15, 7.67e-15, nan ],
[ 5900, 97.39, 2.86, 91.63, 3.04, 47.63, 5.85, 8.62, 32.30, 8.18e-15, 9.25e-15, 8.50e-15, nan ],
[ 6000, 99.27, 2.90, 92.66, 3.11, 48.69, 5.92, 8.36, 34.47, 6.85e-15, 7.70e-15, 7.40e-15, nan ],
[ 6100, 98.34, 3.03, 92.15, 3.23, 48.68, 6.12, 8.32, 35.80, 7.53e-15, 7.95e-15, 7.67e-15, nan ],
[ 6200, 99.10, 3.10, 92.96, 3.31, 43.80, 7.02, 8.77, 35.06, 8.04e-15, 7.28e-15, 7.09e-15, nan ],
[ 6300, 99.16, 3.20, 92.67, 3.43, 45.00, 7.06, 8.64, 36.74, 8.91e-15, 8.75e-15, 8.88e-15, nan ],
[ 6400, 98.99, 3.31, 91.71, 3.57, 45.70, 7.17, 8.64, 37.95, 7.11e-15, 7.39e-15, 7.25e-15, nan ],
[ 6500, 100.53, 3.36, 92.07, 3.67, 45.77, 7.39, 8.11, 41.68, 7.85e-15, 8.97e-15, 8.12e-15, nan ],
[ 6600, 100.24, 3.48, 92.70, 3.76, 45.52, 7.66, 8.63, 40.39, 7.30e-15, 7.44e-15, 7.23e-15, nan ],
[ 6700, 100.08, 3.59, 92.08, 3.90, 46.94, 7.65, 8.33, 43.10, 9.14e-15, 9.40e-15, 9.01e-15, nan ],
[ 6800, 99.89, 3.70, 92.41, 4.00, 46.87, 7.89, 8.30, 44.57, 9.71e-15, 9.71e-15, 9.50e-15, nan ],
[ 6900, 100.20, 3.80, 92.72, 4.11, 47.01, 8.10, 8.25, 46.19, 8.25e-15, 8.55e-15, 8.49e-15, nan ],
[ 7000, 100.51, 3.90, 92.58, 4.24, 48.37, 8.11, 8.58, 45.70, 8.67e-15, 7.43e-15, 7.18e-15, nan ],
[ 7100, 99.50, 4.05, 93.56, 4.31, 48.75, 8.27, 8.22, 49.05, 8.33e-15, 8.20e-15, 7.80e-15, nan ],
[ 7200, 101.15, 4.10, 92.59, 4.48, 45.01, 9.21, 8.57, 48.43, 8.59e-15, 7.78e-15, 8.09e-15, nan ],
[ 7300, 102.98, 4.14, 93.63, 4.55, 45.49, 9.37, 7.83, 54.46, 8.85e-15, 7.86e-15, 8.49e-15, nan ],
[ 7400, 102.21, 4.29, 92.64, 4.73, 45.72, 9.58, 8.21, 53.37, 8.38e-15, 8.65e-15, 8.61e-15, nan ],
[ 7500, 103.83, 4.33, 93.32, 4.82, 45.07, 9.99, 8.10, 55.58, 9.37e-15, 9.78e-15, 9.40e-15, nan ],
[ 7600, 104.14, 4.44, 93.48, 4.94, 46.66, 9.91, 8.06, 57.33, 9.15e-15, 8.27e-15, 8.71e-15, nan ],
[ 7700, 102.22, 4.64, 93.22, 5.09, 47.43, 10.00, 7.98, 59.45, 8.84e-15, 8.34e-15, 9.13e-15, nan ],
[ 7800, 102.55, 4.75, 93.12, 5.23, 46.95, 10.37, 7.58, 64.18, 8.12e-15, 9.13e-15, 8.24e-15, nan ],
[ 7900, 103.03, 4.85, 93.32, 5.35, 47.35, 10.55, 7.67, 65.14, 1.04e-14, 1.05e-14, 1.02e-14, nan ],
[ 8000, 103.28, 4.96, 93.21, 5.49, 48.22, 10.62, 7.51, 68.17, 1.06e-14, 9.34e-15, 1.04e-14, nan ],
[ 8100, 104.39, 5.03, 93.05, 5.64, 48.42, 10.84, 7.51, 69.95, 1.12e-14, 1.27e-14, 1.19e-14, nan ],
[ 8200, 103.54, 5.20, 93.49, 5.75, 44.13, 12.19, 7.39, 72.83, 9.85e-15, 9.72e-15, 9.66e-15, nan ],
[ 8300, 105.09, 5.24, 94.31, 5.85, 45.41, 12.14, 7.23, 76.21, 9.43e-15, 9.61e-15, 8.37e-15, nan ],
[ 8400, 104.40, 5.41, 93.95, 6.01, 45.43, 12.43, 7.60, 74.30, 9.52e-15, 9.29e-15, 1.09e-14, nan ],
[ 8500, 103.71, 5.57, 93.48, 6.18, 46.69, 12.38, 7.50, 77.09, 8.73e-15, 9.08e-15, 9.32e-15, nan ],
[ 8600, 104.80, 5.65, 94.29, 6.28, 47.48, 12.47, 7.70, 76.88, 9.74e-15, 1.02e-14, 9.12e-15, nan ],
[ 8700, 103.79, 5.84, 93.76, 6.46, 46.31, 13.08, 7.42, 81.64, 9.92e-15, 9.88e-15, 9.97e-15, nan ],
[ 8800, 104.70, 5.92, 92.90, 6.67, 48.09, 12.89, 7.97, 77.74, 9.75e-15, 9.41e-15, 9.49e-15, nan ],
[ 8900, 105.58, 6.00, 94.13, 6.73, 47.02, 13.48, 8.09, 78.38, 9.93e-15, 9.80e-15, 1.04e-14, nan ],
[ 9000, 105.37, 6.15, 94.21, 6.88, 47.84, 13.55, 8.09, 80.12, 1.05e-14, 1.03e-14, 1.03e-14, nan ],
[ 10000, 106.67, 7.50, 94.23, 8.49, 47.90, 16.70, 8.34, 95.91, 9.28e-15, 1.01e-14, 9.12e-15, nan ],
[ 12000, 110.03, 10.47, 93.53, 12.32, 48.67, 23.67, 8.28, 139.09, 1.42e-14, 1.38e-14, 1.26e-14, nan ],
[ 14000, 112.51, 13.94, 95.20, 16.47, 47.70, 32.87, 8.44, 185.70, 1.34e-14, 1.27e-14, 1.30e-14, nan ],
[ 16000, 111.07, 18.44, 94.31, 21.72, 46.89, 43.68, 9.01, 227.37, 1.32e-14, 1.36e-14, 1.29e-14, nan ],
[ 18000, 108.35, 23.92, 94.94, 27.31, 47.51, 54.56, 8.41, 308.12, 1.44e-14, 1.48e-14, 1.40e-14, nan ],
[ 20000, 110.84, 28.87, 94.36, 33.92, 47.35, 67.59, 8.33, 383.95, 1.64e-14, 1.48e-14, 1.55e-14, nan ],
])
# numactl --interleave=all ../testing/testing_zhemv -U -N 123 -N 1234 --range 10:90:1 --range 100:900:10 --range 1000:9000:100 --range 10000:20000:2000
zhemv_U = array([
[ 10, 0.03, 0.03, 0.03, 0.03, 0.04, 0.02, 0.48, 0.00, 7.16e-16, 3.66e-16, 7.16e-16, nan ],
[ 11, 0.03, 0.03, 0.04, 0.03, 0.05, 0.02, 0.58, 0.00, 3.33e-16, 2.91e-16, 2.28e-16, nan ],
[ 12, 0.04, 0.03, 0.04, 0.03, 0.06, 0.02, 0.68, 0.00, 5.78e-16, 3.31e-16, 2.96e-16, nan ],
[ 13, 0.05, 0.03, 0.05, 0.03, 0.07, 0.02, 0.79, 0.00, 3.42e-16, 5.51e-16, 2.05e-16, nan ],
[ 14, 0.05, 0.03, 0.06, 0.03, 0.08, 0.02, 0.61, 0.00, 2.84e-16, 2.54e-16, 2.84e-16, nan ],
[ 15, 0.06, 0.04, 0.07, 0.03, 0.09, 0.02, 0.92, 0.00, 3.74e-16, 3.60e-16, 3.74e-16, nan ],
[ 16, 0.07, 0.03, 0.08, 0.03, 0.11, 0.02, 1.17, 0.00, 3.33e-16, 4.44e-16, 3.51e-16, nan ],
[ 17, 0.07, 0.03, 0.08, 0.03, 0.11, 0.02, 1.17, 0.00, 4.31e-16, 2.34e-16, 4.31e-16, nan ],
[ 18, 0.08, 0.04, 0.09, 0.03, 0.12, 0.02, 1.31, 0.00, 2.79e-16, 4.07e-16, 4.07e-16, nan ],
[ 19, 0.09, 0.03, 0.10, 0.03, 0.14, 0.02, 1.09, 0.00, 4.18e-16, 4.18e-16, 4.18e-16, nan ],
[ 20, 0.10, 0.03, 0.11, 0.03, 0.15, 0.02, 1.20, 0.00, 3.97e-16, 5.35e-16, 3.97e-16, nan ],
[ 21, 0.11, 0.03, 0.12, 0.03, 0.16, 0.02, 1.22, 0.00, 3.49e-16, 3.78e-16, 3.49e-16, nan ],
[ 22, 0.12, 0.04, 0.13, 0.03, 0.17, 0.02, 1.33, 0.00, 5.11e-16, 4.91e-16, 4.04e-16, nan ],
[ 23, 0.13, 0.03, 0.15, 0.03, 0.19, 0.02, 1.58, 0.00, 3.18e-16, 5.57e-16, 3.09e-16, nan ],
[ 24, 0.14, 0.03, 0.16, 0.03, 0.20, 0.02, 1.58, 0.00, 4.68e-16, 4.68e-16, 3.05e-16, nan ],
[ 25, 0.16, 0.03, 0.17, 0.03, 0.22, 0.02, 1.85, 0.00, 4.26e-16, 5.68e-16, 4.26e-16, nan ],
[ 26, 0.16, 0.04, 0.19, 0.03, 0.23, 0.03, 2.00, 0.00, 3.42e-16, 4.32e-16, 4.58e-16, nan ],
[ 27, 0.19, 0.03, 0.20, 0.03, 0.27, 0.02, 1.52, 0.00, 5.43e-16, 5.43e-16, 3.96e-16, nan ],
[ 28, 0.20, 0.03, 0.21, 0.03, 0.29, 0.02, 2.13, 0.00, 4.26e-16, 3.83e-16, 4.01e-16, nan ],
[ 29, 0.21, 0.03, 0.24, 0.03, 0.29, 0.02, 1.75, 0.00, 3.68e-16, 3.47e-16, 2.88e-16, nan ],
[ 30, 0.23, 0.03, 0.25, 0.03, 0.30, 0.02, 1.98, 0.00, 3.35e-16, 3.35e-16, 5.92e-16, nan ],
[ 31, 0.24, 0.03, 0.26, 0.03, 0.35, 0.02, 2.11, 0.00, 4.73e-16, 3.44e-16, 3.44e-16, nan ],
[ 32, 0.27, 0.03, 0.28, 0.03, 0.37, 0.02, 2.12, 0.00, 4.58e-16, 4.58e-16, 4.58e-16, nan ],
[ 33, 0.28, 0.03, 0.21, 0.04, 0.38, 0.02, 2.39, 0.00, 4.81e-16, 4.81e-16, 4.44e-16, nan ],
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[ 850, 32.34, 0.18, 29.08, 0.20, 37.08, 0.16, 10.88, 0.53, 2.54e-15, 2.41e-15, 2.56e-15, nan ],
[ 860, 32.75, 0.18, 31.55, 0.19, 36.78, 0.16, 11.00, 0.54, 2.57e-15, 2.39e-15, 2.41e-15, nan ],
[ 870, 33.34, 0.18, 23.51, 0.26, 39.38, 0.15, 10.99, 0.55, 2.61e-15, 2.49e-15, 2.62e-15, nan ],
[ 880, 33.94, 0.18, 32.33, 0.19, 39.26, 0.16, 11.01, 0.56, 2.63e-15, 2.60e-15, 2.52e-15, nan ],
[ 890, 34.13, 0.19, 31.25, 0.20, 39.68, 0.16, 10.87, 0.58, 2.17e-15, 2.23e-15, 2.20e-15, nan ],
[ 900, 33.49, 0.19, 31.36, 0.21, 40.04, 0.16, 10.99, 0.59, 2.92e-15, 2.58e-15, 2.44e-15, nan ],
[ 1000, 37.80, 0.21, 35.45, 0.23, 43.53, 0.18, 10.70, 0.75, 2.75e-15, 2.39e-15, 2.32e-15, nan ],
[ 1100, 41.57, 0.23, 35.79, 0.27, 28.02, 0.35, 10.32, 0.94, 3.10e-15, 3.31e-15, 3.31e-15, nan ],
[ 1200, 45.09, 0.26, 38.34, 0.30, 30.05, 0.38, 9.90, 1.16, 3.27e-15, 3.54e-15, 3.04e-15, nan ],
[ 1300, 47.31, 0.29, 42.30, 0.32, 31.33, 0.43, 9.77, 1.38, 3.71e-15, 4.77e-15, 4.63e-15, nan ],
[ 1400, 53.57, 0.29, 44.33, 0.35, 32.56, 0.48, 9.09, 1.73, 4.06e-15, 4.23e-15, 4.39e-15, nan ],
[ 1500, 55.94, 0.32, 45.06, 0.40, 36.33, 0.50, 8.60, 2.09, 3.79e-15, 3.79e-15, 4.40e-15, nan ],
[ 1600, 61.20, 0.33, 56.94, 0.36, 38.16, 0.54, 8.35, 2.46, 3.87e-15, 3.98e-15, 3.78e-15, nan ],
[ 1700, 63.23, 0.37, 49.77, 0.46, 40.19, 0.58, 8.27, 2.80, 4.07e-15, 3.89e-15, 3.92e-15, nan ],
[ 1800, 67.71, 0.38, 53.47, 0.49, 42.19, 0.61, 7.83, 3.31, 4.04e-15, 4.17e-15, 4.17e-15, nan ],
[ 1900, 71.02, 0.41, 53.83, 0.54, 43.14, 0.67, 8.30, 3.48, 4.44e-15, 4.57e-15, 4.45e-15, nan ],
[ 2000, 74.29, 0.43, 55.32, 0.58, 44.17, 0.73, 8.35, 3.84, 4.12e-15, 4.09e-15, 4.36e-15, nan ],
[ 2100, 77.77, 0.45, 56.84, 0.62, 35.30, 1.00, 8.35, 4.23, 4.33e-15, 4.13e-15, 3.96e-15, nan ],
[ 2200, 79.24, 0.49, 58.63, 0.66, 37.04, 1.05, 8.41, 4.61, 3.99e-15, 4.60e-15, 4.26e-15, nan ],
[ 2300, 85.23, 0.50, 59.80, 0.71, 39.03, 1.09, 7.60, 5.57, 5.14e-15, 5.16e-15, 5.14e-15, nan ],
[ 2400, 86.84, 0.53, 71.39, 0.65, 39.86, 1.16, 8.23, 5.60, 4.63e-15, 4.93e-15, 4.38e-15, nan ],
[ 2500, 88.88, 0.56, 59.28, 0.84, 41.55, 1.20, 8.44, 5.93, 5.48e-15, 5.67e-15, 5.67e-15, nan ],
[ 2600, 92.64, 0.58, 61.71, 0.88, 41.81, 1.29, 8.46, 6.40, 6.89e-15, 6.86e-15, 6.85e-15, nan ],
[ 2700, 93.67, 0.62, 63.08, 0.93, 42.98, 1.36, 8.37, 6.98, 5.06e-15, 5.02e-15, 5.08e-15, nan ],
[ 2800, 97.30, 0.64, 64.77, 0.97, 40.78, 1.54, 8.50, 7.39, 5.39e-15, 5.55e-15, 4.89e-15, nan ],
[ 2900, 100.48, 0.67, 63.39, 1.06, 45.92, 1.47, 8.50, 7.92, 5.04e-15, 5.06e-15, 5.35e-15, nan ],
[ 3000, 106.88, 0.67, 65.73, 1.10, 47.39, 1.52, 8.56, 8.41, 5.21e-15, 5.16e-15, 5.08e-15, nan ],
[ 3100, 110.33, 0.70, 64.15, 1.20, 38.99, 1.97, 8.58, 8.97, 6.08e-15, 5.41e-15, 5.52e-15, nan ],
[ 3200, 111.83, 0.73, 84.42, 0.97, 40.55, 2.02, 9.40, 8.71, 5.40e-15, 5.55e-15, 5.56e-15, nan ],
[ 3300, 114.06, 0.76, 66.99, 1.30, 41.19, 2.12, 8.60, 10.14, 5.67e-15, 6.10e-15, 5.81e-15, nan ],
[ 3400, 116.08, 0.80, 67.44, 1.37, 42.76, 2.16, 8.61, 10.74, 6.46e-15, 6.11e-15, 5.92e-15, nan ],
[ 3500, 120.77, 0.81, 68.14, 1.44, 44.02, 2.23, 8.48, 11.56, 6.11e-15, 5.59e-15, 5.52e-15, nan ],
[ 3600, 122.58, 0.85, 69.43, 1.49, 44.63, 2.32, 8.60, 12.06, 5.81e-15, 5.82e-15, 6.10e-15, nan ],
[ 3700, 124.34, 0.88, 71.06, 1.54, 44.85, 2.44, 8.50, 12.90, 5.44e-15, 5.65e-15, 5.67e-15, nan ],
[ 3800, 127.26, 0.91, 72.17, 1.60, 46.28, 2.50, 8.56, 13.51, 6.23e-15, 6.12e-15, 6.00e-15, nan ],
[ 3900, 127.86, 0.95, 69.05, 1.76, 47.13, 2.58, 8.35, 14.57, 5.81e-15, 6.08e-15, 5.63e-15, nan ],
[ 4000, 125.40, 1.02, 89.42, 1.43, 46.87, 2.73, 8.53, 15.01, 6.16e-15, 6.28e-15, 6.14e-15, nan ],
[ 4100, 118.74, 1.13, 69.89, 1.92, 39.96, 3.37, 7.88, 17.07, 6.16e-15, 6.89e-15, 6.55e-15, nan ],
[ 4200, 119.93, 1.18, 72.32, 1.95, 41.06, 3.44, 8.52, 16.57, 7.28e-15, 6.54e-15, 6.54e-15, nan ],
[ 4300, 123.02, 1.20, 71.07, 2.08, 42.00, 3.52, 8.52, 17.36, 6.06e-15, 6.35e-15, 6.19e-15, nan ],
[ 4400, 123.45, 1.26, 73.15, 2.12, 43.15, 3.59, 8.56, 18.11, 6.31e-15, 6.39e-15, 6.12e-15, nan ],
[ 4500, 122.96, 1.32, 73.50, 2.20, 44.37, 3.65, 8.59, 18.86, 6.75e-15, 6.53e-15, 6.80e-15, nan ],
[ 4600, 124.32, 1.36, 73.46, 2.31, 45.99, 3.68, 8.60, 19.70, 6.95e-15, 7.40e-15, 7.18e-15, nan ],
[ 4700, 122.51, 1.44, 75.13, 2.35, 47.08, 3.76, 8.67, 20.40, 6.19e-15, 6.62e-15, 6.40e-15, nan ],
[ 4800, 123.08, 1.50, 93.44, 1.97, 46.15, 3.99, 8.75, 21.08, 7.21e-15, 6.83e-15, 7.02e-15, nan ],
[ 4900, 122.94, 1.56, 72.59, 2.65, 47.08, 4.08, 8.72, 22.04, 8.24e-15, 7.80e-15, 7.80e-15, nan ],
[ 5000, 121.91, 1.64, 74.09, 2.70, 48.51, 4.12, 8.79, 22.75, 7.47e-15, 7.83e-15, 7.48e-15, nan ],
[ 5100, 123.90, 1.68, 75.27, 2.77, 49.69, 4.19, 8.75, 23.80, 8.39e-15, 8.64e-15, 8.41e-15, nan ],
[ 5200, 123.23, 1.76, 75.03, 2.88, 44.16, 4.90, 8.76, 24.71, 8.76e-15, 8.93e-15, 8.58e-15, nan ],
[ 5300, 123.78, 1.82, 74.62, 3.01, 44.22, 5.08, 8.77, 25.64, 8.93e-15, 8.59e-15, 8.95e-15, nan ],
[ 5400, 122.48, 1.91, 74.60, 3.13, 44.18, 5.28, 8.73, 26.73, 7.28e-15, 7.42e-15, 7.64e-15, nan ],
[ 5500, 123.07, 1.97, 76.99, 3.14, 45.11, 5.37, 8.70, 27.84, 7.53e-15, 6.95e-15, 7.25e-15, nan ],
[ 5600, 125.41, 2.00, 98.53, 2.55, 45.94, 5.46, 8.69, 28.87, 8.12e-15, 8.45e-15, 8.29e-15, nan ],
[ 5700, 124.51, 2.09, 76.33, 3.41, 46.50, 5.59, 8.76, 29.69, 8.20e-15, 8.11e-15, 8.22e-15, nan ],
[ 5800, 124.91, 2.16, 75.87, 3.55, 47.53, 5.66, 8.67, 31.03, 9.28e-15, 8.33e-15, 9.11e-15, nan ],
[ 5900, 124.85, 2.23, 76.86, 3.62, 47.89, 5.82, 8.78, 31.73, 7.88e-15, 7.56e-15, 7.35e-15, nan ],
[ 6000, 127.01, 2.27, 76.88, 3.75, 49.86, 5.78, 8.36, 34.48, 7.60e-15, 7.88e-15, 7.89e-15, nan ],
[ 6100, 126.06, 2.36, 77.08, 3.86, 49.58, 6.01, 8.55, 34.81, 8.49e-15, 9.36e-15, 8.75e-15, nan ],
[ 6200, 128.07, 2.40, 77.78, 3.95, 44.62, 6.89, 8.44, 36.44, 7.79e-15, 7.88e-15, 7.82e-15, nan ],
[ 6300, 127.85, 2.48, 76.00, 4.18, 45.47, 6.98, 8.81, 36.04, 8.40e-15, 8.02e-15, 8.86e-15, nan ],
[ 6400, 130.99, 2.50, 100.48, 3.26, 45.48, 7.21, 9.22, 35.54, 8.53e-15, 8.69e-15, 9.12e-15, nan ],
[ 6500, 129.29, 2.61, 78.02, 4.33, 45.39, 7.45, 8.67, 38.98, 7.96e-15, 7.22e-15, 8.21e-15, nan ],
[ 6600, 128.20, 2.72, 78.97, 4.41, 46.19, 7.55, 8.37, 41.64, 8.75e-15, 9.98e-15, 9.18e-15, nan ],
[ 6700, 129.35, 2.78, 79.03, 4.54, 46.43, 7.74, 8.74, 41.11, 7.78e-15, 7.94e-15, 7.79e-15, nan ],
[ 6800, 129.88, 2.85, 77.61, 4.77, 46.58, 7.94, 8.71, 42.46, 9.95e-15, 1.02e-14, 9.64e-15, nan ],
[ 6900, 132.60, 2.87, 77.75, 4.90, 47.45, 8.03, 8.73, 43.62, 8.70e-15, 8.51e-15, 8.58e-15, nan ],
[ 7000, 130.96, 2.99, 78.26, 5.01, 48.39, 8.10, 8.29, 47.29, 9.23e-15, 8.75e-15, 8.67e-15, nan ],
[ 7100, 131.65, 3.06, 78.31, 5.15, 49.28, 8.19, 8.29, 48.67, 7.97e-15, 8.42e-15, 7.83e-15, nan ],
[ 7200, 132.78, 3.12, 102.98, 4.03, 45.35, 9.15, 8.14, 50.94, 7.83e-15, 8.23e-15, 8.72e-15, nan ],
[ 7300, 132.80, 3.21, 79.19, 5.38, 44.94, 9.49, 7.82, 54.51, 9.41e-15, 9.67e-15, 9.07e-15, nan ],
[ 7400, 132.62, 3.30, 79.55, 5.51, 45.54, 9.62, 8.17, 53.61, 7.64e-15, 7.70e-15, 8.69e-15, nan ],
[ 7500, 134.81, 3.34, 79.02, 5.70, 45.93, 9.80, 8.08, 55.71, 8.08e-15, 8.51e-15, 8.18e-15, nan ],
[ 7600, 134.43, 3.44, 79.45, 5.82, 47.22, 9.79, 8.09, 57.16, 8.45e-15, 8.41e-15, 8.30e-15, nan ],
[ 7700, 134.17, 3.54, 78.14, 6.07, 46.73, 10.15, 8.06, 58.88, 8.20e-15, 8.11e-15, 8.77e-15, nan ],
[ 7800, 135.30, 3.60, 78.70, 6.19, 48.05, 10.13, 7.72, 63.08, 8.40e-15, 8.81e-15, 8.77e-15, nan ],
[ 7900, 135.85, 3.68, 79.45, 6.29, 48.20, 10.36, 7.70, 64.87, 9.47e-15, 9.87e-15, 9.62e-15, nan ],
[ 8000, 136.27, 3.76, 107.74, 4.75, 48.32, 10.60, 7.69, 66.61, 9.00e-15, 8.96e-15, 9.01e-15, nan ],
[ 8100, 135.13, 3.89, 78.97, 6.65, 48.21, 10.89, 7.51, 69.86, 8.90e-15, 9.06e-15, 9.18e-15, nan ],
[ 8200, 132.88, 4.05, 78.76, 6.83, 45.51, 11.82, 7.37, 73.03, 9.72e-15, 8.68e-15, 9.05e-15, nan ],
[ 8300, 135.70, 4.06, 80.81, 6.82, 45.79, 12.04, 7.29, 75.65, 9.44e-15, 9.66e-15, 9.65e-15, nan ],
[ 8400, 133.21, 4.24, 80.84, 6.98, 46.11, 12.24, 7.59, 74.38, 8.45e-15, 8.24e-15, 8.49e-15, nan ],
[ 8500, 135.04, 4.28, 80.91, 7.14, 46.16, 12.52, 7.63, 75.79, 8.87e-15, 9.55e-15, 9.26e-15, nan ],
[ 8600, 133.53, 4.43, 81.05, 7.30, 46.93, 12.61, 7.72, 76.69, 1.07e-14, 1.17e-14, 1.10e-14, nan ],
[ 8700, 134.76, 4.49, 81.66, 7.42, 47.86, 12.65, 7.59, 79.79, 9.70e-15, 9.67e-15, 9.05e-15, nan ],
[ 8800, 132.94, 4.66, 108.00, 5.74, 48.36, 12.81, 8.02, 77.27, 9.47e-15, 9.71e-15, 9.18e-15, nan ],
[ 8900, 133.91, 4.73, 80.20, 7.90, 47.01, 13.48, 8.14, 77.84, 1.21e-14, 1.29e-14, 1.17e-14, nan ],
[ 9000, 133.66, 4.85, 81.75, 7.93, 48.24, 13.43, 8.16, 79.46, 9.94e-15, 9.76e-15, 1.00e-14, nan ],
[ 10000, 133.73, 5.98, 81.51, 9.82, 48.79, 16.40, 8.47, 94.42, 1.00e-14, 1.21e-14, 1.12e-14, nan ],
[ 12000, 134.57, 8.56, 110.76, 10.40, 48.00, 24.00, 8.32, 138.49, 1.21e-14, 1.22e-14, 1.28e-14, nan ],
[ 14000, 130.07, 12.06, 82.60, 18.99, 47.31, 33.15, 8.54, 183.66, 1.34e-14, 1.31e-14, 1.34e-14, nan ],
[ 16000, 127.94, 16.01, 112.71, 18.17, 47.62, 43.01, 8.88, 230.69, 1.39e-14, 1.28e-14, 1.23e-14, nan ],
[ 18000, 124.97, 20.74, 84.81, 30.57, 47.48, 54.60, 7.85, 330.42, 1.60e-14, 1.45e-14, 1.56e-14, nan ],
[ 20000, 122.15, 26.20, 116.89, 27.38, 46.89, 68.25, 8.45, 378.62, 1.66e-14, 1.67e-14, 1.80e-14, nan ],
])
# ------------------------------------------------------------
# file: v2.0.0/cuda7.0-k40c/zpotrf.txt
# numactl --interleave=all ../testing/testing_zpotrf -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
zpotrf = array([
[ 10, nan, nan, 0.33, 0.00, nan ],
[ 20, nan, nan, 1.09, 0.00, nan ],
[ 30, nan, nan, 2.58, 0.00, nan ],
[ 40, nan, nan, 2.57, 0.00, nan ],
[ 50, nan, nan, 3.87, 0.00, nan ],
[ 60, nan, nan, 4.75, 0.00, nan ],
[ 70, nan, nan, 5.18, 0.00, nan ],
[ 80, nan, nan, 5.61, 0.00, nan ],
[ 90, nan, nan, 5.89, 0.00, nan ],
[ 100, nan, nan, 6.06, 0.00, nan ],
[ 200, nan, nan, 28.62, 0.00, nan ],
[ 300, nan, nan, 12.87, 0.00, nan ],
[ 400, nan, nan, 27.16, 0.00, nan ],
[ 500, nan, nan, 43.19, 0.00, nan ],
[ 600, nan, nan, 53.23, 0.01, nan ],
[ 700, nan, nan, 74.00, 0.01, nan ],
[ 800, nan, nan, 79.83, 0.01, nan ],
[ 900, nan, nan, 107.22, 0.01, nan ],
[ 1000, nan, nan, 131.66, 0.01, nan ],
[ 2000, nan, nan, 375.51, 0.03, nan ],
[ 3000, nan, nan, 550.82, 0.07, nan ],
[ 4000, nan, nan, 680.12, 0.13, nan ],
[ 5000, nan, nan, 764.24, 0.22, nan ],
[ 6000, nan, nan, 828.03, 0.35, nan ],
[ 7000, nan, nan, 882.55, 0.52, nan ],
[ 8000, nan, nan, 922.54, 0.74, nan ],
[ 9000, nan, nan, 950.90, 1.02, nan ],
[ 10000, nan, nan, 983.89, 1.36, nan ],
[ 12000, nan, nan, 1032.41, 2.23, nan ],
[ 14000, nan, nan, 1061.08, 3.45, nan ],
[ 16000, nan, nan, 1088.31, 5.02, nan ],
[ 18000, nan, nan, 1105.07, 7.04, nan ],
[ 20000, nan, nan, 1122.98, 9.50, nan ],
])
# numactl --interleave=all ../testing/testing_zpotrf_gpu -N 123 -N 1234 --range 10:90:10 --range 100:900:100 --range 1000:9000:1000 --range 10000:20000:2000
zpotrf_gpu = array([
[ 10, nan, nan, 0.00, 0.00, nan ],
[ 20, nan, nan, 0.01, 0.00, nan ],
[ 30, nan, nan, 0.03, 0.00, nan ],
[ 40, nan, nan, 0.07, 0.00, nan ],
[ 50, nan, nan, 0.14, 0.00, nan ],
[ 60, nan, nan, 0.23, 0.00, nan ],
[ 70, nan, nan, 0.36, 0.00, nan ],
[ 80, nan, nan, 0.52, 0.00, nan ],
[ 90, nan, nan, 0.71, 0.00, nan ],
[ 100, nan, nan, 0.93, 0.00, nan ],
[ 200, nan, nan, 5.95, 0.00, nan ],
[ 300, nan, nan, 10.73, 0.00, nan ],
[ 400, nan, nan, 23.17, 0.00, nan ],
[ 500, nan, nan, 39.91, 0.00, nan ],
[ 600, nan, nan, 50.84, 0.01, nan ],
[ 700, nan, nan, 73.22, 0.01, nan ],
[ 800, nan, nan, 82.49, 0.01, nan ],
[ 900, nan, nan, 111.63, 0.01, nan ],
[ 1000, nan, nan, 138.97, 0.01, nan ],
[ 2000, nan, nan, 439.58, 0.02, nan ],
[ 3000, nan, nan, 640.66, 0.06, nan ],
[ 4000, nan, nan, 781.19, 0.11, nan ],
[ 5000, nan, nan, 858.01, 0.19, nan ],
[ 6000, nan, nan, 916.48, 0.31, nan ],
[ 7000, nan, nan, 967.71, 0.47, nan ],
[ 8000, nan, nan, 1005.48, 0.68, nan ],
[ 9000, nan, nan, 1029.80, 0.94, nan ],
[ 10000, nan, nan, 1055.77, 1.26, nan ],
[ 12000, nan, nan, 1098.56, 2.10, nan ],
[ 14000, nan, nan, 1118.59, 3.27, nan ],
[ 16000, nan, nan, 1140.72, 4.79, nan ],
[ 18000, nan, nan, 1152.94, 6.75, nan ],
[ 20000, nan, nan, 1166.65, 9.14, nan ],
])
| 76.363084 | 886 | 0.443182 |
7940d546489c2c1353ca45d6331d3637f08649dd | 448 | pyde | Python | processing/chapter9/sketch_9_1_L60/sketch_9_1_L60.pyde | brickdonut/2019-fall-polytech-cs | b2830795f35e65ff90cf73e0746551c6efdd1f87 | [
"MIT"
] | null | null | null | processing/chapter9/sketch_9_1_L60/sketch_9_1_L60.pyde | brickdonut/2019-fall-polytech-cs | b2830795f35e65ff90cf73e0746551c6efdd1f87 | [
"MIT"
] | null | null | null | processing/chapter9/sketch_9_1_L60/sketch_9_1_L60.pyde | brickdonut/2019-fall-polytech-cs | b2830795f35e65ff90cf73e0746551c6efdd1f87 | [
"MIT"
] | null | null | null | xCoordinate = [1,2,3,4,5,6,7,8,9,10]
coordinate = 0
def setup():
size(500,500)
smooth()
noStroke()
for i in range(0, len(xCoordinate)):
xCoordinate[i] = 35*i + 90
def draw():
background(50)
for coordinate in xCoordinate:
fill(200)
ellipse(coordinate, 250, 30, 30)
fill(0)
ellipse(coordinate, 250, 3,3)
def keyPressed():
if (key == 's'):
saveFrame("myP")
| 21.333333 | 40 | 0.540179 |
7940d5e63e5badcec8569d3275d3d3fefef1df42 | 844 | py | Python | app/translate.py | raz-m12/python_flask-website | 9f4396a0b436c06174a5d0fc0c83aa2fae6ed277 | [
"MIT"
] | null | null | null | app/translate.py | raz-m12/python_flask-website | 9f4396a0b436c06174a5d0fc0c83aa2fae6ed277 | [
"MIT"
] | null | null | null | app/translate.py | raz-m12/python_flask-website | 9f4396a0b436c06174a5d0fc0c83aa2fae6ed277 | [
"MIT"
] | null | null | null | import json
import requests
from flask import current_app
from flask_babel import _
def translate(text, source_language, dest_language):
if 'MS_TRANSLATOR_KEY' not in current_app.config or \
not current_app.config['MS_TRANSLATOR_KEY']:
return _('Error: the translation service is not configured.')
auth = {
'Ocp-Apim-Subscription-Key': current_app.config['MS_TRANSLATOR_KEY'],
'Ocp-Apim-Subscription-Region': 'westeurope'}
r = requests.post(
'https://api.cognitive.microsofttranslator.com'
'/translate?api-version=3.0&from={}&to={}'.format(
source_language, dest_language), headers=auth, json=[
{'Text': text}])
if r.status_code != 200:
return _('Error: the translation service failed.')
return r.json()[0]['translations'][0]['text']
| 38.363636 | 77 | 0.663507 |
7940d69df3fbe72f3f545c1df20a26c1b2095d9a | 2,964 | py | Python | stackformation/deploy/__init__.py | ibejohn818/stackformation | 7ab5b29b584c64cea31add470c4f6df847d19c1c | [
"MIT"
] | null | null | null | stackformation/deploy/__init__.py | ibejohn818/stackformation | 7ab5b29b584c64cea31add470c4f6df847d19c1c | [
"MIT"
] | 1,396 | 2017-12-24T18:25:05.000Z | 2022-03-31T15:05:19.000Z | stackformation/deploy/__init__.py | ibejohn818/stackformation | 7ab5b29b584c64cea31add470c4f6df847d19c1c | [
"MIT"
] | null | null | null | import time
import logging
from stackformation.utils import (match_stack, _match_stack)
from colorama import Fore, Back, Style # noqa
logger = logging.getLogger(__name__)
class Deploy(object):
"""
Base deploy class
"""
def cli_confirm(self, infra, selector=[], **kwargs):
c = 0
defaults = {
'reverse': False,
'ask': 'Deploy Stack(s)?'
}
defaults.update(kwargs)
stacks = infra.list_stacks(reverse=defaults['reverse'])
results = match_stack(selector, stacks)
for stack in results:
c += 1
print("Stack: {}{}/{}{}".format(
Fore.CYAN + Style.BRIGHT,
stack.get_stack_name(),
stack.get_remote_stack_name(),
Style.RESET_ALL
))
if c <= 0:
print("NO STACKS SELCTED!")
return False
ans = input("{} [y/n]: \n".format(defaults['ask']))
if ans.lower().startswith("y"):
return True
return False
def destroy(self, infra, selector=False, **kw):
stacks = infra.list_stacks(reverse=True)
stacks = match_stack(selector, stacks)
for stack in stacks:
try:
start = stack.start_destroy(infra, stack.infra.context)
if not start:
print("{} Skipping destroy..".format(stack.get_stack_name()))
except Exception as e:
print(str(e))
continue
time.sleep(2)
while stack.deploying(infra):
pass
logger.info("DESTROY COMPLETE: {}".format(stack.get_stack_name()))
def __destroy(self, infra, selector=False, **kwargs):
stacks = infra.list_stacks(reverse=True)
for stack in stacks:
if selector and not _match_stack(selector, stack):
continue
start = stack.start_destroy(infra, stack.infra.context)
if not start:
continue
time.sleep(2)
while stack.deploying(infra):
pass
logger.info("DESTROY COMPLETE: {}".format(stack.get_stack_name()))
class SerialDeploy(Deploy):
"""
Sequential deployment
"""
def deploy(self, infra, selector=False):
stacks = infra.get_stacks()
stacks = match_stack(selector, stacks)
for stack in stacks:
dependent_stacks = infra.get_dependent_stacks(stack)
for k, stk in dependent_stacks.items():
stk.load_stack_outputs(stack.infra)
start = stack.start_deploy(infra, stack.infra.context)
if not start:
print("{} Skipping deploy..".format(stack.get_stack_name()))
time.sleep(2)
while stack.deploying(infra):
pass
logger.info("DEPLOY COMPLETE: {}".format(stack.get_stack_name()))
| 26 | 81 | 0.547571 |
7940d7a6a60ec9f488759063d37b32c9c5e51e8d | 5,271 | py | Python | pluraexercise.py | RajParab/pluradl.py | b1ab480526c698f578e9f4bdd9ca9cddd9876f5e | [
"MIT"
] | 10 | 2020-08-14T03:17:53.000Z | 2021-08-14T13:58:11.000Z | pluraexercise.py | RajParab/pluradl.py | b1ab480526c698f578e9f4bdd9ca9cddd9876f5e | [
"MIT"
] | null | null | null | pluraexercise.py | RajParab/pluradl.py | b1ab480526c698f578e9f4bdd9ca9cddd9876f5e | [
"MIT"
] | 6 | 2020-09-15T18:44:45.000Z | 2021-04-15T10:59:00.000Z | from plura_dl.scrapeutils import (
os,
sys,
re,
sleep,
Path,
clear,
TimeoutException,
set_chrome_driver,
wait_for_access,
enter_hibernation
)
from selenium.webdriver.chrome.options import Options
from pluradl import get_courses, get_usr_pw, flag_parser, arg_parser, set_directory
LOGIN_URL=r'https://app.pluralsight.com/id?'
COURSE_BASE=r'https://app.pluralsight.com/library/courses'
DLPATH, USERNAME, PASSWORD = "", "", ""
USERNAME_INPUT=r'//*[@id="Username"]'
PASSWORD_INPUT=r'//*[@id="Password"]'
LOGIN_SUBMIT=r'//*[@id="login"]'
DOWNLOAD_EXERCISE_FILE=r'//*[@id="ps-main"]/div/div[2]/section/div[3]/div/div/button'
ALT_DOWNLOAD_EXERCISE_FILE=r'/html/body/div[1]/div[3]/div/div[2]/section/div[4]/div/div/button'
def login_routine(driver, LOGIN_URL):
"""Handles WebDriver login into Pluralsight
Arguments:
driver {WebDriver} -- WebDriver object to use
LOGIN_URL {str} -- Login url
"""
driver.get(LOGIN_URL)
wait_for_access(driver, PASSWORD_INPUT)
driver.find_element_by_xpath(USERNAME_INPUT).send_keys(USERNAME)
driver.find_element_by_xpath(PASSWORD_INPUT).send_keys(PASSWORD)
driver.find_element_by_xpath(LOGIN_SUBMIT).click()
def download_routine(driver, course, sleep_time=5):
"""Handling the download of exercise files from Pluralsight
Arguments:
driver {WebDriver} -- WebDriver object to use
excercise_url {str} -- Exercise files page url
"""
sleep(sleep_time)
excercise_url = COURSE_BASE + '/' + course + '/' + 'exercise-files'
no_materals_lookup = r'this course has no materials'
upgrade_lookup = r'Upgrade today'
driver.get(excercise_url)
materials_check=True
try:
wait_for_access(driver, DOWNLOAD_EXERCISE_FILE, timer=sleep_time).click()
except TimeoutException:
try:
course_text = driver.find_element_by_class_name('l-course-page__content').text
except:
course_text = ""
if re.search(no_materals_lookup, course_text):
materials_check=False
print(course, 'did not have any course materials. Tagging it ...')
with open(os.path.join(DLPATH,'tagged_courses.txt'), 'at') as f:
f.write(course + '\n')
elif re.search(upgrade_lookup, course_text):
materials_check=False
print(course, 'are not a part of your subscription. Tagging it ...')
with open(os.path.join(DLPATH,'tagged_courses.txt'), 'at') as f:
f.write(course + '\n')
if materials_check:
try:
wait_for_access(driver, ALT_DOWNLOAD_EXERCISE_FILE, timer=sleep_time).click()
except TimeoutException:
print(course, 'did not succeeded. The course might not be in your subscription or it`s not available anymore. Tagging it ...')
with open(os.path.join(DLPATH,'failed_courses.txt'), 'at') as f:
f.write(course + '\n')
def already_tagged_courses():
"""Courses get tagged if they are already downloaded, if they do
not contain any materials at all or if the do not contain authorized
materials for used subscription. Getting information from tagged_courses.txt.
Returns:
[str] -- List of tagged course_ids
"""
zip_reg = re.compile(r'.+\.zip$')
name_reg = re.compile(r'.*(?=.zip)')
tagged_courses = os.path.join(DLPATH, 'tagged_courses.txt')
course_tags = []
if os.path.exists(tagged_courses):
with open(tagged_courses, 'rt') as f:
for line in f.readlines():
course_tags.append(line.strip())
for element in Path(DLPATH).rglob('*.zip'):
filename = element.name
if zip_reg.match(filename):
course_tags.append(name_reg.search(filename).group())
return course_tags
def main():
"""Main execution
Using Selenium WebDriver along with courselist.txt and Pluralsight
credentials to automate the downloading process of exercise files.
"""
global DLPATH, USERNAME, PASSWORD
scriptpath = os.path.dirname(os.path.abspath(sys.argv[0]))
DLPATH = os.path.join(scriptpath,"exercise_files")
flag_state = flag_parser()
arg_state = arg_parser()
if flag_state[0]:
print("Executing by flag input ..")
USERNAME, PASSWORD = flag_state[1], flag_state[2]
elif arg_state[0]:
print("Executing by user input ..")
USERNAME, PASSWORD = arg_state[1], arg_state[2]
else:
USERNAME, PASSWORD = get_usr_pw()
print("Setting username to:", USERNAME)
courses = get_courses(os.path.dirname(os.path.abspath(sys.argv[0])))
if os.path.exists(DLPATH):
course_tags = already_tagged_courses()
else:
course_tags = []
driver = set_chrome_driver(DLPATH)
set_directory(DLPATH)
login_routine(driver, LOGIN_URL)
for course in courses:
if course[0] not in course_tags:
download_routine(driver, course[0], sleep_time=5)
else:
print(course[0], "is tagged, skipping it.")
print("\nEnd of list reached. Downloads might still be in progress.")
enter_hibernation()
driver.close()
if __name__ == "__main__":
main()
| 35.857143 | 142 | 0.658509 |
7940d7db84ed683eb3d8d25fdbc6a92e05e270cc | 1,479 | py | Python | test/test_inference/test_annotations.py | haoqixu/jedi | ea93dbc08eac0a1b8c39e15c554c0b0c4ce65591 | [
"MIT"
] | 10 | 2020-07-21T21:59:54.000Z | 2021-07-19T11:01:47.000Z | test/test_inference/test_annotations.py | haoqixu/jedi | ea93dbc08eac0a1b8c39e15c554c0b0c4ce65591 | [
"MIT"
] | null | null | null | test/test_inference/test_annotations.py | haoqixu/jedi | ea93dbc08eac0a1b8c39e15c554c0b0c4ce65591 | [
"MIT"
] | 1 | 2021-01-30T18:17:01.000Z | 2021-01-30T18:17:01.000Z | from textwrap import dedent
import pytest
def test_simple_annotations(Script, environment):
"""
Annotations only exist in Python 3.
If annotations adhere to PEP-0484, we use them (they override inference),
else they are parsed but ignored
"""
if environment.version_info.major == 2:
pytest.skip()
source = dedent("""\
def annot(a:3):
return a
annot('')""")
assert [d.name for d in Script(source).infer()] == ['str']
source = dedent("""\
def annot_ret(a:3) -> 3:
return a
annot_ret('')""")
assert [d.name for d in Script(source).infer()] == ['str']
source = dedent("""\
def annot(a:int):
return a
annot('')""")
assert [d.name for d in Script(source).infer()] == ['int']
@pytest.mark.parametrize('reference', [
'assert 1',
'1',
'def x(): pass',
'1, 2',
r'1\n'
])
def test_illegal_forward_references(Script, environment, reference):
if environment.version_info.major == 2:
pytest.skip()
source = 'def foo(bar: "%s"): bar' % reference
assert not Script(source).infer()
def test_lambda_forward_references(Script, environment):
if environment.version_info.major == 2:
pytest.skip()
source = 'def foo(bar: "lambda: 3"): bar'
# For now just receiving the 3 is ok. I'm doubting that this is what we
# want. We also execute functions. Should we only execute classes?
assert Script(source).infer()
| 22.753846 | 77 | 0.613928 |
7940d867a51a6573716d4e0ffa0d7c2691ed6696 | 1,940 | py | Python | nes/ensemble_selection/config.py | automl/nes | 1c54786c30acd6e19eb9708204bffc86b58ea272 | [
"Apache-2.0"
] | 26 | 2020-06-22T16:07:54.000Z | 2022-03-23T08:12:05.000Z | nes/ensemble_selection/config.py | automl/nes | 1c54786c30acd6e19eb9708204bffc86b58ea272 | [
"Apache-2.0"
] | 2 | 2020-07-13T06:23:18.000Z | 2022-03-31T07:30:18.000Z | nes/ensemble_selection/config.py | automl/nes | 1c54786c30acd6e19eb9708204bffc86b58ea272 | [
"Apache-2.0"
] | 4 | 2020-07-06T01:55:16.000Z | 2021-08-02T00:00:14.000Z | from collections import namedtuple
# ======================================
# Some global configs for the experiments.
BUDGET = 400 # Maximum number of networks evaluated.
PLOT_EVERY = 25 # Frequency at which incumbents are chosen (and plotting is done).
MAX_M = 30 # Largest ensemble size used.
model_seeds = namedtuple(typename="model_seeds", field_names=["arch", "init", "scheme"])
dataset_to_budget = {
"cifar10": 400,
"cifar100": 400,
"fmnist": 400,
"tiny": 200,
"imagenet": 1000
}
# deepens_rs not included here yet since the archs are the best ones from the sample trained for nes_rs. See rs_incumbets.py
SCHEMES = ["nes_rs", "nes_re", "deepens_darts", "deepens_gdas",
"nes_rs_oneshot", "nes_re_50k", "nes_rs_darts",
"deepens_minimum", "nes_rs_50k", "deepens_amoebanet_50k",
"deepens_darts_50k", "deepens_amoebanet", "darts_esa", "amoebanet_esa", "nes_rs_esa",
"deepens_darts_anchor", "darts_rs", "darts_hyper", "joint"]
POOLS = {
scheme: [model_seeds(arch=seed, init=seed, scheme=scheme) for seed in range(BUDGET)]
for scheme in SCHEMES
if "nes" in scheme
}
POOLS.update(
{
scheme: [model_seeds(arch=0, init=seed, scheme=scheme) for seed in range(MAX_M)]
for scheme in SCHEMES
if "deepens" in scheme
}
)
POOLS.update(
{
scheme: [model_seeds(arch=0, init=seed, scheme=scheme) for seed in range(BUDGET)]
for scheme in SCHEMES
if scheme in ["darts_esa", "amoebanet_esa"]
}
)
# tiny seed 3
POOLS.update(
{
scheme: [model_seeds(arch=7, init=seed, scheme=scheme) for seed in range(BUDGET)]
for scheme in SCHEMES
if scheme == "nes_rs_esa"
}
)
POOLS.update(
{
scheme: [model_seeds(arch=seed, init=seed, scheme=scheme) for seed in range(BUDGET)]
for scheme in SCHEMES
if scheme in ["darts_rs", "darts_hyper", "joint"]
}
)
| 27.714286 | 124 | 0.641753 |
7940d8a1b0968e4ad027a6773ef338c778ac82a1 | 8,412 | py | Python | code-gen.py | hpsoar/swagger-objc | 1a1b5844e2543d1c5426fb5f180d25e0f12fbe53 | [
"MIT"
] | null | null | null | code-gen.py | hpsoar/swagger-objc | 1a1b5844e2543d1c5426fb5f180d25e0f12fbe53 | [
"MIT"
] | null | null | null | code-gen.py | hpsoar/swagger-objc | 1a1b5844e2543d1c5426fb5f180d25e0f12fbe53 | [
"MIT"
] | null | null | null | # -*- coding:utf8 -*-
import json
from jinja2 import Template
from jinja2 import FileSystemLoader
from jinja2.environment import Environment
jenv = Environment()
jenv.loader = FileSystemLoader('.')
class Property(object):
def __init__(self, name, info):
self.name = name
self.type = ''
self.item_type = None
if info:
self.parse(name, info)
"""
boolean/int32/int64/float/double/string/date/date-time/array
"""
def parse(self, name, definition):
if not definition:
return None
if not name:
name = definition.get('name', None)
self.name = name
definition.update(definition.get('schema', {}))
t = definition.get('type', None)
if 'format' in definition:
t = definition['format']
if '$ref' in definition:
t = definition['$ref']
self.type = t
self.required = definition.get('required', False)
self.default = definition.get('default', None)
self.item_type = Property('item_type', definition.get('items', None))
self.enum = definition.get('enum', None)
self.example = definition.get('example', None)
self.examples = definition.get('examples', None)
self.comment = ''
self.desc = definition.get('description', '')
self.is_native_type = False
self.is_simple_type = False
class Parameter(Property):
def __init__(self, name, info):
super(Parameter, self).__init__(name, info)
self.position = info.get('in', 'query')
class API:
def __init__(self, name, path, method, parameters, responses, summary, desc, tags):
self.name = name
self.path = path
self.method = method
self.parameters = parameters
self.responses = responses
self.summary = summary
self.desc = desc
self.tags = tags
self.merged_response = None
def render_model_header(module, models):
template = jenv.get_template('objc-model-header.template')
o = open('%s_Model.h' % module, 'w')
print >>o, template.render(models=models)
def render_model_body(module, models):
template = jenv.get_template('objc-model-body.template')
o = open('%s_Model.m' % module, 'w')
print >>o, template.render(models=models)
def to_camel_case(s, lower=True):
import re
ret = re.sub(r'(?!^)_([a-zA-Z])', lambda m: m.group(1).upper(), s) if s else s
if not lower:
ret = ret[0].upper() + ret[1:] if ret else ret
return ret
def convert_property_name(name):
if name == 'id':
return 'Id'
else:
return to_camel_case(name)
def flatten_models(models):
return[m for (name, m) in models.items()]
def process_ref_types(p, models):
if p.type and p.type.startswith('#/definitions'):
t = p.type.replace('#/definitions/', '')
p.type = models[t]['name']
def process_property(p, models):
process_ref_types(p, models)
if p.item_type:
process_ref_types(p.item_type, models)
if p.enum:
p.comment = '%s' % '\n'.join(p.enum)
elif p.examples:
p.comment = p.examples
p.name = convert_property_name(p.name)
def process_model_properties(models):
for (name, m) in models.items():
for p in m.get('properties', []):
process_property(p, models)
return models
def convert_to_objc_type(p):
if not p: return (None, False, False, None)
convert_to_objc_type(p.item_type)
m = {
'int32': ('int', True, True),
'int64': ('long long', True, True),
'float': ('float', True, True),
'double': ('double', True, True),
'boolean': ('BOOL', True, True),
'string': ('NSString', False, True),
'date': ('NSString', False, True),
'date-time': ('NSString', False, True),
'array': ('NSArray', False, False),
'byte': ('NSData', False, False),
'binary': ('NSData', False, False),
}
t, is_native_type, is_simple_type = m.get(p.type, (p.type, False, False))
p.type = t
p.is_native_type = is_native_type
p.is_simple_type = is_simple_type
p.item_type = p.item_type and p.item_type.type
def convert_to_objc_types(models):
for m in models:
for p in m.get('properties', []):
convert_to_objc_type(p)
return models
def parse_model(model_name, definition):
props = []
required_props = definition.get('required', [])
for (name, p) in definition.get('properties', {}).items():
prop = Property(name, p)
if not prop.required:
prop.required = name in required_props
props.append(prop)
return { 'name': model_name,
'properties': props }
def parse_definitions(definitions):
models = {}
for (name, d) in definitions.items():
models[name] = parse_model(name, d)
return models
def process_api_names(apis):
for api in apis:
api.name = to_camel_case(api.name, lower=False)
return apis
def process_api_parameters(apis, models):
for api in apis:
for p in api.parameters:
process_property(p, models)
return apis
def process_api_responses(apis, models):
for api in apis:
for p in api.responses:
process_property(p, models)
for p in api.merged_response:
process_property(p, models)
return apis
def merge_response(apis):
import copy
for api in apis:
codes = []
descriptions = []
data = None
for p in api.responses:
codes.append(p.name)
descriptions.append(p.desc)
if p.name == '200':
data = copy.deepcopy(p)
data.name = 'data'
resp = [
Property('code', {'type': 'integer', 'format': 'int32', 'enum': codes }),
Property('description', {'type': 'string', 'examples': descriptions }),
]
if data:
resp.append(data)
api.merged_response = resp
return apis
def convert_api_to_objc(apis):
for api in apis:
for p in api.parameters:
convert_to_objc_type(p)
for p in api.responses:
convert_to_objc_type(p)
for p in api.merged_response:
convert_to_objc_type(p)
return apis
def parse_api(paths):
apis = []
for (path, ops) in paths.items():
for (method, api_info) in ops.items():
parameters = [Parameter(None, p) for p in api_info.get('parameters', [])]
responses = [Property(code, info) for (code, info) in api_info.get('responses', {}).items()]
api = API(api_info.get('operationId', ''),
path,
method,
parameters,
responses,
api_info.get('summary', ''),
api_info.get('description', ''),
api_info.get('tags', []))
apis.append(api)
return apis
def render_api_header(module, apis):
template = jenv.get_template('objc-api-header.template')
o = open('%s_Api.h' % module, 'w')
print >>o, template.render(module=module, apis=apis)
def render_api_body(module, apis):
template = jenv.get_template('objc-api-body.template')
o = open('%s_Api.m' % module, 'w')
print >>o, template.render(module=module, apis=apis)
def main(path, module):
content = json.load(open(path))
for key in content:
print key
parsed_models = parse_definitions(content['definitions'])
parsed_models = process_model_properties(parsed_models)
apis = parse_api(content.get('paths', {}))
apis = process_api_names(apis)
apis = process_api_parameters(apis, parsed_models)
apis = merge_response(apis)
apis = process_api_responses(apis, parsed_models)
apis = convert_api_to_objc(apis)
parsed_models = flatten_models(parsed_models)
parsed_models = convert_to_objc_types(parsed_models)
render_model_header(module, parsed_models)
render_model_body(module, parsed_models)
render_api_header(module, apis)
render_api_body(module, apis)
if __name__ == '__main__':
import sys
module = 'Default'
if len(sys.argv) > 2:
module = sys.argv[2]
main(sys.argv[1], module)
| 28.323232 | 104 | 0.595459 |
7940d9bec71e8f61e360b7c6afe78c8a45f0aa6d | 850 | py | Python | reference/sketchbook/spew/spew.py | JaDogg/__py_playground | 416f88db10e03f5380bcb5cfcad0bca50ffa657c | [
"MIT"
] | 1 | 2015-10-28T00:00:16.000Z | 2015-10-28T00:00:16.000Z | reference/sketchbook/spew/spew.py | JaDogg/__py_playground | 416f88db10e03f5380bcb5cfcad0bca50ffa657c | [
"MIT"
] | null | null | null | reference/sketchbook/spew/spew.py | JaDogg/__py_playground | 416f88db10e03f5380bcb5cfcad0bca50ffa657c | [
"MIT"
] | null | null | null | import random, re
# N.B. the words can be arbitrary text.
dictionary = {word: expansion.strip()
for line in open('menu.text')
for word, expansion in [line.split(':', 1)]}
def expand_word(word, probability=.99):
"""Given a dictionary word, replace it with its definition or not,
randomly. Further expand the definition (but less probably, to
make it eventually stop growing)."""
if random.random() < probability:
return expand_text(dictionary[word], probability * .8)
else:
return word
def expand_text(text, probability):
"Find substrings like [blah], and expand them."
return re.sub(r'\[([^]]*)\]',
lambda match: expand_word(match.group(1), probability),
text)
if __name__ == '__main__':
import sys
print expand_word(sys.argv[1])
| 32.692308 | 73 | 0.630588 |
7940dab525889c3eca9fbcdd4c2c2d8b69a80096 | 450 | py | Python | src/bdbd/src/bdbd/test/circles2.py | rkent/BDBD | c5d391da84faf5607c443078781f8b4e1c017dd5 | [
"MIT"
] | null | null | null | src/bdbd/src/bdbd/test/circles2.py | rkent/BDBD | c5d391da84faf5607c443078781f8b4e1c017dd5 | [
"MIT"
] | null | null | null | src/bdbd/src/bdbd/test/circles2.py | rkent/BDBD | c5d391da84faf5607c443078781f8b4e1c017dd5 | [
"MIT"
] | null | null | null | # test circles in geometry
from bdbd.libpy.geometry import shortestPath
import math
D_TO_R = math.pi / 180. # degrees to radians
x = 3.0
rho = 0.5
# test cases from RKJ notebook 2020-08-26
phis = [60.5, 18.5, 27.0, -90.0]
yes = [2.0, 1.75, -2.0, -2.0]
for i in range(0, len(phis)):
solution = shortestPath(x, yes[i], phis[i]* D_TO_R, rho)
print('Solution: {}'.format(solution))
print('gamma: {:6.2f}'.format(solution['gamma']/D_TO_R))
| 26.470588 | 60 | 0.648889 |
7940dabcfb03cc9eb46f678365685a6e99bcceec | 6,561 | py | Python | python/paddle/fluid/data_feeder.py | limeng357/Paddle | dbd25805c88c48998eb9dc0f4b2ca1fd46326482 | [
"ECL-2.0",
"Apache-2.0"
] | 1 | 2021-08-29T03:39:53.000Z | 2021-08-29T03:39:53.000Z | python/paddle/fluid/data_feeder.py | limeng357/Paddle | dbd25805c88c48998eb9dc0f4b2ca1fd46326482 | [
"ECL-2.0",
"Apache-2.0"
] | 1 | 2022-03-29T21:57:12.000Z | 2022-03-29T21:57:12.000Z | python/paddle/fluid/data_feeder.py | limeng357/Paddle | dbd25805c88c48998eb9dc0f4b2ca1fd46326482 | [
"ECL-2.0",
"Apache-2.0"
] | 2 | 2020-11-04T08:01:39.000Z | 2020-11-06T08:33:28.000Z | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import core
import numpy
import six.moves as six
import multiprocessing
from framework import Variable, default_main_program
__all__ = ['DataFeeder']
class DataToLoDTensorConverter(object):
def __init__(self, place, lod_level, shape, dtype):
self.place = place
self.lod_level = lod_level
self.shape = shape
if dtype == core.VarDesc.VarType.FP32:
self.dtype = 'float32'
elif dtype == core.VarDesc.VarType.INT64:
self.dtype = 'int64'
elif dtype == core.VarDesc.VarType.FP64:
self.dtype = 'float64'
elif dtype == core.VarDesc.VarType.INT32:
self.dtype = 'int32'
elif dtype == core.VarDesc.VarType.UINT8:
self.dtype = 'uint8'
else:
raise ValueError("dtype must be any of [int32, float32, int64, "
"float64, uint8]")
self.data = []
self.lod = []
for i in six.range(lod_level):
self.lod.append([0])
def feed(self, data):
self._feed_impl_(data, self.lod, self.lod_level)
def _feed_impl_(self, data, lod, lod_level):
if lod_level == 0:
self.data.append(data)
else:
cur_lod_len = len(data)
lod[0].append(lod[0][-1] + cur_lod_len)
for each_data in data:
self._feed_impl_(each_data, lod[1:], lod_level - 1)
def done(self):
arr = numpy.array(self.data, dtype=self.dtype).reshape(self.shape)
t = core.LoDTensor()
t.set(arr, self.place)
if self.lod_level > 0:
t.set_lod(self.lod)
return t
class DataFeeder(object):
def __init__(self, feed_list, place, program=None):
self.feed_dtypes = []
self.feed_names = []
self.feed_shapes = []
self.feed_lod_level = []
if program is None:
program = default_main_program()
for each_var in feed_list:
if isinstance(each_var, basestring):
each_var = program.block(0).var(each_var)
if not isinstance(each_var, Variable):
raise TypeError("Feed list should contain a list of variable")
self.feed_dtypes.append(each_var.dtype)
self.feed_names.append(each_var.name)
shape = each_var.shape
batch_size_dim = -1
for i, s in enumerate(shape):
if s < 0:
batch_size_dim = i
break
if batch_size_dim == -1:
raise ValueError("Variable {0} must has a batch size dimension",
each_var.name)
self.feed_lod_level.append(each_var.lod_level)
self.feed_shapes.append(shape)
self.place = place
def feed(self, iterable):
converter = []
for lod_level, shape, dtype in six.zip(
self.feed_lod_level, self.feed_shapes, self.feed_dtypes):
converter.append(
DataToLoDTensorConverter(
place=self.place,
lod_level=lod_level,
shape=shape,
dtype=dtype))
for each_sample in iterable:
assert len(each_sample) == len(converter), (
"The number of fields in data (%s) does not match " +
"len(feed_list) (%s)") % (len(each_sample), len(converter))
for each_converter, each_slot in six.zip(converter, each_sample):
each_converter.feed(each_slot)
ret_dict = {}
for each_name, each_converter in six.zip(self.feed_names, converter):
ret_dict[each_name] = each_converter.done()
return ret_dict
def feed_parallel(self, iterable, num_places=None):
if isinstance(self.place, core.CUDAPlace):
places = [
core.CUDAPlace(i)
for i in six.xrange(self._get_number_of_places_(num_places))
]
else:
places = [
core.CPUPlace()
for _ in six.xrange(self._get_number_of_places_(num_places))
]
if len(iterable) != len(places):
raise ValueError("feed_parallel takes multiple mini-batches. Each "
"mini-batch will be feed on each device. The "
"number of devices and number of mini-batches "
"must be same.")
place = self.place
for p, batch in six.zip(places, iterable):
self.place = p
yield self.feed(batch)
self.place = place
def _get_number_of_places_(self, num_places):
if num_places is not None:
return int(num_places)
elif isinstance(self.place, core.CUDAPlace):
return core.get_cuda_device_count()
else:
return multiprocessing.cpu_count()
def decorate_reader(self,
reader,
multi_devices,
num_places=None,
drop_last=True):
def __reader_creator__():
if not multi_devices:
for item in reader():
yield self.feed(item)
else:
num = self._get_number_of_places_(num_places)
item = []
for batch in reader():
item.append(batch)
if len(item) == num:
yield list(self.feed_parallel(item, num))
item = []
if not drop_last and len(item) != 0:
raise ValueError(
"The data batch which cannot fit for devices will be "
"dropped is not implementation. Other strategies are "
"not implemented")
return __reader_creator__
| 36.653631 | 80 | 0.56089 |
7940dc01970daa7fcd75f3b8fab9bc1acd05edaa | 2,344 | py | Python | experiments/migrations/0001_initial.py | aapris/DjangoHttpBroker-Experiments | 694065d13ef014e4faf102d353041b1c179a3c3c | [
"MIT"
] | null | null | null | experiments/migrations/0001_initial.py | aapris/DjangoHttpBroker-Experiments | 694065d13ef014e4faf102d353041b1c179a3c3c | [
"MIT"
] | null | null | null | experiments/migrations/0001_initial.py | aapris/DjangoHttpBroker-Experiments | 694065d13ef014e4faf102d353041b1c179a3c3c | [
"MIT"
] | null | null | null | # Generated by Django 2.2b1 on 2019-03-23 10:54
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='RfidRead',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('scanned_at', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='RfidScanner',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('devid', models.CharField(db_index=True, max_length=32, unique=True)),
('name', models.CharField(blank=True, max_length=256)),
('description', models.CharField(blank=True, max_length=10000)),
('lat', models.FloatField(blank=True, null=True)),
('lon', models.FloatField(blank=True, null=True)),
('activity_at', models.DateTimeField(blank=True, null=True)),
('created_at', models.DateTimeField(auto_now_add=True)),
('updated_at', models.DateTimeField(auto_now=True)),
],
),
migrations.CreateModel(
name='RfidTag',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('tagid', models.CharField(db_index=True, max_length=256)),
('name', models.CharField(blank=True, max_length=256)),
('reads', models.ManyToManyField(blank=True, related_name='related_rfidtags', through='experiments.RfidRead', to='experiments.RfidScanner', verbose_name='RFID Tag reads')),
],
),
migrations.AddField(
model_name='rfidread',
name='rfidscanner',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='experiments.RfidScanner'),
),
migrations.AddField(
model_name='rfidread',
name='rfidtag',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='experiments.RfidTag'),
),
]
| 41.857143 | 188 | 0.59215 |
7940dc18b963df9968ac2ae766ce8c32e443c520 | 538 | py | Python | shipments/migrations/0003_alter_shipment_transporter.py | Zadigo/mycommerce | 145031ebb359389e680a820577a4b6b2d382646d | [
"MIT"
] | null | null | null | shipments/migrations/0003_alter_shipment_transporter.py | Zadigo/mycommerce | 145031ebb359389e680a820577a4b6b2d382646d | [
"MIT"
] | null | null | null | shipments/migrations/0003_alter_shipment_transporter.py | Zadigo/mycommerce | 145031ebb359389e680a820577a4b6b2d382646d | [
"MIT"
] | null | null | null | # Generated by Django 4.0.1 on 2022-04-20 21:09
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('shipments', '0002_alter_shipment_transporter'),
]
operations = [
migrations.AlterField(
model_name='shipment',
name='transporter',
field=models.CharField(choices=[('In house', 'In House'), ('DHL', 'Dhl'), ('Post office', 'Post Office'), ('Chronopost', 'Chronopost')], default='In house', max_length=150),
),
]
| 28.315789 | 185 | 0.613383 |
7940dd9e9df3d85315325bf08935934ebc208201 | 5,254 | py | Python | troposphere/registry.py | sabakaio/docker-registry | 720a800e5f7f02ff1ec5d9b1d559a2dd6114f7f1 | [
"MIT"
] | null | null | null | troposphere/registry.py | sabakaio/docker-registry | 720a800e5f7f02ff1ec5d9b1d559a2dd6114f7f1 | [
"MIT"
] | null | null | null | troposphere/registry.py | sabakaio/docker-registry | 720a800e5f7f02ff1ec5d9b1d559a2dd6114f7f1 | [
"MIT"
] | null | null | null | from functools import partial
from troposphere import Output, Parameter, Template
from troposphere import GetAtt, Ref, Join
from troposphere import constants as c, ec2, s3
from helpers import iam
from helpers import meta
from helpers.amilookup.resources import ami_lookup
template = Template()
az = template.add_parameter(Parameter(
'AvailabilityZone',
Type=c.AVAILABILITY_ZONE_NAME,
Description='Availability Zone of the Subnet'
))
ssh_key = template.add_parameter(Parameter(
'SSHKeyName',
Type=c.KEY_PAIR_NAME,
Description='Name of an existing EC2 KeyPair to enable SSH access to the instance'
))
ssh_location = template.add_parameter(Parameter(
'SSHLocation',
Description='The IP address range that can be used to SSH to the EC2 instances',
Type=c.STRING,
MinLength='9',
MaxLength='18',
Default='0.0.0.0/0',
AllowedPattern='(\\d{1,3})\\.(\\d{1,3})\\.(\\d{1,3})\\.(\\d{1,3})/(\\d{1,2})',
ConstraintDescription='must be a valid IP CIDR range of the form x.x.x.x/x.'
))
ami_id = GetAtt(ami_lookup(template), 'Id')
ssh_sg = ec2.SecurityGroup(
'SSHSecurityGroup', template,
SecurityGroupIngress=[
{'IpProtocol': 'tcp', 'FromPort': '22', 'ToPort': '22', 'CidrIp': Ref(ssh_location)}
],
GroupDescription='Enable SSH on port 22 for given location'
)
web_sg = ec2.SecurityGroup(
'WebSecurityGroup', template,
SecurityGroupIngress=[
{'IpProtocol': 'tcp', 'FromPort': '443', 'ToPort': '443', 'CidrIp': '0.0.0.0/0'},
],
GroupDescription='Enable HTTPS ports for all incoming traffic'
)
service_name = 'DockerRegistry'
bucket = template.add_resource(s3.Bucket(
service_name + 'Storage',
AccessControl=s3.Private,
))
registry_profile = iam.make_instance_profile(
service_name + 'InstanceProfile', template,
partial(iam.bucket_full_access, bucket)
)
registry_instance_type = template.add_parameter(Parameter(
service_name + 'InstanceType',
Type=c.STRING,
Default=c.T2_MICRO,
AllowedValues=[c.T2_MICRO, c.T2_SMALL, c.T2_MEDIUM, c.M4_LARGE, c.C4_LARGE]
))
registry_block_device_size = template.add_parameter(Parameter(
service_name + 'BlockDeviseSize',
Type=c.STRING,
Default='30',
Description='{n} root file system size (GB)'.format(n=service_name)
))
registry = ec2.Instance(
service_name + 'Instance', template,
AvailabilityZone=Ref(az),
IamInstanceProfile=Ref(registry_profile),
InstanceType=Ref(registry_instance_type),
ImageId=ami_id,
KeyName=Ref(ssh_key),
SecurityGroupIds=[Ref(ssh_sg), Ref(web_sg)],
BlockDeviceMappings=[ec2.BlockDeviceMapping(
DeviceName='/dev/xvda',
Ebs=ec2.EBSBlockDevice(
VolumeSize=Ref(registry_block_device_size),
VolumeType='gp2'
)
)],
Tags=[ec2.Tag('Name', 'docker-registry')],
)
registry_domain = template.add_parameter(Parameter(
'DockerRegistryCertDomainName',
Type=c.STRING,
Description='Domain to issue certificate for'
))
registry_domain_email = template.add_parameter(Parameter(
'DockerRegistryCertEmail',
Type=c.STRING,
Description='Email to use on certificate issue'
))
registry_certs = '/opt/registry/security/'
registry_htpasswd = '/opt/registry/htpasswd'
registry_compose = Join('', [
'version: "2"\n',
'services:\n',
' registry:\n',
' restart: always\n',
' image: registry:2\n',
' container_name: registry\n',
' ports:\n',
' - 443:5000\n',
' environment:\n',
' REGISTRY_STORAGE: s3\n',
' REGISTRY_STORAGE_S3_REGION: ', Ref('AWS::Region'), '\n',
' REGISTRY_STORAGE_S3_ACCESSKEY: ""\n',
' REGISTRY_STORAGE_S3_SECRETKEY: ""\n',
' REGISTRY_STORAGE_S3_BUCKET: ', Ref(bucket), '\n',
' REGISTRY_HTTP_TLS_CERTIFICATE: /certs/fullchain.pem\n',
' REGISTRY_HTTP_TLS_KEY: /certs/privkey.pem\n',
' REGISTRY_AUTH: htpasswd\n',
' REGISTRY_AUTH_HTPASSWD_REALM: "Registry Realm"\n',
' REGISTRY_AUTH_HTPASSWD_PATH: /auth/htpasswd\n',
' volumes:\n',
' - {d}:/certs:ro\n'.format(d=registry_certs),
' - {f}:/auth/htpasswd\n'.format(f=registry_htpasswd),
])
compose_init, compose_file = meta.docker_compose('registry', registry_compose)
compose = '{b} -f {f}'.format(
b='/usr/local/bin/docker-compose',
f=compose_file)
certbot_init = meta.certbot(
Ref(registry_domain),
Ref(registry_domain_email),
copy_to=registry_certs,
pre_hook=('%s stop' % compose),
post_hook=('%s up -d' % compose),
)
meta.add_init(
registry,
meta.docker,
certbot_init,
meta.htpasswd(registry_htpasswd),
compose_init,
)
eip = template.add_parameter(Parameter(
'DockerRegistryEIP',
Type=c.STRING,
Description=(
'Allocation ID for the VPC Elastic IP address you want to associate '
'with Docker Registry instance. You should already have domain name '
'configured for this IP'
)
))
ec2.EIPAssociation(
service_name + 'EIPAccociation', template,
AllocationId=Ref(eip),
InstanceId=Ref(registry),
)
template.add_output(Output(
registry.title + 'Ip',
Value=GetAtt(registry, 'PublicIp')
))
print(template.to_json())
| 30.022857 | 92 | 0.676056 |
7940dde24bf0016b6fd74e9d920e1934d22e0b5e | 991 | py | Python | src/pub_test.py | mijazm/ros_test | f0a70bcf131fd36d2ddbde268fcc85beca9774d0 | [
"MIT"
] | null | null | null | src/pub_test.py | mijazm/ros_test | f0a70bcf131fd36d2ddbde268fcc85beca9774d0 | [
"MIT"
] | null | null | null | src/pub_test.py | mijazm/ros_test | f0a70bcf131fd36d2ddbde268fcc85beca9774d0 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
# This code creates a publisher node which publishes a random number between 0 and 1 to the
# topic /random.
# Author: Mijaz Mukundan
# This code was written to test ROS installation
import rospy
from std_msgs.msg import Float32
import random
def talker():
# A node must have a unique name, if a new node starts with the same name the previous one
# goes poof, the anonymous flag will cause ros to choose a unique name
rospy.init_node('talker', anonymous=True)
# Defining which topic would it publish to
pub = rospy.Publisher(name = '/random', data_class = Float32, queue_size=1)
# An update rate of 10Hz
r = rospy.Rate(1)
while not rospy.is_shutdown():
rand_num = Float32()
rand_num.data = random.random() #Create a random number
pub.publish(rand_num) # Publish the random number
r.sleep()
if __name__=='__main__':
try:
talker()
except rospy.ROSInterruptException:
pass
| 28.314286 | 95 | 0.690212 |
7940df89dccd943056617c0592a923d7561affe4 | 3,436 | py | Python | scripts/generate-supplier-user-csv.py | alphagov-mirror/digitalmarketplace-scripts | 8a7ef9b2b5f5fffea6e012bd676b095a27d35101 | [
"MIT"
] | 1 | 2020-06-23T01:55:31.000Z | 2020-06-23T01:55:31.000Z | scripts/generate-supplier-user-csv.py | alphagov-mirror/digitalmarketplace-scripts | 8a7ef9b2b5f5fffea6e012bd676b095a27d35101 | [
"MIT"
] | 267 | 2015-10-12T12:43:52.000Z | 2021-08-19T10:38:55.000Z | scripts/generate-supplier-user-csv.py | alphagov-mirror/digitalmarketplace-scripts | 8a7ef9b2b5f5fffea6e012bd676b095a27d35101 | [
"MIT"
] | 7 | 2015-11-11T16:47:41.000Z | 2021-04-10T18:03:04.000Z | #!/usr/bin/env python3
"""
PREREQUISITE: You'll need AWS credentials set up for the environment that you're uploading to:
Save your aws_access_key_id and aws_secret_access_key in ~/.aws/credentials
If you have more than one set of credentials in there then be sure to set your AWS_PROFILE environment
variable to reference the right credentials before running the script.
Alternatively, if this script is being run from Jenkins, do not provide any credentials and boto will use
the Jenkins IAM role. It should have the required permissions for the bucket.
This will:
* call the export_<suppliers|users>_for_framework endpoint from the API
* create a CSV file from the results and save to saved to `<output-dir>/<filename>.csv` (see get-model-data.py)
* upload the file to the S3 admin reports bucket for <stage> with the file path:
<framework_slug>/official-details-for-suppliers-<framework_slug>.csv OR
<framework_slug>/user-research-suppliers-on-<framework_slug>.csv OR
<framework_slug>/all-email-accounts-for-suppliers-<framework_slug>.csv
e.g.
g-cloud-10/user-research-suppliers-on-g-cloud-10.csv
Usage: scripts/generate-supplier-user-csv.py <stage> <report_type> <framework_slug> [options]
Options:
--dry-run Don't actually do anything
--user-research-opted-in Only include users who have opted in to user research
--output-dir=<output_dir> Location to store CSV file [default: data]
"""
import os
import sys
sys.path.insert(0, '.')
from dmscripts.helpers.auth_helpers import get_auth_token
from dmscripts.generate_supplier_user_csv import generate_csv_and_upload_to_s3
from dmscripts.helpers import logging_helpers
from dmscripts.helpers.logging_helpers import logging
from dmscripts.helpers.s3_helpers import get_bucket_name
from dmutils.env_helpers import get_api_endpoint_from_stage
from docopt import docopt
from dmapiclient import DataAPIClient
from dmutils.s3 import S3
logger = logging_helpers.configure_logger({
'dmapiclient.base': logging.WARNING,
})
if __name__ == '__main__':
arguments = docopt(__doc__)
stage = arguments['<stage>']
data_api_client = DataAPIClient(get_api_endpoint_from_stage(stage), get_auth_token('api', stage))
report_type = arguments['<report_type>']
framework_slug = arguments['<framework_slug>']
output_dir = arguments['--output-dir']
user_research_opted_in = arguments['--user-research-opted-in'] or None
dry_run = arguments['--dry-run']
if report_type not in ['users', 'suppliers']:
logger.error('Please specify users or suppliers to be exported.')
sys.exit(1)
if not os.path.exists(output_dir):
logger.info("Creating {} directory".format(output_dir))
os.makedirs(output_dir)
if dry_run:
bucket = None
else:
if stage == 'local':
bucket = S3('digitalmarketplace-dev-uploads')
else:
# e.g. preview would give 'digitalmarketplace-reports-preview-preview'
bucket = S3(get_bucket_name(stage, "reports"))
ok = generate_csv_and_upload_to_s3(
bucket,
framework_slug,
report_type,
output_dir,
data_api_client,
dry_run=dry_run,
user_research_opted_in=user_research_opted_in,
logger=logger,
)
if not ok:
sys.exit(1)
| 36.946237 | 119 | 0.708673 |
7940e02278ae9d6779f7972dc5fcf78c5820c7ed | 56 | py | Python | dwpicker/ingest/animschool/__init__.py | markusng/dwpicker | cb63176ac82b0e1c2a1e0695b5554f784387daa5 | [
"MIT"
] | null | null | null | dwpicker/ingest/animschool/__init__.py | markusng/dwpicker | cb63176ac82b0e1c2a1e0695b5554f784387daa5 | [
"MIT"
] | null | null | null | dwpicker/ingest/animschool/__init__.py | markusng/dwpicker | cb63176ac82b0e1c2a1e0695b5554f784387daa5 | [
"MIT"
] | null | null | null | from dwpicker.ingest.animschool.converter import convert | 56 | 56 | 0.892857 |
7940e0706ff7f2bc8922f102fa1e8cc413da4c42 | 5,668 | py | Python | error_analysis.py | vsawal/CS224N-Final-Project | be43a039cefc85568427db2b0cee5ba61fb05157 | [
"MIT"
] | 3 | 2020-09-13T06:56:21.000Z | 2020-11-22T13:32:18.000Z | error_analysis.py | vsawal/CS224N-Final-Project | be43a039cefc85568427db2b0cee5ba61fb05157 | [
"MIT"
] | 1 | 2021-05-05T11:16:37.000Z | 2021-05-05T11:16:37.000Z | error_analysis.py | vsawal/CS224N-Final-Project | be43a039cefc85568427db2b0cee5ba61fb05157 | [
"MIT"
] | null | null | null | import json
import timeit
import uuid
def get_english_arabic_translation():
with open("train-v2.0-questions-arabic.txt", "r") as arfile:
arabic = arfile.readlines()
with open("train-v2.0-questions-copy.txt", "r") as infile:
for i, line in enumerate(infile):
line = line.lower()
if 'how' in line or 'which' in line or 'who' in line or \
'what' in line or 'where' in line or \
'why' in line or 'when' in line:
continue
print("##################")
print("English:", line)
print("Arabic:", arabic[i])
def question_type():
who, when, what, where, why, which, how, other = \
[0,0],[0,0],[0,0],[0,0],[0,0],[0,0],[0,0],[0,0]
with open("final_dict.json", "r") as jsonfile:
a = json.load(jsonfile)
for k, v in a.items():
q = v['question'].lower()
if "who" in q:
who[0] += 1
who[1] += v['correct_ans']
elif "when" in q:
when[0] += 1
when[1] += v['correct_ans']
elif "what" in q:
what[0] += 1
what[1] += v['correct_ans']
elif "where" in q:
where[0] += 1
where[1] += v['correct_ans']
elif "why" in q:
why[0] += 1
why[1] += v['correct_ans']
elif "which" in q:
which[0] += 1
which[1] += v['correct_ans']
elif "how" in q:
how[0] += 1
how[1] += v['correct_ans']
else:
other[0] += 1
other[1] += v['correct_ans']
#print(q)
who.append(who[1]/who[0] * 100)
when.append(when[1]/when[0] * 100)
what.append(what[1]/what[0] * 100)
where.append(where[1]/where[0] * 100)
why.append(why[1]/why[0] * 100)
which.append(which[1]/which[0] * 100)
how.append(how[1]/how[0] * 100)
other.append(other[1]/other[0] * 100)
print("who:", who)
print("when:", when)
print("what:", what)
print("where:", where)
print("why:", why)
print("which:", which)
print("how:", how)
print("other:", other)
def create_dataset_based_on_que_type(outfilename=None, search_criteria=None):
ans = {"version": "v2.0", "data": []}
with open("data/dev-v2.0.json", "r") as jsonFile:
a = json.load(jsonFile)
for i in a["data"]:
for j in i["paragraphs"]:
qas_list = j["qas"]
new_qas_list = []
for item in qas_list:
if 'what' in item['question'].lower():
new_qas_list.append(item)
#print(item['question'].lower())
print("##########################################")
print("Original qas list:", len(qas_list))
j["qas"] = new_qas_list[:]
print(" New qas list:", len(new_qas_list))
print("Modified qas list:", len(j["qas"]))
#for item in qas_list:
#print(item['question'].lower())
with open("data/dev-what.json", "w") as jsonFile:
json.dump(a, jsonFile)
def calculate_change():
a1 = [77.38, 77.36, 74.61, 74.93, 71.07, 69.45, 55.43, 52.54]
b1 = [76.27, 75.40, 74.12, 73.10, 69.75, 66.18, 57.60, 64.40]
a = [80.34, 79.10, 77.59, 76.99, 75.37, 72.10, 68.40, 62.38]
b = [79.26, 77.32, 77.15, 75.18, 73.46, 69.76, 69.87, 72.58]
for i in range(len(a)):
print("diff:", (b[i]-a[i])/a[i])
def get_raw_scores(dataset, preds):
count = 0
min_avg, max_avg = 100, 0
exact_scores = {}
f1_scores = {}
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
qid = qa['id']
gold_answers = [a['text'] for a in qa['answers']
if normalize_answer(a['text'])]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
gold_answers = ['']
#print(gold_answers)
#length = sum([len(x.split()) for x in gold_answers])/len(gold_answers)
if 'how' in qa['question'].lower() or \
'which' in qa['question'].lower() or \
'who' in qa['question'].lower() or \
'what' in qa['question'].lower() or \
'where' in qa['question'].lower() or \
'why' in qa['question'].lower() or \
'when' in qa['question'].lower():
continue
if qid not in preds:
print('Missing prediction for %s' % qid)
continue
a_pred = preds[qid]
# Take max over all gold answers
exact_scores[qid] = max(compute_exact(a, a_pred) for a in gold_answers)
f1_scores[qid] = max(compute_f1(a, a_pred) for a in gold_answers)
print("question:", qa['question'])
print("a_pred:", a_pred)
print("gold:", gold_answers)
print("####################")
return exact_scores, f1_scores
if __name__ == '__main__':
print("Start test ...")
start_time = timeit.default_timer()
get_english_arabic_translation()
question_type()
create_dataset_based_on_que_type()
calculate_change()
with open("data/dev-v2.0.json", 'r') as fd:
dataset_json = json.load(fd)
dataset = dataset_json['data']
with open("output/predictions.json", 'r') as fp:
preds = json.load(fp)
get_raw_scores(dataset, preds)
print("End test.")
print("Total time: ", timeit.default_timer() - start_time)
| 33.341176 | 79 | 0.501764 |
7940e1430766127b75041bd5739e7b0515073f7c | 57 | py | Python | mods/persistence.py | hirusha-adi/UnSillyRAT | 819cc02d3f357a206f6253637ba92f55727ad30d | [
"MIT"
] | null | null | null | mods/persistence.py | hirusha-adi/UnSillyRAT | 819cc02d3f357a206f6253637ba92f55727ad30d | [
"MIT"
] | null | null | null | mods/persistence.py | hirusha-adi/UnSillyRAT | 819cc02d3f357a206f6253637ba92f55727ad30d | [
"MIT"
] | null | null | null | class PERSISTENCE:
def __init__(self):
pass
| 11.4 | 23 | 0.614035 |
7940e31677bf69f77d9091c49244c743238649d1 | 1,074 | py | Python | config.py | valentine-ochieng/Moringa-Overflow | 03d44aaaebca316961e4fb6203e429ee5974d782 | [
"MIT"
] | 1 | 2022-01-10T13:07:50.000Z | 2022-01-10T13:07:50.000Z | config.py | valentine-ochieng/Moringa-Overflow | 03d44aaaebca316961e4fb6203e429ee5974d782 | [
"MIT"
] | null | null | null | config.py | valentine-ochieng/Moringa-Overflow | 03d44aaaebca316961e4fb6203e429ee5974d782 | [
"MIT"
] | null | null | null | import os
class Config:
SECRET_KEY = os.environ.get('SECRET_KEY')
SQLALCHEMY_TRACK_MODIFICATIONS = False
UPLOADED_PHOTOS_DEST = 'app/static/photos'
# email configurations
MAIL_SERVER = 'smtp.googlemail.com'
MAIL_PORT = 587
MAIL_USE_TLS = True
MAIL_USERNAME = os.environ.get("MAIL_USERNAME")
MAIL_PASSWORD = os.environ.get("MAIL_PASSWORD")
# simple mde configurations
SIMPLEMDE_JS_IIFE = True
SIMPLEMDE_USE_CDN = True
@staticmethod
def init_app(app):
pass
class TestConfig(Config):
pass
class ProdConfig(Config):
SQLALCHEMY_DATABASE_URI = os.environ.get("DATABASE_URL")
if SQLALCHEMY_DATABASE_URI and SQLALCHEMY_DATABASE_URI.startswith("postgres://"):
SQLALCHEMY_DATABASE_URI = SQLALCHEMY_DATABASE_URI.replace("postgres://", "postgresql://", 1)
pass
class DevConfig(Config):
SQLALCHEMY_DATABASE_URI = 'postgresql+psycopg2://access:access@localhost/moringamain'
DEBUG = True
config_options = {
'development': DevConfig,
'production': ProdConfig,
'test': TestConfig
} | 33.5625 | 100 | 0.721601 |
7940e3f33c19c94be1143ccd5e2488b56d49fed2 | 1,201 | py | Python | corefacility/core/synchronizations/exceptions.py | serik1987/corefacility | 78d84e19403361e83ef562e738473849f9133bef | [
"RSA-MD"
] | null | null | null | corefacility/core/synchronizations/exceptions.py | serik1987/corefacility | 78d84e19403361e83ef562e738473849f9133bef | [
"RSA-MD"
] | null | null | null | corefacility/core/synchronizations/exceptions.py | serik1987/corefacility | 78d84e19403361e83ef562e738473849f9133bef | [
"RSA-MD"
] | null | null | null | from django.utils.translation import gettext_lazy as _
from rest_framework import status
from rest_framework.exceptions import APIException
class SynchronizationError(APIException):
"""
This is the base class for all synchronization routines
"""
pass
class TeapotError(SynchronizationError):
"""
Trying to synchronize responses when all synchronizers are switched off
"""
status_code = status.HTTP_503_SERVICE_UNAVAILABLE
def __init__(self):
super().__init__(_("Unable to perform synchronization because all synchronizers were switched off"))
class RemoteServerError(SynchronizationError):
"""
Trying to synchronize responses when the remote server can't return the successful response
"""
status_code = status.HTTP_503_SERVICE_UNAVAILABLE
def __init__(self):
super().__init__(_("Unable to perform synchronization due to the temporary problems on remote server"))
class UserRemoveConsistencyError(SynchronizationError):
"""
Trying to remove a user that has been currently logged in
"""
def __init__(self):
super().__init__(_("Failed to remove a user: the user has been currently logged in"))
| 28.595238 | 111 | 0.741882 |
7940e45bb9aa9fff252a360df399a79442c536c5 | 451 | py | Python | rectifai/settings.py | Sushil-Thapa/rectif.ai | b308f613402097dca9734806a8c27ba3eef6a358 | [
"Apache-2.0"
] | null | null | null | rectifai/settings.py | Sushil-Thapa/rectif.ai | b308f613402097dca9734806a8c27ba3eef6a358 | [
"Apache-2.0"
] | null | null | null | rectifai/settings.py | Sushil-Thapa/rectif.ai | b308f613402097dca9734806a8c27ba3eef6a358 | [
"Apache-2.0"
] | null | null | null | import os
import logging
from dotenv import load_dotenv
load_dotenv(verbose=True)
logger = logging.getLogger(__name__)
# The Root Directory of the project
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
POSENET_PATH = os.path.join(ROOT_DIR, 'data','raw','posenet.pth')
POSTURENET_PATH = os.path.join(ROOT_DIR, 'data','raw','posturenet.pth')
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
| 28.1875 | 71 | 0.764967 |
7940e482cc03dd9784e0134b9e2ba122ea6b31ef | 15,268 | py | Python | ironicclient/osc/v1/baremetal_volume_target.py | sapcc/python-ironicclient | 8dcbf5b6d0bc2c2dc3881dbc557e2e403e2fe2b4 | [
"Apache-2.0"
] | 41 | 2015-01-29T20:10:48.000Z | 2022-01-26T10:04:28.000Z | ironicclient/osc/v1/baremetal_volume_target.py | sapcc/python-ironicclient | 8dcbf5b6d0bc2c2dc3881dbc557e2e403e2fe2b4 | [
"Apache-2.0"
] | null | null | null | ironicclient/osc/v1/baremetal_volume_target.py | sapcc/python-ironicclient | 8dcbf5b6d0bc2c2dc3881dbc557e2e403e2fe2b4 | [
"Apache-2.0"
] | 46 | 2015-01-19T17:46:52.000Z | 2021-12-19T01:22:47.000Z | # Copyright 2017 FUJITSU LIMITED
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import itertools
import logging
from osc_lib.command import command
from osc_lib import utils as oscutils
from ironicclient.common.i18n import _
from ironicclient.common import utils
from ironicclient import exc
from ironicclient.v1 import resource_fields as res_fields
class CreateBaremetalVolumeTarget(command.ShowOne):
"""Create a new baremetal volume target."""
log = logging.getLogger(__name__ + ".CreateBaremetalVolumeTarget")
def get_parser(self, prog_name):
parser = super(CreateBaremetalVolumeTarget, self).get_parser(prog_name)
parser.add_argument(
'--node',
dest='node_uuid',
metavar='<uuid>',
required=True,
help=_('UUID of the node that this volume target belongs to.'))
parser.add_argument(
'--type',
dest='volume_type',
metavar="<volume type>",
required=True,
help=_("Type of the volume target, e.g. 'iscsi', "
"'fibre_channel'."))
parser.add_argument(
'--property',
dest='properties',
metavar="<key=value>",
action='append',
help=_("Key/value property related to the type of this volume "
"target. Can be specified multiple times."
))
parser.add_argument(
'--boot-index',
dest='boot_index',
metavar="<boot index>",
type=int,
required=True,
help=_("Boot index of the volume target."))
parser.add_argument(
'--volume-id',
dest='volume_id',
metavar="<volume id>",
required=True,
help=_("ID of the volume associated with this target."))
parser.add_argument(
'--uuid',
dest='uuid',
metavar='<uuid>',
help=_("UUID of the volume target."))
parser.add_argument(
'--extra',
dest='extra',
metavar="<key=value>",
action='append',
help=_("Record arbitrary key/value metadata. "
"Can be specified multiple times."))
return parser
def take_action(self, parsed_args):
self.log.debug("take_action(%s)" % parsed_args)
baremetal_client = self.app.client_manager.baremetal
if parsed_args.boot_index < 0:
raise exc.CommandError(
_('Expected non-negative --boot-index, got %s') %
parsed_args.boot_index)
field_list = ['extra', 'volume_type', 'properties',
'boot_index', 'node_uuid', 'volume_id', 'uuid']
fields = dict((k, v) for (k, v) in vars(parsed_args).items()
if k in field_list and v is not None)
fields = utils.args_array_to_dict(fields, 'properties')
fields = utils.args_array_to_dict(fields, 'extra')
volume_target = baremetal_client.volume_target.create(**fields)
data = dict([(f, getattr(volume_target, f, '')) for f in
res_fields.VOLUME_TARGET_DETAILED_RESOURCE.fields])
return self.dict2columns(data)
class ShowBaremetalVolumeTarget(command.ShowOne):
"""Show baremetal volume target details."""
log = logging.getLogger(__name__ + ".ShowBaremetalVolumeTarget")
def get_parser(self, prog_name):
parser = super(ShowBaremetalVolumeTarget, self).get_parser(prog_name)
parser.add_argument(
'volume_target',
metavar='<id>',
help=_("UUID of the volume target."))
parser.add_argument(
'--fields',
nargs='+',
dest='fields',
metavar='<field>',
action='append',
default=[],
choices=res_fields.VOLUME_TARGET_DETAILED_RESOURCE.fields,
help=_("One or more volume target fields. Only these fields will "
"be fetched from the server."))
return parser
def take_action(self, parsed_args):
self.log.debug("take_action(%s)", parsed_args)
baremetal_client = self.app.client_manager.baremetal
fields = list(itertools.chain.from_iterable(parsed_args.fields))
fields = fields if fields else None
volume_target = baremetal_client.volume_target.get(
parsed_args.volume_target, fields=fields)._info
volume_target.pop("links", None)
return zip(*sorted(volume_target.items()))
class ListBaremetalVolumeTarget(command.Lister):
"""List baremetal volume targets."""
log = logging.getLogger(__name__ + ".ListBaremetalVolumeTarget")
def get_parser(self, prog_name):
parser = super(ListBaremetalVolumeTarget, self).get_parser(prog_name)
parser.add_argument(
'--node',
dest='node',
metavar='<node>',
help=_("Only list volume targets of this node (name or UUID)."))
parser.add_argument(
'--limit',
dest='limit',
metavar='<limit>',
type=int,
help=_('Maximum number of volume targets to return per request, '
'0 for no limit. Default is the maximum number used '
'by the Baremetal API Service.'))
parser.add_argument(
'--marker',
dest='marker',
metavar='<volume target>',
help=_('Volume target UUID (for example, of the last '
'volume target in the list from a previous request). '
'Returns the list of volume targets after this UUID.'))
parser.add_argument(
'--sort',
dest='sort',
metavar='<key>[:<direction>]',
help=_('Sort output by specified volume target fields and '
'directions (asc or desc) (default:asc). Multiple fields '
'and directions can be specified, separated by comma.'))
display_group = parser.add_mutually_exclusive_group(required=False)
display_group.add_argument(
'--long',
dest='detail',
action='store_true',
default=False,
help=_("Show detailed information about volume targets."))
display_group.add_argument(
'--fields',
nargs='+',
dest='fields',
metavar='<field>',
action='append',
default=[],
choices=res_fields.VOLUME_TARGET_DETAILED_RESOURCE.fields,
help=_("One or more volume target fields. Only these fields will "
"be fetched from the server. Can not be used when "
"'--long' is specified."))
return parser
def take_action(self, parsed_args):
self.log.debug("take_action(%s)" % parsed_args)
client = self.app.client_manager.baremetal
columns = res_fields.VOLUME_TARGET_RESOURCE.fields
labels = res_fields.VOLUME_TARGET_RESOURCE.labels
params = {}
if parsed_args.limit is not None and parsed_args.limit < 0:
raise exc.CommandError(
_('Expected non-negative --limit, got %s') %
parsed_args.limit)
params['limit'] = parsed_args.limit
params['marker'] = parsed_args.marker
if parsed_args.node is not None:
params['node'] = parsed_args.node
if parsed_args.detail:
params['detail'] = parsed_args.detail
columns = res_fields.VOLUME_TARGET_DETAILED_RESOURCE.fields
labels = res_fields.VOLUME_TARGET_DETAILED_RESOURCE.labels
elif parsed_args.fields:
params['detail'] = False
fields = itertools.chain.from_iterable(parsed_args.fields)
resource = res_fields.Resource(list(fields))
columns = resource.fields
labels = resource.labels
params['fields'] = columns
self.log.debug("params(%s)" % params)
data = client.volume_target.list(**params)
data = oscutils.sort_items(data, parsed_args.sort)
return (labels,
(oscutils.get_item_properties(s, columns, formatters={
'Properties': oscutils.format_dict},) for s in data))
class DeleteBaremetalVolumeTarget(command.Command):
"""Unregister baremetal volume target(s)."""
log = logging.getLogger(__name__ + ".DeleteBaremetalVolumeTarget")
def get_parser(self, prog_name):
parser = (
super(DeleteBaremetalVolumeTarget, self).get_parser(prog_name))
parser.add_argument(
'volume_targets',
metavar='<volume target>',
nargs='+',
help=_("UUID(s) of the volume target(s) to delete."))
return parser
def take_action(self, parsed_args):
self.log.debug("take_action(%s)", parsed_args)
baremetal_client = self.app.client_manager.baremetal
failures = []
for volume_target in parsed_args.volume_targets:
try:
baremetal_client.volume_target.delete(volume_target)
print(_('Deleted volume target %s') % volume_target)
except exc.ClientException as e:
failures.append(_("Failed to delete volume target "
"%(volume_target)s: %(error)s")
% {'volume_target': volume_target,
'error': e})
if failures:
raise exc.ClientException("\n".join(failures))
class SetBaremetalVolumeTarget(command.Command):
"""Set baremetal volume target properties."""
log = logging.getLogger(__name__ + ".SetBaremetalVolumeTarget")
def get_parser(self, prog_name):
parser = (
super(SetBaremetalVolumeTarget, self).get_parser(prog_name))
parser.add_argument(
'volume_target',
metavar='<volume target>',
help=_("UUID of the volume target."))
parser.add_argument(
'--node',
dest='node_uuid',
metavar='<uuid>',
help=_('UUID of the node that this volume target belongs to.'))
parser.add_argument(
'--type',
dest='volume_type',
metavar="<volume type>",
help=_("Type of the volume target, e.g. 'iscsi', "
"'fibre_channel'."))
parser.add_argument(
'--property',
dest='properties',
metavar="<key=value>",
action='append',
help=_("Key/value property related to the type of this volume "
"target. Can be specified multiple times."))
parser.add_argument(
'--boot-index',
dest='boot_index',
metavar="<boot index>",
type=int,
help=_("Boot index of the volume target."))
parser.add_argument(
'--volume-id',
dest='volume_id',
metavar="<volume id>",
help=_("ID of the volume associated with this target."))
parser.add_argument(
'--extra',
dest='extra',
metavar="<key=value>",
action='append',
help=_("Record arbitrary key/value metadata. "
"Can be specified multiple times."))
return parser
def take_action(self, parsed_args):
self.log.debug("take_action(%s)", parsed_args)
baremetal_client = self.app.client_manager.baremetal
if parsed_args.boot_index is not None and parsed_args.boot_index < 0:
raise exc.CommandError(
_('Expected non-negative --boot-index, got %s') %
parsed_args.boot_index)
properties = []
if parsed_args.node_uuid:
properties.extend(utils.args_array_to_patch(
'add', ["node_uuid=%s" % parsed_args.node_uuid]))
if parsed_args.volume_type:
properties.extend(utils.args_array_to_patch(
'add', ["volume_type=%s" % parsed_args.volume_type]))
if parsed_args.boot_index:
properties.extend(utils.args_array_to_patch(
'add', ["boot_index=%s" % parsed_args.boot_index]))
if parsed_args.volume_id:
properties.extend(utils.args_array_to_patch(
'add', ["volume_id=%s" % parsed_args.volume_id]))
if parsed_args.properties:
properties.extend(utils.args_array_to_patch(
'add', ["properties/" + x for x in parsed_args.properties]))
if parsed_args.extra:
properties.extend(utils.args_array_to_patch(
'add', ["extra/" + x for x in parsed_args.extra]))
if properties:
baremetal_client.volume_target.update(
parsed_args.volume_target, properties)
else:
self.log.warning("Please specify what to set.")
class UnsetBaremetalVolumeTarget(command.Command):
"""Unset baremetal volume target properties."""
log = logging.getLogger(__name__ + "UnsetBaremetalVolumeTarget")
def get_parser(self, prog_name):
parser = (
super(UnsetBaremetalVolumeTarget, self).get_parser(prog_name))
parser.add_argument(
'volume_target',
metavar='<volume target>',
help=_("UUID of the volume target."))
parser.add_argument(
'--extra',
dest='extra',
metavar="<key>",
action='append',
help=_('Extra to unset (repeat option to unset multiple extras)'))
parser.add_argument(
"--property",
dest='properties',
metavar="<key>",
action='append',
help='Property to unset on this baremetal volume target '
'(repeat option to unset multiple properties).',
)
return parser
def take_action(self, parsed_args):
self.log.debug("take_action(%s)", parsed_args)
baremetal_client = self.app.client_manager.baremetal
properties = []
if parsed_args.extra:
properties.extend(utils.args_array_to_patch('remove',
['extra/' + x for x in parsed_args.extra]))
if parsed_args.properties:
properties.extend(utils.args_array_to_patch(
'remove', ['properties/' + x for x in parsed_args.properties]))
if properties:
baremetal_client.volume_target.update(
parsed_args.volume_target, properties)
else:
self.log.warning("Please specify what to unset.")
| 36.968523 | 79 | 0.58449 |
7940e511765f5975386fc8fd99dd39413a0d4248 | 14,271 | py | Python | darglint/parse/grammars/google_arguments_section.py | s-weigand/darglint | 6bc5d764db86626a996de1ff50925f976bf1449e | [
"MIT"
] | 405 | 2017-10-19T11:04:21.000Z | 2022-03-23T07:58:40.000Z | darglint/parse/grammars/google_arguments_section.py | s-weigand/darglint | 6bc5d764db86626a996de1ff50925f976bf1449e | [
"MIT"
] | 186 | 2018-03-26T20:33:37.000Z | 2022-03-20T22:47:54.000Z | darglint/parse/grammars/google_arguments_section.py | s-weigand/darglint | 6bc5d764db86626a996de1ff50925f976bf1449e | [
"MIT"
] | 43 | 2018-10-14T23:49:48.000Z | 2022-02-10T12:39:16.000Z | # Generated on 2020-05-31 10:21:20.274591
from darglint.errors import (
ParameterMalformedError,
)
from darglint.parse.grammar import (
BaseGrammar,
P,
)
from darglint.token import (
TokenType,
)
from darglint.parse.identifiers import (
NoqaIdentifier,
)
from darglint.errors import (
EmptyDescriptionError,
EmptyTypeError,
IndentError,
)
from darglint.parse.identifiers import (
ArgumentIdentifier,
ArgumentItemIdentifier,
ArgumentTypeIdentifier,
)
class ArgumentsGrammar(BaseGrammar):
productions = [
P("arguments-section", ([], "arguments", "arguments-section1", 0)),
P("items-argument", ([], "item-argument", "items-argument0", 0), ([ArgumentItemIdentifier], "head-argument", "item-body", 0), ([ArgumentIdentifier, EmptyDescriptionError], "indent", "head-argument1", 0), ([ArgumentIdentifier, EmptyTypeError, EmptyDescriptionError], "indent", "head-argument3", 2), ([ArgumentIdentifier, ArgumentTypeIdentifier, EmptyDescriptionError], "indent", "head-argument7", 0), ([ArgumentIdentifier, ArgumentTypeIdentifier, EmptyDescriptionError], "indent", "head-argument10", 0), ([ArgumentIdentifier, EmptyDescriptionError], "indent", "head-argument14", 0)),
P("item-argument", ([ArgumentItemIdentifier], "head-argument", "item-body", 0), ([ArgumentIdentifier, EmptyDescriptionError], "indent", "head-argument1", 0), ([ArgumentIdentifier, EmptyTypeError, EmptyDescriptionError], "indent", "head-argument3", 2), ([ArgumentIdentifier, ArgumentTypeIdentifier, EmptyDescriptionError], "indent", "head-argument7", 0), ([ArgumentIdentifier, ArgumentTypeIdentifier, EmptyDescriptionError], "indent", "head-argument10", 0), ([ArgumentIdentifier, EmptyDescriptionError], "indent", "head-argument14", 0)),
P("head-argument", ([ArgumentIdentifier], "indent", "head-argument1", 0), ([ArgumentIdentifier, EmptyTypeError], "indent", "head-argument3", 2), ([ArgumentIdentifier, ArgumentTypeIdentifier], "indent", "head-argument7", 0), ([ArgumentIdentifier, ArgumentTypeIdentifier], "indent", "head-argument10", 0), ([ArgumentIdentifier], "indent", "head-argument14", 0)),
P("item-body", ([], "line", "item-body0", 2), ([], "line", "item-body1", 2), ([], "line", "newline", 2), ([], "word", "line", 2), ([], "word", "noqa-maybe", 2), ([NoqaIdentifier], "hash", "noqa", 2), ([NoqaIdentifier], "noqa-head", "noqa-statement1", 2), (TokenType.INDENT, 2), (TokenType.COLON, 2), (TokenType.HASH, 2), (TokenType.LPAREN, 2), (TokenType.RPAREN, 2), (TokenType.WORD, 2), (TokenType.RAISES, 2), (TokenType.ARGUMENTS, 2), (TokenType.ARGUMENT_TYPE, 2), (TokenType.RETURNS, 2), (TokenType.RETURN_TYPE, 2), (TokenType.YIELDS, 2), (TokenType.YIELD_TYPE, 2), (TokenType.VARIABLES, 2), (TokenType.VARIABLE_TYPE, 2), (TokenType.NOQA, 2), (TokenType.OTHER, 2), (TokenType.RECEIVES, 2), (TokenType.WARNS, 2), (TokenType.SEE, 2), (TokenType.ALSO, 2), (TokenType.NOTES, 2), (TokenType.EXAMPLES, 2), (TokenType.REFERENCES, 2), (TokenType.HEADER, 2), ([IndentError], "line", "item-body4", 0), ([IndentError], "line", "item-body6", 0)),
P("paragraph-indented-two", ([], "indented-two", "paragraph-indented-two0", 0), ([], "indented-two", "line", 0)),
P("paragraph", ([], "indents", "paragraph0", 0), ([], "indents", "line", 0), ([], "line", "paragraph2", 0), ([], "word", "line", 0), ([], "word", "noqa-maybe", 0), ([NoqaIdentifier], "hash", "noqa", 0), ([NoqaIdentifier], "noqa-head", "noqa-statement1", 0), (TokenType.INDENT, 0), (TokenType.COLON, 0), (TokenType.HASH, 0), (TokenType.LPAREN, 0), (TokenType.RPAREN, 0), (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0), (TokenType.HEADER, 0), ([], "line", "paragraph1", 0)),
P("line", ([], "word", "line", 0), ([], "word", "noqa-maybe", 0), ([NoqaIdentifier], "hash", "noqa", 0), ([NoqaIdentifier], "noqa-head", "noqa-statement1", 0), (TokenType.INDENT, 0), (TokenType.COLON, 0), (TokenType.HASH, 0), (TokenType.LPAREN, 0), (TokenType.RPAREN, 0), (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0), (TokenType.HEADER, 0)),
P("indented-two", ([], "indent", "indented-two0", 0)),
P("indents", ([], "indent", "indents", 0), (TokenType.INDENT, 0)),
P("newlines", ([], "newline", "newlines", 0), (TokenType.NEWLINE, 0)),
P("word", (TokenType.COLON, 0), (TokenType.HASH, 0), (TokenType.INDENT, 0), (TokenType.LPAREN, 0), (TokenType.RPAREN, 0), (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0), (TokenType.HEADER, 0)),
P("ident", (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0)),
P("arguments", (TokenType.ARGUMENTS, 0)),
P("colon", (TokenType.COLON, 0)),
P("hash", (TokenType.HASH, 0)),
P("indent", (TokenType.INDENT, 0)),
P("lparen", (TokenType.LPAREN, 0)),
P("newline", (TokenType.NEWLINE, 0)),
P("rparen", (TokenType.RPAREN, 0)),
P("noqa", (TokenType.NOQA, 0)),
P("noqa-maybe", ([NoqaIdentifier], "hash", "noqa", 0), ([NoqaIdentifier], "noqa-head", "noqa-statement1", 0)),
P("noqa-head", ([], "hash", "noqa", 0)),
P("words", ([], "word", "words", 0), (TokenType.COLON, 0), (TokenType.HASH, 0), (TokenType.INDENT, 0), (TokenType.LPAREN, 0), (TokenType.RPAREN, 0), (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0), (TokenType.HEADER, 0)),
P("type-section-parens", ([], "lparen", "type-section-parens0", 0)),
P("type-words-colon", ([], "type-word-colon", "type-words-colon", 0), ([], "type-word-colon", "type-words-colon0", 0), ([ParameterMalformedError], "malformed-type-word", "malformed-type-words", 0), (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0), (TokenType.LPAREN, 0), (TokenType.RPAREN, 0), (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0), (TokenType.COLON, 0), (TokenType.INDENT, 0)),
P("type-word-colon", (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0), (TokenType.COLON, 0), (TokenType.INDENT, 0)),
P("malformed-type-words", ([], "malformed-type-word", "malformed-type-words", 0), (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0), (TokenType.LPAREN, 0), (TokenType.RPAREN, 0)),
P("malformed-type-word", (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0), (TokenType.LPAREN, 0), (TokenType.RPAREN, 0)),
P("arguments-section1", ([], "colon", "arguments-section2", 0)),
P("arguments-section2", ([], "newline", "arguments-section3", 0)),
P("arguments-section3", ([], "items-argument", "newlines", 0), ([], "item-argument", "items-argument0", 0), ([ArgumentItemIdentifier], "head-argument", "item-body", 0), ([ArgumentIdentifier, EmptyDescriptionError], "indent", "head-argument1", 0), ([ArgumentIdentifier, EmptyTypeError, EmptyDescriptionError], "indent", "head-argument3", 2), ([ArgumentIdentifier, ArgumentTypeIdentifier, EmptyDescriptionError], "indent", "head-argument7", 0), ([ArgumentIdentifier, ArgumentTypeIdentifier, EmptyDescriptionError], "indent", "head-argument10", 0), ([ArgumentIdentifier, EmptyDescriptionError], "indent", "head-argument14", 0)),
P("items-argument0", ([], "newline", "items-argument", 0)),
P("head-argument1", ([], "ident", "colon", 0)),
P("head-argument3", ([], "ident", "head-argument4", 0)),
P("head-argument4", ([], "lparen", "head-argument5", 0)),
P("head-argument5", ([], "rparen", "colon", 0)),
P("head-argument7", ([], "ident", "head-argument8", 0)),
P("head-argument8", ([], "type-section-parens", "colon", 0)),
P("head-argument10", ([], "ident", "head-argument11", 0)),
P("head-argument11", ([], "type-section-parens", "head-argument12", 0)),
P("head-argument12", ([], "colon", "newline", 0)),
P("head-argument14", ([], "ident", "head-argument15", 0)),
P("head-argument15", ([], "colon", "newline", 0)),
P("item-body0", ([], "newline", "paragraph-indented-two", 0)),
P("item-body1", ([], "newline", "item-body2", 0)),
P("item-body2", ([], "paragraph-indented-two", "newline", 0)),
P("item-body4", ([], "newline", "paragraph", 0)),
P("item-body6", ([], "newline", "item-body7", 0)),
P("item-body7", ([], "paragraph", "newline", 0)),
P("paragraph-indented-two0", ([], "line", "paragraph-indented-two1", 0)),
P("paragraph-indented-two1", ([], "newline", "paragraph-indented-two", 0)),
P("paragraph0", ([], "line", "paragraph1", 0)),
P("paragraph1", ([], "newline", "paragraph", 0)),
P("paragraph2", ([], "newline", "paragraph", 0)),
P("indented-two0", ([], "indent", "indents", 0), (TokenType.INDENT, 0)),
P("noqa-statement1", ([], "colon", "words", 0)),
P("type-section-parens0", ([], "type-words-colon", "rparen", 0), (TokenType.RPAREN, 0)),
P("type-words-colon0", ([], "newline", "type-words-colon1", 0), (TokenType.NEWLINE, 0)),
P("type-words-colon1", ([], "indents", "type-words-colon", 0), ([], "indent", "indents", 0), (TokenType.INDENT, 0), ([], "type-word-colon", "type-words-colon", 0), ([], "type-word-colon", "type-words-colon0", 0), ([ParameterMalformedError], "malformed-type-word", "malformed-type-words", 0), (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0), (TokenType.LPAREN, 0), (TokenType.RPAREN, 0), (TokenType.WORD, 0), (TokenType.RAISES, 0), (TokenType.ARGUMENTS, 0), (TokenType.ARGUMENT_TYPE, 0), (TokenType.RETURNS, 0), (TokenType.RETURN_TYPE, 0), (TokenType.YIELDS, 0), (TokenType.YIELD_TYPE, 0), (TokenType.VARIABLES, 0), (TokenType.VARIABLE_TYPE, 0), (TokenType.NOQA, 0), (TokenType.OTHER, 0), (TokenType.RECEIVES, 0), (TokenType.WARNS, 0), (TokenType.SEE, 0), (TokenType.ALSO, 0), (TokenType.NOTES, 0), (TokenType.EXAMPLES, 0), (TokenType.REFERENCES, 0), (TokenType.COLON, 0)),
]
start = "arguments-section" | 150.221053 | 1,294 | 0.657557 |
7940e5ad23db95af64198b956199c89063741219 | 73,162 | py | Python | tools/run_tests/run_tests.py | vixadd/grpc | 7c9e8b425166276232653725de32ea0422a39b33 | [
"Apache-2.0"
] | 2 | 2021-07-13T09:16:08.000Z | 2021-11-17T11:07:13.000Z | tools/run_tests/run_tests.py | vixadd/grpc | 7c9e8b425166276232653725de32ea0422a39b33 | [
"Apache-2.0"
] | 1 | 2017-09-12T19:02:08.000Z | 2017-09-12T19:02:08.000Z | tools/run_tests/run_tests.py | vixadd/grpc | 7c9e8b425166276232653725de32ea0422a39b33 | [
"Apache-2.0"
] | 1 | 2021-07-05T12:40:00.000Z | 2021-07-05T12:40:00.000Z | #!/usr/bin/env python
# Copyright 2015 gRPC authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Run tests in parallel."""
from __future__ import print_function
import argparse
import ast
import collections
import glob
import itertools
import json
import logging
import multiprocessing
import os
import os.path
import pipes
import platform
import random
import re
import socket
import subprocess
import sys
import tempfile
import traceback
import time
from six.moves import urllib
import uuid
import six
import python_utils.jobset as jobset
import python_utils.report_utils as report_utils
import python_utils.watch_dirs as watch_dirs
import python_utils.start_port_server as start_port_server
try:
from python_utils.upload_test_results import upload_results_to_bq
except (ImportError):
pass # It's ok to not import because this is only necessary to upload results to BQ.
gcp_utils_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), '../gcp/utils'))
sys.path.append(gcp_utils_dir)
_ROOT = os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), '../..'))
os.chdir(_ROOT)
_FORCE_ENVIRON_FOR_WRAPPERS = {
'GRPC_VERBOSITY': 'DEBUG',
}
_POLLING_STRATEGIES = {
'linux': ['epollex', 'epoll1', 'poll'],
'mac': ['poll'],
}
def platform_string():
return jobset.platform_string()
_DEFAULT_TIMEOUT_SECONDS = 5 * 60
_PRE_BUILD_STEP_TIMEOUT_SECONDS = 10 * 60
def run_shell_command(cmd, env=None, cwd=None):
try:
subprocess.check_output(cmd, shell=True, env=env, cwd=cwd)
except subprocess.CalledProcessError as e:
logging.exception(
"Error while running command '%s'. Exit status %d. Output:\n%s",
e.cmd, e.returncode, e.output)
raise
def max_parallel_tests_for_current_platform():
# Too much test parallelization has only been seen to be a problem
# so far on windows.
if jobset.platform_string() == 'windows':
return 64
return 1024
# SimpleConfig: just compile with CONFIG=config, and run the binary to test
class Config(object):
def __init__(self,
config,
environ=None,
timeout_multiplier=1,
tool_prefix=[],
iomgr_platform='native'):
if environ is None:
environ = {}
self.build_config = config
self.environ = environ
self.environ['CONFIG'] = config
self.tool_prefix = tool_prefix
self.timeout_multiplier = timeout_multiplier
self.iomgr_platform = iomgr_platform
def job_spec(self,
cmdline,
timeout_seconds=_DEFAULT_TIMEOUT_SECONDS,
shortname=None,
environ={},
cpu_cost=1.0,
flaky=False):
"""Construct a jobset.JobSpec for a test under this config
Args:
cmdline: a list of strings specifying the command line the test
would like to run
"""
actual_environ = self.environ.copy()
for k, v in environ.items():
actual_environ[k] = v
if not flaky and shortname and shortname in flaky_tests:
flaky = True
if shortname in shortname_to_cpu:
cpu_cost = shortname_to_cpu[shortname]
return jobset.JobSpec(
cmdline=self.tool_prefix + cmdline,
shortname=shortname,
environ=actual_environ,
cpu_cost=cpu_cost,
timeout_seconds=(self.timeout_multiplier *
timeout_seconds if timeout_seconds else None),
flake_retries=4 if flaky or args.allow_flakes else 0,
timeout_retries=1 if flaky or args.allow_flakes else 0)
def get_c_tests(travis, test_lang):
out = []
platforms_str = 'ci_platforms' if travis else 'platforms'
with open('tools/run_tests/generated/tests.json') as f:
js = json.load(f)
return [
tgt for tgt in js
if tgt['language'] == test_lang and platform_string() in
tgt[platforms_str] and not (travis and tgt['flaky'])
]
def _check_compiler(compiler, supported_compilers):
if compiler not in supported_compilers:
raise Exception('Compiler %s not supported (on this platform).' %
compiler)
def _check_arch(arch, supported_archs):
if arch not in supported_archs:
raise Exception('Architecture %s not supported.' % arch)
def _is_use_docker_child():
"""Returns True if running running as a --use_docker child."""
return True if os.getenv('RUN_TESTS_COMMAND') else False
_PythonConfigVars = collections.namedtuple('_ConfigVars', [
'shell',
'builder',
'builder_prefix_arguments',
'venv_relative_python',
'toolchain',
'runner',
'test_name',
'iomgr_platform',
])
def _python_config_generator(name, major, minor, bits, config_vars):
name += '_' + config_vars.iomgr_platform
return PythonConfig(
name, config_vars.shell + config_vars.builder +
config_vars.builder_prefix_arguments +
[_python_pattern_function(major=major, minor=minor, bits=bits)] +
[name] + config_vars.venv_relative_python + config_vars.toolchain,
config_vars.shell + config_vars.runner + [
os.path.join(name, config_vars.venv_relative_python[0]),
config_vars.test_name
])
def _pypy_config_generator(name, major, config_vars):
return PythonConfig(
name, config_vars.shell + config_vars.builder +
config_vars.builder_prefix_arguments +
[_pypy_pattern_function(major=major)] + [name] +
config_vars.venv_relative_python + config_vars.toolchain,
config_vars.shell + config_vars.runner +
[os.path.join(name, config_vars.venv_relative_python[0])])
def _python_pattern_function(major, minor, bits):
# Bit-ness is handled by the test machine's environment
if os.name == "nt":
if bits == "64":
return '/c/Python{major}{minor}/python.exe'.format(major=major,
minor=minor,
bits=bits)
else:
return '/c/Python{major}{minor}_{bits}bits/python.exe'.format(
major=major, minor=minor, bits=bits)
else:
return 'python{major}.{minor}'.format(major=major, minor=minor)
def _pypy_pattern_function(major):
if major == '2':
return 'pypy'
elif major == '3':
return 'pypy3'
else:
raise ValueError("Unknown PyPy major version")
class CLanguage(object):
def __init__(self, make_target, test_lang):
self.make_target = make_target
self.platform = platform_string()
self.test_lang = test_lang
def configure(self, config, args):
self.config = config
self.args = args
self._make_options = []
self._use_cmake = True
if self.platform == 'windows':
_check_compiler(self.args.compiler, [
'default', 'cmake', 'cmake_vs2015', 'cmake_vs2017',
'cmake_vs2019'
])
_check_arch(self.args.arch, ['default', 'x64', 'x86'])
if self.args.compiler == 'cmake_vs2019':
cmake_generator_option = 'Visual Studio 16 2019'
elif self.args.compiler == 'cmake_vs2017':
cmake_generator_option = 'Visual Studio 15 2017'
else:
cmake_generator_option = 'Visual Studio 14 2015'
cmake_arch_option = 'x64' if self.args.arch == 'x64' else 'Win32'
self._cmake_configure_extra_args = [
'-G', cmake_generator_option, '-A', cmake_arch_option
]
else:
if self.platform == 'linux':
# Allow all the known architectures. _check_arch_option has already checked that we're not doing
# something illegal when not running under docker.
_check_arch(self.args.arch, ['default', 'x64', 'x86'])
else:
_check_arch(self.args.arch, ['default'])
self._docker_distro, self._cmake_configure_extra_args = self._compiler_options(
self.args.use_docker, self.args.compiler)
if self.args.arch == 'x86':
# disable boringssl asm optimizations when on x86
# see https://github.com/grpc/grpc/blob/b5b8578b3f8b4a9ce61ed6677e19d546e43c5c68/tools/run_tests/artifacts/artifact_targets.py#L253
self._cmake_configure_extra_args.append('-DOPENSSL_NO_ASM=ON')
if args.iomgr_platform == "uv":
cflags = '-DGRPC_UV -DGRPC_CUSTOM_IOMGR_THREAD_CHECK -DGRPC_CUSTOM_SOCKET '
try:
cflags += subprocess.check_output(
['pkg-config', '--cflags', 'libuv']).strip() + ' '
except (subprocess.CalledProcessError, OSError):
pass
try:
ldflags = subprocess.check_output(
['pkg-config', '--libs', 'libuv']).strip() + ' '
except (subprocess.CalledProcessError, OSError):
ldflags = '-luv '
self._make_options += [
'EXTRA_CPPFLAGS={}'.format(cflags),
'EXTRA_LDLIBS={}'.format(ldflags)
]
def test_specs(self):
out = []
binaries = get_c_tests(self.args.travis, self.test_lang)
for target in binaries:
if self._use_cmake and target.get('boringssl', False):
# cmake doesn't build boringssl tests
continue
auto_timeout_scaling = target.get('auto_timeout_scaling', True)
polling_strategies = (_POLLING_STRATEGIES.get(
self.platform, ['all']) if target.get('uses_polling', True) else
['none'])
if self.args.iomgr_platform == 'uv':
polling_strategies = ['all']
for polling_strategy in polling_strategies:
env = {
'GRPC_DEFAULT_SSL_ROOTS_FILE_PATH':
_ROOT + '/src/core/tsi/test_creds/ca.pem',
'GRPC_POLL_STRATEGY':
polling_strategy,
'GRPC_VERBOSITY':
'DEBUG'
}
resolver = os.environ.get('GRPC_DNS_RESOLVER', None)
if resolver:
env['GRPC_DNS_RESOLVER'] = resolver
shortname_ext = '' if polling_strategy == 'all' else ' GRPC_POLL_STRATEGY=%s' % polling_strategy
if polling_strategy in target.get('excluded_poll_engines', []):
continue
timeout_scaling = 1
if auto_timeout_scaling:
config = self.args.config
if ('asan' in config or config == 'msan' or
config == 'tsan' or config == 'ubsan' or
config == 'helgrind' or config == 'memcheck'):
# Scale overall test timeout if running under various sanitizers.
# scaling value is based on historical data analysis
timeout_scaling *= 3
if self.config.build_config in target['exclude_configs']:
continue
if self.args.iomgr_platform in target.get('exclude_iomgrs', []):
continue
if self.platform == 'windows':
binary = 'cmake/build/%s/%s.exe' % (_MSBUILD_CONFIG[
self.config.build_config], target['name'])
else:
if self._use_cmake:
binary = 'cmake/build/%s' % target['name']
else:
binary = 'bins/%s/%s' % (self.config.build_config,
target['name'])
cpu_cost = target['cpu_cost']
if cpu_cost == 'capacity':
cpu_cost = multiprocessing.cpu_count()
if os.path.isfile(binary):
list_test_command = None
filter_test_command = None
# these are the flag defined by gtest and benchmark framework to list
# and filter test runs. We use them to split each individual test
# into its own JobSpec, and thus into its own process.
if 'benchmark' in target and target['benchmark']:
with open(os.devnull, 'w') as fnull:
tests = subprocess.check_output(
[binary, '--benchmark_list_tests'],
stderr=fnull)
for line in tests.decode().split('\n'):
test = line.strip()
if not test:
continue
cmdline = [binary,
'--benchmark_filter=%s$' % test
] + target['args']
out.append(
self.config.job_spec(
cmdline,
shortname='%s %s' %
(' '.join(cmdline), shortname_ext),
cpu_cost=cpu_cost,
timeout_seconds=target.get(
'timeout_seconds',
_DEFAULT_TIMEOUT_SECONDS) *
timeout_scaling,
environ=env))
elif 'gtest' in target and target['gtest']:
# here we parse the output of --gtest_list_tests to build up a complete
# list of the tests contained in a binary for each test, we then
# add a job to run, filtering for just that test.
with open(os.devnull, 'w') as fnull:
tests = subprocess.check_output(
[binary, '--gtest_list_tests'], stderr=fnull)
base = None
for line in tests.decode().split('\n'):
i = line.find('#')
if i >= 0:
line = line[:i]
if not line:
continue
if line[0] != ' ':
base = line.strip()
else:
assert base is not None
assert line[1] == ' '
test = base + line.strip()
cmdline = [binary,
'--gtest_filter=%s' % test
] + target['args']
out.append(
self.config.job_spec(
cmdline,
shortname='%s %s' %
(' '.join(cmdline), shortname_ext),
cpu_cost=cpu_cost,
timeout_seconds=target.get(
'timeout_seconds',
_DEFAULT_TIMEOUT_SECONDS) *
timeout_scaling,
environ=env))
else:
cmdline = [binary] + target['args']
shortname = target.get(
'shortname',
' '.join(pipes.quote(arg) for arg in cmdline))
shortname += shortname_ext
out.append(
self.config.job_spec(
cmdline,
shortname=shortname,
cpu_cost=cpu_cost,
flaky=target.get('flaky', False),
timeout_seconds=target.get(
'timeout_seconds',
_DEFAULT_TIMEOUT_SECONDS) * timeout_scaling,
environ=env))
elif self.args.regex == '.*' or self.platform == 'windows':
print('\nWARNING: binary not found, skipping', binary)
return sorted(out)
def make_targets(self):
if self.platform == 'windows':
# don't build tools on windows just yet
return ['buildtests_%s' % self.make_target]
return [
'buildtests_%s' % self.make_target,
'tools_%s' % self.make_target, 'check_epollexclusive'
]
def make_options(self):
return self._make_options
def pre_build_steps(self):
if self.platform == 'windows':
return [['tools\\run_tests\\helper_scripts\\pre_build_cmake.bat'] +
self._cmake_configure_extra_args]
elif self._use_cmake:
return [['tools/run_tests/helper_scripts/pre_build_cmake.sh'] +
self._cmake_configure_extra_args]
else:
return []
def build_steps(self):
return []
def post_tests_steps(self):
if self.platform == 'windows':
return []
else:
return [['tools/run_tests/helper_scripts/post_tests_c.sh']]
def makefile_name(self):
if self._use_cmake:
return 'cmake/build/Makefile'
else:
return 'Makefile'
def _clang_cmake_configure_extra_args(self, version_suffix=''):
return [
'-DCMAKE_C_COMPILER=clang%s' % version_suffix,
'-DCMAKE_CXX_COMPILER=clang++%s' % version_suffix,
]
def _compiler_options(self, use_docker, compiler):
"""Returns docker distro and cmake configure args to use for given compiler."""
if not use_docker and not _is_use_docker_child():
# if not running under docker, we cannot ensure the right compiler version will be used,
# so we only allow the non-specific choices.
_check_compiler(compiler, ['default', 'cmake'])
if compiler == 'gcc4.9' or compiler == 'default' or compiler == 'cmake':
return ('jessie', [])
elif compiler == 'gcc5.3':
return ('ubuntu1604', [])
elif compiler == 'gcc7.4':
return ('ubuntu1804', [])
elif compiler == 'gcc8.3':
return ('buster', [])
elif compiler == 'gcc8.3_openssl102':
return ('buster_openssl102', [
"-DgRPC_SSL_PROVIDER=package",
])
elif compiler == 'gcc_musl':
return ('alpine', [])
elif compiler == 'clang4.0':
return ('ubuntu1604',
self._clang_cmake_configure_extra_args(
version_suffix='-4.0'))
elif compiler == 'clang5.0':
return ('ubuntu1604',
self._clang_cmake_configure_extra_args(
version_suffix='-5.0'))
else:
raise Exception('Compiler %s not supported.' % compiler)
def dockerfile_dir(self):
return 'tools/dockerfile/test/cxx_%s_%s' % (
self._docker_distro, _docker_arch_suffix(self.args.arch))
def __str__(self):
return self.make_target
# This tests Node on grpc/grpc-node and will become the standard for Node testing
class RemoteNodeLanguage(object):
def __init__(self):
self.platform = platform_string()
def configure(self, config, args):
self.config = config
self.args = args
# Note: electron ABI only depends on major and minor version, so that's all
# we should specify in the compiler argument
_check_compiler(self.args.compiler, [
'default', 'node0.12', 'node4', 'node5', 'node6', 'node7', 'node8',
'electron1.3', 'electron1.6'
])
if self.args.compiler == 'default':
self.runtime = 'node'
self.node_version = '8'
else:
if self.args.compiler.startswith('electron'):
self.runtime = 'electron'
self.node_version = self.args.compiler[8:]
else:
self.runtime = 'node'
# Take off the word "node"
self.node_version = self.args.compiler[4:]
# TODO: update with Windows/electron scripts when available for grpc/grpc-node
def test_specs(self):
if self.platform == 'windows':
return [
self.config.job_spec(
['tools\\run_tests\\helper_scripts\\run_node.bat'])
]
else:
return [
self.config.job_spec(
['tools/run_tests/helper_scripts/run_grpc-node.sh'],
None,
environ=_FORCE_ENVIRON_FOR_WRAPPERS)
]
def pre_build_steps(self):
return []
def make_targets(self):
return []
def make_options(self):
return []
def build_steps(self):
return []
def post_tests_steps(self):
return []
def makefile_name(self):
return 'Makefile'
def dockerfile_dir(self):
return 'tools/dockerfile/test/node_jessie_%s' % _docker_arch_suffix(
self.args.arch)
def __str__(self):
return 'grpc-node'
class Php7Language(object):
def configure(self, config, args):
self.config = config
self.args = args
_check_compiler(self.args.compiler, ['default'])
self._make_options = ['EMBED_OPENSSL=true', 'EMBED_ZLIB=true']
def test_specs(self):
return [
self.config.job_spec(['src/php/bin/run_tests.sh'],
environ=_FORCE_ENVIRON_FOR_WRAPPERS)
]
def pre_build_steps(self):
return []
def make_targets(self):
return ['static_c', 'shared_c']
def make_options(self):
return self._make_options
def build_steps(self):
return [['tools/run_tests/helper_scripts/build_php.sh']]
def post_tests_steps(self):
return [['tools/run_tests/helper_scripts/post_tests_php.sh']]
def makefile_name(self):
return 'Makefile'
def dockerfile_dir(self):
return 'tools/dockerfile/test/php7_jessie_%s' % _docker_arch_suffix(
self.args.arch)
def __str__(self):
return 'php7'
class PythonConfig(
collections.namedtuple('PythonConfig', ['name', 'build', 'run'])):
"""Tuple of commands (named s.t. 'what it says on the tin' applies)"""
class PythonLanguage(object):
_TEST_SPECS_FILE = {
'native': ['src/python/grpcio_tests/tests/tests.json'],
'gevent': [
'src/python/grpcio_tests/tests/tests.json',
'src/python/grpcio_tests/tests_gevent/tests.json',
],
'asyncio': ['src/python/grpcio_tests/tests_aio/tests.json'],
}
_TEST_FOLDER = {
'native': 'test',
'gevent': 'test_gevent',
'asyncio': 'test_aio',
}
def configure(self, config, args):
self.config = config
self.args = args
self.pythons = self._get_pythons(self.args)
def test_specs(self):
# load list of known test suites
tests_json = []
for tests_json_file_name in self._TEST_SPECS_FILE[
self.args.iomgr_platform]:
with open(tests_json_file_name) as tests_json_file:
tests_json.extend(json.load(tests_json_file))
environment = dict(_FORCE_ENVIRON_FOR_WRAPPERS)
# TODO(https://github.com/grpc/grpc/issues/21401) Fork handlers is not
# designed for non-native IO manager. It has a side-effect that
# overrides threading settings in C-Core.
if args.iomgr_platform != 'native':
environment['GRPC_ENABLE_FORK_SUPPORT'] = '0'
return [
self.config.job_spec(
config.run,
timeout_seconds=8 * 60,
environ=dict(GRPC_PYTHON_TESTRUNNER_FILTER=str(suite_name),
**environment),
shortname='%s.%s.%s' %
(config.name, self._TEST_FOLDER[self.args.iomgr_platform],
suite_name),
) for suite_name in tests_json for config in self.pythons
]
def pre_build_steps(self):
return []
def make_targets(self):
return []
def make_options(self):
return []
def build_steps(self):
return [config.build for config in self.pythons]
def post_tests_steps(self):
if self.config.build_config != 'gcov':
return []
else:
return [['tools/run_tests/helper_scripts/post_tests_python.sh']]
def makefile_name(self):
return 'Makefile'
def dockerfile_dir(self):
return 'tools/dockerfile/test/python_%s_%s' % (
self._python_manager_name(), _docker_arch_suffix(self.args.arch))
def _python_manager_name(self):
"""Choose the docker image to use based on python version."""
if self.args.compiler in [
'python2.7', 'python3.5', 'python3.6', 'python3.7', 'python3.8'
]:
return 'stretch_' + self.args.compiler[len('python'):]
elif self.args.compiler == 'python_alpine':
return 'alpine'
else:
return 'stretch_default'
def _get_pythons(self, args):
"""Get python runtimes to test with, based on current platform, architecture, compiler etc."""
if args.arch == 'x86':
bits = '32'
else:
bits = '64'
if os.name == 'nt':
shell = ['bash']
builder = [
os.path.abspath(
'tools/run_tests/helper_scripts/build_python_msys2.sh')
]
builder_prefix_arguments = ['MINGW{}'.format(bits)]
venv_relative_python = ['Scripts/python.exe']
toolchain = ['mingw32']
else:
shell = []
builder = [
os.path.abspath(
'tools/run_tests/helper_scripts/build_python.sh')
]
builder_prefix_arguments = []
venv_relative_python = ['bin/python']
toolchain = ['unix']
# Selects the corresponding testing mode.
# See src/python/grpcio_tests/commands.py for implementation details.
if args.iomgr_platform == 'native':
test_command = 'test_lite'
elif args.iomgr_platform == 'gevent':
test_command = 'test_gevent'
elif args.iomgr_platform == 'asyncio':
test_command = 'test_aio'
else:
raise ValueError('Unsupported IO Manager platform: %s' %
args.iomgr_platform)
runner = [
os.path.abspath('tools/run_tests/helper_scripts/run_python.sh')
]
config_vars = _PythonConfigVars(shell, builder,
builder_prefix_arguments,
venv_relative_python, toolchain, runner,
test_command, args.iomgr_platform)
python27_config = _python_config_generator(name='py27',
major='2',
minor='7',
bits=bits,
config_vars=config_vars)
python35_config = _python_config_generator(name='py35',
major='3',
minor='5',
bits=bits,
config_vars=config_vars)
python36_config = _python_config_generator(name='py36',
major='3',
minor='6',
bits=bits,
config_vars=config_vars)
python37_config = _python_config_generator(name='py37',
major='3',
minor='7',
bits=bits,
config_vars=config_vars)
python38_config = _python_config_generator(name='py38',
major='3',
minor='8',
bits=bits,
config_vars=config_vars)
pypy27_config = _pypy_config_generator(name='pypy',
major='2',
config_vars=config_vars)
pypy32_config = _pypy_config_generator(name='pypy3',
major='3',
config_vars=config_vars)
if args.iomgr_platform in ('asyncio', 'gevent'):
if args.compiler not in ('default', 'python3.6', 'python3.7',
'python3.8'):
raise Exception(
'Compiler %s not supported with IO Manager platform: %s' %
(args.compiler, args.iomgr_platform))
if args.compiler == 'default':
if os.name == 'nt':
if args.iomgr_platform == 'gevent':
# TODO(https://github.com/grpc/grpc/issues/23784) allow
# gevent to run on later version once issue solved.
return (python36_config,)
else:
return (python38_config,)
else:
if args.iomgr_platform in ('asyncio', 'gevent'):
return (python36_config, python38_config)
elif os.uname()[0] == 'Darwin':
# NOTE(rbellevi): Testing takes significantly longer on
# MacOS, so we restrict the number of interpreter versions
# tested.
return (
python27_config,
python38_config,
)
else:
return (
python27_config,
python35_config,
python37_config,
python38_config,
)
elif args.compiler == 'python2.7':
return (python27_config,)
elif args.compiler == 'python3.5':
return (python35_config,)
elif args.compiler == 'python3.6':
return (python36_config,)
elif args.compiler == 'python3.7':
return (python37_config,)
elif args.compiler == 'python3.8':
return (python38_config,)
elif args.compiler == 'pypy':
return (pypy27_config,)
elif args.compiler == 'pypy3':
return (pypy32_config,)
elif args.compiler == 'python_alpine':
return (python27_config,)
elif args.compiler == 'all_the_cpythons':
return (
python27_config,
python35_config,
python36_config,
python37_config,
python38_config,
)
else:
raise Exception('Compiler %s not supported.' % args.compiler)
def __str__(self):
return 'python'
class RubyLanguage(object):
def configure(self, config, args):
self.config = config
self.args = args
_check_compiler(self.args.compiler, ['default'])
def test_specs(self):
tests = [
self.config.job_spec(['tools/run_tests/helper_scripts/run_ruby.sh'],
timeout_seconds=10 * 60,
environ=_FORCE_ENVIRON_FOR_WRAPPERS)
]
for test in [
'src/ruby/end2end/sig_handling_test.rb',
'src/ruby/end2end/channel_state_test.rb',
'src/ruby/end2end/channel_closing_test.rb',
'src/ruby/end2end/sig_int_during_channel_watch_test.rb',
'src/ruby/end2end/killed_client_thread_test.rb',
'src/ruby/end2end/forking_client_test.rb',
'src/ruby/end2end/grpc_class_init_test.rb',
'src/ruby/end2end/multiple_killed_watching_threads_test.rb',
'src/ruby/end2end/load_grpc_with_gc_stress_test.rb',
'src/ruby/end2end/client_memory_usage_test.rb',
'src/ruby/end2end/package_with_underscore_test.rb',
'src/ruby/end2end/graceful_sig_handling_test.rb',
'src/ruby/end2end/graceful_sig_stop_test.rb',
'src/ruby/end2end/errors_load_before_grpc_lib_test.rb',
'src/ruby/end2end/logger_load_before_grpc_lib_test.rb',
'src/ruby/end2end/status_codes_load_before_grpc_lib_test.rb',
'src/ruby/end2end/call_credentials_timeout_test.rb',
'src/ruby/end2end/call_credentials_returning_bad_metadata_doesnt_kill_background_thread_test.rb'
]:
tests.append(
self.config.job_spec(['ruby', test],
shortname=test,
timeout_seconds=20 * 60,
environ=_FORCE_ENVIRON_FOR_WRAPPERS))
return tests
def pre_build_steps(self):
return [['tools/run_tests/helper_scripts/pre_build_ruby.sh']]
def make_targets(self):
return []
def make_options(self):
return []
def build_steps(self):
return [['tools/run_tests/helper_scripts/build_ruby.sh']]
def post_tests_steps(self):
return [['tools/run_tests/helper_scripts/post_tests_ruby.sh']]
def makefile_name(self):
return 'Makefile'
def dockerfile_dir(self):
return 'tools/dockerfile/test/ruby_buster_%s' % _docker_arch_suffix(
self.args.arch)
def __str__(self):
return 'ruby'
class CSharpLanguage(object):
def __init__(self):
self.platform = platform_string()
def configure(self, config, args):
self.config = config
self.args = args
if self.platform == 'windows':
_check_compiler(self.args.compiler, ['default', 'coreclr'])
_check_arch(self.args.arch, ['default'])
self._cmake_arch_option = 'x64'
else:
_check_compiler(self.args.compiler, ['default', 'coreclr'])
self._docker_distro = 'buster'
def test_specs(self):
with open('src/csharp/tests.json') as f:
tests_by_assembly = json.load(f)
msbuild_config = _MSBUILD_CONFIG[self.config.build_config]
nunit_args = ['--labels=All', '--noresult', '--workers=1']
assembly_subdir = 'bin/%s' % msbuild_config
assembly_extension = '.exe'
if self.args.compiler == 'coreclr':
assembly_subdir += '/netcoreapp2.1'
runtime_cmd = ['dotnet', 'exec']
assembly_extension = '.dll'
else:
assembly_subdir += '/net45'
if self.platform == 'windows':
runtime_cmd = []
elif self.platform == 'mac':
# mono before version 5.2 on MacOS defaults to 32bit runtime
runtime_cmd = ['mono', '--arch=64']
else:
runtime_cmd = ['mono']
specs = []
for assembly in six.iterkeys(tests_by_assembly):
assembly_file = 'src/csharp/%s/%s/%s%s' % (
assembly, assembly_subdir, assembly, assembly_extension)
if self.config.build_config != 'gcov' or self.platform != 'windows':
# normally, run each test as a separate process
for test in tests_by_assembly[assembly]:
cmdline = runtime_cmd + [assembly_file,
'--test=%s' % test] + nunit_args
specs.append(
self.config.job_spec(
cmdline,
shortname='csharp.%s' % test,
environ=_FORCE_ENVIRON_FOR_WRAPPERS))
else:
# For C# test coverage, run all tests from the same assembly at once
# using OpenCover.Console (only works on Windows).
cmdline = [
'src\\csharp\\packages\\OpenCover.4.6.519\\tools\\OpenCover.Console.exe',
'-target:%s' % assembly_file, '-targetdir:src\\csharp',
'-targetargs:%s' % ' '.join(nunit_args),
'-filter:+[Grpc.Core]*', '-register:user',
'-output:src\\csharp\\coverage_csharp_%s.xml' % assembly
]
# set really high cpu_cost to make sure instances of OpenCover.Console run exclusively
# to prevent problems with registering the profiler.
run_exclusive = 1000000
specs.append(
self.config.job_spec(cmdline,
shortname='csharp.coverage.%s' %
assembly,
cpu_cost=run_exclusive,
environ=_FORCE_ENVIRON_FOR_WRAPPERS))
return specs
def pre_build_steps(self):
if self.platform == 'windows':
return [[
'tools\\run_tests\\helper_scripts\\pre_build_csharp.bat',
self._cmake_arch_option
]]
else:
return [['tools/run_tests/helper_scripts/pre_build_csharp.sh']]
def make_targets(self):
return ['grpc_csharp_ext']
def make_options(self):
return []
def build_steps(self):
if self.platform == 'windows':
return [['tools\\run_tests\\helper_scripts\\build_csharp.bat']]
else:
return [['tools/run_tests/helper_scripts/build_csharp.sh']]
def post_tests_steps(self):
if self.platform == 'windows':
return [['tools\\run_tests\\helper_scripts\\post_tests_csharp.bat']]
else:
return [['tools/run_tests/helper_scripts/post_tests_csharp.sh']]
def makefile_name(self):
if self.platform == 'windows':
return 'cmake/build/%s/Makefile' % self._cmake_arch_option
else:
# no need to set x86 specific flags as run_tests.py
# currently forbids x86 C# builds on both Linux and MacOS.
return 'cmake/build/Makefile'
def dockerfile_dir(self):
return 'tools/dockerfile/test/csharp_%s_%s' % (
self._docker_distro, _docker_arch_suffix(self.args.arch))
def __str__(self):
return 'csharp'
class ObjCLanguage(object):
def configure(self, config, args):
self.config = config
self.args = args
_check_compiler(self.args.compiler, ['default'])
def test_specs(self):
out = []
out.append(
self.config.job_spec(
['src/objective-c/tests/build_one_example_bazel.sh'],
timeout_seconds=10 * 60,
shortname='ios-buildtest-example-sample',
cpu_cost=1e6,
environ={
'SCHEME': 'Sample',
'EXAMPLE_PATH': 'src/objective-c/examples/Sample',
'FRAMEWORKS': 'NO'
}))
# Currently not supporting compiling as frameworks in Bazel
out.append(
self.config.job_spec(
['src/objective-c/tests/build_one_example.sh'],
timeout_seconds=20 * 60,
shortname='ios-buildtest-example-sample-frameworks',
cpu_cost=1e6,
environ={
'SCHEME': 'Sample',
'EXAMPLE_PATH': 'src/objective-c/examples/Sample',
'FRAMEWORKS': 'YES'
}))
out.append(
self.config.job_spec(
['src/objective-c/tests/build_one_example.sh'],
timeout_seconds=20 * 60,
shortname='ios-buildtest-example-switftsample',
cpu_cost=1e6,
environ={
'SCHEME': 'SwiftSample',
'EXAMPLE_PATH': 'src/objective-c/examples/SwiftSample'
}))
out.append(
self.config.job_spec(
['src/objective-c/tests/build_one_example_bazel.sh'],
timeout_seconds=10 * 60,
shortname='ios-buildtest-example-tvOS-sample',
cpu_cost=1e6,
environ={
'SCHEME': 'tvOS-sample',
'EXAMPLE_PATH': 'src/objective-c/examples/tvOS-sample',
'FRAMEWORKS': 'NO'
}))
# Disabled due to #20258
# TODO (mxyan): Reenable this test when #20258 is resolved.
# out.append(
# self.config.job_spec(
# ['src/objective-c/tests/build_one_example_bazel.sh'],
# timeout_seconds=20 * 60,
# shortname='ios-buildtest-example-watchOS-sample',
# cpu_cost=1e6,
# environ={
# 'SCHEME': 'watchOS-sample-WatchKit-App',
# 'EXAMPLE_PATH': 'src/objective-c/examples/watchOS-sample',
# 'FRAMEWORKS': 'NO'
# }))
out.append(
self.config.job_spec(['src/objective-c/tests/run_plugin_tests.sh'],
timeout_seconds=60 * 60,
shortname='ios-test-plugintest',
cpu_cost=1e6,
environ=_FORCE_ENVIRON_FOR_WRAPPERS))
out.append(
self.config.job_spec(
['src/objective-c/tests/run_plugin_option_tests.sh'],
timeout_seconds=60 * 60,
shortname='ios-test-plugin-option-test',
cpu_cost=1e6,
environ=_FORCE_ENVIRON_FOR_WRAPPERS))
out.append(
self.config.job_spec(
['test/core/iomgr/ios/CFStreamTests/build_and_run_tests.sh'],
timeout_seconds=60 * 60,
shortname='ios-test-cfstream-tests',
cpu_cost=1e6,
environ=_FORCE_ENVIRON_FOR_WRAPPERS))
# TODO: replace with run_one_test_bazel.sh when Bazel-Xcode is stable
out.append(
self.config.job_spec(['src/objective-c/tests/run_one_test.sh'],
timeout_seconds=60 * 60,
shortname='ios-test-unittests',
cpu_cost=1e6,
environ={'SCHEME': 'UnitTests'}))
out.append(
self.config.job_spec(['src/objective-c/tests/run_one_test.sh'],
timeout_seconds=60 * 60,
shortname='ios-test-interoptests',
cpu_cost=1e6,
environ={'SCHEME': 'InteropTests'}))
out.append(
self.config.job_spec(['src/objective-c/tests/run_one_test.sh'],
timeout_seconds=60 * 60,
shortname='ios-test-cronettests',
cpu_cost=1e6,
environ={'SCHEME': 'CronetTests'}))
out.append(
self.config.job_spec(['src/objective-c/tests/run_one_test.sh'],
timeout_seconds=30 * 60,
shortname='ios-perf-test',
cpu_cost=1e6,
environ={'SCHEME': 'PerfTests'}))
out.append(
self.config.job_spec(['src/objective-c/tests/run_one_test.sh'],
timeout_seconds=30 * 60,
shortname='ios-perf-test-posix',
cpu_cost=1e6,
environ={'SCHEME': 'PerfTestsPosix'}))
out.append(
self.config.job_spec(['test/cpp/ios/build_and_run_tests.sh'],
timeout_seconds=60 * 60,
shortname='ios-cpp-test-cronet',
cpu_cost=1e6,
environ=_FORCE_ENVIRON_FOR_WRAPPERS))
out.append(
self.config.job_spec(['src/objective-c/tests/run_one_test.sh'],
timeout_seconds=60 * 60,
shortname='mac-test-basictests',
cpu_cost=1e6,
environ={
'SCHEME': 'MacTests',
'PLATFORM': 'macos'
}))
out.append(
self.config.job_spec(['src/objective-c/tests/run_one_test.sh'],
timeout_seconds=30 * 60,
shortname='tvos-test-basictests',
cpu_cost=1e6,
environ={
'SCHEME': 'TvTests',
'PLATFORM': 'tvos'
}))
return sorted(out)
def pre_build_steps(self):
return []
def make_targets(self):
return []
def make_options(self):
return []
def build_steps(self):
return []
def post_tests_steps(self):
return []
def makefile_name(self):
return 'Makefile'
def dockerfile_dir(self):
return None
def __str__(self):
return 'objc'
class Sanity(object):
def configure(self, config, args):
self.config = config
self.args = args
_check_compiler(self.args.compiler, ['default'])
def test_specs(self):
import yaml
with open('tools/run_tests/sanity/sanity_tests.yaml', 'r') as f:
environ = {'TEST': 'true'}
if _is_use_docker_child():
environ['CLANG_FORMAT_SKIP_DOCKER'] = 'true'
environ['CLANG_TIDY_SKIP_DOCKER'] = 'true'
# sanity tests run tools/bazel wrapper concurrently
# and that can result in a download/run race in the wrapper.
# under docker we already have the right version of bazel
# so we can just disable the wrapper.
environ['DISABLE_BAZEL_WRAPPER'] = 'true'
return [
self.config.job_spec(cmd['script'].split(),
timeout_seconds=30 * 60,
environ=environ,
cpu_cost=cmd.get('cpu_cost', 1))
for cmd in yaml.load(f)
]
def pre_build_steps(self):
return []
def make_targets(self):
return ['run_dep_checks']
def make_options(self):
return []
def build_steps(self):
return []
def post_tests_steps(self):
return []
def makefile_name(self):
return 'Makefile'
def dockerfile_dir(self):
return 'tools/dockerfile/test/sanity'
def __str__(self):
return 'sanity'
# different configurations we can run under
with open('tools/run_tests/generated/configs.json') as f:
_CONFIGS = dict(
(cfg['config'], Config(**cfg)) for cfg in ast.literal_eval(f.read()))
_LANGUAGES = {
'c++': CLanguage('cxx', 'c++'),
'c': CLanguage('c', 'c'),
'grpc-node': RemoteNodeLanguage(),
'php7': Php7Language(),
'python': PythonLanguage(),
'ruby': RubyLanguage(),
'csharp': CSharpLanguage(),
'objc': ObjCLanguage(),
'sanity': Sanity()
}
_MSBUILD_CONFIG = {
'dbg': 'Debug',
'opt': 'Release',
'gcov': 'Debug',
}
def _windows_arch_option(arch):
"""Returns msbuild cmdline option for selected architecture."""
if arch == 'default' or arch == 'x86':
return '/p:Platform=Win32'
elif arch == 'x64':
return '/p:Platform=x64'
else:
print('Architecture %s not supported.' % arch)
sys.exit(1)
def _check_arch_option(arch):
"""Checks that architecture option is valid."""
if platform_string() == 'windows':
_windows_arch_option(arch)
elif platform_string() == 'linux':
# On linux, we need to be running under docker with the right architecture.
runtime_arch = platform.architecture()[0]
if arch == 'default':
return
elif runtime_arch == '64bit' and arch == 'x64':
return
elif runtime_arch == '32bit' and arch == 'x86':
return
else:
print(
'Architecture %s does not match current runtime architecture.' %
arch)
sys.exit(1)
else:
if args.arch != 'default':
print('Architecture %s not supported on current platform.' %
args.arch)
sys.exit(1)
def _docker_arch_suffix(arch):
"""Returns suffix to dockerfile dir to use."""
if arch == 'default' or arch == 'x64':
return 'x64'
elif arch == 'x86':
return 'x86'
else:
print('Architecture %s not supported with current settings.' % arch)
sys.exit(1)
def runs_per_test_type(arg_str):
"""Auxiliary function to parse the "runs_per_test" flag.
Returns:
A positive integer or 0, the latter indicating an infinite number of
runs.
Raises:
argparse.ArgumentTypeError: Upon invalid input.
"""
if arg_str == 'inf':
return 0
try:
n = int(arg_str)
if n <= 0:
raise ValueError
return n
except:
msg = '\'{}\' is not a positive integer or \'inf\''.format(arg_str)
raise argparse.ArgumentTypeError(msg)
def percent_type(arg_str):
pct = float(arg_str)
if pct > 100 or pct < 0:
raise argparse.ArgumentTypeError(
"'%f' is not a valid percentage in the [0, 100] range" % pct)
return pct
# This is math.isclose in python >= 3.5
def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
# parse command line
argp = argparse.ArgumentParser(description='Run grpc tests.')
argp.add_argument('-c',
'--config',
choices=sorted(_CONFIGS.keys()),
default='opt')
argp.add_argument(
'-n',
'--runs_per_test',
default=1,
type=runs_per_test_type,
help='A positive integer or "inf". If "inf", all tests will run in an '
'infinite loop. Especially useful in combination with "-f"')
argp.add_argument('-r', '--regex', default='.*', type=str)
argp.add_argument('--regex_exclude', default='', type=str)
argp.add_argument('-j', '--jobs', default=multiprocessing.cpu_count(), type=int)
argp.add_argument('-s', '--slowdown', default=1.0, type=float)
argp.add_argument('-p',
'--sample_percent',
default=100.0,
type=percent_type,
help='Run a random sample with that percentage of tests')
argp.add_argument('-f',
'--forever',
default=False,
action='store_const',
const=True)
argp.add_argument('-t',
'--travis',
default=False,
action='store_const',
const=True)
argp.add_argument('--newline_on_success',
default=False,
action='store_const',
const=True)
argp.add_argument('-l',
'--language',
choices=sorted(_LANGUAGES.keys()),
nargs='+',
required=True)
argp.add_argument('-S',
'--stop_on_failure',
default=False,
action='store_const',
const=True)
argp.add_argument('--use_docker',
default=False,
action='store_const',
const=True,
help='Run all the tests under docker. That provides ' +
'additional isolation and prevents the need to install ' +
'language specific prerequisites. Only available on Linux.')
argp.add_argument(
'--allow_flakes',
default=False,
action='store_const',
const=True,
help=
'Allow flaky tests to show as passing (re-runs failed tests up to five times)'
)
argp.add_argument(
'--arch',
choices=['default', 'x86', 'x64'],
default='default',
help=
'Selects architecture to target. For some platforms "default" is the only supported choice.'
)
argp.add_argument(
'--compiler',
choices=[
'default',
'gcc4.9',
'gcc5.3',
'gcc7.4',
'gcc8.3',
'gcc8.3_openssl102',
'gcc_musl',
'clang4.0',
'clang5.0',
'python2.7',
'python3.5',
'python3.6',
'python3.7',
'python3.8',
'pypy',
'pypy3',
'python_alpine',
'all_the_cpythons',
'electron1.3',
'electron1.6',
'coreclr',
'cmake',
'cmake_vs2015',
'cmake_vs2017',
'cmake_vs2019',
],
default='default',
help=
'Selects compiler to use. Allowed values depend on the platform and language.'
)
argp.add_argument('--iomgr_platform',
choices=['native', 'uv', 'gevent', 'asyncio'],
default='native',
help='Selects iomgr platform to build on')
argp.add_argument('--build_only',
default=False,
action='store_const',
const=True,
help='Perform all the build steps but don\'t run any tests.')
argp.add_argument('--measure_cpu_costs',
default=False,
action='store_const',
const=True,
help='Measure the cpu costs of tests')
argp.add_argument(
'--update_submodules',
default=[],
nargs='*',
help=
'Update some submodules before building. If any are updated, also run generate_projects. '
+
'Submodules are specified as SUBMODULE_NAME:BRANCH; if BRANCH is omitted, master is assumed.'
)
argp.add_argument('-a', '--antagonists', default=0, type=int)
argp.add_argument('-x',
'--xml_report',
default=None,
type=str,
help='Generates a JUnit-compatible XML report')
argp.add_argument('--report_suite_name',
default='tests',
type=str,
help='Test suite name to use in generated JUnit XML report')
argp.add_argument(
'--report_multi_target',
default=False,
const=True,
action='store_const',
help='Generate separate XML report for each test job (Looks better in UIs).'
)
argp.add_argument(
'--quiet_success',
default=False,
action='store_const',
const=True,
help=
'Don\'t print anything when a test passes. Passing tests also will not be reported in XML report. '
+ 'Useful when running many iterations of each test (argument -n).')
argp.add_argument(
'--force_default_poller',
default=False,
action='store_const',
const=True,
help='Don\'t try to iterate over many polling strategies when they exist')
argp.add_argument(
'--force_use_pollers',
default=None,
type=str,
help='Only use the specified comma-delimited list of polling engines. '
'Example: --force_use_pollers epoll1,poll '
' (This flag has no effect if --force_default_poller flag is also used)')
argp.add_argument('--max_time',
default=-1,
type=int,
help='Maximum test runtime in seconds')
argp.add_argument('--bq_result_table',
default='',
type=str,
nargs='?',
help='Upload test results to a specified BQ table.')
args = argp.parse_args()
flaky_tests = set()
shortname_to_cpu = {}
if args.force_default_poller:
_POLLING_STRATEGIES = {}
elif args.force_use_pollers:
_POLLING_STRATEGIES[platform_string()] = args.force_use_pollers.split(',')
jobset.measure_cpu_costs = args.measure_cpu_costs
# update submodules if necessary
need_to_regenerate_projects = False
for spec in args.update_submodules:
spec = spec.split(':', 1)
if len(spec) == 1:
submodule = spec[0]
branch = 'master'
elif len(spec) == 2:
submodule = spec[0]
branch = spec[1]
cwd = 'third_party/%s' % submodule
def git(cmd, cwd=cwd):
print('in %s: git %s' % (cwd, cmd))
run_shell_command('git %s' % cmd, cwd=cwd)
git('fetch')
git('checkout %s' % branch)
git('pull origin %s' % branch)
if os.path.exists('src/%s/gen_build_yaml.py' % submodule):
need_to_regenerate_projects = True
if need_to_regenerate_projects:
if jobset.platform_string() == 'linux':
run_shell_command('tools/buildgen/generate_projects.sh')
else:
print(
'WARNING: may need to regenerate projects, but since we are not on')
print(
' Linux this step is being skipped. Compilation MAY fail.')
# grab config
run_config = _CONFIGS[args.config]
build_config = run_config.build_config
if args.travis:
_FORCE_ENVIRON_FOR_WRAPPERS = {'GRPC_TRACE': 'api'}
languages = set(_LANGUAGES[l] for l in args.language)
for l in languages:
l.configure(run_config, args)
language_make_options = []
if any(language.make_options() for language in languages):
if not 'gcov' in args.config and len(languages) != 1:
print(
'languages with custom make options cannot be built simultaneously with other languages'
)
sys.exit(1)
else:
# Combining make options is not clean and just happens to work. It allows C & C++ to build
# together, and is only used under gcov. All other configs should build languages individually.
language_make_options = list(
set([
make_option for lang in languages
for make_option in lang.make_options()
]))
if args.use_docker:
if not args.travis:
print('Seen --use_docker flag, will run tests under docker.')
print('')
print(
'IMPORTANT: The changes you are testing need to be locally committed'
)
print(
'because only the committed changes in the current branch will be')
print('copied to the docker environment.')
time.sleep(5)
dockerfile_dirs = set([l.dockerfile_dir() for l in languages])
if len(dockerfile_dirs) > 1:
print('Languages to be tested require running under different docker '
'images.')
sys.exit(1)
else:
dockerfile_dir = next(iter(dockerfile_dirs))
child_argv = [arg for arg in sys.argv if not arg == '--use_docker']
run_tests_cmd = 'python tools/run_tests/run_tests.py %s' % ' '.join(
child_argv[1:])
env = os.environ.copy()
env['RUN_TESTS_COMMAND'] = run_tests_cmd
env['DOCKERFILE_DIR'] = dockerfile_dir
env['DOCKER_RUN_SCRIPT'] = 'tools/run_tests/dockerize/docker_run_tests.sh'
if args.xml_report:
env['XML_REPORT'] = args.xml_report
if not args.travis:
env['TTY_FLAG'] = '-t' # enables Ctrl-C when not on Jenkins.
subprocess.check_call(
'tools/run_tests/dockerize/build_docker_and_run_tests.sh',
shell=True,
env=env)
sys.exit(0)
_check_arch_option(args.arch)
def make_jobspec(cfg, targets, makefile='Makefile'):
if platform_string() == 'windows':
return [
jobset.JobSpec([
'cmake', '--build', '.', '--target',
'%s' % target, '--config', _MSBUILD_CONFIG[cfg]
],
cwd=os.path.dirname(makefile),
timeout_seconds=None) for target in targets
]
else:
if targets and makefile.startswith('cmake/build/'):
# With cmake, we've passed all the build configuration in the pre-build step already
return [
jobset.JobSpec(
[os.getenv('MAKE', 'make'), '-j',
'%d' % args.jobs] + targets,
cwd='cmake/build',
timeout_seconds=None)
]
if targets:
return [
jobset.JobSpec(
[
os.getenv('MAKE', 'make'), '-f', makefile, '-j',
'%d' % args.jobs,
'EXTRA_DEFINES=GRPC_TEST_SLOWDOWN_MACHINE_FACTOR=%f' %
args.slowdown,
'CONFIG=%s' % cfg, 'Q='
] + language_make_options +
([] if not args.travis else ['JENKINS_BUILD=1']) + targets,
timeout_seconds=None)
]
else:
return []
make_targets = {}
for l in languages:
makefile = l.makefile_name()
make_targets[makefile] = make_targets.get(makefile, set()).union(
set(l.make_targets()))
def build_step_environ(cfg):
environ = {'CONFIG': cfg}
msbuild_cfg = _MSBUILD_CONFIG.get(cfg)
if msbuild_cfg:
environ['MSBUILD_CONFIG'] = msbuild_cfg
return environ
build_steps = list(
set(
jobset.JobSpec(cmdline,
environ=build_step_environ(build_config),
timeout_seconds=_PRE_BUILD_STEP_TIMEOUT_SECONDS,
flake_retries=2)
for l in languages
for cmdline in l.pre_build_steps()))
if make_targets:
make_commands = itertools.chain.from_iterable(
make_jobspec(build_config, list(targets), makefile)
for (makefile, targets) in make_targets.items())
build_steps.extend(set(make_commands))
build_steps.extend(
set(
jobset.JobSpec(cmdline,
environ=build_step_environ(build_config),
timeout_seconds=None)
for l in languages
for cmdline in l.build_steps()))
post_tests_steps = list(
set(
jobset.JobSpec(cmdline, environ=build_step_environ(build_config))
for l in languages
for cmdline in l.post_tests_steps()))
runs_per_test = args.runs_per_test
forever = args.forever
def _shut_down_legacy_server(legacy_server_port):
try:
version = int(
urllib.request.urlopen('http://localhost:%d/version_number' %
legacy_server_port,
timeout=10).read())
except:
pass
else:
urllib.request.urlopen('http://localhost:%d/quitquitquit' %
legacy_server_port).read()
def _calculate_num_runs_failures(list_of_results):
"""Calculate number of runs and failures for a particular test.
Args:
list_of_results: (List) of JobResult object.
Returns:
A tuple of total number of runs and failures.
"""
num_runs = len(list_of_results) # By default, there is 1 run per JobResult.
num_failures = 0
for jobresult in list_of_results:
if jobresult.retries > 0:
num_runs += jobresult.retries
if jobresult.num_failures > 0:
num_failures += jobresult.num_failures
return num_runs, num_failures
# _build_and_run results
class BuildAndRunError(object):
BUILD = object()
TEST = object()
POST_TEST = object()
def _has_epollexclusive():
binary = 'bins/%s/check_epollexclusive' % args.config
if not os.path.exists(binary):
return False
try:
subprocess.check_call(binary)
return True
except subprocess.CalledProcessError as e:
return False
except OSError as e:
# For languages other than C and Windows the binary won't exist
return False
# returns a list of things that failed (or an empty list on success)
def _build_and_run(check_cancelled,
newline_on_success,
xml_report=None,
build_only=False):
"""Do one pass of building & running tests."""
# build latest sequentially
num_failures, resultset = jobset.run(build_steps,
maxjobs=1,
stop_on_failure=True,
newline_on_success=newline_on_success,
travis=args.travis)
if num_failures:
return [BuildAndRunError.BUILD]
if build_only:
if xml_report:
report_utils.render_junit_xml_report(
resultset, xml_report, suite_name=args.report_suite_name)
return []
if not args.travis and not _has_epollexclusive() and platform_string(
) in _POLLING_STRATEGIES and 'epollex' in _POLLING_STRATEGIES[
platform_string()]:
print('\n\nOmitting EPOLLEXCLUSIVE tests\n\n')
_POLLING_STRATEGIES[platform_string()].remove('epollex')
# start antagonists
antagonists = [
subprocess.Popen(['tools/run_tests/python_utils/antagonist.py'])
for _ in range(0, args.antagonists)
]
start_port_server.start_port_server()
resultset = None
num_test_failures = 0
try:
infinite_runs = runs_per_test == 0
one_run = set(spec for language in languages
for spec in language.test_specs()
if (re.search(args.regex, spec.shortname) and
(args.regex_exclude == '' or
not re.search(args.regex_exclude, spec.shortname))))
# When running on travis, we want out test runs to be as similar as possible
# for reproducibility purposes.
if args.travis and args.max_time <= 0:
massaged_one_run = sorted(one_run, key=lambda x: x.cpu_cost)
else:
# whereas otherwise, we want to shuffle things up to give all tests a
# chance to run.
massaged_one_run = list(
one_run) # random.sample needs an indexable seq.
num_jobs = len(massaged_one_run)
# for a random sample, get as many as indicated by the 'sample_percent'
# argument. By default this arg is 100, resulting in a shuffle of all
# jobs.
sample_size = int(num_jobs * args.sample_percent / 100.0)
massaged_one_run = random.sample(massaged_one_run, sample_size)
if not isclose(args.sample_percent, 100.0):
assert args.runs_per_test == 1, "Can't do sampling (-p) over multiple runs (-n)."
print("Running %d tests out of %d (~%d%%)" %
(sample_size, num_jobs, args.sample_percent))
if infinite_runs:
assert len(massaged_one_run
) > 0, 'Must have at least one test for a -n inf run'
runs_sequence = (itertools.repeat(massaged_one_run) if infinite_runs
else itertools.repeat(massaged_one_run, runs_per_test))
all_runs = itertools.chain.from_iterable(runs_sequence)
if args.quiet_success:
jobset.message(
'START',
'Running tests quietly, only failing tests will be reported',
do_newline=True)
num_test_failures, resultset = jobset.run(
all_runs,
check_cancelled,
newline_on_success=newline_on_success,
travis=args.travis,
maxjobs=args.jobs,
maxjobs_cpu_agnostic=max_parallel_tests_for_current_platform(),
stop_on_failure=args.stop_on_failure,
quiet_success=args.quiet_success,
max_time=args.max_time)
if resultset:
for k, v in sorted(resultset.items()):
num_runs, num_failures = _calculate_num_runs_failures(v)
if num_failures > 0:
if num_failures == num_runs: # what about infinite_runs???
jobset.message('FAILED', k, do_newline=True)
else:
jobset.message('FLAKE',
'%s [%d/%d runs flaked]' %
(k, num_failures, num_runs),
do_newline=True)
finally:
for antagonist in antagonists:
antagonist.kill()
if args.bq_result_table and resultset:
upload_extra_fields = {
'compiler': args.compiler,
'config': args.config,
'iomgr_platform': args.iomgr_platform,
'language': args.language[
0
], # args.language is a list but will always have one element when uploading to BQ is enabled.
'platform': platform_string()
}
try:
upload_results_to_bq(resultset, args.bq_result_table,
upload_extra_fields)
except NameError as e:
logging.warning(
e) # It's fine to ignore since this is not critical
if xml_report and resultset:
report_utils.render_junit_xml_report(
resultset,
xml_report,
suite_name=args.report_suite_name,
multi_target=args.report_multi_target)
number_failures, _ = jobset.run(post_tests_steps,
maxjobs=1,
stop_on_failure=False,
newline_on_success=newline_on_success,
travis=args.travis)
out = []
if number_failures:
out.append(BuildAndRunError.POST_TEST)
if num_test_failures:
out.append(BuildAndRunError.TEST)
return out
if forever:
success = True
while True:
dw = watch_dirs.DirWatcher(['src', 'include', 'test', 'examples'])
initial_time = dw.most_recent_change()
have_files_changed = lambda: dw.most_recent_change() != initial_time
previous_success = success
errors = _build_and_run(check_cancelled=have_files_changed,
newline_on_success=False,
build_only=args.build_only) == 0
if not previous_success and not errors:
jobset.message('SUCCESS',
'All tests are now passing properly',
do_newline=True)
jobset.message('IDLE', 'No change detected')
while not have_files_changed():
time.sleep(1)
else:
errors = _build_and_run(check_cancelled=lambda: False,
newline_on_success=args.newline_on_success,
xml_report=args.xml_report,
build_only=args.build_only)
if not errors:
jobset.message('SUCCESS', 'All tests passed', do_newline=True)
else:
jobset.message('FAILED', 'Some tests failed', do_newline=True)
exit_code = 0
if BuildAndRunError.BUILD in errors:
exit_code |= 1
if BuildAndRunError.TEST in errors:
exit_code |= 2
if BuildAndRunError.POST_TEST in errors:
exit_code |= 4
sys.exit(exit_code)
| 37.596095 | 147 | 0.539064 |
7940e5b632b9146f74c807eae55b8084a5cde85a | 312 | py | Python | lambda-layer/src/common/constants.py | USDOT-SDC/log4sdc | 718875d33fcbd54f2b8f7201d09f761e9afef038 | [
"MIT"
] | null | null | null | lambda-layer/src/common/constants.py | USDOT-SDC/log4sdc | 718875d33fcbd54f2b8f7201d09f761e9afef038 | [
"MIT"
] | 1 | 2021-11-19T18:18:53.000Z | 2021-11-19T18:18:53.000Z | lambda-layer/src/common/constants.py | USDOT-SDC/log4sdc | 718875d33fcbd54f2b8f7201d09f761e9afef038 | [
"MIT"
] | null | null | null | class Constants:
LOGGER_NAME = "log4sdc-common"
LOGGER_LOG_LEVEL_ENV_VAR = "LOG_LEVEL"
LOGGER_DEFAULT_LOG_LEVEL = "WARN"
def __setattr__(self, attr, value):
if hasattr(self, attr):
raise Exception("Attempting to alter read-only value")
self.__dict__[attr] = value
| 24 | 66 | 0.666667 |
7940e5be571d4199306b83bb35c3a271dcd3734c | 164 | py | Python | api/admin.py | nnamdiib/timetable-app | 4184e0d860df6e98ad17d5c3d30d23bbbc15ac58 | [
"MIT"
] | null | null | null | api/admin.py | nnamdiib/timetable-app | 4184e0d860df6e98ad17d5c3d30d23bbbc15ac58 | [
"MIT"
] | 2 | 2020-02-11T23:43:12.000Z | 2020-06-05T17:37:52.000Z | api/admin.py | nnamdiib/timetable-app | 4184e0d860df6e98ad17d5c3d30d23bbbc15ac58 | [
"MIT"
] | null | null | null | from django.contrib import admin
from .models import *
# Register your models here.
admin.site.register(Day)
admin.site.register(Course)
admin.site.register(Class) | 23.428571 | 32 | 0.79878 |
7940e684f03b37e6ab1d2274d2b193edb1c6d23c | 42,349 | py | Python | Python/2 kyu/Evaluate Mathematical Expression/test_calc.py | newtonsspawn/codewars_challenges | 62b20d4e729c8ba79eac7cae6a179af57abd45d4 | [
"MIT"
] | 3 | 2020-05-29T23:29:35.000Z | 2021-08-12T03:16:44.000Z | Python/2 kyu/Evaluate Mathematical Expression/test_calc.py | newtonsspawn/codewars_challenges | 62b20d4e729c8ba79eac7cae6a179af57abd45d4 | [
"MIT"
] | null | null | null | Python/2 kyu/Evaluate Mathematical Expression/test_calc.py | newtonsspawn/codewars_challenges | 62b20d4e729c8ba79eac7cae6a179af57abd45d4 | [
"MIT"
] | 3 | 2020-05-22T12:14:55.000Z | 2021-04-15T12:52:42.000Z | from unittest import TestCase
from calc import calc
class TestCalc(TestCase):
def test_calc_001(self):
self.assertEqual(calc('2 + 3 * 4 / 3 - 6 / 3 * 3 + 8'), 8)
def test_calc_002(self):
self.assertEqual(calc('1 + 2 * 3 * 3 - 8'), 11)
def test_calc_003(self):
self.assertEqual(calc('1 + 2 * 3 * (5 - 2) - 8'), 11)
def test_calc_004(self):
self.assertEqual(calc('1 + 2 * 3 * (5 - (3 - 1)) - 8'), 11)
def test_calc_005(self):
self.assertEqual(calc('4 + -(1)'), 3)
def test_calc_006(self):
self.assertEqual(calc('4 - -(1)'), 5)
def test_calc_007(self):
self.assertEqual(calc('4 * -(1)'), -4)
def test_calc_008(self):
self.assertEqual(calc('4 / -(1)'), -4)
def test_calc_009(self):
self.assertEqual(calc('-1'), -1)
def test_calc_010(self):
self.assertEqual(calc('-(-1)'), 1)
def test_calc_011(self):
self.assertEqual(calc('-(-(-1))'), -1)
def test_calc_012(self):
self.assertEqual(calc('-(-(-(-1)))'), 1)
def test_calc_013(self):
self.assertEqual(calc('(((((-1)))))'), -1)
def test_calc_014(self):
self.assertEqual(calc('5 - 1'), 4)
def test_calc_015(self):
self.assertEqual(calc('5- 1'), 4)
def test_calc_016(self):
self.assertEqual(calc('5 -1'), 4)
def test_calc_017(self):
self.assertEqual(calc('28 + 86 - -4 / 57 - -93 + -48 * -40 / -29'),
140.86327888687237)
def test_calc_018(self):
self.assertEqual(calc('-18 - 96 * 48 + 23 - 2 - 88 / -71 * -33'),
-4645.901408450704)
def test_calc_019(self):
self.assertEqual(calc('49 - -94 / -36 * 94 * 43 * -11 - -26 + 2'),
116172.22222222222)
def test_calc_020(self):
self.assertEqual(calc('-41 - 15 * -56 - 52 - -25 - -8 - -47 + -6'), 821)
def test_calc_021(self):
self.assertEqual(calc('-66 - 85 - 87 + 15 * -8 + -13 - 37 + -6'), -414)
def test_calc_022(self):
self.assertEqual(calc('31 + -43 * 38 * -40 / -82 + -62 + -87 / -41'),
-825.9512195121952)
def test_calc_023(self):
self.assertEqual(calc('-74 - 61 / 36 - 18 - 4 - -63 * 16 * -23'),
-23281.694444444445)
def test_calc_024(self):
self.assertEqual(calc('-6 * -10 + -14 - -97 / 33 / -21 / 41 * -15'),
46.05120895364798)
def test_calc_025(self):
self.assertEqual(calc('-84 / -7 * -3 + -31 / 7 * 52 / 27 - -40'),
-4.529100529100532)
def test_calc_026(self):
self.assertEqual(calc('46 + 92 + -34 + 35 / -72 + -2 * 41 + 26'),
47.513888888888886)
def test_calc_027(self):
self.assertEqual(calc('-4 + 89 - -84 + 75 + 50 + -11 - 64 + 22'), 241)
def test_calc_028(self):
self.assertEqual(calc('-69 * 42 * -25 - -71 + -43 + -73 * 48 - 74'),
68900)
def test_calc_029(self):
self.assertEqual(calc('-70 * -44 * -75 + -38 / 22 * -84 / 2 * -54'),
-234917.45454545456)
def test_calc_030(self):
self.assertEqual(calc('4 - -24 * 79 * -1 * -14 + 62 + 62 - -44'), 26716)
def test_calc_031(self):
self.assertEqual(calc('31 * 57 * 46 * 90 + -63 - 6 - -54 + -45'),
7315320)
def test_calc_032(self):
self.assertEqual(calc('-69 / 72 + 66 * -9 + -76 - -57 / -98 / -14'),
-670.916788143829)
def test_calc_033(self):
self.assertEqual(calc('-91 - 22 - -53 * 19 + -19 - -16 + 30 - 5'), 916)
def test_calc_034(self):
self.assertEqual(calc('-40 - -47 * 3 / -2 * -85 - -11 + 76 + -44'),
5995.5)
def test_calc_035(self):
self.assertEqual(calc('82 + -81 * -62 - 41 * -77 - 12 * -23 + -97'),
8440)
def test_calc_036(self):
self.assertEqual(calc('-92 - 38 - 42 + -70 - -95 - 69 - -60 + 76'), -80)
def test_calc_037(self):
self.assertEqual(calc('72 * -48 * 98 - 65 + -40 / 85 - 58 + 59'),
-338752.4705882353)
def test_calc_038(self):
self.assertEqual(calc('-74 * 45 / 25 - -1 - -66 - -47 * -44 - -37'),
-2097.2)
def test_calc_039(self):
self.assertEqual(calc('-69 / -33 + -58 + 46 + 94 - -51 + 81 + 8'),
224.0909090909091)
def test_calc_040(self):
self.assertEqual(calc('-25 / -5 / -27 + -57 / 8 * 69 * 80 + -64'),
-39394.18518518518)
def test_calc_041(self):
self.assertEqual(calc('8 * -47 + 77 + -98 / -87 / -47 - -91 - 4'),
-212.0239667400342)
def test_calc_042(self):
self.assertEqual(calc('-21 / 31 * -73 / 73 / -93 - 69 * -54 + -97'),
3628.9927159209155)
def test_calc_043(self):
self.assertEqual(calc('41 / -89 / 29 * -55 * 67 / -100 * -41 * -2'),
-48.00065865943432)
def test_calc_044(self):
self.assertEqual(calc('3 + 71 * -31 - 31 + -44 - 94 - 79 - -59'), -2387)
def test_calc_045(self):
self.assertEqual(calc('-9 - -96 * 80 / 56 + 89 / -43 + -25 + -91'),
10.073089700996675)
def test_calc_046(self):
self.assertEqual(calc('18 + -75 * 79 - 93 * -88 * 48 * 92 - -91'),
36134728)
def test_calc_047(self):
self.assertEqual(calc('-91 - 83 - 42 - -76 + -38 - 15 + 9 + 91'), -93)
def test_calc_048(self):
self.assertEqual(calc('-52 + -47 * 50 / 84 / -21 / -41 - -51 / -97'),
-52.55826586774178)
def test_calc_049(self):
self.assertEqual(calc('70 + 35 + 7 - -20 / 68 * 82 * 71 - 48'),
1776.3529411764705)
def test_calc_050(self):
self.assertEqual(calc('1 / 81 + 20 * -64 / -2 - -7 / -53 + 18'),
657.8802702073142)
def test_calc_051(self):
self.assertEqual(calc('-63 * 27 / 62 / 81 + -45 - -87 - 83 + -74'),
-115.33870967741936)
def test_calc_052(self):
self.assertEqual(calc('-49 + -75 / 90 - 89 * 40 / 62 / 12 * -50'),
189.41397849462365)
def test_calc_053(self):
self.assertEqual(calc('79 + -28 - -15 + -100 / 56 + 10 - 93 / 97'),
73.25552282768777)
def test_calc_054(self):
self.assertEqual(calc('40 * -29 + 27 * -71 / 11 - -73 + -3 * 65'),
-1456.2727272727273)
def test_calc_055(self):
self.assertEqual(calc('-28 - -33 / 13 * 27 + 30 / 34 + -61 * -73'),
4494.420814479638)
def test_calc_056(self):
self.assertEqual(calc('-49 - 54 / -41 - -64 / 37 - 54 * 69 / -92'),
-5.453197099538556)
def test_calc_057(self):
self.assertEqual(calc('-7 - -7 * -24 * 47 * -76 * 96 * -100 * -55'),
316850687993)
def test_calc_058(self):
self.assertEqual(calc('10 / -55 + -73 / 46 - 30 + 40 - -82 / -19'),
3.915435822758477)
def test_calc_059(self):
self.assertEqual(calc('19 - -93 - 11 + 45 / -17 + -51 / 23 * -22'),
147.13554987212277)
def test_calc_060(self):
self.assertEqual(calc('10 / 11 - -13 / 93 / -4 * -63 / -29 * 12'),
-0.001921326726665895)
def test_calc_061(self):
self.assertEqual(calc('57 * -41 - -65 - -49 - 75 - -37 / 82 - 76'),
-2373.548780487805)
def test_calc_062(self):
self.assertEqual(calc('-50 - -61 + 76 / 44 / 71 * 8 - -82 * -6'),
-480.8053777208707)
def test_calc_063(self):
self.assertEqual(calc('-43 / 98 / 2 * 53 + 4 / 100 - -48 + 53'),
89.41244897959183)
def test_calc_064(self):
self.assertEqual(calc('-46 * -95 - -30 + 66 / 29 * 71 - 78 * 46'),
973.5862068965516)
def test_calc_065(self):
self.assertEqual(calc('-34 + 35 / -35 + -32 / -16 + -26 * 96 * -6'),
14943.0)
def test_calc_066(self):
self.assertEqual(calc('81 + -14 + 42 + -41 + -100 * -89 + -78 * -22'),
10684)
def test_calc_067(self):
self.assertEqual(calc('-29 + -56 - -53 + -46 - 76 / 24 * 22 * -6'),
339.99999999999994)
def test_calc_068(self):
self.assertEqual(calc('-94 + -28 + 35 + 67 + 31 - 21 - 94 - -31'), -73)
def test_calc_069(self):
self.assertEqual(calc('-96 * -56 / -24 - -42 - 81 + 37 + -9 * 42'),
-604.0)
def test_calc_070(self):
self.assertEqual(calc('17 * 7 * 39 - -35 + 70 * 28 / -83 / -65'),
4676.363299351251)
def test_calc_071(self):
self.assertEqual(calc('-26 * 88 / 91 / 58 + -5 / -45 + -59 / 52'),
-1.4570018104500861)
def test_calc_072(self):
self.assertEqual(calc('-66 * 28 * 32 - -20 - 50 / -52 - 88 + 39'),
-59164.03846153846)
def test_calc_073(self):
self.assertEqual(calc('76 / 32 - 46 * 39 - 35 * 72 / 40 * -54'),
1610.375)
def test_calc_074(self):
self.assertEqual(calc('61 + 81 / 57 * 29 + -52 / 42 * -78 / 46'),
104.30990519777706)
def test_calc_075(self):
self.assertEqual(calc('-32 - 64 - -14 - 44 + 78 * 47 + -81 / -64'),
3541.265625)
def test_calc_076(self):
self.assertEqual(calc('-59 / -54 * 21 + 73 * 52 * 96 - 86 / 77'),
364437.82756132755)
def test_calc_077(self):
self.assertEqual(calc('-93 / -98 + -36 + 20 * 5 - 61 / -51 * 16'),
84.08623449379752)
def test_calc_078(self):
self.assertEqual(calc('12 * 51 + 33 - -7 - -60 * -54 / -88 / 7'),
657.2597402597403)
def test_calc_079(self):
self.assertEqual(calc('1 + -77 - 8 + 73 / -9 - 66 * -37 * 98'),
239223.88888888888)
def test_calc_080(self):
self.assertEqual(calc('15 + 34 - -54 / -7 / -14 - -62 * -64 - 25'),
-3943.4489795918366)
def test_calc_081(self):
self.assertEqual(calc('-9 - 78 * -2 + 93 - -48 * 57 / 87 + 93'),
364.44827586206895)
def test_calc_082(self):
self.assertEqual(calc('16 + -24 - -75 / -7 + 22 * 42 / 37 * -33'),
-842.8223938223938)
def test_calc_083(self):
self.assertEqual(calc('-60 - 68 * 17 - -77 * 31 + 83 / -35 / 18'),
1170.868253968254)
def test_calc_084(self):
self.assertEqual(calc('-36 - -78 * 45 * -69 * 11 - 13 + 88 + -98'),
-2664149)
def test_calc_085(self):
self.assertEqual(calc('71 - -48 / -38 - -97 * 55 - -91 / -22 * -47'),
5599.145933014354)
def test_calc_086(self):
self.assertEqual(calc('-70 + -58 / 20 / -72 + -97 / 42 + -98 * -49'),
4729.730753968254)
def test_calc_087(self):
self.assertEqual(calc('-100 + 93 / 19 - -35 + -57 + -89 + -88 + -82'),
-376.10526315789474)
def test_calc_088(self):
self.assertEqual(calc('52 * -84 + 94 - -64 * -26 / -66 * 3 - -16'),
-4182.363636363636)
def test_calc_089(self):
self.assertEqual(calc('94 + -7 - 70 / 55 - -53 * 87 + 81 + -23'),
4754.727272727273)
def test_calc_090(self):
self.assertEqual(calc('68 * -51 / -40 / -8 * -6 - -89 + 61 * 85'),
5339.025)
def test_calc_091(self):
self.assertEqual(calc('75 + -95 + 85 / 3 * -39 * -88 + -49 - -63'),
97234.0)
def test_calc_092(self):
self.assertEqual(calc('76 / -8 - 48 + -77 / 26 * 93 + -63 / -27'),
-330.58974358974365)
def test_calc_093(self):
self.assertEqual(calc('-24 - -19 + 15 + -61 * 59 * -56 + -28 * 99'),
198782)
def test_calc_094(self):
self.assertEqual(calc('-22 / 50 / 78 + -51 + -8 * 39 / 76 - -64'),
8.889095816464234)
def test_calc_095(self):
self.assertEqual(calc('-41 / -39 - 48 - -98 / 31 - 67 + -48 * 43'),
-2174.7874276261373)
def test_calc_096(self):
self.assertEqual(calc('-13 * -27 - -46 * -99 - -25 - 53 - -41 + 88'),
-4102)
def test_calc_097(self):
self.assertEqual(calc('92 - 81 - 89 + 63 * 51 / -2 + 73 * -66'),
-6502.5)
def test_calc_098(self):
self.assertEqual(calc('-35 / -62 * -43 - 46 + -88 / 38 / -97 + -36'),
-106.2503194301017)
def test_calc_099(self):
self.assertEqual(calc('53 + -84 * -58 / -39 - 82 + -18 * 4 + -95'),
-320.9230769230769)
def test_calc_100(self):
self.assertEqual(calc('-71 - 24 * -19 - 34 + 65 - 89 - -52 - 41'), 338)
def test_calc_101(self):
self.assertEqual(calc('-62 - -38 - 14 * 54 + -92 * 4 - 70 - 66'), -1284)
def test_calc_102(self):
self.assertEqual(calc('60 * 82 / 72 + 81 - 65 * 40 * -91 - 7'),
236742.33333333334)
def test_calc_103(self):
self.assertEqual(calc('-91 + 55 - -26 + -48 / -17 - -25 + 28 * -25'),
-682.1764705882352)
def test_calc_104(self):
self.assertEqual(calc('-10 * 15 * -25 * -7 * 99 / 55 * -3 / -1'),
-141750.0)
def test_calc_105(self):
self.assertEqual(calc('-37 - -43 / 21 * -42 / 3 / -47 - -52 / -73'),
-37.102399689109106)
def test_calc_106(self):
self.assertEqual(calc('73 + 38 + 64 * 44 - 6 + 38 * 81 * -25'), -74029)
def test_calc_107(self):
self.assertEqual(calc('-67 * -42 / -2 + -4 / -49 + 80 - 10 / -82'),
-1326.7964161274267)
def test_calc_108(self):
self.assertEqual(calc('-28 * 23 * 61 / -2 / -90 + 42 / 27 + 11'),
-205.6888888888889)
def test_calc_109(self):
self.assertEqual(calc('-63 / 4 - -72 / -98 * 1 - -17 / 2 + 76'),
68.01530612244898)
def test_calc_110(self):
self.assertEqual(calc('90 + 78 - -5 - -61 * 31 - -9 * 20 + 16'), 2260)
def test_calc_111(self):
self.assertEqual(calc('-92 + -10 / 71 / 95 - -7 - 4 * -77 * 69'),
21166.99851742031)
def test_calc_112(self):
self.assertEqual(calc('-43 / -93 * 51 - 57 / -53 * -6 * -50 * 27'),
8734.90139987827)
def test_calc_113(self):
self.assertEqual(calc('-41 / 90 - -41 - -76 / -32 / 8 - 4 * 82'),
-287.7524305555556)
def test_calc_114(self):
self.assertEqual(calc('67 * 19 / 40 - -37 / -76 / -66 + -43 + -68'),
-79.16762360446572)
def test_calc_115(self):
self.assertEqual(calc('-80 + 2 * -44 + 49 + -16 / -56 / -78 / 14'),
-119.00026164311879)
def test_calc_116(self):
self.assertEqual(calc('-33 + 72 * 64 / 20 - 96 + -80 / 82 / -16'),
101.4609756097561)
def test_calc_117(self):
self.assertEqual(
calc('(15) + (-29 - 71 / (27)) - (47 * ((((-81 / 52)))) * 16)'),
1154.754985754986)
def test_calc_118(self):
self.assertEqual(
calc('-(-9) * (81 / 78 * -(98)) + (-23 + -(((-(-20 - 30)))) / 40)'),
-940.1730769230769)
def test_calc_119(self):
self.assertEqual(
calc('-(81) / (56 * -66 / -(19)) - (-9 - -((((-8 + -19)))) + -63)'),
98.5836038961039)
def test_calc_120(self):
self.assertEqual(
calc('(-77) + (80 + 83 / (12)) + (34 * (((-(77 - -57)))) * 87)'),
-396362.0833333333)
def test_calc_121(self):
self.assertEqual(
calc('(-12) - (-2 / -36 + (2)) / (86 * ((((-86 * -9)))) + -36)'),
-12.000030897600341)
def test_calc_122(self):
self.assertEqual(
calc('(51) / (-24 + -54 * -(31)) + (-40 + (((-(9 - 44)))) - -80)'),
75.03090909090909)
def test_calc_123(self):
self.assertEqual(
calc('(33) / (-82 / 92 - (55)) / (-38 - -(((-(-49 + 56)))) * 46)'),
0.001640088162841955)
def test_calc_124(self):
self.assertEqual(
calc('(-72) - (-37 + -75 + (62)) + (-95 * ((((63 + 79)))) - -93)'),
-13419)
def test_calc_125(self):
self.assertEqual(
calc('-(66) + (70 - -90 - (7)) / (-96 - -((((-88 + 99)))) * 29)'),
-65.31390134529148)
def test_calc_126(self):
self.assertEqual(
calc('(100) / (60 + 6 * (100)) - (-24 / -((((34 - -74)))) / -44)'),
0.15656565656565657)
def test_calc_127(self):
self.assertEqual(
calc('-(51) * (77 - -76 / -(46)) - (97 * (((-(-78 * 90)))) + -46)'),
-684736.7391304348)
def test_calc_128(self):
self.assertEqual(
calc('-(100) * (79 / 9 + (33)) / (77 / (((-(32 + -35)))) / 40)'),
-6510.8225108225115)
def test_calc_129(self):
self.assertEqual(calc(
'(-10) / (59 / -41 * -(91)) * (28 * -((((-80 / -16)))) + -42)'),
13.898305084745763)
def test_calc_130(self):
self.assertEqual(calc(
'-(-73) + (-19 * 91 + (59)) - (31 - -(((-(-15 * 48)))) / -61)'),
-1616.1967213114754)
def test_calc_131(self):
self.assertEqual(
calc('(10) - (-40 * -18 / (98)) - (81 * (((-(-58 - -25)))) - 99)'),
-2571.3469387755104)
def test_calc_132(self):
self.assertEqual(
calc('-(-5) - (84 + -21 + (11)) * (-77 + -((((-6 * 53)))) / 58)'),
5297.275862068966)
def test_calc_133(self):
self.assertEqual(
calc('(-52) - (-8 / -80 / (57)) / (-96 / (((-(62 - -84)))) * 58)'),
-52.00004600221819)
def test_calc_134(self):
self.assertEqual(
calc('-(-85) / (66 * 15 * -(14)) + (-73 * (((-(71 / 63)))) * -10)'),
-822.7045454545454)
def test_calc_135(self):
self.assertEqual(calc(
'-(-59) + (-11 / -79 - -(72)) - (-23 * (((-(77 + 56)))) - -63)'),
-2990.8607594936707)
def test_calc_136(self):
self.assertEqual(
calc('(-39) + (28 * -92 * -(22)) + (-3 / -((((78 * 5)))) * -97)'),
56632.25384615385)
def test_calc_137(self):
self.assertEqual(
calc('(56) / (96 / -77 - (45)) * (71 - ((((-71 * 27)))) / 85)'),
-113.28286502469565)
def test_calc_138(self):
self.assertEqual(calc(
'(-31) + (-33 + -58 - (97)) + (-20 - -(((-(63 / 47)))) + -82)'),
-322.3404255319149)
def test_calc_139(self):
self.assertEqual(
calc('-(-62) * (54 - 51 / -(78)) * (85 * (((-(-24 + -36)))) - 3)'),
17271380.538461536)
def test_calc_140(self):
self.assertEqual(
calc('-(12) / (81 + -27 / -(85)) + (58 / -((((-40 / 55)))) / 21)'),
3.650049603174603)
def test_calc_141(self):
self.assertEqual(
calc('-(-32) * (-63 + -76 - (44)) - (-17 / ((((54 / 63)))) + 64)'),
-5900.166666666667)
def test_calc_142(self):
self.assertEqual(
calc('-(-46) / (55 * 66 * (68)) + (-8 - ((((-41 - 57)))) / 85)'),
-6.846872467995463)
def test_calc_143(self):
self.assertEqual(
calc('-(71) + (48 - 66 + -(50)) / (82 + ((((-60 + 66)))) * -100)'),
-70.86872586872587)
def test_calc_144(self):
self.assertEqual(calc(
'-(-84) * (22 * 66 + -(46)) + (-94 * -(((-(-43 + 10)))) * -3)'),
108798)
def test_calc_145(self):
self.assertEqual(
calc('-(83) * (-73 + -87 + (18)) / (-11 * ((((13 * 42)))) / 30)'),
-58.871128871128874)
def test_calc_146(self):
self.assertEqual(
calc('(-46) / (-82 / 70 * (48)) + (59 + (((-(-11 + -68)))) - 38)'),
100.8180894308943)
def test_calc_147(self):
self.assertEqual(calc(
'(-51) + (-42 + 92 - -(18)) / (-53 * -(((-(-16 / 45)))) + -46)'),
-53.50409165302782)
def test_calc_148(self):
self.assertEqual(
calc('-(-77) / (82 + 14 * (86)) * (-41 / ((((47 + 28)))) - 96)'),
-5.780787973043028)
def test_calc_149(self):
self.assertEqual(
calc('-(-99) / (97 + -71 - -(91)) - (-42 / ((((57 - -69)))) + 32)'),
-30.82051282051282)
def test_calc_150(self):
self.assertEqual(
calc('-(-48) - (87 * 14 / -(23)) + (-83 - (((-(72 * 19)))) / 37)'),
54.92949471210341)
def test_calc_151(self):
self.assertEqual(
calc('(87) - (-92 * 59 + (80)) - (59 + (((-(-20 - -89)))) * 45)'),
8481)
def test_calc_152(self):
self.assertEqual(
calc('(84) - (60 - 36 / -(88)) + (-48 - -((((-50 + -12)))) + 5)'),
-81.4090909090909)
def test_calc_153(self):
self.assertEqual(
calc('(-45) / (1 / 3 / -(54)) + (18 / ((((-100 - 35)))) * 99)'),
7276.8)
def test_calc_154(self):
self.assertEqual(calc(
'(-62) / (-16 * -18 * -(99)) * (-80 * -((((-89 - 75)))) * -30)'),
855.8922558922559)
def test_calc_155(self):
self.assertEqual(
calc('-(-9) + (8 / -64 * -(27)) + (70 + ((((-78 - -41)))) + 74)'),
119.375)
def test_calc_156(self):
self.assertEqual(
calc('-(75) / (74 - -73 * -(76)) - (31 + (((-(-82 * 70)))) - 87)'),
-5683.986298867373)
def test_calc_157(self):
self.assertEqual(
calc('(-49) * (-91 + -2 * (80)) + (-52 - ((((-47 + 99)))) * 26)'),
10895)
def test_calc_158(self):
self.assertEqual(
calc('-(-39) - (-48 / 33 / (49)) + (75 / (((-(65 + 74)))) * -40)'),
60.612418414062816)
def test_calc_159(self):
self.assertEqual(
calc('-(-18) / (-97 / 46 * (64)) * (-51 + -(((-(-20 * 8)))) + 92)'),
15.871778350515463)
def test_calc_160(self):
self.assertEqual(
calc('-(-19) - (37 - -15 - -(17)) - (91 - (((-(31 / 49)))) + -92)'),
-49.63265306122449)
def test_calc_161(self):
self.assertEqual(
calc('(13) / (34 / -62 * (22)) * (-30 - ((((-75 / 13)))) + -58)'),
88.60695187165776)
def test_calc_162(self):
self.assertEqual(
calc('(-86) - (-16 + -74 / -(50)) / (88 + ((((22 * 80)))) * -3)'),
-86.0027966101695)
def test_calc_163(self):
self.assertEqual(
calc('(-13) * (55 / -63 - (50)) / (37 - (((-(2 * -17)))) * 36)'),
-0.5571602412377475)
def test_calc_164(self):
self.assertEqual(
calc('(-37) + (39 / -12 * -(13)) - (72 - ((((28 / -49)))) - -87)'),
-154.32142857142856)
def test_calc_165(self):
self.assertEqual(
calc('-(1) / (87 / -17 * -(76)) / (-57 - ((((-42 + 69)))) / -33)'),
4.576361112579463e-05)
def test_calc_166(self):
self.assertEqual(calc(
'-(-71) * (38 * -88 / (100)) - (-26 + -(((-(88 / 11)))) - -83)'),
-2439.24)
def test_calc_167(self):
self.assertEqual(calc(
'-(-30) * (-45 + -19 * (89)) - (-81 * (((-(-89 - -13)))) / 81)'),
-52004.0)
def test_calc_168(self):
self.assertEqual(
calc('-(86) - (92 + -11 * -(69)) * (82 / -(((-(68 - 62)))) / 24)'),
-570.5972222222222)
def test_calc_169(self):
self.assertEqual(calc(
'(87) + (-23 / 25 + -(43)) / (-84 / -(((-(-91 / -94)))) + -29)'),
87.3793754152824)
def test_calc_170(self):
self.assertEqual(
calc('-(26) + (-27 + 23 * (85)) + (70 - -((((47 * -29)))) * -39)'),
55129)
def test_calc_171(self):
self.assertEqual(
calc('-(25) * (-66 * 33 - (5)) / (88 + (((-(-44 - 23)))) / 94)'),
615.1876723827797)
def test_calc_172(self):
self.assertEqual(
calc('(-25) * (-75 + 44 * -(50)) * (-7 - -((((-94 * -67)))) / 31)'),
11156673.387096774)
def test_calc_173(self):
self.assertEqual(
calc('-(55) - (-74 - -74 * (49)) + (37 - ((((-77 / -89)))) * 72)'),
-3632.2921348314608)
def test_calc_174(self):
self.assertEqual(
calc('(82) - (25 * 36 + (61)) * (65 - -((((4 + 58)))) - 56)'),
-68149)
def test_calc_175(self):
self.assertEqual(
calc('-(22) + (-11 * 20 / (28)) + (33 * ((((71 / 82)))) * -34)'),
-1001.3449477351917)
def test_calc_176(self):
self.assertEqual(
calc('(57) - (-69 / -68 + (80)) * (-8 * -((((-25 * 74)))) + -24)'),
1201019.0)
def test_calc_177(self):
self.assertEqual(
calc('(43) + (-2 / 18 / (70)) - (93 / (((-(-85 * 95)))) - 23)'),
65.98689567054892)
def test_calc_178(self):
self.assertEqual(
calc('-(5) / (61 * -61 * (54)) - (35 / (((-(-50 - -15)))) + 78)'),
-78.99997511620731)
def test_calc_179(self):
self.assertEqual(
calc('-(32) - (4 + 60 - -(84)) / (-16 * ((((79 - -92)))) - -21)'),
-31.94548802946593)
def test_calc_180(self):
self.assertEqual(
calc('-(64) / (-58 + -63 / -(2)) + (96 + -(((-(58 - 2)))) - -40)'),
194.41509433962264)
def test_calc_181(self):
self.assertEqual(calc(
'-(-62) / (-57 + -63 * (2)) / (-82 + -(((-(64 - 83)))) * -73)'),
-0.0002596151833008814)
def test_calc_182(self):
self.assertEqual(calc(
'-(6) * (-69 + -32 * -(13)) * (68 + -(((-(-40 + -55)))) + 35)'),
-16656)
def test_calc_183(self):
self.assertEqual(
calc('(83) + (-96 * -20 / -(48)) / (-73 - -((((21 / 1)))) + 83)'),
81.70967741935483)
def test_calc_184(self):
self.assertEqual(
calc('-(-21) / (65 + -60 - -(51)) / (96 - ((((-12 - 68)))) - -50)'),
0.00165929203539823)
def test_calc_185(self):
self.assertEqual(
calc('-(2) * (69 * 15 + -(6)) - (66 / -(((-(67 * 4)))) * -90)'),
-2035.8358208955224)
def test_calc_186(self):
self.assertEqual(
calc('(-15) * (-58 * 29 + -(64)) - (-66 / ((((-74 / 83)))) - -95)'),
26020.972972972973)
def test_calc_187(self):
self.assertEqual(calc(
'-(17) * (-74 - -83 + -(20)) + (-88 / -((((-34 - 99)))) / -19)'),
187.0348239018599)
def test_calc_188(self):
self.assertEqual(
calc('-(28) / (-22 / 95 - -(53)) - (69 - -((((46 / -10)))) * 94)'),
362.8693796130062)
def test_calc_189(self):
self.assertEqual(
calc('(60) + (37 + 78 / -(25)) / (-54 + -(((-(-56 - -81)))) * 25)'),
60.05933450087566)
def test_calc_190(self):
self.assertEqual(calc(
'-(25) / (-86 - -72 - (55)) / (-48 + -(((-(24 - 66)))) + -21)'),
-0.0032641336989163074)
def test_calc_191(self):
self.assertEqual(
calc('-(45) * (-95 + -46 * -(78)) * (-39 / (((-(35 / 15)))) * 82)'),
-215433269.99999997)
def test_calc_192(self):
self.assertEqual(
calc('(-53) * (14 - 18 - -(70)) * (61 + ((((19 / -13)))) * -5)'),
-238940.3076923077)
def test_calc_193(self):
self.assertEqual(calc(
'(-74) + (-13 / -100 / (30)) - (-74 + ((((-16 - -32)))) - 28)'),
12.004333333333335)
def test_calc_194(self):
self.assertEqual(
calc('-(76) * (66 - 76 + (23)) * (70 * (((-(-15 + -48)))) / 6)'),
-726180.0)
def test_calc_195(self):
self.assertEqual(
calc('(91) + (19 * 75 * (65)) + (-43 + -(((-(-54 + 10)))) / -39)'),
92674.1282051282)
def test_calc_196(self):
self.assertEqual(calc(
'-(-21) + (28 / -32 - (54)) / (26 * -(((-(-17 * -54)))) / 28)'),
20.93562510474275)
def test_calc_197(self):
self.assertEqual(
calc('(-47) / (-97 * -59 * (90)) + (90 + (((-(56 * 76)))) * 9)'),
-38214.000091249734)
def test_calc_198(self):
self.assertEqual(
calc('(-44) - (-16 + -94 / (29)) * (24 * -(((-(-9 / 79)))) / -1)'),
8.609340899170661)
def test_calc_199(self):
self.assertEqual(
calc('-(85) * (-41 / -26 - -(42)) + (50 / -((((65 / 23)))) / 100)'),
-3704.215384615385)
def test_calc_200(self):
self.assertEqual(calc(
'-(40) * (13 * 93 + -(78)) * (-38 * -(((-(-34 * 79)))) - -12)'),
-4618099200)
def test_calc_201(self):
self.assertEqual(
calc('(7) - (73 + 25 * (7)) / (20 + ((((-40 + 97)))) + 29)'),
4.660377358490566)
def test_calc_202(self):
self.assertEqual(
calc('(54) + (13 / -50 * (29)) - (-64 - -(((-(-34 * 52)))) / -50)'),
145.82)
def test_calc_203(self):
self.assertEqual(
calc('(-53) + (100 * -46 - -(28)) * (91 * (((-(-17 - 47)))) / 96)'),
-277421.0)
def test_calc_204(self):
self.assertEqual(
calc('(30) - (67 * 13 * -(97)) + (-24 - ((((-88 / 99)))) * -31)'),
84465.44444444444)
def test_calc_205(self):
self.assertEqual(
calc('-(95) - (-40 + -44 * (77)) / (32 - -((((77 - 82)))) * -8)'),
-47.388888888888886)
def test_calc_206(self):
self.assertEqual(calc(
'-(-97) * (-97 - -86 * -(23)) * (-46 + ((((-69 - 44)))) / -37)'),
8643945.27027027)
def test_calc_207(self):
self.assertEqual(
calc('(-13) + (9 / 14 / (54)) - (-29 - -((((-48 / 38)))) + 10)'),
7.275062656641605)
def test_calc_208(self):
self.assertEqual(
calc('(-40) - (-85 * 22 - (78)) - (2 - (((-(-19 * 62)))) + 60)'),
3024)
def test_calc_209(self):
self.assertEqual(
calc('-(-38) * (8 - 21 * (98)) + (75 - -(((-(-25 + -38)))) * -97)'),
-83936)
def test_calc_210(self):
self.assertEqual(calc(
'(-3) * (-58 * 17 / (40)) * (-84 / -(((-(-42 + -70)))) * -69)'),
-3826.9124999999995)
def test_calc_211(self):
self.assertEqual(
calc('-(1) / (-20 / -53 * -(49)) + (-79 + ((((-57 - 9)))) - -5)'),
-139.94591836734693)
def test_calc_212(self):
self.assertEqual(
calc('-(20) * (-38 - -76 + (96)) + (-18 + (((-(69 + -73)))) / 60)'),
-2697.9333333333334)
def test_calc_213(self):
self.assertEqual(
calc('-(69) * (28 + -76 * (56)) + (90 + -(((-(100 - 2)))) / 61)'),
291823.60655737703)
def test_calc_214(self):
self.assertEqual(calc(
'-(-86) - (64 + 14 / (22)) + (-73 * -(((-(-44 / -80)))) * -20)'),
824.3636363636365)
def test_calc_215(self):
self.assertEqual(calc(
'(-82) / (-3 / 40 * (95)) / (-53 - -(((-(-29 * -20)))) * -12)'),
0.001666247564763944)
def test_calc_216(self):
self.assertEqual(
calc('-(-85) * (31 - -16 * (69)) * (75 * -((((36 - -33)))) / -32)'),
15601816.40625)
def test_calc_217(self):
self.assertEqual(calc('40- 10- 8- -72'), 94)
def test_calc_218(self):
self.assertEqual(calc('-33- 31- 18- 54'), -136)
def test_calc_219(self):
self.assertEqual(calc('-75- -37- 62- 50'), -150)
def test_calc_220(self):
self.assertEqual(calc('-59- -28- 6- 8'), -45)
def test_calc_221(self):
self.assertEqual(calc('62- -57- -78- -95'), 292)
def test_calc_222(self):
self.assertEqual(calc('-15- 94- 60- 4'), -173)
def test_calc_223(self):
self.assertEqual(calc('92- -19- 22- 36'), 53)
def test_calc_224(self):
self.assertEqual(calc('4- 8- 53- -36'), -21)
def test_calc_225(self):
self.assertEqual(calc('21- -71- 51- 80'), -39)
def test_calc_226(self):
self.assertEqual(calc('-12- -11- 66- -70'), 3)
def test_calc_227(self):
self.assertEqual(calc('50- 80- 27- -11'), -46)
def test_calc_228(self):
self.assertEqual(calc('26- 49- -72- 70'), -21)
def test_calc_229(self):
self.assertEqual(calc('40- 36- -48- 100'), -48)
def test_calc_230(self):
self.assertEqual(calc('-79- -58- -35- 67'), -53)
def test_calc_231(self):
self.assertEqual(calc('-2- -74- 6- 89'), -23)
def test_calc_232(self):
self.assertEqual(calc('-31- -85- 36- -51'), 69)
def test_calc_233(self):
self.assertEqual(calc('74- -32- 24- -1'), 83)
def test_calc_234(self):
self.assertEqual(calc('34- -99- 49- -9'), 93)
def test_calc_235(self):
self.assertEqual(calc('41- 51- -19- -10'), 19)
def test_calc_236(self):
self.assertEqual(calc('-76- -26- 19- 77'), -146)
def test_calc_237(self):
self.assertEqual(calc('48- -51- 22- -16'), 93)
def test_calc_238(self):
self.assertEqual(calc('-13- 96- -90- -63'), 44)
def test_calc_239(self):
self.assertEqual(calc('32- -24- -62- 80'), 38)
def test_calc_240(self):
self.assertEqual(calc('49- 63- 5- -48'), 29)
def test_calc_241(self):
self.assertEqual(calc('23- -22- 88- -25'), -18)
def test_calc_242(self):
self.assertEqual(calc('59- -85- 7- 41'), 96)
def test_calc_243(self):
self.assertEqual(calc('-93- 57- -31- -92'), -27)
def test_calc_244(self):
self.assertEqual(calc('-73- -5- 21- 79'), -168)
def test_calc_245(self):
self.assertEqual(calc('-78- -48- 53- 94'), -177)
def test_calc_246(self):
self.assertEqual(calc('-51- -20- 72- 41'), -144)
def test_calc_247(self):
self.assertEqual(calc('-47- -61- 20- 32'), -38)
def test_calc_248(self):
self.assertEqual(calc('-75- -69- -32- -40'), 66)
def test_calc_249(self):
self.assertEqual(calc('-11- -11- 5- 15'), -20)
def test_calc_250(self):
self.assertEqual(calc('-3- -64- -27- -13'), 101)
def test_calc_251(self):
self.assertEqual(calc('21- -52- 7- -38'), 104)
def test_calc_252(self):
self.assertEqual(calc('-11- 78- 31- -81'), -39)
def test_calc_253(self):
self.assertEqual(calc('-4- 75- -23- -54'), -2)
def test_calc_254(self):
self.assertEqual(calc('-84- 50- -60- 34'), -108)
def test_calc_255(self):
self.assertEqual(calc('94- -10- -41- 87'), 58)
def test_calc_256(self):
self.assertEqual(calc('-23- -8- 34- -100'), 51)
def test_calc_257(self):
self.assertEqual(calc('63- -70- 23- 14'), 96)
def test_calc_258(self):
self.assertEqual(calc('67- -75- -38- 23'), 157)
def test_calc_259(self):
self.assertEqual(calc('52- 6- 58- 24'), -36)
def test_calc_260(self):
self.assertEqual(calc('-95- 40- 94- -72'), -157)
def test_calc_261(self):
self.assertEqual(calc('-55- 17- 36- 60'), -168)
def test_calc_262(self):
self.assertEqual(calc('-100- 27- 7- 19'), -153)
def test_calc_263(self):
self.assertEqual(calc('-78- -14- -49- 74'), -89)
def test_calc_264(self):
self.assertEqual(calc('-52- 76- 43- 48'), -219)
def test_calc_265(self):
self.assertEqual(calc('-27- 45- 28- -67'), -33)
def test_calc_266(self):
self.assertEqual(calc('-64- 18- 38- 97'), -217)
def test_calc_267(self):
self.assertEqual(calc('48- 34- -55- -64'), 133)
def test_calc_268(self):
self.assertEqual(calc('-35- 80- -94- 33'), -54)
def test_calc_269(self):
self.assertEqual(calc('-37- 38- 74- -6'), -143)
def test_calc_270(self):
self.assertEqual(calc('53- -17- -55- -26'), 151)
def test_calc_271(self):
self.assertEqual(calc('-29- 23- 57- -80'), -29)
def test_calc_272(self):
self.assertEqual(calc('-31- -61- 25- -6'), 11)
def test_calc_273(self):
self.assertEqual(calc('-28- -57- 69- -76'), 36)
def test_calc_274(self):
self.assertEqual(calc('32- -84- -32- -20'), 168)
def test_calc_275(self):
self.assertEqual(calc('-86- -24- -97- -32'), 67)
def test_calc_276(self):
self.assertEqual(calc('69- 54- -83- -41'), 139)
def test_calc_277(self):
self.assertEqual(calc('-99- -98- -56- -10'), 65)
def test_calc_278(self):
self.assertEqual(calc('-14- 22- -82- -32'), 78)
def test_calc_279(self):
self.assertEqual(calc('-83- 5- -49- 38'), -77)
def test_calc_280(self):
self.assertEqual(calc('49- 33- -25- 93'), -52)
def test_calc_281(self):
self.assertEqual(calc('5- -99- -94- -7'), 205)
def test_calc_282(self):
self.assertEqual(calc('-9- -41- -9- -99'), 140)
def test_calc_283(self):
self.assertEqual(calc('5- 40- 46- 35'), -116)
def test_calc_284(self):
self.assertEqual(calc('-96- 45- -12- -45'), -84)
def test_calc_285(self):
self.assertEqual(calc('-92- -54- -25- 87'), -100)
def test_calc_286(self):
self.assertEqual(calc('-25- 40- 47- -48'), -64)
def test_calc_287(self):
self.assertEqual(calc('27- -75- -67- -57'), 226)
def test_calc_288(self):
self.assertEqual(calc('24- 46- -94- 82'), -10)
def test_calc_289(self):
self.assertEqual(calc('77- -6- 62- 95'), -74)
def test_calc_290(self):
self.assertEqual(calc('27- 95- 96- -84'), -80)
def test_calc_291(self):
self.assertEqual(calc('23- -9- -48- -2'), 82)
def test_calc_292(self):
self.assertEqual(calc('84- 73- 26- 93'), -108)
def test_calc_293(self):
self.assertEqual(calc('30- 67- 88- -59'), -66)
def test_calc_294(self):
self.assertEqual(calc('17- 48- 23- 70'), -124)
def test_calc_295(self):
self.assertEqual(calc('-70- 37- -67- 76'), -116)
def test_calc_296(self):
self.assertEqual(calc('-64- -49- -41- 94'), -68)
def test_calc_297(self):
self.assertEqual(calc('4- 54- -86- -33'), 69)
def test_calc_298(self):
self.assertEqual(calc('-42- 61- 72- 39'), -214)
def test_calc_299(self):
self.assertEqual(calc('13- -52- -69- 96'), 38)
def test_calc_300(self):
self.assertEqual(calc('81- 15- -5- -13'), 84)
def test_calc_301(self):
self.assertEqual(calc('88- -66- 53- 10'), 91)
def test_calc_302(self):
self.assertEqual(calc('-60- -96- 52- 55'), -71)
def test_calc_303(self):
self.assertEqual(calc('-7- -18- -54- -19'), 84)
def test_calc_304(self):
self.assertEqual(calc('73- -47- -59- 60'), 119)
def test_calc_305(self):
self.assertEqual(calc('-43- 7- -64- 17'), -3)
def test_calc_306(self):
self.assertEqual(calc('5- 41- -44- -2'), 10)
def test_calc_307(self):
self.assertEqual(calc('31- -83- 69- 81'), -36)
def test_calc_308(self):
self.assertEqual(calc('37- -73- 49- -53'), 114)
def test_calc_309(self):
self.assertEqual(calc('33- 8- -47- -60'), 132)
def test_calc_310(self):
self.assertEqual(calc('14- 93- 82- -4'), -157)
def test_calc_311(self):
self.assertEqual(calc('-9- -32- -26- 21'), 28)
def test_calc_312(self):
self.assertEqual(calc('-32- 33- 10- -26'), -49)
def test_calc_313(self):
self.assertEqual(calc('89- -81- 5- 1'), 164)
def test_calc_314(self):
self.assertEqual(calc('25- 59- 22- 94'), -150)
def test_calc_315(self):
self.assertEqual(calc('24- -86- -61- -28'), 199)
def test_calc_316(self):
self.assertEqual(calc('-1- -44- 45- 62'), -64)
| 34.097424 | 80 | 0.454509 |
7940e6cad1672aa487f8ed6c103179a0622ce4b6 | 2,050 | py | Python | acurl/tests/test_to_curl.py | markgreene74/mite | 339bdfc39be30534ea2169d8257469bd0ff535fb | [
"MIT"
] | 17 | 2019-11-14T22:32:56.000Z | 2022-02-01T15:38:03.000Z | acurl/tests/test_to_curl.py | markgreene74/mite | 339bdfc39be30534ea2169d8257469bd0ff535fb | [
"MIT"
] | 35 | 2020-01-08T10:50:31.000Z | 2022-02-17T17:00:34.000Z | acurl/tests/test_to_curl.py | markgreene74/mite | 339bdfc39be30534ea2169d8257469bd0ff535fb | [
"MIT"
] | 4 | 2019-11-14T14:48:18.000Z | 2020-05-06T22:09:25.000Z | import pytest
from helpers import create_request
import acurl
def test_to_curl():
r = create_request("GET", "http://foo.com")
assert r.to_curl() == "curl -X GET http://foo.com"
def test_to_curl_headers():
r = create_request(
"GET", "http://foo.com", headers=("Foo: bar", "My-Header: is-awesome")
)
assert (
r.to_curl()
== "curl -X GET -H 'Foo: bar' -H 'My-Header: is-awesome' http://foo.com"
)
def test_to_curl_cookies():
r = create_request(
"GET",
"http://foo.com",
cookies=(acurl._Cookie(False, "foo.com", True, "/", False, 0, "123", "456"),),
)
assert r.to_curl() == "curl -X GET --cookie 123=456 http://foo.com"
def test_to_curl_multiple_cookies():
r = create_request(
"GET",
"http://foo.com",
cookies=(
acurl._Cookie(False, "foo.com", True, "/", False, 0, "123", "456"),
acurl._Cookie(False, "foo.com", True, "/", False, 0, "789", "abc"),
),
)
assert r.to_curl() == "curl -X GET --cookie '123=456;789=abc' http://foo.com"
@pytest.mark.skip(reason="unimplemented")
def test_to_curl_cookies_wrong_domain():
# I'm not sure if this is a valid test case...Request objects should
# probably only be constructed via Session.request, which always creates
# cookies for the domain of the request. So the case this is exercising
# won't ever happen.
r = create_request(
"GET",
"http://foo.com",
cookies=(
acurl._Cookie(
False,
"bar.com", # The domain doesn't match, the cookie should not be passed
True,
"/",
False,
0,
"123",
"456",
),
),
)
assert r.to_curl() == "curl -X GET http://foo.com"
def test_to_curl_auth():
r = create_request("GET", "http://foo.com", auth=("user", "pass"))
assert r.to_curl() == "curl -X GET --user user:pass http://foo.com"
| 28.873239 | 87 | 0.540976 |
7940e70c7114480e2b25391b63da5ddfa8250971 | 6,690 | py | Python | kubernetes/client/models/v1beta1_custom_resource_definition_list.py | anemerovsky-essextec/python | 6e40b9169b27c3f1f9422c0f6dd1cd9caef8d57c | [
"Apache-2.0"
] | null | null | null | kubernetes/client/models/v1beta1_custom_resource_definition_list.py | anemerovsky-essextec/python | 6e40b9169b27c3f1f9422c0f6dd1cd9caef8d57c | [
"Apache-2.0"
] | null | null | null | kubernetes/client/models/v1beta1_custom_resource_definition_list.py | anemerovsky-essextec/python | 6e40b9169b27c3f1f9422c0f6dd1cd9caef8d57c | [
"Apache-2.0"
] | null | null | null | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.12.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from pprint import pformat
from six import iteritems
import re
class V1beta1CustomResourceDefinitionList(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'api_version': 'str',
'items': 'list[V1beta1CustomResourceDefinition]',
'kind': 'str',
'metadata': 'V1ListMeta'
}
attribute_map = {
'api_version': 'apiVersion',
'items': 'items',
'kind': 'kind',
'metadata': 'metadata'
}
def __init__(self, api_version=None, items=None, kind=None, metadata=None):
"""
V1beta1CustomResourceDefinitionList - a model defined in Swagger
"""
self._api_version = None
self._items = None
self._kind = None
self._metadata = None
self.discriminator = None
if api_version is not None:
self.api_version = api_version
self.items = items
if kind is not None:
self.kind = kind
if metadata is not None:
self.metadata = metadata
@property
def api_version(self):
"""
Gets the api_version of this V1beta1CustomResourceDefinitionList.
APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/api-conventions.md#resources
:return: The api_version of this V1beta1CustomResourceDefinitionList.
:rtype: str
"""
return self._api_version
@api_version.setter
def api_version(self, api_version):
"""
Sets the api_version of this V1beta1CustomResourceDefinitionList.
APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/api-conventions.md#resources
:param api_version: The api_version of this V1beta1CustomResourceDefinitionList.
:type: str
"""
self._api_version = api_version
@property
def items(self):
"""
Gets the items of this V1beta1CustomResourceDefinitionList.
Items individual CustomResourceDefinitions
:return: The items of this V1beta1CustomResourceDefinitionList.
:rtype: list[V1beta1CustomResourceDefinition]
"""
return self._items
@items.setter
def items(self, items):
"""
Sets the items of this V1beta1CustomResourceDefinitionList.
Items individual CustomResourceDefinitions
:param items: The items of this V1beta1CustomResourceDefinitionList.
:type: list[V1beta1CustomResourceDefinition]
"""
if items is None:
raise ValueError("Invalid value for `items`, must not be `None`")
self._items = items
@property
def kind(self):
"""
Gets the kind of this V1beta1CustomResourceDefinitionList.
Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/api-conventions.md#types-kinds
:return: The kind of this V1beta1CustomResourceDefinitionList.
:rtype: str
"""
return self._kind
@kind.setter
def kind(self, kind):
"""
Sets the kind of this V1beta1CustomResourceDefinitionList.
Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/api-conventions.md#types-kinds
:param kind: The kind of this V1beta1CustomResourceDefinitionList.
:type: str
"""
self._kind = kind
@property
def metadata(self):
"""
Gets the metadata of this V1beta1CustomResourceDefinitionList.
:return: The metadata of this V1beta1CustomResourceDefinitionList.
:rtype: V1ListMeta
"""
return self._metadata
@metadata.setter
def metadata(self, metadata):
"""
Sets the metadata of this V1beta1CustomResourceDefinitionList.
:param metadata: The metadata of this V1beta1CustomResourceDefinitionList.
:type: V1ListMeta
"""
self._metadata = metadata
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
if not isinstance(other, V1beta1CustomResourceDefinitionList):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
| 31.857143 | 281 | 0.625411 |
7940ebb43087fee2dc062c5637d43fd3569d3fea | 2,366 | py | Python | ulfs/gumbel.py | asappresearch/neural-ilm | fd7e09960525391f4084a5753429deabd7ff00aa | [
"MIT"
] | null | null | null | ulfs/gumbel.py | asappresearch/neural-ilm | fd7e09960525391f4084a5753429deabd7ff00aa | [
"MIT"
] | null | null | null | ulfs/gumbel.py | asappresearch/neural-ilm | fd7e09960525391f4084a5753429deabd7ff00aa | [
"MIT"
] | 2 | 2021-02-25T04:42:14.000Z | 2021-02-25T04:43:06.000Z | import torch
from torch.autograd import Variable
import torch.nn.functional as F
def sample_gumbel(shape, eps=1e-10):
"""
Sample from Gumbel(0, 1)
based on
https://github.com/ericjang/gumbel-softmax/blob/3c8584924603869e90ca74ac20a6a03d99a91ef9/Categorical%20VAE.ipynb ,
(MIT license)
"""
U = torch.rand(shape).float()
return - torch.log(eps - torch.log(U + eps))
def gumbel_softmax_sample(logits, tau, eps=1e-10):
"""
Draw a sample from the Gumbel-Softmax distribution
based on
https://github.com/ericjang/gumbel-softmax/blob/3c8584924603869e90ca74ac20a6a03d99a91ef9/Categorical%20VAE.ipynb
(MIT license)
"""
dims = len(logits.size())
gumbel_noise = sample_gumbel(logits.size(), eps=eps)
y = logits + Variable(gumbel_noise)
res = F.softmax(y / tau)
return res
def gumbel_softmax(logits, tau, hard, eps=1e-10):
"""
Sample from the Gumbel-Softmax distribution and optionally discretize.
Args:
logits: [batch_size, n_class] unnormalized log-probs
tau: non-negative scalar temperature
hard: if True, take argmax, but differentiate w.r.t. soft sample y
Returns:
[batch_size, n_class] sample from the Gumbel-Softmax distribution.
If hard=True, then the returned sample will be one-hot, otherwise it will
be a probability distribution that sums to 1 across classes
Constraints:
- this implementation only works on batch_size x num_features tensor for now
based on
https://github.com/ericjang/gumbel-softmax/blob/3c8584924603869e90ca74ac20a6a03d99a91ef9/Categorical%20VAE.ipynb ,
(MIT license)
"""
shape = logits.size()
assert len(shape) == 2
y_soft = gumbel_softmax_sample(logits, tau=tau, eps=eps)
if hard:
_, k = y_soft.data.max(-1)
# this bit is based on
# https://discuss.pytorch.org/t/stop-gradients-for-st-gumbel-softmax/530/5
y_hard = torch.FloatTensor(*shape).zero_().scatter_(-1, k.view(-1, 1), 1.0)
# this cool bit of code achieves two things:
# - makes the output value exactly one-hot (since we add then
# subtract y_soft value)
# - makes the gradient equal to y_soft gradient (since we strip
# all other gradients)
y = Variable(y_hard - y_soft.data) + y_soft
else:
y = y_soft
return y
| 36.4 | 118 | 0.681319 |
7940ebd0885aea30f08ccb8bf1bdb86250de6fd7 | 6,450 | py | Python | llvm/utils/lit/lit/TestingConfig.py | vusec/typesan | 831ca2af1a629e8ea93bb8c5b4215f12247b595c | [
"Apache-2.0"
] | 30 | 2016-09-06T06:58:43.000Z | 2021-12-23T11:59:38.000Z | llvm/utils/lit/lit/TestingConfig.py | vusec/typesan | 831ca2af1a629e8ea93bb8c5b4215f12247b595c | [
"Apache-2.0"
] | 1 | 2018-05-15T00:55:37.000Z | 2018-05-15T00:55:37.000Z | llvm/utils/lit/lit/TestingConfig.py | vusec/typesan | 831ca2af1a629e8ea93bb8c5b4215f12247b595c | [
"Apache-2.0"
] | 17 | 2016-10-24T06:08:16.000Z | 2022-02-18T17:27:14.000Z | import os
import sys
OldPy = sys.version_info[0] == 2 and sys.version_info[1] < 7
class TestingConfig:
""""
TestingConfig - Information on the tests inside a suite.
"""
@staticmethod
def fromdefaults(litConfig):
"""
fromdefaults(litConfig) -> TestingConfig
Create a TestingConfig object with default values.
"""
# Set the environment based on the command line arguments.
environment = {
'PATH' : os.pathsep.join(litConfig.path +
[os.environ.get('PATH','')]),
'LLVM_DISABLE_CRASH_REPORT' : '1',
}
pass_vars = ['LIBRARY_PATH', 'LD_LIBRARY_PATH', 'SYSTEMROOT', 'TERM',
'LD_PRELOAD', 'ASAN_OPTIONS', 'UBSAN_OPTIONS',
'LSAN_OPTIONS', 'ADB', 'ANDROID_SERIAL']
for var in pass_vars:
val = os.environ.get(var, '')
# Check for empty string as some variables such as LD_PRELOAD cannot be empty
# ('') for OS's such as OpenBSD.
if val:
environment[var] = val
if sys.platform == 'win32':
environment.update({
'INCLUDE' : os.environ.get('INCLUDE',''),
'PATHEXT' : os.environ.get('PATHEXT',''),
'PYTHONUNBUFFERED' : '1',
'TEMP' : os.environ.get('TEMP',''),
'TMP' : os.environ.get('TMP',''),
})
# The option to preserve TEMP, TMP, and TMPDIR.
# This is intended to check how many temporary files would be generated
# (and be not cleaned up) in automated builders.
if 'LIT_PRESERVES_TMP' in os.environ:
environment.update({
'TEMP' : os.environ.get('TEMP',''),
'TMP' : os.environ.get('TMP',''),
'TMPDIR' : os.environ.get('TMPDIR',''),
})
# Set the default available features based on the LitConfig.
available_features = []
if litConfig.useValgrind:
available_features.append('valgrind')
if litConfig.valgrindLeakCheck:
available_features.append('vg_leak')
return TestingConfig(None,
name = '<unnamed>',
suffixes = set(),
test_format = None,
environment = environment,
substitutions = [],
unsupported = False,
test_exec_root = None,
test_source_root = None,
excludes = [],
available_features = available_features,
pipefail = True)
def load_from_path(self, path, litConfig):
"""
load_from_path(path, litConfig)
Load the configuration module at the provided path into the given config
object.
"""
# Load the config script data.
data = None
if not OldPy:
f = open(path)
try:
data = f.read()
except:
litConfig.fatal('unable to load config file: %r' % (path,))
f.close()
# Execute the config script to initialize the object.
cfg_globals = dict(globals())
cfg_globals['config'] = self
cfg_globals['lit_config'] = litConfig
cfg_globals['__file__'] = path
try:
if OldPy:
execfile(path, cfg_globals)
else:
exec(compile(data, path, 'exec'), cfg_globals, None)
if litConfig.debug:
litConfig.note('... loaded config %r' % path)
except SystemExit:
e = sys.exc_info()[1]
# We allow normal system exit inside a config file to just
# return control without error.
if e.args:
raise
except:
import traceback
litConfig.fatal(
'unable to parse config file %r, traceback: %s' % (
path, traceback.format_exc()))
self.finish(litConfig)
def __init__(self, parent, name, suffixes, test_format,
environment, substitutions, unsupported,
test_exec_root, test_source_root, excludes,
available_features, pipefail, limit_to_features = [],
is_early = False):
self.parent = parent
self.name = str(name)
self.suffixes = set(suffixes)
self.test_format = test_format
self.environment = dict(environment)
self.substitutions = list(substitutions)
self.unsupported = unsupported
self.test_exec_root = test_exec_root
self.test_source_root = test_source_root
self.excludes = set(excludes)
self.available_features = set(available_features)
self.pipefail = pipefail
# This list is used by TestRunner.py to restrict running only tests that
# require one of the features in this list if this list is non-empty.
# Configurations can set this list to restrict the set of tests to run.
self.limit_to_features = set(limit_to_features)
# Whether the suite should be tested early in a given run.
self.is_early = bool(is_early)
def finish(self, litConfig):
"""finish() - Finish this config object, after loading is complete."""
self.name = str(self.name)
self.suffixes = set(self.suffixes)
self.environment = dict(self.environment)
self.substitutions = list(self.substitutions)
if self.test_exec_root is not None:
# FIXME: This should really only be suite in test suite config
# files. Should we distinguish them?
self.test_exec_root = str(self.test_exec_root)
if self.test_source_root is not None:
# FIXME: This should really only be suite in test suite config
# files. Should we distinguish them?
self.test_source_root = str(self.test_source_root)
self.excludes = set(self.excludes)
@property
def root(self):
"""root attribute - The root configuration for the test suite."""
if self.parent is None:
return self
else:
return self.parent.root
| 38.622754 | 89 | 0.545116 |
7940ec0266e43981dcef61d5fcd769a14fe8f985 | 5,780 | py | Python | datamanager/transforms.py | nielswart/inetbfa-data-conversion | bf2a28c19608babe32846036122584a1dccd34a1 | [
"Apache-2.0"
] | 1 | 2016-03-03T10:32:47.000Z | 2016-03-03T10:32:47.000Z | datamanager/transforms.py | nielswart/inetbfa-data-conversion | bf2a28c19608babe32846036122584a1dccd34a1 | [
"Apache-2.0"
] | null | null | null | datamanager/transforms.py | nielswart/inetbfa-data-conversion | bf2a28c19608babe32846036122584a1dccd34a1 | [
"Apache-2.0"
] | null | null | null | import pandas as pd
import numpy as np
from functools import partial
from os import path
from datamanager.envs import MASTER_DATA_PATH
'''
This module contains equity indicator and transformation functions for time series data based on pandas DataFrame's
'''
def calc_booktomarket(close, bookvalue):
'''
'''
# should forward fill - the book-value made known at a certain date is valid for the next year / or until the next book value is available
bookval = bookvalue.ffill()
b2m = bookval / close
return b2m
def detrended_oscillator(close):
'''
Calculate the detrended oscillator
'''
ma20 = partial(moving_avg, days=20)
ma50 = partial(moving_avg, days=50)
max20 = partial(max, period = 20)
ixlr = index_log_returns(close)
assert isinstance(ixlr.index, pd.DatetimeIndex)
sma = ma20(ixlr)
lma = ma50(ixlr)
maximum = max20(ixlr)
do = (sma - lma) / maximum
return do
def resample_monthly(data, how = 'last'):
'''
Resample the data to a monthly frequency using a specific aggregation function
'''
if(how == 'sum'):
return data.resample('M').sum()
elif(how == 'mean'):
return data.resample('M').mean()
return data.resample('M').last()
def moving_avg(data, days, min_days = None):
'''
Calculate the moving average of the daily data
Parameters
---------
data : pandas.DataFrame
days : int
min_days : int
Returns
-----------
'''
return pd.rolling_mean(data, days, min_periods=min_days, freq='D')
def momentum_monthly(close, start_lag, end_lag):
'''
Calculate the momentum from monthly closing prices
Parameters
-----
close : pandas.DataFrame
The closing prices to calculate the momentum from (columns are security identifiers and the index is a time series index)
start_lag : int
the starting month to calculate the momentum from relative to the newest (most recent) month in the data set (0). E.g. -12 is 12 months ago -1 is one month ago
end_lag : int
the ending month to calculate the momentum to relative to the most recent month.
Returns
----------
momentum : pandas.DataFrame
The price momentum
'''
assert close.index.freq == "M"
# shift dates to align and calculate momentum
mom = np.log(close.shift(end_lag)) - np.log(close.shift(start_lag))
return mom
def earnings_momentum(ey, close, start_lag, end_lag):
'''
Calculate the momentum in the fundamental earnings of the company derived from the EY and the closing price
'''
def pead_momentum(announcements, close):
'''
Calculate the price momentum from the most recent earnings announcement normalised to annualised returns
Parameters
-----------
announcements : pandas.DataFraem
close : pandas.DataFrame
Returns
-----------
'''
assert len(announcements.index) == len(close.index)
assert len(announcements.columns) == len(close.columns)
# make 1 at every earnings announcement
anndays = announcements.applymap(lambda x: 0 if np.isnan(x) else 1)
ann_data = anndays.as_matrix()
last_ann_price = close * anndays
last_ann_price = last_ann_price.applymap(lambda x: np.NaN if x == 0 else x)
last_ann_price = last_ann_price.ffill()
# convert this to util function taking a predicate
days_since_data = np.ndarray([len(anndays.index), len(anndays.columns)])
col = 0
ann_data = anndays.as_matrix()
for ticker in anndays.columns:
days_since = 0
row = 0
for day in anndays.index:
if (ann_data[row, col] == 1):
days_since = 1.0
else:
days_since += 1.0
days_since_data[row, col] = days_since
row += 1
col += 1
# calculate returns
dsdf = pd.DataFrame(days_since_data, index = anndays.index, columns = anndays.columns)
norm_factor = 252.0 / dsdf
norm_mom = (np.log(close) - np.log(last_ann_price)) * norm_factor
return norm_mom
def earnings_surprise(announcements, close):
'''
Calculate the earnings surprise defined as the the cumulative return after a company earnings announcement for the days 0-2
Returns
-----
surprise : pandas.DataFrame
The cumulative return (surprise) values on the announcement days (index) - all NaN rows removed
'''
ea = announcements.dropna(how='all')
# calculate the log returns
logret = log_returns(close.drop_na(how='all'))
# calculate the 3 day rolling sum of returns
sum3day = pd.rolling_sum(logret.shift(-2), 3)
# get the 3 day sum - the surprise
ann = ea.applymap(lambda val: 0 if np.isnan(val) else 1)
return ann * sum3day
def earnings_surprise_changes(announcements, close):
'''
Calculates the change in earnings suprises by comparing an earnings surprise to a previous surprise
'''
# calculate the earnings surprise
surprise = earnings_surprise(announcements, close)
filled = surprise.ffill()
diffd = filled.diff()
def earnings_surprise_change_momentum(announcements, close):
'''
Calculate the price momentum since the most recent earnings surprise change normalised to annualised returns
'''
def log_returns(data):
'''
Parameters
-----
:window Pandas DataFrame
:returns Pandas DataFrame
'''
assert data.index.freq == "Day"
ret = np.log(data) - np.log(data.tshift(1))
return ret
def index_log_returns(price):
logret = log_returns(price)
logret.dropna(how = 'all', inplace=True)
return np.exp(logret.cumsum())*100 | 27.393365 | 167 | 0.651903 |
7940ed61e5165e294c0f3f71db4e8d7275b733a8 | 645 | py | Python | backend/manage.py | crowdbotics-apps/ideapros-llc-stream-33286 | a60a79687e5f0a2a5ec8cefb059479f587b9694a | [
"FTL",
"AML",
"RSA-MD"
] | null | null | null | backend/manage.py | crowdbotics-apps/ideapros-llc-stream-33286 | a60a79687e5f0a2a5ec8cefb059479f587b9694a | [
"FTL",
"AML",
"RSA-MD"
] | null | null | null | backend/manage.py | crowdbotics-apps/ideapros-llc-stream-33286 | a60a79687e5f0a2a5ec8cefb059479f587b9694a | [
"FTL",
"AML",
"RSA-MD"
] | null | null | null | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'ideapros_llc_stream_33286.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
| 29.318182 | 89 | 0.691473 |
7940f089b0dc96bbc2f67020ac2125f06d6ed674 | 3,472 | py | Python | print_variance.py | tiskw/gaussian-process-bootstrapping-layer | a1c20232ba286aa3245e6aab575a9aaaf274931f | [
"MIT"
] | null | null | null | print_variance.py | tiskw/gaussian-process-bootstrapping-layer | a1c20232ba286aa3245e6aab575a9aaaf274931f | [
"MIT"
] | null | null | null | print_variance.py | tiskw/gaussian-process-bootstrapping-layer | a1c20232ba286aa3245e6aab575a9aaaf274931f | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
"""
Training script for CIFAR10 classifier.
"""
import argparse
import os
import time
import numpy
import sklearn.metrics
import torch
import torchvision
from model import LeNet5, GaussianProcessBootstrapping
class GaussianProcessBootstrappingPrintVar(GaussianProcessBootstrapping):
"""
PyTorch implementation of Gaussian process bootstrapping layer.
"""
def forward(self, X):
"""
Forward function for 2D tensor of shape (n_samples, n_channels).
Args:
inputs (torch.tensor): Input tensor of shape (n_samples, n_channels).
"""
if self.P is None:
self.P = torch.zeros((X.shape[1], X.shape[1]), requires_grad=False, device=X.device)
with torch.no_grad():
X_copy = X.clone().detach()
self.P = self.a * self.P + (1.0 - self.a) * torch.matmul(torch.transpose(X_copy, 0, 1), X_copy)
with torch.no_grad():
e = self.e
P = self.P.clone().detach().double()
I = torch.eye(P.shape[0], device=P.device, dtype=P.dtype)
S = I - torch.linalg.lstsq(P + e * I, P)[0]
M = (I - torch.matmul(P, S) / e).float()
V = torch.sum(torch.matmul(X, M) * X, dim=1, keepdim=True)
if not self.training:
for v in V.clone().detach().cpu().flatten().tolist():
print(v)
return X
def parse_args():
"""
Parse command line arguments.
"""
parser = argparse.ArgumentParser(description=__doc__.strip())
parser.add_argument("--batch_size", default=500, type=int, help="batch size")
parser.add_argument("--device", default="cuda", type=str, help="device type for NN model")
parser.add_argument("--epoch", default=10, type=int, help="number of training epochs")
parser.add_argument("--n_cpus", default=max(1, os.cpu_count()//2), type=int, help="number of available CPUs")
parser.add_argument("--std_error", default=0.2, type=float, help="standard deviation of the gp resampling layer")
parser.add_argument("--model", default=None, type=str, help="path to model")
return parser.parse_args()
def main(args):
"""
Main function.
"""
# Dump arguments.
print("args =", args)
# Create NN model instance.
model = LeNet5(gpb_layer_pos="bottom", std_error=args.std_error)
model[9] = GaussianProcessBootstrappingPrintVar(std_error=args.std_error)
model.load_state_dict(torch.load(args.model))
model.to(args.device)
print(model)
# Setup training dataset.
transform = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
dataset = torchvision.datasets.CIFAR10(root="dataset", train=False, transform=transform, download=True)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.n_cpus)
model.train()
for _ in range(args.epoch):
for images, labels in dataloader:
images = images.to(args.device)
labels = labels.to(args.device)
output = model(images)
model.eval()
for images, labels in dataloader:
images = images.to(args.device)
labels = labels.to(args.device)
output = model(images)
if __name__ == "__main__":
main(parse_args())
# vim: expandtab tabstop=4 shiftwidth=4 fdm=marker
| 32.148148 | 120 | 0.643721 |
7940f1ca33d38ef2e5c607be77e486e8bb64318b | 322 | py | Python | jp.atcoder/abc031/abc031_b/8925627.py | kagemeka/atcoder-submissions | 91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e | [
"MIT"
] | 1 | 2022-02-09T03:06:25.000Z | 2022-02-09T03:06:25.000Z | jp.atcoder/abc031/abc031_b/8925627.py | kagemeka/atcoder-submissions | 91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e | [
"MIT"
] | 1 | 2022-02-05T22:53:18.000Z | 2022-02-09T01:29:30.000Z | jp.atcoder/abc031/abc031_b/8925627.py | kagemeka/atcoder-submissions | 91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e | [
"MIT"
] | null | null | null | import sys
import numpy as np
I = np.array(sys.stdin.read().split(), dtype=np.int64)
L, H, n = I[:3]
a = I[3:]
def main():
res = np.zeros(n, dtype=np.int64)
res = np.maximum(L - a, 0)
res[a > H] = -1
return res
if __name__ == "__main__":
ans = main()
print(*ans, sep="\n")
| 16.1 | 55 | 0.515528 |
7940f36b37afb9c180baab949ffd4a6080b396cb | 11,164 | py | Python | app.py | exandwhy/COVID19_Streamlit | db84b55943e72446dcc9e51dd5ca09f7ce771dee | [
"Apache-2.0"
] | null | null | null | app.py | exandwhy/COVID19_Streamlit | db84b55943e72446dcc9e51dd5ca09f7ce771dee | [
"Apache-2.0"
] | null | null | null | app.py | exandwhy/COVID19_Streamlit | db84b55943e72446dcc9e51dd5ca09f7ce771dee | [
"Apache-2.0"
] | null | null | null | import streamlit as st
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import matplotlib
from plotly.subplots import make_subplots
import warnings
warnings.filterwarnings(action='ignore')
@st.cache(allow_output_mutation=True)
def get_data():
url = 'https://api.covid19india.org/csv/latest/state_wise.csv'
return pd.read_csv(url)
df = get_data()
st.title('COVID-19 Outbreak Monitor')
st.markdown("> and then the whole world walked inside and shut their doors and said we will stop it.")
# Status Table
st.header('\n')
@st.cache(allow_output_mutation=True)
def display_status(df):
df = df[['State', 'Confirmed', 'Recovered', 'Active', 'Deaths', 'Last_Updated_Time']]
df.drop([0], axis=0, inplace=True)
df = df.style.background_gradient(cmap="YlGnBu")
return df
status_table = display_status(df)
st.dataframe(status_table)
# Status of COVID-19
summary = df[df['State'] == 'Total']
summary = summary[['Active', 'Recovered', 'Deaths']]
summary = summary.transpose()
summary = summary.reset_index()
summary = summary.rename(columns = {'index' : 'Property', 0 : 'Numbers'})
fig_summary = px.pie(summary,
values='Numbers',
names='Property')
st.plotly_chart(fig_summary)
# Statewise distribution of confirmed, active, recovered and deceased
st.header('Cases Distribution')
statewise = df.drop([0])
status = ['Confirmed', 'Active', 'Recovered', 'Deaths']
option = st.selectbox('',
status)
@st.cache(allow_output_mutation=True)
def display_status(option):
df = statewise[['State', option]]
fig = px.pie(df,
values=option,
names='State')
fig.update_traces(hoverinfo='label+percent+name', textinfo='none')
return fig
status = display_status(option)
st.plotly_chart(status)
# Spread Trends
st.header('Spread Trends')
@st.cache(allow_output_mutation=True)
def get_trends_data():
url = 'https://api.covid19india.org/csv/latest/state_wise_daily.csv'
return pd.read_csv(url)
trends = get_trends_data()
trends = trends.rename(columns = {'TT' : 'All States',
'AN' : 'Andaman and Nicobar Islands',
'AP' : 'Andhra Pradesh',
'AR' : 'Arunachal Pradesh',
'AS' : 'Assam',
'BR' : 'Bihar',
'CH' : 'Chandigarh',
'CT' : 'Chhattisgarh',
'DN' : 'Dadar and Nagar Haveli',
'DD' : 'Daman and Diu',
'DL' : 'Delhi',
'GA' : 'Goa',
'GJ' : 'Gujarat',
'HR' : 'Haryana',
'HP' : 'Himachal Pradesh',
'JK' : 'Jammu and Kashmir',
'JH' : 'Jharkhand',
'KA' : 'Karnataka',
'KL' : 'Kerala',
'LA' : 'Ladakh',
'LD' : 'Lakshdweep',
'MP' : 'Madhya Pradesh',
'MH' : 'Maharastra',
'MN' : 'Manipur',
'ML' : 'Meghalaya',
'MZ' : 'Mizoram',
'NL' : 'Nagaland',
'OR' : 'Odisa',
'PY' : 'Puducherry',
'PB' : 'Punjab',
'RJ' : 'Rajasthan',
'SK' : 'Sikkim',
'TN' : 'Tamil Nadu',
'TG' : 'Telangana',
'TR' : 'Tripura',
'UP' : 'Uttar Pradesh',
'UT' : 'Uttarakhand',
'WB' : 'West Bengal'})
log = st.checkbox('Logarithmic')
cumulative = st.checkbox('Cumulative')
trends_confirmed = trends[trends['Status'] == 'Confirmed'].drop(columns = ['Status'])
trends_recovered = trends[trends['Status'] == 'Recovered'].drop(columns = ['Status'])
trends_deceased = trends[trends['Status'] == 'Deceased'].drop(columns = ['Status'])
if cumulative:
trends_confirmed = trends_confirmed.set_index('Date')
trends_confirmed = trends_confirmed.cumsum()
trends_confirmed = trends_confirmed.reset_index()
trends_recovered = trends_recovered.set_index('Date')
trends_recovered = trends_recovered.cumsum()
trends_recovered = trends_recovered.reset_index()
trends_deceased = trends_deceased.set_index('Date')
trends_deceased = trends_deceased.cumsum()
trends_deceased = trends_deceased.reset_index()
@st.cache(allow_output_mutation=True)
def trends_plot(state, df):
df = df[['Date', state]]
if log:
fig = go.Figure(data=go.Scatter(x=df['Date'],
y=np.log1p(df[state]),
mode='lines+markers'))
else:
fig = go.Figure(go.Scatter(x=df['Date'],
y=df[state],
mode='lines+markers'))
fig.update_layout(xaxis_title='----->Timeline',
yaxis_title='----->Patients')
return fig
x = list(trends_confirmed.columns)
del x[0]
del x[-1]
states = x
option_c = st.selectbox('Confirmed Cases',states)
confirmed = trends_plot(option_c, trends_confirmed)
st.plotly_chart(confirmed)
option_r = st.selectbox('Recovered Cases',states)
recovered = trends_plot(option_r, trends_recovered)
st.plotly_chart(recovered)
option_d = st.selectbox('Number of Deceased',states)
deceased = trends_plot(option_d, trends_deceased)
st.plotly_chart(deceased)
# comparison
st.header('Compare')
cases = ['Confirmed Cases', 'Recovered Cases', 'Deceased Cases']
cmp = st.selectbox('I want to compare', cases)
state_1 = st.selectbox('in', x)
state_2 = st.selectbox('and', x)
@st.cache(allow_output_mutation=True)
def compare(df, state_1, state_2):
fig = go.Figure()
fig.add_trace(go.Scatter(x=df['Date'],
y=df[state_1],
mode='lines+markers',
name=state_1))
fig.add_trace(go.Scatter(x=df['Date'],
y=df[state_2],
mode='lines+markers',
name=state_2))
return fig
if cmp == cases[0]:
trends_confirmed = trends_confirmed.set_index('Date')
trends_confirmed = trends_confirmed.cumsum()
trends_confirmed = trends_confirmed.reset_index()
fig_cmp = compare(trends_confirmed, state_1, state_2)
elif cmp == cases[1]:
trends_recovered = trends_recovered.set_index('Date')
trends_recovered = trends_recovered.cumsum()
trends_recovered = trends_recovered.reset_index()
fig_cmp = compare(trends_recovered, state_1, state_2)
else:
trends_deceased = trends_deceased.set_index('Date')
trends_deceased = trends_deceased.cumsum()
trends_deceased = trends_deceased.reset_index()
fig_cmp = compare(trends_deceased, state_1, state_2)
st.plotly_chart(fig_cmp)
# Testing
st.header('COVID-19 Testing Status')
@st.cache(allow_output_mutation=True)
def get_test_data():
url = 'https://api.covid19india.org/csv/latest/tested_numbers_icmr_data.csv'
return pd.read_csv(url)
test = get_test_data()
tested_pm = test.copy()
test = test[['Update Time Stamp', 'Total Samples Tested']]
test = test.set_index('Update Time Stamp')
test = test.diff()
test = test.reset_index()
test['Update Time Stamp'] = pd.to_datetime(test['Update Time Stamp'])
test['Update Time Stamp'] = test['Update Time Stamp'].dt.strftime('%d-%m-%Y')
test['Date'] = pd.DatetimeIndex(test['Update Time Stamp']).date
test['Month'] = pd.DatetimeIndex(test['Update Time Stamp']).month
test = test[['Date', 'Month', 'Total Samples Tested']]
test = test.fillna(0)
st.markdown('\n')
st.markdown('**Samples tested daily**')
def test_plot(df):
fig = px.scatter(df,
x = 'Date',
y = 'Total Samples Tested',
color = 'Month',
hover_data = ['Date', 'Total Samples Tested'],
size = 'Total Samples Tested')
fig.update_layout(xaxis_title='-----> Timeline',
yaxis_title='-----> Number of Tests')
st.plotly_chart(fig)
test_plot(test)
st.markdown('**Tests per million**')
tested_pm = tested_pm[['Update Time Stamp', 'Tests per million']]
tested_pm['Date'] = pd.to_datetime(tested_pm['Update Time Stamp'])
tested_pm['Date'] = tested_pm['Date'].dt.strftime('%d-%m-%Y')
tested_pm['Month'] = pd.DatetimeIndex(tested_pm['Date']).month
tested_pm = tested_pm.fillna(0)
tested_pm.rename(columns = {'Tests per million':'Total Samples Tested'}, inplace = True)
test_plot(tested_pm)
# Hospital Data
st.header('Hospitals')
def get_hospital_data():
url = 'https://api.rootnet.in/covid19-in/hospitals/beds.json'
hosp = pd.read_json(url)
return pd.DataFrame(hosp['data']['regional'])
hosp = get_hospital_data()
hosp = hosp[hosp['state'] != 'INDIA']
def urban_rural(hosp, x, y, name):
fig = go.Figure(data=[
go.Bar(name=name,
x=x,
y=y)
])
fig.update_layout(barmode='group')
st.plotly_chart(fig)
def ur_plot(hosp, y_urban, y_rural, name_urban, name_rular):
fig = go.Figure(data=[
go.Bar(name=name_urban, x=hosp['state'], y=y_urban),
go.Bar(name=name_rular, x=hosp['state'], y=y_rural)
])
fig.update_layout(barmode='group')
st.plotly_chart(fig)
urban_button = st.button('Urban Hospital')
if urban_button:
urban_rural(hosp, hosp['state'], hosp['urbanHospitals'], 'Urban Hospital')
else:
pass
rural_button = st.button('Rural Hospital')
if rural_button:
urban_rural(hosp, hosp['state'], hosp['ruralHospitals'], 'Rural Hospital')
else:
pass
ur_button = st.button('Urban VS Rural Hospital')
if ur_button:
ur_plot(hosp, hosp['urbanHospitals'], hosp['ruralHospitals'], 'Urban Hospital', 'Rural Hospital')
else:
pass
# Hospital Beds
st.header('Hospital Beds')
urbanbeds_button = st.button('Urban Beds')
if urbanbeds_button:
urban_rural(hosp, hosp['state'], hosp['urbanBeds'], 'Urban Hospital Beds')
else:
pass
ruralbeds_button = st.button('Rural Beds')
if ruralbeds_button:
urban_rural(hosp, hosp['state'], hosp['ruralBeds'], 'Rural Hospital Beds')
else:
pass
urbeds_button = st.button('Urban vs Rural Beds')
if urbeds_button:
ur_plot(hosp, hosp['urbanBeds'], hosp['ruralBeds'], 'Urban Hospital Beds', 'Rural Hospital Beds')
else:
pass
# Map
# @st.cache
# def on_map():
# url = 'https://api.covid19india.org/csv/latest/state_wise.csv'
# return pd.read_csv(url)
# cnf = on_map()
# fig = px.choropleth(cnf, locations="IND",
# color="Confirmed", # lifeExp is a column of gapminder
# hover_name="Confirmed", # column to add to hover information
# color_continuous_scale=px.colors.sequential.Plasma)
# st.plotly_chart(fig)
| 32.932153 | 102 | 0.597187 |
7940f36c49102a7178559252454817d51ac2cea8 | 3,156 | py | Python | src/qibo/backends/matrices.py | renatomello/qibo | 20c6f3f22effbeccd5d31ed456717f9bee449e0c | [
"Apache-2.0"
] | null | null | null | src/qibo/backends/matrices.py | renatomello/qibo | 20c6f3f22effbeccd5d31ed456717f9bee449e0c | [
"Apache-2.0"
] | null | null | null | src/qibo/backends/matrices.py | renatomello/qibo | 20c6f3f22effbeccd5d31ed456717f9bee449e0c | [
"Apache-2.0"
] | 1 | 2022-03-28T17:52:46.000Z | 2022-03-28T17:52:46.000Z | import numpy as np
class Matrices:
_NAMES = ["I", "H", "X", "Y", "Z", "S", "T", "CNOT", "CZ", "SWAP", "FSWAP", "TOFFOLI"]
def __init__(self, backend):
self.backend = backend
self._I = None
self._H = None
self._X = None
self._Y = None
self._Z = None
self._S = None
self._T = None
self._CNOT = None
self._CZ = None
self._SWAP = None
self._FSWAP = None
self._TOFFOLI = None
self.allocate_matrices()
def allocate_matrices(self):
for name in self._NAMES:
getattr(self, f"_set{name}")()
@property
def dtype(self):
return self.backend._dtypes.get('DTYPECPX')
@property
def I(self):
return self._I
@property
def H(self):
return self._H
@property
def X(self):
return self._X
@property
def Y(self):
return self._Y
@property
def Z(self):
return self._Z
@property
def S(self):
return self._S
@property
def T(self):
return self._T
@property
def CNOT(self):
return self._CNOT
@property
def CZ(self):
return self._CZ
@property
def SWAP(self):
return self._SWAP
@property
def FSWAP(self):
return self._FSWAP
@property
def TOFFOLI(self):
return self._TOFFOLI
def _setI(self):
self._I = self.backend.cast(np.eye(2, dtype=self.dtype))
def _setH(self):
m = np.ones((2, 2), dtype=self.dtype)
m[1, 1] = -1
self._H = self.backend.cast(m / np.sqrt(2))
def _setX(self):
m = np.zeros((2, 2), dtype=self.dtype)
m[0, 1], m[1, 0] = 1, 1
self._X = self.backend.cast(m)
def _setY(self):
m = np.zeros((2, 2), dtype=self.dtype)
m[0, 1], m[1, 0] = -1j, 1j
self._Y = self.backend.cast(m)
def _setZ(self):
m = np.eye(2, dtype=self.dtype)
m[1, 1] = -1
self._Z = self.backend.cast(m)
def _setS(self):
m = np.eye(2, dtype=self.dtype)
m[1, 1] = 1j
self._S = self.backend.cast(m)
def _setT(self):
m = np.eye(2, dtype=self.dtype)
m[1, 1] = np.exp(1j * np.pi / 4.0)
self._T = self.backend.cast(m)
def _setCNOT(self):
m = np.eye(4, dtype=self.dtype)
m[2, 2], m[2, 3] = 0, 1
m[3, 2], m[3, 3] = 1, 0
self._CNOT = self.backend.cast(m)
def _setCZ(self):
m = np.eye(4, dtype=self.dtype)
m[3, 3] = -1
self._CZ = self.backend.cast(m)
def _setSWAP(self):
m = np.eye(4, dtype=self.dtype)
m[1, 1], m[1, 2] = 0, 1
m[2, 1], m[2, 2] = 1, 0
self._SWAP = self.backend.cast(m)
def _setFSWAP(self):
m = np.eye(4, dtype=self.dtype)
m[1, 1], m[1, 2] = 0, 1
m[2, 1], m[2, 2] = 1, 0
m[3, 3] = -1
self._FSWAP = self.backend.cast(m)
def _setTOFFOLI(self):
m = np.eye(8, dtype=self.dtype)
m[-2, -2], m[-2, -1] = 0, 1
m[-1, -2], m[-1, -1] = 1, 0
self._TOFFOLI = self.backend.cast(m)
| 22.225352 | 90 | 0.499049 |
7940f47ef5e5e23289a1a570bfd18f3b8f004e00 | 5,937 | py | Python | roommatefinder/settings.py | smlaming/Roomate-Finder | 864d6633f4303b53596d8fe62572bf7808c6c443 | [
"MIT"
] | null | null | null | roommatefinder/settings.py | smlaming/Roomate-Finder | 864d6633f4303b53596d8fe62572bf7808c6c443 | [
"MIT"
] | null | null | null | roommatefinder/settings.py | smlaming/Roomate-Finder | 864d6633f4303b53596d8fe62572bf7808c6c443 | [
"MIT"
] | null | null | null | """
Django settings for roommatefinder project.
Generated by 'django-admin startproject' using Django 3.1.6.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.1/ref/settings/
"""
from pathlib import Path
import os
import sys
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = ''
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = ['roommate-finder-a-22.herokuapp.com']
#Turn to False when running locally
SECURE_SSL_REDIRECT = True
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'bootstrap4',
'django.contrib.sites',
'roommatefinder',
'allauth',
'allauth.account',
'allauth.socialaccount',
'allauth.socialaccount.providers.google',
'questionnaire.apps.QuestionnaireConfig',
'django.forms',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'roommatefinder.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'roommatefinder.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.1/ref/settings/#databases
#https://www.enterprisedb.com/postgres-tutorials/how-use-postgresql-django
if 'test' in sys.argv:
#Configuration for test database
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'd3rlagcac4cit3',
'USER': 'pkudwynznfiykn',
'PASSWORD': '27ba052fc181835bc04248398dbc09f0962cdd4bbe882df1564d297b1f2392e0',
'HOST': 'ec2-34-225-167-77.compute-1.amazonaws.com',
'PORT': 5432,
'TEST': {
'NAME': 'd3rlagcac4cit3', #This is an important entry
}
}
}
else:
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'd1rgh00vmo1i24',
'USER': 'uoszgxtubbrxrt',
'PASSWORD': 'bd578c0b6ca6d2462219a42e2ab66d2884755fe570d633be21f0db641d77fc0d',
'HOST': 'ec2-3-222-11-129.compute-1.amazonaws.com',
'PORT': '5432',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'EST'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.1/howto/static-files/
STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')
STATIC_URL = '/static/'
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
AWS_LOCATION = 'static'
AWS_ACCESS_KEY_ID =''
AWS_SECRET_ACCESS_KEY = ''
AWS_STORAGE_BUCKET_NAME ='dwltestproj1'
AWS_S3_CUSTOM_DOMAIN='%s.s3.amazonaws.com' % AWS_STORAGE_BUCKET_NAME
AWS_S3_OBJECT_PARAMETERS = {
'CacheControl': 'max-age=86400',
}
DEFAULT_FILE_STORAGE = 'storages.backends.s3boto3.S3Boto3Storage'
STATICFILES_STORAGE = "storages.backends.s3boto3.S3Boto3Storage"
STATICFILES_DIRS = (
os.path.join(BASE_DIR, 'static'),
)
#STATICFILES_DIRS = [
# os.path.join(BASE_DIR, 'static'),
#]
STATIC_URL='https://%s/%s/' % (AWS_S3_CUSTOM_DOMAIN, AWS_LOCATION)
ADMIN_MEDIA_PREFIX = STATIC_URL + 'admin/'
STATICFILES_FINDERS = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder',
)
AWS_DEFAULT_ACL = None
#Google Login Tutorial
#https://medium.com/@whizzoe/in-5-mins-set-up-google-login-to-sign-up-users-on-django-e71d5c38f5d5
AUTHENTICATION_BACKENDS = (
'django.contrib.auth.backends.ModelBackend',
'allauth.account.auth_backends.AuthenticationBackend',
)
SITE_ID = 1
LOGIN_REDIRECT_URL = '/'
SOCIALACCOUNT_PROVIDERS = {
'google': {
'SCOPE': [
'profile',
'email',
],
'AUTH_PARAMS': {
'access_type': 'online',
}
}
}
#FORM_RENDERER = 'django.forms.renderers.TemplatesSetting'
# Activate Django-Heroku.
#CS3240 Resources Page
try:
# Configure Django App for Heroku.
import django_heroku
django_heroku.settings(locals())
except ImportError:
found = False
| 25.925764 | 150 | 0.6857 |
7940f4a951b02381f1745deaf9d984fc6d654579 | 8,837 | py | Python | android/scripts/common.py | hustwei/deqp | 812d768b55dcedf2c0fda63e69db3c05600f379d | [
"Apache-2.0"
] | null | null | null | android/scripts/common.py | hustwei/deqp | 812d768b55dcedf2c0fda63e69db3c05600f379d | [
"Apache-2.0"
] | null | null | null | android/scripts/common.py | hustwei/deqp | 812d768b55dcedf2c0fda63e69db3c05600f379d | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
#-------------------------------------------------------------------------
# drawElements Quality Program utilities
# --------------------------------------
#
# Copyright 2015 The Android Open Source Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#-------------------------------------------------------------------------
import os
import re
import sys
import shlex
import subprocess
import multiprocessing
import string
try:
import threading
except ImportError:
import dummy_threading as threading
class NativeLib:
def __init__ (self, apiVersion, abiVersion, prebuiltDir):
self.apiVersion = apiVersion
self.abiVersion = abiVersion
self.prebuiltDir = prebuiltDir
def __str__ (self):
return "(API: %s, ABI: %s)" % (self.apiVersion, self.abiVersion)
def __repr__ (self):
return "(API: %s, ABI: %s)" % (self.apiVersion, self.abiVersion)
def getPlatform ():
if sys.platform.startswith('linux'):
return 'linux'
else:
return sys.platform
def selectByOS (variants):
platform = getPlatform()
if platform in variants:
return variants[platform]
elif 'other' in variants:
return variants['other']
else:
raise Exception("No configuration for '%s'" % platform)
def isExecutable (path):
return os.path.isfile(path) and os.access(path, os.X_OK)
def which (binName):
for path in os.environ['PATH'].split(os.pathsep):
path = path.strip('"')
fullPath = os.path.join(path, binName)
if isExecutable(fullPath):
return fullPath
return None
def isBinaryInPath (binName):
return which(binName) != None
def selectFirstExistingBinary (filenames):
for filename in filenames:
if filename != None and isExecutable(filename):
return filename
return None
def selectFirstExistingDir (paths):
for path in paths:
if path != None and os.path.isdir(path):
return path
return None
def die (msg):
print msg
exit(-1)
def shellquote(s):
return '"%s"' % s.replace('\\', '\\\\').replace('"', '\"').replace('$', '\$').replace('`', '\`')
def execute (commandLine):
args = shlex.split(commandLine)
retcode = subprocess.call(args)
if retcode != 0:
raise Exception("Failed to execute '%s', got %d" % (commandLine, retcode))
def execArgs (args):
# Make sure previous stdout prints have been written out.
sys.stdout.flush()
retcode = subprocess.call(args)
if retcode != 0:
raise Exception("Failed to execute '%s', got %d" % (str(args), retcode))
def execArgsInDirectory (args, cwd, linePrefix=""):
def readApplyPrefixAndPrint (source, prefix, sink):
while True:
line = source.readline()
if len(line) == 0: # EOF
break;
sink.write(prefix + line)
process = subprocess.Popen(args, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdoutJob = threading.Thread(target=readApplyPrefixAndPrint, args=(process.stdout, linePrefix, sys.stdout))
stderrJob = threading.Thread(target=readApplyPrefixAndPrint, args=(process.stderr, linePrefix, sys.stderr))
stdoutJob.start()
stderrJob.start()
retcode = process.wait()
if retcode != 0:
raise Exception("Failed to execute '%s', got %d" % (str(args), retcode))
def serialApply(f, argsList):
for args in argsList:
f(*args)
def parallelApply(f, argsList):
class ErrorCode:
def __init__ (self):
self.error = None;
def applyAndCaptureError (func, args, errorCode):
try:
func(*args)
except:
errorCode.error = sys.exc_info()
errorCode = ErrorCode()
jobs = []
for args in argsList:
job = threading.Thread(target=applyAndCaptureError, args=(f, args, errorCode))
job.start()
jobs.append(job)
for job in jobs:
job.join()
if errorCode.error:
raise errorCode.error[0], errorCode.error[1], errorCode.error[2]
class Device:
def __init__(self, serial, product, model, device):
self.serial = serial
self.product = product
self.model = model
self.device = device
def __str__ (self):
return "%s: {product: %s, model: %s, device: %s}" % (self.serial, self.product, self.model, self.device)
def getDevices (adb):
proc = subprocess.Popen([adb, 'devices', '-l'], stdout=subprocess.PIPE)
(stdout, stderr) = proc.communicate()
if proc.returncode != 0:
raise Exception("adb devices -l failed, got %d" % proc.returncode)
ptrn = re.compile(r'^([a-zA-Z0-9]+)\s+.*product:([^\s]+)\s+model:([^\s]+)\s+device:([^\s]+)')
devices = []
for line in stdout.splitlines()[1:]:
if len(line.strip()) == 0:
continue
m = ptrn.match(line)
if m == None:
print "WARNING: Failed to parse device info '%s'" % line
continue
devices.append(Device(m.group(1), m.group(2), m.group(3), m.group(4)))
return devices
def getWin32Generator ():
if which("jom.exe") != None:
return "NMake Makefiles JOM"
else:
return "NMake Makefiles"
def isNinjaSupported ():
return which("ninja") != None
def getUnixGenerator ():
if isNinjaSupported():
return "Ninja"
else:
return "Unix Makefiles"
def getExtraBuildArgs (generator):
if generator == "Unix Makefiles":
return ["--", "-j%d" % multiprocessing.cpu_count()]
else:
return []
NDK_HOST_OS_NAMES = [
"windows",
"windows-x86_64",
"darwin-x86",
"darwin-x86_64",
"linux-x86",
"linux-x86_64"
]
def getNDKHostOsName (ndkPath):
for name in NDK_HOST_OS_NAMES:
if os.path.exists(os.path.join(ndkPath, "prebuilt", name)):
return name
raise Exception("Couldn't determine NDK host OS")
# deqp/android path
ANDROID_DIR = os.path.realpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), ".."))
# Build configuration
NATIVE_LIBS = [
# API ABI prebuiltsDir
NativeLib(13, "armeabi-v7a", 'android-arm'), # ARM v7a ABI
NativeLib(13, "x86", 'android-x86'), # x86
NativeLib(21, "arm64-v8a", 'android-arm64'), # ARM64 v8a ABI
NativeLib(21, "x86_64", 'android-x86_64'), # x86_64
]
ANDROID_JAVA_API = "android-22"
NATIVE_LIB_NAME = "libdeqp.so"
def selectNDKPath ():
candidates = [
os.path.expanduser("~/android-ndk-r11"),
"C:/android/android-ndk-r11",
os.environ.get("ANDROID_NDK_PATH", None), # If not defined, return None
]
ndkPath = selectFirstExistingDir(candidates)
if ndkPath == None:
raise Exception("None of NDK directory candidates exist: %s. Check ANDROID_NDK_PATH in common.py" % candidates)
return ndkPath
def noneSafePathJoin (*components):
if None in components:
return None
return os.path.join(*components)
# NDK paths
ANDROID_NDK_PATH = selectNDKPath()
ANDROID_NDK_HOST_OS = getNDKHostOsName(ANDROID_NDK_PATH)
ANDROID_NDK_TOOLCHAIN_VERSION = "r11" # Toolchain file is selected based on this
# Native code build settings
CMAKE_GENERATOR = selectByOS({
'win32': getWin32Generator(),
'other': getUnixGenerator()
})
EXTRA_BUILD_ARGS = getExtraBuildArgs(CMAKE_GENERATOR)
# SDK paths
ANDROID_SDK_PATH = selectFirstExistingDir([
os.environ.get("ANDROID_SDK_PATH", None),
os.path.expanduser("~/android-sdk-linux"),
os.path.expanduser("~/android-sdk-mac_x86"),
"C:/android/android-sdk-windows",
])
ANDROID_BIN = selectFirstExistingBinary([
noneSafePathJoin(ANDROID_SDK_PATH, "tools", "android"),
noneSafePathJoin(ANDROID_SDK_PATH, "tools", "android.bat"),
which('android'),
])
ADB_BIN = selectFirstExistingBinary([
which('adb'), # \note Prefer adb in path to avoid version issues on dev machines
noneSafePathJoin(ANDROID_SDK_PATH, "platform-tools", "adb"),
noneSafePathJoin(ANDROID_SDK_PATH, "platform-tools", "adb.exe"),
])
ZIPALIGN_BIN = selectFirstExistingBinary([
noneSafePathJoin(ANDROID_SDK_PATH, "tools", "zipalign"),
noneSafePathJoin(ANDROID_SDK_PATH, "tools", "zipalign.exe"),
which('zipalign'),
])
JARSIGNER_BIN = which('jarsigner')
# Apache ant
ANT_BIN = selectFirstExistingBinary([
which('ant'),
"C:/android/apache-ant-1.8.4/bin/ant.bat",
"C:/android/apache-ant-1.9.2/bin/ant.bat",
"C:/android/apache-ant-1.9.3/bin/ant.bat",
"C:/android/apache-ant-1.9.4/bin/ant.bat",
])
def makeNameValueTuple (name):
return (name, str(eval(name)))
CONFIG_VAR_NAMES = [
"ANDROID_DIR",
"NATIVE_LIBS",
"ANDROID_JAVA_API",
"NATIVE_LIB_NAME",
"ANDROID_NDK_PATH",
"ANDROID_NDK_HOST_OS",
"ANDROID_NDK_TOOLCHAIN_VERSION",
"CMAKE_GENERATOR",
"EXTRA_BUILD_ARGS",
"ANDROID_SDK_PATH",
"ANDROID_BIN",
"ADB_BIN",
"ZIPALIGN_BIN",
"JARSIGNER_BIN",
"ANT_BIN",
]
CONFIG_STRINGS = [makeNameValueTuple(x) for x in CONFIG_VAR_NAMES]
| 26.860182 | 113 | 0.690619 |
7940f4dcc4b04af088f8ec9f29b0a6c9aed7d386 | 11,485 | py | Python | riboflask_compare.py | skiniry/Trips-Viz | a742a6c7d0c9758e3c439828e804025d7fc44b4f | [
"MIT"
] | 7 | 2019-07-25T14:34:48.000Z | 2021-10-19T07:52:29.000Z | riboflask_compare.py | skiniry/Trips-Viz | a742a6c7d0c9758e3c439828e804025d7fc44b4f | [
"MIT"
] | 3 | 2021-06-07T23:26:38.000Z | 2021-11-15T22:37:43.000Z | riboflask_compare.py | skiniry/Trips-Viz | a742a6c7d0c9758e3c439828e804025d7fc44b4f | [
"MIT"
] | 2 | 2019-09-04T08:51:25.000Z | 2022-03-10T20:58:40.000Z | import matplotlib
matplotlib.use('agg')
from matplotlib import pyplot as plt
import mpld3
from mpld3 import plugins,utils
from new_plugins import InteractiveLegendPlugin,TopToolbar,DownloadProfile,DownloadPNG
from fetch_shelve_reads2 import get_reads
import sqlite3
import config
import os
import config
def merge_dict(dict1,dict2):
master_dict = dict1
for key in dict2:
if key in master_dict:
master_dict[key] += dict2[key]
else:
master_dict[key] = dict2[key]
return master_dict
color_dict = {'frames': ['#FF4A45', '#64FC44', '#5687F9']}
def generate_compare_plot(tran, ambig, min_read, max_read,master_filepath_dict,lite, offset_dict,ribocoverage,organism,normalize, short_code, background_col, hili_start,
hili_stop,comp_uag_col,comp_uga_col,comp_uaa_col, title_size, subheading_size,axis_label_size, marker_size,cds_marker_size,
cds_marker_colour, legend_size,transcriptome):
labels = []
start_visible=[]
line_collections = []
all_stops = ["TAG","TAA","TGA"]
returnstr = "Position,"
y_max = 50
if normalize == True:
y_max = 0
connection = sqlite3.connect('{}/trips.sqlite'.format(config.SCRIPT_LOC))
connection.text_factory = str
cursor = connection.cursor()
cursor.execute("SELECT owner FROM organisms WHERE organism_name = '{}' and transcriptome_list = '{}';".format(organism, transcriptome))
owner = (cursor.fetchone())[0]
if owner == 1:
if os.path.isfile("{0}/{1}/{2}/{2}.{3}.sqlite".format(config.SCRIPT_LOC, config.ANNOTATION_DIR,organism,transcriptome)):
transhelve = sqlite3.connect("{0}/{1}/{2}/{2}.{3}.sqlite".format(config.SCRIPT_LOC, config.ANNOTATION_DIR,organism,transcriptome))
else:
return_str = "Cannot find annotation file {}.{}.sqlite".format(organism,transcriptome)
return {'current': 400, 'total': 100, 'status': 'return_str','result': return_str}
else:
transhelve = sqlite3.connect("{0}transcriptomes/{1}/{2}/{3}/{2}_{3}.sqlite".format(config.UPLOADS_DIR,owner,organism,transcriptome))
cursor = transhelve.cursor()
cursor.execute("SELECT * from transcripts WHERE transcript = '{}'".format(tran))
result = cursor.fetchone()
traninfo = {"transcript":result[0] , "gene":result[1], "length":result[2] , "cds_start":result[3] , "cds_stop":result[4] , "seq":result[5] ,
"strand":result[6], "stop_list":result[7].split(","),"start_list":result[8].split(","), "exon_junctions":result[9].split(","),
"tran_type":result[10], "principal":result[11]}
traninfo["stop_list"] = [int(x) for x in traninfo["stop_list"]]
traninfo["start_list"] = [int(x) for x in traninfo["start_list"]]
if str(traninfo["exon_junctions"][0]) != "":
traninfo["exon_junctions"] = [int(x) for x in traninfo["exon_junctions"]]
else:
traninfo["exon_junctions"] = []
transhelve.close()
gene = traninfo["gene"]
tranlen = traninfo["length"]
cds_start = traninfo["cds_start"]
cds_stop = traninfo["cds_stop"]
strand = traninfo["strand"]
if cds_start == 'NULL' or cds_start == None:
cds_start = 0
if cds_stop == 'NULL' or cds_stop == None:
cds_stop = 0
all_starts = traninfo["start_list"]
all_stops = {"TAG":[],"TAA":[],"TGA":[]}
seq = traninfo["seq"].upper()
for i in range(0,len(seq)):
if seq[i:i+3] in all_stops:
all_stops[seq[i:i+3]].append(i+1)
start_stop_dict = {1:{"starts":[0], "stops":{"TGA":[0],"TAG":[0],"TAA":[0]}},
2:{"starts":[0], "stops":{"TGA":[0],"TAG":[0],"TAA":[0]}},
3:{"starts":[0], "stops":{"TGA":[0],"TAG":[0],"TAA":[0]}}}
for start in all_starts:
rem = ((start-1)%3)+1
start_stop_dict[rem]["starts"].append(start-1)
for stop in all_stops:
for stop_pos in all_stops[stop]:
rem = ((stop_pos-1)%3)+1
start_stop_dict[rem]["stops"][stop].append(stop_pos-1)
fig = plt.figure(figsize=(13,8))
ax_main = plt.subplot2grid((30,1), (0,0),rowspan=22)
if normalize != True:
label = 'Read count'
else:
label = 'Normalized read count'
ax_main.set_ylabel(label, fontsize=axis_label_size, labelpad=30)
label = 'Position (nucleotides)'
ax_main.set_xlabel(label, fontsize=axis_label_size, labelpad=10)
#if normalize is true work out the factors for each colour
if normalize == True:
all_mapped_reads = []
for color in master_filepath_dict:
all_mapped_reads.append(master_filepath_dict[color]["mapped_reads"])
min_reads = float(min(all_mapped_reads))
for color in master_filepath_dict:
factor = min_reads/float(master_filepath_dict[color]["mapped_reads"])
master_filepath_dict[color]["factor"] = factor
# So items can be plotted alphabetically
unsorted_list = []
for color in master_filepath_dict:
input_list = [color, master_filepath_dict[color]["file_names"],master_filepath_dict[color]["file_descs"],master_filepath_dict[color]["file_ids"],master_filepath_dict[color]["filepaths"],master_filepath_dict[color]["file_type"],master_filepath_dict[color]["minread"],master_filepath_dict[color]["maxread"]]
if "factor" in master_filepath_dict[color]:
input_list.append(master_filepath_dict[color]["factor"])
unsorted_list.append(input_list)
sorted_list = sorted(unsorted_list, key=lambda x: x[1][0])
returndict = {}
for item in sorted_list:
# needed to make get_reads accept file_paths
file_paths = {"riboseq":{}}
for i in range(0,len(item[3])):
file_paths["riboseq"][item[3][i]] = item[4][i]
file_names =item[1][0]
file_descs = item[2]
if item[5] == "riboseq":
filename_reads, seqvar_dict = get_reads(ambig, item[6], item[7], tran, file_paths, tranlen, ribocoverage, organism, False, False,"fiveprime","riboseq",1)
else:
filename_reads, seqvar_dict = get_reads(ambig, item[6], item[7], tran, file_paths, tranlen, True, organism, False, False,"fiveprime","riboseq",1)
if normalize == False:
try:
max_val = max(filename_reads.values())*1.1
if max_val > y_max:
y_max = max_val
except Exception as e:
pass
labels.append(file_names)
start_visible.append(True)
plot_filename = ax_main.plot(filename_reads.keys(), filename_reads.values(), alpha=1, label = labels, zorder=1,color=item[0], linewidth=3)
line_collections.append(plot_filename)
returndict[file_names] = {}
for pos in filename_reads:
returndict[file_names][pos] = filename_reads[pos]
else:
normalized_reads = {}
for pos in filename_reads:
normalized_reads[pos] = filename_reads[pos]*item[8]
try:
max_val = max(normalized_reads.values())*1.1
if max_val > y_max:
y_max = max_val
except Exception as e:
pass
labels.append(file_names)
start_visible.append(True)
plot_filename = ax_main.plot(normalized_reads.keys(), normalized_reads.values(), alpha=1, label = labels, zorder=1,color=item[0], linewidth=3)
line_collections.append(plot_filename)
returndict[file_names] = {}
for pos in filename_reads:
returndict[file_names][pos] = normalized_reads[pos]
for plot_filename in returndict:
returnstr += "{},".format(plot_filename)
returnstr += "\n"
for i in range(0,tranlen):
returnstr += "{},".format(i)
for plot_filename in returndict:
returnstr += "{},".format(returndict[plot_filename][i])
returnstr += "\n"
ax_main.set_ylim(0, y_max)
# draw cds start
#plt.plot((cds_start,cds_start), (0, y_max), cds_marker_colour,linestyle = ':',linewidth=cds_marker_size)
# draw cds end
#plt.plot((cds_stop, cds_stop), (0, y_max), cds_marker_colour,linestyle = ':',linewidth=cds_marker_size)
cds_markers = ax_main.plot((cds_start,cds_start), (0, y_max*0.97), color=cds_marker_colour,linestyle = 'solid', linewidth=cds_marker_size)
ax_main.text(cds_start,y_max*0.97,"CDS start",fontsize=18,color="black",ha="center")
#ax_main.annotate('axes fraction',xy=(3, 1), xycoords='data',xytext=(0.8, 0.95), textcoords='axes fraction',arrowprops=dict(facecolor='black', shrink=0.05),horizontalalignment='right', verticalalignment='top')
#trans = blended_transform_factory(ax_main.transData, ax_main.transAxes)
#ax_main.annotate('CDS RELATIVE START',(100,100),transform=trans)
#tform = blended_transform_factory(ax_main.transData, ax_main.transAxes)
#r=10
#ax_main.text(cds_start, 0.9, "CDS START OR WHATEVER", fontsize='xx-large', color='r', transform=tform)
cds_markers += ax_main.plot((cds_stop+1,cds_stop+1), (0, y_max*0.97), color=cds_marker_colour,linestyle = 'solid', linewidth=cds_marker_size)
ax_main.text(cds_stop,y_max*0.97,"CDS stop",fontsize=18,color="black",ha="center")
line_collections.append(cds_markers)
start_visible.append(True)
labels.append("CDS Markers")
ax_f1 = plt.subplot2grid((30,1), (27,0),rowspan=1,sharex=ax_main)
ax_f1.set_facecolor('lightgray')
ax_f2 = plt.subplot2grid((30,1), (28,0),rowspan=1,sharex=ax_main)
ax_f2.set_facecolor('lightgray')
ax_f6 = plt.subplot2grid((30,1), (29,0),rowspan=1,sharex=ax_main)
ax_f6.set_facecolor('lightgray')
ax_f6.set_xlabel('Transcript: {} Length: {} nt'.format(tran, tranlen), fontsize=subheading_size, labelpad=10)
for axis, frame in ((ax_f1, 1), (ax_f2, 2), (ax_f6, 3)):
color = color_dict['frames'][frame - 1]
axis.set_xlim(0, tranlen)
starts = [(item, 1) for item in start_stop_dict[frame]['starts']]
axis.broken_barh(starts, (0.5, 1), color='white',zorder=5, linewidth=2)
stops = [(item, 1) for item in start_stop_dict[frame]['stops']]
uag_stops = [(item, 1) for item in start_stop_dict[frame]['stops']['TAG']]
uaa_stops = [(item, 1) for item in start_stop_dict[frame]['stops']['TAA']]
uga_stops = [(item, 1) for item in start_stop_dict[frame]['stops']['TGA']]
axis.broken_barh(uag_stops, (0, 1), color=comp_uag_col, zorder=2, linewidth=2)
axis.broken_barh(uaa_stops, (0, 1), color=comp_uaa_col, zorder=2, linewidth=2)
axis.broken_barh(uga_stops, (0, 1), color=comp_uga_col, zorder=2, linewidth=2)
axis.set_ylabel('{}'.format(frame),rotation='horizontal', labelpad=10, verticalalignment='center')
axis.set_ylim(0, 1)
axis.tick_params(top=False, left=False, right=False, bottom=False, labeltop=False, labelleft=False, labelright=False, labelbottom=False)
ax_f6.axes.get_yaxis().set_ticks([])
ax_f2.axes.get_yaxis().set_ticks([])
ax_f1.axes.get_yaxis().set_ticks([])
title_str = '{} ({})'.format(gene,short_code)
plt.title(title_str, fontsize=title_size, y=36)
if not (hili_start == 0 and hili_stop == 0):
hili_start = int(hili_start)
hili_stop = int(hili_stop)
hili = ax_main.fill_between([hili_start,hili_stop],[y_max, y_max],zorder=0, alpha=0.75,color="#fffbaf")
labels.append("Highligter")
start_visible.append(True)
line_collections.append(hili)
leg_offset = (legend_size-17)*5
if leg_offset <0:
leg_offset = 0
leg_offset += 230
ilp = InteractiveLegendPlugin(line_collections, labels, alpha_unsel=0,alpha_sel=0.85, xoffset=leg_offset, yoffset=20,start_visible=start_visible,fontsize=legend_size)
plugins.connect(fig, ilp,TopToolbar(yoffset=-50,xoffset=-300),DownloadProfile(returnstr=returnstr),DownloadPNG(returnstr=title_str))
ax_main.set_facecolor(background_col)
# This changes the size of the tick markers, works on both firefox and chrome.
ax_main.tick_params('both', labelsize=marker_size)
ax_main.xaxis.set_major_locator(plt.MaxNLocator(3))
ax_main.yaxis.set_major_locator(plt.MaxNLocator(3))
ax_main.grid(color="white", linewidth=20,linestyle="solid")
graph = "<div style='padding-left: 55px;padding-top: 22px;'> <a href='https://trips.ucc.ie/short/{0}' target='_blank' ><button class='button centerbutton' type='submit'><b>Direct link to this plot</b></button></a> </div>".format(short_code)
tot_prog = 100
graph += mpld3.fig_to_html(fig)
return graph
| 44.688716 | 307 | 0.720853 |
7940f506a415a9b35af8f8725517de78605c7c62 | 4,104 | py | Python | locations/spiders/orangetheory_fitness.py | mfjackson/alltheplaces | 37c90b4041c80a574e6e4c2f886883e97df4b636 | [
"MIT"
] | null | null | null | locations/spiders/orangetheory_fitness.py | mfjackson/alltheplaces | 37c90b4041c80a574e6e4c2f886883e97df4b636 | [
"MIT"
] | null | null | null | locations/spiders/orangetheory_fitness.py | mfjackson/alltheplaces | 37c90b4041c80a574e6e4c2f886883e97df4b636 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import json
import scrapy
from locations.items import GeojsonPointItem
class OrangetheoryFitnessSpider(scrapy.Spider):
name = "orangetheory_fitness"
allowed_domains = ["orangetheory.co"]
start_urls = [
"https://api.orangetheory.co/partners/studios/v2?country=United+States",
"https://api.orangetheory.co/partners/studios/v2?country=Canada",
"https://api.orangetheory.co/partners/studios/v2?country=Australia",
"https://api.orangetheory.co/partners/studios/v2?country=Chile",
"https://api.orangetheory.co/partners/studios/v2?country=China",
"https://api.orangetheory.co/partners/studios/v2?country=Colombia",
"https://api.orangetheory.co/partners/studios/v2?country=Costa+Rica",
"https://api.orangetheory.co/partners/studios/v2?country=Dominican+Republic",
"https://api.orangetheory.co/partners/studios/v2?country=Germany",
"https://api.orangetheory.co/partners/studios/v2?country=Guatemala",
"https://api.orangetheory.co/partners/studios/v2?country=Hong+Kong",
"https://api.orangetheory.co/partners/studios/v2?country=India",
"https://api.orangetheory.co/partners/studios/v2?country=Israel",
"https://api.orangetheory.co/partners/studios/v2?country=Japan",
"https://api.orangetheory.co/partners/studios/v2?country=Kuwait",
"https://api.orangetheory.co/partners/studios/v2?country=Mexico",
"https://api.orangetheory.co/partners/studios/v2?country=New+Zealand",
"https://api.orangetheory.co/partners/studios/v2?country=Peru",
"https://api.orangetheory.co/partners/studios/v2?country=Puerto+Rico",
"https://api.orangetheory.co/partners/studios/v2?country=Singapore",
"https://api.orangetheory.co/partners/studios/v2?country=Spain",
"https://api.orangetheory.co/partners/studios/v2?country=United+Arab+Emirates",
"https://api.orangetheory.co/partners/studios/v2?country=United+Kingdom",
]
download_delay = 0.3
def parse(self, response):
location_data = json.loads(response.text)
locations = location_data["data"]
for location in locations:
# Handle junk data
if (
" live" in location[0]["studioName"].lower()
): # Skip Orangetheory Live virtual records
continue
if location[0]["studioLocation"]["physicalAddress"] in [
"*",
"a",
]: # Skip placeholder records
continue
if location[0]["studioLocation"]["longitude"] in [
"1.00000000",
"0.00000000",
]: # Skip test records
continue
if location[0]["studioName"] == "LatLong": # Skip latlon placeholder record
continue
# Handle coordinates
if (
float(location[0]["studioLocation"]["latitude"]) < -55.0
): # Drop handful of bad coords in Antarctica
lat = lon = ""
elif (
float(location[0]["studioLocation"]["longitude"]) < -180.0
): # Drop handful of bad coords
lat = lon = ""
else:
lat = location[0]["studioLocation"]["latitude"]
lon = location[0]["studioLocation"]["longitude"]
properties = {
"ref": location[0]["studioId"],
"name": location[0]["studioName"],
"addr_full": location[0]["studioLocation"]["physicalAddress"].strip(),
"city": location[0]["studioLocation"]["physicalCity"],
"state": location[0]["studioLocation"]["physicalState"],
"postcode": location[0]["studioLocation"]["physicalPostalCode"],
"country": location[0]["studioLocation"]["physicalCountry"],
"lat": lat,
"lon": lon,
"phone": location[0]["studioLocation"]["phoneNumber"],
}
yield GeojsonPointItem(**properties)
| 46.11236 | 88 | 0.597222 |
7940f5e0a0ba6c34abdda501be57630b2505b6b5 | 69,598 | py | Python | src/gdata/service.py | gauravuniverse/gdata-python-client | c4575d0775ebb83ac2bac2d40319ea921a184f5a | [
"Apache-2.0"
] | 483 | 2015-01-07T18:03:08.000Z | 2021-12-22T00:05:55.000Z | src/gdata/service.py | gauravuniverse/gdata-python-client | c4575d0775ebb83ac2bac2d40319ea921a184f5a | [
"Apache-2.0"
] | 68 | 2015-01-05T17:25:36.000Z | 2021-12-06T20:43:34.000Z | src/gdata/service.py | gauravuniverse/gdata-python-client | c4575d0775ebb83ac2bac2d40319ea921a184f5a | [
"Apache-2.0"
] | 297 | 2015-01-02T20:05:06.000Z | 2022-03-17T22:25:34.000Z | #
# Copyright (C) 2006,2008 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, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""GDataService provides CRUD ops. and programmatic login for GData services.
Error: A base exception class for all exceptions in the gdata_client
module.
CaptchaRequired: This exception is thrown when a login attempt results in a
captcha challenge from the ClientLogin service. When this
exception is thrown, the captcha_token and captcha_url are
set to the values provided in the server's response.
BadAuthentication: Raised when a login attempt is made with an incorrect
username or password.
NotAuthenticated: Raised if an operation requiring authentication is called
before a user has authenticated.
NonAuthSubToken: Raised if a method to modify an AuthSub token is used when
the user is either not authenticated or is authenticated
through another authentication mechanism.
NonOAuthToken: Raised if a method to modify an OAuth token is used when the
user is either not authenticated or is authenticated through
another authentication mechanism.
RequestError: Raised if a CRUD request returned a non-success code.
UnexpectedReturnType: Raised if the response from the server was not of the
desired type. For example, this would be raised if the
server sent a feed when the client requested an entry.
GDataService: Encapsulates user credentials needed to perform insert, update
and delete operations with the GData API. An instance can
perform user authentication, query, insertion, deletion, and
update.
Query: Eases query URI creation by allowing URI parameters to be set as
dictionary attributes. For example a query with a feed of
'/base/feeds/snippets' and ['bq'] set to 'digital camera' will
produce '/base/feeds/snippets?bq=digital+camera' when .ToUri() is
called on it.
"""
__author__ = 'api.jscudder (Jeffrey Scudder)'
import re
import urllib
import urlparse
try:
from xml.etree import cElementTree as ElementTree
except ImportError:
try:
import cElementTree as ElementTree
except ImportError:
try:
from xml.etree import ElementTree
except ImportError:
from elementtree import ElementTree
import atom.service
import gdata
import atom
import atom.http_interface
import atom.token_store
import gdata.auth
import gdata.gauth
AUTH_SERVER_HOST = 'https://www.google.com'
# When requesting an AuthSub token, it is often helpful to track the scope
# which is being requested. One way to accomplish this is to add a URL
# parameter to the 'next' URL which contains the requested scope. This
# constant is the default name (AKA key) for the URL parameter.
SCOPE_URL_PARAM_NAME = 'authsub_token_scope'
# When requesting an OAuth access token or authorization of an existing OAuth
# request token, it is often helpful to track the scope(s) which is/are being
# requested. One way to accomplish this is to add a URL parameter to the
# 'callback' URL which contains the requested scope. This constant is the
# default name (AKA key) for the URL parameter.
OAUTH_SCOPE_URL_PARAM_NAME = 'oauth_token_scope'
# Maps the service names used in ClientLogin to scope URLs.
CLIENT_LOGIN_SCOPES = gdata.gauth.AUTH_SCOPES
# Default parameters for GDataService.GetWithRetries method
DEFAULT_NUM_RETRIES = 3
DEFAULT_DELAY = 1
DEFAULT_BACKOFF = 2
def lookup_scopes(service_name):
"""Finds the scope URLs for the desired service.
In some cases, an unknown service may be used, and in those cases this
function will return None.
"""
if service_name in CLIENT_LOGIN_SCOPES:
return CLIENT_LOGIN_SCOPES[service_name]
return None
# Module level variable specifies which module should be used by GDataService
# objects to make HttpRequests. This setting can be overridden on each
# instance of GDataService.
# This module level variable is deprecated. Reassign the http_client member
# of a GDataService object instead.
http_request_handler = atom.service
class Error(Exception):
pass
class CaptchaRequired(Error):
pass
class BadAuthentication(Error):
pass
class NotAuthenticated(Error):
pass
class NonAuthSubToken(Error):
pass
class NonOAuthToken(Error):
pass
class RequestError(Error):
pass
class UnexpectedReturnType(Error):
pass
class BadAuthenticationServiceURL(Error):
pass
class FetchingOAuthRequestTokenFailed(RequestError):
pass
class TokenUpgradeFailed(RequestError):
pass
class RevokingOAuthTokenFailed(RequestError):
pass
class AuthorizationRequired(Error):
pass
class TokenHadNoScope(Error):
pass
class RanOutOfTries(Error):
pass
class GDataService(atom.service.AtomService):
"""Contains elements needed for GData login and CRUD request headers.
Maintains additional headers (tokens for example) needed for the GData
services to allow a user to perform inserts, updates, and deletes.
"""
# The hander member is deprecated, use http_client instead.
handler = None
# The auth_token member is deprecated, use the token_store instead.
auth_token = None
# The tokens dict is deprecated in favor of the token_store.
tokens = None
def __init__(self, email=None, password=None, account_type='HOSTED_OR_GOOGLE',
service=None, auth_service_url=None, source=None, server=None,
additional_headers=None, handler=None, tokens=None,
http_client=None, token_store=None):
"""Creates an object of type GDataService.
Args:
email: string (optional) The user's email address, used for
authentication.
password: string (optional) The user's password.
account_type: string (optional) The type of account to use. Use
'GOOGLE' for regular Google accounts or 'HOSTED' for Google
Apps accounts, or 'HOSTED_OR_GOOGLE' to try finding a HOSTED
account first and, if it doesn't exist, try finding a regular
GOOGLE account. Default value: 'HOSTED_OR_GOOGLE'.
service: string (optional) The desired service for which credentials
will be obtained.
auth_service_url: string (optional) User-defined auth token request URL
allows users to explicitly specify where to send auth token requests.
source: string (optional) The name of the user's application.
server: string (optional) The name of the server to which a connection
will be opened. Default value: 'base.google.com'.
additional_headers: dictionary (optional) Any additional headers which
should be included with CRUD operations.
handler: module (optional) This parameter is deprecated and has been
replaced by http_client.
tokens: This parameter is deprecated, calls should be made to
token_store instead.
http_client: An object responsible for making HTTP requests using a
request method. If none is provided, a new instance of
atom.http.ProxiedHttpClient will be used.
token_store: Keeps a collection of authorization tokens which can be
applied to requests for a specific URLs. Critical methods are
find_token based on a URL (atom.url.Url or a string), add_token,
and remove_token.
"""
atom.service.AtomService.__init__(self, http_client=http_client,
token_store=token_store)
self.email = email
self.password = password
self.account_type = account_type
self.service = service
self.auth_service_url = auth_service_url
self.server = server
self.additional_headers = additional_headers or {}
self._oauth_input_params = None
self.__SetSource(source)
self.__captcha_token = None
self.__captcha_url = None
self.__gsessionid = None
if http_request_handler.__name__ == 'gdata.urlfetch':
import gdata.alt.appengine
self.http_client = gdata.alt.appengine.AppEngineHttpClient()
def _SetSessionId(self, session_id):
"""Used in unit tests to simulate a 302 which sets a gsessionid."""
self.__gsessionid = session_id
# Define properties for GDataService
def _SetAuthSubToken(self, auth_token, scopes=None):
"""Deprecated, use SetAuthSubToken instead."""
self.SetAuthSubToken(auth_token, scopes=scopes)
def __SetAuthSubToken(self, auth_token, scopes=None):
"""Deprecated, use SetAuthSubToken instead."""
self._SetAuthSubToken(auth_token, scopes=scopes)
def _GetAuthToken(self):
"""Returns the auth token used for authenticating requests.
Returns:
string
"""
current_scopes = lookup_scopes(self.service)
if current_scopes:
token = self.token_store.find_token(current_scopes[0])
if hasattr(token, 'auth_header'):
return token.auth_header
return None
def _GetCaptchaToken(self):
"""Returns a captcha token if the most recent login attempt generated one.
The captcha token is only set if the Programmatic Login attempt failed
because the Google service issued a captcha challenge.
Returns:
string
"""
return self.__captcha_token
def __GetCaptchaToken(self):
return self._GetCaptchaToken()
captcha_token = property(__GetCaptchaToken,
doc="""Get the captcha token for a login request.""")
def _GetCaptchaURL(self):
"""Returns the URL of the captcha image if a login attempt generated one.
The captcha URL is only set if the Programmatic Login attempt failed
because the Google service issued a captcha challenge.
Returns:
string
"""
return self.__captcha_url
def __GetCaptchaURL(self):
return self._GetCaptchaURL()
captcha_url = property(__GetCaptchaURL,
doc="""Get the captcha URL for a login request.""")
def GetGeneratorFromLinkFinder(self, link_finder, func,
num_retries=DEFAULT_NUM_RETRIES,
delay=DEFAULT_DELAY,
backoff=DEFAULT_BACKOFF):
"""returns a generator for pagination"""
yield link_finder
next = link_finder.GetNextLink()
while next is not None:
next_feed = func(str(self.GetWithRetries(
next.href, num_retries=num_retries, delay=delay, backoff=backoff)))
yield next_feed
next = next_feed.GetNextLink()
def _GetElementGeneratorFromLinkFinder(self, link_finder, func,
num_retries=DEFAULT_NUM_RETRIES,
delay=DEFAULT_DELAY,
backoff=DEFAULT_BACKOFF):
for element in self.GetGeneratorFromLinkFinder(link_finder, func,
num_retries=num_retries,
delay=delay,
backoff=backoff).entry:
yield element
def GetOAuthInputParameters(self):
return self._oauth_input_params
def SetOAuthInputParameters(self, signature_method, consumer_key,
consumer_secret=None, rsa_key=None,
two_legged_oauth=False, requestor_id=None):
"""Sets parameters required for using OAuth authentication mechanism.
NOTE: Though consumer_secret and rsa_key are optional, either of the two
is required depending on the value of the signature_method.
Args:
signature_method: class which provides implementation for strategy class
oauth.oauth.OAuthSignatureMethod. Signature method to be used for
signing each request. Valid implementations are provided as the
constants defined by gdata.auth.OAuthSignatureMethod. Currently
they are gdata.auth.OAuthSignatureMethod.RSA_SHA1 and
gdata.auth.OAuthSignatureMethod.HMAC_SHA1
consumer_key: string Domain identifying third_party web application.
consumer_secret: string (optional) Secret generated during registration.
Required only for HMAC_SHA1 signature method.
rsa_key: string (optional) Private key required for RSA_SHA1 signature
method.
two_legged_oauth: boolean (optional) Enables two-legged OAuth process.
requestor_id: string (optional) User email adress to make requests on
their behalf. This parameter should only be set when two_legged_oauth
is True.
"""
self._oauth_input_params = gdata.auth.OAuthInputParams(
signature_method, consumer_key, consumer_secret=consumer_secret,
rsa_key=rsa_key, requestor_id=requestor_id)
if two_legged_oauth:
oauth_token = gdata.auth.OAuthToken(
oauth_input_params=self._oauth_input_params)
self.SetOAuthToken(oauth_token)
def FetchOAuthRequestToken(self, scopes=None, extra_parameters=None,
request_url='%s/accounts/OAuthGetRequestToken' % \
AUTH_SERVER_HOST, oauth_callback=None):
"""Fetches and sets the OAuth request token and returns it.
Args:
scopes: string or list of string base URL(s) of the service(s) to be
accessed. If None, then this method tries to determine the
scope(s) from the current service.
extra_parameters: dict (optional) key-value pairs as any additional
parameters to be included in the URL and signature while making a
request for fetching an OAuth request token. All the OAuth parameters
are added by default. But if provided through this argument, any
default parameters will be overwritten. For e.g. a default parameter
oauth_version 1.0 can be overwritten if
extra_parameters = {'oauth_version': '2.0'}
request_url: Request token URL. The default is
'https://www.google.com/accounts/OAuthGetRequestToken'.
oauth_callback: str (optional) If set, it is assume the client is using
the OAuth v1.0a protocol where the callback url is sent in the
request token step. If the oauth_callback is also set in
extra_params, this value will override that one.
Returns:
The fetched request token as a gdata.auth.OAuthToken object.
Raises:
FetchingOAuthRequestTokenFailed if the server responded to the request
with an error.
"""
if scopes is None:
scopes = lookup_scopes(self.service)
if not isinstance(scopes, (list, tuple)):
scopes = [scopes,]
if oauth_callback:
if extra_parameters is not None:
extra_parameters['oauth_callback'] = oauth_callback
else:
extra_parameters = {'oauth_callback': oauth_callback}
request_token_url = gdata.auth.GenerateOAuthRequestTokenUrl(
self._oauth_input_params, scopes,
request_token_url=request_url,
extra_parameters=extra_parameters)
response = self.http_client.request('GET', str(request_token_url))
if response.status == 200:
token = gdata.auth.OAuthToken()
token.set_token_string(response.read())
token.scopes = scopes
token.oauth_input_params = self._oauth_input_params
self.SetOAuthToken(token)
return token
error = {
'status': response.status,
'reason': 'Non 200 response on fetch request token',
'body': response.read()
}
raise FetchingOAuthRequestTokenFailed(error)
def SetOAuthToken(self, oauth_token):
"""Attempts to set the current token and add it to the token store.
The oauth_token can be any OAuth token i.e. unauthorized request token,
authorized request token or access token.
This method also attempts to add the token to the token store.
Use this method any time you want the current token to point to the
oauth_token passed. For e.g. call this method with the request token
you receive from FetchOAuthRequestToken.
Args:
request_token: gdata.auth.OAuthToken OAuth request token.
"""
if self.auto_set_current_token:
self.current_token = oauth_token
if self.auto_store_tokens:
self.token_store.add_token(oauth_token)
def GenerateOAuthAuthorizationURL(
self, request_token=None, callback_url=None, extra_params=None,
include_scopes_in_callback=False,
scopes_param_prefix=OAUTH_SCOPE_URL_PARAM_NAME,
request_url='%s/accounts/OAuthAuthorizeToken' % AUTH_SERVER_HOST):
"""Generates URL at which user will login to authorize the request token.
Args:
request_token: gdata.auth.OAuthToken (optional) OAuth request token.
If not specified, then the current token will be used if it is of
type <gdata.auth.OAuthToken>, else it is found by looking in the
token_store by looking for a token for the current scope.
callback_url: string (optional) The URL user will be sent to after
logging in and granting access.
extra_params: dict (optional) Additional parameters to be sent.
include_scopes_in_callback: Boolean (default=False) if set to True, and
if 'callback_url' is present, the 'callback_url' will be modified to
include the scope(s) from the request token as a URL parameter. The
key for the 'callback' URL's scope parameter will be
OAUTH_SCOPE_URL_PARAM_NAME. The benefit of including the scope URL as
a parameter to the 'callback' URL, is that the page which receives
the OAuth token will be able to tell which URLs the token grants
access to.
scopes_param_prefix: string (default='oauth_token_scope') The URL
parameter key which maps to the list of valid scopes for the token.
This URL parameter will be included in the callback URL along with
the scopes of the token as value if include_scopes_in_callback=True.
request_url: Authorization URL. The default is
'https://www.google.com/accounts/OAuthAuthorizeToken'.
Returns:
A string URL at which the user is required to login.
Raises:
NonOAuthToken if the user's request token is not an OAuth token or if a
request token was not available.
"""
if request_token and not isinstance(request_token, gdata.auth.OAuthToken):
raise NonOAuthToken
if not request_token:
if isinstance(self.current_token, gdata.auth.OAuthToken):
request_token = self.current_token
else:
current_scopes = lookup_scopes(self.service)
if current_scopes:
token = self.token_store.find_token(current_scopes[0])
if isinstance(token, gdata.auth.OAuthToken):
request_token = token
if not request_token:
raise NonOAuthToken
return str(gdata.auth.GenerateOAuthAuthorizationUrl(
request_token,
authorization_url=request_url,
callback_url=callback_url, extra_params=extra_params,
include_scopes_in_callback=include_scopes_in_callback,
scopes_param_prefix=scopes_param_prefix))
def UpgradeToOAuthAccessToken(self, authorized_request_token=None,
request_url='%s/accounts/OAuthGetAccessToken' \
% AUTH_SERVER_HOST, oauth_version='1.0',
oauth_verifier=None):
"""Upgrades the authorized request token to an access token and returns it
Args:
authorized_request_token: gdata.auth.OAuthToken (optional) OAuth request
token. If not specified, then the current token will be used if it is
of type <gdata.auth.OAuthToken>, else it is found by looking in the
token_store by looking for a token for the current scope.
request_url: Access token URL. The default is
'https://www.google.com/accounts/OAuthGetAccessToken'.
oauth_version: str (default='1.0') oauth_version parameter. All other
'oauth_' parameters are added by default. This parameter too, is
added by default but here you can override it's value.
oauth_verifier: str (optional) If present, it is assumed that the client
will use the OAuth v1.0a protocol which includes passing the
oauth_verifier (as returned by the SP) in the access token step.
Returns:
Access token
Raises:
NonOAuthToken if the user's authorized request token is not an OAuth
token or if an authorized request token was not available.
TokenUpgradeFailed if the server responded to the request with an
error.
"""
if (authorized_request_token and
not isinstance(authorized_request_token, gdata.auth.OAuthToken)):
raise NonOAuthToken
if not authorized_request_token:
if isinstance(self.current_token, gdata.auth.OAuthToken):
authorized_request_token = self.current_token
else:
current_scopes = lookup_scopes(self.service)
if current_scopes:
token = self.token_store.find_token(current_scopes[0])
if isinstance(token, gdata.auth.OAuthToken):
authorized_request_token = token
if not authorized_request_token:
raise NonOAuthToken
access_token_url = gdata.auth.GenerateOAuthAccessTokenUrl(
authorized_request_token,
self._oauth_input_params,
access_token_url=request_url,
oauth_version=oauth_version,
oauth_verifier=oauth_verifier)
response = self.http_client.request('GET', str(access_token_url))
if response.status == 200:
token = gdata.auth.OAuthTokenFromHttpBody(response.read())
token.scopes = authorized_request_token.scopes
token.oauth_input_params = authorized_request_token.oauth_input_params
self.SetOAuthToken(token)
return token
else:
raise TokenUpgradeFailed({'status': response.status,
'reason': 'Non 200 response on upgrade',
'body': response.read()})
def RevokeOAuthToken(self, request_url='%s/accounts/AuthSubRevokeToken' % \
AUTH_SERVER_HOST):
"""Revokes an existing OAuth token.
request_url: Token revoke URL. The default is
'https://www.google.com/accounts/AuthSubRevokeToken'.
Raises:
NonOAuthToken if the user's auth token is not an OAuth token.
RevokingOAuthTokenFailed if request for revoking an OAuth token failed.
"""
scopes = lookup_scopes(self.service)
token = self.token_store.find_token(scopes[0])
if not isinstance(token, gdata.auth.OAuthToken):
raise NonOAuthToken
response = token.perform_request(self.http_client, 'GET', request_url,
headers={'Content-Type':'application/x-www-form-urlencoded'})
if response.status == 200:
self.token_store.remove_token(token)
else:
raise RevokingOAuthTokenFailed
def GetAuthSubToken(self):
"""Returns the AuthSub token as a string.
If the token is an gdta.auth.AuthSubToken, the Authorization Label
("AuthSub token") is removed.
This method examines the current_token to see if it is an AuthSubToken
or SecureAuthSubToken. If not, it searches the token_store for a token
which matches the current scope.
The current scope is determined by the service name string member.
Returns:
If the current_token is set to an AuthSubToken/SecureAuthSubToken,
return the token string. If there is no current_token, a token string
for a token which matches the service object's default scope is returned.
If there are no tokens valid for the scope, returns None.
"""
if isinstance(self.current_token, gdata.auth.AuthSubToken):
return self.current_token.get_token_string()
current_scopes = lookup_scopes(self.service)
if current_scopes:
token = self.token_store.find_token(current_scopes[0])
if isinstance(token, gdata.auth.AuthSubToken):
return token.get_token_string()
else:
token = self.token_store.find_token(atom.token_store.SCOPE_ALL)
if isinstance(token, gdata.auth.ClientLoginToken):
return token.get_token_string()
return None
def SetAuthSubToken(self, token, scopes=None, rsa_key=None):
"""Sets the token sent in requests to an AuthSub token.
Sets the current_token and attempts to add the token to the token_store.
Only use this method if you have received a token from the AuthSub
service. The auth token is set automatically when UpgradeToSessionToken()
is used. See documentation for Google AuthSub here:
http://code.google.com/apis/accounts/AuthForWebApps.html
Args:
token: gdata.auth.AuthSubToken or gdata.auth.SecureAuthSubToken or string
The token returned by the AuthSub service. If the token is an
AuthSubToken or SecureAuthSubToken, the scope information stored in
the token is used. If the token is a string, the scopes parameter is
used to determine the valid scopes.
scopes: list of URLs for which the token is valid. This is only used
if the token parameter is a string.
rsa_key: string (optional) Private key required for RSA_SHA1 signature
method. This parameter is necessary if the token is a string
representing a secure token.
"""
if not isinstance(token, gdata.auth.AuthSubToken):
token_string = token
if rsa_key:
token = gdata.auth.SecureAuthSubToken(rsa_key)
else:
token = gdata.auth.AuthSubToken()
token.set_token_string(token_string)
# If no scopes were set for the token, use the scopes passed in, or
# try to determine the scopes based on the current service name. If
# all else fails, set the token to match all requests.
if not token.scopes:
if scopes is None:
scopes = lookup_scopes(self.service)
if scopes is None:
scopes = [atom.token_store.SCOPE_ALL]
token.scopes = scopes
if self.auto_set_current_token:
self.current_token = token
if self.auto_store_tokens:
self.token_store.add_token(token)
def GetClientLoginToken(self):
"""Returns the token string for the current token or a token matching the
service scope.
If the current_token is a ClientLoginToken, the token string for
the current token is returned. If the current_token is not set, this method
searches for a token in the token_store which is valid for the service
object's current scope.
The current scope is determined by the service name string member.
The token string is the end of the Authorization header, it doesn not
include the ClientLogin label.
"""
if isinstance(self.current_token, gdata.auth.ClientLoginToken):
return self.current_token.get_token_string()
current_scopes = lookup_scopes(self.service)
if current_scopes:
token = self.token_store.find_token(current_scopes[0])
if isinstance(token, gdata.auth.ClientLoginToken):
return token.get_token_string()
else:
token = self.token_store.find_token(atom.token_store.SCOPE_ALL)
if isinstance(token, gdata.auth.ClientLoginToken):
return token.get_token_string()
return None
def SetClientLoginToken(self, token, scopes=None):
"""Sets the token sent in requests to a ClientLogin token.
This method sets the current_token to a new ClientLoginToken and it
also attempts to add the ClientLoginToken to the token_store.
Only use this method if you have received a token from the ClientLogin
service. The auth_token is set automatically when ProgrammaticLogin()
is used. See documentation for Google ClientLogin here:
http://code.google.com/apis/accounts/docs/AuthForInstalledApps.html
Args:
token: string or instance of a ClientLoginToken.
"""
if not isinstance(token, gdata.auth.ClientLoginToken):
token_string = token
token = gdata.auth.ClientLoginToken()
token.set_token_string(token_string)
if not token.scopes:
if scopes is None:
scopes = lookup_scopes(self.service)
if scopes is None:
scopes = [atom.token_store.SCOPE_ALL]
token.scopes = scopes
if self.auto_set_current_token:
self.current_token = token
if self.auto_store_tokens:
self.token_store.add_token(token)
# Private methods to create the source property.
def __GetSource(self):
return self.__source
def __SetSource(self, new_source):
self.__source = new_source
# Update the UserAgent header to include the new application name.
self.additional_headers['User-Agent'] = atom.http_interface.USER_AGENT % (
self.__source,)
source = property(__GetSource, __SetSource,
doc="""The source is the name of the application making the request.
It should be in the form company_id-app_name-app_version""")
# Authentication operations
def ProgrammaticLogin(self, captcha_token=None, captcha_response=None):
"""Authenticates the user and sets the GData Auth token.
Login retreives a temporary auth token which must be used with all
requests to GData services. The auth token is stored in the GData client
object.
Login is also used to respond to a captcha challenge. If the user's login
attempt failed with a CaptchaRequired error, the user can respond by
calling Login with the captcha token and the answer to the challenge.
Args:
captcha_token: string (optional) The identifier for the captcha challenge
which was presented to the user.
captcha_response: string (optional) The user's answer to the captch
challenge.
Raises:
CaptchaRequired if the login service will require a captcha response
BadAuthentication if the login service rejected the username or password
Error if the login service responded with a 403 different from the above
"""
request_body = gdata.auth.generate_client_login_request_body(self.email,
self.password, self.service, self.source, self.account_type,
captcha_token, captcha_response)
# If the user has defined their own authentication service URL,
# send the ClientLogin requests to this URL:
if not self.auth_service_url:
auth_request_url = AUTH_SERVER_HOST + '/accounts/ClientLogin'
else:
auth_request_url = self.auth_service_url
auth_response = self.http_client.request('POST', auth_request_url,
data=request_body,
headers={'Content-Type':'application/x-www-form-urlencoded'})
response_body = auth_response.read()
if auth_response.status == 200:
# TODO: insert the token into the token_store directly.
self.SetClientLoginToken(
gdata.auth.get_client_login_token(response_body))
self.__captcha_token = None
self.__captcha_url = None
elif auth_response.status == 403:
# Examine each line to find the error type and the captcha token and
# captch URL if they are present.
captcha_parameters = gdata.auth.get_captcha_challenge(response_body,
captcha_base_url='%s/accounts/' % AUTH_SERVER_HOST)
if captcha_parameters:
self.__captcha_token = captcha_parameters['token']
self.__captcha_url = captcha_parameters['url']
raise CaptchaRequired, 'Captcha Required'
elif response_body.splitlines()[0] == 'Error=BadAuthentication':
self.__captcha_token = None
self.__captcha_url = None
raise BadAuthentication, 'Incorrect username or password'
else:
self.__captcha_token = None
self.__captcha_url = None
raise Error, 'Server responded with a 403 code'
elif auth_response.status == 302:
self.__captcha_token = None
self.__captcha_url = None
# Google tries to redirect all bad URLs back to
# http://www.google.<locale>. If a redirect
# attempt is made, assume the user has supplied an incorrect authentication URL
raise BadAuthenticationServiceURL, 'Server responded with a 302 code.'
def ClientLogin(self, username, password, account_type=None, service=None,
auth_service_url=None, source=None, captcha_token=None,
captcha_response=None):
"""Convenience method for authenticating using ProgrammaticLogin.
Sets values for email, password, and other optional members.
Args:
username:
password:
account_type: string (optional)
service: string (optional)
auth_service_url: string (optional)
captcha_token: string (optional)
captcha_response: string (optional)
"""
self.email = username
self.password = password
if account_type:
self.account_type = account_type
if service:
self.service = service
if source:
self.source = source
if auth_service_url:
self.auth_service_url = auth_service_url
self.ProgrammaticLogin(captcha_token, captcha_response)
def GenerateAuthSubURL(self, next, scope, secure=False, session=True,
domain='default'):
"""Generate a URL at which the user will login and be redirected back.
Users enter their credentials on a Google login page and a token is sent
to the URL specified in next. See documentation for AuthSub login at:
http://code.google.com/apis/accounts/docs/AuthSub.html
Args:
next: string The URL user will be sent to after logging in.
scope: string or list of strings. The URLs of the services to be
accessed.
secure: boolean (optional) Determines whether or not the issued token
is a secure token.
session: boolean (optional) Determines whether or not the issued token
can be upgraded to a session token.
"""
if not isinstance(scope, (list, tuple)):
scope = (scope,)
return gdata.auth.generate_auth_sub_url(next, scope, secure=secure,
session=session,
request_url='%s/accounts/AuthSubRequest' % AUTH_SERVER_HOST,
domain=domain)
def UpgradeToSessionToken(self, token=None):
"""Upgrades a single use AuthSub token to a session token.
Args:
token: A gdata.auth.AuthSubToken or gdata.auth.SecureAuthSubToken
(optional) which is good for a single use but can be upgraded
to a session token. If no token is passed in, the token
is found by looking in the token_store by looking for a token
for the current scope.
Raises:
NonAuthSubToken if the user's auth token is not an AuthSub token
TokenUpgradeFailed if the server responded to the request with an
error.
"""
if token is None:
scopes = lookup_scopes(self.service)
if scopes:
token = self.token_store.find_token(scopes[0])
else:
token = self.token_store.find_token(atom.token_store.SCOPE_ALL)
if not isinstance(token, gdata.auth.AuthSubToken):
raise NonAuthSubToken
self.SetAuthSubToken(self.upgrade_to_session_token(token))
def upgrade_to_session_token(self, token):
"""Upgrades a single use AuthSub token to a session token.
Args:
token: A gdata.auth.AuthSubToken or gdata.auth.SecureAuthSubToken
which is good for a single use but can be upgraded to a
session token.
Returns:
The upgraded token as a gdata.auth.AuthSubToken object.
Raises:
TokenUpgradeFailed if the server responded to the request with an
error.
"""
response = token.perform_request(self.http_client, 'GET',
AUTH_SERVER_HOST + '/accounts/AuthSubSessionToken',
headers={'Content-Type':'application/x-www-form-urlencoded'})
response_body = response.read()
if response.status == 200:
token.set_token_string(
gdata.auth.token_from_http_body(response_body))
return token
else:
raise TokenUpgradeFailed({'status': response.status,
'reason': 'Non 200 response on upgrade',
'body': response_body})
def RevokeAuthSubToken(self):
"""Revokes an existing AuthSub token.
Raises:
NonAuthSubToken if the user's auth token is not an AuthSub token
"""
scopes = lookup_scopes(self.service)
token = self.token_store.find_token(scopes[0])
if not isinstance(token, gdata.auth.AuthSubToken):
raise NonAuthSubToken
response = token.perform_request(self.http_client, 'GET',
AUTH_SERVER_HOST + '/accounts/AuthSubRevokeToken',
headers={'Content-Type':'application/x-www-form-urlencoded'})
if response.status == 200:
self.token_store.remove_token(token)
def AuthSubTokenInfo(self):
"""Fetches the AuthSub token's metadata from the server.
Raises:
NonAuthSubToken if the user's auth token is not an AuthSub token
"""
scopes = lookup_scopes(self.service)
token = self.token_store.find_token(scopes[0])
if not isinstance(token, gdata.auth.AuthSubToken):
raise NonAuthSubToken
response = token.perform_request(self.http_client, 'GET',
AUTH_SERVER_HOST + '/accounts/AuthSubTokenInfo',
headers={'Content-Type':'application/x-www-form-urlencoded'})
result_body = response.read()
if response.status == 200:
return result_body
else:
raise RequestError, {'status': response.status,
'body': result_body}
def GetWithRetries(self, uri, extra_headers=None, redirects_remaining=4,
encoding='UTF-8', converter=None, num_retries=DEFAULT_NUM_RETRIES,
delay=DEFAULT_DELAY, backoff=DEFAULT_BACKOFF, logger=None):
"""This is a wrapper method for Get with retrying capability.
To avoid various errors while retrieving bulk entities by retrying
specified times.
Note this method relies on the time module and so may not be usable
by default in Python2.2.
Args:
num_retries: Integer; the retry count.
delay: Integer; the initial delay for retrying.
backoff: Integer; how much the delay should lengthen after each failure.
logger: An object which has a debug(str) method to receive logging
messages. Recommended that you pass in the logging module.
Raises:
ValueError if any of the parameters has an invalid value.
RanOutOfTries on failure after number of retries.
"""
# Moved import for time module inside this method since time is not a
# default module in Python2.2. This method will not be usable in
# Python2.2.
import time
if backoff <= 1:
raise ValueError("backoff must be greater than 1")
num_retries = int(num_retries)
if num_retries < 0:
raise ValueError("num_retries must be 0 or greater")
if delay <= 0:
raise ValueError("delay must be greater than 0")
# Let's start
mtries, mdelay = num_retries, delay
while mtries > 0:
if mtries != num_retries:
if logger:
logger.debug("Retrying: %s" % uri)
try:
rv = self.Get(uri, extra_headers=extra_headers,
redirects_remaining=redirects_remaining,
encoding=encoding, converter=converter)
except SystemExit:
# Allow this error
raise
except RequestError, e:
# Error 500 is 'internal server error' and warrants a retry
# Error 503 is 'service unavailable' and warrants a retry
if e[0]['status'] not in [500, 503]:
raise e
# Else, fall through to the retry code...
except Exception, e:
if logger:
logger.debug(e)
# Fall through to the retry code...
else:
# This is the right path.
return rv
mtries -= 1
time.sleep(mdelay)
mdelay *= backoff
raise RanOutOfTries('Ran out of tries.')
# CRUD operations
def Get(self, uri, extra_headers=None, redirects_remaining=4,
encoding='UTF-8', converter=None):
"""Query the GData API with the given URI
The uri is the portion of the URI after the server value
(ex: www.google.com).
To perform a query against Google Base, set the server to
'base.google.com' and set the uri to '/base/feeds/...', where ... is
your query. For example, to find snippets for all digital cameras uri
should be set to: '/base/feeds/snippets?bq=digital+camera'
Args:
uri: string The query in the form of a URI. Example:
'/base/feeds/snippets?bq=digital+camera'.
extra_headers: dictionary (optional) Extra HTTP headers to be included
in the GET request. These headers are in addition to
those stored in the client's additional_headers property.
The client automatically sets the Content-Type and
Authorization headers.
redirects_remaining: int (optional) Tracks the number of additional
redirects this method will allow. If the service object receives
a redirect and remaining is 0, it will not follow the redirect.
This was added to avoid infinite redirect loops.
encoding: string (optional) The character encoding for the server's
response. Default is UTF-8
converter: func (optional) A function which will transform
the server's results before it is returned. Example: use
GDataFeedFromString to parse the server response as if it
were a GDataFeed.
Returns:
If there is no ResultsTransformer specified in the call, a GDataFeed
or GDataEntry depending on which is sent from the server. If the
response is niether a feed or entry and there is no ResultsTransformer,
return a string. If there is a ResultsTransformer, the returned value
will be that of the ResultsTransformer function.
"""
if extra_headers is None:
extra_headers = {}
if self.__gsessionid is not None:
if uri.find('gsessionid=') < 0:
if uri.find('?') > -1:
uri += '&gsessionid=%s' % (self.__gsessionid,)
else:
uri += '?gsessionid=%s' % (self.__gsessionid,)
server_response = self.request('GET', uri,
headers=extra_headers)
result_body = server_response.read()
if server_response.status == 200:
if converter:
return converter(result_body)
# There was no ResultsTransformer specified, so try to convert the
# server's response into a GDataFeed.
feed = gdata.GDataFeedFromString(result_body)
if not feed:
# If conversion to a GDataFeed failed, try to convert the server's
# response to a GDataEntry.
entry = gdata.GDataEntryFromString(result_body)
if not entry:
# The server's response wasn't a feed, or an entry, so return the
# response body as a string.
return result_body
return entry
return feed
elif server_response.status in (301, 302):
if redirects_remaining > 0:
location = (server_response.getheader('Location')
or server_response.getheader('location'))
if location is not None:
m = re.compile('[\?\&]gsessionid=(\w*\-)').search(location)
if m is not None:
self.__gsessionid = m.group(1)
return GDataService.Get(self, location, extra_headers, redirects_remaining - 1,
encoding=encoding, converter=converter)
else:
raise RequestError, {'status': server_response.status,
'reason': '302 received without Location header',
'body': result_body}
else:
raise RequestError, {'status': server_response.status,
'reason': 'Redirect received, but redirects_remaining <= 0',
'body': result_body}
else:
raise RequestError, {'status': server_response.status,
'reason': server_response.reason, 'body': result_body}
def GetMedia(self, uri, extra_headers=None):
"""Returns a MediaSource containing media and its metadata from the given
URI string.
"""
response_handle = self.request('GET', uri,
headers=extra_headers)
return gdata.MediaSource(response_handle, response_handle.getheader(
'Content-Type'),
response_handle.getheader('Content-Length'))
def GetEntry(self, uri, extra_headers=None):
"""Query the GData API with the given URI and receive an Entry.
See also documentation for gdata.service.Get
Args:
uri: string The query in the form of a URI. Example:
'/base/feeds/snippets?bq=digital+camera'.
extra_headers: dictionary (optional) Extra HTTP headers to be included
in the GET request. These headers are in addition to
those stored in the client's additional_headers property.
The client automatically sets the Content-Type and
Authorization headers.
Returns:
A GDataEntry built from the XML in the server's response.
"""
result = GDataService.Get(self, uri, extra_headers,
converter=atom.EntryFromString)
if isinstance(result, atom.Entry):
return result
else:
raise UnexpectedReturnType, 'Server did not send an entry'
def GetFeed(self, uri, extra_headers=None,
converter=gdata.GDataFeedFromString):
"""Query the GData API with the given URI and receive a Feed.
See also documentation for gdata.service.Get
Args:
uri: string The query in the form of a URI. Example:
'/base/feeds/snippets?bq=digital+camera'.
extra_headers: dictionary (optional) Extra HTTP headers to be included
in the GET request. These headers are in addition to
those stored in the client's additional_headers property.
The client automatically sets the Content-Type and
Authorization headers.
Returns:
A GDataFeed built from the XML in the server's response.
"""
result = GDataService.Get(self, uri, extra_headers, converter=converter)
if isinstance(result, atom.Feed):
return result
else:
raise UnexpectedReturnType, 'Server did not send a feed'
def GetNext(self, feed):
"""Requests the next 'page' of results in the feed.
This method uses the feed's next link to request an additional feed
and uses the class of the feed to convert the results of the GET request.
Args:
feed: atom.Feed or a subclass. The feed should contain a next link and
the type of the feed will be applied to the results from the
server. The new feed which is returned will be of the same class
as this feed which was passed in.
Returns:
A new feed representing the next set of results in the server's feed.
The type of this feed will match that of the feed argument.
"""
next_link = feed.GetNextLink()
# Create a closure which will convert an XML string to the class of
# the feed object passed in.
def ConvertToFeedClass(xml_string):
return atom.CreateClassFromXMLString(feed.__class__, xml_string)
# Make a GET request on the next link and use the above closure for the
# converted which processes the XML string from the server.
if next_link and next_link.href:
return GDataService.Get(self, next_link.href,
converter=ConvertToFeedClass)
else:
return None
def Post(self, data, uri, extra_headers=None, url_params=None,
escape_params=True, redirects_remaining=4, media_source=None,
converter=None):
"""Insert or update data into a GData service at the given URI.
Args:
data: string, ElementTree._Element, atom.Entry, or gdata.GDataEntry The
XML to be sent to the uri.
uri: string The location (feed) to which the data should be inserted.
Example: '/base/feeds/items'.
extra_headers: dict (optional) HTTP headers which are to be included.
The client automatically sets the Content-Type,
Authorization, and Content-Length headers.
url_params: dict (optional) Additional URL parameters to be included
in the URI. These are translated into query arguments
in the form '&dict_key=value&...'.
Example: {'max-results': '250'} becomes &max-results=250
escape_params: boolean (optional) If false, the calling code has already
ensured that the query will form a valid URL (all
reserved characters have been escaped). If true, this
method will escape the query and any URL parameters
provided.
media_source: MediaSource (optional) Container for the media to be sent
along with the entry, if provided.
converter: func (optional) A function which will be executed on the
server's response. Often this is a function like
GDataEntryFromString which will parse the body of the server's
response and return a GDataEntry.
Returns:
If the post succeeded, this method will return a GDataFeed, GDataEntry,
or the results of running converter on the server's result body (if
converter was specified).
"""
return GDataService.PostOrPut(self, 'POST', data, uri,
extra_headers=extra_headers, url_params=url_params,
escape_params=escape_params, redirects_remaining=redirects_remaining,
media_source=media_source, converter=converter)
def PostOrPut(self, verb, data, uri, extra_headers=None, url_params=None,
escape_params=True, redirects_remaining=4, media_source=None,
converter=None):
"""Insert data into a GData service at the given URI.
Args:
verb: string, either 'POST' or 'PUT'
data: string, ElementTree._Element, atom.Entry, or gdata.GDataEntry The
XML to be sent to the uri.
uri: string The location (feed) to which the data should be inserted.
Example: '/base/feeds/items'.
extra_headers: dict (optional) HTTP headers which are to be included.
The client automatically sets the Content-Type,
Authorization, and Content-Length headers.
url_params: dict (optional) Additional URL parameters to be included
in the URI. These are translated into query arguments
in the form '&dict_key=value&...'.
Example: {'max-results': '250'} becomes &max-results=250
escape_params: boolean (optional) If false, the calling code has already
ensured that the query will form a valid URL (all
reserved characters have been escaped). If true, this
method will escape the query and any URL parameters
provided.
media_source: MediaSource (optional) Container for the media to be sent
along with the entry, if provided.
converter: func (optional) A function which will be executed on the
server's response. Often this is a function like
GDataEntryFromString which will parse the body of the server's
response and return a GDataEntry.
Returns:
If the post succeeded, this method will return a GDataFeed, GDataEntry,
or the results of running converter on the server's result body (if
converter was specified).
"""
if extra_headers is None:
extra_headers = {}
if self.__gsessionid is not None:
if uri.find('gsessionid=') < 0:
if url_params is None:
url_params = {}
url_params['gsessionid'] = self.__gsessionid
if data and media_source:
if ElementTree.iselement(data):
data_str = ElementTree.tostring(data)
else:
data_str = str(data)
multipart = []
multipart.append('Media multipart posting\r\n--END_OF_PART\r\n' + \
'Content-Type: application/atom+xml\r\n\r\n')
multipart.append('\r\n--END_OF_PART\r\nContent-Type: ' + \
media_source.content_type+'\r\n\r\n')
multipart.append('\r\n--END_OF_PART--\r\n')
extra_headers['MIME-version'] = '1.0'
extra_headers['Content-Length'] = str(len(multipart[0]) +
len(multipart[1]) + len(multipart[2]) +
len(data_str) + media_source.content_length)
extra_headers['Content-Type'] = 'multipart/related; boundary=END_OF_PART'
server_response = self.request(verb, uri,
data=[multipart[0], data_str, multipart[1], media_source.file_handle,
multipart[2]], headers=extra_headers, url_params=url_params)
result_body = server_response.read()
elif media_source or isinstance(data, gdata.MediaSource):
if isinstance(data, gdata.MediaSource):
media_source = data
extra_headers['Content-Length'] = str(media_source.content_length)
extra_headers['Content-Type'] = media_source.content_type
server_response = self.request(verb, uri,
data=media_source.file_handle, headers=extra_headers,
url_params=url_params)
result_body = server_response.read()
else:
http_data = data
if 'Content-Type' not in extra_headers:
content_type = 'application/atom+xml'
extra_headers['Content-Type'] = content_type
server_response = self.request(verb, uri, data=http_data,
headers=extra_headers, url_params=url_params)
result_body = server_response.read()
# Server returns 201 for most post requests, but when performing a batch
# request the server responds with a 200 on success.
if server_response.status == 201 or server_response.status == 200:
if converter:
return converter(result_body)
feed = gdata.GDataFeedFromString(result_body)
if not feed:
entry = gdata.GDataEntryFromString(result_body)
if not entry:
return result_body
return entry
return feed
elif server_response.status == 302:
if redirects_remaining > 0:
location = (server_response.getheader('Location')
or server_response.getheader('location'))
if location is not None:
m = re.compile('[\?\&]gsessionid=(\w*\-)').search(location)
if m is not None:
self.__gsessionid = m.group(1)
return GDataService.PostOrPut(self, verb, data, location,
extra_headers, url_params, escape_params,
redirects_remaining - 1, media_source, converter=converter)
else:
raise RequestError, {'status': server_response.status,
'reason': '302 received without Location header',
'body': result_body}
else:
raise RequestError, {'status': server_response.status,
'reason': 'Redirect received, but redirects_remaining <= 0',
'body': result_body}
else:
raise RequestError, {'status': server_response.status,
'reason': server_response.reason, 'body': result_body}
def Put(self, data, uri, extra_headers=None, url_params=None,
escape_params=True, redirects_remaining=3, media_source=None,
converter=None):
"""Updates an entry at the given URI.
Args:
data: string, ElementTree._Element, or xml_wrapper.ElementWrapper The
XML containing the updated data.
uri: string A URI indicating entry to which the update will be applied.
Example: '/base/feeds/items/ITEM-ID'
extra_headers: dict (optional) HTTP headers which are to be included.
The client automatically sets the Content-Type,
Authorization, and Content-Length headers.
url_params: dict (optional) Additional URL parameters to be included
in the URI. These are translated into query arguments
in the form '&dict_key=value&...'.
Example: {'max-results': '250'} becomes &max-results=250
escape_params: boolean (optional) If false, the calling code has already
ensured that the query will form a valid URL (all
reserved characters have been escaped). If true, this
method will escape the query and any URL parameters
provided.
converter: func (optional) A function which will be executed on the
server's response. Often this is a function like
GDataEntryFromString which will parse the body of the server's
response and return a GDataEntry.
Returns:
If the put succeeded, this method will return a GDataFeed, GDataEntry,
or the results of running converter on the server's result body (if
converter was specified).
"""
return GDataService.PostOrPut(self, 'PUT', data, uri,
extra_headers=extra_headers, url_params=url_params,
escape_params=escape_params, redirects_remaining=redirects_remaining,
media_source=media_source, converter=converter)
def Delete(self, uri, extra_headers=None, url_params=None,
escape_params=True, redirects_remaining=4):
"""Deletes the entry at the given URI.
Args:
uri: string The URI of the entry to be deleted. Example:
'/base/feeds/items/ITEM-ID'
extra_headers: dict (optional) HTTP headers which are to be included.
The client automatically sets the Content-Type and
Authorization headers.
url_params: dict (optional) Additional URL parameters to be included
in the URI. These are translated into query arguments
in the form '&dict_key=value&...'.
Example: {'max-results': '250'} becomes &max-results=250
escape_params: boolean (optional) If false, the calling code has already
ensured that the query will form a valid URL (all
reserved characters have been escaped). If true, this
method will escape the query and any URL parameters
provided.
Returns:
True if the entry was deleted.
"""
if extra_headers is None:
extra_headers = {}
if self.__gsessionid is not None:
if uri.find('gsessionid=') < 0:
if url_params is None:
url_params = {}
url_params['gsessionid'] = self.__gsessionid
server_response = self.request('DELETE', uri,
headers=extra_headers, url_params=url_params)
result_body = server_response.read()
if server_response.status == 200:
return True
elif server_response.status == 302:
if redirects_remaining > 0:
location = (server_response.getheader('Location')
or server_response.getheader('location'))
if location is not None:
m = re.compile('[\?\&]gsessionid=(\w*\-)').search(location)
if m is not None:
self.__gsessionid = m.group(1)
return GDataService.Delete(self, location, extra_headers,
url_params, escape_params, redirects_remaining - 1)
else:
raise RequestError, {'status': server_response.status,
'reason': '302 received without Location header',
'body': result_body}
else:
raise RequestError, {'status': server_response.status,
'reason': 'Redirect received, but redirects_remaining <= 0',
'body': result_body}
else:
raise RequestError, {'status': server_response.status,
'reason': server_response.reason, 'body': result_body}
def ExtractToken(url, scopes_included_in_next=True):
"""Gets the AuthSub token from the current page's URL.
Designed to be used on the URL that the browser is sent to after the user
authorizes this application at the page given by GenerateAuthSubRequestUrl.
Args:
url: The current page's URL. It should contain the token as a URL
parameter. Example: 'http://example.com/?...&token=abcd435'
scopes_included_in_next: If True, this function looks for a scope value
associated with the token. The scope is a URL parameter with the
key set to SCOPE_URL_PARAM_NAME. This parameter should be present
if the AuthSub request URL was generated using
GenerateAuthSubRequestUrl with include_scope_in_next set to True.
Returns:
A tuple containing the token string and a list of scope strings for which
this token should be valid. If the scope was not included in the URL, the
tuple will contain (token, None).
"""
parsed = urlparse.urlparse(url)
token = gdata.auth.AuthSubTokenFromUrl(parsed[4])
scopes = ''
if scopes_included_in_next:
for pair in parsed[4].split('&'):
if pair.startswith('%s=' % SCOPE_URL_PARAM_NAME):
scopes = urllib.unquote_plus(pair.split('=')[1])
return (token, scopes.split(' '))
def GenerateAuthSubRequestUrl(next, scopes, hd='default', secure=False,
session=True, request_url='https://www.google.com/accounts/AuthSubRequest',
include_scopes_in_next=True):
"""Creates a URL to request an AuthSub token to access Google services.
For more details on AuthSub, see the documentation here:
http://code.google.com/apis/accounts/docs/AuthSub.html
Args:
next: The URL where the browser should be sent after the user authorizes
the application. This page is responsible for receiving the token
which is embeded in the URL as a parameter.
scopes: The base URL to which access will be granted. Example:
'http://www.google.com/calendar/feeds' will grant access to all
URLs in the Google Calendar data API. If you would like a token for
multiple scopes, pass in a list of URL strings.
hd: The domain to which the user's account belongs. This is set to the
domain name if you are using Google Apps. Example: 'example.org'
Defaults to 'default'
secure: If set to True, all requests should be signed. The default is
False.
session: If set to True, the token received by the 'next' URL can be
upgraded to a multiuse session token. If session is set to False, the
token may only be used once and cannot be upgraded. Default is True.
request_url: The base of the URL to which the user will be sent to
authorize this application to access their data. The default is
'https://www.google.com/accounts/AuthSubRequest'.
include_scopes_in_next: Boolean if set to true, the 'next' parameter will
be modified to include the requested scope as a URL parameter. The
key for the next's scope parameter will be SCOPE_URL_PARAM_NAME. The
benefit of including the scope URL as a parameter to the next URL, is
that the page which receives the AuthSub token will be able to tell
which URLs the token grants access to.
Returns:
A URL string to which the browser should be sent.
"""
if isinstance(scopes, list):
scope = ' '.join(scopes)
else:
scope = scopes
if include_scopes_in_next:
if next.find('?') > -1:
next += '&%s' % urllib.urlencode({SCOPE_URL_PARAM_NAME:scope})
else:
next += '?%s' % urllib.urlencode({SCOPE_URL_PARAM_NAME:scope})
return gdata.auth.GenerateAuthSubUrl(next=next, scope=scope, secure=secure,
session=session, request_url=request_url, domain=hd)
class Query(dict):
"""Constructs a query URL to be used in GET requests
Url parameters are created by adding key-value pairs to this object as a
dict. For example, to add &max-results=25 to the URL do
my_query['max-results'] = 25
Category queries are created by adding category strings to the categories
member. All items in the categories list will be concatenated with the /
symbol (symbolizing a category x AND y restriction). If you would like to OR
2 categories, append them as one string with a | between the categories.
For example, do query.categories.append('Fritz|Laurie') to create a query
like this feed/-/Fritz%7CLaurie . This query will look for results in both
categories.
"""
def __init__(self, feed=None, text_query=None, params=None,
categories=None):
"""Constructor for Query
Args:
feed: str (optional) The path for the feed (Examples:
'/base/feeds/snippets' or 'calendar/feeds/[email protected]/private/full'
text_query: str (optional) The contents of the q query parameter. The
contents of the text_query are URL escaped upon conversion to a URI.
params: dict (optional) Parameter value string pairs which become URL
params when translated to a URI. These parameters are added to the
query's items (key-value pairs).
categories: list (optional) List of category strings which should be
included as query categories. See
http://code.google.com/apis/gdata/reference.html#Queries for
details. If you want to get results from category A or B (both
categories), specify a single list item 'A|B'.
"""
self.feed = feed
self.categories = []
if text_query:
self.text_query = text_query
if isinstance(params, dict):
for param in params:
self[param] = params[param]
if isinstance(categories, list):
for category in categories:
self.categories.append(category)
def _GetTextQuery(self):
if 'q' in self.keys():
return self['q']
else:
return None
def _SetTextQuery(self, query):
self['q'] = query
text_query = property(_GetTextQuery, _SetTextQuery,
doc="""The feed query's q parameter""")
def _GetAuthor(self):
if 'author' in self.keys():
return self['author']
else:
return None
def _SetAuthor(self, query):
self['author'] = query
author = property(_GetAuthor, _SetAuthor,
doc="""The feed query's author parameter""")
def _GetAlt(self):
if 'alt' in self.keys():
return self['alt']
else:
return None
def _SetAlt(self, query):
self['alt'] = query
alt = property(_GetAlt, _SetAlt,
doc="""The feed query's alt parameter""")
def _GetUpdatedMin(self):
if 'updated-min' in self.keys():
return self['updated-min']
else:
return None
def _SetUpdatedMin(self, query):
self['updated-min'] = query
updated_min = property(_GetUpdatedMin, _SetUpdatedMin,
doc="""The feed query's updated-min parameter""")
def _GetUpdatedMax(self):
if 'updated-max' in self.keys():
return self['updated-max']
else:
return None
def _SetUpdatedMax(self, query):
self['updated-max'] = query
updated_max = property(_GetUpdatedMax, _SetUpdatedMax,
doc="""The feed query's updated-max parameter""")
def _GetPublishedMin(self):
if 'published-min' in self.keys():
return self['published-min']
else:
return None
def _SetPublishedMin(self, query):
self['published-min'] = query
published_min = property(_GetPublishedMin, _SetPublishedMin,
doc="""The feed query's published-min parameter""")
def _GetPublishedMax(self):
if 'published-max' in self.keys():
return self['published-max']
else:
return None
def _SetPublishedMax(self, query):
self['published-max'] = query
published_max = property(_GetPublishedMax, _SetPublishedMax,
doc="""The feed query's published-max parameter""")
def _GetStartIndex(self):
if 'start-index' in self.keys():
return self['start-index']
else:
return None
def _SetStartIndex(self, query):
if not isinstance(query, str):
query = str(query)
self['start-index'] = query
start_index = property(_GetStartIndex, _SetStartIndex,
doc="""The feed query's start-index parameter""")
def _GetMaxResults(self):
if 'max-results' in self.keys():
return self['max-results']
else:
return None
def _SetMaxResults(self, query):
if not isinstance(query, str):
query = str(query)
self['max-results'] = query
max_results = property(_GetMaxResults, _SetMaxResults,
doc="""The feed query's max-results parameter""")
def _GetOrderBy(self):
if 'orderby' in self.keys():
return self['orderby']
else:
return None
def _SetOrderBy(self, query):
self['orderby'] = query
orderby = property(_GetOrderBy, _SetOrderBy,
doc="""The feed query's orderby parameter""")
def ToUri(self):
q_feed = self.feed or ''
category_string = '/'.join(
[urllib.quote_plus(c) for c in self.categories])
# Add categories to the feed if there are any.
if len(self.categories) > 0:
q_feed = q_feed + '/-/' + category_string
return atom.service.BuildUri(q_feed, self)
def __str__(self):
return self.ToUri()
| 40.511059 | 90 | 0.679761 |
7940f7ce9519faa133ecd76ee74693a00d8a5a94 | 1,691 | py | Python | app/aspect/aspect_knowlege_base.py | ahmedAdel202090/Aspect-Model | 2cb21f0385a6b1bbc5f6c7be168783e7fb2c766e | [
"MIT"
] | null | null | null | app/aspect/aspect_knowlege_base.py | ahmedAdel202090/Aspect-Model | 2cb21f0385a6b1bbc5f6c7be168783e7fb2c766e | [
"MIT"
] | null | null | null | app/aspect/aspect_knowlege_base.py | ahmedAdel202090/Aspect-Model | 2cb21f0385a6b1bbc5f6c7be168783e7fb2c766e | [
"MIT"
] | null | null | null | from app.aspect.utils import seq_to_vec
from app.aspect.aspect_extraction import AspectExtraction
class AspectKnowledgeBase(object):
def __init__(self):
self.texts = []
self.knowledge_set = {}
self.aspect_extraction = AspectExtraction()
self.num_keywords=0
def __call__(self,model,texts,ngram,skip_keywords):
self.texts = texts
text_value = list(map(lambda x:x["text"],texts))
text = ' '.join(text_value)
doc_vec = seq_to_vec(model,text)
for text in texts:
aspects = self.aspect_extraction(model,doc_vec,text,ngram,skip_keywords)
for aspect_obj in aspects:
self.num_keywords +=1
if not aspect_obj['aspect'] in self.knowledge_set.keys():
self.knowledge_set[aspect_obj['aspect']] = {'sentiment':[0,0,0,0,0] , 'score':aspect_obj['score']}
sentiment = aspect_obj['sentiment']
self.knowledge_set[aspect_obj['aspect']]['sentiment'][sentiment] +=1
self.rank_normalize()
#self.knowledge_set = dict(sorted(self.knowledge_set.items(), key=lambda item: item[1]['score'],reverse=True))
self.knowledge_set = self.transform_aspect_to_obj(self.knowledge_set)
return self.knowledge_set
def rank_normalize(self):
for keyword in self.knowledge_set.keys():
keyword_freq = sum(self.knowledge_set[keyword]['sentiment'])
self.knowledge_set[keyword]['score'] = self.knowledge_set[keyword]['score'] * (keyword_freq / self.num_keywords)
def transform_aspect_to_obj(self,knowledge_set):
temp_knowledge_set = []
for aspect,value in knowledge_set.items():
value['aspect'] = aspect
temp_knowledge_set.append(value)
return temp_knowledge_set
| 43.358974 | 120 | 0.703726 |
7940f81c08a9717ff9a38f16b516bc37e6349c0c | 12,830 | py | Python | code/python/QuotesAPIforDigitalPortals/v3/fds/sdk/QuotesAPIforDigitalPortals/model/inline_response20052_data.py | factset/enterprise-sdk | 3fd4d1360756c515c9737a0c9a992c7451d7de7e | [
"Apache-2.0"
] | 6 | 2022-02-07T16:34:18.000Z | 2022-03-30T08:04:57.000Z | code/python/QuotesAPIforDigitalPortals/v3/fds/sdk/QuotesAPIforDigitalPortals/model/inline_response20052_data.py | factset/enterprise-sdk | 3fd4d1360756c515c9737a0c9a992c7451d7de7e | [
"Apache-2.0"
] | 2 | 2022-02-07T05:25:57.000Z | 2022-03-07T14:18:04.000Z | code/python/QuotesAPIforDigitalPortals/v3/fds/sdk/QuotesAPIforDigitalPortals/model/inline_response20052_data.py | factset/enterprise-sdk | 3fd4d1360756c515c9737a0c9a992c7451d7de7e | [
"Apache-2.0"
] | null | null | null | """
Quotes API For Digital Portals
The quotes API combines endpoints for retrieving security end-of-day, delayed, and realtime prices with performance key figures and basic reference data on the security and market level. The API supports over 20 different price types for each quote and comes with basic search endpoints based on security identifiers and instrument names. Market coverage is included in the *Sample Use Cases* section below. The Digital Portal use case is focused on high-performance applications that are * serving millions of end-users, * accessible by client browsers via the internet, * supporting subscriptions for streamed updates out-of-the-box, * typically combining a wide variety of *for Digital Portals*-APIs into a highly use-case specific solution for customers, * integrated into complex infrastructures such as existing frontend frameworks, authentication services. All APIs labelled *for Digital Portals* have been designed for direct use by client web applications and feature extreme low latency: The average response time across all endpoints is 30 ms whereas 99% of all requests are answered in close to under 300ms. See the Time Series API for Digital Portals for direct access to price histories, and the News API for Digital Portals for searching and fetching related news. # noqa: E501
The version of the OpenAPI document: 2
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
from fds.sdk.QuotesAPIforDigitalPortals.model_utils import ( # noqa: F401
ApiTypeError,
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
OpenApiModel
)
from fds.sdk.QuotesAPIforDigitalPortals.exceptions import ApiAttributeError
class InlineResponse20052Data(ModelNormal):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
Attributes:
allowed_values (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
with a capitalized key describing the allowed value and an allowed
value. These dicts store the allowed enum values.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
discriminator_value_class_map (dict): A dict to go from the discriminator
variable value to the discriminator class name.
validations (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
that stores validations for max_length, min_length, max_items,
min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
inclusive_minimum, and regex.
additional_properties_type (tuple): A tuple of classes accepted
as additional properties values.
"""
allowed_values = {
}
validations = {
}
@cached_property
def additional_properties_type():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
"""
return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501
_nullable = False
@cached_property
def openapi_types():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.
"""
return {
'id': (float,), # noqa: E501
'name': (str,), # noqa: E501
'description': (str,), # noqa: E501
}
@cached_property
def discriminator():
return None
attribute_map = {
'id': 'id', # noqa: E501
'name': 'name', # noqa: E501
'description': 'description', # noqa: E501
}
read_only_vars = {
}
_composed_schemas = {}
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, *args, **kwargs): # noqa: E501
"""InlineResponse20052Data - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
id (float): Identifier of the type.. [optional] # noqa: E501
name (str): Name of the type.. [optional] # noqa: E501
description (str): Description of the type.. [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
return self
required_properties = set([
'_data_store',
'_check_type',
'_spec_property_naming',
'_path_to_item',
'_configuration',
'_visited_composed_classes',
])
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs): # noqa: E501
"""InlineResponse20052Data - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
id (float): Identifier of the type.. [optional] # noqa: E501
name (str): Name of the type.. [optional] # noqa: E501
description (str): Description of the type.. [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
if var_name in self.read_only_vars:
raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate "
f"class with read only attributes.")
| 48.598485 | 1,302 | 0.593453 |
7940f99daaa46ff6b8d81dc0cb4f2c1e3d302dd7 | 3,683 | py | Python | ui/src/lib/pybitcointools/bitcoin/stealth.py | superzitao/Wallet | a7018511afcf47e04e563640e52b86fd4862f838 | [
"MIT"
] | null | null | null | ui/src/lib/pybitcointools/bitcoin/stealth.py | superzitao/Wallet | a7018511afcf47e04e563640e52b86fd4862f838 | [
"MIT"
] | null | null | null | ui/src/lib/pybitcointools/bitcoin/stealth.py | superzitao/Wallet | a7018511afcf47e04e563640e52b86fd4862f838 | [
"MIT"
] | null | null | null | import main as main
import transaction as tx
# Shared secrets and uncovering pay keys
def shared_secret_sender(scan_pubkey, ephem_privkey):
shared_point = main.multiply(scan_pubkey, ephem_privkey)
shared_secret = main.sha256(main.encode_pubkey(shared_point, 'bin_compressed'))
return shared_secret
def shared_secret_receiver(ephem_pubkey, scan_privkey):
shared_point = main.multiply(ephem_pubkey, scan_privkey)
shared_secret = main.sha256(main.encode_pubkey(shared_point, 'bin_compressed'))
return shared_secret
def uncover_pay_pubkey_sender(scan_pubkey, spend_pubkey, ephem_privkey):
shared_secret = shared_secret_sender(scan_pubkey, ephem_privkey)
return main.add_pubkeys(spend_pubkey, main.privtopub(shared_secret))
def uncover_pay_pubkey_receiver(scan_privkey, spend_pubkey, ephem_pubkey):
shared_secret = shared_secret_receiver(ephem_pubkey, scan_privkey)
return main.add_pubkeys(spend_pubkey, main.privtopub(shared_secret))
def uncover_pay_privkey(scan_privkey, spend_privkey, ephem_pubkey):
shared_secret = shared_secret_receiver(ephem_pubkey, scan_privkey)
return main.add_privkeys(spend_privkey, shared_secret)
# Address encoding
# Functions for basic stealth addresses,
# i.e. one scan key, one spend key, no prefix
def pubkeys_to_basic_stealth_address(scan_pubkey, spend_pubkey, magic_byte=42):
# magic_byte = 42 for mainnet, 43 for testnet.
hex_scankey = main.encode_pubkey(scan_pubkey, 'hex_compressed')
hex_spendkey = main.encode_pubkey(spend_pubkey, 'hex_compressed')
hex_data = '00{0:066x}01{1:066x}0100'.format(int(hex_scankey, 16), int(hex_spendkey, 16))
addr = main.hex_to_b58check(hex_data, magic_byte)
return addr
def basic_stealth_address_to_pubkeys(stealth_address):
hex_data = main.b58check_to_hex(stealth_address)
if len(hex_data) != 140:
raise Exception('Stealth address is not of basic type (one scan key, one spend key, no prefix)')
scan_pubkey = hex_data[2:68]
spend_pubkey = hex_data[70:136]
return scan_pubkey, spend_pubkey
# Sending stealth payments
def mk_stealth_metadata_script(ephem_pubkey, nonce):
op_return = '6a'
msg_size = '26'
version = '06'
return op_return + msg_size + version + '{0:08x}'.format(nonce) + main.encode_pubkey(ephem_pubkey, 'hex_compressed')
def mk_stealth_tx_outputs(stealth_addr, value, ephem_privkey, nonce, network='btc'):
scan_pubkey, spend_pubkey = basic_stealth_address_to_pubkeys(stealth_addr)
if network == 'btc':
btc_magic_byte = 42
if stealth_addr != pubkeys_to_basic_stealth_address(scan_pubkey, spend_pubkey, btc_magic_byte):
raise Exception('Invalid btc mainnet stealth address: ' + stealth_addr)
magic_byte_addr = 0
elif network == 'testnet':
testnet_magic_byte = 43
if stealth_addr != pubkeys_to_basic_stealth_address(scan_pubkey, spend_pubkey, testnet_magic_byte):
raise Exception('Invalid testnet stealth address: ' + stealth_addr)
magic_byte_addr = 111
ephem_pubkey = main.privkey_to_pubkey(ephem_privkey)
output0 = {'script': mk_stealth_metadata_script(ephem_pubkey, nonce),
'value': 0}
pay_pubkey = uncover_pay_pubkey_sender(scan_pubkey, spend_pubkey, ephem_privkey)
pay_addr = main.pubkey_to_address(pay_pubkey, magic_byte_addr)
output1 = {'address': pay_addr,
'value': value}
return [output0, output1]
# Receiving stealth payments
def ephem_pubkey_from_tx_script(stealth_tx_script):
if len(stealth_tx_script) != 80:
raise Exception('Wrong format for stealth tx output')
return stealth_tx_script[14:]
| 36.465347 | 120 | 0.754005 |
7940fa0932898a9c10c15ccba761695b30986434 | 4,671 | py | Python | hevc/convert2.py | dugle80/video-cleanup | 0274435deb74f49f4ff4827bedbc7f9186d22071 | [
"MIT"
] | null | null | null | hevc/convert2.py | dugle80/video-cleanup | 0274435deb74f49f4ff4827bedbc7f9186d22071 | [
"MIT"
] | 1 | 2022-03-18T04:54:47.000Z | 2022-03-18T04:54:47.000Z | hevc/convert2.py | dugle80/video-cleanup | 0274435deb74f49f4ff4827bedbc7f9186d22071 | [
"MIT"
] | null | null | null | from subprocess import PIPE
from ffprobe import FFProbe
from pathlib import Path
import multiprocessing
import time
import os
import signal
import subprocess
import shlex
import psutil
import send2trash
import logging
import shutil
useramdsk = 0
if useramdsk == 0:
ip = "/home/david/Downloads/jdownloader/"
elif useramdsk == 1:
ip = "/tmp/ramdisk/clean/"
trsh11 = "/tmp/ramdisk/trash/"
else:
print("ramdisk setting incorrect, quitting")
quit()
fmpg0 = "/usr/bin/ffmpeg "
fmpg1 = " -hide_banner -hwaccel auto -i " # insert src + 2
fmpg2 = (
" -movflags faststart"
+ " -c:v hevc_nvenc -n -rc 1"
+ " -rc-lookahead 20 -no-scenecut 0 -cq 32 -c:a "
)
logfile = "/home/david/Downloads/convert.log"
logging.basicConfig(
filename=logfile,
format="%(asctime)s - %(message)s",
datefmt="%y-%b-%d %H:%M:%S",
level=logging.INFO,
)
# multiprocessing idea from:
# http://net-informations.com/python/pro/sleep.htm
# pulled nvidia_smi commands needed from GPUtil code
# Could update for windows based on that code
# pip install gputil # (don't think you need to install at this point)
# https://github.com/anderskm/gputil
#
def getTemp():
nvidia_smi = "/usr/bin/nvidia-smi"
gpucmd = subprocess.Popen(
[nvidia_smi, "--query-gpu=temperature.gpu", "--format=csv,noheader,nounits"],
stdout=PIPE,
)
stdout, stderr = gpucmd.communicate()
Temp = stdout.decode("UTF-8").rstrip()
return Temp
def probe_file_codec(filename):
cmnd = "ffprobe -v error -select_streams v:0 -show_entries stream=codec_name -of default=noprint_wrappers=1:nokey=1"
cmnd = cmnd + " " + str(filename)
cmnd = shlex.split(cmnd)
p = subprocess.Popen(cmnd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = p.communicate()
out = out.rstrip()
out = str(out).strip("b'")
out = out.replace("'", "")
return out
def probe_duration(filename):
cmnd = "ffprobe -v error -select_streams v:0 -show_entries format=duration -of default=noprint_wrappers=1:nokey=1"
cmnd = cmnd + " " + str(filename)
cmnd = shlex.split(cmnd)
p = subprocess.Popen(cmnd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = p.communicate()
out = out.rstrip()
out = str(out).strip("b'")
out = out.replace("'", "")
if "." in out:
out = out.split(".")
return out[0]
else:
return out
# return audio codec
def Acdc(srcvid):
for stream in FFProbe(srcvid).streams:
if stream.is_audio():
return stream.codec_name
def convert2hevc(srcvid, parent, stem):
ff_mpeg = (
fmpg0
+ fmpg1
+ srcvid
+ fmpg2
+ Acdc(srcvid)
+ " "
+ parent
+ "/"
+ stem
+ ".HEVC"
+ ".mp4"
)
ffmpeg = shlex.split(ff_mpeg)
p9 = subprocess.Popen(ffmpeg, preexec_fn=os.setsid)
p9.communicate()
return
clean264 = dict()
manager = multiprocessing.Manager()
jobs = []
for path1 in Path(ip).rglob("*"):
if probe_file_codec(path1) != "h264":
continue
return2 = manager.list()
convert = multiprocessing.Process(
target=convert2hevc,
args=(str(path1), str(path1.parent), str(path1.stem)),
)
jobs.append(convert)
logging.info("starting transcode of: {}".format(str(path1)))
convert.start()
clean264[str(path1)] = str(path1.parent) + "/" + str(path1.stem) + ".HEVC.mp4"
while True:
procName = "ffmpeg"
time.sleep(2)
if int(getTemp()) > 90:
procPid = ""
for proc in psutil.process_iter():
if proc.name() == procName:
procPid = proc.pid
logging.info("pausing ffmpeg")
os.kill(procPid, signal.SIGSTOP)
print("paused!")
time.sleep(30)
print("resuming")
logging.info("resuming ffmpeg")
os.kill(procPid, signal.SIGCONT)
if not convert.is_alive():
break
for j1 in jobs:
j1.join()
# clean up another attempt
for k, v in clean264.items():
if probe_duration(k) == probe_duration(v):
sz = os.path.getsize(k) * 0.75
if os.path.getsize(v) < sz:
logging.info(" trashing {}".format(k))
print("trash", k)
if useramdsk == 0:
send2trash.send2trash(k)
elif useramdsk == 1:
kname = k.split(ip)
shutil.move(k, trsh11 + kname[1])
else:
logging.info("check file size on {}".format(k))
print()
print("log, see if worth converting: {}".format(k))
print()
| 27.803571 | 120 | 0.597731 |
7940fa5a8330931272881c3feb58f02432021d8d | 329 | py | Python | shop/urls.py | Zoki92/E-commerce | f5614f54ce06606d99f9b8360cb4f88a97c616f3 | [
"MIT"
] | null | null | null | shop/urls.py | Zoki92/E-commerce | f5614f54ce06606d99f9b8360cb4f88a97c616f3 | [
"MIT"
] | null | null | null | shop/urls.py | Zoki92/E-commerce | f5614f54ce06606d99f9b8360cb4f88a97c616f3 | [
"MIT"
] | 1 | 2019-05-24T06:14:04.000Z | 2019-05-24T06:14:04.000Z | from django.urls import path
from . import views
app_name = 'shop'
urlpatterns = [
path('', views.product_list, name='product_list'),
path('<slug:category_slug>/', views.product_list,
name="product_list_by_category"),
path('<int:id>/<slug:slug>/', views.product_detail,
name="product_detail"),
]
| 23.5 | 55 | 0.665653 |
7940fad5a60338301de94da34338f7699e423e7e | 1,368 | bzl | Python | java/java_grpc_library.bzl | purkhusid/rules_proto_grpc | 943656d049d2932a32d8f882bbb05c024b499020 | [
"Apache-2.0"
] | null | null | null | java/java_grpc_library.bzl | purkhusid/rules_proto_grpc | 943656d049d2932a32d8f882bbb05c024b499020 | [
"Apache-2.0"
] | null | null | null | java/java_grpc_library.bzl | purkhusid/rules_proto_grpc | 943656d049d2932a32d8f882bbb05c024b499020 | [
"Apache-2.0"
] | null | null | null | """Generated definition of java_grpc_library."""
load("//java:java_grpc_compile.bzl", "java_grpc_compile")
load("//internal:compile.bzl", "proto_compile_attrs")
load("@rules_java//java:defs.bzl", "java_library")
def java_grpc_library(name, **kwargs):
# Compile protos
name_pb = name + "_pb"
java_grpc_compile(
name = name_pb,
**{
k: v
for (k, v) in kwargs.items()
if k in ["protos" if "protos" in kwargs else "deps"] + proto_compile_attrs.keys()
} # Forward args
)
# Create java library
java_library(
name = name,
srcs = [name_pb],
deps = GRPC_DEPS + (kwargs.get("deps", []) if "protos" in kwargs else []),
runtime_deps = ["@io_grpc_grpc_java//netty"],
exports = GRPC_DEPS + kwargs.get("exports", []),
visibility = kwargs.get("visibility"),
tags = kwargs.get("tags"),
)
GRPC_DEPS = [
# From https://github.com/grpc/grpc-java/blob/f6c2d221e2b6c975c6cf465d68fe11ab12dabe55/BUILD.bazel#L32-L38
"@io_grpc_grpc_java//api",
"@io_grpc_grpc_java//protobuf",
"@io_grpc_grpc_java//stub",
"@io_grpc_grpc_java//stub:javax_annotation",
"@com_google_code_findbugs_jsr305//jar",
"@com_google_guava_guava//jar",
"@com_google_protobuf//:protobuf_java",
"@com_google_protobuf//:protobuf_java_util",
]
| 33.365854 | 110 | 0.636696 |
7940fb81936a9262fb9c27a126f063374f6481b0 | 1,163 | py | Python | chapters/chp4/mlp.py | Tomspiano/D2L-PyTorch | c748c72a2da66211bd88c1adcd048b05be40e77c | [
"Apache-2.0"
] | 2 | 2021-06-29T15:42:24.000Z | 2021-07-21T08:09:52.000Z | chapters/chp4/mlp.py | Tomspiano/D2L-PyTorch | c748c72a2da66211bd88c1adcd048b05be40e77c | [
"Apache-2.0"
] | null | null | null | chapters/chp4/mlp.py | Tomspiano/D2L-PyTorch | c748c72a2da66211bd88c1adcd048b05be40e77c | [
"Apache-2.0"
] | null | null | null | import torch
from modules import base
from modules import fashionMNIST as fmnist
from torch import nn
from torch.nn import init
from modules import d2lCustom as custom
def train(num_inputs, num_hiddens, num_outputs, train_iter, test_iter, eps):
# epoch 46, loss 0.155, train acc 0.943, test acc 0.883, 546.2 examples/sec
# if eps = 1e-3, learning rate = 0.5
net = nn.Sequential(
custom.FlattenLayer(),
nn.Linear(num_inputs, num_hiddens),
nn.ReLU(),
nn.Linear(num_hiddens, num_outputs)
)
for params in net.parameters():
init.normal_(params, mean=0, std=.01)
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=.5)
base.train(net, train_iter, test_iter, loss, eps=eps, num_epochs=50, optimizer=optimizer)
def main():
batch_size = 256
num_inputs, num_outputs, num_hiddens = 784, 10, 256
eps = 1e-3
# eps = 1e-1
root = '../../Datasets'
train_iter, test_iter = fmnist.load_data(batch_size, root=root)
train(num_inputs, num_hiddens, num_outputs, train_iter, test_iter, eps)
if __name__ == '__main__':
main()
| 25.282609 | 93 | 0.66638 |
7940fc23b1a7a6d6466c51140fa0bcf02baf53b8 | 1,393 | py | Python | PREPROCESSING/manual_labeling/mark_good_datasets_manually.py | MobMonRob/ArmMovementPredictionStudien | 7086f7b044d54b023c7d40e9413c35178a1ad084 | [
"Apache-2.0"
] | 2 | 2020-10-15T07:24:26.000Z | 2022-02-18T05:37:13.000Z | PREPROCESSING/manual_labeling/mark_good_datasets_manually.py | MobMonRob/ArmMovementPredictionStudien | 7086f7b044d54b023c7d40e9413c35178a1ad084 | [
"Apache-2.0"
] | null | null | null | PREPROCESSING/manual_labeling/mark_good_datasets_manually.py | MobMonRob/ArmMovementPredictionStudien | 7086f7b044d54b023c7d40e9413c35178a1ad084 | [
"Apache-2.0"
] | null | null | null | import os
from tkinter import *
from ArmMovementPredictionStudien.PREPROCESSING.utils.utils import open_dataset_numpy
from ArmMovementPredictionStudien.PREPROCESSING.visualisation.visualise_files import visualise
import matplotlib.pyplot as plt
first = True
file_list = os.listdir("../../DATA/0_raw/")
counter = 1090
def write_in_good():
write(file_list[counter], "good")
def write_in_bad():
write(file_list[counter], "bad")
def write(file, good_or_bad):
plt.close('all')
if good_or_bad == "good":
database = open("./good_files.csv", 'a')
elif good_or_bad == "bad":
database = open("./bad_files.csv", 'a')
else:
raise Exception("Enter good or bad")
length = len(open_dataset_numpy(file, "../../DATA/0_raw/"))
database.write(f"{file};{length}\n")
database.close()
show_file()
def show_file():
global counter
print(counter)
counter += 1
visualise(file_selection=file_list[counter], pick_random_file=False)
if __name__ == "__main__":
master = Tk()
master.geometry("200x80+0+0")
good_button = Button(master, text="Good", width=10, bg="green", command=write_in_good)
bad_button = Button(master, text="Bad", width=10, bg="red", command=write_in_bad)
good_button.pack(side="left")
bad_button.pack(side="right")
if first:
show_file()
first = False
mainloop()
| 25.327273 | 94 | 0.676956 |
7940ffa57b85019cb993185eb941fad74f1d58e6 | 824 | py | Python | Examples/Python/SimpleTransformix.py | sorenchr2011/SimpleElastix | 2a79d151894021c66dceeb2c8a64ff61506e7155 | [
"Apache-2.0"
] | 1 | 2021-03-05T22:52:15.000Z | 2021-03-05T22:52:15.000Z | Examples/Python/SimpleTransformix.py | sorenchr2011/SimpleElastix | 2a79d151894021c66dceeb2c8a64ff61506e7155 | [
"Apache-2.0"
] | null | null | null | Examples/Python/SimpleTransformix.py | sorenchr2011/SimpleElastix | 2a79d151894021c66dceeb2c8a64ff61506e7155 | [
"Apache-2.0"
] | 1 | 2021-01-16T08:57:07.000Z | 2021-01-16T08:57:07.000Z | import SimpleITK as sitk
import sys
# Make transform
elastixImageFilter = sitk.ElastixImageFilter()
elastixImageFilter.SetFixedImage(sitk.ReadImage(str(sys.argv[1])))
elastixImageFilter.SetMovingImage(sitk.ReadImage(str(sys.argv[2])))
elastixImageFilter.SetParameterMap(sitk.ReadParameterFile(str(sys.argv[3])))
elastixImageFilter.LogToConsoleOn()
elastixImageFilter.Execute()
# Instantiate SimpleTransformix
transformixImageFilter = sitk.TransformixImageFilter()
# Read Input
transformixImageFilter.SetInputImage(sitk.ReadImage(str(sys.argv[4])))
transformixImageFilter.SetParameterMap(elastixImageFilter.GetTransformParameterMap())
# Perform warp
transformixImageFilter.LogToConsoleOn()
transformixImageFilter.Execute()
# Write result image
sitk.WriteImage(transformixImageFilter.GetResultImage(), str(sys.argv[5]))
| 32.96 | 85 | 0.839806 |
794100e819f99a290a75a4d245839f314058c0bb | 22,254 | py | Python | mrcnn/visualize.py | felixstillger/Mask_RCNN | fab32b45cd104f8df63906157606024c2897ca3e | [
"MIT"
] | null | null | null | mrcnn/visualize.py | felixstillger/Mask_RCNN | fab32b45cd104f8df63906157606024c2897ca3e | [
"MIT"
] | null | null | null | mrcnn/visualize.py | felixstillger/Mask_RCNN | fab32b45cd104f8df63906157606024c2897ca3e | [
"MIT"
] | null | null | null | """
Mask R-CNN
Display and Visualization Functions.
Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
"""
import os
import sys
import random
import itertools
import colorsys
import numpy as np
from skimage.measure import find_contours
import matplotlib.pyplot as plt
from matplotlib import patches, lines
from matplotlib.patches import Polygon
import IPython.display
# Root directory of the project
ROOT_DIR = os.path.abspath("../")
# Import Mask RCNN
sys.path.append(ROOT_DIR) # To find local version of the library
from mrcnn import utils
############################################################
# Visualization
############################################################
def display_images(images, titles=None, cols=4, cmap=None, norm=None,
interpolation=None):
"""Display the given set of images, optionally with titles.
images: list or array of image tensors in HWC format.
titles: optional. A list of titles to display with each image.
cols: number of images per row
cmap: Optional. Color map to use. For example, "Blues".
norm: Optional. A Normalize instance to map values to colors.
interpolation: Optional. Image interpolation to use for display.
"""
titles = titles if titles is not None else [""] * len(images)
rows = len(images) // cols + 1
plt.figure(figsize=(14, 14 * rows // cols))
i = 1
for image, title in zip(images, titles):
plt.subplot(rows, cols, i)
plt.title(title, fontsize=9)
plt.axis('off')
plt.imshow(image.astype(np.uint8), cmap=cmap,
norm=norm, interpolation=interpolation)
i += 1
plt.show()
def random_colors(N, bright=True):
"""
Generate random colors.
To get visually distinct colors, generate them in HSV space then
convert to RGB.
"""
brightness = 1.0 if bright else 0.7
hsv = [(i / N, 1, brightness) for i in range(N)]
colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))
random.shuffle(colors)
return colors
def apply_mask(image, mask, color, alpha=0.5):
"""Apply the given mask to the image.
"""
for c in range(3):
image[:, :, c] = np.where(mask == 1,
image[:, :, c] *
(1 - alpha) + alpha * color[c] * 255,
image[:, :, c])
return image
def display_instances(image, boxes, masks, class_ids, class_names,
scores=None, title="",
figsize=(16, 16), ax=None,
show_mask=True, show_bbox=True,
colors=None, captions=None):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [height, width, num_instances]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
title: (optional) Figure title
show_mask, show_bbox: To show masks and bounding boxes or not
figsize: (optional) the size of the image
colors: (optional) An array or colors to use with each object
captions: (optional) A list of strings to use as captions for each object
"""
# Number of instances
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
# If no axis is passed, create one and automatically call show()
auto_show = False
if not ax:
_, ax = plt.subplots(1, figsize=figsize)
auto_show = True
# Generate random colors
colors = colors or random_colors(N)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
for i in range(N):
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
if show_bbox:
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Label
if not captions:
class_id = class_ids[i]
score = scores[i] if scores is not None else None
label = class_names[class_id]
caption = "{} {:.3f}".format(label, score) if score else label
else:
caption = captions[i]
ax.text(x1, y1 + 8, caption,
color='w', size=11, backgroundcolor="none")
# Mask
mask = masks[:, :, i]
if show_mask:
masked_image = apply_mask(masked_image, mask, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros(
(mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
p = Polygon(verts, facecolor="none", edgecolor=color)
ax.add_patch(p)
ax.imshow(masked_image.astype(np.uint8))
if auto_show:
plt.show()
def display_instances2(image, boxes, masks, class_ids, class_names,
scores=None, title="",
figsize=(16, 16), ax=None,
show_mask=True, show_bbox=True,
colors=None, captions=None, save_dir=None, save_name=None):
"""
boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates.
masks: [height, width, num_instances]
class_ids: [num_instances]
class_names: list of class names of the dataset
scores: (optional) confidence scores for each box
title: (optional) Figure title
show_mask, show_bbox: To show masks and bounding boxes or not
figsize: (optional) the size of the image
colors: (optional) An array or colors to use with each object
captions: (optional) A list of strings to use as captions for each object
"""
# Number of instances
N = boxes.shape[0]
if not N:
print("\n*** No instances to display *** \n")
else:
assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]
# If no axis is passed, create one and automatically call show()
auto_show = False
if not ax:
_, ax = plt.subplots(1, figsize=figsize)
auto_show = True
# Generate random colors
colors = colors or random_colors(N)
# Show area outside image boundaries.
height, width = image.shape[:2]
ax.set_ylim(height + 10, -10)
ax.set_xlim(-10, width + 10)
ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
for i in range(N):
color = colors[i]
# Bounding box
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in image cropping.
continue
y1, x1, y2, x2 = boxes[i]
if show_bbox:
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=0.7, linestyle="dashed",
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Label
if not captions:
class_id = class_ids[i]
score = scores[i] if scores is not None else None
label = class_names[class_id]
caption = "{} {:.3f}".format(label, score) if score else label
else:
caption = captions[i]
ax.text(x1, y1 + 8, caption,
color='w', size=11, backgroundcolor="none")
# Mask
mask = masks[:, :, i]
if show_mask:
masked_image = apply_mask(masked_image, mask, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros(
(mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
p = Polygon(verts, facecolor="none", edgecolor=color)
ax.add_patch(p)
ax.imshow(masked_image.astype(np.uint8))
# save_name = str(random.uniform(1, 1000000000000000))
plt.savefig(f"{save_dir}/img{save_name}")
print(save_name)
if auto_show:
plt.show()
def display_differences(image,
gt_box, gt_class_id, gt_mask,
pred_box, pred_class_id, pred_score, pred_mask,
class_names, title="", ax=None,
show_mask=True, show_box=True,
iou_threshold=0.5, score_threshold=0.5):
"""Display ground truth and prediction instances on the same image."""
# Match predictions to ground truth
gt_match, pred_match, overlaps = utils.compute_matches(
gt_box, gt_class_id, gt_mask,
pred_box, pred_class_id, pred_score, pred_mask,
iou_threshold=iou_threshold, score_threshold=score_threshold)
# Ground truth = green. Predictions = red
colors = [(0, 1, 0, .8)] * len(gt_match)\
+ [(1, 0, 0, 1)] * len(pred_match)
# Concatenate GT and predictions
class_ids = np.concatenate([gt_class_id, pred_class_id])
scores = np.concatenate([np.zeros([len(gt_match)]), pred_score])
boxes = np.concatenate([gt_box, pred_box])
masks = np.concatenate([gt_mask, pred_mask], axis=-1)
# Captions per instance show score/IoU
captions = ["" for m in gt_match] + ["{:.2f} / {:.2f}".format(
pred_score[i],
(overlaps[i, int(pred_match[i])]
if pred_match[i] > -1 else overlaps[i].max()))
for i in range(len(pred_match))]
# Set title if not provided
title = title or "Ground Truth and Detections\n GT=green, pred=red, captions: score/IoU"
# Display
display_instances(
image,
boxes, masks, class_ids,
class_names, scores, ax=ax,
show_bbox=show_box, show_mask=show_mask,
colors=colors, captions=captions,
title=title)
def draw_rois(image, rois, refined_rois, mask, class_ids, class_names, limit=10):
"""
anchors: [n, (y1, x1, y2, x2)] list of anchors in image coordinates.
proposals: [n, 4] the same anchors but refined to fit objects better.
"""
masked_image = image.copy()
# Pick random anchors in case there are too many.
ids = np.arange(rois.shape[0], dtype=np.int32)
ids = np.random.choice(
ids, limit, replace=False) if ids.shape[0] > limit else ids
fig, ax = plt.subplots(1, figsize=(12, 12))
if rois.shape[0] > limit:
plt.title("Showing {} random ROIs out of {}".format(
len(ids), rois.shape[0]))
else:
plt.title("{} ROIs".format(len(ids)))
# Show area outside image boundaries.
ax.set_ylim(image.shape[0] + 20, -20)
ax.set_xlim(-50, image.shape[1] + 20)
ax.axis('off')
for i, id in enumerate(ids):
color = np.random.rand(3)
class_id = class_ids[id]
# ROI
y1, x1, y2, x2 = rois[id]
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
edgecolor=color if class_id else "gray",
facecolor='none', linestyle="dashed")
ax.add_patch(p)
# Refined ROI
if class_id:
ry1, rx1, ry2, rx2 = refined_rois[id]
p = patches.Rectangle((rx1, ry1), rx2 - rx1, ry2 - ry1, linewidth=2,
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Connect the top-left corners of the anchor and proposal for easy visualization
ax.add_line(lines.Line2D([x1, rx1], [y1, ry1], color=color))
# Label
label = class_names[class_id]
ax.text(rx1, ry1 + 8, "{}".format(label),
color='w', size=11, backgroundcolor="none")
# Mask
m = utils.unmold_mask(mask[id], rois[id]
[:4].astype(np.int32), image.shape)
masked_image = apply_mask(masked_image, m, color)
ax.imshow(masked_image)
# Print stats
print("Positive ROIs: ", class_ids[class_ids > 0].shape[0])
print("Negative ROIs: ", class_ids[class_ids == 0].shape[0])
print("Positive Ratio: {:.2f}".format(
class_ids[class_ids > 0].shape[0] / class_ids.shape[0]))
# TODO: Replace with matplotlib equivalent?
def draw_box(image, box, color):
"""Draw 3-pixel width bounding boxes on the given image array.
color: list of 3 int values for RGB.
"""
y1, x1, y2, x2 = box
image[y1:y1 + 2, x1:x2] = color
image[y2:y2 + 2, x1:x2] = color
image[y1:y2, x1:x1 + 2] = color
image[y1:y2, x2:x2 + 2] = color
return image
def display_top_masks(image, mask, class_ids, class_names, limit=4):
"""Display the given image and the top few class masks."""
to_display = []
titles = []
to_display.append(image)
titles.append("H x W={}x{}".format(image.shape[0], image.shape[1]))
# Pick top prominent classes in this image
unique_class_ids = np.unique(class_ids)
mask_area = [np.sum(mask[:, :, np.where(class_ids == i)[0]])
for i in unique_class_ids]
top_ids = [v[0] for v in sorted(zip(unique_class_ids, mask_area),
key=lambda r: r[1], reverse=True) if v[1] > 0]
# Generate images and titles
for i in range(limit):
class_id = top_ids[i] if i < len(top_ids) else -1
# Pull masks of instances belonging to the same class.
m = mask[:, :, np.where(class_ids == class_id)[0]]
m = np.sum(m * np.arange(1, m.shape[-1] + 1), -1)
to_display.append(m)
titles.append(class_names[class_id] if class_id != -1 else "-")
display_images(to_display, titles=titles, cols=limit + 1, cmap="Blues_r")
def plot_precision_recall(AP, precisions, recalls):
"""Draw the precision-recall curve.
AP: Average precision at IoU >= 0.5
precisions: list of precision values
recalls: list of recall values
"""
# Plot the Precision-Recall curve
_, ax = plt.subplots(1)
ax.set_title("Precision-Recall Curve. AP@50 = {:.3f}".format(AP))
ax.set_ylim(0, 1.1)
ax.set_xlim(0, 1.1)
_ = ax.plot(recalls, precisions)
def plot_overlaps(gt_class_ids, pred_class_ids, pred_scores,
overlaps, class_names, threshold=0.5):
"""Draw a grid showing how ground truth objects are classified.
gt_class_ids: [N] int. Ground truth class IDs
pred_class_id: [N] int. Predicted class IDs
pred_scores: [N] float. The probability scores of predicted classes
overlaps: [pred_boxes, gt_boxes] IoU overlaps of predictions and GT boxes.
class_names: list of all class names in the dataset
threshold: Float. The prediction probability required to predict a class
"""
gt_class_ids = gt_class_ids[gt_class_ids != 0]
pred_class_ids = pred_class_ids[pred_class_ids != 0]
plt.figure(figsize=(12, 10))
plt.imshow(overlaps, interpolation='nearest', cmap=plt.cm.Blues)
plt.yticks(np.arange(len(pred_class_ids)),
["{} ({:.2f})".format(class_names[int(id)], pred_scores[i])
for i, id in enumerate(pred_class_ids)])
plt.xticks(np.arange(len(gt_class_ids)),
[class_names[int(id)] for id in gt_class_ids], rotation=90)
thresh = overlaps.max() / 2.
for i, j in itertools.product(range(overlaps.shape[0]),
range(overlaps.shape[1])):
text = ""
if overlaps[i, j] > threshold:
text = "match" if gt_class_ids[j] == pred_class_ids[i] else "wrong"
color = ("white" if overlaps[i, j] > thresh
else "black" if overlaps[i, j] > 0
else "grey")
plt.text(j, i, "{:.3f}\n{}".format(overlaps[i, j], text),
horizontalalignment="center", verticalalignment="center",
fontsize=9, color=color)
plt.tight_layout()
plt.xlabel("Ground Truth")
plt.ylabel("Predictions")
def draw_boxes(image, boxes=None, refined_boxes=None,
masks=None, captions=None, visibilities=None,
title="", ax=None):
"""Draw bounding boxes and segmentation masks with different
customizations.
boxes: [N, (y1, x1, y2, x2, class_id)] in image coordinates.
refined_boxes: Like boxes, but draw with solid lines to show
that they're the result of refining 'boxes'.
masks: [N, height, width]
captions: List of N titles to display on each box
visibilities: (optional) List of values of 0, 1, or 2. Determine how
prominent each bounding box should be.
title: An optional title to show over the image
ax: (optional) Matplotlib axis to draw on.
"""
# Number of boxes
assert boxes is not None or refined_boxes is not None
N = boxes.shape[0] if boxes is not None else refined_boxes.shape[0]
# Matplotlib Axis
if not ax:
_, ax = plt.subplots(1, figsize=(12, 12))
# Generate random colors
colors = random_colors(N)
# Show area outside image boundaries.
margin = image.shape[0] // 10
ax.set_ylim(image.shape[0] + margin, -margin)
ax.set_xlim(-margin, image.shape[1] + margin)
ax.axis('off')
ax.set_title(title)
masked_image = image.astype(np.uint32).copy()
for i in range(N):
# Box visibility
visibility = visibilities[i] if visibilities is not None else 1
if visibility == 0:
color = "gray"
style = "dotted"
alpha = 0.5
elif visibility == 1:
color = colors[i]
style = "dotted"
alpha = 1
elif visibility == 2:
color = colors[i]
style = "solid"
alpha = 1
# Boxes
if boxes is not None:
if not np.any(boxes[i]):
# Skip this instance. Has no bbox. Likely lost in cropping.
continue
y1, x1, y2, x2 = boxes[i]
p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,
alpha=alpha, linestyle=style,
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Refined boxes
if refined_boxes is not None and visibility > 0:
ry1, rx1, ry2, rx2 = refined_boxes[i].astype(np.int32)
p = patches.Rectangle((rx1, ry1), rx2 - rx1, ry2 - ry1, linewidth=2,
edgecolor=color, facecolor='none')
ax.add_patch(p)
# Connect the top-left corners of the anchor and proposal
if boxes is not None:
ax.add_line(lines.Line2D([x1, rx1], [y1, ry1], color=color))
# Captions
if captions is not None:
caption = captions[i]
# If there are refined boxes, display captions on them
if refined_boxes is not None:
y1, x1, y2, x2 = ry1, rx1, ry2, rx2
ax.text(x1, y1, caption, size=11, verticalalignment='top',
color='w', backgroundcolor="none",
bbox={'facecolor': color, 'alpha': 0.5,
'pad': 2, 'edgecolor': 'none'})
# Masks
if masks is not None:
mask = masks[:, :, i]
masked_image = apply_mask(masked_image, mask, color)
# Mask Polygon
# Pad to ensure proper polygons for masks that touch image edges.
padded_mask = np.zeros(
(mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)
padded_mask[1:-1, 1:-1] = mask
contours = find_contours(padded_mask, 0.5)
for verts in contours:
# Subtract the padding and flip (y, x) to (x, y)
verts = np.fliplr(verts) - 1
p = Polygon(verts, facecolor="none", edgecolor=color)
ax.add_patch(p)
ax.imshow(masked_image.astype(np.uint8))
def display_table(table):
"""Display values in a table format.
table: an iterable of rows, and each row is an iterable of values.
"""
html = ""
for row in table:
row_html = ""
for col in row:
row_html += "<td>{:40}</td>".format(str(col))
html += "<tr>" + row_html + "</tr>"
html = "<table>" + html + "</table>"
IPython.display.display(IPython.display.HTML(html))
def display_weight_stats(model):
"""Scans all the weights in the model and returns a list of tuples
that contain stats about each weight.
"""
layers = model.get_trainable_layers()
table = [["WEIGHT NAME", "SHAPE", "MIN", "MAX", "STD"]]
for l in layers:
weight_values = l.get_weights() # list of Numpy arrays
weight_tensors = l.weights # list of TF tensors
for i, w in enumerate(weight_values):
weight_name = weight_tensors[i].name
# Detect problematic layers. Exclude biases of conv layers.
alert = ""
if w.min() == w.max() and not (l.__class__.__name__ == "Conv2D" and i == 1):
alert += "<span style='color:red'>*** dead?</span>"
if np.abs(w.min()) > 1000 or np.abs(w.max()) > 1000:
alert += "<span style='color:red'>*** Overflow?</span>"
# Add row
table.append([
weight_name + alert,
str(w.shape),
"{:+9.4f}".format(w.min()),
"{:+10.4f}".format(w.max()),
"{:+9.4f}".format(w.std()),
])
display_table(table)
| 37.654822 | 92 | 0.580031 |
794100fc628723df100085b233fee0eef09588f7 | 3,454 | py | Python | osidb_bindings/bindings/python_client/api/auth/auth_token_refresh_create.py | RedHatProductSecurity/osidb-bindings | 5cbfbcd7164b4f1eca64c6d0f7154819ec9a4846 | [
"MIT"
] | null | null | null | osidb_bindings/bindings/python_client/api/auth/auth_token_refresh_create.py | RedHatProductSecurity/osidb-bindings | 5cbfbcd7164b4f1eca64c6d0f7154819ec9a4846 | [
"MIT"
] | null | null | null | osidb_bindings/bindings/python_client/api/auth/auth_token_refresh_create.py | RedHatProductSecurity/osidb-bindings | 5cbfbcd7164b4f1eca64c6d0f7154819ec9a4846 | [
"MIT"
] | null | null | null | from typing import Any, Dict, Optional
import httpx
from ...client import Client
from ...models.token_refresh import TokenRefresh
from ...types import UNSET, Response, Unset
def _get_kwargs(
*,
client: Client,
form_data: TokenRefresh,
multipart_data: TokenRefresh,
json_body: TokenRefresh,
) -> Dict[str, Any]:
url = "{}/auth/token/refresh".format(
client.base_url,
)
headers: Dict[str, Any] = client.get_headers()
json_json_body: Dict[str, Any] = UNSET
if not isinstance(json_body, Unset):
json_body.to_dict()
multipart_multipart_data: Dict[str, Any] = UNSET
if not isinstance(multipart_data, Unset):
multipart_data.to_multipart()
return {
"url": url,
"headers": headers,
"data": form_data.to_dict(),
}
def _parse_response(*, response: httpx.Response) -> Optional[TokenRefresh]:
if response.status_code == 200:
_response_200 = response.json()
response_200: TokenRefresh
if isinstance(_response_200, Unset):
response_200 = UNSET
else:
response_200 = TokenRefresh.from_dict(_response_200)
return response_200
return None
def _build_response(*, response: httpx.Response) -> Response[TokenRefresh]:
return Response(
status_code=response.status_code,
content=response.content,
headers=response.headers,
parsed=_parse_response(response=response),
)
def sync_detailed(
*,
client: Client,
form_data: TokenRefresh,
multipart_data: TokenRefresh,
json_body: TokenRefresh,
) -> Response[TokenRefresh]:
kwargs = _get_kwargs(
client=client,
form_data=form_data,
multipart_data=multipart_data,
json_body=json_body,
)
response = httpx.post(
verify=client.verify_ssl,
auth=client.auth,
timeout=client.timeout,
**kwargs,
)
response.raise_for_status()
return _build_response(response=response)
def sync(
*,
client: Client,
form_data: TokenRefresh,
multipart_data: TokenRefresh,
json_body: TokenRefresh,
) -> Optional[TokenRefresh]:
"""Takes a refresh type JSON web token and returns an access type JSON web
token if the refresh token is valid."""
return sync_detailed(
client=client,
form_data=form_data,
multipart_data=multipart_data,
json_body=json_body,
).parsed
async def asyncio_detailed(
*,
client: Client,
form_data: TokenRefresh,
multipart_data: TokenRefresh,
json_body: TokenRefresh,
) -> Response[TokenRefresh]:
kwargs = _get_kwargs(
client=client,
form_data=form_data,
multipart_data=multipart_data,
json_body=json_body,
)
async with httpx.AsyncClient(verify=client.verify_ssl) as _client:
response = await _client.post(**kwargs)
return _build_response(response=response)
async def asyncio(
*,
client: Client,
form_data: TokenRefresh,
multipart_data: TokenRefresh,
json_body: TokenRefresh,
) -> Optional[TokenRefresh]:
"""Takes a refresh type JSON web token and returns an access type JSON web
token if the refresh token is valid."""
return (
await asyncio_detailed(
client=client,
form_data=form_data,
multipart_data=multipart_data,
json_body=json_body,
)
).parsed
| 24.496454 | 78 | 0.657209 |
79410163e2a053e67bd9da0f8807f3560be8e135 | 378 | py | Python | generators/abstract.py | lucasgeorgegustafson/MTG-pack-generator | 495df761f8163373268793cb74e274c42582fe3a | [
"MIT"
] | 1 | 2021-03-25T03:16:40.000Z | 2021-03-25T03:16:40.000Z | generators/abstract.py | lucasgeorgegustafson/MTG-pack-generator | 495df761f8163373268793cb74e274c42582fe3a | [
"MIT"
] | null | null | null | generators/abstract.py | lucasgeorgegustafson/MTG-pack-generator | 495df761f8163373268793cb74e274c42582fe3a | [
"MIT"
] | null | null | null | from abc import ABC, abstractmethod
class AbstractGenerator(ABC):
@abstractmethod
def sort_by_rarity(self):
pass
@abstractmethod
def roll_for_mythic(self):
pass
@abstractmethod
def fix_sort(self):
pass
@abstractmethod
def print_pack(self):
pass
@abstractmethod
def generate_pack(self):
pass
| 15.12 | 35 | 0.634921 |
79410168ad7caffa8737a619c9d3f3d4b5db1dd9 | 10,855 | py | Python | ucsmsdk/mometa/sw/SwEthEstcPc.py | anoop1984/python_sdk | c4a226bad5e10ad233eda62bc8f6d66a5a82b651 | [
"Apache-2.0"
] | null | null | null | ucsmsdk/mometa/sw/SwEthEstcPc.py | anoop1984/python_sdk | c4a226bad5e10ad233eda62bc8f6d66a5a82b651 | [
"Apache-2.0"
] | null | null | null | ucsmsdk/mometa/sw/SwEthEstcPc.py | anoop1984/python_sdk | c4a226bad5e10ad233eda62bc8f6d66a5a82b651 | [
"Apache-2.0"
] | null | null | null | """This module contains the general information for SwEthEstcPc ManagedObject."""
import sys, os
from ...ucsmo import ManagedObject
from ...ucscoremeta import UcsVersion, MoPropertyMeta, MoMeta
from ...ucsmeta import VersionMeta
class SwEthEstcPcConsts():
ADMIN_SPEED_10GBPS = "10gbps"
ADMIN_SPEED_1GBPS = "1gbps"
ADMIN_SPEED_20GBPS = "20gbps"
ADMIN_SPEED_40GBPS = "40gbps"
ADMIN_SPEED_INDETERMINATE = "indeterminate"
ADMIN_STATE_DISABLED = "disabled"
ADMIN_STATE_ENABLED = "enabled"
CDP_DISABLED = "disabled"
CDP_ENABLED = "enabled"
FORGE_MAC_ALLOW = "allow"
FORGE_MAC_DENY = "deny"
IF_ROLE_DIAG = "diag"
IF_ROLE_FCOE_NAS_STORAGE = "fcoe-nas-storage"
IF_ROLE_FCOE_STORAGE = "fcoe-storage"
IF_ROLE_FCOE_UPLINK = "fcoe-uplink"
IF_ROLE_MGMT = "mgmt"
IF_ROLE_MONITOR = "monitor"
IF_ROLE_NAS_STORAGE = "nas-storage"
IF_ROLE_NETWORK = "network"
IF_ROLE_NETWORK_FCOE_UPLINK = "network-fcoe-uplink"
IF_ROLE_SERVER = "server"
IF_ROLE_SERVICE = "service"
IF_ROLE_STORAGE = "storage"
IF_ROLE_UNKNOWN = "unknown"
IF_TYPE_AGGREGATION = "aggregation"
IF_TYPE_PHYSICAL = "physical"
IF_TYPE_UNKNOWN = "unknown"
IF_TYPE_VIRTUAL = "virtual"
LACP_FAST_TIMER_FALSE = "false"
LACP_FAST_TIMER_NO = "no"
LACP_FAST_TIMER_TRUE = "true"
LACP_FAST_TIMER_YES = "yes"
LACP_SUSPEND_INDIVIDUAL_FALSE = "false"
LACP_SUSPEND_INDIVIDUAL_NO = "no"
LACP_SUSPEND_INDIVIDUAL_TRUE = "true"
LACP_SUSPEND_INDIVIDUAL_YES = "yes"
MON_TRAF_DIR_BOTH = "both"
MON_TRAF_DIR_RX = "rx"
MON_TRAF_DIR_TX = "tx"
PORT_MODE_ACCESS = "access"
PORT_MODE_TRUNK = "trunk"
PRIORITY_FLOW_CTRL_AUTO = "auto"
PRIORITY_FLOW_CTRL_ON = "on"
PROTOCOL_LACP = "lacp"
PROTOCOL_STATIC = "static"
RECV_FLOW_CTRL_OFF = "off"
RECV_FLOW_CTRL_ON = "on"
SEND_FLOW_CTRL_OFF = "off"
SEND_FLOW_CTRL_ON = "on"
SWITCH_ID_A = "A"
SWITCH_ID_B = "B"
SWITCH_ID_NONE = "NONE"
UPLINK_FAIL_ACTION_LINK_DOWN = "link-down"
UPLINK_FAIL_ACTION_WARNING = "warning"
class SwEthEstcPc(ManagedObject):
"""This is SwEthEstcPc class."""
consts = SwEthEstcPcConsts()
naming_props = set([u'portId'])
mo_meta = MoMeta("SwEthEstcPc", "swEthEstcPc", "pc-[port_id]", VersionMeta.Version141i, "InputOutput", 0xfff, [], ["read-only"], [u'swEthLanBorder', u'swEthMon'], [u'swEthTargetEp', u'swVlan'], ["Get"])
prop_meta = {
"admin_speed": MoPropertyMeta("admin_speed", "adminSpeed", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, None, None, None, None, ["10gbps", "1gbps", "20gbps", "40gbps", "indeterminate"], []),
"admin_state": MoPropertyMeta("admin_state", "adminState", "string", VersionMeta.Version141i, MoPropertyMeta.READ_WRITE, 0x2, None, None, None, ["disabled", "enabled"], []),
"border_aggr_port_id": MoPropertyMeta("border_aggr_port_id", "borderAggrPortId", "uint", VersionMeta.Version302a, MoPropertyMeta.READ_WRITE, 0x4, None, None, None, [], []),
"border_port_id": MoPropertyMeta("border_port_id", "borderPortId", "uint", VersionMeta.Version141i, MoPropertyMeta.READ_WRITE, 0x8, None, None, None, [], []),
"border_slot_id": MoPropertyMeta("border_slot_id", "borderSlotId", "uint", VersionMeta.Version141i, MoPropertyMeta.READ_WRITE, 0x10, None, None, None, [], []),
"cdp": MoPropertyMeta("cdp", "cdp", "string", VersionMeta.Version142b, MoPropertyMeta.READ_ONLY, None, None, None, None, ["disabled", "enabled"], []),
"child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version141i, MoPropertyMeta.INTERNAL, 0x20, None, None, r"""((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}""", [], []),
"cos_value": MoPropertyMeta("cos_value", "cosValue", "uint", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []),
"dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, 0x40, 0, 256, None, [], []),
"ep_dn": MoPropertyMeta("ep_dn", "epDn", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, None, 0, 256, None, [], []),
"forge_mac": MoPropertyMeta("forge_mac", "forgeMac", "string", VersionMeta.Version142b, MoPropertyMeta.READ_ONLY, None, None, None, None, ["allow", "deny"], []),
"if_role": MoPropertyMeta("if_role", "ifRole", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, None, None, None, None, ["diag", "fcoe-nas-storage", "fcoe-storage", "fcoe-uplink", "mgmt", "monitor", "nas-storage", "network", "network-fcoe-uplink", "server", "service", "storage", "unknown"], []),
"if_type": MoPropertyMeta("if_type", "ifType", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, None, None, None, None, ["aggregation", "physical", "unknown", "virtual"], []),
"lacp_fast_timer": MoPropertyMeta("lacp_fast_timer", "lacpFastTimer", "string", VersionMeta.Version222c, MoPropertyMeta.READ_ONLY, None, None, None, None, ["false", "no", "true", "yes"], []),
"lacp_suspend_individual": MoPropertyMeta("lacp_suspend_individual", "lacpSuspendIndividual", "string", VersionMeta.Version222c, MoPropertyMeta.READ_ONLY, None, None, None, None, ["false", "no", "true", "yes"], []),
"locale": MoPropertyMeta("locale", "locale", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, None, None, None, r"""((defaultValue|unknown|server|chassis|internal|external),){0,5}(defaultValue|unknown|server|chassis|internal|external){0,1}""", [], []),
"mon_traf_dir": MoPropertyMeta("mon_traf_dir", "monTrafDir", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, None, None, None, None, ["both", "rx", "tx"], []),
"name": MoPropertyMeta("name", "name", "string", VersionMeta.Version141i, MoPropertyMeta.READ_WRITE, 0x80, None, None, r"""[\-\.:_a-zA-Z0-9]{0,16}""", [], []),
"peer_dn": MoPropertyMeta("peer_dn", "peerDn", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, None, 0, 256, None, [], []),
"port_id": MoPropertyMeta("port_id", "portId", "uint", VersionMeta.Version141i, MoPropertyMeta.NAMING, 0x100, None, None, None, [], []),
"port_mode": MoPropertyMeta("port_mode", "portMode", "string", VersionMeta.Version141i, MoPropertyMeta.READ_WRITE, 0x200, None, None, None, ["access", "trunk"], []),
"priority_flow_ctrl": MoPropertyMeta("priority_flow_ctrl", "priorityFlowCtrl", "string", VersionMeta.Version211a, MoPropertyMeta.READ_ONLY, None, None, None, None, ["auto", "on"], []),
"protocol": MoPropertyMeta("protocol", "protocol", "string", VersionMeta.Version142b, MoPropertyMeta.READ_ONLY, None, None, None, None, ["lacp", "static"], []),
"recv_flow_ctrl": MoPropertyMeta("recv_flow_ctrl", "recvFlowCtrl", "string", VersionMeta.Version211a, MoPropertyMeta.READ_ONLY, None, None, None, None, ["off", "on"], []),
"rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, 0x400, 0, 256, None, [], []),
"sacl": MoPropertyMeta("sacl", "sacl", "string", VersionMeta.Version302a, MoPropertyMeta.READ_ONLY, None, None, None, r"""((none|del|mod|addchild|cascade),){0,4}(none|del|mod|addchild|cascade){0,1}""", [], []),
"send_flow_ctrl": MoPropertyMeta("send_flow_ctrl", "sendFlowCtrl", "string", VersionMeta.Version211a, MoPropertyMeta.READ_ONLY, None, None, None, None, ["off", "on"], []),
"status": MoPropertyMeta("status", "status", "string", VersionMeta.Version141i, MoPropertyMeta.READ_WRITE, 0x800, None, None, r"""((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}""", [], []),
"switch_id": MoPropertyMeta("switch_id", "switchId", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, None, None, None, None, ["A", "B", "NONE"], []),
"transport": MoPropertyMeta("transport", "transport", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, None, None, None, r"""((defaultValue|unknown|ether|dce|fc),){0,4}(defaultValue|unknown|ether|dce|fc){0,1}""", [], []),
"type": MoPropertyMeta("type", "type", "string", VersionMeta.Version141i, MoPropertyMeta.READ_ONLY, None, None, None, r"""((defaultValue|unknown|lan|san|ipc),){0,4}(defaultValue|unknown|lan|san|ipc){0,1}""", [], []),
"uplink_fail_action": MoPropertyMeta("uplink_fail_action", "uplinkFailAction", "string", VersionMeta.Version142b, MoPropertyMeta.READ_ONLY, None, None, None, None, ["link-down", "warning"], []),
}
prop_map = {
"adminSpeed": "admin_speed",
"adminState": "admin_state",
"borderAggrPortId": "border_aggr_port_id",
"borderPortId": "border_port_id",
"borderSlotId": "border_slot_id",
"cdp": "cdp",
"childAction": "child_action",
"cosValue": "cos_value",
"dn": "dn",
"epDn": "ep_dn",
"forgeMac": "forge_mac",
"ifRole": "if_role",
"ifType": "if_type",
"lacpFastTimer": "lacp_fast_timer",
"lacpSuspendIndividual": "lacp_suspend_individual",
"locale": "locale",
"monTrafDir": "mon_traf_dir",
"name": "name",
"peerDn": "peer_dn",
"portId": "port_id",
"portMode": "port_mode",
"priorityFlowCtrl": "priority_flow_ctrl",
"protocol": "protocol",
"recvFlowCtrl": "recv_flow_ctrl",
"rn": "rn",
"sacl": "sacl",
"sendFlowCtrl": "send_flow_ctrl",
"status": "status",
"switchId": "switch_id",
"transport": "transport",
"type": "type",
"uplinkFailAction": "uplink_fail_action",
}
def __init__(self, parent_mo_or_dn, port_id, **kwargs):
self._dirty_mask = 0
self.port_id = port_id
self.admin_speed = None
self.admin_state = None
self.border_aggr_port_id = None
self.border_port_id = None
self.border_slot_id = None
self.cdp = None
self.child_action = None
self.cos_value = None
self.ep_dn = None
self.forge_mac = None
self.if_role = None
self.if_type = None
self.lacp_fast_timer = None
self.lacp_suspend_individual = None
self.locale = None
self.mon_traf_dir = None
self.name = None
self.peer_dn = None
self.port_mode = None
self.priority_flow_ctrl = None
self.protocol = None
self.recv_flow_ctrl = None
self.sacl = None
self.send_flow_ctrl = None
self.status = None
self.switch_id = None
self.transport = None
self.type = None
self.uplink_fail_action = None
ManagedObject.__init__(self, "SwEthEstcPc", parent_mo_or_dn, **kwargs)
| 60.642458 | 317 | 0.661354 |
7941019bdd915dfdf28221bcd1a6510512c373b0 | 3,120 | py | Python | models/necks/db_fpn.py | ZhenqiSong/OCR_Pytorch | df4e8c53353b6c515509241d4c9af3b153224a10 | [
"MIT"
] | null | null | null | models/necks/db_fpn.py | ZhenqiSong/OCR_Pytorch | df4e8c53353b6c515509241d4c9af3b153224a10 | [
"MIT"
] | null | null | null | models/necks/db_fpn.py | ZhenqiSong/OCR_Pytorch | df4e8c53353b6c515509241d4c9af3b153224a10 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# __author__:Song Zhenqi
# 2021-01-26
import torch
import torch.nn as nn
from torch.nn import functional as F
from . import register_neck
@register_neck('DBFPN')
class DBFPN(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(DBFPN, self).__init__()
self.out_channels = out_channels
self.in2_conv = nn.Conv2d(in_channels=in_channels[0],
out_channels=self.out_channels,
kernel_size=1,
bias=False)
self.in3_conv = nn.Conv2d(in_channels=in_channels[1],
out_channels=self.out_channels,
kernel_size=1,
bias=False)
self.in4_conv = nn.Conv2d(in_channels=in_channels[2],
out_channels=self.out_channels,
kernel_size=1,
bias=False)
self.in5_conv = nn.Conv2d(in_channels=in_channels[3],
out_channels=self.out_channels,
kernel_size=1,
bias=False)
self.p5_conv = nn.Conv2d(in_channels=self.out_channels,
out_channels=self.out_channels//4,
kernel_size=3,
padding=1,
bias=False)
self.p4_conv = nn.Conv2d(in_channels=self.out_channels,
out_channels=self.out_channels//4,
kernel_size=3,
padding=1,
bias=False)
self.p3_conv = nn.Conv2d(in_channels=self.out_channels,
out_channels=self.out_channels//4,
kernel_size=3,
padding=1,
bias=False)
self.p2_conv = nn.Conv2d(in_channels=self.out_channels,
out_channels=self.out_channels//4,
kernel_size=3,
padding=1,
bias=False)
def forward(self, x):
c2, c3, c4, c5 = x
in5 = self.in5_conv(c5)
in4 = self.in4_conv(c4)
in3 = self.in3_conv(c3)
in2 = self.in2_conv(c2)
out4 = in4 + F.interpolate(in5, scale_factor=2, mode='nearest')
out3 = in3 + F.interpolate(out4, scale_factor=2, mode='nearest')
out2 = in2 + F.interpolate(out3, scale_factor=2, mode='nearest')
p5 = self.p5_conv(in5)
p4 = self.p4_conv(out4)
p3 = self.p3_conv(out3)
p2 = self.p2_conv(out2)
p5 = F.interpolate(p5, scale_factor=8, mode='nearest')
p4 = F.interpolate(p4, scale_factor=4, mode='nearest')
p3 = F.interpolate(p3, scale_factor=2, mode='nearest')
fuse = torch.cat([p5, p4, p3, p2], dim=1)
return fuse
| 37.142857 | 72 | 0.476282 |
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