content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
import torch
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from allenact.algorithms.onpolicy_sync.losses import PPO
from allenact.algorithms.onpolicy_sync.losses.imitation import Imitation
from allenact.algorithms.onpolicy_sync.losses.ppo import PPOConfig
from allenact.utils.experiment_utils import (
Builder,
PipelineStage,
TrainingPipeline,
LinearDecay,
)
from projects.tutorials.object_nav_ithor_ppo_one_object import (
ObjectNavThorPPOExperimentConfig,
)
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from numpy import array
from pickle import load
from pandas import read_csv
import os
from BioCAT.src.Combinatorics import multi_thread_shuffling, multi_thread_calculating_scores, make_combine, get_score, get_max_aminochain, skipper
# Importing random forest model
modelpath = os.path.dirname(os.path.abspath(__file__)) + '/RFC.dump'
Rf = load(open(modelpath, 'rb'))
# The function generate list of shuflled matrix
def make_shuffle_matrix(matrix, cpu, iterat):
"""
The functuion generate massive of shuffled matrix.
Parameters
----------
matrix : pandas DataFrame
PSSM profile.
cpu : int
Number of tred used.
iterat : int
Number of iterations of shuffling.
Returns
-------
module_shuffling_matrix : list
List of matrix, shuffled by module.
substrate_shuffling_matrix : list
List of matrix, shuffled by substrate.
"""
module_shuffling_matrix = multi_thread_shuffling(matrix, ShufflingType='module', iterations=iterat, threads=cpu)
substrate_shuffling_matrix = multi_thread_shuffling(matrix, ShufflingType='substrate', iterations=iterat, threads=cpu)
return module_shuffling_matrix, substrate_shuffling_matrix
# The fujnction finds suquence with maximum possible value, results from alignment
def get_MaxSeq(matrix, variant_seq):
"""
The functuion parallel calculation of scores for shuffled matrix.
Parameters
----------
matrix : pandas DataFrame
PSSM profile.
variant_seq : list
Variant of core peptide chain.
Returns
-------
shuffled_scores : list
List of scores for shuffled matrix.
"""
MaxSeq = []
subs = matrix.keys()[1: ]
# Find sequence, wich have maximum alignment score
for idx in matrix.index:
MAX_value = max(list(matrix.iloc[idx][1:]))
for key in subs:
if matrix[key][idx] == MAX_value:
MaxSeq.append(key) # If two smonomer have same value
break
# Making two variants of MaxSeq
MaxSeq_full = MaxSeq.copy()
MaxSeq_nan = MaxSeq.copy()
for max_sub_idx in range(len(MaxSeq)):
if variant_seq[max_sub_idx] == 'nan':
MaxSeq_nan[max_sub_idx] = 'nan' # Adding nan to MaxSeq
return MaxSeq_full, MaxSeq_nan
# The function gives an information about clusters
def get_cluster_info(table, BGC_ID, target_file):
"""
The functuion return information about cluster.
Parameters
----------
table : pandas DataFrame
Table with meta inforamtion about NRPS clusters.
BGC_ID : str
PSSM cluster ID.
target_file : pandas DataFrame
PSSM profile.
Returns
-------
Name : str
Cluster ID.
Coord_cluster : str
Coordinate of cluster.
strand : str
Strand of cluster.
"""
for ind in table[table['ID'].str.contains(BGC_ID)].index:
Name = table[table['ID'].str.contains(target_file.split('.')[0].split('_A_')[1])]['Name'][ind]
Coord_cluster = table['Coordinates of cluster'][ind]
strand = table['Gen strand'][ind]
break
return Name, Coord_cluster, strand
# Calculate scores
def calculate_scores(variant_seq, matrix, substrate_shuffling_matrix, module_shuffling_matrix, cpu, iterat):
"""
Calculating scores.
Parameters
----------
variant_seq : list
Variant of core peptide chain.
matrix : pandas DataFrame
PSSM profile.
substrate_shuffling_matrix : list
List of matrix, shuffled by substrate.
module_shuffling_matrix : list
List of matrix, shuffled by module.
cpu : int
Number of threads used.
iterat : int
Number of iterations of shuffling.
Returns
-------
Sln_score : float
Mln_score : float
Slt_score : float
Mlt_score : float
Sdn_score : float
Mdn_score : float
Sdt_score : float
Mdt_score : float
Scores, which calculated with shuffling matrix by different variants.
M - module shuffling S - substrate shuffling
l - logarithmic transformation of score d - raw score
n - MaxSeq with nan replacement t - MaxSeq without nan replacement
Relative_score : float
Relative score (Probability of target class)
Binary : float
Binary score of cluster matching.
"""
# Finding suquence with maximum possible value, results from alignment
MaxSeq_full, MaxSeq_nan = get_MaxSeq(matrix, variant_seq)
# Calculating shuffled scores
Sln_shuffled_score = array(multi_thread_calculating_scores(MaxSeq_nan, substrate_shuffling_matrix, type_value='log', iterations=iterat, threads=cpu))
Mln_shuffled_score = array(multi_thread_calculating_scores(MaxSeq_nan, module_shuffling_matrix, type_value='log', iterations=iterat, threads=cpu))
Slt_shuffled_score = array(multi_thread_calculating_scores(MaxSeq_full, substrate_shuffling_matrix, type_value='log', iterations=iterat, threads=cpu))
Mlt_shuffled_score = array(multi_thread_calculating_scores(MaxSeq_full, module_shuffling_matrix, type_value='log', iterations=iterat, threads=cpu))
Sdn_shuffled_score = array(multi_thread_calculating_scores(MaxSeq_nan, substrate_shuffling_matrix, type_value=None, iterations=iterat, threads=cpu))
Mdn_shuffled_score = array(multi_thread_calculating_scores(MaxSeq_nan, module_shuffling_matrix, type_value=None, iterations=iterat, threads=cpu))
Sdt_shuffled_score = array(multi_thread_calculating_scores(MaxSeq_full, substrate_shuffling_matrix, type_value=None, iterations=iterat, threads=cpu))
Mdt_shuffled_score = array(multi_thread_calculating_scores(MaxSeq_full, module_shuffling_matrix, type_value=None, iterations=iterat, threads=cpu))
# Calculating scores for target sequence
log_target_score = get_score(variant_seq, matrix, type_value='log')
non_log_target_score = get_score(variant_seq, matrix, type_value=None)
# Calculating features scores
Sln_score = len(Sln_shuffled_score[Sln_shuffled_score < log_target_score])/len(Sln_shuffled_score)
Mln_score = len(Mln_shuffled_score[Mln_shuffled_score < log_target_score])/len(Mln_shuffled_score)
Slt_score = len(Slt_shuffled_score[Slt_shuffled_score < log_target_score])/len(Slt_shuffled_score)
Mlt_score = len(Mlt_shuffled_score[Mlt_shuffled_score < log_target_score])/len(Mlt_shuffled_score)
Sdn_score = len(Sdn_shuffled_score[Sdn_shuffled_score < non_log_target_score])/len(Sdn_shuffled_score)
Mdn_score = len(Mdn_shuffled_score[Mdn_shuffled_score < non_log_target_score])/len(Mdn_shuffled_score)
Sdt_score = len(Sdt_shuffled_score[Sdt_shuffled_score < non_log_target_score])/len(Sdt_shuffled_score)
Mdt_score = len(Mdt_shuffled_score[Mdt_shuffled_score < non_log_target_score])/len(Mdt_shuffled_score)
# Calculating Relative score
Relative_score = round(Rf.predict_proba([[Sln_score, Mln_score,
Sdn_score, Mdn_score,
Sdt_score, Mdt_score,
Slt_score, Mlt_score
]])[0][1], 3)
Binary = Rf.predict([[Sln_score, Mln_score,
Sdn_score, Mdn_score,
Sdt_score, Mdt_score,
Slt_score, Mlt_score
]])[0]
return Sln_score, Mln_score, Slt_score, Mlt_score, Sdn_score, Mdn_score, Sdt_score, Mdt_score, Relative_score, Binary
def give_results(tsv_out, folder, files, table, ID, PeptideSeq, skip, cpu, iterat):
"""
The functuion return information about cluster.
Parameters
----------
tsv_out : dict
Empty dictionary for adding results.
folder : str
Path to PSSMs.
files : list
List of PSSMs.
table : pandas DataFrame
Table with meta inforamtion about NRPS clusters.
ID : str
Name of substance.
PeptideSeq : dict
Core peptide chains for different biosynthesis types (e.g. A, B, or C).
kip : int
Number of presumptive skip.
cpu : int
Number of threads used.
iterat : int
Number of iterations of shuffling.
Returns
-------
tsv_out : dict
Full dictionary for adding results.
"""
for target_file in files:
try:
BGC_ID = target_file.split('.')[0].split('_A_')[1]
except:
continue
if '_A_' not in target_file:
continue
Name, Coord_cluster, strand = get_cluster_info(table, BGC_ID, target_file) # Getting information about cluster
BGC = read_csv(folder + target_file, sep='\t')
# Skipping mode
if skip == 0:
BGC = [BGC]
else:
BGC == skipper(BGC, skip)
for matrix in BGC:
# Check quality of matrix
if len(matrix) == 1:
continue
check = 0
values = matrix.drop(matrix.columns[0], axis=1).values
for i in values:
if all(i) == 0:
check += 1
if check == len(values): # If thes condition is True, the matrix of unrecognized monomers
continue
# Generating shuffling matrix
module_shuffling_matrix, substrate_shuffling_matrix = make_shuffle_matrix(matrix, cpu, iterat)
for BS_type in PeptideSeq:# For every biosynthesis profile pathways
if PeptideSeq[BS_type] == None: # If in sequence only nan monomers
continue
if len(PeptideSeq[BS_type]) == 0: # If have not the variant
continue
# Check correctness of PeptideSeq
length_max= get_max_aminochain(PeptideSeq[BS_type])
EPs = make_combine(PeptideSeq[BS_type], length_max, matrix, delta=3)
if EPs is None: # If length sequnce can't be scaled to cluster size
continue
for variant_seq in EPs:
Sln_score, Mln_score, Slt_score, Mlt_score, Sdn_score, Mdn_score, Sdt_score, Mdt_score, Relative_score, Binary = calculate_scores(variant_seq, matrix, substrate_shuffling_matrix, module_shuffling_matrix, cpu, iterat)
#Recordind dictionary
tsv_out['Chromosome ID'].append(Name)
tsv_out['Coordinates of cluster'].append(Coord_cluster)
tsv_out['Strand'].append(strand)
tsv_out['Substance'].append(ID)
tsv_out['BGC ID'].append(BGC_ID)
tsv_out['Putative linearized NRP sequence'].append('--'.join(variant_seq))
tsv_out['Biosynthesis profile'].append('Type {}'.format(BS_type))
tsv_out['Sln score'].append(Sln_score) #shaffling substrates in matrix with log score and nan in maximally possible sequence
tsv_out['Mln score'].append(Mln_score) #shaffling modules matrix with log score and nan in maximally possible sequence
tsv_out['Sdn score'].append(Sdn_score) #shaffling substrates matrix without log score and nan in maximally possible sequence
tsv_out['Mdn score'].append(Mdn_score) #shaffling modules matrix without log score and nan in maximally possible sequence
tsv_out['Sdt score'].append(Sdt_score) #shaffling substrates matrix without log score in maximally possible sequence
tsv_out['Mdt score'].append(Mdt_score) #shaffling modules matrix without log score in maximally possible sequence
tsv_out['Slt score'].append(Slt_score) #shaffling substrates matrix with log score in maximally possible sequence
tsv_out['Mlt score'].append(Mlt_score) #shaffling modules matrix with log score in maximally possible sequence
tsv_out['Relative score'].append(Relative_score) #Final score
tsv_out['Binary'].append(Binary) #Binary value
return tsv_out
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# built-in
from typing import Optional
# app
from .common import TOKENS, Extractor, Token, traverse
from .value import UNKNOWN, get_value
get_returns = Extractor()
inner_extractor = Extractor()
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# retired
ibmqx2_c_to_tars =\
{
0: [1, 2],
1: [2],
2: [],
3: [2, 4],
4: [2]
} # 6 edges
# retired
ibmqx4_c_to_tars =\
{
0: [],
1: [0],
2: [0, 1, 4],
3: [2, 4],
4: []
} # 6 edges
# retired
ibmq16Rus_c_to_tars = \
{
0: [],
1: [0, 2],
2: [3],
3: [4, 14],
4: [],
5: [4],
6: [5, 7, 11],
7: [10],
8: [7],
9: [8, 10],
10: [],
11: [10],
12: [5, 11, 13],
13: [4, 14],
14: [],
15: [0, 2, 14]
} # 22 edges
ibm20AustinTokyo_c_to_tars = \
{
0: [1, 5],
1: [0, 2, 6, 7],
2: [1, 3, 6, 7],
3: [2, 4, 8, 9],
4: [3, 8, 9],
5: [0, 6, 10, 11],
6: [1, 2, 5, 7, 10, 11],
7: [1, 2, 6, 8, 12, 13],
8: [3, 4, 7, 9, 12, 13],
9: [3, 4, 8, 14],
10: [5, 6, 11, 15],
11: [5, 6, 10, 12, 16, 17],
12: [7, 8, 11, 13, 16, 17],
13: [7, 8, 12, 14, 18, 19],
14: [9, 13, 18, 19],
15: [10, 16],
16: [11, 12, 15, 17],
17: [11, 12, 16, 18],
18: [13, 14, 17, 19],
19: [13, 14, 18]
} # 86 edges
ibmq5YorktownTenerife_c_to_tars = \
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 4],
3: [2, 4],
4: [2, 3]
} # 12 edges
ibmq14Melb_c_to_tars = \
{
0: [1],
1: [0, 2, 13],
2: [1, 3, 12],
3: [2, 4, 11],
4: [3, 5, 10],
5: [4, 6, 9],
6: [5, 8],
7: [8],
8: [6, 7, 9],
9: [5, 8, 10],
10: [4, 9, 11],
11: [3, 10, 12],
12: [2, 11, 13],
13: [1, 12]
} # 36 edges
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43... | 1.533473 | 956 |
import signal
import requests
import time
from math import floor
shutdown = False
MAIN_TAKER = 0.0065
MAIN_MAKER = 0.002
ALT_TAKER = 0.005
ALT_MAKER = 0.0035
TAKER = (MAIN_TAKER + ALT_TAKER)*2
MAKER = MAIN_MAKER + ALT_MAKER
TAKEMAIN = MAIN_TAKER - ALT_MAKER
TAKEALT = ALT_TAKER - MAIN_MAKER
BUFFER = 0.01
NaN = float('nan')
def main():
# price does change in every tick
# check position
# plain arbitradge
# index arbitrage
# shock handling
# wave riding
# pairTickers = [('WMT-M', 'WMT-A'), ('CAT-M', 'CAT-A'), ('MMM-M', 'MMM-A')]
with Session('http://localhost:9998', 'VHK3DEDE') as session:
while session.get_tick():
try:
shock_runner(session)
exchange_arbitrage(session, "WMT-M", "WMT-A")
exchange_arbitrage(session, "CAT-M", "CAT-A")
exchange_arbitrage(session, "MMM-M", "MMM-A")
index_arbitrage(session, ['WMT', 'MMM', 'CAT'])
except Exception as ex:
print("error", str(ex))
# trader = session.getTrader()
# print(trader['nlv'])
# TODO: position cleaner: try to reduce gross position loss-free
# TODO: implement range runner for the last x ticks
TAKER4 = MAIN_TAKER * 5
# TODO: send limit orders and use market to cover unfilled ones after
if __name__ == '__main__':
signal.signal(signal.SIGINT, sigint)
main()
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"""
==========================================
Genrating feedthrough from single instance
==========================================
This example demostrates how to generate a feedthrough wire connection for
a given scalar or vector wires.
**Initial Design**
.. hdl-diagram:: ../../../examples/basic/_initial_design.v
:type: netlistsvg
:align: center
:module: top
**Output1** ``wire0`` feedthough from ``inst_2_1``
.. hdl-diagram:: ../../../examples/basic/_output_wire.v
:type: netlistsvg
:align: center
:module: top
**Output2** ``bus_in`` feedthrough from ``inst_1_0``
.. hdl-diagram:: ../../../examples/basic/_output_bus.v
:type: netlistsvg
:align: center
:module: top
"""
from os import path
import spydrnet as sdn
import spydrnet_physical as sdnphy
netlist = sdnphy.load_netlist_by_name('basic_hierarchy')
top = netlist.top_instance.reference
cable0 = next(top.get_cables("wire0"))
inst2 = next(top.get_instances("inst_2_0"))
sdn.compose(netlist, '_initial_design.v', skip_constraints=True)
top.create_feedthrough(inst2, cable0)
top.create_unconn_wires()
sdn.compose(netlist, '_output_wire.v', skip_constraints=True)
netlist = sdnphy.load_netlist_by_name('basic_hierarchy')
top = netlist.top_instance.reference
bus_in = next(top.get_cables("bus_in"))
inst1 = next(top.get_instances("inst_1_0"))
cables = top.create_feedthrough(inst1, bus_in)
top.create_unconn_wires()
sdn.compose(netlist, '_output_bus.v', skip_constraints=True)
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... | 2.749071 | 538 |
from __future__ import annotations
from typing import Optional, Union
from tools import tools
from exceptions import workflow_exceptions
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from django.apps import apps
from django.test import override_settings
from wagtail_live.signals import live_page_update
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# -*- coding: utf-8 -*-
"""
Script Name:
Author: Do Trinh/Jimmy - 3D artist.
Description:
"""
# -------------------------------------------------------------------------------------------------------------
""" Import """
import os
from PySide2.QtWidgets import (QFrame, QStyle, QAbstractItemView, QSizePolicy, QLineEdit, QPlainTextEdit,
QGraphicsItem, QGraphicsView, QGraphicsScene, QRubberBand, QCalendarWidget, )
from PySide2.QtCore import QEvent, QSettings, QSize, Qt, QDateTime
from PySide2.QtGui import QColor, QPainter, QFont, QTextCursor
SingleSelection = QCalendarWidget.SingleSelection
NoSelection = QCalendarWidget.NoSelection
SingleLetterDay = QCalendarWidget.SingleLetterDayNames
ShortDay = QCalendarWidget.ShortDayNames
LongDay = QCalendarWidget.LongDayNames
NoHoriHeader = QCalendarWidget.NoHorizontalHeader
NoVertHeader = QCalendarWidget.NoVerticalHeader
IsoWeekNum = QCalendarWidget.ISOWeekNumbers
SelectMode = QCalendarWidget.SelectionMode
HoriHeaderFm = QCalendarWidget.HorizontalHeaderFormat
VertHeaderFm = QCalendarWidget.VerticalHeaderFormat
DayOfWeek = Qt.DayOfWeek
Sunday = Qt.Sunday
Monday = Qt.Monday
Tuesday = Qt.Tuesday
Wednesday = Qt.Wednesday
Thursday = Qt.Thursday
Friday = Qt.Friday
Saturday = Qt.Saturday
ICONSIZE = 32
ICONBUFFER = -1
BTNTAGSIZE = QSize(87, 20)
TAGBTNSIZE = QSize(87-1, 20-1)
BTNICONSIZE = QSize(ICONSIZE, ICONSIZE)
ICONBTNSIZE = QSize(ICONSIZE+ICONBUFFER, ICONSIZE+ICONBUFFER)
DAMG_LOGO_COLOR = QColor(0, 114, 188, 255)
# Basic color
GlobalColor = Qt.GlobalColor
WHITE = QColor(Qt.white)
LIGHTGRAY = QColor(Qt.lightGray)
GRAY = QColor(Qt.gray)
DARKGRAY = QColor(Qt.darkGray)
BLACK = QColor(Qt.black)
RED = QColor(Qt.red)
GREEN = QColor(Qt.green)
BLUE = QColor(Qt.blue)
DARKRED = QColor(Qt.darkRed)
DARKGREEN = QColor(Qt.darkGreen)
DARKBLUE = QColor(Qt.darkBlue)
CYAN = QColor(Qt.cyan)
MAGENTA = QColor(Qt.magenta)
YELLOW = QColor(Qt.yellow)
DARKCYAN = QColor(Qt.darkCyan)
DARKMAGENTA = QColor(Qt.darkMagenta)
DARKYELLOW = QColor(Qt.darkYellow)
# Dark Palette color
Color_BACKGROUND_LIGHT = QColor('#505F69')
COLOR_BACKGROUND_NORMAL = QColor('#32414B')
COLOR_BACKGROUND_DARK = QColor('#19232D')
COLOR_FOREGROUND_LIGHT = QColor('#F0F0F0')
COLOR_FOREGROUND_NORMAL = QColor('#AAAAAA')
COLOR_FOREGROUND_DARK = QColor('#787878')
COLOR_SELECTION_LIGHT = QColor('#148CD2')
COLOR_SELECTION_NORMAL = QColor('#1464A0')
COLOR_SELECTION_DARK = QColor('#14506E')
# Nice color
blush = QColor(246, 202, 203, 255)
petal = QColor(247, 170, 189, 255)
petunia = QColor(231, 62, 151, 255)
deep_pink = QColor(229, 2, 120, 255)
melon = QColor(241, 118, 110, 255)
pomegranate = QColor(178, 27, 32, 255)
poppy_red = QColor(236, 51, 39, 255)
orange_red = QColor(240, 101, 53, 255)
olive = QColor(174, 188, 43, 255)
spring = QColor(227, 229, 121, 255)
yellow = QColor(255, 240, 29, 255)
mango = QColor(254, 209, 26, 255)
cantaloupe = QColor(250, 176, 98, 255)
tangelo = QColor(247, 151, 47, 255)
burnt_orange = QColor(236, 137, 36, 255)
bright_orange = QColor(242, 124, 53, 255)
moss = QColor(176, 186, 39, 255)
sage = QColor(212, 219, 145, 255)
apple = QColor(178, 215, 140, 255)
grass = QColor(111, 178, 68, 255)
forest = QColor(69, 149, 62, 255)
peacock = QColor(21, 140, 167, 255)
teal = QColor(24, 157, 193, 255)
aqua = QColor(153, 214, 218, 255)
violet = QColor(55, 52, 144, 255)
deep_blue = QColor(15, 86, 163, 255)
hydrangea = QColor(150, 191, 229, 255)
sky = QColor(139, 210, 244, 255)
dusk = QColor(16, 102, 162, 255)
midnight = QColor(14, 90, 131, 255)
seaside = QColor(87, 154, 188, 255)
poolside = QColor(137, 203, 225, 255)
eggplant = QColor(86, 5, 79, 255)
lilac = QColor(222, 192, 219, 255)
chocolate = QColor(87, 43, 3, 255)
blackout = QColor(19, 17, 15, 255)
stone = QColor(125, 127, 130, 255)
gravel = QColor(181, 182, 185, 255)
pebble = QColor(217, 212, 206, 255)
sand = QColor(185, 172, 151, 255)
ignoreARM = Qt.IgnoreAspectRatio
scrollAsNeed = Qt.ScrollBarAsNeeded
scrollOff = Qt.ScrollBarAlwaysOff
scrollOn = Qt.ScrollBarAlwaysOn
SiPoMin = QSizePolicy.Minimum # Size policy
SiPoMax = QSizePolicy.Maximum
SiPoExp = QSizePolicy.Expanding
SiPoPre = QSizePolicy.Preferred
SiPoIgn = QSizePolicy.Ignored
frameStyle = QFrame.Sunken | QFrame.Panel
center = Qt.AlignCenter # Alignment
right = Qt.AlignRight
left = Qt.AlignLeft
top = Qt.AlignTop
bottom = Qt.AlignBottom
hori = Qt.Horizontal
vert = Qt.Vertical
dockL = Qt.LeftDockWidgetArea # Docking area
dockR = Qt.RightDockWidgetArea
dockT = Qt.TopDockWidgetArea
dockB = Qt.BottomDockWidgetArea
dockAll = Qt.AllDockWidgetAreas
datetTimeStamp = QDateTime.currentDateTime().toString("hh:mm - dd MMMM yy") # datestamp
PRS = dict(password = QLineEdit.Password, center = center , left = left , right = right,
spmax = SiPoMax , sppre = SiPoPre, spexp = SiPoExp, spign = SiPoIgn,
expanding = QSizePolicy.Expanding, spmin = SiPoMin,)
# -------------------------------------------------------------------------------------------------------------
""" Event """
NO_WRAP = QPlainTextEdit.NoWrap
NO_FRAME = QPlainTextEdit.NoFrame
ELIDE_RIGHT = Qt.ElideRight
ELIDE_NONE = Qt.ElideNone
# -------------------------------------------------------------------------------------------------------------
""" Window state """
StateNormal = Qt.WindowNoState
StateMax = Qt.WindowMaximized
StateMin = Qt.WindowMinimized
State_Selected = QStyle.State_Selected
# -------------------------------------------------------------------------------------------------------------
""" Nodegraph setting variables """
ASPEC_RATIO = Qt.KeepAspectRatio
SMOOTH_TRANS = Qt.SmoothTransformation
SCROLLBAROFF = Qt.ScrollBarAlwaysOff # Scrollbar
SCROLLBARON = Qt.ScrollBarAlwaysOn
SCROLLBARNEED = Qt.ScrollBarAsNeeded
WORD_WRAP = Qt.TextWordWrap
INTERSECT_ITEM_SHAPE = Qt.IntersectsItemShape
CONTAIN_ITEM_SHAPE = Qt.ContainsItemShape
MATCH_EXACTLY = Qt.MatchExactly
DRAG_ONLY = QAbstractItemView.DragOnly
# -------------------------------------------------------------------------------------------------------------
""" UI flags """
ITEMENABLE = Qt.ItemIsEnabled
ITEMMOVEABLE = QGraphicsItem.ItemIsMovable
ITEMSENDGEOCHANGE = QGraphicsItem.ItemSendsGeometryChanges
ITEMSCALECHANGE = QGraphicsItem.ItemScaleChange
ITEMPOSCHANGE = QGraphicsItem.ItemPositionChange
DEVICECACHE = QGraphicsItem.DeviceCoordinateCache
SELECTABLE = QGraphicsItem.ItemIsSelectable
MOVEABLE = QGraphicsItem.ItemIsMovable
FOCUSABLE = QGraphicsItem.ItemIsFocusable
PANEL = QGraphicsItem.ItemIsPanel
NOINDEX = QGraphicsScene.NoIndex # Scene
RUBBER_DRAG = QGraphicsView.RubberBandDrag # Viewer
RUBBER_REC = QRubberBand.Rectangle
POS_CHANGE = QGraphicsItem.ItemPositionChange
NODRAG = QGraphicsView.NoDrag
NOFRAME = QGraphicsView.NoFrame
ANCHOR_NO = QGraphicsView.NoAnchor
ANCHOR_UNDERMICE = QGraphicsView.AnchorUnderMouse
ANCHOR_CENTER = QGraphicsView.AnchorViewCenter
CACHE_BG = QGraphicsView.CacheBackground
UPDATE_VIEWRECT = QGraphicsView.BoundingRectViewportUpdate
UPDATE_FULLVIEW = QGraphicsView.FullViewportUpdate
UPDATE_SMARTVIEW = QGraphicsView.SmartViewportUpdate
UPDATE_BOUNDINGVIEW = QGraphicsView.BoundingRectViewportUpdate
UPDATE_MINIMALVIEW = QGraphicsView.MinimalViewportUpdate
STAY_ON_TOP = Qt.WindowStaysOnTopHint
STRONG_FOCUS = Qt.StrongFocus
SPLASHSCREEN = Qt.SplashScreen
FRAMELESS = Qt.FramelessWindowHint
CUSTOMIZE = Qt.CustomizeWindowHint
CLOSEBTN = Qt.WindowCloseButtonHint
MINIMIZEBTN = Qt.WindowMinimizeButtonHint
AUTO_COLOR = Qt.AutoColor
# -------------------------------------------------------------------------------------------------------------
""" Drawing """
ANTIALIAS = QPainter.Antialiasing # Painter
ANTIALIAS_TEXT = QPainter.TextAntialiasing
ANTIALIAS_HIGH_QUALITY = QPainter.HighQualityAntialiasing
SMOOTH_PIXMAP_TRANSFORM = QPainter.SmoothPixmapTransform
NON_COSMETIC_PEN = QPainter.NonCosmeticDefaultPen
NO_BRUSH = Qt.NoBrush # Brush
NO_PEN = Qt.NoPen # Pen
ROUND_CAP = Qt.RoundCap
ROUND_JOIN = Qt.RoundJoin
PATTERN_SOLID = Qt.SolidPattern # Pattern
LINE_SOLID = Qt.SolidLine # Line
LINE_DASH = Qt.DashLine
LINE_DOT = Qt.DotLine
LINE_DASH_DOT = Qt.DashDotDotLine
TRANSPARENT = Qt.transparent
TRANSPARENT_MODE = Qt.TransparentMode
# -------------------------------------------------------------------------------------------------------------
""" Meta Object """
QUEUEDCONNECTION = Qt.QueuedConnection
# -------------------------------------------------------------------------------------------------------------
""" Keyboard and cursor """
TEXT_BOLD = QFont.Bold
TEXT_NORMAL = QFont.Normal
MONO_SPACE = QFont.Monospace
TEXT_MENEOMIC = Qt.TextShowMnemonic
KEY_PRESS = QEvent.KeyPress
KEY_RELEASE = QEvent.KeyRelease
KEY_ALT = Qt.Key_Alt
KEY_DEL = Qt.Key_Delete
KEY_TAB = Qt.Key_Tab
KEY_SHIFT = Qt.Key_Shift
KEY_CTRL = Qt.Key_Control
KEY_BACKSPACE = Qt.Key_Backspace
KEY_ENTER = Qt.Key_Enter
KEY_RETURN = Qt.Key_Return
KEY_F = Qt.Key_F
KEY_S = Qt.Key_S
ALT_MODIFIER = Qt.AltModifier
CTRL_MODIFIER = Qt.ControlModifier
SHIFT_MODIFIER = Qt.ShiftModifier
NO_MODIFIER = Qt.NoModifier
CLOSE_HAND_CUSOR = Qt.ClosedHandCursor
SIZEF_CURSOR = Qt.SizeFDiagCursor
windows = os.name = 'nt'
DMK = Qt.AltModifier if windows else CTRL_MODIFIER
MOUSE_LEFT = Qt.LeftButton
MOUSE_RIGHT = Qt.RightButton
MOUSE_MIDDLE = Qt.MiddleButton
NO_BUTTON = Qt.NoButton
ARROW_NONE = Qt.NoArrow # Cursor
CURSOR_ARROW = Qt.ArrowCursor
CURSOR_SIZEALL = Qt.SizeAllCursor
MOVE_OPERATION = QTextCursor.MoveOperation
MOVE_ANCHOR = QTextCursor.MoveMode.MoveAnchor
KEEP_ANCHOR = QTextCursor.MoveMode.KeepAnchor
ACTION_MOVE = Qt.MoveAction # Action
ignoreARM = Qt.IgnoreAspectRatio
# -------------------------------------------------------------------------------------------------------------
""" Set number """
RELATIVE_SIZE = Qt.RelativeSize # Size
INI = QSettings.IniFormat
NATIVE = QSettings.NativeFormat
INVALID = QSettings.InvalidFormat
SYS_SCOPE = QSettings.SystemScope
USER_SCOPE = QSettings.UserScope
# -------------------------------------------------------------------------------------------------------------
# Created by Trinh Do on 5/6/2020 - 3:13 AM
# 2017 - 2020 DAMGteam. All rights reserved | [
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... | 1.801993 | 8,227 |
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
import sys
import time
import urllib.request
import os
sys.stdin = open('idpwd.txt')
site = input()
id = input()
pwd = input()
# selenium
chromedriver = 'C:\Webdriver\chromedriver.exe'
# selenum webdriver chromedirver .
driver = webdriver.Chrome(chromedriver)
# driver .
driver.get(site)
driver.find_element_by_name('userId').send_keys(id)
driver.find_element_by_name('userPwd').send_keys(pwd)
driver.find_element_by_class_name('form-btn').click()
driver.set_window_size(1600, 800)
driver.find_element_by_xpath("//a[@href='/edu/lectureroom/openlearning/openLearningList.do']/span").click()
# driver.find_element_by_id('searchContNm').send_keys('aps')
#
# driver.find_element_by_xpath("//button[@onclick='fnSearch();']").click()
driver.find_elements_by_xpath("//*[contains(text(), '5_B_Java(1)')]")[0].click()
driver.find_element_by_xpath("//span[@class='file-name']").click()
driver.switch_to.window(driver.window_handles[1])
print(driver.find_elements_by_xpath("//button[@title=' ']")[0].get_attribute('disabled'))
# driver.find_elements_by_xpath("//button[@title=' ']")[0].click()
# print(driver.find_elements_by_xpath("//button[@title=' ']")[0].get_attribute('disabled'))
# url + find
# pre = driver.current_url
# find = pre.find('/index.html')
# url = pre[:find]
# src = driver.find_element_by_class_name("background").get_attribute('src')
# print(src)
##
# for i in driver.find_elements_by_xpath("//button[@title=' ']"):
# print(i)
cnt = 1
# url = driver.find_elements_by_class_name("background")[-1].get_attribute('src')
# print(url)
# urllib.request.urlretrieve(url, '123.jpg')
# os.system("curl " + url + " > test.jpg")
time.sleep(2)
driver.get_screenshot_as_file("hi.png")
# for i in driver.find_elements_by_class_name("background"):
# time.sleep(2)
# print(i.get_attribute('style'))
# i.screenshot(str(cnt)+'.png')
# cnt += 1
while 1:
time.sleep(0.4)
driver.save_screenshot('APS/C/'+str(cnt)+'.png')
# print(driver.find_element_by_class_name("background").get_attribute('src'))
# driver.find_element_by_class_name("background").screenshot(str(cnt)+'.png')
driver.find_elements_by_xpath("//button[@title=' ']")[0].click()
cnt += 1
if driver.find_elements_by_xpath("//button[@title=' ']")[0].get_attribute('disabled') == 'disabled':
break
| [
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1... | 2.5 | 968 |
from io import StringIO
# StringIO
f = StringIO()
f.write('Python-100')
str = f.getvalue() #
print(':%s' %str)
f.write('\n') #
f.write('100')
f.close() #
f1 = StringIO('Python-100' + '\n' + '100')
#
print(f1.read())
f1.close()
# outputData()
# dataStr
dataStr = outputData()
# 1. outputData()
dataIO = StringIO(dataStr)
# outputData()
# dataStr
dataStr = outputData()
# 1. outputData()
dataIO = StringIO(dataStr)
# 1.1 StringIO
print('1.1:\n%s' %dataIO.getvalue())
# 1.2
print('1.2:')
for data in dataIO.readlines():
print(data.strip('\n')) #
# 1.2
print('1.2:')
for data in dataIO.readlines():
print(data.strip('\n')) #
# 1.3
# (32)
print(':%d' %dataIO.tell())
#
dataIO.seek(0)
print('1.3:')
for data in dataIO:
print(data.strip('\n')) | [
198,
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69,
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13,
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3419,
1303,
220,
198,
4798,
7,
10... | 2.134409 | 372 |
import biothings.hub.dataload.uploader as uploader
| [
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] | 3 | 17 |
"""Contains tests for finpack/core/cli.py
"""
__copyright__ = "Copyright (C) 2021 Matt Ferreira"
import os
import unittest
from importlib import metadata
from docopt import docopt
from finpack.core import cli
| [
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198,
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555,
715,
395,
198,
6... | 3.227273 | 66 |
enemy = Yaratik()
enemy.move_left()
# ejderha also includes all functions from parent class (yaratik)
ejderha = Ejderha()
ejderha.move_left()
ejderha.Ates_puskurtme()
# Zombie is called the (child class), inherits from Yaratik (parent class)
zombie = Zombie()
zombie.move_right()
zombie.Isirmak()
| [
628,
198,
198,
46970,
796,
575,
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1134,
3419,
198,
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8,
198,
68,
73,
1082,
3099,
796,
412,
73,
108... | 2.657895 | 114 |
"""
Author: Peratham Wiriyathammabhum
"""
import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors
def affinity_graph(X):
'''
This function returns a numpy array.
'''
ni, nd = X.shape
A = np.zeros((ni, ni))
for i in range(ni):
for j in range(i+1, ni):
dist = ((X[i] - X[j])**2).sum() # compute L2 distance
A[i][j] = dist
A[j][i] = dist # by symmetry
return A
def knn_graph(X, knn=4):
'''
This function returns a numpy array.
'''
ni, nd = X.shape
nbrs = NearestNeighbors(n_neighbors=(knn+1), algorithm='ball_tree').fit(X)
distances, indices = nbrs.kneighbors(X)
A = np.zeros((ni, ni))
for dist, ind in zip(distances, indices):
i0 = ind[0]
for i in range(1,knn+1):
d = dist[i]
A[i0, i] = d
A[i, i0] = d # by symmetry
return A
def sparse_affinity_graph(X):
'''
TODO: This function returns a numpy sparse matrix.
'''
ni, nd = X.shape
A = np.zeros((ni, ni))
for i in range(ni):
for j in range(i+1, ni):
dist = ((X[i] - X[j])**2).sum() # compute L2 distance
A[i][j] = dist
A[j][i] = dist # by symmetry
return A
def laplacian_graph(X, mode='affinity', knn=3, eta=0.01, sigma=2.5):
'''
The unnormalized graph Laplacian, L = D W.
'''
if mode == 'affinity':
W = affinity_graph(X)
W[abs(W) > eta] = 0
elif mode == 'nearestneighbor':
W = knn_graph(X, knn=knn)
elif mode == 'gaussian':
W = affinity_graph(X)
bandwidth = 2.0*(sigma**2)
W = np.exp(W) / bandwidth
else:
pass
D = np.diag(W.sum(axis=1))
L = D - W
return L
| [
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1... | 2.131285 | 716 |
# Copyright 2019 The LUCI Authors. All rights reserved.
# Use of this source code is governed under the Apache License, Version 2.0
# that can be found in the LICENSE file.
"""This package houses all subcommands for the recipe engine.
See implementation_details.md for the expectations of the modules in this
directory.
"""
import argparse
import errno
import logging
import os
import pkgutil
import sys
if sys.version_info >= (3, 5): # we're running python > 3.5
OS_WALK = os.walk
else:
# From vpython
from scandir import walk as OS_WALK
# pylint: disable=wrong-import-position
from .. import simple_cfg
from ..recipe_deps import RecipeDeps
from ..recipe_module_importer import RecipeModuleImporter
LOG = logging.getLogger(__name__)
# This incantation finds all loadable submodules of ourself. The
# `prefix=__name__` bit is so that these modules get loaded with the correct
# import names, i.e.
#
# recipe_engine.internal.commands.<submodule>
#
# If omitted, then these submodules can get double loaded as both:
#
# <submodule> AND
# recipe_engine.internal.commands.<submodule>
#
# Which can both interfere with the global python module namespace, and lead to
# strange errors when doing type assertions (since all data in these modules
# will be loaded under two different names; classes will fail isinstance checks
# even though they are "the same").
_COMMANDS = [
loader.find_module(module_name).load_module(module_name)
for (loader, module_name, _) in pkgutil.walk_packages(
__path__, prefix=__name__+'.')
if '.' not in module_name[len(__name__)+1:]
]
# Order all commands by an optional __cmd_priority__ field, and then by module
# name.
_COMMANDS.sort(
key=lambda mod: (
not hasattr(mod, '__cmd_priority__'), # modules defining priority first
getattr(mod, '__cmd_priority__', None), # actual priority
mod.__name__ # name
))
# Now actually set these commands on ourself so that 'mock' works correctly.
#
# This is needed to allow some tests (though it may be worth adjusting these
# tests later to not need this. Just delete this function and see which tests
# fail to find the dependencies on this behavior).
_patch_our_attrs()
def _check_recipes_cfg_consistency(recipe_deps):
"""Checks all recipe.cfg files for the loaded recipe_deps and logs
inconsistent dependencies.
Args:
recipe_deps (RecipeDeps) - The loaded+fetched recipe deps
for the current run.
"""
actual = recipe_deps.main_repo.simple_cfg.deps
# For every repo we loaded
for repo_name in actual:
required_deps = recipe_deps.repos[repo_name].simple_cfg.deps
for req_repo_name, req_spec in required_deps.iteritems():
# If this depends on something we didn't load, log an error.
if req_repo_name not in actual:
LOG.error(
'%r depends on %r, but your recipes.cfg is missing an '
'entry for this.', repo_name, req_repo_name)
continue
actual_spec = actual[req_repo_name]
if req_spec.revision == actual_spec.revision:
# They match, it's all good.
continue
LOG.warn(
'recipes.cfg depends on %r @ %s, but %r depends on version %s.',
req_repo_name, actual_spec.revision, repo_name, req_spec.revision)
def _cleanup_pyc(recipe_deps):
"""Removes any .pyc files from the recipes/recipe_module directories.
Args:
* recipe_deps (RecipeDeps) - The loaded recipe dependencies.
"""
for repo in recipe_deps.repos.itervalues():
for to_walk in (repo.recipes_dir, repo.modules_dir):
for root, _dirs, files in OS_WALK(to_walk):
for fname in files:
if not fname.endswith('.pyc'):
continue
try:
to_clean = os.path.join(root, fname)
LOG.info('cleaning %r', to_clean)
os.unlink(to_clean)
except OSError as ex:
# If multiple things are cleaning pyc's at the same time this can
# race. Fortunately we only care that SOMETHING deleted the pyc :)
if ex.errno != errno.ENOENT:
raise
def parse_and_run():
"""Parses the command line and runs the chosen subcommand.
Returns the command's return value (either int or None, suitable as input to
`os._exit`).
"""
parser = argparse.ArgumentParser(
description='Interact with the recipe system.')
_add_common_args(parser)
subp = parser.add_subparsers(dest='command')
for module in _COMMANDS:
description = module.__doc__
helplines = []
for line in description.splitlines():
line = line.strip()
if not line:
break
helplines.append(line)
module.add_arguments(subp.add_parser(
module.__name__.split('.')[-1], # use module's short name
formatter_class=argparse.RawDescriptionHelpFormatter,
help=' '.join(helplines),
description=description,
))
args = parser.parse_args()
_common_post_process(args)
args.postprocess_func(parser.error, args)
return args.func(args)
| [
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2,
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307,
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287,
262,
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... | 2.739414 | 1,842 |
# Copyright (C) 2020-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""STCPipelinemodule."""
import numpy as np
import gzip as gz
from .pipeline import TransformationPipeline, Transformer
| [
2,
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375,
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526,
15931,
198,
198,
11748,
299,
32152,
355,
... | 3.028986 | 69 |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# ------------------------------------------------------------
# File: __init__.py.py
# Created Date: 2020/6/24
# Created Time: 0:12
# Author: Hypdncy
# Author Mail: hypdncy@outlook.com
# Copyright (c) 2020 Hypdncy
# ------------------------------------------------------------
# .::::.
# .::::::::.
# :::::::::::
# ..:::::::::::'
# '::::::::::::'
# .::::::::::
# '::::::::::::::..
# ..::::::::::::.
# ``::::::::::::::::
# ::::``:::::::::' .:::.
# ::::' ':::::' .::::::::.
# .::::' :::: .:::::::'::::.
# .:::' ::::: .:::::::::' ':::::.
# .::' :::::.:::::::::' ':::::.
# .::' ::::::::::::::' ``::::.
# ...::: ::::::::::::' ``::.
# ````':. ':::::::::' ::::..
# '.:::::' ':'````..
# ------------------------------------------------------------ | [
2,
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14629,
14,
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2,
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9,
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2,
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25,
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14,
... | 1.851852 | 621 |
import numpy as np
import pytest
from pytest import approx
from pymt.component.grid import GridMixIn
| [
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628,
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198
] | 3.166667 | 36 |
import sys
if __name__ == "__main__":
mylog = sys.argv[1]
correctlog = sys.argv[2]
mylog_sp = load_log_sp(mylog)
correctlog_sp = load_log_sp(correctlog)
for (i, ((nb1, sp1), (nb2, sp2))) in enumerate(zip(mylog_sp, correctlog_sp)):
print('{} {} - {} vs {}'.format(
nb1, nb2, sp1, sp2))
if sp1.lower() != sp2.lower() or int(nb1.lower(),16) != int(nb2.lower(), 16):
break
| [
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... | 1.981567 | 217 |
"""Exercise 1
Usage:
$ CUDA_VISIBLE_DEVICES=2 python practico_1_train_petfinder.py --dataset_dir ../ --epochs 30 --dropout 0.1 0.1 --hidden_layer_sizes 200 100
To know which GPU to use, you can check it with the command
$ nvidia-smi
"""
import argparse
import os
import mlflow
import pickle
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers, models
import warnings
warnings.filterwarnings("ignore")
from auxiliary import process_features, load_dataset, build_columns, log_dir_name
TARGET_COL = 'AdoptionSpeed'
###########################################################################################################
print('All operations completed')
if __name__ == '__main__':
main()
| [
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... | 3.32377 | 244 |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from pkg_resources import parse_version
from warnings import warn
from copy import deepcopy
import networkx as nx
from networkx.readwrite import json_graph
from catpy.applications.base import CatmaidClientApplication
NX_VERSION_INFO = parse_version(nx.__version__)._key[1]
err_msg = (
"Tried to treat the edge's source/target fields as indices into the list of nodes, but failed. "
"See issue #26 [1]. "
"Has CATMAID upgraded to networkx 2.x? [2]\n\n"
"[1]: https://github.com/catmaid/catpy/issues/26\n"
"[2]: https://github.com/catmaid/CATMAID/blob/master/django/requirements.txt"
)
def convert_nodelink_data(jso):
"""NetworkX serialises graphs differently in v1.x and v2.x.
This converts v1-style data (as emitted by CATMAID) to v2-style data.
See issue #26 https://github.com/catmaid/catpy/issues/26
Parameters
----------
jso : dict
Returns
-------
dict
"""
if NX_VERSION_INFO < (2, 0):
warn(
"You are converting networkx v1-style JSON (emitted by CATMAID) to v2-style JSON,"
" but you are using networkx v1"
)
out = deepcopy(jso)
for edge in out["links"]:
for label in ["source", "target"]:
try:
edge[label] = out["nodes"][edge[label]]["id"]
except (KeyError, IndexError):
raise RuntimeError(err_msg)
return out
| [
2,
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9,
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25,
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14601,
1330,
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198,
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2769,
... | 2.456667 | 600 |
from typing import Optional
from watchmen_auth import PrincipalService
from watchmen_data_kernel.cache import CacheService
from watchmen_data_kernel.common import DataKernelException
from watchmen_data_kernel.external_writer import find_external_writer_create, register_external_writer_creator
from watchmen_meta.common import ask_meta_storage, ask_snowflake_generator
from watchmen_meta.system import ExternalWriterService as ExternalWriterStorageService
from watchmen_model.common import ExternalWriterId
from watchmen_model.system import ExternalWriter
| [
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7948,... | 4.140741 | 135 |
nota1 = float(input('Digite a nota da primeira nota '))
peso1 = float(input('Digite o peso da primeira nota '))
nota2 = float(input('Digite a nota da seugundo nota '))
peso2 = float(input('Digite o peso da segundo nota '))
media = (nota1/peso1+nota2/peso2)/2
print('A mdia das duas notas :', media)
| [
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166... | 2.416 | 125 |
from importlib import import_module
from typing import Any
def import_string(path: str) -> Any:
"""Imports a dotted path name and returns the class/attribute.
Parameters
----------
path: str
Dotted module path to retrieve.
Returns
-------
Class/attribute at the given import path.
Raises
------
ImportError
If the path does not exist.
"""
try:
module_path, class_name = path.rsplit('.', 1)
except ValueError as error:
raise ImportError(
f"{path} does not look like a module path",
) from error
module = import_module(module_path)
try:
return getattr(module, class_name)
except AttributeError as error:
raise ImportError(
f"Module '{module_path}' does not define a '{class_name}' "
"attribute/class",
) from error
| [
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423... | 2.512821 | 351 |
#!/usr/bin/env python
import json
import os
import re
from enum import Enum
from os.path import join
from le_utils.constants import content_kinds
from le_utils.constants import exercises
from le_utils.constants import file_formats
from le_utils.constants import licenses
from ricecooker.chefs import SushiChef
from ricecooker.classes import files
from ricecooker.classes import nodes
from ricecooker.classes import questions
from ricecooker.classes.licenses import get_license
from ricecooker.exceptions import InvalidFormatException
from ricecooker.exceptions import raise_for_invalid_channel
from ricecooker.exceptions import UnknownContentKindError
from ricecooker.exceptions import UnknownFileTypeError
from ricecooker.exceptions import UnknownQuestionTypeError
# CHANNEL SETTINGS
SOURCE_DOMAIN = "<yourdomain.org>" # content provider's domain
SOURCE_ID = "<yourid>" # an alphanumeric channel ID
CHANNEL_TITLE = "Testing Ricecooker Channel" # a humand-readbale title
CHANNEL_LANGUAGE = "en" # language code of channel
# LOCAL DIRS
EXAMPLES_DIR = os.path.dirname(os.path.realpath(__file__))
DATA_DIR = os.path.join(EXAMPLES_DIR, "data")
CONTENT_DIR = os.path.join(EXAMPLES_DIR, "content")
#
# A utility function to manage absolute paths that allows us to refer to files
# in the CONTENT_DIR (subdirectory `content/' in current directory) using content://
def get_abspath(path, content_dir=CONTENT_DIR):
"""
Replaces `content://` with absolute path of `content_dir`.
By default looks for content in subdirectory `content` in current directory.
"""
if path:
file = re.search("content://(.+)", path)
if file:
return os.path.join(content_dir, file.group(1))
return path
FILE_TYPE_MAPPING = {
content_kinds.AUDIO: {
file_formats.MP3: FileTypes.AUDIO_FILE,
file_formats.PNG: FileTypes.THUMBNAIL,
file_formats.JPG: FileTypes.THUMBNAIL,
file_formats.JPEG: FileTypes.THUMBNAIL,
},
content_kinds.DOCUMENT: {
file_formats.PDF: FileTypes.DOCUMENT_FILE,
file_formats.PNG: FileTypes.THUMBNAIL,
file_formats.JPG: FileTypes.THUMBNAIL,
file_formats.JPEG: FileTypes.THUMBNAIL,
},
content_kinds.HTML5: {
file_formats.HTML5: FileTypes.HTML_ZIP_FILE,
file_formats.PNG: FileTypes.THUMBNAIL,
file_formats.JPG: FileTypes.THUMBNAIL,
file_formats.JPEG: FileTypes.THUMBNAIL,
},
content_kinds.H5P: {
file_formats.H5P: FileTypes.H5P_FILE,
file_formats.PNG: FileTypes.THUMBNAIL,
file_formats.JPG: FileTypes.THUMBNAIL,
file_formats.JPEG: FileTypes.THUMBNAIL,
},
content_kinds.VIDEO: {
file_formats.MP4: FileTypes.VIDEO_FILE,
file_formats.VTT: FileTypes.SUBTITLE_FILE,
file_formats.PNG: FileTypes.THUMBNAIL,
file_formats.JPG: FileTypes.THUMBNAIL,
file_formats.JPEG: FileTypes.THUMBNAIL,
},
content_kinds.EXERCISE: {
file_formats.PNG: FileTypes.THUMBNAIL,
file_formats.JPG: FileTypes.THUMBNAIL,
file_formats.JPEG: FileTypes.THUMBNAIL,
},
}
def guess_file_type(kind, filepath=None, youtube_id=None, web_url=None, encoding=None):
"""guess_file_class: determines what file the content is
Args:
filepath (str): filepath of file to check
Returns: string indicating file's class
"""
if youtube_id:
return FileTypes.YOUTUBE_VIDEO_FILE
elif web_url:
return FileTypes.WEB_VIDEO_FILE
elif encoding:
return FileTypes.BASE64_FILE
else:
ext = os.path.splitext(filepath)[1][1:].lower()
if kind in FILE_TYPE_MAPPING and ext in FILE_TYPE_MAPPING[kind]:
return FILE_TYPE_MAPPING[kind][ext]
return None
def guess_content_kind(path=None, web_video_data=None, questions=None):
"""guess_content_kind: determines what kind the content is
Args:
files (str or list): files associated with content
Returns: string indicating node's kind
"""
# If there are any questions, return exercise
if questions and len(questions) > 0:
return content_kinds.EXERCISE
# See if any files match a content kind
if path:
ext = os.path.splitext(path)[1][1:].lower()
if ext in content_kinds.MAPPING:
return content_kinds.MAPPING[ext]
raise InvalidFormatException(
"Invalid file type: Allowed formats are {0}".format(
[key for key, value in content_kinds.MAPPING.items()]
)
)
elif web_video_data:
return content_kinds.VIDEO
else:
return content_kinds.TOPIC
# LOAD sample_tree.json (as dict)
with open(join(DATA_DIR, "sample_tree.json"), "r") as json_file:
SAMPLE_TREE = json.load(json_file)
# LOAD JSON DATA (as string) FOR PERSEUS QUESTIONS
SAMPLE_PERSEUS_1_JSON = open(join(DATA_DIR, "sample_perseus01.json"), "r").read()
# SAMPLE_PERSEUS_2_JSON = open(join(DATA_DIR,'sample_perseus02.json'),'r').read()
# ADD EXERCISES
EXERCISES_NODES = [
{
"title": "Rice Cookers",
"id": "d98752",
"description": "Start cooking rice today!",
"children": [
{
"title": "Rice Chef",
"id": "6cafe2",
"author": "Revision 3",
"description": "Become a master rice cooker",
"file": "https://ia600209.us.archive.org/27/items/RiceChef/Rice Chef.mp4",
"license": licenses.CC_BY_NC_SA,
"copyright_holder": "Learning Equality",
"files": [
{
"path": "https://ia600209.us.archive.org/27/items/RiceChef/Rice Chef.mp4"
},
{
"encoding": "data:image/png;base64,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"
},
],
},
{
"title": "Rice Exercise",
"id": "6cafe3",
"description": "Test how well you know your rice",
"license": licenses.CC_BY_NC_SA,
"copyright_holder": "Learning Equality",
"mastery_model": exercises.DO_ALL,
"files": [
{
"path": "http://www.publicdomainpictures.net/pictures/110000/nahled/bowl-of-rice.jpg"
}
],
"questions": [
{
"id": "eeeee",
"question": "Which rice is your favorite? \\_\\_\\_ ",
"type": exercises.MULTIPLE_SELECTION,
"correct_answers": [
"White rice",
"Brown rice",
"Sushi rice <p>abc</p>",
],
"all_answers": ["White rice", "Quinoa", "Brown rice", "<"],
},
{
"id": "bbbbb",
"question": "Which rice is the crunchiest?",
"type": exercises.SINGLE_SELECTION,
"correct_answer": "Rice Krispies \n",
"all_answers": [
"White rice",
"Brown rice \n",
"Rice Krispies \n",
],
"hints": "It's delicious",
},
{
"id": "aaaaa",
"question": "How many minutes does it take to cook rice? <img src='https://upload.wikimedia.org/wikipedia/commons/5/5e/Jeera-rice.JPG'>",
"type": exercises.INPUT_QUESTION,
"answers": ["20", "25", "15"],
"hints": [
"Takes roughly same amount of time to install kolibri on Windows machine",
"Does this help?\n",
],
},
{
"id": "ddddd",
"type": exercises.PERSEUS_QUESTION,
"item_data": SAMPLE_PERSEUS_1_JSON,
},
],
},
{
"title": "Rice Exercise 2",
"id": "6cafe4",
"description": "Test how well you know your rice",
"license": licenses.CC_BY_NC_SA,
"copyright_holder": "Learning Equality",
"mastery_model": exercises.M_OF_N,
"files": [
{
"path": "https://c1.staticflickr.com/5/4021/4302326650_b11f0f0aaf_b.jpg"
}
],
"questions": [
{
"id": "11111",
"question": "<h3 id=\"rainbow\" style=\"font-weight:bold\">RICE COOKING!!!</h3><script type='text/javascript'><!-- setInterval(function() {$('#rainbow').css('color', '#'+((1<<24)*Math.random()|0).toString(16));}, 300); --></script>",
"type": exercises.SINGLE_SELECTION,
"all_answers": ["Answer"],
"correct_answer": "Answer",
},
{
"id": "121212",
"question": "<math> <mrow> <msup><mi> a </mi><mn>2</mn></msup> <mo> + </mo> <msup><mi> b </mi><mn>2</mn></msup> <mo> = </mo> <msup><mi> c </mi><mn>2</mn></msup> </mrow> </math>",
"type": exercises.SINGLE_SELECTION,
"all_answers": ["Answer"],
"correct_answer": "Answer",
},
],
},
{
"title": "HTML Sample",
"id": "abcdef",
"description": "An example of how html can be imported from the ricecooker",
"license": licenses.PUBLIC_DOMAIN,
"files": [{"path": "content://htmltest.zip"}],
},
{
"title": "Rice Exercise 3",
"id": "6cafe5",
"description": "Test how well you know your rice",
"license": licenses.CC_BY_NC_SA,
"copyright_holder": "Learning Equality",
"mastery_model": exercises.M_OF_N,
"files": [
{
"path": "https://upload.wikimedia.org/wikipedia/commons/b/b7/Rice_p1160004.jpg"
}
],
"questions": [
{
"id": "123456",
"question": "Solve: $$(111^{x+1}\\times111^\\frac14)\\div111^\\frac12=111^3$$",
"type": exercises.SINGLE_SELECTION,
"all_answers": ["Yes", "No", "Rice!"],
"correct_answer": "Rice!",
}
],
},
],
}
]
SAMPLE_TREE.extend(EXERCISES_NODES)
def _build_tree(node, sourcetree):
"""
Parse nodes given in `sourcetree` and add as children of `node`.
"""
for child_source_node in sourcetree:
try:
main_file = (
child_source_node["files"][0] if "files" in child_source_node else {}
)
kind = guess_content_kind(
path=main_file.get("path"),
web_video_data=main_file.get("youtube_id") or main_file.get("web_url"),
questions=child_source_node.get("questions"),
)
except UnknownContentKindError:
continue
if kind == content_kinds.TOPIC:
child_node = nodes.TopicNode(
source_id=child_source_node["id"],
title=child_source_node["title"],
author=child_source_node.get("author"),
description=child_source_node.get("description"),
thumbnail=child_source_node.get("thumbnail"),
)
node.add_child(child_node)
source_tree_children = child_source_node.get("children", [])
_build_tree(child_node, source_tree_children)
elif kind == content_kinds.VIDEO:
child_node = nodes.VideoNode(
source_id=child_source_node["id"],
title=child_source_node["title"],
license=get_license(
child_source_node.get("license"),
description="Description of license",
copyright_holder=child_source_node.get("copyright_holder"),
),
author=child_source_node.get("author"),
description=child_source_node.get("description"),
derive_thumbnail=True, # video-specific data
thumbnail=child_source_node.get("thumbnail"),
)
add_files(child_node, child_source_node.get("files") or [])
node.add_child(child_node)
elif kind == content_kinds.AUDIO:
child_node = nodes.AudioNode(
source_id=child_source_node["id"],
title=child_source_node["title"],
license=child_source_node.get("license"),
author=child_source_node.get("author"),
description=child_source_node.get("description"),
thumbnail=child_source_node.get("thumbnail"),
copyright_holder=child_source_node.get("copyright_holder"),
)
add_files(child_node, child_source_node.get("files") or [])
node.add_child(child_node)
elif kind == content_kinds.DOCUMENT:
child_node = nodes.DocumentNode(
source_id=child_source_node["id"],
title=child_source_node["title"],
license=child_source_node.get("license"),
author=child_source_node.get("author"),
description=child_source_node.get("description"),
thumbnail=child_source_node.get("thumbnail"),
copyright_holder=child_source_node.get("copyright_holder"),
)
add_files(child_node, child_source_node.get("files") or [])
node.add_child(child_node)
elif kind == content_kinds.EXERCISE:
mastery_model = (
child_source_node.get("mastery_model")
and {"mastery_model": child_source_node["mastery_model"]}
) or {}
child_node = nodes.ExerciseNode(
source_id=child_source_node["id"],
title=child_source_node["title"],
license=child_source_node.get("license"),
author=child_source_node.get("author"),
description=child_source_node.get("description"),
exercise_data=mastery_model,
thumbnail=child_source_node.get("thumbnail"),
copyright_holder=child_source_node.get("copyright_holder"),
)
add_files(child_node, child_source_node.get("files") or [])
for q in child_source_node.get("questions"):
question = create_question(q)
child_node.add_question(question)
node.add_child(child_node)
elif kind == content_kinds.HTML5:
child_node = nodes.HTML5AppNode(
source_id=child_source_node["id"],
title=child_source_node["title"],
license=child_source_node.get("license"),
author=child_source_node.get("author"),
description=child_source_node.get("description"),
thumbnail=child_source_node.get("thumbnail"),
copyright_holder=child_source_node.get("copyright_holder"),
)
add_files(child_node, child_source_node.get("files") or [])
node.add_child(child_node)
elif kind == content_kinds.H5P:
child_node = nodes.H5PAppNode(
source_id=child_source_node["id"],
title=child_source_node["title"],
license=child_source_node.get("license"),
author=child_source_node.get("author"),
description=child_source_node.get("description"),
thumbnail=child_source_node.get("thumbnail"),
copyright_holder=child_source_node.get("copyright_holder"),
)
add_files(child_node, child_source_node.get("files") or [])
node.add_child(child_node)
else: # unknown content file format
continue
return node
if __name__ == "__main__":
"""
This code will run when the sushi chef is called from the command line.
"""
chef = SampleChef()
chef.main()
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
11748,
33918,
198,
11748,
28686,
198,
11748,
302,
198,
6738,
33829,
1330,
2039,
388,
198,
6738,
28686,
13,
6978,
1330,
4654,
198,
198,
6738,
443,
62,
26791,
13,
9979,
1187,
1330,
2695,
... | 1.808682 | 11,541 |
# ##### BEGIN GPL LICENSE BLOCK #####
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# ##### END GPL LICENSE BLOCK #####
# <pep8 compliant>
import enchant
import os
import pickle
import re
| [
2,
46424,
347,
43312,
38644,
38559,
24290,
9878,
11290,
46424,
198,
2,
198,
2,
220,
770,
1430,
318,
1479,
3788,
26,
345,
460,
17678,
4163,
340,
290,
14,
273,
198,
2,
220,
13096,
340,
739,
262,
2846,
286,
262,
22961,
3611,
5094,
1378... | 3.680851 | 235 |
import numpy as np
import os
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from naslib.utils.utils import AverageMeterGroup
from naslib.predictors.utils.encodings import encode
from naslib.predictors import Predictor
# NOTE: faster on CPU
device = torch.device("cpu")
print("device:", device)
| [
11748,
299,
32152,
355,
45941,
198,
11748,
28686,
198,
11748,
33918,
198,
11748,
28034,
198,
11748,
28034,
13,
20471,
355,
299,
77,
198,
11748,
28034,
13,
20471,
13,
45124,
355,
376,
198,
11748,
28034,
13,
40085,
355,
6436,
198,
6738,
2... | 3.393443 | 122 |
from pythonforandroid.toolchain import Recipe, shprint, current_directory, ArchARM
from os.path import exists, join, realpath
from os import uname
import glob
import sh
recipe = LibX264Recipe()
| [
6738,
21015,
1640,
19411,
13,
25981,
7983,
1330,
26694,
11,
427,
4798,
11,
1459,
62,
34945,
11,
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198,
6738,
28686,
13,
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1330,
7160,
11,
4654,
11,
1103,
6978,
198,
6738,
28686,
1330,
555,
480,
198,
11748,
15095,
198,
1... | 3.62963 | 54 |
#coding=utf-8
try:
if __name__.startswith('qgb.Win'):
from .. import py
else:
import py
except Exception as ei:
raise ei
raise EnvironmentError(__name__)
if py.is2():
import _winreg as winreg
from _winreg import *
else:
import winreg
from winreg import *
def get(skey,name,root=HKEY_CURRENT_USER,returnType=True):
''' from qgb.Win import reg
reg.get(r'Software\Microsoft\Windows\CurrentVersion\Internet Settings','ProxyEnable')
reg.get(r'HKLM\SYSTEM\CurrentControlSet\Services\LanmanServer\Parameters\Size' )
There are seven predefined root keys, traditionally named according to their constant handles defined in the Win32 API
skey name FileNotFoundError: [WinError 2]
'''
r = OpenKey(root,skey)
r = QueryValueEx(r,name)
if returnType:return r[0],'{} : {}'.format(REG_TYPE[r[1]],r[1])
else :return r[0]
REG_TYPE={ 0 : 'REG_NONE',
1 : 'REG_SZ',
2 : 'REG_EXPAND_SZ',
3 : 'REG_BINARY',
4 : 'REG_DWORD',
5 : 'REG_DWORD_BIG_ENDIAN',
6 : 'REG_LINK',
7 : 'REG_MULTI_SZ',
8 : 'REG_RESOURCE_LIST',
9 : 'REG_FULL_RESOURCE_DESCRIPTOR',
10: 'REG_RESOURCE_REQUIREMENTS_LIST',
11: 'REG_QWORD'}
| [
2,
66,
7656,
28,
40477,
12,
23,
198,
28311,
25,
198,
197,
361,
11593,
3672,
834,
13,
9688,
2032,
342,
10786,
80,
22296,
13,
16643,
6,
2599,
198,
197,
197,
6738,
11485,
1330,
12972,
198,
197,
17772,
25,
198,
197,
197,
11748,
12972,
... | 2.265896 | 519 |
import unittest
import paramiko
from tornado.httputil import HTTPServerRequest
from tests.utils import read_file, make_tests_data_path
from webssh.handler import MixinHandler, IndexHandler, InvalidValueError
| [
11748,
555,
715,
395,
198,
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5772,
12125,
198,
198,
6738,
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13,
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1996,
346,
1330,
38288,
18497,
18453,
198,
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13,
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1330,
1100,
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11,
787,
62,
41989,
62,
7890,
62,
6978,
198,
6738,
3992,
45824,
13... | 3.767857 | 56 |
import logging
from unittest import mock
from unittest.mock import call
from django.conf import settings
from django.contrib.auth import get_user_model
from django.core.signing import Signer
from django.urls import reverse
from django.http import Http404
from django.test import RequestFactory
from braces.views import LoginRequiredMixin
from django.test import override_settings
from model_mommy import mommy
from apps.notifications.models import Log, StatusUpdate, MemberUpdate, ReadLog, \
ActionContextQuerySet
from apps.notifications.views import LogListView, LogCountView, ReadLogUpdateView, \
LogQuestionnairesListView, LogInformationUpdateCreateView, \
LogSubscriptionPreferencesView, SignedLogSubscriptionPreferencesView
from apps.qcat.tests import TestCase
#def test_add_user_aware_data_is_todo(self):
# data = self._test_add_user_aware_data()
# self.assertTrue(data[1]['is_todo'])
def test_get_status_invalid(self):
request = RequestFactory().get('{}?statuses=foo'.format(self.url_path))
view = self.setup_view(self.view, request)
self.assertEqual(view.get_statuses(), [])
class ReadLogUpdateViewTest(TestCase):
| [
11748,
18931,
198,
6738,
555,
715,
395,
1330,
15290,
198,
6738,
555,
715,
395,
13,
76,
735,
1330,
869,
198,
198,
6738,
42625,
14208,
13,
10414,
1330,
6460,
198,
6738,
42625,
14208,
13,
3642,
822,
13,
18439,
1330,
651,
62,
7220,
62,
... | 3.072165 | 388 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import sys
import os
sys.path.insert(0, os.path.abspath('..'))
from clint import resources
resources.init('kennethreitz', 'clint')
lorem = 'Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.'
print('%s created.' % resources.user.path)
resources.user.write('lorem.txt', lorem)
print('lorem.txt created')
assert resources.user.read('lorem.txt') == lorem
print('lorem.txt has correct contents')
resources.user.delete('lorem.txt')
print('lorem.txt deleted')
assert resources.user.read('lorem.txt') == None
print('lorem.txt deletion confirmed')
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
6738,
11593,
37443,
834,
1330,
3601,
62,
8818,
198,
198,
11748,
25064,
198,
11748,
28686,
198,
198,
17597,
13,
697... | 2.874652 | 359 |
from django.conf.urls import url
from django.conf import settings
from django.conf.urls.static import static
from . import views
urlpatterns = [
url(r'^$', views.index, name='index'),
url(r'^image/$', views.add_image, name='upload_image'),
url(r'^profile/$', views.profile_info, name='profile'),
url(r'^update/$', views.profile_update, name='update'),
url(r'^comment/(?P<image_id>\d+)', views.comment, name='comment'),
url(r'^search/', views.search_results, name = 'search_results'),
url(r'^follow/(?P<user_id>\d+)', views.follow, name = 'follow'),
url(r'^unfollow/(?P<user_id>\d+)', views.unfollow, name='unfollow'),
url(r'^likes/(\d+)/$', views.like_images,name='likes')
]
if settings.DEBUG:
urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
| [
6738,
42625,
14208,
13,
10414,
13,
6371,
82,
1330,
19016,
198,
6738,
42625,
14208,
13,
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1330,
6460,
198,
6738,
42625,
14208,
13,
10414,
13,
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82,
13,
12708,
1330,
220,
9037,
198,
6738,
764,
1330,
5009,
198,
198,
6371,
33279,
... | 2.564669 | 317 |
import random
import math
from fractions import Fraction
from datetime import datetime
from jinja2 import Template
# empty class for passing to template engine
# returns flour percent using flour type
def get_special_flour_percent(flourType: str, breadFlourPercent:int) -> int:
if flourType == 'Hard Red Whole Wheat' or flourType == 'Hard White Wheat':
percentages = [0,25,30,35,40,45,50]
percentages = list(filter(lambda x: 100-breadFlourPercent >= x, percentages))
return random.choice(percentages)
elif flourType == 'Rye' and breadFlourPercent >= 75:
percentages = [0,10,15,20]
percentages = list(filter(lambda x: 100-breadFlourPercent >= x, percentages))
return random.choice(percentages)
else:
percentages = [0,10,15,20,25.30]
percentages = list(filter(lambda x: 100-breadFlourPercent >= x, percentages))
return random.choice(percentages)
# returns multiplied spoon units from teaspoon fraction input, 3 tsp = 1 tbsp
# returns amount given the type of flavoring(spices)
# returns list of spices using number of spices
# check if extract is nut
# checks if extract1 and extract2 are both allowed based on zest/extract same flavor
# return list of extracts using number of extracts
# return percentage of enrichment
# return liquid percent from liquid tpye
# return fruit puree fruit choice(s), omitted fruit chance weighting for now
# retrun fruit puree percent from 0-2 fruitPurees using random generation
# returns rounded ml conversion from percent, used in template
# takes filename and writes an html recipe file | [
11748,
4738,
198,
11748,
10688,
198,
6738,
49876,
1330,
376,
7861,
198,
6738,
4818,
8079,
1330,
4818,
8079,
198,
6738,
474,
259,
6592,
17,
1330,
37350,
198,
198,
2,
6565,
1398,
329,
6427,
284,
11055,
3113,
220,
198,
198,
2,
5860,
1060... | 3.521445 | 443 |
import datetime
from unittest.mock import patch
import dns.resolver
import dns.rrset
import pytest
import pytz
from django.utils import timezone
from freezegun import freeze_time
from rest_framework import status
from posthog.models import Organization, OrganizationDomain, OrganizationMembership, Team
from posthog.test.base import APIBaseTest, BaseTest
def test_cannot_list_or_retrieve_domains_for_other_org(self):
self.organization_membership.level = OrganizationMembership.Level.ADMIN
self.organization_membership.save()
response = self.client.get(f"/api/organizations/@current/domains/{self.another_domain.id}")
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
self.assertEqual(response.json(), self.not_found_response())
response = self.client.get(f"/api/organizations/{self.another_org.id}/domains/{self.another_domain.id}")
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
self.assertEqual(response.json(), self.permission_denied_response())
# Create domains
def test_cannot_request_verification_for_verified_domains(self):
self.organization_membership.level = OrganizationMembership.Level.ADMIN
self.organization_membership.save()
self.domain.verified_at = timezone.now()
self.domain.save()
response = self.client.post(f"/api/organizations/@current/domains/{self.domain.id}/verify")
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertEqual(
response.json(),
{
"type": "validation_error",
"code": "already_verified",
"detail": "This domain has already been verified.",
"attr": None,
},
)
def test_only_admin_can_create_verified_domains(self):
count = OrganizationDomain.objects.count()
response = self.client.post("/api/organizations/@current/domains/", {"domain": "evil.posthog.com"})
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
self.assertEqual(
response.json(), self.permission_denied_response("Your organization access level is insufficient."),
)
self.assertEqual(OrganizationDomain.objects.count(), count)
def test_only_admin_can_request_verification(self):
response = self.client.post(f"/api/organizations/@current/domains/{self.domain.id}/verify")
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
self.assertEqual(
response.json(), self.permission_denied_response("Your organization access level is insufficient."),
)
self.domain.refresh_from_db()
self.assertEqual(self.domain.verified_at, None)
# Update domains
# Delete domains
| [
11748,
4818,
8079,
198,
6738,
555,
715,
395,
13,
76,
735,
1330,
8529,
198,
198,
11748,
288,
5907,
13,
411,
14375,
198,
11748,
288,
5907,
13,
21062,
2617,
198,
11748,
12972,
9288,
198,
11748,
12972,
22877,
198,
6738,
42625,
14208,
13,
... | 2.542525 | 1,117 |
import numpy as np
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
from os import listdir
from tensorflow.keras.callbacks import ModelCheckpoint
dataDir = "./data/trainSmallFA/"
files = listdir(dataDir)
files.sort()
totalLength = len(files)
inputs = np.empty((len(files), 3, 64, 64))
targets = np.empty((len(files), 3, 64, 64))
for i, file in enumerate(files):
npfile = np.load(dataDir + file)
d = npfile['a']
inputs[i] = d[0:3] # inx, iny, mask
targets[i] = d[3:6] # p, velx, vely
# print("inputs shape = ", inputs.shape)
print(np.shape(targets[:, 1, :, :].flatten()))
maxvel = np.amax(np.sqrt(targets[:, 1, :, :]* targets[:, 1, :, :]
+ targets[:, 2, :, :]* targets[:, 2, :, :]))
print(maxvel)
targets[:, 1:3, :, :] /= maxvel
targets[:, 0, :, :] /= np.amax(targets[:, 0, :, :])
for input in inputs:
plt.figure(num=None, figsize=(20, 10), dpi=80, facecolor='w', edgecolor='k')
# predicted data
plt.subplot(331)
plt.title('x vel')
plt.imshow(input[0, :, :], cmap='jet') # vmin=-100,vmax=100, cmap='jet')
plt.colorbar()
plt.subplot(332)
plt.title('y vel')
plt.imshow(input[1, :, :], cmap='jet')
plt.colorbar()
plt.show() | [
11748,
299,
32152,
355,
45941,
198,
11748,
11192,
273,
11125,
355,
48700,
198,
6738,
11192,
273,
11125,
1330,
41927,
292,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
198,
6738,
28686,
1330,
1351,
15908,
198,
6738,
11192,... | 2.189474 | 570 |
from pepper.framework import *
from pepper import logger
from pepper.language import Utterance
from pepper.language.generation.thoughts_phrasing import phrase_thoughts
from pepper.language.generation.reply import reply_to_question
from .responder import Responder, ResponderType
from pepper.language import UtteranceType
from pepper.knowledge import sentences, animations
from random import choice
import re
from typing import Optional, Union, Tuple, Callable
| [
6738,
13385,
13,
30604,
1330,
1635,
198,
6738,
13385,
1330,
49706,
198,
198,
6738,
13385,
13,
16129,
1330,
7273,
353,
590,
198,
6738,
13385,
13,
16129,
13,
20158,
13,
2016,
912,
62,
746,
81,
2313,
1330,
9546,
62,
2016,
912,
198,
6738,... | 4.160714 | 112 |
# -*- coding: utf-8 -*-
import re
from unicodedata import normalize
from flask import Blueprint, render_template, current_app
from flask import redirect, url_for, g, abort
from sqlalchemy import desc
from fedora_college.core.database import db
from fedora_college.modules.content.forms import * # noqa
from fedora_college.core.models import * # noqa
from fedora_college.fedmsgshim import publish
from flask_fas_openid import fas_login_required
bundle = Blueprint('content', __name__, template_folder='templates')
from fedora_college.modules.content.media import * # noqa
_punct_re = re.compile(r'[\t !"#$%&\'()*\-/<=>?@\[\\\]^_`{|},.]+')
# Verify if user is authenticated
# generate url slug
def slugify(text, delim=u'-'):
"""Generates an slightly worse ASCII-only slug."""
result = []
for word in _punct_re.split(text.lower()):
word = normalize('NFKD', word).encode('ascii', 'ignore')
if word:
result.append(word)
return unicode(delim.join(result))
# attach tags to a content entry
# delete content
# add / edit more content
# View Blog post
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
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302,
198,
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9043,
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198,
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1330,
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11,
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62,
28243,
11,
1459,
62,
1324,
198,
6738,
42903,
1330,
18941,
11,
19... | 2.838462 | 390 |
"""Test the Airthings config flow."""
from unittest.mock import patch
import airthings
from homeassistant import config_entries
from homeassistant.components.airthings.const import CONF_ID, CONF_SECRET, DOMAIN
from homeassistant.core import HomeAssistant
from homeassistant.data_entry_flow import RESULT_TYPE_CREATE_ENTRY, RESULT_TYPE_FORM
from tests.common import MockConfigEntry
TEST_DATA = {
CONF_ID: "client_id",
CONF_SECRET: "secret",
}
| [
37811,
14402,
262,
317,
3333,
654,
4566,
5202,
526,
15931,
198,
6738,
555,
715,
395,
13,
76,
735,
1330,
8529,
198,
198,
11748,
1633,
27971,
198,
198,
6738,
1363,
562,
10167,
1330,
4566,
62,
298,
1678,
198,
6738,
1363,
562,
10167,
13,
... | 3.019737 | 152 |
# ------------------------------------------------------------------------------
# Copyright (c) NKU
# Licensed under the MIT License.
# Written by Xuanyi Li (xuanyili.edu@gmail.com)
# ------------------------------------------------------------------------------
import os
import torch
import torch.nn.functional as F
#import cv2 as cv
import numpy as np
if __name__ == '__main__':
pass
# import matplotlib.pyplot as plt
# image = cv.imread('/media/lxy/sdd1/ActiveStereoNet/StereoNet_pytorch/results/forvideo/iter-122.jpg')
#im_gray = cv.imread('/media/lxy/sdd1/ActiveStereoNet/StereoNet_pytorch/results/forvideo/iter-133.jpg', cv.IMREAD_GRAYSCALE)
# print(im_gray.shape)
#im_color = cv.applyColorMap(im_gray*2, cv.COLORMAP_JET)
# cv.imshow('test', im_color)
# cv.waitKey(0)
#cv.imwrite('test.png',im_color)
# print(image.shape)
# plt.figure('Image')
# sc =plt.imshow(image)
# sc.set_cmap('hsv')
# plt.colorbar()
# plt.axis('off')
# plt.show()
# print('end')
# image[:,:,0].save('/media/lxy/sdd1/ActiveStereoNet/StereoNet_pytorch/results/pretrained_StereoNet_single/it1er-151.jpg')
| [
2,
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15532,
31,
14816,
13,
785,
8,
198,
2,
16529,
26171,
198,
1... | 2.54386 | 456 |
from aiohttp import web
import base64
import io
import face_recognition
main()
| [
6738,
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] | 3.333333 | 24 |
# ~~~
# This file is part of the paper:
#
# " An Online Efficient Two-Scale Reduced Basis Approach
# for the Localized Orthogonal Decomposition "
#
# https://github.com/TiKeil/Two-scale-RBLOD.git
#
# Copyright 2019-2021 all developers. All rights reserved.
# License: Licensed as BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
# Authors:
# Stephan Rave
# Tim Keil
# ~~~
from setuptools import setup
setup(name='rblod',
version='2021.1',
description='Pymor support for RBLOD',
author='Tim Keil',
author_email='tim.keil@wwu.de',
license='MIT',
packages=['rblod'])
| [
2,
220,
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93,
198,
2,
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318,
636,
286,
262,
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2,
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2,
220,
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220,
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... | 2.406593 | 273 |
import os
import shutil
import numpy as np
import pandas as pd
import seaborn as sns
import cosmicfish as cf
import matplotlib.pyplot as plt
import dill
# Instruct pyplot to use seaborn
sns.set()
# Set project, data, CLASS directories
projectdir = os.environ['STORAGE_DIR']
datastore = os.environ['DATASTORE_DIR']
classpath = os.environ['CLASS_DIR']
fidx = int(os.environ['FORECAST_INDEX'])
# Generate output paths
fp_resultsdir = projectdir
cf.makedirectory(fp_resultsdir)
# Specify resolution of numerical integrals
derivative_step = 0.008 # How much to vary parameter to calculate numerical derivative
g_derivative_step = 0.1
mu_integral_step = 0.05 # For calculating numerical integral wrt mu between -1 and 1
# Linda Fiducial Cosmology
fp_fid = {
"A_s" : 2.2321e-9,
"n_s" : 0.967,
"omega_b" : 0.02226,
"omega_cdm" : 0.1127,
"tau_reio" : 0.0598,
"h" : 0.701,
"T_cmb" : 2.726, # Units [K]
"N_ncdm" : 4.,
"deg_ncdm" : 1.0,
"T_ncdm" : (0.79/2.726), # Units [T_cmb].
"m_ncdm" : 0.01, # Units [eV]
"b0" : 1.0,
"beta0" : 1.7,
"beta1" : 1.0,
"alphak2" : 1.0,
"sigma_fog_0" : 250000, #Units [m s^-2]
"N_eff" : 0.0064, #We allow relativistic neutrinos in addition to our DM relic
"relic_vary" : "N_ncdm", # Fix T_ncdm or m_ncdm
"m_nu" : 0.02
}
# EUCLID values
z_table = np.array([0.65, 0.75, 0.85, 0.95, 1.05, 1.15, 1.25, 1.35, 1.45, 1.55, 1.65, 1.75, 1.85, 1.95])
dNdz = np.array([2434.280, 4364.812, 4728.559, 4825.798, 4728.797, 4507.625, 4269.851, 3720.657, 3104.309,
2308.975, 1514.831, 1474.707, 893.716, 497.613])
skycover = 0.3636
# Run Fisher Forecast
full_masses = np.geomspace(0.01, 10., 21)
full_temps = np.array([0.79, 0.91, 0.94, 1.08])
mass_index=(fidx % 21)
temp_index=(fidx // 21)
masses = np.array([full_masses[mass_index]])
temps = np.array([full_temps[temp_index]])
omegacdm_set = np.array([
fp_fid['omega_cdm']
- ((masses/cf.NEUTRINO_SCALE_FACTOR)* np.power(tval / 1.95, 3.))
for tidx, tval in enumerate(temps)])
fp_fiducialset = [[
dict(fp_fid, **{
'm_ncdm' : masses[midx],
'omega_cdm' : omegacdm_set[tidx, midx],
'T_ncdm' : temps[tidx]/2.726})
for midx, mval in enumerate(masses)]
for tidx, tval in enumerate(temps)]
fp_forecastset = [[cf.forecast(
classpath,
datastore,
'2relic',
fidval,
z_table,
"EUCLID",
dNdz,
fsky=skycover,
dstep=derivative_step,
gstep=g_derivative_step,
RSD=True,
FOG=True,
AP=True,
COV=True)
for fididx, fidval in enumerate(fidrowvals)]
for fidrowidx, fidrowvals in enumerate(fp_fiducialset)]
#dill.load_session('')
for frowidx, frowval in enumerate(fp_forecastset):
for fidx, fcst in enumerate(frowval):
if type(fcst.fisher)==type(None):
fcst.gen_pm()
fcst.gen_fisher(
fisher_order=[
'omega_b',
'omega_cdm',
'n_s',
'A_s',
'tau_reio',
'h',
'N_ncdm',
'M_ncdm',
'sigma_fog',
'beta0',
'beta1',
'alpha_k2'],
mu_step=mu_integral_step,
skipgen=False)
print("Relic Forecast ", fidx, " complete...")
dill.dump_session(os.path.join(fp_resultsdir, 'fp_'+str(temp_index)+'_'+str(mass_index)+'.db'))
else:
print('Fisher matrix already generated!')
| [
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220,
220,
220,
220,
220,
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220,
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220,
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220,... | 1.25741 | 6,410 |
import os
if __name__ == "__main__":
dims = ["32", "64", "128", "256", "512", "1024", "2048"]
for dim in dims:
os.system(
f"perf stat -e r11 -x, -r 10 ../matmul.out ../data/mat-A-{dim}.txt ../data/mat-B-{dim}.txt ./out/out-{dim}.txt 2>>res_arm.csv"
)
print(f"Finished {dim}")
print("Finished.") | [
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... | 1.965909 | 176 |
# coding: utf-8
# Copyright (c) 2016, 2021, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license.
from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401
from oci.decorators import init_model_state_from_kwargs
| [
2,
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1... | 3.097561 | 164 |
# Copyright (c) 2022, salesforce.com, inc.
# All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
import os
import re
import time
import random
import argparse
import torch
from transformers import GPT2TokenizerFast
from jaxformer.hf.codegen.modeling_codegen import CodeGenForCausalLM
########################################################################
# util
########################################################################
# model
########################################################################
# sample
########################################################################
# main
if __name__ == '__main__':
test_truncate()
main()
print('done.')
| [
2,
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198,
2,
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2... | 4.024155 | 207 |
import unittest
from services import RoverRunnerService
from tests.test_environment.marses import small_mars_with_one_rover_empty_commands
from tests.test_environment import mocks as m
from data_objects import Rover
| [
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92... | 3.677966 | 59 |
import argparse
import warnings
warnings.simplefilter("ignore", UserWarning)
import files
from tensorboardX import SummaryWriter
import os
import numpy as np
import time
import torch
import torch.optim
import torch.nn as nn
import torch.utils.data
import torchvision
import torchvision.transforms as tfs
from data import DataSet,return_model_loader
from util import weight_init, write_conv, setup_runtime, AverageMeter, MovingAverage
if __name__ == "__main__":
args = get_parser().parse_args()
name = "%s" % args.comment.replace('/', '_')
try:
args.device = [int(item) for item in args.device.split(',')]
except AttributeError:
args.device = [int(args.device)]
setup_runtime(seed=42, cuda_dev_id=args.device)
print(args, flush=True)
print()
print(name,flush=True)
writer = SummaryWriter('./runs/%s/%s'%(args.data,name))
writer.add_text('args', " \n".join(['%s %s' % (arg, getattr(args, arg)) for arg in vars(args)]))
# Setup model and train_loader
print('Commencing!', flush=True)
model, train_loader = return_model_loader(args)
train_loader = RotationDataLoader(args.imagenet_path, is_validation=False,
crop_size=224, batch_size=args.batch_size, num_workers=args.workers,
shuffle=True)
# add additional head to the network for RotNet loss.
if args.arch == 'alexnet':
if args.hc == 1:
model.__setattr__("top_layer0", nn.Linear(4096, args.ncl))
model.top_layer = None
model.headcount = args.hc+1
model.__setattr__("top_layer%s" % args.hc, nn.Linear(4096, 4))
else:
if args.hc == 1:
model.__setattr__("top_layer0", nn.Linear(2048*int(args.archspec), args.ncl))
model.top_layer = None
model.headcount = args.hc+1
model.__setattr__("top_layer%s" % args.hc, nn.Linear(2048*int(args.archspec), 4))
if args.init:
for mod in model.modules():
mod.apply(weight_init)
# Setup optimizer
o = Optimizer()
o.writer = writer
o.lr = args.lr
o.num_epochs = args.epochs
o.resume = True
o.log_interval = args.log_interval
o.checkpoint_dir = os.path.join(args.exp, 'checkpoints')
# Optimize
o.optimize(model, train_loader)
| [
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198,
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299,
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355,
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19... | 2.311573 | 1,011 |
# -*- coding: utf-8 -*-
from tests import HangulizeTestCase
from hangulize.langs.vie import Vietnamese
| [
2,
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8181,
377,
1096,
13,
17204,
82,
13,
85,
494,
1330,
23618,
198
] | 2.861111 | 36 |
from mathlibpy.functions import *
import unittest
if __name__ == "__main__":
unittest.main()
| [
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198,
220,
220,
220,
555,
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395,
13,
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3419,
198
] | 2.615385 | 39 |
if __name__ == '__main__':
letters = ["c", "f", "j"]
target = "a"
print(f"Input: letters = {letters}, target = {target}")
print(f"Output: {Solution().nextGreatestLetter(letters, target)}")
| [
198,
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2496,
796,
366,
64,
1,
198,
220,
220,
220,
3601,
7,
69,
1,
205... | 2.555556 | 81 |
import nonebot
from nonebot import on_command
from nonebot.rule import to_me
from nonebot.typing import T_State
from nonebot.adapters import Bot, Event
from nonebot.log import logger
from .config import global_config
from .schedule import anti_cpdaily_check_routine
cpdaily = on_command('cpdaily')
scheduler = nonebot.require("nonebot_plugin_apscheduler").scheduler
| [
11748,
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284,
62,
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309,
62,
9012,
198,
6738,
4844,
13645,
13,
324,
12126,
1330,
18579,
... | 3.3125 | 112 |
"""
============
Dollar Ticks
============
Use a `~.ticker.FormatStrFormatter` to prepend dollar signs on y axis labels.
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
# Fixing random state for reproducibility
np.random.seed(19680801)
fig, ax = plt.subplots()
ax.plot(100*np.random.rand(20))
formatter = ticker.FormatStrFormatter('$%1.2f')
ax.yaxis.set_major_formatter(formatter)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_visible(False)
tick.label2.set_visible(True)
tick.label2.set_color('green')
plt.show()
#############################################################################
#
# ------------
#
# References
# """"""""""
#
# The use of the following functions, methods, classes and modules is shown
# in this example:
import matplotlib
matplotlib.ticker
matplotlib.ticker.FormatStrFormatter
matplotlib.axis.Axis.set_major_formatter
matplotlib.axis.Axis.get_major_ticks
matplotlib.axis.Tick
| [
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198,
25609,
198,
35,
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3378,
198,
25609,
198,
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16488,
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37811,
198,
11748,
299,
32152,
355... | 2.910714 | 336 |
import json
import re
from flask import request, abort, jsonify
from Chibrary import config
from Chibrary.config import logger
from Chibrary.exceptions import *
from functools import wraps
from urllib import parse
from Chibrary.server import db
"""
{
code: ...,
message: ...,
data: ...,
}
"""
# headerAuthorization: {token}
# headerAuthorization: {token}
# request
# def url_check(url: str):
# url = url.lower()
# reg = "^(https|http|ftp|rtsp|mms)\\://?([a-zA-Z0-9\\.\\-]+(\\:[a-zA-Z0-9\\.&%\\$\\-]+)*@)?((25[0-5]|2" \
# "[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]{1}[0-9]{1}|[1-9])\\.(25[0-5]|2[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]" \
# "{1}[0-9]{1}|[1-9]|0)\\.(25[0-5]|2[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]{1}[0-9]{1}|[1-9]|0)\\.(25[0-5]|" \
# "2[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]{1}[0-9]{1}|[0-9])|([a-zA-Z0-9\\-]+\\.)*[a-zA-Z0-9\\-]+\\.[a-zA-Z]" \
# "{2,4})(\\:[0-9]+)?(/[^/][a-zA-Z0-9\\.\\,\\?\\'\\\\/\\+&%\\$\\=~_\\-@]*)*$"
# print(re.search(url, reg))
if __name__ == '__main__':
print(parse_url_query('http://blog.com/sss/ssss/s?wd=dsfa&a=fdsa&a=1&b=1.1&a=s'))
print(format_file_size(20250000))
# print(url_check('http://www.bilibili.com/'))
| [
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1330,
1635,
198,
6738,
1257,... | 1.697931 | 725 |
import numpy as np
from something import Top
i = 0
while i < 10:
a = np.ndarray((10,4))
b = np.ones((10, Top))
i += 1
del Top
# show_store()
| [
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... | 2.279412 | 68 |
from aiohttp_admin2.mappers import Mapper
from aiohttp_admin2.mappers import fields
def test_correct_float_type():
"""
In this test we check success convert to float type.
"""
mapper = FloatMapper({"field": 1})
mapper.is_valid()
assert mapper.data["field"] == 1.0
mapper = FloatMapper({"field": 2})
mapper.is_valid()
assert mapper.data["field"] == 2.0
mapper = FloatMapper({"field": -3})
mapper.is_valid()
assert mapper.data["field"] == -3.0
mapper = FloatMapper({"field": 0})
mapper.is_valid()
assert mapper.data["field"] == 0.0
def test_wrong_float_type():
"""
In this test we check error when we received wrong float type.
"""
assert FloatMapper({"field": "string"}).is_valid() is False
assert FloatMapper({"field": []}).is_valid() is False
| [
6738,
257,
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4023,
62,
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17,
13,
76,
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1330,
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62,
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62,
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62,
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... | 2.623824 | 319 |
"""
Try to load all of the MODFLOW-USG examples in ../examples/data/mfusg_test.
These are the examples that are distributed with MODFLOW-USG.
"""
import os
import flopy
# make the working directory
tpth = os.path.join("temp", "t038")
if not os.path.isdir(tpth):
os.makedirs(tpth)
# build list of name files to try and load
usgpth = os.path.join("..", "examples", "data", "mfusg_test")
usg_files = []
for path, subdirs, files in os.walk(usgpth):
for name in files:
if name.endswith(".nam"):
usg_files.append(os.path.join(path, name))
#
# function to load a MODFLOW-USG model and then write it back out
def load_model(namfile, model_ws):
m = flopy.modflow.Modflow.load(
namfile, model_ws=model_ws, version="mfusg", verbose=True, check=False
)
assert m, f"Could not load namefile {namfile}"
assert m.load_fail is False
m.change_model_ws(tpth)
m.write_input()
return
if __name__ == "__main__":
for fusg in usg_files:
d, f = os.path.split(fusg)
load_model(f, d)
| [
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2... | 2.352018 | 446 |
#!/usr/bin/env python3
import os
from argparse import ArgumentParser, ArgumentTypeError, FileType, Namespace
from typing import Any
| [
2,
48443,
14629,
14,
8800,
14,
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11,
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198,
6738,
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4377,
628,
198
] | 3.722222 | 36 |
from typing import Optional
from asn1crypto import core, x509, cms
__all__ = [
'Target', 'TargetCert', 'Targets', 'SequenceOfTargets',
'AttrSpec', 'AAControls'
]
# Blame X.509...
def _make_tag_explicit(field_decl):
tag_dict = field_decl[2]
if 'explicit' in tag_dict:
return
tag_dict['explicit'] = tag_dict['implicit']
del tag_dict['implicit']
def _make_tag_implicit(field_decl):
tag_dict = field_decl[2]
if 'implicit' in tag_dict:
return
tag_dict['implicit'] = tag_dict['explicit']
del tag_dict['explicit']
# Deal with wbond/asn1crypto#218
_make_tag_explicit(cms.RoleSyntax._fields[1])
_make_tag_explicit(cms.SecurityCategory._fields[1])
# Deal with wbond/asn1crypto#220
_make_tag_implicit(cms.AttCertIssuer._alternatives[1])
# patch in attribute certificate extensions
# Note: unlike in Certomancer, we don't do this one conditionally, since
# we need the actual Python types to agree with what we export
ext_map = x509.ExtensionId._map
ext_specs = x509.Extension._oid_specs
ext_map['2.5.29.55'] = 'target_information'
ext_specs['target_information'] = SequenceOfTargets
ext_map['2.5.29.56'] = 'no_rev_avail'
ext_specs['no_rev_avail'] = core.Null
ext_map['1.3.6.1.5.5.7.1.6'] = 'aa_controls'
ext_specs['aa_controls'] = AAControls
ext_map['1.3.6.1.5.5.7.1.4'] = 'audit_identity'
ext_specs['audit_identity'] = core.OctetString
| [
6738,
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1330,
32233,
198,
198,
6738,
355,
77,
16,
29609,
78,
1330,
4755,
11,
2124,
29022,
11,
269,
907,
198,
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834,
439,
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220,
220,
705,
21745,
3256,
705,
21745,
37608,
3256,
705,
51,
853,
1039,
3256,... | 2.408935 | 582 |
from model.group import Group
import random
| [
6738,
2746,
13,
8094,
1330,
4912,
198,
11748,
4738,
198
] | 4.4 | 10 |
'''
Autor: Gurkirt Singh
Start data: 15th May 2016
purpose: of this file is read frame level predictions and process them to produce a label per video
'''
from sklearn.svm import LinearSVC
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import pickle
import os
import time,json
import pylab as plt
from eval_detection import ANETdetection
import scipy.io as sio
#######baseDir = "/mnt/sun-alpha/actnet/";
baseDir = "/data/shared/solar-machines/actnet/";
#baseDir = "/mnt/solar-machines/actnet/";
########imgDir = "/mnt/sun-alpha/actnet/rgb-images/";
######## imgDir = "/mnt/DATADISK2/ss-workspace/actnet/rgb-images/";
annotPklFile = "../Evaluation/data/actNet200-V1-3.pkl"
if __name__=="__main__":
#processOnePredictions()
# saveAps()
# plotmAPs()
evalALL()
| [
7061,
6,
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273,
25,
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606,
284,
4439,
257,
6167,
583,
2008,
198,
198,
7061,
6,
198... | 2.640523 | 306 |
import csv
if __name__ == '__main__':
data = '''book_title,author,publisher,pub_date,isbn
Python 101,Mike Driscoll, Mike Driscoll,2020,123456789
wxPython Recipes,Mike Driscoll,Apress,2018,978-1-4842-3237-8
Python Interviews,Mike Driscoll,Packt Publishing,2018,9781788399081'''
records = []
for line in data.splitlines():
records.append(line.strip().split(','))
headers = records.pop(0)
list_of_dicts = []
for row in records:
my_dict = dict(zip(headers, row))
list_of_dicts.append(my_dict)
csv_dict_writer('output_dict.csv', headers, list_of_dicts)
| [
11748,
269,
21370,
628,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1366,
796,
705,
7061,
2070,
62,
7839,
11,
9800,
11,
12984,
8191,
11,
12984,
62,
4475,
11,
271,
9374,
198,
220,
220,
220,... | 2.382239 | 259 |
# SPDX-FileCopyrightText: 2017 Fermi Research Alliance, LLC
# SPDX-License-Identifier: Apache-2.0
import pytest
from decisionengine.framework.modules import Publisher, Source
from decisionengine.framework.modules.Module import verify_products
from decisionengine.framework.modules.Source import Parameter
| [
2,
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55,
12,
8979,
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25,
2177,
376,
7780,
72,
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10302,
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2,
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198,
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12972,
9288,
198,
198,
6738,
2551,
18392,
13,
30604,
... | 3.949367 | 79 |
import torch
import torch.nn as nn
from torch.nn.functional import max_pool1d
from utility.model_parameter import Configuration, ModelParameter
| [
11748,
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28034,
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62,
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11,
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36301,
628
] | 3.842105 | 38 |
'''
<xs:complexType name="backup">
<xs:annotation>
<xs:documentation></xs:documentation>
</xs:annotation>
<xs:sequence>
<xs:group ref="duration"/>
<xs:group ref="editorial"/>
</xs:sequence>
</xs:complexType>
'''
from musicscore.dtd.dtd import Sequence, GroupReference, Element
from musicscore.musicxml.groups.common import Editorial
from musicscore.musicxml.elements.note import Duration
from musicscore.musicxml.types.complextypes.complextype import ComplexType
| [
7061,
6,
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197,
27,
34223,
25,
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6030,
1438,
2625,
1891,
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198,
197,
197,
27,
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25,
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29,
198,
197,
197,
197,
27,
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25,
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341,
12240,
34223,
25,
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341,
29,
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197,
197,
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... | 2.841176 | 170 |
import nltk
import re
import csv
import string
import collections
import numpy as np
from nltk.corpus import wordnet
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import WordPunctTokenizer
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
""""Pre - Processing: tokenization, stopwords removal, remove words(with size 1), lower capitalization & lemmatization"""
""""Read Data"""
# Open sms corpus.
sms_file = open('SMSSpamCollection.txt', encoding="utf8") # Check the structure of this file!
sms_data = []
sms_labels = []
# CSV Reader LABEL & DATA are separated by TAB.
csv_reader = csv.reader(sms_file,delimiter='\t')
# Store labels and data.
for line in csv_reader:
sms_text = preprocessing(line[1])
if ( sms_text != None):
# adding the sms_id
sms_labels.append( line[0])
# adding the cleaned text We are calling preprocessing method
sms_data.append(sms_text)
sms_file.close()
"""Sampling steps (70:30)"""
trainset_size = int(round(len(sms_data)*0.70))
# I chose this threshold for 70:30 train and test split.
print('The training set size for this classifier is ' + str(trainset_size) + '\n')
x_train = np.array([''.join(el) for el in sms_data[0:trainset_size]]) # train sms_data (70%).
y_train = np.array([el for el in sms_labels[0:trainset_size]]) # train sms_labels (70%).
x_test = np.array([''.join(el) for el in sms_data[trainset_size+1:len(sms_data)]]) # test sms_data (30%).
y_test = np.array([el for el in sms_labels[trainset_size+1:len(sms_labels)]]) # test sms_labels (30%).
"""We are building a TFIDF vectorizer here"""
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(min_df=2, ngram_range=(1, 2), stop_words='english', strip_accents='unicode', norm='l2')
X_train = vectorizer.fit_transform(x_train)
X_test = vectorizer.transform(x_test)
"""Text Clustering - K Means"""
from sklearn.cluster import KMeans, MiniBatchKMeans
print('--> Text Clustering - K Means')
true_k = 5
km = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1)
kmini = MiniBatchKMeans(n_clusters=true_k, init='k-means++', n_init=1, init_size=1000, batch_size=1000, verbose=False) #verbose=opts.verbose
# we are using the same test,train data in TFIDF form as we did in text classification
km_model = km.fit(X_train)
print("For K-mean clustering ")
clustering = collections.defaultdict(list)
for idx, label in enumerate(km_model.labels_):
clustering[label].append(idx)
print(clustering)
kmini_model = kmini.fit(X_train)
print("For K-mean Mini batch clustering ")
clustering = collections.defaultdict(list)
for idx, label in enumerate(kmini_model.labels_):
clustering[label].append(idx)
print(clustering)
| [
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201,
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198,
6738,
299,
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74,
13,
10215,
79,
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1573,
... | 2.538124 | 1,141 |
import torch
import numpy as np
import hashlib
from torch.autograd import Variable
import os
| [
11748,
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220,
220,
220,
220,
220,
220,
220,
220,
628,
628,
628,
628,
628... | 2.813953 | 43 |
from ibm_watson import TextToSpeechV1, SpeechToTextV1, DetailedResponse
from os import system
from json import loads
if __name__ == '__main__':
main()
| [
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5248,
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628,
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198,
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... | 2.962963 | 54 |
from subprocess import run
| [
6738,
850,
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1330,
1057,
628
] | 4.666667 | 6 |
# Generated by Django 2.1.7 on 2019-02-23 23:45
from django.db import migrations
| [
2,
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13,
22,
319,
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12,
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198,
198,
6738,
42625,
14208,
13,
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1330,
15720,
602,
628
] | 2.766667 | 30 |
from . import (emoji as emj,
keyboards as kb,
telegram as tg,
phrases as phr,
finance as fin,
utils,
glossary,
bots,
gcp,
sed,
db)
| [
6738,
764,
1330,
357,
368,
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355,
795,
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220,
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220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
573... | 1.448454 | 194 |
#!/usr/bin/env python
# -*- coding: utf-8
"""
:mod:`question.serializers` -- serializers
"""
from rest_framework import serializers
from .models import Question, PossibleAnswer
from category.models import Category
| [
2,
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14,
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9,
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13... | 3.421875 | 64 |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'src/ui_ShowResultDialog.ui'
#
# Created: Sat May 16 17:05:43 2015
# by: PyQt5 UI code generator 5.4
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
from widgets.ImageLabel import ImageLabel
| [
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2,
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2,
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25,
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1737,
1467,
1596,
25... | 2.852174 | 115 |
"""
mixcoatl.admin.api_key
----------------------
Implements access to the DCM ApiKey API
"""
from mixcoatl.resource import Resource
from mixcoatl.decorators.lazy import lazy_property
from mixcoatl.decorators.validations import required_attrs
from mixcoatl.utils import uncamel, camelize, camel_keys, uncamel_keys
import json
| [
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5... | 3.31 | 100 |
my_set = {1, 3, 5}
my_dict = {'name': 'Jose', 'age': 90}
another_dict = {1: 15, 2: 75, 3: 150}
lottery_players = [
{
'name': 'Rolf',
'numbers': (13, 45, 66, 23, 22)
},
{
'name': 'John',
'numbers': (14, 56, 80, 23, 22)
}
]
universities = [
{
'name': 'Oxford',
'location': 'UK'
},
{
'name': 'MIT',
'location': 'US'
}
] | [
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62,
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6... | 1.746835 | 237 |
"""Class :py:class:`QWTable` is a QTableView->QWidget for tree model
======================================================================
Usage ::
# Run test: python lcls2/psdaq/psdaq/control_gui/QWTable.py
from psdaq.control_gui.QWTable import QWTable
w = QWTable()
Created on 2019-03-28 by Mikhail Dubrovin
Re-designed after copy psana/graphqt/QWTable.py -> psdaq/control_gui/
"""
import logging
logger = logging.getLogger(__name__)
from PyQt5.QtWidgets import QTableView, QVBoxLayout, QAbstractItemView, QSizePolicy
from PyQt5.QtGui import QStandardItemModel, QStandardItem
from PyQt5.QtCore import Qt, QModelIndex
from psdaq.control_gui.QWIcons import icon
if __name__ == '__main__':
import sys
from PyQt5.QtWidgets import QApplication
logging.basicConfig(format='%(asctime)s %(name)s %(levelname)s: %(message)s', datefmt='%H:%M:%S', level=logging.DEBUG)
app = QApplication(sys.argv)
w = QWTable()
#w.setGeometry(100, 100, 700, 300)
w.setWindowTitle('QWTable')
w.move(100,50)
w.show()
app.exec_()
del w
del app
# EOF
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8... | 2.569087 | 427 |
"""Extension loader for filetype handlers.
The extension objects provided by MIMEExtensionLoader objects have four
attributes: parse, embed, add_options, and update_options. The first two
are used as handlers for supporting the MIME type as primary and embeded
resources. The last two are (currently) only used for printing.
"""
__version__ = '$Revision: 2.4 $'
from . import extloader
import string
| [
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... | 3.803738 | 107 |
# coding: utf-8
# (C) Copyright IBM Corp. 2021.
#
# 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.
# IBM OpenAPI SDK Code Generator Version: 3.25.0-2b3f843a-20210115-164628
"""
The administration REST API for IBM Event Streams on Cloud.
"""
from typing import Dict, List
import json
from ibm_cloud_sdk_core import BaseService, DetailedResponse
from ibm_cloud_sdk_core.authenticators.authenticator import Authenticator
from ibm_cloud_sdk_core.get_authenticator import get_authenticator_from_environment
from ibm_cloud_sdk_core.utils import convert_model
from .common import get_sdk_headers
##############################################################################
# Service
##############################################################################
##############################################################################
# Models
##############################################################################
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this ReplicaAssignmentBrokers object."""
return json.dumps(self.to_dict(), indent=2)
def __eq__(self, other: 'ReplicaAssignmentBrokers') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'ReplicaAssignmentBrokers') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
class ConfigCreate():
"""
ConfigCreate.
:attr str name: (optional) The name of the config property.
:attr str value: (optional) The value for a config property.
"""
def __init__(self,
*,
name: str = None,
value: str = None) -> None:
"""
Initialize a ConfigCreate object.
:param str name: (optional) The name of the config property.
:param str value: (optional) The value for a config property.
"""
self.name = name
self.value = value
def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'value') and self.value is not None:
_dict['value'] = self.value
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this ConfigCreate object."""
return json.dumps(self.to_dict(), indent=2)
def __eq__(self, other: 'ConfigCreate') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'ConfigCreate') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
class ConfigUpdate():
"""
ConfigUpdate.
:attr str name: (optional) The name of the config property.
:attr str value: (optional) The value for a config property.
:attr bool reset_to_default: (optional) When true, the value of the config
property is reset to its default value.
"""
def __init__(self,
*,
name: str = None,
value: str = None,
reset_to_default: bool = None) -> None:
"""
Initialize a ConfigUpdate object.
:param str name: (optional) The name of the config property.
:param str value: (optional) The value for a config property.
:param bool reset_to_default: (optional) When true, the value of the config
property is reset to its default value.
"""
self.name = name
self.value = value
self.reset_to_default = reset_to_default
def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'value') and self.value is not None:
_dict['value'] = self.value
if hasattr(self, 'reset_to_default') and self.reset_to_default is not None:
_dict['reset_to_default'] = self.reset_to_default
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this ConfigUpdate object."""
return json.dumps(self.to_dict(), indent=2)
def __eq__(self, other: 'ConfigUpdate') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'ConfigUpdate') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
class MirroringActiveTopics():
"""
Topics that are being actively mirrored.
:attr List[str] active_topics: (optional)
"""
def __init__(self,
*,
active_topics: List[str] = None) -> None:
"""
Initialize a MirroringActiveTopics object.
:param List[str] active_topics: (optional)
"""
self.active_topics = active_topics
def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'active_topics') and self.active_topics is not None:
_dict['active_topics'] = self.active_topics
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MirroringActiveTopics object."""
return json.dumps(self.to_dict(), indent=2)
def __eq__(self, other: 'MirroringActiveTopics') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MirroringActiveTopics') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
class MirroringTopicSelection():
"""
Mirroring topic selection payload.
:attr List[str] includes: (optional)
"""
def __init__(self,
*,
includes: List[str] = None) -> None:
"""
Initialize a MirroringTopicSelection object.
:param List[str] includes: (optional)
"""
self.includes = includes
def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'includes') and self.includes is not None:
_dict['includes'] = self.includes
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this MirroringTopicSelection object."""
return json.dumps(self.to_dict(), indent=2)
def __eq__(self, other: 'MirroringTopicSelection') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'MirroringTopicSelection') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
class ReplicaAssignment():
"""
ReplicaAssignment.
:attr int id: (optional) The ID of the partition.
:attr ReplicaAssignmentBrokers brokers: (optional)
"""
def __init__(self,
*,
id: int = None,
brokers: 'ReplicaAssignmentBrokers' = None) -> None:
"""
Initialize a ReplicaAssignment object.
:param int id: (optional) The ID of the partition.
:param ReplicaAssignmentBrokers brokers: (optional)
"""
self.id = id
self.brokers = brokers
def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'id') and self.id is not None:
_dict['id'] = self.id
if hasattr(self, 'brokers') and self.brokers is not None:
_dict['brokers'] = self.brokers.to_dict()
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this ReplicaAssignment object."""
return json.dumps(self.to_dict(), indent=2)
def __eq__(self, other: 'ReplicaAssignment') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'ReplicaAssignment') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
class TopicConfigs():
"""
TopicConfigs.
:attr str cleanup_policy: (optional) The value of config property
'cleanup.policy'.
:attr str min_insync_replicas: (optional) The value of config property
'min.insync.replicas'.
:attr str retention_bytes: (optional) The value of config property
'retention.bytes'.
:attr str retention_ms: (optional) The value of config property 'retention.ms'.
:attr str segment_bytes: (optional) The value of config property
'segment.bytes'.
:attr str segment_index_bytes: (optional) The value of config property
'segment.index.bytes'.
:attr str segment_ms: (optional) The value of config property 'segment.ms'.
"""
def __init__(self,
*,
cleanup_policy: str = None,
min_insync_replicas: str = None,
retention_bytes: str = None,
retention_ms: str = None,
segment_bytes: str = None,
segment_index_bytes: str = None,
segment_ms: str = None) -> None:
"""
Initialize a TopicConfigs object.
:param str cleanup_policy: (optional) The value of config property
'cleanup.policy'.
:param str min_insync_replicas: (optional) The value of config property
'min.insync.replicas'.
:param str retention_bytes: (optional) The value of config property
'retention.bytes'.
:param str retention_ms: (optional) The value of config property
'retention.ms'.
:param str segment_bytes: (optional) The value of config property
'segment.bytes'.
:param str segment_index_bytes: (optional) The value of config property
'segment.index.bytes'.
:param str segment_ms: (optional) The value of config property
'segment.ms'.
"""
self.cleanup_policy = cleanup_policy
self.min_insync_replicas = min_insync_replicas
self.retention_bytes = retention_bytes
self.retention_ms = retention_ms
self.segment_bytes = segment_bytes
self.segment_index_bytes = segment_index_bytes
self.segment_ms = segment_ms
def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'cleanup_policy') and self.cleanup_policy is not None:
_dict['cleanup.policy'] = self.cleanup_policy
if hasattr(self, 'min_insync_replicas') and self.min_insync_replicas is not None:
_dict['min.insync.replicas'] = self.min_insync_replicas
if hasattr(self, 'retention_bytes') and self.retention_bytes is not None:
_dict['retention.bytes'] = self.retention_bytes
if hasattr(self, 'retention_ms') and self.retention_ms is not None:
_dict['retention.ms'] = self.retention_ms
if hasattr(self, 'segment_bytes') and self.segment_bytes is not None:
_dict['segment.bytes'] = self.segment_bytes
if hasattr(self, 'segment_index_bytes') and self.segment_index_bytes is not None:
_dict['segment.index.bytes'] = self.segment_index_bytes
if hasattr(self, 'segment_ms') and self.segment_ms is not None:
_dict['segment.ms'] = self.segment_ms
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this TopicConfigs object."""
return json.dumps(self.to_dict(), indent=2)
def __eq__(self, other: 'TopicConfigs') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'TopicConfigs') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
class TopicDetail():
"""
TopicDetail.
:attr str name: (optional) The name of the topic.
:attr int partitions: (optional) The number of partitions.
:attr int replication_factor: (optional) The number of replication factor.
:attr int retention_ms: (optional) The value of config property 'retention.ms'.
:attr str cleanup_policy: (optional) The value of config property
'cleanup.policy'.
:attr TopicConfigs configs: (optional)
:attr List[ReplicaAssignment] replica_assignments: (optional) The replia
assignment of the topic.
"""
def __init__(self,
*,
name: str = None,
partitions: int = None,
replication_factor: int = None,
retention_ms: int = None,
cleanup_policy: str = None,
configs: 'TopicConfigs' = None,
replica_assignments: List['ReplicaAssignment'] = None) -> None:
"""
Initialize a TopicDetail object.
:param str name: (optional) The name of the topic.
:param int partitions: (optional) The number of partitions.
:param int replication_factor: (optional) The number of replication factor.
:param int retention_ms: (optional) The value of config property
'retention.ms'.
:param str cleanup_policy: (optional) The value of config property
'cleanup.policy'.
:param TopicConfigs configs: (optional)
:param List[ReplicaAssignment] replica_assignments: (optional) The replia
assignment of the topic.
"""
self.name = name
self.partitions = partitions
self.replication_factor = replication_factor
self.retention_ms = retention_ms
self.cleanup_policy = cleanup_policy
self.configs = configs
self.replica_assignments = replica_assignments
def to_dict(self) -> Dict:
"""Return a json dictionary representing this model."""
_dict = {}
if hasattr(self, 'name') and self.name is not None:
_dict['name'] = self.name
if hasattr(self, 'partitions') and self.partitions is not None:
_dict['partitions'] = self.partitions
if hasattr(self, 'replication_factor') and self.replication_factor is not None:
_dict['replicationFactor'] = self.replication_factor
if hasattr(self, 'retention_ms') and self.retention_ms is not None:
_dict['retentionMs'] = self.retention_ms
if hasattr(self, 'cleanup_policy') and self.cleanup_policy is not None:
_dict['cleanupPolicy'] = self.cleanup_policy
if hasattr(self, 'configs') and self.configs is not None:
_dict['configs'] = self.configs.to_dict()
if hasattr(self, 'replica_assignments') and self.replica_assignments is not None:
_dict['replicaAssignments'] = [x.to_dict() for x in self.replica_assignments]
return _dict
def _to_dict(self):
"""Return a json dictionary representing this model."""
return self.to_dict()
def __str__(self) -> str:
"""Return a `str` version of this TopicDetail object."""
return json.dumps(self.to_dict(), indent=2)
def __eq__(self, other: 'TopicDetail') -> bool:
"""Return `true` when self and other are equal, false otherwise."""
if not isinstance(other, self.__class__):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other: 'TopicDetail') -> bool:
"""Return `true` when self and other are not equal, false otherwise."""
return not self == other
| [
2,
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2,
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8,
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366,
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15341,
198,
2,
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743,
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428,
... | 2.512379 | 7,149 |
import functions
from pytest import approx
from bcca.test import should_print
def test_min_insurance():
assert functions.min_insurance(100000) == approx(80000.0)
assert functions.min_insurance(123456789) == approx(98765431.2)
assert functions.min_insurance(0) == approx(0.0)
assert functions.min_insurance(-54317890) == approx(-43454312.0)
def test_bmi():
assert functions.bmi(160, 67) == approx(25.05680552)
assert functions.bmi(200, 72) == approx(27.12191358)
assert functions.bmi(120, 60) == approx(23.43333333)
def test_calories():
assert functions.calories(5, 20) == 125
assert functions.calories(1, 1) == 13
def test_earnings():
assert functions.earnings(100, 100, 100) == 3600
assert functions.earnings(50, 75, 100) == 2550
assert functions.earnings(0, 1000, 79) == 12711
| [
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38... | 2.686709 | 316 |
from django.contrib import admin
from .models import Product
admin.site.register(Product) | [
6738,
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13,
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764,
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13,
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] | 3.75 | 24 |
# coding=utf-8
from __future__ import unicode_literals
from .base import BasePlugin
| [
2,
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] | 3.307692 | 26 |
#!/usr/bin/env python3
# Project: VUT FIT SUI Project - Dice Wars
# Authors:
# - Josef Kol <xkolar71@stud.fit.vutbr.cz>
# - Dominik Harmim <xharmi00@stud.fit.vutbr.cz>
# - Petr Kapoun <xkapou04@stud.fit.vutbr.cz>
# - Jindich estk <xsesta05@stud.fit.vutbr.cz>
# Year: 2020
# Description: Generates game configurations.
import random
import sys
from argparse import ArgumentParser
import time
from signal import signal, SIGCHLD
from utils import run_ai_only_game, BoardDefinition
parser = ArgumentParser(prog='Dice_Wars')
parser.add_argument('-p', '--port', help="Server port", type=int, default=5005)
parser.add_argument('-a', '--address', help="Server address", default='127.0.0.1')
procs = []
def signal_handler():
""" Handler for SIGCHLD signal that terminates server and clients. """
for p in procs:
try:
p.kill()
except ProcessLookupError:
pass
PLAYING_AIs = [
'xkolar71_orig',
'xkolar71_2',
'xkolar71_3',
'xkolar71_4',
]
if __name__ == '__main__':
main()
| [
2,
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14,
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2,
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532,
5264,
69,
25910,
220,
220,
220,
220,
220,
1279,
87,
74,
... | 2.402715 | 442 |
import functools
import sys
from contextlib import contextmanager
import pytest
_orig_trace = None
# if _orig_trace and not hasattr(sys, "pypy_version_info"):
# Fails with PyPy2 (https://travis-ci.org/antocuni/pdb/jobs/509624590)?!
| [
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175... | 2.711111 | 90 |
""" Various generic env utilties. """
def center_crop_img(img, crop_zoom):
""" crop_zoom is amount to "zoom" into the image. E.g. 2.0 would cut out half of the width,
half of the height, and only give the center. """
raw_height, raw_width = img.shape[:2]
center = raw_height // 2, raw_width // 2
crop_size = raw_height // crop_zoom, raw_width // crop_zoom
min_y, max_y = int(center[0] - crop_size[0] // 2), int(center[0] + crop_size[0] // 2)
min_x, max_x = int(center[1] - crop_size[1] // 2), int(center[1] + crop_size[1] // 2)
img_cropped = img[min_y:max_y, min_x:max_x]
return img_cropped
def crop_img(img, relative_corners):
""" relative_corners are floats between 0 and 1 designating where the corners of a crop box
should be ([[top_left_x, top_left_y], [bottom_right_x, bottom_right_y]]).
e.g. [[0, 0], [1, 1]] would be the full image, [[0.5, 0.5], [1, 1]] would be bottom right."""
rc = relative_corners
raw_height, raw_width = img.shape[:2]
top_left_pix = [int(rc[0][0] * raw_width), int(rc[0][1] * raw_height)]
bottom_right_pix = [int(rc[1][0] * raw_width), int(rc[1][1] * raw_height)]
img_cropped = img[top_left_pix[1]:bottom_right_pix[1], top_left_pix[0]:bottom_right_pix[0]]
return img_cropped | [
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1,
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... | 2.377323 | 538 |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
import mock
from exam import fixture
from sentry import options
from sentry.models import Project
from sentry.testutils import TestCase
from sentry.utils.http import (
is_same_domain, is_valid_origin, get_origins, absolute_uri, is_valid_ip,
)
| [
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5... | 3.067308 | 104 |
# -*- coding: utf-8 -*-
"""WebHelpers used in project."""
#from webhelpers import date, feedgenerator, html, number, misc, text
from markupsafe import Markup | [
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... | 3 | 53 |
import sys;
from Thesis.load.loadBenchmark import runLoadBenchmarkAsBatch;
from Thesis.cluster.RiakCluster import RiakCluster;
NORMAL_BINDING = 'riak';
CONSISTENCY_BINDING = 'riak_consistency';
IPS_IN_CLUSTER = ['172.16.33.14', '172.16.33.15', '172.16.33.16', '172.16.33.17', '172.16.33.18'];
cluster = RiakCluster(NORMAL_BINDING, CONSISTENCY_BINDING, IPS_IN_CLUSTER);
runLoadBenchmarkAsBatch(cluster, ['172.16.33.10'], '/root/YCSB/workloads/workload_load',
3, '/root/YCSB/loads/riak',
['1000000000'], ['1'], ['1']);
# main(); | [
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... | 2.071429 | 280 |
# coding: utf-8
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this file,
# You can obtain one at http://mozilla.org/MPL/2.0/.
import unittest
from mock import patch
from auto_nag.people import People
from auto_nag.round_robin import BadFallback, RoundRobin
| [
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... | 3.211009 | 109 |
# should re-write compiled functions to take a local and global dict
# as input.
from __future__ import absolute_import, print_function
import sys
import os
from . import ext_tools
from . import catalog
from . import common_info
from numpy.core.multiarray import _get_ndarray_c_version
ndarray_api_version = '/* NDARRAY API VERSION %x */' % (_get_ndarray_c_version(),)
# not an easy way for the user_path_list to come in here.
# the PYTHONCOMPILED environment variable offers the most hope.
function_catalog = catalog.catalog()
function_cache = {}
def inline(code,arg_names=[],local_dict=None, global_dict=None,
force=0,
compiler='',
verbose=0,
support_code=None,
headers=[],
customize=None,
type_converters=None,
auto_downcast=1,
newarr_converter=0,
**kw):
"""
Inline C/C++ code within Python scripts.
``inline()`` compiles and executes C/C++ code on the fly. Variables
in the local and global Python scope are also available in the
C/C++ code. Values are passed to the C/C++ code by assignment
much like variables passed are passed into a standard Python
function. Values are returned from the C/C++ code through a
special argument called return_val. Also, the contents of
mutable objects can be changed within the C/C++ code and the
changes remain after the C code exits and returns to Python.
inline has quite a few options as listed below. Also, the keyword
arguments for distutils extension modules are accepted to
specify extra information needed for compiling.
Parameters
----------
code : string
A string of valid C++ code. It should not specify a return
statement. Instead it should assign results that need to be
returned to Python in the `return_val`.
arg_names : [str], optional
A list of Python variable names that should be transferred from
Python into the C/C++ code. It defaults to an empty string.
local_dict : dict, optional
If specified, it is a dictionary of values that should be used as
the local scope for the C/C++ code. If local_dict is not
specified the local dictionary of the calling function is used.
global_dict : dict, optional
If specified, it is a dictionary of values that should be used as
the global scope for the C/C++ code. If `global_dict` is not
specified, the global dictionary of the calling function is used.
force : {0, 1}, optional
If 1, the C++ code is compiled every time inline is called. This
is really only useful for debugging, and probably only useful if
your editing `support_code` a lot.
compiler : str, optional
The name of compiler to use when compiling. On windows, it
understands 'msvc' and 'gcc' as well as all the compiler names
understood by distutils. On Unix, it'll only understand the
values understood by distutils. (I should add 'gcc' though to
this).
On windows, the compiler defaults to the Microsoft C++ compiler.
If this isn't available, it looks for mingw32 (the gcc compiler).
On Unix, it'll probably use the same compiler that was used when
compiling Python. Cygwin's behavior should be similar.
verbose : {0,1,2}, optional
Specifies how much information is printed during the compile
phase of inlining code. 0 is silent (except on windows with msvc
where it still prints some garbage). 1 informs you when compiling
starts, finishes, and how long it took. 2 prints out the command
lines for the compilation process and can be useful if your having
problems getting code to work. Its handy for finding the name of
the .cpp file if you need to examine it. verbose has no effect if
the compilation isn't necessary.
support_code : str, optional
A string of valid C++ code declaring extra code that might be
needed by your compiled function. This could be declarations of
functions, classes, or structures.
headers : [str], optional
A list of strings specifying header files to use when compiling
the code. The list might look like ``["<vector>","'my_header'"]``.
Note that the header strings need to be in a form than can be
pasted at the end of a ``#include`` statement in the C++ code.
customize : base_info.custom_info, optional
An alternative way to specify `support_code`, `headers`, etc. needed
by the function. See :mod:`scipy.weave.base_info` for more
details. (not sure this'll be used much).
type_converters : [type converters], optional
These guys are what convert Python data types to C/C++ data types.
If you'd like to use a different set of type conversions than the
default, specify them here. Look in the type conversions section
of the main documentation for examples.
auto_downcast : {1,0}, optional
This only affects functions that have numpy arrays as input
variables. Setting this to 1 will cause all floating point values
to be cast as float instead of double if all the Numeric arrays
are of type float. If even one of the arrays has type double or
double complex, all variables maintain their standard
types.
newarr_converter : int, optional
Unused.
Other Parameters
----------------
Relevant :mod:`distutils` keywords. These are duplicated from Greg Ward's
:class:`distutils.extension.Extension` class for convenience:
sources : [string]
List of source filenames, relative to the distribution root
(where the setup script lives), in Unix form (slash-separated)
for portability. Source files may be C, C++, SWIG (.i),
platform-specific resource files, or whatever else is recognized
by the "build_ext" command as source for a Python extension.
.. note:: The `module_path` file is always appended to the front of
this list
include_dirs : [string]
List of directories to search for C/C++ header files (in Unix
form for portability).
define_macros : [(name : string, value : string|None)]
List of macros to define; each macro is defined using a 2-tuple,
where 'value' is either the string to define it to or None to
define it without a particular value (equivalent of "#define
FOO" in source or -DFOO on Unix C compiler command line).
undef_macros : [string]
List of macros to undefine explicitly.
library_dirs : [string]
List of directories to search for C/C++ libraries at link time.
libraries : [string]
List of library names (not filenames or paths) to link against.
runtime_library_dirs : [string]
List of directories to search for C/C++ libraries at run time
(for shared extensions, this is when the extension is loaded).
extra_objects : [string]
List of extra files to link with (e.g. object files not implied
by 'sources', static libraries that must be explicitly specified,
binary resource files, etc.)
extra_compile_args : [string]
Any extra platform- and compiler-specific information to use
when compiling the source files in 'sources'. For platforms and
compilers where "command line" makes sense, this is typically a
list of command-line arguments, but for other platforms it could
be anything.
extra_link_args : [string]
Any extra platform- and compiler-specific information to use
when linking object files together to create the extension (or
to create a new static Python interpreter). Similar
interpretation as for 'extra_compile_args'.
export_symbols : [string]
List of symbols to be exported from a shared extension. Not
used on all platforms, and not generally necessary for Python
extensions, which typically export exactly one symbol: "init" +
extension_name.
swig_opts : [string]
Any extra options to pass to SWIG if a source file has the .i
extension.
depends : [string]
List of files that the extension depends on.
language : string
Extension language (i.e. "c", "c++", "objc"). Will be detected
from the source extensions if not provided.
See Also
--------
distutils.extension.Extension : Describes additional parameters.
"""
# this grabs the local variables from the *previous* call
# frame -- that is the locals from the function that called
# inline.
global function_catalog
call_frame = sys._getframe().f_back
if local_dict is None:
local_dict = call_frame.f_locals
if global_dict is None:
global_dict = call_frame.f_globals
if force:
module_dir = global_dict.get('__file__',None)
func = compile_function(code,arg_names,local_dict,
global_dict,module_dir,
compiler=compiler,
verbose=verbose,
support_code=support_code,
headers=headers,
customize=customize,
type_converters=type_converters,
auto_downcast=auto_downcast,
**kw)
function_catalog.add_function(code,func,module_dir)
results = attempt_function_call(code,local_dict,global_dict)
else:
# 1. try local cache
try:
results = apply(function_cache[code],(local_dict,global_dict))
return results
except TypeError as msg:
msg = str(msg).strip()
if msg[:16] == "Conversion Error":
pass
else:
raise TypeError(msg)
except NameError as msg:
msg = str(msg).strip()
if msg[:16] == "Conversion Error":
pass
else:
raise NameError(msg)
except KeyError:
pass
# 2. try function catalog
try:
results = attempt_function_call(code,local_dict,global_dict)
# 3. build the function
except ValueError:
# compile the library
module_dir = global_dict.get('__file__',None)
func = compile_function(code,arg_names,local_dict,
global_dict,module_dir,
compiler=compiler,
verbose=verbose,
support_code=support_code,
headers=headers,
customize=customize,
type_converters=type_converters,
auto_downcast=auto_downcast,
**kw)
function_catalog.add_function(code,func,module_dir)
results = attempt_function_call(code,local_dict,global_dict)
return results
| [
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1... | 2.577616 | 4,387 |
# Copyright 2015 Tesora Inc.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import abc
import os
import re
import six
from trove.guestagent.common import guestagent_utils
from trove.guestagent.common import operating_system
from trove.guestagent.common.operating_system import FileMode
| [
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... | 3.436735 | 245 |
import pathlib
import yaml
documentations = {"Our Platform": "QuantConnect-Platform-2.0.0.yaml",
"Alpha Streams": "QuantConnect-Alpha-0.8.yaml"}
for section, source in documentations.items():
yaml_file = open(source)
doc = yaml.load(yaml_file, Loader=yaml.Loader)
paths = doc["paths"]
for api_call, result in paths.items():
j = 1
content = result["post"] if "post" in result else result["get"]
# Create path if not exist
destination_folder = pathlib.Path("/".join(content["tags"]))
destination_folder.mkdir(parents=True, exist_ok=True)
# Create Introduction part
with open(destination_folder / f'{j:02} Introduction.html', "w") as html_file:
html_file.write("<p>\n")
html_file.write(f"{content['summary']}\n")
html_file.write("</p>\n")
j += 1
# Create Description part if having one
if "description" in content:
with open(destination_folder / f'{j:02} Description.html', "w") as html_file:
html_file.write('<p>\n')
html_file.write(f'{content["description"]}\n')
html_file.write('</p>\n')
j += 1
# Create Request part
with open(destination_folder / f'{j:02} Request.html', "w") as html_file:
description_ = ""
if "parameters" in content:
writeUp = RequestTable(api_call, content["parameters"])
elif "requestBody" in content:
if "description" in content["requestBody"]:
description_ = str(content["requestBody"]["description"])
if description_[-1] != ".":
description_ += "."
description_ += " "
writeUp = ResponseTable(content["requestBody"])
else:
writeUp = '<table class="table qc-table">\n<thead>\n<tr>\n'
writeUp += f'<th colspan="1"><code>{api_call}</code> Method</th>\n</tr>\n</thead>\n'
writeUp += f'</tr>\n<td><code>{api_call}</code> method takes no parameters.</td>\n</tr>\n</table>'
description_ += f'The <code>{api_call}</code> API accepts requests in the following format:\n'
html_file.write("<p>\n" + description_ + "</p>\n")
html_file.write(writeUp)
j += 1
# Create Response part
with open(destination_folder / f'{j:02} Responses.html', "w") as html_file:
html_file.write('<p>\n')
html_file.write(f'The <code>{api_call}</code> API provides a response in the following format:\n')
html_file.write('</p>\n')
request_body = content["responses"]
for code, properties in request_body.items():
if code == "200":
html_file.write('<h4>200 Success</h4>\n')
elif code == "401":
html_file.write('<h4>401 Authentication Error</h4>\n<table class="table qc-table">\n<thead>\n<tr>\n')
html_file.write('<th colspan="2"><code>UnauthorizedError</code> Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.</th>\n')
html_file.write('</tr>\n</thead>\n<tr>\n<td width="20%">www_authenticate</td> <td> <code>string</code> <br/> Header</td>\n</tr>\n</table>\n')
continue
elif code == "404":
html_file.write('<h4>404 Not Found Error</h4>\n')
html_file.write('<p>The requested item, index, page was not found.</p>\n')
continue
elif code == "default":
html_file.write('<h4>Default Generic Error</h4>\n')
writeUp = ResponseTable(properties)
html_file.write(writeUp)
print(f"Documentation of {section} is generated and inplace!") | [
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... | 1.896811 | 2,258 |
from .utils import get_request, authorized
| [
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] | 4.3 | 10 |
from django.conf.urls import url
# from .views import BaseIndexView
urlpatterns = [
# url(r'^$', BaseIndexView.as_view(), name="index"),
] | [
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6... | 2.769231 | 52 |
# Copyright (c) 2018, Palo Alto Networks
#
# Permission to use, copy, modify, and/or distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in all copies.
#
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
# Author: Scott Shoaf <sshoaf@paloaltonetworks.com>
'''
Palo Alto Networks create_loadable_configs.py
Provides rendering of configuration templates with user defined values
Output is a set of loadable full configurations and set commands for Panos and Panorama
Edit the config_variables.yaml values and then run the script
This software is provided without support, warranty, or guarantee.
Use at your own risk.
'''
import datetime
import os
import shutil
import sys
import time
import getpass
import oyaml
from jinja2 import Environment, FileSystemLoader
from passlib.hash import des_crypt
from passlib.hash import md5_crypt
from passlib.hash import sha256_crypt
from passlib.hash import sha512_crypt
defined_filters = ['md5_hash', 'des_hash', 'sha512_hash']
def myconfig_newdir(myconfigdir_name, foldertime):
'''
create a new main loadable_configs folder if required then new subdirectories for configs
:param myconfigdir_name: prefix folder name from the my_variables.py file
:param foldertime: datetime when script run; to be used as suffix of folder name
:return: the myconfigdir full path name
'''
# get the full path to the config directory we want (panos / panorama)
myconfigpath = os.path.abspath(os.path.join('..', 'loadable_configs'))
if os.path.isdir(myconfigpath) is False:
os.mkdir(myconfigpath, mode=0o755)
print('created new loadable config directory')
# check that configs folder exists and if not create a new one
# then create snippets and full sub-directories
myconfigdir = '{0}/{1}-{2}'.format(myconfigpath, myconfigdir_name, foldertime)
if os.path.isdir(myconfigdir) is False:
os.mkdir(myconfigdir, mode=0o755)
print('\ncreated new archive folder {0}-{1}'.format(myconfigdir_name, foldertime))
if os.path.isdir('{0}/{1}'.format(myconfigdir, config_type)) is False:
os.mkdir('{0}/{1}'.format(myconfigdir, config_type))
print('created new subdirectories for {0}'.format(config_type))
return myconfigdir
def template_render(filename, template_path, render_type, context):
'''
render the jinja template using the context value from config_variables.yaml
:param filename: name of the template file
:param template_path: path for the template file
:param render_type: type if full or set commands; aligns with folder name
:param context: dict of variables to render
:return: return the rendered xml file and set conf file
'''
print('..creating template for {0}'.format(filename))
env = Environment(loader=FileSystemLoader('{0}/{1}'.format(template_path, render_type)))
# load our custom jinja filters here, see the function defs below for reference
env.filters['md5_hash'] = md5_hash
env.filters['des_hash'] = des_hash
env.filters['sha512_hash'] = sha512_hash
template = env.get_template(filename)
rendered_template = template.render(context)
return rendered_template
def template_save(snippet_name, myconfigdir, config_type, element):
'''
after rendering the template save to the myconfig directory
each run saves with a unique prefix name + datetime
:param snippet_name: name of the output file
:param myconfigdir: path to the my_config directory
:param config_type: based on initial run list; eg. panos or panorama
:param element: xml element rendered based on input variables; used as folder name
:param render_type: type eg. if full or snippets; aligns with folder name
:return: no value returned (future could be success code)
'''
print('..saving template for {0}'.format(snippet_name))
filename = snippet_name
with open('{0}/{1}/{2}'.format(myconfigdir, config_type, filename), 'w') as configfile:
configfile.write(element)
# copy the variables file used for the render into the my_template folder
var_file = 'loadable_config_vars/config_variables.yaml'
if os.path.isfile('{0}/{1}'.format(myconfigdir, var_file)) is False:
vfilesrc = var_file
vfiledst = '{0}/{1}'.format(myconfigdir, var_file)
shutil.copy(vfilesrc, vfiledst)
return
# define functions for custom jinja filters
def md5_hash(txt):
'''
Returns the MD5 Hashed secret for use as a password hash in the PanOS configuration
:param txt: text to be hashed
:return: password hash of the string with salt and configuration information. Suitable to place in the phash field
in the configurations
'''
return md5_crypt.hash(txt)
def des_hash(txt):
'''
Returns the DES Hashed secret for use as a password hash in the PanOS configuration
:param txt: text to be hashed
:return: password hash of the string with salt and configuration information. Suitable to place in the phash field
in the configurations
'''
return des_crypt.hash(txt)
def sha256_hash(txt):
'''
Returns the SHA256 Hashed secret for use as a password hash in the PanOS configuration
:param txt: text to be hashed
:return: password hash of the string with salt and configuration information. Suitable to place in the
phash field in the configurations
'''
return sha256_crypt.hash(txt)
def sha512_hash(txt):
'''
Returns the SHA512 Hashed secret for use as a password hash in the PanOS configuration
:param txt: text to be hashed
:return: password hash of the string with salt and configuration information. Suitable to place in the
phash field in the configurations
'''
return sha512_crypt.hash(txt)
def replace_variables(config_type, render_type, input_var):
'''
get the input variables and render the output configs with jinja2
inputs are read from the template directory and output to my_config
:param config_type: panos or panorama to read/write to the respective directories
:param archivetime: datetimestamp used for the output my_config folder naming
'''
config_variables = 'config_variables.yaml'
# create dict of values for the jinja template render
context = create_context(config_variables)
# update context dict with variables from user input
for snippet_var in input_var:
context[snippet_var] = input_var[snippet_var]
# get the full path to the output directory we want (panos / panorama)
template_path = os.path.abspath(os.path.join('..',
'templates', config_type))
# append to the sys path for module lookup
sys.path.append(template_path)
# output subdir located in loadable_configs dir
myconfig_path = myconfig_newdir(input_var['output_dir'], input_var['archive_time'])
# render full and set conf files
print('\nworking with {0} config template'.format(render_type))
if render_type == 'full':
filename = 'iron_skillet_{0}_full.xml'.format(config_type)
if render_type == 'set_commands':
filename = 'iron_skillet_{0}_full.conf'.format(config_type)
element = template_render(filename, template_path, render_type, context)
template_save(filename, myconfig_path, config_type, element)
print('\nconfigs have been created and can be found in {0}'.format(myconfig_path))
print('along with the metadata values used to render the configs\n')
return
if __name__ == '__main__':
# Use the timestamp to create a unique folder name
print('=' * 80)
print(' ')
print('Welcome to Iron-Skillet'.center(80))
print(' ')
print('=' * 80)
input_var = {}
# archive_time used as part of the my_config directory name
input_var['archive_time'] = datetime.datetime.fromtimestamp(time.time()).strftime('%Y%m%d_%H%M%S')
print('\ndatetime used for folder creation: {0}\n'.format(input_var['archive_time']))
# this prompts for the prefix name of the output directory
input_var['output_dir'] = input('Enter the name of the output directory: ')
# this prompts for the superuser username to be added into the configuration; no default admin/admin used
input_var['ADMINISTRATOR_USERNAME'] = input('Enter the superuser administrator account username: ')
print('\na phash will be created for superuser {0} and added to the config file\n'.format(
input_var['ADMINISTRATOR_USERNAME']))
passwordmatch = False
# prompt for the superuser password to create a phash and store in the my_config files; no default admin/admin
while passwordmatch is False:
password1 = getpass.getpass("Enter the superuser administrator account password: ")
password2 = getpass.getpass("Enter password again to verify: ")
if password1 == password2:
input_var['ADMINISTRATOR_PASSWORD'] = password1
passwordmatch = True
else:
print('\nPasswords do not match. Please try again.\n')
# loop through all config types that have their respective template folders
for config_type in ['panos', 'panorama']:
for render_type in ['full', 'set_commands']:
replace_variables(config_type, render_type, input_var) | [
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2... | 3.003967 | 3,277 |
import glob
import logging
import os
import warnings
import pytest
from _pytest.outcomes import Failed
from _pytest.reports import TestReport
from .broker_pact import BrokerPact, BrokerPacts, PactBrokerConfig
from .result import PytestResult, log
# Future options to be implemented. Listing them here so naming consistency can be a thing.
# group.addoption("--pact-publish-pacts", action="store_true", default=False,
# help="publish pacts to pact broker")
# group.addoption("--pact-consumer-version", default=None,
# help="consumer version to use when publishing pacts to the broker")
# group.addoption("--pact-consumer-version-source", default=None,
# help="generate consumer version from source 'git-tag' or 'git-hash'")
# group.addoption("--pact-consumer-version-tag", metavar='TAG', action="append",
# help="tag(s) that should be applied to the consumer version when pacts "
# "are uploaded to the broker; multiple tags may be supplied")
# add the pact broker URL to the pytest output if running verbose
def test_id(identifier):
interaction, _ = identifier
return str(interaction)
def pytest_generate_tests(metafunc):
if "pact_verifier" in metafunc.fixturenames:
broker_url = get_broker_url(metafunc.config)
if not broker_url:
pact_files_location = metafunc.config.getoption("pact_files")
if not pact_files_location:
raise ValueError("need a --pact-broker-url or --pact-files option")
pact_files = load_pact_files(pact_files_location)
metafunc.parametrize(
"pact_verifier", flatten_pacts(pact_files), ids=test_id, indirect=True
)
else:
provider_name = get_provider_name(metafunc.config)
if not provider_name:
raise ValueError("--pact-broker-url requires the --pact-provider-name option")
broker = PactBrokerConfig(
broker_url,
metafunc.config.getoption("pact_broker_token"),
metafunc.config.getoption("pact_verify_consumer_tag", []),
)
broker_pacts = BrokerPacts(
provider_name, pact_broker=broker, result_factory=PytestResult
)
pacts = broker_pacts.consumers()
filter_consumer_name = metafunc.config.getoption("pact_verify_consumer")
if not filter_consumer_name:
filter_consumer_name = metafunc.config.getoption("pact_consumer_name")
if filter_consumer_name:
warnings.warn(
"The --pact-consumer-name command-line option is deprecated "
"and will be removed in the 3.0.0 release.",
DeprecationWarning,
)
if filter_consumer_name:
pacts = [pact for pact in pacts if pact.consumer == filter_consumer_name]
metafunc.parametrize("pact_verifier", flatten_pacts(pacts), ids=test_id, indirect=True)
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198,
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... | 2.245513 | 1,393 |
# -*- coding: utf-8 -*-
def docstring_property(class_doc):
"""Property attribute for docstrings.
Took from: https://gist.github.com/bfroehle/4041015
>>> class A(object):
... '''Main docstring'''
... def __init__(self, x):
... self.x = x
... @docstring_property(__doc__)
... def __doc__(self):
... return "My value of x is %s." % self.x
>>> A.__doc__
'Main docstring'
>>> a = A(10)
>>> a.__doc__
'My value of x is 10.'
"""
return wrapper
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220,
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309,
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3740,... | 2.164659 | 249 |
import warnings
try:
import cudasift as cs
except:
cs = None
import numpy as np
import pandas as pd
def match(edge, aidx=None, bidx=None, **kwargs):
"""
Apply a composite CUDA matcher and ratio check. If this method is used,
no additional ratio check is necessary and no symmetry check is required.
The ratio check is embedded on the cuda side and returned as an
ambiguity value. In testing symmetry is not required as it is expensive
without significant gain in accuracy when using this implementation.
"""
source_kps = edge.source.get_keypoints(index=aidx)
source_des = edge.source.descriptors[aidx]
source_map = {k:v for k, v in enumerate(source_kps.index)}
destin_kps = edge.destination.get_keypoints(index=bidx)
destin_des = edge.destination.descriptors[bidx]
destin_map = {k:v for k, v in enumerate(destin_kps.index)}
s_siftdata = cs.PySiftData.from_data_frame(source_kps, source_des)
d_siftdata = cs.PySiftData.from_data_frame(destin_kps, destin_des)
cs.PyMatchSiftData(s_siftdata, d_siftdata)
matches, _ = s_siftdata.to_data_frame()
# Matches are reindexed 0-n, but need to be remapped to the source_kps,
# destin_kps indices. This is the mismatch)
source = np.empty(len(matches))
source[:] = edge.source['node_id']
destination = np.empty(len(matches))
destination[:] = edge.destination['node_id']
df = pd.concat([pd.Series(source), pd.Series(matches.index),
pd.Series(destination), matches.match,
matches.score, matches.ambiguity], axis=1)
df.columns = ['source_image', 'source_idx', 'destination_image',
'destination_idx', 'score', 'ambiguity']
df.source_idx = df.source_idx.map(source_map)
df.destination_idx = df.destination_idx.map(destin_map)
# Set the matches and set the 'ratio' (ambiguity) mask
edge.matches = df
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... | 2.5722 | 741 |
from flask_restx import Api
from app.apis.hello import api as hello
api = Api(
title='api',
version='1.0',
description='',
prefix='/api',
doc='/api'
)
api.add_namespace(hello)
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220,
220,
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16,
... | 2.341176 | 85 |
# coding: utf-8
import unittest
from test.support import captured_stdout
from brainfuck import BrainFuck
if __name__ == "__main__":
unittest.main()
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83... | 2.854545 | 55 |