blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 3 288 | content_id stringlengths 40 40 | detected_licenses listlengths 0 112 | license_type stringclasses 2 values | repo_name stringlengths 5 115 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 684 values | visit_date timestamp[us]date 2015-08-06 10:31:46 2023-09-06 10:44:38 | revision_date timestamp[us]date 1970-01-01 02:38:32 2037-05-03 13:00:00 | committer_date timestamp[us]date 1970-01-01 02:38:32 2023-09-06 01:08:06 | github_id int64 4.92k 681M ⌀ | star_events_count int64 0 209k | fork_events_count int64 0 110k | gha_license_id stringclasses 22 values | gha_event_created_at timestamp[us]date 2012-06-04 01:52:49 2023-09-14 21:59:50 ⌀ | gha_created_at timestamp[us]date 2008-05-22 07:58:19 2023-08-21 12:35:19 ⌀ | gha_language stringclasses 147 values | src_encoding stringclasses 25 values | language stringclasses 1 value | is_vendor bool 2 classes | is_generated bool 2 classes | length_bytes int64 128 12.7k | extension stringclasses 142 values | content stringlengths 128 8.19k | authors listlengths 1 1 | author_id stringlengths 1 132 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3c01565ab29a50b5e2ab3b2e0ebc8671fa00fa1d | 84fbca1dd86aa1d7d65afd65bc65a71420513fb8 | /queriesapp/migrations/0002_auto_20200313_1226.py | 4fc15376311aefa9eef66940a093094bb9bb41e9 | [] | no_license | corri-golden/queries | 40a7415868149014dcf9d20dedd17801b4552f0b | fc7a4f26f185e3a6018cd257a12abe74019db531 | refs/heads/master | 2021-09-26T16:47:14.470875 | 2021-03-20T04:23:57 | 2021-03-20T04:23:57 | 245,893,279 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 927 | py | # Generated by Django 3.0.4 on 2020-03-13 12:26
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('queriesapp', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Status',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('status_name', models.CharField(max_length=50)),
],
options={
'verbose_name': 'status',
'verbose_name_plural': 'statuses',
},
),
migrations.AddField(
model_name='query',
name='status',
field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to='queriesapp.Status'),
preserve_default=False,
),
]
| [
"corri.golden@gmail.com"
] | corri.golden@gmail.com |
a65d310a45773ea11f0818aa6af806ea9d490511 | 3de1e940512394a6aebe499a9ce07a33a427ea7b | /tools/stats/monitor.py | 972d0dbea038bda843c1db9f7728bd4e8bbd1fa5 | [
"BSD-3-Clause",
"LicenseRef-scancode-generic-cla",
"BSL-1.0",
"Apache-2.0",
"BSD-2-Clause"
] | permissive | shinh/pytorch | 3719d1a6129db16e932452bec9b6646edf153226 | c6b7c33885eeff9dc125f87c7134772d59d0ba21 | refs/heads/master | 2022-10-21T22:03:14.775982 | 2022-10-10T05:47:11 | 2022-10-10T05:47:11 | 220,897,452 | 1 | 0 | NOASSERTION | 2019-11-11T03:56:40 | 2019-11-11T03:56:39 | null | UTF-8 | Python | false | false | 2,871 | py | #!/usr/bin/env python3
import datetime
import json
import signal
import time
from typing import Any, Dict, List
import psutil # type: ignore[import]
import pynvml # type: ignore[import]
def get_processes_running_python_tests() -> List[Any]:
python_processes = []
for process in psutil.process_iter():
try:
if "python" in process.name() and process.cmdline():
python_processes.append(process)
except (psutil.NoSuchProcess, psutil.AccessDenied):
# access denied or the process died
pass
return python_processes
def get_per_process_cpu_info() -> List[Dict[str, Any]]:
processes = get_processes_running_python_tests()
per_process_info = []
for p in processes:
info = {
"pid": p.pid,
"cmd": " ".join(p.cmdline()),
"cpu_percent": p.cpu_percent(),
"rss_memory": p.memory_info().rss,
"uss_memory": p.memory_full_info().uss,
}
if "pss" in p.memory_full_info():
# only availiable in linux
info["pss_memory"] = p.memory_full_info().pss
per_process_info.append(info)
return per_process_info
def get_per_process_gpu_info(handle: Any) -> List[Dict[str, Any]]:
processes = pynvml.nvmlDeviceGetComputeRunningProcesses(handle)
per_process_info = []
for p in processes:
info = {"pid": p.pid, "gpu_memory": p.usedGpuMemory}
per_process_info.append(info)
return per_process_info
if __name__ == "__main__":
handle = None
try:
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
except pynvml.NVMLError:
# no pynvml avaliable, probably because not cuda
pass
kill_now = False
def exit_gracefully(*args: Any) -> None:
global kill_now
kill_now = True
signal.signal(signal.SIGTERM, exit_gracefully)
while not kill_now:
try:
stats = {
"time": datetime.datetime.utcnow().isoformat("T") + "Z",
"total_cpu_percent": psutil.cpu_percent(),
"per_process_cpu_info": get_per_process_cpu_info(),
}
if handle is not None:
stats["per_process_gpu_info"] = get_per_process_gpu_info(handle)
# https://docs.nvidia.com/deploy/nvml-api/structnvmlUtilization__t.html
gpu_utilization = pynvml.nvmlDeviceGetUtilizationRates(handle)
stats["total_gpu_utilization"] = gpu_utilization.gpu
stats["total_gpu_mem_utilization"] = gpu_utilization.memory
except Exception as e:
stats = {
"time": datetime.datetime.utcnow().isoformat("T") + "Z",
"error": str(e),
}
finally:
print(json.dumps(stats))
time.sleep(1)
| [
"pytorchmergebot@users.noreply.github.com"
] | pytorchmergebot@users.noreply.github.com |
fc15825c91764aeef1e3c0d91ecb2bc9384037cb | d5fc28473e41a11e3ee793362e80f2db83b1d386 | /tests/unit/cli.py | 1f4810cb82091b028bc3a6c0ceb35d0071fb39b7 | [
"Apache-2.0"
] | permissive | starbops/haas | 71335ea29dbf06579381c7745176ee9f7c86d423 | 751d4fc27732ac7d660886b7c47948300c606460 | refs/heads/master | 2020-05-29T11:37:40.484664 | 2016-03-17T03:04:11 | 2016-03-17T03:04:11 | 53,773,276 | 0 | 0 | null | 2016-03-13T07:41:34 | 2016-03-13T07:41:34 | null | UTF-8 | Python | false | false | 1,877 | py | import pytest
import tempfile
import os
import signal
from subprocess import check_call, Popen
from time import sleep
config = """
[headnode]
base_imgs = base-headnode, img1, img2, img3, img4
[database]
uri = sqlite:///haas.db
[extensions]
haas.ext.auth.null =
haas.ext.network_allocators.null =
"""
@pytest.fixture(autouse=True)
def make_config(request):
tmpdir = tempfile.mkdtemp()
cwd = os.getcwd()
os.chdir(tmpdir)
with open('haas.cfg', 'w') as f:
f.write(config)
def cleanup():
os.remove('haas.cfg')
os.remove('haas.db')
os.chdir(cwd)
os.rmdir(tmpdir)
request.addfinalizer(cleanup)
def test_init_db():
check_call(['haas', 'init_db'])
def runs_for_seconds(cmd, seconds=1):
"""Test if the command ``cmd`` runs for at least ``seconds`` seconds.
``cmd`` is a list containing the name of a command and its arguments.
``seconds`` is a number of seconds (by default 1).
``run_for_seconds`` will execute ``cmd``, wait for ``seconds`` seconds,
send SIGTERM to the process, and then wait() for it. If the exit status
indicates that it stopped for any reason other than SIGTERM,
``run_for_seconds`` returns False, otherwise it returns True.
This is useful to check that a server process does not immediately die on
startup, though it's a bit of a hack --- checking rigorously would require
extra knowledge of the workings of that process (hooray for the halting
problem).
"""
proc = Popen(cmd)
sleep(seconds)
proc.terminate()
status = proc.wait()
return status == -signal.SIGTERM
def test_serve():
check_call(['haas', 'init_db'])
assert runs_for_seconds(['haas', 'serve', '5000'], seconds=1)
def test_serve_networks():
check_call(['haas', 'init_db'])
assert runs_for_seconds(['haas', 'serve_networks'], seconds=1)
| [
"ian@zenhack.net"
] | ian@zenhack.net |
dd1c933da5c0589e147cfac927c95849c3d02401 | 07c75f8717683b9c84864c446a460681150fb6a9 | /back_cursor/S-scrapy/zhilian/zhilian/pipelines.py | ce305e87f0e027bad9b1bb740e708506227f2072 | [] | no_license | laomu/py_1709 | 987d9307d9025001bd4386381899eb3778f9ccd6 | 80630e6ac3ed348a2a6445e90754bb6198cfe65a | refs/heads/master | 2021-05-11T09:56:45.382526 | 2018-01-19T07:08:00 | 2018-01-19T07:08:00 | 118,088,974 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,236 | py | # -*- coding: utf-8 -*-
# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html
# 引入sqlalchemy模块
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
# 进入pymysql模块，用于替代sqlalchemy底层的mysqldb
import pymysql
pymysql.install_as_MySQLdb()
class ZhilianPipeline(object):
'''
智联招聘爬虫管道模块，进行数据验证和存储
'''
def __init__(self):
# 打开和数据库的连接引擎，获取连接会话对象
engine = create_engine("mysql://root:@localhost/py1709_spider?charset=utf8")
Session = sessionmaker(bind=engine)
self.session = Session()
def process_item(self, item, spider):
# 生成sql语句
zl_sql = "insert into jobs(job_name, company, salary) values('%s', '%s', '%s')" % \
(item['job_name'], item['company'], item['salary'])
# 执行sql语句
self.session.execute(zl_sql)
return item
def close_spider(self, spider):
# 提交数据并关闭数据库连接会话
self.session.commit()
self.session.close()
| [
"1007821300@qq.com"
] | 1007821300@qq.com |
7f5466358e1afe8affcb50531035a634f09d47f2 | 3e3bf98840d133e56f0d0eb16ba85678ddd6ca45 | /.history/iss_20200102114952.py | b10444b89fa1b6c3096e6c74cc748aa78096e84a | [] | no_license | Imraj423/backend-iss-location-assessment | a05d3cc229a5fc4857483ae466348c1f8c23c234 | b0565c089a445ccffcb8d0aab3c0be3bb0c1d5b8 | refs/heads/master | 2020-12-03T17:04:58.512124 | 2020-06-24T16:02:02 | 2020-06-24T16:02:02 | 231,400,854 | 0 | 0 | null | 2020-06-24T16:02:04 | 2020-01-02T14:43:44 | null | UTF-8 | Python | false | false | 920 | py | import requests
import turtle
import time
screen = turtle.Screen()
image = "iss.gif"
screen.addshape(image)
raf = turtle.Turtle()
raf.shape(image)
raf.setheading(90)
raf.penup()
screen.bgpic("map.gif")
screen.screensize(4000, 3000)
screen.setup(width=800, height=600, startx=0, starty=0)
screen.exitonclick()
while True:
s = requests.get('http://api.open-notify.org/iss-now.json')
s.raise_for_status
result = json.loads(s.read())
print(s.text)
#Let's extract the required information
location = result["iss_position"]
lat = location["latitude"]
lon = location["longitude"]
#Output informationon screen
print("\nLatitude: " + str(lat))
print("Longitude: " + str(lon))
#Plot the ISS on the map
raf.goto(lon, lat)
#refresh position every 5 seconds
time.sleep(5)
r = requests.get('http://api.open-notify.org/astros.json')
r.raise_for_status()
print(r.text)
| [
"dahqniss@gmail.com"
] | dahqniss@gmail.com |
a802808f5187909756b07ccfd2d5e6956da34179 | 866b7169c069c153bacfa7961dce8909aa391faa | /blog/admin.py | e7b63a18af39c397f3075c4067021ab8e245f26b | [] | no_license | nikolasribeiro/pagina-jovenes-40 | d2502b5d1569000eb7dc059f62b66978ea8642ab | 5618cbc1ac03f6dd47eba6360dbdbb2ead70f268 | refs/heads/main | 2023-03-08T18:45:08.323432 | 2021-02-27T22:36:47 | 2021-02-27T22:36:47 | 336,833,170 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 984 | py | from django.contrib import admin
from .models import Blog, Categoria
# Import Export para guardar datos
from import_export import resources
from import_export.admin import ImportExportModelAdmin
# Clases del import Export
class BlogResource(resources.ModelResource):
class Meta:
model = Blog
class BlogAdmin(ImportExportModelAdmin, admin.ModelAdmin):
list_display = (
'titulo_blog',
'subtitulo_blog',
'imagen_blog',
'slug',
'descripcion_breve',
'contenido_blog',
'autor',
'fecha_publicacion',
)
resource_class = BlogResource
class CategoriaResource(resources.ModelResource):
class Meta:
model = Blog
class CategoriaAdmin(ImportExportModelAdmin, admin.ModelAdmin):
list_display = (
'nombre_categoria',
)
resource_class = CategoriaResource
# Register your models here.
admin.site.register(Blog, BlogAdmin)
admin.site.register(Categoria, CategoriaAdmin) | [
"nikolasribeiro2@outlook.com"
] | nikolasribeiro2@outlook.com |
cf43f252196aa5e91466d9092828816745be5ca3 | 3851d5eafcc5fd240a06a7d95a925518412cafa0 | /Django_Code/gs25/gs25/asgi.py | cb53a37a73a74e22db638ccc220b4e981d1b6bca | [] | no_license | Ikshansaleem/DjangoandRest | c0fafaecde13570ffd1d5f08019e04e1212cc2f3 | 0ccc620ca609b4ab99a9efa650b5893ba65de3c5 | refs/heads/master | 2023-01-31T04:37:57.746016 | 2020-12-10T06:27:24 | 2020-12-10T06:27:24 | 320,180,735 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 401 | py | """
ASGI config for gs25 project.
It exposes the ASGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.0/howto/deployment/asgi/
"""
import os
from django.core.asgi import get_asgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'gs25.settings')
application = get_asgi_application()
| [
"ikshan3108@gmail.com"
] | ikshan3108@gmail.com |
c67b2260e2dab5a6ed7a5447cb5d23fbae2047c7 | 07131e91dcf2529e9c7058f8a8f239d419c8f7e0 | /1447.simplified-fractions.py | 240c2196f222b390151a2ea343df329835207f3d | [] | no_license | Code-Wen/LeetCode_Notes | 5194c5c5306cb9f4a0fac85e06fefe6c02d65d44 | 791fc1b43beef89d668788de6d12f5c643431b8f | refs/heads/master | 2021-07-04T14:41:00.830723 | 2020-09-27T16:31:22 | 2020-09-27T16:31:22 | 178,456,323 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,492 | py | #
# @lc app=leetcode id=1447 lang=python3
#
# [1447] Simplified Fractions
#
# https://leetcode.com/problems/simplified-fractions/description/
#
# algorithms
# Medium (59.85%)
# Likes: 50
# Dislikes: 6
# Total Accepted: 7.5K
# Total Submissions: 12.5K
# Testcase Example: '2\r'
#
# Given an integer n, return a list of all simplified fractions between 0 and 1
# (exclusive) such that the denominator is less-than-or-equal-to n. The
# fractions can be in any order.
#
#
# Example 1:
#
#
# Input: n = 2
# Output: ["1/2"]
# Explanation: "1/2" is the only unique fraction with a denominator
# less-than-or-equal-to 2.
#
# Example 2:
#
#
# Input: n = 3
# Output: ["1/2","1/3","2/3"]
#
#
# Example 3:
#
#
# Input: n = 4
# Output: ["1/2","1/3","1/4","2/3","3/4"]
# Explanation: "2/4" is not a simplified fraction because it can be simplified
# to "1/2".
#
# Example 4:
#
#
# Input: n = 1
# Output: []
#
#
#
# Constraints:
#
#
# 1 <= n <= 100
#
#
# @lc code=start
class Solution:
def simplifiedFractions(self, n: int) -> List[str]:
res = []
def gcd(n1,n2):
if n1 < n2:
n1, n2 = n2, n1
while n2 > 0:
n2, n1 = n1%n2, n2
return n1
for denom in range(2, n+1):
for numerator in range(1, denom):
if gcd(denom, numerator) == 1:
res.append(str(numerator)+"/"+str(denom))
return res
# @lc code=end
| [
"chenxu.wen.math@gmail.com"
] | chenxu.wen.math@gmail.com |
6924ccab05426697554fea2c956596a548469849 | 4d2475135f5fc9cea73572b16f59bfdc7232e407 | /prob130_surrounded_regions.py | cbb05c421a1740056d03c472983e5335f0f064ce | [] | no_license | Hu-Wenchao/leetcode | 5fa0ae474aadaba372756d234bc5ec397c8dba50 | 31b2b4dc1e5c3b1c53b333fe30b98ed04b0bdacc | refs/heads/master | 2021-06-24T04:57:45.340001 | 2017-06-17T02:33:09 | 2017-06-17T02:33:09 | 45,328,724 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,128 | py | """
Given a 2D board containing 'X' and 'O', capture
all regions surrounded by 'X'.
A region is captured by flipping all 'O's into 'X's
in that surrounded region.
For example,
X X X X
X O O X
X X O X
X O X X
After running your function, the board should be:
X X X X
X X X X
X X X X
X O X X
"""
class Solution(object):
def solve(self, board):
"""
:type board: List[List[str]]
:rtype: void Do not return anything, modify board in-place instead.
"""
if not board:
return
m, n = len(board), len(board[0])
stack = [(i, 0) for i in range(m)] + [(i, n-1) for i in range(m)]
stack += [(0, j) for j in range(1, n-1)] + \
[(m-1, j) for j in range(1, n-1)]
while stack:
i, j = stack.pop()
if 0 <= i < m and 0 <= j < n and board[i][j] == 'O':
board[i][j] = 'S'
stack.append((i-1, j))
stack.append((i+1, j))
stack.append((i, j+1))
stack.append((i, j-1))
board[:] = [['XO'[node=='S'] for node in row] for row in board]
| [
"huwcbill@gmail.com"
] | huwcbill@gmail.com |
73e6a2ec930cc79de0a569f1fe7e1d0881098d19 | 4a52362d2a46c747af74e3c321b1bd9d73bd0116 | /virtual/bin/static | fcf01443e92188fc7d4641feefe749e4664b5659 | [
"MIT"
] | permissive | TonyKioko/PichaZa | 1420242b48c204637a166778084aaa4cb3776938 | 8e2e8f3d002a624fe64ce089e4581265080975d6 | refs/heads/master | 2020-03-30T01:31:53.034302 | 2018-10-03T07:35:30 | 2018-10-03T07:35:30 | 150,580,180 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 244 | #!/home/tony/Desktop/PichaZa/virtual/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from static import command
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(command())
| [
"tonykioko384@gmail.com"
] | tonykioko384@gmail.com | |
a754658047ec2d9bd7b8997aa6df168e5080f297 | 0fc2b99fd8414dbce5f1f6057b9b800c968d5d05 | /tests/pysge/test_pysge.py | a54c3b00256e1b2b635b0b89ce3728218a840e3b | [
"MIT"
] | permissive | widdowquinn/lpbio | 9df898cb9580f62da1f66d5736cbf7a984633561 | 8b95642396d05a56c1c54389e3de6d88d7cbffb5 | refs/heads/master | 2020-03-29T02:08:56.675473 | 2019-11-07T14:27:44 | 2019-11-07T14:27:44 | 149,422,654 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,938 | py | # -*- coding: utf-8 -*-
"""Tests of SGE job submission"""
import shutil
import time
import unittest
import pytest
from lpbio import pysge
class TestPysge(unittest.TestCase):
"""Class collecting tests for pysge"""
@staticmethod
def test_create_job():
"""Create Job for SGE-like scheduler"""
pysge.Job(name="test_job", command="echo {}".format(time.asctime()))
@staticmethod
def test_create_job_dependencies():
"""Create Job with dependencies for SGE-like scheduler"""
job = pysge.Job(
name="test_job_dependencies", command="echo {}".format(time.asctime())
)
depjobs = [
pysge.Job(
name="dependency {}".format(i), command="echo {}".format(time.asctime())
)
for i in range(3)
]
[job.add_dependency(depjob) for depjob in depjobs]
@staticmethod
def test_create_jobgroup():
"""Create parameter-sweep JobGroup for SGE-like scheduler"""
args = {"arg1": ["a", "b", "c"]}
pysge.JobGroup(name="test_jobgroup", command="echo", arguments=args)
@staticmethod
def test_create_jobgroup_dependencies():
"""Create parameter-sweep JobGroup with dependencies for SGE-like scheduler"""
args = {"arg1": ["a", "b", "c"]}
jobgroup = pysge.JobGroup(
name="test_jobgroup_dependencies", command="echo", arguments=args
)
depjobs = [
pysge.Job(
name="dependency {}".format(i), command="echo {}".format(time.asctime())
)
for i in range(3)
]
for depjob in depjobs:
jobgroup.add_dependency(depjob)
@pytest.mark.skipif(
shutil.which(pysge.QSUB_DEFAULT) is None,
reason="qsub executable ({}) could not be found".format(pysge.QSUB_DEFAULT),
)
def test_create_run_job(self):
"""Create and run Job with SGE-like scheduler"""
job = pysge.Job(
name="test_run_job",
command="echo {} \\(test_create_run_job\\)".format(time.asctime()),
)
pysge.build_and_submit_jobs(job)
@pytest.mark.skipif(
shutil.which(pysge.QSUB_DEFAULT) is None,
reason="qsub executable ({}) could not be found".format(pysge.QSUB_DEFAULT),
)
def test_create_run_job_badname(self):
"""Create and run a Job using SGE-like scheduler
This job has undesirable characters in the name
"""
job = pysge.Job(
name="test run job #|!;,.?",
command="echo This was a bad name! \\(test_create_run_job_badname\\)",
)
pysge.build_and_submit_jobs(job)
@pytest.mark.skipif(
shutil.which(pysge.QSUB_DEFAULT) is None,
reason="qsub executable ({}) could not be found".format(pysge.QSUB_DEFAULT),
)
def test_create_run_jobgroup(self):
"""Create and run JobGroup with SGE-like scheduler"""
args = {"arg1": ["a", "b", "c"]}
jobgroup = pysge.JobGroup(
name="test_run_jobgroup",
command="echo $arg1 \\(test_create_run_jobgroup\\)",
arguments=args,
)
pysge.build_and_submit_jobs(jobgroup)
@pytest.mark.skipif(
shutil.which(pysge.QSUB_DEFAULT) is None,
reason="qsub executable ({}) could not be found".format(pysge.QSUB_DEFAULT),
)
def test_create_run_job_dependencies(self):
"""Create and run Job with dependencies for SGE-like scheduler"""
job = pysge.Job(
name="test_run_job_dependencies",
command="echo {} \\(test_create_run_job_dependencies\\)".format(
time.asctime()
),
)
depjobs = [
pysge.Job(
name="testjob_dependency_{}".format(i),
command="echo {}".format(time.asctime()),
)
for i in range(3)
]
for depjob in depjobs:
job.add_dependency(depjob)
pysge.build_and_submit_jobs([job] + depjobs)
@pytest.mark.skipif(
shutil.which(pysge.QSUB_DEFAULT) is None,
reason="qsub executable ({}) could not be found".format(pysge.QSUB_DEFAULT),
)
def test_create_run_jobgroup_dependencies(self):
"""Create parameter-sweep JobGroup with dependencies for SGE-like scheduler"""
args = {"arg1": ["a", "b", "c"]}
jobgroup = pysge.JobGroup(
name="test_run_jobgroup_dependencies",
command="echo $arg1 \\(test_create_run_jobgroup_dependencies\\)",
arguments=args,
)
depjobs = [
pysge.Job(
name="testjobgroup_dependency_{}".format(i),
command="echo {}".format(time.asctime()),
)
for i in range(3)
]
for depjob in depjobs:
jobgroup.add_dependency(depjob)
pysge.build_and_submit_jobs([jobgroup] + depjobs)
| [
"leighton.pritchard@hutton.ac.uk"
] | leighton.pritchard@hutton.ac.uk |
d2297f166ccb241eecdd47f1c9b46e9d9faad85c | 11a246743073e9d2cb550f9144f59b95afebf195 | /kattis/chopin.py | 67513e777cb40682ed9aa5468bad04aa7e93f38a | [] | no_license | ankitpriyarup/online-judge | b5b779c26439369cedc05c045af5511cbc3c980f | 8a00ec141142c129bfa13a68dbf704091eae9588 | refs/heads/master | 2020-09-05T02:46:56.377213 | 2019-10-27T20:12:25 | 2019-10-27T20:12:25 | 219,959,932 | 0 | 1 | null | 2019-11-06T09:30:58 | 2019-11-06T09:30:57 | null | UTF-8 | Python | false | false | 538 | py | import sys
def main():
tc = 1
flip = {'major': {}, 'minor': {}}
flip['A#'] = 'Bb'
flip['Bb'] = 'A#'
flip['C#'] = 'Db'
flip['Db'] = 'C#'
flip['D#'] = 'Eb'
flip['Eb'] = 'D#'
flip['F#'] = 'Gb'
flip['Gb'] = 'F#'
flip['G#'] = 'Ab'
flip['Ab'] = 'G#'
for line in sys.stdin:
note, tone = line.strip().split()
if note in flip:
print('Case {}: {} {}'.format(tc, flip[note], tone))
else:
print('Case {}: UNIQUE'.format(tc))
tc += 1
main()
| [
"arnavsastry@gmail.com"
] | arnavsastry@gmail.com |
e0049f98e7e82d3f7e4ed64035a39d25d6443025 | d1c67f2031d657902acef4411877d75b992eab91 | /swagger_client/models/list_escalations_response.py | f2b5efd0d2aa78275881ff42e8022c09b496ba2d | [] | no_license | Certn/opsgenie-python | c6e6a7f42394499e5224d679cc9a449042fcf9c3 | bd5f402f97d591e4082b38c938cbabca4cf29787 | refs/heads/master | 2023-01-01T10:45:13.132455 | 2020-10-27T17:40:01 | 2020-10-27T17:40:01 | 307,769,432 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,621 | py | # coding: utf-8
"""
Opsgenie REST API
Opsgenie OpenAPI Specification # noqa: E501
OpenAPI spec version: 2.0.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class ListEscalationsResponse(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'request_id': 'str',
'took': 'float',
'data': 'list[Escalation]'
}
attribute_map = {
'request_id': 'requestId',
'took': 'took',
'data': 'data'
}
def __init__(self, request_id=None, took=0.0, data=None): # noqa: E501
"""ListEscalationsResponse - a model defined in Swagger""" # noqa: E501
self._request_id = None
self._took = None
self._data = None
self.discriminator = None
self.request_id = request_id
self.took = took
if data is not None:
self.data = data
@property
def request_id(self):
"""Gets the request_id of this ListEscalationsResponse. # noqa: E501
:return: The request_id of this ListEscalationsResponse. # noqa: E501
:rtype: str
"""
return self._request_id
@request_id.setter
def request_id(self, request_id):
"""Sets the request_id of this ListEscalationsResponse.
:param request_id: The request_id of this ListEscalationsResponse. # noqa: E501
:type: str
"""
if request_id is None:
raise ValueError("Invalid value for `request_id`, must not be `None`") # noqa: E501
self._request_id = request_id
@property
def took(self):
"""Gets the took of this ListEscalationsResponse. # noqa: E501
:return: The took of this ListEscalationsResponse. # noqa: E501
:rtype: float
"""
return self._took
@took.setter
def took(self, took):
"""Sets the took of this ListEscalationsResponse.
:param took: The took of this ListEscalationsResponse. # noqa: E501
:type: float
"""
if took is None:
raise ValueError("Invalid value for `took`, must not be `None`") # noqa: E501
self._took = took
@property
def data(self):
"""Gets the data of this ListEscalationsResponse. # noqa: E501
:return: The data of this ListEscalationsResponse. # noqa: E501
:rtype: list[Escalation]
"""
return self._data
@data.setter
def data(self, data):
"""Sets the data of this ListEscalationsResponse.
:param data: The data of this ListEscalationsResponse. # noqa: E501
:type: list[Escalation]
"""
self._data = data
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(ListEscalationsResponse, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, ListEscalationsResponse):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| [
"john@oram.ca"
] | john@oram.ca |
8fde5030c14e3eb60ee0aae8b332acfc19a8dc8b | 758bf41e46a3093f4923af603f1f7f8063408b9c | /website/testFromRemoteRepo/_bsch3398/museum/python/user.py | 719bf82b3ee747cef3765681b6702717b4d1b1a9 | [] | no_license | mpetyx/mpetyx.com | 4033d97b21c9227a6ba505980fd0c1b57254e8fb | d50c379b4fe09e0135656573f7049225fc90ae36 | refs/heads/master | 2021-01-10T19:50:15.488371 | 2014-01-22T09:04:14 | 2014-01-22T09:04:14 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,103 | py | #!/usr/local/bin/python
from util import *
import connect
import sessions
#django for templates only
from django.conf import settings
from django.template import Template, Context
methods = ("login", "logout", "add", "delete", "view", "update")
def login(fields, cookie):
if fields.has_key('user') and fields.has_key('password'):
user = fields['user'].value #.value
password = fields['password'].value #.value
db = connect.connect()
temp = db.read('users', {'userid': user})
#print temp # testing
# user does exist and password matches
if temp and temp[0]['PASSWORD'] == password:
# create session cookie
sid = sessions.create(user)
newcookie = 'id=' + str(sid)
# redirect to catalogue menu page
t = loader('loggedin')
c = Context({}) #TODO
print http_response(t.render(c), newcookie)
# no match
else:
t = loader('login')
c = Context({'errors': 'Incorrect username or password. Also, I slept with your sister.'})
print http_response(t.render(c))
# go back to login page with error message
else:
t = loader('login')
c = Context({})
print http_response(t.render(c))
def logout(fields, cookie):
pass
def add(fields, cookie):
pass
def delete(fields, cookie):
pass
def view(fields, cookie):
pass
def update(fields, cookie):
pass
def run(fields, cookie):
if fields.has_key('method'):
method = fields['method'].value
if method in methods:
if method == "login":
login(fields, cookie)
elif method == "logout":
logout(fields, cookie)
elif method == "add":
add(fields, cookie)
elif method == "delete":
delete(fields, cookie)
elif method == "view":
view(fields, cookie)
elif method == "update":
update(fields, cookie)
if __name__ == "__main__":
pass
| [
"mpetyx@gmail.com"
] | mpetyx@gmail.com |
49ef083dd7476bf8ff926498dd04773df7b9d6f1 | f7c4084ddb4b26ac6005e569c907e94ce63f9993 | /项目/api_sh/data_01/migrations/0001_initial.py | 991a987ed1f657a88f79ba7cc71819dc786de534 | [] | no_license | LDZ-RGZN/- | 01caeb008bab16e1f7dd1c02137def2e030e1636 | 696be7f7a33f009eac92dff504365eb386060df1 | refs/heads/master | 2020-04-13T14:56:43.565470 | 2019-01-13T11:19:28 | 2019-01-13T11:19:28 | 162,986,641 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,352 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11 on 2019-01-08 11:35
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='content',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=100, verbose_name='章节名称')),
('bookname', models.CharField(max_length=150, verbose_name='书名')),
('author', models.CharField(max_length=100, verbose_name='作者')),
('WordNumber', models.CharField(max_length=100, verbose_name='本章字数')),
('FaBuData', models.CharField(max_length=200, verbose_name='发布时间')),
('content', models.TextField(verbose_name='内容')),
],
options={
'verbose_name': '详情',
'verbose_name_plural': '详情',
},
),
migrations.CreateModel(
name='liebiao',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('bookname', models.CharField(max_length=200, verbose_name='书名')),
('img_url', models.TextField(verbose_name='图片链接')),
('author', models.CharField(max_length=100, verbose_name='作者')),
('State', models.CharField(max_length=100, verbose_name='状态')),
('WordNumber', models.CharField(max_length=100, verbose_name='字数')),
('introduce', models.TextField(verbose_name='简介')),
],
options={
'verbose_name': '列表',
'verbose_name_plural': '列表',
},
),
migrations.CreateModel(
name='zhangjie',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=100, verbose_name='章节名称')),
('zj_link', models.TextField(verbose_name='链接')),
('mf', models.CharField(max_length=100, verbose_name='状态')),
],
options={
'verbose_name': '章节',
'verbose_name_plural': '章节',
},
),
migrations.CreateModel(
name='zonglei',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('category', models.CharField(max_length=100, verbose_name='类别')),
('CGurl', models.TextField(verbose_name='类别链接')),
],
options={
'verbose_name': '总类别',
'verbose_name_plural': '总类别',
},
),
migrations.AddField(
model_name='liebiao',
name='category',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='data_01.zonglei', verbose_name='类别'),
),
]
| [
"2654213432@qq.com"
] | 2654213432@qq.com |
a448f9c388862144f132bcfef5eeb24bb2bad601 | 638af6b8c580eeae23fc1034882c4b514195137a | /Packages/vcs_legacy/Test/test_ps_hatching.py | 4716cae2fa5f0c4e53cb057cd6ace51ee97ef4e6 | [] | no_license | doutriaux1/uvcdat | 83684a86b514b8cac4d8900a503fc13d557fc4d2 | 37e9635f988696c346b4c3cdb49144d1e21dab5d | refs/heads/master | 2021-01-17T07:57:22.897539 | 2015-02-02T22:52:12 | 2015-02-02T22:52:12 | 14,878,320 | 1 | 0 | null | 2015-02-19T20:54:25 | 2013-12-02T23:44:46 | C | UTF-8 | Python | false | false | 792 | py | # Adapted for numpy/ma/cdms2 by convertcdms.py
import cdms2 as cdms,vcs_legacy,sys,time,support,os
bg=support.bg
x=vcs_legacy.init()
x.portrait()
#x.setdefaultfont(2)
f=cdms.open(os.path.join(cdms.__path__[0],'..','..','..','..','sample_data','clt.nc'))
s=f('clt')
iso = x.createisofill('my')
levs = range(0,95,5)
#print len(levs)
colors = vcs_legacy.getcolors(levs)
hatch = []
iso.levels=levs
iso.fillareacolors=colors
iso.fillareastyle='pattern'
iso.fillareastyle='hatch'
iso.fillareaindices=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]
#print iso.fillareaindices
#iso.fillareaindices=[17,]*21
#print colors
#iso.list()
l = x.createline('my')
l.x=[.001,.999,.999,.001,.001]
l.y=[.001,.001,.999,.999,.001]
x.plot(l,bg=bg)
support.check_plot(x)
x.plot(s,iso,bg=bg)
support.check_plot(x)
| [
"doutriaux1@llnl.gov"
] | doutriaux1@llnl.gov |
c3fc957bd8157028fc72a63f5e48786b003b968b | 321b4ed83b6874eeb512027eaa0b17b0daf3c289 | /77/77.combinations.664188670.Wrong-Answer.leetcode.python3.py | f9d4b9c263f19faa1a2c5bbe5831661b9d091441 | [] | no_license | huangyingw/submissions | 7a610613bdb03f1223cdec5f6ccc4391149ca618 | bfac1238ecef8b03e54842b852f6fec111abedfa | refs/heads/master | 2023-07-25T09:56:46.814504 | 2023-07-16T07:38:36 | 2023-07-16T07:38:36 | 143,352,065 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 353 | py | class Solution(object):
def combine(self, n, k):
res = []
self.get_combine(res, [], n, k, 1)
return res
def get_combine(self, res, prefix, n, k, start):
if k == 0:
res.append(list(prefix))
for idx in range(start, n + 1):
self.get_combine(res, prefix + [idx], n, k - 1, start + 1)
| [
"huangyingw@gmail.com"
] | huangyingw@gmail.com |
35651e65aea695ef813cc1faf53c12e1c4efeff5 | cbe264842df4eae3569b28ed4aae9489014ed23c | /codeit/algorithm/greedy_min_fee.py | cc107882b08662f5008275ceceac29457af32609 | [
"MIT"
] | permissive | zeroam/TIL | 31e176c2f4c3e1ef72b1155353690cc2f7160f96 | 43e3573be44c7f7aa4600ff8a34e99a65cbdc5d1 | refs/heads/master | 2021-07-23T01:43:34.135033 | 2021-07-10T06:47:17 | 2021-07-10T06:47:17 | 167,952,375 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 521 | py | def min_fee(pages_to_print):
sorted_pages_to_print = sorted(pages_to_print)
total_fee = 0
while sorted_pages_to_print:
size = len(sorted_pages_to_print)
minute = sorted_pages_to_print.pop(0)
total_fee += size * minute
return total_fee
if __name__ == '__main__':
from util import test_value
test_value(min_fee([6, 11, 4, 1]), 39)
test_value(min_fee([3, 2, 1]), 10)
test_value(min_fee([3, 1, 4, 3, 2]), 32)
test_value(min_fee([8, 4, 2, 3, 9, 23, 6, 8]), 188) | [
"imdff0803@gmail.com"
] | imdff0803@gmail.com |
df3c79870df90f3021b5a630eb28b3efd6fa07c0 | 6fcfb638fa725b6d21083ec54e3609fc1b287d9e | /python/numenta_nupic/nupic-master/tests/unit/nupic/algorithms/sp_overlap_test.py | 1ca3af5d62178bc471696f4ea91f01904d2a56cf | [] | no_license | LiuFang816/SALSTM_py_data | 6db258e51858aeff14af38898fef715b46980ac1 | d494b3041069d377d6a7a9c296a14334f2fa5acc | refs/heads/master | 2022-12-25T06:39:52.222097 | 2019-12-12T08:49:07 | 2019-12-12T08:49:07 | 227,546,525 | 10 | 7 | null | 2022-12-19T02:53:01 | 2019-12-12T07:29:39 | Python | UTF-8 | Python | false | false | 7,048 | py | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero Public License version 3 as
# published by the Free Software Foundation.
#
# 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 Affero Public License for more details.
#
# You should have received a copy of the GNU Affero Public License
# along with this program. If not, see http://www.gnu.org/licenses.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------
"""
This is a legacy test from trunk and may replicate spatial pooler tests.
The allocation of cells to new patterns is explored. After all the cells
have been allocated, cells must be reused. This test makes sure that the
allocation of new cells is such that we achieve maximum generality and
predictive power.
Note: Since the sp pooler has 2048 cells with a sparsity of 40 cells active
per iteration, 100% allocation is reached at the 51st unique pattern.
"""
import unittest2 as unittest
import random as rnd
import time
import numpy
from nupic.bindings.math import GetNTAReal
from nupic.encoders import scalar
from nupic.bindings.algorithms import SpatialPooler
realDType = GetNTAReal()
SEED = 42
class TestSPFrequency(unittest.TestCase):
def testCategory(self):
"""Test that the most frequent possible option is chosen for a scalar
encoded field """
self.frequency(n=100, w=21, seed=SEED, numColors=90, encoder = 'scalar')
def testScalar(self):
"""Test that the most frequent possible option is chosen for a category
encoded field """
self.frequency(n=30, w=21, seed=SEED, numColors=90, encoder = 'category')
@unittest.skip("Not working...")
def testScalarLong(self):
"""Test that the most frequent possible option is chosen for a scalar
encoded field. Run through many different numbers of patterns and random
seeds"""
for n in [52, 70, 80, 90, 100, 110]:
self.frequency(n=100, w=21, seed=SEED, numColors=n, encoder='scalar')
@unittest.skip("Not working...")
def testCategoryLong(self):
"""Test that the most frequent possible option is chosen for a category
encoded field. Run through many different numbers of patterns and random
seeds"""
for n in [52, 70, 80, 90, 100, 110]:
self.frequency(n=100, w=21, seed=SEED, numColors=n)
def frequency(self,
n=15,
w=7,
columnDimensions = 2048,
numActiveColumnsPerInhArea = 40,
stimulusThreshold = 0,
spSeed = 1,
spVerbosity = 0,
numColors = 2,
seed=42,
minVal=0,
maxVal=10,
encoder = 'category',
forced=True):
""" Helper function that tests whether the SP predicts the most
frequent record """
print "\nRunning SP overlap test..."
print encoder, 'encoder,', 'Random seed:', seed, 'and', numColors, 'colors'
#Setting up SP and creating training patterns
# Instantiate Spatial Pooler
spImpl = SpatialPooler(
columnDimensions=(columnDimensions, 1),
inputDimensions=(1, n),
potentialRadius=n/2,
numActiveColumnsPerInhArea=numActiveColumnsPerInhArea,
spVerbosity=spVerbosity,
stimulusThreshold=stimulusThreshold,
potentialPct=0.5,
seed=spSeed,
globalInhibition=True,
)
rnd.seed(seed)
numpy.random.seed(seed)
colors = []
coincs = []
reUsedCoincs = []
spOutput = []
patterns = set([])
# Setting up the encodings
if encoder=='scalar':
enc = scalar.ScalarEncoder(name='car', w=w, n=n, minval=minVal,
maxval=maxVal, periodic=False, forced=True) # forced: it's strongly recommended to use w>=21, in the example we force skip the check for readibility
for y in xrange(numColors):
temp = enc.encode(rnd.random()*maxVal)
colors.append(numpy.array(temp, dtype=realDType))
else:
for y in xrange(numColors):
sdr = numpy.zeros(n, dtype=realDType)
# Randomly setting w out of n bits to 1
sdr[rnd.sample(xrange(n), w)] = 1
colors.append(sdr)
# Training the sp
print 'Starting to train the sp on', numColors, 'patterns'
startTime = time.time()
for i in xrange(numColors):
# TODO: See https://github.com/numenta/nupic/issues/2072
spInput = colors[i]
onCells = numpy.zeros(columnDimensions)
spImpl.compute(spInput, True, onCells)
spOutput.append(onCells.tolist())
activeCoincIndices = set(onCells.nonzero()[0])
# Checking if any of the active cells have been previously active
reUsed = activeCoincIndices.intersection(patterns)
if len(reUsed) == 0:
# The set of all coincidences that have won at least once
coincs.append((i, activeCoincIndices, colors[i]))
else:
reUsedCoincs.append((i, activeCoincIndices, colors[i]))
# Adding the active cells to the set of coincs that have been active at
# least once
patterns.update(activeCoincIndices)
if (i + 1) % 100 == 0:
print 'Record number:', i + 1
print "Elapsed time: %.2f seconds" % (time.time() - startTime)
print len(reUsedCoincs), "re-used coinc(s),"
# Check if results match expectations
summ = []
for z in coincs:
summ.append(sum([len(z[1].intersection(y[1])) for y in reUsedCoincs]))
zeros = len([x for x in summ if x==0])
factor = max(summ)*len(summ)/sum(summ)
if len(reUsed) < 10:
self.assertLess(factor, 41,
"\nComputed factor: %d\nExpected Less than %d" % (
factor, 41))
self.assertLess(zeros, 0.99*len(summ),
"\nComputed zeros: %d\nExpected Less than %d" % (
zeros, 0.99*len(summ)))
else:
self.assertLess(factor, 8,
"\nComputed factor: %d\nExpected Less than %d" % (
factor, 8))
self.assertLess(zeros, 12,
"\nComputed zeros: %d\nExpected Less than %d" % (
zeros, 12))
def hammingDistance(s1, s2):
assert len(s1) == len(s2)
return sum(ch1 != ch2 for ch1, ch2 in zip(s1, s2))
if __name__ == '__main__':
unittest.main()
| [
"659338505@qq.com"
] | 659338505@qq.com |
d56407a6b0db2c1f4b194d83c021e874ad9414d2 | 4e7db10524c938c8c6e687521def2889e20ec646 | /P16/1-7.py | 62f18ba79452ebca3058388918cdc5cc264b5f6c | [] | no_license | mpigrobot/python | e5cf60ca438e0d5e63a1e87a266a9e255bc07271 | bf9262657a7401f37de38318db768e630fab97a9 | refs/heads/master | 2020-03-15T15:37:03.228080 | 2018-03-31T07:33:07 | 2018-03-31T07:33:07 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 520 | py | #!/usr/bin/env python
# coding:utf-8
import sys
sys.path.append("../")
import jieba
print ", ".join(jieba.cut("大连美容美发学校中君意是你值得信赖的选择"))
jieba.load_userdict( 'C:\Users\Administrator\Desktop\dir1.txt')
print ", ".join(jieba.cut("大连美容美发学校中君意是你值得信赖的选择"))
# jieba.suggest_freq('君意', True)
jieba.add_word("君意", freq = 20000, tag = None)
print ", ".join(jieba.cut("大连美容美发学校中君意是你值得信赖的选择")) | [
"noreply@github.com"
] | mpigrobot.noreply@github.com |
eda674ee22d94b233e6ae2676f25b8efc5cdcd5b | ca5e3595a9949abba08c642842166f82d768c153 | /modulation.py | f70357fbf07fdbf03eccb00b5de4dd5781fa470c | [] | no_license | MaksimKulya/PromaX | f36980300ec564a0c8d523f3e07ebf9ed2a9b262 | e723cf4390f9cdcc4bbda627dff90a2185322a54 | refs/heads/master | 2023-06-25T13:13:09.968726 | 2021-07-13T15:09:37 | 2021-07-13T15:09:37 | 385,638,675 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 612 | py | import matplotlib.pyplot as plt
import math
import cmath
import pylab
from matplotlib import mlab
from PIL import Image
import numpy as np
import scipy.constants
import numba as nb
pi=math.pi
@nb.njit
def modulation(Nx,Ny,am,ph,nu,n,c,h):
AM = np.zeros(shape=(nu.shape[0], Nx, Ny))
PH = np.zeros(shape=(nu.shape[0], Nx, Ny))
G_object = np.zeros(shape=(nu.shape[0], Nx, Ny), dtype=nb.types.complex64)
for k in range(nu.shape[0]):
AM[k, :, :] = am
PH[k, :, :] = ph * (2*pi*nu[k]*(n-1)*h/c)
G_object[k, :, :] = AM[k, :, :] * np.exp(1j * PH[k, :, :])
return G_object
| [
"maxk2350@yandex.ru"
] | maxk2350@yandex.ru |
182d7ea8a9a297586f8be5758698b81f665b8e65 | 5c7da7dabdc076ad7113ccd20561a8bbf5f9a70e | /portfolios/migrations/0007_auto_20200215_1347.py | 9bae61e5b98ca60a0561156c6c192a93b6cb9cd2 | [] | no_license | aqcloudacio/cloudaciofeez | 2499fb5fc5334fa871daab2abea6c34bfa8c7667 | 8399560ece9aa10a6d6801f42c027dca26a65936 | refs/heads/master | 2023-02-27T22:36:20.501159 | 2021-02-11T00:03:46 | 2021-02-11T00:03:46 | 337,887,413 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 788 | py | # Generated by Django 2.2.7 on 2020-02-15 02:47
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('portfolios', '0006_auto_20200215_1344'),
]
operations = [
migrations.AlterField(
model_name='portfolio',
name='platform',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='portfolios', to='platforms.Platform'),
),
migrations.AlterField(
model_name='portfolio',
name='platform_fee_group',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='portfolios', to='platforms.PlatformFees'),
),
]
| [
"alejandro.quintero@clouxter.com"
] | alejandro.quintero@clouxter.com |
855afa2e49764022f6489b06d28787448896f33d | 84a96dbd96e926ebb5c658e3cb897db276c32d6c | /tensorflow/python/keras/optimizer_v2/adamax.py | 9166f637c1e9a7f1cefb35436a6db667ff59ab84 | [
"Apache-2.0"
] | permissive | MothCreations/gavlanWheels | bc9189092847369ad291d1c7d3f4144dd2239359 | 01d8a43b45a26afec27b971f686f79c108fe08f9 | refs/heads/master | 2022-12-06T09:27:49.458800 | 2020-10-13T21:56:40 | 2020-10-13T21:56:40 | 249,206,716 | 6 | 5 | Apache-2.0 | 2022-11-21T22:39:47 | 2020-03-22T14:57:45 | C++ | UTF-8 | Python | false | false | 8,043 | py | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Adamax for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.keras import backend_config
from tensorflow.python.keras.optimizer_v2 import optimizer_v2
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.training import training_ops
from tensorflow.python.util.tf_export import keras_export
@keras_export('keras.optimizers.Adamax')
class Adamax(optimizer_v2.OptimizerV2):
"""Optimizer that implements the Adamax algorithm.
It is a variant of Adam based on the infinity norm.
Default parameters follow those provided in the paper.
Adamax is sometimes superior to adam, specially in models with embeddings.
Initialization:
```
m_0 <- 0 (Initialize initial 1st moment vector)
v_0 <- 0 (Initialize the exponentially weighted infinity norm)
t <- 0 (Initialize timestep)
```
The update rule for `variable` with gradient `g` uses an optimization
described at the end of section 7.1 of the paper:
```
t <- t + 1
m_t <- beta1 * m_{t-1} + (1 - beta1) * g
v_t <- max(beta2 * v_{t-1}, abs(g))
variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon)
```
Similar to AdamOptimizer, the epsilon is added for numerical stability
(especially to get rid of division by zero when v_t = 0).
Contrast to AdamOptimizer, the sparse implementation of this algorithm
(used when the gradient is an IndexedSlices object, typically because of
`tf.gather` or an embedding lookup in the forward pass) only updates
variable slices and corresponding `m_t`, `v_t` terms when that part of
the variable was used in the forward pass. This means that the sparse
behavior is contrast to the dense behavior (similar to some momentum
implementations which ignore momentum unless a variable slice was actually
used).
References
see Section 7 of [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
([pdf](http://arxiv.org/pdf/1412.6980.pdf)).
"""
_HAS_ALL_REDUCE_SUM_GRAD = True
def __init__(self,
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-7,
name='Adamax',
**kwargs):
"""Construct a new Adamax optimizer.
Args:
learning_rate: A `Tensor`, floating point value, or a schedule that is a
`tf.keras.optimizers.schedules.LearningRateSchedule`. The learning rate.
beta_1: A float value or a constant float tensor. The exponential decay
rate for the 1st moment estimates.
beta_2: A float value or a constant float tensor. The exponential decay
rate for the exponentially weighted infinity norm.
epsilon: A small constant for numerical stability.
name: Optional name for the operations created when applying gradients.
Defaults to "Adamax".
**kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`,
`decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip
gradients by value, `decay` is included for backward compatibility to
allow time inverse decay of learning rate. `lr` is included for backward
compatibility, recommended to use `learning_rate` instead.
"""
super(Adamax, self).__init__(name, **kwargs)
self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
self._set_hyper('decay', self._initial_decay)
self._set_hyper('beta_1', beta_1)
self._set_hyper('beta_2', beta_2)
self.epsilon = epsilon or backend_config.epsilon()
def _create_slots(self, var_list):
# Separate for-loops to respect the ordering of slot variables from v1.
for var in var_list:
self.add_slot(var, 'm') # Create slots for the first moments.
for var in var_list:
self.add_slot(var, 'v') # Create slots for the second moments.
def _prepare_local(self, var_device, var_dtype, apply_state):
super(Adamax, self)._prepare_local(var_device, var_dtype, apply_state)
local_step = math_ops.cast(self.iterations + 1, var_dtype)
beta_1_t = array_ops.identity(self._get_hyper('beta_1', var_dtype))
beta_2_t = array_ops.identity(self._get_hyper('beta_2', var_dtype))
beta_1_power = math_ops.pow(beta_1_t, local_step)
lr_t = apply_state[(var_device, var_dtype)]['lr_t']
apply_state[(var_device, var_dtype)].update(
dict(
neg_scaled_lr=-lr_t / (1 - beta_1_power),
epsilon=ops.convert_to_tensor_v2(self.epsilon, var_dtype),
beta_1_t=beta_1_t,
beta_1_power=beta_1_power,
one_minus_beta_1_t=1 - beta_1_t,
beta_2_t=beta_2_t,
zero=array_ops.zeros((), dtype=dtypes.int64)))
def _resource_apply_dense(self, grad, var, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype))
or self._fallback_apply_state(var_device, var_dtype))
m = self.get_slot(var, 'm')
v = self.get_slot(var, 'v')
return training_ops.resource_apply_ada_max(
var.handle,
m.handle,
v.handle,
coefficients['beta_1_power'],
coefficients['lr_t'],
coefficients['beta_1_t'],
coefficients['beta_2_t'],
coefficients['epsilon'],
grad,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = ((apply_state or {}).get((var_device, var_dtype))
or self._fallback_apply_state(var_device, var_dtype))
# m_t = beta1 * m + (1 - beta1) * g_t
m = self.get_slot(var, 'm')
m_slice = array_ops.gather(m, indices, axis=coefficients['zero'])
m_t_slice = (m_slice * coefficients['beta_1_t'] +
grad * coefficients['one_minus_beta_1_t'])
with ops.control_dependencies([m_t_slice]):
m_t = self._resource_scatter_update(m, indices, m_t_slice)
# u_t = max(beta2 * u, abs(g_t))
v = self.get_slot(var, 'v')
v_slice = array_ops.gather(v, indices, axis=coefficients['zero'])
v_t_slice = math_ops.maximum(v_slice * coefficients['beta_2_t'],
math_ops.abs(grad))
with ops.control_dependencies([v_t_slice]):
v_t = self._resource_scatter_update(v, indices, v_t_slice)
# theta_t = theta - lr / (1 - beta1^t) * m_t / u_t
var_slice = coefficients['neg_scaled_lr'] * (
m_t_slice / (v_t_slice + coefficients['epsilon']))
with ops.control_dependencies([var_slice]):
var_update = self._resource_scatter_add(var, indices, var_slice)
return control_flow_ops.group(*[var_update, m_t, v_t])
def get_config(self):
config = super(Adamax, self).get_config()
config.update({
'learning_rate': self._serialize_hyperparameter('learning_rate'),
'decay': self._serialize_hyperparameter('decay'),
'beta_1': self._serialize_hyperparameter('beta_1'),
'beta_2': self._serialize_hyperparameter('beta_2'),
'epsilon': self.epsilon,
})
return config
| [
"gardener@tensorflow.org"
] | gardener@tensorflow.org |
5e873a127a5b816d0d8b4e502262fb066ca2608d | 2d82d4c6574bd6d32f2cf1c781615f7951f55f66 | /muntjac/demo/sampler/features/trees/TreeSingleSelect.py | d07a2e355077b83ac72f563a77bb096ebf2612c1 | [
"Apache-2.0"
] | permissive | metaperl/muntjac | f83f745ee03942a61af92ee7fba7285aa9c46f3c | 8db97712edd81b4d25deaaa48587d2a08010f2c8 | refs/heads/master | 2021-01-15T22:04:25.057862 | 2012-11-09T03:52:59 | 2012-11-09T03:52:59 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 887 | py |
from muntjac.ui.tree import Tree
from muntjac.demo.sampler.features.trees.TreeMultiSelect import TreeMultiSelect
from muntjac.demo.sampler.features.trees.TreeActions import TreeActions
from muntjac.demo.sampler.APIResource import APIResource
from muntjac.demo.sampler.Feature import Feature, Version
class TreeSingleSelect(Feature):
def getSinceVersion(self):
return Version.OLD
def getName(self):
return 'Tree, single selection'
def getDescription(self):
return ('In this example, you can select any single tree node and '
'modify its \'name\' property. Click again to de-select.')
def getRelatedAPI(self):
return [APIResource(Tree)]
def getRelatedFeatures(self):
return [TreeMultiSelect, TreeActions]
def getRelatedResources(self):
# TODO Auto-generated method stub
return None
| [
"r.w.lincoln@gmail.com"
] | r.w.lincoln@gmail.com |
3fb6faabd50d6e4fec8f682bbab921a976447f7b | 60a831fb3c92a9d2a2b52ff7f5a0f665d4692a24 | /IronPythonStubs/release/stubs.min/Autodesk/Revit/UI/__init___parts/TaskDialogResult.py | 6464317c49a8ed24e79672214c635e272cf50cb8 | [
"MIT"
] | permissive | shnlmn/Rhino-Grasshopper-Scripts | a9411098c5d1bbc55feb782def565d535b27b709 | 0e43c3c1d09fb12cdbd86a3c4e2ba49982e0f823 | refs/heads/master | 2020-04-10T18:59:43.518140 | 2020-04-08T02:49:07 | 2020-04-08T02:49:07 | 161,219,695 | 11 | 2 | null | null | null | null | UTF-8 | Python | false | false | 1,153 | py | class TaskDialogResult(Enum,IComparable,IFormattable,IConvertible):
"""
Enum to specify the task dialog result.
enum TaskDialogResult,values: Cancel (2),Close (8),CommandLink1 (1001),CommandLink2 (1002),CommandLink3 (1003),CommandLink4 (1004),No (7),None (0),Ok (1),Retry (4),Yes (6)
"""
def __eq__(self,*args):
""" x.__eq__(y) <==> x==yx.__eq__(y) <==> x==yx.__eq__(y) <==> x==y """
pass
def __format__(self,*args):
""" __format__(formattable: IFormattable,format: str) -> str """
pass
def __ge__(self,*args):
pass
def __gt__(self,*args):
pass
def __init__(self,*args):
""" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """
pass
def __le__(self,*args):
pass
def __lt__(self,*args):
pass
def __ne__(self,*args):
pass
def __reduce_ex__(self,*args):
pass
def __str__(self,*args):
pass
Cancel=None
Close=None
CommandLink1=None
CommandLink2=None
CommandLink3=None
CommandLink4=None
No=None
None=None
Ok=None
Retry=None
value__=None
Yes=None
| [
"magnetscoil@gmail.com"
] | magnetscoil@gmail.com |
a6a2cdc64be78791ddd99b63741b386489d36ecf | 5746d26f891270c1bb407a244d9a942534298d96 | /fastreid/data/build.py | e7005a90fcb391336d9acc7f3280546059c9cbf6 | [
"Apache-2.0"
] | permissive | winterxx/fast-reid | 1463253c43876249dd55a3adb0a3e71fa8037aa3 | 727a7468311949efbbc7be360c2c1afaf440bb22 | refs/heads/master | 2022-10-20T17:50:39.245472 | 2020-06-16T03:46:22 | 2020-06-16T03:46:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,131 | py | # encoding: utf-8
"""
@author: l1aoxingyu
@contact: sherlockliao01@gmail.com
"""
import torch
from torch._six import container_abcs, string_classes, int_classes
from torch.utils.data import DataLoader
from . import samplers
from .common import CommDataset
from .datasets import DATASET_REGISTRY
from .transforms import build_transforms
def build_reid_train_loader(cfg):
train_transforms = build_transforms(cfg, is_train=True)
train_items = list()
for d in cfg.DATASETS.NAMES:
dataset = DATASET_REGISTRY.get(d)(combineall=cfg.DATASETS.COMBINEALL)
dataset.show_train()
train_items.extend(dataset.train)
train_set = CommDataset(train_items, train_transforms, relabel=True)
num_workers = cfg.DATALOADER.NUM_WORKERS
batch_size = cfg.SOLVER.IMS_PER_BATCH
num_instance = cfg.DATALOADER.NUM_INSTANCE
if cfg.DATALOADER.PK_SAMPLER:
if cfg.DATALOADER.NAIVE_WAY:
data_sampler = samplers.NaiveIdentitySampler(train_set.img_items, batch_size, num_instance)
else:
data_sampler = samplers.BalancedIdentitySampler(train_set.img_items, batch_size, num_instance)
else:
data_sampler = samplers.TrainingSampler(len(train_set))
batch_sampler = torch.utils.data.sampler.BatchSampler(data_sampler, batch_size, True)
train_loader = torch.utils.data.DataLoader(
train_set,
num_workers=num_workers,
batch_sampler=batch_sampler,
collate_fn=fast_batch_collator,
)
return train_loader
def build_reid_test_loader(cfg, dataset_name):
test_transforms = build_transforms(cfg, is_train=False)
dataset = DATASET_REGISTRY.get(dataset_name)()
dataset.show_test()
test_items = dataset.query + dataset.gallery
test_set = CommDataset(test_items, test_transforms, relabel=False)
num_workers = cfg.DATALOADER.NUM_WORKERS
batch_size = cfg.TEST.IMS_PER_BATCH
data_sampler = samplers.InferenceSampler(len(test_set))
batch_sampler = torch.utils.data.BatchSampler(data_sampler, batch_size, False)
test_loader = DataLoader(
test_set,
batch_sampler=batch_sampler,
num_workers=num_workers,
collate_fn=fast_batch_collator)
return test_loader, len(dataset.query)
def trivial_batch_collator(batch):
"""
A batch collator that does nothing.
"""
return batch
def fast_batch_collator(batched_inputs):
"""
A simple batch collator for most common reid tasks
"""
elem = batched_inputs[0]
if isinstance(elem, torch.Tensor):
out = torch.zeros((len(batched_inputs), *elem.size()), dtype=elem.dtype)
for i, tensor in enumerate(batched_inputs):
out[i] += tensor
return out
elif isinstance(elem, container_abcs.Mapping):
return {key: fast_batch_collator([d[key] for d in batched_inputs]) for key in elem}
elif isinstance(elem, float):
return torch.tensor(batched_inputs, dtype=torch.float64)
elif isinstance(elem, int_classes):
return torch.tensor(batched_inputs)
elif isinstance(elem, string_classes):
return batched_inputs
| [
"sherlockliao01@gmail.com"
] | sherlockliao01@gmail.com |
5350c7e34a18d8cebb9c9bcc45be9ec798fde418 | 684a7d56589f7b96002646dfc26ba2de52eb7d80 | /source/callback/eval_mscoco.py | ecc1b5db5b7e1be53cffa13367cc079c536f8f70 | [
"Apache-2.0"
] | permissive | adewin/lambda-deep-learning-demo | 7a42b935ca1ab1e92a0170bf28c7e526cffa5cb6 | ebbbd63c0abf87a1a4155b17cef145039b7a1ef7 | refs/heads/master | 2020-07-08T13:15:51.476791 | 2019-04-26T21:25:44 | 2019-04-26T21:25:44 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,269 | py | """
Copyright 2018 Lambda Labs. All Rights Reserved.
Licensed under
==========================================================================
"""
import os
import numpy as np
from scipy import misc
import tensorflow as tf
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
from .callback import Callback
DATASET_DIR = "/mnt/data/data/mscoco"
# DATASET_META = "val2017"
DATASET_META = "val2014"
COCO_ID_MAP = np.asarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38,
39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,
62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76,
77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88,
89, 90])
class EvalMSCOCO(Callback):
def __init__(self, config):
super(EvalMSCOCO, self).__init__(config)
self.detection = []
self.image_ids = []
def before_run(self, sess):
self.graph = tf.get_default_graph()
def after_run(self, sess):
print("Detection Finished ...")
# for item in self.detection:
# print(item)
if len(self.detection) > 0:
annotation_file = os.path.join(
DATASET_DIR,
"annotations",
"instances_" + DATASET_META + ".json")
coco = COCO(annotation_file)
coco_results = coco.loadRes(self.detection)
# DETECTION_FILE = "/home/ubuntu/data/mscoco/results/SSD_512x512_score/detections_minival_ssd512_results.json"
# coco_results = coco.loadRes(DETECTION_FILE)
cocoEval = COCOeval(coco, coco_results, "bbox")
cocoEval.params.imgIds = self.image_ids
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
else:
print("Found no valid detection. Consider re-train your model.")
def after_step(self, sess, outputs_dict, feed_dict=None):
num_images = len(outputs_dict["image_id"])
# print(num_images)
# print('----------------------')
for i in range(num_images):
file_name = outputs_dict["file_name"][i][0]
# print(file_name)
num_detections = len(outputs_dict["labels"][i])
translation = outputs_dict["translations"][i]
scale = outputs_dict["scales"][i]
input_image = misc.imread(file_name)
h, w = input_image.shape[:2]
# COCO evaluation is based on per detection
for d in range(num_detections):
box = outputs_dict["bboxes"][i][d]
box = box * [float(w), float(h), float(w), float(h)]
box[0] = np.clip(box[0], 0, w)
box[1] = np.clip(box[1], 0, h)
box[2] = np.clip(box[2], 0, w)
box[3] = np.clip(box[3], 0, h)
box[2] = box[2] - box[0]
box[3] = box[3] - box[1]
result = {
"image_id": outputs_dict["image_id"][i][0],
"category_id": COCO_ID_MAP[outputs_dict["labels"][i][d]],
"bbox": box,
"score": outputs_dict["scores"][i][d]
}
self.detection.append(result)
self.image_ids.append(outputs_dict["image_id"][i][0])
def build(config):
return EvalMSCOCO(config)
| [
"cl.chuanli@gmail.com"
] | cl.chuanli@gmail.com |
d7a7d2dea431fd8d3dc35b48022975e66ec20183 | f281d0d6431c1b45c6e5ebfff5856c374af4b130 | /DAY001~099/DAY46-BOJ2533-사회망 서비스(SNS)/younghoon.py | 38fcc0c3116cb0e7f54d7a6dd1b9073362b4c486 | [] | no_license | tachyon83/code-rhino | ec802dc91dce20980fac401b26165a487494adb4 | b1af000f5798cd12ecdab36aeb9c7a36f91c1101 | refs/heads/master | 2022-08-13T09:10:16.369287 | 2022-07-30T11:27:34 | 2022-07-30T11:27:34 | 292,142,812 | 5 | 6 | null | null | null | null | UTF-8 | Python | false | false | 634 | py | '''
이해못했습니당 ㅠ
dp공부를 더하고 봐야겠어요
'''
import sys
sys.setrecursionlimit(10**9)
N=int(sys.stdin.readline())
Tree=[[] for _ in range(N+1)]
check=[0 for _ in range(N+1)]
for _ in range(N-1):
u,v=map(int,sys.stdin.readline().split())
Tree[u].append(v)
Tree[v].append(u)
DP=[[0,0] for _ in range(N+1)]
check=[True for _ in range(N+1)]
def DFS(cur):
check[cur]=False
DP[cur][0]=1
DP[cur][1]=0
for i in Tree[cur]:
if check[i]:
DFS(i)
DP[cur][0]+=DP[i][1]
DP[cur][1]+=max(DP[i][0],DP[i][1])
DFS(1)
print(N-max(DP[1][0],DP[1][1]))
| [
"noreply@github.com"
] | tachyon83.noreply@github.com |
9a74952d6bcbbe9d6c9c34e92a33ccbe56808a6b | 70e9a7da3d4e2a41b30544516e166dab2495253c | /payment_trustcode/controllers/main.py | 050edccfc195b6c8248b0c16dcd7b39d3caf7dfa | [
"MIT"
] | permissive | Trust-Code/odoo-brasil | bf06ea58a4e0376cb5c297c18bf48eaf97104e54 | d456a10e32f56e259061afbd989942ea1aae2c2d | refs/heads/16.0 | 2023-08-31T16:06:21.038792 | 2023-01-26T19:31:31 | 2023-01-26T19:31:31 | 72,882,959 | 206 | 253 | MIT | 2023-08-18T17:05:49 | 2016-11-04T20:28:03 | Python | UTF-8 | Python | false | false | 757 | py | import logging
from odoo import http
from odoo.http import request
from werkzeug.utils import redirect
_logger = logging.getLogger(__name__)
class IuguController(http.Controller):
_notify_url = '/iugu/notificacao/'
@http.route(
'/iugu/notificacao/', type='http', auth="none",
methods=['GET', 'POST'], csrf=False)
def iugu_form_feedback(self, **post):
request.env['payment.transaction'].sudo().form_feedback(post, 'iugu')
return "<status>OK</status>"
@http.route(
'/iugu/checkout/redirect', type='http',
auth='none', methods=['GET', 'POST'])
def iugu_checkout_redirect(self, **post):
post = post
if 'secure_url' in post:
return redirect(post['secure_url'])
| [
"danimaribeiro@gmail.com"
] | danimaribeiro@gmail.com |
18480acf2489cd737fa1a54137dc34a18873c149 | fc43470de13ff8f03105efc2a3660a1ed6a1a553 | /LeetCode/207_CourseSchedule.py | 168da2eb51e923ae31c007d923cd1b462ab06a0c | [] | no_license | youseop/Problem_solutions | 5a05597f188b4ef8f7d8483b46bf05fbf2158d01 | 1fba638d9520bca4354bca01f194f80b159e26aa | refs/heads/master | 2023-06-24T05:12:45.060086 | 2021-07-24T14:22:33 | 2021-07-24T14:22:33 | 298,317,735 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 841 | py | class Solution:
def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:
from collections import deque as dq
course = list(0 for _ in range(numCourses))
need = dict()
for i in range(numCourses):
need[i] = []
for a, b in prerequisites:
course[a] += 1
need[b].append(a)
cnt = 0
point = dq([])
for i in range(numCourses):
if course[i] == 0:
point.append(i)
cnt += 1
while point:
a = point.popleft()
for i in need[a]:
course[i] -= 1
if course[i] == 0:
point.append(i)
cnt += 1
if cnt == numCourses:
return True
else:
return False
| [
"66366941+youseop@users.noreply.github.com"
] | 66366941+youseop@users.noreply.github.com |
aa316048e3e95c342aba666bf410bbb7cf9b543b | a667b52cb8d2ec857c55d33f04fc0e81d36dc681 | /options/data/real/Reco16_Run179101_DV.py | 4672a9ee45d24e2d7f95dfea3e8b6b5847ee3163 | [] | no_license | wenyanyin/CP_violation_simulation | 639d73333a3795654275cb43cc7dad7c742d1be1 | 7b93b2fe1050fb30d0b809b758cd5a3b2824b875 | refs/heads/master | 2022-04-29T14:19:23.744004 | 2022-04-01T13:05:18 | 2022-04-01T13:05:18 | 168,570,282 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 286 | py | from Configurables import DaVinci
from Gaudi.Configuration import importOptions
DaVinci().DataType = '2016'
DaVinci().InputType = 'RDST'
DaVinci().DDDBtag = 'dddb-20150724'
DaVinci().CondDBtag = 'cond-20170325'
importOptions('$APPCONFIGOPTS/DaVinci/DV-RawEventJuggler-0_3-to-4_2.py')
| [
"Michael.Alexander@glasgow.ac.uk"
] | Michael.Alexander@glasgow.ac.uk |
65b74da4eede267474cc9a56a3e2901994358f6a | 06c367fe2d2233c6efb64f323e15bebd7f48c625 | /saleor/product/urls.py | c3159a1923f41052ea09b230dba75e681991402d | [
"BSD-3-Clause"
] | permissive | AkioSky/FishMart | ce630bc4addf63bc105e4f3e13e92c15b119b558 | 1d01d7e79812dc7cccb1b26ffc6457af6104d9f2 | refs/heads/master | 2022-12-11T16:13:38.277080 | 2019-04-22T03:44:22 | 2019-04-22T03:44:22 | 182,615,627 | 0 | 0 | BSD-3-Clause | 2022-12-08T01:44:37 | 2019-04-22T03:20:03 | Python | UTF-8 | Python | false | false | 585 | py | from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^$', views.product_list, name='list'),
url(r'^(?P<slug>[a-z0-9-_]+?)-(?P<product_id>[0-9]+)/$',
views.product_details, name='details'),
url(r'^category/(?P<slug>[a-z0-9-_]+?)-(?P<category_id>[0-9]+)/$',
views.category_index, name='category'),
url(r'(?P<slug>[a-z0-9-_]+?)-(?P<product_id>[0-9]+)/add/$',
views.product_add_to_cart, name='add-to-cart'),
url(r'^collection/(?P<slug>[a-z0-9-_/]+?)-(?P<pk>[0-9]+)/$',
views.collection_index, name='collection')]
| [
"whitebirdinbluesky1990@gmail.com"
] | whitebirdinbluesky1990@gmail.com |
5221a3cd94c2ccee498f920816799911eaf1cb15 | de24f83a5e3768a2638ebcf13cbe717e75740168 | /moodledata/vpl_data/380/usersdata/277/103384/submittedfiles/principal.py | e2edee6fa3b2efd552caa85372bac86a66d41972 | [] | no_license | rafaelperazzo/programacao-web | 95643423a35c44613b0f64bed05bd34780fe2436 | 170dd5440afb9ee68a973f3de13a99aa4c735d79 | refs/heads/master | 2021-01-12T14:06:25.773146 | 2017-12-22T16:05:45 | 2017-12-22T16:05:45 | 69,566,344 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 522 | py | # 1. Ler uma matriz m,n de inteiros
while(True):
m = int(input('Digite a quantidade de linhas: '))
if m >= 1 and m <= 100:
break
else:
print('Numero invalido. Digite entre 1 e 100 (inclusive)')
while(True):
n = int(input('Digite a quantidade de colunas: '))
if n >= 1 and n <= 100:
break
else:
print('Numero invalido. Digite entre 1 e 100 (inclusive)')
# 2. Receber um intinerario a percorrer
# - uma lista de inteiros
# 3. Calcular o custo do trajeto informado | [
"rafael.mota@ufca.edu.br"
] | rafael.mota@ufca.edu.br |
3aacfc56559b1cc3148120eb9b169bee48276dd4 | 9e988c0dfbea15cd23a3de860cb0c88c3dcdbd97 | /sdBs/AllRun/pg_1536+690/sdB_pg_1536+690_lc.py | 110380af2de8d87ee8d50b1b95d32ee08d79b9b4 | [] | no_license | tboudreaux/SummerSTScICode | 73b2e5839b10c0bf733808f4316d34be91c5a3bd | 4dd1ffbb09e0a599257d21872f9d62b5420028b0 | refs/heads/master | 2021-01-20T18:07:44.723496 | 2016-08-08T16:49:53 | 2016-08-08T16:49:53 | 65,221,159 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 345 | py | from gPhoton.gAperture import gAperture
def main():
gAperture(band="NUV", skypos=[234.203458,68.869067], stepsz=30., csvfile="/data2/fleming/GPHOTON_OUTPU/LIGHTCURVES/sdBs/sdB_pg_1536+690/sdB_pg_1536+690_lc.csv", maxgap=1000., overwrite=True, radius=0.00555556, annulus=[0.005972227,0.0103888972], verbose=3)
if __name__ == "__main__":
main()
| [
"thomas@boudreauxmail.com"
] | thomas@boudreauxmail.com |
ea3a6a6002d4c5c1dd9374b6e70e4f7feb25bf0a | 41f39d013ae3cb2b3ca4230c77b9037cc9c894f6 | /gym/gym/envs/tests/spec_list.py | b9596408afb70ecdad341bf20530ecc72bf0ef7c | [
"MIT",
"LicenseRef-scancode-generic-cla"
] | permissive | sokol1412/rllab_hierarchical_rl | 162aec9bb06e271d12333fa072fb44d692c26301 | 6d46c02e32c3d7e9ac55d753d6a3823ff86c5a57 | refs/heads/master | 2020-03-07T07:37:39.510301 | 2018-08-19T11:54:56 | 2018-08-19T11:54:56 | 127,353,660 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,181 | py | from gym.gym import envs
import os
import logging
logger = logging.getLogger(__name__)
def should_skip_env_spec_for_tests(spec):
# We skip tests for envs that require dependencies or are otherwise
# troublesome to run frequently
ep = spec._entry_point
# Skip mujoco tests for pull request CI
skip_mujoco = not (os.environ.get('MUJOCO_KEY_BUNDLE') or os.path.exists(os.path.expanduser('~/.mujoco')))
if skip_mujoco and ep.startswith('gym.envs.mujoco:'):
return True
if ( 'GoEnv' in ep or
'HexEnv' in ep or
ep.startswith('gym.envs.box2d:') or
ep.startswith('gym.envs.box2d:') or
ep.startswith('gym.envs.parameter_tuning:') or
ep.startswith('gym.envs.safety:Semisuper') or
(ep.startswith("gym.envs.atari") and not spec.id.startswith("Pong") and not spec.id.startswith("Seaquest"))
):
logger.warning("Skipping tests for env {}".format(ep))
return True
return False
spec_list = [spec for spec in sorted(envs.registry.all(), key=lambda x: x.id) if spec._entry_point is not None and not should_skip_env_spec_for_tests(spec)]
| [
"wlasek1412@gmail.com"
] | wlasek1412@gmail.com |
e279ab7f4f8a7694db03f67ed2b49a5684138c0b | 70bee1e4e770398ae7ad9323bd9ea06f279e2796 | /openapi_client/models/types_console_certificate_settings.py | ce30f655952ee5e232890ddeccf76d3bd9a86194 | [] | no_license | hi-artem/twistlock-py | c84b420b1e582b3c4cf3631eb72dac6d659d4746 | 9888e905f5b9d3cc00f9b84244588c0992f8e4f4 | refs/heads/main | 2023-07-18T07:57:57.705014 | 2021-08-22T04:36:33 | 2021-08-22T04:36:33 | 398,637,698 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,974 | py | # coding: utf-8
"""
Prisma Cloud Compute API
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: 21.04.439
Generated by: https://openapi-generator.tech
"""
try:
from inspect import getfullargspec
except ImportError:
from inspect import getargspec as getfullargspec
import pprint
import re # noqa: F401
import six
from openapi_client.configuration import Configuration
class TypesConsoleCertificateSettings(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
openapi_types = {
'check_revocation': 'bool',
'console_ca_cert': 'str',
'console_custom_cert': 'CommonSecret',
'hpkp': 'TypesHPKPSettings'
}
attribute_map = {
'check_revocation': 'checkRevocation',
'console_ca_cert': 'consoleCaCert',
'console_custom_cert': 'consoleCustomCert',
'hpkp': 'hpkp'
}
def __init__(self, check_revocation=None, console_ca_cert=None, console_custom_cert=None, hpkp=None, local_vars_configuration=None): # noqa: E501
"""TypesConsoleCertificateSettings - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration.get_default_copy()
self.local_vars_configuration = local_vars_configuration
self._check_revocation = None
self._console_ca_cert = None
self._console_custom_cert = None
self._hpkp = None
self.discriminator = None
if check_revocation is not None:
self.check_revocation = check_revocation
if console_ca_cert is not None:
self.console_ca_cert = console_ca_cert
if console_custom_cert is not None:
self.console_custom_cert = console_custom_cert
if hpkp is not None:
self.hpkp = hpkp
@property
def check_revocation(self):
"""Gets the check_revocation of this TypesConsoleCertificateSettings. # noqa: E501
CheckRevocation indicates whether cert revocation status is required. # noqa: E501
:return: The check_revocation of this TypesConsoleCertificateSettings. # noqa: E501
:rtype: bool
"""
return self._check_revocation
@check_revocation.setter
def check_revocation(self, check_revocation):
"""Sets the check_revocation of this TypesConsoleCertificateSettings.
CheckRevocation indicates whether cert revocation status is required. # noqa: E501
:param check_revocation: The check_revocation of this TypesConsoleCertificateSettings. # noqa: E501
:type check_revocation: bool
"""
self._check_revocation = check_revocation
@property
def console_ca_cert(self):
"""Gets the console_ca_cert of this TypesConsoleCertificateSettings. # noqa: E501
ConsoleCACert is a custom CA certificate for the console. # noqa: E501
:return: The console_ca_cert of this TypesConsoleCertificateSettings. # noqa: E501
:rtype: str
"""
return self._console_ca_cert
@console_ca_cert.setter
def console_ca_cert(self, console_ca_cert):
"""Sets the console_ca_cert of this TypesConsoleCertificateSettings.
ConsoleCACert is a custom CA certificate for the console. # noqa: E501
:param console_ca_cert: The console_ca_cert of this TypesConsoleCertificateSettings. # noqa: E501
:type console_ca_cert: str
"""
self._console_ca_cert = console_ca_cert
@property
def console_custom_cert(self):
"""Gets the console_custom_cert of this TypesConsoleCertificateSettings. # noqa: E501
:return: The console_custom_cert of this TypesConsoleCertificateSettings. # noqa: E501
:rtype: CommonSecret
"""
return self._console_custom_cert
@console_custom_cert.setter
def console_custom_cert(self, console_custom_cert):
"""Sets the console_custom_cert of this TypesConsoleCertificateSettings.
:param console_custom_cert: The console_custom_cert of this TypesConsoleCertificateSettings. # noqa: E501
:type console_custom_cert: CommonSecret
"""
self._console_custom_cert = console_custom_cert
@property
def hpkp(self):
"""Gets the hpkp of this TypesConsoleCertificateSettings. # noqa: E501
:return: The hpkp of this TypesConsoleCertificateSettings. # noqa: E501
:rtype: TypesHPKPSettings
"""
return self._hpkp
@hpkp.setter
def hpkp(self, hpkp):
"""Sets the hpkp of this TypesConsoleCertificateSettings.
:param hpkp: The hpkp of this TypesConsoleCertificateSettings. # noqa: E501
:type hpkp: TypesHPKPSettings
"""
self._hpkp = hpkp
def to_dict(self, serialize=False):
"""Returns the model properties as a dict"""
result = {}
def convert(x):
if hasattr(x, "to_dict"):
args = getfullargspec(x.to_dict).args
if len(args) == 1:
return x.to_dict()
else:
return x.to_dict(serialize)
else:
return x
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
attr = self.attribute_map.get(attr, attr) if serialize else attr
if isinstance(value, list):
result[attr] = list(map(
lambda x: convert(x),
value
))
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], convert(item[1])),
value.items()
))
else:
result[attr] = convert(value)
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, TypesConsoleCertificateSettings):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, TypesConsoleCertificateSettings):
return True
return self.to_dict() != other.to_dict()
| [
"aakatev@virtru.com"
] | aakatev@virtru.com |
bf08004a8dc519aed4684436e2e47168caab9220 | f3b233e5053e28fa95c549017bd75a30456eb50c | /mcl1_input/L37/37-67_wat_20Abox/set_1ns_equi_m.py | 9a30abf2ff76750baf8c5f509e5e70f035965f6b | [] | no_license | AnguseZhang/Input_TI | ddf2ed40ff1c0aa24eea3275b83d4d405b50b820 | 50ada0833890be9e261c967d00948f998313cb60 | refs/heads/master | 2021-05-25T15:02:38.858785 | 2020-02-18T16:57:04 | 2020-02-18T16:57:04 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 923 | py | import os
dir = '/mnt/scratch/songlin3/run/mcl1/L37/wat_20Abox/ti_one-step/37_67/'
filesdir = dir + 'files/'
temp_equiin = filesdir + 'temp_equi_m.in'
temp_pbs = filesdir + 'temp_1ns_equi_m.pbs'
lambd = [ 0.00922, 0.04794, 0.11505, 0.20634, 0.31608, 0.43738, 0.56262, 0.68392, 0.79366, 0.88495, 0.95206, 0.99078]
for j in lambd:
os.system("rm -r %6.5f" %(j))
os.system("mkdir %6.5f" %(j))
os.chdir("%6.5f" %(j))
os.system("rm *")
workdir = dir + "%6.5f" %(j) + '/'
#equiin
eqin = workdir + "%6.5f_equi_m.in" %(j)
os.system("cp %s %s" %(temp_equiin, eqin))
os.system("sed -i 's/XXX/%6.5f/g' %s" %(j, eqin))
#PBS
pbs = workdir + "%6.5f_1ns_equi.pbs" %(j)
os.system("cp %s %s" %(temp_pbs, pbs))
os.system("sed -i 's/XXX/%6.5f/g' %s" %(j, pbs))
#top
os.system("cp ../37-67_merged.prmtop .")
os.system("cp ../0.5_equi_0_3.rst .")
#submit pbs
os.system("qsub %s" %(pbs))
os.chdir(dir)
| [
"songlin3@msu.edu"
] | songlin3@msu.edu |
1055636efad751d50373b46a2e7170cabd726762 | 15f321878face2af9317363c5f6de1e5ddd9b749 | /solutions_python/Problem_124/121.py | 1ee579e02a9298b03bc8eb97a5888b8339bc66a9 | [] | no_license | dr-dos-ok/Code_Jam_Webscraper | c06fd59870842664cd79c41eb460a09553e1c80a | 26a35bf114a3aa30fc4c677ef069d95f41665cc0 | refs/heads/master | 2020-04-06T08:17:40.938460 | 2018-10-14T10:12:47 | 2018-10-14T10:12:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,380 | py | import math
def test(rank, left, right, n, x, y) :
if n == 0 :
if x > 0 :
if right > y :
return 1.0
else :
return 0.0
else :
if left > y :
return 1.0
else :
return 0.0
# left case
leftres = 0
if left < rank * 2 :
leftres = test(rank, left + 1, right, n - 1, x, y)
# right case
rightres = 0
if right < rank * 2 :
rightres = test(rank, left, right + 1, n - 1, x, y)
if left < rank * 2 :
if right < rank * 2 :
return leftres * 0.5 + rightres * 0.5
else :
return leftres
else :
return rightres
t = int(raw_input())
for casenum in range(1, t + 1) :
n, x, y = [int(z) for z in raw_input().split()]
if (x == 0) and (y == 0) :
print "Case #%d: 1.0" % casenum
continue
rank = (abs(x) + y) / 2
maxn = (2 * rank * rank) + (3 * rank ) + 1
minn = (2 * rank - 1) * rank
if n >= maxn :
print "Case #%d: 1.0" % casenum
continue
elif y == rank * 2 :
print "Case #%d: 0.0" % casenum
continue
elif n <= minn :
print "Case #%d: 0.0" % casenum
else :
print "Case #%d: %F" % (casenum, test(rank, 0, 0, n - minn, x, y))
| [
"miliar1732@gmail.com"
] | miliar1732@gmail.com |
73e9576f7c22061ccd62f5111e442fa156109f2d | 38cacbe9ec2f8ea4540f5aed31da60ac3595c08b | /tests/utils.py | b2b5b9aa0b054a7399086693022be53f9da50f24 | [
"Apache-2.0"
] | permissive | naure/YaP | 14bac663cdf31bda58dd5288f1f297ffa164a742 | e4f9c8b00a463b4fedceb6d9241dd9c723607562 | refs/heads/master | 2020-04-06T06:56:17.027479 | 2016-08-23T19:34:15 | 2016-08-23T19:34:15 | 24,347,882 | 6 | 1 | null | null | null | null | UTF-8 | Python | false | false | 968 | py | import difflib
def red(s):
return '\033[91m' + s + '\033[0m'
def green(s):
return '\033[92m' + s + '\033[0m'
def color_diffline(line):
if line.startswith('-'): # Red
return red(line)
if line.startswith('+'): # Green
return green(line)
return line
def diff(a, b, **kwargs):
return '\n'.join(map(
color_diffline,
difflib.unified_diff(
a.splitlines(), b.splitlines(), **kwargs
)))
def diff_paths(pa, pb):
with open(pa) as fa, open(pb) as fb:
a = fa.read()
b = fb.read()
if a != b:
return diff(a, b, fromfile=pa, tofile=pb)
else:
return False
def compare_paths(ref_path, test_path, what='Output'):
test_diff = diff_paths(ref_path, test_path)
if test_diff:
print(red('{} {} is different than reference {}'.format(
what, test_path, ref_path)))
print(test_diff)
return 1
else:
return 0
| [
"devnull@localhost"
] | devnull@localhost |
60112dc2b60f73bf2d1353313923ac3433aa5be3 | 30cffb7452220c2ac2961dd2e0f42e3b359a59c0 | /simscale_sdk/models/ground_relative.py | 9f70ec5cb7f1b769bb77f048419ec92fffa6e3f3 | [
"MIT"
] | permissive | vpurcarea/simscale-python-sdk | 0bf892d8824f8d4599caa0f345d5ba28e038f5eb | 6f2d12b2d21142bd854042c0fb402c2c797629e4 | refs/heads/master | 2023-03-14T04:31:06.226337 | 2021-03-03T16:20:01 | 2021-03-03T16:20:01 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,290 | py | # coding: utf-8
"""
SimScale API
The version of the OpenAPI document: 0.0.0
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import six
from simscale_sdk.configuration import Configuration
class GroundRelative(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
openapi_types = {
'type': 'str',
'topological_reference': 'TopologicalReference'
}
attribute_map = {
'type': 'type',
'topological_reference': 'topologicalReference'
}
def __init__(self, type='GROUND_RELATIVE', topological_reference=None, local_vars_configuration=None): # noqa: E501
"""GroundRelative - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._type = None
self._topological_reference = None
self.discriminator = None
self.type = type
if topological_reference is not None:
self.topological_reference = topological_reference
@property
def type(self):
"""Gets the type of this GroundRelative. # noqa: E501
:return: The type of this GroundRelative. # noqa: E501
:rtype: str
"""
return self._type
@type.setter
def type(self, type):
"""Sets the type of this GroundRelative.
:param type: The type of this GroundRelative. # noqa: E501
:type: str
"""
if self.local_vars_configuration.client_side_validation and type is None: # noqa: E501
raise ValueError("Invalid value for `type`, must not be `None`") # noqa: E501
self._type = type
@property
def topological_reference(self):
"""Gets the topological_reference of this GroundRelative. # noqa: E501
:return: The topological_reference of this GroundRelative. # noqa: E501
:rtype: TopologicalReference
"""
return self._topological_reference
@topological_reference.setter
def topological_reference(self, topological_reference):
"""Sets the topological_reference of this GroundRelative.
:param topological_reference: The topological_reference of this GroundRelative. # noqa: E501
:type: TopologicalReference
"""
self._topological_reference = topological_reference
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, GroundRelative):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, GroundRelative):
return True
return self.to_dict() != other.to_dict()
| [
"simscale"
] | simscale |
6e14b31178f64e5288bfaa4ef92323615e3ab96c | ed6cb6f58b36f16d38e0f7909173c67c15575a3f | /smart_compress/models/base.py | a381b2ad5ac4abdb3dfa1faf51075fcd05ebbe9f | [] | no_license | gthparch/etri-quant | 4698e50ed1f835ced5b69f928cda5bc2e357657f | 36cfb72f50937b65762fac0f12f044714b755a66 | refs/heads/main | 2023-08-14T21:42:10.007259 | 2021-10-09T01:28:40 | 2021-10-09T01:28:40 | 415,169,674 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,904 | py | from abc import abstractmethod
from argparse import ArgumentParser, Namespace
from typing import Iterator, Type, Union
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from argparse_utils.mapping import mapping_action
from smart_compress.util.pytorch.hooks import wrap_optimizer
from torch import nn
from torch.optim import SGD, Adam, AdamW
from torch.optim.lr_scheduler import MultiStepLR
from torch.optim.optimizer import Optimizer
def make_optimizer_args(
hparams: Namespace,
**kwargs,
):
optimizer_args = dict(
lr=hparams.learning_rate,
momentum=hparams.momentum,
weight_decay=hparams.weight_decay,
)
if hparams.beta1 and hparams.beta2:
optimizer_args.update(dict(betas=(hparams.beta1, hparams.beta2)))
if hparams.epsilon:
optimizer_args.update(dict(eps=hparams.epsilon))
optimizer_args.update(kwargs)
return optimizer_args
def make_multistep_scheduler(optimizer: Optimizer, hparams: Namespace):
return MultiStepLR(
optimizer,
milestones=hparams.scheduler_milestones,
gamma=hparams.scheduler_gamma,
)
class BaseModule(pl.LightningModule):
@staticmethod
def add_argparse_args(parent_parser):
parser = ArgumentParser(parents=[parent_parser], add_help=False)
parser.add_argument(
"--optimizer_type",
action=mapping_action(dict(adam=Adam, adamw=AdamW, sgd=SGD)),
default="sgd",
dest="optimizer_cls",
)
parser.add_argument(
"--scheduler_type",
action=mapping_action(dict(multi_step=make_multistep_scheduler)),
dest="make_scheduler_fn",
),
parser.add_argument("--scheduler_gamma", type=float, default=0.1)
parser.add_argument(
"--scheduler_milestones",
type=int,
nargs="+",
default=[100, 150, 200],
)
parser.add_argument("--learning_rate", type=float, default=0.1)
parser.add_argument("--weight_decay", type=float, default=0)
parser.add_argument("--momentum", type=float, default=0.9)
parser.add_argument("--beta1", type=float)
parser.add_argument("--beta2", type=float)
parser.add_argument("--epsilon", type=float)
parser.add_argument("--measure_average_grad_norm", action="store_true")
return parser
def __init__(self, *args, compression=None, **kwargs):
super().__init__()
self.compression = compression
if self.compression is None:
from smart_compress.compress.fp32 import FP32
self.compression = FP32(self.hparams)
self.save_hyperparameters()
if self.hparams.measure_average_grad_norm:
self._grads = []
def training_epoch_end(self, *args, **kwargs):
if not self.hparams.measure_average_grad_norm:
return super().training_epoch_end(*args, **kwargs)
try:
avg = torch.mean(torch.tensor(self._grads))
print(f"AVERAGE: {avg}")
except:
pass
return super().training_epoch_end(*args, **kwargs)
def loss_function(self, outputs, ground_truth):
return F.cross_entropy(outputs, ground_truth)
def accuracy_function(self, outputs, ground_truth):
return dict()
@abstractmethod
def forward(self, x):
raise Exception("Not implemented")
def calculate_loss(self, batch):
inputs, labels = batch
outputs = self(inputs)
loss = self.loss_function(outputs, labels)
if self.hparams.compress_loss:
loss.data = self.compression(loss.data, tag="loss")
return labels, loss, outputs
def training_step(self, batch, _batch_idx):
labels, loss, outputs = self.calculate_loss(batch)
self.log("train_loss", loss)
for metric, value in self.accuracy_function(outputs, labels).items():
self.log(f"train_{metric}", value, on_epoch=True, prog_bar=True)
return dict(loss=loss)
def validation_step(self, batch, _batch_idx):
labels, loss, outputs = self.calculate_loss(batch)
self.log("val_loss", loss)
for metric, value in self.accuracy_function(outputs, labels).items():
self.log(f"val_{metric}", value, on_epoch=True, prog_bar=True)
return dict(loss=loss)
def configure_optimizers(self):
base_args = make_optimizer_args(self.hparams)
params_bn = []
params_no_bn = []
for child in self.modules():
params = params_bn if type(child) == nn.BatchNorm2d else params_no_bn
params.extend(child.parameters(recurse=False))
optimizer = self.hparams.optimizer_cls(
[
dict(params=params_bn, no_weight_compression=True, **base_args),
dict(params=params_no_bn, **base_args),
]
)
if (
self.hparams.compress_weights
or self.hparams.compress_gradients
or self.hparams.compress_momentum_vectors
):
optimizer = wrap_optimizer(optimizer, self.compression, self.hparams)
if self.hparams.make_scheduler_fn:
scheduler = self.hparams.make_scheduler_fn(optimizer, self.hparams)
return [optimizer], [scheduler]
return [optimizer], []
def optimizer_zero_grad(self, *args, **kwargs):
if not self.hparams.measure_average_grad_norm:
return super().optimizer_zero_grad(*args, **kwargs)
norms = torch.tensor(
[
parameter.grad.norm()
for parameter in self.parameters()
if parameter.grad is not None
]
)
if len(norms):
self._grads.append(torch.mean(norms))
return super().optimizer_zero_grad(*args, **kwargs)
| [
"caojiashen24@gmail.com"
] | caojiashen24@gmail.com |
4e7a822623cae02b8a770d73d3e214da5a0056c0 | d8edd97f8f8dea3f9f02da6c40d331682bb43113 | /networks1216.py | 05847acaccb6e806629fb44d584e05d7924634a3 | [] | no_license | mdubouch/noise-gan | bdd5b2fff3aff70d5f464150443d51c2192eeafd | 639859ec4a2aa809d17eb6998a5a7d217559888a | refs/heads/master | 2023-07-15T09:37:57.631656 | 2021-08-27T11:02:45 | 2021-08-27T11:02:45 | 284,072,311 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,579 | py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
__version__ = 205
# Number of continuous features (E, t, dca)
n_features = 3
geom_dim = 3
class SelfAttn(nn.Module):
""" Self attention Layer"""
def __init__(self, dim):
super().__init__()
self.query_conv = nn.Conv1d(dim, dim, 1, 1, 0)
self.key_conv = nn.Conv1d(dim, dim, 1, 1, 0)
self.value_conv = nn.Conv1d(dim, dim, 1, 1, 0)
def forward(self,x):
"""
inputs :
x : input feature maps (BxCxL)
returns :
out : self attention value + input feature
attention: B x L x L
"""
Q = self.query_conv(x).permute(0,2,1) # (B, L, C)
K = self.key_conv(x) # (B, C, L)
attn = torch.softmax(torch.bmm(Q, K), dim=-1) # (B, L, L)
V = self.value_conv(x) # (B, C, L)
out = torch.bmm(V, attn) # (B, C, L)
out = out + x
return out, attn
def wire_hook(grad):
print('wg %.2e %.2e' % (grad.abs().mean().item(), grad.std().item()))
return grad
class Gen(nn.Module):
def __init__(self, ngf, latent_dims, seq_len, encoded_dim, n_wires):
super().__init__()
self.ngf = ngf
self.seq_len = seq_len
self.version = __version__
# Input: (B, latent_dims, 1)
self.act = nn.ReLU()
n512 = 256
self.lin0 = nn.Linear(latent_dims, seq_len//32*n512, bias=True)
self.n512 = n512
n256 = n512 // 2
n128 = n512 // 4
n64 = n512 // 8
n32 = n512 // 16
n16 = n512 // 32
class ResBlockUp(nn.Module):
def __init__(self, in_c, out_c):
super().__init__()
self.conv1 = nn.ConvTranspose1d(in_c, out_c, 3, 2, 1, output_padding=1)
self.conv2 = nn.ConvTranspose1d(out_c, out_c, 3, 1, 1)
self.convp = nn.ConvTranspose1d(in_c, out_c, 2, 2, 0, bias=False)
self.bn1 = nn.InstanceNorm1d(out_c)
self.bn2 = nn.InstanceNorm1d(out_c)
self.act = nn.ReLU()
def forward(self, x):
y = self.bn1(self.act(self.conv1(x)))
y = self.conv2(y)
xp = self.convp(x)
y = self.bn2(self.act(xp + y))
return y
self.convu1 = ResBlockUp(n512, n512)
self.convu2 = ResBlockUp(n512, n512)
self.convu3 = ResBlockUp(n512, n512//2)
# Common branch
self.convu4 = ResBlockUp(n512//2, n512//4)
self.convu5 = ResBlockUp(n512//4, n512//8)
# W branch
self.convuw1 = ResBlockUp(n512//2, n512//4)
self.convuw2 = ResBlockUp(n512//4, n512//8)
# P branch
self.convup1 = ResBlockUp(n512//2, n512//4)
self.convup2 = ResBlockUp(n512//4, n512//8)
self.attnw2 = SelfAttn(n512//4)
self.convw2 = nn.Conv1d(n512//4, n512//8, 7, 1, 3)
self.attnw1 = SelfAttn(n512//8)
self.convw1 = nn.Conv1d(n512//8, n_wires, 1, 1, 0)
self.attnp2 = SelfAttn(n512//4)
self.convp2 = nn.Conv1d(n512//4, n512//8, 3, 1, 1)
self.attnp1 = SelfAttn(n512//8)
self.convp1 = nn.Conv1d(n512//8, n_features, 7, 1, 3)
def forward(self, z):
#print('latent space %.2e %.2e' % (z.mean().item(), z.std().item()))
# z: random point in latent space
x = self.act(self.lin0(z).reshape(-1, self.n512, self.seq_len // 32))
x = self.convu1(x)
x = self.convu2(x)
x0 = self.convu3(x)
# Common
x = self.convu4(x0)
x = self.convu5(x)
# W
w = self.convuw1(x0)
w = self.convuw2(w)
# P
p = self.convup1(x0)
p = self.convup2(p)
w0 = torch.cat([x, w], dim=1)
w, w_attn = self.attnw2(w0)
w = self.act(self.convw2(w))
w, w_attn = self.attnw1(w)
w = self.convw1(w)
wg = torch.softmax(w, dim=1)
p0 = torch.cat([x, p], dim=1)
p, p_attn = self.attnp2(p0)
p = self.act(self.convp2(p))
p, p_attn = self.attnp1(p)
p = self.convp1(p)
#return torch.cat([self.out(p), xy], dim=1), wg
return torch.tanh(p), wg
def xy_hook(grad):
print('xy %.2e %.2e' % (grad.abs().mean().item(), grad.std().item()))
return grad
class Disc(nn.Module):
def __init__(self, ndf, seq_len, encoded_dim, n_wires):
super().__init__()
self.version = __version__
# (B, n_features, 256)
self.act = nn.LeakyReLU(0.2)
n768 = 256
n512 = 256
n256 = 256
n128 = 128
n64 = 64
nproj = 8
class ResBlock(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv1 = nn.Conv1d(channels, channels, 3, 1, 1, padding_mode='circular')
self.conv2 = nn.Conv1d(channels, channels, 3, 1, 1, padding_mode='circular')
self.act = nn.LeakyReLU(0.2)
def forward(self, x):
y = self.act(self.conv1(x))
y = self.conv2(y)
y = self.act(y + x)
return y
class ResBlockDown(nn.Module):
def __init__(self, channels):
super().__init__()
self.conv1 = nn.Conv1d(channels, channels*2, 3, 2, 1, padding_mode='circular')
self.conv2 = nn.Conv1d(channels*2, channels*2, 3, 1, 1, padding_mode='circular')
self.convp = nn.Conv1d(channels, channels*2, 2, 2, 0, bias=False)
self.act = nn.LeakyReLU(0.2)
def forward(self, x):
y = self.act(self.conv1(x))
y = self.conv2(y)
xp = self.convp(x)
y = self.act(y + xp)
return y
self.convw0 = nn.Conv1d(n_wires, nproj, 1, 1, 0, bias=False)
self.conv1 = nn.Conv1d(nproj+geom_dim+n_features, n64, 17, 1, 8, padding_mode='circular')
self.attn1 = SelfAttn(n64)
self.rb2 = ResBlockDown(n64)#nn.Conv1d(n64, n64*2, 3, 2, 1, padding_mode='circular')
self.attn2 = SelfAttn(n64*2)
self.rb3 = ResBlockDown(n64*2)#nn.Conv1d(n64*2, n64*4, 3, 2, 1, padding_mode='circular')
self.attn3 = SelfAttn(n64*4)
self.rb4 = ResBlockDown(n64*4)#nn.Conv1d(n64*4, n64*8, 3, 2, 1, padding_mode='circular')
self.lin0 = nn.Linear(n64*8, 1)
self.dropout = nn.Dropout(0.2)
self.out = nn.Identity()
self.padleft = nn.ConstantPad1d((1, 0), 0.)
def forward(self, x_):
# x_ is concatenated tensor of p_ and w_, shape (batch, features+n_wires, seq_len)
# p_ shape is (batch, features, seq_len),
# w_ is AE-encoded wire (batch, encoded_dim, seq_len)
seq_len = x_.shape[2]
#dist = ((xy - nn.ConstantPad1d((1, 0), 0.0)(xy[:,:,:-1]))**2).sum(dim=1).unsqueeze(1)
p = x_[:,:n_features]
xy = x_[:,n_features:n_features+geom_dim]
wg = x_[:,n_features+geom_dim:]
w0 = self.convw0(wg)
x = torch.cat([w0, p, xy], dim=1)
x = self.act(self.conv1(x))
x, x_attn = self.attn1(x)
x = self.rb2(x)
x, x_attn = self.attn2(x)
x = self.rb3(x)
x, x_attn = self.attn3(x)
x = self.rb4(x)
x = self.lin0(x.mean(2)).squeeze()
return self.out(x)
def get_n_params(model):
return sum(p.reshape(-1).shape[0] for p in model.parameters())
| [
"m.dubouchet18@imperial.ac.uk"
] | m.dubouchet18@imperial.ac.uk |
bc0fa8f250c68c7f999971227b923c04b645ab61 | 4768e4ad67416e8b93344ccf647954398fd69561 | /microblog/db_helpers.py | 3a122e3cead9c5f0c55ae35f8a93a6433a264d6e | [] | no_license | cleartext/Enterprise-Microblogging-Server | ee0624c004faa8c6ade282a28949fe9a17438e89 | befff4b9ec64b395a9ef55b52ed563a4168bc07f | refs/heads/master | 2016-09-06T16:23:39.454558 | 2011-01-25T21:56:46 | 2011-01-25T21:56:46 | 1,288,330 | 3 | 2 | null | null | null | null | UTF-8 | Python | false | false | 678 | py | """
Different database helpers, to retrive
information about users.
"""
from microblog.models import User
from microblog.exceptions import UserNotFound
def get_user_by_jid(jid, session):
jid = jid.split('/', 1)[0]
user = session.query(User).filter(User.jid==jid).scalar()
if user is None:
raise UserNotFound('User with jid "%s" not found.' % jid)
return user
def get_all_users(session):
return session.query(User)
def get_user_by_username(username, session):
user = session.query(User).filter(User.username==username).scalar()
if user is None:
raise UserNotFound('User with username "%s" not found.' % username)
return user
| [
"svetlyak.40wt@gmail.com"
] | svetlyak.40wt@gmail.com |
a4de822d452f9b30465f5be889f1f3b10fb5bd39 | 6c10c6e229014dc3bf14efaec2ea8bf07c406752 | /AILearning/ComputerVision/ImageClassification.py | 5ddaebaa3e190d8957c87cd97df64729df342429 | [] | no_license | GuyRobot/AIPythonExamples | e59c6edb355d9cadee2b3f19a087b1b656956262 | 4acdd0d4966e31a616910554bc075b641aa152df | refs/heads/master | 2021-05-21T13:05:49.615593 | 2021-02-28T06:41:04 | 2021-02-28T06:41:04 | 252,662,467 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,674 | py | import collections
from d2l import AllDeepLearning as d2l
from mxnet import gluon, init, nd, autograd
from mxnet.gluon import nn
import os
import pandas as pd
import shutil
import time
import math
import tarfile
from pathlib import Path
data_dir = "E:/Python_Data/cifar-10/"
# tiny_data_dir = "E:/Python_Data/kaggle_cifar10_tiny/"
# data_dir = tiny_data_dir
# data_dir = "E:/Python_Data/cifar-10/"
tiny_data_dir = data_dir
a = "http://d2l-data.s3-accelerate.amazonaws.com/kaggle_cifar10_tiny.zip"
demo = False
# def download_voc_pascal(data_dir='../data'):
# """Download the VOC2012 segmentation dataset."""
# voc_dir = os.path.join(data_dir, 'Cifar_Tiny')
# url = "http://d2l-data.s3-accelerate.amazonaws.com/kaggle_cifar10_tiny.zip"
# sha1 = '2068874e4b9a9f0fb07ebe0ad2b29754449ccacd'
# fname = gluon.utils.download(url, data_dir, sha1_hash=sha1)
# with tarfile.open(fname, 'r') as f:
# f.extractall(data_dir)
# return voc_dir
def read_csv_labels(fname):
with open(fname) as f:
lines = f.readlines()[1:]
tokens = [l.rstrip().split(',') for l in lines]
return dict(((name, label) for name, label in tokens))
def copyfile(filename, target_dir):
Path("%s" % target_dir).mkdir(parents=True, exist_ok=True)
shutil.copy(filename, target_dir)
def reorg_train_valid(data_dir, labels, valid_ratio):
n = collections.Counter(labels.values()).most_common()[-1][1]
n_valid_per_label = max(1, math.floor(n * valid_ratio))
label_count = {}
for train_file in os.listdir(data_dir + 'train'):
label = labels[train_file.split('.')[0]]
fname = data_dir + 'train/' + train_file
copyfile(fname, data_dir + 'train_valid_test/train_valid/' + label)
if label not in label_count or label_count[label] < n_valid_per_label:
copyfile(fname, data_dir + 'train_valid_test/valid/' + label)
label_count[label] = label_count.get(label, 0) + 1
else:
copyfile(fname, data_dir + 'train_valid_test/train/' + label)
return n_valid_per_label
def reorg_test(data_dir):
for test_file in os.listdir(data_dir + 'test'):
copyfile(data_dir + 'test/' + test_file,
data_dir + 'train_valid_test/test/unknown/')
def reorg_cifar10_data(data_dir, valid_ratio):
labels = read_csv_labels(data_dir + 'trainLabels.csv')
reorg_train_valid(data_dir, labels, valid_ratio)
reorg_test(data_dir)
batch_size = 1 if demo else 128
valid_ratio = 0.1
reorg_cifar10_data(tiny_data_dir, valid_ratio)
transform_train = gluon.data.vision.transforms.Compose([
gluon.data.vision.transforms.Resize(40),
gluon.data.vision.transforms.RandomResizedCrop(32, scale=(0.64, 0.1), ratio=(1.0, 1.0)),
gluon.data.vision.transforms.RandomFlipLeftRight(),
gluon.data.vision.transforms.ToTensor(),
gluon.data.vision.transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))])
transform_test = gluon.data.vision.transforms.Compose([
gluon.data.vision.transforms.ToTensor(),
gluon.data.vision.transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])
train_ds, valid_ds, train_valid_ds, test_ds = [gluon.data.vision.ImageFolderDataset(
data_dir+'train_valid_test/'+folder) for folder in ['train', 'valid', 'train_valid', 'test']]
train_iter, train_valid_iter = [gluon.data.DataLoader(
dataset.transform_first(transform_train), batch_size, shuffle=True,
last_batch='keep') for dataset in (train_ds, train_valid_ds)]
valid_iter, test_iter = [gluon.data.DataLoader(
dataset.transform_first(transform_test), batch_size, shuffle=False,
last_batch='keep') for dataset in [valid_ds, test_ds]]
class Residual(nn.HybridBlock):
def __init__(self, num_channels, use_1x1_conv=False, strides=1, **kwargs):
super(Residual, self).__init__(**kwargs)
self.conv1 = nn.Conv2D(num_channels, kernel_size=3, padding=1, strides=strides)
self.conv2 = nn.Conv2D(num_channels, kernel_size=3, padding=1)
if use_1x1_conv:
self.conv3 = nn.Conv2D(num_channels, kernel_size=1, strides=strides)
else:
self.conv3 = None
self.bn1 = nn.BatchNorm()
self.bn2 = nn.BatchNorm()
def hybrid_forward(self, F, X, *args, **kwargs):
Y = F.relu(self.bn1(self.conv1(X)))
Y = self.bn2(self.conv2(Y))
if self.conv3:
X = self.conv3(X)
return F.relu(Y + X)
def resnet18(num_classes):
net = nn.HybridSequential()
net.add(nn.Conv2D(64, kernel_size=3, strides=1, padding=1),
nn.BatchNorm(),
nn.Activation('relu'))
def resnet_block(num_channels, num_residuals, first_block=False):
blk = nn.HybridSequential()
for i in range(num_residuals):
if i == 0 and not first_block:
blk.add(Residual(num_channels, use_1x1_conv=True, strides=2))
else:
blk.add(Residual(num_channels))
return blk
net.add(resnet_block(64, 2, first_block=True),
resnet_block(128, 2),
resnet_block(256, 2),
resnet_block(512, 2))
net.add(nn.GlobalAvgPool2D(), nn.Dense(num_classes))
return net
def get_net(ctx):
num_classes = 10
net = resnet18(num_classes)
net.initialize(init.Xavier(), ctx=ctx)
return net
def train(net, train_iter, valid_iter, num_epochs, lr,
wd, ctx, lr_period, lr_decay):
loss = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr,
"momentum": 0.9, 'wd': wd})
for epoch in range(num_epochs):
train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()
if epoch > 0 and epoch % lr_period == 0:
trainer.set_learning_rate(trainer.learning_rate * lr_decay)
for X, y in train_iter:
y = y.astype('float32').as_in_context(ctx)
with autograd.record():
y_hat = net(X.as_in_context(ctx))
l = loss(y_hat, y)
l.backward()
trainer.step(batch_size)
train_l_sum += float(l.sum().asscalar())
train_acc_sum += float((y_hat.argmax(axis=1) == y).sum().asscalar())
n += y.size
time_s = "time %.2f sec" % (time.time() - start)
if valid_iter is not None:
valid_acc = d2l.evaluate_accuracy_gpu(net, valid_iter, ctx=ctx)
epoch_s = ("epoch %d, loss %.2f, train acc %f, valid acc %f, " %
(epoch + 1, train_l_sum / n, train_acc_sum / n, valid_acc))
else:
epoch_s = ("epoch %d, loss %f, train acc %f" %
(epoch + 1, train_l_sum / n, train_acc_sum / n))
print(epoch_s + time_s + ', lr ' + str(trainer.learning_rate))
ctx, num_epochs, lr, wd = d2l.try_gpu(), 100, 0.1, 5e-4
lr_period, lr_decay, net = 80, 0.1, get_net(ctx)
net.hybridize()
train(net, train_iter, valid_iter, num_epochs, lr, wd, ctx, lr_period, lr_decay)
net, preds = get_net(ctx), []
net.hybridize()
train(net, train_valid_iter, None, num_epochs, lr, wd, ctx, lr_period,
lr_decay)
# for X, _ in test_iter:
# y_hat = net(X.as_in_context(ctx))
# preds.extend(y_hat.argmax(axis=1).astype(int).asnumpy())
# sorted_ids = list(range(1, len(test_ds) + 1))
# sorted_ids.sort(key=lambda x: str(x))
# df = pd.DataFrame({'id': sorted_ids, 'label': preds})
# df['label'] = df['label'].apply(lambda x: train_valid_ds.synsets[x])
# df.to_csv('submission.csv', index=False) | [
"bluexker@gmail.com"
] | bluexker@gmail.com |
0ff320ce0727df5f0904b4620d4c6d0545a34571 | 9743d5fd24822f79c156ad112229e25adb9ed6f6 | /xai/brain/wordbase/otherforms/_mirroring.py | a70d8db0f6d067749196aa854f55063eb436422f | [
"MIT"
] | permissive | cash2one/xai | de7adad1758f50dd6786bf0111e71a903f039b64 | e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6 | refs/heads/master | 2021-01-19T12:33:54.964379 | 2017-01-28T02:00:50 | 2017-01-28T02:00:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 226 | py |
#calss header
class _MIRRORING():
def __init__(self,):
self.name = "MIRRORING"
self.definitions = mirror
self.parents = []
self.childen = []
self.properties = []
self.jsondata = {}
self.basic = ['mirror']
| [
"xingwang1991@gmail.com"
] | xingwang1991@gmail.com |
b4c1978dceeb02f1e9f67909e5ca91c0b929cef9 | b007d88e6726452ffa8fe80300614f311ae5b318 | /educative.io/coding_patterns/two_pointers/triplet_sum_to_zero.py | cf9fb664ace0958b7075ee5471e82813d78f042d | [] | no_license | jinurajan/Datastructures | ec332b12b8395f42cb769e771da3642f25ba7e7f | 647fea5d2c8122468a1c018c6829b1c08717d86a | refs/heads/master | 2023-07-06T14:42:55.168795 | 2023-07-04T13:23:22 | 2023-07-04T13:23:22 | 76,943,162 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,271 | py | """
Given an array of unsorted numbers, find all unique triplets in it that add up to zero.
Example 1:
Input: [-3, 0, 1, 2, -1, 1, -2]
Output: [-3, 1, 2], [-2, 0, 2], [-2, 1, 1], [-1, 0, 1]
Explanation: There are four unique triplets whose sum is equal to zero.
Example 2:
Input: [-5, 2, -1, -2, 3]
Output: [[-5, 2, 3], [-2, -1, 3]]
Explanation: There are two unique triplets whose sum is equal to zero.
"""
def search_triplets(arr):
triplets = []
arr.sort()
n = len(arr)
def two_sum(target, left, triplets):
right = len(arr) - 1
while left < right:
curr = arr[left] + arr[right]
if curr == target:
triplets.append([-target, arr[left], arr[right]])
left += 1
right -= 1
while left < right and arr[left] == arr[left - 1]:
left += 1
while right >= 0 and arr[right] == arr[right + 1]:
right -= 1
elif target > curr:
left += 1 # we need a pair with a bigger sum
else:
right -= 1
for i in range(n - 3):
if i > 0 and arr[i] == arr[i - 1]:
continue
two_sum(-arr[i], i + 1, triplets)
return triplets
| [
"jinu.p.r@gmail.com"
] | jinu.p.r@gmail.com |
8f3d58a6187c5c0b08864b3b0efa195d47915b34 | dabf4121ac793c2cfe87ff525a8a0f7305ea2c59 | /plugins/maya/publish/extract_pointcache_abc.py | a086790b49e5224c1d72527853aaeadeeda9c52b | [
"MIT"
] | permissive | Lynn5160/reveries-config | 9d91210ebde47a69bb00614f95341a7ce313118f | 1928e4d41acc4861ffa3260fa855ca77561285b0 | refs/heads/master | 2022-11-13T21:13:47.665939 | 2020-07-02T08:24:21 | 2020-07-02T08:24:21 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,148 | py |
import contextlib
import pyblish.api
class ExtractPointCacheABC(pyblish.api.InstancePlugin):
"""
"""
order = pyblish.api.ExtractorOrder
hosts = ["maya"]
label = "Extract PointCache (abc)"
families = [
"reveries.pointcache.abc",
]
def process(self, instance):
from maya import cmds
from reveries import utils
staging_dir = utils.stage_dir(dir=instance.data["_sharedStage"])
filename = "%s.abc" % instance.data["subset"]
outpath = "%s/%s" % (staging_dir, filename)
instance.data["repr.Alembic._stage"] = staging_dir
instance.data["repr.Alembic._hardlinks"] = [filename]
instance.data["repr.Alembic.entryFileName"] = filename
if instance.data.get("staticCache"):
start = cmds.currentTime(query=True)
end = cmds.currentTime(query=True)
else:
context_data = instance.context.data
start = context_data["startFrame"]
end = context_data["endFrame"]
instance.data["startFrame"] = start
instance.data["endFrame"] = end
euler_filter = instance.data.get("eulerFilter", False)
root = instance.data["outCache"]
instance.data["repr.Alembic._delayRun"] = {
"func": self.export_alembic,
"args": [
root, outpath, start, end, euler_filter
],
}
def export_alembic(self, root, outpath, start, end, euler_filter):
from reveries.maya import io, lib, capsule
from maya import cmds
with contextlib.nested(
capsule.no_undo(),
capsule.no_refresh(),
capsule.evaluation("off"),
capsule.maintained_selection(),
):
# Selection may change if there are duplicate named nodes and
# require instancing them to resolve
with capsule.delete_after() as delete_bin:
# (NOTE) We need to check any duplicate named nodes, or
# error will raised during Alembic export.
result = lib.ls_duplicated_name(root)
duplicated = [n for m in result.values() for n in m]
if duplicated:
self.log.info("Duplicate named nodes found, resolving...")
# Duplicate it so we could have a unique named new node
unique_named = list()
for node in duplicated:
new_nodes = cmds.duplicate(node,
inputConnections=True,
renameChildren=True)
new_nodes = cmds.ls(new_nodes, long=True)
unique_named.append(new_nodes[0])
# New nodes will be deleted after the export
delete_bin.extend(new_nodes)
# Replace duplicate named nodes with unique named
root = list(set(root) - set(duplicated)) + unique_named
for node in set(root):
# (NOTE) If a descendent is instanced, it will appear only
# once on the list returned.
root += cmds.listRelatives(node,
allDescendents=True,
fullPath=True,
noIntermediate=True) or []
root = list(set(root))
cmds.select(root, replace=True, noExpand=True)
def _export_alembic():
io.export_alembic(
outpath,
start,
end,
selection=True,
renderableOnly=True,
writeVisibility=True,
writeCreases=True,
worldSpace=True,
eulerFilter=euler_filter,
attr=[
lib.AVALON_ID_ATTR_LONG,
],
attrPrefix=[
"ai", # Write out Arnold attributes
"avnlook_", # Write out lookDev controls
],
)
auto_retry = 1
while auto_retry:
try:
_export_alembic()
except RuntimeError as err:
if auto_retry:
# (NOTE) Auto re-try export
# For unknown reason, some artist may encounter
# runtime error when exporting but re-run the
# publish without any change will resolve.
auto_retry -= 1
self.log.warning(err)
self.log.warning("Retrying...")
else:
raise err
else:
break
| [
"davidlatwe@gmail.com"
] | davidlatwe@gmail.com |
499c1470a1433ed4086dcaf206216e5fda9b4ec6 | a839135eae95f745f1d9edb370ac459854042cce | /tests/test_feed.py | 4b9c19439fe949c370936b6b86e25b4acde5b1bb | [
"MIT"
] | permissive | d21d3q/thermalprinter | 8afae538fa81055bf47710390af1c213b86455fc | a502fe8a7b7ab5a0773e92a37e6539f73b34b950 | refs/heads/master | 2023-03-16T01:49:52.478726 | 2022-06-02T10:52:23 | 2022-06-02T10:52:23 | 164,908,229 | 0 | 0 | MIT | 2019-01-09T17:31:32 | 2019-01-09T17:31:25 | Python | UTF-8 | Python | false | false | 771 | py | # coding: utf-8
import pytest
from thermalprinter.exceptions import ThermalPrinterValueError
def test_changing_no_value(printer):
printer.feed()
assert printer.feeds == 1
def test_changing_good_value(printer):
printer.feed(42)
assert printer.feeds == 42 + 1
def test_bad_value__not_int(printer):
with pytest.raises(ThermalPrinterValueError):
printer.feed('42')
assert printer.feeds == 42 + 1
def test_changing_bad_value__not_in_range_low(printer):
with pytest.raises(ThermalPrinterValueError):
printer.feed(-42)
assert printer.feeds == 42 + 1
def test_changing_bad_value__not_in_range_high(printer):
with pytest.raises(ThermalPrinterValueError):
printer.feed(512)
assert printer.feeds == 42 + 1
| [
"contact@tiger-222.fr"
] | contact@tiger-222.fr |
756caf90edd534e5f336c64ff1742c1aa610a6d9 | 945f9c5c34b42fd7863c525f7e54d2c88a5950e6 | /pyppl_strict.py | c29178d57ce1b3515b5241aa48a033f133e73c5d | [
"MIT"
] | permissive | stjordanis/pyppl_strict | 0c517a4e803e039b09602c385a75cbcd773514fc | efd4d361ddda3f95b1249cee612ef9f5d7b46123 | refs/heads/master | 2022-09-28T02:09:19.641392 | 2020-06-06T05:14:33 | 2020-06-06T05:14:33 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,010 | py | """More strict check of job success for PyPPL
Features:
1. make sure all outputs have been generated
2. allow custom returncode settings
3. allow a custom script to check the output file
"""
from os import utime
import random
import cmdy
from pyppl.plugin import hookimpl
from pyppl.config import config
from pyppl.jobmgr import STATES
from pyppl.utils import always_list, fs, filesig
from pyppl._proc import OUT_VARTYPE
from pyppl._job import RC_NO_RCFILE
__version__ = "0.0.7"
RC_NO_OUTFILE = 5000
RC_EXPECT_FAIL = 10000
config.config.strict_rc = [0]
config.config.strict_expect = ""
def strict_rc_converter(rc):
"""Convert return code from input"""
if not rc:
return [0]
if isinstance(rc, str):
rc = always_list(rc)
rc = list(rc)
if 0 not in rc:
rc.insert(0, 0)
return rc
def show_error(job, total):
"""Show error message for a job"""
if job.rc >= RC_EXPECT_FAIL:
msg = '%s (Expectation failed)' % (job.rc - RC_EXPECT_FAIL)
elif job.rc >= RC_NO_OUTFILE:
msg = '%s (Output file/dir not generated)' % (job.rc - RC_NO_OUTFILE)
elif job.rc == RC_NO_RCFILE:
msg = '- (No RC file generated)'
else:
msg = '%s (Script failed)' % job.rc
if job.proc.errhow == 'ignore':
job.logger(
f'Failed but ignored (totally {total}). Return code: {msg}.',
level='warning',
plugin='strict'
)
return
job.logger(f'Failed (totally {total}). Return code: {msg}.',
level='failed',
plugin='strict')
job.logger(f'Script: {job.dir / "job.script"}',
level='failed', plugin='strict')
job.logger(f'Stdout: {job.dir / "job.stdout"}',
level='failed', plugin='strict')
job.logger(f'Stderr: {job.dir / "job.stderr"}',
level='failed', plugin='strict')
# errors are not echoed, echo them out
if (job.index not in job.proc.config.get('echo_jobs', [])
or 'stderr' not in job.proc.config.get('echo_types', {})):
job.logger('Check STDERR below:', level='failed', plugin='strict')
errmsgs = []
if job.dir.joinpath('job.stderr').exists():
errmsgs = job.dir.joinpath('job.stderr').read_text().splitlines()
if not errmsgs:
errmsgs = ['<EMPTY STDERR>']
for errmsg in errmsgs[-20:] if len(errmsgs) > 20 else errmsgs:
job.logger(errmsg, level='failed', plugin='strict')
if len(errmsgs) > 20:
job.logger(
'[ Top {top} line(s) ignored, see all in stderr file. ]'.
format(top=len(errmsgs) - 20),
level='failed', plugin='strict'
)
@hookimpl
def logger_init(logger):
"""Add log levels"""
logger.add_level('FAILED', 'ERROR')
logger.add_sublevel('OUTFILE_NOT_EXISTS', -1)
logger.add_sublevel('EXPECTATION_FAILED', -1)
@hookimpl
def proc_init(proc):
"""Add configs"""
def strict_expect_converter(expect):
if isinstance(expect, proc.template):
return expect
return proc.template(expect, **proc.envs)
proc.add_config('strict_rc', default=0, converter=strict_rc_converter)
proc.add_config('strict_expect',
default='',
converter=strict_expect_converter)
@hookimpl
def job_succeeded(job):
"""Check rc, expect and outfiles to tell if a job is really succeeded"""
if job.rc not in job.proc.config.strict_rc:
return False
# check if all outputs are generated
# refresh stat
outdir, mtime = filesig(job.dir.joinpath('output'), job.proc.dirsig)
utime(outdir, (mtime, mtime))
for outtype, outdata in job.output.values():
if outtype not in OUT_VARTYPE and not fs.exists(outdata):
job.rc += RC_NO_OUTFILE
job.logger('Outfile not generated: {}'.format(outdata),
slevel="OUTFILE_NOT_EXISTS",
level='debug',
plugin='strict')
return False
expect_cmd = job.proc.config.strict_expect.render(job.data)
if expect_cmd:
cmd = cmdy.bash(c=expect_cmd, _raise=False) # pylint: disable=no-member
if cmd.rc != 0:
job.rc += RC_EXPECT_FAIL
job.logger(expect_cmd,
slevel="EXPECTATION_FAILED",
level='error',
plugin='strict')
return False
return True
@hookimpl
def proc_postrun(proc, status):
"""Show error message for failed jobs"""
if status == 'failed':
failed_jobs = [
job for job in proc.jobs
if job.state in (STATES.ENDFAILED, STATES.DONEFAILED,
STATES.SUBMITFAILED, STATES.BUILTFAILED,
STATES.KILLED, STATES.KILLFAILED)
]
failed_jobs = failed_jobs or [proc.jobs[0]]
show_error(random.choice(failed_jobs), total=len(failed_jobs))
| [
"pwwang@pwwang.com"
] | pwwang@pwwang.com |
a5fb8ff0526b379fe6f367eb993bd6f0943b7aac | f8b4461f66801fa624ec1798c4547b6f5c9bdf51 | /SpaceInvaders/train.py | e950d4b70255c9c88363bf6d9d3eb624142b5460 | [] | no_license | jcw024/NEAT-retro | 5c713aea81efecc108c88e7dde434586c66e35a1 | fdf9a6044e934fb658ad86cad2730690b7af8975 | refs/heads/master | 2021-10-24T13:57:41.696033 | 2021-10-23T02:42:49 | 2021-10-23T02:42:49 | 165,330,293 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 3,121 | py | #! /usr/bin/env python
from __future__ import division
import retro
import numpy as np
import cv2
import neat
import pickle
import cProfile
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', '-c', type=str, help='checkpoint file to continue previous run')
args = parser.parse_args()
#trains single genome within generation
def eval_genomes(genome, config):
ob = env.reset()
ac = env.action_space.sample()
inx, iny, inc = env.observation_space.shape
inx = int(inx/5)
iny = int(iny/5)
net = neat.nn.recurrent.RecurrentNetwork.create(genome, config)
current_max_fitness = 0
fitness_current = 0
counter = 0
lives_tot = 3
#cv2.namedWindow("network_input", cv2.WINDOW_NORMAL) #show input pixels to neat
done = False
while not done:
env.render()
#shrink screen for fewer pixel observations per loop
#scaledimg = cv2.cvtColor(ob, cv2.COLOR_BGR2GRAY)
#scaledimg = cv2.resize(scaledimg, (iny, inx))
ob = cv2.resize(ob, (inx, iny))
ob = cv2.cvtColor(ob, cv2.COLOR_BGR2GRAY)
ob = np.reshape(ob, (inx,iny))
#cv2.imshow('network_input', scaledimg)
#cv2.waitKey(1)
imgarray = np.ndarray.flatten(ob)
nnOutput = net.activate(imgarray)
ob, rew1, done1, info = env.step(nnOutput) #3 steps to skip some frames
ob, rew2, done2, info = env.step(nnOutput)
ob, rew3, done3, info = env.step(nnOutput)
rew = (rew1 + rew2 + rew3)
lives = info['lives']
if lives < lives_tot:
#fitness_current -= 100
lives_tot = lives
fitness_current += rew
if fitness_current > current_max_fitness:
current_max_fitness = fitness_current
counter = 0
else:
counter += 1
if any([done1, done2, done3]) or counter == 400:
done = True
genome.fitness = fitness_current
print(genome.fitness)
return genome.fitness
#setup training population parameters
def main(checkpoint=None):
if checkpoint is not None:
p = neat.checkpoint.Checkpointer.restore_checkpoint(checkpoint)
else:
p = neat.Population(config)
p.add_reporter(neat.StdOutReporter(True))
stats = neat.StatisticsReporter()
p.add_reporter(stats)
p.add_reporter(neat.Checkpointer(generation_interval=10,time_interval_seconds=3600))
pe = neat.ParallelEvaluator(5, eval_genomes) #run on multiple cores
winner = p.run(pe.evaluate, 40) #do training for 40 generations
with open('winner_pop50_gen40.pkl', 'wb') as output:
print('writing winner gen to ', output)
pickle.dump(winner, output)
if __name__ == '__main__':
env = retro.make('SpaceInvaders-Snes', '1Player.ClassicMode.UprightCabinet.state')
imgarray = []
config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
neat.DefaultSpeciesSet, neat.DefaultStagnation,
'config')
main(args.checkpoint)
| [
"="
] | = |
0e0a01939fd310d7aafb5cccf0c79513c9cf03ab | a7bc6a7c45c58ac08e295b77d4a19769bfd97463 | /NMT/Transformers_NMT/process_data.py | 98081e75fabdfaea0b0488852ea04fb70cffe3ca | [] | no_license | pnageshkar/NLP_pytorch_project | cc2a3fb8a41e7d2d8a794561e1b9a971b36f2cfa | 57d2ac65a0cf21dca43a9329f6c25e6a23854e15 | refs/heads/master | 2022-11-14T14:59:03.099067 | 2020-07-09T09:05:10 | 2020-07-09T09:05:10 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,056 | py | """
@file : process_data.py
@author: xiaolu
@time : 2019-12-26
"""
'''
主要功能:
1. 对中文文本和英文文本建立词典 然后存到vocab.pkl
2. 将训练集和验证集转化为id序列 然后存到data.pkl
'''
import os
import pickle
from collections import Counter
import jieba
import nltk
from tqdm import tqdm
from config import Config
from utils import normalizeString, encode_text
def build_vocab(token, word2idx, idx2char):
if token not in word2idx:
next_index = len(word2idx)
word2idx[token] = next_index
idx2char[next_index] = token
def process(file, lang='zh'):
'''
建立词表
:param file:
:param lang:
:return:
'''
print('processing {}...'.format(file))
with open(file, 'r', encoding='utf-8') as f:
data = f.readlines()
word_freq = Counter()
lengths = []
for line in tqdm(data):
sentence = line.strip()
if lang == 'en':
# 若是英文 转小写 然后切分
sentence_en = sentence.lower()
tokens = [normalizeString(s) for s in nltk.word_tokenize(sentence_en)] # 得到token然后再清洗
word_freq.update(list(tokens))
vocab_size = Config.n_src_vocab # 是由超参数给出的
else:
# 若是中文 使用jieba进行分词
seg_list = jieba.cut(sentence.strip())
tokens = list(seg_list)
word_freq.update(list(tokens))
vocab_size = Config.n_tgt_vocab
lengths.append(len(tokens)) # 得到每个句子的真实长度
words = word_freq.most_common(vocab_size - 4) # vocab_size 统计出词频最高的这么多个词
word_map = {k[0]: v + 4 for v, k in enumerate(words)} # 词->id
word_map['<pad>'] = 0
word_map['<sos>'] = 1
word_map['<eos>'] = 2
word_map['<unk>'] = 3
print(len(word_map))
print(words[:100])
word2idx = word_map
idx2char = {v: k for k, v in word2idx.items()}
return word2idx, idx2char
def get_data(in_file, out_file):
'''
加载语料
:param in_file: 中文数据集路径
:param out_file: 英文数据集路径
:return:
'''
print('getting data {}->{}...'.format(in_file, out_file))
with open(in_file, 'r', encoding='utf-8') as file:
in_lines = file.readlines()
with open(out_file, 'r', encoding='utf-8') as file:
out_lines = file.readlines()
samples = []
for i in tqdm(range(len(in_lines))):
sentence_zh = in_lines[i].strip()
tokens = jieba.cut(sentence_zh.strip())
in_data = encode_text(src_char2idx, tokens) # encode_text(src_char2idx, tokens) 将语料转为id序列
sentence_en = out_lines[i].strip().lower()
tokens = [normalizeString(s.strip()) for s in nltk.word_tokenize(sentence_en)] # 将英文单词预处理
out_data = [Config.sos_id] + encode_text(tgt_char2idx, tokens) + [Config.eos_id] # 转为id 并加上开始和结束标志
# 这里的maxlen_in=50 和 maxlen_out=100 也是有超参数给出的
if len(in_data) < Config.maxlen_in and len(out_data) < Config.maxlen_out and Config.unk_id not in in_data and Config.unk_id not in out_data:
samples.append({'in': in_data, 'out': out_data})
return samples
if __name__ == '__main__':
# 加载词表 没有的话 我们建立词表
if os.path.isfile(Config.vocab_file):
with open(Config.vocab_file, 'rb') as file:
data = pickle.load(file)
src_char2idx = data['dict']['src_char2idx']
src_idx2char = data['dict']['src_idx2char']
tgt_char2idx = data['dict']['tgt_char2idx']
tgt_idx2char = data['dict']['tgt_idx2char']
else:
src_char2idx, src_idx2char = process(Config.train_translation_zh_filename, lang='zh')
tgt_char2idx, tgt_idx2char = process(Config.train_translation_en_filename, lang='en')
print("输入文本字典的大小:", len(src_char2idx))
print("输出文本字典的大小:", len(tgt_char2idx))
data = {
'dict': {
'src_char2idx': src_char2idx,
'src_idx2char': src_idx2char,
'tgt_char2idx': tgt_char2idx,
'tgt_idx2char': tgt_idx2char
}
}
with open(Config.vocab_file, 'wb') as file:
pickle.dump(data, file)
# 加载训练集和验证集
train = get_data(Config.train_translation_zh_filename, Config.train_translation_en_filename)
valid = get_data(Config.valid_translation_zh_filename, Config.valid_translation_en_filename)
data = {
'train': train,
'valid': valid
}
# 这里面存的数据: 中文已映射成对应得id保存, 英文也已映射成id 并且加了其实标志和结束标志.他们都没有进行padding 只是有一个最大长度
print('num_train: ' + str(len(train)))
print('num_valid: ' + str(len(valid)))
with open(Config.data_file, 'wb') as file:
pickle.dump(data, file)
| [
"lu.xiao@tcl.com"
] | lu.xiao@tcl.com |
ea1091477d699b137e6ab824d5bb4743d7cd9fe0 | 18f672d3239d199770756ebb8000f6544b5093fb | /stock/migrations/0002_product_name_short.py | ea5dd79e743fe0fc8e7ce026a967bf1d7ae275ab | [
"Apache-2.0"
] | permissive | pkimber/old-stock-migrated-to-gitlab | a8cc4adca8c90fe9fff134ff5fd31b37e914d3db | e712dd19684cdc2028bfea42c373c19ab3aab152 | refs/heads/master | 2021-06-15T10:58:03.884840 | 2017-04-13T15:23:00 | 2017-04-13T15:23:00 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 454 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.10.1 on 2016-09-30 12:46
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('stock', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='product',
name='name_short',
field=models.CharField(blank=True, max_length=100),
),
]
| [
"code@pkimber.net"
] | code@pkimber.net |
d8e6164c4728fb4b27177e08f4714d8ae4094824 | 10123c03954bfd57e6b9ee0acbe93e61c11dc6d0 | /Permutations.py | 01368d7730607adce93caeb2b458812778a12f72 | [] | no_license | ramchinta/python | 4a720d27fd137d32d83326338ad1748c17cd5998 | e82305a822ea200086a0978a29c18ab65a3b18fb | refs/heads/master | 2020-09-02T06:49:30.365372 | 2020-05-09T08:22:57 | 2020-05-09T08:22:57 | 219,160,098 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,022 | py | '''Given a collection of distinct integers, return all possible permutations.
Example:
Input: [1,2,3]
Output:
[
[1,2,3],
[1,3,2],
[2,1,3],
[2,3,1],
[3,1,2],
[3,2,1]
]'''
class Solution:
def permute(self, nums):
"""
:type nums: List[int]
:rtype: List[List[int]]
"""
def backtrack(first=0):
# if all integers are used up
if first == n:
output.append(nums[:])
for i in range(first, n):
# place i-th integer first
# in the current permutation
nums[first], nums[i] = nums[i], nums[first]
# use next integers to complete the permutations
backtrack(first + 1)
# backtrack
nums[first], nums[i] = nums[i], nums[first]
n = len(nums)
output = []
backtrack()
return output
print(Solution().permute([1,2,3]))
#[[1, 2, 3], [1, 3, 2], [2, 1, 3], [2, 3, 1], [3, 2, 1], [3, 1, 2]] | [
"mithrachinta@gmail.com"
] | mithrachinta@gmail.com |
9ea9c27397cba8d779b2f23ebe41b720d863300d | 632efa9e1991bc632aaba4783e05c942afc77712 | /tests/py/test_goal.py | e180004421eec2fb754be4f7d4fe2dadeaa2aba3 | [
"CC0-1.0",
"LicenseRef-scancode-public-domain"
] | permissive | astiob/liberapay.com | 82e2785b17a104197c483bf5dd1c1cc85cc1cb26 | 29842f7aeaaaca99f4427ff43ebdefd41223ff9c | refs/heads/master | 2020-04-05T23:39:24.843313 | 2016-04-19T13:02:07 | 2016-04-19T13:02:07 | 56,925,959 | 0 | 1 | null | 2016-04-23T15:22:00 | 2016-04-23T15:22:00 | null | UTF-8 | Python | false | false | 2,309 | py | from __future__ import print_function, unicode_literals
from decimal import Decimal
from liberapay.testing import Harness
class Tests(Harness):
def setUp(self):
self.alice = self.make_participant('alice')
def change_goal(self, goal, goal_custom="", auth_as="alice"):
return self.client.PxST(
"/alice/goal",
{'goal': goal, 'goal_custom': goal_custom},
auth_as=self.alice if auth_as == 'alice' else auth_as
)
def test_changing_to_minus_1_asks_confirmation(self):
r = self.client.PxST('/alice/goal', {'goal': '-1'}, auth_as=self.alice)
assert "Warning: Doing this will remove all the tips you are currently receiving." in r.text
def test_wonky_custom_amounts_are_standardized(self):
self.change_goal("custom", ",100,100.00001")
alice = self.alice.from_id(self.alice.id)
assert alice.goal == 100100
def test_anonymous_gets_403(self):
response = self.change_goal("100.00", auth_as=None)
assert response.code == 403, response.code
def test_invalid_is_400(self):
response = self.change_goal("cheese")
assert response.code == 400, response.code
def test_invalid_custom_amount_is_400(self):
response = self.change_goal("custom", "cheese")
assert response.code == 400, response.code
def test_change_goal(self):
self.change_goal("custom", "100")
self.change_goal("0")
self.change_goal("custom", "1,100.00")
self.change_goal("null", "")
self.change_goal("custom", "400")
actual = self.db.one("SELECT goal FROM participants")
assert actual == Decimal("400.00")
actual = self.db.all("""
SELECT payload
FROM events
WHERE type = 'set_goal'
ORDER BY ts DESC
""")
assert actual == ['400', None, '1100.00', '0', '100']
def test_team_member_can_change_team_goal(self):
team = self.make_participant('team', kind='group')
team.add_member(self.alice)
r = self.client.PxST(
'/team/goal',
{'goal': 'custom', 'goal_custom': '99.99'},
auth_as=self.alice
)
assert r.code == 302
assert team.refetch().goal == Decimal('99.99')
| [
"changaco@changaco.oy.lc"
] | changaco@changaco.oy.lc |
086dd62330fcf5b767d3e54b7e8ca44c0db75ec7 | ec1aa6a0217c29301b08c8b9559be1f8a346502b | /correctiv_eurosfueraerzte/admin/__init__.py | e4eabaa7c8211dc838ab3b52a39e8e2ede241a5f | [
"MIT"
] | permissive | correctiv/correctiv-eurosfueraerzte | 091039881130fa6c7a78811cdc9bf00893aa6906 | 291c358d65eccf06034e409d888de56a4545c7b7 | refs/heads/master | 2022-12-16T03:24:41.366597 | 2017-08-16T13:37:33 | 2017-08-16T13:37:33 | 53,336,927 | 5 | 0 | MIT | 2022-11-22T01:53:29 | 2016-03-07T15:39:45 | Python | UTF-8 | Python | false | false | 788 | py | from django.contrib import admin
from ..models import (PharmaCompany, Drug, ObservationalStudy,
PaymentRecipient, PharmaPayment, ZeroDoctor,
ZeroDocSubmission)
from .base import (PharmaCompanyAdmin, DrugAdmin, ObservationalStudyAdmin,
PaymentRecipientAdmin, PharmaPaymentAdmin)
from .zerodocs import ZeroDoctorAdmin, ZeroDocSubmissionAdmin
admin.site.register(PharmaCompany, PharmaCompanyAdmin)
admin.site.register(Drug, DrugAdmin)
admin.site.register(ObservationalStudy, ObservationalStudyAdmin)
admin.site.register(PaymentRecipient, PaymentRecipientAdmin)
admin.site.register(PharmaPayment, PharmaPaymentAdmin)
admin.site.register(ZeroDoctor, ZeroDoctorAdmin)
admin.site.register(ZeroDocSubmission, ZeroDocSubmissionAdmin)
| [
"mail@stefanwehrmeyer.com"
] | mail@stefanwehrmeyer.com |
f7f852b199e0f51ab15fa49c3bcdb5463ef18a76 | f0d713996eb095bcdc701f3fab0a8110b8541cbb | /NbZ2cMeEfH3KpQRku_24.py | d4f52eab2e0c8c08f9daf43571dcc4231657dce1 | [] | no_license | daniel-reich/turbo-robot | feda6c0523bb83ab8954b6d06302bfec5b16ebdf | a7a25c63097674c0a81675eed7e6b763785f1c41 | refs/heads/main | 2023-03-26T01:55:14.210264 | 2021-03-23T16:08:01 | 2021-03-23T16:08:01 | 350,773,815 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,541 | py | """
You are given a list of `0`s and `1`s, like the one below:
[0, 1, 0, 0, 0, 1, 1, 1, 0, 1]
# The first element, a 0, and the last element, a 1 are both unhappy.
# The second element, a 1 is unhappy.
# The second-to-last element, a 0 is unhappy.
# All other numbers in this list are happy.
A `1` is **unhappy** if the digit to its left and the digit to its right are
both 0s. A `0` is **unhappy** if the digit to its left and the digit to its
right are both 1s. If a number has only one neighbor, it is **unhappy** if its
only neighbor is different. Otherwise, a number is **happy**.
Write a function that takes in a list of `0`s and `1`s and outputs the
**portion of numbers which are happy**. The total portion of numbers which are
happy can be represented as:
portion of happy 0s = # happy 0s / total # 0s
portion of happy 1s = # happy 1s / total # 1s
portion of happy numbers = (portion of happy 0s + portion of happy 1s) / 2
In the example above, `0.6` is the number of happy numbers.
### Examples
portion_happy([0, 1, 0, 1, 0]) ➞ 0
portion_happy([0, 1, 1, 0]) ➞ 0.5
portion_happy([0, 0, 0, 1, 1]) ➞ 1
portion_happy([1, 0, 0, 1, 1]) ➞ 0.8
### Notes
* Remember: a `0` border number is unhappy if its only neighbor is a `1` and vice versa.
* A list will contain at least two elements.
"""
import re
def portion_happy(n):
n = ''.join(map(str,n))
u = lambda x: len(re.findall("(?<!%s)%s(?!%s)"%(x,x,x),n))
return 1-((u(0)+u(1))/len(n))
| [
"daniel.reich@danielreichs-MacBook-Pro.local"
] | daniel.reich@danielreichs-MacBook-Pro.local |
fccf0b7bd873beb81a2e03f845b1ab61e0cd8002 | a2dc75a80398dee58c49fa00759ac99cfefeea36 | /bluebottle/funding/migrations/0023_bankpayoutaccount.py | 4c2896a6cfd4e6e3a3b9b656a801977f9e27dd97 | [
"BSD-2-Clause"
] | permissive | onepercentclub/bluebottle | e38b0df2218772adf9febb8c6e25a2937889acc0 | 2b5f3562584137c8c9f5392265db1ab8ee8acf75 | refs/heads/master | 2023-08-29T14:01:50.565314 | 2023-08-24T11:18:58 | 2023-08-24T11:18:58 | 13,149,527 | 15 | 9 | BSD-3-Clause | 2023-09-13T10:46:20 | 2013-09-27T12:09:13 | Python | UTF-8 | Python | false | false | 1,430 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.15 on 2019-08-26 10:30
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('funding', '0022_auto_20190804_1022'),
]
operations = [
migrations.CreateModel(
name='BankPayoutAccount',
fields=[
('payoutaccount_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='funding.PayoutAccount')),
('account_number', models.CharField(blank=True, max_length=100, null=True, verbose_name='bank account number')),
('account_holder_name', models.CharField(blank=True, max_length=100, null=True, verbose_name='account holder name')),
('account_holder_address', models.CharField(blank=True, max_length=500, null=True, verbose_name='account holder address')),
('account_bank_country', models.CharField(blank=True, max_length=100, null=True, verbose_name='bank country')),
('account_details', models.CharField(blank=True, max_length=500, null=True, verbose_name='account details')),
],
options={
'abstract': False,
},
bases=('funding.payoutaccount',),
),
]
| [
"loek@goodup.com"
] | loek@goodup.com |
c4fe86c6132760476a28ff976caa14c7b657506f | 691793de7d07b17918d076b319281c706f7275c0 | /test/test_notification_event.py | 547cb7800d3e17e7eddff3c860fb253bd77ae619 | [
"MIT"
] | permissive | signingtoday/signingtoday-sdk-python | 1ddfae5340690c80760c500436631d4a8ff9c87f | ed267279622fb59f2ad8fa289157fc9cdf9d8a5b | refs/heads/master | 2020-12-03T15:32:35.755222 | 2020-03-24T08:27:11 | 2020-03-24T08:27:11 | 231,372,803 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,254 | py | # coding: utf-8
"""
Signing Today Web
*Signing Today* is the perfect Digital Signature Gateway. Whenever in Your workflow You need to add one or more Digital Signatures to Your document, *Signing Today* is the right choice. You prepare Your documents, *Signing Today* takes care of all the rest: send invitations (`signature tickets`) to signers, collects their signatures, send You back the signed document. Integrating *Signing Today* in Your existing applications is very easy. Just follow these API specifications and get inspired by the many examples presented hereafter. # noqa: E501
The version of the OpenAPI document: 2.0.0
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import unittest
import datetime
import signing_today_client
from signing_today_client.models.notification_event import NotificationEvent # noqa: E501
from signing_today_client.rest import ApiException
class TestNotificationEvent(unittest.TestCase):
"""NotificationEvent unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def make_instance(self, include_optional):
"""Test NotificationEvent
include_option is a boolean, when False only required
params are included, when True both required and
optional params are included """
# model = signing_today_client.models.notification_event.NotificationEvent() # noqa: E501
if include_optional :
return NotificationEvent(
id = 1,
time = '2007-04-02T19:30:10Z',
dst_id = 'd6bb4c8f-37bf-4900-a1e4-dd9b0939cafb',
user_id = '1ca229c8-2a99-4f3e-9421-36377fd8d9e5',
dst_title = 'Contract',
username = 'johndoe',
email = 'jdo@bit4id.com',
event = 'evt_dst_status_signed'
)
else :
return NotificationEvent(
)
def testNotificationEvent(self):
"""Test NotificationEvent"""
inst_req_only = self.make_instance(include_optional=False)
inst_req_and_optional = self.make_instance(include_optional=True)
if __name__ == '__main__':
unittest.main()
| [
"smartcloud@bit4id.com"
] | smartcloud@bit4id.com |
6c5d71530c58702155e5b75c3fe0457827dc44a5 | b8e885e1546dfb7a45dc7da7718d73ae4103196e | /nebula/__init__.py | 7fd8272fa8e9757aa84f2b70df4bef4fdf4bb229 | [] | no_license | dannon/nebula | b4655b3b5401c528d25ae973763c4dd82d367ab6 | 5ca94404894f9a64b8adf2afbc37381757ae6b7a | refs/heads/master | 2021-01-18T16:00:41.768529 | 2014-09-23T20:29:55 | 2014-09-23T20:29:55 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 911 | py |
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
from parser import NebulaCompile
from scheduler import Scheduler
from website import WebSite
from workrepo import WorkRepo
class Config:
def __init__(self, mesos=None, port=9999, host='localhost', workdir="/tmp", docker=None):
self.mesos = mesos
self.port = port
self.host = host
self.workdir = workdir
self.docker = docker
| [
"kellrott@gmail.com"
] | kellrott@gmail.com |
0d5998dc0bdef8074798becd45da509feac2c687 | 2f0aa66e14c6595289f6a0de2bdf71e9922052a7 | /nextApi/company/serializers.py | a8e40069f87047ad03eef4ae796f22982d9f0f9f | [] | no_license | aimethierry/NextApi | 8f83a2b0f499fdf5118eb930baa051584cfd9aa5 | 90884ee6d900ce71116b40276dda0e97bec0b521 | refs/heads/master | 2022-12-11T09:03:54.981284 | 2020-09-19T12:40:36 | 2020-09-19T12:40:36 | 296,866,571 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 246 | py | from rest_framework import serializers
from .models import Company
class companySerializer(serializers.ModelSerializer):
class Meta:
model = Company
fields = ['name', 'country', 'city', 'street', 'pbox', 'description']
| [
"aime.thierry97@gmail.com"
] | aime.thierry97@gmail.com |
89859a5f2de10fec32b6d37f696243b99ef2ff8e | 0c785a2601f2b02c1636d57c70039f0c4f08294a | /pybles/PySrc/tests/test_jyserver.py | 81f3ce332de8e93c0ed3f6cf8c9c62c15242b1c1 | [] | no_license | SoftwareEngineeringToolDemos/ICSE-2012-CodeBubbles | bc26d9655fbd56e5f61364db1c176a3539653d7f | 6da209c1ff0f7fbfa958c97dc22ec478b2b5219c | refs/heads/master | 2021-01-17T13:35:48.729810 | 2016-06-24T19:42:07 | 2016-06-24T19:42:07 | 45,094,073 | 0 | 2 | null | null | null | null | UTF-8 | Python | false | false | 5,869 | py | '''
@author Fabio Zadrozny
'''
import sys
import unittest
import socket
import urllib
IS_JYTHON = sys.platform.find('java') != -1
if IS_JYTHON:
import os
#make it as if we were executing from the directory above this one (so that we can use jycompletionserver
#without the need for it being in the pythonpath)
sys.argv[0] = os.path.dirname(sys.argv[0])
#twice the dirname to get the previous level from this file.
sys.path.insert(1, os.path.join(os.path.dirname(sys.argv[0])))
import pycompletionserver as jycompletionserver
DEBUG = 0
def dbg(s):
if DEBUG:
sys.stdout.write('TEST %s\n' % s)
class Test(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
def tearDown(self):
unittest.TestCase.tearDown(self)
def testIt(self):
dbg('ok')
def testMessage(self):
t = jycompletionserver.T(0, 0)
l = []
l.append(('Def', 'description' , 'args'))
l.append(('Def1', 'description1', 'args1'))
l.append(('Def2', 'description2', 'args2'))
msg = t.processor.formatCompletionMessage('test_jyserver.py', l)
self.assertEquals('@@COMPLETIONS(test_jyserver.py,(Def,description,args),(Def1,description1,args1),(Def2,description2,args2))END@@', msg)
l = []
l.append(('Def', 'desc,,r,,i()ption', ''))
l.append(('Def(1', 'descriptio(n1', ''))
l.append(('De,f)2', 'de,s,c,ription2', ''))
msg = t.processor.formatCompletionMessage(None, l)
expected = '@@COMPLETIONS(None,(Def,desc%2C%2Cr%2C%2Ci%28%29ption, ),(Def%281,descriptio%28n1, ),(De%2Cf%292,de%2Cs%2Cc%2Cription2, ))END@@'
self.assertEquals(expected, msg)
def testCompletionSocketsAndMessages(self):
dbg('testCompletionSocketsAndMessages')
t, sToWrite, sToRead, self.connToRead, addr = self.createConnections()
dbg('connections created')
try:
#now that we have the connections all set up, check the code completion messages.
msg = urllib.quote_plus('math')
toWrite = '@@IMPORTS:%sEND@@' % msg
dbg('writing' + str(toWrite))
sToWrite.send(toWrite) #math completions
completions = self.readMsg()
dbg(urllib.unquote_plus(completions))
start = '@@COMPLETIONS('
self.assert_(completions.startswith(start), '%s DOESNT START WITH %s' % (completions, start))
self.assert_(completions.find('@@COMPLETIONS') != -1)
self.assert_(completions.find('END@@') != -1)
msg = urllib.quote_plus('__builtin__.str')
toWrite = '@@IMPORTS:%sEND@@' % msg
dbg('writing' + str(toWrite))
sToWrite.send(toWrite) #math completions
completions = self.readMsg()
dbg(urllib.unquote_plus(completions))
start = '@@COMPLETIONS('
self.assert_(completions.startswith(start), '%s DOESNT START WITH %s' % (completions, start))
self.assert_(completions.find('@@COMPLETIONS') != -1)
self.assert_(completions.find('END@@') != -1)
finally:
try:
self.sendKillMsg(sToWrite)
while not hasattr(t, 'ended'):
pass #wait until it receives the message and quits.
sToRead.close()
sToWrite.close()
self.connToRead.close()
except:
pass
def createConnections(self, p1=50002, p2=50003):
'''
Creates the connections needed for testing.
'''
t = jycompletionserver.T(p1, p2)
t.start()
sToWrite = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sToWrite.connect((jycompletionserver.HOST, p1))
sToRead = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sToRead.bind((jycompletionserver.HOST, p2))
sToRead.listen(1) #socket to receive messages.
connToRead, addr = sToRead.accept()
return t, sToWrite, sToRead, connToRead, addr
def readMsg(self):
msg = '@@PROCESSING_END@@'
while msg.startswith('@@PROCESSING'):
msg = self.connToRead.recv(1024)
if msg.startswith('@@PROCESSING:'):
dbg('Status msg:' + str(msg))
while msg.find('END@@') == -1:
msg += self.connToRead.recv(1024)
return msg
def sendKillMsg(self, socket):
socket.send(jycompletionserver.MSG_KILL_SERVER)
#"C:\Program Files\Java\jdk1.5.0_04\bin\java.exe" -Dpython.path="C:\bin\jython21\Lib";"C:\bin\jython21";"C:\Program Files\Java\jdk1.5.0_04\jre\lib\rt.jar" -classpath C:/bin/jython21/jython.jar org.python.util.jython D:\eclipse_workspace\org.python.pydev\PySrc\pycompletionserver.py 53795 58659
#
#"C:\Program Files\Java\jdk1.5.0_04\bin\java.exe" -Dpython.path="C:\bin\jython21\Lib";"C:\bin\jython21";"C:\Program Files\Java\jdk1.5.0_04\jre\lib\rt.jar" -classpath C:/bin/jython21/jython.jar org.python.util.jython D:\eclipse_workspace\org.python.pydev\PySrc\tests\test_jyserver.py
#
#"C:\Program Files\Java\jdk1.5.0_04\bin\java.exe" -Dpython.path="C:\bin\jython21\Lib";"C:\bin\jython21";"C:\Program Files\Java\jdk1.5.0_04\jre\lib\rt.jar" -classpath C:/bin/jython21/jython.jar org.python.util.jython d:\runtime-workbench-workspace\jython_test\src\test.py
if __name__ == '__main__':
if IS_JYTHON:
suite = unittest.makeSuite(Test)
unittest.TextTestRunner(verbosity=1).run(suite)
else:
sys.stdout.write('Not running jython tests for non-java platform: %s' % sys.platform)
| [
"you@example.com"
] | you@example.com |
e6f76838cc8948c487e9b5d7f982f891fd930d1a | 7c74ceb9f8addcc0816d012e0b84b174b96e0def | /src/azure-cli/azure/cli/command_modules/aro/_rbac.py | 3b993a002cd0239d028eae1e6aec6d2af2013b6b | [
"MIT",
"LGPL-2.1-only",
"LGPL-2.1-or-later",
"GPL-1.0-or-later",
"MPL-2.0",
"LGPL-2.0-or-later",
"BSD-3-Clause",
"Apache-2.0",
"BSD-2-Clause"
] | permissive | microsoft/azure-cli | 4c826290e7a6f6bd27da3829b05e4f02ff6dc8d9 | 9ba64b33f6f78e2c3e42f8a147f59484300e8779 | refs/heads/dev | 2023-08-31T08:51:39.526556 | 2022-11-28T19:08:23 | 2022-11-28T19:08:23 | 370,900,439 | 7 | 7 | MIT | 2023-08-01T23:34:50 | 2021-05-26T03:59:41 | Python | UTF-8 | Python | false | false | 2,862 | py | # --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
import uuid
from azure.cli.core.commands.client_factory import get_mgmt_service_client
from azure.cli.core.commands.client_factory import get_subscription_id
from azure.cli.core.profiles import get_sdk
from azure.cli.core.profiles import ResourceType
from knack.log import get_logger
from msrest.exceptions import ValidationError
from msrestazure.tools import resource_id
NETWORK_CONTRIBUTOR = '4d97b98b-1d4f-4787-a291-c67834d212e7'
logger = get_logger(__name__)
def _gen_uuid():
return uuid.uuid4()
def _create_role_assignment(auth_client, resource, params):
# retry "ValidationError: A hash conflict was encountered for the role Assignment ID. Please use a new Guid."
max_retries = 3
retries = 0
while True:
try:
return auth_client.role_assignments.create(resource, _gen_uuid(), params)
except ValidationError as ex:
if retries >= max_retries:
raise
retries += 1
logger.warning("%s; retry %d of %d", ex, retries, max_retries)
def assign_network_contributor_to_resource(cli_ctx, resource, object_id):
auth_client = get_mgmt_service_client(cli_ctx, ResourceType.MGMT_AUTHORIZATION)
RoleAssignmentCreateParameters = get_sdk(cli_ctx, ResourceType.MGMT_AUTHORIZATION,
'RoleAssignmentCreateParameters', mod='models',
operation_group='role_assignments')
role_definition_id = resource_id(
subscription=get_subscription_id(cli_ctx),
namespace='Microsoft.Authorization',
type='roleDefinitions',
name=NETWORK_CONTRIBUTOR,
)
_create_role_assignment(auth_client, resource, RoleAssignmentCreateParameters(
role_definition_id=role_definition_id,
principal_id=object_id,
principal_type='ServicePrincipal',
))
def has_network_contributor_on_resource(cli_ctx, resource, object_id):
auth_client = get_mgmt_service_client(cli_ctx, ResourceType.MGMT_AUTHORIZATION)
role_definition_id = resource_id(
subscription=get_subscription_id(cli_ctx),
namespace='Microsoft.Authorization',
type='roleDefinitions',
name=NETWORK_CONTRIBUTOR,
)
for assignment in auth_client.role_assignments.list_for_scope(resource):
if assignment.role_definition_id.lower() == role_definition_id.lower() and \
assignment.principal_id.lower() == object_id.lower():
return True
return False
| [
"noreply@github.com"
] | microsoft.noreply@github.com |
52ac0359f5fb4a6ae85782c49c80f98062649017 | 34a70bf642b6f678dce2b22efc598656a1a7d90a | /GraphCodes/CyclesUndirectedGraph.py | 5a36ee8efbc7fa46ab572f4fc8f242ead3212579 | [] | no_license | CodeForContribute/Algos-DataStructures | ce89a313b3e32de57f23b263966a84bb020e6a18 | d0ddc7c8f94270f9269a9a5233b3a07fe59c9a1f | refs/heads/master | 2022-03-28T03:08:17.934077 | 2019-11-23T21:58:03 | 2019-11-23T21:58:03 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,240 | py | from collections import defaultdict
class Graph:
def __init__(self, vertices):
self.vertices = vertices
self.graph = defaultdict(list)
def addEdge(self, u, v):
self.graph[u].append(v)
# me Complexity of this method is same as time complexity of DFS traversal which is O(V+E)
def isCycleIndirected(self, v, visited, recStack):
visited[v] = True
recStack[v] = True
for neighbor in self.graph[v]:
if visited[neighbor] and recStack[neighbor]:
return True
if self.isCycleIndirected(neighbor, visited, recStack):
return True
def isCycle(self):
visited = [False for i in range(self.vertices)]
recStack = [False for i in range(self.vertices)]
for i in range(self.vertices):
if not visited[i]:
if self.isCycleIndirected(i, visited, recStack):
return True
return False
if __name__ == '__main__':
g = Graph(8)
g.addEdge(0, 1)
g.addEdge(0, 2)
g.addEdge(1, 2)
g.addEdge(2, 5)
g.addEdge(2, 3)
g.addEdge(3, 7)
if g.isCycle() == 1:
print("Graph has a cycle")
else:
print("Graph has no cycle")
| [
"RAUSHAN.KUMAR2@otis.COM"
] | RAUSHAN.KUMAR2@otis.COM |
b107848bc925c50961146c951860c3bfc5fbe2c3 | 397e125e94f4f139f2bf5055824d81f24b8b1757 | /ABC/137/E.py | 7c09e79bfacdc5cdd6e89aa3d749feeb7e189c9a | [] | no_license | tails1434/Atcoder | ecbab6ee238e3f225551297db961b1b502841fa4 | e7c7fed36be46bbaaf020a70997842240ba98d62 | refs/heads/master | 2021-07-07T00:31:49.235625 | 2020-09-30T01:42:01 | 2020-09-30T01:42:01 | 189,009,622 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,580 | py | from collections import deque
import sys
input = sys.stdin.readline
def main():
N, M, P = map(int, input().split())
edge = []
g = [[] for _ in range(N)]
rg = [[] for _ in range(N)]
for _ in range(M):
A, B, C = map(int, input().split())
A -= 1
B -= 1
C -= P
C = -C
edge.append((A,B,C))
g[A].append(B)
rg[B].append(A)
# 0 => N-1の間にある閉路を検出したいので
# 0とN-1からたどりつけない場所は前処理で取り除く
visited1 = set()
visited2 = set()
visited1.add(0)
visited2.add(N-1)
Q = deque()
Q.append(0)
while Q:
v = Q.popleft()
for dest in g[v]:
if dest in visited1:
continue
visited1.add(dest)
Q.append(dest)
Q.append(N-1)
while Q:
v = Q.popleft()
for dest in rg[v]:
if dest in visited2:
continue
visited2.add(dest)
Q.append(dest)
OK = visited1 & visited2
flag = True
d = [float('inf')] * N
d[0] = 0
step = 0
while flag:
flag = False
for A, B, C in edge:
if not A in OK:
continue
if not B in OK:
continue
newD = d[A] + C
if newD < d[B]:
d[B] = newD
flag = True
step += 1
if step > N:
print(-1)
exit()
print(max(0,-d[N-1]))
if __name__ == "__main__":
main() | [
"sososo1333@gmail.com"
] | sososo1333@gmail.com |
a09fbeb5fff9be004c863cc1d188a6c2e4edecd2 | ce196aba0adde47ea2767eae1d7983a1ef548bb8 | /T30-turtle-带轴sin(x).py | 65b1abeeb749519d38055915ef35d8a1c0f3e5ca | [] | no_license | xiang-daode/Python3_codes | 5d2639ffd5d65065b98d029e79b8f3608a37cf0b | 06c64f85ce2c299aef7f9311e9473e0203a05b09 | refs/heads/main | 2023-08-30T14:59:55.123128 | 2021-11-03T05:12:24 | 2021-11-03T05:12:24 | 333,632,892 | 0 | 2 | null | null | null | null | UTF-8 | Python | false | false | 756 | py | import math
from turtle import *
N = 100
def f(x):
return x
def jumpto(x, y):
penup()
goto(x,y)
def line(x1, y1, x2, y2):
jumpto(x1, y1)
pendown()
goto(x2, y2)
def coosys():
width(4)
pencolor('red')
line(-N, 0, N+1, 0)
pencolor('blue')
line(0, -2, 0, 2.1)
def plot(fun, y, color):
pencolor(color)
width(2)
jumpto(-N, 0)
pendown()
#dot(5)
for i in range(-N,N,2):
yi=math.cos(i/10)
goto(i,yi)
#dot(5)
def main():
reset()
setworldcoordinates(-100,-2, 101, 2.1)
speed(0)
hideturtle()
coosys()
plot(f, 0, "green")
return "Done!"
if __name__ == "__main__":
main()
mainloop()
| [
"noreply@github.com"
] | xiang-daode.noreply@github.com |
a24ea528d1d4837e64a95d27a224929645603346 | 90c2619937019bb1145edfb2d9d6a7cdea460b57 | /src/783.py | 4a993223e7ea44eab55e13ecca14d8d860f897c5 | [
"MIT"
] | permissive | zhaoyi3264/leetcode-solutions | 2d289a7e5c74cfe7f8b019c6056ce16485ae057b | 1a3a2d441cdd07a17e80b0ea43b7b266844f530c | refs/heads/main | 2023-06-03T11:35:25.054669 | 2021-06-28T02:58:07 | 2021-06-28T02:58:07 | 349,618,427 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 588 | py | # Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
prev = -float('inf')
ans = float('inf')
def minDiffInBST(self, root: TreeNode) -> int:
def inorder(root):
if not root:
return
inorder(root.left)
self.ans = min(self.ans, root.val - self.prev)
self.prev = root.val
inorder(root.right)
inorder(root)
return self.ans
| [
"zhaoyi3264@gmail.com"
] | zhaoyi3264@gmail.com |
51f1741be15eb364a9879aef8b51ed191d5ebdfa | de577e64440d2c330ff0018e8bfb7cf3abf11b70 | /fsleyes/plugins/tools/addroihistogram.py | e388e0aa2128585e355eae1a475c08d34bb2f652 | [
"Apache-2.0",
"CC-BY-3.0",
"BSD-3-Clause"
] | permissive | CGSchwarzMayo/fsleyes | bb887bf8e8dd46bb9e0e3d5c3028d97811fabad1 | 37b45d034d60660b6de3e4bdf5dd6349ed6d853b | refs/heads/master | 2023-09-01T01:39:38.508051 | 2023-08-21T18:21:34 | 2023-08-21T18:21:34 | 272,476,938 | 0 | 0 | NOASSERTION | 2020-06-15T15:34:36 | 2020-06-15T15:34:34 | null | UTF-8 | Python | false | false | 5,615 | py | #!/usr/bin/env python
#
# addroihistogram.py - The AddROIHistogramAction class.
#
# Author: Paul McCarthy <pauldmccarthy@gmail.com>
#
"""This module provides the :class:`AddROIHistogramAction` class, an
action used by the :class:`.HistogramPanel`.
"""
import wx
import numpy as np
import fsl.data.image as fslimage
import fsleyes.views.histogrampanel as histogrampanel
import fsleyes.strings as strings
import fsleyes.plotting.dataseries as dataseries
import fsleyes.plotting.histogramseries as histogramseries
import fsleyes.plugins.tools.addmaskdataseries as addmaskdataseries
import fsleyes.actions.base as base
class AddROIHistogramAction(base.Action):
"""The ``AddROIHistogramAction`` class is used by the
:class:`.HistogramPanel`.
It performs a very similar task to the :class:`.AddMaskDataSeriesAction` -
the user selects a binary mask, the data within the base image is extracted
for that mask, and the histogram of that data is added to the plot.
"""
@staticmethod
def supportedViews():
"""The ``AddROIHistogramAction`` is restricted for use with the
:class:`.HistogramPanel`.
"""
return [histogrampanel.HistogramPanel]
def __init__(self, overlayList, displayCtx, plotPanel):
"""Create an ``AddROIHistogramAction``.
:arg overlayList: The :class:`.OverlayList`.
:arg displayCtx: The :class:`.DisplayContext`.
:arg plotPanel: The :class:`.HistogramPanel`.
"""
base.Action.__init__(
self, overlayList, displayCtx, self.__addROIHistogram)
self.__plotPanel = plotPanel
self.__roiOptions = []
overlayList.addListener('overlays',
self.name,
self.__overlayListChanged)
displayCtx .addListener('selectedOverlay',
self.name,
self.__overlayListChanged)
self.__overlayListChanged()
def destroy(self):
"""Must be called when this ``AddROIHistogramAction`` is no
longer in use.
"""
if self.destroyed:
return
self.overlayList.removeListener('overlays', self.name)
self.displayCtx .removeListener('selectedOverlay', self.name)
self.__plotPanel = None
self.__roiOptions = None
base.Action.destroy(self)
def __overlayListChanged(self, *a):
"""Called when the :class:`.OverlayList` or the
:attr:`.DisplayContext.selectedOverlay` changes. Updates a list of
valid mask images for the currently selected overlay.
"""
overlay = self.displayCtx.getSelectedOverlay()
if (len(self.overlayList) == 0 or
(not isinstance(overlay, fslimage.Image))):
self.enabled = False
return
self.__roiOptions = [o for o in self.overlayList if
isinstance(o, fslimage.Image) and
o is not overlay and
o.sameSpace(overlay)]
self.enabled = len(self.__roiOptions) > 0
def __addROIHistogram(self):
"""Prompts the user to select an ROI mask, calculates the histogram
of that mask on the currently selected overlay, and adds the result
to the ``HistogramPanel``.
"""
overlay = self.displayCtx.getSelectedOverlay()
opts = self.displayCtx.getOpts(overlay)
roiOptions = self.__roiOptions
frame = wx.GetApp().GetTopWindow()
msg = strings.messages[self, 'selectMask'].format(overlay.name)
title = strings.titles[ self, 'selectMask'].format(overlay.name)
dlg = addmaskdataseries.MaskDialog(
frame,
[o.name for o in roiOptions],
title=title,
message=msg,
checkbox=False)
if dlg.ShowModal() != wx.ID_OK:
return
maskimg = roiOptions[dlg.GetChoice()]
mask = maskimg[:] > 0
if overlay.ndim > 3: data = overlay[opts.index()][mask]
else: data = overlay[mask]
count = self.__plotPanel.histType == 'count'
drange = (np.nanmin(data), np.nanmax(data))
nbins = histogramseries.autoBin(data, drange)
xdata, ydata, _ = histogramseries.histogram(data,
nbins,
drange,
drange,
includeOutliers=False,
count=count)
ds = dataseries.DataSeries(overlay,
self.overlayList,
self.displayCtx,
self.__plotPanel)
ds.colour = self.__plotPanel.getOverlayPlotColour(overlay)
ds.lineStyle = self.__plotPanel.getOverlayPlotStyle(overlay)
ds.lineWidth = 2
ds.alpha = 1
ds.label = '{} [mask: {}]'.format(overlay.name, maskimg.name)
# We have to run the data through
# prepareDataSeries to preprocess
# (e.g. smooth) it
ds.setData(xdata, ydata)
ds.setData(*self.__plotPanel.prepareDataSeries(ds))
self.__plotPanel.canvas.dataSeries.append(ds)
| [
"pauldmccarthy@gmail.com"
] | pauldmccarthy@gmail.com |
ff1b4b20c92851e1bdcfd180c5b9f4b46b22dbdb | 0ea15da8de03fa9ad7acce50c03824ced868a4e7 | /awesome_itech_project/awesome_itech_project/wsgi.py | e4fd12ce9b44444d43b7c3da64df723fa319c0de | [] | no_license | Zhouhao12345/AwesomeITECH | 6b23d2c40c2c74be237eba2396b83188b7750e13 | 57f1f9bc304f407edbcb9f64ff289037d9aff7f2 | refs/heads/master | 2021-01-10T14:43:04.994235 | 2016-03-25T21:15:09 | 2016-03-25T21:15:09 | 52,460,370 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 417 | py | """
WSGI config for awesome_itech_project project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.7/howto/deployment/wsgi/
"""
import os
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "awesome_itech_project.settings")
from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()
| [
"root@localhost.localdomain"
] | root@localhost.localdomain |
b7d41e8d6776f9362fcd567214b789af65441908 | 1719920a92f7194766624474b98d59ef8d6eddaf | /models/mobile_app_content_file.py | 51e891805d2376f4dff51e5a5f0d891a13e2d963 | [
"MIT"
] | permissive | MIchaelMainer/msgraph-v10-models-python | cfa5e3a65ba675383975a99779763211ed9fa0a9 | adad66363ebe151be2332f3ef74a664584385748 | refs/heads/master | 2020-03-19T12:51:06.370673 | 2018-06-08T00:16:12 | 2018-06-08T00:16:12 | 136,544,573 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,626 | py | # -*- coding: utf-8 -*-
'''
# Copyright (c) Microsoft Corporation. All Rights Reserved. Licensed under the MIT License. See License in the project root for license information.
#
# This file was generated and any changes will be overwritten.
'''
from __future__ import unicode_literals
from ..model.mobile_app_content_file_upload_state import MobileAppContentFileUploadState
from datetime import datetime
from ..one_drive_object_base import OneDriveObjectBase
class MobileAppContentFile(OneDriveObjectBase):
def __init__(self, prop_dict={}):
self._prop_dict = prop_dict
@property
def azure_storage_uri(self):
"""
Gets and sets the azureStorageUri
Returns:
str:
The azureStorageUri
"""
if "azureStorageUri" in self._prop_dict:
return self._prop_dict["azureStorageUri"]
else:
return None
@azure_storage_uri.setter
def azure_storage_uri(self, val):
self._prop_dict["azureStorageUri"] = val
@property
def is_committed(self):
"""
Gets and sets the isCommitted
Returns:
bool:
The isCommitted
"""
if "isCommitted" in self._prop_dict:
return self._prop_dict["isCommitted"]
else:
return None
@is_committed.setter
def is_committed(self, val):
self._prop_dict["isCommitted"] = val
@property
def created_date_time(self):
"""
Gets and sets the createdDateTime
Returns:
datetime:
The createdDateTime
"""
if "createdDateTime" in self._prop_dict:
return datetime.strptime(self._prop_dict["createdDateTime"].replace("Z", ""), "%Y-%m-%dT%H:%M:%S.%f")
else:
return None
@created_date_time.setter
def created_date_time(self, val):
self._prop_dict["createdDateTime"] = val.isoformat()+"Z"
@property
def name(self):
"""
Gets and sets the name
Returns:
str:
The name
"""
if "name" in self._prop_dict:
return self._prop_dict["name"]
else:
return None
@name.setter
def name(self, val):
self._prop_dict["name"] = val
@property
def size(self):
"""
Gets and sets the size
Returns:
int:
The size
"""
if "size" in self._prop_dict:
return self._prop_dict["size"]
else:
return None
@size.setter
def size(self, val):
self._prop_dict["size"] = val
@property
def size_encrypted(self):
"""
Gets and sets the sizeEncrypted
Returns:
int:
The sizeEncrypted
"""
if "sizeEncrypted" in self._prop_dict:
return self._prop_dict["sizeEncrypted"]
else:
return None
@size_encrypted.setter
def size_encrypted(self, val):
self._prop_dict["sizeEncrypted"] = val
@property
def azure_storage_uri_expiration_date_time(self):
"""
Gets and sets the azureStorageUriExpirationDateTime
Returns:
datetime:
The azureStorageUriExpirationDateTime
"""
if "azureStorageUriExpirationDateTime" in self._prop_dict:
return datetime.strptime(self._prop_dict["azureStorageUriExpirationDateTime"].replace("Z", ""), "%Y-%m-%dT%H:%M:%S.%f")
else:
return None
@azure_storage_uri_expiration_date_time.setter
def azure_storage_uri_expiration_date_time(self, val):
self._prop_dict["azureStorageUriExpirationDateTime"] = val.isoformat()+"Z"
@property
def upload_state(self):
"""
Gets and sets the uploadState
Returns:
:class:`MobileAppContentFileUploadState<onedrivesdk.model.mobile_app_content_file_upload_state.MobileAppContentFileUploadState>`:
The uploadState
"""
if "uploadState" in self._prop_dict:
if isinstance(self._prop_dict["uploadState"], OneDriveObjectBase):
return self._prop_dict["uploadState"]
else :
self._prop_dict["uploadState"] = MobileAppContentFileUploadState(self._prop_dict["uploadState"])
return self._prop_dict["uploadState"]
return None
@upload_state.setter
def upload_state(self, val):
self._prop_dict["uploadState"] = val
| [
"mmainer@microsoft.com"
] | mmainer@microsoft.com |
ff268df9cf570e57179bf90ed24e13ec67a171d6 | 98ac0b139301285ece1a4bc9f13b75433d263419 | /torchreid/models/mlfn.py | ec712d66f781745c4f0c329560cfd572515eda3e | [
"MIT"
] | permissive | sovrasov/deep-person-reid | eb0e6b2f0bb3fa6dc22205fd443fd583f9951cd2 | 79773b88986c26e9ac2407af5999923426298a8f | refs/heads/master | 2021-09-13T04:31:15.620950 | 2019-11-14T08:06:34 | 2019-11-14T08:06:34 | 202,522,569 | 1 | 1 | MIT | 2019-11-14T08:06:36 | 2019-08-15T10:35:17 | Python | UTF-8 | Python | false | false | 7,569 | py | from __future__ import absolute_import
from __future__ import division
__all__ = ['mlfn']
import torch
from torch import nn
from torch.nn import functional as F
import torch.utils.model_zoo as model_zoo
model_urls = {
# training epoch = 5, top1 = 51.6
'imagenet': 'https://mega.nz/#!YHxAhaxC!yu9E6zWl0x5zscSouTdbZu8gdFFytDdl-RAdD2DEfpk',
}
class MLFNBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, fsm_channels, groups=32):
super(MLFNBlock, self).__init__()
self.groups = groups
mid_channels = out_channels // 2
# Factor Modules
self.fm_conv1 = nn.Conv2d(in_channels, mid_channels, 1, bias=False)
self.fm_bn1 = nn.BatchNorm2d(mid_channels)
self.fm_conv2 = nn.Conv2d(mid_channels, mid_channels, 3, stride=stride, padding=1, bias=False, groups=self.groups)
self.fm_bn2 = nn.BatchNorm2d(mid_channels)
self.fm_conv3 = nn.Conv2d(mid_channels, out_channels, 1, bias=False)
self.fm_bn3 = nn.BatchNorm2d(out_channels)
# Factor Selection Module
self.fsm = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, fsm_channels[0], 1),
nn.BatchNorm2d(fsm_channels[0]),
nn.ReLU(inplace=True),
nn.Conv2d(fsm_channels[0], fsm_channels[1], 1),
nn.BatchNorm2d(fsm_channels[1]),
nn.ReLU(inplace=True),
nn.Conv2d(fsm_channels[1], self.groups, 1),
nn.BatchNorm2d(self.groups),
nn.Sigmoid(),
)
self.downsample = None
if in_channels != out_channels or stride > 1:
self.downsample = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1, stride=stride, bias=False),
nn.BatchNorm2d(out_channels),
)
def forward(self, x):
residual = x
s = self.fsm(x)
# reduce dimension
x = self.fm_conv1(x)
x = self.fm_bn1(x)
x = F.relu(x, inplace=True)
# group convolution
x = self.fm_conv2(x)
x = self.fm_bn2(x)
x = F.relu(x, inplace=True)
# factor selection
b, c = x.size(0), x.size(1)
n = c // self.groups
ss = s.repeat(1, n, 1, 1) # from (b, g, 1, 1) to (b, g*n=c, 1, 1)
ss = ss.view(b, n, self.groups, 1, 1)
ss = ss.permute(0, 2, 1, 3, 4).contiguous()
ss = ss.view(b, c, 1, 1)
x = ss * x
# recover dimension
x = self.fm_conv3(x)
x = self.fm_bn3(x)
x = F.relu(x, inplace=True)
if self.downsample is not None:
residual = self.downsample(residual)
return F.relu(residual + x, inplace=True), s
class MLFN(nn.Module):
"""Multi-Level Factorisation Net.
Reference:
Chang et al. Multi-Level Factorisation Net for
Person Re-Identification. CVPR 2018.
Public keys:
- ``mlfn``: MLFN (Multi-Level Factorisation Net).
"""
def __init__(self, num_classes, loss='softmax', groups=32, channels=[64, 256, 512, 1024, 2048], embed_dim=1024, **kwargs):
super(MLFN, self).__init__()
self.loss = loss
self.groups = groups
# first convolutional layer
self.conv1 = nn.Conv2d(3, channels[0], 7, stride=2, padding=3)
self.bn1 = nn.BatchNorm2d(channels[0])
self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
# main body
self.feature = nn.ModuleList([
# layer 1-3
MLFNBlock(channels[0], channels[1], 1, [128, 64], self.groups),
MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
# layer 4-7
MLFNBlock(channels[1], channels[2], 2, [256, 128], self.groups),
MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
# layer 8-13
MLFNBlock(channels[2], channels[3], 2, [512, 128], self.groups),
MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
# layer 14-16
MLFNBlock(channels[3], channels[4], 2, [512, 128], self.groups),
MLFNBlock(channels[4], channels[4], 1, [512, 128], self.groups),
MLFNBlock(channels[4], channels[4], 1, [512, 128], self.groups),
])
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
# projection functions
self.fc_x = nn.Sequential(
nn.Conv2d(channels[4], embed_dim, 1, bias=False),
nn.BatchNorm2d(embed_dim),
nn.ReLU(inplace=True),
)
self.fc_s = nn.Sequential(
nn.Conv2d(self.groups * 16, embed_dim, 1, bias=False),
nn.BatchNorm2d(embed_dim),
nn.ReLU(inplace=True),
)
self.classifier = nn.Linear(embed_dim, num_classes)
self.init_params()
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(x, inplace=True)
x = self.maxpool(x)
s_hat = []
for block in self.feature:
x, s = block(x)
s_hat.append(s)
s_hat = torch.cat(s_hat, 1)
x = self.global_avgpool(x)
x = self.fc_x(x)
s_hat = self.fc_s(s_hat)
v = (x + s_hat) * 0.5
v = v.view(v.size(0), -1)
if not self.training:
return v
y = self.classifier(v)
if self.loss == 'softmax':
return y
elif self.loss == 'triplet':
return y, v
else:
raise KeyError('Unsupported loss: {}'.format(self.loss))
def init_pretrained_weights(model, model_url):
"""Initializes model with pretrained weights.
Layers that don't match with pretrained layers in name or size are kept unchanged.
"""
pretrain_dict = model_zoo.load_url(model_url)
model_dict = model.state_dict()
pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
def mlfn(num_classes, loss='softmax', pretrained=True, **kwargs):
model = MLFN(num_classes, loss, **kwargs)
if pretrained:
#init_pretrained_weights(model, model_urls['imagenet'])
import warnings
warnings.warn('The imagenet pretrained weights need to be manually downloaded from {}'.format(model_urls['imagenet']))
return model | [
"k.zhou@qmul.ac.uk"
] | k.zhou@qmul.ac.uk |
ca0e2147614ffa23c1d12256ceb2c465f8ef9ee1 | 8cefaf15f2b70bc3457047351151f85dbffc191e | /tools.py | 2a0c12b25219e29de93b58540ba2a51db4d1e70a | [] | no_license | tjacek/ortho_selection | 183fa86088d94f343b191538493035ad31c4d1d7 | ee923bfda63262ce62033f8e633f1ac37804ce21 | refs/heads/master | 2023-01-21T09:18:07.671478 | 2020-12-02T21:53:22 | 2020-12-02T21:53:22 | 265,209,732 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,182 | py | import itertools
import feats
def filtered_dict(names,dic):
return { name_i:dic[name_i] for name_i in names}
def split(names,selector=None):
if(type(names)==dict):
train,test=split(names.keys(),selector)
return filtered_dict(train,names),filtered_dict(test,names)
if(not selector):
selector=get_person
train,test=[],[]
for name_i in names:
if(selector(name_i)):
train.append(name_i)
else:
test.append(name_i)
return train,test
def get_person(name_i):
return (int(name_i.split('_')[1])%2)==1
def person_cats(y):
return ["%s_%d" %(y_i.split("_")[1],i)
for i,y_i in enumerate(y)]
def read_datasets(in_path):
if(type(in_path)==tuple):
common_path,deep_path=in_path
if(type(common_path)==list):
return multi_dataset(common_path,deep_path)
return combined_dataset(common_path,deep_path)
return feats.read(in_path)
def combined_dataset(common_path,deep_path,sub_datasets=False):
if(not common_path):
return feats.read(deep_path)
if(not deep_path):
return feats.read(common_path)
common_data=feats.read(common_path)[0]
deep_data=feats.read(deep_path)
datasets=[common_data+ data_i
for data_i in deep_data]
if(sub_datasets):
return datasets,common_data,deep_data
return datasets
def multi_dataset(common_path,deep_path):
datasets=[combined_dataset(common_i,deep_path)
for common_i in common_path]
return itertools.chain.from_iterable(datasets)
def concat_dataset(in_path):
if(type(in_path)==tuple):
common_path,deep_path=in_path
# raise Exception(type(common_path))
if(type(common_path)==list):
common_data=feats.read_unified(common_path)
else:
common_data=feats.read(common_path)
# return multi_dataset(common_path,deep_path)
# return combined_dataset(common_path,deep_path)
deep_data=feats.read(deep_path)
datasets=[common_data+ data_i
for data_i in deep_data]
return datasets
return feats.read(in_path) | [
"tjacek@agh.edu.pl"
] | tjacek@agh.edu.pl |
892a16eacf42bc8b91b6898224179f9044e23c40 | 4dc5944ffad7f251b467200ba5101000561c2bdf | /iniciante/2879.py | 0dab20c1683c9fc7cb5621f11332df7ce287b7bd | [] | no_license | DyogoBendo/URI-Python | 3215337229fdb5ef1a446231925c72da4d1ea64b | 40376e0fbb2e7dd97ba316a20863826b4753c601 | refs/heads/master | 2023-06-03T08:36:06.745086 | 2021-06-16T02:00:11 | 2021-06-16T02:00:11 | 314,667,743 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 163 | py | if __name__ == "__main__":
n = int(input())
w = 0
for i in range(n):
p = int(input())
if p != 1:
w += 1
print(w) | [
"dyogoromagnabendo@gmail.com"
] | dyogoromagnabendo@gmail.com |
89049661b471aab41331bcd78d322b1768555aa0 | 44e14881c8e248c347dd81f0574c4f306c684d64 | /mysite/settings.py | c800c4c0e67641a4ccd20b759ee29dff8311daf4 | [] | no_license | eduarde/OrderHelperApp | f38af40fc492c6300c3ac70ba2e740789b1d7261 | 83938a7f50519f45dcbb96d6dbf1ea49559e28fa | refs/heads/master | 2021-01-10T12:30:47.142918 | 2016-03-17T20:05:38 | 2016-03-17T20:05:38 | 52,556,674 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,709 | py | """
Django settings for mysite project.
Generated by 'django-admin startproject' using Django 1.9.
For more information on this file, see
https://docs.djangoproject.com/en/1.9/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.9/ref/settings/
"""
import os
import django.contrib.auth
django.contrib.auth.LOGIN_URL = '/'
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/1.9/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'yan%@fw$+%fg*coibl7gnyog30wj$l5-uumhzl%8tt8r!lfxwd'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = False
ALLOWED_HOSTS = ['orderhelper.pythonanywhere.com']
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'orderhelper',
'widget_tweaks',
'datetimewidget',
'pure_pagination',
]
MIDDLEWARE_CLASSES = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.auth.middleware.SessionAuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'mysite.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'mysite.wsgi.application'
# Database
# https://docs.djangoproject.com/en/1.9/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/1.9/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/1.9/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'Europe/Bucharest'
USE_I18N = True
USE_L10N = True
USE_TZ = True
MESSAGE_STORAGE = 'django.contrib.messages.storage.cookie.CookieStorage'
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.9/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
LOGIN_URL ='/'
LOGIN_REDIRECT_URL = '/pending'
PAGINATION_SETTINGS = {
'PAGE_RANGE_DISPLAYED': 5,
'MARGIN_PAGES_DISPLAYED': 2,
'SHOW_FIRST_PAGE_WHEN_INVALID': True,
}
# handler404 = 'orderhelper.views.handler404'
| [
"eduard.erja@gmail.com"
] | eduard.erja@gmail.com |
2ea70bfaa953a1c8cc5b12b1a97ff742bf1dc11c | 96dcea595e7c16cec07b3f649afd65f3660a0bad | /tests/components/picnic/test_services.py | bc80ff73a11f0e084c64a9bd9984de5caa683527 | [
"Apache-2.0"
] | permissive | home-assistant/core | 3455eac2e9d925c92d30178643b1aaccf3a6484f | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | refs/heads/dev | 2023-08-31T15:41:06.299469 | 2023-08-31T14:50:53 | 2023-08-31T14:50:53 | 12,888,993 | 35,501 | 20,617 | Apache-2.0 | 2023-09-14T21:50:15 | 2013-09-17T07:29:48 | Python | UTF-8 | Python | false | false | 6,777 | py | """Tests for the Picnic services."""
from unittest.mock import MagicMock, patch
import pytest
from homeassistant.components.picnic import CONF_COUNTRY_CODE, DOMAIN
from homeassistant.components.picnic.const import SERVICE_ADD_PRODUCT_TO_CART
from homeassistant.components.picnic.services import PicnicServiceException
from homeassistant.const import CONF_ACCESS_TOKEN
from homeassistant.core import HomeAssistant
from tests.common import MockConfigEntry
UNIQUE_ID = "295-6y3-1nf4"
def create_picnic_api_client(unique_id):
"""Create PicnicAPI mock with set response data."""
auth_token = "af3wh738j3fa28l9fa23lhiufahu7l"
auth_data = {
"user_id": unique_id,
"address": {
"street": "Teststreet",
"house_number": 123,
"house_number_ext": "b",
},
}
picnic_mock = MagicMock()
picnic_mock.session.auth_token = auth_token
picnic_mock.get_user.return_value = auth_data
return picnic_mock
async def create_picnic_config_entry(hass: HomeAssistant, unique_id):
"""Create a Picnic config entry."""
config_entry = MockConfigEntry(
domain=DOMAIN,
data={
CONF_ACCESS_TOKEN: "x-original-picnic-auth-token",
CONF_COUNTRY_CODE: "NL",
},
unique_id=unique_id,
)
config_entry.add_to_hass(hass)
await hass.config_entries.async_setup(config_entry.entry_id)
await hass.async_block_till_done()
return config_entry
@pytest.fixture
def picnic_api_client():
"""Return the default picnic api client."""
with patch(
"homeassistant.components.picnic.create_picnic_client"
) as create_picnic_client_mock:
picnic_client_mock = create_picnic_api_client(UNIQUE_ID)
create_picnic_client_mock.return_value = picnic_client_mock
yield picnic_client_mock
@pytest.fixture
async def picnic_config_entry(hass: HomeAssistant):
"""Generate the default Picnic config entry."""
return await create_picnic_config_entry(hass, UNIQUE_ID)
async def test_add_product_using_id(
hass: HomeAssistant,
picnic_api_client: MagicMock,
picnic_config_entry: MockConfigEntry,
) -> None:
"""Test adding a product by id."""
await hass.services.async_call(
DOMAIN,
SERVICE_ADD_PRODUCT_TO_CART,
{
"config_entry_id": picnic_config_entry.entry_id,
"product_id": "5109348572",
"amount": 3,
},
blocking=True,
)
# Check that the right method is called on the api
picnic_api_client.add_product.assert_called_with("5109348572", 3)
async def test_add_product_using_name(
hass: HomeAssistant,
picnic_api_client: MagicMock,
picnic_config_entry: MockConfigEntry,
) -> None:
"""Test adding a product by name."""
# Set the return value of the search api endpoint
picnic_api_client.search.return_value = [
{
"items": [
{
"id": "2525404",
"name": "Best tea",
"display_price": 321,
"unit_quantity": "big bags",
},
{
"id": "2525500",
"name": "Cheap tea",
"display_price": 100,
"unit_quantity": "small bags",
},
]
}
]
await hass.services.async_call(
DOMAIN,
SERVICE_ADD_PRODUCT_TO_CART,
{"config_entry_id": picnic_config_entry.entry_id, "product_name": "Tea"},
blocking=True,
)
# Check that the right method is called on the api
picnic_api_client.add_product.assert_called_with("2525404", 1)
async def test_add_product_using_name_no_results(
hass: HomeAssistant,
picnic_api_client: MagicMock,
picnic_config_entry: MockConfigEntry,
) -> None:
"""Test adding a product by name that can't be found."""
# Set the search return value and check that the right exception is raised during the service call
picnic_api_client.search.return_value = []
with pytest.raises(PicnicServiceException):
await hass.services.async_call(
DOMAIN,
SERVICE_ADD_PRODUCT_TO_CART,
{
"config_entry_id": picnic_config_entry.entry_id,
"product_name": "Random non existing product",
},
blocking=True,
)
async def test_add_product_using_name_no_named_results(
hass: HomeAssistant,
picnic_api_client: MagicMock,
picnic_config_entry: MockConfigEntry,
) -> None:
"""Test adding a product by name for which no named results are returned."""
# Set the search return value and check that the right exception is raised during the service call
picnic_api_client.search.return_value = [{"items": [{"attr": "test"}]}]
with pytest.raises(PicnicServiceException):
await hass.services.async_call(
DOMAIN,
SERVICE_ADD_PRODUCT_TO_CART,
{
"config_entry_id": picnic_config_entry.entry_id,
"product_name": "Random product",
},
blocking=True,
)
async def test_add_product_multiple_config_entries(
hass: HomeAssistant,
picnic_api_client: MagicMock,
picnic_config_entry: MockConfigEntry,
) -> None:
"""Test adding a product for a specific Picnic service while multiple are configured."""
with patch(
"homeassistant.components.picnic.create_picnic_client"
) as create_picnic_client_mock:
picnic_api_client_2 = create_picnic_api_client("3fj9-9gju-236")
create_picnic_client_mock.return_value = picnic_api_client_2
picnic_config_entry_2 = await create_picnic_config_entry(hass, "3fj9-9gju-236")
await hass.services.async_call(
DOMAIN,
SERVICE_ADD_PRODUCT_TO_CART,
{"product_id": "5109348572", "config_entry_id": picnic_config_entry_2.entry_id},
blocking=True,
)
# Check that the right method is called on the api
picnic_api_client.add_product.assert_not_called()
picnic_api_client_2.add_product.assert_called_with("5109348572", 1)
async def test_add_product_device_doesnt_exist(
hass: HomeAssistant,
picnic_api_client: MagicMock,
picnic_config_entry: MockConfigEntry,
) -> None:
"""Test adding a product for a specific Picnic service, which doesn't exist."""
with pytest.raises(ValueError):
await hass.services.async_call(
DOMAIN,
SERVICE_ADD_PRODUCT_TO_CART,
{"product_id": "5109348572", "config_entry_id": 12345},
blocking=True,
)
# Check that the right method is called on the api
picnic_api_client.add_product.assert_not_called()
| [
"noreply@github.com"
] | home-assistant.noreply@github.com |
dfe1ced20ccf2b601d73682bc0dc97808d4d7108 | 4e82bbef275a42ea7c9d58cab546de938fd82064 | /pywad/browser/firefox.py | 3f6db16f9a6599f59f12f7e7f4568f41eea8103a | [] | no_license | TakesxiSximada/pywad | 9a1f115f92f362ebaf5ee4767dd6b468ba9a1f51 | 8ad88595a8f00f4232c24167b5517db8e7c4993e | refs/heads/master | 2016-09-02T04:39:17.736770 | 2015-03-16T09:37:45 | 2015-03-16T09:37:45 | 27,847,444 | 2 | 0 | null | 2015-03-16T09:34:31 | 2014-12-11T00:57:55 | Python | UTF-8 | Python | false | false | 2,066 | py | # -*- coding: utf-8 -*-
from selenium.webdriver import Firefox, Proxy, FirefoxProfile
class FirefoxFactory(object):
"""The borwser factory class of Firefox.
"""
default_profile = {
'security.warn_entering_secure': False,
'security.warn_entering_secure.show_once': True,
'security.warn_entering_weak': False,
'security.warn_entering_weak._show_once': True,
'security.warn_leaving_secure': False,
'security.warn_leaving_secure.show_once': True,
'security.warn_leaving_weak': False,
'security.warn_leaving_weak._show_once': True,
'security.warn_submit_insecure': False,
'security.warn_viewing_mixed': False,
'security.warn_viewing_mixed.show_once': True,
}
def __init__(self, proxy=None, implicitly_wait=10, clear_cookies=False):
"""Constructor.
"""
self.implicitly_wait = implicitly_wait
self.clear_cookies = clear_cookies
self.proxy = proxy
def _create_proxy_setting(self):
"""Create proxy object.
"""
proxy = Proxy()
if self.proxy:
proxy.ftp_proxy = proxy.ssl_proxy = proxy.http_proxy = self.proxy
return proxy
def _create_profile(self):
"""Create profile object.
"""
profile = FirefoxProfile()
for name, value in self.default_profile.items():
profile.set_preference(name, value)
return profile
def _create_browser_instance(self):
"""Start browser.
"""
profile = self._create_profile()
proxy = self._create_proxy_setting()
return Firefox(firefox_profile=profile, proxy=proxy)
def create(self):
"""The browser factory method.
"""
browser = self._create_browser_instance()
browser.implicitly_wait(self.implicitly_wait)
if self.clear_cookies:
browser.delete_allcookies()
return browser
def __call__(self):
"""Emurate factory function.
"""
return self.create()
| [
"takesxi.sximada@gmail.com"
] | takesxi.sximada@gmail.com |
618d07f2b7895b24d0458ce034dfafb50ecff6dd | 39597cb5c9a04470381b630a070217506e054d3b | /deadfish.py | 018611f4b61da3bb3a2a40c0f60595dc75f65877 | [] | no_license | tahentx/gridshift | bc3aef88ac7736f62d187c486d079543ce0b8d68 | 9cc8fcc06f0784a321faee103f8ccfe3b3aa13d2 | refs/heads/master | 2021-06-28T23:05:20.059776 | 2020-05-17T16:46:46 | 2020-05-17T16:46:46 | 237,519,217 | 0 | 0 | null | 2021-06-10T15:47:36 | 2020-01-31T21:28:49 | Jupyter Notebook | UTF-8 | Python | false | false | 488 | py | def parse(data):
data = list(data)
box = []
value = 5
for char in data:
if char == "i":
value = value + 1
box.append(value)
elif char == "d":
value = value - 1
box.append(value)
elif char == "s":
value = value ** 2
box.append(value)
elif char == "o":
if len(box) == 0:
box.append(0)
return box
return box
parse("ii22ds") | [
"hendricks.ta@gmail.com"
] | hendricks.ta@gmail.com |
f576cc3486e53efcdc3ec4c131c5bba9f36f9abd | bd08d0532f20b7285b437c9bf620de1bbcd5b9ea | /aalh_iit_jdoylewitgencollection/debug-convert-dates.py | bd1f624cc039e31df7b49158095233069740cbae | [
"Unlicense"
] | permissive | johndewees/iitmigration | a9e8a31ba6ceb541ce12c22fd612596cc243dbca | 4dadfbecda719d6e7d60af076a231aedec3c862f | refs/heads/main | 2023-03-14T17:06:58.777683 | 2021-03-27T20:44:58 | 2021-03-27T20:44:58 | 320,086,321 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,787 | py | from openpyxl import load_workbook
filename = 'aalh_iit_jdoylewitgencollection.xlsx'
wb = load_workbook(filename)
ws = wb['Metadata Template']
minimumcol = 31
maximumcol = 31
minimumrow = 7
maximumrow = 200
iterationrow = 7
targetcol = 31
for row in ws.iter_rows(min_row=minimumrow, min_col=minimumcol, max_row=maximumrow, max_col=maximumcol):
for cell in row:
print(iterationrow)
testvar = ws.cell(row=iterationrow, column=targetcol).value
print(testvar)
if testvar == None:
print('No Date Digitized')
elif testvar.find('/') != -1:
testvarlist = testvar.split('/')
testvaryear = testvarlist[2]
testvaryear = testvaryear.strip()
testvarmonth = testvarlist[0]
testvarmonth = testvarmonth.strip()
testvarmonth = int(testvarmonth)
if testvarmonth < 10:
testvarmonth = str(testvarmonth)
testvarmonth = '0' + testvarmonth
else:
testvarmonth = str(testvarmonth)
testvarday = testvarlist[1]
testvarday = testvarday.strip()
testvarday = int(testvarday)
if testvarday < 10:
testvarday = str(testvarday)
testvarday = '0' + testvarday
else:
testvarday = str(testvarday)
isodate = testvaryear + '-' + testvarmonth + '-' + testvarday
ws.cell(row=iterationrow, column=targetcol).value = isodate
else:
print('Date is already formatted correctly')
print(ws.cell(row=iterationrow, column=targetcol).value)
iterationrow = iterationrow + 1
wb.save('aalh_iit_jdoylewitgencollection.xlsx') | [
"noreply@github.com"
] | johndewees.noreply@github.com |
d08c52d6ec07887f972f0b6586973801cc248350 | 6cbaade56c5db347d1be9a3422a69af52df39b97 | /python_workspace/3_bigdata/02_Standardization_Analysis/2_Excel/12_excel_introspect_all_workbooks.py | c11c104903944b5e2737168189535a1e3837ade5 | [] | no_license | baewonje/iot_bigdata_- | b54e3772f64b9695efee8632183590628b679e11 | 2ce1af67d2f05abeb2ecd442b7299f349bdb9753 | refs/heads/master | 2020-09-06T09:53:53.018320 | 2019-12-06T08:19:33 | 2019-12-06T08:19:33 | 220,390,928 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 682 | py | # !/usr/bin/env python 3
import glob
import sys
import os
from xlrd import open_workbook
input_directory = sys.argv[1] # Parameters = .
workbook_counter = 0
for input_file in glob.glob(os.path.join(input_directory, '*.xls*')):
workbook = open_workbook(input_file)
print('Workbook: {}'.format(os.path.basename(input_file)))
print('Number of worksheets: {}'.format(workbook.nsheets))
for worksheet in workbook.sheets():
print('Worksheet name:', worksheet.name, '\tRows', worksheet.nrows, '\tColumns:', worksheet.ncols)
workbook_counter += 1
print('Number of Excel workbooks: {}'.format(workbook_counter))
| [
"50129576+baewonje@users.noreply.github.com"
] | 50129576+baewonje@users.noreply.github.com |
e08c2dc8691e3a89398eb76dc78a075bebb81438 | 7e616a3b1928940467ec09a82b52d5b4d83984a1 | /MODULE1/Activities/PREFECT/create_dw.py | 1dede11f56bea15c470fed5c431d3cb08e61a576 | [] | no_license | gustavo32/DataEngineeringBootcamp | 70e4c2fb06a387418718df2929b89820a0a76c0d | 704dbe11f33f27ab9eda5649990685f153048429 | refs/heads/main | 2023-01-28T17:38:36.182545 | 2020-12-07T20:12:37 | 2020-12-07T20:12:37 | 316,643,488 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,135 | py | from datetime import datetime, timedelta
import pendulum
import prefect
from prefect import task, Flow
from prefect.schedules import CronSchedule
import pandas as pd
from io import BytesIO
import zipfile
import requests
import sqlalchemy
import psycopg2
schedule = CronSchedule(
cron= '*/10 * * * *',
start_date=pendulum.datetime(2020, 12, 5, 14, tz="America/Sao_Paulo")
)
@task
def get_raw_date():
url = 'http://download.inep.gov.br/microdados/Enade_Microdados/microdados_enade_2019.zip'
filebytes = BytesIO(requests.get(url).content)
zipped = zipfile.ZipFile(filebytes)
zipped.extractall()
return './microdados_enade_2019/2019/3.DADOS/'
@task
def apply_filters(path):
interested_cols = ['CO_GRUPO', 'NU_IDADE', 'TP_SEXO', 'NT_GER',
'NT_FG', 'NT_CE', 'QE_I01', 'QE_I02', 'QE_I04', 'QE_I05', 'QE_I08']
df = pd.read_csv(path + 'microdados_enade_2019.txt', sep=';', decimal=',',
usecols=interested_cols)
df[(df.NU_IDADE > 20) &
(df.NU_IDADE < 40) &
(df.NT_GER > 0)]
return df
@task
def get_mean_normalized_age(df):
coppied_df = df.copy()
coppied_df['mean_normalized_age'] = coppied_df.NU_IDADE - coppied_df.NU_IDADE.mean()
return coppied_df[['mean_normalized_age']]
@task
def get_squared_mean_normalized_age(df):
coppied_df = df.copy()
coppied_df['squared_mean_normalized_age'] = coppied_df['mean_normalized_age'] ** 2
return coppied_df[['squared_mean_normalized_age']]
@task
def get_marital_status(df):
coppied_df = df.copy()
coppied_df['marital_status'] = coppied_df.QE_I01.replace({
'A': 'SINGLE',
'B': 'MARRIED',
'C': 'DIVORCED',
'D': 'WIDOWED',
'E': 'OTHERS'
})
return coppied_df[['marital_status']]
@task
def get_skin_color(df):
coppied_df = df.copy()
coppied_df['skin_color'] = coppied_df.QE_I01.replace({
'A': 'WHITE',
'B': 'BLACK',
'C': 'YELLOW',
'D': 'BROWN',
'E': 'INDIGENOUS',
'F': '',
' ': ''
})
return coppied_df[['skin_color']]
@task
def join_data(dfs):
final = pd.concat(dfs, axis=1)
logger = prefect.context.get('logger')
logger.info(final.head(2).to_json())
return final
@task
def write_dw(df):
engine = sqlalchemy.create_engine(
'postgresql://postgres:123456@localhost:5432/enade')
df.to_sql('enade', con=engine, index=False, if_exists='replace', method='multi', chunksize=100000)
with Flow('Enade', schedule) as flow:
path = get_raw_date()
df = apply_filters(path)
normalized_mean_age = get_mean_normalized_age(df)
normalized_squared_mean_age = get_squared_mean_normalized_age(normalized_mean_age)
marital_status = get_marital_status(df)
skin_color = get_skin_color(df)
final = join_data([
df,
normalized_mean_age,
normalized_squared_mean_age,
marital_status,
skin_color
])
dw = write_dw(final)
flow.register(project_name='IGTI', idempotency_key=flow.serialized_hash())
flow.run_agent(token='D_0wWDFgx0e67I2IIbf7Ew')
| [
"you@example.com"
] | you@example.com |
e50542f228d6759629bd4a34572c3eb303e86f74 | 3f1a6ad5c0c9015653206f45f5395d006b663a14 | /Q Learning/MTM/ARVIND/Run 9/GlobalVariables.py | 3e2d5879ffdeb20c3f0fd1e851beb570cc01e9b0 | [] | no_license | ciddhijain/Results | 075c9c51bf8bebefdf3ca87f66a50c9c39d8461e | a3ce350885264a6bf5c277bd811ad1a9931ced18 | refs/heads/master | 2021-01-10T19:23:17.764638 | 2015-07-03T14:51:48 | 2015-07-03T14:51:48 | 37,335,721 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,572 | py | __author__ = 'Ciddhi'
from datetime import timedelta, datetime
databaseName = 'QL_ARVIND' # This is database name to which connection is made
userName = 'root' # This is the user name for database connection
password = 'controljp' # This is the password for database connection
dbHost = '127.0.0.1' # This is host address for database connection
dbPort = '3306' # This is port for database connection
dbConnector = 'mysqlconnector' # This is the connector string to be used, depending upon python package
startDate = datetime(2014, 12, 1).date() # This is the start of training period
endDate = datetime(2015, 4, 10).date() # This is the end of training period
rankingDays = 15 # This is the number of days for which ranking is done
initializationDays = 15 # This is the number of days for which q_matrix is initilaized
liveDays = 15 # This is the number of days for which live trading is done
logFileName = "QLearningARVIND"
maxProcesses = 3 # This is the number of maximum processes
#-----------------------------------------------------------------------------------------------------------------------------------------
# These variables need to contain list values
alpha = [0.2, 0.4, 0.5, 0.6, 0.8] # This defines the weightage to long trades as compared to short trades while constructing reward matrix
gamma = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8] # This defines the weightage of old data as compared to latest observations of reward matrix
#maxGreedyLevel = [5] # This defines the number of future states for which reward is to be maximized in construction of q matrix
#individualFactor = [8] # This defines the factor of total asset which is to be allocated to each strategy
zeroRange = [0.002, 0.006, 0.01] # This determines the spread between states 0, 1, 2
factorLevelPairs = [[5, 12], [10, 12], [10, 8]]
#------------------------------------------------------------------------------------------------------------------------------------------
dummyIndividualId = -1 # This is to keep a track of max total capital that is invested in the portfolio
unitQty = 250000 # This is the amount of each decrement in asset
hourWindow = 1 # This is the window after which re-allocation is done
maxTotalAsset = 10000000 # This is the total asset deployed
trainingFactor = 2
trainingMaxTotalAsset = maxTotalAsset*trainingFactor # This is the total asset deployed while training
dummyPerformance = -50000
performanceMonthlyOutfileNameBase = 'arvind mtm monthly 15 15 15'
performanceOutfileNameBase = 'arvind mtm 15 15 15'
latestIndividualTableBase = "latest_individual_table"
trainingTradesheetTableBase = "training_tradesheet_data_table"
trainingAssetTableBase = "training_asset_allocation_table"
rankingTableBase = "ranking_table"
dailyAssetTableBase = "asset_daily_allocation_table"
newTradesheetTableBase = "tradesheet_data_table"
assetTableBase = "asset_allocation_table"
qMatrixTableBase = "q_matrix_table"
reallocationTableBase = "reallocation_table"
performanceTableBase = "performance_table"
rankingWalkforwardTableBase = "ranking_walkforward_table"
dailyMtmTableBase = "daily_mtm_table"
| [
"ciddhijain@gmail.com"
] | ciddhijain@gmail.com |
cac0bf55441435485f57281a5edd5505e43adbd5 | 53db22afe7b2dc8344dd2ab0636644109708128f | /clab/nninit/__init__.py | f4854ab3143e64ac2d0a2683aa9e6858db9ca291 | [
"Apache-2.0"
] | permissive | Erotemic/clab | 5b5c11b2e4d39642f071c560e0e7caf3397c372d | 89af79816d219cbecefefa209c0f6dc1fe390375 | refs/heads/master | 2018-10-09T18:04:31.762389 | 2018-08-13T14:23:38 | 2018-08-13T14:23:38 | 110,250,463 | 3 | 3 | null | null | null | null | UTF-8 | Python | false | false | 747 | py | """
python -c "import ubelt._internal as a; a.autogen_init('clab.nninit')"
python -m clab
"""
# flake8: noqa
from clab.nninit import base
from clab.nninit import lsuv
from clab.nninit.base import (HeNormal, KaimingNormal, KaimingUniform, NoOp,
Orthogonal, Pretrained, VGG16, apply_initializer,
constant, he_normal, he_uniform, init_he_normal,
kaiming_normal, kaiming_uniform,
load_partial_state, normal, orthogonal, shock,
shock_he, sparse, trainable_layers, uniform,
xavier_normal, xavier_uniform,)
from clab.nninit.lsuv import (LSUV, Orthonormal, svd_orthonormal,)
| [
"crallj@rpi.edu"
] | crallj@rpi.edu |
9c198d82430f363233c68b084eddf9cf7586ae1e | d61183674ed7de0de626490cfba77d67c298d1be | /py_scripts/bench_plot_neighbors.py | 54b896c2395f7e0598e4cd69e6dd4c51357c11ad | [] | no_license | Giannos-G/python_dataset | bc670a53143d92cf781e88dee608da38b0e63886 | 18e24cbef16ada1003a3e15a2ed2a3f995f25e46 | refs/heads/main | 2023-07-25T20:24:31.988271 | 2021-09-09T10:31:41 | 2021-09-09T10:31:41 | 363,489,911 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,405 | py | """
Plot the scaling of the nearest neighbors algorithms with k, D, and N
"""
from time import time
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
from sklearn import neighbors, datasets
def get_data(N, D, dataset='dense'):
if dataset == 'dense':
np.random.seed(0)
return np.random.random((N, D))
elif dataset == 'digits':
X, _ = datasets.load_digits(return_X_y=True)
i = np.argsort(X[0])[::-1]
X = X[:, i]
return X[:N, :D]
else:
raise ValueError("invalid dataset: %s" % dataset)
def barplot_neighbors(Nrange=2 ** np.arange(1, 11),
Drange=2 ** np.arange(7),
krange=2 ** np.arange(10),
N=1000,
D=64,
k=5,
leaf_size=30,
dataset='digits'):
algorithms = ('kd_tree', 'brute', 'ball_tree')
fiducial_values = {'N': N,
'D': D,
'k': k}
#------------------------------------------------------------
# varying N
N_results_build = {alg: np.zeros(len(Nrange))
for alg in algorithms}
N_results_query = {alg: np.zeros(len(Nrange))
for alg in algorithms}
for i, NN in enumerate(Nrange):
print("N = %i (%i out of %i)" % (NN, i + 1, len(Nrange)))
X = get_data(NN, D, dataset)
for algorithm in algorithms:
nbrs = neighbors.NearestNeighbors(n_neighbors=min(NN, k),
algorithm=algorithm,
leaf_size=leaf_size)
t0 = time()
nbrs.fit(X)
t1 = time()
nbrs.kneighbors(X)
t2 = time()
N_results_build[algorithm][i] = (t1 - t0)
N_results_query[algorithm][i] = (t2 - t1)
#------------------------------------------------------------
# varying D
D_results_build = {alg: np.zeros(len(Drange))
for alg in algorithms}
D_results_query = {alg: np.zeros(len(Drange))
for alg in algorithms}
for i, DD in enumerate(Drange):
print("D = %i (%i out of %i)" % (DD, i + 1, len(Drange)))
X = get_data(N, DD, dataset)
for algorithm in algorithms:
nbrs = neighbors.NearestNeighbors(n_neighbors=k,
algorithm=algorithm,
leaf_size=leaf_size)
t0 = time()
nbrs.fit(X)
t1 = time()
nbrs.kneighbors(X)
t2 = time()
D_results_build[algorithm][i] = (t1 - t0)
D_results_query[algorithm][i] = (t2 - t1)
#------------------------------------------------------------
# varying k
k_results_build = {alg: np.zeros(len(krange))
for alg in algorithms}
k_results_query = {alg: np.zeros(len(krange))
for alg in algorithms}
X = get_data(N, DD, dataset)
for i, kk in enumerate(krange):
print("k = %i (%i out of %i)" % (kk, i + 1, len(krange)))
for algorithm in algorithms:
nbrs = neighbors.NearestNeighbors(n_neighbors=kk,
algorithm=algorithm,
leaf_size=leaf_size)
t0 = time()
nbrs.fit(X)
t1 = time()
nbrs.kneighbors(X)
t2 = time()
k_results_build[algorithm][i] = (t1 - t0)
k_results_query[algorithm][i] = (t2 - t1)
plt.figure(figsize=(8, 11))
for (sbplt, vals, quantity,
build_time, query_time) in [(311, Nrange, 'N',
N_results_build,
N_results_query),
(312, Drange, 'D',
D_results_build,
D_results_query),
(313, krange, 'k',
k_results_build,
k_results_query)]:
ax = plt.subplot(sbplt, yscale='log')
plt.grid(True)
tick_vals = []
tick_labels = []
bottom = 10 ** np.min([min(np.floor(np.log10(build_time[alg])))
for alg in algorithms])
for i, alg in enumerate(algorithms):
xvals = 0.1 + i * (1 + len(vals)) + np.arange(len(vals))
width = 0.8
c_bar = plt.bar(xvals, build_time[alg] - bottom,
width, bottom, color='r')
q_bar = plt.bar(xvals, query_time[alg],
width, build_time[alg], color='b')
tick_vals += list(xvals + 0.5 * width)
tick_labels += ['%i' % val for val in vals]
plt.text((i + 0.02) / len(algorithms), 0.98, alg,
transform=ax.transAxes,
ha='left',
va='top',
bbox=dict(facecolor='w', edgecolor='w', alpha=0.5))
plt.ylabel('Time (s)')
ax.xaxis.set_major_locator(ticker.FixedLocator(tick_vals))
ax.xaxis.set_major_formatter(ticker.FixedFormatter(tick_labels))
for label in ax.get_xticklabels():
label.set_rotation(-90)
label.set_fontsize(10)
title_string = 'Varying %s' % quantity
descr_string = ''
for s in 'NDk':
if s == quantity:
pass
else:
descr_string += '%s = %i, ' % (s, fiducial_values[s])
descr_string = descr_string[:-2]
plt.text(1.01, 0.5, title_string,
transform=ax.transAxes, rotation=-90,
ha='left', va='center', fontsize=20)
plt.text(0.99, 0.5, descr_string,
transform=ax.transAxes, rotation=-90,
ha='right', va='center')
plt.gcf().suptitle("%s data set" % dataset.capitalize(), fontsize=16)
plt.figlegend((c_bar, q_bar), ('construction', 'N-point query'),
'upper right')
if __name__ == '__main__':
barplot_neighbors(dataset='digits')
barplot_neighbors(dataset='dense')
#plt.show()
| [
"giannos.gavrielides@gmail.com"
] | giannos.gavrielides@gmail.com |
fb518482a9258267dfecca4ed56546ae95d000cf | 03b98276b252aa0e94142b65fd4b9dcd0a4d12ee | /Project.py | 4d44581f8e3cc0aa66a52956f2641de9b4be4d85 | [] | no_license | FrancescoSaverioZuppichini/scientists-keypoints-classification | b2c0ec30f5f7067d3809955f26a2f0cb051cdc2a | d1c4b6010e8c0f13271cd3304a137febf927c51d | refs/heads/main | 2023-03-02T07:44:40.767193 | 2021-01-31T15:56:12 | 2021-01-31T15:56:12 | 334,368,509 | 0 | 0 | null | 2021-01-31T15:56:13 | 2021-01-30T08:49:52 | Jupyter Notebook | UTF-8 | Python | false | false | 518 | py | from dataclasses import dataclass
from pathlib import Path
@dataclass
class Project:
"""
This class represents our project. It stores useful information about the structure, e.g. paths.
"""
base_dir: Path = Path(__file__).parents[0]
data_dir: Path = base_dir / 'dataset'
checkpoint_dir: Path = base_dir / 'checkpoint'
def __post_init__(self):
# create the directories if they don't exist
self.data_dir.mkdir(exist_ok=True)
self.checkpoint_dir.mkdir(exist_ok=True) | [
"zuppif@usi.ch"
] | zuppif@usi.ch |
a56e5646913bda05dbd46183d6ccfa161a785312 | 7a0f7ce00528b103c7b8b501f1e8333fc3a0836c | /Class Based Views/CBV/CBV/CBV_app/urls.py | e9a5006b2991b23bdefa7672692ccb6ffcd53603 | [] | no_license | StefanDimitrovDimitrov/DjangoRESTFramework | 605ea044f4039d5bb8017ffe2f33c54ea0ebacc0 | 076cac204d2b5b1a278b68d3883394d5dcc2c040 | refs/heads/main | 2023-07-15T01:55:52.550274 | 2021-08-27T16:34:40 | 2021-08-27T16:34:40 | 382,106,004 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 269 | py | from django.urls import path
from CBV.CBV_app.views import CBVList, Details, Create
urlpatterns = [
path('', CBVList.as_view(), name='CBVList'),
path('details/<int:pk>', Details.as_view(), name='details'),
path('create', Create.as_view(), name='create')
] | [
"54206891+StefanDimitrovDimitrov@users.noreply.github.com"
] | 54206891+StefanDimitrovDimitrov@users.noreply.github.com |
e9441712b4248e5f710a43bc2f7edd338493dca2 | 781e2692049e87a4256320c76e82a19be257a05d | /all_data/exercism_data/python/bob/5250fabed22b4ae590e1fc2c10ff7469.py | 1d05c10071c4c66d5b3f7de258699ffdc3b7ceb5 | [] | no_license | itsolutionscorp/AutoStyle-Clustering | 54bde86fe6dbad35b568b38cfcb14c5ffaab51b0 | be0e2f635a7558f56c61bc0b36c6146b01d1e6e6 | refs/heads/master | 2020-12-11T07:27:19.291038 | 2016-03-16T03:18:00 | 2016-03-16T03:18:42 | 59,454,921 | 4 | 0 | null | 2016-05-23T05:40:56 | 2016-05-23T05:40:56 | null | UTF-8 | Python | false | false | 465 | py | #
# Skeleton file for the Python "Bob" exercise.
#
def hey(what):
#Strip whitespace
what=what.strip()
if (what==''):
return 'Fine. Be that way!'
elif (any(c.isalpha() for c in what) and what.upper()==what):
#if the string contains letters, and all letters are uppercase (i.e. applying .upper() doesn't change the string)
return 'Whoa, chill out!'
elif (what[-1]=='?'):
#if asked a question, return sure.
return 'Sure.'
else:
return 'Whatever.'
| [
"rrc@berkeley.edu"
] | rrc@berkeley.edu |
9f455b8429dea02b5edeba4e91d0ff72aa48f3b9 | 5a52ccea88f90dd4f1acc2819997fce0dd5ffb7d | /alipay/aop/api/response/AlipayDataDataserviceHellobikeLogSyncResponse.py | da91fbf326c81f03835b551c7221a1f8723c99fd | [
"Apache-2.0"
] | permissive | alipay/alipay-sdk-python-all | 8bd20882852ffeb70a6e929038bf88ff1d1eff1c | 1fad300587c9e7e099747305ba9077d4cd7afde9 | refs/heads/master | 2023-08-27T21:35:01.778771 | 2023-08-23T07:12:26 | 2023-08-23T07:12:26 | 133,338,689 | 247 | 70 | Apache-2.0 | 2023-04-25T04:54:02 | 2018-05-14T09:40:54 | Python | UTF-8 | Python | false | false | 742 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.response.AlipayResponse import AlipayResponse
class AlipayDataDataserviceHellobikeLogSyncResponse(AlipayResponse):
def __init__(self):
super(AlipayDataDataserviceHellobikeLogSyncResponse, self).__init__()
self._biz_code = None
@property
def biz_code(self):
return self._biz_code
@biz_code.setter
def biz_code(self, value):
self._biz_code = value
def parse_response_content(self, response_content):
response = super(AlipayDataDataserviceHellobikeLogSyncResponse, self).parse_response_content(response_content)
if 'biz_code' in response:
self.biz_code = response['biz_code']
| [
"liuqun.lq@alibaba-inc.com"
] | liuqun.lq@alibaba-inc.com |
8a01fbab7ddd08d936daca4f2b151f92b88385c5 | e9ff112a590a2707e66c518328ba71a4d964846a | /train_scripts/train_c2i.py | f980e7fb7c32ab9e357e2dc4a9e4acdc8522b720 | [
"MIT"
] | permissive | n644t031/fastMRI-kspace | 60a6ca9679ede25f0db89f174647a8451a578331 | 4c484b3183e9f06838b5ee108af283611c2e1e77 | refs/heads/master | 2022-08-30T17:19:23.105996 | 2020-05-24T13:55:40 | 2020-05-24T13:55:40 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,068 | py | import torch
from torch import nn, optim
from pathlib import Path
from utils.run_utils import initialize, save_dict_as_json, get_logger, create_arg_parser
from utils.data_loaders import create_prefetch_data_loaders
from train.subsample import RandomMaskFunc, UniformMaskFunc
from data.input_transforms import PreProcessCMG
from data.output_transforms import PostProcessCMG
from train.new_model_trainers.img_only import ModelTrainerIMG
# from models.att_unet import UNet
from models.unet_no_norm import UNet
from metrics.new_1d_ssim import SSIMLoss, LogSSIMLoss
def train_cmg_to_img(args):
# Creating checkpoint and logging directories, as well as the run name.
ckpt_path = Path(args.ckpt_root)
ckpt_path.mkdir(exist_ok=True)
ckpt_path = ckpt_path / args.train_method
ckpt_path.mkdir(exist_ok=True)
run_number, run_name = initialize(ckpt_path)
ckpt_path = ckpt_path / run_name
ckpt_path.mkdir(exist_ok=True)
log_path = Path(args.log_root)
log_path.mkdir(exist_ok=True)
log_path = log_path / args.train_method
log_path.mkdir(exist_ok=True)
log_path = log_path / run_name
log_path.mkdir(exist_ok=True)
logger = get_logger(name=__name__)
# Assignment inside running code appears to work.
if (args.gpu is not None) and torch.cuda.is_available():
device = torch.device(f'cuda:{args.gpu}')
logger.info(f'Using GPU {args.gpu} for {run_name}')
else:
device = torch.device('cpu')
logger.info(f'Using CPU for {run_name}')
# Saving peripheral variables and objects in args to reduce clutter and make the structure flexible.
args.run_number = run_number
args.run_name = run_name
args.ckpt_path = ckpt_path
args.log_path = log_path
args.device = device
save_dict_as_json(vars(args), log_dir=log_path, save_name=run_name)
if args.random_sampling:
mask_func = RandomMaskFunc(args.center_fractions, args.accelerations)
else:
mask_func = UniformMaskFunc(args.center_fractions, args.accelerations)
input_train_transform = PreProcessCMG(mask_func, args.challenge, device, augment_data=args.augment_data,
use_seed=False, crop_center=args.crop_center)
input_val_transform = PreProcessCMG(mask_func, args.challenge, device, augment_data=False,
use_seed=True, crop_center=args.crop_center)
output_train_transform = PostProcessCMG(challenge=args.challenge, residual_acs=args.residual_acs)
output_val_transform = PostProcessCMG(challenge=args.challenge, residual_acs=args.residual_acs)
# DataLoaders
train_loader, val_loader = create_prefetch_data_loaders(args)
losses = dict(
img_loss=LogSSIMLoss(filter_size=7).to(device)
# img_loss=SSIMLoss(filter_size=7).to(device=device)
# img_loss=nn.L1Loss()
)
# model = UNet(
# in_chans=30, out_chans=30, chans=args.chans, num_pool_layers=args.num_pool_layers, num_groups=args.num_groups,
# negative_slope=args.negative_slope, use_residual=args.use_residual, interp_mode=args.interp_mode,
# use_ca=args.use_ca, reduction=args.reduction, use_gap=args.use_gap, use_gmp=args.use_gmp).to(device)
model = UNet(in_chans=30, out_chans=30, chans=args.chans, num_pool_layers=args.num_pool_layers,
num_depth_blocks=args.num_depth_blocks, use_residual=args.use_residual, use_ca=args.use_ca,
reduction=args.reduction, use_gap=args.use_gap, use_gmp=args.use_gmp).to(device)
optimizer = optim.Adam(model.parameters(), lr=args.init_lr)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_red_epochs, gamma=args.lr_red_rate)
trainer = ModelTrainerIMG(args, model, optimizer, train_loader, val_loader, input_train_transform,
input_val_transform, output_train_transform, output_val_transform, losses, scheduler)
try:
trainer.train_model()
except KeyboardInterrupt:
trainer.writer.close()
logger.warning('Closing summary writer due to KeyboardInterrupt.')
if __name__ == '__main__':
project_name = 'fastMRI-kspace'
assert Path.cwd().name == project_name, f'Current working directory set at {Path.cwd()}, not {project_name}!'
settings = dict(
# Variables that almost never change.
challenge='multicoil',
data_root='/media/veritas/D/FastMRI',
log_root='./logs',
ckpt_root='./checkpoints',
batch_size=1, # This MUST be 1 for now.
save_best_only=True,
smoothing_factor=8,
# Variables that occasionally change.
center_fractions=[0.08, 0.04],
accelerations=[4, 8],
random_sampling=True,
num_pool_layers=5,
verbose=False,
use_gt=True,
augment_data=True,
crop_center=True,
# Model specific parameters.
train_method='C2I', # Weighted semi-k-space to complex-valued image.
# num_groups=16, # Maybe try 16 now since chans is 64.
chans=64,
num_depth_blocks=1,
# negative_slope=0.1,
# interp_mode='nearest',
use_residual=True,
residual_acs=False,
# TensorBoard related parameters.
max_images=8, # Maximum number of images to save.
shrink_scale=1, # Scale to shrink output image size.
# Channel Attention.
use_ca=False,
reduction=8,
use_gap=False,
use_gmp=False,
# Learning rate scheduling.
lr_red_epochs=[20, 25],
lr_red_rate=0.1,
# Variables that change frequently.
use_slice_metrics=True,
num_epochs=30,
gpu=0, # Set to None for CPU mode.
num_workers=4,
init_lr=2E-4,
max_to_keep=1,
prev_model_ckpt='',
sample_rate_train=1,
start_slice_train=0,
sample_rate_val=1,
start_slice_val=0,
)
arguments = create_arg_parser(**settings).parse_args()
train_cmg_to_img(arguments)
| [
"veritas9872@gmail.com"
] | veritas9872@gmail.com |
2c562bda934b4f62ab5ded428de590c8313dcc52 | 0b9e884be78ecc22a44a94e2c1cabefd637b9ed0 | /Python_Talk/mpt-master/ch12/guess_letter.py | 7c0efa9b99992a2bcda705aca8885edea8980335 | [] | no_license | marcovnyc/penguin-code | 6ba3faa5f21186918e2d08f5a0fcacebb2697e56 | a0c1f91219ff74a8bb8e9fd3375b03b667056b54 | refs/heads/master | 2021-12-22T15:04:26.002512 | 2021-12-16T04:01:40 | 2021-12-16T04:01:40 | 7,264,458 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,798 | py | import turtle as t
from random import choice
# Set up the board
t.setup(600,500)
t.hideturtle()
t.tracer(False)
t.bgcolor("lavender")
t.title("Guess the Word Game in Turtle Graphics")
# Define a variable to count how many guesses left
score = 6
# Create a second turtle to show guesses left
left = t.Turtle()
left.up()
left.hideturtle()
left.goto(-290,200)
left.write(f"guesses left: {score}", font = ('Arial',20,'normal'))
# Put incorrect guesses on top
t.up()
t.goto(-290,150)
t.write("incorrect guesses:", font = ('Arial',20,'normal'))
# Put four empty spaces for the four letters at bottom
for x in range(4):
t.goto(-275+150*x,-200)
t.down()
t.goto(-175+150*x,-200)
t.up()
t.update()
# Put words in a dictionary and randomly pick one
words = ['that', 'with', 'have', 'this', 'will', 'your',
'from', 'they', 'know', 'want', 'been',
'good', 'much', 'some', 'time']
word = choice(words)
# Create a missed list
missed = []
# Start the game loop
while True:
# Take written input
inp = input("What's your guess?\n").lower()
# Stop the loop if you key in "done"
if inp == "done":
break
# Check if the letter is in the word
elif inp in list(word):
# If yes, put it in the right position(s)
for w in range(4):
if inp == list(word)[w]:
t.goto(-250+150*w,-190)
t.write(inp, font = ('Arial',60,'normal'))
# If the letter is not in the word, show it at the top
else:
missed.append(inp)
t.goto(-290+80*len(missed),60)
t.write(inp, font = ('Arial',60,'normal'))
# Update everything that happens in the iteration
t.update()
try:
t.bye()
except t.Terminator:
print('exit turtle')
| [
"penguin@penguin.com"
] | penguin@penguin.com |
ee7d13e00cccdc88d49ad555a163e514fa7a1276 | aad51b0ea59c38b23ed419e10b86c44aa947f117 | /156/rrhood.py | cec768264e83a157a3edbe67b33fa6193bb99896 | [] | no_license | berubejd/PyBites | 3a1d7144f59f67a0996dbe224b69bc0b6da439d6 | 439446e8b67612a603713723b2d4a021677341d2 | refs/heads/master | 2021-07-14T14:03:51.819347 | 2020-11-01T14:59:02 | 2020-11-01T14:59:02 | 221,087,519 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,346 | py | #!/usr/bin/env python3.8
CHARACTERS = ['Red Riding Hood',
# we're omitting 'mother' here for simplicity
# (= substring grandmother)
('Grandmother', 'Grandma', 'Granny'),
'wolf', 'woodsman']
text = """
Once upon a time, there was a little girl who lived in a village near the forest. Whenever she went out, the little girl wore a red riding cloak, so everyone in the village called her Little Red Riding Hood.
One morning, Little Red Riding Hood asked her mother if she could go to visit her grandmother as it had been awhile since they'd seen each other.
"That's a good idea," her mother said. So they packed a nice basket for Little Red Riding Hood to take to her grandmother.
When the basket was ready, the little girl put on her red cloak and kissed her mother goodbye.
"Remember, go straight to Grandma's house," her mother cautioned. "Don't dawdle along the way and please don't talk to strangers! The woods are dangerous."
"Don't worry, mommy," said Little Red Riding Hood, "I'll be careful."
But when Little Red Riding Hood noticed some lovely flowers in the woods, she forgot her promise to her mother. She picked a few, watched the butterflies flit about for awhile, listened to the frogs croaking and then picked a few more.
Little Red Riding Hood was enjoying the warm summer day so much, that she didn't notice a dark shadow approaching out of the forest behind her...
Suddenly, the wolf appeared beside her.
"What are you doing out here, little girl?" the wolf asked in a voice as friendly as he could muster.
"I'm on my way to see my Grandma who lives through the forest, near the brook," Little Red Riding Hood replied.
Then she realized how late she was and quickly excused herself, rushing down the path to her Grandma's house.
The wolf, in the meantime, took a shortcut...
The wolf, a little out of breath from running, arrived at Grandma's and knocked lightly at the door.
"Oh thank goodness dear! Come in, come in! I was worried sick that something had happened to you in the forest," said Grandma thinking that the knock was her granddaughter.
The wolf let himself in. Poor Granny did not have time to say another word, before the wolf gobbled her up!
The wolf let out a satisfied burp, and then poked through Granny's wardrobe to find a nightgown that he liked. He added a frilly sleeping cap, and for good measure, dabbed some of Granny's perfume behind his pointy ears.
A few minutes later, Red Riding Hood knocked on the door. The wolf jumped into bed and pulled the covers over his nose. "Who is it?" he called in a cackly voice.
"It's me, Little Red Riding Hood."
"Oh how lovely! Do come in, my dear," croaked the wolf.
When Little Red Riding Hood entered the little cottage, she could scarcely recognize her Grandmother.
"Grandmother! Your voice sounds so odd. Is something the matter?" she asked.
"Oh, I just have touch of a cold," squeaked the wolf adding a cough at the end to prove the point.
"But Grandmother! What big ears you have," said Little Red Riding Hood as she edged closer to the bed.
"The better to hear you with, my dear," replied the wolf.
"But Grandmother! What big eyes you have," said Little Red Riding Hood.
"The better to see you with, my dear," replied the wolf.
"But Grandmother! What big teeth you have," said Little Red Riding Hood her voice quivering slightly.
"The better to eat you with, my dear," roared the wolf and he leapt out of the bed and began to chase the little girl.
Almost too late, Little Red Riding Hood realized that the person in the bed was not her Grandmother, but a hungry wolf.
She ran across the room and through the door, shouting, "Help! Wolf!" as loudly as she could.
A woodsman who was chopping logs nearby heard her cry and ran towards the cottage as fast as he could.
He grabbed the wolf and made him spit out the poor Grandmother who was a bit frazzled by the whole experience, but still in one piece."Oh Grandma, I was so scared!" sobbed Little Red Riding Hood, "I'll never speak to strangers or dawdle in the forest again."
"There, there, child. You've learned an important lesson. Thank goodness you shouted loud enough for this kind woodsman to hear you!"
The woodsman knocked out the wolf and carried him deep into the forest where he wouldn't bother people any longer.
Little Red Riding Hood and her Grandmother had a nice lunch and a long chat.
"""
def make_character_index(text=text, characters=CHARACTERS):
"""Return a dict with keys are characters (lowercased) and values
the lines they appear in sorted order.
Matches should be case insensitive.
If a character has multiple synonyms
- e.g. ('Grandmother', 'Grandma', 'Granny') -
then return the former as key.
"""
from collections import defaultdict
index = defaultdict(list)
line_number = 0
for line in text.lower().strip().splitlines():
line_number += 1
for character in characters:
if isinstance(character, str):
character = (character,)
for syn in character:
if syn.lower() in line:
if not line_number in index[character[0].lower()]:
index[character[0].lower()].append(line_number)
return index
print(make_character_index())
| [
"berubejd@gmail.com"
] | berubejd@gmail.com |
59ab9c4e25ae4f4d32c497ff085285a02ce8eae2 | 5b86d752424e303a5115ded25892776d3781bddf | /tools/forecast_data_select.py | c4ceec80ef4eccac6ff982ff5fac063d8bf44513 | [] | no_license | GlassyWing/weather_predict_torch | 3318e441768ebbaaac408cdd3c48133f7bc03495 | 0cbd0af4b787bb8624d65b9e31970161e29d05a2 | refs/heads/master | 2020-05-17T08:04:28.473337 | 2019-05-04T13:54:27 | 2019-05-04T13:54:27 | 183,597,527 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 274 | py | import pandas as pd
if __name__ == '__main__':
weather = pd.read_csv("../data/weather.csv")
weather = weather[(weather['year'] == 2017) & (weather['month'] == 5) & (weather['county'] == '仁和')][::-1]
weather.to_csv("../data/forecast_test.csv", index=False)
| [
"1490215053@qq.com"
] | 1490215053@qq.com |
beb9bd4c2cd0d682ed67c40ad31749fdf94ea87d | 432ea480327c3e0ce37d605d1c4ac29a8b653853 | /src/visions/backends/python/types/file.py | 2886a3bce3435a710a17540736818199fafd230b | [
"BSD-4-Clause",
"BSD-2-Clause"
] | permissive | dylan-profiler/visions | 3f7f99b06cc8a7b90cb4df988dbbec6c329a8e0a | a0b55bbf95e6efe001195e4b497358d6283966b5 | refs/heads/develop | 2022-11-27T01:17:01.735418 | 2022-10-30T10:44:37 | 2022-10-30T10:44:37 | 227,633,867 | 188 | 23 | NOASSERTION | 2022-10-05T23:06:31 | 2019-12-12T15:09:01 | Python | UTF-8 | Python | false | false | 246 | py | import pathlib
from typing import Sequence
from visions.types.file import File
@File.contains_op.register
def file_contains(sequence: Sequence, state: dict) -> bool:
return all(isinstance(p, pathlib.Path) and p.exists() for p in sequence)
| [
"sfbbrugman@gmail.com"
] | sfbbrugman@gmail.com |
4b6960d22383d7c69b3c5e10c4b64ac23a1c83e8 | 230553326780c93f60d552a95d50018025724b4b | /py-code/迭代器.py | 0fa3e53eaa24b154fab2aaba25a75e2ac9777164 | [] | no_license | g-lyc/PRACTICE | 55108dbeb75893e4e6631ce3968420f1da0266ef | 492f72a1c14b53982ada478890e6a5487a67c46e | refs/heads/master | 2022-08-21T17:33:29.682865 | 2022-08-15T02:58:17 | 2022-08-15T02:58:17 | 51,586,693 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 553 | py | #coding:utf-8
import os
import sys
# 生成器都是迭代器，迭代器不一定是生成器
l = [1,2,3,4,5] #可迭代对象，不是迭代器
d = iter(l) # l.__iter__()
print(d) #<listiterator object at 0x0000000004DA5518>
#什么是迭代器？
#满足两个条件 1、有iter()方法 2、有next()方法
# for 循环内部三件事：
# 1、 调用可迭代对象的iter方法返回一个迭代器对象
# 2、 不断调用迭代器对象的next方法
# 3、 处理StopIteration
# Iterator 迭代器
# Iterable 可迭代对象
| [
"309080979@qq.com"
] | 309080979@qq.com |
2b27287a81ce34c6bdfd3b3077d0ad39cac86b10 | 1f4204f903657884d9cccfd44b19ecb531b59ded | /test_settings/66_1_3_200.py | c083982ec3213706b72bcdccb9b5721887a8d8a3 | [] | no_license | fmcc/StylometricAnalyser | 795a8e4abe264ee18ab3bcb34bd128bcd06ac5ca | e86305a63c95d8b533cab4a3be0010c2fee0ff14 | refs/heads/master | 2021-01-23T08:38:44.961082 | 2013-08-31T20:23:36 | 2013-08-31T20:23:36 | 11,097,508 | 3 | 1 | null | null | null | null | UTF-8 | Python | false | false | 540 | py |
import os
DB_PATH = os.getcwd() + '/database/greek_texts.db'
LOGGING = True
DB_LOGGING = False
NGRAM_WORDS = False
NGRAM_LENGTHS = {
'MIN': 1,
'MAX': 3
}
NO_SPACES = True
RESTRICT_VECTOR_SPACE = 200
# If selected, texts will be divided according to their original top-level divisions (Books etc. 'div1' in Perseus's TEI XML
USE_ORIGINAL_DIVISIONS = False
#If USE_ORIGINAL_DIVISIONS is False, the text will be divided into chunks the length defined here. If O the text will not be divided.
DIVISION_LENGTH = 5000
| [
"finlaymccourt@gmail.com"
] | finlaymccourt@gmail.com |
db000f476282e2536b72f3be77dc90f773225eb7 | 83544ef94ce2c1a05b6028ae2ce58ef8acfb6fa8 | /pmca-console.spec | 78c3eff77d7b1c5610639372fbb7074d05cd63eb | [
"MIT"
] | permissive | ma1co/Sony-PMCA-RE | 9ae44c5b09580d62e860c3acff24bd1fac28a31e | a82f5baaa8e9c3d9f28f94699e860fb2e48cc8e0 | refs/heads/master | 2023-08-07T07:54:13.763912 | 2022-08-18T12:46:04 | 2022-08-18T12:46:04 | 35,510,548 | 1,788 | 228 | MIT | 2022-11-05T06:45:01 | 2015-05-12T20:18:25 | Python | UTF-8 | Python | false | false | 177 | spec | # Run `pyinstaller pmca-console.spec` to generate an executable
input = 'pmca-console.py'
output = 'pmca-console'
console = True
with open('build.spec') as f:
exec(f.read())
| [
"ma1co@users.noreply.github.com"
] | ma1co@users.noreply.github.com |
20ed4c2c7261e9b4442d61d4810acd5d0b5743e6 | 385ab972316b41cb0643f1050f9220b8eaeb4647 | /findDigits.py | 41ec66dee85addf385ce1e7b73749ac75dcd3cb8 | [] | no_license | Rpratik13/HackerRank | 09174c9b331e25cec33848a80e9109800cdbc894 | 38b9a39261bfb3b2fc208ad1e3d8a485585b419a | refs/heads/master | 2020-03-22T05:24:03.516086 | 2020-01-31T16:08:19 | 2020-01-31T16:08:19 | 139,563,106 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 274 | py | def findDigits(n):
temp = n
count = 0
while temp!=0:
d = temp%10
if d == 0:
temp = temp//10
continue
if n%d==0:
count+=1
temp = temp//10
return count
t = int(input())
for t_itr in range(t):
n = int(input())
result = findDigits(n)
print(result) | [
"r.pratik013@gmail.com"
] | r.pratik013@gmail.com |
a277c31b1f5861dc5d0a8cf2c169ac2f24ea9e6b | 2c143ba64032f65c7f7bf1cbd567a1dcf13d5bb1 | /树/验证二叉树.py | 52f08e37e653a6186347565b6ffa4d6440299a4a | [] | no_license | tx991020/MyLeetcode | 5b6121d32260fb30b12cc8146e44e6c6da03ad89 | cfe4f087dfeb258caebbc29fc366570ac170a68c | refs/heads/master | 2020-04-09T21:43:41.403553 | 2019-03-27T18:54:35 | 2019-03-27T18:54:35 | 160,611,089 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,076 | py | '''
给定一个二叉树，判断其是否是一个有效的二叉搜索树。
假设一个二叉搜索树具有如下特征：
节点的左子树只包含小于当前节点的数。
节点的右子树只包含大于当前节点的数。
所有左子树和右子树自身必须也是二叉搜索树。
示例 1:
输入:
2
/ \
1 3
输出: true
示例 2:
输入:
5
/ \
1 4
/ \
3 6
输出: false
解释: 输入为: [5,1,4,null,null,3,6]。
根节点的值为 5 ，但是其右子节点值为 4 。
'''
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution:
def isValidBST(self, root):
"""
:type root: TreeNode
:rtype: bool
"""
inorder = self.inorder(root)
return inorder == list(sorted(set(inorder)))
def inorder(self, root):
if root is None:
return []
return self.inorder(root.left) + [root.val] + self.inorder(root.right)
| [
"wudi@hetao101.com"
] | wudi@hetao101.com |
60662f03bae36268062d3d500b1a75f366f385cd | 91824d746654fe12881b4fc3b55c553aae0d22ac | /py/fizz-buzz.py | 94d8d1eecae70b8c48ce696fb08d2f16527ea5db | [
"Apache-2.0"
] | permissive | ckclark/leetcode | a1a173c67a36a3256b198f853fcd3d15aa5abbb7 | 844c6f18d06dcb397db76436e5f4b8ddcb1beddc | refs/heads/master | 2021-01-15T08:14:43.368516 | 2020-02-14T07:25:05 | 2020-02-14T07:30:10 | 42,386,911 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 427 | py | import sys
class Solution(object):
def fizzBuzz(self, n):
"""
:type n: int
:rtype: List[str]
"""
ret = []
for i in xrange(1, n + 1):
s = ''
if i % 3 == 0:
s += 'Fizz'
if i % 5 == 0:
s += 'Buzz'
if i % 3 > 0 and i % 5 > 0:
s += str(i)
ret.append(s)
return ret
| [
"clark.ck@gmail.com"
] | clark.ck@gmail.com |
40018adf13b332b79d6fb3be3827cb089212a630 | 610dedfb6e21d297e8cdbcba599a4e564bd785cb | /EstruturaDeRepeticao/estruturaderepeticao-29.py | 5e9e0dce39884add04858ba5c6bdaa07905a59e9 | [] | no_license | zumbipy/PythonExercicios | f7b2ddf2376b9ecb2aedc77531e3571dc746a12b | 7a17b78cf927a2889b93238542e90e00810c43e6 | refs/heads/master | 2021-01-23T10:43:47.997462 | 2018-07-22T14:58:44 | 2018-07-22T14:58:44 | 93,086,120 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,182 | py | # Telegram: @ZumbiPy __ _ ___
# /_ / __ ____ _ / / (_) _ \__ __
# / /_/ // / ' \/ _ \/ / ___/ // /
# /___/\_,_/_/_/_/_.__/_/_/ \_, /
# E-mail: zumbipy@gmail.com /___/
"""
29 - O Sr. Manoel Joaquim possui uma grande loja de artigos de R$ 1,99,
com cerca de 10 caixas. Para agilizar o cálculo de quanto cada cliente
deve pagar ele desenvolveu um tabela que contém o número de itens que o
cliente comprou e ao lado o valor da conta. Desta forma a atendente do
caixa precisa apenas contar quantos itens o cliente está levando e olhar
na tabela de preços. Você foi contratado para desenvolver o programa que
monta esta tabela de preços, que conterá os preços de 1 até 50 produtos,
conforme o exemplo abaixo:
Lojas Quase Dois - Tabela de preços
1 - R$ 1.99
2 - R$ 3.98
...
50 - R$ 99.50
"""
# ================================================================================
# Logica e variavel.
# ================================================================================
print("Lojas Quase Dois - Tabela de preços")
for produtos in range(1, 51):
valor = 1.99 * produtos
print("{:>2} - R$ {:.2f}".format(produtos, valor))
| [
"zumbipy@gmail.com"
] | zumbipy@gmail.com |
ae3a0a0301704c185845a2ce5ed9681188f3d08e | 9dee94907e6456a4af9855d358693923c17b4e0d | /0015_3Sum.py | 159dafa89798c41df6a1700328fa7950dc93e7eb | [] | no_license | chien-wei/LeetCode | e215915a8103e56f182040dacc9fb0d6996c86ec | 0d6f414e7610fedb2ec4818ecf88d51aa69e1355 | refs/heads/master | 2021-05-13T14:48:22.891100 | 2019-08-20T05:52:59 | 2019-08-20T05:52:59 | 116,749,327 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,277 | py | class Solution:
def threeSum(self, nums):
"""
:type nums: List[int]
:rtype: List[List[int]]
"""
res = []
nums.sort()
self.nSum(nums, 0, 3, [], res)
return res
def nSum(self, nums, target, n, result, results):
#print(nums, target, n, result, results)
if len(nums) < n or n < 2:
return
if n == 2:
l, r = 0, len(nums)-1
while r > l:
if nums[l] + nums[r] == target:
results.append(result + [nums[l], nums[r]])
l += 1
r -= 1
while r > l and nums[l] == nums[l-1]:
l += 1
while r > l and nums[r] == nums[r+1]:
r -= 1
elif nums[l] + nums[r] < target:
l += 1
else:
r -= 1
else:
for i in range(len(nums)-n+1):
if target < nums[i] * n or target > nums[-1] * n:
break
if i == 0 or i > 0 and nums[i-1] != nums[i]:
self.nSum(nums[i+1:], target - nums[i], n-1, result+[nums[i]], results)
return | [
"chien-wei@outlook.com"
] | chien-wei@outlook.com |
d2029e11c506652cd6919211d3bae4de432c9fb8 | ba2a05f20454bda428f140634bc602699f164fc4 | /00.SSAFY/1.first-semester/07_django/API/api/settings.py | 871671aa9e43287bfe37e62f0f3a9b4c9fa4751f | [] | no_license | snowink1137/TIL | 734da402e99afa52f1af4ef996a6b274b1bcce0b | 9e9c78eb0c892affc88e2d46e143cef98af743fb | refs/heads/master | 2023-01-08T18:26:34.311579 | 2021-11-14T11:04:22 | 2021-11-14T11:04:22 | 162,255,934 | 0 | 0 | null | 2023-01-07T11:09:09 | 2018-12-18T08:32:44 | Jupyter Notebook | UTF-8 | Python | false | false | 3,183 | py | """
Django settings for api project.
Generated by 'django-admin startproject' using Django 2.1.7.
For more information on this file, see
https://docs.djangoproject.com/en/2.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.1/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'o+kn&$uu)urs-*1*6z_bj5#_q1ps$uws7fi$nx*asvmnv)!sbd'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'django_extensions',
'rest_framework',
'rest_framework_swagger',
'movie_api',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'api.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'api.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.1/topics/i18n/
LANGUAGE_CODE = 'ko-kr'
TIME_ZONE = 'Asia/Seoul'
USE_I18N = True
USE_L10N = True
USE_TZ = False
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.1/howto/static-files/
STATIC_URL = '/static/'
| [
"snowink1137@gmail.com"
] | snowink1137@gmail.com |
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