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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
91efd913c270d343c4b45b6d1eb44d4aa58f912c | 35a6b6b5cabcf9fb39527bab020ef7c96265a026 | /p3.py | 5911e61bf240cc3e917c3377949ca16c9c46851d | []
| no_license | mepky/data-structure-and-algorithm | 9a1324142276e6966692c51734613f15234f5300 | 96f64e657f97e46fc2d32cca5294fa0f104d5d01 | refs/heads/master | 2020-03-24T08:57:41.692564 | 2020-02-10T12:40:13 | 2020-02-10T12:40:13 | 142,614,071 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 476 | py | from collections import defaultdict
t=int(input())
for _ in range(t):
l=defaultdict(int)
n=int(input())
d=2**20
t=0
s=input()
a=[-1]*27
for i in range(n):
if a[ord(s[i])-97]==-1:
a[ord(s[i])-97]=i
else:
d=min(d,i-a[ord(s[i])-97])
t=1
a[ord(s[i])-97]=i
if t==0:
print(0)
else:
print(n-d)
| [
"[email protected]"
]
| |
f4cc030b9c8573c816c10160ff087a8c68c9d808 | e00cf0bf72421ec31e4d3608c615aeeba5064731 | /wows/move.py | 3165d0d74b85208a58ea1b2ed7ee70fd489a053c | []
| no_license | lorne-luo/auto-wows | b4a84c7d99585c84a635fb5be11fd0f03a5f37fd | 992ad473f1d5a78686e1c4c939c6c218e72373d7 | refs/heads/master | 2020-12-30T00:52:17.497039 | 2020-02-25T11:10:30 | 2020-02-25T11:10:30 | 238,803,938 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,190 | py | import time
from random import randint
import pyautogui as pag
import settings as settings
from helper import search_template, get_map_image
class WOWS_Move(object):
def move_ship(self):
global MOVE_TO
pag.press('m', presses=1, interval=0.25)
pag.sleep(1.5)
if not MOVE_TO:
map_image = get_map_image()
self_loc = search_template(map_image, 'map_self_icon.bmp')
print('self_loc', self_loc)
if self_loc:
MOVE_TO = (settings.BATTLE_MAP_TOPLEFT[0] + settings.BATTLE_MAP_SIZE[0] - self_loc[1],
settings.BATTLE_MAP_TOPLEFT[1] + settings.BATTLE_MAP_SIZE[1] - self_loc[0])
else:
MOVE_TO = (settings.BATTLE_MAP_TOPLEFT[0] + settings.BATTLE_MAP_SIZE[0] / 2,
settings.BATTLE_MAP_TOPLEFT[1] + settings.BATTLE_MAP_SIZE[1] / 2)
for i in range(4):
loc = (MOVE_TO[0] + randint(-50, 50),
MOVE_TO[1] + randint(-50, 50))
pag.moveTo(loc)
pag.click(clicks=2, interval=0.5, button='left')
time.sleep(1)
pag.press('esc')
time.sleep(2)
| [
"[email protected]"
]
| |
327de0fb6195fa9d70bb2f59a1b649c60f9ad8da | 31900bdf5648061a3093230711c5394e20b90436 | /usr/lib/enigma2/python/Plugins/Extensions/MediaPortal/additions/porn/cliphunter.py | f0ad44408f3a495045054598cafc29b1ceb97fb7 | []
| no_license | linuxbox10/enigma2-plugin-extensions-mediaportal | aa6f14ecfc42ce91e22c487070541459a1ab820c | e6b388918c186442718e7200e03c83d0db260831 | refs/heads/master | 2021-05-01T18:50:50.332850 | 2018-02-10T11:33:48 | 2018-02-10T11:33:48 | 121,009,954 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 10,746 | py | # -*- coding: utf-8 -*-
###############################################################################################
#
# MediaPortal for Dreambox OS
#
# Coded by MediaPortal Team (c) 2013-2018
#
# This plugin is open source but it is NOT free software.
#
# This plugin may only be distributed to and executed on hardware which
# is licensed by Dream Property GmbH. This includes commercial distribution.
# In other words:
# It's NOT allowed to distribute any parts of this plugin or its source code in ANY way
# to hardware which is NOT licensed by Dream Property GmbH.
# It's NOT allowed to execute this plugin and its source code or even parts of it in ANY way
# on hardware which is NOT licensed by Dream Property GmbH.
#
# This applies to the source code as a whole as well as to parts of it, unless
# explicitely stated otherwise.
#
# If you want to use or modify the code or parts of it,
# you have to keep OUR license and inform us about the modifications, but it may NOT be
# commercially distributed other than under the conditions noted above.
#
# As an exception regarding execution on hardware, you are permitted to execute this plugin on VU+ hardware
# which is licensed by satco europe GmbH, if the VTi image is used on that hardware.
#
# As an exception regarding modifcations, you are NOT permitted to remove
# any copy protections implemented in this plugin or change them for means of disabling
# or working around the copy protections, unless the change has been explicitly permitted
# by the original authors. Also decompiling and modification of the closed source
# parts is NOT permitted.
#
# Advertising with this plugin is NOT allowed.
# For other uses, permission from the authors is necessary.
#
###############################################################################################
from Plugins.Extensions.MediaPortal.plugin import _
from Plugins.Extensions.MediaPortal.resources.imports import *
agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.115 Safari/537.36'
default_cover = "file://%s/cliphunter.png" % (config.mediaportal.iconcachepath.value + "logos")
class cliphunterGenreScreen(MPScreen):
def __init__(self, session):
MPScreen.__init__(self, session, skin='MP_PluginDescr', default_cover=default_cover)
self["actions"] = ActionMap(["MP_Actions"], {
"ok" : self.keyOK,
"0" : self.closeAll,
"cancel" : self.keyCancel,
"up" : self.keyUp,
"down" : self.keyDown,
"right" : self.keyRight,
"left" : self.keyLeft
}, -1)
self['title'] = Label("cliphunter.com")
self['ContentTitle'] = Label("Genre:")
self.keyLocked = True
self.suchString = ''
self.genreliste = []
self.ml = MenuList([], enableWrapAround=True, content=eListboxPythonMultiContent)
self['liste'] = self.ml
self.onLayoutFinish.append(self.layoutFinished)
def layoutFinished(self):
url = "http://www.cliphunter.com/categories/"
getPage(url, agent=agent).addCallback(self.genreData).addErrback(self.dataError)
def genreData(self, data):
Cats = re.findall(' <a href="(/categories/.*?)" title="(.*?)">.*?<img src="(.*?)"/>', data, re.S)
if Cats:
for (Url, Title, Image) in Cats:
Url = 'http://www.cliphunter.com%s/' % Url.replace(' ','%20')
if not Title == "All":
self.genreliste.append((Title, Url, Image))
self.genreliste.sort()
self.genreliste.insert(0, ("Pornstars", 'http://www.cliphunter.com/pornstars/top/overview/', default_cover))
self.genreliste.insert(0, ("Top Year", 'http://www.cliphunter.com/popular/ratings/year/', default_cover))
self.genreliste.insert(0, ("Top Month", 'http://www.cliphunter.com/popular/ratings/month/', default_cover))
self.genreliste.insert(0, ("Top Week", 'http://www.cliphunter.com/popular/ratings/week/', default_cover))
self.genreliste.insert(0, ("Top Yesterday", 'http://www.cliphunter.com/popular/ratings/yesterday/', default_cover))
self.genreliste.insert(0, ("Top Today", 'http://www.cliphunter.com/popular/ratings/today/', default_cover))
self.genreliste.insert(0, ("Hall of Fame", 'http://www.cliphunter.com/popular/ratings/all/', default_cover))
self.genreliste.insert(0, ("Newest", 'http://www.cliphunter.com/categories/All/', default_cover))
self.genreliste.insert(0, ("--- Search ---", "callSuchen", default_cover))
self.ml.setList(map(self._defaultlistcenter, self.genreliste))
self.ml.moveToIndex(0)
self.keyLocked = False
self.showInfos()
def showInfos(self):
Image = self['liste'].getCurrent()[0][2]
CoverHelper(self['coverArt']).getCover(Image)
def keyOK(self):
if self.keyLocked:
return
Name = self['liste'].getCurrent()[0][0]
Link = self['liste'].getCurrent()[0][1]
if Name == "--- Search ---":
self.suchen()
elif Name == "Pornstars":
self.session.open(cliphunterPornstarScreen, Link, Name)
else:
self.session.open(cliphunterFilmScreen, Link, Name)
def SuchenCallback(self, callback = None, entry = None):
if callback is not None and len(callback):
self.suchString = callback.replace(' ', '%20')
Link = '%s' % (self.suchString)
Name = "--- Search ---"
self.session.open(cliphunterFilmScreen, Link, Name)
class cliphunterPornstarScreen(MPScreen, ThumbsHelper):
def __init__(self, session, Link, Name):
self.Link = Link
self.Name = Name
MPScreen.__init__(self, session, skin='MP_PluginDescr', default_cover=default_cover)
ThumbsHelper.__init__(self)
self["actions"] = ActionMap(["MP_Actions"], {
"ok" : self.keyOK,
"0" : self.closeAll,
"cancel" : self.keyCancel,
"5" : self.keyShowThumb,
"up" : self.keyUp,
"down" : self.keyDown,
"right" : self.keyRight,
"left" : self.keyLeft,
"nextBouquet" : self.keyPageUp,
"prevBouquet" : self.keyPageDown,
"green" : self.keyPageNumber
}, -1)
self['title'] = Label("cliphunter.com")
self['ContentTitle'] = Label("Genre: %s" % self.Name)
self['F2'] = Label(_("Page"))
self['Page'] = Label(_("Page:"))
self.keyLocked = True
self.page = 1
self.lastpage = 1
self.genreliste = []
self.ml = MenuList([], enableWrapAround=True, content=eListboxPythonMultiContent)
self['liste'] = self.ml
self.onLayoutFinish.append(self.loadPage)
def loadPage(self):
self.keyLocked = True
self.genreliste = []
url = "%s%s" % (self.Link, str(self.page))
getPage(url, agent=agent).addCallback(self.genreData).addErrback(self.dataError)
def genreData(self, data):
self.getLastPage(data, '', 'maxPages="(.*?)"')
Parse = re.search('photoGrid">(.*?)class="clearfix">', data, re.S)
Cats = re.findall('href="(.*?)">.*?src=\'(.*?)\'/>.*?<span>(.*?)</span>', Parse.group(1), re.S)
if Cats:
for (Url, Image, Title) in Cats:
Url = "http://www.cliphunter.com" + Url + "/movies/"
self.genreliste.append((Title.title(), Url, Image))
self.ml.setList(map(self._defaultlistleft, self.genreliste))
self.ml.moveToIndex(0)
self.keyLocked = False
self.th_ThumbsQuery(self.genreliste, 0, 1, 2, None, None, self.page, int(self.lastpage), mode=1)
self.showInfos()
def showInfos(self):
Title = self['liste'].getCurrent()[0][0]
Image = self['liste'].getCurrent()[0][2]
self['name'].setText(Title)
CoverHelper(self['coverArt']).getCover(Image)
def keyOK(self):
if self.keyLocked:
return
Name = self['liste'].getCurrent()[0][0]
Link = self['liste'].getCurrent()[0][1]
self.session.open(cliphunterFilmScreen, Link, Name)
class cliphunterFilmScreen(MPScreen, ThumbsHelper):
def __init__(self, session, Link, Name):
self.Link = Link
self.Name = Name
MPScreen.__init__(self, session, skin='MP_PluginDescr', default_cover=default_cover)
ThumbsHelper.__init__(self)
self["actions"] = ActionMap(["MP_Actions"], {
"ok" : self.keyOK,
"0" : self.closeAll,
"cancel" : self.keyCancel,
"5" : self.keyShowThumb,
"up" : self.keyUp,
"down" : self.keyDown,
"right" : self.keyRight,
"left" : self.keyLeft,
"nextBouquet" : self.keyPageUp,
"prevBouquet" : self.keyPageDown,
"green" : self.keyPageNumber
}, -1)
self['title'] = Label("cliphunter.com")
self['ContentTitle'] = Label("Genre: %s" % self.Name)
self['F2'] = Label(_("Page"))
self['Page'] = Label(_("Page:"))
self.keyLocked = True
self.page = 1
self.lastpage = 1
self.filmliste = []
self.ml = MenuList([], enableWrapAround=True, content=eListboxPythonMultiContent)
self['liste'] = self.ml
self.onLayoutFinish.append(self.loadPage)
def loadPage(self):
self.keyLocked = True
self['name'].setText(_('Please wait...'))
self.filmliste = []
if re.match(".*?Search", self.Name):
url = "http://www.cliphunter.com/search/%s/%s" % (self.Link, str(self.page))
else:
url = "%s%s" % (self.Link, str(self.page))
getPage(url, agent=agent).addCallback(self.loadData).addErrback(self.dataError)
def loadData(self, data):
self.getLastPage(data, '', 'maxPages="(.*?)"')
Movies = re.findall('class="t"\shref="(/w/\d+/(.*?))".*?class="i"\ssrc="(.*?)".*?class="tr">(.*?)</div>.*?class="vttl.*?">(.*?)</a>', data, re.S)
if Movies:
for (Url, TitleUrl, Image, Runtime, Title) in Movies:
Url = "http://www.cliphunter.com" + Url
self.filmliste.append((TitleUrl.replace('_',' '), Url, Image, Runtime))
if len(self.filmliste) == 0:
self.filmliste.append((_('No videos found!'), '', None, ''))
self.ml.setList(map(self._defaultlistleft, self.filmliste))
self.ml.moveToIndex(0)
self.keyLocked = False
self.th_ThumbsQuery(self.filmliste, 0, 1, 2, None, None, self.page, int(self.lastpage), mode=1)
self.showInfos()
def showInfos(self):
title = self['liste'].getCurrent()[0][0]
url = self['liste'].getCurrent()[0][1]
pic = self['liste'].getCurrent()[0][2]
runtime = self['liste'].getCurrent()[0][3]
self['handlung'].setText("Runtime: %s" % runtime)
self['name'].setText(title)
CoverHelper(self['coverArt']).getCover(pic)
def keyOK(self):
if self.keyLocked:
return
Link = self['liste'].getCurrent()[0][1]
self.keyLocked = True
getPage(Link, agent=agent).addCallback(self.getVideoPage).addErrback(self.dataError)
def getVideoPage(self, data):
url = re.findall('"url":"(.*?)"}', data, re.S)
if url:
url = url[-1]
url = url.replace('\u0026', '.')
translation_table = {
'a': 'h', 'd': 'e', 'e': 'v', 'f': 'o', 'g': 'f', 'i': 'd', 'l': 'n',
'm': 'a', 'n': 'm', 'p': 'u', 'q': 't', 'r': 's', 'v': 'p', 'x': 'r',
'y': 'l', 'z': 'i',
'$': ':', '&': '.', '(': '=', '^': '&', '=': '/',
}
url = ''.join(translation_table.get(c, c) for c in url)
self.keyLocked = False
Title = self['liste'].getCurrent()[0][0]
self.session.open(SimplePlayer, [(Title, url)], showPlaylist=False, ltype='cliphunter') | [
"[email protected]"
]
| |
12e05ceaac7c5c4174fb21ada9bdbb1e70c90c54 | ffb05b145989e01da075e2a607fb291955251f46 | /pypers/oxford/non_cooperative.py | 6c7b293967ae50f89ebf7f90ccccdc8e62ba6d40 | []
| no_license | micheles/papers | a5e7f2fa0cf305cd3f8face7c7ecc0db70ce7cc7 | be9070f8b7e8192b84a102444b1238266bdc55a0 | refs/heads/master | 2023-06-07T16:46:46.306040 | 2018-07-14T04:17:51 | 2018-07-14T04:17:51 | 32,264,461 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 255 | py | # non_cooperative.py
class B1(object):
def __init__(self, **kw):
print "B1.__init__"
super(B1, self).__init__(**kw)
class B2(object):
def __init__(self, **kw):
print "B2.__init__"
super(B2, self).__init__(**kw)
| [
"[email protected]"
]
| |
94f2093636ae67fdc8ec2d5431c2b52cbd51d7c2 | ac5e52a3fc52dde58d208746cddabef2e378119e | /exps-gsn-edf/gsn-edf_ut=3.0_rd=0.5_rw=0.06_rn=4_u=0.075-0.35_p=harmonic-2/sched=RUN_trial=25/params.py | 1e06a0ee411f4cd8e4e96c1df8f010d7336d6730 | []
| no_license | ricardobtxr/experiment-scripts | 1e2abfcd94fb0ef5a56c5d7dffddfe814752eef1 | 7bcebff7ac2f2822423f211f1162cd017a18babb | refs/heads/master | 2023-04-09T02:37:41.466794 | 2021-04-25T03:27:16 | 2021-04-25T03:27:16 | 358,926,457 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 254 | py | {'cpus': 4,
'duration': 30,
'final_util': '3.041500',
'max_util': '3.0',
'periods': 'harmonic-2',
'release_master': False,
'res_distr': '0.5',
'res_nmb': '4',
'res_weight': '0.06',
'scheduler': 'GSN-EDF',
'trial': 25,
'utils': 'uni-medium-3'}
| [
"[email protected]"
]
| |
6cd613bcdd91d3c252c77c5671f432f525d64cfc | bee2af5228232ce94f418b61810cecd93af62615 | /virtual/bin/django-admin.py | d6b83b56c12b4e16e7f24824f482b665a071f57a | []
| no_license | thuitafaith/djangoapp | b64c2e1a05c67b1135d4d9dd7975c17522238a69 | e06280b34a7b1ec012d0baab6f0fb153875a39b4 | refs/heads/master | 2022-12-11T19:06:08.540528 | 2019-08-29T12:36:45 | 2019-08-29T12:36:45 | 203,321,071 | 0 | 0 | null | 2022-11-22T04:13:07 | 2019-08-20T07:15:28 | Python | UTF-8 | Python | false | false | 158 | py | #!/home/faith/Desktop/django-rem/virtual/bin/python
from django.core import management
if __name__ == "__main__":
management.execute_from_command_line()
| [
"[email protected]"
]
| |
f3467f1043b80a0ea9337c61aa83eb37180e440c | 98c6ea9c884152e8340605a706efefbea6170be5 | /examples/data/Assignment_2/mphnok005/question3.py | 8ab32014734be33c45000ec60015c87758483dae | []
| no_license | MrHamdulay/csc3-capstone | 479d659e1dcd28040e83ebd9e3374d0ccc0c6817 | 6f0fa0fa1555ceb1b0fb33f25e9694e68b6a53d2 | refs/heads/master | 2021-03-12T21:55:57.781339 | 2014-09-22T02:22:22 | 2014-09-22T02:22:22 | 22,372,174 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 207 | py | from math import*
x=sqrt(2)
a=2
pi=2*(a/x)
while x<2:
x=(sqrt(2+x))
pi=(pi*a/x)
print("Approximation of pi:",round(pi,3))
c=eval(input("Enter the radius:\n"))
print("Area:",round(c**2*pi,3)) | [
"[email protected]"
]
| |
3da3e17495525b485fd627a5d52d55b261e728ec | 8d50cc4f37c153fcb51de4501f3fa50c00394d9b | /test/benchmark/resnet_tl_benchmark.py | 0273e724c53f6d0c0598924637c84431b5b3fe0c | [
"MIT"
]
| permissive | liujuanLT/InsightFace_TF | dbd239dfdda1866c348e82211932884f73cb3067 | 257b6e0dcf7e7c3523dc7e1c08ba529fab1bf75b | refs/heads/master | 2022-04-27T21:24:01.458277 | 2022-03-17T12:28:15 | 2022-03-17T12:28:15 | 463,040,192 | 0 | 0 | MIT | 2022-02-24T06:51:16 | 2022-02-24T06:51:15 | null | UTF-8 | Python | false | false | 1,255 | py | import tensorflow as tf
import tensorflow.contrib.slim.nets as nets
import numpy as np
from nets.resnet import get_resnet
slim = tf.contrib.slim
resnet = nets.resnet_v1
if __name__ == '__main__':
output_shape = 85164
batch_size = 128
image = tf.placeholder(name='input_x', shape=[None, 224, 224, 3], dtype=tf.float32)
labels = tf.placeholder(name='input_label', shape=[None, output_shape], dtype=tf.float32)
with slim.arg_scope(nets.resnet_utils.resnet_arg_scope()):
nets = get_resnet(image, output_shape, 50, type='resnet', sess=None, pretrained=False)
print(nets.outputs)
probabilities = tf.reduce_mean(tf.nn.softmax(nets.outputs, dim=-1))
print(probabilities)
losses = tf.norm(tf.subtract(probabilities, labels))
train_op = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(losses)
sess = tf.Session()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
while True:
datasets = np.random.randn(batch_size, 224, 224, 3).astype(np.float32)
datasets_labels = np.random.randn(batch_size, output_shape).astype(np.float32)
losses_val, _ = sess.run([losses, train_op], feed_dict={image: datasets, labels: datasets_labels})
print(losses_val) | [
"[email protected]"
]
| |
51d5ae1fa2d5ae73a65d826bd1113e9b57cef767 | 03383b657ad6d526e7e6aa6639fe41019cd39ea2 | /recursion/palandrome.py | 985c2d63e7528bf16d0978634606988d462fbf30 | []
| no_license | ahmedmeshref/Leetcode-Solutions | 1c5f908cb2f6487c9dfadcc8f91192dedbb5a17e | 28f848cb25e4aa22e6d8c9d715488f191ed15137 | refs/heads/main | 2023-05-26T14:33:11.246122 | 2021-06-07T21:32:13 | 2021-06-07T21:32:13 | 356,045,965 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 402 | py | def isPalindrome(s: str) -> bool:
l = 0
r = len(s) - 1
while l != r:
if not s[l].isalpha():
l += 1
elif not s[r].isalpha():
r -= 1
elif s[l].lower() != s[r].lower():
return False
else:
l += 1
r -= 1
return s[l] == s[r]
print(isPalindrome("A man, a plan, a canal: Panama"))
| [
"[email protected]"
]
| |
e49952bb3039c47341a3a2001f153c1fcea8521c | 05169e203974411667ab947298a74575b8a179e0 | /packages/jet_bridge_base/jet_bridge_base/serializers/relationship_override.py | c985788ccf2e5eeaf886f08eb8bb093846f356d8 | [
"MIT"
]
| permissive | jet-admin/jet-bridge | f6b563e1801985063483ddb02e9e1c3301dc0612 | c53d30fb308eed5822083eaf71f641c4098610cc | refs/heads/master | 2023-09-01T14:31:42.261427 | 2023-08-24T13:54:34 | 2023-08-24T13:54:34 | 163,167,532 | 1,564 | 166 | MIT | 2023-03-18T03:20:04 | 2018-12-26T10:27:33 | Python | UTF-8 | Python | false | false | 4,873 | py | from jet_bridge_base.models.model_relation_override import ModelRelationOverrideModel
from jet_bridge_base.store import store
from sqlalchemy import inspect
from jet_bridge_base import fields
from jet_bridge_base.db import get_mapped_base, reload_request_graphql_schema, get_request_connection
from jet_bridge_base.exceptions.validation_error import ValidationError
from jet_bridge_base.serializers.serializer import Serializer
from jet_bridge_base.logger import logger
class ModelDescriptionRelationOverrideSerializer(Serializer):
direction = fields.CharField()
local_field = fields.CharField()
related_model = fields.CharField()
related_field = fields.CharField()
class ModelDescriptionRelationOverridesSerializer(Serializer):
model = fields.CharField()
relations = ModelDescriptionRelationOverrideSerializer(many=True)
def get_model(self, request, name):
MappedBase = get_mapped_base(request)
return MappedBase.classes.get(name)
def generate_many_to_one_name(self, mapper, local_field, related_model, related_field):
name = '__'.join([local_field, 'to', related_model, related_field])
if name in mapper.columns:
name = name + '_relation'
logger.warning('Already detected column name, using {}'.format(name))
return name
def generate_one_to_many_name(self, mapper, local_field, related_model, related_field):
name = '__'.join([related_model, related_field, 'to', local_field])
if name in mapper.columns:
name = name + '_relation'
logger.warning('Already detected column name, using {}'.format(name))
return name
def validate(self, attrs):
request = self.context.get('request')
Model = self.get_model(request, attrs['model'])
if Model is None:
raise ValidationError('Unknown relation override model: {}'.format(attrs['model']))
mapper = inspect(Model)
for item in attrs['relations']:
if item['direction'] == 'MANYTOONE':
item['name'] = self.generate_many_to_one_name(mapper, item['local_field'], item['related_model'], item['related_field'])
elif item['direction'] == 'ONETOMANY':
item['name'] = self.generate_one_to_many_name(mapper, item['local_field'], item['related_model'], item['related_field'])
else:
raise ValidationError('Unknown relation direction: {}'.format(item['direction']))
return attrs
def save(self):
request = self.context.get('request')
connection = get_request_connection(request)
draft = bool(request.get_argument('draft', False))
with store.session() as session:
with session.begin():
for item in self.validated_data:
set_overrides = sorted(item['relations'], key=lambda x: x['name'])
existing_overrides = session.query(ModelRelationOverrideModel).filter(
ModelRelationOverrideModel.connection_id == connection['id'],
ModelRelationOverrideModel.model == item['model'],
draft == draft
).order_by(ModelRelationOverrideModel.name).all()
existing_overrides = list(existing_overrides)
for i, override in enumerate(set_overrides):
existing_override = existing_overrides[i] if i < len(existing_overrides) else None
if existing_override:
existing_override.name = override.get('name')
existing_override.direction = override.get('direction')
existing_override.local_field = override.get('local_field')
existing_override.related_model = override.get('related_model')
existing_override.related_field = override.get('related_field')
else:
session.add(ModelRelationOverrideModel(
connection_id=connection['id'],
model=item['model'],
draft=draft,
name=override.get('name'),
direction=override.get('direction'),
local_field=override.get('local_field'),
related_model=override.get('related_model'),
related_field=override.get('related_field')
))
delete_overrides = existing_overrides[len(item['relations']):]
for override in delete_overrides:
session.delete(override)
reload_request_graphql_schema(request, draft)
| [
"[email protected]"
]
| |
8ba80ac4b037dde92443141d60bd35bf1f98031e | e4414bd8152e52855db7ab9065ae12b7329143e0 | /python/src/hangman.py | 0dd38bbfdc6501bc39f632a253400dd40bbf2d07 | []
| no_license | catalinc/programmingpraxis-solutions | 39cb847877ec46d2fb85740791c24889ab5654a8 | c0b13906aa76ffac705bf108db138fb9a38bc16a | refs/heads/master | 2021-03-27T16:46:47.781839 | 2017-09-09T15:17:38 | 2017-09-09T15:17:38 | 53,532,233 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,889 | py | #!/usr/bin/env python
# See http://programmingpraxis.com/2011/12/20/hangman/
import random
import sys
HANGMAN = [
"",
"""
O
""",
"""
O
|
""",
"""
_O
|
""",
"""
_O_
|
""",
"""
_O_
|
/
""",
"""
_O_
|
/ \\
"""
]
def play_game():
secret_word = random_word().upper()
guessed_letters = set()
failed_attempts = 0
print_matches(secret_word, guessed_letters)
while True:
try:
letter = raw_input("Your guess ? ").upper()
except KeyboardInterrupt:
exit_game()
if letter in secret_word:
guessed_letters.add(letter)
else:
failed_attempts += 1
print_hangman(failed_attempts)
if lose(failed_attempts):
print("Sorry, you lose...")
print("The word was: %s" % (" ".join(list(secret_word))))
break
print_matches(secret_word, guessed_letters)
if win(secret_word, guessed_letters):
print("You nail it !")
break
def random_word(words_file='words.lst'):
word = None
n = 0
with open(words_file) as f:
for line in f:
n += 1
if random.random() < 1.0 / n:
word = line
return word
def print_matches(word, letters):
out = []
for l in word:
if l in letters:
out.append(l)
else:
out.append("_")
print(" ".join(out))
def exit_game():
print("Bye !")
sys.exit(0)
def print_hangman(guess_attempts):
print HANGMAN[guess_attempts]
def win(secret_word, guessed_letters):
return len(secret_word) == len(guessed_letters)
def lose(failed_attempts):
return failed_attempts == len(HANGMAN) - 1
if __name__ == '__main__':
print("Let's play Hangman !")
while True:
play_game()
if raw_input("Play another ? [Y]/N ").upper() == "N":
exit_game()
| [
"[email protected]"
]
| |
74242c01bb15f0739920d399519d7227f57b8f8a | fc91e867bb74cbebcb0ee608f1477ae16af91631 | /.venv/bin/django-admin.py | 96ac186b1a98732e5519c8d218d286e285191802 | []
| no_license | karthik018/FacebookPosts | 7580afbfab066c6bd09a43be086e4ce9621bbd65 | 2fbe59e8640ca2da01e0028f7b10c4f9d7b62b65 | refs/heads/master | 2020-06-12T05:29:43.493418 | 2019-07-16T06:16:25 | 2019-07-16T06:16:25 | 194,204,422 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 157 | py | #!/home/ib_admin/FacebookPosts/.venv/bin/python3.7
from django.core import management
if __name__ == "__main__":
management.execute_from_command_line()
| [
"[email protected]"
]
| |
900ce188b2d480f02d08d8fe381e861ddc612129 | acb8e84e3b9c987fcab341f799f41d5a5ec4d587 | /langs/4/j33.py | 32bc61a86b88039321d406fcdd145e2fdf95b792 | []
| no_license | G4te-Keep3r/HowdyHackers | 46bfad63eafe5ac515da363e1c75fa6f4b9bca32 | fb6d391aaecb60ab5c4650d4ae2ddd599fd85db2 | refs/heads/master | 2020-08-01T12:08:10.782018 | 2016-11-13T20:45:50 | 2016-11-13T20:45:50 | 73,624,224 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 486 | py | import sys
def printFunction(lineRemaining):
if lineRemaining[0] == '"' and lineRemaining[-1] == '"':
if len(lineRemaining) > 2:
#data to print
lineRemaining = lineRemaining[1:-1]
print ' '.join(lineRemaining)
else:
print
def main(fileName):
with open(fileName) as f:
for line in f:
data = line.split()
if data[0] == 'j33':
printFunction(data[1:])
else:
print 'ERROR'
return
if __name__ == '__main__':
main(sys.argv[1]) | [
"[email protected]"
]
| |
99023c5533e743afb8349cd031816969f2e0f52e | 6527b66fd08d9e7f833973adf421faccd8b765f5 | /yuancloud/recicler/event/tests/test_mail_schedule.py | 7b92308e184f89e0d7bc6436545f7d9324c6b05d | []
| no_license | cash2one/yuancloud | 9a41933514e57167afb70cb5daba7f352673fb4d | 5a4fd72991c846d5cb7c5082f6bdfef5b2bca572 | refs/heads/master | 2021-06-19T22:11:08.260079 | 2017-06-29T06:26:15 | 2017-06-29T06:26:15 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,952 | py | # -*- coding: utf-8 -*-
import datetime
from dateutil.relativedelta import relativedelta
from yuancloud import fields, tools
from yuancloud.addons.event.tests.common import TestEventCommon
from yuancloud.tools import mute_logger
class TestMailSchedule(TestEventCommon):
@mute_logger('yuancloud.addons.base.ir.ir_model', 'yuancloud.models')
def test_00_event_mail_schedule(self):
""" Test mail scheduling for events """
self.env['ir.values'].set_default('event.config.settings', 'auto_confirmation', True)
now = fields.datetime.now()
event_date_begin = now + relativedelta(days=1)
event_date_end = now + relativedelta(days=3)
test_event = self.Event.sudo(self.user_eventmanager).create({
'name': 'TestEventMail',
'date_begin': event_date_begin,
'date_end': event_date_end,
'seats_max': 10,
'event_mail_ids': [
(0, 0, { # right at subscription
'interval_unit': 'now',
'interval_type': 'after_sub',
'template_id': self.env['ir.model.data'].xmlid_to_res_id('event.event_subscription')}),
(0, 0, { # 2 days before event
'interval_nbr': 2,
'interval_unit': 'days',
'interval_type': 'before_event',
'template_id': self.env['ir.model.data'].xmlid_to_res_id('event.event_reminder')}),
]
})
# create some registrations
self.Registration.sudo(self.user_eventuser).create({
'event_id': test_event.id,
'name': 'Reg0',
'email': '[email protected]',
})
self.Registration.sudo(self.user_eventuser).create({
'event_id': test_event.id,
'name': 'Reg1',
'email': '[email protected]',
})
# check subscription scheduler
schedulers = self.EventMail.search([('event_id', '=', test_event.id), ('interval_type', '=', 'after_sub')])
self.assertEqual(len(schedulers), 1, 'event: wrong scheduler creation')
self.assertEqual(schedulers[0].scheduled_date, test_event.create_date, 'event: incorrect scheduled date for checking controller')
# verify that subscription scheduler was auto-executed after each registration
self.assertEqual(len(schedulers[0].mail_registration_ids), 2, 'event: incorrect number of mail scheduled date')
mails = self.env['mail.mail'].search([('subject', 'ilike', 'subscription'), ('date', '>=', datetime.datetime.strftime(now, tools.DEFAULT_SERVER_DATETIME_FORMAT))], order='date DESC', limit=3)
self.assertEqual(len(mails), 2, 'event: wrong number of subscription mail sent')
for registration in schedulers[0].mail_registration_ids:
self.assertTrue(registration.mail_sent, 'event: wrongly confirmed mailing on subscription')
# check before event scheduler
schedulers = self.EventMail.search([('event_id', '=', test_event.id), ('interval_type', '=', 'before_event')])
self.assertEqual(len(schedulers), 1, 'event: wrong scheduler creation')
self.assertEqual(schedulers[0].scheduled_date, datetime.datetime.strftime(event_date_begin + relativedelta(days=-2), tools.DEFAULT_SERVER_DATETIME_FORMAT), 'event: incorrect scheduled date')
# execute event reminder scheduler explicitly
schedulers[0].execute()
self.assertTrue(schedulers[0].mail_sent, 'event: reminder scheduler should have sent an email')
self.assertTrue(schedulers[0].done, 'event: reminder scheduler should be done')
mails = self.env['mail.mail'].search([('subject', 'ilike', 'reminder'), ('date', '>=', datetime.datetime.strftime(now, tools.DEFAULT_SERVER_DATETIME_FORMAT))], order='date DESC', limit=3)
self.assertEqual(len(mails), 2, 'event: wrong number of reminders in outgoing mail queue')
| [
"[email protected]"
]
| |
f2b1502e69ebbcb83d168b947b4e1536ab2f5ca9 | 2e682fd72e3feaa70e3f7bf2a3b83c50d783ec02 | /PyTorch/dev/nlp/Speech_Transformer_ID0487_for_PyTorch/test/test_lr.py | 50038c0c6c0a3d30f8e5cf25a74a0eebf9c1ebc4 | [
"GPL-1.0-or-later",
"MIT",
"Apache-2.0",
"BSD-2-Clause",
"BSD-3-Clause",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-unknown-license-reference"
]
| permissive | Ascend/ModelZoo-PyTorch | 4c89414b9e2582cef9926d4670108a090c839d2d | 92acc188d3a0f634de58463b6676e70df83ef808 | refs/heads/master | 2023-07-19T12:40:00.512853 | 2023-07-17T02:48:18 | 2023-07-17T02:48:18 | 483,502,469 | 23 | 6 | Apache-2.0 | 2022-10-15T09:29:12 | 2022-04-20T04:11:18 | Python | UTF-8 | Python | false | false | 1,717 | py | # Copyright 2017 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.
# ============================================================================
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import matplotlib.pyplot as plt
if __name__ == '__main__':
k = 0.2
warmup_steps = 4000
d_model = 512
init_lr = d_model ** (-0.5)
lr_list = []
for step_num in range(1, 500000):
lr = k * init_lr * min(step_num ** (-0.5),
step_num * (warmup_steps ** (-1.5)))
lr_list.append(lr)
print(lr_list[:100])
print(lr_list[-100:])
plt.plot(lr_list)
plt.show()
| [
"[email protected]"
]
| |
7d82abc23d5e3d4bf5e54cd6ec2da4a4d1a8768f | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p02631/s475540632.py | 3a87c44b29ca2411c0463a78d1676b61c5e7616c | []
| no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 267 | py | N = int(input())
a = list(map(int, input().split()))
# XOR演算子 ^
# aの要素全てのXORを計算、それをSとする
S = 0
for aa in a:
S ^= aa
# i番目の番号はaiとSのXORで表される
ans = []
for ai in a:
ans.append(S ^ ai)
print(*ans) | [
"[email protected]"
]
| |
8319e02dd8e51c0f3c972288a559d15a0f3bb1c5 | acd41dc7e684eb2e58b6bef2b3e86950b8064945 | /res/packages/scripts/scripts/common/Lib/plat-mac/Carbon/Cm.py | 81888a1e6189f6251d73285153430da7c7720a3a | []
| no_license | webiumsk/WoT-0.9.18.0 | e07acd08b33bfe7c73c910f5cb2a054a58a9beea | 89979c1ad547f1a1bbb2189f5ee3b10685e9a216 | refs/heads/master | 2021-01-20T09:37:10.323406 | 2017-05-04T13:51:43 | 2017-05-04T13:51:43 | 90,268,530 | 0 | 0 | null | null | null | null | WINDOWS-1250 | Python | false | false | 362 | py | # 2017.05.04 15:34:09 Střední Evropa (letní čas)
# Embedded file name: scripts/common/Lib/plat-mac/Carbon/Cm.py
from _Cm import *
# okay decompyling C:\Users\PC\wotmods\files\originals\res\packages\scripts\scripts\common\Lib\plat-mac\Carbon\Cm.pyc
# decompiled 1 files: 1 okay, 0 failed, 0 verify failed
# 2017.05.04 15:34:09 Střední Evropa (letní čas)
| [
"[email protected]"
]
| |
8e3269fafdfc4c4927faaa47a88c3a3c531bf398 | 676f6f2d02db6aeeaa1bb0b28ab49e8c73923d0e | /venv/Lib/site-packages/falcon/bench/bench.py | b0c60863ab0aaefa5eee2e548c536b1e6bc55c82 | [
"Apache-2.0"
]
| permissive | vrian/orsen | ce34f74ea3a14c95d37ffa5c694b7c66725925df | 9c10148aba62868fad4b679a4b9b717829586e96 | refs/heads/master | 2023-01-21T21:47:06.210918 | 2018-06-23T04:46:26 | 2018-06-23T04:46:26 | 120,284,869 | 1 | 0 | Apache-2.0 | 2023-01-09T09:39:16 | 2018-02-05T09:44:03 | Python | UTF-8 | Python | false | false | 11,546 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2014 by Rackspace Hosting, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import argparse
from collections import defaultdict, deque
from decimal import Decimal
import gc
import inspect
import platform
import random
import sys
import tempfile
import timeit
try:
import cProfile
except ImportError:
import profile as cProfile
try:
import guppy
except ImportError:
heapy = None
else:
heapy = guppy.hpy()
try:
import pprofile
except ImportError:
pprofile = None
try:
import vmprof
from vmshare.service import Service
except ImportError:
vmprof = None
from falcon.bench import create # NOQA
import falcon.testing as helpers
# NOTE(kgriffs): Based on testing, these values provide a ceiling that's
# several times higher than fast x86 hardware can achieve today.
ITER_DETECTION_MAX_ATTEMPTS = 27
ITER_DETECTION_MULTIPLIER = 1.7
ITER_DETECTION_STARTING = 3000
# NOTE(kgriffs): Benchmark duration range, in seconds, to target
ITER_DETECTION_DURATION_MIN = 1.0
ITER_DETECTION_DURATION_MAX = 5.0
JIT_WARMING_MULTIPLIER = 30
PYPY = platform.python_implementation() == 'PyPy'
BODY = helpers.rand_string(10240, 10240).encode('utf-8') # NOQA
HEADERS = {'X-Test': 'Funky Chicken'} # NOQA
class StartResponseMockLite(object):
"""Mock object representing a WSGI `start_response` callable."""
def __init__(self):
self._called = 0
self.status = None
self.headers = None
self.exc_info = None
def __call__(self, status, headers, exc_info=None):
"""Implements the PEP-3333 `start_response` protocol."""
self._called += 1
self.status = status
self.headers = headers
self.exc_info = exc_info
@property
def call_count(self):
return self._called
def bench(func, iterations, stat_memory):
gc.collect()
heap_diff = None
if heapy and stat_memory:
heap_before = heapy.heap()
total_sec = timeit.timeit(func, setup=gc.enable, number=iterations)
if heapy and stat_memory:
heap_diff = heapy.heap() - heap_before
sec_per_req = Decimal(str(total_sec)) / Decimal(str(iterations))
return (sec_per_req, heap_diff)
def determine_iterations(func):
# NOTE(kgriffs): Algorithm adapted from IPython's magic timeit
# function to determine iterations so that 0.2 <= total time < 2.0
iterations = ITER_DETECTION_STARTING
for __ in range(1, ITER_DETECTION_MAX_ATTEMPTS):
gc.collect()
total_sec = timeit.timeit(
func,
setup=gc.enable,
number=int(iterations)
)
if total_sec >= ITER_DETECTION_DURATION_MIN:
assert total_sec < ITER_DETECTION_DURATION_MAX
break
iterations *= ITER_DETECTION_MULTIPLIER
return int(iterations)
def profile(name, env, filename=None, verbose=False):
if filename:
filename = name + '-' + filename
print('Profiling %s ==> %s' % (name, filename))
else:
filename = None
title = name + ' profile'
print()
print('=' * len(title))
print(title)
print('=' * len(title))
func = create_bench(name, env)
gc.collect()
num_iterations = 100000
if PYPY:
print('JIT warmup...')
# TODO(kgriffs): Measure initial time, and keep iterating until
# performance increases and then steadies
for x in range(num_iterations * JIT_WARMING_MULTIPLIER):
func()
print('Ready.')
code = 'for x in range({0}): func()'.format(num_iterations)
if verbose:
if pprofile is None:
print('pprofile not found. Please install pprofile and try again.')
return
pprofile.runctx(code, locals(), globals(), filename=filename)
else:
cProfile.runctx(code, locals(), globals(),
sort='tottime', filename=filename)
def profile_vmprof(name, env):
if vmprof is None:
print('vmprof not found. Please install vmprof and try again.')
return
func = create_bench(name, env)
gc.collect()
#
# Based on: https://github.com/vmprof/vmprof-python/blob/master/vmprof/__main__.py
#
prof_file = tempfile.NamedTemporaryFile(delete=False)
filename = prof_file.name
vmprof.enable(prof_file.fileno())
try:
for __ in range(1000000):
func()
except BaseException as e:
if not isinstance(e, (KeyboardInterrupt, SystemExit)):
raise
vmprof.disable()
service = Service('vmprof.com')
service.post({
Service.FILE_CPU_PROFILE: filename,
Service.FILE_JIT_PROFILE: filename + '.jit',
'argv': ' '.join(sys.argv[:]),
'VM': platform.python_implementation(),
})
prof_file.close()
def exhaust(iterator_or_generator):
# from https://docs.python.org/dev/library/itertools.html#itertools-recipes
deque(iterator_or_generator, maxlen=0)
def create_bench(name, env):
srmock = StartResponseMockLite()
function = name.lower().replace('-', '_')
app = eval('create.{0}(BODY, HEADERS)'.format(function))
def bench():
app(env, srmock)
assert srmock.status == '200 OK'
def bench_generator():
exhaust(app(env, srmock))
assert srmock.status == '200 OK'
if inspect.isgeneratorfunction(app):
return bench_generator
else:
return bench
def consolidate_datasets(datasets):
results = defaultdict(list)
for dataset in datasets:
for name, sec_per_req, _ in dataset:
results[name].append(sec_per_req)
return [(name, min(vector)) for name, vector in results.items()]
def round_to_int(dec):
return int(dec.to_integral_value())
def avg(array):
return sum(array) / len(array)
def hello_env():
request_headers = {'Content-Type': 'application/json'}
return helpers.create_environ('/hello/584/test',
query_string='limit=10&thing=ab',
headers=request_headers)
def queues_env():
request_headers = {'Content-Type': 'application/json'}
path = ('/v1/852809/queues/0fd4c8c6-bd72-11e2-8e47-db5ebd4c8125'
'/claims/db5ebd4c8125')
qs = 'limit=10&thing=a+b&x=%23%24'
return helpers.create_environ(path, query_string=qs,
headers=request_headers)
def get_env(framework):
return queues_env() if framework == 'falcon-ext' else hello_env()
def run(frameworks, trials, iterations, stat_memory):
# Skip any frameworks that are not installed
for name in frameworks:
try:
create_bench(name, hello_env())
except ImportError as ex:
print(ex)
print('Skipping missing library: ' + name)
del frameworks[frameworks.index(name)]
print()
datasets = []
if not frameworks:
print('Nothing to do.\n')
return datasets
benchmarks = []
for name in frameworks:
bm = create_bench(name, get_env(name))
bm_iterations = iterations if iterations else determine_iterations(bm)
if PYPY:
print('{}: JIT warmup'.format(name))
# TODO(kgriffs): Measure initial time, and keep iterating until
# performance increases and then steadies
bench(bm, bm_iterations * JIT_WARMING_MULTIPLIER, False)
bm_iterations = iterations if iterations else determine_iterations(bm)
benchmarks.append((name, bm_iterations, bm))
print('{}: {} iterations'.format(name, bm_iterations))
print()
for r in range(trials):
random.shuffle(frameworks)
sys.stdout.write('Benchmarking, Trial %d of %d' %
(r + 1, trials))
sys.stdout.flush()
dataset = []
for name, bm_iterations, bm in benchmarks:
sec_per_req, heap_diff = bench(
bm,
bm_iterations,
stat_memory
)
dataset.append((name, sec_per_req, heap_diff))
sys.stdout.write('.')
sys.stdout.flush()
datasets.append(dataset)
print('done.')
return datasets
def main():
frameworks = [
'bottle',
'django',
'falcon',
'falcon-ext',
'flask',
'pecan',
'werkzeug',
]
parser = argparse.ArgumentParser(description='Falcon benchmark runner')
parser.add_argument('-b', '--benchmark', type=str, action='append',
choices=frameworks, dest='frameworks', nargs='+')
parser.add_argument('-i', '--iterations', type=int, default=0)
parser.add_argument('-t', '--trials', type=int, default=10)
parser.add_argument('-p', '--profile', type=str,
choices=['standard', 'verbose', 'vmprof'])
parser.add_argument('-o', '--profile-output', type=str, default=None)
parser.add_argument('-m', '--stat-memory', action='store_true')
args = parser.parse_args()
if args.stat_memory and heapy is None:
print('WARNING: Guppy not installed; memory stats are unavailable.\n')
if args.frameworks:
frameworks = args.frameworks
# Normalize frameworks type
normalized_frameworks = []
for one_or_many in frameworks:
if isinstance(one_or_many, list):
normalized_frameworks.extend(one_or_many)
else:
normalized_frameworks.append(one_or_many)
frameworks = normalized_frameworks
# Profile?
if args.profile:
framework = 'falcon-ext'
if args.profile == 'vmprof':
profile_vmprof(framework, get_env(framework))
else:
profile(framework, get_env(framework),
filename=args.profile_output,
verbose=(args.profile == 'verbose'))
print()
return
# Otherwise, benchmark
datasets = run(frameworks, args.trials, args.iterations,
args.stat_memory)
if not datasets:
return
dataset = consolidate_datasets(datasets)
dataset = sorted(dataset, key=lambda r: r[1])
baseline = dataset[-1][1]
print('\nResults:\n')
for i, (name, sec_per_req) in enumerate(dataset):
req_per_sec = round_to_int(Decimal(1) / sec_per_req)
us_per_req = (sec_per_req * Decimal(10 ** 6))
factor = round_to_int(baseline / sec_per_req)
print('{3}. {0:.<20s}{1:.>06d} req/sec or {2: >3.2f} μs/req ({4}x)'.
format(name, req_per_sec, us_per_req, i + 1, factor))
if heapy and args.stat_memory:
print()
for name, _, heap_diff in datasets[0]:
title = 'Memory change induced by ' + name
print()
print('=' * len(title))
print(title)
print('=' * len(title))
print(heap_diff)
print()
if __name__ == '__main__':
main()
| [
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| |
b3df535e0bf14619764330e153f9691f97ebfe7a | ae3df32afc258c80cb2ce504ce87fa5bb7740ea7 | /main/apps.py | a1c166fbd7a6ef9873d13d2341e00132f5d8b9dd | []
| no_license | chensandiego/elast-python | 622251d806b947899d74dc064c19193b418ac505 | 8c28a47acfc5ef540a017abcd786cf815591b163 | refs/heads/master | 2020-12-24T05:40:38.917432 | 2016-08-08T09:16:39 | 2016-08-08T09:16:39 | 65,190,208 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 240 | py | from __future__ import unicode_literals
from django.apps import AppConfig
from elasticsearch_dsl.connections import connections
class MainConfig(AppConfig):
name = 'main'
def ready(self):
connections.create_connection() | [
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]
| |
03077baac22100638f1f73d6914d61d5790e636d | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03804/s359892659.py | cf5a2561cd5c03f89d2bfa0dc2d375e6139544c1 | []
| no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 477 | py | n, m = map(int, input().split())
a = [""] * n
b = [""] * m
for i in range(n):
a[i] = input()
for i in range(m):
b[i] = input()
for i in range(n):
for j in range(n):
if i + m > n or j + m > n:
continue
flag = True
for k in range(m):
for l in range(m):
if a[i + k][j + l] != b[k][l]:
flag = False
if flag is True:
print("Yes")
exit(0)
print("No")
| [
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| |
d5ca2bcbd5de3c1b9c9bac46eab8058ddbdaa268 | 15f321878face2af9317363c5f6de1e5ddd9b749 | /solutions_python/Problem_200/1005.py | d1785f14535df9f9f8739a47a08da1ea17308063 | []
| 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 | 405 | py | import numpy as np
def solve(n, j):
front = 0
for i in range(1, len(n)):
if n[-i-1] > n[-i]:
n[-i-1] -= 1
front = i
if front:
n[-front:] = 9
if not n[0]:
n = n[1:]
print('Case #{}: {}'.format(j+1, ''.join(map(str, n))))
def main():
T = int(input())
for i in range(T):
solve(np.array(list(map(int, list(input())))), i)
if __name__ == '__main__':
main()
| [
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| |
4b4fb06a5c7779a15bbde10c3ca456691d7aa16b | 2ed6ad4a736879a47d192159da45ca56610c089a | /tests/test_utils.py | 5322f50e74f0e19c141fd1adbdd2a5b05e92fb39 | [
"MIT"
]
| permissive | poonyisaTH/gsheets-db-api | a82bd35984766697757cc96aa74a1281d948f019 | f023b32986d4da9a501fca8d435f2b6edc153353 | refs/heads/master | 2023-05-29T15:01:10.604324 | 2021-02-17T20:59:41 | 2021-02-17T20:59:41 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,952 | py | # -*- coding: utf-8 -*-
import unittest
from moz_sql_parser import parse
import pyparsing
from .context import format_gsheet_error, format_moz_error
class UtilsTestSuite(unittest.TestCase):
def test_format_moz_error(self):
query = 'SELECT ))) FROM table'
with self.assertRaises(pyparsing.ParseException) as context:
parse(query)
result = format_moz_error(query, context.exception)
expected = (
'SELECT ))) FROM table\n'
' ^\n'
'Expected {{expression1 [{[as] column_name1}]} | "*"} '
'(at char 7), (line:1, col:8)'
)
self.assertEqual(result, expected)
def test_format_gsheet_error(self):
query = 'SELECT A + B FROM "http://docs.google.com"'
translated_query = 'SELECT A + B'
errors = [{
'reason': 'invalid_query',
'detailed_message': (
"Invalid query: Can't perform the function sum on values that "
"are not numbers"
),
'message': 'INVALID_QUERY',
}]
result = format_gsheet_error(query, translated_query, errors)
expected = (
'Original query:\n'
'SELECT A + B FROM "http://docs.google.com"\n\n'
'Translated query:\n'
'SELECT A + B\n\n'
'Error:\n'
"Invalid query: Can't perform the function sum on values that "
"are not numbers"
)
self.assertEqual(result, expected)
def test_format_gsheet_error_caret(self):
query = 'SELECT A IS NULL FROM "http://docs.google.com"'
translated_query = 'SELECT A IS NULL'
errors = [{
'reason': 'invalid_query',
'detailed_message': (
'Invalid query: PARSE_ERROR: Encountered " "is" "IS "" at '
'line 1, column 10.\nWas expecting one of:\n'
' <EOF> \n'
' "where" ...\n'
' "group" ...\n'
' "pivot" ...\n'
' "order" ...\n'
' "skipping" ...\n'
' "limit" ...\n'
' "offset" ...\n'
' "label" ...\n'
' "format" ...\n'
' "options" ...\n'
' "," ...\n'
' "*" ...\n'
' "+" ...\n'
' "-" ...\n'
' "/" ...\n'
' "%" ...\n'
' "*" ...\n'
' "/" ...\n'
' "%" ...\n'
' "+" ...\n'
' "-" ...\n'
' '
),
'message': 'INVALID_QUERY',
}]
result = format_gsheet_error(query, translated_query, errors)
expected = (
'Original query:\n'
'SELECT A IS NULL FROM "http://docs.google.com"\n\n'
'Translated query:\n'
'SELECT A IS NULL\n\n'
'Error:\n'
'SELECT A IS NULL\n'
' ^\n'
'Invalid query: PARSE_ERROR: Encountered " "is" "IS "" at line 1, '
'column 10.\n'
'Was expecting one of:\n'
' <EOF> \n'
' "where" ...\n'
' "group" ...\n'
' "pivot" ...\n'
' "order" ...\n'
' "skipping" ...\n'
' "limit" ...\n'
' "offset" ...\n'
' "label" ...\n'
' "format" ...\n'
' "options" ...\n'
' "," ...\n'
' "*" ...\n'
' "+" ...\n'
' "-" ...\n'
' "/" ...\n'
' "%" ...\n'
' "*" ...\n'
' "/" ...\n'
' "%" ...\n'
' "+" ...\n'
' "-" ...'
)
self.assertEqual(result, expected)
| [
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| |
26c3ed7037c5e7c99c281a9602db0848de390886 | ce55c319f5a78b69fefc63595d433864a2e531b5 | /前后端分离-vue-DRF/houfen_DRF-projects/15day周末作业/booklogin/user/views.py | ea76a3b29e6788ab22cbcb4e135039d76dd5f722 | []
| no_license | Suijng/1809_data | a072c875e8746190e3b715e53f1afe3323f4666b | 45f8a57089f5c30ccc1a3cddb03b76dc95355417 | refs/heads/master | 2022-12-21T12:38:30.458291 | 2019-09-27T01:14:41 | 2019-09-27T01:14:41 | 211,207,071 | 0 | 0 | null | 2022-11-22T03:16:18 | 2019-09-27T00:55:21 | HTML | UTF-8 | Python | false | false | 7,016 | py | from django.shortcuts import render
# Create your views here.
from rest_framework.views import APIView
from rest_framework.response import Response
from user.serializers import ResgsterUserSerializer,CategorySerializer,\
BookDetailSerializer,BookSerializer,\
ChpaterListSerializer,ChpaterDetailSerializer
from user.models import User,Token,Category,Book,Chpater
from utils.pagination import MyPageNumberPagination
# 注册
# class RegisterView(APIView):
#
# def post(self,request,*args,**kwargs):
# ret = {
# 'code':1,
# 'msg':'注册成功'
# }
# # 获取post请求参数
# data = request.data
# # 序列化请求参数
# ser = ResgsterUserSerializer(data=data)
# if ser.is_valid(): # 验证字段
# print(ser.validated_data)
# ser.save()
# else:
# # 验证失败打印错误信息
# print(ser.errors)
# ret['code'] = 0
# ret['msg'] = '参数错误,注册失败'
#
# return Response(ret)
from rest_framework.viewsets import GenericViewSet
from rest_framework.mixins import CreateModelMixin,ListModelMixin,RetrieveModelMixin
from rest_framework import status
# 注册
class RegisterView(CreateModelMixin,GenericViewSet):
queryset = User.objects.all()
serializer_class = ResgsterUserSerializer
# 重写内部创建方法
def create(self, request, *args, **kwargs):
ret = {
'code': 1,
'msg': '注册成功'
}
serializer = self.get_serializer(data=request.data)
if serializer.is_valid():
self.perform_create(serializer)
headers = self.get_success_headers(serializer.data)
return Response(ret,status=status.HTTP_201_CREATED, headers=headers)
else:
# 验证失败打印错误信息
print(serializer.errors)
ret['code'] = 0
ret['msg'] = '参数错误,注册失败'
return Response(ret)
#************************** 登录
# 生成token
import time,hashlib
def get_token(name,password):
add_time = str(int(time.time() * 1000))
md5_obj = hashlib.md5(add_time.encode('utf8'))
md5_obj.update(name.encode('utf8'))
md5_obj.update(password.encode('utf8'))
return md5_obj.hexdigest()
# 登录
class LoginView(APIView):
def post(self,request,*args,**kwargs):
ret = {
'code': 1,
'msg': '登录成功'
}
# 获取post请求
data = request.data
# 获取用户名
name = data['name']
# 获取密码
password = data['password']
try:
obj = User.objects.filter(name=name).first()
if obj:
# 用户存在的
if obj.password == password:
# 登录成功 生成登录标识
token = get_token(name,password)
Token.objects.update_or_create(user=obj,defaults={'token':token})
ret['token'] = token
else:
# 密码错误
ret['msg'] = '账号或密码错误'
ret['code'] = 0
else:
ret['msg'] = '该用户不存在'
ret['code'] = 0
except Exception as e:
print(e)
ret['msg'] = '捕获异常'
ret['code'] = 0
return Response(ret)
#****************** 书籍分类
class CategoryView(ListModelMixin,RetrieveModelMixin,GenericViewSet):
queryset = Category.objects.all()
serializer_class = CategorySerializer
pagination_class = MyPageNumberPagination
def get_serializer_class(self):
# 动态设置序列化的类
if self.action == 'list':
return CategorySerializer
elif self.action == 'retrieve':
return BookSerializer
# 给前端展示的字典套列表套字典
def list(self, request, *args, **kwargs):
print(request.version) # 打印版本
ret = {
'code': 1,
}
queryset = self.filter_queryset(self.get_queryset())
# 没有分页展示所有数据
serializer = self.get_serializer(queryset, many=True)
ret['data'] = serializer.data
return Response(ret)
#***** 书籍分类下的书
def retrieve(self, request, *args, **kwargs):
category_id = kwargs.get('pk')
if category_id:
books = Book.objects.filter(category=category_id)
# 调用paginate_queryset方法获取当前分页数据
page = self.paginate_queryset(books)
# 通过判断page结果 判断是否使用了分页
if page is not None:
serializer = self.get_serializer(page,many=True)
return self.get_paginated_response(serializer.data)
#******** 书籍详情视图 获取每本book书的url地址
class BookDetailView(RetrieveModelMixin,GenericViewSet):
queryset = Book.objects.all()
serializer_class = BookDetailSerializer
def retrieve(self, request, *args, **kwargs):
instance = self.get_object()
serializer = self.get_serializer(instance)
ret = {'code':1,'data':serializer.data}
return Response(ret)
# 章节列表视图
from utils.authenandpermission import MyPermission,MyAuthentication
class ChapterView(ListModelMixin,RetrieveModelMixin,GenericViewSet):
queryset = Chpater.objects.all()
serializer_class = ChpaterListSerializer
pagination_class = MyPageNumberPagination
def get_serializer_class(self):
if self.action == 'list':
return ChpaterListSerializer
elif self.action == 'retrieve':
return ChpaterDetailSerializer
# 认证
def get_authenticators(self):
if self.kwargs.get('pk'):
# 根据章节id获取，章节详情
return [MyAuthentication(),]
return []
# 权限
def get_permissions(self):
if self.kwargs.get('pk'):
# 根据章节id获取，章节详情，返回权限类
return [MyPermission(), ]
return []
def list(self, request, *args, **kwargs):
book_id = kwargs.get('bookid')
if book_id:
chpaters = Chpater.objects.filter(book=book_id)
# 调用paginate_queryset方法获取当前分页数据
page = self.paginate_queryset(chpaters)
# 通过判断page结果 判断是否使用了分页
if page is not None:
serializer = self.get_serializer(page, many=True)
return self.get_paginated_response(serializer.data)
def retrieve(self, request, *args, **kwargs):
# 根据章节的id获取章节详情信息
instance = self.get_object()
serializer = self.get_serializer(instance)
ret = {'code':1,'data':serializer.data}
return Response(ret)
| [
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]
| |
bcf4694b4be4de84974a88f8c1e0c68664a56527 | 4913fb7fd32c3dd0da53af7a012569ec2254b35a | /59.集合数据的操作.py | 75c83dbf6875876bad10856772cd2746191883a6 | []
| no_license | puhaoran12/python_note | 8a21954050ba3126f2ef6d5d1e4a2904df954b9b | b807e7b7dd90c87cee606f50421400c8f3d0ba03 | refs/heads/master | 2023-07-07T20:20:04.546541 | 2021-08-21T02:17:12 | 2021-08-21T02:17:12 | 398,439,725 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 325 | py | #交集intersection() 或者 &
s1={10,20,30}
s2={30,40,50}
print(s1.intersection(s2))
print(s1 & s2)
#并集union() 或者 |
print(s1.union(s2))
print(s1 | s2)
#差集difference() 或者 -
print(s1.difference(s2))
print(s1 - s2)
#对称差集symmetric_difference() 或者 ^
print(s1.symmetric_difference(s2))
print(s1 ^ s2)
| [
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]
| |
cbc7ff8ebf0f55d2d54140505ce153702a872ce4 | 9784a90cac667e8e0aaba0ca599b4255b215ec67 | /convert_models.py | d3b743be02b1e3c57c11c482699a1440c12daa44 | [
"MIT"
]
| permissive | osmr/imgclsmob | d2f48f01ca541b20119871393eca383001a96019 | f2993d3ce73a2f7ddba05da3891defb08547d504 | refs/heads/master | 2022-07-09T14:24:37.591824 | 2021-12-14T10:15:31 | 2021-12-14T10:15:31 | 140,285,687 | 3,017 | 624 | MIT | 2022-07-04T15:18:37 | 2018-07-09T12:57:46 | Python | UTF-8 | Python | false | false | 87,933 | py | """
Script for converting models between frameworks (MXNet, Gluon, PyTroch, Chainer, Keras, TensorFlow).
"""
import argparse
import logging
import re
import numpy as np
from common.logger_utils import initialize_logging
def parse_args():
parser = argparse.ArgumentParser(description="Convert models (Gluon/PyTorch/Chainer/MXNet/Keras/TF/TF2)",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"--src-fwk",
type=str,
required=True,
help="source model framework name")
parser.add_argument(
"--dst-fwk",
type=str,
required=True,
help="destination model framework name")
parser.add_argument(
"--src-model",
type=str,
required=True,
help="source model name")
parser.add_argument(
"--dst-model",
type=str,
required=True,
help="destination model name")
parser.add_argument(
"--src-params",
type=str,
default="",
help="source model parameter file path")
parser.add_argument(
"--dst-params",
type=str,
default="",
help="destination model parameter file path")
parser.add_argument(
"--load-ignore-extra",
action="store_true",
help="ignore extra layers in the source PyTroch model")
parser.add_argument(
"--remove-module",
action="store_true",
help="enable if stored PyTorch model has module")
parser.add_argument(
"--src-num-classes",
type=int,
default=1000,
help="number of classes for source model")
parser.add_argument(
"--src-in-channels",
type=int,
default=3,
help="number of input channels for source model")
parser.add_argument(
"--dst-num-classes",
type=int,
default=1000,
help="number of classes for destination model")
parser.add_argument(
"--dst-in-channels",
type=int,
default=3,
help="number of input channels for destination model")
parser.add_argument(
"--model-type",
type=str,
default="image",
help="model type (image or audio)")
parser.add_argument(
"--save-dir",
type=str,
default="",
help="directory of saved models and log-files")
parser.add_argument(
"--logging-file-name",
type=str,
default="train.log",
help="filename of training log")
args = parser.parse_args()
return args
def prepare_src_model(src_fwk,
src_model,
src_params_file_path,
dst_fwk,
ctx,
use_cuda,
load_ignore_extra=False,
remove_module=False,
num_classes=None,
in_channels=None):
ext_src_param_keys = None
ext_src_param_keys2 = None
src_net = None
if src_fwk == "gluon":
from gluon.utils import prepare_model as prepare_model_gl
src_net = prepare_model_gl(
model_name=src_model,
use_pretrained=False,
pretrained_model_file_path=src_params_file_path,
dtype=np.float32,
tune_layers="",
classes=(num_classes if num_classes > 0 else None),
in_channels=in_channels,
ctx=ctx)
src_params = src_net._collect_params_with_prefix()
src_param_keys = list(src_params.keys())
if src_model in ["oth_resnet50_v1", "oth_resnet101_v1", "oth_resnet152_v1", "oth_resnet50_v1b",
"oth_resnet101_v1b", "oth_resnet152_v1b"]:
src_param_keys = [key for key in src_param_keys if
not (key.startswith("features.") and key.endswith(".bias"))]
if src_model in ["oth_resnet50_v1", "oth_resnet101_v1", "oth_resnet152_v1", "oth_resnet50_v1b",
"oth_resnet101_v1b", "oth_resnet152_v1b"]:
src_param_keys = [key for key in src_param_keys if
not (key.startswith("features.") and key.endswith(".bias"))]
if src_model.startswith("wrn20_10_1bit") or src_model.startswith("wrn20_10_32bit"):
src_param_keys = [key for key in src_param_keys if
not (key.startswith("features.") and
(key.endswith(".bn.gamma") or key.endswith(".bn.beta")))]
if dst_fwk == "chainer":
src_param_keys_ = src_param_keys.copy()
src_param_keys = [key for key in src_param_keys_ if (not key.endswith(".running_mean")) and
(not key.endswith(".running_var"))]
ext_src_param_keys = [key for key in src_param_keys_ if (key.endswith(".running_mean")) or
(key.endswith(".running_var"))]
if src_model in ["condensenet74_c4_g4", "condensenet74_c8_g8"]:
src_param_keys_ = src_param_keys.copy()
src_param_keys = [key for key in src_param_keys_ if (not key.endswith(".index"))]
ext_src_param_keys2 = [key for key in src_param_keys_ if (key.endswith(".index"))]
elif src_model.startswith("xdensenet"):
src_param_keys_ = src_param_keys.copy()
src_param_keys = [key for key in src_param_keys_ if (not key.endswith(".mask"))]
ext_src_param_keys2 = [key for key in src_param_keys_ if (key.endswith(".mask"))]
elif src_model.startswith("jasper") or src_model.startswith("quartznet"):
src_param_keys_ = src_param_keys.copy()
src_param_keys = [key for key in src_param_keys_ if (not key.endswith(".window")) and
(not key.endswith(".fb"))]
ext_src_param_keys2 = [key for key in src_param_keys_ if (key.endswith(".window")) or
(key.endswith(".fb"))]
elif src_fwk == "pytorch":
from pytorch.utils import prepare_model as prepare_model_pt
src_net = prepare_model_pt(
model_name=src_model,
use_pretrained=False,
pretrained_model_file_path=src_params_file_path,
use_cuda=use_cuda,
use_data_parallel=False,
load_ignore_extra=load_ignore_extra,
num_classes=(num_classes if num_classes > 0 else None),
in_channels=in_channels,
remove_module=remove_module)
src_params = src_net.state_dict()
src_param_keys = list(src_params.keys())
if dst_fwk != "pytorch":
src_param_keys = [key for key in src_param_keys if not key.endswith("num_batches_tracked")]
if src_model in ["oth_shufflenetv2_wd2"]:
src_param_keys = [key for key in src_param_keys if not key.startswith("network.0.")]
if src_model.startswith("oth_dla"):
src1 = list(filter(re.compile("\.project").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src2 = []
for i in range(2, 6):
src1_i = list(filter(re.compile("level{}".format(i)).search, src1))
if len(src1_i) == 0:
continue
max_len = max([len(k) for k in src1_i])
pattern_i = [k for k in src1_i if len(k) == max_len][0][:-21]
src2_i = list(filter(re.compile(pattern_i).search, src1))
src2 += src2_i
src_param_keys = src2 + src1n
elif src_fwk == "mxnet":
import mxnet as mx
src_sym, src_arg_params, src_aux_params = mx.model.load_checkpoint(
prefix=src_params_file_path,
epoch=0)
src_params = {}
src_params.update(src_arg_params)
src_params.update(src_aux_params)
src_param_keys = list(src_params.keys())
elif src_fwk == "tensorflow":
# import tensorflow as tf
# from tensorflow_.utils import prepare_model as prepare_model_tf
# src_net = prepare_model_tf(
# model_name=src_model,
# classes=num_classes,
# use_pretrained=False,
# pretrained_model_file_path=src_params_file_path)
# src_param_keys = [v.name for v in tf.global_variables()]
# src_params = {v.name: v for v in tf.global_variables()}
src_net = None
src_params = dict(np.load(src_params_file_path))
src_param_keys = list(src_params.keys())
elif (src_fwk == "tf2") and (dst_fwk == "tfl"):
import tensorflow as tf
from tensorflow2.utils import prepare_model as prepare_model_tf2
gpus = tf.config.experimental.list_physical_devices("GPU")
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
src_net = prepare_model_tf2(
model_name=src_model,
use_pretrained=True,
pretrained_model_file_path="")
batch_size = 1
input_shape = ((batch_size, 3, src_net.in_size[0], src_net.in_size[1]) if
src_net.data_format == "channels_first" else
(batch_size, src_net.in_size[0], src_net.in_size[1], 3))
src_net(tf.random.normal(input_shape))
src_params = None
src_param_keys = None
else:
raise ValueError("Unsupported src fwk: {}".format(src_fwk))
return src_params, src_param_keys, ext_src_param_keys, ext_src_param_keys2, src_net
def prepare_dst_model(dst_fwk,
dst_model,
src_fwk,
ctx,
use_cuda,
num_classes=None,
in_channels=None,
model_type="image"):
if dst_fwk == "gluon":
from gluon.utils import prepare_model as prepare_model_gl
dst_net = prepare_model_gl(
model_name=dst_model,
use_pretrained=False,
pretrained_model_file_path="",
dtype=np.float32,
tune_layers="",
classes=(num_classes if num_classes > 0 else None),
in_channels=in_channels,
ctx=ctx)
dst_params = dst_net._collect_params_with_prefix()
dst_param_keys = list(dst_params.keys())
elif dst_fwk == "pytorch":
from pytorch.utils import prepare_model as prepare_model_pt
dst_net = prepare_model_pt(
model_name=dst_model,
use_pretrained=False,
pretrained_model_file_path="",
use_cuda=use_cuda,
use_data_parallel=False,
num_classes=(num_classes if num_classes > 0 else None),
in_channels=in_channels)
dst_params = dst_net.state_dict()
dst_param_keys = list(dst_params.keys())
if src_fwk != "pytorch":
dst_param_keys = [key for key in dst_param_keys if not key.endswith("num_batches_tracked")]
elif dst_fwk == "chainer":
from chainer_.utils import prepare_model as prepare_model_ch
dst_net = prepare_model_ch(
model_name=dst_model,
use_pretrained=False,
pretrained_model_file_path="")
dst_params = {i[0]: i[1] for i in dst_net.namedparams()}
dst_param_keys = list(dst_params.keys())
elif dst_fwk == "keras":
from keras_.utils import prepare_model as prepare_model_ke
dst_net = prepare_model_ke(
model_name=dst_model,
use_pretrained=False,
pretrained_model_file_path="")
# dst_param_keys = list(dst_net._arg_names) + list(dst_net._aux_names)
dst_param_keys = [v.name for v in dst_net.weights]
dst_params = {}
for layer in dst_net.layers:
if layer.name:
for weight in layer.weights:
if weight.name:
dst_params.setdefault(weight.name, []).append(weight)
dst_params[weight.name] = (layer, weight)
elif dst_fwk == "tensorflow":
import tensorflow as tf
from tensorflow_.utils import prepare_model as prepare_model_tf
dst_net = prepare_model_tf(
model_name=dst_model,
use_pretrained=False,
pretrained_model_file_path="")
dst_param_keys = [v.name for v in tf.global_variables()]
dst_params = {v.name: v for v in tf.global_variables()}
elif dst_fwk == "tf2":
import tensorflow as tf
from tensorflow2.utils import prepare_model as prepare_model_tf2
gpus = tf.config.experimental.list_physical_devices("GPU")
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
dst_net = prepare_model_tf2(
model_name=dst_model,
use_pretrained=False,
pretrained_model_file_path="")
batch_size = 1
if model_type == "image":
input_shape = ((batch_size, 3, dst_net.in_size[0], dst_net.in_size[1]) if
dst_net.data_format == "channels_first" else
(batch_size, dst_net.in_size[0], dst_net.in_size[1], 3))
dst_net(tf.random.normal(input_shape))
else:
seq_len = 100 * 640
# input_shape = ((batch_size, dst_net.in_channels, seq_len) if
# dst_net.data_format == "channels_first" else
# (batch_size, seq_len, dst_net.in_channels))
input_shape = (batch_size, seq_len)
x_len = tf.convert_to_tensor(np.array([seq_len - 0], dtype=np.long))
dst_net(tf.random.normal(input_shape), x_len)
dst_param_keys = [v.name for v in dst_net.weights]
dst_params = {v.name: v for v in dst_net.weights}
elif dst_fwk == "tfl":
dst_net = None
dst_params = None
dst_param_keys = None
else:
raise ValueError("Unsupported dst fwk: {}".format(dst_fwk))
return dst_params, dst_param_keys, dst_net
def convert_mx2gl(dst_net,
dst_params_file_path,
dst_params,
dst_param_keys,
src_params,
src_param_keys,
src_model,
ctx):
if src_model in ["crunet56", "crunet116"]:
src_param_keys.sort()
src_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
src_param_keys = [re.sub("^conv", "features.", key) for key in src_param_keys]
src_param_keys = [re.sub("^fc6", "output.1.", key) for key in src_param_keys]
src_param_keys = [re.sub('_c1x1-a', '.body.conv1.', key) for key in src_param_keys]
src_param_keys = [re.sub('_c3x3-b', '.body.conv2A.', key) for key in src_param_keys]
src_param_keys = [re.sub('_c1x1-b', '.body.conv2B.', key) for key in src_param_keys]
src_param_keys = [re.sub('_c1x1-c', '.body.conv3.', key) for key in src_param_keys]
src_param_keys = [re.sub('_x__x_1x1_bases\[dim3\]_weight$', '_x__1.body.conv1.convT.weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__x_3x3_bases\[dim21\]_weight$', '_x__1.body.conv2.convT.weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__\(1\)_1x1_bases\[dim3\]_weight$', '_x__1.body.conv1.convQ.weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__\(1\)_3x3_bases\[dim21\]_weight$', '_x__1.body.conv2.convQ.weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__\(2\)_1x1_bases\[dim3\]_weight$', '_x__7.body.conv1.convQ.weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__\(2\)_3x3_bases\[dim21\]_weight$', '_x__7.body.conv2.convQ.weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__\(3\)_1x1_bases\[dim3\]_weight$', '_x__14.body.conv1.convQ.weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__\(3\)_3x3_bases\[dim21\]_weight$', '_x__14.body.conv2.convQ.weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_c1x1-w\(s\/2\)', '.input_convZ.', key) for key in src_param_keys]
src_param_keys = [re.sub('_c1x1-w_weight$', '.input_convZ.conv.weight', key) for key in src_param_keys]
src_param_keys = [re.sub('_c1x1-w\(s\/1\)', '.input_conv.', key) for key in src_param_keys]
src_param_keys = [re.sub('_c1x1-w\(s\/key\)', '.identity_conv.', key) for key in src_param_keys]
src_param_keys = [re.sub('__conv_weight$', '.conv.weight', key) for key in src_param_keys]
src_param_keys = [re.sub('__bn__bn_beta$', '.bn.beta', key) for key in src_param_keys]
src_param_keys = [re.sub('__bn__bn_gamma$', '.bn.gamma', key) for key in src_param_keys]
src_param_keys = [re.sub('__bn__bn_moving_mean$', '.bn.running_mean', key) for key in src_param_keys]
src_param_keys = [re.sub('__bn__bn_moving_var$', '.bn.running_var', key) for key in src_param_keys]
src_param_keys = [re.sub('1_x_1__relu-sp__bn_', '1_x_1.conv.bnA.', key) for key in src_param_keys]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
dst_param_keys.sort()
dst_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
src_param_keys = [re.sub("^features\.", "conv", key) for key in src_param_keys]
src_param_keys = [re.sub('^output\.1\.', 'fc6', key) for key in src_param_keys]
src_param_keys = [re.sub('_x__1\.body\.conv1\.convT\.weight$', '_x__x_1x1_bases[dim3]_weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__1\.body\.conv2\.convT\.weight$', '_x__x_3x3_bases[dim21]_weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__1\.body\.conv1\.convQ\.weight$', '_x__(1)_1x1_bases[dim3]_weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__1\.body\.conv2\.convQ\.weight$', '_x__(1)_3x3_bases[dim21]_weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__7\.body\.conv1\.convQ\.weight$', '_x__(2)_1x1_bases[dim3]_weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__7\.body\.conv2\.convQ\.weight$', '_x__(2)_3x3_bases[dim21]_weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__14\.body\.conv1\.convQ\.weight$', '_x__(3)_1x1_bases[dim3]_weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('_x__14\.body\.conv2\.convQ\.weight$', '_x__(3)_3x3_bases[dim21]_weight', key)
for key in src_param_keys]
src_param_keys = [re.sub('\.body\.conv1\.', '_c1x1-a', key) for key in src_param_keys]
src_param_keys = [re.sub('\.body\.conv2A\.', '_c3x3-b', key) for key in src_param_keys]
src_param_keys = [re.sub('\.body\.conv2B\.', '_c1x1-b', key) for key in src_param_keys]
src_param_keys = [re.sub('\.body\.conv3\.', '_c1x1-c', key) for key in src_param_keys]
src_param_keys = [re.sub('\.input_convZ\.conv\.weight$', '_c1x1-w_weight', key) for key in src_param_keys]
src_param_keys = [re.sub('\.input_convZ\.', '_c1x1-w(s/2)', key) for key in src_param_keys]
src_param_keys = [re.sub('\.input_conv\.', '_c1x1-w(s/1)', key) for key in src_param_keys]
src_param_keys = [re.sub('\.identity_conv\.', '_c1x1-w(s/key)', key) for key in src_param_keys]
src_param_keys = [re.sub('\.conv\.weight$', '__conv_weight', key) for key in src_param_keys]
src_param_keys = [re.sub('\.bn\.beta$', '__bn__bn_beta', key) for key in src_param_keys]
src_param_keys = [re.sub('\.bn\.gamma$', '__bn__bn_gamma', key) for key in src_param_keys]
src_param_keys = [re.sub('\.bn\.running_mean$', '__bn__bn_moving_mean', key) for key in src_param_keys]
src_param_keys = [re.sub('\.bn\.running_var$', '__bn__bn_moving_var', key) for key in src_param_keys]
src_param_keys = [re.sub('1_x_1\.conv\.bnA\.', '1_x_1__relu-sp__bn_', key) for key in src_param_keys]
dst_i = 0
for src_i, src_key in enumerate(src_param_keys):
dst_key = dst_param_keys[dst_i]
for tt in range(10):
if (dst_key.split('.')[-1].split('_')[-1] == src_key.split('_')[-1]) and\
(dst_params[dst_key].shape == src_params[src_key].shape):
break
assert (dst_key.split('.')[-1].split('_')[-1] == "weight")
dst_i += 1
dst_key = dst_param_keys[dst_i]
dst_i += 1
assert (dst_key.split('.')[-1].split('_')[-1] == src_key.split('_')[-1])
assert (dst_params[dst_key].shape == src_params[src_key].shape), \
"src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
src_key, dst_key, src_params[src_key].shape, dst_params[dst_key].shape)
dst_params[dst_key]._load_init(src_params[src_key], ctx)
for param in dst_net.collect_params().values():
if param._data is not None:
continue
print("param={}".format(param))
param.initialize(ctx=ctx)
dst_net.save_parameters(dst_params_file_path)
return
elif src_model in ["igcv3_w1"]:
src_param_keys = [key.replace("seq-", "features.") for key in src_param_keys]
src_param_keys = [key.replace("fc_", "output.1.") for key in src_param_keys]
src_param_keys = [key.replace('-batchnorm_beta', '.bn.beta') for key in src_param_keys]
src_param_keys = [key.replace('-batchnorm_gamma', '.bn.gamma') for key in src_param_keys]
src_param_keys = [key.replace('-batchnorm_moving_mean', '.bn.running_mean') for key in src_param_keys]
src_param_keys = [key.replace('-batchnorm_moving_var', '.bn.running_var') for key in src_param_keys]
src_param_keys = [key.replace('-conv2d_weight', '.conv.weight') for key in src_param_keys]
src_param_keys = [key.replace('first-3x3-conv', 'features.A') for key in src_param_keys]
src_param_keys = [key.replace('last-1x1-conv', 'features.B') for key in src_param_keys]
src_param_keys = [key.replace('-exp', '.conv1') for key in src_param_keys]
src_param_keys = [key.replace('-depthwise', '.conv2') for key in src_param_keys]
src_param_keys = [key.replace('-linear', '.conv3') for key in src_param_keys]
src_param_keys = [key.replace("-block", ".block") for key in src_param_keys]
dst_param_keys = [key.replace('features.0.', 'features.A.') for key in dst_param_keys]
dst_param_keys = [key.replace('features.6.', 'features.B.') for key in dst_param_keys]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
dst_param_keys.sort()
dst_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
src_param_keys = [key.replace('.bn.beta', '-batchnorm_beta') for key in src_param_keys]
src_param_keys = [key.replace('.bn.gamma', '-batchnorm_gamma') for key in src_param_keys]
src_param_keys = [key.replace('.bn.running_mean', '-batchnorm_moving_mean') for key in src_param_keys]
src_param_keys = [key.replace('.bn.running_var', '-batchnorm_moving_var') for key in src_param_keys]
src_param_keys = [key.replace('.conv.weight', '-conv2d_weight') for key in src_param_keys]
src_param_keys = [key.replace('features.A', 'first-3x3-conv') for key in src_param_keys]
src_param_keys = [key.replace('features.B', 'last-1x1-conv') for key in src_param_keys]
src_param_keys = [key.replace('.conv1', '-exp') for key in src_param_keys]
src_param_keys = [key.replace('.conv2', '-depthwise', ) for key in src_param_keys]
src_param_keys = [key.replace('.conv3', '-linear') for key in src_param_keys]
src_param_keys = [key.replace("features.", "seq-") for key in src_param_keys]
src_param_keys = [key.replace("output.1.", "fc_") for key in src_param_keys]
src_param_keys = [key.replace(".block", "-block") for key in src_param_keys]
dst_param_keys = [key.replace('features.A.', 'features.0.') for key in dst_param_keys]
dst_param_keys = [key.replace('features.B.', 'features.6.') for key in dst_param_keys]
elif src_model in ["preresnet269b"]:
dst_net.features[1][0].body.conv1a.bn.initialize(ctx=ctx, verbose=True, force_reinit=True)
dst1 = list(filter(re.compile("^features.1.0.body.conv1.bn.").search, dst_param_keys))
dst_param_keys = [key for key in dst_param_keys if key not in dst1]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
src_param_keys = [re.sub('^classifier_', "output.", key) for key in src_param_keys]
src_param_keys = [re.sub('^res', "features.", key) for key in src_param_keys]
src_param_keys = [re.sub('_conv1_weight$', '_conv1_aweight', key) for key in src_param_keys]
src_param_keys = [re.sub('_conv2_weight$', '_conv2_aweight', key) for key in src_param_keys]
src_param_keys = [re.sub('_conv3_weight$', '_conv3_aweight', key) for key in src_param_keys]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
dst_param_keys.sort()
dst_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
src_param_keys = [re.sub("^output\.", "classifier_", key) for key in src_param_keys]
src_param_keys = [re.sub("^features\.", "res", key) for key in src_param_keys]
src_param_keys = [re.sub('_conv1_aweight$', '_conv1_weight', key) for key in src_param_keys]
src_param_keys = [re.sub('_conv2_aweight$', '_conv2_weight', key) for key in src_param_keys]
src_param_keys = [re.sub('_conv3_aweight$', '_conv3_weight', key) for key in src_param_keys]
for src_i, (src_key, dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
assert (dst_key.split('.')[-1].split('_')[-1] == src_key.split('_')[-1]), \
"src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
src_key, dst_key, src_params[src_key].shape, dst_params[dst_key].shape)
assert (dst_params[dst_key].shape == src_params[src_key].shape), \
"src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
src_key, dst_key, src_params[src_key].shape, dst_params[dst_key].shape)
dst_params[dst_key]._load_init(src_params[src_key], ctx)
for param in dst_net.collect_params().values():
if param._data is not None:
continue
print("param={}".format(param))
param.initialize(ctx=ctx)
dst_net.save_parameters(dst_params_file_path)
def convert_gl2ch(dst_net,
dst_params_file_path,
dst_params,
dst_param_keys,
src_params,
src_param_keys,
ext_src_param_keys,
ext_src_param_keys2,
src_model):
if src_model.startswith("diares") or src_model.startswith("diapreres"):
src1 = list(filter(re.compile("^features\.[0-9]*\.\d*[1-9]\d*\.attention").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src_param_keys = src1n
assert (len(src_param_keys) == len(dst_param_keys))
if src_model.startswith("quartznet") or src_model.startswith("jasper"):
dst_param_keys = [key.replace("features/final_block/", "features/zfinal_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/W", "/weight") for key in dst_param_keys]
dst_param_keys = [key.replace("/post_activ/", "/stageN/post_activ/") for key in dst_param_keys]
dst_param_keys = [key.replace("/features/body/", "/features/zbody/") for key in dst_param_keys]
dst_param_keys = [key.replace("features/final_postactiv/", "features/stageN/final_postactiv/") for key in dst_param_keys]
dst_param_keys = [key.replace("features/final_block/", "features/stageN/final_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/final_block/", "/zfinal_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("features/final_conv/", "features/stageN/final_conv/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stem1_unit/", "/stage0/stem1_unit/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stem2_unit/", "/stage0/stem2_unit/") for key in dst_param_keys]
if not src_model.startswith("ibppose_coco"):
dst_param_keys = [key.replace("/hg/", "/stage1_hg/") for key in dst_param_keys]
if src_model.startswith("centernet"):
dst_param_keys = [key.replace("/unit", "/a_unit") for key in dst_param_keys]
dst_param_keys = [key.replace("/reg_block/", "/z_reg_block/") for key in dst_param_keys]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
dst_param_keys.sort()
dst_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
if src_model.startswith("quartznet") or src_model.startswith("jasper"):
dst_param_keys = [key.replace("features/zfinal_block/", "features/final_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/weight", "/W") for key in dst_param_keys]
dst_param_keys = [key.replace("/zfinal_block/", "/final_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stageN/post_activ/", "/post_activ/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stageN/final_postactiv/", "/final_postactiv/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stageN/final_block/", "/final_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/features/zbody/", "/features/body/") for key in dst_param_keys]
dst_param_keys = [key.replace("features/stageN/final_conv/", "features/final_conv/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stage0/stem1_unit/", "/stem1_unit/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stage0/stem2_unit/", "/stem2_unit/") for key in dst_param_keys]
if not src_model.startswith("ibppose_coco"):
dst_param_keys = [key.replace("/stage1_hg/", "/hg/") for key in dst_param_keys]
if src_model.startswith("centernet"):
dst_param_keys = [key.replace("/a_unit", "/unit") for key in dst_param_keys]
dst_param_keys = [key.replace("/z_reg_block/", "/reg_block/") for key in dst_param_keys]
if src_model.startswith("wrn20_10_1bit") or src_model.startswith("wrn20_10_32bit"):
ext2_src_param_keys = [key.replace('.conv.weight', '.bn.beta') for key in src_param_keys if
key.endswith(".conv.weight")]
ext2_src_param_keys.append("features.4.bn.beta")
ext2_dst_param_keys = [key.replace("/conv/W", "/bn/beta") for key in dst_param_keys if key.endswith("/conv/W")]
ext2_dst_param_keys.append("/features/post_activ/bn/beta")
ext3_src_param_keys = {".".join(v.split(".")[:-1]): i for i, v in enumerate(ext2_src_param_keys)}
ext3_dst_param_keys = list(map(lambda x: x.split("/")[1:-1], ext2_dst_param_keys))
else:
ext2_src_param_keys = [key for key in src_param_keys if key.endswith(".beta")]
ext2_dst_param_keys = [key for key in dst_param_keys if key.endswith("/beta")]
ext3_src_param_keys = {".".join(v.split(".")[:-1]): i for i, v in enumerate(ext2_src_param_keys)}
ext3_dst_param_keys = list(map(lambda x: x.split("/")[1:-1], ext2_dst_param_keys))
for i, src_key in enumerate(ext_src_param_keys):
src_key1 = src_key.split(".")[-1]
src_key2 = ".".join(src_key.split(".")[:-1])
dst_ind = ext3_src_param_keys[src_key2]
dst_path = ext3_dst_param_keys[dst_ind]
obj = dst_net
for j, sub_path in enumerate(dst_path):
obj = getattr(obj, sub_path)
if src_key1 == 'running_mean':
assert (obj.avg_mean.shape == src_params[src_key].shape), \
"src_key={}, dst_path={}, src_shape={}, obj.avg_mean.shape={}".format(
src_key, dst_path, src_params[src_key].shape, obj.avg_mean.shape)
obj.avg_mean = src_params[src_key]._data[0].asnumpy()
elif src_key1 == 'running_var':
assert (obj.avg_var.shape == src_params[src_key].shape)
obj.avg_var = src_params[src_key]._data[0].asnumpy()
if src_model in ["condensenet74_c4_g4", "condensenet74_c8_g8"]:
assert (dst_net.output.fc.index.shape == src_params["output.1.index"].shape)
dst_net.output.fc.index = src_params["output.1.index"]._data[0].asnumpy().astype(np.int32)
ext_src_param_keys2.remove("output.1.index")
ext2_src_param_keys = [key for key in src_param_keys if key.endswith(".conv1.conv.weight")]
ext2_dst_param_keys = [key for key in dst_param_keys if key.endswith("/conv1/conv/W")]
ext3_src_param_keys = {".".join(v.split(".")[:-2]): i for i, v in enumerate(ext2_src_param_keys)}
ext3_dst_param_keys = list(map(lambda x: x.split("/")[1:-2], ext2_dst_param_keys))
for i, src_key in enumerate(ext_src_param_keys2):
src_key2 = ".".join(src_key.split(".")[:-1])
dst_ind = ext3_src_param_keys[src_key2]
dst_path = ext3_dst_param_keys[dst_ind]
obj = dst_net
for j, sub_path in enumerate(dst_path):
obj = getattr(obj, sub_path)
assert (obj.index.shape == src_params[src_key].shape), \
"src_key={}, dst_path={}, src_shape={}, obj.index.shape={}".format(
src_key, dst_path, src_params[src_key].shape, obj.index.shape)
obj.index = src_params[src_key]._data[0].asnumpy().astype(np.int32)
elif src_model.startswith("xdensenet"):
ext2_src_param_keys = [key for key in src_param_keys if key.endswith(".conv1.conv.weight")] +\
[key for key in src_param_keys if key.endswith(".conv2.conv.weight")]
ext2_dst_param_keys = [key for key in dst_param_keys if key.endswith("/conv1/conv/W")] +\
[key for key in dst_param_keys if key.endswith("/conv2/conv/W")]
ext3_src_param_keys = {".".join(v.split(".")[:-1]): i for i, v in enumerate(ext2_src_param_keys)}
ext3_dst_param_keys = list(map(lambda x: x.split("/")[1:-1], ext2_dst_param_keys))
for i, src_key in enumerate(ext_src_param_keys2):
src_key2 = ".".join(src_key.split(".")[:-1])
dst_ind = ext3_src_param_keys[src_key2]
dst_path = ext3_dst_param_keys[dst_ind]
obj = dst_net
for j, sub_path in enumerate(dst_path):
obj = getattr(obj, sub_path)
assert (obj.mask.shape == src_params[src_key].shape), \
"src_key={}, dst_path={}, src_shape={}, obj.index.shape={}".format(
src_key, dst_path, src_params[src_key].shape, obj.mask.shape)
obj.mask = src_params[src_key]._data[0].asnumpy()
for i, (src_key, dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
assert (dst_params[dst_key].array.shape == src_params[src_key].shape), \
"src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
src_key, dst_key, src_params[src_key].shape, dst_params[dst_key].array.shape)
dst_params[dst_key].array = src_params[src_key]._data[0].asnumpy()
# print("src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
# src_key, dst_key, src_params[src_key].shape, dst_params[dst_key].array.shape))
from chainer.serializers import save_npz
save_npz(
file=dst_params_file_path,
obj=dst_net)
def convert_gl2gl(dst_net,
dst_params_file_path,
dst_params,
dst_param_keys,
src_params,
src_param_keys,
finetune,
src_model,
ctx):
if src_model.startswith("oth_danet_resnet"):
src6 = list(filter(re.compile("^head.sa.gamma").search, src_param_keys))
src6n = [key for key in src_param_keys if key not in src6]
src_param_keys = src6n + src6
src7 = list(filter(re.compile("^head.conv51").search, src_param_keys))
src7n = [key for key in src_param_keys if key not in src7]
src_param_keys = src7n + src7
src8 = list(filter(re.compile("^head.conv6").search, src_param_keys))
src8n = [key for key in src_param_keys if key not in src8]
src_param_keys = src8n + src8
src1 = list(filter(re.compile("^head.conv5c").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src_param_keys = src1n + src1
src2 = list(filter(re.compile("^head.sc").search, src_param_keys))
src2n = [key for key in src_param_keys if key not in src2]
src_param_keys = src2n + src2
src3 = list(filter(re.compile("^head.conv52").search, src_param_keys))
src3n = [key for key in src_param_keys if key not in src3]
src_param_keys = src3n + src3
src4 = list(filter(re.compile("^head.conv7").search, src_param_keys))
src4n = [key for key in src_param_keys if key not in src4]
src_param_keys = src4n + src4
src5 = list(filter(re.compile("^head.conv8").search, src_param_keys))
src5n = [key for key in src_param_keys if key not in src5]
src_param_keys = src5n + src5
elif src_model.startswith("oth_icnet_resnet50_citys"):
src1 = list(filter(re.compile("^conv_sub1").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src_param_keys = src1 + src1n
src2 = list(filter(re.compile("^head").search, src_param_keys))
src2n = [key for key in src_param_keys if key not in src2]
src_param_keys = src2n + src2
elif src_model.startswith("oth_fastscnn_citys"):
src1 = list(filter(re.compile("^feature_fusion").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src_param_keys = src1n + src1
dst0 = list(filter(re.compile("^fusion").search, dst_param_keys))
dst0n = [key for key in dst_param_keys if key not in dst0]
dst_param_keys = dst0n + dst0
dst1 = list(filter(re.compile("^fusion.low_pw_conv.bn").search, dst_param_keys))
dst1n = [key for key in dst_param_keys if key not in dst1]
dst_param_keys = dst1n + dst1
dst2 = list(filter(re.compile("^fusion.high_conv.bn").search, dst_param_keys))
dst2n = [key for key in dst_param_keys if key not in dst2]
dst_param_keys = dst2n + dst2
for i, (src_key, dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
if dst_params[dst_key].shape != src_params[src_key].shape:
logging.warning(
"dst_param.shape != src_param.shape, src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
src_key, dst_key, src_params[src_key].shape, dst_params[dst_key].shape))
if finetune:
continue
else:
raise ValueError
if dst_key.split('.')[-1] != src_key.split('.')[-1]:
logging.warning(
"dst_key.suff != src_key.suff, src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
src_key, dst_key, src_params[src_key].shape, dst_params[dst_key].shape))
dst_params[dst_key]._load_init(src_params[src_key]._data[0], ctx)
dst_net.save_parameters(dst_params_file_path)
def convert_gl2ke(dst_net,
dst_params_file_path,
dst_params,
dst_param_keys,
src_params,
src_param_keys):
import mxnet as mx
dst_param_keys = [key.replace("/post_activ/", "/stageN/post_activ/") for key in dst_param_keys]
dst_param_keys = [key.replace("/final_block/", "/stageN/final_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stem1_unit/", "/stage0/stem1_unit/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stem2_unit/", "/stage0/stem2_unit/") for key in dst_param_keys]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
dst_param_keys.sort()
dst_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
dst_param_keys = [key.replace("/stageN/post_activ/", "/post_activ/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stageN/final_block/", "/final_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stage0/stem1_unit/", "/stem1_unit/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stage0/stem2_unit/", "/stem2_unit/") for key in dst_param_keys]
dst_param_keys_orig = dst_param_keys.copy()
dst_param_keys = [s[:(s.find("convgroup") + 9)] + "/" + s.split("/")[-1] if s.find("convgroup") >= 0 else s
for s in dst_param_keys]
dst_param_keys_uniq, dst_param_keys_index = np.unique(dst_param_keys, return_index=True)
dst_param_keys = list(dst_param_keys_uniq[dst_param_keys_index.argsort()])
# dst_param_keys = list(np.unique(dst_param_keys))
assert (len(src_param_keys) == len(dst_param_keys))
def process_width(src_key, dst_key, src_weight):
dst_layer = dst_params[dst_key][0]
dst_weight = dst_params[dst_key][1]
if (dst_layer.__class__.__name__ in ["Conv2D"]) and dst_key.endswith("kernel1") and\
(dst_layer.data_format == "channels_last"):
src_weight = np.transpose(src_weight, (2, 3, 1, 0))
if (dst_layer.__class__.__name__ in ["DepthwiseConv2D"]) and dst_key.endswith("kernel1") and\
(dst_layer.data_format == "channels_last"):
src_weight = np.transpose(src_weight, (2, 3, 0, 1))
if (dst_layer.__class__.__name__ in ["Dense"]) and dst_key.endswith("kernel1"):
src_weight = np.transpose(src_weight, (1, 0))
assert (dst_weight._keras_shape == src_weight.shape), \
"src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
src_key, dst_key, src_weight.shape, dst_weight._keras_shape)
# print("src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
# src_key, dst_key, src_weight.shape, dst_weight._keras_shape))
dst_weight.bind(mx.nd.array(src_weight))
for i, (src_key, dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
if dst_key.find("convgroup") >= 0:
dst_key_stem = dst_key[:(dst_key.find("convgroup") + 9)]
dst_keys = [s for s in dst_param_keys_orig if s.startswith(dst_key_stem)]
if src_key.endswith("weight"):
dst_keys = [s for s in dst_keys if s.endswith("kernel1")]
elif src_key.endswith("bias"):
dst_keys = [s for s in dst_keys if s.endswith("bias1")]
groups = len(dst_keys)
src_weight0 = src_params[src_key]._data[0]
src_weight0_list = mx.nd.split(src_weight0, axis=0, num_outputs=groups)
for gi in range(groups):
src_weight_gi = src_weight0_list[gi].asnumpy()
dst_key_gi = dst_keys[gi]
process_width(src_key, dst_key_gi, src_weight_gi)
else:
src_weight = src_params[src_key]._data[0].asnumpy()
process_width(src_key, dst_key, src_weight)
dst_net.save_weights(dst_params_file_path)
def convert_gl2tf(dst_params_file_path,
dst_params,
dst_param_keys,
src_params,
src_param_keys):
import mxnet as mx
dst_param_keys = [key.replace("/kernel:", "/weight:") for key in dst_param_keys]
dst_param_keys = [key.replace("/dw_kernel:", "/weight_dw:") for key in dst_param_keys]
dst_param_keys = [key.replace("/post_activ/", "/stageN/post_activ/") for key in dst_param_keys]
dst_param_keys = [key.replace("/final_block/", "/stageN/final_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stem1_unit/", "/stage0/stem1_unit/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stem2_unit/", "/stage0/stem2_unit/") for key in dst_param_keys]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
dst_param_keys.sort()
dst_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
dst_param_keys = [key.replace("/weight:", "/kernel:") for key in dst_param_keys]
dst_param_keys = [key.replace("/weight_dw:", "/dw_kernel:") for key in dst_param_keys]
dst_param_keys = [key.replace("/stageN/post_activ/", "/post_activ/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stageN/final_block/", "/final_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stage0/stem1_unit/", "/stem1_unit/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stage0/stem2_unit/", "/stem2_unit/") for key in dst_param_keys]
dst_param_keys_orig = dst_param_keys.copy()
dst_param_keys = [s[:(s.find("convgroup") + 9)] + "/" + s.split("/")[-1] if s.find("convgroup") >= 0 else s
for s in dst_param_keys]
dst_param_keys_uniq, dst_param_keys_index = np.unique(dst_param_keys, return_index=True)
dst_param_keys = list(dst_param_keys_uniq[dst_param_keys_index.argsort()])
assert (len(src_param_keys) == len(dst_param_keys))
import tensorflow as tf
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
def process_width(src_key, dst_key, src_weight):
if len(src_weight.shape) == 4:
if dst_key.split("/")[-1][:-2] == "dw_kernel":
src_weight = np.transpose(src_weight, axes=(2, 3, 0, 1))
else:
src_weight = np.transpose(src_weight, axes=(2, 3, 1, 0))
elif len(src_weight.shape) == 2:
src_weight = np.transpose(src_weight, axes=(1, 0))
assert (tuple(dst_params[dst_key].get_shape().as_list()) == src_weight.shape)
sess.run(dst_params[dst_key].assign(src_weight))
# print(dst_params[dst_key].eval(sess))
for i, (src_key, dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
if dst_key.find("convgroup") >= 0:
dst_key_stem = dst_key[:(dst_key.find("convgroup") + 9)]
dst_keys = [s for s in dst_param_keys_orig if s.startswith(dst_key_stem)]
if src_key.endswith("weight"):
dst_keys = [s for s in dst_keys if s.endswith("kernel:0")]
elif src_key.endswith("bias"):
dst_keys = [s for s in dst_keys if s.endswith("bias:0")]
groups = len(dst_keys)
src_weight0 = src_params[src_key]._data[0]
src_weight0_list = mx.nd.split(src_weight0, axis=0, num_outputs=groups)
for gi in range(groups):
src_weight_gi = src_weight0_list[gi].asnumpy()
dst_key_gi = dst_keys[gi]
process_width(src_key, dst_key_gi, src_weight_gi)
else:
src_weight = src_params[src_key]._data[0].asnumpy()
process_width(src_key, dst_key, src_weight)
# saver = tf.train.Saver()
# saver.save(
# sess=sess,
# save_path=dst_params_file_path)
from tensorflow_.utils import save_model_params
save_model_params(
sess=sess,
file_path=dst_params_file_path)
def convert_gl2tf2(dst_net,
dst_params_file_path,
dst_params,
dst_param_keys,
src_params,
src_param_keys,
src_model):
if src_model.startswith("hrnet"):
src_param_keys = [key.replace(".transition.", ".atransition.") for key in src_param_keys]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
if src_model.startswith("hrnet"):
src_param_keys = [key.replace(".atransition.", ".transition.") for key in src_param_keys]
dst_param_keys = [key.replace("/kernel:", "/weight:") for key in dst_param_keys]
dst_param_keys = [key.replace("/depthwise_kernel:", "/weight_depthwise:") for key in dst_param_keys]
dst_param_keys = [key.replace("/post_activ/", "/stageN/post_activ/") for key in dst_param_keys]
if (not src_model.startswith("pspnet_")) and (not src_model.startswith("deeplabv3_")) and\
(not src_model.startswith("simplepose_")) and (not src_model.startswith("alphapose_")) and\
(not src_model.startswith("lwopenpose")) and (not src_model.startswith("quartznet")) and\
(not src_model.startswith("jasper")):
dst_param_keys = [key.replace("/final_block/", "/stageN/final_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/final_block/", "/zfinal_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stem1_unit/", "/stage0/stem1_unit/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stem2_unit/", "/stage0/stem2_unit/") for key in dst_param_keys]
if src_model.startswith("hrnet"):
dst_param_keys = [key.replace("/transition/", "/atransition/") for key in dst_param_keys]
if src_model.startswith("hardnet"):
# dst_param_keys = [key.replace('/dw_conv/', '/z_dw_conv/') for key in dst_param_keys]
dst_param_keys = [key.replace("features/down", "features/z_down") for key in dst_param_keys]
if src_model.startswith("centernet"):
dst_param_keys = [key.replace("/unit", "/a_unit") for key in dst_param_keys]
dst_param_keys = [key.replace("/reg_block/", "/z_reg_block/") for key in dst_param_keys]
# if src_model.startswith("danet"):
# dst_param_keys = [key.replace("da_net/head/", "z_da_net/head/") for key in dst_param_keys]
dst_param_keys.sort()
dst_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
dst_param_keys = [key.replace("/weight:", "/kernel:") for key in dst_param_keys]
dst_param_keys = [key.replace("/weight_depthwise:", "/depthwise_kernel:") for key in dst_param_keys]
dst_param_keys = [key.replace("/zfinal_block/", "/final_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stageN/post_activ/", "/post_activ/") for key in dst_param_keys]
if (not src_model.startswith("pspnet_")) and (not src_model.startswith("deeplabv3_")) and\
(not src_model.startswith("simplepose_")) and (not src_model.startswith("alphapose_")) and\
(not src_model.startswith("lwopenpose")) and (not src_model.startswith("quartznet")) and\
(not src_model.startswith("jasper")):
dst_param_keys = [key.replace("/stageN/final_block/", "/final_block/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stage0/stem1_unit/", "/stem1_unit/") for key in dst_param_keys]
dst_param_keys = [key.replace("/stage0/stem2_unit/", "/stem2_unit/") for key in dst_param_keys]
if src_model.startswith("hrnet"):
dst_param_keys = [key.replace("/atransition/", "/transition/") for key in dst_param_keys]
if src_model.startswith("hardnet"):
# dst_param_keys = [key.replace('/z_dw_conv/', '/dw_conv/') for key in dst_param_keys]
dst_param_keys = [key.replace("features/z_down", "features/down") for key in dst_param_keys]
if src_model.startswith("centernet"):
dst_param_keys = [key.replace("/a_unit", "/unit") for key in dst_param_keys]
dst_param_keys = [key.replace("/z_reg_block/", "/reg_block/") for key in dst_param_keys]
# if src_model.startswith("danet"):
# dst_param_keys = [key.replace("z_da_net/head/", "da_net/head/") for key in dst_param_keys]
dst_param_keys_orig = dst_param_keys.copy()
dst_param_keys = [s[:(s.find("convgroup") + 9)] + "/" + s.split("/")[-1] if s.find("convgroup") >= 0 else s
for s in dst_param_keys]
dst_param_keys_uniq, dst_param_keys_index = np.unique(dst_param_keys, return_index=True)
dst_param_keys = list(dst_param_keys_uniq[dst_param_keys_index.argsort()])
assert (len(src_param_keys) == len(dst_param_keys))
def process_width(src_key, dst_key, src_weight):
if len(src_weight.shape) == 4:
if dst_key.split("/")[-1][:-2] == "depthwise_kernel":
src_weight = np.transpose(src_weight, axes=(2, 3, 0, 1))
else:
src_weight = np.transpose(src_weight, axes=(2, 3, 1, 0))
elif len(src_weight.shape) == 2:
src_weight = np.transpose(src_weight, axes=(1, 0))
elif len(src_weight.shape) == 3:
if not ((src_model.startswith("jasper") or src_model.startswith("quartznet")) and
dst_key.split("/")[-1][:-2] == "fb"):
src_weight = np.transpose(src_weight, axes=(2, 1, 0))
if dst_key.split("/")[-1][:-2] == "depthwise_kernel":
assert(len(dst_params[dst_key].shape) == 4)
src_weight = np.expand_dims(src_weight, -1)
dst_weight = dst_params[dst_key]
assert (tuple(dst_weight.shape) == src_weight.shape), \
"src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
src_key, dst_key, src_weight.shape, tuple(dst_weight.shape))
dst_weight.assign(src_weight)
for i, (src_key, dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
# print("src_key={},\tsrc_key2={},\tdst_key={}".format(src_key, src_params[src_key].name, dst_key))
if dst_key.find("convgroup") >= 0:
import mxnet as mx
dst_key_stem = dst_key[:(dst_key.find("convgroup") + 9)]
dst_keys = [s for s in dst_param_keys_orig if s.startswith(dst_key_stem)]
if src_key.endswith("weight"):
dst_keys = [s for s in dst_keys if s.endswith("kernel:0")]
elif src_key.endswith("bias"):
dst_keys = [s for s in dst_keys if s.endswith("bias:0")]
groups = len(dst_keys)
src_weight0 = src_params[src_key]._data[0]
src_weight0_list = mx.nd.split(src_weight0, axis=0, num_outputs=groups)
for gi in range(groups):
src_weight_gi = src_weight0_list[gi].asnumpy()
dst_key_gi = dst_keys[gi]
process_width(src_key, dst_key_gi, src_weight_gi)
else:
src_weight = src_params[src_key]._data[0].asnumpy()
process_width(src_key, dst_key, src_weight)
dst_net.save_weights(dst_params_file_path)
def convert_pt2pt(dst_params_file_path,
dst_params,
dst_param_keys,
src_params,
src_param_keys,
src_model,
dst_model):
import torch
if src_model.startswith("oth_quartznet") or src_model.startswith("oth_jasper"):
src1 = list(filter(re.compile("\.res\.").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src_param_keys = src1n + src1
dst1 = list(filter(re.compile("\.identity_block\.").search, dst_param_keys))
dst1n = [key for key in dst_param_keys if key not in dst1]
dst_param_keys = dst1n + dst1
elif src_model.startswith("oth_dicenet"):
src1 = list(filter(re.compile("\.conv_height\.").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src2 = list(filter(re.compile("\.conv_width\.").search, src1n))
src2n = [key for key in src1n if key not in src2]
src3 = list(filter(re.compile("\.linear_comb_layer\.").search, src2n))
src3n = [key for key in src2n if key not in src3]
src4 = list(filter(re.compile("\.proj_layer\.").search, src3n))
src4n = [key for key in src3n if key not in src4]
src_param_keys = src4n + src1 + src2 + src3 + src4
dst1 = list(filter(re.compile("\.h_conv\.").search, dst_param_keys))
dst1n = [key for key in dst_param_keys if key not in dst1]
dst2 = list(filter(re.compile("\.w_conv\.").search, dst1n))
dst2n = [key for key in dst1n if key not in dst2]
dst3 = list(filter(re.compile("\.att\.").search, dst2n))
dst3n = [key for key in dst2n if key not in dst3]
dst4 = list(filter(re.compile("\.proj_conv\.").search, dst3n))
dst4n = [key for key in dst3n if key not in dst4]
dst_param_keys = dst4n + dst1 + dst2 + dst3 + dst4
elif src_model.startswith("oth_proxyless"):
src1 = src_param_keys[5]
del src_param_keys[5]
src_param_keys.insert(0, src1)
src2 = src_param_keys[-3]
del src_param_keys[-3]
src_param_keys.insert(-7, src2)
elif src_model.startswith("oth_scnet"):
pass
src1 = list(filter(re.compile(".k1.").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src2 = list(filter(re.compile(".scconv.").search, src1n))
src2n = [key for key in src1n if key not in src2]
src_param_keys = src2n + src1 + src2
dst1 = list(filter(re.compile(".conv2a.").search, dst_param_keys))
dst1n = [key for key in dst_param_keys if key not in dst1]
dst2 = list(filter(re.compile(".conv2b.").search, dst1n))
dst2n = [key for key in dst1n if key not in dst2]
dst_param_keys = dst2n + dst1 + dst2
elif src_model == "oth_bisenet":
src1 = list(filter(re.compile("^cp.conv_avg").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src2 = list(filter(re.compile("^cp.arm32").search, src1n))
src2n = [key for key in src1n if key not in src2]
src3 = list(filter(re.compile("^cp.conv_head32").search, src2n))
src3n = [key for key in src2n if key not in src3]
src4 = list(filter(re.compile("^cp.arm16").search, src3n))
src4n = [key for key in src3n if key not in src4]
src5 = list(filter(re.compile("^cp.conv_head16").search, src4n))
src5n = [key for key in src4n if key not in src5]
src6 = list(filter(re.compile("^ffm").search, src5n))
src6n = [key for key in src5n if key not in src6]
src_param_keys = src6n + src1 + src2 + src3 + src4 + src5 + src6
dst1 = list(filter(re.compile("^pool").search, dst_param_keys))
dst1n = [key for key in dst_param_keys if key not in dst1]
dst_param_keys = dst1n + dst1
elif src_model.startswith("oth_dla"):
src1 = list(filter(re.compile("\.project").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src_param_keys = src1 + src1n
dst1 = list(filter(re.compile("\.project_conv").search, dst_param_keys))
dst1n = [key for key in dst_param_keys if key not in dst1]
dst_param_keys = dst1 + dst1n
elif dst_model == "ntsnet":
src1 = list(filter(re.compile("^proposal_net").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src_param_keys = src1 + src1n
dst1 = list(filter(re.compile("^navigator_unit\.branch\d+\.down").search, dst_param_keys))
dst1n = [key for key in dst_param_keys if key not in dst1]
dst2 = list(filter(re.compile("^navigator_unit\.branch\d+\.tidy").search, dst1n))
dst2n = [key for key in dst1n if key not in dst2]
dst_param_keys = dst1 + dst2 + dst2n
elif dst_model == "fishnet150":
src1 = list(filter(re.compile("^(conv|fish\.fish\.[0-2])").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src2 = list(filter(re.compile("^fish\.fish\.6\.1").search, src1n))
src2n = [key for key in src1n if key not in src2]
src3 = list(filter(re.compile("^fish\.fish\.5\.1").search, src2n))
src3n = [key for key in src2n if key not in src3]
src4 = list(filter(re.compile("^fish\.fish\.4\.1").search, src3n))
src4n = [key for key in src3n if key not in src4]
src5 = list(filter(re.compile("^fish\.fish\.3\.[0-1]").search, src4n))
src5n = [key for key in src4n if key not in src5]
src6 = list(filter(re.compile("^fish\.fish\.3\.3").search, src5n))
src6n = [key for key in src5n if key not in src6]
src7 = list(filter(re.compile("^fish\.fish\.[3-6]").search, src6n))
src7n = [key for key in src6n if key not in src7]
src8 = list(filter(re.compile("^fish\.fish\.9\.1").search, src7n))
src8n = [key for key in src7n if key not in src8]
src9 = list(filter(re.compile("^fish\.fish\.8\.1").search, src8n))
src9n = [key for key in src8n if key not in src9]
src10 = list(filter(re.compile("^fish\.fish\.7\.1").search, src9n))
src10n = [key for key in src9n if key not in src10]
src_param_keys = src1 + src2 + src3 + src4 + src5 + src6 + src7 + src8 + src9 + src10 + src10n
elif dst_model == "bam_resnet50":
src_bams = list(filter(re.compile("^bam").search, src_param_keys))
src_param_keys = [key for key in src_param_keys if key not in src_bams]
src_param_keys = src_param_keys + src_bams
dst_bams = list(filter(re.compile("^features.stage[0-9].unit1.bam.").search, dst_param_keys))
dst_param_keys = [key for key in dst_param_keys if key not in dst_bams]
dst_param_keys = dst_param_keys + dst_bams
elif dst_model.startswith("sinet"):
src1 = list(filter(re.compile("\.vertical.weight").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src_param_keys = src1n + src1
src2 = list(filter(re.compile("\.horizontal.weight").search, src_param_keys))
src2n = [key for key in src_param_keys if key not in src2]
src_param_keys = src2n + src2
src3 = list(filter(re.compile("\.B_v\.").search, src_param_keys))
src3n = [key for key in src_param_keys if key not in src3]
src_param_keys = src3n + src3
src4 = list(filter(re.compile("\.B_h\.").search, src_param_keys))
src4n = [key for key in src_param_keys if key not in src4]
src_param_keys = src4n + src4
src5 = list(filter(re.compile("bn_4\.").search, src_param_keys))
src5n = [key for key in src_param_keys if key not in src5]
src_param_keys = src5n + src5
src6 = list(filter(re.compile("bn_3\.").search, src_param_keys))
src6n = [key for key in src_param_keys if key not in src6]
src_param_keys = src6n + src6
dst1 = list(filter(re.compile("\.v_conv.conv\.").search, dst_param_keys))
dst1n = [key for key in dst_param_keys if key not in dst1]
dst_param_keys = dst1n + dst1
dst2 = list(filter(re.compile("\.h_conv.conv\.").search, dst_param_keys))
dst2n = [key for key in dst_param_keys if key not in dst2]
dst_param_keys = dst2n + dst2
dst3 = list(filter(re.compile("\.v_conv.bn\.").search, dst_param_keys))
dst3n = [key for key in dst_param_keys if key not in dst3]
dst_param_keys = dst3n + dst3
dst4 = list(filter(re.compile("\.h_conv.bn\.").search, dst_param_keys))
dst4n = [key for key in dst_param_keys if key not in dst4]
dst_param_keys = dst4n + dst4
dst5 = list(filter(re.compile("decoder.decode1.bn\.").search, dst_param_keys))
dst5n = [key for key in dst_param_keys if key not in dst5]
dst_param_keys = dst5n + dst5
dst6 = list(filter(re.compile("decoder.decode2.bn\.").search, dst_param_keys))
dst6n = [key for key in dst_param_keys if key not in dst6]
dst_param_keys = dst6n + dst6
elif src_model.startswith("oth_ibppose"):
def sort_hg(src2):
src2b1 = list(filter(re.compile("^hourglass.[0-9].hg.0.1.").search, src2))
src2b2 = list(filter(re.compile("^hourglass.[0-9].hg.1.1.").search, src2))
src2b3 = list(filter(re.compile("^hourglass.[0-9].hg.2.1.").search, src2))
src2b4 = list(filter(re.compile("^hourglass.[0-9].hg.3.1.").search, src2))
src2b5 = list(filter(re.compile("^hourglass.[0-9].hg.3.2.").search, src2))
src2b6 = list(filter(re.compile("^hourglass.[0-9].hg.3.3.").search, src2))
src2b7 = list(filter(re.compile("^hourglass.[0-9].hg.2.2.").search, src2))
src2b8 = list(filter(re.compile("^hourglass.[0-9].hg.2.3.").search, src2))
src2b9 = list(filter(re.compile("^hourglass.[0-9].hg.1.2.").search, src2))
src2b10 = list(filter(re.compile("^hourglass.[0-9].hg.1.3.").search, src2))
src2b11 = list(filter(re.compile("^hourglass.[0-9].hg.0.2.").search, src2))
src2b12 = list(filter(re.compile("^hourglass.[0-9].hg.0.3.").search, src2))
src2b13 = list(filter(re.compile("^hourglass.[0-9].hg.0.0.").search, src2))
src2b14 = list(filter(re.compile("^hourglass.[0-9].hg.1.0.").search, src2))
src2b15 = list(filter(re.compile("^hourglass.[0-9].hg.2.0.").search, src2))
src2b16 = list(filter(re.compile("^hourglass.[0-9].hg.3.0.").search, src2))
src2b17 = list(filter(re.compile("^hourglass.[0-9].hg.3.4.").search, src2))
return src2b1 + src2b2 + src2b3 + src2b4 +\
src2b11 + src2b12 + src2b9 + src2b10 + src2b7 + src2b8 + src2b5 + src2b6 +\
src2b13 + src2b14 + src2b15 + src2b16 + src2b17
src1 = list(filter(re.compile("^pre.").search, src_param_keys))
src1n = [key for key in src_param_keys if key not in src1]
src_param_keys = src1n + src1
src2 = list(filter(re.compile("^hourglass.").search, src_param_keys))
src2n = [key for key in src_param_keys if key not in src2]
src2b1 = sort_hg(list(filter(re.compile("^hourglass.0.hg.").search, src2)))
src2b2 = sort_hg(list(filter(re.compile("^hourglass.1.hg.").search, src2)))
src2b3 = sort_hg(list(filter(re.compile("^hourglass.2.hg.").search, src2)))
src2b4 = sort_hg(list(filter(re.compile("^hourglass.3.hg.").search, src2)))
src_param_keys = src2n + src2b1 + src2b2 + src2b3 + src2b4
src3 = list(filter(re.compile("^features.[0-9].before_regress").search, src_param_keys))
src3n = [key for key in src_param_keys if key not in src3]
src3b = list(filter(re.compile("^features.[0-9].before_regress.0.").search, src3))
src_param_keys = src3n + src3b
src4 = list(filter(re.compile("^outs.[0-9].").search, src_param_keys))
src4n = [key for key in src_param_keys if key not in src4]
src4b = list(filter(re.compile("^outs.[0-9].0.").search, src4))
src_param_keys = src4n + src4b
src5 = list(filter(re.compile("^merge_features.[0-9].").search, src_param_keys))
src5n = [key for key in src_param_keys if key not in src5]
src5b = list(filter(re.compile("^merge_features.[0-9].0.").search, src5))
src_param_keys = src5n + src5b
src6 = list(filter(re.compile("^merge_preds.[0-9].").search, src_param_keys))
src6n = [key for key in src_param_keys if key not in src6]
src6b = list(filter(re.compile("^merge_preds.[0-9].0.").search, src6))
src_param_keys = src6n + src6b
dst1 = list(filter(re.compile("^backbone.").search, dst_param_keys))
dst1n = [key for key in dst_param_keys if key not in dst1]
dst_param_keys = dst1n + dst1
dst2 = list(filter(re.compile("^decoder.pass[1-9].hg.").search, dst_param_keys))
dst2n = [key for key in dst_param_keys if key not in dst2]
dst_param_keys = dst2n + dst2
dst3 = list(filter(re.compile("^decoder.pass[1-9].pre_block.").search, dst_param_keys))
dst3n = [key for key in dst_param_keys if key not in dst3]
dst_param_keys = dst3n + dst3
dst4 = list(filter(re.compile("^decoder.pass[1-9].post_block.").search, dst_param_keys))
dst4n = [key for key in dst_param_keys if key not in dst4]
dst_param_keys = dst4n + dst4
dst5 = list(filter(re.compile("^decoder.pass[1-9].pre_merge_block.").search, dst_param_keys))
dst5n = [key for key in dst_param_keys if key not in dst5]
dst_param_keys = dst5n + dst5
dst6 = list(filter(re.compile("^decoder.pass[1-9].post_merge_block.").search, dst_param_keys))
dst6n = [key for key in dst_param_keys if key not in dst6]
dst_param_keys = dst6n + dst6
assert (len(src_param_keys) == len(dst_param_keys))
for i, (src_key, dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
if (src_model == "oth_shufflenetv2_wd2" and dst_model == "shufflenetv2_wd2") and \
(src_key == "network.8.weight"):
dst_params[dst_key] = torch.from_numpy(src_params[src_key].numpy()[:, :, 0, 0])
else:
# print("src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
# src_key, dst_key, tuple(src_params[src_key].size()), tuple(dst_params[dst_key].size())))
assert (tuple(dst_params[dst_key].size()) == tuple(src_params[src_key].size())), \
"src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
src_key, dst_key, tuple(src_params[src_key].size()), tuple(dst_params[dst_key].size()))
assert (dst_key.split('.')[-1] == src_key.split('.')[-1])
dst_params[dst_key] = torch.from_numpy(src_params[src_key].numpy())
torch.save(
obj=dst_params,
f=dst_params_file_path)
def convert_gl2pt(dst_params_file_path,
dst_params,
dst_param_keys,
src_params,
src_param_keys):
import torch
for i, (src_key, dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
assert (tuple(dst_params[dst_key].size()) == src_params[src_key].shape)
dst_params[dst_key] = torch.from_numpy(src_params[src_key]._data[0].asnumpy())
torch.save(
obj=dst_params,
f=dst_params_file_path)
def convert_pt2gl(dst_net,
dst_params_file_path,
dst_params,
dst_param_keys,
src_params,
src_param_keys,
ctx):
import mxnet as mx
for i, (src_key, dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
assert (dst_params[dst_key].shape == tuple(src_params[src_key].size())), \
"src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
src_key, dst_key, tuple(src_params[src_key].size()), dst_params[dst_key].shape)
dst_params[dst_key]._load_init(mx.nd.array(src_params[src_key].numpy(), ctx), ctx)
dst_net.save_parameters(dst_params_file_path)
def convert_tf2tf(dst_params_file_path,
dst_params,
dst_param_keys,
src_params,
src_param_keys):
import re
src_param_keys = [key.replace("/W:", "/kernel:") for key in src_param_keys]
src_param_keys = [key.replace("/b:", "/bias:") for key in src_param_keys]
src_param_keys = [key.replace("linear/", "output/") for key in src_param_keys]
src_param_keys = [key.replace("stage", "features/stage") for key in src_param_keys]
src_param_keys = [re.sub("^conv1/", "features/init_block/conv/", key) for key in src_param_keys]
src_param_keys = [re.sub("^conv5/", "features/final_block/conv/", key) for key in src_param_keys]
src_param_keys = [key.replace('/dconv_bn/', '/dconv/bn/') for key in src_param_keys]
src_param_keys = [key.replace('/shortcut_dconv_bn/', '/shortcut_dconv/bn/') for key in src_param_keys]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
dst_param_keys.sort()
dst_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
src_param_keys = [key.replace("/kernel:", "/W:") for key in src_param_keys]
src_param_keys = [key.replace("/bias:", "/b:") for key in src_param_keys]
src_param_keys = [key.replace("output/", "linear/") for key in src_param_keys]
src_param_keys = [key.replace("features/stage", "stage") for key in src_param_keys]
src_param_keys = [key.replace("features/init_block/conv/", 'conv1/') for key in src_param_keys]
src_param_keys = [key.replace("features/final_block/conv/", 'conv5/') for key in src_param_keys]
src_param_keys = [key.replace('/dconv/bn/', '/dconv_bn/') for key in src_param_keys]
src_param_keys = [key.replace('/shortcut_dconv/bn/', '/shortcut_dconv_bn/') for key in src_param_keys]
assert (len(src_param_keys) == len(dst_param_keys))
import tensorflow as tf
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i, (src_key, dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
assert (src_params[src_key].shape == tuple(dst_params[dst_key].get_shape().as_list()))
sess.run(dst_params[dst_key].assign(src_params[src_key]))
from tensorflow_.utils import save_model_params
save_model_params(
sess=sess,
file_path=dst_params_file_path)
def convert_tf2gl(dst_net,
dst_params_file_path,
dst_params,
dst_param_keys,
src_params,
src_param_keys,
ctx):
import mxnet as mx
src_param_keys = [key.replace("/kernel:", "/weight:") for key in src_param_keys]
src_param_keys = [key.replace("/dw_kernel:", "/weight_dw:") for key in src_param_keys]
src_param_keys = [key.replace("/post_activ/", "/stageN/post_activ/") for key in src_param_keys]
src_param_keys = [key.replace("/final_block/", "/stageN/final_block/") for key in src_param_keys]
src_param_keys = [key.replace("/stem1_unit/", "/stage0/stem1_unit/") for key in src_param_keys]
src_param_keys = [key.replace("/stem2_unit/", "/stage0/stem2_unit/") for key in src_param_keys]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
dst_param_keys.sort()
dst_param_keys.sort(key=lambda var: ["{:10}".format(int(x)) if
x.isdigit() else x for x in re.findall(r"[^0-9]|[0-9]+", var)])
src_param_keys = [key.replace("/weight:", "/kernel:") for key in src_param_keys]
src_param_keys = [key.replace("/weight_dw:", "/dw_kernel:") for key in src_param_keys]
src_param_keys = [key.replace("/stageN/post_activ/", "/post_activ/") for key in src_param_keys]
src_param_keys = [key.replace("/stageN/final_block/", "/final_block/") for key in src_param_keys]
src_param_keys = [key.replace("/stage0/stem1_unit/", "/stem1_unit/") for key in src_param_keys]
src_param_keys = [key.replace("/stage0/stem2_unit/", "/stem2_unit/") for key in src_param_keys]
assert (len(src_param_keys) == len(dst_param_keys))
for i, (src_key, dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
src_weight = src_params[src_key]
if len(src_weight.shape) == 4:
if src_key.split("/")[-1][:-2] == "dw_kernel":
dst_weight = np.transpose(src_weight, axes=(2, 3, 0, 1))
else:
dst_weight = np.transpose(src_weight, axes=(3, 2, 0, 1))
elif len(src_weight.shape) == 2:
dst_weight = np.transpose(src_weight, axes=(1, 0))
else:
dst_weight = src_weight
assert (dst_weight.shape == dst_params[dst_key].shape), \
"src_key={}, dst_key={}, src_shape={}, dst_shape={}".format(
src_key, dst_key, dst_weight.shape, dst_params[dst_key].shape)
dst_params[dst_key]._load_init(mx.nd.array(dst_weight, ctx), ctx)
dst_net.save_parameters(dst_params_file_path)
def convert_tf22tfl(src_net,
dst_params_file_path):
import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_keras_model(src_net)
tflite_model = converter.convert()
open(dst_params_file_path, "wb").write(tflite_model)
# batch_size = 1
# input_shape = ((batch_size, 3, src_net.in_size[0], src_net.in_size[1]) if
# src_net.data_format == "channels_first" else
# (batch_size, src_net.in_size[0], src_net.in_size[1], 3))
# input_data = tf.random.normal(input_shape)
# tf_results = src_net(input_data)
# interpreter = tf.lite.Interpreter(model_content=tflite_model)
# interpreter.allocate_tensors()
# input_details = interpreter.get_input_details()
# output_details = interpreter.get_output_details()
# input_data = np.array(np.random.random_sample(input_details[0]["shape"]), dtype=np.float32)
# interpreter.set_tensor(input_details[0]["index"], input_data)
# interpreter.invoke()
# tflite_results = interpreter.get_tensor(output_details[0]["index"])
# for tf_result, tflite_result in zip(tf_results, tflite_results):
# np.testing.assert_almost_equal(tf_result.numpy(), tflite_result, decimal=5)
def _init_ctx(args):
ctx = None
if args.src_fwk in ("gluon", "mxnet", "keras") or args.dst_fwk in ("gluon", "mxnet", "keras"):
import mxnet as mx
ctx = mx.cpu()
return ctx
def _prepare_src_model(args, ctx, use_cuda):
return prepare_src_model(
src_fwk=args.src_fwk,
src_model=args.src_model,
src_params_file_path=args.src_params,
dst_fwk=args.dst_fwk,
ctx=ctx,
use_cuda=use_cuda,
load_ignore_extra=args.load_ignore_extra,
remove_module=args.remove_module,
num_classes=args.src_num_classes,
in_channels=args.src_in_channels)
def _prepare_dst_model(args, ctx, use_cuda):
return prepare_dst_model(
dst_fwk=args.dst_fwk,
dst_model=args.dst_model,
src_fwk=args.src_fwk,
ctx=ctx,
use_cuda=use_cuda,
num_classes=args.dst_num_classes,
in_channels=args.dst_in_channels,
model_type=args.model_type)
def update_and_initialize_logging(args):
"""
Update arguments ans initialize logging.
Parameters:
----------
args : ArgumentParser
Main script arguments.
"""
packages = []
pip_packages = []
if (args.src_fwk == "gluon") or (args.dst_fwk == "gluon"):
packages += ["mxnet, numpy"]
pip_packages += ["mxnet-cu110", "mxnet-cu112"]
if (args.src_fwk == "pytorch") or (args.dst_fwk == "pytorch"):
packages += ["torch", "torchvision"]
if (args.src_fwk == "chainer") or (args.dst_fwk == "chainer"):
packages += ["chainer"]
pip_packages += ["cupy-cuda110", "cupy-cuda112", "chainer"]
if (args.src_fwk == "keras") or (args.dst_fwk == "keras"):
packages += ["keras"]
pip_packages += ["keras", "keras-mxnet", "mxnet-cu110", "mxnet-cu112"]
if (args.src_fwk == "tensorflow") or (args.dst_fwk == "tensorflow"):
packages += ["tensorflow-gpu"]
pip_packages += ["tensorflow", "tensorflow-gpu", "tensorpack"]
if (args.src_fwk == "tf2") or (args.dst_fwk == "tf2") or (args.dst_fwk == "tfl"):
packages += ["tensorflow"]
pip_packages += ["tensorflow", "tensorflow-gpu"]
_, log_file_exist = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
script_args=args,
log_packages=packages,
log_pip_packages=pip_packages)
def main():
args = parse_args()
ctx = None
use_cuda = False
if args.dst_fwk == "tf2":
dst_params, dst_param_keys, dst_net = _prepare_dst_model(args, ctx, use_cuda)
update_and_initialize_logging(args=args)
ctx = _init_ctx(args)
src_params, src_param_keys, ext_src_param_keys, ext_src_param_keys2, src_net =\
_prepare_src_model(args, ctx, use_cuda)
if args.dst_fwk != "tf2":
dst_params, dst_param_keys, dst_net = _prepare_dst_model(args, ctx, use_cuda)
if ((args.dst_fwk in ["keras", "tensorflow", "tf2"]) and any([s.find("convgroup") >= 0 for s in dst_param_keys]))\
or ((args.src_fwk == "mxnet") and (args.src_model in ["crunet56", "crunet116", "preresnet269b"])):
assert (len(src_param_keys) <= len(dst_param_keys))
elif ((args.dst_fwk == "chainer") and
(args.src_model.startswith("diaresnet") or args.src_model.startswith("diapreresnet"))) or\
args.src_model.startswith("oth_ibppose"):
assert (len(src_param_keys) >= len(dst_param_keys))
elif args.dst_fwk == "tfl":
pass
else:
assert (len(src_param_keys) == len(dst_param_keys))
if args.src_fwk == "gluon" and args.dst_fwk == "gluon":
convert_gl2gl(
dst_net=dst_net,
dst_params_file_path=args.dst_params,
dst_params=dst_params,
dst_param_keys=dst_param_keys,
src_params=src_params,
src_param_keys=src_param_keys,
finetune=((args.src_num_classes != args.dst_num_classes) or (args.src_in_channels != args.dst_in_channels)),
src_model=args.src_model,
ctx=ctx)
elif args.src_fwk == "pytorch" and args.dst_fwk == "pytorch":
convert_pt2pt(
dst_params_file_path=args.dst_params,
dst_params=dst_params,
dst_param_keys=dst_param_keys,
src_params=src_params,
src_param_keys=src_param_keys,
src_model=args.src_model,
dst_model=args.dst_model)
elif args.src_fwk == "gluon" and args.dst_fwk == "pytorch":
convert_gl2pt(
dst_params_file_path=args.dst_params,
dst_params=dst_params,
dst_param_keys=dst_param_keys,
src_params=src_params,
src_param_keys=src_param_keys)
elif args.src_fwk == "gluon" and args.dst_fwk == "chainer":
convert_gl2ch(
dst_net=dst_net,
dst_params_file_path=args.dst_params,
dst_params=dst_params,
dst_param_keys=dst_param_keys,
src_params=src_params,
src_param_keys=src_param_keys,
ext_src_param_keys=ext_src_param_keys,
ext_src_param_keys2=ext_src_param_keys2,
src_model=args.src_model)
elif args.src_fwk == "gluon" and args.dst_fwk == "keras":
convert_gl2ke(
dst_net=dst_net,
dst_params_file_path=args.dst_params,
dst_params=dst_params,
dst_param_keys=dst_param_keys,
src_params=src_params,
src_param_keys=src_param_keys)
elif args.src_fwk == "gluon" and args.dst_fwk == "tensorflow":
convert_gl2tf(
dst_params_file_path=args.dst_params,
dst_params=dst_params,
dst_param_keys=dst_param_keys,
src_params=src_params,
src_param_keys=src_param_keys)
elif args.src_fwk == "gluon" and args.dst_fwk == "tf2":
convert_gl2tf2(
dst_net=dst_net,
dst_params_file_path=args.dst_params,
dst_params=dst_params,
dst_param_keys=dst_param_keys,
src_params=src_params,
src_param_keys=src_param_keys,
src_model=args.src_model)
elif args.src_fwk == "pytorch" and args.dst_fwk == "gluon":
convert_pt2gl(
dst_net=dst_net,
dst_params_file_path=args.dst_params,
dst_params=dst_params,
dst_param_keys=dst_param_keys,
src_params=src_params,
src_param_keys=src_param_keys,
ctx=ctx)
elif args.src_fwk == "mxnet" and args.dst_fwk == "gluon":
convert_mx2gl(
dst_net=dst_net,
dst_params_file_path=args.dst_params,
dst_params=dst_params,
dst_param_keys=dst_param_keys,
src_params=src_params,
src_param_keys=src_param_keys,
src_model=args.src_model,
ctx=ctx)
elif args.src_fwk == "tensorflow" and args.dst_fwk == "tensorflow":
convert_tf2tf(
dst_params_file_path=args.dst_params,
dst_params=dst_params,
dst_param_keys=dst_param_keys,
src_params=src_params,
src_param_keys=src_param_keys)
elif args.src_fwk == "tensorflow" and args.dst_fwk == "gluon":
convert_tf2gl(
dst_net=dst_net,
dst_params_file_path=args.dst_params,
dst_params=dst_params,
dst_param_keys=dst_param_keys,
src_params=src_params,
src_param_keys=src_param_keys,
ctx=ctx)
elif args.src_fwk == "tf2" and args.dst_fwk == "tfl":
convert_tf22tfl(
src_net=src_net,
dst_params_file_path=args.dst_params)
else:
raise NotImplementedError
logging.info("Convert {}-model {} into {}-model {}".format(
args.src_fwk, args.src_model, args.dst_fwk, args.dst_model))
if __name__ == '__main__':
main()
| [
"[email protected]"
]
| |
687597834631051ff823fce8248de22de124ebb5 | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p02553/s242742740.py | 19cb61040111d626c69f31e200e94dcd971f47c1 | []
| no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 78 | py | a,b,c,d = map(int,input().split())
s = max(max(a*c,a*d),max(b*c,b*d))
print(s) | [
"[email protected]"
]
| |
4e860abb10b550c48e16c59c692326f5f75730fa | f08177abce14672891c34c0eecc064f2b42f2441 | /hydrus/client/db/ClientDBMappingsCounts.py | 5df0ef9fa636429b38e960148136fbb76dc8a01a | [
"WTFPL"
]
| permissive | bbappserver/hydrus-userpatch | d217006cc7691a08c11c98ddfd2415da56d6b27d | ef19e2167e24433d960a0811a93a683d62203518 | refs/heads/master | 2022-09-19T03:36:16.575489 | 2022-05-04T21:40:27 | 2022-05-04T21:40:27 | 179,595,032 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 22,801 | py | import collections
import sqlite3
import typing
from hydrus.core import HydrusConstants as HC
from hydrus.core import HydrusDBBase
from hydrus.client import ClientData
from hydrus.client.db import ClientDBModule
from hydrus.client.db import ClientDBServices
from hydrus.client.metadata import ClientTags
def GenerateCombinedFilesMappingsCountsCacheTableName( tag_display_type, tag_service_id ):
if tag_display_type == ClientTags.TAG_DISPLAY_STORAGE:
name = 'combined_files_ac_cache'
elif tag_display_type == ClientTags.TAG_DISPLAY_ACTUAL:
name = 'combined_files_display_ac_cache'
suffix = str( tag_service_id )
combined_counts_cache_table_name = 'external_caches.{}_{}'.format( name, suffix )
return combined_counts_cache_table_name
def GenerateSpecificCountsCacheTableName( tag_display_type, file_service_id, tag_service_id ):
if tag_display_type == ClientTags.TAG_DISPLAY_STORAGE:
name = 'specific_ac_cache'
elif tag_display_type == ClientTags.TAG_DISPLAY_ACTUAL:
name = 'specific_display_ac_cache'
suffix = '{}_{}'.format( file_service_id, tag_service_id )
specific_counts_cache_table_name = 'external_caches.{}_{}'.format( name, suffix )
return specific_counts_cache_table_name
class ClientDBMappingsCounts( ClientDBModule.ClientDBModule ):
CAN_REPOPULATE_ALL_MISSING_DATA = True
def __init__( self, cursor: sqlite3.Cursor, modules_services: ClientDBServices.ClientDBMasterServices ):
self.modules_services = modules_services
ClientDBModule.ClientDBModule.__init__( self, 'client mappings counts', cursor )
self._missing_storage_tag_service_pairs = set()
self._missing_display_tag_service_pairs = set()
def _GetServiceTableGenerationDictSingle( self, tag_display_type, file_service_id, tag_service_id ):
table_dict = {}
table_name = self.GetCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
# the version was earlier here but we updated when adding combined delete files and ipfs to these tables
version = 465
table_dict[ table_name ] = ( 'CREATE TABLE IF NOT EXISTS {} ( tag_id INTEGER PRIMARY KEY, current_count INTEGER, pending_count INTEGER );', version )
return table_dict
def _GetServiceTableGenerationDict( self, service_id ) -> dict:
tag_service_id = service_id
table_dict = {}
file_service_ids = list( self.modules_services.GetServiceIds( HC.FILE_SERVICES_WITH_SPECIFIC_MAPPING_CACHES ) )
file_service_ids.append( self.modules_services.combined_file_service_id )
for file_service_id in file_service_ids:
for tag_display_type in ( ClientTags.TAG_DISPLAY_STORAGE, ClientTags.TAG_DISPLAY_ACTUAL ):
single_table_dict = self._GetServiceTableGenerationDictSingle( tag_display_type, file_service_id, tag_service_id )
table_dict.update( single_table_dict )
return table_dict
def _GetServiceIdsWeGenerateDynamicTablesFor( self ):
return self.modules_services.GetServiceIds( HC.REAL_TAG_SERVICES )
def _RepairRepopulateTables( self, table_names, cursor_transaction_wrapper: HydrusDBBase.DBCursorTransactionWrapper ):
file_service_ids = list( self.modules_services.GetServiceIds( HC.FILE_SERVICES_WITH_SPECIFIC_TAG_LOOKUP_CACHES ) )
file_service_ids.append( self.modules_services.combined_file_service_id )
tag_service_ids = list( self.modules_services.GetServiceIds( HC.REAL_TAG_SERVICES ) )
for tag_service_id in tag_service_ids:
for file_service_id in file_service_ids:
storage_table_dict_for_this = self._GetServiceTableGenerationDictSingle( ClientTags.TAG_DISPLAY_STORAGE, file_service_id, tag_service_id )
storage_table_names_for_this = set( storage_table_dict_for_this.keys() )
if not storage_table_names_for_this.isdisjoint( table_names ):
self._missing_storage_tag_service_pairs.add( ( file_service_id, tag_service_id ) )
display_table_dict_for_this = self._GetServiceTableGenerationDictSingle( ClientTags.TAG_DISPLAY_ACTUAL, file_service_id, tag_service_id )
display_table_names_for_this = set( display_table_dict_for_this.keys() )
if not display_table_names_for_this.isdisjoint( table_names ):
self._missing_display_tag_service_pairs.add( ( file_service_id, tag_service_id ) )
def AddCounts( self, tag_display_type, file_service_id, tag_service_id, ac_cache_changes ):
counts_cache_table_name = self.GetCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
new_tag_ids = set()
new_local_tag_ids = set()
for ( tag_id, current_delta, pending_delta ) in ac_cache_changes:
self._Execute( 'INSERT OR IGNORE INTO {} ( tag_id, current_count, pending_count ) VALUES ( ?, ?, ? );'.format( counts_cache_table_name ), ( tag_id, current_delta, pending_delta ) )
if self._GetRowCount() > 0:
new_tag_ids.add( tag_id )
if file_service_id == self.modules_services.combined_local_file_service_id: # and tag_service_id = all known tags
new_local_tag_ids.add( tag_id )
if len( new_tag_ids ) < len( ac_cache_changes ):
self._ExecuteMany( 'UPDATE {} SET current_count = current_count + ?, pending_count = pending_count + ? WHERE tag_id = ?;'.format( counts_cache_table_name ), ( ( num_current, num_pending, tag_id ) for ( tag_id, num_current, num_pending ) in ac_cache_changes if tag_id not in new_tag_ids ) )
return ( new_tag_ids, new_local_tag_ids )
def ClearCounts( self, tag_display_type, file_service_id, tag_service_id, keep_current = False, keep_pending = False ):
table_name = self.GetCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
if keep_current:
self._Execute( 'UPDATE {} SET pending_count = 0 WHERE pending_count > 0;'.format( table_name ) )
self._Execute( 'DELETE FROM {} WHERE current_count = 0 AND pending_count = 0;'.format( table_name ) )
elif keep_pending:
self._Execute( 'UPDATE {} SET current_count = 0 WHERE current_count > 0;'.format( table_name ) )
self._Execute( 'DELETE FROM {} WHERE current_count = 0 AND pending_count = 0;'.format( table_name ) )
else:
self._Execute( 'DELETE FROM {};'.format( table_name ) )
def CreateTables( self, tag_display_type, file_service_id, tag_service_id, populate_from_storage = False ):
table_generation_dict = self._GetServiceTableGenerationDictSingle( tag_display_type, file_service_id, tag_service_id )
for ( table_name, ( create_query_without_name, version_added ) ) in table_generation_dict.items():
self._Execute( create_query_without_name.format( table_name ) )
#
if tag_display_type == ClientTags.TAG_DISPLAY_ACTUAL and populate_from_storage:
display_table_name = self.GetCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
storage_table_name = self.GetCountsCacheTableName( ClientTags.TAG_DISPLAY_STORAGE, file_service_id, tag_service_id )
self._Execute( 'INSERT OR IGNORE INTO {} ( tag_id, current_count, pending_count ) SELECT tag_id, current_count, pending_count FROM {};'.format( display_table_name, storage_table_name ) )
def DropTables( self, tag_display_type, file_service_id, tag_service_id ):
table_name = self.GetCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
self._Execute( 'DROP TABLE IF EXISTS {};'.format( table_name ) )
def FilterExistingTagIds( self, tag_display_type, file_service_id, tag_service_id, tag_ids_table_name ):
counts_cache_table_name = self.GetCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
return self._STS( self._Execute( 'SELECT tag_id FROM {} CROSS JOIN {} USING ( tag_id );'.format( tag_ids_table_name, counts_cache_table_name ) ) )
def GetAutocompleteCountEstimate( self, tag_display_type: int, tag_service_id: int, file_service_id: int, tag_ids: typing.Collection[ int ], include_current_tags: bool, include_pending_tags: bool ):
count = 0
if not include_current_tags and not include_pending_tags:
return count
( current_count, pending_count ) = self.GetAutocompleteCountEstimateStatuses( tag_display_type, tag_service_id, file_service_id, tag_ids )
if include_current_tags:
count += current_count
if include_current_tags:
count += pending_count
return count
def GetAutocompleteCountEstimateStatuses( self, tag_display_type: int, tag_service_id: int, file_service_id: int, tag_ids: typing.Collection[ int ] ):
include_current_tags = True
include_pending_tags = True
ids_to_count = self.GetCounts( tag_display_type, tag_service_id, file_service_id, tag_ids, include_current_tags, include_pending_tags )
current_count = 0
pending_count = 0
for ( current_min, current_max, pending_min, pending_max ) in ids_to_count.values():
current_count += current_min
pending_count += pending_min
return ( current_count, pending_count )
def GetCounts( self, tag_display_type, tag_service_id, file_service_id, tag_ids, include_current, include_pending, domain_is_cross_referenced = True, zero_count_ok = False, job_key = None, tag_ids_table_name = None ):
if len( tag_ids ) == 0:
return {}
if tag_service_id == self.modules_services.combined_tag_service_id and file_service_id == self.modules_services.combined_file_service_id:
ids_to_count = {}
return ids_to_count
if tag_service_id == self.modules_services.combined_tag_service_id:
search_tag_service_ids = self.modules_services.GetServiceIds( HC.REAL_TAG_SERVICES )
else:
search_tag_service_ids = [ tag_service_id ]
cache_results = []
if len( tag_ids ) > 1:
if tag_ids_table_name is None:
with self._MakeTemporaryIntegerTable( tag_ids, 'tag_id' ) as temp_tag_id_table_name:
for search_tag_service_id in search_tag_service_ids:
if job_key is not None and job_key.IsCancelled():
return {}
cache_results.extend( self.GetCountsForTags( tag_display_type, file_service_id, search_tag_service_id, temp_tag_id_table_name ) )
else:
for search_tag_service_id in search_tag_service_ids:
if job_key is not None and job_key.IsCancelled():
return {}
cache_results.extend( self.GetCountsForTags( tag_display_type, file_service_id, search_tag_service_id, tag_ids_table_name ) )
else:
( tag_id, ) = tag_ids
for search_tag_service_id in search_tag_service_ids:
cache_results.extend( self.GetCountsForTag( tag_display_type, file_service_id, search_tag_service_id, tag_id ) )
#
ids_to_count = {}
for ( tag_id, current_count, pending_count ) in cache_results:
if not include_current:
current_count = 0
if not include_pending:
pending_count = 0
if current_count == 0 and pending_count == 0 and not zero_count_ok:
continue
current_max = current_count
pending_max = pending_count
if domain_is_cross_referenced:
# file counts are perfectly accurate
current_min = current_count
pending_min = pending_count
else:
# for instance this is a search for 'my files' deleted files, but we are searching on 'all deleted files' domain
current_min = 0
pending_min = 0
if tag_id in ids_to_count:
( existing_current_min, existing_current_max, existing_pending_min, existing_pending_max ) = ids_to_count[ tag_id ]
( current_min, current_max ) = ClientData.MergeCounts( existing_current_min, existing_current_max, current_min, current_max )
( pending_min, pending_max ) = ClientData.MergeCounts( existing_pending_min, existing_pending_max, pending_min, pending_max )
ids_to_count[ tag_id ] = ( current_min, current_max, pending_min, pending_max )
if zero_count_ok:
for tag_id in tag_ids:
if tag_id not in ids_to_count:
ids_to_count[ tag_id ] = ( 0, 0, 0, 0 )
return ids_to_count
def GetCountsCacheTableName( self, tag_display_type, file_service_id, tag_service_id ):
if file_service_id == self.modules_services.combined_file_service_id:
counts_cache_table_name = GenerateCombinedFilesMappingsCountsCacheTableName( tag_display_type, tag_service_id )
else:
counts_cache_table_name = GenerateSpecificCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
return counts_cache_table_name
def GetCountsEstimate( self, tag_display_type: int, tag_service_id: int, file_service_id: int, tag_ids: typing.Collection[ int ], include_current_tags: bool, include_pending_tags: bool ):
ids_to_count = collections.Counter()
if not include_current_tags and not include_pending_tags:
return ids_to_count
ids_to_count_statuses = self.GetCountsEstimateStatuses( tag_display_type, tag_service_id, file_service_id, tag_ids )
for ( tag_id, ( current_count, pending_count ) ) in ids_to_count_statuses.items():
count = 0
if include_current_tags:
count += current_count
if include_current_tags:
count += pending_count
ids_to_count[ tag_id ] = count
return ids_to_count
def GetCountsEstimateStatuses( self, tag_display_type: int, tag_service_id: int, file_service_id: int, tag_ids: typing.Collection[ int ] ):
include_current_tags = True
include_pending_tags = True
ids_to_count_full = self.GetCounts( tag_display_type, tag_service_id, file_service_id, tag_ids, include_current_tags, include_pending_tags )
ids_to_count_statuses = collections.defaultdict( lambda: ( 0, 0 ) )
for ( tag_id, ( current_min, current_max, pending_min, pending_max ) ) in ids_to_count_full.items():
ids_to_count_statuses[ tag_id ] = ( current_min, pending_min )
return ids_to_count_statuses
def GetCountsForTag( self, tag_display_type, file_service_id, tag_service_id, tag_id ):
counts_cache_table_name = self.GetCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
return self._Execute( 'SELECT tag_id, current_count, pending_count FROM {} WHERE tag_id = ?;'.format( counts_cache_table_name ), ( tag_id, ) ).fetchall()
def GetCountsForTags( self, tag_display_type, file_service_id, tag_service_id, temp_tag_id_table_name ):
counts_cache_table_name = self.GetCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
# temp tags to counts
return self._Execute( 'SELECT tag_id, current_count, pending_count FROM {} CROSS JOIN {} USING ( tag_id );'.format( temp_tag_id_table_name, counts_cache_table_name ) ).fetchall()
def GetCurrentPendingPositiveCountsAndWeights( self, tag_display_type, file_service_id, tag_service_id, tag_ids, tag_ids_table_name = None ):
include_current = True
include_pending = True
ids_to_count = self.GetCounts( tag_display_type, tag_service_id, file_service_id, tag_ids, include_current, include_pending, tag_ids_table_name = tag_ids_table_name )
current_tag_ids = set()
current_tag_weight = 0
pending_tag_ids = set()
pending_tag_weight = 0
for ( tag_id, ( current_min, current_max, pending_min, pending_max ) ) in ids_to_count.items():
if current_min > 0:
current_tag_ids.add( tag_id )
current_tag_weight += current_min
if pending_min > 0:
pending_tag_ids.add( tag_id )
pending_tag_weight += pending_min
return ( current_tag_ids, current_tag_weight, pending_tag_ids, pending_tag_weight )
def GetMissingTagCountServicePairs( self ):
return ( self._missing_storage_tag_service_pairs, self._missing_display_tag_service_pairs )
def GetQueryPhraseForCurrentTagIds( self, tag_display_type, file_service_id, tag_service_id ):
counts_cache_table_name = self.GetCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
return 'SELECT tag_id FROM {} WHERE current_count > 0'.format( counts_cache_table_name )
def GetTablesAndColumnsThatUseDefinitions( self, content_type: int ) -> typing.List[ typing.Tuple[ str, str ] ]:
tables_and_columns = []
if content_type == HC.CONTENT_TYPE_TAG:
table_dict = self._GetServicesTableGenerationDict()
for table_name in table_dict.keys():
tables_and_columns.append( ( table_name, 'tag_id' ) )
return tables_and_columns
def GetTotalCurrentCount( self, tag_display_type, file_service_id, tag_service_id ):
counts_cache_table_name = self.GetCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
result = self._Execute( 'SELECT SUM( current_count ) FROM {};'.format( counts_cache_table_name ) ).fetchone()
if result is None or result[0] is None:
count = 0
else:
( count, ) = result
return count
def ReduceCounts( self, tag_display_type, file_service_id, tag_service_id, ac_cache_changes ):
# this takes positive counts, despite ultimately being a reduce guy
counts_cache_table_name = self.GetCountsCacheTableName( tag_display_type, file_service_id, tag_service_id )
deleted_tag_ids = set()
deleted_local_tag_ids = set()
for ( tag_id, current_delta, pending_delta ) in ac_cache_changes:
self._Execute( 'DELETE FROM {} WHERE tag_id = ? AND current_count = ? AND pending_count = ?;'.format( counts_cache_table_name ), ( tag_id, current_delta, pending_delta ) )
if self._GetRowCount() > 0:
deleted_tag_ids.add( tag_id )
if file_service_id == self.modules_services.combined_local_file_service_id: # and tag_service_id = all known tags
deleted_local_tag_ids.add( tag_id )
if len( deleted_tag_ids ) < len( ac_cache_changes ):
self._ExecuteMany( 'UPDATE {} SET current_count = current_count - ?, pending_count = pending_count - ? WHERE tag_id = ?;'.format( counts_cache_table_name ), ( ( current_delta, pending_delta, tag_id ) for ( tag_id, current_delta, pending_delta ) in ac_cache_changes if tag_id not in deleted_tag_ids ) )
return ( deleted_tag_ids, deleted_local_tag_ids )
| [
"[email protected]"
]
| |
1265bb8736bd9b11afc120fcc3bdcb77428869ec | 29a4c1e436bc90deaaf7711e468154597fc379b7 | /modules/ieee/doc2/nextpow2.py | 7c5fcf15c55e96875561be4f21550ed813ecbc7a | [
"BSL-1.0"
]
| permissive | brycelelbach/nt2 | 31bdde2338ebcaa24bb76f542bd0778a620f8e7c | 73d7e8dd390fa4c8d251c6451acdae65def70e0b | refs/heads/master | 2021-01-17T12:41:35.021457 | 2011-04-03T17:37:15 | 2011-04-03T17:37:15 | 1,263,345 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,923 | py | [{'functor': {'arity': '1',
'call_types': [],
'ret_arity': '0',
'rturn': {'default': 'typename nt2::meta::as_integer<typename boost::result_of<nt2::meta::floating(T)>::type, signed>::type'},
'type_defs': [],
'types': ['real_', 'unsigned_int_', 'signed_int_']},
'unit': {'global_header': {'first_stamp': 'modified by jt the 04/12/2010',
'included': [],
'notes': [],
'ranges': {'real_': [['T(-10)', 'T(10)']],
'signed_int_': [['-100', '100']],
'unsigned_int_': [['0', '100']]},
'specific_values': {'default': {},
'real_': {'nt2::Inf<T>()': 'nt2::Zero<r_t>()',
'nt2::Minf<T>()': 'nt2::Zero<r_t>()',
'nt2::Mone<T>()': 'nt2::Zero<r_t>()',
'nt2::One<T>()': 'nt2::Zero<r_t>()',
'nt2::Zero<T>()': 'nt2::Zero<r_t>()'},
'signed_int_': {'nt2::Mone<T>()': 'nt2::Zero<r_t>()',
'nt2::One<T>()': 'nt2::Zero<r_t>()',
'nt2::Zero<T>()': 'nt2::Zero<r_t>()'},
'unsigned_int_': {'nt2::One<T>()': 'nt2::Zero<r_t>()',
'nt2::Zero<T>()': 'nt2::Zero<r_t>()'}},
'stamp': 'modified by jt the 12/12/2010',
'verif_test': {}}},
'version': '0.1'}] | [
"[email protected]"
]
| |
969ff18c3b0c3ebd06ccfc2dc0dfe97216e6a725 | 6a47ec6800610ea93479f91505e73a3eb4f34ae0 | /user/serviced.py | 74e25a9df84b86e320e670d436afb861e42769b5 | []
| no_license | risification/queue_project | 1158aac7bae3b04f98c106c23c27281c96bcaf41 | e85f9f2d1835f10a0247a569f88d4cb29803538a | refs/heads/master | 2023-04-26T08:05:25.573243 | 2021-06-07T21:03:47 | 2021-06-07T21:03:47 | 374,119,444 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 579 | py | from django.contrib.auth.models import User
from django.core.mail import EmailMessage
def mailing(username):
email_list = []
obj = User.objects.filter(is_superuser=True)
for user in obj:
email_list.append(user.email)
subjects = 'hi'
body = f'User with {username} register in database, pls check him !'
email = EmailMessage(subject=subjects, body=body, to=email_list)
email.send()
def validate_password(password):
if len(password) >= 8 and password.isdigit() and password.isalpha():
return True
else:
return False
| [
"[email protected]"
]
| |
5a4d8c674b599a2c01fdc8fd795bf0ea39b3d9b4 | 0ddcfcbfc3faa81c79e320c34c35a972dab86498 | /puzzles/orderly_queue.py | a373cf1f75c26e6261bdd30af8d0855a2660bb45 | []
| no_license | IvanWoo/coding-interview-questions | 3311da45895ac4f3c394b22530079c79a9215a1c | 1312305b199b65a11804a000432ebe28d1fba87e | refs/heads/master | 2023-08-09T19:46:28.278111 | 2023-06-21T01:47:07 | 2023-06-21T01:47:07 | 135,307,912 | 0 | 0 | null | 2023-07-20T12:14:38 | 2018-05-29T14:24:43 | Python | UTF-8 | Python | false | false | 1,048 | py | # https://leetcode.com/problems/orderly-queue/
"""
ou are given a string s and an integer k. You can choose one of the first k letters of s and append it at the end of the string..
Return the lexicographically smallest string you could have after applying the mentioned step any number of moves.
Example 1:
Input: s = "cba", k = 1
Output: "acb"
Explanation:
In the first move, we move the 1st character 'c' to the end, obtaining the string "bac".
In the second move, we move the 1st character 'b' to the end, obtaining the final result "acb".
Example 2:
Input: s = "baaca", k = 3
Output: "aaabc"
Explanation:
In the first move, we move the 1st character 'b' to the end, obtaining the string "aacab".
In the second move, we move the 3rd character 'c' to the end, obtaining the final result "aaabc".
Constraints:
1 <= k <= s.length <= 1000
s consist of lowercase English letters.
"""
def orderly_queue(s: str, k: int) -> str:
if k == 1:
return min([s[i:] + s[:i] for i in range(len(s))])
else:
return "".join(sorted(s))
| [
"[email protected]"
]
| |
7267956f1f7b465699fb043dc755525ce97b5ccf | 2c73882fc59ca85f4854a43bcda8cc9edd282b8d | /polls_api/views.py | 2664dfc220c7e377fed156deed7d18e979f75115 | []
| no_license | mjstealth/guide-to-backbonejs-with-django | 540236f3535ee171c3aa4c43a1be9394a8a7e4bc | e7d5016c800e1e0e282da0386cc6112d4eed63c1 | refs/heads/master | 2021-01-17T22:40:28.191509 | 2012-09-04T22:17:12 | 2012-09-04T22:17:12 | 5,679,419 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 990 | py | from django.shortcuts import get_object_or_404
from django.core.urlresolvers import reverse
from djangorestframework import views
from djangorestframework.response import Response
from polls.forms import PollForm
from polls.models import Poll
from .resources import PollResource
class PollResults (views.View):
def get(self, request, poll_id):
poll = get_object_or_404(Poll.objects.all(), pk=poll_id)
results = PollResource().serialize(poll)
return results
class PollVotes (views.View):
def post(self, request, poll_id):
poll = get_object_or_404(Poll.objects.all(), pk=poll_id)
form = PollForm(request.POST, instance=poll)
if form.is_valid():
form.save()
else:
return Response(content=form.errors, status=400)
return Response(status=303, headers={'Location': reverse('polls_api_results', args=[poll_id])})
poll_results_view = PollResults.as_view()
poll_votes_view = PollVotes.as_view()
| [
"[email protected]"
]
| |
42a8dac1509c16a1f9ee4746a23db2e89449bf64 | 11d265eba2ced9de43c339e4014c779b521320cd | /accounts/migrations/0004_auto_20200423_2253.py | eccb31bc3dd7e0a1872e9574429fc5cdc2edd129 | []
| no_license | Sloshpit/budget_old | d9271de625cd7e3aa66ccbec501b005e50cd2812 | a5603996b026542adb3bc8c578c03bcb843bea01 | refs/heads/master | 2022-04-23T08:42:43.377827 | 2020-04-25T14:40:39 | 2020-04-25T14:40:39 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 606 | py | # Generated by Django 3.0.5 on 2020-04-24 02:53
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('accounts', '0003_auto_20200423_2251'),
]
operations = [
migrations.RenameField(
model_name='account',
old_name='transaction_date',
new_name='balance_date',
),
migrations.RemoveField(
model_name='account',
name='transaction',
),
migrations.RemoveField(
model_name='account',
name='transaction_amount',
),
]
| [
"[email protected]"
]
| |
f71387df36af9f3c0cb4897aa762c93b0ccbdb5f | 3f60b999ea7bda83c9586f75f52463dc20337f24 | /sensitive_user_portrait/weibo_rank/Offline_task.py | de919db3a2449e8b9e35b521386aa9943040a4ae | []
| no_license | jianjian0dandan/sensitive_user_portrait | 629e49ce71db92b50634bac9c828811cdb5381e9 | cacc30267ebc0e621b1d48d4f1206277a0f48123 | refs/heads/master | 2021-01-20T23:18:07.138057 | 2016-05-22T12:09:40 | 2016-05-22T12:09:40 | 42,869,287 | 0 | 0 | null | 2015-09-21T13:55:12 | 2015-09-21T13:55:11 | null | UTF-8 | Python | false | false | 6,454 | py | #-*-coding: utf-8 -*-
import datetime
import json
import time as TIME
from elasticsearch import Elasticsearch
from time_utils import ts2datetime, datetime2ts, ts2date
from global_utils import es_user_portrait as es
WEIBO_RANK_KEYWORD_TASK_INDEX = 'weibo_rank_keyword_task'
WEIBO_RANK_KEYWORD_TASK_TYPE = 'weibo_rank_task'
MAX_ITEMS = 2 ** 10
def add_task(user_name , type="keyword", range="all", pre='flow_text_', during='1', start_time='2013-09-07', end_time='2013-09-07', keyword='hello,world', sort_norm='reposts_count', sort_scope='all_limit_keyword', time=1, number=100):
time_now = int(TIME.time())
task_id = user_name + "-" + str(time_now)
tmp_list = keyword.split(',')
keyword_list = []
for item in tmp_list:
if item:
keyword_list.append(item)
body_json = {
'submit_user' : user_name ,
'keyword' : json.dumps(keyword_list),
'keyword_string': "&".join(keyword_list),
'submit_time' : ts2datetime(time_now),
'create_time': time_now,
'end_time' : datetime2ts(end_time),
'search_type' : type,
'status':0,
'range' : range ,
'user_ts' : user_name + '-'+ str(time_now),
'pre' : pre,
'during' : during ,
'start_time' : datetime2ts(start_time) ,
'sort_norm' : sort_norm ,
'sort_scope' : sort_scope,
'time' : time ,
'isall' : isall,
'number': number
}
es.index(index = WEIBO_RANK_KEYWORD_TASK_INDEX , doc_type=WEIBO_RANK_KEYWORD_TASK_TYPE , id=task_id, body=body_json)
return body_json["user_ts"]
def search_weibo_task(user_name):
c_result = {}
query = {"query":{"bool":{"must":[{"term":{"user_rank_task.submit_user":user_name}}]}},"size":MAX_ITEMS,"sort":[{"create_time":{"order":"desc"}}],"fields":["status","search_type","keyword","submit_user","sort_scope","sort_norm","start_time","user_ts","end_time", "create_time", 'number']}
if 1:
return_list = []
result = es.search(index=WEIBO_RANK_KEYWORD_TASK_INDEX , doc_type=WEIBO_RANK_KEYWORD_TASK_TYPE , body=query)['hits']
c_result['flag'] = True
for item in result['hits']:
result_temp = {}
result_temp['submit_user'] = item['fields']['submit_user'][0]
result_temp['search_type'] = item['fields']['search_type'][0]
result_temp['keyword'] = json.loads(item['fields']['keyword'][0])
result_temp['sort_scope'] = item['fields']['sort_scope'][0]
result_temp['sort_norm'] = item['fields']['sort_norm'][0]
result_temp['start_time'] = ts2datetime(item['fields']['start_time'][0])
result_temp['end_time'] = ts2datetime(item['fields']['end_time'][0])
result_temp['status'] = item['fields']['status'][0]
result_temp['create_time'] = ts2date(item['fields']['create_time'][0])
result_temp['search_id'] = item['fields']['user_ts'][0]
tmp = item['fields'].get('number', 0)
if tmp:
result_temp['number'] = int(tmp[0])
else:
result_temp['number'] = 100
return_list.append(result_temp)
c_result['data'] = return_list
return c_result
def getResult(search_id):
item = es.get(index=WEIBO_RANK_KEYWORD_TASK_INDEX , doc_type=WEIBO_RANK_KEYWORD_TASK_TYPE , id=search_id)
try:
result_obj = {}
result_obj['keyword'] = json.loads(item['_source']['keyword'])
result_obj['sort_scope'] = item['_source']['sort_scope']
result_obj['sort_norm'] = item['_source']['sort_norm']
result_obj['start_time'] = ts2datetime(item['_source']['start_time'])
result_obj['end_time'] =ts2datetime(item['_source']['end_time'])
result_obj['result'] = json.loads(item['_source']['result'])
result_obj['text_results'] = json.loads(item['_source']['text_results'])
result_obj['number'] = item['_source']['number']
return result_obj
except :
return []
def delOfflineTask(search_id):
es.delete(index=WEIBO_RANK_KEYWORD_TASK_INDEX , doc_type=WEIBO_RANK_KEYWORD_TASK_TYPE , id = search_id )
return True
def sort_task(user, keyword, status, start_time, end_time, submit_time):
query_body = {
"query":{
"filtered":{
"filter":{
"bool":{
"must":[
{"term":{"submit_user": user}}
]
}
}
}
},
"size": 10000,
"sort":{"submit_time":{"order":"desc"}}
}
query_list = []
if keyword:
keyword_list = keyword.split(',')
query_list.append({"terms":{"keyword_string":keyword_list}})
if status != 2:
query_list.append({"term":{"status": status}})
if start_time and end_time:
start_ts = datetime2ts(start_time)
end_ts = datetime2ts(end_time)
query_list.append({"range":{"start_time":{"gte":start_ts, "lte":end_ts}}})
query_list.append({"range":{"end_time":{"gte":start_ts, "lte":end_ts}}})
if submit_time:
query_list.append({"term":{"submit_time": submit_time}})
if query_list:
query_body["query"]["filtered"]["filter"]["bool"]["must"].extend(query_list)
#print query_body
search_results = es.search(index=WEIBO_RANK_KEYWORD_TASK_INDEX, doc_type=WEIBO_RANK_KEYWORD_TASK_TYPE, body=query_body)["hits"]["hits"]
results = []
if search_results:
for item in search_results:
iter_item = item['_source']
tmp = []
tmp.append(iter_item['search_type'])
tmp.append(json.loads(iter_item['keyword']))
tmp.append(ts2datetime(iter_item['start_time']))
tmp.append(ts2datetime(iter_item['end_time']))
tmp.append(iter_item['range'])
tmp.append(ts2date(iter_item['create_time']))
tmp.append(iter_item['status'])
tmp.append(iter_item['sort_norm'])
tmp.append(iter_item['sort_scope'])
tmp.append(item['_id']) # task_name
results.append(tmp)
return results
if __name__ == "__main__":
print search_task("[email protected]", [], 0, '', '', '2016-04-12')
| [
"[email protected]"
]
| |
43bf411f069beff4b058247c875c82e5f19f01bc | 4b1965b3d831ab54998973afb26f4327ed010336 | /info/user/views.py | 4edbf7fa25d4329141c2449cb244798b16174185 | []
| no_license | yc12192057/information11_mm | 7d353dfe61962eb0bd2c29b7f0b54a2a62953262 | 2e4052d130b200797aa8a57a0d37f8267d523a8b | refs/heads/master | 2020-03-21T10:22:23.558714 | 2018-06-24T02:34:25 | 2018-06-24T02:34:25 | 138,447,718 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,383 | py | from flask import current_app
from flask import g
from flask import request
from flask import session
from info.utils.image_storage import storage
from info import constants
from info import db
from info.models import Category, News
from info.utils.response_code import RET
from . import profile_blue
from flask import render_template,redirect,jsonify
from info.utils.common import user_login_data
from info.utils.image_storage import storage
@profile_blue.route("/news_list")
@user_login_data
def news_list():
page = request.args.get("p",1)
try:
page = int(page)
except Exception as e:
current_app.logger.error(e)
page = 1
user = g.user
paginate = News.query.filter(News.user_id == user.id).paginate(page,2,False)
items = paginate.items
current_page = paginate.page
total_page = paginate.pages
news_list = []
for item in items:
news_list.append(item.to_review_dict())
data = {
"current_page":current_page,
"total_page":total_page,
"news_list":news_list
}
return render_template("news/user_news_list.html",data = data)
@profile_blue.route("/news_release",methods = ["GET","POST"])
@user_login_data
def news_release():
if request.method == "GET":
# 首先获取到新闻分类，然后传递到模板页码，进行展示
category_list = Category.query.all()
categorys = []
for category in category_list:
categorys.append(category.to_dict())
# 删除列表当中０的元素
categorys.pop(0)
data = {
"categories":categorys
}
return render_template("news/user_news_release.html",data = data)
# 获取到表单页码提交过来的数据，获取的是用户发布的新闻数据
title = request.form.get("title")
category_id = request.form.get("category_id")
digest = request.form.get("digest")
index_image = request.files.get("index_image")
content = request.form.get("content")
if not all([title,category_id,digest,index_image,content]):
return jsonify(errno = RET.PARAMERR,errmsg = "参数错误")
user = g.user
index_image = index_image.read()
key = storage(index_image)
# 用户发布完成之后，我们需要把当前发布的新闻存储到数据
news = News()
news.title = title
news.source = "个人来源"
news.digest = digest
news.content = content
news.index_image_url = constants.QINIU_DOMIN_PREFIX + key
news.category_id = category_id
news.user_id = user.id
# 当前的状态１表示正在审核中
news.status = 1
db.session.add(news)
db.session.commit()
return jsonify(errno = RET.OK,errmsg = "发布成功")
@profile_blue.route("/collection")
@user_login_data
def collection():
# 当前表示用户所有收藏的新闻，获取所有新闻涉及到分页，那么肯定是从第一页开始
page = request.args.get("p",1)
try:
page = int(page)
except Exception as e:
current_app.logger.error(e)
page = 1
user = g.user
# 获取到当前登陆用户的所有的收藏新闻列表
# 第一个参数表示页码
# 第二个参数表示当前每个页码一共有多少条数据
paginate = user.collection_news.paginate(page,10,False)
items = paginate.items
current_page = paginate.page
total_page = paginate.pages
collections = []
for item in items:
collections.append(item.to_dict())
data = {
"collections":collections,
"current_page":current_page,
"total_page":total_page,
}
return render_template("news/user_collection.html",data = data)
"""修改密码"""
@profile_blue.route("/pass_info",methods = ["GET","POST"])
@user_login_data
def pass_info():
if request.method == "GET":
return render_template("news/user_pass_info.html")
user = g.user
old_password = request.json.get("old_password")
new_password = request.json.get("new_password")
if not all([old_password,new_password]):
return jsonify(errno = RET.PARAMERR,errmsg = "请输入密码")
# 判断旧的密码是否正确，只有当旧密码正确，才能修改新的密码
if not user.check_password(old_password):
return jsonify(errno = RET.PARAMERR,errmsg = "旧密码错误")
# 如果旧密码正确，那么直接更新到当前的数据库里面
user.password = new_password
db.session.commit()
return jsonify(errno = RET.OK,errmsg = "密码修改成功")
@profile_blue.route("/pic_info",methods= ["GET","POST"])
@user_login_data
def pic_info():
user = g.user
if request.method == "GET":
data = {
"user_info": user.to_dict() if user else None
}
return render_template("news/user_pic_info.html", data=data)
avatar = request.files.get("avatar").read()
# 如果上传成功,那么就会返回一个url地址,或者叫做key
# 如果想在浏览器里面浏览刚刚 上传的图片,那么必须通过
# 七牛的地址 + 刚刚返回的url
# http: // oyucyko3w.bkt.clouddn.com / + url
url = storage(avatar)
user.avatar_url = url
db.session.commit()
return jsonify(errno = RET.OK,errmsg = "上传成功",data={"avatar_url": constants.QINIU_DOMIN_PREFIX + url})
"""修改个人信息"""
@profile_blue.route("/base_info",methods = ["GET","POST"])
@user_login_data
def base_info():
user = g.user
if request.method == "GET":
data = {
"user_info": user.to_dict() if user else None
}
return render_template("news/user_base_info.html",data = data)
nick_name = request.json.get("nick_name")
signature = request.json.get("signature")
gender = request.json.get("gender")
user.nick_name = nick_name
user.signature = signature
user.gender = gender
# 更新数据库
db.session.commit()
# 更新session里面的数据
session["nick_name"] = user.nick_name
return jsonify(errno = RET.OK,errmsg = "修改成功")
@profile_blue.route("/info")
@user_login_data
def info():
user = g.user
if not user:
# 重新跳转到首页
return redirect("/")
data = {
"user_info": user.to_dict() if user else None
}
return render_template("news/user.html",data = data) | [
"[email protected]"
]
| |
1fcb9a5bc116b70cacd5ddbd3646b35b3f6e0d8c | e0527bce5c53a196752d3a16adf50cb60754de5f | /05-How to Repeat Actions demos/02-dotty_dots.py | 47bb00a38d29385492c81d3cb4b98ea027472cab | []
| no_license | ARWA-ALraddadi/python-tutorial-for-beginners | ddeb657f419fbc176bea273bc9fb6b88d1894191 | 21cedfc47871ca4d25c2382464c60ab0a2121205 | refs/heads/master | 2023-06-30T20:24:30.688800 | 2021-08-08T08:22:29 | 2021-08-08T08:22:29 | 193,094,651 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,825 | py | #---------------------------------------------------------------------
#
# Dotty dots - Repeating actions with minor variations
#
# Up until now the only repetition we've seen has been the same action
# done many times. This simple demonstration shows how actions can
# be repeated with minor variations for each different value in a
# list.
#
# The program simply draws a grid of multi-coloured dots. Experiment
# with the code to produce different patterns!
#
# Some useful constant values, all in pixels
canvas_size = 600
max_coord = 250
grid_size = 20
dot_size = 15
# Set up a drawing canvas with a black background
from turtle import *
setup(canvas_size, canvas_size)
title("Dotty dots")
bgcolor('black')
# Set up some drawing characteristics
penup()
speed('fastest')
# Define a list of colours
column_colours = ['red', 'green', 'blue', 'yellow', 'white', 'orange',
'aqua', 'olive', 'misty rose', 'salmon', 'spring green',
'fuchsia', 'deep sky blue', 'silver', 'aquamarine',
'orange red', 'seashell', 'chocolate', 'light steel blue',
'tomato', 'chartreuse', 'bisque', 'dark orchid',
'powder blue', 'gainsboro']
# Determine how many rows we can fit between the maximum
# and minimum y-coords, separated by the given grid size
number_of_rows = max_coord * 2 // grid_size
# Do the same action multiple times, with the only
# difference being the row number
for row_number in range(number_of_rows):
# Go to the start of the row
goto(-max_coord, max_coord - row_number * grid_size)
# Do the same action multiple times, with the only
# difference being the colour
for colour in column_colours:
color(colour)
dot(dot_size)
forward(grid_size)
# Exit gracefully
hideturtle()
done()
| [
"[email protected]"
]
| |
93d1c4b038d428ed57ee5e22dfd6aa42a7abb5be | d0168d08221da5bf95c0dd511efeecddc9b0a73d | /profiles/migrations/0001_initial.py | cdaa8070d22ad1710c0d0041619d3e087f6b3285 | []
| no_license | alexarirok/roret-farm-software | 900b5842c7b39c4a19543e138a719e4b496531a9 | aa23fd729351f0d045b2e310dc839a8b4d639c6d | refs/heads/master | 2021-04-08T21:03:59.709224 | 2020-05-01T00:07:53 | 2020-05-01T00:07:53 | 248,808,990 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 853 | py | # Generated by Django 3.0.5 on 2020-04-23 21:43
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Profile',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('firstName', models.CharField(max_length=50, null=True)),
('lastName', models.CharField(max_length=50)),
('email', models.EmailField(blank=True, max_length=254)),
('phoneNumber', models.IntegerField(blank=True)),
('department', models.CharField(blank=True, max_length=30)),
('bio', models.TextField(blank=True, max_length=500)),
],
),
]
| [
"[email protected]"
]
| |
22f3c9cd4a9a1004dd7c7bb512643d2bbf2cbdb2 | 048405bfa0b48eaf78dd2298bdfe61472bd74eef | /scripts/multiproc.py | d0bf77fe6d0ed47a785ac752a9bddf3529d5e1ed | []
| no_license | sousa-edvan/greedy_grasp_ant | 2218ae20f707baa8d5428db76129e5c758a21d07 | 12f5ac99b4d0e9599a2ecd138f8f6a3551fe2473 | refs/heads/master | 2022-01-27T02:30:35.977782 | 2019-07-19T16:39:20 | 2019-07-19T16:39:20 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 654 | py | import os
import pandas as pd
import subprocess
from multiprocessing import cpu_count, Pool
from auto_tqdm import tqdm
from notipy_me import Notipy
def score(data):
csv = "scores/{data}.csv".format(data=data)
subprocess.run([
"./gga/greedy_grasp_ant",
"--data=data/{data}".format(data=data),
"--log={csv}".format(csv=csv),
"--all"
])
df = pd.read_csv(csv, index_col=0)[["mean"]]
df.columns = [data]
return df.T
data = os.listdir("data")
with Notipy():
with Pool(cpu_count()) as p:
df = pd.concat(list(tqdm(p.imap(score, data), total=len(data))))
df.to_csv("scores/all_scores.csv")
| [
"[email protected]"
]
| |
6e29a6e6c458214a2a869d88d2a4615e11373078 | adf2e802c7563e4b7b7cc279a54deceb6a803098 | /openapi_client/models/pdf_annotate_parameters.py | 9dc6b17b59f3a07476f94e6f5e9c90548eeb50bf | []
| no_license | Orpalis/passportpdfsdk-python | 2466f7568becf2bd386bd9e4e00b4e3c1e642727 | 257d305ca9e6508d44fe521a1e4721f1835e8d0e | refs/heads/master | 2022-04-24T15:58:21.257112 | 2020-04-27T11:09:37 | 2020-04-27T11:09:37 | 254,665,250 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,981 | py | # coding: utf-8
"""
PassportPDF API
Another brick in the cloud # noqa: E501
The version of the OpenAPI document: 1.0.1
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import six
from openapi_client.configuration import Configuration
class PdfAnnotateParameters(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 = {
'file_id': 'str',
'page_range': 'str',
'annotation_type': 'AnnotationType',
'sticky_note_annotation_parameters': 'StickyNoteAnnotationParameters',
'link_annotation_parameters': 'LinkAnnotationParameters',
'free_text_annotation_parameters': 'FreeTextAnnotationParameters',
'line_annotation_parameters': 'LineAnnotationParameters',
'square_and_circle_annotation_parameters': 'SquareAndCircleAnnotationParameters',
'rubber_stamp_annotation_parameters': 'RubberStampAnnotationParameters'
}
attribute_map = {
'file_id': 'FileId',
'page_range': 'PageRange',
'annotation_type': 'AnnotationType',
'sticky_note_annotation_parameters': 'StickyNoteAnnotationParameters',
'link_annotation_parameters': 'LinkAnnotationParameters',
'free_text_annotation_parameters': 'FreeTextAnnotationParameters',
'line_annotation_parameters': 'LineAnnotationParameters',
'square_and_circle_annotation_parameters': 'SquareAndCircleAnnotationParameters',
'rubber_stamp_annotation_parameters': 'RubberStampAnnotationParameters'
}
def __init__(self, file_id=None, page_range=None, annotation_type=None, sticky_note_annotation_parameters=None, link_annotation_parameters=None, free_text_annotation_parameters=None, line_annotation_parameters=None, square_and_circle_annotation_parameters=None, rubber_stamp_annotation_parameters=None, local_vars_configuration=None): # noqa: E501
"""PdfAnnotateParameters - 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._file_id = None
self._page_range = None
self._annotation_type = None
self._sticky_note_annotation_parameters = None
self._link_annotation_parameters = None
self._free_text_annotation_parameters = None
self._line_annotation_parameters = None
self._square_and_circle_annotation_parameters = None
self._rubber_stamp_annotation_parameters = None
self.discriminator = None
self.file_id = file_id
self.page_range = page_range
if annotation_type is not None:
self.annotation_type = annotation_type
if sticky_note_annotation_parameters is not None:
self.sticky_note_annotation_parameters = sticky_note_annotation_parameters
if link_annotation_parameters is not None:
self.link_annotation_parameters = link_annotation_parameters
if free_text_annotation_parameters is not None:
self.free_text_annotation_parameters = free_text_annotation_parameters
if line_annotation_parameters is not None:
self.line_annotation_parameters = line_annotation_parameters
if square_and_circle_annotation_parameters is not None:
self.square_and_circle_annotation_parameters = square_and_circle_annotation_parameters
if rubber_stamp_annotation_parameters is not None:
self.rubber_stamp_annotation_parameters = rubber_stamp_annotation_parameters
@property
def file_id(self):
"""Gets the file_id of this PdfAnnotateParameters. # noqa: E501
The identifier of the previously uploaded file to be processed. # noqa: E501
:return: The file_id of this PdfAnnotateParameters. # noqa: E501
:rtype: str
"""
return self._file_id
@file_id.setter
def file_id(self, file_id):
"""Sets the file_id of this PdfAnnotateParameters.
The identifier of the previously uploaded file to be processed. # noqa: E501
:param file_id: The file_id of this PdfAnnotateParameters. # noqa: E501
:type: str
"""
if self.local_vars_configuration.client_side_validation and file_id is None: # noqa: E501
raise ValueError("Invalid value for `file_id`, must not be `None`") # noqa: E501
self._file_id = file_id
@property
def page_range(self):
"""Gets the page_range of this PdfAnnotateParameters. # noqa: E501
Specifies the page or the range of page to be annotated. # noqa: E501
:return: The page_range of this PdfAnnotateParameters. # noqa: E501
:rtype: str
"""
return self._page_range
@page_range.setter
def page_range(self, page_range):
"""Sets the page_range of this PdfAnnotateParameters.
Specifies the page or the range of page to be annotated. # noqa: E501
:param page_range: The page_range of this PdfAnnotateParameters. # noqa: E501
:type: str
"""
if self.local_vars_configuration.client_side_validation and page_range is None: # noqa: E501
raise ValueError("Invalid value for `page_range`, must not be `None`") # noqa: E501
self._page_range = page_range
@property
def annotation_type(self):
"""Gets the annotation_type of this PdfAnnotateParameters. # noqa: E501
:return: The annotation_type of this PdfAnnotateParameters. # noqa: E501
:rtype: AnnotationType
"""
return self._annotation_type
@annotation_type.setter
def annotation_type(self, annotation_type):
"""Sets the annotation_type of this PdfAnnotateParameters.
:param annotation_type: The annotation_type of this PdfAnnotateParameters. # noqa: E501
:type: AnnotationType
"""
self._annotation_type = annotation_type
@property
def sticky_note_annotation_parameters(self):
"""Gets the sticky_note_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:return: The sticky_note_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:rtype: StickyNoteAnnotationParameters
"""
return self._sticky_note_annotation_parameters
@sticky_note_annotation_parameters.setter
def sticky_note_annotation_parameters(self, sticky_note_annotation_parameters):
"""Sets the sticky_note_annotation_parameters of this PdfAnnotateParameters.
:param sticky_note_annotation_parameters: The sticky_note_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:type: StickyNoteAnnotationParameters
"""
self._sticky_note_annotation_parameters = sticky_note_annotation_parameters
@property
def link_annotation_parameters(self):
"""Gets the link_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:return: The link_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:rtype: LinkAnnotationParameters
"""
return self._link_annotation_parameters
@link_annotation_parameters.setter
def link_annotation_parameters(self, link_annotation_parameters):
"""Sets the link_annotation_parameters of this PdfAnnotateParameters.
:param link_annotation_parameters: The link_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:type: LinkAnnotationParameters
"""
self._link_annotation_parameters = link_annotation_parameters
@property
def free_text_annotation_parameters(self):
"""Gets the free_text_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:return: The free_text_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:rtype: FreeTextAnnotationParameters
"""
return self._free_text_annotation_parameters
@free_text_annotation_parameters.setter
def free_text_annotation_parameters(self, free_text_annotation_parameters):
"""Sets the free_text_annotation_parameters of this PdfAnnotateParameters.
:param free_text_annotation_parameters: The free_text_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:type: FreeTextAnnotationParameters
"""
self._free_text_annotation_parameters = free_text_annotation_parameters
@property
def line_annotation_parameters(self):
"""Gets the line_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:return: The line_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:rtype: LineAnnotationParameters
"""
return self._line_annotation_parameters
@line_annotation_parameters.setter
def line_annotation_parameters(self, line_annotation_parameters):
"""Sets the line_annotation_parameters of this PdfAnnotateParameters.
:param line_annotation_parameters: The line_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:type: LineAnnotationParameters
"""
self._line_annotation_parameters = line_annotation_parameters
@property
def square_and_circle_annotation_parameters(self):
"""Gets the square_and_circle_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:return: The square_and_circle_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:rtype: SquareAndCircleAnnotationParameters
"""
return self._square_and_circle_annotation_parameters
@square_and_circle_annotation_parameters.setter
def square_and_circle_annotation_parameters(self, square_and_circle_annotation_parameters):
"""Sets the square_and_circle_annotation_parameters of this PdfAnnotateParameters.
:param square_and_circle_annotation_parameters: The square_and_circle_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:type: SquareAndCircleAnnotationParameters
"""
self._square_and_circle_annotation_parameters = square_and_circle_annotation_parameters
@property
def rubber_stamp_annotation_parameters(self):
"""Gets the rubber_stamp_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:return: The rubber_stamp_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:rtype: RubberStampAnnotationParameters
"""
return self._rubber_stamp_annotation_parameters
@rubber_stamp_annotation_parameters.setter
def rubber_stamp_annotation_parameters(self, rubber_stamp_annotation_parameters):
"""Sets the rubber_stamp_annotation_parameters of this PdfAnnotateParameters.
:param rubber_stamp_annotation_parameters: The rubber_stamp_annotation_parameters of this PdfAnnotateParameters. # noqa: E501
:type: RubberStampAnnotationParameters
"""
self._rubber_stamp_annotation_parameters = rubber_stamp_annotation_parameters
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, PdfAnnotateParameters):
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, PdfAnnotateParameters):
return True
return self.to_dict() != other.to_dict()
| [
"[email protected]"
]
| |
46716e05f494d85df10a692e589e37f999ee1bdd | 487ce91881032c1de16e35ed8bc187d6034205f7 | /codes/CodeJamCrawler/CJ/16_0_2_anrieff_b.py | 9217d8d49f04d08baad00e10f7695015df8cedd7 | []
| no_license | DaHuO/Supergraph | 9cd26d8c5a081803015d93cf5f2674009e92ef7e | c88059dc66297af577ad2b8afa4e0ac0ad622915 | refs/heads/master | 2021-06-14T16:07:52.405091 | 2016-08-21T13:39:13 | 2016-08-21T13:39:13 | 49,829,508 | 2 | 0 | null | 2021-03-19T21:55:46 | 2016-01-17T18:23:00 | Python | UTF-8 | Python | false | false | 570 | py | #!/usr/bin/env python
# Contestant: Veselin 'anrieff' Georgiev
# Round: Google Code Jam Qualification 2016
# Task: B. Revenge of the pancakes
# Solution: Greedy. At each step, find the largest single-colored block at the top, and flip it, until we finish.
TC = int(raw_input().strip())
for tc in xrange(1, TC + 1):
print "Case #%d:" % tc,
a = list(raw_input().strip())
n = len(a)
steps = 0
while a.count('-') != 0:
steps += 1
i = 0
while i < n and a[i] == a[0]:
i += 1
for j in xrange(i):
a[j] = '-' if a[j] == '+' else '+' # reverse
print steps
| [
"[[email protected]]"
]
| |
e82f2bd71cc0846186353d8c20817723d286fc4f | 4d4fcde3efaa334f7aa56beabd2aa26fbcc43650 | /server/src/uds/reports/lists/__init__.py | 2cb963d21ee727e9b5b0bcc891ec8e5716d7db72 | []
| no_license | xezpeleta/openuds | a8b11cb34eb0ef7bb2da80f67586a81b2de229ef | 840a7a02bd7c9894e8863a8a50874cdfdbf30fcd | refs/heads/master | 2023-08-21T17:55:48.914631 | 2021-10-06T10:39:06 | 2021-10-06T10:39:06 | 414,489,331 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,712 | py | # -*- coding: utf-8 -*-
#
# Copyright (c) 2015-2020 Virtual Cable S.L.U.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# * Neither the name of Virtual Cable S.L. nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
@author: Adolfo Gómez, dkmaster at dkmon dot com
"""
# Make reports visible to autoloader
from . import users
| [
"[email protected]"
]
| |
630d1956e0b63d64523db795b9be2b9148d32cde | 5cb98473ea9972d0a9a0278cde9b6ee8264f9bac | /01. Jump to python/chap05/5_6/260.py | 3716f386db385102e9e74387bd614dd0a58e1cff | []
| no_license | libus1204/bigdata2019 | fd85dbcd8c89db991ab5c3efa11ff85466a823f8 | 5e9a6fa2c340c1fcd2840889ba40c7b805926558 | refs/heads/master | 2020-04-21T10:56:33.519490 | 2019-04-15T05:28:19 | 2019-04-15T05:28:19 | 169,503,607 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 83 | py | import time
index = 1
while True:
print(index)
index += 1
time.sleep(5) | [
"[email protected]"
]
| |
89dffaba38711b93fdcb658ebbf0b28432889f78 | 113b962bd5e2eb770067bd374a15dfe8a1c2d09f | /py_scripts/get_mappedcount_byLibID.py | 8a5cee444afcf4ef2e1d1d2b47beaaa11f6be665 | []
| no_license | aungthurhahein/biotech_script | ecce51950bcef69405843da12ece2f84ea5541d6 | 2fda699343e6c46543fa1df2412c8ca2f2622cda | refs/heads/master | 2020-12-24T06:20:13.028141 | 2016-07-06T15:23:34 | 2016-07-06T15:23:34 | 25,574,741 | 5 | 0 | null | null | null | null | UTF-8 | Python | false | false | 827 | py | """
# get occurences of ref_ids by Lib_IDs
# modification tips: file type, column of file ids
# __author__ = 'atrx'
# Date: 22012015
"""
import sys
from Bio import SeqIO
usage = "Usage %s infile" % sys.argv[0] # specific massage for no input
try:
fastafile = sys.argv[1]
contigid = sys.argv[2]
except:
print usage, sys.exit(1)
fasta_file = open(fastafile, 'r')
ref_file = open(contigid, 'r')
id_list = []
contig_list = []
id_key = []
for l in ref_file:
id = l.split()
id_list.append(l)
id_key.append(id[1].strip())
for seq in SeqIO.parse(fasta_file, "fasta"):
contig_list.append(seq.id)
for seq_record in contig_list:
contigid = seq_record.strip()
if contigid in id_key:
lo = id_key.index(contigid)
print id_list[lo].strip()
else:
print "0 " + seq_record
| [
"[email protected]"
]
| |
fa568dcd357b037a884e720bb3f4b2961b3d5e46 | 343413e76c09d2bd3d009f382d9dcd19c984d58f | /.history/main_20201229180214.py | e7c1f84e7a234bab2a2ddd0a968647204387eebe | []
| no_license | rozbeh1212/cipher | 7b81e640501639cefb0fe6bf100647dd2602291e | abdebdd7d1e155ffab78ce38be8bf28074366c42 | refs/heads/master | 2023-02-04T13:44:36.892470 | 2020-12-29T14:44:10 | 2020-12-29T14:44:10 | 325,314,022 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 991 | py | alphabet = [
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o',
'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o',
'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'
]
direction = input("Type 'encode' to encrypt, type 'decode' to decrypt:\n")
text = input("Type your message:\n").lower()
shift = int(input("Type the shift number:\n"))
def caeser(start_text, shipft_amount, cipher_direction):
end_text = ""
for letter in start_text:
position = alphabet.index(position)
if cipher_direction == "decode":
shift_amount *= -1
new_position = position + shift_amount
end_text += alphabet[new_position]
print(f"the {c}d text is {plane_text}")
import art
caeser(start_text=text, shift_amount=shift, cipher_direction=direction)
| [
"[email protected]"
]
| |
21a65c73620f2a40477d64a11550fc36704d99f4 | 85a9ffeccb64f6159adbd164ff98edf4ac315e33 | /pysnmp/A3COM-HUAWEI-RS485-MIB.py | 6e4c22f4a88026cd33571d0023c92ae07bce922d | [
"Apache-2.0"
]
| permissive | agustinhenze/mibs.snmplabs.com | 5d7d5d4da84424c5f5a1ed2752f5043ae00019fb | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | refs/heads/master | 2020-12-26T12:41:41.132395 | 2019-08-16T15:51:41 | 2019-08-16T15:53:57 | 237,512,469 | 0 | 0 | Apache-2.0 | 2020-01-31T20:41:36 | 2020-01-31T20:41:35 | null | UTF-8 | Python | false | false | 10,571 | py | #
# PySNMP MIB module A3COM-HUAWEI-RS485-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/A3COM-HUAWEI-RS485-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 16:52:12 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
h3cCommon, = mibBuilder.importSymbols("A3COM-HUAWEI-OID-MIB", "h3cCommon")
Integer, OctetString, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "Integer", "OctetString", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ValueRangeConstraint, SingleValueConstraint, ConstraintsIntersection, ConstraintsUnion, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "SingleValueConstraint", "ConstraintsIntersection", "ConstraintsUnion", "ValueSizeConstraint")
ifIndex, = mibBuilder.importSymbols("IF-MIB", "ifIndex")
InetAddress, InetAddressType = mibBuilder.importSymbols("INET-ADDRESS-MIB", "InetAddress", "InetAddressType")
ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup")
Counter64, Counter32, iso, IpAddress, MibIdentifier, ObjectIdentity, TimeTicks, Integer32, ModuleIdentity, MibScalar, MibTable, MibTableRow, MibTableColumn, Unsigned32, NotificationType, Bits, Gauge32 = mibBuilder.importSymbols("SNMPv2-SMI", "Counter64", "Counter32", "iso", "IpAddress", "MibIdentifier", "ObjectIdentity", "TimeTicks", "Integer32", "ModuleIdentity", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Unsigned32", "NotificationType", "Bits", "Gauge32")
DisplayString, RowStatus, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "RowStatus", "TextualConvention")
h3cRS485 = ModuleIdentity((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109))
if mibBuilder.loadTexts: h3cRS485.setLastUpdated('200910210000Z')
if mibBuilder.loadTexts: h3cRS485.setOrganization('Hangzhou H3C Technologies Co., Ltd.')
h3cRS485Properties = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1))
h3cRS485PropertiesTable = MibTable((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1), )
if mibBuilder.loadTexts: h3cRS485PropertiesTable.setStatus('current')
h3cRS485PropertiesEntry = MibTableRow((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: h3cRS485PropertiesEntry.setStatus('current')
h3cRS485RawSessionNextIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 64))).setMaxAccess("readonly")
if mibBuilder.loadTexts: h3cRS485RawSessionNextIndex.setStatus('current')
h3cRS485BaudRate = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9, 10))).clone(namedValues=NamedValues(("bautRate300", 1), ("bautRate600", 2), ("bautRate1200", 3), ("bautRate2400", 4), ("bautRate4800", 5), ("bautRate9600", 6), ("bautRate19200", 7), ("bautRate38400", 8), ("bautRate57600", 9), ("bautRate115200", 10))).clone('bautRate9600')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: h3cRS485BaudRate.setStatus('current')
h3cRS485DataBits = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4))).clone(namedValues=NamedValues(("five", 1), ("six", 2), ("seven", 3), ("eight", 4))).clone('eight')).setUnits('bit').setMaxAccess("readwrite")
if mibBuilder.loadTexts: h3cRS485DataBits.setStatus('current')
h3cRS485Parity = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5))).clone(namedValues=NamedValues(("none", 1), ("odd", 2), ("even", 3), ("mark", 4), ("space", 5))).clone('none')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: h3cRS485Parity.setStatus('current')
h3cRS485StopBits = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("one", 1), ("two", 2), ("oneAndHalf", 3))).clone('one')).setUnits('bit').setMaxAccess("readwrite")
if mibBuilder.loadTexts: h3cRS485StopBits.setStatus('current')
h3cRS485FlowControl = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1, 6), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("none", 1), ("hardware", 2), ("xonOrxoff", 3))).clone('none')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: h3cRS485FlowControl.setStatus('current')
h3cRS485TXCharacters = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1, 7), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: h3cRS485TXCharacters.setStatus('current')
h3cRS485RXCharacters = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1, 8), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: h3cRS485RXCharacters.setStatus('current')
h3cRS485TXErrCharacters = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1, 9), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: h3cRS485TXErrCharacters.setStatus('current')
h3cRS485RXErrCharacters = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1, 10), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: h3cRS485RXErrCharacters.setStatus('current')
h3cRS485ResetCharacters = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 1, 1, 1, 11), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("counting", 1), ("clear", 2))).clone('counting')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: h3cRS485ResetCharacters.setStatus('current')
h3cRS485RawSessions = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2))
h3cRS485RawSessionSummary = MibIdentifier((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 1))
h3cRS485RawSessionMaxNum = MibScalar((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 64))).setMaxAccess("readonly")
if mibBuilder.loadTexts: h3cRS485RawSessionMaxNum.setStatus('current')
h3cRS485RawSessionTable = MibTable((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 2), )
if mibBuilder.loadTexts: h3cRS485RawSessionTable.setStatus('current')
h3cRS485RawSessionEntry = MibTableRow((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 2, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "A3COM-HUAWEI-RS485-MIB", "h3cRS485SessionIndex"))
if mibBuilder.loadTexts: h3cRS485RawSessionEntry.setStatus('current')
h3cRS485SessionIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 2, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 64)))
if mibBuilder.loadTexts: h3cRS485SessionIndex.setStatus('current')
h3cRS485SessionType = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 2, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("udp", 1), ("tcpClient", 2), ("tcpServer", 3)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: h3cRS485SessionType.setStatus('current')
h3cRS485SessionAddType = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 2, 1, 3), InetAddressType().clone('ipv4')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: h3cRS485SessionAddType.setStatus('current')
h3cRS485SessionRemoteIP = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 2, 1, 4), InetAddress()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: h3cRS485SessionRemoteIP.setStatus('current')
h3cRS485SessionRemotePort = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 2, 1, 5), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1024, 65535))).setMaxAccess("readcreate")
if mibBuilder.loadTexts: h3cRS485SessionRemotePort.setStatus('current')
h3cRS485SessionLocalPort = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 2, 1, 6), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1024, 65535))).setMaxAccess("readcreate")
if mibBuilder.loadTexts: h3cRS485SessionLocalPort.setStatus('current')
h3cRS485SessionStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 2, 1, 7), RowStatus()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: h3cRS485SessionStatus.setStatus('current')
h3cRS485RawSessionErrInfoTable = MibTable((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 3), )
if mibBuilder.loadTexts: h3cRS485RawSessionErrInfoTable.setStatus('current')
h3cRS485RawSessionErrInfoEntry = MibTableRow((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 3, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "A3COM-HUAWEI-RS485-MIB", "h3cRS485SessionIndex"))
if mibBuilder.loadTexts: h3cRS485RawSessionErrInfoEntry.setStatus('current')
h3cRS485RawSessionErrInfo = MibTableColumn((1, 3, 6, 1, 4, 1, 43, 45, 1, 10, 2, 109, 2, 3, 1, 1), DisplayString()).setMaxAccess("readonly")
if mibBuilder.loadTexts: h3cRS485RawSessionErrInfo.setStatus('current')
mibBuilder.exportSymbols("A3COM-HUAWEI-RS485-MIB", h3cRS485RawSessionMaxNum=h3cRS485RawSessionMaxNum, h3cRS485StopBits=h3cRS485StopBits, h3cRS485SessionRemotePort=h3cRS485SessionRemotePort, h3cRS485RawSessionErrInfoEntry=h3cRS485RawSessionErrInfoEntry, h3cRS485TXErrCharacters=h3cRS485TXErrCharacters, h3cRS485=h3cRS485, h3cRS485RXCharacters=h3cRS485RXCharacters, h3cRS485PropertiesEntry=h3cRS485PropertiesEntry, h3cRS485SessionAddType=h3cRS485SessionAddType, h3cRS485FlowControl=h3cRS485FlowControl, h3cRS485Properties=h3cRS485Properties, h3cRS485PropertiesTable=h3cRS485PropertiesTable, h3cRS485ResetCharacters=h3cRS485ResetCharacters, PYSNMP_MODULE_ID=h3cRS485, h3cRS485RawSessionEntry=h3cRS485RawSessionEntry, h3cRS485RawSessionNextIndex=h3cRS485RawSessionNextIndex, h3cRS485RawSessionTable=h3cRS485RawSessionTable, h3cRS485RawSessionErrInfo=h3cRS485RawSessionErrInfo, h3cRS485TXCharacters=h3cRS485TXCharacters, h3cRS485SessionStatus=h3cRS485SessionStatus, h3cRS485RawSessions=h3cRS485RawSessions, h3cRS485SessionRemoteIP=h3cRS485SessionRemoteIP, h3cRS485SessionLocalPort=h3cRS485SessionLocalPort, h3cRS485SessionIndex=h3cRS485SessionIndex, h3cRS485RXErrCharacters=h3cRS485RXErrCharacters, h3cRS485RawSessionSummary=h3cRS485RawSessionSummary, h3cRS485BaudRate=h3cRS485BaudRate, h3cRS485DataBits=h3cRS485DataBits, h3cRS485SessionType=h3cRS485SessionType, h3cRS485RawSessionErrInfoTable=h3cRS485RawSessionErrInfoTable, h3cRS485Parity=h3cRS485Parity)
| [
"[email protected]"
]
| |
6b98012278b2ef3e02f3bdbc33e146865eb26807 | de24f83a5e3768a2638ebcf13cbe717e75740168 | /moodledata/vpl_data/59/usersdata/201/46743/submittedfiles/testes.py | 3378d84c86f02731a2be48d3b0f834f8d9cb8366 | []
| 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 | 111 | py | # -*- coding: utf-8 -*-
#COMECE AQUI ABAIXO
r=float(input('Digite um raio:'))
pi=3,14
a=pi*(r**2):
print(a) | [
"[email protected]"
]
| |
3ab2ab497849cf95c4e137ee698165d20cbe687f | 1939f5b78e6dbd0675f6f2a9a0f4f49c2b069389 | /instagram/src/instabot.py | 91bd65f0544dd187a1655c570d6dcb9774751ecf | [
"MIT"
]
| permissive | pavel-malin/instagram | cb7988d9fbfad14911bf39567f7b2f6336b1fb34 | b53f00c20521f46f5836946a499f476859d431f5 | refs/heads/master | 2021-03-16T07:54:57.942684 | 2017-11-20T11:23:46 | 2017-11-20T11:23:46 | 111,401,293 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 36,349 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import atexit
import datetime
import itertools
import json
import logging
import random
import signal
import sys
if 'threading' in sys.modules:
del sys.modules['threading']
import time
import requests
from unfollow_protocol import unfollow_protocol
from userinfo import UserInfo
class InstaBot:
"""
Instagram bot v 1.1.0
like_per_day=1000 - How many likes set bot in one day.
media_max_like=0 - Don't like media (photo or video) if it have more than
media_max_like likes.
media_min_like=0 - Don't like media (photo or video) if it have less than
media_min_like likes.
tag_list = ['cat', 'car', 'dog'] - Tag list to like.
max_like_for_one_tag=5 - Like 1 to max_like_for_one_tag times by row.
log_mod = 0 - Log mod: log_mod = 0 log to console, log_mod = 1 log to file,
log_mod = 2 no log.
https://github.com/LevPasha/instabot.py
"""
url = 'https://www.instagram.com/'
url_tag = 'https://www.instagram.com/explore/tags/%s/?__a=1'
url_likes = 'https://www.instagram.com/web/likes/%s/like/'
url_unlike = 'https://www.instagram.com/web/likes/%s/unlike/'
url_comment = 'https://www.instagram.com/web/comments/%s/add/'
url_follow = 'https://www.instagram.com/web/friendships/%s/follow/'
url_unfollow = 'https://www.instagram.com/web/friendships/%s/unfollow/'
url_login = 'https://www.instagram.com/accounts/login/ajax/'
url_logout = 'https://www.instagram.com/accounts/logout/'
url_media_detail = 'https://www.instagram.com/p/%s/?__a=1'
url_user_detail = 'https://www.instagram.com/%s/?__a=1'
user_agent = ("Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 "
"(KHTML, like Gecko) Chrome/48.0.2564.103 Safari/537.36")
accept_language = 'ru-RU,ru;q=0.8,en-US;q=0.6,en;q=0.4'
# If instagram ban you - query return 400 error.
error_400 = 0
# If you have 3 400 error in row - looks like you banned.
error_400_to_ban = 3
# If InstaBot think you are banned - going to sleep.
ban_sleep_time = 2 * 60 * 60
# All counter.
bot_mode = 0
like_counter = 0
follow_counter = 0
unfollow_counter = 0
comments_counter = 0
current_user = 'hajka'
current_index = 0
current_id = 'abcds'
# List of user_id, that bot follow
bot_follow_list = []
user_info_list = []
user_list = []
ex_user_list = []
unwanted_username_list = []
is_checked = False
is_selebgram = False
is_fake_account = False
is_active_user = False
is_following = False
is_follower = False
is_rejected = False
is_self_checking = False
is_by_tag = False
is_follower_number = 0
self_following = 0
self_follower = 0
# Log setting.
log_file_path = ''
log_file = 0
# Other.
user_id = 0
media_by_tag = 0
media_on_feed = []
media_by_user = []
login_status = False
# For new_auto_mod
next_iteration = {"Like": 0, "Follow": 0, "Unfollow": 0, "Comments": 0}
def __init__(self,
login,
password,
like_per_day=1000,
media_max_like=50,
media_min_like=0,
follow_per_day=0,
follow_time=5 * 60 * 60,
unfollow_per_day=0,
comment_list=[["this", "the", "your"],
["photo", "picture", "pic", "shot", "snapshot"],
["is", "looks", "feels", "is really"],
["great", "super", "good", "very good", "good",
"wow", "WOW", "cool", "GREAT", "magnificent",
"magical", "very cool", "stylish", "beautiful",
"so beautiful", "so stylish", "so professional",
"lovely", "so lovely", "very lovely", "glorious",
"so glorious", "very glorious", "adorable",
"excellent", "amazing"],[".", "..", "...", "!",
"!!", "!!!"]],
comments_per_day=0,
tag_list=['cat', 'car', 'dog'],
max_like_for_one_tag=5,
unfollow_break_min=15,
unfollow_break_max=30,
log_mod=0,
proxy="",
user_blacklist={},
tag_blacklist=[],
unwanted_username_list=[],
unfollow_whitelist=[]):
self.bot_start = datetime.datetime.now()
self.unfollow_break_min = unfollow_break_min
self.unfollow_break_max = unfollow_break_max
self.user_blacklist = user_blacklist
self.tag_blacklist = tag_blacklist
self.unfollow_whitelist = unfollow_whitelist
self.comment_list = comment_list
self.time_in_day = 24 * 60 * 60
# Like
self.like_per_day = like_per_day
if self.like_per_day != 0:
self.like_delay = self.time_in_day / self.like_per_day
# Follow
self.follow_time = follow_time
self.follow_per_day = follow_per_day
if self.follow_per_day != 0:
self.follow_delay = self.time_in_day / self.follow_per_day
# Unfollow
self.unfollow_per_day = unfollow_per_day
if self.unfollow_per_day != 0:
self.unfollow_delay = self.time_in_day / self.unfollow_per_day
# Comment
self.comments_per_day = comments_per_day
if self.comments_per_day != 0:
self.comments_delay = self.time_in_day / self.comments_per_day
# Don't like if media have more than n likes.
self.media_max_like = media_max_like
# Don't like if media have less than n likes.
self.media_min_like = media_min_like
# Auto mod seting:
# Default list of tag.
self.tag_list = tag_list
# Get random tag, from tag_list, and like (1 to n) times.
self.max_like_for_one_tag = max_like_for_one_tag
# log_mod 0 to console, 1 to file
self.log_mod = log_mod
self.s = requests.Session()
# if you need proxy make something like this:
# self.s.proxies = {"https" : "http://proxyip:proxyport"}
# by @ageorgios
if proxy != "":
proxies = {
'http': 'http://' + proxy,
'https': 'http://' + proxy,
}
self.s.proxies.update(proxies)
# convert login to lower
self.user_login = login.lower()
self.user_password = password
self.bot_mode = 0
self.media_by_tag = []
self.media_on_feed = []
self.media_by_user = []
self.unwanted_username_list = unwanted_username_list
now_time = datetime.datetime.now()
log_string = 'Instabot v1.1.0 started at %s:\n' % \
(now_time.strftime("%d.%m.%Y %H:%M"))
self.write_log(log_string)
self.login()
self.populate_user_blacklist()
signal.signal(signal.SIGTERM, self.cleanup)
atexit.register(self.cleanup)
def populate_user_blacklist(self):
for user in self.user_blacklist:
user_id_url = self.url_user_detail % (user)
info = self.s.get(user_id_url)
# prevent error if 'Account of user was deleted or link is invalid
from json import JSONDecodeError
try:
all_data = json.loads(info.text)
except JSONDecodeError as e:
self.write_log('Account of user %s was deleted or link is '
'invalid' % (user))
else:
# prevent exception if user have no media
id_user = all_data['user']['id']
# Update the user_name with the user_id
self.user_blacklist[user] = id_user
log_string = "Blacklisted user %s added with ID: %s" % (user,
id_user)
self.write_log(log_string)
time.sleep(5 * random.random())
def login(self):
log_string = 'Trying to login as %s...\n' % (self.user_login)
self.write_log(log_string)
self.s.cookies.update({
'sessionid': '',
'mid': '',
'ig_pr': '1',
'ig_vw': '1920',
'csrftoken': '',
's_network': '',
'ds_user_id': ''
})
self.login_post = {
'username': self.user_login,
'password': self.user_password
}
self.s.headers.update({
'Accept-Encoding': 'gzip, deflate',
'Accept-Language': self.accept_language,
'Connection': 'keep-alive',
'Content-Length': '0',
'Host': 'www.instagram.com',
'Origin': 'https://www.instagram.com',
'Referer': 'https://www.instagram.com/',
'User-Agent': self.user_agent,
'X-Instagram-AJAX': '1',
'X-Requested-With': 'XMLHttpRequest'
})
r = self.s.get(self.url)
self.s.headers.update({'X-CSRFToken': r.cookies['csrftoken']})
time.sleep(5 * random.random())
login = self.s.post(
self.url_login, data=self.login_post, allow_redirects=True)
self.s.headers.update({'X-CSRFToken': login.cookies['csrftoken']})
self.csrftoken = login.cookies['csrftoken']
time.sleep(5 * random.random())
if login.status_code == 200:
r = self.s.get('https://www.instagram.com/')
finder = r.text.find(self.user_login)
if finder != -1:
ui = UserInfo()
self.user_id = ui.get_user_id_by_login(self.user_login)
self.login_status = True
log_string = '%s login success!' % (self.user_login)
self.write_log(log_string)
else:
self.login_status = False
self.write_log('Login error! Check your login data!')
else:
self.write_log('Login error! Connection error!')
def logout(self):
now_time = datetime.datetime.now()
log_string = 'Logout: likes - %i, follow - %i, unfollow - %i, comments - %i.' % \
(self.like_counter, self.follow_counter,
self.unfollow_counter, self.comments_counter)
self.write_log(log_string)
work_time = datetime.datetime.now() - self.bot_start
log_string = 'Bot work time: %s' % (work_time)
self.write_log(log_string)
try:
logout_post = {'csrfmiddlewaretoken': self.csrftoken}
logout = self.s.post(self.url_logout, data=logout_post)
self.write_log("Logout success!")
self.login_status = False
except:
self.write_log("Logout error!")
def cleanup(self, *_):
# Unfollow all bot follow
if self.follow_counter >= self.unfollow_counter:
for f in self.bot_follow_list:
log_string = "Trying to unfollow: %s" % (f[0])
self.write_log(log_string)
self.unfollow_on_cleanup(f[0])
sleeptime = random.randint(self.unfollow_break_min,
self.unfollow_break_max)
log_string = "Pausing for %i seconds... %i of %i" % (
sleeptime, self.unfollow_counter, self.follow_counter)
self.write_log(log_string)
time.sleep(sleeptime)
self.bot_follow_list.remove(f)
# Logout
if (self.login_status):
self.logout()
exit(0)
def get_media_id_by_tag(self, tag):
""" Get media ID set, by your hashtag """
if (self.login_status):
log_string = "Get media id by tag: %s" % (tag)
self.write_log(log_string)
if self.login_status == 1:
url_tag = self.url_tag % (tag)
try:
r = self.s.get(url_tag)
all_data = json.loads(r.text)
self.media_by_tag = list(all_data['tag']['media']['nodes'])
except:
self.media_by_tag = []
self.write_log("Except on get_media!")
else:
return 0
def like_all_exist_media(self, media_size=-1, delay=True):
""" Like all media ID that have self.media_by_tag """
if self.login_status:
if self.media_by_tag != 0:
i = 0
for d in self.media_by_tag:
# Media count by this tag.
if media_size > 0 or media_size < 0:
media_size -= 1
l_c = self.media_by_tag[i]['likes']['count']
if ((l_c <= self.media_max_like and
l_c >= self.media_min_like) or
(self.media_max_like == 0 and
l_c >= self.media_min_like) or
(self.media_min_like == 0 and
l_c <= self.media_max_like) or
(self.media_min_like == 0 and
self.media_max_like == 0)):
for blacklisted_user_name, blacklisted_user_id in self.user_blacklist.items(
):
if self.media_by_tag[i]['owner'][
'id'] == blacklisted_user_id:
self.write_log(
"Not liking media owned by blacklisted user: "
+ blacklisted_user_name)
return False
if self.media_by_tag[i]['owner'][
'id'] == self.user_id:
self.write_log(
"Keep calm - It's your own media ;)")
return False
try:
caption = self.media_by_tag[i][
'caption'].encode(
'ascii', errors='ignore')
tag_blacklist = set(self.tag_blacklist)
if sys.version_info[0] == 3:
tags = {
str.lower(
(tag.decode('ASCII')).strip('#'))
for tag in caption.split()
if (tag.decode('ASCII')
).startswith("#")
}
else:
tags = {
unicode.lower(
(tag.decode('ASCII')).strip('#'))
for tag in caption.split()
if (tag.decode('ASCII')
).startswith("#")
}
if tags.intersection(tag_blacklist):
matching_tags = ', '.join(
tags.intersection(tag_blacklist))
self.write_log(
"Not liking media with blacklisted tag(s): "
+ matching_tags)
return False
except:
self.write_log(
"Couldn't find caption - not liking")
return False
log_string = "Trying to like media: %s" % \
(self.media_by_tag[i]['id'])
self.write_log(log_string)
like = self.like(self.media_by_tag[i]['id'])
# comment = self.comment(self.media_by_tag[i]['id'], 'Cool!')
# follow = self.follow(self.media_by_tag[i]["owner"]["id"])
if like != 0:
if like.status_code == 200:
# Like, all ok!
self.error_400 = 0
self.like_counter += 1
log_string = "Liked: %s. Like #%i." % \
(self.media_by_tag[i]['id'],
self.like_counter)
self.write_log(log_string)
elif like.status_code == 400:
log_string = "Not liked: %i" \
% (like.status_code)
self.write_log(log_string)
# Some error. If repeated - can be ban!
if self.error_400 >= self.error_400_to_ban:
# Look like you banned!
time.sleep(self.ban_sleep_time)
else:
self.error_400 += 1
else:
log_string = "Not liked: %i" \
% (like.status_code)
self.write_log(log_string)
return False
# Some error.
i += 1
if delay:
time.sleep(self.like_delay * 0.9 +
self.like_delay * 0.2 *
random.random())
else:
return True
else:
return False
else:
return False
else:
return False
else:
self.write_log("No media to like!")
def like(self, media_id):
""" Send http request to like media by ID """
if self.login_status:
url_likes = self.url_likes % (media_id)
try:
like = self.s.post(url_likes)
last_liked_media_id = media_id
except:
self.write_log("Except on like!")
like = 0
return like
def unlike(self, media_id):
""" Send http request to unlike media by ID """
if self.login_status:
url_unlike = self.url_unlike % (media_id)
try:
unlike = self.s.post(url_unlike)
except:
self.write_log("Except on unlike!")
unlike = 0
return unlike
def comment(self, media_id, comment_text):
""" Send http request to comment """
if self.login_status:
comment_post = {'comment_text': comment_text}
url_comment = self.url_comment % (media_id)
try:
comment = self.s.post(url_comment, data=comment_post)
if comment.status_code == 200:
self.comments_counter += 1
log_string = 'Write: "%s". #%i.' % (comment_text,
self.comments_counter)
self.write_log(log_string)
return comment
except:
self.write_log("Except on comment!")
return False
def follow(self, user_id):
""" Send http request to follow """
if self.login_status:
url_follow = self.url_follow % (user_id)
try:
follow = self.s.post(url_follow)
if follow.status_code == 200:
self.follow_counter += 1
log_string = "Followed: %s #%i." % (user_id,
self.follow_counter)
self.write_log(log_string)
return follow
except:
self.write_log("Except on follow!")
return False
def unfollow(self, user_id):
""" Send http request to unfollow """
if self.login_status:
url_unfollow = self.url_unfollow % (user_id)
try:
unfollow = self.s.post(url_unfollow)
if unfollow.status_code == 200:
self.unfollow_counter += 1
log_string = "Unfollow: %s #%i." % (user_id,
self.unfollow_counter)
self.write_log(log_string)
return unfollow
except:
self.write_log("Exept on unfollow!")
return False
def unfollow_on_cleanup(self, user_id):
""" Unfollow on cleanup by @rjmayott """
if self.login_status:
url_unfollow = self.url_unfollow % (user_id)
try:
unfollow = self.s.post(url_unfollow)
if unfollow.status_code == 200:
self.unfollow_counter += 1
log_string = "Unfollow: %s #%i of %i." % (
user_id, self.unfollow_counter, self.follow_counter)
self.write_log(log_string)
else:
log_string = "Slow Down - Pausing for 5 minutes so we don't get banned!"
self.write_log(log_string)
time.sleep(300)
unfollow = self.s.post(url_unfollow)
if unfollow.status_code == 200:
self.unfollow_counter += 1
log_string = "Unfollow: %s #%i of %i." % (
user_id, self.unfollow_counter,
self.follow_counter)
self.write_log(log_string)
else:
log_string = "Still no good :( Skipping and pausing for another 5 minutes"
self.write_log(log_string)
time.sleep(300)
return False
return unfollow
except:
log_string = "Except on unfollow... Looks like a network error"
self.write_log(log_string)
return False
def auto_mod(self):
""" Star loop, that get media ID by your tag list, and like it """
if self.login_status:
while True:
random.shuffle(self.tag_list)
self.get_media_id_by_tag(random.choice(self.tag_list))
self.like_all_exist_media(random.randint \
(1, self.max_like_for_one_tag))
def new_auto_mod(self):
while True:
# ------------------- Get media_id -------------------
if len(self.media_by_tag) == 0:
self.get_media_id_by_tag(random.choice(self.tag_list))
self.this_tag_like_count = 0
self.max_tag_like_count = random.randint(
1, self.max_like_for_one_tag)
# ------------------- Like -------------------
self.new_auto_mod_like()
# ------------------- Follow -------------------
self.new_auto_mod_follow()
# ------------------- Unfollow -------------------
self.new_auto_mod_unfollow()
# ------------------- Comment -------------------
self.new_auto_mod_comments()
# Bot iteration in 1 sec
time.sleep(3)
# print("Tic!")
def new_auto_mod_like(self):
if time.time() > self.next_iteration["Like"] and self.like_per_day != 0 \
and len(self.media_by_tag) > 0:
# You have media_id to like:
if self.like_all_exist_media(media_size=1, delay=False):
# If like go to sleep:
self.next_iteration["Like"] = time.time() + \
self.add_time(self.like_delay)
# Count this tag likes:
self.this_tag_like_count += 1
if self.this_tag_like_count >= self.max_tag_like_count:
self.media_by_tag = [0]
# Del first media_id
del self.media_by_tag[0]
def new_auto_mod_follow(self):
if time.time() > self.next_iteration["Follow"] and \
self.follow_per_day != 0 and len(self.media_by_tag) > 0:
if self.media_by_tag[0]["owner"]["id"] == self.user_id:
self.write_log("Keep calm - It's your own profile ;)")
return
log_string = "Trying to follow: %s" % (
self.media_by_tag[0]["owner"]["id"])
self.write_log(log_string)
if self.follow(self.media_by_tag[0]["owner"]["id"]) != False:
self.bot_follow_list.append(
[self.media_by_tag[0]["owner"]["id"], time.time()])
self.next_iteration["Follow"] = time.time() + \
self.add_time(self.follow_delay)
def new_auto_mod_unfollow(self):
if time.time() > self.next_iteration["Unfollow"] and \
self.unfollow_per_day != 0 and len(self.bot_follow_list) > 0:
if self.bot_mode == 0:
for f in self.bot_follow_list:
if time.time() > (f[1] + self.follow_time):
log_string = "Trying to unfollow #%i: " % (
self.unfollow_counter + 1)
self.write_log(log_string)
self.auto_unfollow()
self.bot_follow_list.remove(f)
self.next_iteration["Unfollow"] = time.time() + \
self.add_time(self.unfollow_delay)
if self.bot_mode == 1:
unfollow_protocol(self)
def new_auto_mod_comments(self):
if time.time() > self.next_iteration["Comments"] and self.comments_per_day != 0 \
and len(self.media_by_tag) > 0 \
and self.check_exisiting_comment(self.media_by_tag[0]['code']) == False:
comment_text = self.generate_comment()
log_string = "Trying to comment: %s" % (self.media_by_tag[0]['id'])
self.write_log(log_string)
if self.comment(self.media_by_tag[0]['id'], comment_text) != False:
self.next_iteration["Comments"] = time.time() + \
self.add_time(self.comments_delay)
def add_time(self, time):
""" Make some random for next iteration"""
return time * 0.9 + time * 0.2 * random.random()
def generate_comment(self):
c_list = list(itertools.product(*self.comment_list))
repl = [(" ", " "), (" .", "."), (" !", "!")]
res = " ".join(random.choice(c_list))
for s, r in repl:
res = res.replace(s, r)
return res.capitalize()
def check_exisiting_comment(self, media_code):
url_check = self.url_media_detail % (media_code)
check_comment = self.s.get(url_check)
all_data = json.loads(check_comment.text)
if all_data['graphql']['shortcode_media']['owner']['id'] == self.user_id:
self.write_log("Keep calm - It's your own media ;)")
# Del media to don't loop on it
del self.media_by_tag[0]
return True
comment_list = list(all_data['graphql']['shortcode_media']['edge_media_to_comment']['edges'])
for d in comment_list:
if d['node']['owner']['id'] == self.user_id:
self.write_log("Keep calm - Media already commented ;)")
# Del media to don't loop on it
del self.media_by_tag[0]
return True
return False
def auto_unfollow(self):
chooser = 1
current_user = 'abcd'
current_id = '12345'
checking = True
self.media_on_feed = []
if len(self.media_on_feed) < 1:
self.get_media_id_recent_feed()
if len(self.media_on_feed) != 0:
chooser = random.randint(0, len(self.media_on_feed) - 1)
current_id = self.media_on_feed[chooser]['node']["owner"]["id"]
current_user = self.media_on_feed[chooser]['node']["owner"][
"username"]
while checking:
for wluser in self.unfollow_whitelist:
if wluser == current_user:
chooser = random.randint(0,
len(self.media_on_feed) - 1)
current_id = self.media_on_feed[chooser]['node'][
"owner"]["id"]
current_user = self.media_on_feed[chooser]['node'][
"owner"]["username"]
log_string = (
"found whitelist user, starting search again")
self.write_log(log_string)
break
else:
checking = False
if self.login_status:
now_time = datetime.datetime.now()
log_string = "%s : Get user info \n%s" % (
self.user_login, now_time.strftime("%d.%m.%Y %H:%M"))
self.write_log(log_string)
if self.login_status == 1:
url_tag = self.url_user_detail % (current_user)
try:
r = self.s.get(url_tag)
all_data = json.loads(r.text)
self.user_info = all_data['user']
i = 0
log_string = "Checking user info.."
self.write_log(log_string)
while i < 1:
follows = self.user_info['follows']['count']
follower = self.user_info['followed_by']['count']
media = self.user_info['media']['count']
follow_viewer = self.user_info['follows_viewer']
followed_by_viewer = self.user_info[
'followed_by_viewer']
requested_by_viewer = self.user_info[
'requested_by_viewer']
has_requested_viewer = self.user_info[
'has_requested_viewer']
log_string = "Follower : %i" % (follower)
self.write_log(log_string)
log_string = "Following : %s" % (follows)
self.write_log(log_string)
log_string = "Media : %i" % (media)
self.write_log(log_string)
if follower / follows > 2:
self.is_selebgram = True
self.is_fake_account = False
print(' >>>This is probably Selebgram account')
elif follows / follower > 2:
self.is_fake_account = True
self.is_selebgram = False
print(' >>>This is probably Fake account')
else:
self.is_selebgram = False
self.is_fake_account = False
print(' >>>This is a normal account')
if follows / media < 10 and follower / media < 10:
self.is_active_user = True
print(' >>>This user is active')
else:
self.is_active_user = False
print(' >>>This user is passive')
if follow_viewer or has_requested_viewer:
self.is_follower = True
print(" >>>This account is following you")
else:
self.is_follower = False
print(' >>>This account is NOT following you')
if followed_by_viewer or requested_by_viewer:
self.is_following = True
print(' >>>You are following this account')
else:
self.is_following = False
print(' >>>You are NOT following this account')
i += 1
except:
media_on_feed = []
self.write_log("Except on get_info!")
time.sleep(20)
return 0
else:
return 0
if self.is_selebgram is not False or self.is_fake_account is not False or self.is_active_user is not True or self.is_follower is not True:
print(current_user)
self.unfollow(current_id)
try:
del self.media_on_feed[chooser]
except:
self.media_on_feed = []
self.media_on_feed = []
def get_media_id_recent_feed(self):
if self.login_status:
now_time = datetime.datetime.now()
log_string = "%s : Get media id on recent feed" % (self.user_login)
self.write_log(log_string)
if self.login_status == 1:
url_tag = 'https://www.instagram.com/?__a=1'
try:
r = self.s.get(url_tag)
all_data = json.loads(r.text)
self.media_on_feed = list(
all_data['graphql']['user']['edge_web_feed_timeline'][
'edges'])
log_string = "Media in recent feed = %i" % (
len(self.media_on_feed))
self.write_log(log_string)
except:
self.media_on_feed = []
self.write_log("Except on get_media!")
time.sleep(20)
return 0
else:
return 0
def write_log(self, log_text):
""" Write log by print() or logger """
if self.log_mod == 0:
try:
print(log_text)
except UnicodeEncodeError:
print("Your text has unicode problem!")
elif self.log_mod == 1:
# Create log_file if not exist.
if self.log_file == 0:
self.log_file = 1
now_time = datetime.datetime.now()
self.log_full_path = '%s%s_%s.log' % (
self.log_file_path, self.user_login,
now_time.strftime("%d.%m.%Y_%H:%M"))
formatter = logging.Formatter('%(asctime)s - %(name)s '
'- %(message)s')
self.logger = logging.getLogger(self.user_login)
self.hdrl = logging.FileHandler(self.log_full_path, mode='w')
self.hdrl.setFormatter(formatter)
self.logger.setLevel(level=logging.INFO)
self.logger.addHandler(self.hdrl)
# Log to log file.
try:
self.logger.info(log_text)
except UnicodeEncodeError:
print("Your text has unicode problem!")
| [
"[email protected]"
]
| |
84a2a9db3cd847433912ae84459035f42045f6bc | da3e36172daaf863ef73372f8c36cc2629ec1769 | /UMDC/03/17g.py | ce55196990dd77c97e38c5ebc70122baad56ce1d | []
| no_license | mentecatoDev/python | 08eef1cb5a6ca2f16b01ee98192ccf1a65b9380a | 80ddf541d3d1316ba8375db8f6ec170580e7831b | refs/heads/master | 2021-06-30T07:03:51.957376 | 2021-02-22T09:40:46 | 2021-02-22T09:40:46 | 222,322,503 | 3 | 4 | null | null | null | null | UTF-8 | Python | false | false | 2,333 | py | """
Ejercicio 17g
Escribir funciones que resuelvan los siguientes problemas:
g) Dadas dos fechas (dia1, mes1, año1, dia2, mes2, año2), indicar el tiempo
transcurrido entre ambas, en años, meses y dias. Nota: en todos los casos,
involucrar las funciones escritas previamente cuando sea posible.
"""
def bisiesto(anio):
"""Devuelve True si el anio es bisiesto."""
if anio % 4:
return False
else:
if anio % 100:
return True
else:
if anio % 400:
return False
else:
return True
def dias_mes(mes, anio):
"""Devuelve los días de cualquier mes teniendo en cuenta el anio."""
if mes in (1, 3, 5, 7, 8, 10, 12):
return 31
elif mes in (4, 6, 9, 11):
return 30
elif mes == 2:
if bisiesto(anio):
return 29
else:
return 28
else:
return -1
def validar_fecha(dia, mes, anio):
dm = dias_mes(mes, anio)
if dm == -1:
return -1
if dm < dia:
return False
elif mes > 12:
return False
else:
return True
def dias_faltan(dia, mes, anio):
if validar_fecha(dia, mes, anio):
return dias_mes(mes, anio)-dia
else:
return -1
print(dias_faltan(1, 1, 2000))
def dias_fin_anio(dia, mes, anio):
if validar_fecha(dia, mes, anio):
dias = 0
for m in range(mes+1, 12+1):
dias += dias_mes(m, anio)
dias += dias_faltan(dia, mes, anio)
return dias
else:
return -1
def dias_principio(dia, mes, anio):
if validar_fecha(dia, mes, anio):
if bisiesto(anio):
return 365 - dias_fin_anio(dia, mes, anio)
else:
return 364 - dias_fin_anio(dia, mes, anio)
else:
return -1
def dias_transcurridos(dia1, mes1, anio1, dia2, mes2, anio2):
if anio1 == anio2:
total = -dias_principio(dia1, mes1, anio1) + \
dias_principio(dia2, mes2, anio2)
else:
total = dias_fin_anio(dia1, mes1, anio1) + \
dias_principio(dia2, mes2, anio2)+1
for a in range(anio1+1, anio2):
if bisiesto(a):
total += 366
else:
total += 365
return total
print(dias_transcurridos(1, 1, 2001, 31, 12, 2002))
| [
"[email protected]"
]
| |
eee6c26c594ab5b9fa6e26288db0e7e9dee3d498 | ff886f5f947460576feaec2a049f6a9f78f2a63f | /core/management/commands/wait_for_db.py | 460989d579a419bc219cba5e76cc9fcb204aa701 | [
"MIT"
]
| permissive | devendraprasad1984/loan_payment_app | 2bc927afbc084504bb10a959105d72f6f419e2c8 | 1a4c31d03a8c5ecf4dae2a981373649f4f699aa3 | refs/heads/main | 2023-07-21T19:00:40.692978 | 2021-09-09T03:36:04 | 2021-09-09T03:36:04 | 400,111,124 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 707 | py | import time
from django.db import connections
from django.db.utils import OperationalError
from django.core.management.base import BaseCommand
class Command(BaseCommand):
"""django overriding default app run until database is made available"""
def handle(self, *args, **options):
self.stdout.write('waiting for db connection...')
db_conn = None
while not db_conn:
try:
db_conn = connections['default']
except OperationalError:
self.stdout.write(self.style.ERROR('database is not available, re-checking in 1sec'))
time.sleep(1)
self.stdout.write(self.style.SUCCESS('database is available'))
| [
"[email protected]"
]
| |
0d543afecf4a0144548d3602be45f8e851f1657b | a61dae5e34605f708cec4ba661a3e6d2ed78ab73 | /Weather/GUI.py | 595c6a018becfbd6c4cf78563b0fa83b0d9947dd | []
| no_license | ChyiYaqing/PythonPrj | a475c2f83db2a607377d24a78a8c0aa1220229e3 | a7f8e9c0263b4f0f3827c5488ab4fed002962a1b | refs/heads/master | 2020-06-02T01:27:05.095901 | 2017-06-25T12:48:30 | 2017-06-25T12:48:30 | 94,090,551 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,424 | py | #!/usr/bin/env python3
#-*- coding: utf-8 -*-
try:
# for Python2
from Tkinter import * ## notice capitalized T in Tkinter
except ImportError:
# for Python3
from tkinter import * ## notice lowercase 't' in tkinter here
from Weathers import *
import urllib2
from threading import Thread
import tkMessageBox
from sys import exit
class MyThread(Thread):
def run(self):
tkMessageBox.showinfo("Error", "The City Is Not Exist!")
class Top(Frame):
def __init__(self, master=None, content=None, chart=None, today=None):
Frame.__init__(self, master)
self.content = content
self.master = master
self.chart = chart
self.today = today
self.createWidget()
def createWidget(self):
self.e = StringVar()
self.top = LabelFrame(self, text = 'City Name', padx = 5, pady = 5)
#create the LabelFrame widget with string 'City Name'
self.e.set(self.content.weathers.city)
#set the varible of type of StringVar as self.cityname
self.entry = Entry(self.top, width=29, textvariable=self.e)
#create the Entry widget
self.submitbutton = Button(self.top, text = "submit", command=self.submitcity)
#create the Button widget
self.submitbutton.bind("<Return>", self.submitcity)
#bind the Button namely submit with Enter Key in keyboard
self.entry.pack(side='left')
self.submitbutton.pack(side="right")
self.top.pack(fill=X)
#place the widgets on frame namely Top
#define the function namely submit and it is the activity of button namely submit
def submitcity(self):
lastcityname = self.content.weathers.city
#backup the cityname as lastcityname
cityname = self.entry.get().capitalize()
#make cityname as a same formate
if self.content.updateWeathers(cityname) == 1:
MyThread().start()
cityname = lastcityname
self.e.set(lastcityname)
else:
self.chart.updateLineChart()
self.today.updateToday()
class Today(Frame):
def __init__(self, master=None, content=None):
Frame.__init__(self, master)
self.content = content
self.createWidget()
def createWidget(self):
self.today = LabelFrame(self, text='today')
self.img = PhotoImage(file='Today.gif')
self.canvas = Canvas(self.today, height=90)
self.item1 = self.canvas.create_image(150, 50, image=self.img)
self.item2 = self.canvas.create_text(20, 10, text=self.content.weathers.day[0])
self.item3 = self.canvas.create_text(80, 10, text=self.content.weathers.date[0])
self.item4 = self.canvas.create_text(150, 80, text=self.content.weathers[0].mindegree + 'C ~ ' + self.content.weathers[0].maxdegree + 'C')
self.canvas.pack(fill=X)
self.today.pack(fill=X)
def updateToday(self):
self.img = PhotoImage(file='Today.gif')
self.canvas.itemconfigure(self.item1, image=self.img)
self.canvas.itemconfigure(self.item2, text=self.content.weathers.day[0])
self.canvas.itemconfigure(self.item3, text=self.content.weathers.date[0])
self.canvas.itemconfigure(self.item4, text=self.content.weathers[0].mindegree + 'C ~ ' + self.content.weathers[0].maxdegree + 'C')
class Content(Frame):
def __init__(self, master=None):
Frame.__init__(self, master)
self.master = master
self.weathers = Weathers()
self.weathers.setDefaultCity()
self.createWidget()
self.initWeathers()
def createWidget(self):
self.labels = [];
#self.today = LabelFrame(self, text = 'Today Weather', padx = 5, pady = 5)
self.feture = LabelFrame(self, text = 'Feture Weather', padx = 5, pady = 5)
#self.label.pack()
self.labels.append(Label(self, justify = 'left', anchor = 'w', fg = 'red'))
#self.labels[0].pack(fill=X)
for i in range(1, 5):
self.labels.append(Label(self.feture, justify = 'left', anchor = 'w'))
self.labels[i].pack(fill=X)
#self.today.pack(fill=X)
self.feture.pack(fill=X)
def initWeathers(self):
try:
self.weathers.setURL()
self.weathers.setWeathersFromInternet()
except urllib2.URLError:
tkMessageBox.showinfo("Error", "Please check connect!")
exit(0)
for i, wea in enumerate(self.weathers, start=0):
self.labels[i]['text'] = wea.message
#fill in message in the text of labels
def updateWeathers(self, cityname):
self.weathers.setCity(cityname)
self.weathers.setURL()
try:
if self.weathers.setWeathersFromInternet() == 1:
return 1
except urllib2.URLError:
tkMessageBox.showinfo("Error", "Please check connect!")
exit(0)
for i, wea in enumerate(self.weathers, start=0):
self.labels[i]['text'] = wea.message
self.weathers.saveWeathers()
return 0
class LineChart(Frame):
def __init__(self, master=None, content=None):
Frame.__init__(self, master)
self.content = content
self.createWidget()
self.initChart()
self.drawLineChart()
def createWidget(self):
self.chartframe = LabelFrame(self, text = 'LineChart', padx = 5, pady = 5);
self.chart = Canvas(self.chartframe, height=200)
self.chartframe.pack(fill=X)
self.chart.pack()
img = PhotoImage(file='./icon/purple_retina.gif')
self.label = Label(self)
self.label.configure(image=img)
self.label.image = img
self.label.pack(side='right')
def initChart(self):
self.chart.create_line(20, 20, 20, 180, fill='black')
#y
self.chart.create_line(10, 170, 270, 170, fill='black')
#x
self.chart.create_line(15, 25, 20, 20, fill='black');
self.chart.create_line(25, 25, 20, 20, fill='black');
#y
self.chart.create_line(265, 165, 270, 170, fill='black');
self.chart.create_line(265, 175, 270, 170, fill='black');
for i in range(0, 5):
self.chart.create_line(40 * i + 60, 170, 40 * i + 60, 165, fill='black')
def drawLineChart(self):
self.pointmax = []
self.pointmin = []
self.minmin = sorted(self.content.weathers.mindegree)[0]
self.maxmax = sorted(self.content.weathers.maxdegree)[len(self.content.weathers.maxdegree) - 1]
self.gap = 150 / (self.maxmax - self.minmin) - 1
for i, d in enumerate(self.content.weathers.maxdegree[0:6]):
self.pointmax.append((170 - ((d - self.minmin) * self.gap) - 10, 60 + i * 40))
for i, d in enumerate(self.content.weathers.mindegree[0:6]):
self.pointmin.append((170 - ((d - self.minmin) * self.gap) - 10, 60 + i * 40))
for i, wea in enumerate(self.content.weathers, start=0):
self.chart.create_text(40 * i + 60, 175, text=wea.date.split()[0])
for i in range(1, len(self.pointmax)):
self.chart.create_line(20, self.pointmax[i - 1][0], 25, self.pointmax[i - 1][0], fill='red')
self.chart.create_text(30, self.pointmax[i - 1][0], text=self.content.weathers[i - 1].maxdegree, fill='red')
self.chart.create_line(self.pointmax[i - 1][1], self.pointmax[i - 1][0], self.pointmax[i][1], self.pointmax[i][0], fill='red')
self.chart.create_line(20, self.pointmax[len(self.pointmax) - 1][0], 25, self.pointmax[len(self.pointmax) - 1][0], fill='red')
self.chart.create_text(30, self.pointmax[len(self.pointmax) - 1][0], text=self.content.weathers[len(self.pointmax) - 1].maxdegree, fill='red');
for i in range(1, len(self.pointmin)):
self.chart.create_line(15, self.pointmin[i - 1][0], 20, self.pointmin[i - 1][0], fill='blue')
self.chart.create_text(10, self.pointmin[i - 1][0], text=self.content.weathers[i - 1].mindegree, fill='blue');
self.chart.create_line(self.pointmin[i - 1][1], self.pointmin[i - 1][0], self.pointmin[i][1], self.pointmin[i][0], fill='blue')
self.chart.create_line(15, self.pointmin[len(self.pointmin) - 1][0], 20, self.pointmin[len(self.pointmin) - 1][0], fill='blue')
self.chart.create_text(10, self.pointmin[len(self.pointmin) - 1][0], text=self.content.weathers[len(self.pointmin) - 1].mindegree, fill='blue');
def updateLineChart(self):
self.chart.delete(ALL)
self.initChart()
self.drawLineChart()
def GUI():
root = Tk(className='MyWeather')
root.resizable(width = False, height = False)
content = Content(root)
today = Today(root, content)
chart = LineChart(root, content)
top = Top(root, content, chart, today)
top.pack(fill=X)
today.pack(fill=X)
content.pack(fill=X)
chart.pack(fill=X)
root.mainloop()
| [
"[email protected]"
]
| |
95414f7ed3b48f7baf6bd13799ea4698d7f6093f | 199522cb43b4e2c7e3bf034a0e604794258562b1 | /0x0F-python-object_relational_mapping/3-my_safe_filter_states.py | 3659d402edd14791ff0d3dce555884770499752c | []
| no_license | jormao/holbertonschool-higher_level_programming | a0fd92f2332f678e6fe496057c04f2995d24a4ac | 360b3a7294e9e0eadcadb57d4c48c22369c05111 | refs/heads/master | 2020-09-29T01:36:20.094209 | 2020-05-15T03:27:06 | 2020-05-15T03:27:06 | 226,915,744 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 607 | py | #!/usr/bin/python3
"""
script that takes in arguments and displays all values in the
states table of hbtn_0e_0_usa where name matches the argument.
But this time, write one that is safe from MySQL injections!
"""
import MySQLdb
from sys import argv
if __name__ == "__main__":
db = MySQLdb.connect(host="localhost", port=3306, user=argv[1],
passwd=argv[2], db=argv[3])
cur = db.cursor()
cur.execute("SELECT * FROM states\
WHERE name = %s\
ORDER BY id", (argv[4],))
rows = cur.fetchall()
for row in rows:
print(row)
cur.close()
db.close()
| [
"[email protected]"
]
| |
041350efe6b160a115e9e22c301c74a34ff53193 | 71257430418ed7410ddffb6df692a5e816eb53b7 | /61hunter.py | ed47aa6e3c043824bfe5d9b810408fd96bd965c2 | []
| no_license | aarthisandhiya/aarthi | 917283541b9aa133db5d50a3b68eda2a10c38af7 | 00b31831832ea573dfd886eb0001ad824325136d | refs/heads/master | 2020-04-15T05:10:42.585357 | 2019-07-21T13:57:58 | 2019-07-21T13:57:58 | 164,411,781 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 199 | py | a=int(input())
c=0
b=[int(a) for a in input().split()]
u,v=map(int,input().split())
for i in range(0,len(b)):
if b[i]==u:
while b[i]<int(v):
c=c+1
i=i+1
print(c)
| [
"[email protected]"
]
| |
0ac0305052893eb0942f039d2bc543f72d5454e5 | cf09d6430e37b5460d7208d6cae6d3af0fa15925 | /jsonbot/jsb/lib/reboot.py | 9209bd73573898a076abeb23cc8bd8fa26b3fd6a | [
"MIT"
]
| permissive | Lujeni/old-projects | 2bbf0ff89852a3e4a9677475a615d2ee4b07d635 | 657304c8b017a98935de9728fc695abe8be7cc4f | refs/heads/master | 2021-03-12T23:08:34.054777 | 2014-10-16T23:10:15 | 2014-10-16T23:10:15 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,632 | py | # jsb/reboot.py
#
#
""" reboot code. """
## jsb imports
from jsb.lib.fleet import getfleet
from jsb.imports import getjson
json = getjson()
## basic imports
import os
import sys
import pickle
import tempfile
import logging
import time
## reboot function
def reboot():
""" reboot the bot. """
logging.warn("reboot - rebooting")
os.execl(sys.argv[0], *sys.argv)
## reboot_stateful function
def reboot_stateful(bot, ievent, fleet, partyline):
""" reboot the bot, but keep the connections (IRC only). """
logging.warn("reboot - doing statefull reboot")
session = {'bots': {}, 'name': bot.cfg.name, 'channel': ievent.channel, 'partyline': []}
fleet = getfleet()
for i in fleet.bots:
logging.warn("reboot - updating %s" % i.cfg.name)
data = i._resumedata()
if not data: continue
session['bots'].update(data)
if i.type == "sxmpp": i.exit() ; continue
if i.type == "convore": i.exit() ; continue
if i.type == "tornado":
i.exit()
time.sleep(0.1)
for socketlist in i.websockets.values():
for sock in socketlist: sock.stream.close()
session['partyline'] = partyline._resumedata()
sfile, sessionfile = tempfile.mkstemp('-session', 'jsb-', text=True)
logging.warn("writing session file %s" % sessionfile)
json.dump(session, open(sessionfile, "w"))
args = []
skip = False
for a in sys.argv[1:]:
if skip: skip = False ; continue
if a == "-r": skip = True ; continue
args.append(a)
os.execl(sys.argv[0], sys.argv[0], '-r', sessionfile, *args)
| [
"[email protected]"
]
| |
e25ee8942b20a0704262265705ad3ad2b5b7b407 | f99f30752e9bb9e023b37c731f64fb2155ac3daf | /03/zip.py | 69160f8e55ebbe1da4acd417e0cd571fe8488b3e | []
| no_license | chu83/python-basics | 148ff6977f5ca04775951d90ed1f5f763c51a9ff | 19fe0937842c668f604876be0aeb0962a2630dd2 | refs/heads/master | 2023-01-19T01:29:25.203738 | 2020-11-29T18:34:33 | 2020-11-29T18:34:33 | 311,258,549 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 335 | py | print('========== zip() 함수 사용 예 ===========')
s1 = ['foo', 'bar', 'baz']
s2 = ['one', 'two', 'three', 'four']
z = zip(s1, s2)
print(z, type(z))
print('========== 순회1 ===========')
for t in z:
print(t, type(t))
z = zip(s1, s2)
for a, b in z:
print(a, b)
print('========== 순회2 ===========')
z = zip(s1, s2) | [
"[email protected]"
]
| |
318d59a2c7fd3d07c465da350c7d3b65dd8f4934 | 91d1a6968b90d9d461e9a2ece12b465486e3ccc2 | /ivs_write_f/playback-key-pair_import.py | e6227c4b54f4786793a1d735b01d2cf516e72ad9 | []
| no_license | lxtxl/aws_cli | c31fc994c9a4296d6bac851e680d5adbf7e93481 | aaf35df1b7509abf5601d3f09ff1fece482facda | refs/heads/master | 2023-02-06T09:00:33.088379 | 2020-12-27T13:38:45 | 2020-12-27T13:38:45 | 318,686,394 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 773 | py | #!/usr/bin/python
# -*- codding: utf-8 -*-
import os
import sys
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
from common.execute_command import write_parameter
# url : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/describe-instances.html
if __name__ == '__main__':
"""
delete-playback-key-pair : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ivs/delete-playback-key-pair.html
get-playback-key-pair : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ivs/get-playback-key-pair.html
list-playback-key-pairs : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ivs/list-playback-key-pairs.html
"""
write_parameter("ivs", "import-playback-key-pair") | [
"[email protected]"
]
| |
4cdb8d4ce152583225c607c387d527a82eced8d3 | 7c9707f0f1cb8e633ac605934f3dbd8036790868 | /projet/rpi_manager/models.py | f61c7c196096c5f2351a5ccd5919b2269e0b3f2e | []
| no_license | ometeore/hydropo | 891e1abd4c1b8ccd0a3b27a043abf894b70ceb5b | 324076d4b7ddbd14e718c424eb24d129c2a2243c | refs/heads/master | 2023-06-14T08:35:55.838469 | 2021-07-04T16:28:09 | 2021-07-04T16:28:09 | 290,198,666 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,146 | py | from django.db import models
from django import forms
from datetime import datetime
from channels.layers import get_channel_layer
from asgiref.sync import async_to_sync
class Rpi(models.Model):
name = models.CharField(max_length=200)
uid_name = models.CharField(max_length=200)
last_connect = models.DateTimeField()
is_conected = models.BooleanField()
# plutot que de comparer des str sources de bugs
# import datetime
# regler le passage a minuit aussi
# date_time_obj = datetime.datetime.strptime(date_time_str, '%Y-%m-%d %H:%M:%S.%f')
def compare_time(self, begin_test, end_test, cat):
if cat:
schedule = self.water.all()
else:
schedule = self.lights.all()
for times in schedule:
if begin_test > str(times.begin) and begin_test < str(times.end):
return False
if end_test > str(times.begin) and end_test < str(times.end):
return False
if begin_test < str(times.begin) and end_test > str(times.end):
return False
return True
def broadcast_schedule(self):
message = {}
message["manual"] = False
schedule_water_list = [
[str(elm.begin), str(elm.end)] for elm in self.water.all()
]
message["water"] = schedule_water_list
schedule_lights_list = [
[str(elm.begin), str(elm.end)] for elm in self.lights.all()
]
message["lights"] = schedule_lights_list
objectif_ph = self.ph.filter(objectif=True)
message["ph"] = objectif_ph[0].value
objectif_ec = self.ec.filter(objectif=True)
message["ec"] = objectif_ec[0].value
####### This part is sending the message to the websocket in group call "group0"
channel_layer = get_channel_layer()
async_to_sync(channel_layer.group_send)(
self.uid_name, {"type": "send_message", "message": message}
)
def broadcast_manual(self, tool):
message = {}
message["manual"] = True
message["tool"] = tool
print("ASK FOR MANUAL MODE FOR ID: {}".format(self.uid_name))
print(message)
channel_layer = get_channel_layer()
async_to_sync(channel_layer.group_send)(
self.uid_name, {"type": "send_message", "message": message}
)
class WaterSchedule(models.Model):
begin = models.TimeField()
end = models.TimeField()
rpi = models.ForeignKey(Rpi, on_delete=models.CASCADE, related_name="water")
class LightSchedule(models.Model):
begin = models.TimeField()
end = models.TimeField()
rpi = models.ForeignKey(Rpi, on_delete=models.CASCADE, related_name="lights")
class Ph(models.Model):
date = models.DateTimeField()
value = models.FloatField()
objectif = models.BooleanField()
rpi = models.ForeignKey(Rpi, on_delete=models.CASCADE, related_name="ph")
class Ec(models.Model):
date = models.DateTimeField()
value = models.IntegerField()
objectif = models.BooleanField()
rpi = models.ForeignKey(Rpi, on_delete=models.CASCADE, related_name="ec")
| [
"[email protected]"
]
| |
6d21cd382eeb98e10bb5bc8a2a202726211def5f | ce6ace34704e74c2a53e9b38b2630876d9cd52e2 | /mdias_addons/metro_park_maintenance/models/day_plan_limit.py | 8f25985b0d5a8f7e281a0c359a8c74c657e8ef34 | []
| no_license | rezaghanimi/main_mdias | e3cfd8033204d8e7e484041f506892621a3e3479 | 13b428a5c4ade6278e3e5e996ef10d9fb0fea4b9 | refs/heads/master | 2022-09-17T20:15:42.305452 | 2020-05-29T05:38:35 | 2020-05-29T05:38:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 861 | py |
# -*- coding: utf-8 -*-
from odoo import models, fields, api
class DayPlanLimit(models.Model):
'''
日计划限制
'''
_name = 'metro_park_maintenance.day_plan_limit'
location = fields.Many2one(string='地点', comodel_name='metro_park_base.location')
max_repair_after_high_run = fields.Integer(string='高峰车最大检修数量')
max_repair_back_time = fields.Char(string="返回时间", help='最大返回时间')
class DayPlanLimit(models.TransientModel):
'''
日计划向导限制
'''
_name = 'metro_park_maintenance.day_plan_wizard_limit'
location = fields.Many2one(string='地点', comodel_name='metro_park_base.location')
max_repair_after_high_run = fields.Integer(string='高峰车最大检修数量')
max_repair_back_time = fields.Char(string="返回时间", help='最大返回时间')
| [
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]
| |
f23d62dafdbb77a295d93ac632a4441e517a6c10 | c92d5b8509f23444622529aa24d4bc85bf1d3c9f | /main/question47/book1.py | 44b832b082eb8ed9403996e4f3f5e5ee8a3f4ad1 | []
| no_license | qcymkxyc/JZoffer | 75dfb747394018f14552f521413b01a5faa9c07f | 28628616589061653a8322d5b400f9af32f2249d | refs/heads/master | 2021-07-15T00:53:00.711360 | 2019-02-10T03:16:52 | 2019-02-10T03:16:52 | 149,714,958 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 926 | py | #!/usr/bin/env python
# _*_coding:utf-8_*_
"""
@Time : 19-1-24 上午10:58
@Author: qcymkxyc
@File: book1.py
@Software: PyCharm
"""
def max_value(matrix):
"""
动态规划
:param matrix: List[List[int]]
矩阵
:return: int
最大值
"""
n_row, n_col = len(matrix), len(matrix[0])
value_matrix = list()
for i, v in enumerate(matrix):
value_matrix.append([0] * len(v))
# 第一行初始化
for i in range(n_col):
value_matrix[0][i] += sum(matrix[0][:i + 1])
# 第一列初始化
for row in range(n_row):
value_matrix[row][0] = sum(map(lambda x:x[0], matrix[:row + 1]))
for row in range(1,n_row):
for col in range(1,n_col):
value_matrix[row][col] = max(value_matrix[row - 1][col], value_matrix[row][col - 1]) + \
matrix[row][col]
return value_matrix[-1][-1]
| [
"[email protected]"
]
| |
5802eeeb0ab8d6f8d89deb95c876c1ac27840b2a | 4fc9cb4cf01e41c4ed3de89f13d213e95c87dd33 | /angr/procedures/definitions/win32_wdsclientapi.py | dcbb07c74c314d0c5708a98cc0f55187713044fc | [
"BSD-2-Clause"
]
| permissive | mborgerson/angr | ea5daf28576c3d31b542a0e229139ab2494326e9 | 8296578e92a15584205bfb2f7add13dd0fb36d56 | refs/heads/master | 2023-07-24T22:41:25.607215 | 2022-10-19T19:46:12 | 2022-10-20T18:13:31 | 227,243,942 | 1 | 2 | BSD-2-Clause | 2021-04-07T22:09:51 | 2019-12-11T00:47:55 | Python | UTF-8 | Python | false | false | 14,784 | py | # pylint:disable=line-too-long
import logging
from ...sim_type import SimTypeFunction, SimTypeShort, SimTypeInt, SimTypeLong, SimTypeLongLong, SimTypeDouble, SimTypeFloat, SimTypePointer, SimTypeChar, SimStruct, SimTypeFixedSizeArray, SimTypeBottom, SimUnion, SimTypeBool
from ...calling_conventions import SimCCStdcall, SimCCMicrosoftAMD64
from .. import SIM_PROCEDURES as P
from . import SimLibrary
_l = logging.getLogger(name=__name__)
lib = SimLibrary()
lib.set_default_cc('X86', SimCCStdcall)
lib.set_default_cc('AMD64', SimCCMicrosoftAMD64)
lib.set_library_names("wdsclientapi.dll")
prototypes = \
{
#
'WdsCliClose': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["Handle"]),
#
'WdsCliRegisterTrace': SimTypeFunction([SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="SByte"), offset=0)], SimTypeBottom(label="Void"), arg_names=["pwszFormat", "Params"]), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pfn"]),
#
'WdsCliFreeStringArray': SimTypeFunction([SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["ppwszArray", "ulCount"]),
#
'WdsCliFindFirstImage': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hSession", "phFindHandle"]),
#
'WdsCliFindNextImage': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["Handle"]),
#
'WdsCliGetEnumerationFlags': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["Handle", "pdwFlags"]),
#
'WdsCliGetImageHandleFromFindHandle': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["FindHandle", "phImageHandle"]),
#
'WdsCliGetImageHandleFromTransferHandle': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hTransfer", "phImageHandle"]),
#
'WdsCliCreateSession': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimStruct({"pwszUserName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pwszDomain": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pwszPassword": SimTypePointer(SimTypeChar(label="Char"), offset=0)}, name="WDS_CLI_CRED", pack=False, align=None), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pwszServer", "pCred", "phSession"]),
#
'WdsCliAuthorizeSession': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimStruct({"pwszUserName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pwszDomain": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pwszPassword": SimTypePointer(SimTypeChar(label="Char"), offset=0)}, name="WDS_CLI_CRED", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hSession", "pCred"]),
#
'WdsCliInitializeLog': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="CPU_ARCHITECTURE"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hSession", "ulClientArchitecture", "pwszClientId", "pwszClientAddress"]),
#
'WdsCliLog': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hSession", "ulLogLevel", "ulMessageCode"]),
#
'WdsCliGetImageName': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]),
#
'WdsCliGetImageDescription': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]),
#
'WdsCliGetImageType': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=False, label="WDS_CLI_IMAGE_TYPE"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pImageType"]),
#
'WdsCliGetImageFiles': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pppwszFiles", "pdwCount"]),
#
'WdsCliGetImageLanguage': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]),
#
'WdsCliGetImageLanguages': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypePointer(SimTypeChar(label="SByte"), offset=0), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pppszValues", "pdwNumValues"]),
#
'WdsCliGetImageVersion': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]),
#
'WdsCliGetImagePath': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]),
#
'WdsCliGetImageIndex': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pdwValue"]),
#
'WdsCliGetImageArchitecture': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=False, label="CPU_ARCHITECTURE"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pdwValue"]),
#
'WdsCliGetImageLastModifiedTime': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimStruct({"wYear": SimTypeShort(signed=False, label="UInt16"), "wMonth": SimTypeShort(signed=False, label="UInt16"), "wDayOfWeek": SimTypeShort(signed=False, label="UInt16"), "wDay": SimTypeShort(signed=False, label="UInt16"), "wHour": SimTypeShort(signed=False, label="UInt16"), "wMinute": SimTypeShort(signed=False, label="UInt16"), "wSecond": SimTypeShort(signed=False, label="UInt16"), "wMilliseconds": SimTypeShort(signed=False, label="UInt16")}, name="SYSTEMTIME", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppSysTimeValue"]),
#
'WdsCliGetImageSize': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeLongLong(signed=False, label="UInt64"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pullValue"]),
#
'WdsCliGetImageHalName': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]),
#
'WdsCliGetImageGroup': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]),
#
'WdsCliGetImageNamespace': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]),
#
'WdsCliGetImageParameter': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="WDS_CLI_IMAGE_PARAM_TYPE"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ParamType", "pResponse", "uResponseLen"]),
#
'WdsCliGetTransferSize': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeLongLong(signed=False, label="UInt64"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pullValue"]),
#
'WdsCliSetTransferBufferSize': SimTypeFunction([SimTypeInt(signed=False, label="UInt32")], SimTypeBottom(label="Void"), arg_names=["ulSizeInBytes"]),
#
'WdsCliTransferImage': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeFunction([SimTypeInt(signed=False, label="PFN_WDS_CLI_CALLBACK_MESSAGE_ID"), SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypeBottom(label="Void"), arg_names=["dwMessageId", "wParam", "lParam", "pvUserData"]), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hImage", "pwszLocalPath", "dwFlags", "dwReserved", "pfnWdsCliCallback", "pvUserData", "phTransfer"]),
#
'WdsCliTransferFile': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeFunction([SimTypeInt(signed=False, label="PFN_WDS_CLI_CALLBACK_MESSAGE_ID"), SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypeBottom(label="Void"), arg_names=["dwMessageId", "wParam", "lParam", "pvUserData"]), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pwszServer", "pwszNamespace", "pwszRemoteFilePath", "pwszLocalFilePath", "dwFlags", "dwReserved", "pfnWdsCliCallback", "pvUserData", "phTransfer"]),
#
'WdsCliCancelTransfer': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hTransfer"]),
#
'WdsCliWaitForTransfer': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hTransfer"]),
#
'WdsCliObtainDriverPackages': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), SimTypePointer(SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hImage", "ppwszServerName", "pppwszDriverPackages", "pulCount"]),
#
'WdsCliObtainDriverPackagesEx': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), SimTypePointer(SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hSession", "pwszMachineInfo", "ppwszServerName", "pppwszDriverPackages", "pulCount"]),
#
'WdsCliGetDriverQueryXml': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pwszWinDirPath", "ppwszDriverQuery"]),
}
lib.set_prototypes(prototypes)
| [
"[email protected]"
]
| |
8168b5bf889b97e447da255e86d69e116f571d47 | c8975f8bbe32637399a3ca00ad21e8e6602e358d | /aoc/year2021/day14/day14.py | bf40f0314c32c659180cf4eaa5ae69c3475ea98d | [
"Unlicense"
]
| permissive | Godsmith/adventofcode | 0e8e0beb813300206b2810b523b54a6c40ca936f | 3c59ea66830f82b63881e0ea19bfe3076f2a500d | refs/heads/master | 2021-12-28T13:05:42.579374 | 2021-12-26T22:19:55 | 2021-12-26T22:24:01 | 225,074,419 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 977 | py | from collections import Counter
from aocd import get_data
from more_itertools import pairwise
def run(data, iterations):
new_element_from_pair = {tuple(line.split(" -> ")[0]): line.split(" -> ")[1] for line in data.splitlines()[2:]}
new_pairs_from_pair = {(e1, e2): [(e1, inserted), (inserted, e2)] for (e1, e2), inserted in new_element_from_pair.items()}
template = data.splitlines()[0]
element_counter = Counter(template)
pair_counter = Counter(pairwise(template))
for _ in range(iterations):
new_pair_counter = Counter()
for pair in pair_counter:
for new_pair in new_pairs_from_pair[pair]:
new_pair_counter[new_pair] += pair_counter[pair]
element_counter[new_element_from_pair[pair]] += pair_counter[pair]
pair_counter = new_pair_counter
return element_counter.most_common()[0][1] - element_counter.most_common()[-1][1]
print(run(get_data(), 10))
print(run(get_data(), 40))
| [
"[email protected]"
]
| |
f32d24f9fdb32d8eb2a1eef5c82ae7102d01c864 | cc5f2ee6a5de6faf141f10b1b10717243821a0a5 | /problems/problem 106.py | e9b15f310b5e23ecf09e8a1864e6f1ea2dd3f449 | []
| no_license | alexandrepoulin/ProjectEulerInPython | faf634025c86bc74fc764d315813bf1706e58f63 | 97cb52cdd7508f2db891d1644e3d247814571718 | refs/heads/master | 2020-04-22T11:47:04.594328 | 2019-02-16T00:17:40 | 2019-02-16T00:17:40 | 170,341,883 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 793 | py | print("Starting")
import useful
## only need to check subset pairs which have the same number of elements
## there are useful.nChooseK(n,s)*useful.nChooseK(n-s,s)*0.5 such pairs
## for a specific subset, we only need to look at pairs which have interweining elements
## such as (1,3)(2,4)
## number of times this doesn't happend is given by Catalan numbers given by c
## multiply that by the total number of ways to make two subsets of that size
## or useful.nChooseK(n,2*s)
## and you find how many pairs you need to check for a subset size
def c(s):
return useful.nChooseK(2*s,s)/(s+1)
def x(n,s):
return useful.nChooseK(n,s)*useful.nChooseK(n-s,s)*0.5-c(s)*useful.nChooseK(n,2*s)
answer = 0
N= 12
for s in range(2,7):
answer += x(N,s)
print(answer)
| [
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3cd515eca280170fe3a32456a2936ef77006c086 | 286b6dc56323f982092ffafbfac8a32dbbaeb7ef | /Day_09/sample_pyvmomi.py | 10033c34292fb7b9d33af55fc34b5e48284d85bb | []
| no_license | learndevops19/pythonTraining-CalsoftInc | ccee0d90aadc00bfdb17f9578620f6bf92f80a4c | c5f61516b835339b394876edd1c6f62e7cc6f0c3 | refs/heads/master | 2021-02-05T04:27:17.590913 | 2019-11-20T17:27:06 | 2019-11-20T17:27:06 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,761 | py | import ssl
from pyVim import connect
from pyVmomi import vim
def connectVcenter(vCenterHost, username, password, portNum=443):
"""
Description : Performs vCenter connection.
Parameters : vCenterHost - vCenter server ip address (STRING)
username - vCenter server username (STRING)
password - vCenter server password (STRING)
portNum - Port number for connection, default is 443 (INT)
Returns : Service instance object
"""
context = ssl._create_unverified_context()
si = connect.SmartConnect(
host=vCenterHost, user=username, pwd=password, port=portNum, sslContext=context
)
return si
def getObj(content, vimtype, name):
"""
Description: Get the vsphere object associated with a given text name
Parameters : content - Data object having properties for the
ServiceInstance managed object (OBJECT)
vimtype - Managed object type (OBJECT)
name - Managed object entity name (STRING)
Return: Matched Managed object (OBJECT)
"""
container = content.viewManager.CreateContainerView(
content.rootFolder, vimtype, True
)
for vmObj in container.view:
if vmObj.name == name:
return vmObj
def getDatacenterByName(si, name):
"""
Description: Find a datacenter by it's name and return it
Parameters : si - vCenter connection session (OBJECT)
name - datacenter name (STRING)
Return: datacenter Object (OBJECT)
"""
return getObj(si.RetrieveContent(), [vim.Datacenter], name)
def getClusterByName(si, name):
"""
Description: Find a cluster by it's name and return it
Parameters : si - vCenter connection session (OBJECT)
name - cluster name (STRING)
Return: cluster Object (OBJECT)
"""
return getObj(si.RetrieveContent(), [vim.ClusterComputeResource], name)
def getHostByName(si, name):
"""
Description: Find a host by it's name and return it
Parameters : si - vCenter connection session (OBJECT)
name - host name (STRING)
Return: host Object (OBJECT)
"""
return getObj(si.RetrieveContent(), [vim.HostSystem], name)
def getVirtualMachineByName(si, name):
"""
Description: Find a vm by it's name and return it
Parameters : si - vCenter connection session (OBJECT)
name - vm name (STRING)
Return: virtual machine Object (OBJECT)
"""
return getObj(si.RetrieveContent(), [vim.VirtualMachine], name)
def getDatastoreByName(si, name):
"""
Description: Find a datastore by it's name and return it
Parameters : si - vCenter connection session (OBJECT)
name - datastore name (STRING)
Return: datastore Object (OBJECT)
"""
return getObj(si.RetrieveContent(), [vim.Datastore], name)
def getNetworkByName(si, name, isVDS=False):
"""
Description: Find a network by it's name and return it
Parameters : si - vCenter connection session (OBJECT)
name - network name (STRING)
Return: network Object
"""
if isVDS is False:
networkObj = getObj(si.RetrieveContent(), [vim.Network], name)
else:
networkObj = getObj(
si.RetrieveContent(), [vim.dvs.DistributedVirtualPortgroup], name
)
return networkObj
# connect vcenter
siObj = connectVcenter(vcenterIp, vcenterUsername, vcenterPassword)
# print(siObj.content.about)
# get datacenter by name
datacenterName = "UCP CI Datacenter"
datacenterObj = getDatacenterByName(siObj, datacenterName)
print("datacenterName is", datacenterObj.name, datacenterObj.datastore[0].name)
# get cluster by name
# clusterName = 'Dockerized'
# clusterObj = getClusterByName(siObj, clusterName)
# print("clusterName is", clusterObj.name)
# get host by name
# hostName = '192.168.25.205'
# hostObj = getHostByName(siObj, hostName)
# print("hostName is", hostObj.name)
# get datastore by name
# datastoreName = 'ds1'
# datastoreObj = getDatastoreByName(siObj, datastoreName)
# print("datastoreName is", datastoreObj.name)
# get network by name
# networkName = 'VM Network'
# networkObj = getNetworkByName(siObj, networkName)
# print("networkName is", networkObj.name)
# print("Vm's in this network", [vm.name for vm in networkObj.vm])
# get all vms inside datacenter
# vmsList = datacenterObj.vmFolder.childEntity
# for vm in vmsList:
# print("Virtual Machine - ", vm.name)
# get vm by name
# vmObj = getVirtualMachineByName(siObj, 'k8s-master')
# print('VirtualMachineName', vmObj.name, dir(vmObj))
# poweroff the above virtual machine
# vmObj.PowerOff()
# poweron the above virtual machine
# vmObj.PowerOn()
| [
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]
| |
aaf684914f88ee47e002fe6283aad1328b10f3ad | 6cce023315d4083c7df0fcdeea2a037b00818878 | /py-files/data_setup.py | 3a1efa85492db400854022be0137e9d4defafa58 | []
| no_license | Limmen/Distributed_ML | e02e865a123e552d3795c76a4a0846f2da6f3a55 | d5b65a0bcb89182e3ac773b0a3cec46625dabccb | refs/heads/master | 2021-05-09T03:52:41.530823 | 2018-01-28T12:21:47 | 2018-01-28T12:21:47 | 119,255,519 | 5 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,735 | py |
import argparse
import pyspark
from pyspark.sql.functions import udf
from pyspark.sql.types import *
import tensorflow as tf
import pandas as pd
import numpy as np
SEQ_LABELS_TRAIN = "data/y_train.csv"
SEQ_FEATURES_TRAIN = "data/x_train.csv"
SEQ_LABELS_TEST = "data/y_test.csv"
SEQ_FEATURES_TEST = "data/x_test.csv"
conf = pyspark.SparkConf()
conf = conf.setAppName("har_data_setup").set("spark.hadoop.validateOutputSpecs", "false")
sc = pyspark.SparkContext(conf=conf)
sql = pyspark.SQLContext(sc)
CLEANED_DATA_PATH = "./cleaned_data"
def read_raw_data(sql):
seq_features_train_raw = sql.read.format("com.databricks.spark.csv").options(header="false").load(SEQ_FEATURES_TRAIN)
seq_labels_train_raw = sql.read.format("com.databricks.spark.csv").options(header="false").load(SEQ_LABELS_TRAIN)
seq_features_test_raw = sql.read.format("com.databricks.spark.csv").options(header="false").load(SEQ_FEATURES_TEST)
seq_labels_test_raw = sql.read.format("com.databricks.spark.csv").options(header="false").load(SEQ_LABELS_TEST)
return seq_features_train_raw,seq_labels_train_raw, seq_features_test_raw, seq_labels_test_raw
seq_features_train_raw, seq_labels_train_raw,seq_features_test_raw,seq_labels_test_raw = read_raw_data(sql)
features_train_size = seq_features_train_raw.count()
labels_train_size = seq_labels_train_raw.count()
features_test_size = seq_features_test_raw.count()
labels_test_size = seq_labels_test_raw.count()
print("train feat size: {0}, train label size: {1}, test feat size {2}, test label size {3}".format(features_train_size, labels_train_size, features_test_size, labels_test_size))
seq_labels_test_raw.printSchema
classes = seq_labels_train_raw.unionAll(seq_labels_test_raw).select("_c0").distinct().rdd.map(lambda row: row._c0).zipWithIndex().collectAsMap()
seq_labels_train_clean = seq_labels_train_raw.select("_c0").rdd.map(lambda row: classes[row._c0])
seq_labels_test_clean = seq_labels_test_raw.select("_c0").rdd.map(lambda row: classes[row._c0])
labels_train_np = seq_labels_train_clean.collect()
labels_test_np = seq_labels_test_clean.collect()
np.savetxt(CLEANED_DATA_PATH + "/train/labels/y_train.csv", np.array(labels_train_np).astype(int), fmt='%i', delimiter=",")
np.savetxt(CLEANED_DATA_PATH + "/test/labels/y_test.csv", np.array(labels_test_np).astype(int), fmt='%i', delimiter=",")
np.savetxt(CLEANED_DATA_PATH + "/classes/classes.csv", np.array([[k,v] for k,v in classes.items()]),fmt="%s", delimiter=",")
np.savetxt(CLEANED_DATA_PATH + "/size/sizes.csv", np.array([["features_train_size", features_train_size], ["labels_train_size", labels_train_size], ["features_test_size", features_test_size], ["labels_test_size", labels_test_size]]), fmt="%s", delimiter=",")
| [
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]
| |
df7e6169ccdea122ba78000885ec1008e3579ebd | bc539788b876773e294383863252c1637de9eb7f | /Pscrapy/PycharmProjects/Reptile/Practise/practise4.py | a3249cb05379978d945554dfcee685df198bd50c | []
| no_license | umsung/scrapy | 4eb56bf74f3e617e49dcdec61cf77010eb912f4f | deacd9f289159c5af114b0dd3110448ad7eb43e8 | refs/heads/master | 2020-05-31T14:11:46.530793 | 2019-10-16T01:32:25 | 2019-10-16T01:32:25 | 190,321,772 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 120 | py |
num = input('输入数字：').strip()
num = int(num)
for i in range(1, num):
print(' '*(num-i),'* '*i)
| [
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]
| |
a3abadf3e5c58d3196f6ac02b55a07ff35093bd4 | 16c77266859989d156fe3f4d0ce3a37a1898ad38 | /ml/sk/__init__.py | 2e9e9ccc69e32e7f29ce91893b4883b61084bc5d | [
"MIT"
]
| permissive | SRHerzog/ut | 92620e66be2ea9707d9cd3cf390179326ed2eefe | 894bd5607eb76676aaea7a37ed8a91b5fb5e805e | refs/heads/master | 2021-06-30T19:15:46.131299 | 2017-09-15T20:47:35 | 2017-09-15T20:47:35 | 103,696,926 | 0 | 0 | null | 2017-09-15T20:08:10 | 2017-09-15T20:08:10 | null | UTF-8 | Python | false | false | 54 | py | from __future__ import division
__author__ = 'thor'
| [
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]
| |
8cf423d1f9f0257fa371e065ae2d57628aeedaf2 | ee4db47ccecd23559b3b6f3fce1822c9e5982a56 | /Analyse Data/NumpPy.py | d2bf919d8ad04f330f143dfea2c477d7387bd3ee | []
| no_license | meoclark/Data-Science-DropBox | d51e5da75569626affc89fdcca1975bed15422fd | 5f365cedc8d0a780abeb4e595cd0d90113a75d9d | refs/heads/master | 2022-10-30T08:43:22.502408 | 2020-06-16T19:45:05 | 2020-06-16T19:45:05 | 265,558,242 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 4,021 | py | # Introduction to NumPy: Numerical Python
#NumPy is great at storing and manipulating numerical data in arrays.
import numpy as np
# NumPy Arrays
#A NumPy array is a special type of list. It’s a data structure that organizes multiple items. Each item can be of any type (strings, numbers, or even other arrays).
test_1 = np.array([92, 94, 88, 91, 87])
# test_1 is now a numpy array
#Creating an Array from a CSV
# Note the delimiter can be in other formats such as semi colon and tabs.
test_2 = np.genfromtxt('test_2.csv', delimiter=',')
# Operations with NumPy Arrays
# Let’s compare how to add a number to each value in a python list versus a NumPy array:
# With a list
A = [1, 2, 3, 4, 5,6]
A_plus_3 = []
for i in range(len(A)):
l_plus_3.append(A[i] + 3)
# With an array
a = np.array(l)
a_plus_3 = a + 3
#Squaring each value:
a ** 2
#array([ 1, 4, 9, 16, 25, 36])
#Taking the square root of each value:
np.sqrt(a)
#array([ 1, 1.41421356, 1.73205081, 2, 2.23606798, 2.44948974])
# Add extra 2 points to test_3
test_3 = np.array([87, 85, 72, 90, 92])
test_3_fixed = test_3 + 2
# Operations with NumPy Arrays II
# Arrays can also be added to or subtracted from each other in NumPy,
# assuming the arrays have the same number of elements.
a = np.array([1, 2, 3, 4, 5])
b = np.array([6, 7, 8, 9, 10])
a + b
#array([ 7, 9, 11, 13, 15])
test_1 = np.array([92, 94, 88, 91, 87])
test_2 = np.array([79, 100, 86, 93, 91])
test_3 = np.array([87, 85, 72, 90, 92])
test_3_fixed = test_3 + 2
total_grade = test_1 + test_2 + test_3_fixed
# average score
final_grade = total_grade / 3
print(final_grade)
# Two-Dimensional Arrays
# in NumPy we can create an array of arrays.
# If the arrays that make up our bigger array are all the same size, then it has a special name: a two-dimensional array.
#we could have also stored all of this data in a single, two-dimensional array: Notice the double square brackets syntax [[]]
np.array([[92, 94, 88, 91, 87],
[79, 100, 86, 93, 91],
[87, 85, 72, 90, 92]])
coin_toss_again = np.array([[1,0,0,1,0],[0,0,1,1,1]])
# Selecting Elements from a 1-D Array
# This uses normal indexing
test_1 = np.array([92, 94, 88, 91, 87])
test_2 = np.array([79, 100, 86, 93, 91])
test_3 = np.array([87, 85, 72, 90, 92])
jeremy_test_2 = test_2[-2]
#grabs 93
manual_adwoa_test_1 = test_1[1:3]
# grabs 94 88
# Selecting Elements from a 2-D Array
# The syntax for selecting from a 2-d array is a[row,column] where a is the array.
a = np.array([[32, 15, 6, 9, 14],
[12, 10, 5, 23, 1],
[2, 16, 13, 40, 37]])
a[2,1]
#16
# selects the first column
a[:,0]
#array([32, 12, 2])
a[1,:]
#array([12, 10, 5, 23, 1])
# selects the first three elements of the first row
a[0,0:3]
#array([32, 15, 6])
student_scores = np.array([[92, 94, 88, 91, 87],
[79, 100, 86, 93, 91],
[87, 85, 72, 90, 92]])
tanya_test_3 = student_scores[2,0]
cody_test_scores = student_scores[:,4]
# Logical Operations with Arrays
# < > == != | &
porridge = np.array([79, 65, 50, 63, 56, 90, 85, 98, 79, 51])
cold = porridge[porridge < 60]
hot = porridge[porridge > 80]
just_right = porridge[(porridge >= 60) & (porridge <= 80)]
print(cold,
hot,
just_right
)
# Review
import numpy as np
temperatures = np.genfromtxt('temperature_data.csv',delimiter=',')
print(temperatures)
temperatures_fixed = temperatures + 3.0
monday_temperatures = temperatures_fixed[0,:]
thursday_friday_morning = temperatures_fixed[3:,1]
temperature_extremes = temperatures_fixed[(temperatures_fixed < 50) | (temperatures_fixed > 60)]
# Project Bettys Bakery
import numpy as np
cupcakes = np.array([2,0.75,2,1,0.5])
recipes = np.genfromtxt('recipes.csv',delimiter=',')
print(recipes)
eggs = recipes[:,2]
print(eggs)
#egg = recipes[recipes[:,2] == 1]
cookies = recipes[2,:]
print(cookies)
double_batch = cupcakes * 2
print(double_batch)
grocery_list = cookies + double_batch
print(grocery_list)
| [
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]
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e1e458abdbc5777af32bf1194e4add3db39fd867 | 36b9fa9f2d8ff655546a33cb47ddacd009bc00c9 | /autogalaxy/profiles/light/linear/__init__.py | bb275334df92a6bf4b34d18f21e6aa123ae5dc62 | [
"MIT"
]
| permissive | Jammy2211/PyAutoGalaxy | 67b76968b8516309b2ebdbff7affd5c1923cf0b1 | d1a2e400b7ac984a21d972f54e419d8783342454 | refs/heads/main | 2023-08-19T01:00:22.320073 | 2023-08-17T15:39:46 | 2023-08-17T15:39:46 | 216,190,501 | 27 | 9 | MIT | 2023-09-13T14:07:43 | 2019-10-19T10:45:44 | Python | UTF-8 | Python | false | false | 401 | py | from .abstract import LightProfile, LightProfileLinear, LightProfileLinearObjFuncList
from .gaussian import Gaussian, GaussianSph
from .moffat import Moffat
from .sersic import Sersic, SersicSph
from .exponential import Exponential, ExponentialSph
from .dev_vaucouleurs import DevVaucouleurs, DevVaucouleursSph
from .sersic_core import SersicCore
from .exponential_core import ExponentialCore
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7a8252f05c1ee87e900b5ed853a3cabc43688b96 | 74081581575e80b2b0f6b75ba912d58ea4f37ac6 | /maskrcnn_benchmark/modeling/detector/detectors.py | 35064d2f9992fd2c2e08d4a29ad31d1e5a229f8f | []
| no_license | youngfly11/LCMCG-PyTorch | 5f6b9f231613b86ac7b250ca0f34229402e1615e | e95299b9a9f1b13e21750ef0dcde0941d703d009 | refs/heads/master | 2021-10-25T19:29:12.967318 | 2021-10-25T03:35:14 | 2021-10-25T03:35:14 | 221,908,808 | 56 | 12 | null | null | null | null | UTF-8 | Python | false | false | 419 | py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .generalized_rcnn import GeneralizedRCNN
from .generalized_rcnn_det import GeneralizedRCNNDet
_DETECTION_META_ARCHITECTURES = {"GeneralizedRCNN": GeneralizedRCNN, "GeneralizedRCNNDet": GeneralizedRCNNDet}
def build_detection_model(cfg):
meta_arch = _DETECTION_META_ARCHITECTURES[cfg.MODEL.META_ARCHITECTURE]
return meta_arch(cfg)
| [
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]
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cbf3083dd8ea5ae4718b4b154ac624468f4e7c15 | 68b23f776fddb77de735419cbf30f33a49e9def2 | /backend/terminus/home/urls.py | 501046c917470aac71074c89c9f1d1a75f5cceac | []
| no_license | vash512/terminus | cbd00f74a600a13fd52aa2206c3eb1e7b5301ec7 | 4eb86d853bc76c22cd1af3c86fed1bc10d457c88 | refs/heads/master | 2016-09-05T14:49:42.655635 | 2015-07-09T03:34:38 | 2015-07-09T03:34:38 | 32,414,141 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,155 | py | # -*- coding: utf-8 -*-
from django.conf.urls import patterns, include, url
from django.views.generic import TemplateView
urlpatterns=patterns('home.views',
url(r'^$', 'index_view', name='index'),
url(r'^humans.txt$', TemplateView.as_view(template_name='statics/humans.txt', content_type='text/plain; charset=utf-8')),
url(r'^robots.txt$', TemplateView.as_view(template_name='statics/robots.txt', content_type='text/plain; charset=utf-8')),
url(r'^sitemap.xml$', TemplateView.as_view(template_name='statics/sitemap.xml', content_type='application/xml; charset=utf-8')),
url(r'^contacto/', 'contacto' ),
url(r'^acercade/', 'acercade'),
url(r'^corpuscontable', 'corpus'),
url(r'^ayuda', 'ayuda'),
#terminos urls de prueba
url(r'^terminos', 'terminos'),
url(r'^terminos/termino', 'termino_detalle'),
url(r'^q/$', 'busqueda'),
url(r'^q/termino', 'busqueda_list'),
url(r'^docs/doc', 'doc_detalle'),
url(r'^docs/$', 'docs'),
#estas direcciones las debe administrar terminos.urls y terminos.views
url(r'^login/', 'log_in'),
url(r'^registro/', 'registro'),
url(r'^logout/', 'log_out'),
) | [
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]
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a05385930991319e2dc5ebf3029f337f10410b3a | ffba5c4a64a87214160c5904b220be8a6e88cd58 | /python-packages/maizy_f/r.py | 5e83d68deeca516eed184231752129e90e707f19 | []
| no_license | maizy/dev-setup | 6e8ae5bc9b56dd85f7612b453e92e31043816189 | 5eb8473cf9c66c66ff8fd9e8c72cecf931f62494 | refs/heads/master | 2022-11-13T22:08:00.184435 | 2022-11-13T08:33:25 | 2022-11-13T08:33:25 | 7,286,016 | 2 | 0 | null | 2017-12-22T11:57:00 | 2012-12-22T13:20:57 | Python | UTF-8 | Python | false | false | 361 | py | # coding: utf-8
# Copyright (c) Nikita Kovaliov, maizy.ru, 2013
from __future__ import print_function, absolute_import, unicode_literals
from fabric.api import task, run
@task
def info():
run('uname -a')
run('hostname')
lsb_rel = run('which lsb_release')
if lsb_rel != '':
print('Debian like os found')
run('lsb_release -a')
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9290a1f679623cb6793f2eaef635da4b4689e597 | 6fce025097cebfd9d1dd37f6611e7fdfdbea90e6 | /rainfields/model_conv.py | 8a22d603ee800aed0a84aee26d7728f6111a7b66 | []
| no_license | ANU-WALD/pluvi_pondus | ec0439d19acdcf4fdf712d6b14a1714297d661b2 | ff8680f7115ab2cb75138bf6705abb59618e47d1 | refs/heads/master | 2021-07-01T14:32:14.501631 | 2020-08-22T09:41:28 | 2020-08-22T09:41:28 | 138,804,652 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,287 | py | from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten
from tensorflow.keras.optimizers import Adam, SGD
import numpy as np
x = np.load("x_conv.npy")[:10000000]
print(x.shape)
y = np.load("y_conv.npy")[:10000000,None]
print(y.shape)
prec_mask = np.nonzero(y>0)
print(prec_mask)
print(len(prec_mask))
#print(prec_mask.shape)
#print(prec_mask[0])
print(prec_mask[0].shape)
x_prec = x[prec_mask[0], :]
y_prec = y[prec_mask[0], :]
print(x_prec.shape, y_prec.shape)
zero_mask = np.nonzero(y==0)
x_dry = x[zero_mask[0], :]
y_dry = y[zero_mask[0], :]
print(x_dry.shape, y_dry.shape)
idxs = np.arange(x_dry.shape[0])
np.random.seed(0)
np.random.shuffle(idxs)
n = x_prec.shape[0] * 2
x_dry = x_dry[idxs[:n],:]
y_dry = y_dry[idxs[:n],:]
print(x_dry.shape, y_dry.shape)
x = np.concatenate((x_prec, x_dry), axis=0)
y = np.concatenate((y_prec, y_dry), axis=0)
print(x.shape, y.shape)
idxs = np.arange(x.shape[0])
np.random.shuffle(idxs)
x = x[idxs,:]
x = np.reshape(x, (x.shape[0], -1))
y = y[idxs,:]
print(x.shape, y.shape)
model= Sequential()
model.add(Dense(100, activation='relu', input_dim=100))
model.add(Dense(200, activation='relu'))
model.add(Dense(400, activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(1, activation='relu'))
"""
model= Sequential()
model.add(Conv2D(16, kernel_size=3, activation='relu', padding='same', input_shape=(5,5,4)))
model.add(Conv2D(32, kernel_size=3, activation='relu', padding='same'))
model.add(Conv2D(64, kernel_size=3, activation='relu', padding='same'))
model.add(Conv2D(32, kernel_size=3, activation='relu', padding='same'))
model.add(Flatten())
model.add(Dense(1, activation='relu'))
"""
x_train = x[:175000,:]
x_test = x[175000:,:]
y_train = y[:175000,:]
y_test = y[175000:,:]
print(y_train.shape, y_test.shape)
print(np.square(y_train).mean(axis=0))
print(np.square(y_test).mean(axis=0))
print(np.abs(y_train).mean(axis=0))
print(np.abs(y_test).mean(axis=0))
#classifier.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.compile(optimizer=Adam(lr=0.000001), loss='mse', metrics=['mae', 'mse'])
model.fit(x_train, y_train, batch_size=32, nb_epoch=10, validation_data=(x_test, y_test))
| [
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| |
53b09cda44362a8837373232a16c18428dcb871d | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p02585/s497439083.py | c64c92fb3dffd1704057e5332cba0a7d2217f5e0 | []
| no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,416 | py | n,k = map(int,input().split())
P = list(map(int,input().split()))
C = list(map(int,input().split()))
g = [[0]*(n) for _ in range(n)]
A = [n]*n
# for i in range(n):
# tmp = 0
# idx = i
# cnt = 0
# set_ =set()
# while cnt<n:
# if C[idx] not in set_:
# tmp += C[idx]
# set_.add(C[idx])
# g[i][cnt] = tmp
# idx = P[idx]-1
# cnt += 1
# else:
# p = len(set_)
# A[i] = p
# break
ans = -float('inf')
for i in range(n):
S = []
idx = P[i]-1
S.append(C[idx])
while idx != i:
idx = P[idx]-1
S.append(S[-1] +C[idx])
v,w = k//len(S),k%len(S)
if k<=len(S):
val = max(S[:k])
elif S[-1]<=0:
val = max(S)
else:
val1 = S[-1] *(v-1)
val1 += max(S)
val2 = S[-1]*v
if w!=0:
val2 += max(0,max(S[:w]))
val = max(val1,val2)
ans = max(ans,val)
# for i in range(n):
# v,w = k//A[i],k%A[i]
# if A[i]<k:
# if g[i][A[i]-1]<=0:
# val = max(g[i][:A[i]])
# else:
# val1 = (v-1)*g[i][A[i]-1]
# val1 += max(g[i][:A[i]])
# val2 = v*g[i][A[i]-1]
# if w!=0:
# val2 += max(0,max(g[i][:w]))
# val = max(val1,val2)
# else:
# val = max(g[i][:k])
# ans = max(ans,val)
print(ans) | [
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]
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9a79bc2d121ab5020b5787e81900966444fc6e38 | 76fefdb20c453e830b8db86f32a1b42d79108cdd | /venv/bin/django-admin.py | a02fc84b3fe69bf065a0bd2537f44fe524e8d4d0 | []
| no_license | Belie06Loryn/chaty-final-master | 35f6762e9bf45e1086db6280cd0bd7dc0828fb96 | 86e839c069eb54dad3390e84c6b7534d165a3942 | refs/heads/master | 2022-12-09T14:31:19.478090 | 2019-12-08T16:00:45 | 2019-12-08T16:00:45 | 226,365,156 | 0 | 0 | null | 2022-12-08T06:59:44 | 2019-12-06T16:08:51 | Python | UTF-8 | Python | false | false | 166 | py | #!/home/alexie/Documents/chaty-final-master/venv/bin/python
from django.core import management
if __name__ == "__main__":
management.execute_from_command_line()
| [
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| |
5f28d3473174758f29072135291cc13603f342ab | 94bb77d0847df86ead773650cf4aa0885ed3ad4e | /dappcrowd/restapi/submissions_endpoint.py | d2fb9fe38acbfc271ba77225c557ec7a4ae17f5a | []
| no_license | Tribler/dappcoder | 3766f0b252ac38d889ad3596b5b6335669d31100 | 8ae43d51a284929bc081c87debc9ef003d1f9116 | refs/heads/master | 2020-04-01T07:53:38.236183 | 2019-01-17T13:07:53 | 2019-01-17T13:07:53 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,196 | py | import json
from twisted.web import http
from twisted.web.server import NOT_DONE_YET
from dappcrowd.restapi.root_endpoint import DAppCrowdEndpoint
class SubmissionsEndpoint(DAppCrowdEndpoint):
def render_GET(self, request):
"""
Get all submissions.
"""
dappcrowd_overlay = self.get_dappcrowd_overlay()
return json.dumps({"submissions": dappcrowd_overlay.persistence.get_submissions()})
def getChild(self, path, request):
return SubmissionPKEndpoint(self.ipv8, self.ipfs_api, path)
def render_PUT(self, request):
"""
Create a new submission for an app request.
"""
parameters = http.parse_qs(request.content.read(), 1)
required_params = ['project_pk', 'project_id', 'submission']
for required_param in required_params:
if required_param not in parameters:
request.setResponseCode(http.BAD_REQUEST)
return json.dumps({"error": "missing parameter %s" % required_param})
def on_block_created(blocks):
request.write(json.dumps({"success": True}))
request.finish()
self.get_dappcrowd_overlay().create_submission(parameters['project_pk'][0].decode('hex'), parameters['project_id'][0], parameters['submission'][0]).addCallback(on_block_created)
return NOT_DONE_YET
class SubmissionPKEndpoint(DAppCrowdEndpoint):
def __init__(self, ipv8, ipfs_api, public_key):
DAppCrowdEndpoint.__init__(self, ipv8, ipfs_api)
self.public_key = public_key.decode('hex')
def getChild(self, path, request):
return SpecificSubmissionEndpoint(self.ipv8, self.ipfs_api, self.public_key, path)
class SpecificSubmissionEndpoint(DAppCrowdEndpoint):
def __init__(self, ipv8, ipfs_api, public_key, submission_id):
DAppCrowdEndpoint.__init__(self, ipv8, ipfs_api)
self.public_key = public_key
self.submission_id = submission_id
self.putChild("reviews", SpecificSubmissionReviewsEndpoint(ipv8, ipfs_api, public_key, submission_id))
def render_GET(self, request):
if not self.get_dappcrowd_overlay().persistence.has_submission(self.public_key, self.submission_id):
request.setResponseCode(http.NOT_FOUND)
return json.dumps({"error": "the submission is not found"})
return json.dumps({
"submission": self.get_dappcrowd_overlay().persistence.get_submission(self.public_key, self.submission_id)
})
class SpecificSubmissionReviewsEndpoint(DAppCrowdEndpoint):
def __init__(self, ipv8, ipfs_api, public_key, submission_id):
DAppCrowdEndpoint.__init__(self, ipv8, ipfs_api)
self.public_key = public_key
self.submission_id = submission_id
def render_GET(self, request):
if not self.get_dappcrowd_overlay().persistence.has_submission(self.public_key, self.submission_id):
request.setResponseCode(http.NOT_FOUND)
return json.dumps({"error": "the submission is not found"})
return json.dumps({
"reviews": self.get_dappcrowd_overlay().persistence.get_reviews(self.public_key, self.submission_id)
})
| [
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]
| |
b4b798b4b5b230c3088019cf13bf6acb5fe54680 | 95d4c8f4cda7ad1c7623a2df02da8cee1ad0941d | /src/classifier/german_pos_tagger.py | a66192956c55f524f2b1142c45afdf0df24c1383 | [
"MIT"
]
| permissive | tiefenauer/ip7-python | 8f587b7b77430facff19b24441490756b01d5b17 | 512105ba39110ec77d2ea0961dd7c2a42d4ec26d | refs/heads/master | 2021-09-09T20:37:21.647146 | 2018-02-20T12:07:46 | 2018-02-20T12:07:46 | 107,635,390 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,382 | py | # -*- coding: utf-8 -*-
#
# Natural Language Toolkit: ClassifierBasedGermanTagger
#
# URL: <http://www.experimentallabor.de/>
#
# Copyright 2011 Philipp Nolte
#
# 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.
"""
Tag German text.
"""
import re
from nltk.tag.sequential import ClassifierBasedTagger
class ClassifierBasedGermanTagger(ClassifierBasedTagger):
"""A classifier based German part-of-speech tagger. It has an accuracy of
96.09% after being trained on 90% of the German TIGER corpus. The tagger
extends the NLTK ClassifierBasedTagger and implements a slightly modified
feature detector.
"""
def feature_detector(self, tokens, index, history):
"""Implementing a slightly modified feature detector.
@param tokens: The tokens from the sentence to tag.
@param index: The current token index to tag.
@param history: The previous tagged tokens.
"""
word = tokens[index]
if index == 0: # At the beginning of the sentence
prevword = prevprevword = None
prevtag = prevprevtag = None
# word = word.lower() # Lowercase at the beginning of sentence
elif index == 1:
prevword = tokens[index - 1] # Note: no lowercase
prevprevword = None
prevtag = history[index - 1]
prevprevtag = None
else:
prevword = tokens[index - 1]
prevprevword = tokens[index - 2]
prevtag = history[index - 1]
prevprevtag = history[index - 2]
if re.match('[0-9]+([\.,][0-9]*)?|[0-9]*[\.,][0-9]+$', word):
# Included "," as decimal point
shape = 'number'
elif re.compile('\W+$', re.UNICODE).match(word):
# Included unicode flag
shape = 'punct'
elif re.match('([A-ZÄÖÜ]+[a-zäöüß]*-?)+$', word):
# Included dash for dashed words and umlauts
shape = 'upcase'
elif re.match('[a-zäöüß]+', word):
# Included umlauts
shape = 'downcase'
elif re.compile("\w+", re.UNICODE).match(word):
# Included unicode flag
shape = 'mixedcase'
else:
shape = 'other'
features = {
'prevtag': prevtag,
'prevprevtag': prevprevtag,
'word': word,
'word.lower': word.lower(),
'suffix3': word.lower()[-3:],
# 'suffix2': word.lower()[-2:],
# 'suffix1': word.lower()[-1:],
'preffix1': word[:1], # included
'prevprevword': prevprevword,
'prevword': prevword,
'prevtag+word': '%s+%s' % (prevtag, word),
'prevprevtag+word': '%s+%s' % (prevprevtag, word),
'prevword+word': '%s+%s' % (prevword, word),
'shape': shape
}
return features
| [
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]
| |
73b8eea0e247cc2fb5986af3fd0beca8578749f2 | f0d713996eb095bcdc701f3fab0a8110b8541cbb | /E9FwvGyad5CDbiH4C_9.py | d750b3eefe4c93d1c5db878cb337dcc042cf9e95 | []
| 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 | 1,348 | py | """
Create a function that takes a 2D array as an argument and returns the number
of people whose view is blocked by a tall person. The concert stage is pointed
towards the top of the 2D array and the tall person (represented by a 2)
blocks the view of all the people (represented by a 1) behind them.
### Examples
block([
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 2],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]
]) ➞ 2
# The tall person blocks 2 people behind him thus
# the function returns 2.
block([
[1, 2, 1, 1],
[1, 1, 1, 2],
[1, 1, 1, 1],
[1, 1, 1, 1],
]) ➞ 5
# There are 2 tall people that block everyone behind
# them. The first tall person in the first row blocks 3
# people behind him while the second tall person in
# the second row blocks 2 people behind him thus the
# function returns 5.
block([
[1, 1, 1, 1],
[2, 1, 1, 2],
[1, 1, 1, 1],
[1, 1, 1, 1],
]) ➞ 4
### Notes
1. There is only a maximum of 1 tall person in every column.
2. No view is blocked if the tall person is in the last row.
"""
def block(lst):
total = 0
for x in range(len(lst[0])):
for y in range(len(lst)-1, 0, -1):
if lst[y][x] < lst[y-1][x]:
total += len(lst) - y
return total
| [
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| |
2d4b29a8afb8ba840c0c97a4e5296c98779b4382 | 1f696631898e0279951709e150da6d87045e4bc4 | /mysite/blog/migrations/0003_auto_20201018_1329.py | 55a90dffcfe644d3983ed6e04e389fcac44cd412 | []
| no_license | henryfrstr/django_project_blog | 9f50b004b2fed59304c3f5a1f05247d44a232992 | 0f3c391a3cd790ae504fb84a786158a1d775abda | refs/heads/main | 2022-12-31T05:05:20.989719 | 2020-10-25T11:26:27 | 2020-10-25T11:26:27 | 305,067,579 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 357 | py | # Generated by Django 3.0.8 on 2020-10-18 10:29
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('blog', '0002_auto_20201018_1327'),
]
operations = [
migrations.RenameField(
model_name='post',
old_name='titles',
new_name='title',
),
]
| [
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| |
10177a53490eb98107c90432833b44de0dc5241f | 36e3d735e06d0642f1e8c26bff57305a01cc627c | /nms/priClient/settings.py | 70dc9c3a439ae7865ae186e64032e891229bbeb1 | []
| no_license | WilsonWangTHU/ipv6_server | 5c768cdaeaf22ee508c5fff162b208481a42f95d | 5088f58ab25061e65127699ed328ddaab24f9aac | refs/heads/master | 2021-01-18T21:18:39.653994 | 2016-05-27T04:22:23 | 2016-05-27T04:22:23 | 55,656,523 | 4 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,819 | py | """
Django settings for subClient project.
Generated by 'django-admin startproject' using Django 1.9.5.
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
# 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 = '2+ytb#pjeh*g!9_3m(id@&mn$c+f56$q6fp=*%lkr)wp8hpfz%'
# 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',
'net_data'
]
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 = 'priClient.urls'
TEMPLATE_DEBUG = True
TEMPLATE_DIRS = (
os.path.join(BASE_DIR, 'templates'),
)
WSGI_APPLICATION = 'priClient.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 = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.9/howto/static-files/
STATIC_URL = os.path.join(BASE_DIR, 'static/')
| [
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]
| |
ee692b4e30708d7c40cc7efe0f041f668c08dcb4 | f63314b4852fb97ad740e53e450110fcd08a515b | /app.py | ce08afaac1bc0e74d4fe1216c543d83672b30fd1 | []
| no_license | xsomam/askfm-scrapper | ee0e2c4439d5be3e4ebd7fceb27d01fbff3aa4da | 2e997268f40cd37dfc56bc7adc496d609106b327 | refs/heads/master | 2023-04-09T03:45:56.074797 | 2020-08-07T23:48:31 | 2020-08-07T23:48:31 | 384,990,560 | 0 | 0 | null | 2023-03-17T21:32:19 | 2021-07-11T15:56:16 | null | WINDOWS-1250 | Python | false | false | 7,611 | py | # Basic imports
import os
import time
import json
import logging
# Modules imports
import requests
from bs4 import BeautifulSoup
# Imports from files
from answer import SinglePost
from locators import *
logging.basicConfig(format='%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',
datefmt='%d-%m-%Y %H:%M:%S',
level=logging.INFO,
filename='logs.txt')
logger = logging.getLogger('app')
ask_url = 'https://ask.fm' #Base Ask URL
BASE_DIR = os.getcwd()
# List with pending profile links to scrape.
# First link is simply base URL: ask.fm/profile.
# After first visit, if there is "next" button on page (next page)
# Then this link is appended to this array and program continues scraping
_pending_list = []
# Array for each Singlequestion_obj object.
# Singlequestion_obj object contains every answer encapsulated within one question_obj
# With info about question, answer etc.
_question = []
# Retrieving function.
# First loops through profile, and stops looping if there are no more "next" pages left
# If there are none, starts writing text content to drive by iterating through questions array
def retrieve(askfm_nick, dl_img, to_csv, to_json):
logger.info('Running "retrieve" function.')
_n = 1
# Append base Askfm profile link to pending list to be scraped first
_pending_list.append(ask_url + '/' + askfm_nick)
# scraping będzie trwał tak długo, jak istnieje chociaż 1 link w liście "_pending_list".
# len = 0 oznacza, że nie ma więcej linków
# Trial of creating folder for downloaded content with user's nickname as name
try:
logger.info('Trial of creation of directory for scraping: '+ BASE_DIR)
os.mkdir(askfm_nick)
except Exception:
logger.info('FAIL of creation of directory for scraping: ' + BASE_DIR)
print('Directory already exists or another error happened. Skipping...')
pass
# Loop runs as long as there is some page to scrape.
# If there was no "next" page, loop ends
while len(_pending_list) > 0:
for link in _pending_list:
print(f'Connecting : {link}')
logger.info('Establishing connection to: ' + link)
# Get content of page, parse it with BS4
try:
site = requests.get(link).content
soup = BeautifulSoup(site, 'html.parser')
# Select each indivifual question on profile
all_questions = soup.select(SiteLocators.QUESTIONS)
except Exception:
print(f'Connection error at: {link}. Retrial in 5 seconds...')
# logger.info(f'Connection error at: {link}. Retrial in 5 seconds...')
time.sleep(5)
continue
# From array of questions we crate actual objects which will contain
# Only important data (like question, answer in text etc.)
# WHich we will perform operations on later
for question in all_questions:
question_obj = SinglePost(question)
_question.append(question_obj)
logger.info(f'Adding question #{_n}.')
# If given question had image, we access it and download it
if question_obj.image:
save_one_image(askfm_nick, question_obj)
_n += 1
# Remove already scraped profile from pedning list
_pending_list.remove(link)
print(f'{link} removed from temp...')
logger.info(f'{link} removed from temp.')
# If there is next page, we again start looping
next_page = soup.select_one(SiteLocators.NEXT)
logger.info('Retrieving next page link')
print('Retrieving next page link')
if next_page:
_pending_list.append(ask_url + next_page.attrs['href'])
logger.info(f"Link to next site appended to temp list: {ask_url}{next_page.attrs['href']}")
print(f"{ask_url}{next_page.attrs['href']} appending successful! Looping back...")
else:
logger.info(f'No "Next" link found. Retrieving done.')
print('No "Next" link found. Retrieving done.')
perform_file_operations(askfm_nick, dl_img, to_csv, to_json)
# Function dispatching file operations
def perform_file_operations(askfm_nick, dl_img, to_csv, to_json):
for each in _question:
if to_csv:
save_to_csv(askfm_nick)
if to_json:
save_to_json(askfm_nick)
def save_to_json(askfm_nick):
logger.info('Running "save_to_json" function.')
print('Saving to JSON')
_list = []
file_name = f'{askfm_nick}.json'
with open(os.path.join(BASE_DIR, askfm_nick,file_name), 'w') as file:
for each in _question:
json_dict = {
'question': each.question,
'answer': each.answer,
'likes': each.likes,
'time': each.date,
'link': each.link,
'img': f"{askfm_nick}-{each.link.split('/')[-1]}.{each.image_extension}" if each.image else None,
'asker_url': each.asker
}
_list.append(json_dict)
# if each.image:
# save_images(each.image_link, each.image_extension, each.link)
json.dump(_list, file, indent=4, ensure_ascii=True)
print(f'Saved to JSON: {file_name}')
def save_to_csv(askfm_nick):
logger.info('Running "save_to_csv" function.')
pass
def save_images(askfm_nick):
logger.info('Running "save_images" function.')
for each in _question:
if each.image:
print('Saving image....')
_photo_name = f"{askfm_nick}-{each.link.split('/')[-1]}.{each.image_extension}"
try:
logger.info('Trial of saving image begins.')
logger.info('Requesting image from: ' + each.image_link)
photo_file = requests.get(each.image_link).content
img_path = os.path.join(BASE_DIR, askfm_nick, _photo_name)
with open(img_path, 'wb') as f:
f.write(photo_file)
logger.info('Saving image to: ' + img_path)
print(f"Image saved: {_photo_name}")
except Exception:
print(f"Could not get image {_photo_name}. Skipping...")
logger.info('Error with saving image: ' + _photo_name)
pass
def save_one_image(askfm_nick, question_obj):
logger.info('Running "save_one_image" function.')
print('Saving image....')
_photo_name = f"{askfm_nick}-{question_obj.link.split('/')[-1]}.{question_obj.image_extension}"
try:
logger.info('Trial of saving image begins.')
logger.info('Requesting image from: ' + question_obj.image_link)
photo_file = requests.get(question_obj.image_link).content
img_path = os.path.join(BASE_DIR, askfm_nick,_photo_name)
with open(img_path, 'wb') as f:
f.write(photo_file)
logger.info('Saving image to: ' + img_path)
print(f"Image saved: {_photo_name}")
except Exception:
print(f"Could not get image {_photo_name}. Skipping...")
logger.info('Error with saving image: ' + _photo_name)
pass
| [
"[email protected]"
]
| |
023d1d8dece7491ac60d165dac3295008bf0a004 | 6109a95a284891792c35d0d19906ab8d1697f9c7 | /src/k8s-configuration/azext_k8s_configuration/vendored_sdks/v2021_05_01_preview/aio/operations/_operations.py | 3c82397333fb155b013c9017f213f97b26d8e9e6 | [
"MIT",
"LicenseRef-scancode-generic-cla"
]
| permissive | Tatsinnit/azure-cli-extensions | 3e5a1752edced00d7c33660027d2c17fae074569 | a1959b123d4c11149adae2728ab5791949889d54 | refs/heads/master | 2022-10-05T17:40:10.825889 | 2022-03-16T10:33:56 | 2022-03-16T10:33:56 | 250,102,909 | 0 | 0 | MIT | 2020-03-25T22:12:01 | 2020-03-25T22:12:01 | null | UTF-8 | Python | false | false | 4,949 | py | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.async_paging import AsyncItemPaged, AsyncList
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest
from azure.mgmt.core.exceptions import ARMErrorFormat
from ... import models as _models
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class Operations:
"""Operations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.mgmt.kubernetesconfiguration.v2021_05_01_preview.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
def list(
self,
**kwargs: Any
) -> AsyncIterable["_models.ResourceProviderOperationList"]:
"""List all the available operations the KubernetesConfiguration resource provider supports.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either ResourceProviderOperationList or the result of cls(response)
:rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.kubernetesconfiguration.v2021_05_01_preview.models.ResourceProviderOperationList]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ResourceProviderOperationList"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-05-01-preview"
accept = "application/json"
def prepare_request(next_link=None):
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
# Construct URL
url = self.list.metadata['url'] # type: ignore
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {} # type: Dict[str, Any]
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('ResourceProviderOperationList', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, AsyncList(list_of_elem)
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, response)
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(
get_next, extract_data
)
list.metadata = {'url': '/providers/Microsoft.KubernetesConfiguration/operations'} # type: ignore
| [
"[email protected]"
]
| |
cee01a1f512e64d11d177b39003a6d66c4c62798 | f375899369ba86aed1da89101c31817168ffec40 | /cinema/urls.py | f73d16e3686a88eb9e7b40b41d324fb2021b3100 | []
| no_license | sheremilbekov/cinema | f596a7d1e41f03161a3ddf0c3594f39619c812df | d8baac5c7e25a90340a35e1e0b0cce093014f965 | refs/heads/master | 2023-03-30T10:19:20.654540 | 2021-04-07T12:10:54 | 2021-04-07T12:10:54 | 354,850,726 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 941 | py | """cinema URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.1/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.conf import settings
from django.conf.urls.static import static
from django.contrib import admin
from django.urls import path, include
urlpatterns = [
path('admin/', admin.site.urls),
path('cooking/', include('main.urls')),
] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
| [
"[email protected]"
]
| |
c3a87e73a1c9e46f95c2c5bbf037974632f09470 | 747f759311d404af31c0f80029e88098193f6269 | /addons/esale_joomla/__terp__.py | 0b9ba5da767aab77ff4bccd6f9b18701e5bdec38 | []
| no_license | sgeerish/sirr_production | 9b0d0f7804a928c0c582ddb4ccb7fcc084469a18 | 1081f3a5ff8864a31b2dcd89406fac076a908e78 | refs/heads/master | 2020-05-19T07:21:37.047958 | 2013-09-15T13:03:36 | 2013-09-15T13:03:36 | 9,648,444 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 62 | py | /home/openerp/production/extra-addons/esale_joomla/__terp__.py | [
"[email protected]"
]
| |
16effac639ce13ca5ccf22f2cfad0658eac06638 | 547ba955855ff623a8ef6e80fcfaddebadf34bed | /Chapter08/B06246_08_14-slope.py | a5a8f6c01c4af2755f280d506db81016afd04122 | []
| no_license | CodedQuen/QGIS-Python-Programming-Cookbook | 94a36d265d0336d5bb36ac02d637ba17ee765b04 | f84e0159f7c8ec81a29573a7fd2e03b046efce33 | refs/heads/master | 2022-05-28T05:04:35.053121 | 2020-05-05T09:44:50 | 2020-05-05T09:44:50 | 261,414,871 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,009 | py | # Computing Road Slope using Elevation Data
# https://github.com/GeospatialPython/Learn/raw/master/road.zip
from PyQt4.QtCore import *
from PyQt4.QtGui import *
import processing
dem = "/qgis_data/road/dem.asc"
road = "/qgis_data/road/road.shp"
slope = "/qgis_data/road/slope.tif"
segRoad = "/qgis_data/road/segRoad.shp"
steepness = "/qgis_data/road/steepness.shp"
hillshade = "/qgis_data/road/hillshade.tif"
demLyr = QgsRasterLayer(dem, "DEM")
roadLyr = QgsVectorLayer(road, "Road", "ogr")
ext = demLyr.extent()
xmin = ext.xMinimum()
ymin = ext.yMinimum()
xmax = ext.xMaximum()
ymax = ext.yMaximum()
demBox = "{},{},{},{}".format(xmin,xmax,ymin,ymax)
processing.runalg("grass7:r.slope",dem,0,False,1,0,demBox,0,slope)
ext = roadLyr.extent()
xmin = ext.xMinimum()
ymin = ext.yMinimum()
xmax = ext.xMaximum()
ymax = ext.yMaximum()
roadBox = "{},{},{},{}".format(xmin,xmax,ymin,ymax)
processing.runalg("grass7:v.split.length",road,500,roadBox,-1,0.0001,0,segRoad)
slopeLyr = QgsRasterLayer(slope, "Slope")
segRoadLyr = QgsVectorLayer(segRoad, "Segmented Road", "ogr")
QgsMapLayerRegistry.instance().addMapLayers([segRoadLyr,slopeLyr], False)
processing.runalg("saga:addgridvaluestoshapes",segRoad,slope,0,steepness)
steepLyr = QgsVectorLayer(steepness, "Road Gradient", "ogr")
roadGrade = (
("Rolling Hill", 0.0, 20.0, "green"),
("Steep", 20.0, 40.0, "yellow"),
("Very Steep", 40.0, 90.0, "red"))
ranges = []
for label, lower, upper, color in roadGrade:
sym = QgsSymbolV2.defaultSymbol(steepLyr.geometryType())
sym.setColor(QColor(color))
sym.setWidth(3.0)
rng = QgsRendererRangeV2(lower, upper, sym, label)
ranges.append(rng)
field = "slopetif"
renderer = QgsGraduatedSymbolRendererV2(field, ranges)
steepLyr.setRendererV2(renderer)
processing.runalg("saga:analyticalhillshading",dem,0,158,45,4,hillshade)
hs = QgsRasterLayer(hillshade, "Terrain")
QgsMapLayerRegistry.instance().addMapLayers([steepLyr, hs]) | [
"[email protected]"
]
| |
9620af649f65a0c0002935d9e24ea87dd7578b35 | b0cdab54c5e81681125c01801148c287605ee8d0 | /speciality/migrations/0005_auto_20181228_2150.py | ad4d5210ff776dbc55eeccf74f5266e8a064ed44 | []
| no_license | lpd76/rdavid2 | 5528746749acc51d4d0f5efd77886929798e2569 | 18aa5120fe4ba0ea44f611dd52b008db52641f17 | refs/heads/master | 2020-04-13T20:47:58.141579 | 2019-01-17T16:51:31 | 2019-01-17T16:51:31 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 637 | py | # Generated by Django 2.1.4 on 2018-12-28 21:50
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('speciality', '0004_auto_20181228_2140'),
]
operations = [
migrations.AlterModelOptions(
name='speciality',
options={'verbose_name_plural': 'specialities'},
),
migrations.AlterField(
model_name='specialitydetails',
name='speciality',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='speciality.Speciality'),
),
]
| [
"[email protected]"
]
| |
b68987bce2f40abf5a5b3be0d046f49f02354bc8 | 650f9e246de38d0ceaee8726f27801f3337e24ce | /string-trainer/simple/csimple.py | 7d3e49c4a22d4dd82a8951120a8f4e6961a58054 | [
"MIT"
]
| permissive | paulscottrobson/old-trainer-archive | 57b6fbb5886e5fe526c37f40b7fb9c179176bce7 | b3659d3f36b7443594202e0ae9439e80e493a22c | refs/heads/master | 2021-01-20T02:46:44.751050 | 2017-04-26T07:26:34 | 2017-04-26T07:26:34 | 89,451,106 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,861 | py | # *****************************************************************************************
#
# Simple compiler for TAB1 format
#
# *****************************************************************************************
import re,sys
# *****************************************************************************************
# Compiler / Processor Exception
# *****************************************************************************************
class CompilerException(Exception):
def __init__(self,message):
self.message = message
Exception.__init__(self)
# *****************************************************************************************
# Strum class
# *****************************************************************************************
class Strum:
def __init__(self,strumDef,qbTime,voices,label = ""):
self.strum = strumDef
self.qbTime = qbTime
self.label = label
self.preRender = self.convertToRender(strumDef,voices)
def getStrum(self):
return self.strum
def getQuarterBeatTime(self):
return self.qbTime
def getLabel(self):
return self.label
def toString(self):
s = self.strum+"@"+str(self.time)
if self.label != "":
s = s + "("+self.label+")"
return s
def convertToRender(self,strum,voices):
strum = strum.upper().strip()
r = []
while strum != "":
if strum[0] == 'X':
r.append(-1)
strum = strum[1:]
elif strum[0] in Strum.FRETS:
diatonic = Strum.FRETS.index(strum[0])
r.append(Strum.TOCHROMATIC[diatonic % 7]+int(diatonic / 7) * 12)
strum = strum[1:]
if (strum+" ")[0] == '+':
r[-1] += 1
strum = strum[1:]
else:
raise CompilerException("Bad strum "+strum)
# first strum given is the treble so make it the last.
r.reverse()
# right pad
while len(r) < voices:
r.insert(0,-1)
return "".join([chr(x+97) if x >= 0 else "-" for x in r])
def render(self):
return self.preRender
Strum.TOCHROMATIC = [
0, 2, 4, 5, 7, 9, 10
# D E F# G A B C
]
Strum.FRETS = "0123456789TLWHF"
Strum.QBOFFSETS = { "O":8, "o":8, "-":-2, "=":-3, ".":2 }
# *****************************************************************************************
# Bar class
# *****************************************************************************************
class Bar:
def __init__(self,barNumber,beats,voices):
self.barNumber = barNumber
self.beats = beats
self.strums = []
self.voices = voices
self.qbPosition = 0
def add(self,strumDef,label = ""):
self.strums.append(Strum(strumDef,self.qbPosition,self.voices,label))
self.qbPosition += 4
return self
def toString(self):
s = "#{0} B:{1} V:{2} C:{3} {{".format(self.barNumber,self.beats,self.voices,len(self.strums))
s = s + " ".join([x.toString() for x in self.strums]) + "}"
return s
def isOffset(self,c):
return c in Strum.QBOFFSETS
def offset(self,c):
if not self.isOffset(c):
raise CompilerException("Unknown offset "+c)
self.qbPosition += Strum.QBOFFSETS[c]
def render(self):
r = ""
qbPosition = 0
for strum in self.strums:
qbElapsed = strum.getQuarterBeatTime() - qbPosition
while qbElapsed > 0:
amt = min(8,qbElapsed)
r = r + str(amt)
qbElapsed = qbElapsed - amt
r = r + strum.render()
qbPosition = strum.getQuarterBeatTime()
return r
# *****************************************************************************************
# Song Class
# *****************************************************************************************
class Song:
def __init__(self,sourceFile):
self.reset()
self.loadTab1(sourceFile)
self.compileBody()
if self.get("title") == "":
raise CompilerException("No title provided")
def reset(self):
self.bars = []
self.keys = { "title":"","author":"","beats":"4","tempo":"100", \
"version":"1", "tuning":"d3,a4,d4", "type":"dulcimer" }
def get(self,key):
return self.keys[key.strip().lower()]
def loadTab1(self,sourceFile):
# pre process file - tabs, spaces, comments
source = open(sourceFile).readlines()
source = [x if x.find("//") < 0 else x[:x.find("//")] for x in source]
source = [x.replace("\t"," ").strip() for x in source]
# key updates.
for assign in [x for x in source if x.find(":=") >= 0]:
assign = [x.strip() for x in assign.split(":=")]
if assign[0] == '"' and assign[-1] == '"':
assign = assign[1:-1]
self.keys[assign[0].lower()] = assign[1]
source = [x for x in source if x.find(":=") < 0]
self.source = source
def compileBody(self):
for line in range(0,len(self.source)):
if self.source[line] != "":
for barPart in [x.strip() for x in self.source[line].split("|") if x.strip() != ""]:
newBar = Bar(len(self.bars),int(self.get("beats")),3)
self.bars.append(newBar)
try:
self.compileTab1(newBar,barPart.upper())
except CompilerException as cEx:
newMsg = cEx.message+" @ "+str(line+1)
raise Exception(newMsg)
def compileTab1(self,bar,src):
while src != "":
m = re.match("^([X"+Strum.FRETS+"\\+]+)\\s*(.*)$",src)
if m is not None:
strum = m.group(1)
bar.add(strum)
src = m.group(2)
elif src[0] in Strum.QBOFFSETS:
bar.offset(src[0])
src = src[1:].strip()
else:
raise CompilerException("Unknown command "+src)
def exportToJSON(self,handle):
handle.write("{ \n")
keys = [x for x in self.keys.keys()]
keys.sort()
for k in keys:
handle.write(' {0:14}:"{1}",\n'.format('"'+k+'"',self.keys[k]))
handle.write(' "bars": [\n')
for n in range(0,len(self.bars)):
r = self.bars[n].render()
handle.write('{0:14}"{1}"{2}\n'.format("",r,"," if n < len(self.bars)-1 else ""))
handle.write("\n ]\n")
handle.write("} \n")
s = Song("twinkle.tab1")
s.exportToJSON(sys.stdout)
s.exportToJSON(open("../app/music.json","w"))
| [
"[email protected]"
]
| |
10252106e1b1114e8e4adf56f12d670ac5aee1e0 | 397c9e2743c41cf591692c4fc37f43a9070119bd | /build/env/lib/python2.7/site-packages/SQLAlchemy-1.2.0b3-py2.7-linux-x86_64.egg/sqlalchemy/cutils.py | a62e8adc17fa043f78d6b8b32d3c703fd2682408 | [
"Apache-2.0"
]
| permissive | bopopescu/myhue | cf41238c782d12b3a1a0ee9ef70196359bb67894 | 5f566970a5a1fa5af9f01832c9e9808c47634bc7 | refs/heads/master | 2022-11-18T05:37:24.467150 | 2019-11-23T16:16:22 | 2019-11-23T16:16:22 | 282,390,507 | 0 | 0 | Apache-2.0 | 2020-07-25T07:03:40 | 2020-07-25T07:03:39 | null | UTF-8 | Python | false | false | 282 | py | def __bootstrap__():
global __bootstrap__, __loader__, __file__
import sys, pkg_resources, imp
__file__ = pkg_resources.resource_filename(__name__, 'cutils.so')
__loader__ = None; del __bootstrap__, __loader__
imp.load_dynamic(__name__,__file__)
__bootstrap__()
| [
"[email protected]"
]
| |
93c4453f26512207811cdba404053b9a07b2e9c1 | b68887f55cfcd0225d732acfbfcc7f3724e49d5d | /pages/factories.py | a59431ddc5e91da1966fb1ba58c8d6ad49dcbfb0 | [
"MIT"
]
| permissive | rds0751/nhsuk-content-store | 0ac7eb06f85cc97cd57e58a3f24e19db9991a8a2 | 7bd6a386e3583779ddba2347a4b3a80fdf75b368 | refs/heads/master | 2020-04-19T08:53:54.273378 | 2019-01-29T05:08:18 | 2019-01-29T05:08:18 | 168,092,530 | 0 | 0 | null | 2019-01-29T05:05:33 | 2019-01-29T05:05:33 | null | UTF-8 | Python | false | false | 1,515 | py | import factory
from home.factories import HomePageFactory, ParentBasedFactory
from . import models
class ConditionsPageFactory(ParentBasedFactory, factory.django.DjangoModelFactory):
title = 'Conditions'
slug = 'conditions'
path = '000100010002'
depth = 3
_ParentFactory = HomePageFactory
_unique = True
class Meta:
model = models.FolderPage
class ConditionPageFactory(ParentBasedFactory, factory.django.DjangoModelFactory):
path = factory.Sequence(lambda n: '000100010002%04d' % (n + 1))
depth = 4
_ParentFactory = ConditionsPageFactory
_unique = False
class Meta:
model = models.EditorialPage
class SymptomsPageFactory(ParentBasedFactory, factory.django.DjangoModelFactory):
title = 'Symptoms'
slug = 'symptoms'
path = '000100010001'
depth = 3
_ParentFactory = HomePageFactory
_unique = True
class Meta:
model = models.FolderPage
class SymptomPageFactory(ParentBasedFactory, factory.django.DjangoModelFactory):
path = factory.Sequence(lambda n: '000100010001%04d' % (n + 1))
depth = 4
_ParentFactory = SymptomsPageFactory
_unique = False
class Meta:
model = models.EditorialPage
class ConditionFolderPageFactory(ParentBasedFactory, factory.django.DjangoModelFactory):
path = factory.Sequence(lambda n: '000100010002%04d' % (n + 1))
depth = 4
_ParentFactory = ConditionsPageFactory
_unique = False
class Meta:
model = models.FolderPage
| [
"[email protected]"
]
| |
93f72136ac32eeb64dafcf570b2025f0c7a8aca7 | a51b2cfd2fcfce0e800fba591efb675d7e83da61 | /src/multi_gpu_train.py | 6bb22be2ec00116833cd6539d7ba2be1e6fb2546 | []
| no_license | yyht/tf-center-loss | a3c4164b9de151b970ec3efaf38f79609e1f8f34 | 5cdcb638f1cec355f1938bf3a646338596bc21ef | refs/heads/master | 2020-05-05T08:05:36.618949 | 2018-08-05T04:29:33 | 2018-08-05T04:29:33 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 9,137 | py | # auther : lxy
# time : 2017.12.15 /09:56
#project:
# tool: python2
#version: 0.1
#modify:
#name: center loss
#citations: https://github.com/ydwen/caffe-face
#############################
import numpy as np
import tensorflow as tf
from read_tfrecord_v2 import read_single_tfrecord
from net import *
from Center_loss_custom import *
from mnist import mnist_data
import argparse
CENTER_LOSS_ALPHA = 0.9
def argument():
parser = argparse.ArgumentParser(description="face resnet center loss")
parser.add_argument('--batch_size',type=int,default=16,help='the batch_size num')
parser.add_argument('--epoch_num',type=int,default=10,\
help='the epoch num should bigger than 10000')
parser.add_argument('--save_model_name',type=str,default='./face_model/model.ckpt',\
help='model Parameters saved name and directory')
parser.add_argument('--lr',type=float,default=0.001,help='the Learning rate begin')
parser.add_argument('--sta',type=str,default='train',help="input should 'train' or 'test' ")
parser.add_argument('--img_shape',type=int,default='300',help="the input image reshape size")
args = parser.parse_args()
return args
def build_network(input_images, labels):
num_class = 526
sta = 'train'
ratio = 0.003
net = face_net(input_images,num_class,sta)
#logits, features = net.inference()
logits, features = net.get_resnet18()
#res1 = net.res1
assert num_class== net.num_classes,"net class should be equal to loss"
with tf.name_scope('loss'):
with tf.name_scope('center_loss'):
center_loss, centers, centers_update_op = get_center_loss(features,logits, labels, CENTER_LOSS_ALPHA, num_class)
with tf.name_scope('softmax_loss'):
#softmax_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits))
labels_onehot = tf.one_hot(labels,on_value=1,off_value=0,depth=num_class)
entropy_loss = tf.nn.softmax_cross_entropy_with_logits(labels=labels_onehot, logits=logits)
print("entropy_loss ",entropy_loss.shape)
softmax_loss = tf.reduce_mean(entropy_loss)
with tf.name_scope('total_loss'):
regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = softmax_loss + ratio * center_loss+0.01 * sum(regularization_losses)
#total_loss = softmax_loss
with tf.name_scope('acc'):
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.cast(tf.arg_max(logits, 1),tf.int32), labels), tf.float32))
with tf.name_scope('pred_class'):
pred_class = tf.arg_max(logits, 1)
with tf.name_scope('loss/'):
tf.summary.scalar('CenterLoss', center_loss)
tf.summary.scalar('SoftmaxLoss', softmax_loss)
tf.summary.scalar('TotalLoss', total_loss)
#return total_loss, accuracy, centers_update_op, center_loss, softmax_loss,pred_class
return total_loss
def make_parallel(model,num_gpus,**kwargs):
in_splits = {}
for k,v in kwargs.items():
in_splits[k] = tf.split(v,num_gpus)
out_splits = []
for i in range(num_gpus):
with tf.device(tf.DeviceSpec(device_type="GPU",device_index=i)):
with tf.variable_scope(tf.get_variable_scope(),reuse=i>0):
out_splits.append(model(**{k:v[i] for k,v in in_splits.items()}))
return tf.stack(out_splits,axis=0)
def main():
LAMBDA = 0.001
num_class = 526
args = argument()
checkpoint_dir = args.save_model_name
lr = args.lr
batch_size = args.batch_size
epoch_num = args.epoch_num
sta = args.sta
img_shape = args.img_shape
num_gpus = 4
#train_batch_loader = BatchLoader("./data/facescrub_train.list", batch_size,img_shape)
#test_batch_loader = BatchLoader("./data/facescrub_val.list", batch_size,img_shape)
#(Height,Width) = (train_batch_loader.height,train_batch_loader.width)
#train_batch_loader = mnist_data(batch_size)
tfrecord_file = './data/MegaFace_train.tfrecord_shuffle'
val_file = './data/MegaFace_val.tfrecord_shuffle'
image_batch, label_batch = read_single_tfrecord(tfrecord_file, batch_size, img_shape)
val_image_batch, val_label_batch = read_single_tfrecord(val_file, batch_size, img_shape)
print("img shape",img_shape)
with tf.name_scope('input'):
input_images = tf.placeholder(tf.float32, shape=(batch_size,img_shape,img_shape,3), name='input_images')
labels = tf.placeholder(tf.int32, shape=(batch_size), name='labels')
learn_rate = tf.placeholder(tf.float32,shape=(None),name='learn_rate')
with tf.name_scope('var'):
global_step = tf.Variable(0, trainable=False, name='global_step')
#total_loss, accuracy, centers_update_op, center_loss, softmax_loss,pred_class = build_network(input_images,labels)
#total_loss, accuracy, centers_update_op, center_loss, softmax_loss,pred_class = make_parallel(build_network,num_gpus,input_images=input_images,labels=labels)
total_loss = make_parallel(build_network,num_gpus,input_images=input_images,labels=labels)
#optimizer = tf.train.AdamOptimizer(learn_rate)
optimizer = tf.train.GradientDescentOptimizer(learn_rate)
#with tf.control_dependencies([centers_update_op]):
train_op = optimizer.minimize(tf.reduce_mean(total_loss), colocate_gradients_with_ops=True)
#train_op = optimizer.minimize(total_loss, global_step=global_step)
summary_op = tf.summary.merge_all()
with tf.Session(config=tf.ConfigProto(log_device_placement=False)) as sess:
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter('./tmp/face_log', sess.graph)
saver = tf.train.Saver()
#begin
coord = tf.train.Coordinator()
#begin enqueue thread
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
step = sess.run(global_step)
epoch_idx =0
graph_step=0
item = './data/facescrub_train.list'
imagelist = open(item, 'r')
files_item = imagelist.readlines()
file_len = len(files_item)
batch_num = np.ceil(file_len / batch_size)
while epoch_idx <= epoch_num:
step = 0
ckpt_fg = 'True'
ps_loss=0.0
pc_loss=0.0
acc_sum = 0.0
while step < batch_num:
train_img_batch, train_label_batch = sess.run([image_batch,label_batch])
#print("data in ",in_img[0,:2,:2,0])
_, summary_str,Center_loss = sess.run(
[train_op, summary_op,total_loss],
feed_dict={
input_images: train_img_batch,
labels: train_label_batch,
learn_rate: lr
})
step += 1
#print("step",step, str(Softmax_loss),str(Center_loss))
#print("res1",res1_o[0,:20])
#print("step label",step, str(batch_labels))
graph_step+=1
if step %10 ==0 :
writer.add_summary(summary_str, global_step=graph_step)
pc_loss+=Center_loss
#ps_loss+=Softmax_loss
#acc_sum+=train_acc
if step % 100 == 0:
#lr = lr*0.1
#c_loss+=c_loss
#s_loss+=s_loss
print ("****** Epoch {} Step {}: ***********".format(str(epoch_idx),str(step)) )
print ("center loss: {}".format(pc_loss/100.0))
print ("softmax_loss: {}".format(ps_loss/100.0))
print ("train_acc: {}".format(acc_sum/100.0))
print ("*******************************")
if (Center_loss<0.1 and ckpt_fg=='True'):
print("******************************************************************************")
saver.save(sess, checkpoint_dir, global_step=epoch_idx)
ckpt_fg = 'False'
ps_loss=0.0
pc_loss=0.0
acc_sum=0.0
epoch_idx +=1
if epoch_idx % 5 ==0:
print("******************************************************************************")
saver.save(sess, checkpoint_dir, global_step=epoch_idx)
#writer.add_summary(summary_str, global_step=step)
if epoch_idx % 5 == 0:
lr = lr*0.5
if epoch_idx:
val_img_batch,val_label_batch = sess.run([val_image_batch,val_label_batch])
vali_acc = sess.run(
total_loss,
feed_dict={
input_images: val_img_batch,
labels: val_label_batch
})
print(("epoch: {}, train_acc:{:.4f}, vali_acc:{:.4f}".
format(epoch_idx, Center_loss, vali_acc)))
coord.join(threads)
sess.close()
if __name__ == '__main__':
main()
| [
"[email protected]"
]
| |
6acd9d44dc1191828b5807335b648d30c0e9194d | 0eda43d797abfc69ad28000b3c3599af44049bdf | /setup.py | 21f2ea356d1b8e4b3e0b98a7bd61d346e529cf0b | [
"CC0-1.0",
"LicenseRef-scancode-public-domain"
]
| permissive | biomodels/BIOMD0000000048 | d8d23b0491ac80e27692b6e115b9884ee46397d6 | 6d17577fdde45ed5c0ec8457eacb860458e30215 | refs/heads/master | 2021-01-18T14:19:32.446581 | 2014-10-16T05:18:50 | 2014-10-16T05:18:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 377 | py | from setuptools import setup, find_packages
setup(name='BIOMD0000000048',
version=20140916,
description='BIOMD0000000048 from BioModels',
url='http://www.ebi.ac.uk/biomodels-main/BIOMD0000000048',
maintainer='Stanley Gu',
maintainer_url='[email protected]',
packages=find_packages(),
package_data={'': ['*.xml', 'README.md']},
) | [
"[email protected]"
]
| |
a03f688cd3bb6ceef3f26b749170bc2c0ac710d7 | 82770c7bc5e2f27a48b8c370b0bab2ee41f24d86 | /microblog/flask/venv/lib/python2.7/site-packages/billiard/forking.py | 57fc9795e47d83e6c656232b9ddde88438c6ec12 | [
"Apache-2.0"
]
| permissive | johankaito/fufuka | 77ddb841f27f6ce8036d7b38cb51dc62e85b2679 | 32a96ecf98ce305c2206c38443e58fdec88c788d | refs/heads/master | 2022-07-20T00:51:55.922063 | 2015-08-21T20:56:48 | 2015-08-21T20:56:48 | 39,845,849 | 2 | 0 | Apache-2.0 | 2022-06-29T23:30:11 | 2015-07-28T16:39:54 | Python | UTF-8 | Python | false | false | 17,515 | py | #
# Module for starting a process object using os.fork() or CreateProcess()
#
# multiprocessing/forking.py
#
# Copyright (c) 2006-2008, R Oudkerk
# Licensed to PSF under a Contributor Agreement.
#
from __future__ import absolute_import
import os
import sys
import signal
import warnings
from pickle import load, HIGHEST_PROTOCOL
from billiard import util
from billiard import process
from billiard.five import int_types
from .reduction import dump
from .compat import _winapi as win32
__all__ = ['Popen', 'assert_spawning', 'exit',
'duplicate', 'close']
try:
WindowsError = WindowsError # noqa
except NameError:
class WindowsError(Exception): # noqa
pass
W_OLD_DJANGO_LAYOUT = """\
Will add directory %r to path! This is necessary to accommodate \
pre-Django 1.4 layouts using setup_environ.
You can skip this warning by adding a DJANGO_SETTINGS_MODULE=settings \
environment variable.
"""
#
# Choose whether to do a fork or spawn (fork+exec) on Unix.
# This affects how some shared resources should be created.
#
_forking_is_enabled = sys.platform != 'win32'
#
# Check that the current thread is spawning a child process
#
def assert_spawning(self):
if not Popen.thread_is_spawning():
raise RuntimeError(
'%s objects should only be shared between processes'
' through inheritance' % type(self).__name__
)
#
# Unix
#
if sys.platform != 'win32':
try:
import thread
except ImportError:
import _thread as thread # noqa
import select
WINEXE = False
WINSERVICE = False
exit = os._exit
duplicate = os.dup
close = os.close
_select = util._eintr_retry(select.select)
#
# We define a Popen class similar to the one from subprocess, but
# whose constructor takes a process object as its argument.
#
class Popen(object):
_tls = thread._local()
def __init__(self, process_obj):
# register reducers
from billiard import connection # noqa
_Django_old_layout_hack__save()
sys.stdout.flush()
sys.stderr.flush()
self.returncode = None
r, w = os.pipe()
self.sentinel = r
if _forking_is_enabled:
self.pid = os.fork()
if self.pid == 0:
os.close(r)
if 'random' in sys.modules:
import random
random.seed()
code = process_obj._bootstrap()
os._exit(code)
else:
from_parent_fd, to_child_fd = os.pipe()
cmd = get_command_line() + [str(from_parent_fd)]
self.pid = os.fork()
if self.pid == 0:
os.close(r)
os.close(to_child_fd)
os.execv(sys.executable, cmd)
# send information to child
prep_data = get_preparation_data(process_obj._name)
os.close(from_parent_fd)
to_child = os.fdopen(to_child_fd, 'wb')
Popen._tls.process_handle = self.pid
try:
dump(prep_data, to_child, HIGHEST_PROTOCOL)
dump(process_obj, to_child, HIGHEST_PROTOCOL)
finally:
del(Popen._tls.process_handle)
to_child.close()
# `w` will be closed when the child exits, at which point `r`
# will become ready for reading (using e.g. select()).
os.close(w)
util.Finalize(self, os.close, (r,))
def poll(self, flag=os.WNOHANG):
if self.returncode is None:
try:
pid, sts = os.waitpid(self.pid, flag)
except os.error:
# Child process not yet created. See #1731717
# e.errno == errno.ECHILD == 10
return None
if pid == self.pid:
if os.WIFSIGNALED(sts):
self.returncode = -os.WTERMSIG(sts)
else:
assert os.WIFEXITED(sts)
self.returncode = os.WEXITSTATUS(sts)
return self.returncode
def wait(self, timeout=None):
if self.returncode is None:
if timeout is not None:
r = _select([self.sentinel], [], [], timeout)[0]
if not r:
return None
# This shouldn't block if select() returned successfully.
return self.poll(os.WNOHANG if timeout == 0.0 else 0)
return self.returncode
def terminate(self):
if self.returncode is None:
try:
os.kill(self.pid, signal.SIGTERM)
except OSError:
if self.wait(timeout=0.1) is None:
raise
@staticmethod
def thread_is_spawning():
if _forking_is_enabled:
return False
else:
return getattr(Popen._tls, 'process_handle', None) is not None
@staticmethod
def duplicate_for_child(handle):
return handle
#
# Windows
#
else:
try:
import thread
except ImportError:
import _thread as thread # noqa
import msvcrt
try:
import _subprocess
except ImportError:
import _winapi as _subprocess # noqa
#
#
#
TERMINATE = 0x10000
WINEXE = (sys.platform == 'win32' and getattr(sys, 'frozen', False))
WINSERVICE = sys.executable.lower().endswith("pythonservice.exe")
exit = win32.ExitProcess
close = win32.CloseHandle
#
#
#
def duplicate(handle, target_process=None, inheritable=False):
if target_process is None:
target_process = _subprocess.GetCurrentProcess()
h = _subprocess.DuplicateHandle(
_subprocess.GetCurrentProcess(), handle, target_process,
0, inheritable, _subprocess.DUPLICATE_SAME_ACCESS
)
if sys.version_info[0] < 3 or (
sys.version_info[0] == 3 and sys.version_info[1] < 3):
h = h.Detach()
return h
#
# We define a Popen class similar to the one from subprocess, but
# whose constructor takes a process object as its argument.
#
class Popen(object):
'''
Start a subprocess to run the code of a process object
'''
_tls = thread._local()
def __init__(self, process_obj):
_Django_old_layout_hack__save()
# create pipe for communication with child
rfd, wfd = os.pipe()
# get handle for read end of the pipe and make it inheritable
rhandle = duplicate(msvcrt.get_osfhandle(rfd), inheritable=True)
os.close(rfd)
# start process
cmd = get_command_line() + [rhandle]
cmd = ' '.join('"%s"' % x for x in cmd)
hp, ht, pid, tid = _subprocess.CreateProcess(
_python_exe, cmd, None, None, 1, 0, None, None, None
)
close(ht) if isinstance(ht, int_types) else ht.Close()
(close(rhandle) if isinstance(rhandle, int_types)
else rhandle.Close())
# set attributes of self
self.pid = pid
self.returncode = None
self._handle = hp
self.sentinel = int(hp)
# send information to child
prep_data = get_preparation_data(process_obj._name)
to_child = os.fdopen(wfd, 'wb')
Popen._tls.process_handle = int(hp)
try:
dump(prep_data, to_child, HIGHEST_PROTOCOL)
dump(process_obj, to_child, HIGHEST_PROTOCOL)
finally:
del Popen._tls.process_handle
to_child.close()
@staticmethod
def thread_is_spawning():
return getattr(Popen._tls, 'process_handle', None) is not None
@staticmethod
def duplicate_for_child(handle):
return duplicate(handle, Popen._tls.process_handle)
def wait(self, timeout=None):
if self.returncode is None:
if timeout is None:
msecs = _subprocess.INFINITE
else:
msecs = max(0, int(timeout * 1000 + 0.5))
res = _subprocess.WaitForSingleObject(int(self._handle), msecs)
if res == _subprocess.WAIT_OBJECT_0:
code = _subprocess.GetExitCodeProcess(self._handle)
if code == TERMINATE:
code = -signal.SIGTERM
self.returncode = code
return self.returncode
def poll(self):
return self.wait(timeout=0)
def terminate(self):
if self.returncode is None:
try:
_subprocess.TerminateProcess(int(self._handle), TERMINATE)
except WindowsError:
if self.wait(timeout=0.1) is None:
raise
#
#
#
if WINSERVICE:
_python_exe = os.path.join(sys.exec_prefix, 'python.exe')
else:
_python_exe = sys.executable
def set_executable(exe):
global _python_exe
_python_exe = exe
def is_forking(argv):
'''
Return whether commandline indicates we are forking
'''
if len(argv) >= 2 and argv[1] == '--billiard-fork':
assert len(argv) == 3
os.environ["FORKED_BY_MULTIPROCESSING"] = "1"
return True
else:
return False
def freeze_support():
'''
Run code for process object if this in not the main process
'''
if is_forking(sys.argv):
main()
sys.exit()
def get_command_line():
'''
Returns prefix of command line used for spawning a child process
'''
if process.current_process()._identity == () and is_forking(sys.argv):
raise RuntimeError('''
Attempt to start a new process before the current process
has finished its bootstrapping phase.
This probably means that have forgotten to use the proper
idiom in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce a Windows executable.''')
if getattr(sys, 'frozen', False):
return [sys.executable, '--billiard-fork']
else:
prog = 'from billiard.forking import main; main()'
return [_python_exe, '-c', prog, '--billiard-fork']
def _Django_old_layout_hack__save():
if 'DJANGO_PROJECT_DIR' not in os.environ:
try:
settings_name = os.environ['DJANGO_SETTINGS_MODULE']
except KeyError:
return # not using Django.
conf_settings = sys.modules.get('django.conf.settings')
configured = conf_settings and conf_settings.configured
try:
project_name, _ = settings_name.split('.', 1)
except ValueError:
return # not modified by setup_environ
project = __import__(project_name)
try:
project_dir = os.path.normpath(_module_parent_dir(project))
except AttributeError:
return # dynamically generated module (no __file__)
if configured:
warnings.warn(UserWarning(
W_OLD_DJANGO_LAYOUT % os.path.realpath(project_dir)
))
os.environ['DJANGO_PROJECT_DIR'] = project_dir
def _Django_old_layout_hack__load():
try:
sys.path.append(os.environ['DJANGO_PROJECT_DIR'])
except KeyError:
pass
def _module_parent_dir(mod):
dir, filename = os.path.split(_module_dir(mod))
if dir == os.curdir or not dir:
dir = os.getcwd()
return dir
def _module_dir(mod):
if '__init__.py' in mod.__file__:
return os.path.dirname(mod.__file__)
return mod.__file__
def main():
'''
Run code specifed by data received over pipe
'''
global _forking_is_enabled
_Django_old_layout_hack__load()
assert is_forking(sys.argv)
_forking_is_enabled = False
handle = int(sys.argv[-1])
if sys.platform == 'win32':
fd = msvcrt.open_osfhandle(handle, os.O_RDONLY)
else:
fd = handle
from_parent = os.fdopen(fd, 'rb')
process.current_process()._inheriting = True
preparation_data = load(from_parent)
prepare(preparation_data)
# Huge hack to make logging before Process.run work.
try:
os.environ["MP_MAIN_FILE"] = sys.modules["__main__"].__file__
except KeyError:
pass
except AttributeError:
pass
loglevel = os.environ.get("_MP_FORK_LOGLEVEL_")
logfile = os.environ.get("_MP_FORK_LOGFILE_") or None
format = os.environ.get("_MP_FORK_LOGFORMAT_")
if loglevel:
from billiard import util
import logging
logger = util.get_logger()
logger.setLevel(int(loglevel))
if not logger.handlers:
logger._rudimentary_setup = True
logfile = logfile or sys.__stderr__
if hasattr(logfile, "write"):
handler = logging.StreamHandler(logfile)
else:
handler = logging.FileHandler(logfile)
formatter = logging.Formatter(
format or util.DEFAULT_LOGGING_FORMAT,
)
handler.setFormatter(formatter)
logger.addHandler(handler)
self = load(from_parent)
process.current_process()._inheriting = False
from_parent.close()
exitcode = self._bootstrap()
exit(exitcode)
def get_preparation_data(name):
'''
Return info about parent needed by child to unpickle process object
'''
from billiard.util import _logger, _log_to_stderr
d = dict(
name=name,
sys_path=sys.path,
sys_argv=sys.argv,
log_to_stderr=_log_to_stderr,
orig_dir=process.ORIGINAL_DIR,
authkey=process.current_process().authkey,
)
if _logger is not None:
d['log_level'] = _logger.getEffectiveLevel()
if not WINEXE and not WINSERVICE:
main_path = getattr(sys.modules['__main__'], '__file__', None)
if not main_path and sys.argv[0] not in ('', '-c'):
main_path = sys.argv[0]
if main_path is not None:
if (not os.path.isabs(main_path) and
process.ORIGINAL_DIR is not None):
main_path = os.path.join(process.ORIGINAL_DIR, main_path)
d['main_path'] = os.path.normpath(main_path)
return d
#
# Prepare current process
#
old_main_modules = []
def prepare(data):
'''
Try to get current process ready to unpickle process object
'''
old_main_modules.append(sys.modules['__main__'])
if 'name' in data:
process.current_process().name = data['name']
if 'authkey' in data:
process.current_process()._authkey = data['authkey']
if 'log_to_stderr' in data and data['log_to_stderr']:
util.log_to_stderr()
if 'log_level' in data:
util.get_logger().setLevel(data['log_level'])
if 'sys_path' in data:
sys.path = data['sys_path']
if 'sys_argv' in data:
sys.argv = data['sys_argv']
if 'dir' in data:
os.chdir(data['dir'])
if 'orig_dir' in data:
process.ORIGINAL_DIR = data['orig_dir']
if 'main_path' in data:
main_path = data['main_path']
main_name = os.path.splitext(os.path.basename(main_path))[0]
if main_name == '__init__':
main_name = os.path.basename(os.path.dirname(main_path))
if main_name == '__main__':
main_module = sys.modules['__main__']
main_module.__file__ = main_path
elif main_name != 'ipython':
# Main modules not actually called __main__.py may
# contain additional code that should still be executed
import imp
if main_path is None:
dirs = None
elif os.path.basename(main_path).startswith('__init__.py'):
dirs = [os.path.dirname(os.path.dirname(main_path))]
else:
dirs = [os.path.dirname(main_path)]
assert main_name not in sys.modules, main_name
file, path_name, etc = imp.find_module(main_name, dirs)
try:
# We would like to do "imp.load_module('__main__', ...)"
# here. However, that would cause 'if __name__ ==
# "__main__"' clauses to be executed.
main_module = imp.load_module(
'__parents_main__', file, path_name, etc
)
finally:
if file:
file.close()
sys.modules['__main__'] = main_module
main_module.__name__ = '__main__'
# Try to make the potentially picklable objects in
# sys.modules['__main__'] realize they are in the main
# module -- somewhat ugly.
for obj in list(main_module.__dict__.values()):
try:
if obj.__module__ == '__parents_main__':
obj.__module__ = '__main__'
except Exception:
pass
| [
"[email protected]"
]
| |
47f0abfaceb11e660d4f305e745db7fe9fee819f | da84fa23cc4cf2e81e50892085ac162508bff155 | /nestris_ocr/capturing/linux/linux_mgr.py | 6381209b8dd85f1880aca2b7eb9cbc653ec9f0cd | []
| no_license | alex-ong/NESTrisOCR | 83ddaba55b100f0ee20e924731459e547e321887 | 488beeb30e596ccd0548152e241e1c6f772e717b | refs/heads/master | 2023-01-10T08:02:41.702538 | 2021-07-07T06:33:39 | 2021-07-07T06:33:39 | 169,196,192 | 25 | 8 | null | 2022-12-27T15:37:40 | 2019-02-05T05:44:34 | Python | UTF-8 | Python | false | false | 1,202 | py | import Xlib
import Xlib.display
from Xlib import X
class WindowMgr:
"""Encapsulates some calls for window management"""
def __init__(self, hwnd=None):
self.handle = hwnd
def checkWindow(self, hwnd):
"""checks if a window still exists"""
return hwnd
def getWindows(self):
"""
Return a list of tuples (handler, window name) for each real window.
"""
windows = []
def getWindowHierarchy(window, windows):
children = window.query_tree().children
for w in children:
try:
w.get_image(0, 0, 1, 1, X.ZPixmap, 0xFFFFFFFF)
windows.append(
(
w.id,
w.get_wm_class()[1] if w.get_wm_class() is not None else "",
)
)
except Xlib.error.BadMatch:
pass
finally:
windows = getWindowHierarchy(w, windows)
return windows
root = Xlib.display.Display().screen().root
windows = getWindowHierarchy(root, windows)
return windows
| [
"[email protected]"
]
| |
11065362a8ac77972c519aadeae585300bb5085d | 7bededcada9271d92f34da6dae7088f3faf61c02 | /pypureclient/flasharray/FA_2_25/models/active_directory_get_response.py | ee529854e041a3ff612ccf174315845d4e2c49ef | [
"BSD-2-Clause"
]
| permissive | PureStorage-OpenConnect/py-pure-client | a5348c6a153f8c809d6e3cf734d95d6946c5f659 | 7e3c3ec1d639fb004627e94d3d63a6fdc141ae1e | refs/heads/master | 2023-09-04T10:59:03.009972 | 2023-08-25T07:40:41 | 2023-08-25T07:40:41 | 160,391,444 | 18 | 29 | BSD-2-Clause | 2023-09-08T09:08:30 | 2018-12-04T17:02:51 | Python | UTF-8 | Python | false | false | 5,613 | py | # coding: utf-8
"""
FlashArray REST API
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: 2.25
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re
import six
import typing
from ....properties import Property
if typing.TYPE_CHECKING:
from pypureclient.flasharray.FA_2_25 import models
class ActiveDirectoryGetResponse(object):
"""
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 = {
'more_items_remaining': 'bool',
'total_item_count': 'int',
'continuation_token': 'str',
'items': 'list[ActiveDirectory]'
}
attribute_map = {
'more_items_remaining': 'more_items_remaining',
'total_item_count': 'total_item_count',
'continuation_token': 'continuation_token',
'items': 'items'
}
required_args = {
}
def __init__(
self,
more_items_remaining=None, # type: bool
total_item_count=None, # type: int
continuation_token=None, # type: str
items=None, # type: List[models.ActiveDirectory]
):
"""
Keyword args:
more_items_remaining (bool): Returns a value of `true` if subsequent items can be retrieved.
total_item_count (int): The total number of records after applying all filter query parameters. The `total_item_count` will be calculated if and only if the corresponding query parameter `total_item_count` is set to `true`. If this query parameter is not set or set to `false`, a value of `null` will be returned.
continuation_token (str): Continuation token that can be provided in the `continuation_token` query param to get the next page of data. If you use the continuation token to page through data you are guaranteed to get all items exactly once regardless of how items are modified. If an item is added or deleted during the pagination then it may or may not be returned. The continuation token is generated if the limit is less than the remaining number of items, and the default sort is used (no sort is specified).
items (list[ActiveDirectory]): A list of Active Directory computer account configuration objects.
"""
if more_items_remaining is not None:
self.more_items_remaining = more_items_remaining
if total_item_count is not None:
self.total_item_count = total_item_count
if continuation_token is not None:
self.continuation_token = continuation_token
if items is not None:
self.items = items
def __setattr__(self, key, value):
if key not in self.attribute_map:
raise KeyError("Invalid key `{}` for `ActiveDirectoryGetResponse`".format(key))
self.__dict__[key] = value
def __getattribute__(self, item):
value = object.__getattribute__(self, item)
if isinstance(value, Property):
raise AttributeError
else:
return value
def __getitem__(self, key):
if key not in self.attribute_map:
raise KeyError("Invalid key `{}` for `ActiveDirectoryGetResponse`".format(key))
return object.__getattribute__(self, key)
def __setitem__(self, key, value):
if key not in self.attribute_map:
raise KeyError("Invalid key `{}` for `ActiveDirectoryGetResponse`".format(key))
object.__setattr__(self, key, value)
def __delitem__(self, key):
if key not in self.attribute_map:
raise KeyError("Invalid key `{}` for `ActiveDirectoryGetResponse`".format(key))
object.__delattr__(self, key)
def keys(self):
return self.attribute_map.keys()
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
if hasattr(self, attr):
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(ActiveDirectoryGetResponse, 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, ActiveDirectoryGetResponse):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| [
"[email protected]"
]
| |
e0cf8c5298a8ee4e8a3b21eb3b1fe65504c3047e | 204ec78fcebcea9e1e1da4905cf3fad0a514b01f | /test/unit/test_timeout.py | 4990b87aad1b2d40888f75acc3481c349d3eb4e0 | [
"Apache-2.0"
]
| permissive | ARMmbed/pyOCD | 659340bf8753aa8e15a72890b8bea64dff2c2f42 | d4cdcf7e532cae17caad866839287bbe1e0d952b | refs/heads/master | 2023-05-31T13:45:15.797588 | 2020-10-12T13:55:47 | 2020-10-12T13:55:47 | 190,203,829 | 3 | 1 | Apache-2.0 | 2019-07-05T11:05:40 | 2019-06-04T13:09:56 | Python | UTF-8 | Python | false | false | 1,942 | py | # pyOCD debugger
# Copyright (c) 2017-2019 Arm Limited
# SPDX-License-Identifier: Apache-2.0
#
# 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 time import (time, sleep)
import pytest
from pyocd.utility.timeout import Timeout
class TestTimeout:
def test_no_timeout(self):
with Timeout(0.05) as to:
cnt = 0
while to.check():
sleep(0.01)
cnt += 1
if cnt == 4:
break
else:
assert False
assert not to.did_time_out
def test_timeout_a(self):
s = time()
with Timeout(0.05) as to:
while to.check():
sleep(0.01)
assert to.did_time_out
assert (time() - s) >= 0.05
def test_timeout_b(self):
timedout = False
s = time()
with Timeout(0.05) as to:
cnt = 0
while cnt < 10:
if to.did_time_out:
timedout = True
sleep(0.02)
cnt += 1
assert timedout
assert to.did_time_out
assert (time() - s) >= 0.05
def test_timeout_c(self):
timedout = False
with Timeout(0.05) as to:
cnt = 0
while cnt < 10:
if to.did_time_out:
timedout = True
cnt += 1
assert not timedout
assert not to.did_time_out
| [
"[email protected]"
]
| |
f158c6821e350e490fa25d9eda4fc880f01fe9d0 | 6e2dc82bcfbc420ce6fd8e890f9f254e8e594902 | /www/cursivedata/migrations/0005_auto__add_field_pipeline_anim_loop.py | 92063d7e04206acf824ce406bdbd7eabe0b2d325 | [
"CC-BY-4.0"
]
| permissive | mattvenn/cursivedata | 8ea86bde4a58a5678b1116953d17f0ae3600daf6 | 43e43263bef6f01698166d87bcff00b246957277 | refs/heads/master | 2021-01-17T08:06:38.715586 | 2016-07-22T16:04:10 | 2016-07-22T16:04:10 | 5,599,674 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,340 | py | # -*- coding: utf-8 -*-
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding field 'Pipeline.anim_loop'
db.add_column('cursivedata_pipeline', 'anim_loop',
self.gf('django.db.models.fields.BooleanField')(default=False),
keep_default=False)
def backwards(self, orm):
# Deleting field 'Pipeline.anim_loop'
db.delete_column('cursivedata_pipeline', 'anim_loop')
models = {
'cursivedata.cosmsource': {
'Meta': {'object_name': 'COSMSource'},
'add_feed_id': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'add_feed_title': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'add_location': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'api_key': ('django.db.models.fields.CharField', [], {'default': "'WsH6oBOmVbflt5ytsSYHYVGQzCaSAKw0Ti92WHZzajZHWT0g'", 'max_length': '400'}),
'cosm_trigger_id': ('django.db.models.fields.CharField', [], {'max_length': '50', 'blank': 'True'}),
'cosm_url': ('django.db.models.fields.CharField', [], {'default': "'http://api.cosm.com/v2/triggers/'", 'max_length': '200'}),
'feed_id': ('django.db.models.fields.CharField', [], {'default': "'96779'", 'max_length': '400'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'last_updated': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'last_value': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '200'}),
'name': ('django.db.models.fields.CharField', [], {'default': "'Unknown Source'", 'max_length': '100'}),
'pipelines': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['cursivedata.Pipeline']", 'symmetrical': 'False', 'blank': 'True'}),
'stream_id': ('django.db.models.fields.CharField', [], {'default': "'1'", 'max_length': '400'}),
'use_stream_id': ('django.db.models.fields.BooleanField', [], {'default': 'False'})
},
'cursivedata.datapoint': {
'Meta': {'object_name': 'DataPoint'},
'current': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'data': ('jsonfield.fields.JSONField', [], {'default': '{}'}),
'datastore': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cursivedata.DataStore']"}),
'date': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'})
},
'cursivedata.datastore': {
'Meta': {'object_name': 'DataStore'},
'available': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'})
},
'cursivedata.endpoint': {
'Meta': {'object_name': 'Endpoint'},
'device': ('django.db.models.fields.CharField', [], {'default': "'web'", 'max_length': '200'}),
'full_image_file': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}),
'full_svg_file': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}),
'generate_gcode': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'height': ('django.db.models.fields.FloatField', [], {'default': '200', 'max_length': '200'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'img_height': ('django.db.models.fields.IntegerField', [], {'default': '500'}),
'img_width': ('django.db.models.fields.IntegerField', [], {'default': '500'}),
'last_image_file': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}),
'last_svg_file': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}),
'last_updated': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2015, 5, 25, 0, 0)'}),
'location': ('django.db.models.fields.CharField', [], {'default': "'default'", 'max_length': '200'}),
'name': ('django.db.models.fields.CharField', [], {'default': "'default'", 'max_length': '200'}),
'paused': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'robot_svg_file': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}),
'run_id': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'side_margin': ('django.db.models.fields.FloatField', [], {'default': '10', 'max_length': '200'}),
'status': ('django.db.models.fields.CharField', [], {'default': "'default'", 'max_length': '200'}),
'status_updated': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'top_margin': ('django.db.models.fields.FloatField', [], {'default': '10', 'max_length': '200'}),
'width': ('django.db.models.fields.FloatField', [], {'default': '200', 'max_length': '200'})
},
'cursivedata.gcodeoutput': {
'Meta': {'object_name': 'GCodeOutput'},
'endpoint': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cursivedata.Endpoint']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'served': ('django.db.models.fields.BooleanField', [], {'default': 'False'})
},
'cursivedata.generator': {
'Meta': {'object_name': 'Generator'},
'description': ('django.db.models.fields.CharField', [], {'default': "'Unknown'", 'max_length': '2000'}),
'file_path': ('django.db.models.fields.CharField', [], {'default': "'./generators'", 'max_length': '200'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'image': ('django.db.models.fields.CharField', [], {'default': "'No Image'", 'max_length': '200'}),
'last_updated': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2015, 5, 25, 0, 0)'}),
'last_used': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2015, 5, 25, 0, 0)'}),
'module_name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '200'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'})
},
'cursivedata.generatorstate': {
'Meta': {'object_name': 'GeneratorState'},
'generator': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cursivedata.Generator']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'params': ('jsonfield.fields.JSONField', [], {'default': '{}'}),
'state': ('jsonfield.fields.JSONField', [], {'default': '{}'})
},
'cursivedata.parameter': {
'Meta': {'object_name': 'Parameter'},
'data_type': ('django.db.models.fields.CharField', [], {'default': "'float'", 'max_length': '20'}),
'default': ('django.db.models.fields.CharField', [], {'default': "'0'", 'max_length': '200', 'blank': 'True'}),
'description': ('django.db.models.fields.CharField', [], {'default': "'Some parameter'", 'max_length': '1000', 'blank': 'True'}),
'generator': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cursivedata.Generator']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'})
},
'cursivedata.pipeline': {
'Meta': {'object_name': 'Pipeline'},
'anim_autoplay': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'anim_loop': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'anim_speed': ('django.db.models.fields.IntegerField', [], {'default': '1000'}),
'auto_begin_days': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'data_store': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['cursivedata.DataStore']", 'unique': 'True'}),
'description': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '2000', 'blank': 'True'}),
'endpoint': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cursivedata.Endpoint']"}),
'full_image_file': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}),
'full_svg_file': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}),
'generator': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cursivedata.Generator']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'img_height': ('django.db.models.fields.IntegerField', [], {'default': '500'}),
'img_width': ('django.db.models.fields.IntegerField', [], {'default': '500'}),
'last_image_file': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}),
'last_svg_file': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}),
'last_updated': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2015, 5, 25, 0, 0)'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'next_auto_begin_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'paused': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'print_top_left_x': ('django.db.models.fields.FloatField', [], {'default': '0'}),
'print_top_left_y': ('django.db.models.fields.FloatField', [], {'default': '0'}),
'print_width': ('django.db.models.fields.FloatField', [], {'default': '500'}),
'run_id': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'sources': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['cursivedata.COSMSource']", 'symmetrical': 'False', 'blank': 'True'}),
'state': ('django.db.models.fields.related.OneToOneField', [], {'to': "orm['cursivedata.GeneratorState']", 'unique': 'True'})
},
'cursivedata.storedoutput': {
'Meta': {'object_name': 'StoredOutput'},
'endpoint': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cursivedata.Endpoint']", 'null': 'True', 'blank': 'True'}),
'filename': ('django.db.models.fields.CharField', [], {'default': "'output/none'", 'max_length': '200'}),
'filetype': ('django.db.models.fields.CharField', [], {'default': "'unknown'", 'max_length': '10'}),
'generator': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cursivedata.Generator']", 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'pipeline': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['cursivedata.Pipeline']", 'null': 'True', 'blank': 'True'}),
'run_id': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'status': ('django.db.models.fields.CharField', [], {'default': "'complete'", 'max_length': '10'})
}
}
complete_apps = ['cursivedata'] | [
"[email protected]"
]
| |
4a1c309a93de9647a0f1adc90e88ad9c8624b3be | 2b8c88dfee5c5a784357515eafe8cd5f997c8774 | /leetcode/dynamic_programming/code-84.py | 1b9e7013a5652e79e6603e09d069daf7eb6aa134 | []
| no_license | archenRen/learnpy | e060f3aa2f77c35fc1b12345720af6c8b528da57 | 934ef76b97297f746a722a48c76672c7bc744cd9 | refs/heads/master | 2022-04-28T20:25:59.114036 | 2020-05-03T02:16:03 | 2020-05-03T02:16:14 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,187 | py |
# This is a TLE solution.
def largestRectangleArea2(heights: 'List[int]') -> int:
n = len(heights)
max_val = 0
for i in range(n):
min_val = heights[i]
max_val = max(max_val, min_val)
for j in range(i-1, -1, -1):
min_val = min(heights[j], min_val)
max_val = max(max_val, min_val * (i - j + 1))
return max_val
def largestRectangleArea(heights: 'List[int]') -> int:
# The stack maintain the indexes of buildings with ascending height.
n = len(heights)
heights.append(0)
stack = []
ans = 0
i = 0
while i <= n:
if not stack or heights[i] >= heights[stack[-1]]:
stack.append(i)
else:
tp = stack.pop()
if stack:
ans = max(ans, heights[tp] * (i - stack[-1] - 1))
else:
ans = max(ans, heights[tp] * i)
i -= 1
i += 1
return ans
# print(largestRectangleArea([2, 1, 5, 6, 2, 3])) # expect 10 (2*5)
# print(largestRectangleArea([2, 1, 3, 6, 2, 3]))# expect 8 (4*2)
# print(largestRectangleArea([2,3]))
# print(largestRectangleArea([3]))
print(largestRectangleArea(list(range(10))))
| [
"[email protected]"
]
| |
610ac8671393a3cc93c8ac2f5fb7cbe982e9e849 | 96090102d5e87f1771ba5a90f7b676f4ccb0afa6 | /src/profiles/forms.py | ef4d8adbf95e2f2acf6f725493fe0bef6afcef2b | []
| no_license | rahulsayon/SocialWedding | b4b37ad69b89236784c6fb983ab27b4cd2e4266e | ab96b6a5d381936463065e75f74d0c8ffd3b1907 | refs/heads/master | 2022-12-18T15:08:39.380348 | 2020-09-27T14:49:40 | 2020-09-27T14:49:40 | 299,053,233 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 197 | py | from django import forms
from . models import Profile
class ProfileModalForm(forms.ModelForm):
class Meta:
model = Profile
fields = [ 'first_name','last_name','bio','avatar' ] | [
"[email protected]"
]
| |
ffd4ff39507434f06cbbc5a0767aeadf66cdf5a4 | 2bdedcda705f6dcf45a1e9a090377f892bcb58bb | /src/main/output/idea/party/issue/line_friend/group_lot_guy_lombok_kind/door/oauth.py | 555d4e970019c6d7f81128a63b321c2efb7bdedb | []
| no_license | matkosoric/GenericNameTesting | 860a22af1098dda9ea9e24a1fc681bb728aa2d69 | 03f4a38229c28bc6d83258e5a84fce4b189d5f00 | refs/heads/master | 2021-01-08T22:35:20.022350 | 2020-02-21T11:28:21 | 2020-02-21T11:28:21 | 242,123,053 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,672 | py | const request = require('request')
const uuidv4 = require('uuid/v4')
const { LimitReachedError } = require('./errors')
const ITEMS_IN_REQUEST_LIMIT = 25
const REQUEST_CHAR_LIMIT = 5000
const CHAR_PER_HOUR_LIMIT = 2000000
// const subscriptionKey = process.env.TRANSLATOR_TEXT_KEY;
// if (!subscriptionKey) {
// throw new Error('Environment variable for your subscription key is not set.')
// }
const subscriptionKey = 'a674785ff843a278a87995ef4ee1659b'
function MicrosoftTranslator () {}
MicrosoftTranslator.prototype.translate = function (strings, targetLang) {
console.log(`Microsoft: translating ${strings.length} strings to ${targetLang}...`)
let options = {
method: 'POST',
baseUrl: 'https://api.cognitive.microsofttranslator.com/',
url: 'translate',
qs: {
'api-version': '3.0',
to: targetLang
},
headers: {
'5bb321a1b738949e8bace956a490028a': subscriptionKey,
'Content-type': 'application/json',
'X-ClientTraceId': uuidv4().toString()
},
body: strings.map(str => ({ text: str })),
json: true
}
return new Promise((resolve, reject) => {
request(options, (err, res, body) => {
if (err) {
reject(err)
return
}
if (body.error) {
console.log('body', body)
if (body.error.code === 400077) {
reject(new LimitReachedError('Microsoft', 'Maximum request size'))
} else if (body.error.code === 403001) {
reject(new LimitReachedError('Microsoft', 'Quota per hour'))
} else {
reject(new Error(body.error.message))
}
} else {
let translations = body
.reduce((accum, item) => accum.concat(item.translations), [])
.map(i => i.text)
resolve(translations)
}
})
}).then(translations => {
console.log(`Microsoft: Translation succeed. Got ${translations.length} translations.`)
return translations
})
}
MicrosoftTranslator.prototype.getRequestLimit = function () {
return REQUEST_CHAR_LIMIT
}
MicrosoftTranslator.prototype.getRequestItemsCountLimit = function () {
return ITEMS_IN_REQUEST_LIMIT
}
MicrosoftTranslator.prototype.getMaxLimit = function () {
return CHAR_PER_HOUR_LIMIT
}
module.exports = MicrosoftTranslator
// new MicrosoftTranslator()
// .translate([(new Array(5001)).join('a'), 'b'], 'ru')
// .then(translations => console.log('Result', translations))
// .catch(err => console.error(err))
/*
* Limits: https://docs.microsoft.com/en-us/azure/cognitive-services/translator/request-limits
* https://docs.microsoft.com/en-us/azure/cognitive-services/translator/reference/v3-0-translate?tabs=curl
* */
| [
"[email protected]"
]
| |
7094d4bbe7a500eb46faa9fac35c316ada1389af | 77fc5af96da1d461c86c7f9668b64b99ca04a1b6 | /codes/horner.py | 4458f960d38c57f60ba6940082b190afccdbd331 | []
| no_license | rene-d/edupython | 5b6bc8ddb5eb8ec896ee70fb961d4e689af1075a | 1261d0c7aae17bb2d4ff3370860768b73ba4172d | refs/heads/master | 2020-11-24T10:07:18.504472 | 2019-12-21T21:03:08 | 2019-12-21T21:03:08 | 228,099,675 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 425 | py | # Méthode de Hörner
# https://edupython.tuxfamily.org/sources/view.php?code=horner
# Créé par IANTE, le 12/07/2011
from lycee import *
P=liste_demande('entrez les coefficients de P(x) par ordre des puissances croissantes')
r=demande('Entrez une racine évidente')
Q=[0]*(len(P)-1)
v=0
for d in range(len(P)-2,-1,-1):
v=P[d+1]+r*v
Q[d]=v
print (affiche_poly(P)+'=('+affiche_poly([-r,1])+')('+affiche_poly(Q)+')')
| [
"[email protected]"
]
| |
39d5e277eb935eee8876c1af0b0557edcf5f6b91 | 146012dda21ab72badad6daa8f98e6b26fedb128 | /13day/9-名片系统.py | c647a413c352cb726036cb58e94329648c26b284 | []
| no_license | fengshuai1/1805 | 41786c3561beca580ba82d9e9d4347571e38e198 | 8dc3e6605cc1d6f91685ae45bfebfc062f0aa489 | refs/heads/master | 2020-03-19T07:41:40.608389 | 2018-06-28T01:45:43 | 2018-06-28T01:45:43 | 136,140,329 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,124 | py | list = []#存放名字
print("名片管理系统".center(50,"*"))
while True:
print("1:添加名片".center(50," "))
print("2:查找名片".center(50," "))
print("3:修改名片".center(50," "))
print("4:删除名片".center(50," "))
print("5:打印名片".center(50," "))
num = int(input("请选择功能"))
if num == 1:
d = {}#空字典
while True:
name = input("请输入要添加的名字")
if len(name) > 4:
print("太长，请重新输入")
continue
job = input("请输入要添加的职位")
if len(job) > 4:
print("太长，请重新输入")
continue
phone = input("请输入手机号")
if len(phone) != 11 or phone.startswith("1") == False:
print("手机号输入有误，请重新输入")
continue
d["name"] = name
d["job"] = job
d["phone"] = phone
#添加到列表
list.append(d)
print("添加成功")
break
elif num == 2:
name = input("请输入要查找的姓名")
flag = False#假设没有咱们要找的人
for i in list:
if name == i["name"]:
print("姓名:%s\n职位:%s\n电话:%s"%(i["name"],i["job"],i["phone"]))
flag = True#找到了
break
if flag == False:
print("查无此人")
elif num == 3:
#要改之前，你得先查到你要找的那个
name = input("请输入你要改的人的姓名")
flag = False
for i in list:
if name == i["name"]:
print("1:修改名字")
print("2:修改职位")
print("3:修改电话")
num_1 = int(input("请选择功能"))
if num_1 == 1:
new_name = input("请输入新的名字")
i["name"] = new_name
elif num_1 == 2:
new_job = input("请输入新的职位")
i["job"] = new_job
elif num_1 == 3:
new_phone = input("请输入新的电话")
i["phone"] = new_phone
flag = True
break
if flag == False:
print("查无此人")
elif num == 4:
name = input("请输入你要删除的名字")
flag = False
for position,i in enumerate(list):#把索引遍历出来
if name == i["name"]:
flag = True#找到了
print("1:确认删除")
print("2:取消删除")
num_2 = int(input("请选择序号"))
if num_2 == 1:
list.pop(position)#直接删除
break
if flag == False:
print("查无此人")
elif num == 5:#打印名片
print("名字\t职位\t电话")
for i in list:
print(" "+i["name"]+"\t "+i["job"]+"\t "+i["phone"])
| [
"[email protected]"
]
| |
c441941156bd0808bc93eb34a0c6ef9a076dbaee | 06164402e4a9c46a03d579175e588519dbd4048d | /experiments/experiments_gdsc/cross_validation/vb_nmf/linesearch_xval_vb.py | 013c70a9a0481ff098be2e4b97b6fb3098dc6e91 | [
"Apache-2.0"
]
| permissive | XuanHeIIIS/BNMTF | 19547e36466ecee8d45fb0002d305ee6b7ba6c23 | 34df0c3cebc5e67a5e39762b9305b75d73a2a0e0 | refs/heads/master | 2020-03-27T12:47:58.375964 | 2018-06-10T10:22:19 | 2018-06-10T10:22:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,825 | py | """
Run the cross validation with line search for model selection using VB-NMF on
the Sanger dataset.
"""
import sys, os
project_location = os.path.dirname(__file__)+"/../../../../../"
sys.path.append(project_location)
import numpy, random
from BNMTF.code.models.bnmf_vb_optimised import bnmf_vb_optimised
from BNMTF.code.cross_validation.line_search_cross_validation import LineSearchCrossValidation
from BNMTF.data_drug_sensitivity.gdsc.load_data import load_gdsc
# Settings
standardised = False
iterations = 1000
init_UV = 'random'
K_range = [15,20,25,30]
no_folds = 10
restarts = 1
quality_metric = 'AIC'
output_file = "./results.txt"
alpha, beta = 1., 1.
lambdaU = 1./10.
lambdaV = 1./10.
priors = { 'alpha':alpha, 'beta':beta, 'lambdaU':lambdaU, 'lambdaV':lambdaV }
# Load in the Sanger dataset
(_,X_min,M,_,_,_,_) = load_gdsc(standardised=standardised,sep=',')
# Run the cross-validation framework
#random.seed(42)
#numpy.random.seed(9000)
nested_crossval = LineSearchCrossValidation(
classifier=bnmf_vb_optimised,
R=X_min,
M=M,
values_K=K_range,
folds=no_folds,
priors=priors,
init_UV=init_UV,
iterations=iterations,
restarts=restarts,
quality_metric=quality_metric,
file_performance=output_file
)
nested_crossval.run()
"""
all_MSE = [2.2242309355503416, 2.3108126630384804, 2.4095896447817631, 2.2188694213830114, 2.4185938516134278, 2.1808748510586002, 2.2503432196374651, 2.2305023229025145, 2.3595465204422488, 2.2186318302878667]
all_R2 = [0.8123419361488506, 0.8011409466575017, 0.7943028271877304, 0.8125046212085996, 0.7934881370166628, 0.8111969927756486, 0.8058878338360765, 0.811089129626958, 0.798953276136085, 0.8151865445946502]
Average MSE: 2.2821995260695718 +- 0.0066998949966021598
Average R^2: 0.80560922451887629 +- 5.8495363723835686e-05
""" | [
"[email protected]"
]
| |
72dde4d0cca5ada32dd37e6e36d79b7dc6680cba | 685e7dc080a383d12dd526a510a8f74c34ef2e71 | /tests/nonci/test_compare_pretrained.py | cf57a0df969679003ebcb54d6d1f3d881dc8170d | [
"MIT"
]
| permissive | 18813055625/bert-for-tf2 | f1b86351675861ebe710bb4f94e99b89a639f83a | e71d108f0bd8c5af0c4e0b8427b144e996c02fdb | refs/heads/master | 2020-07-29T08:24:33.635201 | 2019-09-09T11:56:37 | 2019-09-09T11:56:37 | 209,729,589 | 0 | 1 | MIT | 2019-09-20T07:16:54 | 2019-09-20T07:16:53 | null | UTF-8 | Python | false | false | 8,706 | py | # coding=utf-8
#
# created by kpe on 27.Mar.2019 at 15:37
#
from __future__ import absolute_import, division, print_function
import unittest
import re
import os
import numpy as np
import tensorflow as tf
from tensorflow.python import keras
from tensorflow.python.keras import backend as K
import params
from bert import BertModelLayer
from bert.loader import map_from_stock_variale_name, map_to_stock_variable_name, load_stock_weights
from bert.loader import StockBertConfig, map_stock_config_to_params
from bert.tokenization import FullTokenizer
tf.compat.v1.disable_eager_execution()
class TestCompareBertsOnPretrainedWeight(unittest.TestCase):
bert_ckpt_dir = ".models/uncased_L-12_H-768_A-12/"
bert_ckpt_file = bert_ckpt_dir + "bert_model.ckpt"
bert_config_file = bert_ckpt_dir + "bert_config.json"
def test_bert_original_weights(self):
print("bert checkpoint: ", self.bert_ckpt_file)
bert_vars = tf.train.list_variables(self.bert_ckpt_file)
for ndx, var in enumerate(bert_vars):
print("{:3d}".format(ndx), var)
def create_bert_model(self, max_seq_len=18):
bc = None
with tf.io.gfile.GFile(self.bert_config_file, "r") as reader:
bc = StockBertConfig.from_json_string(reader.read())
bert = BertModelLayer.from_params(map_stock_config_to_params(bc),
name="bert")
input_ids = keras.layers.Input(shape=(max_seq_len,), dtype='int32', name="input_ids")
token_type_ids = keras.layers.Input(shape=(max_seq_len,), dtype='int32', name="token_type_ids")
output = bert([input_ids, token_type_ids])
model = keras.Model(inputs=[input_ids, token_type_ids], outputs=output)
return model, bert, (input_ids, token_type_ids)
def test_keras_weights(self):
max_seq_len = 18
model, bert, inputs = self.create_bert_model(18)
model.build(input_shape=[(None, max_seq_len),
(None, max_seq_len)])
model.summary()
for ndx, var in enumerate(bert.trainable_variables):
print("{:3d}".format(ndx), var.name, var.shape)
#for ndx, var in enumerate(model.trainable_variables):
# print("{:3d}".format(ndx), var.name, var.shape)
def test___compare_weights(self):
#tf.reset_default_graph()
max_seq_len = 18
model, bert, inputs = self.create_bert_model(18)
model.build(input_shape=[(None, max_seq_len),
(None, max_seq_len)])
stock_vars = tf.train.list_variables(self.bert_ckpt_file)
stock_vars = {name: list(shape) for name, shape in stock_vars}
keras_vars = model.trainable_variables
keras_vars = {var.name.split(":")[0]: var.shape.as_list() for var in keras_vars}
matched_vars = set()
unmatched_vars = set()
shape_errors = set()
for name in stock_vars:
bert_name = name
keras_name = map_from_stock_variale_name(bert_name)
if keras_name in keras_vars:
if keras_vars[keras_name] == stock_vars[bert_name]:
matched_vars.add(bert_name)
else:
shape_errors.add(bert_name)
else:
unmatched_vars.add(bert_name)
print("bert -> keras:")
print(" matched count:", len(matched_vars))
print(" unmatched count:", len(unmatched_vars))
print(" shape error count:", len(shape_errors))
print("unmatched:\n", "\n ".join(unmatched_vars))
self.assertEqual(197, len(matched_vars))
self.assertEqual(9, len(unmatched_vars))
self.assertEqual(0, len(shape_errors))
matched_vars = set()
unmatched_vars = set()
shape_errors = set()
for name in keras_vars:
keras_name = name
bert_name = map_to_stock_variable_name(keras_name)
if bert_name in stock_vars:
if stock_vars[bert_name] == keras_vars[keras_name]:
matched_vars.add(keras_name)
else:
shape_errors.add(keras_name)
else:
unmatched_vars.add(keras_name)
print("keras -> bert:")
print(" matched count:", len(matched_vars))
print(" unmatched count:", len(unmatched_vars))
print(" shape error count:", len(shape_errors))
print("unmatched:\n", "\n ".join(unmatched_vars))
self.assertEqual(197, len(matched_vars))
self.assertEqual(0, len(unmatched_vars))
self.assertEqual(0, len(shape_errors))
def predict_on_keras_model(self, input_ids, input_mask, token_type_ids):
max_seq_len = input_ids.shape[-1]
model, bert, k_inputs = self.create_bert_model(max_seq_len)
model.build(input_shape=[(None, max_seq_len),
(None, max_seq_len)])
load_stock_weights(bert, self.bert_ckpt_file)
k_res = model.predict([input_ids, token_type_ids])
return k_res
def predict_on_stock_model(self, input_ids, input_mask, token_type_ids):
from tests.ext.modeling import BertModel, BertConfig, get_assignment_map_from_checkpoint
tf.compat.v1.reset_default_graph()
tf_placeholder = tf.compat.v1.placeholder
max_seq_len = input_ids.shape[-1]
pl_input_ids = tf.compat.v1.placeholder(tf.int32, shape=(1, max_seq_len))
pl_mask = tf.compat.v1.placeholder(tf.int32, shape=(1, max_seq_len))
pl_token_type_ids = tf.compat.v1.placeholder(tf.int32, shape=(1, max_seq_len))
bert_config = BertConfig.from_json_file(self.bert_config_file)
tokenizer = FullTokenizer(vocab_file=os.path.join(self.bert_ckpt_dir, "vocab.txt"))
s_model = BertModel(config=bert_config,
is_training=False,
input_ids=pl_input_ids,
input_mask=pl_mask,
token_type_ids=pl_token_type_ids,
use_one_hot_embeddings=False)
tvars = tf.compat.v1.trainable_variables()
(assignment_map, initialized_var_names) = get_assignment_map_from_checkpoint(tvars, self.bert_ckpt_file)
tf.compat.v1.train.init_from_checkpoint(self.bert_ckpt_file, assignment_map)
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
s_res = sess.run(
s_model.get_sequence_output(),
feed_dict={pl_input_ids: input_ids,
pl_token_type_ids: token_type_ids,
pl_mask: input_mask,
})
return s_res
def test_direct_keras_to_stock_compare(self):
from tests.ext.modeling import BertModel, BertConfig, get_assignment_map_from_checkpoint
bert_config = BertConfig.from_json_file(self.bert_config_file)
tokenizer = FullTokenizer(vocab_file=os.path.join(self.bert_ckpt_dir, "vocab.txt"))
# prepare input
max_seq_len = 6
input_str = "Hello, Bert!"
input_tokens = tokenizer.tokenize(input_str)
input_tokens = ["[CLS]"] + input_tokens + ["[SEP]"]
input_ids = tokenizer.convert_tokens_to_ids(input_tokens)
input_ids = input_ids + [0]*(max_seq_len - len(input_tokens))
input_mask = [1]*len(input_tokens) + [0]*(max_seq_len - len(input_tokens))
token_type_ids = [0]*len(input_tokens) + [0]*(max_seq_len - len(input_tokens))
input_ids = np.array([input_ids], dtype=np.int32)
input_mask = np.array([input_mask], dtype=np.int32)
token_type_ids = np.array([token_type_ids], dtype=np.int32)
print(" tokens:", input_tokens)
print("input_ids:{}/{}:{}".format(len(input_tokens), max_seq_len, input_ids), input_ids.shape, token_type_ids)
s_res = self.predict_on_stock_model(input_ids, input_mask, token_type_ids)
k_res = self.predict_on_keras_model(input_ids, input_mask, token_type_ids)
np.set_printoptions(precision=9, threshold=20, linewidth=200, sign="+", floatmode="fixed")
print("s_res", s_res.shape)
print("k_res", k_res.shape)
print("s_res:\n {}".format(s_res[0, :2, :10]), s_res.dtype)
print("k_res:\n {}".format(k_res[0, :2, :10]), k_res.dtype)
adiff = np.abs(s_res-k_res).flatten()
print("diff:", np.max(adiff), np.argmax(adiff))
self.assertTrue(np.allclose(s_res, k_res, atol=1e-6))
| [
"[email protected]"
]
|
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