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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2d923a516024796b02d871f62f60e7086f316a5a | 1691647418b8ab019ccb76abfd3c807e1f2fe593 | /d11/11_2.py | 4e216ae9c7efee7d734d25e56aca450f4779f7ce | [
"CC0-1.0"
] | permissive | szeko34/Adventofcode2020 | 0e54f24056e50190df8e78b9354ae38cdf593bbd | 4896df41a0664f2424bb8466a3b80a181bb44e68 | refs/heads/main | 2023-02-16T08:56:49.367731 | 2020-12-25T22:42:09 | 2020-12-25T22:42:09 | 319,099,532 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,379 | py | subor=open("11_1input.txt","r")
pole = []
riadky=[x.strip() for x in subor.readlines()]
pole.append(list("."*(len(riadky[0])+2)))
for x in riadky:
pole.append(list("."+x+"."))
pole.append(list("."*(len(riadky[0])+2)))
#print(pole)
def susedov(p,x,y):
tmp = 0
for i in -1,0,1:
for j in -1,0,1:
c=1
while 0<=x+c*i<len(p) and 0<=y+c*j<len(p[0]) and p[x+c*i][y+c*j] == ".":
c+=1
if 0<=x+c*i<len(p) and 0<=y+c*j<len(p[0]):
tmp += p[x+c*i][y+c*j] == "#"
if p[x][y] == "#":
return tmp - 1
else:
return tmp
def vypis(pole):
for x in pole:
print(x)
def krok(p):
nove=[list("."*(len(riadky[0])+2))]
for i in range(1,len(p)-1):
r=["."]
for j in range(1,len(p[0])-1):
#print(susedov(p,i,j))
if p[i][j] == "L" and susedov(p,i,j) == 0:
r.append("#")
elif p[i][j] == "#" and susedov(p,i,j) >= 5:
r.append("L")
else:
r.append(p[i][j])
r.append(".")
nove.append(r)
nove.append(list("."*(len(riadky[0])+2)))
#vypis(nove)
return(nove)
nove=pole
stare = []
k=0
while nove != stare:
stare=nove
nove=krok(stare)
k += 1
#print(k)
sedadiel = 0
for x in nove:
sedadiel+= x.count("#")
print(sedadiel)
| [
"42772637+szeko34@users.noreply.github.com"
] | 42772637+szeko34@users.noreply.github.com |
a6909b832a4db8007dd5d57edd5f98c63f804d21 | bda849e79d847dcc0015388dac2617fec0fd873f | /puzzle18.py | 4e904a825e6a678829b84dcde3f0fee6394801f5 | [] | no_license | ferretj/adventofcode2017 | 5044c3c4bdbe1bee6da281025bc0889843478adc | c1262d7ec3f56e6e5265740108c17420cc874125 | refs/heads/master | 2021-09-10T20:07:29.215943 | 2018-04-01T10:50:58 | 2018-04-01T10:50:58 | 112,783,547 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,949 | py | import sys
from cStringIO import StringIO
from collections import defaultdict
# PROBLEM 1
# we store register states in a dictionary and overwrite a
# frequency variable each time a sound is played
#
# instructions are represented by functions that are applied following
# the order and values defined in input
#
# we simply return the freq variable at the first rcv instruction
def recover_freq(f, n_iter=100000):
# updates the register, frequency and cursor after applying next instruction
def update(reg, freq, curr, ops, args1, args2):
# returns value or register value depending on the nature
# of the input string
def retrieve_val(arg, reg):
if not arg.isdigit() and not '-' in arg:
arg = reg[arg]
return int(arg)
# retrieve next instruction parameters
op = ops[curr]
arg1 = args1[curr]
arg2 = args2[curr]
curr += 1
stop = False
# apply instruction
if op == 'set':
arg2 = retrieve_val(arg2, reg)
reg[arg1] = arg2
elif op == 'add':
arg2 = retrieve_val(arg2, reg)
reg[arg1] += arg2
elif op == 'mul':
arg2 = retrieve_val(arg2, reg)
reg[arg1] *= arg2
elif op == 'mod':
arg2 = retrieve_val(arg2, reg)
reg[arg1] = reg[arg1] % arg2
elif op == 'snd':
arg1 = retrieve_val(arg1, reg)
freq = arg1
elif op == 'rcv':
arg1 = retrieve_val(arg1, reg)
# only apply if first arg is nonzero
if arg1 != 0:
stop = True
elif op == 'jgz':
arg1 = retrieve_val(arg1, reg)
arg2 = retrieve_val(arg2, reg)
# only apply if first arg is strictly positive
if arg1 > 0:
curr += arg2 - 1
else:
raise ValueError('Unknown operation : {}'.format(op))
# stop if reaching either end of instruction list
npg = len(ops)
if curr >= npg or curr < 0:
stop = True
return reg, freq, curr, stop
reg = defaultdict(int)
freq = None
# parse input
ops = []
args1 = []
args2 = []
for line in f.readlines():
line = line.rstrip('\n')
elems = line.split()
ops.append(elems[0])
args1.append(elems[1])
if len(elems) == 3:
args2.append(elems[2])
else:
args2.append(None)
# sequentially eval operations
curr = 0
for i in xrange(n_iter):
reg, freq, curr, stop = update(reg, freq, curr, ops, args1, args2)
# stop and return latest frequency at first rcv instruction
if stop:
return freq
raise ValueError('Exceeded max number of iterations without finding rcv instruction.')
# PROBLEM 2
# the above script is slightly modified : register, cursor are duplicated
# and buffers are added for communication between both programs
#
# as before, we sequentially apply the instructions in the list until a rcv
# instruction on an empty buffer is called, at which point we go on to the second
# program and only stop when both programs are locked
def duet(f, n_iter=10000000):
def inverse(p):
if p == 0:
return 1
return 0
# updates the registers, buffers and cursors after applying next instruction
def update(regs, buffs, currs, ops, args1, args2, p, cpt):
# returns value or register value depending on the nature
# of the input string
def retrieve_val(arg, regs, p):
if not arg.isdigit() and not '-' in arg:
arg = regs[p][arg]
return int(arg)
# recv instruction
def receive_val(arg, regs, buffs, p):
pinv = inverse(p)
print 'pinv : ', pinv
buff = buffs[pinv]
stop = False
print buff
print len(buff)
if len(buff) == 0:
stop = True
else:
val = buff.pop()
regs[p][arg] = val
return buffs, stop
# snd instruction
def send_val(val, buffs, p, cpt):
buffs[p].insert(0, val)
if p == 1:
cpt += 1
return buffs, cpt
# retrieve next instruction parameters
op = ops[currs[p]]
arg1 = args1[currs[p]]
arg2 = args2[currs[p]]
currs[p] += 1
stop = False
# apply instruction
if op == 'set':
arg2 = retrieve_val(arg2, regs, p)
regs[p][arg1] = arg2
elif op == 'add':
arg2 = retrieve_val(arg2, regs, p)
regs[p][arg1] += arg2
elif op == 'mul':
arg2 = retrieve_val(arg2, regs, p)
regs[p][arg1] *= arg2
elif op == 'mod':
arg2 = retrieve_val(arg2, regs, p)
regs[p][arg1] = regs[p][arg1] % arg2
elif op == 'snd':
arg1 = retrieve_val(arg1, regs, p)
buffs, cpt = send_val(arg1, buffs, p, cpt)
elif op == 'rcv':
buffs, stop = receive_val(arg1, regs, buffs, p)
if stop:
currs[p] -= 1
elif op == 'jgz':
arg1 = retrieve_val(arg1, regs, p)
arg2 = retrieve_val(arg2, regs, p)
if arg1 > 0:
currs[p] += arg2 - 1
else:
raise ValueError('Unknown operation : {}'.format(op))
return regs, buffs, currs, stop, cpt
regs = [defaultdict(int) for _ in xrange(2)]
regs[1]['p'] = 1
buffs = [[] for _ in xrange(2)]
# parse input
ops = []
args1 = []
args2 = []
for line in f.readlines():
line = line.rstrip('\n')
elems = line.split()
ops.append(elems[0])
args1.append(elems[1])
if len(elems) == 3:
args2.append(elems[2])
else:
args2.append(None)
# sequentially eval operations
cpt = 0
p = 0
currs = [0] * 2
fstp = False
for i in xrange(n_iter):
regs, buffs, currs, stop, cpt = update(regs, buffs, currs, ops, args1, args2, p, cpt)
if stop:
# stop when both programs are deadlocked
if fstp:
return cpt
else:
p = inverse(p)
fstp = True
else:
fstp = False
raise ValueError('Exceeded max number of iterations without finding rcv instruction.')
if __name__ == '__main__':
if int(sys.argv[1]) == 0:
with open('data/puzzle18.txt', 'r') as input:
with open('sols/puzzle18.txt', 'w') as f:
f.write(str(recover_freq(input)))
elif int(sys.argv[1]) == 1:
with open('data/puzzle18.txt', 'r') as input:
with open('sols/puzzle18b.txt', 'w') as f:
f.write(str(duet(input)))
| [
"johan.ferret1@gmail.com"
] | johan.ferret1@gmail.com |
a44cd5735a6d4ffd2ad97cf097d7bfc89d468eff | f7c3b1f65cc4d6dba1dc9dcf70f523497a14d791 | /mujoco/learner/baselines/baselines/common/vec_env/dummy_vec_env.py | 447f78ad49cd9c078bd1690e9dfd4e2c4d30fa19 | [
"MIT"
] | permissive | hiwonjoon/ICML2019-TREX | b9cac1ac5d97b22374a92f2f3cf5d8956cdb2482 | 44f92b61ca6c79ac22d468382d4f2fbee164fb7a | refs/heads/master | 2021-06-16T15:52:33.325054 | 2021-04-10T04:57:24 | 2021-04-10T04:57:24 | 184,654,702 | 76 | 24 | MIT | 2020-01-28T22:11:19 | 2019-05-02T21:36:24 | Python | UTF-8 | Python | false | false | 3,304 | py | import numpy as np
from gym import spaces
from . import VecEnv
from .util import copy_obs_dict, dict_to_obs, obs_space_info
class DummyVecEnv(VecEnv):
"""
VecEnv that does runs multiple environments sequentially, that is,
the step and reset commands are send to one environment at a time.
Useful when debugging and when num_env == 1 (in the latter case,
avoids communication overhead)
"""
def __init__(self, env_fns):
"""
Arguments:
env_fns: iterable of callables functions that build environments
"""
self.envs = [fn() for fn in env_fns]
env = self.envs[0]
VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
obs_space = env.observation_space
self.keys, shapes, dtypes = obs_space_info(obs_space)
self.buf_obs = { k: np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k]) for k in self.keys }
self.buf_dones = np.zeros((self.num_envs,), dtype=np.bool)
self.buf_rews = np.zeros((self.num_envs,), dtype=np.float32)
self.buf_infos = [{} for _ in range(self.num_envs)]
self.actions = None
def step_async(self, actions):
listify = True
try:
if len(actions) == self.num_envs:
listify = False
except TypeError:
pass
if not listify:
self.actions = actions
else:
assert self.num_envs == 1, "actions {} is either not a list or has a wrong size - cannot match to {} environments".format(actions, self.num_envs)
self.actions = [actions]
self.last_actions = actions
def step_wait(self):
for e in range(self.num_envs):
action = self.actions[e]
if isinstance(self.envs[e].action_space, spaces.Discrete):
action = int(action)
obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(action)
if self.buf_dones[e]:
obs = self.envs[e].reset()
self._save_obs(e, obs)
return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones),
self.buf_infos.copy())
def reset(self):
for e in range(self.num_envs):
obs = self.envs[e].reset()
self._save_obs(e, obs)
return self._obs_from_buf()
def _save_obs(self, e, obs):
for k in self.keys:
if k is None:
self.buf_obs[k][e] = obs
else:
self.buf_obs[k][e] = obs[k]
def _obs_from_buf(self):
return dict_to_obs(copy_obs_dict(self.buf_obs))
def get_images(self):
return [env.render(mode='rgb_array') for env in self.envs]
def render(self, mode='human'):
if self.num_envs == 1:
try:
viewer = self.envs[0].env.env._get_viewer(mode)
except AttributeError:
viewer = None
if viewer and hasattr(viewer,'_ncam') and viewer._ncam >= 1:
from mujoco_py.generated import const
viewer.cam.fixedcamid = 0
viewer.cam.type = const.CAMERA_FIXED
return self.envs[0].render(mode=mode)
else:
return super().render(mode=mode)
| [
"hi.wonjoon@gmail.com"
] | hi.wonjoon@gmail.com |
27f4fdc29c4407956cd4ba901e109a5a92fbefce | 0678320c0a67f8f8733232a35e1dddecf009781e | /FRIOC_kmedoids.py | 208504ddd5d4b119bbe92d03c822ebc628d80f10 | [] | no_license | shengsoft/k-medoids-cluster-method | 769a96ab075d8f80683f3176e068b0e340b309f6 | aa8360cf65afb341fae876a7e0f7a1b0c57d90da | refs/heads/master | 2022-12-24T07:38:55.408499 | 2020-09-28T00:07:18 | 2020-09-28T00:07:18 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,355 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 9 14:53:13 2020
@author: NoufAlghanmi
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 25 14:49:34 2020
@author: NoufAlghanmi
"""
from pyclustering.cluster.kmedoids import kmedoids
import numpy as np
import random
class FRIOC_kmedoids:
def __init__(self, X,Y,initial_medoids,threshold=1.5):
self.X = X
self.Y=Y
self.threshold=threshold
self.initial_medoids=initial_medoids
def fit(self):
Final_cluster=[]
Temp_cluster=[]
ToCheck_cluster=[]
#threshold=0.9529#0.01 #0.5
K=4#int(y.max()-y.min()/threshold)
Final_medoids=[]
Check_medoids=[]
Temp_medoids=[]
kmedoids_instance = kmedoids(self.X.values, self.initial_medoids,ccore=False,
data_type='distance_matrix')
# run cluster analysis and obtain results
kmedoids_instance.process()
ToCheck_cluster = kmedoids_instance.get_clusters()
Check_medoids=kmedoids_instance.get_medoids()
OC=[]
for i in range(len(Check_medoids)):
STD=np.std(self.Y.iloc[ToCheck_cluster[i]]) #it is a number
if STD<=self.threshold:
Final_cluster.append(ToCheck_cluster[i])
Final_medoids.append(Check_medoids[i])
else:
Temp_cluster.append(ToCheck_cluster[i])
Temp_medoids.append(Check_medoids[i])
ToCheck_cluster=Temp_cluster
Check_medoids=Temp_medoids
while ToCheck_cluster:
L=len(ToCheck_cluster)
Temp_cluster=[]
Temp_medoids=[]
for i in range(0,L):
list= ToCheck_cluster[i]
if len(list)==0: continue
if len(list)<=2:
Final_cluster.append(list)
Final_medoids.append(Check_medoids[i])
continue
OC=self.Y.iloc[list]
STD=np.std(OC)
if STD<=self.threshold:
Final_cluster.append(list)
Final_medoids.append(Check_medoids[i])
else:
data=self.X.iloc[list, list]
new_medoids=random.sample(range(len(list)), K)
kmedoids_instance = kmedoids(data.values, new_medoids, ccore=False,
data_type='distance_matrix')
# run cluster analysis and obtain results
kmedoids_instance.process()
cluster=kmedoids_instance.get_clusters()
for i in range(len(cluster)):
for j in range(len(cluster[i])):
cluster[i][j]=list[cluster[i][j]]
Temp_cluster.append(cluster[i])
medoids=kmedoids_instance.get_medoids()
for i in range(len(medoids)):
Temp_medoids.append(list[medoids[i]])
ToCheck_cluster=Temp_cluster
Check_medoids=Temp_medoids
return Final_medoids,Final_cluster
| [
"evgeniyeremenkoe.mentor@gmail.com"
] | evgeniyeremenkoe.mentor@gmail.com |
23af4eb3d098b3aee766117532d1b5bf1e9dffef | 853ed913766013f63c86c1093b2a8b3327941326 | /CNN_on_Minist/utils/CAM.py | 3f498f03efe5646a8c7369d29c1fa820b8563bcb | [] | no_license | SY-Ma/CNN-on-Minist | c492ec31a2f6a188f1ab4839e5e2fe865a77bb34 | 214da992d639989884e3b0f49264bce0f95a1b11 | refs/heads/main | 2023-03-29T09:43:56.897072 | 2021-03-31T13:32:27 | 2021-03-31T13:32:27 | 336,547,211 | 6 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,734 | py | # @Time : 2021/02/16 05:58
# @Author : SY.M
# @FileName: CAM.py
import torch
import numpy as np
from cv2 import cv2
from datetime import datetime
def cam_figure(draw_data, labels, net):
"""
绘制热力图
:param draw_data: 最后一层卷积之后的 四维向量 batch channel H W
:param labels: 对应标签向量
:param net: 使用的网络，获取其linear层的权重
:return: 无
"""
m = 0 # 表示获取一个batch中第几个样本
draw_data = draw_data[m]
# weight的下标所对应的权重 是 使得特征预测出来的 数字 为下标的权重 所以图片中数字是几，这个下标应该就是几
weight = net.linear.weight[int(labels[m])]
# 乘以相应的权重
for index, (i, j) in enumerate(zip(draw_data, weight)):
draw_data[index] = torch.mul(i, j)
# 所有channel相加求平均
# draw_data = torch.mean(draw_data, dim=0)
# mean = torch.mean(draw_data.unsqueeze(dim=0), dim=0)
# std = torch.std(draw_data.unsqueeze(dim=0), dim=0)
# draw_data = (draw_data - mean) / std * 255
draw_data = torch.mean(draw_data, dim=0) * 200
draw_data = draw_data.numpy().astype(np.uint8)
# 获取图片的矩阵 用于 与图片的交叠显示
# img = cv2.imread('x0 7.jpg')
# height, width, _ = img.shape
# heatmap = cv2.applyColorMap(cv2.resize(draw_data, (height, width)), cv2.COLORMAP_JET)
# result = heatmap * 0.3 + img * 0.5
heatmap = cv2.applyColorMap(draw_data, cv2.COLORMAP_JET)
result = heatmap
time = datetime.strftime(datetime.now(), '%Y-%m-%d %H-%M-%S')
cv2.imwrite(f'CAM_result_figure/number={int(labels[m])} {time}.jpg', result)
| [
"noreply@github.com"
] | SY-Ma.noreply@github.com |
f8756bb104f73e67fdce5e1320a6fbfdeb3956ae | 6bd8c1eb7d88f8690924ae489782be46404a67d2 | /ml/config.py | ec7d1857fcbec77e65f27096723fee1ea4107c0e | [] | no_license | chridey/altlex | 167b289aba4beadc97a91c00c95600170ded43da | dd89175eb980b96d6f0f2822cb8b6cb86f294709 | refs/heads/master | 2020-04-10T06:44:29.482486 | 2018-04-04T18:53:20 | 2018-04-04T18:53:20 | 52,225,321 | 11 | 5 | null | null | null | null | UTF-8 | Python | false | false | 3,152 | py | from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import BernoulliNB
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC, LinearSVC
from sklearn.linear_model import LogisticRegression, SGDClassifier
try:
from sklearn.neural_network import MLPClassifier
except ImportError:
MLPClassifier = None
from altlex.ml.miniBatchSGD import MiniBatchSGD
class Config:
@property
def classifiers(self):
return {'random_forest': RandomForestClassifier,
'naive_bayes': BernoulliNB,
'ada_boost': AdaBoostClassifier,
'svm': SVC,
'linear_svm': LinearSVC,
'sgd': SGDClassifier,
'mini_batch_sgd': MiniBatchSGD,
'mlp': MLPClassifier,
'logistic_regression': LogisticRegression,
}
@property
def classifierSettings(self):
return {'random_forest': dict(n_estimators=1000,
#tried 10, 100, and 500
#higher number increases precision
n_jobs=1,
oob_score=False,
bootstrap=True,
#False reduces recall
max_features="sqrt",
#using all reduces precision
#log2 reduces recall w/o p incr
#.5 reduces precision (lt max tho)
min_samples_split=2
#1 and 2 no diff
#3 is worse
),
'naive_bayes': {},
'ada_boost': dict(n_estimators=500),
'sgd': {},
'mlp': {},
'mini_batch_sgd': {},
'svm': dict(verbose=True,
#kernel='poly',
#C=10, gamma=1)
#C=.01, gamma=.1)
#C=.01)
C=10),
#degree=3),
'linear_svm': {},
'logistic_regression': dict(C=.1),
'grid_search': {},
'grid_search_svm': {},
'grid_search_logistic_regression': {},
'grid_search_sgd': {},
'grid_search_label_spreading': {},
'grid_search_pipeline': {},
'tfkld': {},
'label_spreading': LabelSpreader,
'gmm': GMM,
'transductive_svm': TransductiveSVM,
'constrained_gmm': MixtureClient,
'rnn': RNNLearner}
@property
def semisupervised(self):
return {'label_spreading',
'grid_search_label_spreading',
'transductive_svm',
'constrained_gmm'}
@property
def unsupervised(self):
return {'gmm'}
| [
"chidey@cs.columbia.edu"
] | chidey@cs.columbia.edu |
0da1ffb7c4fef9cf29fa5547dee86d63309e0e51 | 26255e522e3aa341c0d0f2a2b62639295ffa037a | /else_statement.py | 1a80a185662047185ab0554d622dd4fff483575c | [] | no_license | dishant470266/hacker_rank | 2b74267dffd7c7a2845615b20f749e45d5d58db4 | f880d8f37831feb0cefa6406e1b5596cfa02ac17 | refs/heads/master | 2020-08-09T12:19:06.112506 | 2019-12-12T10:34:09 | 2019-12-12T10:34:09 | 214,086,075 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 127 | py | age = int(input("enter your age"))
if(age>=14):
print("you are aboue 14")
else:
print("sorry you can not above 14") | [
"noreply@github.com"
] | dishant470266.noreply@github.com |
7a276e1d3bec4ce95f9b4a892b2031fa589f4dbc | 7ff2c7224d5ccaffb9ed31b0678d67feaf791a94 | /build/universal_robot/ur10_e_moveit_config/catkin_generated/pkg.installspace.context.pc.py | 9e40b3463b8bfa9346ecde1cabb2a2736aab8e92 | [
"Apache-2.0",
"BSD-3-Clause"
] | permissive | Orwatronic/MAS_513 | 77e40544f837deb60beef0cfa95248b815ad5ba4 | d6c2ffd4ef604fe814b32ee08335da5ea718dad9 | refs/heads/master | 2023-08-27T14:57:58.356248 | 2021-11-15T09:36:25 | 2021-11-15T09:36:25 | 424,366,171 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 383 | py | # generated from catkin/cmake/template/pkg.context.pc.in
CATKIN_PACKAGE_PREFIX = ""
PROJECT_PKG_CONFIG_INCLUDE_DIRS = "".split(';') if "" != "" else []
PROJECT_CATKIN_DEPENDS = "".replace(';', ' ')
PKG_CONFIG_LIBRARIES_WITH_PREFIX = "".split(';') if "" != "" else []
PROJECT_NAME = "ur10_e_moveit_config"
PROJECT_SPACE_DIR = "/home/orwah/catkin_ws/install"
PROJECT_VERSION = "1.2.5"
| [
"orwah_92@hotmail.com"
] | orwah_92@hotmail.com |
c7dd6e6ee742438cce821562dfac0ff4ae39d0b6 | a7963cef9176a6a73255e98bee9b1cda45a91423 | /python2/implementation/findDigits/findDigits.py | 126ce646d193fa0c2a4f3a0b0cf66a5519249aea | [] | no_license | ayazhemani/hackerrank-py | a1394fb35f8b4d2c3e68e18b5c8d5976850adb2d | fcf3755b62fe0644af763875e3a00be962941a6d | refs/heads/master | 2021-06-05T05:09:26.047540 | 2021-04-25T01:54:51 | 2021-04-25T01:54:51 | 131,791,980 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 785 | py | """Solution for HackerRank challenge: Find Digits
"""
def find_digits(number):
"""Count the digits in a number that divide into that number cleanly.
Args:
number (int): Number whose digits will be counted
Returns:
int: Count of digits in number that are divisible.
"""
count = 0
for digit in str(number):
if int(digit) == 0:
continue
elif number % int(digit) == 0:
count += 1
return count
def main():
"""Receives input from stdin, provides output to stdout.
"""
tests_count = int(raw_input())
results = []
for i in xrange(tests_count):
results.append(find_digits(int(raw_input())))
for result in results:
print result
if __name__ == '__main__':
main()
| [
"ayazhemani@gmail.com"
] | ayazhemani@gmail.com |
63fafdc0c526719ba3957ec88239cbf44de85f76 | dfca5d9e5d62f0898261e015d18e7527fe19395e | /todoapp/urls.py | 567ade8c19624048fe36dd769b4d0a56f328b6a7 | [] | no_license | keshavsharma98/TodoApp | 2d8dbd837c357f20d5ba7c991230c0288673ceb0 | 7ccdba672cd917df3e011f21892610a50e16b32e | refs/heads/master | 2020-04-29T03:04:11.142013 | 2019-03-15T10:17:26 | 2019-03-15T10:17:26 | 175,795,080 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 960 | py | """todoapp URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.1/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.conf.urls import url, include
from django.contrib import admin
from todolist.views import index
from todolist.resources import NoteResource
note_resource = NoteResource()
urlpatterns = [
url(r'^admin/', admin.site.urls),
url(r'^$', index, name="TodoList"),
url(r'^api/', include(note_resource.urls)),
]
| [
"16ucs092@lnmiit.ac.in"
] | 16ucs092@lnmiit.ac.in |
399c7e83ea6bbed0640c8024c90550fe02841b5e | d6b556ff44b9cfcb54971ee5b8e4a45a2178e2a6 | /ies_base/filters.py | e56ef15e12341b70a8d6be1d3e0b3d64b9d0d33e | [
"MIT"
] | permissive | InstanteSports/ies-django-base | 51db29735a0c4df5e0d7e6fc2d80af7ddf03bdc9 | 283fe741dd190ae13a256543bff09aa169607b78 | refs/heads/master | 2020-04-07T05:29:54.616249 | 2017-04-21T20:15:52 | 2017-04-21T20:15:52 | 32,108,200 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 955 | py | from django_filters import Filter
from django_filters.fields import Lookup
from django.db.models import Q
from functools import reduce
class ListFilter(Filter):
def filter(self, qs, value):
return super(ListFilter, self).filter(qs, Lookup(value.split(u","), "in"))
class MultipleFieldListFilter(Filter):
def __init__(self, combiner="OR", *args, **kwargs):
self.combiner = combiner
super(MultipleFieldListFilter, self).__init__(*args, **kwargs)
def filter(self, qs, value):
value = value.split(u",")
if not value:
return qs
q_list = []
for name in self.name.split(','):
q = Q()
for v in value:
q |= Q(**{name: v})
q_list.append(q)
if self.combiner == "OR":
q = reduce(lambda a, b: a | b, q_list)
else:
q = reduce(lambda a, b: a & b, q_list)
return qs.filter(q).distinct() | [
"jonzlin95@gmail.com"
] | jonzlin95@gmail.com |
2f099ec6af52ee2c90fd48ee346f03e471790fa3 | dd229f5bd008c3dc011efddfa981dbc250dd6d4c | /detection.py | 0264d6fdc40654b745a6139af4ee32ea72791c98 | [] | no_license | prat96/crude-lane-detection | 019bed6de91eb02f16a64f9f498eefeab7d76d19 | b8636779756d3c4cc37449456069c13fb3dce42f | refs/heads/master | 2020-04-29T00:33:44.415499 | 2019-03-14T20:58:17 | 2019-03-14T20:58:17 | 175,698,246 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,792 | py | import numpy as np
#import matplotlib
#import matplotlib.pyplot as plt
#import argparse
import cv2
import imutils
import time
cap = cv2.VideoCapture('testvideo2.mp4')
# loop through until entire video file is played
while(cap.isOpened()):
# read video frame & show on screen
ret, frame = cap.read()
snip = frame[500:700,300:900]
cv2.imshow("Snip",snip)
# create polygon (trapezoid) mask to select region of interest
mask = np.zeros((snip.shape[0], snip.shape[1]), dtype="uint8")
pts = np.array([[25, 190], [275, 50], [380, 50], [575, 190]], dtype=np.int32)
cv2.fillConvexPoly(mask, pts, 255)
cv2.imshow("Mask", mask)
# apply mask and show masked image on screen
masked = cv2.bitwise_and(snip, snip, mask=mask)
cv2.imshow("Region of Interest", masked)
# convert to grayscale then black/white to binary image
frame = cv2.cvtColor(masked, cv2.COLOR_BGR2GRAY)
thresh = 200
frame = cv2.threshold(frame, thresh, 255, cv2.THRESH_BINARY)[1]
cv2.imshow("Black/White", frame)
# blur image to help with edge detection
blurred = cv2.GaussianBlur(frame, (11, 11), 0)
# cv2.imshow("Blurred", blurred)
# identify edges & show on screen
edged = cv2.Canny(blurred, 30, 150)
cv2.imshow("Edged", edged)
# perform full Hough Transform to identify lane lines
lines = cv2.HoughLines(edged, 1, np.pi / 180, 25)
# define arrays for left and right lanes
rho_left = []
theta_left = []
rho_right = []
theta_right = []
if lines is not None:
# loop through all of the lines found by cv2.HoughLines
for i in range(0, len(lines)):
# evaluate each row of cv2.HoughLines output 'lines'
for rho, theta in lines[i]:
# collect left lanes
if theta < np.pi/2 and theta > np.pi/4:
rho_left.append(rho)
theta_left.append(theta)
# collect right lanes
if theta > np.pi/2 and theta < 3*np.pi/4:
rho_right.append(rho)
theta_right.append(theta)
# statistics to identify median lane dimensions
left_rho = np.median(rho_left)
left_theta = np.median(theta_left)
right_rho = np.median(rho_right)
right_theta = np.median(theta_right)
# plot median lane on top of scene snip
if left_theta > np.pi/4:
a = np.cos(left_theta); b = np.sin(left_theta)
x0 = a * left_rho; y0 = b * left_rho
offset1 = 250; offset2 = 800
x1 = int(x0 - offset1 * (-b)); y1 = int(y0 - offset1 * (a))
x2 = int(x0 + offset2 * (-b)); y2 = int(y0 + offset2 * (a))
cv2.line(snip, (x1, y1), (x2, y2), (0, 255, 0), 6)
if right_theta > np.pi/4:
a = np.cos(right_theta); b = np.sin(right_theta)
x0 = a * right_rho; y0 = b * right_rho
offset1 = 290; offset2 = 800
x3 = int(x0 - offset1 * (-b)); y3 = int(y0 - offset1 * (a))
x4 = int(x0 - offset2 * (-b)); y4 = int(y0 - offset2 * (a))
cv2.line(snip, (x3, y3), (x4, y4), (255, 0, 0), 6)
# overlay semi-transparent lane outline on original
if left_theta > np.pi/4 and right_theta > np.pi/4:
pts = np.array([[x1, y1], [x2, y2], [x3, y3], [x4, y4]], dtype=np.int32)
# (1) create a copy of the original:
overlay = snip.copy()
# (2) draw shapes:
cv2.fillConvexPoly(overlay, pts, (0, 255, 0))
# (3) blend with the original:
opacity = 0.4
cv2.addWeighted(overlay, opacity, snip, 1 - opacity, 0, snip)
cv2.imshow("Lined", snip)
# press the q key to break out of video
if cv2.waitKey(25) & 0xFF == ord('q'):
break
# clear everything once finished
cap.release()
cv2.destroyAllWindows()
| [
"pratheek.b@tonboimaging.com"
] | pratheek.b@tonboimaging.com |
3e38218f72b83a274a3c9d0dd2a44e3340d34023 | 4d52634ea8ac23cd1b4e4b3c476962bce7120d08 | /setup.py | 2b9f17e6e86f0a919432259e775ac76ea32c5eb4 | [
"MIT"
] | permissive | FiMDP/FiMDPEnv | 5d3064f740ae2289ffe095d1b38d83b7cf37dab8 | 86445155a89304927ddf4d6714319edd50c7c4f2 | refs/heads/master | 2023-02-21T03:36:42.424987 | 2021-01-23T19:15:56 | 2021-01-23T19:15:56 | 279,793,889 | 2 | 1 | MIT | 2020-10-24T00:34:17 | 2020-07-15T07:11:27 | Jupyter Notebook | UTF-8 | Python | false | false | 931 | py | from setuptools import setup
import pathlib
from fimdpenv import __version__ as version
# The directory containing this file
HERE = pathlib.Path(__file__).parent
# The text of the README file
README = (HERE / "README.md").read_text()
setup(
name='fimdpenv',
version=version,
description='Simulation environments for FiMDP',
long_description=README,
long_description_content_type="text/markdown",
keywords='simulation-environment, grid-world, agent, cmdp, uuv-dynamics, street-network, formal-methods',
url='https://github.com/FiMDP/FiMDPEnv',
author="Pranay Thangeda",
author_email="contact@prny.me",
license="MIT",
python_requires=">=3.6.0",
packages=['fimdpenv'],
install_requires=[
'fimdp>=2.0.0',
'numpy>=1.18.5',
'matplotlib>=3.2.2',
'scipy>=1.5.0',
'ipython>=7.16.1',
'networkx>=2.5',
'folium>=0.11.0'
]
)
| [
"contact@prny.me"
] | contact@prny.me |
bb07e28b1ad61718f3d83d3badd3e2a813c4fd6e | a06c598f64521b3c8fd0361fd18ce442285e6a2c | /readNovel.py | f3f0a0729b9636bf7c9edb119b68ea7d248ddfdd | [] | no_license | jefftsai8049/ML-Deep_Learning_HW3 | 37bbe7507dabd7c86cac0eda4f4e6933441d82b8 | 2d8c4ce2fc9ff0311503ad3d5a137255c71c892f | refs/heads/master | 2021-01-22T03:39:49.544368 | 2015-05-20T10:15:12 | 2015-05-20T10:15:12 | 35,473,756 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,882 | py | __author__ = 'jefftsai'
import csv
class readNovel:
# def __init__(self):
# # open pre-processing raw file
def findStrat(self):
# print("find start")
# find where is the article start
i=0
while 1:
str = self.file.readline()
# find does the line exist "Chapter of CHAPTER"
if str.find("Chapter") >= 0 or str.find("CHAPTER") >= 0:
break
else:
self.useless.write(str+"\n")
i = i+1
# if read 300 lines,but dosn't find the start of the article
if i>300:
break
# print(i)
return i+1
def findLines(self,inFileName):
file = open(inFileName,"r")
i=0
while 1:
text = file.readline()
if len(text) < 1:
break
i = i+1
return i
def readSplit(self ,inFileName, outFileName, useless):
self.file = open(inFileName, "r", errors='ignore')
# for save ignore text
self.useless = open(useless,"w")
# for save remain text
outFile = open(outFileName,"w")
# find start position
self.findStrat()
# initialize
string = ""
oldText = ""
j=0
while 1:
# read one line
text = self.file.readline()
# replace exception condition
content = text.replace("\n"," ")
content = content.replace("Mr.","Mr")
content = content.replace("Mrs.","Mrs")
content = content.replace("--"," ")
content = content.replace("-"," ")
content = content.replace('(',' ')
content = content.replace(')',' ')
content = content.replace("---"," ")
content = content.replace(","," ")
# if EOF break out the while loop
if text == "":
break
# if this line is the start of the article
if content.find("Chapter") >= 0 or content.find("CHAPTER") >= 0 :
content = " "
# if this line is the end of the article
if content.find("End of") >= 0 or content.find("THE END") >= 0 or content.find("*END") >= 0:
self.findStrat()
# if two lines is \n, change line
if oldText == "\n" and text == "\n":
string = self.checkString(string)
string = string.lower()
outFile.write(string+"\n")
string = ""
# read the text char by char
else:
for i in range(0,len(content)):
if content[i] !="." and content[i] !="?" and content[i] !="!":
if (ord(content[i]) > 64 and ord(content[i]) < 91) or (ord(content[i]) > 96 and ord(content[i]) < 123) or content[i] == " ":
string = string+content[i]
else:
string = self.checkString(string)
string = string.lower()
outFile.write(string+"\n")
string = ""
oldText = text
print(j)
j = j+1
# if j > 1000:
# break
outFile.close()
self.useless.close()
self.file.close()
return True
def checkString(self,string):
# string = string.replace('\"','')
# # string = string.replace("\'",'')
# string = string.replace(',',' ,')
# string = string.replace(';',' ;')
# string = string.replace('(',' ')
# string = string.replace(')',' ')
# string = string.replace("---"," ")
# string = string.replace("--"," ")
# string = string.replace("-"," ")
if len(string) > 0:
if string[0] == " " or string[0] == "\'":
if len(string) == 1:
string = ""
else:
string = string[1:]
if string[0] == " " or string[0] == "\'":
string = string[1:]
# print(string)
# print(len(string))
# print(string)
return string
def preprocessing(self,filename,outFileName):
splitData = open(filename, "r")
outFile = open(outFileName, "w")
# j=0
while 1:
string = splitData.readline()
if len(string) > 1:
string = string.replace("\n", "")
if string[0] == "":
while 1:
string = string[1:]
if string[0] !=" ":
break
else:
string = string.lower()
data = string.split(" ")
for i in range(0,data.count("")):
data.remove("")
# print(data)
# outFile.write(str(j)+",")
outFile.write("START,")
outFile.write(",".join(data))
outFile.write(",END"+"\n")
# j =j +1
elif len(string) == 0:
break
return True
def word2index(self,inFileName,outFileName,mapFileName):
inFile = open(inFileName,"r")
outFile = open(outFileName,"w")
map = dict([["START",0],["END",1]])
i = 2
k = 0
while 1:
string = inFile.readline()
if len(string) < 1:
break
string = string.replace("\n","")
data = string.split(",")
# print(data)
for j in range(0,len(data)):
if ~(data[j] in map):
if map.setdefault(data[j],i) == i:
i = i+1
k = k+1
# if k > 3:
# break
print(map)
self.writeDict(mapFileName,map)
inFile.seek(0)
n=0
while 1:
string = inFile.readline()
string = string.replace("\n","")
data = string.split(",")
temp = ""
if len(string) < 1:
break
else:
# data = map[data]
# print(data)
for m in range(0,len(data)):
data[m] = map[data[m]]
temp = temp + str(data[m])+","
temp = temp[:-1]+"\n"
outFile.write(temp)
n = n +1
# if n > 3:
# break
# print(data)
outFile.close()
return True
def writeDict(self,mapFileName,map):
mapFile = open(mapFileName,"w")
for key, val in map.items():
mapFile.write(key+","+str(val)+"\n")
return True
def loadDict(self,mapFileName):
mapFile = open(mapFileName,"r")
map = dict()
while 1:
text = mapFile.readline()
if len(text) < 1:
break
else:
mapElement = text.split(",")
map.setdefault(mapElement[0],int(mapElement[1]))
return map
def word2VectorIndex(self,mapFileName,trainOutFileName,trainIndexFileName):
map = self.loadWord2VectorMap(mapFileName)
inFile = open(trainOutFileName,"r")
outFile = open(trainIndexFileName,"w")
j=0
while 1:
# if j > 5:
# break
# j = j +1
text = inFile.readline()
string = ""
if len(text)<2:
break
else:
text = text[:-1]
data = text.split(",")
for i in range(0,len(data)):
if i == 0:
data[i] = -2
elif i == len(data)-1:
data[i] = -3
else:
if data[i] in map:
data[i] = map[data[i]]
else:
data[i] = -1
string = string+str(data[i])+","
string = string[:-1]
outFile.write(string+"\n")
inFile.close()
outFile.close()
return "word2vector index ok"
def loadWord2VectorMap(self,mapFileName):
mapFile = open(mapFileName,"r")
set = []
i = 0
while 1:
text = mapFile.readline()
if len(text)<1:
break
else:
text = text[:-1]
element = text.split(" ")
element[1] = int(element[1])
set.append(element)
# if i > 10:
# break
# i=i+1
# print(set)
map = dict(set)
return map | [
"jefftsai8049@gmail.com"
] | jefftsai8049@gmail.com |
3bb75f203a23abf95a1933aa156d793213e197ed | 1ed734e04efe2547fc33f0220ca4b97a74f08d48 | /google_play_worker.py | dd592c8d9494667a948e10afb397b3374838cb09 | [] | no_license | tramX/google_play | 1a8d30e9b8aa40a2f8e9acd50c1e03e0d957e043 | 45a4459d8bd95d661ec8eee1e71606791ad9bc3a | refs/heads/master | 2020-03-28T19:37:39.527915 | 2018-09-16T13:08:09 | 2018-09-16T13:08:09 | 148,993,828 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 657 | py | from selenium import webdriver
from selenium.webdriver.chrome.options import Options
import os
import time
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
options = Options()
options.add_argument('--headless')
options.add_argument('--disable-gpu')
driver = webdriver.Chrome('{}/chromedriver'.format(BASE_DIR), chrome_options=options)
def get_html(url):
driver.get(url)
for a in driver.find_elements_by_css_selector('a'):
if a.get_attribute('jsname') == 'Hly47e':
a.click()
time.sleep(0.5)
view_details_div = driver.find_element_by_class_name('fnLizd')
return view_details_div.get_attribute('innerHTML')
| [
"podwar2008@gmail.com"
] | podwar2008@gmail.com |
5617b15208b32212a8b16540764583c46d362158 | 403d1f4acae8edc4ac24bc4830c6f1dababfecaa | /layers/dynamicGroup.py | 4b0e63e338a27726c65f8758262b3bc8b8e8d0e2 | [] | no_license | coleary9/RockBrawl | b5539b01670f5aad3c86f245df346b74bc37bdfa | 1bc7d983481a62ee178ba4774f445e37b16d3b0c | refs/heads/master | 2021-01-01T15:55:36.569836 | 2014-04-09T17:49:00 | 2014-04-09T17:49:00 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,167 | py | # BitCity Studios:
# Cameron O'Leary <coleary9@jhu.edu>
# Steve Griffin <sgriff27@jhu.edu>
# Jeremy Dolinko <j.dolinko@gmail.com>
# Jonathan Rivera <jriver21@jhu.edu>
# Michael Shavit <shavitmichael@gmail.com>
import pygame
from pygame.locals import *
import glob
from pygame.sprite import Group
class DynamicGroup(Group):
"""The basic empty group interface."""
def __init__(self):
"""
Constructor.
"""
Group.__init__(self)
def draw(self, camera):
"""Perform all graphical tasks for this frame."""
for obj in self:
camera.draw(obj)
def update(self, dt):
"""Perform all calculations for the amount of time that has passed."""
for obj in self:
if not obj.update(dt):
self.remove(obj)
class EnemiesGroup(DynamicGroup):
"""A group with a reference to the player."""
def addPlayerReference(self, player):
self.player = player
def update(self, dt):
pos = self.player.pos()
for obj in self:
if not obj.update(dt, pos):
self.remove(obj)
if __name__ == "__main__":
main()
| [
"coleary9@jhu.edu"
] | coleary9@jhu.edu |
104b94d81875cb9335562f944b7bdd291bad5647 | b2933c228900b157b4996ae0fccd20fecd9de07f | /habu/cli/cmd_crack_luhn.py | c48e6e6ca310fe8026a9f89388b3cc2bb87a144c | [
"BSD-3-Clause"
] | permissive | dolboBobo/habu | 841638d233ac7ef97ebbd105d5cea9a20801f103 | d9f2df6ac66b7527f7a731f943af07726ff7e2e2 | refs/heads/master | 2020-12-04T22:04:06.396948 | 2020-01-23T12:06:23 | 2020-01-23T12:06:23 | 231,915,598 | 1 | 0 | BSD-3-Clause | 2020-01-23T12:06:24 | 2020-01-05T12:40:39 | null | UTF-8 | Python | false | false | 1,564 | py | #!/usr/bin/env python3
import sys
import click
def luhn_validate(number):
""" Source code from: https://en.wikipedia.org/wiki/Luhn_algorithm"""
sum = 0
parity = len(number) % 2
for i, digit in enumerate([int(x) for x in number]):
if i % 2 == parity:
digit *= 2
if digit > 9:
digit -= 9
sum += digit
return sum % 10 == 0
@click.command()
@click.argument('number')
def cmd_crack_luhn(number):
"""Having known values for a Luhn validated number, obtain the possible unknown numbers.
Numbers that use the Luhn algorithm for validation are Credit Cards, IMEI,
National Provider Identifier in the United States, Canadian Social
Insurance Numbers, Israel ID Numbers and Greek Social Security Numbers (ΑΜΚΑ).
The '-' characters are ignored.
Define the missing numbers with the 'x' character.
Reference: https://en.wikipedia.org/wiki/Luhn_algorithm
Example:
\b
$ habu.crack.luhn 4509-xxxx-3160-6445
"""
number = number.replace('-', '')
unknown_count = number.count('x')
if not number.replace('x', '').isdigit():
print('Invalid format. Please, read the documentation.', file=sys.stderr)
sys.exit(1)
for n in range(10 ** unknown_count):
candidate = number
for item in '{:0{count}}'.format(n, count=unknown_count):
candidate = candidate.replace('x', item, 1)
if luhn_validate(candidate):
print(candidate)
if __name__ == '__main__':
cmd_crack_luhn()
| [
"fabian@portantier.com"
] | fabian@portantier.com |
216973cccfef4cf492f226bef5f4b07a8f34663e | a35132aafead5d02307d7b35818e6ad9f370c792 | /testPWM.py | ebf96ab396df82995652c2f93f2e471082e74d2d | [] | no_license | CidrolinF/Livent-Maido | 9ae7449d1c87372c4d0e56ed552eb3e2e1f2d088 | e19141bbbb8a08fa6f94f419524c13e434ed52d9 | refs/heads/master | 2021-06-30T21:27:04.986047 | 2021-06-20T12:48:20 | 2021-06-20T12:54:16 | 238,229,056 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 192 | py | import RPi.GPIO as GPIO
import time
GPIO.cleanup()
GPIO.setmode(GPIO.BOARD)
GPIO.setup(12, GPIO.OUT)
rapport = 100
p = GPIO.PWM(12, 40) #Channel, Frequence
p.start(rapport)
time.sleep(100) | [
"brousse.emmanuel@gmail.com"
] | brousse.emmanuel@gmail.com |
f69d78501aeee4313bc452aba57e92f092e7065e | af9b483e99c69613ba903d7817f111d7ff89b197 | /venv/bin/wheel | 371261a0565c97b2573e19e1191123b3847a6dc9 | [] | no_license | arnabdutta73/DC | c3dc9f53572456a0938f59e2b01191d5efff98c2 | fcb404b2f5c32f26c4c952686ac9aaeeb24fd573 | refs/heads/master | 2023-02-26T02:09:28.009838 | 2021-02-05T06:50:21 | 2021-02-05T06:50:21 | 113,568,916 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 240 | #!/root/PycharmProjects/DC/venv/bin/python3.4
# -*- coding: utf-8 -*-
import re
import sys
from wheel.tool import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
"arnabdutta73@gmail.com"
] | arnabdutta73@gmail.com | |
89fbc7f52117689c4483dbcb5398d8bc7ddec367 | b7eed26cf8a0042a61f555eed1e9bf0a3227d490 | /students/piatkowska_anna/lesson_05_lists/coma_code.py | d70d664d3dab57917ac70321777fd49a79eeb24b | [] | no_license | jedzej/tietopythontraining-basic | e8f1ac5bee5094c608a2584ab19ba14060c36dbe | a68fa29ce11942cd7de9c6bbea08fef5541afa0f | refs/heads/master | 2021-05-11T11:10:05.110242 | 2018-08-20T12:34:55 | 2018-08-20T12:34:55 | 118,122,178 | 14 | 84 | null | 2018-08-24T15:53:04 | 2018-01-19T12:23:02 | Python | UTF-8 | Python | false | false | 1,077 | py | """
Comma Code
Say you have a list value like this:
spam = ['apples', 'bananas', 'tofu', 'cats']
Write a function that takes a list value as an argument
and returns a string with all the items separated by
a comma and a space, with and inserted before the last item.
For example, passing the previous spam list to the function
would return 'apples, bananas, tofu, and cats'.
But your function should be able to work with any list value passed to it.
"""
def coma_separate_string(l):
if not isinstance(l, list):
raise ValueError("Expected list argument")
result = ""
for i in range(len(l) - 1):
result += str(l[i]) + ", "
result += "and " + l[len(l) - 1]
return result
def coma_separet_string_without_last_coma(l):
if not isinstance(l, list):
raise ValueError
result = ''
result = ', '.join(l[:-1])
result += ' and ' + l[-1]
return result
if __name__ == "__main__":
spam = ['apples', 'bananas', 'tofu', 'cats']
print(coma_separate_string(spam))
print(coma_separet_string_without_last_coma(spam))
| [
"anna.piatkowska@tieto.com"
] | anna.piatkowska@tieto.com |
1af31878fc9c13d021ae10057dec2f1535d3594a | a61bf859ceeb1ba98de3863225e07b29e1d7ce8a | /thonny/plugins/micropython/base_api_stubs/framebuf.pyi | c048df53f12bfad86fd3291b592a2dc18112ffa1 | [
"MIT",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | thonny/thonny | 3974b1860703e8450b837863682117f525a886c6 | 8fc9f5c7cbbe1d1c82aa5503ec4b684e28aa608c | refs/heads/master | 2023-08-31T03:04:34.685140 | 2023-08-24T11:38:36 | 2023-08-24T11:38:36 | 163,728,962 | 2,788 | 1,048 | MIT | 2023-08-10T18:59:37 | 2019-01-01T10:29:50 | Python | UTF-8 | Python | false | false | 8,369 | pyi | """
Frame buffer manipulation.
Descriptions taken from:
https://raw.githubusercontent.com/micropython/micropython/master/docs/library/framebuf.rst.
This module provides a general frame buffer which can be used to create
bitmap images, which can then be sent to a display.
"""
__author__ = "Howard C Lovatt"
__copyright__ = "Howard C Lovatt, 2020 onwards."
__license__ = "MIT https://opensource.org/licenses/MIT (as used by MicroPython)."
__version__ = "7.3.9" # Version set by https://github.com/hlovatt/tag2ver
from typing import overload, Final
from uio import AnyWritableBuf
MONO_VLSB: Final[int] = ...
"""
Monochrome (1-bit) color format
This defines a mapping where the bits in a byte are vertically mapped with
bit 0 being nearest the top of the screen. Consequently each byte occupies
8 vertical pixels. Subsequent bytes appear at successive horizontal
locations until the rightmost edge is reached. Further bytes are rendered
at locations starting at the leftmost edge, 8 pixels lower.
"""
MONO_HLSB: Final[int] = ...
"""
Monochrome (1-bit) color format
This defines a mapping where the bits in a byte are horizontally mapped.
Each byte occupies 8 horizontal pixels with bit 7 being the leftmost.
Subsequent bytes appear at successive horizontal locations until the
rightmost edge is reached. Further bytes are rendered on the next row, one
pixel lower.
"""
MONO_HMSB: Final[int] = ...
"""
Monochrome (1-bit) color format
This defines a mapping where the bits in a byte are horizontally mapped.
Each byte occupies 8 horizontal pixels with bit 0 being the leftmost.
Subsequent bytes appear at successive horizontal locations until the
rightmost edge is reached. Further bytes are rendered on the next row, one
pixel lower.
"""
RGB565: Final[int] = ...
"""
Red Green Blue (16-bit, 5+6+5) color format
"""
GS2_HMSB: Final[int] = ...
"""
Grayscale (2-bit) color format
"""
GS4_HMSB: Final[int] = ...
"""
Grayscale (4-bit) color format
"""
GS8: Final[int] = ...
"""
Grayscale (8-bit) color format
"""
class FrameBuffer:
"""
The FrameBuffer class provides a pixel buffer which can be drawn upon with
pixels, lines, rectangles, text and even other FrameBuffer's. It is useful
when generating output for displays.
For example::
import framebuf
# FrameBuffer needs 2 bytes for every RGB565 pixel
fbuf = framebuf.FrameBuffer(bytearray(100 * 10 * 2), 100, 10, framebuf.RGB565)
fbuf.fill(0)
fbuf.text('MicroPython!', 0, 0, 0xffff)
fbuf.hline(0, 9, 96, 0xffff)
"""
def __init__(
self,
buffer: AnyWritableBuf,
width: int,
height: int,
format: int,
stride: int = ...,
/,
):
"""
Construct a FrameBuffer object. The parameters are:
- *buffer* is an object with a buffer protocol which must be large
enough to contain every pixel defined by the width, height and
format of the FrameBuffer.
- *width* is the width of the FrameBuffer in pixels
- *height* is the height of the FrameBuffer in pixels
- *format* specifies the type of pixel used in the FrameBuffer;
permissible values are listed under Constants below. These set the
number of bits used to encode a color value and the layout of these
bits in *buffer*.
Where a color value c is passed to a method, c is a small integer
with an encoding that is dependent on the format of the FrameBuffer.
- *stride* is the number of pixels between each horizontal line
of pixels in the FrameBuffer. This defaults to *width* but may
need adjustments when implementing a FrameBuffer within another
larger FrameBuffer or screen. The *buffer* size must accommodate
an increased step size.
One must specify valid *buffer*, *width*, *height*, *format* and
optionally *stride*. Invalid *buffer* size or dimensions may lead to
unexpected errors.
"""
def fill(self, c: int, /) -> None:
"""
Fill the entire FrameBuffer with the specified color.
"""
@overload
def pixel(self, x: int, y: int, /) -> int:
"""
If *c* is not given, get the color value of the specified pixel.
If *c* is given, set the specified pixel to the given color.
"""
@overload
def pixel(self, x: int, y: int, c: int, /) -> None:
"""
If *c* is not given, get the color value of the specified pixel.
If *c* is given, set the specified pixel to the given color.
"""
def hline(self, x: int, y: int, w: int, c: int, /) -> None:
"""
Draw a line from a set of coordinates using the given color and
a thickness of 1 pixel. The `line` method draws the line up to
a second set of coordinates whereas the `hline` and `vline`
methods draw horizontal and vertical lines respectively up to
a given length.
"""
def vline(self, x: int, y: int, h: int, c: int, /) -> None:
"""
Draw a line from a set of coordinates using the given color and
a thickness of 1 pixel. The `line` method draws the line up to
a second set of coordinates whereas the `hline` and `vline`
methods draw horizontal and vertical lines respectively up to
a given length.
"""
def line(self, x1: int, y1: int, x2: int, y2: int, c: int, /) -> None:
"""
Draw a line from a set of coordinates using the given color and
a thickness of 1 pixel. The `line` method draws the line up to
a second set of coordinates whereas the `hline` and `vline`
methods draw horizontal and vertical lines respectively up to
a given length.
"""
def rect(self, x: int, y: int, w: int, h: int, c: int, /) -> None:
"""
Draw a rectangle at the given location, size and color. The `rect`
method draws only a 1 pixel outline whereas the `fill_rect` method
draws both the outline and interior.
"""
def fill_rect(self, x: int, y: int, w: int, h: int, c: int, /) -> None:
"""
Draw a rectangle at the given location, size and color. The `rect`
method draws only a 1 pixel outline whereas the `fill_rect` method
draws both the outline and interior.
"""
def text(self, s: str, x: int, y: int, c: int = 1, /) -> None:
"""
Write text to the FrameBuffer using the the coordinates as the upper-left
corner of the text. The color of the text can be defined by the optional
argument but is otherwise a default value of 1. All characters have
dimensions of 8x8 pixels and there is currently no way to change the font.
"""
def scroll(self, xstep: int, ystep: int, /) -> None:
"""
Shift the contents of the FrameBuffer by the given vector. This may
leave a footprint of the previous colors in the FrameBuffer.
"""
def blit(
self,
fbuf: FrameBuffer,
x: int,
y: int,
key: int = -1,
pallet: FrameBuffer | None = None,
/,
) -> None:
"""
Draw another FrameBuffer on top of the current one at the given coordinates.
If *key* is specified then it should be a color integer and the
corresponding color will be considered transparent: all pixels with that
color value will not be drawn.
The *palette* argument enables blitting between FrameBuffers with differing
formats. Typical usage is to render a monochrome or grayscale glyph/icon to
a color display. The *palette* is a FrameBuffer instance whose format is
that of the current FrameBuffer. The *palette* height is one pixel and its
pixel width is the number of colors in the source FrameBuffer. The *palette*
for an N-bit source needs 2**N pixels; the *palette* for a monochrome source
would have 2 pixels representing background and foreground colors. The
application assigns a color to each pixel in the *palette*. The color of the
current pixel will be that of that *palette* pixel whose x position is the
color of the corresponding source pixel.
"""
| [
"aivar.annamaa@gmail.com"
] | aivar.annamaa@gmail.com |
559af56ed2651ded432e425c33ff602ed70a46f0 | a3cd7bce0c691d1904ba9d7651fdeda6dbdbff89 | /Intro Scripts (LPTHW)/ex15.py | fd4447dd2b8c211340f42f9cdf9951bb830ddbe7 | [] | no_license | mkyle1121/python-git | 4a0bc7b72a39f5cb20ffc542c626eebffedf0b95 | e30d4d2177d50fa4054a968a168e8cc4862931ab | refs/heads/master | 2020-03-26T00:51:02.249446 | 2019-02-25T13:34:19 | 2019-02-25T13:34:19 | 144,070,266 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 252 | py | from sys import argv
script, filename = argv
txt = open(filename)
print "Here's your file %r:" % filename
print txt.read()
print "I'll also ask you to type it again."
fileagain = raw_input("> ")
txtagain = open(fileagain)
print txtagain.read()
| [
"mkyle1121@gmail.com"
] | mkyle1121@gmail.com |
5ce07c778f84bbeedac4634536f06c036966a6c8 | ae258e3207a5a14f43c3a290cc0da4f1be4d805f | /project/User/migrations/0001_initial.py | aee0cad4eaebcf200cea3344d491da90a97029d2 | [] | no_license | abbnm2017/django_blog | d1d87985c71b4f957efca8f835bad2abc55bc321 | 9611cd01cf27cd570fe38c7d34e95475f9b59154 | refs/heads/master | 2021-08-07T15:27:46.774695 | 2021-07-19T13:43:22 | 2021-07-19T13:43:22 | 239,752,902 | 2 | 0 | null | 2021-07-19T13:43:22 | 2020-02-11T12:08:27 | JavaScript | UTF-8 | Python | false | false | 13,807 | py | # Generated by Django 3.0.1 on 2021-03-01 15:27
from django.conf import settings
import django.contrib.auth.models
import django.contrib.auth.validators
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
class Migration(migrations.Migration):
initial = True
dependencies = [
('contenttypes', '0002_remove_content_type_name'),
('auth', '0011_update_proxy_permissions'),
]
operations = [
migrations.CreateModel(
name='abbnm',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('md', models.IntegerField(default=1)),
],
),
migrations.CreateModel(
name='allclass',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('cls_title', models.CharField(max_length=1000)),
],
),
migrations.CreateModel(
name='Boy',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=32)),
],
),
migrations.CreateModel(
name='DayNumber',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('day', models.DateField(default=django.utils.timezone.now, verbose_name='日期')),
('count', models.IntegerField(default=0, verbose_name='网站访问次数')),
],
options={
'verbose_name': '网站日访问量统计',
'verbose_name_plural': '网站日访问量统计',
},
),
migrations.CreateModel(
name='Girl',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('nick', models.CharField(max_length=32)),
],
),
migrations.CreateModel(
name='ImgK',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('img', models.ImageField(upload_to='img')),
('name', models.CharField(max_length=1000)),
],
),
migrations.CreateModel(
name='MarryBoth',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('nickname', models.CharField(max_length=32)),
('username', models.CharField(max_length=32)),
('password', models.CharField(max_length=64)),
('gender', models.IntegerField(choices=[(1, '男'), (2, '女')])),
],
),
migrations.CreateModel(
name='MarryBoy',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('nickname', models.CharField(max_length=32)),
('username', models.CharField(max_length=32)),
('password', models.CharField(max_length=64)),
],
),
migrations.CreateModel(
name='MarryGirl',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('nickname', models.CharField(max_length=32)),
('username', models.CharField(max_length=32)),
('password', models.CharField(max_length=64)),
],
),
migrations.CreateModel(
name='MessageBoard',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=30)),
('date', models.DateTimeField(auto_now_add=True)),
('content', models.CharField(max_length=255)),
],
),
migrations.CreateModel(
name='PcImgK',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('img', models.ImageField(upload_to='keke')),
('name', models.CharField(max_length=1000)),
],
),
migrations.CreateModel(
name='student',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=1000)),
('class_id', models.IntegerField()),
],
),
migrations.CreateModel(
name='tea2class',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('teacher_id', models.IntegerField()),
('class_id', models.IntegerField()),
],
),
migrations.CreateModel(
name='teacher',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('teacher_name', models.CharField(max_length=1000)),
],
),
migrations.CreateModel(
name='testdata',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=64)),
],
),
migrations.CreateModel(
name='UserInfo',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('username', models.CharField(max_length=32)),
('email', models.EmailField(max_length=254)),
('c_time', models.DateTimeField(null=True)),
('color', models.IntegerField(choices=[(1, '黑色'), (2, '白色'), (3, '蓝色')])),
],
),
migrations.CreateModel(
name='Userip',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('ip', models.CharField(max_length=30, verbose_name='IP地址')),
('count', models.IntegerField(default=0, verbose_name='访问次数')),
],
options={
'verbose_name': '访问用户信息',
'verbose_name_plural': '访问用户信息',
},
),
migrations.CreateModel(
name='VisitNumber',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('count', models.IntegerField(default=0, verbose_name='网站访问总次数')),
],
options={
'verbose_name': '网站访问总次数',
'verbose_name_plural': '网站访问总次数',
},
),
migrations.CreateModel(
name='UserProfile',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('password', models.CharField(max_length=128, verbose_name='password')),
('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')),
('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')),
('username', models.CharField(error_messages={'unique': 'A user with that username already exists.'}, help_text='Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.', max_length=150, unique=True, validators=[django.contrib.auth.validators.UnicodeUsernameValidator()], verbose_name='username')),
('first_name', models.CharField(blank=True, max_length=30, verbose_name='first name')),
('last_name', models.CharField(blank=True, max_length=150, verbose_name='last name')),
('email', models.EmailField(blank=True, max_length=254, verbose_name='email address')),
('is_staff', models.BooleanField(default=False, help_text='Designates whether the user can log into this admin site.', verbose_name='staff status')),
('is_active', models.BooleanField(default=True, help_text='Designates whether this user should be treated as active. Unselect this instead of deleting accounts.', verbose_name='active')),
('date_joined', models.DateTimeField(default=django.utils.timezone.now, verbose_name='date joined')),
('mobile', models.CharField(max_length=11, unique=True, verbose_name='手机号码')),
('icon', models.ImageField(upload_to='uploads/%Y/%m/%d')),
('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')),
('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')),
],
options={
'verbose_name': '用户表',
'verbose_name_plural': '用户表',
'db_table': 'userprofile',
},
managers=[
('objects', django.contrib.auth.models.UserManager()),
],
),
migrations.CreateModel(
name='U2U',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('b', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='girls', to='User.MarryBoth')),
('g', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='boys', to='User.MarryBoth')),
],
),
migrations.CreateModel(
name='testinfo',
fields=[
('nid', models.BigAutoField(primary_key=True, serialize=False)),
('user', models.CharField(max_length=32)),
('password', models.CharField(max_length=64)),
('age', models.IntegerField(default=1)),
('ug', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='User.testdata')),
],
),
migrations.CreateModel(
name='MarryB2G',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('b', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='User.MarryBoy')),
('g', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='User.MarryGirl')),
],
),
migrations.CreateModel(
name='Love',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('b', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='User.Boy')),
('g', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='User.Girl')),
],
options={
'unique_together': {('b', 'g')},
},
),
migrations.CreateModel(
name='LikeRecord',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('object_id', models.PositiveIntegerField()),
('like_time', models.DateTimeField(auto_now_add=True)),
('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.ContentType')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='LikeCount',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('object_id', models.PositiveIntegerField()),
('liked_num', models.IntegerField(default=0)),
('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.ContentType')),
],
),
migrations.CreateModel(
name='Comment',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('news_id', models.IntegerField()),
('content', models.CharField(max_length=32)),
('user', models.CharField(max_length=32)),
('reply', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='xxxx', to='User.Comment')),
],
),
migrations.AddField(
model_name='boy',
name='m',
field=models.ManyToManyField(through='User.Love', to='User.Girl'),
),
]
| [
"437627679@qq.com"
] | 437627679@qq.com |
e5564c0432e2b79a7ad496285d4f20729c559f06 | 657f9303df782f57f91504cdd4760437de29713d | /venv/ven/bin/pip3 | 446f414893a5b2e41486d2b53c706d05d452d7d7 | [] | no_license | bopopescu/django_mysite | 3c737a97b842cc2b825dfe0bd9b31a1b12cad4c8 | f98172691e8f4e335f0ee0b1bad719a011b1eb60 | refs/heads/master | 2022-11-16T09:14:03.669614 | 2018-07-19T14:15:24 | 2018-07-19T14:15:24 | 281,803,244 | 0 | 0 | null | 2020-07-22T23:24:21 | 2020-07-22T23:24:20 | null | UTF-8 | Python | false | false | 418 | #!/Users/shuikuhiro/myPythonProject/django_mysite/venv/ven/bin/python
# EASY-INSTALL-ENTRY-SCRIPT: 'pip==10.0.1','console_scripts','pip3'
__requires__ = 'pip==10.0.1'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('pip==10.0.1', 'console_scripts', 'pip3')()
)
| [
"dustin_chu@hotmail.com"
] | dustin_chu@hotmail.com | |
86a9d878da99500d17d294e4526e7f601a7a2b1f | c17b41ed6bf7e9c180772a67b77e1194ab38bf77 | /DialogFlow/admin.py | daca1b8c6caed0504df565d7643423e1fa26938a | [] | no_license | Avish34/SuggestionSystem | 8c337d350c248524b2249892e39deb027d66cf39 | 21de04cebb19af86c1ccfb9ae94d921f5e3621c0 | refs/heads/master | 2023-06-10T02:42:11.414510 | 2021-07-02T17:00:06 | 2021-07-02T17:00:06 | 375,990,391 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 170 | py | from django.contrib import admin
from .models import Suggestion,accuracy_db
# Register your models here.
admin.site.register(Suggestion)
admin.site.register(accuracy_db)
| [
"porwalavish@gmail.com"
] | porwalavish@gmail.com |
19729a470a6e68a8af30ae2ee2f1e593f9e228a0 | 62442c2547b22aae27f3bb3a0d3f84a9e8e535a0 | /python/patterns/kwargs.py | 9c00601baf68dfe7fad45e8fb51dffd185df5ce1 | [] | no_license | jsdelivrbot/demos | 935729fe9afde33709c4e4e74863b64c16c33b33 | 01a97eda371c2d832c9f2c907a945310662e0710 | refs/heads/master | 2020-04-10T10:35:44.039560 | 2018-12-08T10:53:29 | 2018-12-08T10:53:29 | 160,970,588 | 0 | 0 | null | 2018-12-08T19:13:00 | 2018-12-08T19:13:00 | null | UTF-8 | Python | false | false | 244 | py | def print_params(**kwargs):
for key, value in kwargs.iteritems():
print "%s = %s" %(key, value)
print_params(name="Amjed", age = "27")
_dict = {'location':'Berlin', 'interests':['Swimming', 'Hacking','Paragliding']}
print_params(**_dict)
| [
"amjedonline@gmail.com"
] | amjedonline@gmail.com |
6cc9a2bf1d6c4aa1f54516ab07cd440e380f4ff6 | aa7e7be67d3f6b91ef753996577e6b0fe5ef712b | /api.py | 8c14203afb5b5248d187d632f2024a7463b52ea8 | [] | no_license | x2y2/salt-api | 53715698ffd7002eef8c3435cede33cc57a61e4d | 8e68f8659716a06fc713ea04a0719bde7b1a6223 | refs/heads/master | 2016-08-08T20:40:51.296020 | 2016-02-24T07:00:54 | 2016-02-24T07:00:54 | 51,803,957 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,574 | py | #!/usr/bin/python
#salt-api
#wangpei
#20160215
import sys
import pycurl
import StringIO
import time
import json
def api_login(url):
ch = pycurl.Curl()
ch.setopt(ch.URL,url + '/login')
info = StringIO.StringIO()
ch.setopt(ch.WRITEFUNCTION,info.write)
ch.setopt(ch.POST,True)
ch.setopt(ch.SSL_VERIFYPEER,0)
ch.setopt(ch.SSL_VERIFYHOST,0)
ch.setopt(ch.HTTPHEADER,['Accept: application/x-yaml'])
ch.setopt(ch.POSTFIELDS,'username=saltapi&password=123456&eauth=pam')
ch.setopt(ch.HEADER,False)
ch.perform()
html = info.getvalue()
token = html.split('\n')[-3].replace('\n','')
token = token.split(' ')[3]
info.close()
ch.close()
#print html
#print token
return token
def get_jid(url,token,*args):
tgt = args[1]
fun = args[2]
ch = pycurl.Curl()
ch.setopt(ch.URL,url)
info = StringIO.StringIO()
ch.setopt(ch.WRITEFUNCTION,info.write)
ch.setopt(ch.POST,True)
ch.setopt(ch.SSL_VERIFYPEER,0)
ch.setopt(ch.SSL_VERIFYHOST,0)
ch.setopt(ch.HTTPHEADER,['Accept: application/x-yaml','X-Auth-Token: {0}'.format(token)])
data = 'client=local_async&tgt={0}&expr_form=nodegroup&fun={1}'.format(tgt,fun)
if len(args) >= 3:
for key in range(3,len(args)):
data += '&arg={0}'.format(args[key])
ch.setopt(ch.POSTFIELDS,data)
ch.setopt(ch.HEADER,False)
ch.perform()
html = info.getvalue()
jid = html.split('\n')[1].replace('\n','')
jid = jid.split('\'')[1]
info.close()
ch.close()
#print jid
return jid
def get_jobinfo(url,token,jid):
ch = pycurl.Curl()
#ch.setopt(ch.URL,url+'/jobs/{0}'.format(jid))
ch.setopt(ch.URL,url)
info = StringIO.StringIO()
ch.setopt(ch.WRITEFUNCTION,info.write)
ch.setopt(ch.POST,True)
ch.setopt(ch.SSL_VERIFYPEER,0)
ch.setopt(ch.SSL_VERIFYHOST,0)
ch.setopt(ch.HTTPHEADER,['Accept: application/x-yaml','X-Auth-Token: {0}'.format(token)])
data = 'client=runner&fun=jobs.lookup_jid&jid={0}'.format(jid)
ch.setopt(ch.POSTFIELDS,data)
ch.setopt(ch.HEADER,False)
ch.perform()
html = info.getvalue()
info.close()
ch.close()
print html
def job_state(url,token,jid):
ch = pycurl.Curl()
ch.setopt(ch.URL,url)
info = StringIO.StringIO()
ch.setopt(ch.WRITEFUNCTION,info.write)
ch.setopt(ch.POST,True)
ch.setopt(ch.SSL_VERIFYPEER,0)
ch.setopt(ch.SSL_VERIFYHOST,0)
ch.setopt(ch.HTTPHEADER,['Accept: application/json','X-Auth-Token: {0}'.format(token)])
data = 'client=runner&fun=jobs.print_job&jid={0}'.format(jid)
ch.setopt(ch.POSTFIELDS,data)
ch.setopt(ch.HEADER,False)
ch.perform()
html = info.getvalue()
ret = json.loads(html)
target = ret['return'][0][jid]['Target'].split(',')
ltarget = len(target)
result = ret['return'][0][jid]['Result']
lresult = len(result)
info.close()
ch.close()
#print html
#print target
#print result
if ltarget == lresult:
return result
else:
return
if __name__ == '__main__':
url = 'http://116.228.151.160:1238'
args = sys.argv[:]
if len(args) < 3:
print 'input 2 arguments at least'
sys.exit(1)
token = api_login(url)
jid = get_jid(url,token,*args)
for timer in range(300):
if timer < 300:
state = job_state(url,token,jid)
if state:
get_jobinfo(url,token,jid)
break
else:
time.sleep(1)
timer = timer +1
continue
| [
"wangpei-it@bestpay.com.cn"
] | wangpei-it@bestpay.com.cn |
621243ff90c4c5230de219342c957a5f7985ff33 | 94690cf598d314a23a6147bbf6e02c6277f97081 | /in_progress/82_pathsum_3ways/pathsum_3ways.py | 0fec107514a89fd09b1dded51e6104cfd3ed939e | [] | no_license | kcollett1/ProjectEuler | 7e3923acb4ca01c07f21278fc30afaf9b95ef4be | 3ae613dd5124ec31e6b16cb9693cae3481e7b307 | refs/heads/master | 2020-09-12T17:19:39.802759 | 2020-02-29T04:48:57 | 2020-02-29T04:48:57 | 222,492,028 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,396 | py | #!/Users/kcolletti1/opt/anaconda3/bin/python3
# load data into variables to work with ###
import sys
with open('matrix.txt','r') as f: rows = f.readlines()
for i,row in enumerate(rows):
rows[i] = [int(i) for i in row.strip().split(',')]
# done loading data #######################
# start algorithm #########################
x_len = len(rows)
y_len = len(rows[0])
minsum = 10 ** 10
for start in range(0, x_len):
for end in range(0, x_len):
# calculate here the DP problem for the matrix
# using the rows[start][0] and rows[end][y_len - 1]
# locations as source and sink
sum_trckr = [[0 for i in range(y_len)] for j in range(x_len)]
sum_trckr[0][0] = rows[0][0]
for x in range(1, x_len):
sum_trckr[x][0] = sum_trckr[x - 1][0] + rows[x][0]
for y in range(1, y_len):
sum_trckr[0][y] = sum_trckr[0][y - 1] + rows[0][y]
for x in range(1, x_len):
for y in range(1, y_len):
sum_trckr[x][y] = rows[x][y]
if sum_trckr[x - 1][y] <= sum_trckr[x][y - 1]:
sum_trckr[x][y] += sum_trckr[x - 1][y]
else:
sum_trckr[x][y] += sum_trckr[x][y - 1]
if sum_trckr[end][y_len - 1] < minsum: minsum = sum_trckr[end][y_len - 1]
print('answer:', minsum)
# done with algorithm #####################
| [
"collettikatrina@gmail.com"
] | collettikatrina@gmail.com |
bf162809e894ae9f2f2a007b772c95208446e4b6 | cc4b47a37a7e7df6a3d126c204dcd4d5d63679c6 | /instagram/urls.py | 856786a5b906a971ef9ad7ebfe2c446064bd4de5 | [] | no_license | dev-jromero/clone-instagram | 3f6b18f4694c5f7e3a5a96c7e18100030c61cbd3 | ba98551c0b9f67af9e7e91e69ffcd92efc32f4e7 | refs/heads/master | 2023-01-27T20:36:08.810001 | 2020-12-07T04:51:28 | 2020-12-07T04:51:28 | 312,429,429 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 983 | py | #django
from django.contrib import admin
from django.conf import settings
from django.conf.urls.static import static
from django.urls import path
from instagram import views as local_views
from posts import views as posts_views
from users import views as users_views
urlpatterns = [
path('admin/', admin.site.urls),
path('hello-world/',local_views.hello_world, name='hello_world'),
path('sorted/',local_views.sort_integers, name='sort'),
path('hi/<str:name>/<int:age>/', local_views.say_hi, name='hi'),
path('', posts_views.list_posts, name='feed'),
path('posts/new', posts_views.create_post, name='create_post'),
path('users/login/', users_views.login_view, name='login'),
path('users/logout/', users_views.logout_view, name='logout'),
path('users/signup/',users_views.signup, name='signup'),
path('users/me/profile/', users_views.update_profile, name='update_profile')
]+ static(settings.MEDIA_URL,document_root=settings.MEDIA_ROOT)
| [
"n.bjhon@gmail.com"
] | n.bjhon@gmail.com |
5c3ee9a10b870a6960b1be14bb8646e09129068e | aeb04c24ce11d1bcec83a9d191b386c6674b667b | /q4/main.py | e470d7c74a66bdadfacfe33a7dd40251fbb24b5c | [] | no_license | PPL-IIITA/ppl-assignment-LuD1161 | 3525a42f8cc899e4d332e6b90ac4aa194f6437c6 | 212d5faf3be83fc553659ca06086523b28fe05e2 | refs/heads/master | 2021-01-21T05:14:45.164943 | 2017-04-09T21:28:13 | 2017-04-09T21:28:13 | 83,157,709 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,544 | py | import csv
from boys import Boys
from girls import Girls
from gifts import Gift
from couple import Couple
from ffunctions import calc_happiness
from utils import create_log
from utils import test_cases
# Generate Test Cases
test_cases()
def main():
boys = open('./boys_list.csv')
getBoy = csv.reader(boys, delimiter=',')
girls = open('./girls_list.csv')
getGirls = csv.reader(girls, delimiter=',')
boy_list = []
girl_list = []
couple_list = []
virtual_couple_list = []
after_breakup_girl_list = []
for i in getBoy:
boy_list += [Boys(i[0], int(i[1]), int(i[2]), int(i[3]), int(i[4]), i[5])]
for i in getGirls:
girl_list += [Girls(i[0], int(i[1]), int(i[2]), int(i[3]), i[4])]
# Create gift list to be sent to happiness calculator
with open('./gift_list.csv', 'r') as gift_file:
gift_list = csv.reader(gift_file, delimiter=',')
for row in gift_list:
gifts = [Gift(row[0], int(row[1]), int(row[2]), row[3])]
gift_file.close()
gifts = sorted(gifts, key=lambda item: item.cost)
for girl in girl_list:
for boy in boy_list:
# Create Log
create_log(boy.name + ' is trying for ' + girl.name)
if boy.status == 'Single' and girl.status == 'Single' and \
boy.check_eligibility(girl.maintenance_budget, girl.attractiveness):
boy.gf = girl.name
girl.bf = boy.name
# Update relationship status
boy.status = 'Committed'
girl.status = 'Committed'
text = girl.name + ' is in relationship with ' + boy.name
print(text)
# Create Log
create_log(text)
couple_list += [(girl, boy)]
break
for i in couple_list:
# Create couples first to be sent to happiness calculator
virtual_couple_list += [Couple(i[0], i[1])]
print("Couples in relationship are : ")
for couple in virtual_couple_list:
print(couple.girl.name + " is in a relationship with " + couple.boy.name)
calc_happiness(virtual_couple_list, gifts)
for girl in girl_list:
for i in range(0, 10):
if girl == virtual_couple_list[i].girl:
# Add the boy to girl's blacklist , thus removing any chance
# of their matching up again
girl.blacklist = virtual_couple_list[i].boy.name
# Create the new list of girls after breakup
after_breakup_girl_list.append(girl)
# Remove them from couple list
virtual_couple_list.remove(virtual_couple_list[i])
# Now rerun the matchup loop after breakup
for girl in after_breakup_girl_list:
for boy in boy_list:
# Create Log
create_log(boy.name + ' is trying for ' + girl.name)
if boy.status == 'Single' and girl.status == 'Single' and \
boy.check_eligibility(girl.maintenance_budget, girl.attractiveness):
boy.gf = girl.name
girl.bf = boy.name
# Update relationship status
boy.status = 'Committed'
girl.status = 'Committed'
text = girl.name + ' is in relationship with ' + boy.name
print(text)
# Create Log
create_log(text)
virtual_couple_list.append(Couple(girl, boy))
break
main()
| [
"noreply@github.com"
] | PPL-IIITA.noreply@github.com |
fb985a0496b4586135a299d0421e853779a9e9c9 | f5a8866ca08727fa99e8dce3c7d4eb413191e035 | /python/coupled/upscaling/scenario_classic.py | e7e8d5a6fea29101926c44d7e2854892c73bb65d | [] | no_license | Plant-Root-Soil-Interactions-Modelling/dumux-rosi | fecb3f48963e102a0257fbf5939b0e2973a6336c | cf6a6e5fa450b306e1ae134284419b1558c9349e | refs/heads/master | 2023-08-16T22:19:14.152346 | 2023-08-02T08:16:51 | 2023-08-02T08:16:51 | 80,874,606 | 6 | 3 | null | 2023-08-31T11:10:28 | 2017-02-03T22:24:47 | Jupyter Notebook | UTF-8 | Python | false | false | 6,360 | py | """
New reference scenario for upscaling (DL 29.3.2023)
static root system in soil (1D or 3D) outer radii with Voronoi method, or via densities
full hydraulic model with perirhizal nonlinear resistances
(steady rate approach and fixed-point-iteration in new manuscript notation)
"""
import sys; sys.path.append("../../modules"); sys.path.append("../../../build-cmake/cpp/python_binding/");
sys.path.append("../../../../CPlantBox"); sys.path.append("../../../../CPlantBox/src")
import argparse
import timeit
import numpy as np
import matplotlib.pyplot as plt
from scipy import sparse
from functional.xylem_flux import sinusoidal2
import visualisation.vtk_plot as vp
from rhizo_models import plot_transpiration
from scenario_setup import *
def simulate_classic(sim_time, r, rho_, rs_age, trans, wilting_point, soil, s, sra_table_lookup, mapping):
print("\nInitial root sytstem age", rs_age)
# print("rs_age", rs_age)
# rs_age = r.get_ages(rs_age)
# print("rs_age", np.max(rs_age))
dt = 360 / (24 * 3600) # days
skip = 1
max_error = 10
max_iter = 10
ns = len(r.rs.segments)
nodes = r.get_nodes()
# seg_length = r.rs.segLength()
assert len(nodes) - 1 == ns, "Number of nodes should be equal one less than number of segments"
""" Doussan """
A_dirichlet, Kr, kx0 = r.doussan_system_matrix(rs_age)
Id = sparse.identity(ns).tocsc() # identity matrix
col_ind = r.collar_index()
print("collar_index (segment index)", r.collar_index())
print("kx0:", kx0)
print()
print("invert matrix start")
Ainv_dirichlet = sparse.linalg.inv(A_dirichlet).todense() # dense
A_neumann = A_dirichlet.copy()
A_neumann[col_ind, col_ind] -= kx0
Ainv_neumann = sparse.linalg.inv(A_neumann).todense() # dense
C_comp_dirichlet = Kr @ (Id - Ainv_dirichlet @ Kr) # Neumann, Hess, Eqn (24)
c_dirichlet = (Kr @ Ainv_dirichlet)[:, col_ind] * (-kx0) # # Hess (25)
# print("C_comp_dirichlet", type(C_comp_dirichlet), C_comp_dirichlet.shape)
# print("c_dirichlet", type(c_dirichlet), c_dirichlet.shape)
C_comp_neumann = Kr @ (Id - Ainv_neumann @ Kr) # Neumann, Hess, Eqn (32)
c_neumann = (Kr @ Ainv_neumann)[:, col_ind] # Hess (33)
# print("C_comp_neumann", type(C_comp_neumann), C_comp_neumann.shape)
# print("c_neumann", type(c_neumann), c_neumann.shape)
print("invert matrix stop")
""" Numerical solution """
start_time = timeit.default_timer()
x_, y_, z_, sink_, hs_, hx_, hsr_ = [], [], [], [], [], [], []
sx = s.getSolutionHead() # inital condition, solverbase.py
N = round(sim_time / dt)
t = 0.
t_pot = -trans * sinusoidal2(0., dt)
hs = np.transpose(np.array([[sx[mapping[j]][0] for j in range(0, ns)]]))
for j in range(0, len(nodes) - 1): # from matric to total
hs[j, 0] += nodes[j + 1][2]
rx = Ainv_dirichlet.dot(Kr.dot(hs)) + Ainv_dirichlet[:, 0] * kx0 * wilting_point
q_dirichlet = -Kr.dot(hs - rx)
if np.sum(q_dirichlet) < t_pot:
rx = Ainv_neumann.dot(Kr.dot(hs)) + Ainv_neumann[:, 0] * t_pot # # Hess Eqn (29)
for i in range(0, N):
t_pot = -trans * sinusoidal2(t, dt) # potential transpiration ...
print("t_pot", t_pot)
hs = np.transpose(np.array([[sx[mapping[j]][0] for j in range(0, ns)]]))
for j in range(0, ns): # from matric to total potential
hs[j, 0] += nodes[j + 1][2]
rx = Ainv_dirichlet.dot(Kr.dot(hs)) + Ainv_dirichlet[:, 0] * kx0 * wilting_point
q_dirichlet = -Kr.dot(hs - rx)
if np.sum(q_dirichlet) <= t_pot:
rx = Ainv_neumann.dot(Kr.dot(hs)) + Ainv_neumann[:, 0] * t_pot # # Hess Eqn (29)
if np.sum(q_dirichlet) > t_pot:
print("dirichlet:", np.sum(q_dirichlet), t_pot)
fluxes = r.sumSegFluxes(q_dirichlet[:, 0])
sum_root_flux = np.sum(q_dirichlet)
else:
q_neumann = -Kr.dot(hs - rx)
print("neumann:", np.sum(q_neumann), t_pot, np.sum(q_dirichlet))
fluxes = r.sumSegFluxes(q_neumann[:, 0])
sum_root_flux = np.sum(q_neumann)
water = s.getWaterVolume()
s.setSource(fluxes.copy()) # richards.py
s.solve(dt)
sum_soil_flux = (s.getWaterVolume() - water) / dt
old_sx = sx.copy()
sx = s.getSolutionHead() # richards.py
water = s.getWaterVolume()
# remember results
x_.append(t)
y_.append(sum_soil_flux) # cm3/day (soil uptake)
z_.append(sum_root_flux) # cm3/day (root system uptake)
if i % skip == 0:
n = round(float(i) / float(N) * 100.)
print("t_pot", t_pot, "q_root", sum_root_flux, "soil", sum_soil_flux)
print("[" + ''.join(["*"]) * n + ''.join([" "]) * (100 - n) + "], soil [{:g}, {:g}] cm, root [{:g}, {:g}] cm, {:g} days {:g}\n"
.format(np.min(sx), np.max(sx), np.min(rx), np.max(rx), s.simTime, rx[0, 0]))
""" remember results """
sink = np.zeros(sx.shape)
for k, v in fluxes.items():
sink[k] += v
sink_.append(sink) # [cm3/day] per soil cell
hs_.append(sx.copy()) # [cm] per soil cell
hx_.append(rx) # [cm] per segment
t += dt
print ("Coupled benchmark solved in ", timeit.default_timer() - start_time, " s")
return hx_, hs_, sink_, x_, y_, z_, hs_, dt
if __name__ == "__main__":
# TODO parse from args?
plant = "soybean" # soybean, maize
dim = "3D" # 1D, 3D
soil = "hydrus_loam" # hydrus_loam, hydrus_clay, hydrus_sand
outer_method = "surface" # voronoi, length, surface, volume
initial = -200 # cm total potential
sim_time = 7.
name = "classic_" + plant + "_" + dim + "_" + soil + "_" + outer_method
print(name, "\n")
r, rho_, rs_age, trans, wilting_point, soil, s, sra_table_lookup, mapping = set_scenario(plant, dim, initial, soil, outer_method)
print("trans", trans)
hx_, hsr_, sink_, x_, y_, z_, hs_, dt = simulate_classic(sim_time, r, rho_, rs_age, trans, wilting_point, soil, s, sra_table_lookup, mapping)
""" write and plot """
s.writeDumuxVTK("results/" + name)
write_files(name, hx_, hsr_, sink_, x_, y_, z_, hs_)
plot_transpiration(x_, y_, z_, lambda t: trans * sinusoidal2(t, dt), name)
| [
"d.leitner@fz-juelich.de"
] | d.leitner@fz-juelich.de |
63225bbdc48bbc1d2cf30ed8e8055676a3283a10 | 61ec5dfb4dc1a541f2d76cc4f9387f911ab97604 | /Reto_5_P72_Definicion.py | 9ffa45993b1b3f07e74df03f10fdf0a1a2f782e2 | [] | no_license | mysuarez/retos | 44702fe4aa36365f00f7996567e23d40a89352c6 | 5ef47fd2fd6041aa28cb40e0bff213756da66df5 | refs/heads/main | 2023-05-28T07:36:55.669628 | 2021-06-20T00:21:02 | 2021-06-20T00:21:02 | 378,524,484 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,241 | py | import pandas as pd
def listaPeliculas(rutaFileCsv: str)-> str:
if rutaFileCsv.split('.')[-1] != 'csv':
try:
csv = pd.read_csv(rutaFileCsv)
#print(csv)
#Se selecciona la columna 'Year' como indice y la columna 'Gross Earnings' para aplicar la fórmula de resumen
subGrupoPeliculas = csv[['Year', 'Gross Earnings']]
#print(subGrupoPeliculas)
#Con pivot_table indexamos 'Year'
gananciaAno = subGrupoPeliculas.pivot_table(index=['Year'])
return gananciaAno.head(10)
#Se importa la libreria matplotlib.pyplot
#import matplotlib.pyplot as plt
#gananciaAno.plot()
#Se muestra la columna 'Net Earnings' [76 rows x 1 columns]
#print('\n Ganancia por Año')
#Se muestra el resultado con la grafica
#plt.show()
except:
print('Error al leer el archivo de datos.')
else:
return 'Extensión inválida.'
#ruta file csv
rutaFileCsv = 'https://github.com/luisguillermomolero/MisionTIC2022/blob/3f3847bbf2dbe4b2cf4dcceb96a455d92c88f9c5/movies.csv?raw=true'
print(listaPeliculas(rutaFileCsv))
| [
"noreply@github.com"
] | mysuarez.noreply@github.com |
d4682ee987290491b8b6b3876046b353b18161df | e4a108638231da73887f4309ebc86d2298bcc92b | /device/xiaomi/lavender.py | 3139e5f923c7094b5fb6c2dbfc3f1b2aba09b06b | [] | no_license | indi121/android-kernel-builder | 1ad72d081722d62212844b3a482bd0288050714f | cf083d889801411e63ad853eefe43cfc221c5e9d | refs/heads/master | 2023-08-29T05:42:59.839542 | 2021-10-11T08:11:00 | 2021-10-11T14:24:08 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 138 | py | # Source common kernel config
from device.xiaomi.sdm660 import *
# Device info
CODENAME = "lavender"
DEFCONFIG = f"{CODENAME}_defconfig"
| [
"barezzisebastiano@gmail.com"
] | barezzisebastiano@gmail.com |
3ce37f63bf6d0bffe9ace1740550a08181d37070 | 03dd284dc87d2cd363434fc88fc72ef25fac2673 | /lib/models/nets_32x32_small_v2.py | 52814935151130f77d04c540e746705f2ff1a326 | [
"Apache-2.0"
] | permissive | HymEric/One-GORD | 49db9f71976e352bd222d283445c17841a11a3dc | a63ecaf2b9538789ca0e761d55608a28d7194c4d | refs/heads/main | 2023-06-01T16:53:54.200546 | 2021-06-13T14:26:22 | 2021-06-13T14:26:22 | 305,567,777 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 15,013 | py | '''Based on https://github.com/igul222/improved_wgan_training/blob/master/gan_64x64.py'''
import functools
import tensorflow as tf
from lib.models.distributions import Product, Distribution, Gaussian, Categorical, Bernoulli, MeanBernoulli
from lib.models.ops.linear import Linear
from lib.models.ops.conv2d import Conv2D
from lib.models.ops.deconv2d import Deconv2D
from lib.models.ops import linear, conv2d, deconv2d
from lib.models.ops.batchnorm import Batchnorm
from lib.models.ops.layernorm import Layernorm
DIM = 32
CPU = False # True: data_format='NHWC', False: data_format='NCHW'
def LeakyReLU(x, alpha=0.2):
return tf.maximum(alpha*x, x)
def Normalize(name, axes, inputs, is_training, mode):
if ('Discriminator' in name) and (mode == 'wgan-gp'):
if axes != [0,2,3]:
raise Exception('Layernorm over non-standard axes is unsupported')
return Layernorm(name,[1,2,3],inputs)
else:
return Batchnorm(name,axes,inputs,fused=True, cpu=CPU, is_training=is_training)
def ConvMeanPool(name, input_dim, output_dim, filter_size, inputs, he_init=True, biases=True):
output = Conv2D(name, input_dim, output_dim, filter_size, inputs, he_init=he_init, biases=biases, cpu=CPU)
output = tf.add_n([output[:,:,::2,::2], output[:,:,1::2,::2], output[:,:,::2,1::2], output[:,:,1::2,1::2]]) / 4.
return output
def MeanPoolConv(name, input_dim, output_dim, filter_size, inputs, he_init=True, biases=True):
output = inputs
output = tf.add_n([output[:,:,::2,::2], output[:,:,1::2,::2], output[:,:,::2,1::2], output[:,:,1::2,1::2]]) / 4.
output = Conv2D(name, input_dim, output_dim, filter_size, output, he_init=he_init, biases=biases, cpu=CPU)
return output
def UpsampleConv(name, input_dim, output_dim, filter_size, inputs, he_init=True, biases=True):
output = inputs
output = tf.concat([output, output, output, output], axis=1)
output = tf.transpose(output, [0,2,3,1])
output = tf.depth_to_space(output, 2)
output = tf.transpose(output, [0,3,1,2])
output = Conv2D(name, input_dim, output_dim, filter_size, output, he_init=he_init, biases=biases, cpu=CPU)
return output
def ResidualBlock(name, input_dim, output_dim, filter_size, inputs, is_training, mode,
resample=None, he_init=True, norm_inputs=False):
"""
resample: None, 'down', or 'up'
"""
if resample=='down':
conv_shortcut = MeanPoolConv
conv_1 = functools.partial(Conv2D, input_dim=input_dim, output_dim=input_dim, cpu=CPU)
conv_2 = functools.partial(ConvMeanPool, input_dim=input_dim, output_dim=output_dim)
elif resample=='up':
conv_shortcut = UpsampleConv
conv_1 = functools.partial(UpsampleConv, input_dim=input_dim, output_dim=output_dim)
conv_2 = functools.partial(Conv2D, input_dim=output_dim, output_dim=output_dim, cpu=CPU)
elif resample==None:
conv_shortcut = Conv2D
conv_1 = functools.partial(Conv2D, input_dim=input_dim, output_dim=input_dim, cpu=CPU)
conv_2 = functools.partial(Conv2D, input_dim=input_dim, output_dim=output_dim, cpu=CPU)
else:
raise Exception('invalid resample value')
if output_dim==input_dim and resample==None:
shortcut = inputs # Identity skip-connection
else:
shortcut = conv_shortcut(name+'.Shortcut', input_dim=input_dim, output_dim=output_dim, filter_size=1,
he_init=False, biases=True, inputs=inputs)
output = inputs
if not norm_inputs:
output = Normalize(name+'.BN1', [0,2,3], output, is_training, mode)
output = tf.nn.relu(output)
output = conv_1(name+'.Conv1', filter_size=filter_size, inputs=output, he_init=he_init, biases=False)
output = Normalize(name+'.BN2', [0,2,3], output, is_training, mode)
output = tf.nn.relu(output)
output = conv_2(name+'.Conv2', filter_size=filter_size, inputs=output, he_init=he_init)
return shortcut + output
# --- Low capacity net: Arch from 'B-VAE' paper: https://openreview.net/pdf?id=Sy2fzU9gl ---
def low_capacity_encoder(name, inputs, n_channels, latent_dim, is_training, mode=None, nonlinearity=tf.nn.relu):
output = tf.reshape(inputs, [-1, n_channels, DIM, DIM])
output = Conv2D(name + '.0', n_channels, DIM // 2, 4, output, stride=2)
output = Normalize(name + '.BN0', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Conv2D(name + '.1', DIM // 2, DIM // 2, 4, output, stride=2)
output = Normalize(name + '.BN1', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Conv2D(name + '.2', DIM // 2, DIM , 4, output, stride=2)
output = Normalize(name + '.BN2', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Conv2D(name + '.3', DIM, DIM, 4, output, stride=2)
output = Normalize(name + '.BN3', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = tf.reshape(output, [-1, DIM*4*4])
output = Linear(name + '.FC', DIM*4*4, DIM*4, output)
output = Normalize(name + '.BNFC', [0], output, is_training, mode)
output = nonlinearity(output)
output = Linear(name + '.Output', DIM*4, latent_dim, output)
return output
def low_capacity_decoder(name, z, n_channels, is_training, mode=None, nonlinearity=tf.nn.relu):
output = Linear(name + '.Input', z.get_shape().as_list()[1], DIM*4, z)
output = Normalize(name + '.BN0', [0], output, is_training, mode)
output = nonlinearity(output)
output = tf.reshape(output, [-1, DIM // 4, 4, 4])
output = Deconv2D(name + '.1', DIM // 4, DIM, 4, output)
output = Normalize(name + '.BN1', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name + '.2', DIM, DIM, 4, output)
output = Normalize(name + '.BN2', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name + '.3', DIM, DIM // 2, 4, output)
output = Normalize(name + '.BN3', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name + '.4', DIM // 2, n_channels, 4, output)
output = tf.reshape(output, [-1, n_channels*DIM*DIM])
return output
# --- High capacity net: Arch from 'autoencoding beyond pixels' paper: https://arxiv.org/pdf/1512.09300.pdf ---
def high_capacity_encoder(name, inputs, n_channels, latent_dim, is_training, mode=None, nonlinearity=tf.nn.relu):
output = tf.reshape(inputs, [-1, n_channels, DIM, DIM])
output = Conv2D(name + '.0', n_channels, DIM, 5, output, stride=2)
output = Normalize(name + '.BN0', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Conv2D(name + '.1', DIM, DIM*2, 5, output, stride=2)
output = Normalize(name + '.BN1', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Conv2D(name + '.2', DIM*2, DIM*4, 5, output, stride=2)
output = Normalize(name + '.BN2', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = tf.reshape(output, [-1, DIM*4*8*8])
output = Linear(name + '.FC', DIM*4*8*8, DIM*4*8, output)
output = Normalize(name + '.BNFC', [0], output, is_training, mode)
output = nonlinearity(output)
output = Linear(name + '.Output', DIM*4*8, latent_dim, output)
return output
def high_capacity_decoder(name, z, n_channels, is_training, mode=None, nonlinearity=tf.nn.relu):
output = Linear(name + '.Input', z.get_shape().as_list()[1], DIM*4*8*8, z)
output = Normalize(name + '.BN0', [0], output, is_training, mode)
output = nonlinearity(output)
output = tf.reshape(output, [-1, DIM*4, 8, 8])
output = Deconv2D(name + '.1', DIM*4, DIM*4, 5, output)
output = Normalize(name + '.BN1', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name + '.2', DIM*4, DIM*2, 5, output)
output = Normalize(name + '.BN2', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name + '.3', DIM*2, DIM // 2, 5, output)
output = Normalize(name + '.BN3', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Conv2D(name + '.4', DIM // 2, n_channels, 5, output)
output = tf.reshape(output, [-1, n_channels*DIM*DIM])
return output
# --- ResNet: Arch from improved WGAN paper: https://arxiv.org/pdf/1704.00028.pdf ---
def resnet_encoder(name, inputs, n_channels, latent_dim, is_training, mode=None, nonlinearity=tf.nn.relu):
output = tf.reshape(inputs, [-1, n_channels, DIM, DIM])
output = Conv2D(name + '.Input', n_channels, DIM, 3, output, he_init=False, cpu=CPU)
output = ResidualBlock(name + '.Res1', DIM, 2*DIM, 3, output, is_training, mode, resample='down')
output = ResidualBlock(name + '.Res2', 2*DIM, 4*DIM, 3, output, is_training, mode, resample='down')
output = ResidualBlock(name + '.Res3', 4*DIM, 8*DIM, 3, output, is_training, mode, resample='down')
output = ResidualBlock(name + '.Res4', 8*DIM, 8*DIM, 3, output, is_training, mode, resample='down')
output = tf.reshape(output, [-1, 2*2*8*DIM])
output = Linear(name + '.Output', 2*2*8*DIM, latent_dim, output)
return output
def resnet_decoder(name, z, n_channels, is_training, mode=None, nonlinearity=tf.nn.relu):
output = Linear(name + '.Input', z.get_shape().as_list()[1], 2*2*8*DIM, z)
output = Normalize(name + '.BN0', [0], output, is_training, mode)
output = nonlinearity(output)
output = tf.reshape(output, [-1, 8*DIM, 2, 2])
output = ResidualBlock(name + '.Res1', 8*DIM, 8*DIM, 3, output, is_training, mode, resample='up', norm_inputs=True)
output = ResidualBlock(name + '.Res2', 8*DIM, 4*DIM, 3, output, is_training, mode, resample='up')
output = ResidualBlock(name + '.Res3', 4*DIM, 2*DIM, 3, output, is_training, mode, resample='up')
output = ResidualBlock(name + '.Res4', 2*DIM, 1*DIM, 3, output, is_training, mode, resample='up')
output = Normalize(name + '.BN5', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Conv2D(name + '.Output', DIM, n_channels, 3, output)
output = tf.reshape(output, [-1, n_channels*DIM*DIM])
return output
def classification_net(name, z,class_net_unit_num,class_num, is_training, mode=None, nonlinearity=tf.nn.relu):
#print('In classification network')
output = Linear(name + '.Input', z.get_shape().as_list()[1], class_net_unit_num, z)
#print output.shape
output = Normalize(name + '.BN0', [0], output, is_training, mode)
#print output.shape
output = nonlinearity(output)
#last layer
output = Linear(name + '.Input1', class_net_unit_num, class_num, output)
#print output.shape
return output
def gan_discriminator(name,inputs, n_channels,is_training, mode=None,nonlinearity=LeakyReLU):
output = tf.reshape(inputs, [-1, n_channels, DIM, DIM])
output = Conv2D(name + '.discriminator1', n_channels, DIM, 5, output,stride=2)
output = nonlinearity(output)
output = Conv2D(name + '.discriminator2', DIM, 2*DIM, 5, output,stride=2)
output = nonlinearity(output)
output = Conv2D(name + '.discriminator3', 2*DIM, 4*DIM, 5, output,stride=2)
output = nonlinearity(output)
output = tf.reshape(output, [-1, 4*4*4*DIM])
output = Linear(name +'.Output', 4*4*4*DIM, 1, output)
return output
# --- DCGAN: Arch from DCGAN paper: https://arxiv.org/pdf/1511.06434.pdf ---
# i.e. Discriminator + Q(c|x)
def dcgan_encoder(name, inputs, n_channels, latent_dim, is_training, mode=None, nonlinearity=LeakyReLU):
conv2d.set_weights_stdev(0.02)
deconv2d.set_weights_stdev(0.02)
linear.set_weights_stdev(0.02)
output = tf.reshape(inputs, [-1, n_channels, DIM, DIM])
output = Conv2D(name + '.1', 3, DIM, 5, output, stride=2)
output = nonlinearity(output)
output = Conv2D(name + '.2', DIM, 2*DIM, 5, output, stride=2)
output = Normalize(name + '.BN2', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Conv2D(name + '.3', 2*DIM, 4*DIM, 5, output, stride=2)
output = Normalize(name + '.BN3', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Conv2D(name + '.4', 4*DIM, 8*DIM, 5, output, stride=2)
output = Normalize(name + '.BN4', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = tf.reshape(output, [-1, 4*4*8*DIM])
output = Linear(name + '.Output', 4*4*8*DIM, latent_dim, output)
conv2d.unset_weights_stdev()
deconv2d.unset_weights_stdev()
linear.unset_weights_stdev()
return output
# i.e. Generator
def dcgan_decoder(name, z, n_channels, is_training, mode=None, nonlinearity=tf.nn.relu):
conv2d.set_weights_stdev(0.02)
deconv2d.set_weights_stdev(0.02)
linear.set_weights_stdev(0.02)
output = Linear(name + '.Input', z.get_shape().as_list()[1], 4*4*8*DIM, z)
output = tf.reshape(output, [-1, 8*DIM, 4, 4])
output = Normalize(name + '.BN1', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name +'.2', 8*DIM, 4*DIM, 5, output)
output = Normalize(name + '.BN2', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name +'.3', 4*DIM, 2*DIM, 5, output)
output = Normalize(name + '.BN3', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name +'.4', 2*DIM, DIM, 5, output)
output = Normalize(name + '.BN4', [0,2,3], output, is_training, mode)
output = nonlinearity(output)
output = Deconv2D(name +'.5', DIM, n_channels, 5, output)
output = tf.reshape(output, [-1, n_channels*DIM*DIM])
conv2d.unset_weights_stdev()
deconv2d.unset_weights_stdev()
linear.unset_weights_stdev()
return output
def NetsRetreiver(arch):
if arch == 'low_cap':
return low_capacity_encoder, low_capacity_decoder
if arch == 'high_cap':
return high_capacity_encoder, high_capacity_decoder
if arch == 'resnet':
return resnet_encoder, resnet_decoder
if arch == 'dcgan':
return dcgan_encoder, dcgan_decoder
raise Exception()
def NetsRetreiverWithClassifier(arch):
if arch == 'low_cap':
return low_capacity_encoder, low_capacity_decoder
if arch == 'high_cap':
return high_capacity_encoder, high_capacity_decoder
if arch == 'resnet':
return resnet_encoder, resnet_decoder,classification_net,gan_discriminator
if arch == 'dcgan':
return dcgan_encoder, dcgan_decoder
raise Exception() | [
"noreply@github.com"
] | HymEric.noreply@github.com |
1146907bbd75471624b44319947b4ecff58ca904 | 1f0ee3ff78e369e832b66d20ddbb31049cf1c1bd | /001 - Foundations of AI/006 - Simulated Annealing/AIND-Simulated_Annealing.py | 754343630a4cdda7cfff85fa8a6f182658a61752 | [
"LicenseRef-scancode-public-domain",
"MIT"
] | permissive | New-Skills/18_udacity_aind | 1edfc8814c2d1cde7f904ddfb432d2d2502e6d8d | 3bbaa7db3744e9817c55c2c84080e1d8b14de3d9 | refs/heads/master | 2021-09-26T09:58:31.967668 | 2018-10-28T19:59:54 | 2018-10-28T19:59:54 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,368 | py | import json
import copy
import numpy as np # contains helpful math functions like numpy.exp()
import numpy.random as random # see numpy.random module
# import random # alternative to numpy.random module
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from traveling_salesman_problem import *
"""Read input data and define helper functions for visualization."""
# Map services and data available from U.S. Geological Survey, National Geospatial Program.
# Please go to http://www.usgs.gov/visual-id/credit_usgs.html for further information
map = mpimg.imread("map.png") # US States & Capitals map
# List of 30 US state capitals and corresponding coordinates on the map
with open('capitals.json', 'r') as capitals_file:
capitals = json.load(capitals_file)
capitals_list = list(capitals.items())
def show_path(path, starting_city, w=12, h=8):
"""Plot a TSP path overlaid on a map of the US States & their capitals."""
x, y = list(zip(*path))
_, (x0, y0) = starting_city
plt.imshow(map)
plt.plot(x0, y0, 'y*', markersize=15) # y* = yellow star for starting point
plt.plot(x + x[:1], y + y[:1]) # include the starting point at the end of path
plt.axis("off")
fig = plt.gcf()
fig.set_size_inches([w, h])
def simulated_annealing(problem, schedule):
"""The simulated annealing algorithm, a version of stochastic hill climbing
where some downhill moves are allowed. Downhill moves are accepted readily
early in the annealing schedule and then less often as time goes on. The
schedule input determines the value of the temperature T as a function of
time. [Norvig, AIMA Chapter 3]
Parameters
----------
problem : Problem
An optimization problem, already initialized to a random starting state.
The Problem class interface must implement a callable method
"successors()" which returns states in the neighborhood of the current
state, and a callable function "get_value()" which returns a fitness
score for the state. (See the `TravelingSalesmanProblem` class below
for details.)
schedule : callable
A function mapping time to "temperature". "Time" is equivalent in this
case to the number of loop iterations.
Returns
-------
Problem
An approximate solution state of the optimization problem
Notes
-----
(1) DO NOT include the MAKE-NODE line from the AIMA pseudocode
(2) Modify the termination condition to return when the temperature
falls below some reasonable minimum value (e.g., 1e-10) rather than
testing for exact equality to zero
See Also
--------
AIMA simulated_annealing() pseudocode
https://github.com/aimacode/aima-pseudocode/blob/master/md/Simulated-Annealing.md
"""
current_value = problem.get_value()
for time in range(1, int(1e100)):
temperature = schedule(time)
if temperature <= 1e-10:
return problem
next = random.choice(problem.successors())
next_value = next.get_value()
delta_e = next_value - current_value
if delta_e >= 0:
current_value = next_value
problem = next
else:
success_probab = np.exp(delta_e / temperature)
if random.choice([1, 0], p=[success_probab, 1 - success_probab]):
current_value = next_value
problem = next
# These are presented as globals so that the signature of schedule()
# matches what is shown in the AIMA textbook; you could alternatively
# define them within the schedule function, use a closure to limit
# their scope, or define an object if you would prefer not to use
# global variables
alpha = 0.95
temperature=1e4
def schedule(time):
return ((alpha**time)*temperature)
if __name__ == '__main__':
# Construct an instance of the TravelingSalesmanProblem
test_cities = [('DC', (11, 1)), ('SF', (0, 0)), ('PHX', (2, -3)), ('LA', (0, -4))]
tsp = TravelingSalesmanProblem(test_cities)
assert (tsp.path == test_cities)
# Test the successors() method -- no output means the test passed
successor_paths = [x.path for x in tsp.successors()]
assert (all(x in [[('LA', (0, -4)), ('SF', (0, 0)), ('PHX', (2, -3)), ('DC', (11, 1))],
[('SF', (0, 0)), ('DC', (11, 1)), ('PHX', (2, -3)), ('LA', (0, -4))],
[('DC', (11, 1)), ('PHX', (2, -3)), ('SF', (0, 0)), ('LA', (0, -4))],
[('DC', (11, 1)), ('SF', (0, 0)), ('LA', (0, -4)), ('PHX', (2, -3))]]
for x in successor_paths))
# test the schedule() function -- no output means that the tests passed
assert (np.allclose(alpha, 0.95, atol=1e-3))
assert (np.allclose(schedule(0), temperature, atol=1e-3))
assert (np.allclose(schedule(10), 5987.3694, atol=1e-3))
# Failure implies that the initial path of the test case has been changed
assert (tsp.path == [('DC', (11, 1)), ('SF', (0, 0)), ('PHX', (2, -3)), ('LA', (0, -4))])
result = simulated_annealing(tsp, schedule)
print("Initial score: {}\nStarting Path: {!s}".format(tsp.get_value(), tsp.path))
print("Final score: {}\nFinal Path: {!s}".format(result.get_value(), result.path))
assert (tsp.path != result.path)
assert (result.get_value() > tsp.get_value()) | [
"32975318+Axel-Bravo@users.noreply.github.com"
] | 32975318+Axel-Bravo@users.noreply.github.com |
c90f97db69e685c02a6b3c5a458d35bfec02f7f4 | d77f3560c0903e01d350f53fad37381e82c251c9 | /lm/lm/unigram/lm.py | 989eb6a1f87dbee1d4c595ab3e75a7e3fdfee7ff | [] | no_license | nymwa/ConLangLM | 160cc6f83ed2b7fea756fc2545566b93a6f92276 | de6c2be9de72fa36b29b343f2ea563885d647c43 | refs/heads/master | 2023-03-07T22:02:16.752203 | 2021-02-16T11:26:26 | 2021-02-16T11:26:26 | 288,083,457 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 75 | py |
class UnigramLM:
def __init__():
def train():
pass
def score():
pass
| [
"nymwa0@gmail.com"
] | nymwa0@gmail.com |
75d6d63c300a5e16d4a7ed4351fb194dfdbcc64d | d35a25115a0de3a0d17b5af4cf7228d8656ef684 | /staff/models.py | 09dfb733bcbe5c254b0cc1fb145af7e62aee57b9 | [] | no_license | samCyan/lenden-assignment-school | 5ac0e129b9dc2b33729a1cd9525db433723521f3 | 4eadee252d72bdecfffa3c98be879573a953af0a | refs/heads/main | 2023-03-06T17:52:28.579562 | 2021-02-11T20:36:53 | 2021-02-11T20:36:53 | 335,974,168 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,576 | py | from django.db import models
from django.utils import timezone
from django.urls import reverse
from django.core.validators import RegexValidator
# from subject import models as subject_models
class Staff(models.Model):
STATUS = [
('active', 'Active'),
('inactive', 'Inactive')
]
GENDER = [
('male', 'Male'),
('female', 'Female')
]
current_status = models.CharField(max_length=10, choices=STATUS, default='active')
surname = models.CharField(max_length=200)
firstname = models.CharField(max_length=200)
other_name = models.CharField(max_length=200, blank=True)
gender = models.CharField(max_length=10, choices=GENDER, default='male')
date_of_birth = models.DateField(default=timezone.now)
date_of_admission = models.DateField(default=timezone.now)
salary = models.IntegerField(blank=False, default=5000)
mobile_num_regex = RegexValidator(regex="^[0-9]{10,15}$", message="Entered mobile number isn't in a right format!")
mobile_number = models.CharField(validators=[mobile_num_regex], max_length=13, blank=True)
subjects = models.ManyToManyField('subject.Subject', blank=True, default=[])
address = models.TextField(blank=True)
others = models.TextField(blank=True)
takes_web_lecture = models.BooleanField(default=False)
def __str__(self):
return f'{self.surname} {self.firstname} {self.other_name}'
def get_absolute_url(self):
return reverse('staff-detail', kwargs={'pk': self.pk})
| [
"emproarsam.sa@gmail.com"
] | emproarsam.sa@gmail.com |
0d8f0929c9fd6a1133841be35c2ccd4e36bd9902 | b7898d6021a756d06d44c1a94331d64d1954878a | /04-Python-input/fun_input.py | 803dbf1ea6dae58ad223ebfb41d38a468afd0a98 | [] | no_license | plvnamor/python_from_zero | a09860c82c1ab4845c1ee67a7c2edcec27cef1cd | 32bbb9d2ec3784c13ef3cdbdb3c5b27c27caad1c | refs/heads/master | 2023-07-19T13:02:31.856218 | 2021-09-05T07:26:40 | 2021-09-05T07:26:40 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,339 | py | # name = input("Введите ваше имя: ")
# print(name)
# birth_date = input("Введите дату рождения в формате дд.мм.гг(пример: 01.01.1970): ")
# print(birth_date)
# my_var = input("Введите данные: ")
# print(type(my_var))
# name = input("Введите ваше имя: ")
# print(name)
# print(type(name))
#
# age = input("Введите ваш возраст: ")
# print(age)
# print(type(age))
# age = age * 3
# print(age)
# age = input("Введите ваш возраст: ")
# print(age)
# print(type(age))
#
# age = int(age)
# print(type(age))
#
# age = age * 3
# print(age)
# age = int(input("Введите ваш возраст: "))
# age = age / 2
# print(age)
# amount = float(input("Процент по вкладу составит: "))
# print(amount)
# shape_len = float(input("Введите длину прямоугольника: "))
# shape_width = float(input("Введите ширину прямоугольника: "))
# shape_area = shape_len * shape_width
#
# print(f"Площадь прямоугольника равна: {shape_area}")
# your_age = int(input("Сколько вам лет? - "))
#
# if your_age < 18:
# print("Подрасти сынок")
# else:
# print("Добро пожаловать на наш ХАБ :))") | [
"pythontodaynet@gmail.com"
] | pythontodaynet@gmail.com |
c5e8e1a02936891689e43ab2b3a34523a1b77e23 | 4459c567c2c6f0722dca40bf96f45247aa42b42c | /paratope/atrous_run.py | bdfb14e40598c80a3d3a545c698fcf1d66688b45 | [] | no_license | chasingtim/attentive-parapred | b7492bdd5633bea3d87ba4e8aff8cbd8cf46d240 | 7cc895017b942c0ef5a600f170f7a0e574377132 | refs/heads/master | 2023-03-20T21:05:44.551106 | 2020-11-16T03:39:58 | 2020-11-16T03:39:58 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,334 | py | from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
from torch import index_select
from sklearn.metrics import confusion_matrix, roc_auc_score, matthews_corrcoef
from atrous import *
from constants import *
from evaluation_tools import *
def atrous_run(cdrs_train, lbls_train, masks_train, lengths_train, weights_template, weights_template_number,
cdrs_test, lbls_test, masks_test, lengths_test):
print("dilated run", file=print_file)
model = DilatedConv()
ignored_params = list(map(id, [model.conv1.weight]))
base_params = filter(lambda p: id(p) not in ignored_params,
model.parameters())
optimizer = optim.Adam([
{'params': base_params},
{'params': model.conv1.weight, 'weight_decay': 0.01},
], lr=0.01)
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[5], gamma=0.1)
total_input = cdrs_train
total_lbls = lbls_train
total_masks = masks_train
total_lengths = lengths_train
if use_cuda:
print("using cuda")
model.cuda()
total_input = total_input.cuda()
total_lbls = total_lbls.cuda()
total_masks = total_masks.cuda()
cdrs_test = cdrs_test.cuda()
lbls_test = lbls_test.cuda()
masks_test = masks_test.cuda()
for epoch in range(epochs):
model.train(True)
scheduler.step()
epoch_loss = 0
batches_done=0
total_input, total_masks, total_lengths, total_lbls = \
permute_training_data(total_input, total_masks, total_lengths, total_lbls)
for j in range(0, cdrs_train.shape[0], batch_size):
batches_done +=1
interval = [x for x in range(j, min(cdrs_train.shape[0], j + batch_size))]
interval = torch.LongTensor(interval)
if use_cuda:
interval = interval.cuda()
input = index_select(total_input, 0, interval)
masks = index_select(total_masks, 0, interval)
lengths = total_lengths[j:j + batch_size]
lbls = index_select(total_lbls, 0, interval)
input, masks, lengths, lbls = sort_batch(input, masks, list(lengths), lbls)
unpacked_masks = masks
packed_masks = pack_padded_sequence(masks, lengths, batch_first=True)
masks, _ = pad_packed_sequence(packed_masks, batch_first=True)
unpacked_lbls = lbls
packed_lbls = pack_padded_sequence(lbls, lengths, batch_first=True)
lbls, _ = pad_packed_sequence(packed_lbls, batch_first=True)
output = model(input, unpacked_masks)
loss_weights = (unpacked_lbls * 1.5 + 1) * unpacked_masks
max_val = (-output).clamp(min=0)
loss = loss_weights * (output - output * unpacked_lbls + max_val + ((-max_val).exp() + (-output - max_val).exp()).log())
masks_added = masks.sum()
loss = loss.sum() / masks_added
#print("Epoch %d - Batch %d has loss %d " % (epoch, j, loss.data), file=monitoring_file)
epoch_loss +=loss
model.zero_grad()
loss.backward()
optimizer.step()
print("Epoch %d - loss is %f : " % (epoch, epoch_loss.data[0]/batches_done))
model.eval()
cdrs_test2, masks_test2, lengths_test2, lbls_test2 = sort_batch(cdrs_test, masks_test, list(lengths_test),
lbls_test)
unpacked_masks_test2 = masks_test2
probs_test2 = model(cdrs_test2, unpacked_masks_test2)
# K.mean(K.equal(lbls_test, K.round(y_pred)), axis=-1)
sigmoid = nn.Sigmoid()
probs_test2 = sigmoid(probs_test2)
probs_test2 = probs_test2.data.cpu().numpy().astype('float32')
lbls_test2 = lbls_test2.data.cpu().numpy().astype('int32')
probs_test2 = flatten_with_lengths(probs_test2, lengths_test2)
lbls_test2 = flatten_with_lengths(lbls_test2, lengths_test2)
print("Roc", roc_auc_score(lbls_test2, probs_test2))
torch.save(model.state_dict(), weights_template.format(weights_template_number))
print("test", file=track_f)
model.eval()
cdrs_test, masks_test, lengths_test, lbls_test = sort_batch(cdrs_test, masks_test, list(lengths_test), lbls_test)
unpacked_masks_test = masks_test
packed_input = pack_padded_sequence(masks_test, list(lengths_test), batch_first=True)
masks_test, _ = pad_packed_sequence(packed_input, batch_first=True)
probs_test = model(cdrs_test, unpacked_masks_test)
# K.mean(K.equal(lbls_test, K.round(y_pred)), axis=-1)
sigmoid = nn.Sigmoid()
probs_test = sigmoid(probs_test)
probs_test1 = probs_test.data.cpu().numpy().astype('float32')
lbls_test1 = lbls_test.data.cpu().numpy().astype('int32')
probs_test1 = flatten_with_lengths(probs_test1, list(lengths_test))
lbls_test1 = flatten_with_lengths(lbls_test1, list(lengths_test))
print("Roc", roc_auc_score(lbls_test1, probs_test1))
return probs_test, lbls_test, probs_test1, lbls_test1 # get them in kfold, append, concatenate do roc on them
| [
"andreeadeac22@gmail.com"
] | andreeadeac22@gmail.com |
556aa5e7687d52b1ac3a9ba0190a1ff58e8a531d | 9e22f3f2d5d0fba163bde6bd618f99aa282e0a05 | /Assignment6/mongo_Q1.py | e6c1caf3b315b424125ec0eae64855141ee0d200 | [] | no_license | sarthak-chakraborty/Advanced-Database-Coursework | 213b1c6e0bed24e363565596d46f29300e545ec1 | 0776b05bc6cfa7b1f7488f30ae0571212bf20c04 | refs/heads/master | 2023-01-18T17:58:43.018608 | 2020-11-28T19:41:11 | 2020-11-28T19:41:11 | 293,569,111 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,221 | py | import sys
from pymongo import MongoClient
def readFile(filename):
friend, follower = {}, {}
f = open(filename)
for line in f:
t = line.split(',')
u1 = t[0].strip()
u2 = t[1].strip()
if u1 not in friend:
friend[u1] = 1
else:
friend[u1] += 1
if u2 not in follower:
follower[u2] = 1
else:
follower[u2] += 1
f.close()
return friend, follower
def main():
if len(sys.argv) < 2:
print("Filename not given")
exit()
filename = sys.argv[1]
client = MongoClient('mongodb://localhost:27017/')
db = client.twitter
collection = db['tutorial']
print("Reading file to get friend and follower counts...")
friend_count, follower_count = readFile(filename)
print("Updating the database with friend and follower counts...")
for user, count in friend_count.items():
collection.update_many({'user.id_str' : user}, {'$set':{'user.new_friends_count':count}})
for user, count in follower_count.items():
collection.update_many({'user.id_str':user}, {'$set':{'user.new_followers_count':count}})
if __name__ == "__main__":
main() | [
"sarthak.chakraborty@gmail.com"
] | sarthak.chakraborty@gmail.com |
9e52afd59b71f7801a87f9a95b3bf7ea5f791347 | ee2f57ffb3c0bec9a196090022a623a342a9ce96 | /PythonApp/PythonApp/GPOIDEmo.py | 5aa331abd1392316c0643b97e968365fc63900aa | [] | no_license | cherho0/pythonApp | c313f2b2869530a79b0cba26d68e2c61df5d5ad1 | 9c7c6fb851358bc85956e9c512ba23b80d8cc3b3 | refs/heads/master | 2021-01-20T03:54:37.721785 | 2017-09-07T03:49:49 | 2017-09-07T03:49:49 | 101,373,987 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 16 | py | import RPI.GPIO
| [
"czy11807@ly.com"
] | czy11807@ly.com |
527fb50223d631d209522b9c5cb81dad7094ea5a | ad9521d33838efa82772172bbc93fdf6db5e1746 | /app01/migrations/0002_auto_20180513_1838.py | 6297158fb35ff712213aac6d767c88208c96cd25 | [] | no_license | luobiao12/crm | cfdc8efe81d38b582d9aecfb9887c9b46e413151 | c8350a9bae9b2b71cacad26f78ce0d6633c40755 | refs/heads/master | 2020-03-19T06:26:05.227092 | 2018-09-02T09:45:50 | 2018-09-02T09:45:50 | 136,019,291 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 550 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.11 on 2018-05-13 10:38
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('app01', '0001_initial'),
]
operations = [
migrations.AlterModelOptions(
name='userinfo',
options={'verbose_name': '员工表', 'verbose_name_plural': '员工表'},
),
migrations.AlterModelTable(
name='userinfo',
table='员工表',
),
]
| [
"593215393@qq.com"
] | 593215393@qq.com |
d1b0af35eefcb79c3d768faaf00cd5b6373b56dd | 26eabfc87d278cbefd31fde1bd531553d218f580 | /SerialRead/SerialRead.pyde | 7f3762a8fdc40ef955e6e2e4ea64b6f04bb98097 | [] | no_license | tomasdecamino/CS_TOLIS | 2a7ae35c06715c4520e24f05f7a51a94cb6ff7cb | 43b5275c47a87b5b1a6fa54ba0996e254772dc75 | refs/heads/main | 2023-08-02T19:19:15.361645 | 2021-09-16T18:13:56 | 2021-09-16T18:13:56 | 336,251,497 | 4 | 1 | null | null | null | null | UTF-8 | Python | false | false | 841 | pyde | # Tomas de Camino Beck
# simple example to connect Micro:Bit using serial to Processing
# At the en d micro:Bit code
add_library('serial')
inString = "0"
def setup():
size(800,300)
println(Serial.list())
#Choose proper serial device from list
myPort = Serial(this,Serial.list()[5], 115200)
myPort.bufferUntil(10)
def serialEvent(port):
global inString
inString = port.readString()
def draw():
background(0)
#nothing yet
ellipse(400+int(inString),150,100,100)
text(inString,10,10)
"""
# Micro:Bit Simple code sending accelerometer data to Processing
from microbit import *
while True:
reading = accelerometer.get_x()
if reading > 20:
display.show("R")
elif reading < -20:
display.show("L")
else:
display.show("-")
print(reading)
"""
| [
"noreply@github.com"
] | tomasdecamino.noreply@github.com |
0ec67c650b1c198c29e7b42669f61908f326ecf1 | 0e1275f4f7acaec3f9ef0b8d8c287c6e4ddbb985 | /get_bs_localizations/get_bs_localizations.py | 7839ec10fb62d24191cd7419ec639ad9ce916e3f | [] | no_license | ArnaudStigliani/get_interdistances | 7c79cfeeec2b8b401b8aec3c3d4ee3d801da89f7 | cbfd991c9eaf1d45471c98364c5fb482fcdfb9a0 | refs/heads/master | 2021-01-20T08:27:29.317958 | 2017-05-02T12:26:56 | 2017-05-02T12:26:56 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 19,797 | py | ### ----------------------------
### TFBS Interdistances program
### ----------------------------
'''
This program allows to localize transcription factor binding sites on sequences such as promoters.
You need a matrix with frequency values and fasta sequences (bound sequences (i.e. peaks)).
This program was written by Adrien Bessy, Arnaud Stigliani and Francois Parcy, and was inspired by Morpheus program written by Eugenio Gomez Minguet
and (4-scores.py and CalculateScoreAboveTh_and_Interdistances.py) programs written by Laura Gregoire.
'''
import os,sys,inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0,parentdir)
from interdistances_functions import *
import numpy as np
from Bio import SeqIO
import time
import sys
from operator import truediv
import operator
from collections import Counter
import matplotlib.pyplot as plt
from matplotlib.offsetbox import AnchoredOffsetbox, TextArea, HPacker, VPacker
import matplotlib.patches as mpatches
from matplotlib import pylab
import types
import argparse
import logging
from optparse import OptionParser
from scipy import stats
from docx import Document
from docx.shared import Inches
from docx.shared import RGBColor
from docx.shared import Pt
parser = argparse.ArgumentParser()
parser.add_argument("--factor", "-fac", type=str, default= "ARF2")
parser.add_argument("--pseudoCount", "-pc",type=float,default= 0.001)
parser.add_argument("--threshold", "-th",nargs='+',type=int,default= -12)
parser.add_argument("--Interdistance_maxValue", "-maxInter", type=int,default= 20)
args = parser.parse_args()
#python get_bs_localizations.py -fac "ARF5" -pc 0.001 -maxInter 20 -th -12
#python get_bs_localizations.py -fac "ARF2" -pc 0.001 -maxInter 20 -th -12
factorTranscription = args.factor
pseudoCount = args.pseudoCount
threshold = args.threshold
Interdistance_maxValue = args.Interdistance_maxValue
###################Parameters we can change#################
if factorTranscription == "ARF2" :
#FastaFile = "IR7.fas"
FastaFile = "IAA19.fas"
MatrixFile = "../matrix/ARF2_OMalley_matrixC.txt"
matrixType = "freq"
if factorTranscription == "ARF5" :
FastaFile = "IAA19.fas"
MatrixFile = "../matrix/ARF5_OMalley_Cmatrix.txt"
matrixType = "freq"
###############################################################################
document = Document()
document.add_heading("Localization of the "+factorTranscription+" binding sites on "+FastaFile, 0)
def interdistance_calcul(DR,ER,IR,good_score_positions) :
for first in range(0,len(good_score_positions)-1) :
firstSubSeq = good_score_positions[first]
for second in range(first+1,len(good_score_positions)) :
secondSubSeq = good_score_positions[second]
'''
Here we do different substractions according to we get a Direct Repeat (DR), an Inverted Repeat (IR) or an Everted Repeat (ER).
Because In the litterature, The interdistance calculation was made between subsequences from our matrix
and not between the whole sequences from our matrix.
So according to your specific interdistance calculations you can change these lines.
'''
if factorTranscription == "ARF2" :
if firstSubSeq[1] == secondSubSeq[1] :
d = ( int(secondSubSeq[0]) +2 ) -( int(firstSubSeq[0]) + lenMotif -2)
if Interdistance_maxValue >= d >= 0 :
DR.append(["DR "+str(d),firstSubSeq[1],firstSubSeq[2],firstSubSeq[3],firstSubSeq[4],firstSubSeq[5],secondSubSeq[1],secondSubSeq[2],secondSubSeq[3],secondSubSeq[4],secondSubSeq[5],firstSubSeq[2]+secondSubSeq[2]])
if firstSubSeq[1] == ">" and secondSubSeq[1] == "<" :
d = ( int(secondSubSeq[0]) +2 ) -( int(firstSubSeq[0]) + lenMotif -2 )
if Interdistance_maxValue >= d >= 0 :
ER.append(["ER "+str(d),firstSubSeq[1],firstSubSeq[2],firstSubSeq[3],firstSubSeq[4],firstSubSeq[5],secondSubSeq[1],secondSubSeq[2],secondSubSeq[3],secondSubSeq[4],secondSubSeq[5],firstSubSeq[2]+secondSubSeq[2]])
if firstSubSeq[1] == "<" and secondSubSeq[1] == ">" :
d = ( int(secondSubSeq[0]) +2 ) -( int(firstSubSeq[0]) + lenMotif -2 )
if Interdistance_maxValue >= d >= 0 :
IR.append(["IR "+str(d),firstSubSeq[1],firstSubSeq[2],firstSubSeq[3],firstSubSeq[4],firstSubSeq[5],secondSubSeq[1],secondSubSeq[2],secondSubSeq[3],secondSubSeq[4],secondSubSeq[5],firstSubSeq[2]+secondSubSeq[2]])
if factorTranscription == "ARF5" :
if firstSubSeq[1] == ">" and secondSubSeq[1] == ">" :
d = (int(secondSubSeq[0]) +3 ) - (int(firstSubSeq[0]) -1 )
if Interdistance_maxValue >= d >= 0 :
print("firstSubSeq[2] : ",firstSubSeq[2])
print("secondSubSeq[2] : ",secondSubSeq[2])
DR.append(["DR "+str(d),firstSubSeq[1],firstSubSeq[2],firstSubSeq[3],firstSubSeq[4],firstSubSeq[5],secondSubSeq[1],secondSubSeq[2],secondSubSeq[3],secondSubSeq[4],secondSubSeq[5],firstSubSeq[2]+secondSubSeq[2]])
if firstSubSeq[1] == "<" and secondSubSeq[1] == "<" :
d = (int(secondSubSeq[0]) +1 ) - (int(firstSubSeq[0]) -3 )
if Interdistance_maxValue >= d >= 0 :
print("firstSubSeq[2] : ",firstSubSeq[2])
print("secondSubSeq[2] : ",secondSubSeq[2])
DR.append(["DR "+str(d),firstSubSeq[1],firstSubSeq[2],firstSubSeq[3],firstSubSeq[4],firstSubSeq[5],secondSubSeq[1],secondSubSeq[2],secondSubSeq[3],secondSubSeq[4],secondSubSeq[5],firstSubSeq[2]+secondSubSeq[2]])
if firstSubSeq[1] == ">" and secondSubSeq[1] == "<" :
#print("ER")
d = (int(secondSubSeq[0]) +1 ) - (int(firstSubSeq[0]) -1 )
if Interdistance_maxValue >= d >= 0 :
print("firstSubSeq[2] : ",firstSubSeq[2])
print("secondSubSeq[2] : ",secondSubSeq[2])
ER.append(["ER "+str(d),firstSubSeq[1],firstSubSeq[2],firstSubSeq[3],firstSubSeq[4],firstSubSeq[5],secondSubSeq[1],secondSubSeq[2],secondSubSeq[3],secondSubSeq[4],secondSubSeq[5],firstSubSeq[2]+secondSubSeq[2]])
if firstSubSeq[1] == "<" and secondSubSeq[1] == ">" :
#print("IR")
d = (int(secondSubSeq[0]) +3 ) - (int(firstSubSeq[0]) -3 )
if Interdistance_maxValue >= d >= 0 :
print("firstSubSeq[2] : ",firstSubSeq[2])
print("secondSubSeq[2] : ",secondSubSeq[2])
IR.append(["IR "+str(d),firstSubSeq[1],firstSubSeq[2],firstSubSeq[3],firstSubSeq[4],firstSubSeq[5],secondSubSeq[1],secondSubSeq[2],secondSubSeq[3],secondSubSeq[4],secondSubSeq[5],firstSubSeq[2]+secondSubSeq[2]])
return(DR,ER,IR)
# These 3 lines allows to retrieve the matrix from the file
F = open(MatrixFile,"r")
matrix = F.read().replace("\r","\n") + "\n"
F.close()
# These 3 lines allows to retrieve all the individual frequency values from the matrix and put them in order into a list
import re
num = re.compile(r"([+-]?\d+[.,]\d+)")
Mdata = num.findall(matrix)
matF, lenMotif = get_score_matrix(Mdata,matrixType,pseudoCount)
# The following line allows to produce the reversed matrix
'''if we take the example given before : A T G C
Position 1: 0.4444 0.155 0.654 0.645
Position 2: 0.1645 0.1565 0.21614 0.16456
Now, we can notice that scores change between the positions 1 and 2, and between A and T, and between G and C.
So we can calculate with this reverse matrix, the score of the complementary strand.
'''
matRev = list(reversed(matF))
# This line allows to retrieve all the sequences from the fasta file
sequences = SeqIO.to_dict(SeqIO.parse(FastaFile, "fasta"))
print(sequences)
print " There are %s sequence(s) to analyze"%(len(sequences))
all_data = []
sequences_ = ""
explanation = ["Position of the T of the \"TGTCNN\" or of the N of the \"NNGACA\" if the motif is on the complementary strand" , "strand", "score", "binding site"]
# We look at all the fasta sequences:
for s in sorted(sequences):
# We will store in this list all the best scores (see the threshold after) found for subsequences of one sequence
#if type(threshold) is list:
good_score_positions = []
DR = []
ER = []
IR = []
# This line allows to retrieve the DNA sequence
seq = sequences[s].seq
seq2 = ""
seq_id = sequences[s].id
a = 0
p = document.add_paragraph("")
p.add_run("Command line: "+str(sys.argv).replace("[","").replace("]","").replace("'","").replace(",",""))
p = document.add_paragraph("")
p.add_run("Legend: ").bold = True
run = p.add_run("strand + ")
run.font.color.rgb = RGBColor(0xFF, 0x00, 0xFF)
run = p.add_run("strand -")
run.font.color.rgb = RGBColor(0x00, 0x00, 0xFF)
p = document.add_paragraph("")
# We look at each sub-sequences of the whole sequence. Each sub-sequence has the same length that the matrix length.
for c in range(len(seq) - (lenMotif -1)):
strandPos = seq[c:c+lenMotif].upper()
test = 0
for nu in strandPos :
if nu not in ["A","C","G","T"]:
test = 1
if test == 1:
score = "NA"
else :
index = 0
#These lines allows to calculate a score for one sub-sequence
scoreStrandPos = 0
scoreStrandNeg = 0
while index < lenMotif:
if strandPos[index] == 'A':
scoreStrandPos = scoreStrandPos + matF[index*4]
scoreStrandNeg = scoreStrandNeg + matRev[index*4]
elif strandPos[index] == 'C':
scoreStrandPos = scoreStrandPos + matF[index*4+1]
scoreStrandNeg = scoreStrandNeg + matRev[index*4+1]
elif strandPos[index] == 'G':
scoreStrandPos = scoreStrandPos + matF[index*4+2]
scoreStrandNeg = scoreStrandNeg + matRev[index*4+2]
elif strandPos[index] == 'T':
scoreStrandPos = scoreStrandPos + matF[index*4+3]
scoreStrandNeg = scoreStrandNeg + matRev[index*4+3]
index += 1
#These lines allows to retrieve the position and the strand where there is a predicted binding site.
#You can change the threshold.
#print("scoreStrandPos : ",scoreStrandPos)
#print("str(strandPos[3:9]) : ",str(strandPos[3:9]))
#print("scoreStrandNeg : ",scoreStrandNeg)
#print("str(strandPos[3:9]) : ",str(strandPos[1:7]))
if scoreStrandPos > threshold:
if factorTranscription == "ARF2" :
if a ==0 :
seq2 = seq2 + seq[a:c+2].upper()
run1 = p.add_run(seq2)
run1.font.name = 'Courier 10 pitch'
run = p.add_run(seq[c+2:c+8])
run.font.color.rgb = RGBColor(0xFF, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
if a != 0 and c+2 - a > -1 :
seq2 = seq[a:c+2].upper()
run1 = p.add_run(seq2)
run1.font.name = 'Courier 10 pitch'
run = p.add_run(seq[c+2:c+8])
run.font.color.rgb = RGBColor(0xFF, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
if a != 0 and c+2 - a < 0 :
run = p.add_run(seq[a:c+8])
run.font.color.rgb = RGBColor(0xFF, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
a = c + 8
rest = int((c+3)/65) + 1
column = c+3 - int((c+3)/65) * 65
good_score_positions.append([c+3,">",round(scoreStrandPos,2),str(strandPos[2:8]),rest,column])
if factorTranscription == "ARF5" :
#print("scoreStrandPos : ",scoreStrandPos)
if a ==0 :
seq2 = seq2 + seq[a:c+3].upper()
run1 = p.add_run(seq2)
run1.font.name = 'Courier 10 pitch'
run = p.add_run(seq[c+3:c+9])
run.font.color.rgb = RGBColor(0xFF, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
if a != 0 and c+2 - a > -1 :
seq2 = seq[a:c+3].upper()
run1 = p.add_run(seq2)
run1.font.name = 'Courier 10 pitch'
run = p.add_run(seq[c+3:c+9])
run.font.color.rgb = RGBColor(0xFF, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
if a != 0 and c+2 - a < 0 :
run = p.add_run(seq[a:c+9])
run.font.color.rgb = RGBColor(0xFF, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
a = c + 9
rest = int((c+4)/65) + 1
column = c+4 - int((c+4)/65) * 65
good_score_positions.append([c+4,">",round(scoreStrandPos,2),str(strandPos[3:9]),rest,column])
#seq2 = seq2 + seq[a:c+3].upper() + " "
#a = c+3
#print("c+4 : ",c+4)
#good_score_positions.append([c+4,">",round(scoreStrandPos,2),str(strandPos[3:9])])
if scoreStrandNeg > threshold:
if factorTranscription == "ARF2" :
if a ==0 :
seq2 = seq2 + seq[a:c+2].upper()
run1 = p.add_run(seq2)
run1.font.name = 'Courier 10 pitch'
run = p.add_run(seq[c+2:c+8])
run.font.color.rgb = RGBColor(0x00, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
if a != 0 and c+2 - a > -1 :
seq2 = seq[a:c+2].upper()
run1 = p.add_run(seq2)
run1.font.name = 'Courier 10 pitch'
run = p.add_run(seq[c+2:c+8])
run.font.color.rgb = RGBColor(0x00, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
if a != 0 and c+2 - a < 0 :
run = p.add_run(seq[a:c+8])
run.font.color.rgb = RGBColor(0x00, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
a = c + 8
#p.add_run(str(strandPos[2:8])).bold = True
rest = int((c+3)/65) + 1
column = c+3 - int((c+3)/65) * 65
good_score_positions.append([c+3,"<",round(scoreStrandNeg,2),str(strandPos[2:8]),rest,column])
if factorTranscription == "ARF5" :
#print("scoreStrandNeg : ",scoreStrandNeg)
if a ==0 :
seq2 = seq2 + seq[a:c+1].upper()
run1 = p.add_run(seq2)
run1.font.name = 'Courier 10 pitch'
run = p.add_run(seq[c+1:c+7])
run.font.color.rgb = RGBColor(0x00, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
if a != 0 and c+2 - a > -1 :
seq2 = seq[a:c+1].upper()
run1 = p.add_run(seq2)
run1.font.name = 'Courier 10 pitch'
run = p.add_run(seq[c+1:c+7])
run.font.color.rgb = RGBColor(0x00, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
if a != 0 and c+2 - a < 0 :
run = p.add_run(seq[a:c+7])
run.font.color.rgb = RGBColor(0x00, 0x00, 0xFF)
run.font.name = 'Courier 10 pitch'
a = c + 7
rest = int((c+2)/65) + 1
column = c+2 - int((c+2)/65) * 65
good_score_positions.append([c+2,"<",round(scoreStrandNeg,2),str(strandPos[1:7]),rest,column])
#seq2 = seq2 + seq[a:c+1].upper() + " "
#a = c+1
#print("c+2 : ",c+2)
#good_score_positions.append([c+2,"<",round(scoreStrandNeg,2),str(strandPos[1:7])])
run = p.add_run(seq[a:len(seq)])
run.font.name = 'Courier 10 pitch'
run = document.add_paragraph().add_run("1 10 20 30 40 50 60")
run.font.name = 'Courier 10 pitch'
font = run.font
font.size = Pt(8)
seq2 = seq2 + seq[a:].upper()
DR,ER,IR = interdistance_calcul(DR,ER,IR,good_score_positions)
#print("DR : ",DR)
#print("ER : ",ER)
#print("IR : ",IR)
#sequences_ = sequences_ + seq_id + "\n" + seq2 + "\n\n" + str(explanation) + "\nDR : " + str(DR) + "\nER : "+ str(ER) + "\nIR : " + str(IR) + "\n" + "\n\nAll the sites:\n" + str(good_score_positions) + "\n\n\n"
########################################### About the main matrix #######################
''' The sens of the matrix is important: The positions are on the vertical sens and the bases are on the horizontal sens as described in the example.
separation between numbers can be spaces, tabulation, comas...
Example : A C G T
position 1: 0.16456 0.21614 0.1565,0.1645
position 2: 0.645; 0.654 0.155 |||||| 0.4444
...
'''
####################################################################################
###############################################################################################
########## get INTERDISTANCE VALUES for POSITIVE sets:
#sequences_ = get_interdist(matScore,matRev,FastaFile,threshold,factorTranscription,Interdistance_maxValue)
#output_presentation = "Here the binding sites are coloured. For example, with the sequence \n\"AGTAGTCAT TGT CAGATAGAAAGAAAGAG CAGACAAAAGGATCG\"\nBecause a binding site has a length of 6 bp, this means there is a first binding site: \nTGTCAGA, a second one: CAGATA and a last one: CAGACA.\nThe matrix used is: "+MatrixFile + "\n\n\n\n"
#text_file = open("localization_of_the_"+factorTranscription+"_binding_sites_on_"+FastaFile+".txt", "w")
#text_file.write(output_presentation+str(sequences_))
#text_file.close()
#p = document.add_paragraph('A plain paragraph having some ')
#p.add_run('bold').bold = True
#p.add_run(' and some ')
#p.add_run('italic.').italic = True
#document.add_heading('Heading, level 1', level=1)
#document.add_paragraph('Intense quote', style='IntenseQuote')
#document.add_paragraph(
#'first item in ordered list', style='ListNumber'
#)
p = document.add_paragraph("")
p.add_run("DR table sorted by decreasing order for score sum: ").bold = True
table = document.add_table(rows=1, cols=6)
hdr_cells = table.rows[0].cells
hdr_cells[0].text = 'DR'
hdr_cells[1].text = 'score sum'
hdr_cells[2].text = 'line'
hdr_cells[3].text = 'column'
hdr_cells[4].text = 'bs 1'
hdr_cells[5].text = 'bs 2'
DR = sorted(DR, key = operator.itemgetter(11), reverse=True)
for item in DR:
row_cells = table.add_row().cells
row_cells[0].text = str(item[0])
row_cells[1].text = str(item[11])
row_cells[2].text = str(item[4])
row_cells[3].text = str(item[5])
row_cells[4].text = str(item[3])
row_cells[5].text = str(item[8])
document.add_page_break()
p = document.add_paragraph("")
p.add_run("ER table sorted by decreasing order for score sum: ").bold = True
table = document.add_table(rows=1, cols=6)
hdr_cells = table.rows[0].cells
hdr_cells[0].text = 'ER'
hdr_cells[1].text = 'score sum'
hdr_cells[2].text = 'line'
hdr_cells[3].text = 'column'
hdr_cells[4].text = 'bs 1'
hdr_cells[5].text = 'bs 2'
ER = sorted(ER, key = operator.itemgetter(11), reverse=True)
for item in ER:
row_cells = table.add_row().cells
row_cells[0].text = str(item[0])
row_cells[1].text = str(item[11])
row_cells[2].text = str(item[4])
row_cells[3].text = str(item[5])
row_cells[4].text = str(item[3])
row_cells[5].text = str(item[8])
document.add_page_break()
p = document.add_paragraph("")
p.add_run("IR table sorted by decreasing order for score sum: ").bold = True
table = document.add_table(rows=1, cols=6)
hdr_cells = table.rows[0].cells
hdr_cells[0].text = 'IR'
hdr_cells[1].text = 'score sum'
hdr_cells[2].text = 'line'
hdr_cells[3].text = 'column'
hdr_cells[4].text = 'bs 1'
hdr_cells[5].text = 'bs 2'
IR = sorted(IR, key = operator.itemgetter(11), reverse=True)
for item in IR:
row_cells = table.add_row().cells
row_cells[0].text = str(item[0])
row_cells[1].text = str(item[11])
row_cells[2].text = str(item[4])
row_cells[3].text = str(item[5])
row_cells[4].text = str(item[3])
row_cells[5].text = str(item[8])
document.add_page_break()
p = document.add_paragraph("")
p.add_run("All bs table: ").bold = True
table = document.add_table(rows=1, cols=6)
hdr_cells = table.rows[0].cells
hdr_cells[0].text = 'Index'
hdr_cells[1].text = 'sens'
hdr_cells[2].text = 'score'
hdr_cells[3].text = 'sequence'
hdr_cells[4].text = 'line'
hdr_cells[5].text = 'column'
good_score_positions = sorted(good_score_positions, key=operator.itemgetter(2), reverse=True)
for item in good_score_positions:
row_cells = table.add_row().cells
row_cells[0].text = str(item[0])
row_cells[1].text = str(item[1])
row_cells[2].text = str(item[2])
row_cells[3].text = str(item[3])
row_cells[4].text = str(item[4])
row_cells[5].text = str(item[5])
document.add_page_break()
document.save("localization_of_the_"+factorTranscription+"_binding_sites_on_"+FastaFile+".docx")
| [
"adrien.bessy@cea.fr"
] | adrien.bessy@cea.fr |
840eefdaf2de38d9b8988b8994e23875ae86ed98 | 3b1406633ec4497aab35b60f68a73bca86e268a4 | /src/datamodel/item_data.py | edff380f2172a9cd722baa2ee2487beee1631679 | [] | no_license | sujeetbansal/time_it | c7d105c5eed609ba4d92f68df68b22fa5123bedc | d3c359e858b23595ff8f19ad4c4d16e4d51a6500 | refs/heads/master | 2021-01-10T03:06:43.036352 | 2015-06-10T05:55:56 | 2015-06-10T05:55:56 | 36,936,670 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,174 | py | class PriceInfo(object):
def __init__(self, time=None, price=None):
super(PriceInfo, self).__init__()
self.time = time
self.price = price
class ItemKey(object):
def __init__(self, url=None, seller_info=None):
self.url = url
self.seller_info = seller_info
class ItemInfo(object):
def __init__(self, sku=None, title=None, description=None):
super(ItemInfo, self).__init__()
self.sku = sku
self.title = title
self.description = description
class SellerInfo(object):
def __init__(self, name=None):
super(SellerInfo, self).__init__()
self.name = name
class EtailerInfo(object):
def __init__(self, name=None):
super(EtailerInfo, self).__init__()
self.name = name
class Item(object):
def __init__(self, item_key=None, item_info=None, price_history=None,
seller_info=None, etailer_info=None,
related_items=None):
super(Item, self).__init__()
self.item_key = item_key
self.item_info = item_info
self.price_history = price_history # List of PriceInfo.
self.etailer_info = etailer_info
self.seller_info = seller_info
self.related_items = related_items
| [
"sujeetb@sujeetb-macbookpro3.roam.corp.google.com"
] | sujeetb@sujeetb-macbookpro3.roam.corp.google.com |
334ad3411895b727e30fc249dc903ae37cf532d2 | 3365e4d4fc67bbefe4e8c755af289c535437c6f4 | /.history/src/core/app/support_functions_20170809162416.py | 536f175bc0efdec61940749b0c5f4ab767c59396 | [] | no_license | kiranhegde/OncoPlotter | f3ab9cdf193e87c7be78b16501ad295ac8f7d2f1 | b79ac6aa9c6c2ca8173bc8992ba3230aa3880636 | refs/heads/master | 2021-05-21T16:23:45.087035 | 2017-09-07T01:13:16 | 2017-09-07T01:13:16 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,771 | py | '''
Support functions handling data import and formatting for plotting
'''
import pandas as pd
from pprint import pprint
def import_plot_data(file_path):
'''
Imports raw data from the Excel template file and returns:
1) Formatted data for waterfall plotting
2) Formatted data for spider plotting
3) Formatted data for swimmer plotting
'''
xl = pd.ExcelFile(file_path)
df_waterfall_data = xl.parse('Waterfall_data')
df_spider_data = xl.parse('Spider_data')
df_swimmer_data = xl.parse('Swimmer_data')
#Spider
spider_headers = list(df_spider_data)
spider_data = {}
for header in spider_headers:
spider_data[header] = df_spider_data[header]
#Swimmer
swimmer_headers = list(df_swimmer_data)
swimmer_data = {}
for header in swimmer_headers:
swimmer_data[header] = df_swimmer_data[header]
waterfall_data = parse_df_waterfall(df_waterfall_data)
return waterfall_data
def parse_df_swimmer(df):
'''
Parse dataframe of swimmer plot data and extract dates and events
'''
#Sort df_swimmer_data by their totals
numcols = len(list(df)) #number of keys
swimmer_data = []
for key in list(df):
swimmer_data.append(df[key])
pt_list = swimmer_data[0] #list of the patients
pt_stacks = swimmer_data[1:] #their corresponding 'stacks', each col is a dosage level (if applicable)
def parse_df_waterfall(df_waterfall_data):
'''
Prepare waterfall plot data for plotting
'''
df_waterfall_data = df_waterfall_data.sort_values('Best response percent change', ascending = False)
waterfall_data = []
for key in list(df_waterfall_data):
waterfall_data.append(df_waterfall_data[key])
return waterfall_data | [
"ngoyal95@terpmail.umd.edu"
] | ngoyal95@terpmail.umd.edu |
f57d60ae5363094c0333fe733c6af480da5f25e5 | ab5890a10c801da2c8fcb78be513b86628f61e8c | /Fino_ISIs_no_NMDA.py | 3035e4ee09976fad1cc8bfbbc9d9d67c8d0c2002 | [] | no_license | ModelDBRepository/189153 | f27a7d9156d7e9360125ab51c159897972699a36 | 90f5d579d52a19f35bfc79e66fdc52dcbc4703b0 | refs/heads/master | 2020-03-12T17:40:22.657892 | 2016-10-10T15:54:50 | 2016-10-10T15:54:50 | 130,741,980 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 4,579 | py | import numpy as np
import make_timetables as mp
import subprocess
sim_name = 'MSsim'
ISIs = [-.2,-.1,-.05,-.03,-.02,-0.01,-0.005,0.005,.01,.02,.03,.05,.1,.2]
text_sim_1 = '''include SimParams.g
include MScell/globals.g
include MScell/Ca_constants.g
include MScell/SynParamsCtx.g
include MScell/spineParams.g
include MScell/MScellSynSpines // access make_MS_cell
include InOut/add_output.g //functions for ascii file output
include InOut/IF.g //function to create pulse generator for current injection, also IF & IV curves
include InOut/HookUp.g
setclock 0 {simdt}
setclock 1 {simdt}
str DA = "UI"
str Location = "tertdend1_1"
str jitter = "No"
int jitter_int = 0
int seedvalue = 5757538
'''
#here Protocol and Timing
text_sim_2 = """
GABAYesNo = 0
NMDAgmax = 0
str diskpath="SimData/No_NMDA_"@{Protocol}@"_"@{DA}@"_"@{Timing}@"_"@{Location}@"_randseed_"@{seedvalue}@"_high_res"
int totspine={make_MS_cell_SynSpine {neuronname} {pfile} {spinesYesNo} {DA}}
reset
Vmhead={add_outputVm {comps} {Vmfile} {neuronname}}
if ({CaOut})
if ( {calciumdye} == 0)
Cafile = "Ca_plasticity"
elif ( {calciumdye} == 1)
Cafile = "Ca_Fura_2_plasticity"
elif ( {calciumdye} == 2)
Cafile = "Ca_Fluo_5f_plasticity"
elif ( {calciumdye} == 3)
Cafile = "Ca_Fluo_4_plasticity"
else
Cafile = "Ca_unnkown_dye_plasticity"
end
Cahead={add_outputCal {comps} {CaBufs} {Cafile} {neuronname}}
else
Cafile="X_plasticity"
Cahead=""
end
if ({GkOut})
Gkfile="Gk_plasticity"
Gkhead={add_outputGk {comps} {chans} {Gkfile} {neuronname}}
else
Gkfile="X_plasticity"
Gkhead=""
end
str stimcomp= {Location}
str spinefile="spine_plasticity"
ce /
"""
#here include paradigm
#and change ISI
text_sim_3 = '''
float newTrainFreq = {burstFreq}/{numbursts}
echo {newTrainFreq}
HookUp {prestim} {Protocol} {Timing} {stimcomp} {diskpath} {numAP} {inject} {AP_durtime} {APinterval} {ISI} {pulseFreq} {pulses} {burstFreq} {numbursts} {newTrainFreq} 1 {jitter_int}
reset
step 1.5 -time
fileFLUSH {Vmfile}
fileFLUSH {Cafile}
fileFLUSH {Gkfile}
fileFLUSH {spinefile}
fileFLUSH {somainjfile}
'''
if __name__ == '__main__':
params = mp.read_file('SimParams.g')
gabaYesNo = mp.find_value(params,'GABAYesNo','Pre')
spines = mp.find_string(params,'whichSpines','Pre')
stim_start = mp.find_value(params,'initSim','Pre')
too_fast = mp.find_value(params,'TooFast','Pre')
print gabaYesNo, spines, stim_start, too_fast
protocols = ['InOut/Fino.g','InOut/P_K.g']
timings = ['Pre']
paradigm_names = ['Fino','P_and_K']
for ISI in ISIs:
for timing in timings:
for i, protocol in enumerate(protocols):
print protocol
text = mp.read_file(protocol)
#find pulseFreq
freq = mp.find_value(text, 'pulseFreq',timing)
npulses = mp.find_value(text, 'pulses',timing)
nbursts = mp.find_value(text, 'numbursts',timing)
burstfreq = mp.find_value(text, 'burstFreq',timing)
ntrains = mp.find_value(text, 'numtrains',timing)
trainfreq = mp.find_value(text, 'trainFreq',timing)
print freq, npulses, nbursts, burstfreq, ntrains, trainfreq
if npulses == None:
npulses = 1
fname_base = paradigm_names[i]+'_'+timing+'_stimulation_'
spine_list = ['spine_1']
mp.distribute(freq,npulses,burstfreq,nbursts,trainfreq,ntrains,stim_start,fname_base,spine_list)
sim_file = 'No_NMDA_'+sim_name+'_'+paradigm_names[i]+'_ISI_'+str(ISI)+'.g'
print sim_file
fil = open(sim_file,'w')
fil.write(text_sim_1)
fil.write('str Protocol = "'+paradigm_names[i]+'"\n')
fil.write('str Timing = "'+timing+'"\n')
fil.write(text_sim_2)
fil.write("include "+protocol+'\n')
fil.write("ISI = "+str(ISI)+'\n')
fil.write(text_sim_3)
fil.close()
process = subprocess.Popen(['/home/asia/genesis-2.4/chemesis','-nox','-notty','-batch',sim_file])
ret = process.wait()
| [
"tom.morse@yale.edu"
] | tom.morse@yale.edu |
c77ca8e6e48099ccf7e5b58de592ec992418b2c3 | 916e2370c899520a37ce72f8559db513a50826b0 | /info/utils/pic_storage.py | 9edbc7959f2a57f9928c65a53ed3f790cf3912ae | [] | no_license | zhiliangsu/NewsInfo | 63d4c96add76d7c6df5b3a19335951b8b7ae8ae2 | acb5e50e8c9d847090bc9cbcd4689b4318ce07cc | refs/heads/master | 2022-12-12T11:54:00.193809 | 2019-01-08T09:43:20 | 2019-01-08T09:43:20 | 166,170,696 | 0 | 0 | null | 2022-12-08T01:32:40 | 2019-01-17T06:12:13 | Python | UTF-8 | Python | false | false | 1,445 | py | import qiniu
# 直接使用老师创建的空间
access_key = "W0oGRaBkAhrcppAbz6Nc8-q5EcXfL5vLRashY4SI"
secret_key = "tsYCBckepW4CqW0uHb9RdfDMXRDOTEpYecJAMItL"
# 上传的图片的空间名称
bucket_name = "information22"
# data --> 上传图片的二进制数据
def pic_storage(data):
"""上传用户图片数据到七牛云平台"""
if not data:
raise AttributeError("图片数据为空")
# 权限验证
q = qiniu.Auth(access_key, secret_key)
# 上传的图片名称
# 如果不设置key,七牛云会自动给图片分配一个图片名称,而且是唯一的
# key = 'hello'
token = q.upload_token(bucket_name)
# 上传图片数据到七牛云
ret, info = qiniu.put_data(token, None, data)
print(ret)
print("-----------------------")
print(info)
# 封装的工具类,给别人调用,一旦产生异常一定要抛出不能私自解决,如果私自解决了,别人就不知道错误发生在什么地方
if ret is not None:
print('All is OK')
else:
# 抛出异常
raise AttributeError("上传图片到七牛云失败")
if info.status_code == 200:
print("上传图片成功")
else:
raise AttributeError("上传图片到七牛云失败")
return ret['key']
if __name__ == '__main__':
file_name = input("输入上传的文件:")
with open(file_name, "rb") as f:
print(pic_storage(f.read()))
| [
"zhiliangsuzhiliang@gmail.com"
] | zhiliangsuzhiliang@gmail.com |
996a4d8b92259dfe1541dcf13e0dd29de1879fa8 | f67b58f9e3eb5769fe9e6c20d40b5fe407a8754e | /codecademy_scripts/word_file.py | 7f4cb03674d4e510590d53e086077ac49857a614 | [
"MIT"
] | permissive | Faraaz54/python_training_problems | 870b92f1c66ff51d3040474ca3cad63e53ee5c22 | 24c7b42daaf54366759e1d7c4b42f9936316e94b | refs/heads/master | 2021-01-01T05:56:08.095410 | 2017-07-19T18:15:43 | 2017-07-19T18:15:43 | 97,311,778 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 332 | py | fin = open('words.txt', 'r')
total = 0
count = 0
while True:
line = fin.readline()
total = total + 1
word = line.strip()
if 'e' not in word:
count = count + 1
if not line:
break
print count
print total
percentage = float(count) / total * 100.00
print percentage
fin.close()
| [
"noreply@github.com"
] | Faraaz54.noreply@github.com |
b01eee70d1be29720cb8de8e44af94fb9e7611a5 | 7b858e09aada0d85d223311838137cea4feaf763 | /transform_data/spark_dataframe_func.py | 5d742ac6299de81f88b57dcc660e8986fc8e5e24 | [] | no_license | sarkaraj/Timeseries-processing-with-Spark | 2a86d0e5867e9c174b183613f7ea9c22e8603d7a | 4ffac09a9661aba1b7d4e2eba3f201903321c7db | refs/heads/master | 2020-04-06T18:08:26.930604 | 2018-10-15T10:10:25 | 2018-10-15T10:10:25 | 157,678,464 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,769 | py | from pyspark.sql.types import *
def final_select_dataset(join_dataframe, sqlContext):
join_dataframe_return = join_dataframe \
.rdd \
.map(lambda _row: column_selector(_row))
_final_df = sqlContext.createDataFrame(join_dataframe_return, schema=_schema())
return _final_df
def column_selector(row_object):
# CASE I: When there is a match
if (row_object.customernumber_prophet == row_object.customernumber_arima):
customernumber = row_object.customernumber_prophet
matno = row_object.mat_no_prophet
error_arima = row_object.error_arima
pred_arima = row_object.pred_arima
arima_params = row_object.arima_params
error_prophet = row_object.error_prophet
pred_prophet = row_object.pred_prophet
prophet_params = row_object.prophet_params
pdt_cat = row_object.pdt_cat_prophet
result = customernumber, matno, error_arima, pred_arima, arima_params, error_prophet, pred_prophet, prophet_params, pdt_cat
return result
elif row_object.customernumber_prophet != row_object.customernumber_arima:
# CASE 2 : When Prophet_model is not present
if row_object.customernumber_prophet == None:
customernumber = row_object.customernumber_arima
matno = row_object.mat_no_arima
error_arima = row_object.error_arima
pred_arima = row_object.pred_arima
arima_params = row_object.arima_params
error_prophet = row_object.error_prophet
pred_prophet = row_object.pred_prophet
prophet_params = row_object.prophet_params
pdt_cat = row_object.pdt_cat_arima
result = customernumber, matno, error_arima, pred_arima, arima_params, error_prophet, pred_prophet, prophet_params, pdt_cat
return result
# CASE 3 : When arima_model is not present
elif row_object.customernumber_arima == None:
customernumber = row_object.customernumber_prophet
matno = row_object.mat_no_prophet
error_arima = row_object.error_arima
pred_arima = row_object.pred_arima
arima_params = row_object.arima_params
error_prophet = row_object.error_prophet
pred_prophet = row_object.pred_prophet
prophet_params = row_object.prophet_params
pdt_cat = row_object.pdt_cat_prophet
result = customernumber, matno, error_arima, pred_arima, arima_params, error_prophet, pred_prophet, prophet_params, pdt_cat
return result
def _schema():
customernumber = StructField("customernumber", StringType(), nullable=False)
mat_no = StructField("mat_no", StringType(), nullable=False)
_error_arima = StructField("error_arima", MapType(StringType(), FloatType()), nullable=True)
# week_num_year = StructType([StructField("week_or_month", IntegerType()), StructField("year", IntegerType())])
_pred_arima = StructField("pred_arima", MapType(StringType(), FloatType()), nullable=True)
_opt_param_arima = StructField("arima_params", MapType(StringType(), ArrayType(IntegerType())), nullable=True)
_error_prophet = StructField("error_prophet", MapType(StringType(), FloatType()), nullable=True)
_pred_prophet = StructField("pred_prophet", MapType(StringType(), FloatType()), nullable=True)
_opt_param_prophet = StructField("prophet_params", MapType(StringType(), StringType()), nullable=True)
_pdt_category = StructField("pdt_cat", MapType(StringType(), StringType()), nullable=False)
schema = StructType(
[customernumber, mat_no, _error_arima, _pred_arima, _opt_param_arima, _error_prophet, _pred_prophet,
_opt_param_prophet, _pdt_category])
return schema
| [
"rikirockers@gmail.com"
] | rikirockers@gmail.com |
6c35738d50833f629ce033cf25eb9071b1e45ad4 | de77a51bf27f6fb5d7fce32ee9950d3244dcc1ef | /fault_example/Model/gen_observation_data.py | 2096c125ff13ed1f82a0f867258bf08238978266 | [] | no_license | andrewtbanks/PEST_examples | a42963c88dd7c00ea40dcbeb3cc587e13ce05808 | dce93ad9cb2a19f3b900dc2f8ab03071cb0574bd | refs/heads/master | 2020-05-17T10:49:43.896245 | 2019-06-12T20:01:11 | 2019-06-12T20:01:11 | 183,667,421 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,833 | py | import matplotlib.pyplot as plt
import os
import numpy as np
import flopy
import flopy.utils.reference as srf
import flopy.utils.binaryfile as bf
import pykrige.kriging_tools as kt
from pykrige.ok import OrdinaryKriging
# sample TRUTH MODEL to get observtions (i.e. field measurements) for use in PEST
# also fit variograms based on sample data for kriging step in pilot point approach
fmain = r'C:\PEST_examples\fault_example'
# load TRUTH modflow model
mf_path = fmain + os.sep+'Truth'+os.sep+'modflow' # path to true model
mf_modelname = 'example_Truth'
mf = flopy.modflow.Modflow.load(mf_path+os.sep+mf_modelname+ '.nam')# import modflow object
hk= mf.lpf.hk.array[0,:,:]# load K field
hdobj = bf.HeadFile(mf_path+os.sep+mf_modelname+'.hds', precision = 'single')# load heads
kstpkper =hdobj.get_kstpkper() #list of valid kstep kper from TRUTH model
hds = hdobj.get_data(kstpkper = kstpkper[0]) # array of heads from final timestep of SS stress period
# create reference grid object corresponding to TRUTH modflow model
xul = -sum(mf.dis.delr)/2 # modflow model spatial domain upper left x coordinate
yul = sum(mf.dis.delc)/2 # modflow model spatial domain upper left y coordinate
grid_ref = srf.SpatialReference(delr = mf.dis.delr,
delc = mf.dis.delc,
lenuni = mf.dis.lenuni,
xul = xul,
yul = yul)
# get grid of x, y position of center of each cell
Xc = grid_ref.get_xcenter_array() #
Yc = grid_ref.get_ycenter_array()
X,Y = np.meshgrid(Xc,Yc)
# determine sampling (pilot) points - uniformly spaced on a grid throuout domain
nx_samp = 5
ny_samp = 8
nsamp = nx_samp*ny_samp
sclx = nx_samp/mf.dis.delc[0]#4.5
scly = ny_samp/mf.dis.delr[0]#1E6#7.5
xsamp = np.linspace(xul-xul/sclx,-xul+xul/sclx,nx_samp)
ysamp = np.linspace(yul-yul/scly,-yul+yul/scly,ny_samp)
Xsamp,Ysamp = np.meshgrid(xsamp,ysamp)
r,c = grid_ref.get_rc(Xsamp.reshape(nsamp,),Ysamp.reshape(nsamp,))
r = r.reshape((ny_samp,nx_samp))
c = c.reshape((ny_samp,nx_samp))
## SAMPLE HEADS - (experimental/field data for PEST input)
## and SAMPLE K - (for fitting experimental variogram and kriging intermediate K values from pilot point estimates)
hds_samp = np.zeros((ny_samp,nx_samp)) #experimental head measurements from corresponding cells in TRUTH model
hk_samp = np.zeros((ny_samp,nx_samp)) #experimental K measurements from corresponding cells in TRUTH model
for row in np.arange(0,ny_samp):
for col in np.arange(0,nx_samp):
hds_samp[row,col] = hds[0,r[row,col],c[row,col]]# Sample head
hk_samp[row,col] = hk[r[row,col],c[row,col]]# Sample K
# Krige experimental K data
variogram_model = 'spherical'
OK_init = OrdinaryKriging(Xsamp.reshape(nsamp,),
Ysamp.reshape(nsamp,),
hk_samp.reshape(nsamp,),
variogram_model=variogram_model,
verbose=False,
enable_plotting=False)
# get variogram parameters
sill,rng,nug = OK_init.variogram_model_parameters
variogram_parameters = {'sill': sill, 'range': rng, 'nugget':nug}
#hk_pred = ax.imshow(z.data, cmap = 'jet', extent =[xul, -xul, -yul, yul])
# convert pilot point locations from TRUTH to row, col indicies in model used by PEST
# load modflow model used by PEST
mfmdl_path = os.getcwd()+os.sep+'modflow' # path to true model
mfmdl_modelname = 'example'
mfmdl = flopy.modflow.Modflow.load(mfmdl_path+os.sep+mfmdl_modelname+ '.nam')# import modflow object
grid_ref_mdl = srf.SpatialReference(delr = mfmdl.dis.delr,
delc = mfmdl.dis.delc,
lenuni = mfmdl.dis.lenuni,
xul = xul,
yul = yul)
rmdl,cmdl = grid_ref_mdl.get_rc(Xsamp.reshape(nsamp,),Ysamp.reshape(nsamp,))
rmdl = rmdl.reshape((ny_samp,nx_samp))
cmdl = cmdl.reshape((ny_samp,nx_samp))
## save pilot point data and variogram parameters
pp = {'nx':nx_samp,
'ny':ny_samp,
'x':Xsamp,
'y':Ysamp,
'r':rmdl,
'c':cmdl,
'head':hds_samp,
'variogram_model':variogram_model,
'variogram_parameters' : variogram_parameters}
np.save('pilot_point_and_variogram_data', pp)
#####################################################
##### test variogram input parameters feature
OK = OrdinaryKriging(Xsamp.reshape(nsamp,),
Ysamp.reshape(nsamp,),
hk_samp.reshape(nsamp,),
variogram_model=variogram_model,
variogram_parameters = variogram_parameters,
verbose=False,
enable_plotting=False)
# kriging predictions (for testing)
nx_pred = 80
ny_pred = 50
npred = nx_pred*ny_pred
xpred = np.linspace(xul,-xul,nx_pred)
ypred = np.linspace(yul,-yul,ny_pred)
Xpred,Ypred = np.meshgrid(xpred,ypred)
hk_krig,ss = OK.execute('grid',xpred,ypred)
## PLOTS ##
# true K
fig = plt.figure()
ax = fig.gca()
hk_true = ax.imshow(hk, cmap = 'jet', extent =[xul, -xul, -yul, yul])
plt.colorbar(mappable = hk_true,ax = ax, label = 'True K')
ax.set_title('Pilot Point Locations')
sample_pts = ax.scatter(Xsamp,Ysamp, s = 15, facecolor = 'white', edgecolor = 'black', linewidth = 1)
ax.set_xlabel('x (m)', fontweight = 'bold', fontsize = 8)
ax.set_ylabel('y (m)', fontweight = 'bold', fontsize = 8)
# kriged K
fig = plt.figure()
ax = fig.gca()
hk_krig = ax.imshow(hk_krig.data, cmap = 'jet', extent =[xul, -xul, -yul, yul])
plt.colorbar(mappable = hk_krig,ax = ax, label = 'Kriged True K ')
ax.set_title('Pilot Point Locations')
sample_pts = ax.scatter(Xsamp,Ysamp, s = 15, facecolor = 'white', edgecolor = 'black', linewidth = 1)
ax.set_xlabel('x (m)', fontweight = 'bold', fontsize = 8)
ax.set_ylabel('y (m)', fontweight = 'bold', fontsize = 8)
# true heads
fig = plt.figure()
ax = fig.gca()
heads_true = ax.imshow(hds[0,:,:], cmap = 'jet', extent =[xul, -xul, -yul, yul], vmin = 10, vmax =12)
plt.colorbar(mappable = heads_true,ax = ax, label = 'True Head')
sample_pts = ax.scatter(Xsamp,Ysamp, s = 15, facecolor = 'white', edgecolor = 'black', linewidth = 1)
ax.set_title('Pilot Point Locations')
ax.set_xlabel('x (m)', fontweight = 'bold', fontsize = 8)
ax.set_ylabel('y (m)', fontweight = 'bold', fontsize = 8)
#plt.imshow(hds_samp, cmap = 'jet', extent =[xul, -xul, -yul, yul], vmin = 10, vmax =12)
###plt.imshow(hk, cmap = 'jet', extent =[xul, -xul, -yul, yul])
##
##plt.colorbar()
##plt.show()
plt.show()
| [
"noreply@github.com"
] | andrewtbanks.noreply@github.com |
88bcf722bf36eaa3bb2d33a58a45982947d04238 | ec8792f814a71bd7048d7121940631debcccdf73 | /moviEase/movieApp/migrations/0001_initial.py | 2bc07944c8f650459c06e407ddae5faf5baf8a3a | [
"Apache-2.0"
] | permissive | cs-fullstack-fall-2018/django-template-blocks1-jpark1914 | 4cb8da66aafca9ec633919cdd87f90f8440d0861 | 74586bb5b01c04f2513ca6d1aa2f7cef39e880cb | refs/heads/master | 2020-03-31T23:17:46.898296 | 2018-10-12T03:29:13 | 2018-10-12T03:29:13 | 152,649,682 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 658 | py | # Generated by Django 2.0.6 on 2018-10-11 16:08
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Movie',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=200)),
('yearReleased', models.IntegerField()),
('ageAllowed', models.IntegerField()),
('genre', models.CharField(max_length=200)),
],
),
]
| [
"parkerj4321@gmail.com"
] | parkerj4321@gmail.com |
5963e85f285677d3ba0718ab314b440f7d1e0962 | 01cc5be0b2d0b9d34dba8ae3b1c46c0cc7a320c1 | /random question.py | c4517566a76801cd37f8e9a2dca7a6bea26907a3 | [] | no_license | Kristof910/test-gitdesktop-repo | 21cb46a399c337a56a693421a407e363788b9a95 | 97513482977433804fd7d778b3fee410d85089f3 | refs/heads/master | 2022-04-03T10:26:31.371056 | 2020-02-09T22:07:56 | 2020-02-09T22:07:56 | 239,381,666 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,285 | py | import random
import os
def main():
questions = ["What is the purpose of a list (array in some programming languages) data structure? Name some methods of it!",
"What is the difference between a list/array and a set?",
"What is the purpose and methods of a dictionary/map data structure?",
"Fibonacci sequences. Write a method (or pseudo code), that generates the Fibonacci sequences.",
"How do you find the average of values in a list/array if you can’t use any built-in functions?",
"What do we call an *in-place* sort?",
"Explain an algorithm which sorts a list!",
"What is the call stack?",
"What is “Stack overflow”?",
"What are the main parts of a function?",
"How do you use a dictionary in Python?",
"What does it mean that an object is immutable in Python?",
"What is conditional expression in Python?",
"What are different types of arguments in Python?",
"What is variable shadowing? (context: variable scope)",
"What can happen if you try to delete/drop/add an item from a List, while you are iterating over it in Python?",
"What is the golden rule of variable scoping in Python (context: LEGB)? What is the lifetime of variables?",
"If you need to access the iterator variable after a for loop, how would you do it in Python?",
"What type of elements can a list contain in Python?",
"What is slice operator in Python and how to use?",
"What arithmetic operators (+,*,-,/) can be used on lists in Python? What do they do?",
"What is the purpose of the in and not in membership operators in Python?",
"What does the + operator mean when used with strings in Python?",
"Explain f strings in Python?",
"Name 4 iterable types in Python!",
"What is the difference between list/set/dictionary comprehension and a generator expression in Python?",
"Does the order of the function definitions matter in Python? Why?",
"What does unpacking mean in Python?",
"What happens when you try to assign the result of a function which has no return statement to a variable in Python?",
"What techniques can you use while debugging a program in Python?",
"What does step over, step into and step out mean while using the debugger?",
"How can you start to debug a program from a certain line using the debugger?",
"What are the advantages of using a version control system?",
"What is the difference between the working directory, the staging area and the repository in git?",
"What are remote repositories in git?",
"Why does a merge conflict occur?",
"Through what series of commands could you put a new file into a remote repository connected to your existing local repository?",
"What does it mean atomic commits and descriptive commit messages?",
"What’s the difference between git and GitHub?",
"What does clean code mean?",
"What steps do we usually do during a clean code refactoring?",
"What is exception handling?",
"What are the basics of exception handling in Python?",
"In which case should we catch an exception? Why?",
"What can/should we do with an exception in the ‘except’ block?",
"What does the else and finally statement do in a try-except block in Python?",
"What is the main goal of a retrospective meeting?",
"What is UNIX and what is Linux?",
"What do we call the shell in Linux?",
"What does root means in a Linux environment?",
"How do you access your personal files in Linux?",
"How can you install an application in Linux?",
"What is package management in Linux, what are repositories?",
"How do you navigate in the filesystem with the command line?",
"What does the following commands do: mkdir, rm, cat, cp, touch?",
"How can you look up what does a command do in Linux if you have no internet connection?",
"What does the following commands do: head, tail, more, less?",
"How do you download a file from internet using the terminal?"]
counter = 0
while True:
counter += 1
os.system("clear")
print(questions[random.randint(0, len(questions) - 1)])
print("\nQuestion asked: ", counter)
input("Press ENTER to get a new quesiton!")
if __name__ == "__main__":
main()
| [
"kristof910@icloud.com"
] | kristof910@icloud.com |
94c86e9730072b32529e4bd24889478de2d09576 | 0cc0bb76ecf846dd912a474aef1e12d2bc1d9c1e | /usexy/middlewares.py | dffcb959415f4f01562035136d16f3df7edc2460 | [] | no_license | Coder-Leo/usexy | f444b80b2ef5b1df465a613e1c9925755b260803 | 1771811ace0fd3125ccedc61d748bd7cf947f9ab | refs/heads/master | 2020-04-04T22:16:54.491129 | 2018-11-06T03:03:08 | 2018-11-06T03:03:08 | 156,317,457 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,595 | py | # -*- coding: utf-8 -*-
# Define here the models for your spider middleware
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/spider-middleware.html
from scrapy import signals
class UsexySpiderMiddleware(object):
# Not all methods need to be defined. If a method is not defined,
# scrapy acts as if the spider middleware does not modify the
# passed objects.
@classmethod
def from_crawler(cls, crawler):
# This method is used by Scrapy to create your spiders.
s = cls()
crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
return s
def process_spider_input(self, response, spider):
# Called for each response that goes through the spider
# middleware and into the spider.
# Should return None or raise an exception.
return None
def process_spider_output(self, response, result, spider):
# Called with the results returned from the Spider, after
# it has processed the response.
# Must return an iterable of Request, dict or Item objects.
for i in result:
yield i
def process_spider_exception(self, response, exception, spider):
# Called when a spider or process_spider_input() method
# (from other spider middleware) raises an exception.
# Should return either None or an iterable of Response, dict
# or Item objects.
pass
def process_start_requests(self, start_requests, spider):
# Called with the start requests of the spider, and works
# similarly to the process_spider_output() method, except
# that it doesn’t have a response associated.
# Must return only requests (not items).
for r in start_requests:
yield r
def spider_opened(self, spider):
spider.logger.info('Spider opened: %s' % spider.name)
class UsexyDownloaderMiddleware(object):
# Not all methods need to be defined. If a method is not defined,
# scrapy acts as if the downloader middleware does not modify the
# passed objects.
@classmethod
def from_crawler(cls, crawler):
# This method is used by Scrapy to create your spiders.
s = cls()
crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
return s
def process_request(self, request, spider):
# Called for each request that goes through the downloader
# middleware.
# Must either:
# - return None: continue processing this request
# - or return a Response object
# - or return a Request object
# - or raise IgnoreRequest: process_exception() methods of
# installed downloader middleware will be called
return None
def process_response(self, request, response, spider):
# Called with the response returned from the downloader.
# Must either;
# - return a Response object
# - return a Request object
# - or raise IgnoreRequest
return response
def process_exception(self, request, exception, spider):
# Called when a download handler or a process_request()
# (from other downloader middleware) raises an exception.
# Must either:
# - return None: continue processing this exception
# - return a Response object: stops process_exception() chain
# - return a Request object: stops process_exception() chain
pass
def spider_opened(self, spider):
spider.logger.info('Spider opened: %s' % spider.name)
| [
"2427755908@qq.com"
] | 2427755908@qq.com |
dd9b23ca9906e95806ee9c082e6d68f696591cb5 | c799d4b2b864f64392287a56861be613646261e1 | /dashboards/views/question.py | bcb7a0432fa90546cec77f99a9fd41d2abc08f55 | [
"MIT"
] | permissive | losdwind/WellBeingAnalysis | 35773d169239b981af39b7d5ad609ce1377d1459 | d8d8241fcba913ea550f18ad0a85c731591ed18d | refs/heads/main | 2023-06-09T01:40:25.224391 | 2021-06-28T16:17:08 | 2021-06-28T16:17:08 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,975 | py | import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import numpy as np
from dash.dependencies import Input, Output
from dashboards.maindash import app
from dashboards.data.collector import question_col
from dashboards.utils.graph import create_graph
from dashboards.utils.stats import question_stats
from dashboards.views.dropdowns import dropdowns
from dashboards.views.panel import panel
max_rating, min_rating, average_rating, size = question_stats(question_col.find())
fig = create_graph(average_rating)
def question_view():
return html.Div(
children=[
dropdowns(
"dropdown-1",
"dropdown-2",
"Domain",
question_col.distinct("question.domain"),
),
dropdowns(
"dropdown-3",
"dropdown-4",
"Subdomain",
question_col.distinct("question.subdomain"),
),
dropdowns(
"dropdown-5", "dropdown-6", "Date", question_col.distinct("date.date")
),
dropdowns(
"dropdown-7", "dropdown-8", "User", question_col.distinct("user")
),
panel(
size,
max_rating,
min_rating,
fig,
"entries",
"max-elem",
"min-elem",
"updated-graph",
),
]
)
@app.callback(
Output("dropdown-2", "options"),
Input("dropdown-1", "value"),
)
def update_domain_dropdown_options(selected_domain):
return [
{"label": k, "value": k}
for k in question_col.distinct("question.domain")
if selected_domain <= k
]
@app.callback(
Output("dropdown-4", "options"),
Input("dropdown-3", "value"),
)
def update_subdomain_dropdown_options(selected_subdomain):
return [
{"label": k, "value": k}
for k in question_col.distinct("question.subdomain")
if selected_subdomain <= k
]
@app.callback(
Output("dropdown-6", "options"),
Input("dropdown-5", "value"),
)
def update_date_dropdown_options(selected_date):
return [
{"label": k, "value": k}
for k in question_col.distinct("date.date")
if selected_date <= k
]
@app.callback(
Output("dropdown-8", "options"),
Input("dropdown-7", "value"),
)
def update_user_dropdown_options(selected_user):
return [
{"label": k, "value": k}
for k in question_col.distinct("user")
if selected_user <= k
]
@app.callback(
Output("entries", "children"),
Output("max-elem", "children"),
Output("min-elem", "children"),
Output("updated-graph", "figure"),
Input("dropdown-1", "value"),
Input("dropdown-2", "value"),
Input("dropdown-3", "value"),
Input("dropdown-4", "value"),
Input("dropdown-5", "value"),
Input("dropdown-6", "value"),
Input("dropdown-7", "value"),
Input("dropdown-8", "value"),
)
def update_figure(
first_domain,
second_domain,
first_subdomain,
second_subdomain,
first_date,
second_date,
first_user,
second_user,
):
cursor = question_col.find(
{
"question.domain": {"$gte": first_domain, "$lte": second_domain},
"question.subdomain": {
"$gte": first_subdomain,
"$lte": second_subdomain,
},
"date.date": {"$gte": first_date, "$lte": second_date},
"user": {"$gte": first_user, "$lte": second_user},
}
)
if cursor.count() <= 0:
fig = create_graph(0.00)
size = 0
max_rating = "No value"
min_rating = "No value"
return size, max_rating, min_rating, fig
max_rating, min_rating, average_rating, size = question_stats(cursor)
fig = create_graph(average_rating)
return size, max_rating, min_rating, fig
| [
"rmilhazesveloso@gmail.com"
] | rmilhazesveloso@gmail.com |
7fdad68d37f374cadf6b37e0f5f43a6d37e0cd7a | cfb773fb42ec401681f172a8d02a393fde5ba2aa | /preprocessing.py | 77691517459f00e4e4fb8cee7ec0d1eba7071037 | [] | no_license | Telomeresis/ns-project-1 | 19033527a91c5c1d190205965c63d8d200bf4ec3 | 36e1cbcfd250fa5e7d8879ba68dc93950d020a64 | refs/heads/main | 2023-07-03T20:35:47.023661 | 2021-08-12T06:17:38 | 2021-08-12T06:17:38 | 339,268,668 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 20,313 | py | # Import for scraping
import pandas as pd
import numpy as np
from bs4 import BeautifulSoup
import requests
import re
import pickle
import random
# Import for network and plot creation
import networkx as nx
from itertools import combinations
import matplotlib.pyplot as plt
def preprocessing():
# Step 1: Loading Provided Faculty Details
def load_faculty():
# Import faculty details into df
faculty_df = pd.read_excel('Faculty.xlsx')
# Select relevant columns
faculty_df = faculty_df[['Faculty', 'Position', 'Gender', 'Management', 'DBLP', 'Area']]
return faculty_df
faculty_df = load_faculty()
print('Step 1 Completed\n')
# Step 2: Scraping DBLP HTML
def scrape_dblp(faculty_df):
# Check if content_list.pkl exists.
try:
f = open("content_list.pkl")
with open('content_list.pkl', 'rb') as f:
content_list = pickle.load(f)
# Do something with the file
except IOError:
f = None
print("File not accessible")
# Create empty list for storing modified DBLP link to access XML variant w/ their API
xml_list = []
# Iterate over faculty_df and replace .html w/ .xml - updated to append .xml for missing .html cases
for each in faculty_df['DBLP']:
if '.html' in each:
replaced_each = each.replace(".html", ".xml")
xml_list.append(replaced_each)
# Declare list to store extracted content
content_list = []
i = 0
# Iterate using q_list to make a GET request to fetch raw HTML content
for each in xml_list:
html_content = requests.get(each).text
content_list.append(html_content)
i+=1
if (i % 10 == 0):
print(str(i)+' Records Retrieved')
# Store content_list with pickle
with open('content_list.pkl', 'wb') as f:
pickle.dump(content_list, f)
finally:
f.close()
return content_list
content_list = scrape_dblp(faculty_df)
print('Step 2 Completed\n')
# Step 3: Parsing DBLP HTML w/ BS4
def parse_dblp(content_list):
# Declare empty list for storing soups
pretty_soup_list = []
for each in content_list:
soup = BeautifulSoup(each, "lxml")
pretty_soup_list.append(soup.prettify())
# Store pretty_soup_list with pickle
with open('pretty_soup_list.pkl', 'wb') as f:
pickle.dump(pretty_soup_list, f)
return pretty_soup_list
pretty_soup_list = parse_dblp(content_list)
def parse_articles_conf(pretty_soup_list):
# Declare empty all_article_list
all_article_list = []
faculty_dblp_name_list = []
# Iterate over pretty_soup_list + extract names because given names in excel aren't same as DBLP lmao
for each in pretty_soup_list:
converted_each = BeautifulSoup(each, "lxml") # need to convert lmao
individual_article_list = converted_each.find_all('article')
individual_article_list += converted_each.find_all('inproceedings')
all_article_list.append(individual_article_list)
try:
faculty_dblp_name = converted_each.dblpperson['name']
except:
faculty_dblp_name = converted_each.title.text.strip().strip('dblp: ') # omg cancerous code sorry
finally:
#print(faculty_dblp_name)
faculty_dblp_name_list.append(faculty_dblp_name)
return all_article_list, faculty_dblp_name_list
all_article_list, faculty_dblp_name_list = parse_articles_conf(pretty_soup_list)
print('Step 3 Completed\n')
# Step 4.1: Declare DF for DBLP
COLUMN_NAMES=[
'f_index',
'Faculty',
'key',
'Year',
'Full Authors List'
]
dblp_df = pd.DataFrame(columns=COLUMN_NAMES)
# Step 4.2: DF population w/ Parsed DBLP Data:
def df_population(dblp_df):
# Index for doing dict mapping later
faculty_index = 0
# declare empty lists for DF
article_key_list = []
article_mdate_list = []
faculty_index_list = []
title_list = []
year_list = []
authors_list = []
for each in all_article_list:
for article in each:
# Article Tag Extraction w/ Array Indexing
article_key = article["key"]
article_mdate = article["mdate"]
# Strip processing
stripped_year = article.year.text.strip()
stripped_authors = [each.text.strip() for each in article.find_all('author')] # list comprehension; bad space and time complexity
# Append to df
append_dict = {'f_index': faculty_index, 'Faculty': '', 'key': article_key, 'Year': stripped_year, 'Full Authors List': stripped_authors}
dblp_df = dblp_df.append(append_dict, ignore_index=True)
faculty_index+=1
# Create dict mapping for Faculty, len is used as f_index.
faculty_dict_mapping = dict(zip(range(len(faculty_df['Faculty'])), faculty_df['Faculty'],))
dblp_df['Faculty'] = dblp_df['f_index'].map(faculty_dict_mapping)
# Store dblp_df with pickle
with open('dblp_12k_df.pkl', 'wb') as f:
pickle.dump(dblp_df, f)
return dblp_df
dblp_df = df_population(dblp_df)
print('Step 4 Completed\n')
# Step 5: Dataframe Post Processing (NLP & Metrics)
def find_name_form_in_list(fac_name, authors_list, dblp_df):
mod_fac_name = fac_name.replace("-", " ")
mod_fac_name_list = mod_fac_name.split(" ")
#print(mod_fac_name_list)
best_i = 0
best_count = 0
for i in range(len(authors_list)):
#check matchability
author = authors_list[i]
count = 0
for w in mod_fac_name_list:
if w in author:
count += 1
if count > best_count:
best_count = count
best_i = i
return authors_list[best_i]
dblp_df["published_name"] = dblp_df.apply(lambda row: find_name_form_in_list(row["Faculty"], row["Full Authors List"], dblp_df), axis = 1)
def ci_calculation(dblp_df):
# Code for inserting contribution into DF
contribution_index_list = []
for i, row in dblp_df.iterrows():
if (row['published_name'] in row['Full Authors List']): # check if DBLP name exists in Full Authors List
ci = row['Full Authors List'].index(row['published_name'])+1 # if so, retrieve index, +1 (to acccount for 0), then append to contribution_index_list
else:
ci = '-'
contribution_index_list.append(ci)
dblp_df['Author Contribution Index'] = contribution_index_list # assigns a value based on how much contribution the author has made for a publication. 1 = Highest (Main)
return dblp_df
dblp_df = ci_calculation(dblp_df)
authors_list = dblp_df['Full Authors List'].tolist()
dblp_df['Other Authors'] = dblp_df['Full Authors List'].copy() # assigns a value based on how much contribution the author has made for a publication. 1 = Highest (Main)
def prune_name_from_other_authors_list(fac_name, authors_list, dblp_df):
mod_fac_name = fac_name.replace("-", " ")
mod_fac_name_list = mod_fac_name.split(" ")
#print(mod_fac_name_list)
best_i = 0
best_count = 0
for i in range(len(authors_list)):
#check matchability
author = authors_list[i]
count = 0
for w in mod_fac_name_list:
if w in author:
count += 1
if count > best_count:
best_count = count
best_i = i
authors_list.remove(authors_list[best_i])
return authors_list
dblp_df.apply(lambda row: prune_name_from_other_authors_list(row["Faculty"], row["Other Authors"], dblp_df), axis = 1)
print('Step 5 Completed\n')
# Step 5.5: Output
# Store dblp_df with pickle
dblp_12k_processed_df = dblp_df
dblp_df.to_csv(r'dblp_df.csv', index = False)
with open('dblp_12k_processed_df.pkl', 'wb') as f:
pickle.dump(dblp_df, f)
# print('Step 5.5 Completed: Output Complete\n')
# Step 6: Retrieve unique, non-SCSE authors with distinguishable names
def filter_unique_authors(dblp_df):
# Iterate to fill up list of other authors
other_authors_list = []
for i, row in dblp_df.iterrows():
for each in row['Other Authors']:
other_authors_list.append(each)
# Convert to retrieve only unique author entries
other_authors_set = set(other_authors_list)
unique_authors_list = list(other_authors_set)
# Iterate to retrieve unique authors with distinguishable names
unique_authors_numberless_list = []
for each in unique_authors_list:
if re.search(r'\d', each) == None:
unique_authors_numberless_list.append(each)
return unique_authors_numberless_list
unique_authors_numberless_list = filter_unique_authors(dblp_df)
def remove_scse(main_list):
faculty_list = dblp_df['Faculty'].tolist()
unique_faculty_list = list(set(faculty_list))
for each in unique_faculty_list:
mod_fac_name = each.replace("-", " ")
mod_fac_name_list = mod_fac_name.split(" ")
#print(mod_fac_name_list)
best_i = 0
best_count = 0
for i in range(len(main_list)):
#check matchability
author = main_list[i]
count = 0
for w in mod_fac_name_list:
if w in author:
count += 1
if count > best_count:
best_count = count
best_i = i
main_list.remove(main_list[best_i])
return main_list
unique_authors_numberless_scseless_list = remove_scse(unique_authors_numberless_list)
print('Step 6 Completed\n')
# Step 7: Retrieve faculty members of interest
def retrieve_thousand_faculty(dblp_df):
ssb_df = dblp_df[dblp_df['Faculty'] == 'Sourav S Bhowmick']
# Iterate to fill up list of other authors
ssb_other_authors_list = []
for i, row in ssb_df.iterrows():
for each in row['Other Authors']:
ssb_other_authors_list.append(each)
# Convert to retrieve only unique author entries
ssb_unique_authors_list = list(set(ssb_other_authors_list))
# Iterate to retrieve unique authors with distinguishable names
ssb_unique_authors_numberless_list = []
for each in ssb_unique_authors_list:
if re.search(r'\d', each) == None:
ssb_unique_authors_numberless_list.append(each)
# Cross-compare and retrieve only non-scse sourav pals
sourav_pal_list = []
for each in unique_authors_numberless_scseless_list:
for ssb_each in ssb_unique_authors_numberless_list:
if ssb_each == each:
sourav_pal_list.append(ssb_each)
# Retrieve non-sourav pal authors from main list
remainder_authors_list = list(set(unique_authors_numberless_scseless_list).symmetric_difference(set(sourav_pal_list)))
# randomly chosen remainder people
random_chosen_list = random.sample(remainder_authors_list, 1000-len(sourav_pal_list))
return sourav_pal_list + random_chosen_list
thousand_apostles_list = retrieve_thousand_faculty(dblp_df)
#print(len(thousand_apostles_list))
print('Step 7 Completed\n')
# Step -: Store thousand faculty members with pickle
with open('1000_faculty.pkl', 'wb') as f:
pickle.dump(thousand_apostles_list, f)
print('Step 8 Completed\n')
# Step 9: From here on out, we need to write code do perform 2 kinds of actions:
# - load faculty graph and create initial graph of 85 people
# - load faculty graph and create augmented graph of 1085 people
# Step 9: Load faculty members
# Step 8.1: Create Faculty Graph
def create_faculty_network():
# Import faculty details into df
faculty_df = pd.read_excel('Faculty.xlsx')
# Select relevant columns
faculty_df = faculty_df[['Faculty', 'Position', 'Gender', 'Management', 'DBLP', 'Area']]
# Create dictionary
faculty_dict = faculty_df.to_dict('index')
# Declare empty List
faculty_list = []
# Iterate over faculty_dict to fill up faculty_list
for each in faculty_dict.items():
node_no = each[0]
faculty = each[1]['Faculty']
position = each[1]['Position']
gender = each[1]['Gender']
management = each[1]['Management']
dblp = each[1]['DBLP']
area = each[1]['Area']
faculty_list.append((node_no, {'Faculty': faculty}))
faculty_list.append((node_no, {'Position': position}))
faculty_list.append((node_no, {'Gender': gender}))
faculty_list.append((node_no, {'Management': management}))
faculty_list.append((node_no, {'DBLP': dblp}))
faculty_list.append((node_no, {'Area': area}))
# Declare empty new graph for faculty network
faculty_graph = nx.MultiGraph()
# Fill up empty graph w/ faculty_list
faculty_graph.add_nodes_from(faculty_list)
return faculty_graph
faculty_graph = create_faculty_network()
print('Step 9 Completed\n')
# Step 8.2: Plot and Save Faculty Network
def save_plot_faculty_network(faculty_graph):
# Set figure for graph
plt.figure(figsize=(35, 20))
# Draw the graph
nx.draw(faculty_graph, with_labels=True, font_size=10,
node_color='red', font_color='white', edge_color='grey', node_size=250)
# Save the graph
plt.savefig("collab_graph.png", dpi=326)
print('Step 10 Completed\n')
# Step KW
def kw_analysis(dblp_df):
dblp_multi_df = dblp_df[dblp_df['key'].duplicated(keep=False)]
dblp_multi_df.sort_values(by=['key'])
# get categorical uniques in dataframe
categorical_list = dblp_multi_df['key'].drop_duplicates().tolist()
# create list to store key:f_index_list mappins
key_findex_list = []
# use categorical uniques to return df records
for each in categorical_list:
# extract f_index values from returned df records into a list
mappings = dblp_multi_df[dblp_multi_df['key'] == each]['f_index'].tolist()
# create unique pair-wise combinations for mappings (needed for networkx)
mappings_pair = list(combinations(mappings, 2))
year = dblp_multi_df[dblp_multi_df['key'] == each]['Year'].iloc[0]
key_findex_list.append([mappings_pair, each, year])
# list comprehensions
pairings = np.array(key_findex_list)[:,[0,2]]
distinct_paired_edges = [x for x in pairings if x[0][0][0] != x[0][0][1]]
distinct_paired_edges = [[list(set(x[0])), x[1]] for x in distinct_paired_edges]
distinct_paired_edges = [[[y, x[1]]for y in x[0]] for x in distinct_paired_edges]
# removing the flipped ones i.e I only want (0,16) and not (16,0)
distinct_paired_edges = [[y for y in x if y[0][0] < y[0][1]] for x in distinct_paired_edges]
distinct_paired_edges = [[x[0][0], x[0][0], x[0][1]] for x in distinct_paired_edges]
edge_year_pairings = [[x[0][0], x[0][1], x[1]] for x in distinct_paired_edges]
edge_year_pairings = [tuple(x) for x in edge_year_pairings]
# save key_findex_list into pickle file for easy replication
file_name = "edge_year_pairings.pkl"
open_file = open(file_name, "wb")
pickle.dump(edge_year_pairings, open_file)
open_file.close()
# iterate over key_findex_list to populate initial network
for edge in edge_year_pairings:
faculty_graph.add_edge(edge[0], edge[1], label = edge[2])
# save faculty_graph into pickle file for easy replication
file_name = "faculty_graph.pkl"
open_file = open(file_name, "wb")
pickle.dump(faculty_graph, open_file)
open_file.close()
# Step 9.1: Create Augmented Faculty Graph
def create_augmented_faculty_network(dblp_12k_processed_df, thousand_apostles_list):
# Import faculty details into df
faculty_df = pd.read_excel('Faculty.xlsx')
# Select relevant columns
faculty_df = faculty_df[['Faculty', 'Position', 'Gender', 'Management', 'DBLP', 'Area']]
# Augment Previously created faculty_df w/ 1000 apostles
for new_faculty in thousand_apostles_list:
row = pd.Series([new_faculty, '-', '-', '-', '-', '-'], index=faculty_df.columns)
faculty_df = faculty_df.append(row, ignore_index=True)
# Create dictionary
faculty_dict = faculty_df.to_dict('index')
# Declare empty List
faculty_list = []
# Iterate over faculty_dict to fill up faculty_list
for each in faculty_dict.items():
node_no = each[0]
faculty = each[1]['Faculty']
position = each[1]['Position']
gender = each[1]['Gender']
management = each[1]['Management']
dblp = each[1]['DBLP']
area = each[1]['Area']
faculty_list.append((node_no, {'Faculty': faculty}))
faculty_list.append((node_no, {'Position': position}))
faculty_list.append((node_no, {'Gender': gender}))
faculty_list.append((node_no, {'Management': management}))
faculty_list.append((node_no, {'DBLP': dblp}))
faculty_list.append((node_no, {'Area': area}))
# Declare empty new graph for faculty network
faculty_graph_1k = nx.MultiGraph()
# Fill up empty graph w/ faculty_list
faculty_graph_1k.add_nodes_from(faculty_list)
# Assign f_index to all augmented faculty members list
faculty_df.insert(0, 'f_index', faculty_df.index.values.tolist())
# Segment to use previously-extracted DBLP raw data to map collaborations between faculty staff and 1k apostles
# Import dblp_df_2.csv as DF
dblp_df = dblp_12k_processed_df
unique_oa_list = dblp_df['Other Authors'].tolist()
faculty_1k_graph = create_augmented_faculty_network
print('Step 11 Completed\n')
# Step 9.2: Plot and Save Faculty Network
def save_plot_faculty_1k_network(faculty_1k_graph):
plt = faculty_1k_graph
# Set figure for graph
plt.figure(figsize=(200, 100))
# Draw the graph (with isolates removed)
nx.spring_layout(faculty_graph_1k, k=0.25, iterations=20)
nx.draw(faculty_graph_1k, with_labels=True, font_size=20,
node_color='red', font_color='white', edge_color='grey', node_size=1500)
# Save the graph
plt.savefig("faculty_graph_1k.png", dpi=50)
print('Step 12 Completed\n')
# Step 13: Output df and graph files
faculty_df = pd.read_excel('Faculty.xlsx')
faculty_df.to_csv('faculty_df.csv', index=False)
try:
with open('faculty_1k_graph.pkl', 'wb') as f:
pickle.dump(faculty_1k_graph, f)
except:
print()
print('Step 13 Completed\n')
| [
"telomeresis@gmail.com"
] | telomeresis@gmail.com |
84ee400f23c9aefc9ada9fc5f095513aefc36b5d | a860ad7bf734f446aa8386145c724be326df18e5 | /test_check_alive.py | 2ea7a61994aebc521551d40accb853b6b4ef3b13 | [] | no_license | jsylew/doctorwho-game | 300549ef43703fc07d4cb0f1b44e698b557728d7 | e807bfb2b493cfe96bc628f96e8f8ef2b625fede | refs/heads/master | 2020-12-10T05:11:16.286382 | 2020-01-13T04:18:18 | 2020-01-13T04:18:18 | 233,509,784 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 315 | py | from unittest import TestCase
from A3.fight import check_alive
class TestCheck_alive(TestCase):
def test_check_alive_yes(self):
player = {"HP": 5}
self.assertTrue(check_alive(player))
def test_check_alive_no(self):
player = {"HP": 0}
self.assertFalse(check_alive(player)) | [
"noreply@github.com"
] | jsylew.noreply@github.com |
cbd6e1bf2f4ef5451bfc4f4c2a31bd38dfeaa3e1 | 1952466ac4e09ba029dc7c0f764c8646457a62d2 | /t-shirt.py | 431096f91ada39fee3905bae907d89731230d280 | [] | no_license | marshalloffutt/functions | b38190fba39b6706b593cf50852600fd1e933153 | 05de247def352183fb25aceb9811ebd7f44fa492 | refs/heads/master | 2020-05-23T17:43:15.358514 | 2019-05-15T20:46:26 | 2019-05-15T20:46:26 | 186,873,301 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 259 | py | # 8-3 & 8-4
def make_shirt(text, size = 'large'):
print("One " + size + " t-shirt that says: ")
print("\t" + text)
make_shirt("hello world", "medium")
make_shirt(text="Hello world", size="small")
make_shirt("Hello world")
make_shirt("O'Doyle rules") | [
"MOffutt@trcintl.com"
] | MOffutt@trcintl.com |
23e21edc9da16c847fd7a7e47d1658b3ef0630dc | 5b9e510ae4ec7d2a25f82427a771217081755d19 | /downloadDPD.py | 15e25a7cf8c6c453e50e621c4d0dd8c6d711d896 | [] | no_license | jpike97/CFBAnalysis | b98d97fb15352325890dcdc9f34117ba69855c8f | 3539873528bf44ef257312b7a0732289cca375fa | refs/heads/main | 2023-09-03T00:34:19.741795 | 2021-11-19T23:03:46 | 2021-11-19T23:03:46 | 417,656,850 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 587 | py | from bs4 import BeautifulSoup
import requests
import json
import csv
dpdDogsPage = requests.get("http://dpdsdogs.com/ncaa/fbsrank.htm")
dpdDogsHTML = dpdDogsPage.content
soup = BeautifulSoup(dpdDogsHTML, 'html.parser')
data = []
table = soup.find('table')
rows = table.find_all('tr')
jsonfile = open('predictions/dpdRankings.json', 'w')
for row in rows:
cols = row.find_all('td')
cols = [ele.text.strip() for ele in cols]
data.append([ele for ele in cols if ele])
with open("dpdRankings.csv", "w", newline="") as f:
writer = csv.writer(f)
writer.writerows(data)
| [
"jpike@sagepath.com"
] | jpike@sagepath.com |
8c9eb7022dd4eea5d900989e2db7934c1d3ab201 | ba8c3052a8cf65f454f3b34076df6ec47ff12523 | /jsonpipelines.py | 3fd0ec1db27c46493079c588a4f3ad04f83f61c8 | [] | no_license | mybnzs/python_project | 044e58cbdba601f16ba50a19360e223fa13e65eb | 9926ed7b5591033461ec7d59e803c38abe56fe9f | refs/heads/master | 2020-03-27T22:59:37.776945 | 2018-09-04T06:08:30 | 2018-09-04T06:08:30 | 147,279,804 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 512 | py | # -*- coding: utf-8 -*-
# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html
import json
class ScrapydemoexamplePipeline(object):
def process_item(self, item, spider):
#print('item----',item)
jsonInfo=json.dumps(dict(item),ensure_ascii=False)
with open('musicInfo.json','a',encoding="utf-8") as wrStream:
wrStream.write(jsonInfo+"\n")
return item
| [
"mybnzs1229@163.com"
] | mybnzs1229@163.com |
bfc52e8bfdd29e2e465282d59f9833c791eb0446 | 2284069eb2617edaae325d50a1c56fd1bb5cef00 | /sr_topologies/__init__.py | c086e492a9fcb4b15ba1c82449847f7137c18242 | [] | no_license | gkalogeridou/P33 | a1324a73c44b0f0d31ba08c47b15e586fe625d7e | 6b9842e6a2f99ee00898725ae0e096672b1345a0 | refs/heads/master | 2020-12-27T09:43:05.543205 | 2014-09-04T14:21:25 | 2014-09-04T14:21:25 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 66 | py | """
CaMT - Cambridge Mininet Topologies
See README for details
""" | [
"ga288@cl.cam.ac.uk"
] | ga288@cl.cam.ac.uk |
fcb35e9d67611de681ee61b51b31f046ba484d02 | 456f2527f837ac12cff2bfc9ef1d4f65c1f24eeb | /webdjango/src/samsung_elasticsearch/wsgi.py | 4eabd7cd17b56984b41f1cf9d1774cfefdc175bc | [] | no_license | ammyblabla/basic_web_crawler | 435295169b3b27a3b3bd689bcb251c15dd449ecc | 4384e47514f21c2741c21cab1f27c178d70a6297 | refs/heads/master | 2020-03-27T04:37:46.130626 | 2018-11-23T04:46:49 | 2018-11-23T04:46:49 | 145,956,008 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 419 | py | """
WSGI config for samsung_elasticsearch project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/2.1/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'samsung_elasticsearch.settings')
application = get_wsgi_application()
| [
"tanaporn.tha@ku.th"
] | tanaporn.tha@ku.th |
216cb426bf718771d50f2175809be44e24a500e9 | fdd9e3131ead660db9485304438993a2a249fb1f | /src/npc/campaign/reorganizers/character_reorganizer.py | 8dd371ec895f29069f2f9a0994705dbfe635f0a1 | [
"MIT"
] | permissive | aurule/npc | 6807aa0723e765cb33fe5f5b49b0f579a6207153 | 2e1b2e92e2a4908d791846f184ee7e4de2f6682e | refs/heads/develop | 2023-09-02T02:46:47.900892 | 2023-08-30T17:31:00 | 2023-08-30T17:31:00 | 47,045,977 | 14 | 2 | MIT | 2023-08-18T20:49:12 | 2015-11-29T01:40:18 | Python | UTF-8 | Python | false | false | 1,926 | py | from npc.db import DB, character_repository
from npc.campaign import Pathfinder
from .base_reorganizer import BaseReorganizer
from .relocation_class import Relocation
class CharacterReorganizer(BaseReorganizer):
"""Reorganizer for characters
Gets the paths for all character files in the campaign.
"""
def __init__(self, campaign, db: DB = None, exists: bool = True):
"""Create a new CharacterReorganizer
Args:
campaign (Campaign): Campaign this is for
db (DB): Database to use. Defaults to global singleton (default: `None`)
exists (bool): Whether to limit ideal paths to existing directories (default: `True`)
"""
super().__init__()
self.campaign = campaign
self.db = db if db else DB()
self.exists = exists
self.pathfinder = Pathfinder(self.campaign, self.db)
def gather_paths(self):
"""Gather the ideal paths for character records
Iterates each character and uses our pathfinder to generate an ideal path. The current path is just
the record's file_path, so that needs to be populated correctly beforehand.
"""
for character in self.campaign.characters.all():
ideal_path = self.pathfinder.build_character_path(character, exists=self.exists)
ideal_filename = self.pathfinder.make_filename(character)
self.add_relocation(character.id, character.file_path, ideal_path / ideal_filename)
def after_move(self, relocation: Relocation):
"""Update the character's path after it's been moved
Args:
relocation (Relocation): The relocation that was just executed.
"""
query = character_repository.update_attrs_by_id(relocation.id,
{"file_loc": str(relocation.ideal_path)})
self.campaign.characters.apply_query(query)
| [
"paige.andrews@modolabs.com"
] | paige.andrews@modolabs.com |
1c21a62dadc880ee9c53de78cabfaa122f8ebdb3 | 5973c1f9c8946bd0c89396156984ace77b4795af | /ok_tests/q09.py | a36b0f60917d0e2e1bb68833d4f88ecfc9dfa390 | [] | no_license | braddelong/PS4S18E101b | 07eb87d68bc14b70d9662158267b05e4534b44c9 | ab4f3ee28d9d4324080e42c4e053a42c95d09ab9 | refs/heads/master | 2021-05-08T11:03:41.324602 | 2020-05-08T14:26:05 | 2020-05-08T14:26:05 | 119,880,518 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,054 | py | test = {
'name': '',
'points': 1,
'suites': [
{
'cases': [
{
'code': r"""
>>> import numpy as np
>>> np.isclose(YoverLfourone, 5)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> import numpy as np
>>> np.isclose(YKverLfourone, 25)
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> import numpy as np
>>> np.isclose(YoverLfourtwo, 4.5
True
""",
'hidden': False,
'locked': False
},
{
'code': r"""
>>> import numpy as np
>>> np.isclose(KoverLfourtwo, 22.4)
True
""",
'hidden': False,
'locked': False
}
],
'scored': True,
'setup': '',
'teardown': '',
'type': 'doctest'
}
]
}
| [
"brad.delong@gmail.com"
] | brad.delong@gmail.com |
dde263c1f634e501a3be5b9d48d1aa8402888cc7 | 1d57d947cb57f9170d21d757a862acc50e3ab025 | /teddybearcollector/settings.py | 04d740652eab84c1e08c4a236f9804db47037a65 | [] | no_license | xhaferllari11/teddy_bear_collector | 269329f0f23e6b1d44f71469c6a56f80ba6fbcc6 | fc9627b1845e242788b03c0df0dbfee71b6ee963 | refs/heads/master | 2022-12-08T23:49:19.743181 | 2020-01-05T22:39:52 | 2020-01-05T22:39:52 | 228,254,099 | 1 | 0 | null | 2022-12-08T03:22:46 | 2019-12-15T21:19:07 | Python | UTF-8 | Python | false | false | 3,232 | py | """
Django settings for teddybearcollector project.
Generated by 'django-admin startproject' using Django 2.2.6.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.2/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '*c6-58#%c9ju^d*@!x5q8ad-_hl-_^(q%n%h*#vrj0-891__v9'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'main_app',
'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 = 'teddybearcollector.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 = 'teddybearcollector.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'tbcollector',
}
}
# Password validation
# https://docs.djangoproject.com/en/2.2/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/2.2/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/2.2/howto/static-files/
STATIC_URL = '/static/'
LOGIN_REDIRECT_URL = '/teddys/'
LOGOUT_REDIRECT_URL = '/'
import django_heroku
django_heroku.settings(locals()) | [
"alban.xhaferllari@gmail.com"
] | alban.xhaferllari@gmail.com |
3dd068856c18b39fa67d0eb6e00557e6ea1988b0 | ca9973566bae31dfd6be75062b5b7f058b0db11d | /game_stats.py | 0d0d38dab97aa8ccff0abac52ede47931552f589 | [] | no_license | IkarGhosts/Old_Galaxy | 30869e090dba15028554f23bb888cc0af1da1e51 | f5cc89fc55a1f75fada49bcd72cf928a545eec7f | refs/heads/master | 2020-05-05T01:38:50.391599 | 2019-06-07T10:14:10 | 2019-06-07T10:14:10 | 179,610,022 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 278 | py | class GameStats():
"""Отслеживание статистики для игры"""
def __init__(self, ai_settings):
self.ai_settings = ai_settings
self.reset_stats()
def reset_stats(self):
self.ships_left = self.ai_settings.ship_limit
| [
"ikarghosts@hotmail.com"
] | ikarghosts@hotmail.com |
4c109c431be0f005cacdb8fcdccf408189403022 | c0f5ccd1e9fbffff1133ffcfabaa71b4931a92cf | /scripts/partial3.py | 8ae3d85da675c25d04a1d0ac45695a3503d23b6f | [] | no_license | shirtd/prelim | 9ed82bba34531ffa28bb6daf1386ad675066fcd1 | c789713c851563726c19932af454f36f7a3fee62 | refs/heads/master | 2023-03-27T06:18:04.523941 | 2021-03-25T21:17:27 | 2021-03-25T21:17:27 | 257,415,279 | 0 | 0 | null | 2020-08-27T16:36:20 | 2020-04-20T22:09:00 | TeX | UTF-8 | Python | false | false | 8,216 | py | import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import numpy.linalg as la
import numpy as np
import os, sys
from util.plot import SurfacePlot, SurfaceCut
from util.data import *
import dionysus as dio
from mayavi import mlab
SEED = 4869361 # np.random.randint(10000000) #
print('seed: %d' % SEED)
np.random.seed(SEED)
SURF_ARGS = { 'A' : {'min' : CUTS[0], 'max' : CUTS[1], 'color' : COLOR['green'], 'opacity' : 0.5},#, 'backface_culling' : True},
'B' : {'min' : CUTS[1], 'max' : CUTS[2], 'color' : COLOR['blue'], 'opacity' : 0.5},
'C' : {'min' : CUTS[2], 'max' : CUTS[3], 'color' : COLOR['purple'], 'opacity' : 0.5},
'D' : {'min' : CUTS[3], 'max' : CUTS[4], 'color' : COLOR['yellow'], 'opacity' : 0.5}}
CONT_ARGS = { 'A_c' : {'scalar' : [CUTS[0]], 'color' : COLOR['black']},
'B_c' : {'scalar' : [CUTS[2]], 'color' : COLOR['blue']},
'C_c' : {'scalar' : [CUTS[3]], 'color' : COLOR['purple']}}
VIEW = {'default' : 'top',
'side' : { 'view' : (-11.800830502323867, 80.88795919149756, 9.035877007511106,
np.array([-1.00787402, 1.01587307, 0.6490444])),
'zoom' : 1.6, 'roll' : -89},
'top' : { 'view' : (0.0, 0.0, 8.291977298839994,
np.array([-1.00648859, 1.05123171, 0.67399999])),
'zoom' : 1.6, 'roll' : -80}}
def grid_coord(v, N):
return [v//N, v%N]
if __name__ == "__main__":
# DIR = os.path.join('figures', 'cover')
DIR = os.path.join('figures', 'partial3')
if not os.path.exists(DIR):
os.mkdir(DIR)
SAVE = True
AMT = 2
OFF = 1.25
N, WIDTH, HEIGHT = 32, 2, 1
NOISE_AMT = 0.05
THRESH = OFF * (np.sqrt(2 * (2 / N) ** 2) / 2 + NOISE_AMT)
# N, WIDTH, HEIGHT = 512, 2, 1
X_RNG = np.linspace(-WIDTH,WIDTH,WIDTH*N)
Y_RNG = np.linspace(-HEIGHT,HEIGHT,HEIGHT*N)
X, Y = np.meshgrid(X_RNG, Y_RNG)
G = mk_gauss(X, Y, GAUSS_ARGS)
surf = SurfacePlot(X, Y, G, SURF_ARGS, CONT_ARGS, VIEW)
surf.reset_view('top')
# surf['cont']['A_c']['visible'] = True
# surf['cont']['A_c']._o.actor.property.line_width = 10
# P = np.array([[Y[i,j]-1, X[i,j]+1, G[i,j]] for i in range(G.shape[0]) for j in range(G.shape[1]) if G[i,j] > 0.03 and np.random.rand() > 1/AMT]) # *G.max()
P = np.array([[Y[i,j]-1, X[i,j]+1, G[i,j]] for i in range(G.shape[0]) for j in range(G.shape[1]) if np.random.rand() < 1/AMT and G[i,j] >= CUTS[0]]) # *G.max()
Z = (1.1+np.zeros(len(P))) * G.max()
NOISE = np.hstack(((2*np.random.rand(len(P), 2) - 1) * NOISE_AMT/2, np.zeros((len(P),1))))
PN = P + NOISE
p = mlab.points3d(PN[:,0], PN[:,1], Z, color=(0,0,0), scale_factor=0.02)
p.actor.property.lighting = False
p.glyph.glyph_source.glyph_source.phi_resolution = 16
p.glyph.glyph_source.glyph_source.theta_resolution = 16
D = np.vstack((Y.flatten()-1, X.flatten()+1)).T
I = [i for i,v in enumerate(G.flatten()) if 0.0 < v <= 0.06]
B = D[I]
Qidx = [i for i,p in enumerate(PN) if min(la.norm(p[:2] - q) for q in B) <= 2*THRESH]# or p[2] <= 0.05]
Nidx = list(set(range(len(PN))) - set(Qidx))
b = mlab.points3d(PN[:,0], PN[:,1], Z[:], color=COLOR['red'], scale_factor=2*THRESH, opacity=0.2)
b.actor.property.lighting = False
b.actor.property.frontface_culling = True
b.glyph.glyph_source.glyph_source.phi_resolution = 32
b.glyph.glyph_source.glyph_source.theta_resolution = 32
# b = mlab.points3d(PN[Nidx,0], PN[Nidx,1], Z[Nidx], color=COLOR['red'], scale_factor=2*THRESH, opacity=0.2)
# b.actor.property.lighting = False
# b.actor.property.frontface_culling = True
# b.glyph.glyph_source.glyph_source.phi_resolution = 32
# b.glyph.glyph_source.glyph_source.theta_resolution = 32
#
# c = mlab.points3d(PN[Qidx,0], PN[Qidx,1], Z[Qidx], color=COLOR['blue'], scale_factor=2*THRESH, opacity=0.2)
# c.actor.property.lighting = False
# c.actor.property.frontface_culling = True
# c.glyph.glyph_source.glyph_source.phi_resolution = 32
# c.glyph.glyph_source.glyph_source.theta_resolution = 32
b.visible = False
# c.visible = False
R = dio.fill_rips(PN[:,:2], 2, 2*THRESH)
T = [list(s) for s in R if s.dimension()==2]
# val = np.array([[G[v//G.shape[0], v%G.shape[1]] for v in t] for t in T])
t = mlab.triangular_mesh(PN[:,0], PN[:,1], Z, T, opacity=0.5, scalars=PN[:,2], color=COLOR['red'])
t.actor.property.lighting = False
t.actor.mapper.scalar_visibility = False
t.enable_contours = True
TRI_ARGS = {k : v for k,v in SURF_ARGS.items() if v['max'] <= t.contour.maximum_contour and v['min'] >= t.contour.minimum_contour}
TRI_ARGS['A'] = SURF_ARGS['A'].copy()
TRI_ARGS['D'] = SURF_ARGS['D'].copy()
TRI_ARGS['A']['min'] = t.contour.minimum_contour
TRI_ARGS['D']['max'] = t.contour.maximum_contour
t.enable_contours = False
t.actor.mapper.scalar_visibility = False
tri_cuts = {k : SurfaceCut(t.parent, k, **v) for k,v in TRI_ARGS.items()}
# for k,v in tri_cuts.items():
# v._o.actor.property.edge_visibility = True
# v._o.actor.property.line_width = 0.1
# t.
t.actor.property.representation = 'wireframe'
t.actor.property.color = (0,0,0)
# t.visible = False
# t.actor.property.lighting = False
# t.actor.mapper.scalar_visibility = False
# t.enable_contours = True
# TRI_ARGS = {'P' : {'min' : 0, 'max' : 0.1, 'color' : COLOR['red'], 'opacity' : 0.2},
# 'Q' : {'min' : 0.1, 'max' : 1, 'color' : COLOR['blue'], 'opacity' : 0.2}}
# t.enable_contours = False
# t.actor.mapper.scalar_visibility = False
# tri_cuts = {k : SurfaceCut(t.parent, k, **v) for k,v in TRI_ARGS.items()}
# t.visible = False
#
# tri_cuts['Q']._o.contour.auto_update_range = False
# tri_cuts['Q']._o.contour._data_max = 2.0
# tri_cuts['Q']._o.contour.maximum_contour = 2.0
# SurfaceCut(t.parent, 'A', min=t.contour.minimum_contour, max=0.3, color=SURF_ARGS['A']['color'], opacity=0.5)
# surf.save(os.path.join(DIR, 'dump.png'), (10,10))
# surf.save(os.path.join(DIR, 'dump.png'), (10,10))
# surf.save(os.path.join(DIR, 'complex.png'), (3000,3000))
if SAVE:
surf.save(os.path.join(DIR, 'dump.png'), (10,10))
surf.save(os.path.join(DIR, 'dump.png'), (10,10))
surf.save(os.path.join(DIR, 'complex.png'), (3000,3000))
t.parent.visible = False
b.visible = True
# c.visible = True
surf.save(os.path.join(DIR, 'cover.png'), (3000,3000))
b.visible = False
# c.visible = False
surf.save(os.path.join(DIR, 'samples.png'), (3000,3000))
p.visible = False
surf.save(os.path.join(DIR, 'surf.png'), (3000,3000))
#
# if SAVE:
# surf.save(os.path.join(DIR, 'complex1.png'), (3000,3000))
#
# tri_cuts = {k : SurfaceCut(t.parent, k, **v) for k,v in TRI_ARGS.items()}
# t.visible = False
#
# if SAVE:
# surf.save(os.path.join(DIR, 'scalar1.png'), (3000,3000))
#
# # for k,v in tri_cuts.items():
# # v['visible'] = False
# #
# # R = dio.fill_rips(PN[:,:2], 2, 4*THRESH)
# # T = [list(s) for s in R if s.dimension()==2]
# # val = np.array([[G[v//G.shape[0], v%G.shape[1]] for v in t] for t in T])
# # t = mlab.triangular_mesh(PN[:,0], PN[:,1], Z, T, opacity=0.5, scalars=PN[:,2], color=COLOR['red'])
# # t.actor.property.lighting = False
# # t.actor.mapper.scalar_visibility = False
# # t.enable_contours = True
# # TRI_ARGS = {k : v for k,v in SURF_ARGS.items() if v['max'] < t.contour.maximum_contour and v['min'] > t.contour.minimum_contour}
# # t.enable_contours = False
# # t.actor.mapper.scalar_visibility = False
# #
# # if SAVE:
# # surf.save(os.path.join(DIR, 'complex2.png'), (3000,3000))
# #
# # tri_cuts = {k : SurfaceCut(t.parent, k, **v) for k,v in TRI_ARGS.items()}
# # t.visible = False
# #
# # if SAVE:
# # surf.save(os.path.join(DIR, 'scalar2.png'), (3000,3000))
# #
# # # s = mlab.surf(X.T, Y.T, G)
| [
"kirk@apple.local"
] | kirk@apple.local |
29df1b79e74bd7267776cc1b058070ef3e71e7db | 150d9e4cee92be00251625b7f9ff231cc8306e9f | /ShortestWordDistance.py | b282416e497d2bd6465870fb8734ed834ae5c3ea | [] | no_license | JerinPaulS/Python-Programs | 0d3724ce277794be597104d9e8f8becb67282cb0 | d0778178d89d39a93ddb9b95ca18706554eb7655 | refs/heads/master | 2022-05-12T02:18:12.599648 | 2022-04-20T18:02:15 | 2022-04-20T18:02:15 | 216,547,245 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,215 | py | '''
243. Shortest Word Distance
Given a list of words and two words word1 and word2, return the shortest distance between these two words in the list.
Example:
Assume that words= ["practice", "makes", "perfect", "coding", "makes"].
Input: word1 = "coding", word2 = "practice"
Output: 3
Input: word1 = "makes", word2 = "coding"
Output: 1
Note:
You may assume that word1 does not equal to word2 and word1 and word2 are both in the list.
'''
class Solution(object):
def shortestDistance(self, words, word1, word2):
"""
:type words: list
:type word1: str
:type word2: str
:rtype: int
"""
word_dict = {}
for index in range(len(words)):
if word_dict.has_key(words[index]):
word_dict[words[index]].append(index)
else:
temp = [index]
word_dict[words[index]] = temp
temp_list1 = word_dict[str(word1)]
temp_list2 = word_dict[word2]
result = len(words)
for i in temp_list1:
for j in temp_list2:
result = min(abs(i - j), result)
return result
obj = Solution()
print(obj.shortestDistance(["practice", "makes", "perfect", "coding", "makes"], "coding", "practice"))
print(obj.shortestDistance(["practice", "makes", "perfect", "coding", "makes"], "makes", "coding")) | [
"jerinsprograms@gmail.com"
] | jerinsprograms@gmail.com |
3e4763fc02a1b9d1e3dc377bef8ce6a4f0c4f819 | 09a58fcb72ff3ac697f8a51059dafc680ed9f700 | /aula7/teste2.py | 6ec88b7ec7d90a33ae36863f2551fa212c9133df | [] | no_license | brunozupp/TreinamentoPython | af0e7b59265c720d58858edd767729e83856d17e | 93cac18c0249b5b80c3b406308b6102e6c7fb48b | refs/heads/master | 2023-08-04T09:31:30.667397 | 2021-09-21T20:02:25 | 2021-09-21T20:02:25 | 402,187,048 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 474 | py | if __name__ == '__main__':
nome = input("Qual é seu nome: ")
print('Prazer em te conhecer {:20}!'.format(nome))
print('Prazer em te conhecer {:>20}!'.format(nome)) # alinhado a direita
print('Prazer em te conhecer {:<20}!'.format(nome)) # alinhado a esquerda
print('Prazer em te conhecer {:^20}!'.format(nome)) # centralizado em 20 espaços
print('Prazer em te conhecer {:=^20}!'.format(nome)) # centralizado preenchendo os espaços em branco com = | [
"brunimzuppwolf@gmail.com"
] | brunimzuppwolf@gmail.com |
403ac671cd2d453ae487fd222034d913875fbf84 | acd41dc7e684eb2e58b6bef2b3e86950b8064945 | /res/packages/scripts/scripts/client/CustomEffectManager.py | c48c868ffdc6cfeced96947c76740f0f529b2281 | [] | no_license | webiumsk/WoT-0.9.18.0 | e07acd08b33bfe7c73c910f5cb2a054a58a9beea | 89979c1ad547f1a1bbb2189f5ee3b10685e9a216 | refs/heads/master | 2021-01-20T09:37:10.323406 | 2017-05-04T13:51:43 | 2017-05-04T13:51:43 | 90,268,530 | 0 | 0 | null | null | null | null | WINDOWS-1250 | Python | false | false | 9,781 | py | # 2017.05.04 15:19:58 Střední Evropa (letní čas)
# Embedded file name: scripts/client/CustomEffectManager.py
import math
import Math
import material_kinds
from helpers.PixieNode import PixieCache
from CustomEffect import EffectSettings
from svarog_script.py_component import Component
from vehicle_systems.tankStructure import TankNodeNames
from AvatarInputHandler import mathUtils
_ENABLE_VALUE_TRACKER = False
_ENABLE_VALUE_TRACKER_ENGINE = False
_ENABLE_PIXIE_TRACKER = False
class CustomEffectManager(Component):
_LEFT_TRACK = 1
_RIGHT_TRACK = 2
_DRAW_ORDER_IDX = 50
@property
def variables(self):
return self.__variableArgs
def __init__(self, appearance):
if _ENABLE_VALUE_TRACKER or _ENABLE_VALUE_TRACKER_ENGINE or _ENABLE_PIXIE_TRACKER:
from helpers.ValueTracker import ValueTracker
self.__vt = ValueTracker.instance()
else:
self.__vt = None
self.__selectors = []
self.__variableArgs = dict()
self.__vehicle = None
self.__appearance = appearance
self.__engineState = appearance.detailedEngineState
self.__prevWaterHeight = None
self.__gearUP = False
self.__trailParticleNodes = None
self.__engineState.setGearUpCallback(self.__gearUp)
args = {}
args['chassis'] = {}
args['chassis']['model'] = appearance.compoundModel
args['hull'] = {}
args['hull']['model'] = appearance.compoundModel
args['drawOrderBase'] = CustomEffectManager._DRAW_ORDER_IDX
for desc in appearance.typeDescriptor.hull['customEffects']:
if desc is not None:
selector = desc.create(args)
if selector is not None:
self.__selectors.append(selector)
for desc in appearance.typeDescriptor.chassis['customEffects']:
if desc is not None:
selector = desc.create(args)
if selector is not None:
self.__selectors.append(selector)
self.__createChassisCenterNodes()
PixieCache.incref()
return
def getParameter(self, name):
return self.__variableArgs.get(name, 0.0)
def destroy(self):
self.deactivate()
for effectSelector in self.__selectors:
effectSelector.destroy()
PixieCache.decref()
self.__engineState.delGearUpCallback()
self.__trailParticleNodes = None
self.__selectors = None
self.__engineState = None
self.__appearance = None
self.__variableArgs = None
if _ENABLE_PIXIE_TRACKER:
self.__vt.addValue2('Pixie Count', PixieCache.pixiesCount)
if _ENABLE_VALUE_TRACKER or _ENABLE_VALUE_TRACKER_ENGINE or _ENABLE_PIXIE_TRACKER:
self.__vt = None
return
def enable(self, enable, settingsFlags = EffectSettings.SETTING_DUST):
for effectSelector in self.__selectors:
if effectSelector.settingsFlags() == settingsFlags:
if enable:
effectSelector.start()
else:
effectSelector.stop()
def setVehicle(self, vehicle):
self.__vehicle = vehicle
def activate(self):
super(CustomEffectManager, self).activate()
for effectSelector in self.__selectors:
effectSelector.start()
def deactivate(self):
for effectSelector in self.__selectors:
effectSelector.stop()
self.__vehicle = None
super(CustomEffectManager, self).deactivate()
return
def __createChassisCenterNodes(self):
compoundModel = self.__appearance.compoundModel
self.__trailParticleNodes = [compoundModel.node(TankNodeNames.TRACK_LEFT_MID), compoundModel.node(TankNodeNames.TRACK_RIGHT_MID)]
def getTrackCenterNode(self, trackIdx):
return self.__trailParticleNodes[trackIdx]
def __gearUp(self):
self.__gearUP = True
def update(self):
vehicleSpeed = self.__vehicle.speedInfo.value[2]
appearance = self.__appearance
self.__variableArgs['speed'] = vehicleSpeed
self.__variableArgs['isPC'] = self.__vehicle.isPlayerVehicle
direction = 1 if vehicleSpeed >= 0.0 else -1
self.__variableArgs['direction'] = direction
self.__variableArgs['rotSpeed'] = self.__vehicle.speedInfo.value[1]
matKindsUnderTracks = getCorrectedMatKinds(appearance)
self.__variableArgs['deltaR'], self.__variableArgs['directionR'], self.__variableArgs['matkindR'] = self.__getScrollParams(appearance.trackScrollController.rightScroll(), appearance.trackScrollController.rightContact(), matKindsUnderTracks[CustomEffectManager._RIGHT_TRACK], direction)
self.__variableArgs['deltaL'], self.__variableArgs['directionL'], self.__variableArgs['matkindL'] = self.__getScrollParams(appearance.trackScrollController.leftScroll(), appearance.trackScrollController.leftContact(), matKindsUnderTracks[CustomEffectManager._LEFT_TRACK], direction)
matInv = Math.Matrix(self.__vehicle.matrix)
matInv.invert()
velocityLocal = matInv.applyVector(self.__vehicle.filter.velocity)
velLen = velocityLocal.length
if velLen > 1.0:
vehicleDir = Math.Vector3(0.0, 0.0, 1.0)
velocityLocal = Math.Vector2(velocityLocal.z, velocityLocal.x)
cosA = velocityLocal.dot(Math.Vector2(vehicleDir.z, vehicleDir.x)) / velLen
self.__variableArgs['hullAngle'] = math.acos(mathUtils.clamp(0.0, 1.0, math.fabs(cosA)))
else:
self.__variableArgs['hullAngle'] = 0.0
self.__variableArgs['isUnderWater'] = 1 if appearance.isUnderwater else 0
self.__correctWaterNodes()
self.__variableArgs['gearUp'] = self.__gearUP
self.__variableArgs['RPM'] = self.__engineState.relativeRPM
self.__gearUP = False
self.__variableArgs['engineLoad'] = self.__engineState.mode
self.__variableArgs['engineState'] = self.__engineState.engineState
self.__variableArgs['engineStart'] = self.__engineState.starting
self.__variableArgs['physicLoad'] = self.__engineState.physicLoad
for effectSelector in self.__selectors:
effectSelector.update(self.__variableArgs)
if _ENABLE_VALUE_TRACKER:
self.__vt.addValue2('speed', self.__variableArgs['speed'])
self.__vt.addValue2('direction', self.__variableArgs['direction'])
self.__vt.addValue2('rotSpeed', self.__variableArgs['rotSpeed'])
self.__vt.addValue2('deltaR', self.__variableArgs['deltaR'])
self.__vt.addValue2('deltaL', self.__variableArgs['deltaL'])
self.__vt.addValue2('hullAngle', self.__variableArgs['hullAngle'])
self.__vt.addValue2('isUnderWater', self.__variableArgs['isUnderWater'])
self.__vt.addValue2('directionR', self.__variableArgs['directionR'])
self.__vt.addValue2('directionL', self.__variableArgs['directionL'])
if self.__variableArgs['matkindL'] > -1:
materialL = material_kinds.EFFECT_MATERIAL_INDEXES_BY_IDS[self.__variableArgs['matkindL']]
self.__vt.addValue('materialL', material_kinds.EFFECT_MATERIALS[materialL])
else:
self.__vt.addValue('materialL', 'No')
if self.__variableArgs['matkindR'] > -1:
materialR = material_kinds.EFFECT_MATERIAL_INDEXES_BY_IDS[self.__variableArgs['matkindR']]
self.__vt.addValue('materialR', material_kinds.EFFECT_MATERIALS[materialR])
else:
self.__vt.addValue('materialR', 'No')
if _ENABLE_VALUE_TRACKER_ENGINE:
self.__vt.addValue2('engineStart', self.__variableArgs['engineStart'])
self.__vt.addValue2('gearUP', self.__variableArgs['gearUp'])
self.__vt.addValue2('RPM', self.__variableArgs['RPM'])
self.__vt.addValue2('engineLoad', self.__engineState.mode)
self.__vt.addValue2('physicLoad', self.__engineState.physicLoad)
if _ENABLE_PIXIE_TRACKER:
self.__vt.addValue2('Pixie Count', PixieCache.pixiesCount)
@staticmethod
def __getScrollParams(trackScrolldelta, hasContact, matKindsUnderTrack, direction):
matKind = -1
scrollDelta = 0.0
if hasContact:
scrollDelta = trackScrolldelta
matKind = matKindsUnderTrack
if scrollDelta != 0.0:
direction = 1 if scrollDelta >= 0.0 else -1
scrollDelta = abs(scrollDelta)
return (scrollDelta, direction, matKind)
def __correctWaterNodes(self):
waterHeight = 0.0 if not self.__appearance.isInWater else self.__appearance.waterHeight
if waterHeight != self.__prevWaterHeight:
invVehicleMatrix = Math.Matrix(self.__appearance.compoundModel.matrix)
invVehicleMatrix.invert()
waterShiftRel = invVehicleMatrix.applyVector(Math.Vector3(0, waterHeight, 0))
for effectSelector in self.__selectors:
for node in effectSelector.effectNodes:
if node is not None:
node.correctWater(waterShiftRel)
self.__prevWaterHeight = waterHeight
return
def getCorrectedMatKinds(vehicleAppearance):
correctedMatKinds = [ (material_kinds.WATER_MATERIAL_KIND if vehicleAppearance.isInWater else matKind) for matKind in vehicleAppearance.terrainMatKind ]
return correctedMatKinds
# okay decompyling C:\Users\PC\wotmods\files\originals\res\packages\scripts\scripts\client\CustomEffectManager.pyc
# decompiled 1 files: 1 okay, 0 failed, 0 verify failed
# 2017.05.04 15:19:58 Střední Evropa (letní čas)
| [
"info@webium.sk"
] | info@webium.sk |
a7625641cc8660014bc8bbe5ff844087c7cb99dd | 471bf60a824d5466c0a917e175528aec811e6c01 | /2016-01-Shenzhen/Team 08 - Service-Chaining in Openflow/odl_service_chain/src/services/flowmanage.py | 4cd85f69a5a1f3d1f4adc7909b97c7deb41a06b3 | [] | no_license | donaldh/opendaylight-bootcamps | 4468c5629218c4274be5656bbd9cb0015c9035f7 | 03268f9b8db88e03692f44590960f97185fa05d3 | refs/heads/master | 2020-12-25T21:56:01.486848 | 2016-05-13T01:27:24 | 2016-05-13T01:27:24 | 58,688,772 | 0 | 1 | null | 2016-05-13T01:20:03 | 2016-05-13T01:20:03 | null | UTF-8 | Python | false | false | 6,432 | py | __author__ = 'cmcc'
import utils.openflowutils as ofutils
import utils.constants as const
from utils.restconf import ODLRestRequest
from xml.dom.minidom import Document
global auto_flow_id
auto_flow_id = 0
class OFManager(object):
def __init__(self, ctr_ip=None):
self.controller_ip = ctr_ip if ctr_ip else const.DEFAULT_ODL_CONTROLLER_IP
self.odlRequest = ODLRestRequest(self.controller_ip)
def write_flow(self, flowbody):
return self.odlRequest.config_openflow(self.controller_ip, flowbody)
def get_input_elem(self, doc):
input = doc.createElement('input')
input.setAttribute("xmlns","urn:opendaylight:flow:service")
doc.appendChild(input)
return input
def get_node_elem(self, node_id, doc):
node = doc.createElement("node")
node.setAttribute("xmlns:inv","urn:opendaylight:inventory")
node_id = doc.createTextNode(node_id)
node.appendChild(node_id)
return node
def get_table_elem(self, table_id, doc):
table = doc.createElement("table_id")
table_id = doc.createTextNode(str(table_id))
table.appendChild(table_id)
return table
def get_priority_elem(self, priority, doc):
priority_node = doc.createElement("priority")
prio_value = doc.createTextNode(str(priority))
priority_node.appendChild(prio_value)
return priority_node
def convert_dict_to_xml(self, dict_obj, doc):
node_list = []
for key, value in dict_obj.iteritems():
key_node = doc.createElement(key)
if isinstance(value, dict):
value_nodes = self.convert_dict_to_xml(value,doc)
for node in value_nodes:
key_node.appendChild(node)
elif isinstance(value, list):
value_nodes = self.get_list_node(value, doc)
for node in value_nodes:
key_node.appendChild(node)
else:
value = str(value)
value_node = doc.createTextNode(str(value))
key_node.appendChild(value_node)
node_list.append(key_node)
return node_list
def get_flow_match(self, match, doc):
match_nodes = self.convert_dict_to_xml(match, doc)
return match_nodes
def get_list_node(self, value_list, doc):
node_list = []
for value in value_list:
if isinstance(value, str) or isinstance(value, int):
value_node = doc.createTextNode(value)
node_list.append(value_node)
elif isinstance(value,dict):
value_nodes = self.convert_dict_to_xml(value, doc)
node_list.extend(value_nodes)
return node_list
def convert_flow_toxml(self, node_id, table_id, priority, matches, action_list):
doc = Document()
input = self.get_input_elem(doc)
node_id = "/inv:nodes/inv:node[inv:id=\"%s\"]"%node_id
node = self.get_node_elem(node_id, doc)
input.appendChild(node)
table = self.get_table_elem(table_id,doc)
input.appendChild(table)
priority = self.get_priority_elem(priority, doc)
input.appendChild(priority)
match = self.get_flow_match(matches, doc)
for node in match:
input.appendChild(node)
instructions = doc.createElement("instructions")
instruction = doc.createElement("instruction")
appl_action = doc.createElement("apply-actions")
appl_action_order = doc.createElement("order")
appl_acion_order_value = doc.createTextNode("0")
appl_action_order.appendChild(appl_acion_order_value)
action_list_nodes = self.convert_dict_to_xml({"action":action_list}, doc)
for action_node in action_list_nodes:
appl_action.appendChild(action_node)
instruction.appendChild(appl_action_order)
instruction.appendChild(appl_action)
instructions.appendChild(instruction)
input.appendChild(instructions)
return input,doc
def get_flow(self, node_id, table_id, priority=0, flow_id = None,flow_name=None, output_port=None, mod_dmac=None, ret_pkt=False, **kwargs):
action_list = []
if output_port:
action_list.append(ofutils.set_output_action(output_port))
if mod_dmac:
action_list.append(ofutils.set_mod_dmac_action(mod_dmac))
if ret_pkt:
action_list.append(ofutils.set_return_pkt_action())
if not flow_id:
flow_id = 1
flow = {"input":
{
"transaction-uri":"odlTraining8",
"flow-ref":ofutils.set_flow_ref(node_id,table_id, flow_id),
"node":ofutils.set_node(node_id),
"flow-table":ofutils.set_flow_table(node_id, table_id),
"flow":{
"priority":priority,
"table_id":table_id,
"flow-name":flow_name if flow_name else ofutils.generate_flow_name(node_id,table_id,flow_id),
"match":ofutils.set_match_field(**kwargs),
"instructions": ofutils.set_actions(action_list)
}
}
}
return flow
def construct_flow(self, node_id, table_id, priority=0, flow_name=None, output_port=None, mod_dmac=None, ret_pkt=False, **kwargs):
flow = self.get_flow(node_id, table_id, priority, flow_name=flow_name, output_port=output_port, mod_dmac=mod_dmac, ret_pkt=ret_pkt, **kwargs)
matches = {"match": flow["input"]["flow"]["match"]}
action_list =flow["input"]["flow"]["instructions"]["instruction"][0]["apply-actions"]["action"]
flow_xml,doc = self.convert_flow_toxml(node_id, table_id, priority, matches, action_list)
flow_body = doc.toprettyxml()
print flow_body
self.write_flow(flow_body)
return flow_body
def construct_flow_to_json(self, node_id, table_id, flow_id, priority=0, flow_name=None,output_port=None, mod_dmac=None, ret_pkt=False,**kwargs):
flow = self.get_flow(node_id, table_id, flow_id, priority, flow_name, output_port, mod_dmac, ret_pkt, **kwargs)
print(flow)
self.write_flow(flow)
return flow
| [
"ermcgann@cisco.com"
] | ermcgann@cisco.com |
caf1c52fc0df7cec7a621cb608368de64420f42c | 46fa8d974aea2edfc27d73e367ebf01db988e173 | /backgroundSubtraction.py | bb1fc3c1aaaf921cdd5a2520603b259ae03294d9 | [
"MIT"
] | permissive | MayankShrivastava17/opencv-operation-on-image | 696b36cca831aa700c1541a0e34c70805813c832 | c5ade8fa527ad05d99b65016678d8d25db201132 | refs/heads/main | 2023-06-09T22:40:11.231239 | 2021-06-24T18:40:45 | 2021-06-24T18:40:45 | 347,404,375 | 1 | 0 | null | 2021-06-24T18:40:45 | 2021-03-13T15:18:24 | Python | UTF-8 | Python | false | false | 456 | py | # This is used for object subtraction, security enhancement, pedestrian tracking
import cv2 as cv
import numpy as np
cap = cv.VideoCapture(0)
fbgb = cv.createBackgroundSubtractorMOG2()
while 1:
ret, frame = cap.read()
fgmask = fbgb.apply(frame)
cv.imshow('fgmask', fgmask)
# cv.waitKey(0)
cv.imshow('frame', frame)
# cv.waitKey(0)
k = cv.waitKey(30) & 0xff
if k == 27:
break
cap.release()
cv.destroyAllWindows()
| [
"mayanksri910@gmail.com"
] | mayanksri910@gmail.com |
7688fb7f88563eeb185f6d8b24f0e5caf1fbd99b | e574dce6be0d212bf974148d550cc3349dcaf64f | /formtest/urls.py | 85794274dd619dd78b5c0a8fd589e61d76b9d489 | [] | no_license | da91love/django_ajax_tutorial | edd6717faffa647fbeab88dab6ea0435fb59feef | e4c0cb558ca447a23a5c30af0d4c69dc51520d9a | refs/heads/master | 2020-07-23T04:51:54.527117 | 2019-10-12T05:32:05 | 2019-10-12T05:32:05 | 207,450,805 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 189 | py | from django.urls import path
from . import views
app_name = 'formtest'
urlpatterns = [
path('', views.get_name, name='form'),
path('success', views.get_success, name='success'),
]
| [
"daseul.kim@accenture.com"
] | daseul.kim@accenture.com |
fa47095466b95f5493bef006ca22b41146adaaa9 | acd2b9919729ea5e26b84f81c61cf4b3105c97de | /logestic_classifier.py | 786ffd8ecc016a922936f02252f9512517bed010 | [] | no_license | Xiaoxiao9321/Detect-Lead-Agency | e7bf176f1b1013d6841f86181926fc1f416e0aa2 | d913a5233837b039373dfeedcb8bcf066579a69a | refs/heads/master | 2022-06-10T13:01:35.354875 | 2020-05-03T09:56:45 | 2020-05-03T09:56:45 | 260,864,250 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,314 | py | import pandas as pd
import spacy
import numpy as np
from sklearn.linear_model import LogisticRegression
import csv
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import normalize
from sklearn.feature_extraction import text
from sklearn.metrics import precision_recall_curve
import matplotlib.pyplot as plt
from sklearn.preprocessing import label_binarize
from sklearn.metrics import average_precision_score
from sklearn.metrics import classification_report
def split(dataset):
train = dataset.sample(frac=0.7, random_state=0)
test = dataset.drop(train.index)
return train, test
def create(lst, voca_index, g_label):
xp = []
for key in lst:
content = key[0].split()
for i in content:
if i not in voca_index.keys():
continue
position = voca_index[i]
key[1][position] = 0
if i in g_label:
c_indexs = content.index(i)
index_word3 = 300 - c_indexs
position3 = voca_index[i]
key[1][position3] = index_word3
xp.append(key[1])
return xp
if __name__=="__main__":
sp = spacy.load('en_core_web_sm')
# d_train = pd.read_csv('train.csv')
# d_test = pd.read_csv('test.csv')
df = pd.read_csv('data.csv')
d_train, d_test = split(df)
x_train = d_train['content'].tolist()
y_train = d_train['target'].tolist()
x_test = d_test['content'].tolist()
y_test = d_test['target'].tolist()
g_label = list(set(y_train))
print(len(y_train))
print(len(set(y_test)))
additional_stopwords = ['action', 'chapter', 'eis', 'statement', 'appendix', 'environment', 'area', 'study',
'impact', 'scoping', 'feasibility', 'notice']
sw = text.ENGLISH_STOP_WORDS.union(additional_stopwords)
vect = CountVectorizer(analyzer='word', stop_words=sw)
a_x_train = vect.fit(x_train)
voca_index = a_x_train.vocabulary_
new_x_train = vect.fit_transform(x_train)
new_train = new_x_train.toarray()
train_data = np.copy(new_train)
train_lst = list(zip(x_train, new_train))
train_instances = create(train_lst, voca_index, g_label)
train_instances_array = np.array(train_instances)
# concatenate position feature
train_array = np.concatenate((train_data, train_instances_array), axis=1)
# train logistic regression
clf = LogisticRegression(solver='liblinear', multi_class='auto', max_iter=500)
clf.fit(train_array, y_train)
#test data
new_x_test = vect.transform(x_test)
new_test = new_x_test.toarray()
test_data = np.copy(new_test)
test_lst = list(zip(x_test, new_test))
test_instances = create(test_lst, voca_index, g_label)
test_instances_array = np.array(test_instances)
test_array = np.concatenate((test_data, test_instances_array), axis=1)
train_pre = clf.predict(train_array)
test_pre = clf.predict(test_array)
print("train_accuracy:", accuracy_score(y_train, train_pre))
print("test_accuracy", accuracy_score(y_test, test_pre))
# performance metrics
targets = list(set(y_test))
report = classification_report(y_test, test_pre, labels=targets)
print(report)
| [
"xiaoxiaochen@dhcp-10-134-162-242.uawifi.arizona.edu"
] | xiaoxiaochen@dhcp-10-134-162-242.uawifi.arizona.edu |
9772ebe57e6ed1743860407f5afac69952cbcd2b | 4021ed4a11981f132e173a304c2e0258f0165f75 | /Bioinformatics_stronghold/49_NWCK/NWCK2.py | 3b9362d0665cf61b881bc086104bc4eaf6d1b28f | [] | no_license | dpflieger/Rosalind | d7289eb367d14707435c44906a96a250a48ff0f2 | d01c295a7a13d109ab4525faa3832a07e0c8aa54 | refs/heads/master | 2021-01-13T09:36:59.431150 | 2019-12-03T20:09:21 | 2019-12-03T20:09:21 | 72,040,231 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 383 | py | from Bio import Phylo
from io import StringIO
c = open('rosalind_nwck.txt').read().rstrip().split('\n\n')
for i in c:
t, x, y = i.split()
tree = Phylo.read(StringIO(t), 'newick')
ancestor = tree.common_ancestor(x, y)
print(len(ancestor.get_path(x)) + len(ancestor.get_path(y)), end = " ")
#open('rosalind_nwck_sub.txt', 'wt').write(' '.join([str(i) for i in out])) | [
"pflieger.dav@gmail.com"
] | pflieger.dav@gmail.com |
a980109793514b0f95ee8fd3df0ecdfa9e12e53f | 7fbd5c1229bc0fb40c6ec4e0cd21d6701a293588 | /model/mask_rcnn/lib/roi_pool/build.py | 1bcd75870b5c9dd779b707390906a7020da2f456 | [] | no_license | liruilong940607/carnava-image-masking-challenge | 5f318a2481a1da9f50d26470cd4687d2d3e435d5 | 56800803bb3172cc6b47fc66004ea31250bf3f4b | refs/heads/master | 2021-07-16T20:07:08.322438 | 2017-10-15T13:37:54 | 2017-10-15T13:37:54 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 807 | py | import os
import torch
from torch.utils.ffi import create_extension
if not torch.cuda.is_available():
raise ValueError('this version is for cuda only! cuda not found')
curr_dir = os.path.dirname(os.path.realpath(__file__))
print('curr_dir is:',curr_dir)
print('Including CUDA code.')
sources = ['src/roi_pool_cuda.c']
headers = ['src/roi_pool_cuda.h']
defines = [('WITH_CUDA', None)]
with_cuda = True
extra_objects = ['src/roi_pool_kernel.cu.o']
extra_objects = [os.path.join(curr_dir, fname) for fname in extra_objects]
ffi = create_extension(
'roi_pool_cuda',
headers = headers,
sources = sources,
define_macros = defines,
relative_to = __file__,
with_cuda = with_cuda,
extra_objects = extra_objects
)
if __name__ == '__main__':
ffi.build()
| [
"hchaolee@gmail.com"
] | hchaolee@gmail.com |
498024e186f1d7a4fb2ed543996c1d5f388a7398 | 39763829d9a2c72c4fc882e8dd7b60850cb3278e | /nba_shotchart.py | c962c0952825304d162c1e1e85382b8c64c19e72 | [] | no_license | WilliamYe6/NBA_Shot_Chart_Visualizer | 55b05315bbc15b682ab850dc5cc41bc39f962e78 | 1bbcedebf0e919985c825f16edcc468801228c8d | refs/heads/main | 2023-09-01T13:24:29.593976 | 2021-10-11T21:02:38 | 2021-10-11T21:02:38 | 416,074,422 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,659 | py | import sys
import numpy as np
import pandas as pd
# nba_api
from nba_api.stats.static import players
from nba_api.stats.endpoints import shotchartdetail
from nba_api.stats.endpoints import playercareerstats
# matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import cm
from matplotlib.patches import Circle, Rectangle, Arc, ConnectionPatch
# get_player_shotchartdetail: player_name, season_id -> player_shotchart_df, league_avg
def get_player_shotchartdetail(player_name, season_id):
# player dictionary
nba_players = players.get_players()
player_dict = [player for player in nba_players if player['full_name'] == player_name][0]
# career dataframe
career = playercareerstats.PlayerCareerStats(player_id=player_dict['id'])
career_df = career.get_data_frames()[0]
# team id during the season
team_id = career_df[career_df['SEASON_ID'] == season_id]['TEAM_ID']
# shotchartdetail endpoints
shotchartlist = shotchartdetail.ShotChartDetail(team_id=int(team_id),
player_id=int(player_dict['id']),
season_type_all_star='Regular Season',
season_nullable=season_id,
context_measure_simple="FGA").get_data_frames()
return shotchartlist[0], shotchartlist[1]
# draw court function
def draw_court(ax=None, color="black", lw=1, outer_lines=False):
if ax is None:
ax = plt.gca()
# Basketball Hoop
hoop = Circle((0,0), radius=7.5, linewidth=lw, color=color, fill=False)
# Backboard
backboard = Rectangle((-30, -12.5), 60, 0, linewidth=lw, color=color)
# The paint
# outer box
outer_box = Rectangle((-80, -47.5), 160, 190, linewidth=lw, color=color, fill=False)
# inner box
inner_box = Rectangle((-60, -47.5), 120, 190, linewidth=lw, color=color, fill=False)
# Free Throw Top Arc
top_free_throw = Arc((0, 142.5), 120, 120, theta1=0, theta2=180, linewidth=lw, color=color, fill=False)
# Free Bottom Top Arc
bottom_free_throw = Arc((0, 142.5), 120, 120, theta1=180, theta2=0, linewidth=lw, color=color)
# Restricted Zone
restricted = Arc((0, 0), 80, 80, theta1=0, theta2=180, linewidth=lw, color=color)
# Three Point Line
corner_three_a = Rectangle((-220, -47.5), 0, 140, linewidth=lw, color=color)
corner_three_b = Rectangle((220, -47.5), 0, 140, linewidth=lw, color=color)
three_arc = Arc((0, 0), 475, 475, theta1=22, theta2=158, linewidth=lw, color=color)
# Center Court
center_outer_arc = Arc((0, 422.5), 120, 120, theta1=180, theta2=0, linewidth=lw, color=color)
center_inner_arc = Arc((0, 422.5), 40, 40, theta1=180, theta2=0, linewidth=lw, color=color)
# list of court shapes
court_elements = [hoop, backboard, outer_box, inner_box, top_free_throw, bottom_free_throw, restricted, corner_three_a, corner_three_b, three_arc, center_outer_arc, center_inner_arc]
#outer_lines=True
if outer_lines:
outer_lines = Rectangle((-250, -47.5), 500, 470, linewidth=lw, color=color, fill=False)
court_elements.append(outer_lines)
for element in court_elements:
ax.add_patch(element)
# Shot Chart Function
def shot_chart(data, title="", color="b", xlim=(-250, 250), ylim=(422.5, -47.5), line_color="blue",
court_color="white", court_lw=2, outer_lines=False,
flip_court=False, gridsize=None,
ax=None, despine=False):
if ax is None:
ax = plt.gca()
if not flip_court:
ax.set_xlim(xlim)
ax.set_ylim(ylim)
else:
ax.set_xlim(xlim[::-1])
ax.set_ylim(ylim[::-1])
ax.tick_params(labelbottom="off", labelleft="off")
ax.set_title(title, fontsize=18)
# draws the court using the draw_court()
draw_court(ax, color=line_color, lw=court_lw, outer_lines=outer_lines)
# separate color by make or miss
x_missed = data[data['EVENT_TYPE'] == 'Missed Shot']['LOC_X']
y_missed = data[data['EVENT_TYPE'] == 'Missed Shot']['LOC_Y']
x_made = data[data['EVENT_TYPE'] == 'Made Shot']['LOC_X']
y_made = data[data['EVENT_TYPE'] == 'Made Shot']['LOC_Y']
# Plot missed shots
ax.scatter(x_missed, y_missed, c='r', marker="x", s=50, linewidths=2)
# Plot made shots
ax.scatter(x_made, y_made, facecolors='none', edgecolors='g', marker='o', s=50, linewidths=2)
# Set the spines to match the rest of court lines, makes outer_lines
# somewhat unnecessary
for spine in ax.spines:
ax.spines[spine].set_lw(court_lw)
ax.spines[spine].set_color(line_color)
if despine:
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
return ax
if __name__ == "__main__":
# Accept 2 arguments
## First argument is the player name
## Second argument is the season id
### if there is no argument
if (len(sys.argv) == 1):
player_name = "LeBron James"
season_id = "2012-13"
else:
player_name = sys.argv[1]
season_id = sys.argv[2]
# title
title = player_name + " Shot Chart " + season_id
# Get Shotchart Data using nba_api
player_shotchart_df, league_avg = get_player_shotchartdetail(player_name, season_id)
# Draw Court and plot Shot Chart
shot_chart(player_shotchart_df, title=title)
# Set the size for our plots
plt.rcParams['figure.figsize'] = (12, 11)
plt.show()
| [
"williamxye2001@gmail.com"
] | williamxye2001@gmail.com |
69a17ce6fa30b56a604c09ced64fdd5ba04925b5 | 8681719ab6c69efd055e20424bf02f141c054a24 | /CommoditySpider/entrypoint.py | c2e47eff636a75d85e4fdc7460dd3f9cca953d84 | [] | no_license | Hiboboo/CommoditySpider | 36935ac5f95f855cd875a8c1e56077f01ab6b221 | c0666a041e2a9e7536f518dbf708e77e7afb1238 | refs/heads/master | 2021-06-18T11:19:36.548410 | 2021-02-02T07:14:42 | 2021-02-02T07:14:42 | 150,951,601 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,156 | py | # -*- coding: utf-8 -*-
from scrapy.crawler import CrawlerProcess
from scrapy.utils.project import get_project_settings
"""
快速启动爬虫
目的是在爬取 目标站点 之前, 先爬取代理网站的代理地址
"""
class SpiderManager(object):
def __init__(self):
self.setting = get_project_settings()
self.process = None
def start_spider(self):
""" 在同一进程里面执行多个爬虫程序 """
self.process = CrawlerProcess(self.setting)
for spider in spider_list:
self.process.crawl(spider)
self.process.start()
def start(self):
"""
启动爬虫和代理池
"""
self.start_spider()
if __name__ == '__main__':
manager = SpiderManager()
"""
Demospider() 是 Scrapy 项目 spider 目录下的爬虫脚本名字
这里需要更换成 你项目的 爬虫名
"""
# 'mercado_livre', 'aliexpress'
spider_list = ['mercado_livre']
manager.start()
# from scrapy.cmdline import execute
# # -s JOBDIR=crawls/commspider-1
# execute(['scrapy', 'crawlall', 'commspider -s JOBDIR=crawls/commspider-1'])
| [
"loveboboo@vip.qq.com"
] | loveboboo@vip.qq.com |
ec43467506273359e38ad38a663818a3ba951eea | ce4128e47d20f55be4bb1d0bfe8364f790aa84dc | /walkd_infer.py | 0a83c9577c220af1fee800044cefa87a0d6ee20e | [] | no_license | SunTzunami/Walking_data_inferences | 204d72f8c7f4ba492ce524f82d6c675284b69fb8 | 17eaf93a65dfbc230649a3152295da95ca16f4ed | refs/heads/master | 2022-11-09T07:26:20.889472 | 2020-06-16T04:06:55 | 2020-06-16T04:06:55 | 262,536,690 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,551 | py | import numpy as np
from scipy import stats
import pandas as pd
from pylab import *
import seaborn as sns
df=pd.read_csv('walkd.txt') #reading the data file
y=[x for x in df['distance'] ] #list of distance traversed on various days
#######
nd=[ a+1 for a in range(len(y))] #list of number of days
#######
mavgdis=[] #list of moving average distance
avgd=0
n=0
for d in y:
avgd+=d
n+=1
mavgdis.append(avgd/n)
#######
acc=0
Cdfdist=[] #list of cumulative distance traversed by each day
for d in y:
acc+=d
Cdfdist.append(acc)
dis=np.array(y)
#######
avgd=[np.mean(dis) for x in range(len(y))] #absolute average distance (a constant but must have a list for plotting]
#######
T=[int(t/60)+((t%60)/60) for t in df['time'] if t!=0] #list of different walk durations
#######
V=[s[0]/s[1] for s in zip(y,T) if (s[1]!=0)] #list of the various speeds that I had on various days
#######
V1=[s[0]/s[1] for s in zip(y,T) if (s[1]!=0) and s[0]>1] #list of the various speeds that I had on various days excluding the days I didn't walk/jog
#######)
vel=np.array(V)
#######
acc=0
mavgV=[] #moving average velocity
n=0
for v in vel:
acc+=v
n+=1
mavgV.append(acc/n)
#######
avgV=[np.mean(vel) for x in range(len(vel))] #absolute average velocity
#fig = plt.figure()
#fig.patch.set_facecolor('xkcd:mint green')
plt.rcParams['axes.facecolor'] = 'black'
####### can test now
#plt.figure(figsize=(8,8))
#x=df['time']
#plt.plot(V1)
#plt.plot(y) #distance traversed
#plt.plot(avgd) #absolute average distance
#plt.plot(mavgV) #absolute average velocity
#plt.plot(mavgdis) #moving average distance
#plt.scatter(V, y) #distance vs velocity
#plt.scatter(T, V)
plt.plot(nd, Cdfdist)
#plt.scatter(nd, Cdfdist)
#plt.plot(T, Cdfdist)
#sns.jointplot(x=T, y=y, kind='kde').annotate(stats.pearsonr)
#sns.regplot(x=T, y=y, fit_reg=True).annotate(xy=(T, y), stats.pearsonr)
#plt.bar(nd, Cdfdist)
#plt.scatter(T, V) #velocity vs time
#plt.plot(T)
#plt.scatter(T, y)
plt.xlabel('Number of days')
#plt.xlabel('Time')
plt.ylabel('Cumulative distance (km)')
for i in range(5):
plt.axvline(x=(i+1)*10, color='red', linestyle='--')
plt.axvline(x=1, color='red', linestyle='--')
plt.axhline(y=Cdfdist[0], color='green', linestyle='--')
for i in range(5):
plt.axhline(y=Cdfdist[9+10*i], color='green', linestyle='--')
#plt.legend(['distance traversed'])
#plt.title('Net distance traversed')
print(df.iloc[:, :2].corr())
print('Average Speed= ', str(np.mean(vel))+' kmph.')
print('Average Distance= ', str(np.mean(dis))+' km.')
print('Average Duration= ', str(np.mean(T))+ ' hrs.')
print('Total Distance= ', str(sum(y))+'km.')
print('Total number of days= ', str(nd[-1]))
plt.show()
| [
"noreply@github.com"
] | SunTzunami.noreply@github.com |
800334b3a21a24eba39c814e0cb7ed493b609c35 | 752c3f99a03ca50fd50fa56736eba69c05287534 | /SudokuSolver.py | da79f1ea04414fc7cd1e66a906ade44768089a95 | [] | no_license | mokana99/SudokuSolver | 4dcf575307a557c12fa7aba5fe3463757049a409 | 1ca12a10f8d5361bebaef9c575cc2b98f5439c17 | refs/heads/master | 2022-09-20T04:03:30.102657 | 2020-05-29T10:24:00 | 2020-05-29T10:24:00 | 267,829,657 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,651 | py | board = [
[7,8,0,4,0,0,1,2,0],
[6,0,0,0,7,5,0,0,9],
[0,0,0,6,0,1,0,7,8],
[0,0,7,0,4,0,2,6,0],
[0,0,1,0,5,0,9,3,0],
[9,0,4,0,6,0,0,0,5],
[0,7,0,3,0,0,0,1,2],
[1,2,0,0,0,7,4,0,0],
[0,4,9,2,0,6,0,0,7]
]
def solve(bo):
find = find_emty(bo)
if not find:
return True
else:
row, col = find
for i in range(1,10):
if valid(bo, i, (row, col)):
bo[row][col] = i
if solve(bo):
return True
bo[row][col] = 0
return False
def valid(bo, num, pos):
# Check row
for i in range(len(bo[0])):
if bo[pos[0]][i] == num and pos[1] != i:
return False
# Check column
for i in range(len(bo)):
if bo[i][pos[1]] == num and pos[0] != i:
return False
# Check box
box_x = pos[1] // 3
box_y = pos[0] // 3
for i in range(box_y*3, box_y*3 + 3):
for j in range(box_x * 3, box_x*3 + 3):
if bo[i][j] == num and pos[0] != (i, j):
return False
return True
def print_board(bo):
for i in range(len(bo)):
if i % 3 == 0 and i != 0:
print('- - - - - - - - - - - - ')
for y in range(len(bo[0])):
if y % 3 == 0 and y != 0:
print(' | ', end="")
if y == 8:
print(bo[i][y])
else:
print(str(bo[i][y]) + " ", end="")
def find_emty(bo):
for i in range(len(bo)):
for y in range(len(bo[0])):
if bo[i][y] == 0:
return (i, y) #return "x" and "y"
return None
print_board(board)
solve(board)
print('.................................................')
print_board(board)
| [
"noreply@github.com"
] | mokana99.noreply@github.com |
09d88d911425835b75e4bfff931485def12cf4cc | a57e9c1a75dd1b036b631afec8dce6086297f46d | /assignment-8.4.py | 216ed1415f9242671433b03145790a484553163b | [] | no_license | gibosky/PythonlearnExercises | 2f6a30467dd3fb2152f41f6044ed3e73ad8705b2 | 5d20d936a6082da2c58ae70b73846508988832fd | refs/heads/master | 2021-01-10T10:46:37.448306 | 2016-03-09T14:40:52 | 2016-03-09T14:40:52 | 52,033,265 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 389 | py | def openFile (fname) :
try :
fh = open(fname)
except IOError, e:
print 'File cannot be opened:', fname
exit()
return fh
fname = raw_input("Enter file name: ")
fh = openFile(fname)
lstAll = list()
lstLine = list()
for line in fh :
lstLine = line.split()
for word in lstLine :
if word not in lstAll :
lstAll.append(word)
lstAll.sort()
print lstAll
#print line.rstrip()
| [
"rogeriopaula@rogerios-MacBook-Pro.local"
] | rogeriopaula@rogerios-MacBook-Pro.local |
dd71af29cdd85503a32d82e89bb9ebeea6df17bc | 6393d3c53f5d6907e161d9991e79acab90bca14c | /server1.py | 3824056531c9c1d0751e6a46fefe9e4a4044131c | [] | no_license | fossbalaji/haproxysetup | 45f653599dbca7349a0b21f28a6fea31149ff3ac | 5836c6be38b512614161be0bc34a83c77f18eac4 | refs/heads/master | 2021-01-17T22:09:35.596519 | 2017-03-07T11:21:32 | 2017-03-07T11:21:32 | 84,189,284 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 404 | py | import BaseHTTPServer
class MyHandler(BaseHTTPServer.BaseHTTPRequestHandler):
def do_GET(s):
s.send_response(200)
s.send_header("Content-type", "text/plain")
s.end_headers()
s.wfile.write("Hello From Server 1")
if __name__ == '__main__':
server_class = BaseHTTPServer.HTTPServer
httpd = server_class(('localhost', 8001),MyHandler)
httpd.serve_forever() | [
"fossbalaji@gmail.com"
] | fossbalaji@gmail.com |
3e9d621f11c9d317958e65c2dc8cc4bfef1f6de3 | a564ec14337391f1da7fbf09974364a8354ce541 | /algoritimos/Python/fibbo-high.py | 0e2a72141310447b4a6e1c6516c88a7c05742869 | [
"BSD-2-Clause"
] | permissive | Gigers/data-struct | 37b931204e5460b17d9a7060a072a1035eea167e | f54917247a4e75ffe15783b0a025185d2215309a | refs/heads/master | 2021-06-26T11:55:01.686092 | 2019-09-07T17:23:55 | 2019-09-07T17:23:55 | 99,754,591 | 5 | 5 | null | 2017-11-21T09:06:32 | 2017-08-09T02:03:46 | C | UTF-8 | Python | false | false | 264 | py | #Definição de dicionario
dic = {}
#Definição da função
def fib(n):
print ('fib(%d)' %n) #Print para mostrar o valor de n
if n <= 2:
return 1
else:
if n not in dic:
dic[n] = fib(n - 1) + fib(n - 2)
return dic[n]
print (fib(8))
| [
"phelpsmemo@gmail.com"
] | phelpsmemo@gmail.com |
0130b4fb788ec2f71d43c55a1381aa65d006bd39 | 6673bd9425e7670d9d9949cea6845f5fdecfda3d | /scripts/change instance db prefix.py | 5a7290ba1832766ce0557823a95c59e4070d17ad | [
"MIT"
] | permissive | datopian/ckan-cloud-operator | 19b9feae7ac607f37de24c00a65aac841df9eb80 | 30715b32a00bace400df8d6dce763b9469800f83 | refs/heads/master | 2022-12-24T14:07:33.778058 | 2022-03-19T20:23:01 | 2022-03-19T20:23:01 | 163,747,850 | 16 | 8 | MIT | 2022-12-08T03:29:59 | 2019-01-01T15:46:50 | Python | UTF-8 | Python | false | false | 8,115 | py | import datetime
from ruamel import yaml
from ckan_cloud_operator.helpers import scripts as scripts_helpers
from ckan_cloud_operator import kubectl
from ckan_cloud_operator import logs
from ckan_cloud_operator.drivers.jenkins import driver as jenkins_driver
from ckan_cloud_operator.providers.ckan import manager as ckan_manager
from ckan_cloud_operator.providers.db.gcloudsql import manager as gcloudsql_manager
from ckan_cloud_operator.providers.cluster import manager as cluster_manager
from ckan_cloud_operator.drivers.gcloud import driver as gcloud_driver
from ckan_cloud_operator.config import manager as config_manager
INSTANCE_ID, OLD_DB_PREFIX, NEW_DB_PREFIX, DOWN_TIME_APPROVAL_CODE, NEW_INSTANCE_ID, GITLAB_REPO, SOLR_COLLECTION_NAME, STORAGE_PATH = scripts_helpers.get_env_vars(
'INSTANCE_ID', 'OLD_DB_PREFIX', 'NEW_DB_PREFIX', 'DOWN_TIME_APPROVAL_CODE', 'NEW_INSTANCE_ID', 'GITLAB_REPO',
'SOLR_COLLECTION_NAME', 'STORAGE_PATH'
)
def _create_down_time_approval_code(instance_id, old_db_prefix, new_db_prefix, db_name, datastore_name):
jenkins_user_token = ckan_manager.get_jenkins_token('ckan-cloud-operator-jenkins-creds')
backups_console_log = jenkins_driver.curl(
*jenkins_user_token,
'https://cc-p-jenkins.ckan.io/job/check%20individual%20DB%20backups/lastBuild/console',
raw=True
)
backups_console_log = backups_console_log.split('Finished: SUCCESS')[0].split('Updated property [compute/zone].')[1]
backups = yaml.safe_load(backups_console_log)
backups = {k: v for k, v in backups.items()
if (
k == db_name or k == datastore_name
or k == f'{db_name}-total-size'
or k == f'{datastore_name}-total-size'
)}
logs.info('last instance backups')
logs.print_yaml_dump(backups)
return scripts_helpers.create_file_based_approval_code({
'instance-id': instance_id,
'old-db-prefix': old_db_prefix,
'new-db-prefix': new_db_prefix,
'db-name': db_name,
'datastore-name': datastore_name
})
def _check_down_time_approval_code(instance_id, old_db_prefix, new_db_prefix, down_time_approval_code, db_name,
datastore_name):
assert scripts_helpers.check_file_based_approval_code(down_time_approval_code, {
'instance-id': instance_id,
'old-db-prefix': old_db_prefix,
'new-db-prefix': new_db_prefix,
'db-name': db_name,
'datastore-name': datastore_name
}), 'invalid down time approval code'
def _get_latest_backups(db_name, datastore_name):
gs_base_url = config_manager.get(key='backups-gs-base-url', secret_name='ckan-cloud-provider-db-gcloudsql-credentials')
output = gcloud_driver.check_output(
*cluster_manager.get_provider().get_project_zone(),
f"ls {gs_base_url}/`date +%Y/%m/%d`/'*'/ | grep {db_name}", gsutil=True
).decode() + '\n' + gcloud_driver.check_output(
*cluster_manager.get_provider().get_project_zone(),
f"ls {gs_base_url}/`date +%Y/%m/%d`/'*'/ | grep {datastore_name}", gsutil=True
).decode()
datastore_backup_url, datastore_backup_datetime = None, None
db_backup_url, db_backup_datetime = None, None
for line in output.splitlines():
line = line.strip()
if len(line) < 10: continue
backup_name, backup_datetime = line.split('/')[-1].split('.')[0].split('_')
backup_datetime = datetime.datetime.strptime(backup_datetime, '%Y%m%d%H%M')
if backup_name == db_name:
is_datastore = False
elif backup_name == datastore_name:
is_datastore = True
else:
continue
logs.info(backup_name=backup_name, backup_datetime=backup_datetime, is_datastore=is_datastore)
if is_datastore and (datastore_backup_datetime is None or datastore_backup_datetime < backup_datetime):
datastore_backup_datetime, datastore_backup_url = backup_datetime, line
if not is_datastore and (db_backup_datetime is None or db_backup_datetime < backup_datetime):
db_backup_datetime, db_backup_url = backup_datetime, line
logs.info(db_backup_datetime=db_backup_datetime, db_backup_url=db_backup_url)
logs.info(datastore_backup_datetime=datastore_backup_datetime, datastore_backup_url=datastore_backup_url)
return db_backup_url, datastore_backup_url
def main(instance_id, old_db_prefix, new_db_prefix, down_time_approval_code, new_instance_id, gitlab_repo, solr_collection_name, storage_path):
logs.info(instance_id=instance_id, old_db_prefix=old_db_prefix, new_db_prefix=new_db_prefix,
down_time_approval_code=down_time_approval_code)
instance = kubectl.get('ckancloudckaninstance', instance_id)
current_db_prefix = instance['spec']['db'].get('dbPrefix', 'prod')
current_datastore_prefix = instance['spec']['datastore'].get('dbPrefix', 'prod')
assert current_db_prefix == current_datastore_prefix, 'different prefix for datastore and DB is not supported yet'
db_name = instance['spec']['db']['name']
datastore_name = instance['spec']['datastore']['name']
logs.info(current_db_prefix=current_db_prefix, db_name=db_name,
current_datastore_prefix=current_datastore_prefix, datastore_name=datastore_name)
if current_db_prefix == old_db_prefix:
if down_time_approval_code:
_check_down_time_approval_code(instance_id, old_db_prefix, new_db_prefix, down_time_approval_code,
db_name, datastore_name)
logs.info(f'Deleting instance deployment (namespace={instance_id} deployment={instance_id}')
kubectl.call(f'delete deployment {instance_id} --wait=false', namespace=instance_id)
logs.info('Creating DB backups')
assert gcloudsql_manager.create_backup(db_name), 'failed db backup'
assert gcloudsql_manager.create_backup(datastore_name), 'failed datastore backup'
db_backup_url, datastore_backup_url = _get_latest_backups(db_name, datastore_name)
logs.important_log(logs.INFO, db_backup_url=db_backup_url)
logs.important_log(logs.INFO, datastore_backup_url=datastore_backup_url)
logs.info('Creating parameters file to trigger copy instance job to create the new instance')
with open('copy_instance_params', 'w') as f:
f.write(
f'OLD_INSTANCE_ID={instance_id}\n'
f'NEW_INSTANCE_ID={new_instance_id}\n'
f'NEW_GITLAB_REPO={gitlab_repo}\n'
f'NEW_DB_PREFIX={new_db_prefix}\n'
f'IMPORT_DATE_PATH=\n'
f'IMPORT_HOUR=\n'
f'SKIP_MINIO_MIRROR=no\n'
f'SKIP_CREATE=no\n'
f'SKIP_ROUTER=no\n'
f'USE_EXISTING_MIGRATION=no\n'
f'NEW_SOLR_COLLECTION_NAME={solr_collection_name}\n'
f'NEW_STORAGE_PATH={storage_path}\n'
f'DATABASE_IMPORT_URL={db_backup_url}\n'
f'DATASTORE_IMPORT_URL={datastore_backup_url}\n'
f'DRY_RUN=yes\n'
)
with open('change_instance_routes_params', 'w') as f:
f.write(
f'OLD_INSTANCE_ID={instance_id}\n'
f'NEW_INSTANCE_ID={new_instance_id}\n'
f'DRY_RUN=yes\n'
)
logs.exit_great_success()
else:
down_time_approval_code = _create_down_time_approval_code(
instance_id, old_db_prefix, new_db_prefix,
db_name, datastore_name
)
logs.important_log(logs.INFO, f'DOWN_TIME_APPROVAL_CODE={down_time_approval_code}')
logs.exit_great_success(quiet=True)
else:
raise NotImplementedError()
if __name__ == '__main__':
main(INSTANCE_ID, OLD_DB_PREFIX, NEW_DB_PREFIX, DOWN_TIME_APPROVAL_CODE, NEW_INSTANCE_ID, GITLAB_REPO, SOLR_COLLECTION_NAME, STORAGE_PATH)
| [
"noreply@github.com"
] | datopian.noreply@github.com |
d8f32d3e9b84c94a5d8883b3f2a1418fb6539d9b | f6596f7eb49b378e1eb7e5e0947c8cfd40df4379 | /demo/predict-taxi-trip-duration-nb/demo/train_sql.py | c0511c6148f7a3b3ba8bba33fb9f190fda1cbd2c | [
"Apache-2.0"
] | permissive | RhnSharma/OpenMLDB | 413bfc85181008f66369a388efb5fcc51edac871 | 14f662558880f0784699eb8339677e5afa6df6cf | refs/heads/main | 2023-07-18T08:37:20.313164 | 2021-09-01T06:30:59 | 2021-09-01T06:30:59 | 401,995,081 | 1 | 0 | Apache-2.0 | 2021-09-01T08:56:49 | 2021-09-01T08:56:48 | null | UTF-8 | Python | false | false | 2,951 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2021 4Paradigm
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pyspark.sql import SparkSession
import numpy as np
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
spark = SparkSession.builder.appName("Dataframe demo").getOrCreate()
parquet_predict = "./data/taxi_tour_table_predict_simple.snappy.parquet"
parquet_train = "./data/taxi_tour_table_train_simple.snappy.parquet"
predict = spark.read.parquet(parquet_predict)
predict.createOrReplaceTempView("t2")
train = spark.read.parquet(parquet_train)
train.createOrReplaceTempView("t1")
sql_tpl = """select trip_duration, passenger_count,
sum(pickup_latitude) over w as vendor_sum_pl,
max(pickup_latitude) over w as vendor_max_pl,
min(pickup_latitude) over w as vendor_min_pl,
avg(pickup_latitude) over w as vendor_avg_pl,
sum(pickup_latitude) over w2 as pc_sum_pl,
max(pickup_latitude) over w2 as pc_max_pl,
min(pickup_latitude) over w2 as pc_min_pl,
avg(pickup_latitude) over w2 as pc_avg_pl ,
count(vendor_id) over w2 as pc_cnt,
count(vendor_id) over w as vendor_cnt
from {}
window w as (partition by vendor_id order by pickup_datetime ROWS_RANGE BETWEEN 1d PRECEDING AND CURRENT ROW),
w2 as (partition by passenger_count order by pickup_datetime ROWS_RANGE BETWEEN 1d PRECEDING AND CURRENT ROW)"""
train_sql = sql_tpl.format('t1')
predict_sql = sql_tpl.format('t2')
train_df = spark.sql(train_sql)
predict_df = spark.sql(predict_sql)
train_set = train_df.toPandas()
predict_set = predict_df.toPandas()
y_train = train_set['trip_duration']
x_train = train_set.drop(columns=['trip_duration'])
y_predict = predict_set['trip_duration']
x_predict = predict_set.drop(columns=['trip_duration'])
lgb_train = lgb.Dataset(x_train, y_train)
lgb_eval = lgb.Dataset(x_predict, y_predict, reference=lgb_train)
# specify your configurations as a dict
params = {
'boosting_type': 'gbdt',
'objective': 'regression',
'metric': {'l2', 'l1'},
'num_leaves': 31,
'learning_rate': 0.05,
'feature_fraction': 0.9,
'bagging_fraction': 0.8,
'bagging_freq': 5,
'verbose': 0
}
print('Starting training...')
gbm = lgb.train(params,
lgb_train,
num_boost_round=20,
valid_sets=lgb_eval,
early_stopping_rounds=5)
gbm.save_model('/tmp/model.txt')
print("save model.txt done")
| [
"noreply@github.com"
] | RhnSharma.noreply@github.com |
c5762aa90cf8cd12cfdfeab9833778e1413529a8 | af77c05777611f3875ef06edaaea88d0253cd997 | /src/pykeen/models/baseline/models.py | 480d4a09f0e53947b6f3c3445996cacb8cd4adbd | [
"MIT"
] | permissive | xlives/pykeen | 5a73433f5c9b65435eb4dfa40217162e0f41281a | 3b6b11e1555218f2bf33883bf7f78bb34ce4dece | refs/heads/master | 2023-08-20T01:43:20.585140 | 2021-10-18T09:48:24 | 2021-10-18T09:48:24 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,725 | py | # -*- coding: utf-8 -*-
"""Non-parametric baseline models."""
from abc import ABC
import numpy
import torch
from .utils import get_csr_matrix, marginal_score
from ..base import Model
from ...triples import CoreTriplesFactory
__all__ = [
'EvaluationOnlyModel',
'MarginalDistributionBaseline',
]
class EvaluationOnlyModel(Model, ABC):
"""A model which only implements the methods used for evaluation."""
def __init__(self, triples_factory: CoreTriplesFactory):
"""Non-parametric models take a minimal set of arguments.
:param triples_factory: The training triples factory is used to assign the number of entities, relations,
and inverse condition in the non-parametric model.
"""
super().__init__(
triples_factory=triples_factory,
# These operations are deterministic and a random seed can be fixed
# just to avoid warnings
random_seed=0,
# These operations do not need to be performed on a GPU
preferred_device='cpu',
)
def _reset_parameters_(self):
"""Non-parametric models do not implement :meth:`Model._reset_parameters_`."""
raise RuntimeError
def collect_regularization_term(self): # noqa:D102
"""Non-parametric models do not implement :meth:`Model.collect_regularization_term`."""
raise RuntimeError
def score_hrt(self, hrt_batch: torch.LongTensor): # noqa:D102
"""Non-parametric models do not implement :meth:`Model.score_hrt`."""
raise RuntimeError
def score_r(self, ht_batch: torch.LongTensor): # noqa:D102
"""Non-parametric models do not implement :meth:`Model.score_r`."""
raise RuntimeError
class MarginalDistributionBaseline(EvaluationOnlyModel):
r"""
Score based on marginal distributions.
To predict scores for the tails, we make the following simplification of $P(t | h, r)$:
.. math ::
P(t | h, r) \sim P(t | h) * P(t | r)
Depending on the settings, we either set $P(t | *) = \frac{1}{n}$ where $n$ is the number of entities,
or estimate them by counting occurrences in the training triples.
.. note ::
This model cannot make use of GPU acceleration, since internally it uses scipy's sparse matrices.
---
name: Marginal Distribution Baseline
citation:
author: Berrendorf
year: 2021
link: https://github.com/pykeen/pykeen/pull/514
github: pykeen/pykeen
"""
def __init__(
self,
triples_factory: CoreTriplesFactory,
entity_margin: bool = True,
relation_margin: bool = True,
):
"""
Initialize the model.
:param triples_factory:
The triples factory containing the training triples.
:param entity_margin:
...
:param relation_margin:
...
If you set ``entity_margin=False`` and ``relation_margin=False``, it will
lead to a uniform distribution, i.e. equal scores for all entities.
"""
super().__init__(triples_factory=triples_factory)
h, r, t = numpy.asarray(triples_factory.mapped_triples).T
if relation_margin:
self.head_per_relation, self.tail_per_relation = [
get_csr_matrix(
row_indices=r,
col_indices=col_indices,
shape=(triples_factory.num_relations, triples_factory.num_entities),
)
for col_indices in (h, t)
]
else:
self.head_per_relation = self.tail_per_relation = None
if entity_margin:
self.head_per_tail, self.tail_per_head = [
get_csr_matrix(
row_indices=row_indices,
col_indices=col_indices,
shape=(triples_factory.num_entities, triples_factory.num_entities),
)
for row_indices, col_indices in ((t, h), (h, t))
]
else:
self.head_per_tail = self.tail_per_head = None
def score_t(self, hr_batch: torch.LongTensor) -> torch.FloatTensor: # noqa:D102
return marginal_score(
entity_relation_batch=hr_batch,
per_entity=self.tail_per_head,
per_relation=self.tail_per_relation,
num_entities=self.num_entities,
)
def score_h(self, rt_batch: torch.LongTensor) -> torch.FloatTensor: # noqa:D102
return marginal_score(
entity_relation_batch=rt_batch.flip(1),
per_entity=self.head_per_tail,
per_relation=self.head_per_relation,
num_entities=self.num_entities,
)
| [
"noreply@github.com"
] | xlives.noreply@github.com |
2ed73877064935d985f2c23e1fff78cbb939d79a | c141efabf44b948c31dfbc65cdaf3d69f3c3a371 | /app/methods.py | 1871dda88dc3f86c323b69755408382278cddc47 | [] | no_license | eengoron/close-crm-razorsync-integration | 552c28f43bf3aaf458f87a67ecf1a91b57006009 | 214a855aff859f10e3bd5d9e51a31fe8f5182bb7 | refs/heads/master | 2023-05-24T21:45:52.505341 | 2020-01-23T17:49:50 | 2020-01-23T17:49:50 | 219,886,006 | 0 | 0 | null | 2023-05-22T22:32:29 | 2019-11-06T01:33:05 | Python | UTF-8 | Python | false | false | 22,188 | py | from closeio_api import Client as CloseIO_API, APIError
import os
import requests
import json
import time
import copy
from .format_rs_to_close import format_address_as_string, format_address, format_new_contacts_array, format_opportunity_data, format_note_data, convert_epoch_to_dt
import logging
## Initiate Logger
log_format = "[%(asctime)s] %(levelname)s %(message)s"
logging.basicConfig(level=logging.INFO, format=log_format)
## Initiate Close API
api = CloseIO_API(os.environ.get('CLOSE_API_KEY'))
org_id = api.get('api_key/' + os.environ.get('CLOSE_API_KEY'))['organization_id']
dev_api = CloseIO_API(os.environ.get('CLOSE_DEV_API_KEY'))
##############################################
# Close Methods
##############################################
## Method to get most recent completed sync time from the Master Lead in the Development Sandbox
## If we cannot get the most recent completed sync time, we default to 5 minutes before the
## current time
def get_sync_time_from_close(current_time):
if os.environ.get('MASTER_LEAD_ID'):
try:
lead = dev_api.get('lead/' + os.environ.get('MASTER_LEAD_ID'), params={ '_fields': 'custom' })
if lead['custom'].get('last_sync_time'):
return int(lead['custom']['last_sync_time'])
except APIError as e:
logging.error("No Master Lead could be found...")
return (current_time - 300000)
## Method to set the sync time on the master lead in Close once a RazorSync sync has been completed at a particular time.
def set_sync_time_in_close(last_sync_time):
if os.environ.get('MASTER_LEAD_ID'):
try:
dev_api.put('lead/' + os.environ.get('MASTER_LEAD_ID'), data={ 'custom.last_sync_time': last_sync_time })
except APIError as e:
logging.error("Could not update sync time on lead because we could not get the master lead...")
## Method to update an existing address lead in Close with new information if the Customer in RazorSync has been updated.
## We check the differences in lead name, contacts, and billing address, and make updates where appropriate.
def update_lead(lead, addr, cust):
lead_updates = {}
name = format_address_as_string(addr)
contact_names_in_close = { k['display_name'].lower() : k for k in lead['contacts'] }
if name.strip() != lead['display_name'].strip():
lead_updates = { 'name': name }
new_contacts = format_new_contacts_array(cust)
for contact in new_contacts:
if contact['name'].lower() in contact_names_in_close:
close_contact = contact_names_in_close[contact['name'].lower()]
close_phones_and_emails = [i['email'] for i in close_contact['emails']] + [i['phone'] for i in close_contact['phones']]
new_contact_phones_and_emails = [i['email'] for i in contact['emails']] + [i['phone'] for i in contact['phones']]
difference = [i for i in new_contact_phones_and_emails if i not in close_phones_and_emails]
if difference:
try:
if contact.get('date_created'):
del contact['date_created']
api.put('contact/' + contact_names_in_close[contact['name'].lower()]['id'], data=contact)
logging.info(f"Updated Contact {contact['name']}")
except APIError as e:
logging.error(f"Failed to update contact {contact_names_in_close[contact['name'].lower()]['id']} because {str(e)}...")
else:
contact['lead_id'] = lead['id']
try:
api.post('contact', data=contact)
logging.info(f"Posted new contact {contact['name']} on {contact['lead_id']}")
except APIError as e:
logging.error(f"Failed to post new contact {contact['name']} to lead because {str(e)}")
if len(cust['Addresses']) > 1:
billing_address = [i for i in cust['Addresses'] if i.get('AddressTypeId') and address_types.get(i['AddressTypeId']) == "Billing"]
if billing_address:
billing_address = format_address_as_string(billing_address[0])
if billing_address != lead['custom'].get('RS Billing Address'):
lead_updates['custom.RS Billing Address'] = billing_address
if lead_updates != {}:
try:
api.put('lead/' + lead['id'], data=lead_updates)
logging.info(f"Successfully Updated {lead['display_name']}")
except APIError as e:
logging.error(f"Failed to PUT updates on lead because {str(e)}")
## Method to create a new lead from a new non-billing address in RazorSync
def post_new_close_lead(post_addr, post_cust):
lead_data = {}
addresses = []
## Note: Appartment is intentionally misspelled here because they have a typo in their API
lead_data['name'] = format_address_as_string(post_addr)
lead_data['custom.RS Address ID'] = post_addr['Id']
lead_data['contacts'] = format_new_contacts_array(post_cust)
if lead_data['name']:
addresses.append(format_address(post_addr))
if addresses:
lead_data['addresses'] = addresses
## Check if Lead has a billing address
if len(post_cust['Addresses']) > 1:
billing_address = [i for i in post_cust['Addresses'] if i.get('AddressTypeId') and address_types.get(i['AddressTypeId']) == "Billing"]
if billing_address:
lead_data['custom.RS Billing Address'] = format_address_as_string(billing_address[0])
if lead_data:
lead_data['custom.Created via RS Integration'] = 'Yes'
lead_data['status'] = 'Created via RS Integration'
try:
lead = api.post('lead', data=lead_data)
logging.info(f"Successfully posted {lead['display_name']}")
return lead
except APIError as e:
logging.error(f"Failed to post {lead_data['name']} because {str(e)}")
return None
## Method to find a close lead using the RS Address ID custom field and searching for an exact match
def find_close_lead_from_rs_address_id(address_id):
resp = api.get('lead', params={ 'query': f"\"custom.RS Address ID\":\"{address_id}\"", '_fields': 'id,contacts,opportunities,display_name,custom' })
if resp['data']:
return resp['data'][0]
else:
return None
## Method to find or create a new lead in Close from an RS address and customer record
## First, we try to find a lead using find_close_lead_from_rs_address_id. If no lead is found,
## we post one. If a lead is found, we try to update it.
def find_or_create_close_address_lead_from_customer(addr, cust):
lead = find_close_lead_from_rs_address_id(addr['Id'])
if not lead:
lead = post_new_close_lead(addr, cust)
else:
update_lead(lead, addr, cust)
return lead
## This method takes a service item ID that was deleted in RazorSync, found below, and
## marks the opportunity on the Close lead that reflects that service item as "Removed From Work Order".
def update_deleted_service_item_to_deleted(serv_id, opportunities):
opp = find_potential_close_opp_from_work_order_service_item_id(opportunities, serv_id)
if opp and opp['status_label'] != 'Removed From Work Order':
try:
api.put('opportunity/' + opp['id'], data={ 'status': 'Removed From Work Order' })
logging.info(f"Successfully updated {opp['id']} to status Removed From Work Order")
except APIError as e:
logging.error(f"Failed to update {opp['id']}'s status to removed from work order' because {str(e)}")
return None
## Given a Close lead's opportunities and a service item ID as input, this method tries to match
## a Close opoortunity to a service item ID for updating via note parsing for the given ID.
def find_potential_close_opp_from_work_order_service_item_id(opportunities, serv_item_id):
potential_opps = [i for i in opportunities if f"Work Order Service Item ID: {serv_item_id}" in i['note'] ]
if potential_opps:
return potential_opps[0]
return None
## This method finds all service items in Close given a lead and a Work Order ID.
def get_list_of_service_items_in_close(lead, work_order_id):
service_item_ids = []
for opp in lead['opportunities']:
try:
## Check to see if there's a service item ID in the opp note. This is the identifier for RS opportunities. Also check to make sure the Work Order ID itself
## matches because a lead can have multiple work orders attached.
if 'Work Order Service Item ID: ' in opp['note'] and opp['note'].split('Work Order Service Item ID: ')[1].split('\n')[0].strip() and f"Work Order ID: {work_order_id}" in opp['note']:
service_item_ids.append(opp['note'].split('Work Order Service Item ID: ')[1].split('\n')[0].strip())
except IndexError as e:
logging.error(f"Failed to find service item ID on {opp['id']} - {lead['id']}")
return service_item_ids
## This method tries to update a Close opportunity when a work order service item is updated.
## We try to find a potential opportunity by service item ID, and if we find one we format the opp_data
## array like the opps are formatted in Close. If no potential opp is found, we post a new opportunity to the lead.
## If an opp is found, we try to update it if the notes or value don't match.
def create_or_update_close_opportunity_from_service_item(serv_item, w_o, lead_data):
potential_opp = find_potential_close_opp_from_work_order_service_item_id(lead_data['opportunities'], serv_item['Id'])
opp_data = format_opportunity_data(serv_item, w_o)
if not potential_opp:
opp_data['lead_id'] = lead_data['id']
try:
opp = api.post('opportunity', data=opp_data)
logging.info(f"Successfully created new opportunity for Service Item {serv_item['Id']} - {w_o['Id']} - {opp['id']}")
except APIError as e:
logging.error(f"Failed to create new opportunity for Service Item {serv_item['Id']} - {w_o['Id']} because {str(e)}")
else:
opp = potential_opp
if opp['note'].strip() != opp_data['note'].strip() or opp.get('value') != opp_data.get('value'):
try:
if opp_data.get('date_created'):
del opp_data['date_created']
api.put('opportunity/' + opp['id'], data=opp_data)
logging.info(f"Successfully updated opportunity for Service Item {serv_item['Id']} - {opp['id']}")
except APIError as e:
logging.error(f"Failed to update opportunity for Service Item {serv_item['Id']} - {opp['id']} because {str(e)}")
## Method to find a note in Close via a work_order_id so we can properly update work order notes and
## not create duplicates. If the note is meant to be a completed note, we look for was completed on.
## Otherwise, we look for the pattern "Work Order ID: IDHERE" in the note.
def find_note_for_work_order_by_id(lead_id, work_order_id, was_completed=False):
try:
notes = []
has_more = True
offset = 0
while has_more:
resp = api.get('activity/note', params={ 'lead_id': lead_id, '_skip': offset })
for note in resp['data']:
if was_completed:
if f"Work Order ID: {work_order_id} was completed on:" in note['note']:
return note
else:
try:
if f"Work Order ID: {work_order_id}" in note['note'] and not f"Work Order ID: {work_order_id} was completed on:" in note['note']:
return note
except IndexError as e:
logging.error(f"Failed to parse Work Order note on {lead_id} - {work_order_id} - {note['id']}")
offset += len(resp['data'])
has_more = resp['has_more']
except APIError as e:
logging.error(f"Failed to get notes for {lead_id} because {str(e)}")
return None
## This method creates or updates work order notes on a lead for a synced work order
def create_or_update_close_work_order_notes(serv_items, w_o, lead_data):
## Create "was completed" note on the lead.
if w_o['StatusName'] == 'Complete' and not find_note_for_work_order_by_id(lead_id=lead_data['id'], work_order_id=w_o['Id'], was_completed=True):
note_data = format_note_data(work_order=w_o, was_completed=True)
note_data['lead_id'] = lead_data['id']
api.post('activity/note', data=note_data)
## For any Work Order update, try to find the work order not that was previously created. If not, create one.
potential_note = find_note_for_work_order_by_id(lead_id=lead_data['id'], work_order_id=w_o['Id'])
note_data = format_note_data(work_order=w_o, was_completed=False, service_items=serv_items)
if not potential_note:
note_data['lead_id'] = lead_data['id']
try:
api.post('activity/note', data=note_data)
except APIError as e:
logging.error(f"Failed to post note to {note_data['lead_id']} because {str(e)}")
else:
if potential_note['note'].strip() != note_data['note'].strip():
try:
if note_data.get('date_created'):
del note_data['date_created']
api.put('activity/note/' + potential_note['id'], data=note_data)
except APIError as e:
logging.error(f"Failed to update note {potential_note['id']} because {str(e)}")
## Method to sync RS Work Order statuses to statuses in Close based on a list of RS statuses defined below
def find_work_order_statuses_in_close(work_order_statuses):
try:
opportunity_statuses = api.get(f"organization/{org_id}", params={ '_fields': 'opportunity_statuses'})['opportunity_statuses']
status_names_in_close = [i['label'] for i in opportunity_statuses]
if 'Removed From Work Order' not in status_names_in_close:
try:
api.post('status/opportunity', data={ 'label': 'Removed From Work Order', 'type': 'lost' })
except APIError as e:
logging.error(f'Failed to Create Removed From Work Order Status because {str(e)}')
for work_order_status in work_order_statuses:
if work_order_status['Name'] not in status_names_in_close:
status_data = { 'label': work_order_status['Name']}
status_data['type'] = 'active'
if 'Complete' in status_data['label']:
status_data['type'] = 'won'
elif 'No ' in status_data['label'] or 'cancelled' in status_data['label'].lower():
status_data['type'] = 'lost'
try:
api.post('status/opportunity', data=status_data)
except APIError as e:
logging.error(f"Failed to post status to Close {status_data['label']} because {str(e)}")
except Exception as e:
logging.error(f"Failed to sync Work Order Statuses to Close because {str(e)}")
##############################################
# RazorSync Methods
##############################################
address_types = {}
work_order_statuses = {}
service_item_dictionary = {}
rs_users_dictionary = {}
# Initiate RazorSync Request Headers
razorsync_headers = {
"Host": f"{os.environ.get('RAZORSYNC_SERVERNAME').lower()}.0.razorsync.com",
"Token": f"{os.environ.get('RAZORSYNC_TOKEN')}",
"Content-type": 'application/json',
"ServerName": f"{os.environ.get('RAZORSYNC_SERVERNAME')}"
}
# Method to make request to RazorSync API
def make_rs_request(method, url_path, params={}, data=None):
url = f"https://{os.environ.get('RAZORSYNC_SERVERNAME').lower()}.0.razorsync.com/ApiService.svc/{url_path}"
if method.lower() == 'post':
r = requests.post(url, headers=razorsync_headers, data=json.dumps(data))
if r.status_code >= 400:
logging.error(f"Failed to complete POST for RS Path {url_path}")
return [] if 'List' in url_path else []
try:
r.json()
return r.json()
except Exception as e:
logging.error(f"Failed post request to {url_path} because {str(e)}")
logging.error(f"Url: {url}\nHeaders: {razorsync_headers}\nData: {data}")
return None
if method.lower() == 'get':
r = requests.get(url, headers=razorsync_headers, params=params)
if r.status_code >= 400:
logging.error(f"Failed to complete GET for RS Path {url_path}")
return [] if 'List' in url_path else {}
try:
r.json()
return r.json()
except Exception as e:
logging.error(f"Failed get request to {url_path} because {str(e)}")
logging.error(f"{r.text}")
return None
# This method gets the address types and work order statuses of the RazorSync organization
# It also makes sure all work order statuses are correctly reflected in Close
def get_settings_models():
try:
settings_resp = make_rs_request(method="GET", url_path='Settings')
for k in settings_resp['AddressTypeModels']:
address_types[k['Id']] = k['Name']
for k in settings_resp['Users']:
rs_users_dictionary[k['Id']] = f"{k.get('FirstName') or ''} {k.get('LastName') or ''}".strip()
## Make sure all work order statuses are in Close
find_work_order_statuses_in_close(settings_resp['WorkOrderCustomStatuses'])
for k in settings_resp['WorkOrderCustomStatuses']:
work_order_statuses[k['Id']] = k['Name']
except Exception as e:
logging.error(f"Could not get RS Settings Models because {str(e)}")
# This method gets all the possible service items in the RazorSync organization
def get_service_item_dictionary():
service_items = make_rs_request(method='GET', url_path='ServiceItem/List')
for k in service_items:
service_item_dictionary[k['Id']] = k
## This method gets every service item for a work_order_id
def get_service_items_for_work_order_id(work_order_id):
work_order_service_items = make_rs_request(method='GET', url_path=f'WorkOrderServiceItem/List/{work_order_id}')
for work_order_service_item in work_order_service_items:
if work_order_service_item['ServiceItemId'] not in service_item_dictionary:
get_service_item_dictionary()
if work_order_service_item['ServiceItemId'] in service_item_dictionary:
work_order_service_item['specifics'] = service_item_dictionary[work_order_service_item['ServiceItemId']]
return work_order_service_items
## This method finds an address_id based on a service_request_id from a work_order
def find_address_id_using_service_request_id(service_request_id):
if service_request_id:
service_request = make_rs_request(method="GET", url_path=f"ServiceRequest/{service_request_id}")
return service_request['AddressId']
return None
## This method processes work order updates where applicable.
def process_work_order_updates(work_order, recently_found_leads):
if (work_order['StatusId'] not in work_order_statuses) or (work_order.get('FieldWorkerId') and work_order['FieldWorkerId'] not in rs_users_dictionary):
get_settings_models()
work_order['StatusName'] = work_order_statuses.get(work_order['StatusId'])
work_order['WorkerName'] = rs_users_dictionary.get(work_order['FieldWorkerId'], "") if work_order.get('FieldWorkerId') else "No Technician Yet"
address_id = find_address_id_using_service_request_id(work_order['ServiceRequestId'])
if not address_id:
return None
if recently_found_leads.get(address_id):
lead = recently_found_leads[address_id]
else:
lead = find_close_lead_from_rs_address_id(address_id)
## Implement SMS if address doesn't exist for work order
if not lead:
return None
service_items = get_service_items_for_work_order_id(work_order['Id'])
close_service_item_ids = get_list_of_service_items_in_close(lead, work_order['Id'])
current_work_order_service_item_ids = [str(i.get('Id')) for i in service_items]
removed_service_items = [i for i in close_service_item_ids if str(i) not in current_work_order_service_item_ids]
for service_item in service_items:
create_or_update_close_opportunity_from_service_item(service_item, work_order, lead)
create_or_update_close_work_order_notes(service_items, work_order, lead)
for serv_id in removed_service_items:
update_deleted_service_item_to_deleted(serv_id, lead['opportunities'])
# Start Timezone Capped Search of RS. This is a job that runs on AP Scheduler
def search_in_rs():
leads_found_this_search = {}
current_time = int(time.time()*1000)
last_sync_time = get_sync_time_from_close(current_time)
mod_dates = { "FromModifiedDate": f"/Date({last_sync_time})/", "ToModifiedDate": f"/Date({current_time})/" }
customer_list = make_rs_request(method='POST', url_path="Customer/List", data=mod_dates)
if customer_list:
for customer in customer_list:
for address in customer['Addresses']:
if address_types.get(address['AddressTypeId'], "No Type") != "Billing":
lead = find_or_create_close_address_lead_from_customer(address, customer)
leads_found_this_search[address['Id']] = lead
logging.info(f"Found, created, or updated leads for {customer_list.index(customer) + 1} of {len(customer_list)}")
work_orders = make_rs_request(method='POST', url_path="WorkOrder/List", data=mod_dates)
for order in work_orders:
process_work_order_updates(order, leads_found_this_search)
logging.info(f"Proccessed Work Order {work_orders.index(order) + 1} of {len(work_orders)}: {order['Id']}")
leads_found_this_search = {}
set_sync_time_in_close(current_time)
logging.info(f"Ran sync between {convert_epoch_to_dt(last_sync_time, '%x %I:%M:%S %p')} - {convert_epoch_to_dt(current_time, '%x %I:%M:%S %p')}")
get_settings_models()
get_service_item_dictionary()
| [
"eric.engoron@gmail.com"
] | eric.engoron@gmail.com |
1b5c6a64f387fffe68ba362f24dc301ab91a3129 | ef85c7bb57412c86d9ab28a95fd299e8411c316e | /tools/pot/openvino/tools/pot/data_loaders/image_loader.py | 4ba603555e669616ca5883e8b36958c1e9f5749a | [
"Apache-2.0"
] | permissive | SDxKeeper/dldt | 63bf19f01d8021c4d9d7b04bec334310b536a06a | a7dff0d0ec930c4c83690d41af6f6302b389f361 | refs/heads/master | 2023-01-08T19:47:29.937614 | 2021-10-22T15:56:53 | 2021-10-22T15:56:53 | 202,734,386 | 0 | 1 | Apache-2.0 | 2022-12-26T13:03:27 | 2019-08-16T13:41:06 | C++ | UTF-8 | Python | false | false | 1,146 | py | # Copyright (C) 2020-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from cv2 import imread, IMREAD_GRAYSCALE
from ..api.data_loader import DataLoader
from ..data_loaders.utils import prepare_image, collect_img_files
class ImageLoader(DataLoader):
def __init__(self, config):
super().__init__(config)
self._img_files = collect_img_files(config.data_source)
self._shape = None
self._crop_central_fraction = config.get('central_fraction', None)
def __getitem__(self, idx):
return self._read_and_preproc_image(self._img_files[idx])
def __len__(self):
return len(self._img_files)
@property
def shape(self):
return self._shape
@shape.setter
def shape(self, shape):
self._shape = tuple(shape)
def _read_and_preproc_image(self, img_path):
image = imread(img_path, IMREAD_GRAYSCALE)\
if self._shape[1] == 1 else imread(img_path)
if image is None:
raise Exception('Can not read the image: {}'.format(img_path))
return prepare_image(image, self.shape[-2:], self._crop_central_fraction)
| [
"noreply@github.com"
] | SDxKeeper.noreply@github.com |
3b166ef430ea2084a0b38b6d370739251eb5f175 | 5bf75a46e354b584faa11d508fe4a9e930754879 | /results/mrcpsp/rcpsp_test/plot_smt.py | a0ea809b95d76245d5089b4bcc916934846a1d1b | [] | no_license | gokberkkocak/phd_experiments | 7c5ceda7209e7e26c407e62d2ec6a56ee77657b3 | 4e831eeea6e9a5353287994c478251296848cafb | refs/heads/main | 2022-07-30T01:24:53.510669 | 2022-01-31T15:48:07 | 2022-01-31T15:48:07 | 454,059,633 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,461 | py | #!/usr/bin/env python3
import json
import sys
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
TYPE = "solver_time"
show = ['normal','inter'][0]
with open("results_native.json", "r") as json_file:
results = json.load(json_file)
print(len(results.keys()))
scatter = []
for instance in results:
row = []
try:
row.append(instance)
row.append(results[instance]["normal_bisect_yices"][TYPE]) # 1
row.append(results[instance]["normal_linear_yices"][TYPE]) # 2
row.append(results[instance]["normal_unsat_yices"][TYPE]) # 3
row.append(results[instance]["inter_bisect_yices"][TYPE]) # 4
row.append(results[instance]["inter_linear_yices"][TYPE]) # 5
row.append(results[instance]["inter_unsat_yices"][TYPE]) # 6
row.append(results[instance]["z3"][TYPE]) # 7
row.append(results[instance]["chuffed"][TYPE]) # 8
scatter.append(row)
except:
print("ski")
SORT_KEY = 6
scatter = sorted(scatter, key=lambda x: x[SORT_KEY])
fig, ax = plt.subplots(figsize=(8,4))
r1 = np.arange(len(scatter))
#colors = ["lime", "green", "black", "orange", "magenta", "cyan", "red", "blue"]
colors = ["orange", "magenta", "cyan", "orange", "magenta", "cyan", "red", "blue"]
names = ["normal_bisect_yices", "normal_linear_yices", "normal_unsat_yices", "inter_bisect_yices", "inter_linear_yices", "inter_unsat_yices", "z3", "chuffed"]
markers = ['o','v','+', 'o','v','+', 'x','*']
if show == 'inter':
r = range(4,9)
else:
r = list(range(1,4))
r.extend(range(7,9))
for i in r:
plt.scatter(r1, [row[i] for row in scatter],30, label=names[i-1], marker=markers[i-1], color=colors[i-1] ,alpha=0.5, edgecolors ="none")
#plt.scatter(r1, [row[i] for row in scatter],50, label=names[i-1], marker=markers[i-1], color='blue', alpha=0.5, edgecolors ="none")
plt.title('')
plt.ylabel("Time (s)",fontsize=12)
plt.xlabel("Instances",fontsize=12)
plt.yscale('log')
plt.xticks([])
plt.yticks([0.2,1,10,50,150])
ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.minorticks_off()
matplotlib.rcParams.update({'legend.fontsize':12,'legend.markerscale':1.5})
ax.legend(labels=['yices-bisect','yices-linear','yices-UNSAT','z3','Chuffed'], loc='center left',bbox_to_anchor=(1,0.5)) # for i in range(4,9) only
plt.tight_layout()
plotFormat = ['pdf','png'][0]
plt.savefig("results-" + show + '.' + plotFormat, format=plotFormat)
| [
"gk34@st-andrews.ac.uk"
] | gk34@st-andrews.ac.uk |
a1afbbc3c78c8d6c64a19c175c0c40f8cb9546e9 | 413684cf1e2c1ce637ba77f7b7399b0ec14abece | /7/7.1.py | 0b350ee46b3d5afa569dfe3c4c9d58ea6d89340b | [] | no_license | Prozen/AdventOfCode2016 | 1998558ee3371b81833e7854737d0d24c74ae91c | de3fbac45eff3050cdeae8c5a61d35b954eaa230 | refs/heads/master | 2020-06-13T01:15:38.148196 | 2016-12-18T06:04:49 | 2016-12-18T06:04:49 | 75,470,097 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 180,615 | py | data = """dnwtsgywerfamfv[gwrhdujbiowtcirq]bjbhmuxdcasenlctwgh
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cygilweroxmbmbmx[hopxissehjarmezawol]exywzaffjuhehvmbm
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xuabbxdwkutpsogcfea[tgetfqpgstsxrokcemk]cbftstsldgcqbxf[vwjejomptmifhdulc]ejeroshnazbwjjzofbe""".splitlines()
def getSSLs(ip):
if len(ip) > 2:
if ip[0]==ip[2] and ip[0]!= ip[1]: return [ip[0:3]] + getSSLs(ip[1:])
else: return getSSLs(ip[1:])
else: return []
def split(s):
left = []
right = []
while s != "":
start_of_bracket = s.find('[')
if start_of_bracket!= -1:
left.append(s[:start_of_bracket])
s = s[start_of_bracket+1:]
end_of_bracket = s.find(']')
if end_of_bracket != -1:
right.append(s[:end_of_bracket])
s = s[end_of_bracket+1:]
else :
return left + [s], right
def isSSl(ip):
good, bad = split(ip)
goodl = [ssl[1] + ssl[:2] for part in good for ssl in getSSLs(part)]
return any(ssl in x for x in bad for ssl in goodl)
print(sum(isSSl(s) for s in data))
| [
"pavel.rozenblioum@jayway.com"
] | pavel.rozenblioum@jayway.com |
2da7c78192808cd7ab3882adb48f76e3c9e465c1 | 8c30945dad70dbef65131034ee18f5106d507131 | /djangogirls/urls.py | c13250824af4e05209b01eab42f41f5328482438 | [] | no_license | SamiBilal/second_first | e29bf0f4ea1b19e228c24da9bfc5459e1eb80329 | 827ec4ad9a7599cfb681e48f44f2c866b374d472 | refs/heads/master | 2020-07-15T07:22:23.653419 | 2019-09-03T12:00:00 | 2019-09-03T12:00:00 | 205,511,550 | 0 | 0 | null | 2019-09-03T12:02:21 | 2019-08-31T07:27:18 | Python | UTF-8 | Python | false | false | 797 | py | """djangogirls URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.2/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,include
urlpatterns = [
path('', include('blog.urls')),
path('admin/', admin.site.urls),
]
| [
"samibilal120@gmail.com"
] | samibilal120@gmail.com |
90c212ea86a3574a9503bd9b197917209e803260 | 6256a3fd97d87f94b47e7b2f85caf654e56b9f94 | /day01/test08.py | fa9423f80a966cb4fc8552c6384b1db6ddfac044 | [] | no_license | PythonLxg/spider | 68374e8640b148fe238555ab54fbf6681a39a293 | 63b09c93a9d1ef347a68a7fb7214188754a5ab66 | refs/heads/master | 2020-04-30T20:40:36.208203 | 2019-03-22T04:59:49 | 2019-03-22T04:59:49 | 177,073,947 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 754 | py | # -*- coding utf-8 -*-
# C:\Users\lxg\Documents\Python
# Author:李小根
# Time:2019/3/15
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
browser = webdriver.Chrome()
try:
browser.get('https://www.baidu.com')
input = browser.find_element_by_id('kw')
input.send_keys("python")
input.send_keys(Keys.ENTER)
wait = WebDriverWait(browser, 10)
wait.until(EC.presence_of_element_located((By.ID, 'content_left')))
print(browser.current_url)
print(browser.get_cookies())
print(browser.page_source)
finally:
browser.close()
| [
"1812852877@qq.com"
] | 1812852877@qq.com |
7d02c2a07c49057a3080847b57e8e673bf857b98 | c8f4dd3c094a2f0e262ba880a4e62e75b121119b | /Chapter03/0305.py | 20bd3bea6ff4598577d14af6b37c592991a2318b | [
"MIT"
] | permissive | 0201shj/Python-OpenCV | 2a87888fe571f6aef0554e629022e74ed4ab0bf1 | 249f8cc9404e547da0f5c68000f29f2e598562a5 | refs/heads/main | 2023-08-30T21:10:14.477881 | 2021-10-01T09:17:55 | 2021-10-01T09:17:55 | 399,083,772 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 370 | py | # 0305.py
import cv2
import numpy as np
img = np.zeros(shape = (512, 512, 3), dtype = np.uint8) + 255
pts1 = np.array([[100, 100], [200, 100], [200, 200], [100, 200]])
pts2 = np.array([[300, 200], [400, 100], [400, 200]])
cv2.polylines(img, [pts1, pts2], isClosed = True, color = (255, 0, 0))
cv2.imshow('img', img)
cv2.waitKey()
cv2.destroyAllWindows() | [
"noreply@github.com"
] | 0201shj.noreply@github.com |
b541b3836b17fbc61b9b41e819e855ef917a64b2 | d158e9255024945aa62bf85bc84e3cf08ce78c52 | /eeg/eeg-report-annotations/src/main/python/relation/relnet/serving.py | 9721c538c6d3cb38905b0b07e859fcb189ce09e2 | [] | no_license | utd-hltri/l-pcrs | dae2eda0b287e62341e54e3f703a178b1a92e8df | f91f1466fa1a7f5257597fd32b3de270836dbca8 | refs/heads/master | 2021-09-07T17:27:49.819346 | 2018-02-26T21:50:07 | 2018-02-26T21:50:07 | 115,817,252 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,847 | py | """
Serving input function definition.
"""
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import tensorflow as tf
from tensorflow.contrib.learn import utils
def generate_serving_input_fn(metadata):
"Returns _serving_input_fn for use with an export strategy."
max_story_length = metadata['max_story_length']
max_sentence_length = metadata['max_sentence_length']
num_blocks = metadata['max_entities_length']
def _serving_input_fn():
story_placeholder = tf.placeholder(
shape=[max_story_length, max_sentence_length],
dtype=tf.int64,
name='story')
entity_index_placeholder = tf.placeholder(
shape=[num_blocks],
dtype=tf.int64,
name='entity_indexes')
keys_placeholder = tf.placeholder(
shape=[num_blocks],
dtype=tf.int64,
name='key_indexes')
feature_placeholders = {
'story': story_placeholder,
'entity_indexes': entity_index_placeholder,
'keys': keys_placeholder
}
features = {
key: tf.expand_dims(tensor, axis=0)
for key, tensor in feature_placeholders.items()
}
input_fn_ops = utils.input_fn_utils.InputFnOps(
features=features,
labels=None,
default_inputs=feature_placeholders)
return input_fn_ops
return _serving_input_fn
def generate_serving_input_fn_attr(metadata):
"Returns _serving_input_fn for use with an export strategy."
max_story_length = metadata['max_story_length']
max_sentence_length = metadata['max_sentence_length']
num_blocks = metadata['max_entities_length']
num_attrs = metadata['num_attrs'] + 1
def _serving_input_fn():
story_placeholder = tf.placeholder(
shape=[max_story_length, max_sentence_length, num_attrs],
dtype=tf.int64,
name='story')
entity_index_placeholder = tf.placeholder(
shape=[num_blocks, num_blocks],
dtype=tf.int64,
name='entity_indexes')
keys_placeholder = tf.placeholder(
shape=[num_blocks, num_attrs],
dtype=tf.int64,
name='key_indexes')
feature_placeholders = {
'story': story_placeholder,
'entity_indexes': entity_index_placeholder,
'keys': keys_placeholder
}
features = {
key: tf.expand_dims(tensor, axis=0)
for key, tensor in feature_placeholders.items()
}
input_fn_ops = utils.input_fn_utils.InputFnOps(
features=features,
labels=None,
default_inputs=feature_placeholders)
return input_fn_ops
return _serving_input_fn
| [
"travis@hlt.utdallas.edu"
] | travis@hlt.utdallas.edu |
3d4fadd95be4d55758b0ce02e416a77a97530949 | 43a27b80ce1a8cf422142f5a43044917ff17a7cf | /python/leetcode/MinWindowSubstring.py | b0599246769de55011acb2d6e6433f9e173e299d | [] | no_license | bignamehyp/interview | 321cbe4c5763b2fc6d2ba31354d627af649fe4ed | 73c3a3e94c96994afdbc4236888456c8c08b6ee4 | refs/heads/master | 2021-01-25T08:49:30.249961 | 2015-03-07T07:34:00 | 2015-03-07T07:34:00 | 23,125,534 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,120 | py | class MinWindowSubstring:
def minWindow(self, S, T):
toFind = dict()
for c in T:
if c in toFind:
toFind[c] += 1
else:
toFind[c] = 1
begin = 0
pos = 0
numMatches = 0
found = dict()
minWin = ""
minLength = 1 << 32
while pos < len(S):
if S[pos] in toFind:
if S[pos] in found:
found[S[pos]] += 1
else:
found[S[pos]] = 1
if found[S[pos]] <= toFind[S[pos]]:
numMatches += 1
if numMatches == len(T):
while not S[begin] in found or found[S[begin]] > toFind[S[begin]]:
if S[begin] in found:
found[S[begin]] -= 1
begin += 1
if pos - begin + 1 < minLength:
minLength = pos - begin + 1
minWin = S[begin:pos+1]
pos += 1
return minWin
| [
"huangyp@kalman.(none)"
] | huangyp@kalman.(none) |
90b1a75ac0556371c65e2569c6cee7e8f8d66994 | 919aa09eafc944844d6d99f86122bea96a8ae5a5 | /memcrypt_analysis.py | 02d85b60d3b680c498e3084b485e467b1d22ce1c | [] | no_license | GuyCockitt/MemCrypt-Task | d54e16dab576dfcd52bd4470926054b95a89b5d4 | 08ed804a7e31aa1ed881b03cf31b5951fab30bac | refs/heads/main | 2023-06-27T10:35:39.134675 | 2021-07-31T22:58:02 | 2021-07-31T22:58:02 | 391,477,574 | 0 | 0 | null | 2021-07-31T22:56:41 | 2021-07-31T22:52:32 | null | UTF-8 | Python | false | false | 4,822 | py | import os
from Cryptodome.Cipher import AES
from itertools import permutations
#Below are header values for common file types.
JPEG_HDR = b'\xFF\xD8\xFF\xE0'
MS_OFFICE_HDR = b'\x50\x4B\x03\x04\x14\x00\x06\x00'
PNG_HDR = b'\x89\x50\x4E\x47\x0D\x0A\x1A\x0A'
PDF_HDR = b'%PDF-'
def isIncremental(buffer):
"""TODO 1: Please implement a function which will:
1) Check if the 16-byte buffer contains incremental values (at 1-step intervals).
2) Return True if incremental values are detected. Otherwise, the function should return False.
Examples of the many incremental values, found in memory_dump.bin, include:
40 41 42 43 44 45 46 47 48 49 4A 4B 4C 4D 4E 4F (ASCII: @ABCDEFGHIJKLMNO)
58 59 5A 5B 5C 5D 5E 5F 60 61 62 63 64 65 66 67 (ASCII: XYZ[\]^_`abcdefg)
6A 6B 6C 6D 6E 6F 70 71 72 73 74 75 76 77 78 79 (ASCII: jklmnopqrstuvwxy)
Keyword arguments:
buffer -- the buffer to check for incremental values. 16-byte size buffers are passed in by default.
"""
relations = []
for i in range(15):
relations.append(buffer[i+1]-buffer[i]==1)
if all(relations):
return True
else:
return False
def decryptFile(candidates):
"""TODO 2: Please implement a function which will:
1) Generate all permutations of candidate values (this has been done for you in the code below)
2) Test each candidate value against 'data\encrypted_file' using the Cryptodome.Cipher.AES decrypt* function. Ensure mode AES.MODE_CBC is used.
3) Check the header of each decryption attempt to determine if decryption was successful. The isKnownHeader() function can be used for this purpose.
4) Output the correct key and IV (via a standard print statement) on successful decryption.
5) Write the decrypted file to the 'data' directory.
6) Consider extending the code to append the correct extension based on isKnownHeader() function match.
(e.g. if the function determines the decrypted file to be JPG, add the .jpg extension).
*See https://pycryptodome.readthedocs.io/en/latest/src/cipher/aes.html for example usage.
Keyword arguments:
candidates -- The candidate keys and initialisation vectors (IVs) one wishes to test.
"""
permu = list(permutations(candidates, 2)) #This function may be used to generate all permutations of candidate values.
file = r"data\encrypted_file"
filesize = os.path.getsize(file)
with open(file, "rb") as fh:
ciphertext=fh.read(filesize)
for pair in permu:
cipher = AES.new(pair[0], AES.MODE_CBC, pair[1])
plaintext = cipher.decrypt(ciphertext)
x = isKnownHeader(plaintext)
if x[0]:
with open("data\decrypted_file"+x[1], "wb") as fw:
fw.write(plaintext)
break
def isKnownHeader(buffer):
"""This function performs analysis on the decrypted buffer to determine if it matches a known header (i.e. file type).
If a match is detected, then it is likely the decryption process was successful.
Keyword arguments:
buffer -- The buffer we wish to determine if decryption was successful.
"""
if JPEG_HDR in buffer[0:len(JPEG_HDR)]:
return True, ".jpg"
if MS_OFFICE_HDR in buffer[0:len(MS_OFFICE_HDR)]:
return True, ".docx"
if PNG_HDR in buffer[0:len(PNG_HDR)]:
return True, ".png"
if PDF_HDR in buffer[0:len(PDF_HDR)]:
return True, ".pdf"
return False, ""
def memoryAnalysis(file, offset):
"""This function iterates through the memory_dump.bin file and reads the content (buffer) of the file at 16-byte offsets.
The 16-byte buffer will be checked by the isIncremental() function to determine if the data is a candidate cryptographic value or benign.
If isIncremental() returns false, the 16-byte buffer is considered a candidate value and will be added to the candidates list.
Keyword arguments:
file -- the memory dump file we wish to perform analysis on.
offset -- the offset value to operate against the memory dump file. Fixed at 16 bytes for this task.
"""
candidates = []
filesize = os.path.getsize(file)
with open(file, "rb") as fh:
for i in range(0, filesize, offset):
read = fh.read(offset)
if isIncremental(read) == False:
candidates.append(read)
return candidates
def main():
#We begin by analysing the memory dump file. A list of candidate values will be returned by the function.
candidates = memoryAnalysis(r"data\memory_dump.bin", 16)
#We then attempt to decrypt the encypted_file by trying all possible permutation of candidate values.
decryptFile(candidates)
if __name__ == "__main__":
main()
| [
"noreply@github.com"
] | GuyCockitt.noreply@github.com |
4d5e3db219926f973bb90de678b961cc12ebd31d | cb046e0bcaa2fb64d1f7d3f59a6660cdde787e7f | /question/test.py | b19e6764fe8030eed9dc32db69fc97f83c839574 | [] | no_license | Xyandraa9/truth-or-dare-bot | c0bf0ea445e2611cf90613644a29c84730d084b6 | 79c031bba4838f9b7f6aa76992f5938727c38ed6 | refs/heads/main | 2023-07-28T06:12:58.501619 | 2021-08-23T10:44:58 | 2021-08-23T10:44:58 | 378,641,211 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 912 | py | import random
count = 0
with open('dare_question.txt','r+') as question:
for i in question:
count+=1
question.seek(0)
rand = random.randint(0,count)
check = 0
for i in question:
check +=1
q = i.split('Q.')
x = q[1].split("\n")
if(check == rand):
print(x[0])
# print('1',ctx.args)
# print('2',ctx.author)
# print('3',ctx.bot)
# print('4',ctx.channel)
# print('5',ctx.cog)
# print('6',ctx.command)
# print('7',ctx.command_failed)
# print('8',ctx.guild)
# print('9',ctx.invoked_subcommand)
# print('10',ctx.invoked_with)
# print('11',ctx.kwargs)
# print('12',ctx.me)
# print(ctx.message.guild.id)
# print('14',ctx.prefix)
# print('15',ctx.subcommand_passed)
# print('16',ctx.valid)
# print('17',ctx.voice_client) | [
"rockingbinitagarwal@gamil.com"
] | rockingbinitagarwal@gamil.com |
a20003843812b2540d416661e556290a59c928b8 | 60710a2b6794a028524a997fa9b5c21eb6fe8d70 | /tweeter.py | 8c672e2158e68b07901bac5d7aaba97271e5f59f | [] | no_license | ZachLynch123/miscellaneous-scripts | a68fb0ade4fb0ab328af1ea8f8a04ea3a91467f6 | 8624667e75512523b5d5202648b3c5e8cac3d73c | refs/heads/master | 2021-09-03T12:09:38.389806 | 2018-01-09T00:28:48 | 2018-01-09T00:28:48 | 104,949,207 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,347 | py | import tweepy, os
def getStatus():
lines = []
while True:
line = raw_input()
if line:
lines.append(line)
else:
break
status = '\n'.join(lines)
return status
def tweetthis(type):
if type == "text":
print "Enter your tweet "+user.name
tweet = getStatus()
try:
api.update_status(tweet)
except Exception as e:
print e
return
elif type == "pic":
print "Enter pic path "+user.name
pic = os.path.abspath(raw_input())
print "Enter status "+user.name
title = getStatus()
try:
api.update_with_media(pic, status=title)
except Exception as e:
print e
return
print "\n\nDONE!!"
def initialize():
global api, auth, user
ck = "egCBYLUaFz2O1uSkOaMztm6Ij" # consumer key
cks = "EZgngtEU0o6617NAjYJ0P7fb5K73c8vjYrG2ss0cOxctrgxNh1" # consumer key SECRET
at = "4308126313-2uN18LdAAD8qbiGDGSONze59lMkyaHN2S2Ary4a" # access token
ats = "ZNXOHQvHZkPthDyNhzOgvAE8g02zRviJcteUnuDvdVQtH" # access token SECRET
auth = tweepy.OAuthHandler(ck,cks)
auth.set_access_token(at,ats)
api = tweepy.API(auth)
user = api.me()
def main():
doit = int(raw_input("\n1. text\n2. picture\n"))
initialize()
if doit == 1:
tweetthis("text")
print "OK, Let's try again!"
main()
elif doit == 2:
tweetthis("pic")
print "OK, Let's try again!"
main()
main() | [
"Zachary.Lynch1337@gmail.com"
] | Zachary.Lynch1337@gmail.com |
fa7e34e125908a2f0c71c968c0f028d320fc0b7d | 357cb2b92e51657e679a1ae96277d5bdf24e928f | /src/quotationtool/referatory/bibtex/field.py | 3bc1eae29d7740e6f80d9ad5b34f8b806423ccb9 | [] | no_license | quotationtool/quotationtool.referatory | d917342744e90fb1f3d8fb8d4cf6e3d1b02c4372 | c52dc61c3c8370ca67d4d740855104adbe1195b3 | refs/heads/master | 2020-03-08T06:43:51.644618 | 2011-01-26T18:38:14 | 2011-01-26T18:38:14 | 127,978,534 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,762 | py | # -*- coding: utf-8 -*-
from zope.interface import implements
from zope import schema
from quotationtool.referatory.i18n import _
import ifield
## # boilerplate code
## class Xxx(schema.TextLine):
## __doc__ = ifield.IXxx.__doc__
## implements(ifield.IXxx)
## def __init__(self, *args, **kw):
## d = {
## 'title': _('bibtex-xxx-title', u""),
## 'description': _('bibtex-xxx-desc', u""),
## }
## for key, value in kw.items():
## d[key] = value
## super(Xxx, self).__init__(*args, **d)
class Language(schema.Choice):
__doc__ = ifield.ILanguage.__doc__
implements(ifield.ILanguage)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-language-title', u"Language"),
'description': _('bibtex-language-desc', u"The language of the publication."),
'vocabulary': 'z3c.i18n.iso.languages',
'default': 'de',
}
for key, value in kw.items():
d[key] = value
super(Language, self).__init__(*args, **d)
class Name(schema.TextLine):
__doc__ = ifield.IName.__doc__
implements(ifield.IName)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-name-title', u"Name"),
'description': _('bibtex-name-desc', u"Enter <i>last name, first name</i>."),
}
for key, value in kw.items():
d[key] = value
super(Name, self).__init__(*args, **d)
class Author(schema.Tuple):
__doc__ = ifield.IAuthor.__doc__
implements(ifield.IAuthor)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-author-title', u"Author"),
'description': _('bibtex-author-desc', u"Enter <i>last name, first name</i>. Each name goes into a seperate line."),
'value_type': Name(required = True),
}
for key, value in kw.items():
d[key] = value
super(Author, self).__init__(*args, **d)
class Editor(schema.Tuple):
__doc__ = ifield.IEditor.__doc__
implements(ifield.IEditor)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-editor-title', u"Editor"),
'description': _('bibtex-editor-desc', u"Enter <i>last name, first name</i>. Each name goes into a seperate line. If there is also an author field, then the editor field gives the editor of the book or collection in which the reference appears."),
'value_type': Name(required = True),
}
for key, value in kw.items():
d[key] = value
super(Editor, self).__init__(*args, **d)
class TitleTupleItem(schema.TextLine):
__doc__ = ifield.ITitleTupleItem.__doc__
implements(ifield.ITitleTupleItem)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-titleitem-title', u"Title"),
'description': _('bibtex-titleitem-desc', u"Without trailing dot or comma."),
}
for key, value in kw.items():
d[key] = value
super(TitleTupleItem, self).__init__(*args, **d)
class Title(schema.Tuple):
__doc__ = ifield.ITitle.__doc__
implements(ifield.ITitle)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-title-title', u"Title"),
'description': _('bibtex-title-desc', u"Title, maintitle, subtitle, second subtitle etc. They all go into seperate lines. Without trailing dot or comma."),
'value_type': TitleTupleItem(required = True),
}
for key, value in kw.items():
d[key] = value
super(Title, self).__init__(*args, **d)
class Publisher(schema.TextLine):
__doc__ = ifield.IPublisher.__doc__
implements(ifield.IPublisher)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-publisher-title', u"Publisher"),
'description': _('bibtex-publisher-desc', u"The name of the publisher or publishing house."),
}
for key, value in kw.items():
d[key] = value
super(Publisher, self).__init__(*args, **d)
class Year(schema.TextLine):
__doc__ = ifield.IYear.__doc__
implements(ifield.IYear)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-year-title', u"Year"),
'description': _('bibtex-year-desc', u"The year of publication or, for an unpublished work, the year it was written. Generally it should consist of four numerals, such as 1984, although this website can handle any year whose last four nonpunctuation characters are numerals, such as `(about 1984)'."),
}
for key, value in kw.items():
d[key] = value
super(Year, self).__init__(*args, **d)
class Volume(schema.TextLine):
__doc__ = ifield.IVolume.__doc__
implements(ifield.IVolume)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-volume-title', u"Volume"),
'description': _('bibtex-volume-desc', u"The volume of a journal or multivolume book."),
}
for key, value in kw.items():
d[key] = value
super(Volume, self).__init__(*args, **d)
class Number(schema.TextLine):
__doc__ = ifield.INumber.__doc__
implements(ifield.INumber)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-number-title', u"Number"),
'description': _('bibtex-number-desc', u"The number of a journal, magazine, technical report, or of a work in a series. An issue of a journal or magazine is usually identified by its volume and number; the organization that issues a technical report usually gives it a number; and sometimes books are given numbers in a named series."),
}
for key, value in kw.items():
d[key] = value
super(Number, self).__init__(*args, **d)
class Series(schema.TextLine):
__doc__ = ifield.ISeries.__doc__
implements(ifield.ISeries)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-series-title', u"Series"),
'description': _('bibtex-series-desc', u"The name of a series or set of books. When citing an entire book, then the title field gives its title and an optional series field gives the name of a series or multi-volume set in which the book is published."),
}
for key, value in kw.items():
d[key] = value
super(Series, self).__init__(*args, **d)
class Address(schema.TextLine):
__doc__ = ifield.IAddress.__doc__
implements(ifield.IAddress)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-address-title', u"Address"),
'description': _('bibtex-address-desc', u"Usually the address (town) of the publisher or other type of institution. For major publishing houses, van Leunen recommends omitting the information entirely. For small publishers, on the other hand, you can help the reader by giving the complete address."),
}
for key, value in kw.items():
d[key] = value
super(Address, self).__init__(*args, **d)
class Edition(schema.TextLine):
__doc__ = ifield.IEdition.__doc__
implements(ifield.IEdition)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-edition-title', u"Edition"),
'description': _('bibtex-edition-desc', u"The edition of a book--for example, ``Second''. This should be an ordinal, and should have the first letter capitalized, as shown here; the standard styles convert to lower case when necessary."),
}
for key, value in kw.items():
d[key] = value
super(Edition, self).__init__(*args, **d)
class Month(schema.TextLine):
__doc__ = ifield.IMonth.__doc__
implements(ifield.IMonth)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-month-title', u"Month"),
'description': _('bibtex-month-desc', u"The month in which the work was published or, for an unpublished work, in which it was written. You should use the standard three-letter abbreviation, as described in Appendix B.1.3 of the LaTeX book."),
}
for key, value in kw.items():
d[key] = value
super(Month, self).__init__(*args, **d)
class Note(schema.TextLine):
__doc__ = ifield.INote.__doc__
implements(ifield.INote)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-note-title', u"Note"),
'description': _('bibtex-note-desc', u"Any additional information that can help the reader. The first word should be capitalized."),
}
for key, value in kw.items():
d[key] = value
super(Note, self).__init__(*args, **d)
class Journal(schema.TextLine):
__doc__ = ifield.IJournal.__doc__
implements(ifield.IJournal)
def __init__(self, *args, **kw):
d = {
'title': _("bibtex-journal-title",
u"Journal"),
'description': _('bibtex-journal-desc',
u"A journal name. Abbreviations are provided for many journals; see the Local Guide."),
}
for key, value in kw.items():
d[key] = value
super(Journal, self).__init__(*args, **d)
class Pages(schema.TextLine):
__doc__ = ifield.IPages.__doc__
implements(ifield.IPages)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-pages-title',
u"Pages"),
'description': _('bibtex-pages-desc',
u"One or more page numbers or range of numbers, such as 42-111 or 7,41,73-97 or 43+ (the `+' in this last example indicates pages following that don't form a simple range). To make it easier to maintain Scribe-compatible databases, the standard styles convert a single dash (as in 7-33) to the double dash used in TeX to denote number ranges (as in 7-33)."),
}
for key, value in kw.items():
d[key] = value
super(Pages, self).__init__(*args, **d)
class Booktitle(schema.Tuple):
__doc__ = ifield.IBooktitle.__doc__
implements(ifield.IBooktitle)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-booktitle-title',
u"Book Title"),
'description': _('bibtex-booktitle-desc',
u"Title of a book, part of which is being cited."),
'value_type': TitleTupleItem(required = True),
}
for key, value in kw.items():
d[key] = value
super(Booktitle, self).__init__(*args, **d)
class Type(schema.TextLine):
__doc__ = ifield.IType.__doc__
implements(ifield.IType)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-type-title',
u"Type"),
'description': _('bibtex-type-desc',
u"The type of a technical report--for example, ``Research Note''."),
}
for key, value in kw.items():
d[key] = value
super(Type, self).__init__(*args, **d)
class Chapter(schema.TextLine):
__doc__ = ifield.IChapter.__doc__
implements(ifield.IChapter)
def __init__(self, *args, **kw):
d = {
'title': _('bibtex-chapter-title',
u"Chapter"),
'description': _('bibtex-chapter-desc',
u"A chapter (or section or whatever) number."),
}
for key, value in kw.items():
d[key] = value
super(Chapter, self).__init__(*args, **d)
| [
"christian.lueck@ruhr-uni-bochum.de"
] | christian.lueck@ruhr-uni-bochum.de |
787cabc03551bbc17b236046a6149d8744460138 | 92beb48b5fd7a57353cc03b5656663ccf0645079 | /nkbill/settings.py | 3b4d2db8df992fb12c44380a7b97571cb040f513 | [] | no_license | Nitanshubajpai/InvoiceGenerator | 1d83c2b95e1f97b34c364fa3e865a6a8b98484d2 | 1f02e5b6f277a1cc5e433466ebdf68624082a282 | refs/heads/main | 2023-06-23T01:11:16.472872 | 2021-07-19T15:39:46 | 2021-07-19T15:39:46 | 386,213,558 | 0 | 0 | null | 2021-07-19T13:49:23 | 2021-07-15T08:07:54 | Python | UTF-8 | Python | false | false | 3,583 | py | """
Django settings for nkbill project.
Generated by 'django-admin startproject' using Django 3.2.4.
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/
"""
import os
from pathlib import Path
import django_heroku
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'django-insecure-cmf2%0bly7#3!28%o_l)xyig5h9zza)nbqyr+@2p%+$p#f20un'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = False
ALLOWED_HOSTS = ['127.0.0.1']
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'billmanage'
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'whitenoise.middleware.WhiteNoiseMiddleware',
'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 = 'nkbill.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 = 'nkbill.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.2/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/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_ROOT = os.path.join(BASE_DIR, 'staticfiles')
STATIC_URL = '/static/'
# Extra places for collectstatic to find static files.
STATICFILES_DIRS = (
os.path.join(BASE_DIR, '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'
django_heroku.settings(locals()) | [
"nitanshubajpai@gmail.com"
] | nitanshubajpai@gmail.com |
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