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37c24b3960134c61b5a8710012b9ad3ebf8a62fe
|
55c250525bd7198ac905b1f2f86d16a44f73e03a
|
/Python/Python/Scripts/Auto py to exe/build/lib/auto_py_to_exe/dialogs.py
|
08ced7a66201b6e9c57607cc3cabb9a7329be462
|
[] |
no_license
|
NateWeiler/Resources
|
213d18ba86f7cc9d845741b8571b9e2c2c6be916
|
bd4a8a82a3e83a381c97d19e5df42cbababfc66c
|
refs/heads/master
| 2023-09-03T17:50:31.937137
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| 2023-08-28T23:50:57
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UTF-8
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py
|
version https://git-lfs.github.com/spec/v1
oid sha256:17632a1084b74f79b082631a021c864a01bee63a94b1fb5768945e30f05a405b
size 2899
|
[
"nateweiler84@gmail.com"
] |
nateweiler84@gmail.com
|
7461b94a60fcbe15ed116a2853262476e06aaafd
|
c06d18ac5b87b3b82fc486454c422b119d6c1ee9
|
/src/demo/_tensorflow/linear/linear.py
|
70f197e8d2ad5074603c813b803127c0355fe803
|
[
"MIT"
] |
permissive
|
tangermi/nlp
|
b3a4c9612e6049463bf12bc9abb7aff06a084ace
|
aa36b8b20e8c91807be73a252ff7799789514302
|
refs/heads/master
| 2022-12-09T12:33:15.009413
| 2020-04-03T04:03:24
| 2020-04-03T04:03:24
| 252,056,010
| 0
| 0
| null | 2022-12-08T07:26:55
| 2020-04-01T02:55:05
|
Jupyter Notebook
|
UTF-8
|
Python
| false
| false
| 1,092
|
py
|
# -*- coding: utf-8 -*-
import tensorflow as tf
class Linear(tf.keras.Model):
def __init__(self):
super().__init__()
self.dense = tf.keras.layers.Dense(
units=1,
activation=None,
kernel_initializer=tf.zeros_initializer(),
bias_initializer=tf.zeros_initializer()
)
def call(self, input):
output = self.dense(input)
return output
if __name__ == '__main__':
X = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
y = tf.constant([[10.0], [20.0]])
model = Linear()
optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)
for i in range(100):
with tf.GradientTape() as tape:
y_pred = model(X) # 调用模型 y_pred = model(X) 而不是显式写出 y_pred = a * X + b
loss = tf.reduce_mean(tf.square(y_pred - y))
grads = tape.gradient(loss, model.variables) # 使用 model.variables 这一属性直接获得模型中的所有变量
optimizer.apply_gradients(grads_and_vars=zip(grads, model.variables))
print(model.variables)
|
[
"n10057862@qut.edu.au"
] |
n10057862@qut.edu.au
|
237b5db6e779a7de6c8b385bcac3bf982604e07e
|
931aa9c6a44f86e86440c17de62801b26b66fce8
|
/constance/LV/getLineUnbalanceAndLosses.py
|
f92c4871027b8e1d87960321b14354a1e8ea4bb7
|
[] |
no_license
|
Constancellc/epg-psopt
|
3f1b4a9f9dcaabacf0c7d2a5dbc10947ac0e0510
|
59bdc7951bbbc850e63e813ee635474012a873a4
|
refs/heads/master
| 2021-06-08T11:33:57.467689
| 2020-04-01T13:19:18
| 2020-04-01T13:19:18
| 96,895,185
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 7,785
|
py
|
import csv
import random
import copy
import numpy as np
import matplotlib.pyplot as plt
from lv_optimization_new import LVTestFeeder
import pickle
#outfile = '../../../Documents/simulation_results/LV/voltages.csv'
stem = '../../../Documents/ccModels/eulv/'
alpha = 0.328684513701
g = open(stem+'lnsYprims.pkl','rb')
data = pickle.load(g)
g.close()
# first get phases
lds = np.load('../../../Documents/ccModels/loadBuses/eulvLptloadBusesCc-24.npy')
lds = lds.flatten()[0]
phase = []
for i in range(len(lds)):
bus = lds['load'+str(i+1)]
if bus[-1] == '1':
phase.append('A')
elif bus[-1] == '2':
phase.append('B')
elif bus[-1] == '3':
phase.append('C')
# data is a dictionary where the key is the line number and it points to
# [bus a, bus b, Yprim]
# so we need to build up a dictionary of the voltages
a = np.load(stem+'eulvLptaCc060.npy')
My = np.load(stem+'eulvLptMyCc060.npy')
v0 = np.load(stem+'eulvLptV0Cc060.npy')
Y = np.load(stem+'eulvLptYbusCc060.npy')
Y = Y.flatten()[0]
Y = Y.conj()
YNodeOrder = np.load(stem+'eulvNmtYNodeOrderCc060.npy')
buses = []
for node in YNodeOrder:
buses = buses+[node.split('.')[0]]
def get_losses(Vtot):
losses = {}
for line in data:
data0 = data[line]
bus1 = data0[0]
bus2 = data0[1]
Yprim = data0[2]
idx1 = [i for i, x in enumerate(buses) if x == bus1]
idx2 = [i for i, x in enumerate(buses) if x == bus2]
Vidx = Vtot[idx1+idx2]
Iphs = Yprim.dot(Vidx)
Sinj = Vidx*(Iphs.conj())
Sloss = sum(Sinj)
losses[line] = [bus1,bus2,Sloss.real]
return losses
def get_unbalance(Vtot):
unbalance = {}
a = complex(-0.5,0.866)
A = np.array([[complex(1,0),complex(1,0),complex(1,0)],
[complex(1,0),a,a*a],
[complex(1,0),a*a,a]])
A = A*0.333
for line in data:
data0 = data[line]
bus1 = data0[0]
bus2 = data0[1]
Yprim = data0[2]
idx1 = [i for i, x in enumerate(buses) if x == bus1]
idx2 = [i for i, x in enumerate(buses) if x == bus2]
Vidx = Vtot[idx1+idx2]
Iphs = Yprim.dot(Vidx)
Is = np.matmul(A,Iphs[:3])
unbalance[line] = [bus1,bus2,abs(Is[0]),abs(Is[1]),abs(Is[2])]
return unbalance
fdr = LVTestFeeder('manc_models/1',1)
fdr.set_households_NR('../../../Documents/netrev/TC2a/03-Dec-2013.csv')
fdr.set_evs_MEA('../../../Documents/My_Electric_Avenue_Technical_Data/'+
'constance/ST1charges/')
voltages = fdr.get_all_voltages(My,a,alpha,v0)
losses_no_evs = {}
ub_no_evs = {}
print(fdr.predict_losses())
for t in voltages:
ls = get_losses(voltages[t])
ub = get_unbalance(voltages[t])
for l in ls:
if l not in losses_no_evs:
losses_no_evs[l] = 0
ub_no_evs[l] = [0]*3
losses_no_evs[l] += ls[l][2]
for i in range(3):
ub_no_evs[l][i] += ub[l][2+i]
fdr.uncontrolled()
voltages = fdr.get_all_voltages(My,a,alpha,v0)
losses_unc = {}
ub_unc = {}
print(fdr.predict_losses())
for t in voltages:
ls = get_unbalance(voltages[t])
ub = get_unbalance(voltages[t])
for l in ls:
if l not in losses_unc:
losses_unc[l] = 0
ub_unc[l] = [0]*3
losses_unc[l] += ls[l][2]
for i in range(3):
ub_unc[l][i] += ub[l][2+i]
fdr.load_flatten()
voltages = fdr.get_all_voltages(My,a,alpha,v0)
losses_lf = {}
ub_lf = {}
print(fdr.predict_losses())
for t in voltages:
ls = get_unbalance(voltages[t])
ub = get_unbalance(voltages[t])
for l in ls:
if l not in losses_lf:
losses_lf[l] = 0
ub_lf[l] = [0]*3
losses_lf[l] += ls[l][2]
for i in range(3):
ub_lf[l][i] += ub[l][2+i]
fdr.loss_minimise()
voltages = fdr.get_all_voltages(My,a,alpha,v0)
losses_lm = {}
ub_lm = {}
print(fdr.predict_losses())
for t in voltages:
ls = get_unbalance(voltages[t])
ub = get_unbalance(voltages[t])
for l in ls:
if l not in losses_lm:
losses_lm[l] = 0
ub_lm[l] = [0]*3
losses_lm[l] += ls[l][2]
for i in range(3):
ub_lm[l][i] += ub[l][2+i]
fdr.balance_phase2(phase)
voltages = fdr.get_all_voltages(My,a,alpha,v0)
losses_p = {}
ub_p = {}
print(fdr.predict_losses())
for t in voltages:
ls = get_unbalance(voltages[t])
ub = get_unbalance(voltages[t])
for l in ls:
if l not in losses_p:
losses_p[l] = 0
ub_p[l] = [0]*3
losses_p[l] += ls[l][2]
for i in range(3):
ub_p[l][i] += ub[l][2+i]
for i in range(3):
with open('lv test/branch_'+str(i)+'.csv','w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['line','no evs','unc','lf','lm','p'])
for l in losses_unc:
writer.writerow([l,ub_no_evs[l][i],ub_unc[l][i],ub_lf[l][i],
ub_lm[l][i],ub_p[l][i]])
with open('lv test/branch_losses.csv','w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['line','no evs','unc','lf','lm','p'])
for l in losses_unc:
writer.writerow([l,losses_no_evs[l],losses_unc[l],losses_lf[l],
losses_lm[l],losses_p[l]])
'''
busV = {}
for i in range(907):
busV[i+1] = [complex(0,0)]*3
for i in range(3):
busV[1][i] = v0[i]
for i in range(len(voltages)):
bn = int(i/3)+2
pn = i%3
busV[bn][pn] = voltages[i]
lineI = {}
for l in data:
b1 = data[l][0]
b2 = data[l][1]
Yp = data[l][2]
v_ = np.hstack((busV[int(b1)],busV[int(b2)]))
i = np.matmul(Yp,v_)[:3]
iT = 0
for ii in range(3):
iT += abs(i[ii]/1000)
lineI[l] = iT
with open('lv test/no_evs.csv','w') as csvfile:
writer = csv.writer(csvfile)
for l in lineI:
writer.writerow([l,lineI[l]])
busV = {}
for i in range(907):
busV[i+1] = [complex(0,0)]*3
for i in range(3):
busV[1][i] = v0[i]
for i in range(len(voltages)):
bn = int(i/3)+2
pn = i%3
busV[bn][pn] = voltages[i]
lineI = {}
for l in data:
b1 = data[l][0]
b2 = data[l][1]
Yp = data[l][2]
v_ = np.hstack((busV[int(b1)],busV[int(b2)]))
i = np.matmul(Yp,v_)[:3]
iT = 0
for ii in range(3):
iT += abs(i[ii]/1000)
lineI[l] = iT
with open('lv test/uncontrolled.csv','w') as csvfile:
writer = csv.writer(csvfile)
for l in lineI:
writer.writerow([l,lineI[l]])
busV = {}
for i in range(907):
busV[i+1] = [complex(0,0)]*3
for i in range(3):
busV[1][i] = v0[i]
for i in range(len(voltages)):
bn = int(i/3)+2
pn = i%3
busV[bn][pn] = voltages[i]
lineI = {}
for l in data:
b1 = data[l][0]
b2 = data[l][1]
Yp = data[l][2]
v_ = np.hstack((busV[int(b1)],busV[int(b2)]))
i = np.matmul(Yp,v_)[:3]
iT = 0
for ii in range(3):
iT += abs(i[ii]/1000)
lineI[l] = iT
with open('lv test/lf.csv','w') as csvfile:
writer = csv.writer(csvfile)
for l in lineI:
writer.writerow([l,lineI[l]])
busV = {}
for i in range(907):
busV[i+1] = [complex(0,0)]*3
for i in range(3):
busV[1][i] = v0[i]
for i in range(len(voltages)):
bn = int(i/3)+2
pn = i%3
busV[bn][pn] = voltages[i]
lineI = {}
for l in data:
b1 = data[l][0]
b2 = data[l][1]
Yp = data[l][2]
v_ = np.hstack((busV[int(b1)],busV[int(b2)]))
i = np.matmul(Yp,v_)[:3]
iT = 0
for ii in range(3):
iT += abs(i[ii]/1000)
lineI[l] = iT
with open('lv test/lm.csv','w') as csvfile:
writer = csv.writer(csvfile)
for l in lineI:
writer.writerow([l,lineI[l]])
# now I need to work out the line flows from the current injections
'''
|
[
"constancellc@gmail.com"
] |
constancellc@gmail.com
|
9c541ff8948b8d049f61e4e3e61cfa30a9bb0056
|
33170e7fc26b6af2ab61b67aa520c307bbd0e118
|
/py/trash/947_predict_0228-4.py
|
09ef21e955ea5f5f8ebc8ba007660cc1fa85d498
|
[
"MIT"
] |
permissive
|
alaskaw/Microsoft-Malware-Prediction
|
26e56adb803184328d1a8f5a3423d5edda7fc400
|
103cbf7c4fc98ae584e1aa9d1c220bb79ddbbd80
|
refs/heads/master
| 2020-04-28T21:22:06.403542
| 2019-03-14T04:36:01
| 2019-03-14T04:36:01
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 7,407
|
py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 28 16:52:33 2019
@author: kazuki.onodera
"""
import numpy as np
import pandas as pd
import os, gc
from glob import glob
from tqdm import tqdm
import sys
sys.path.append(f'/home/{os.environ.get("USER")}/PythonLibrary')
import lgbextension as ex
import lightgbm as lgb
from sklearn.externals import joblib
from sklearn.metrics import roc_auc_score
import utils , utils_cat
utils.start(__file__)
#==============================================================================
SUBMIT_FILE_PATH = '../output/0228-4.csv.gz'
COMMENT = 'nejumi + f009 f014 top50(f019)'
EXE_SUBMIT = True
SEED = np.random.randint(9999)
print('SEED:', SEED)
param = {
'boosting_type': 'gbdt',
'class_weight': None,
'colsample_bytree': 0.71,
'learning_rate': 0.05,
'max_depth': -1,
'min_child_samples': 10,
'min_child_weight': 5,
'min_split_gain': 0,
# 'n_estimators': n_estimators,
'n_jobs': -1,
'num_leaves': 64,
'objective': 'binary',
# 'random_state': seed,
'reg_alpha': 0,
'reg_lambda': 0,
'subsample': 0.71,
'subsample_for_bin': 50000,
'subsample_freq': 1,
'max_bin': 255,
'metric': 'auc',
'nthread': -1,
'verbose': -1,
# 'seed': seed,
# 'device': 'gpu',
# 'gpu_use_dp': False
}
NROUND = 19999
NFOLD = 5
VERBOSE_EVAL = 100
ESR = 100
col_drop = [
'Census_SystemVolumeTotalCapacity',
]
USE_PREF_f019 = ['f019']
feature_f019 = pd.read_csv('LOG/imp_f019.csv').head(50).feature.tolist()
USE_PREF_all = ['f009', 'f014']
RESULT_DICT = {}
RESULT_DICT['file'] = SUBMIT_FILE_PATH
# =============================================================================
# def
# =============================================================================
def get_files(search:str, prefs:list):
files = sorted(glob(search))
# USE_PREF
li = []
for i in files:
for j in prefs:
if j in i:
li.append(i)
break
files = li
[print(i,f) for i,f in enumerate(files)]
return files
# =============================================================================
# load
# =============================================================================
files_tr_f019 = get_files('../data/train_f*.f', USE_PREF_f019)
X_train_f019 = pd.concat([
pd.read_feather(f) for f in tqdm(files_tr_f019, mininterval=30)
], axis=1)[feature_f019]
files_tr_all = get_files('../data/train_f*.f', USE_PREF_all)
X_train_all = pd.concat([
pd.read_feather(f) for f in tqdm(files_tr_all, mininterval=30)
], axis=1)
X_train = pd.concat([X_train_f019, X_train_all, joblib.load('../external/X_train_nejumi.pkl.gz')],
axis=1)
del X_train_f019, X_train_all; gc.collect()
y_train = utils.load_target()['HasDetections']
# drop
if len(col_drop) > 0:
X_train.drop(col_drop, axis=1, inplace=True)
if X_train.columns.duplicated().sum()>0:
raise Exception(f'duplicated!: { X_train.columns[X_train.columns.duplicated()] }')
print('no dup :) ')
print(f'X_train.shape {X_train.shape}')
gc.collect()
CAT = list( set(X_train.columns)&set(utils_cat.ALL))
print(f'CAT: {CAT}')
COL = X_train.columns.tolist()
RESULT_DICT['feature size'] = len(COL)
RESULT_DICT['category feature size'] = len(CAT)
# =============================================================================
# all sample
# =============================================================================
dtrain = lgb.Dataset(X_train, y_train.values,
categorical_feature=CAT,
free_raw_data=False)
gc.collect()
#models = []
#for i in range(LOOP):
# param['seed'] = np.random.randint(9999)
# model = lgb.train(params=param, train_set=dtrain,
# num_boost_round=NROUND,
# )
# model.save_model(f'../data/lgb{i}.model')
# models.append(model)
# CV
param['seed'] = np.random.randint(9999)
ret, models = lgb.cv(param, dtrain, NROUND,
nfold=NFOLD,
stratified=True, shuffle=True,
feval=ex.eval_auc,
early_stopping_rounds=ESR,
verbose_eval=VERBOSE_EVAL,
categorical_feature=CAT,
seed=SEED)
for i, model in enumerate(models):
model.save_model(f'../data/lgb{i}.model')
#models = []
#for i in range(LOOP):
# model = lgb.Booster(model_file=f'../data/lgb{i}.model')
# models.append(model)
imp = ex.getImp(models)
imp['split'] /= imp['split'].max()
imp['gain'] /= imp['gain'].max()
imp['total'] = imp['split'] + imp['gain']
imp.sort_values('total', ascending=False, inplace=True)
imp.reset_index(drop=True, inplace=True)
imp.to_csv(f'LOG/imp_{__file__}.csv', index=False)
utils.savefig_imp(imp, f'LOG/imp_{__file__}.png', x='total')
RESULT_DICT['nfold'] = NFOLD
RESULT_DICT['seed'] = SEED
RESULT_DICT['eta'] = param['learning_rate']
RESULT_DICT['NROUND'] = NROUND
RESULT_DICT['train AUC'] = ret['auc-mean'][-1]
del dtrain, X_train, y_train; gc.collect()
# =============================================================================
# test
# =============================================================================
files_te = get_files('../data/test_f*.f', USE_PREF_f019+USE_PREF_all)
X_test = pd.concat([
pd.read_feather(f) for f in tqdm(files_te, mininterval=30)
]+[joblib.load('../external/X_test_nejumi.pkl.gz')], axis=1)[COL]
gc.collect()
if X_test.columns.duplicated().sum()>0:
raise Exception(f'duplicated!: { X_test.columns[X_test.columns.duplicated()] }')
print('no dup :) ')
print(f'X_test.shape {X_test.shape}')
y_pred = pd.Series(0, index=X_test.index)
for model in tqdm(models):
y_pred += pd.Series(model.predict(X_test)).rank()
y_pred /= y_pred.max()
sub = pd.read_csv('../input/sample_submission.csv.zip')
sub['HasDetections'] = y_pred.values
print('corr with best')
sub_best = pd.read_csv(utils.SUB_BEST)
print('with mybest:', sub['HasDetections'].corr( sub_best['HasDetections'], method='spearman') )
sub_best['HasDetections'] = np.load(utils.SUB_nejumi)
print('with nejumi:', sub['HasDetections'].corr( sub_best['HasDetections'], method='spearman') )
print("""
# =============================================================================
# write down these info to benchmark.xlsx
# =============================================================================
""")
[print(f'{k:<25}: {RESULT_DICT[k]}') for k in RESULT_DICT]
print("""
# =============================================================================
""")
# save
sub.to_csv(SUBMIT_FILE_PATH, index=False, compression='gzip')
#utils.to_pkl_gzip(sub[['HasDetections']], SUBMIT_FILE_PATH.replace('.csv.gz', f'_{SEED}.pkl'))
# =============================================================================
# submission
# =============================================================================
if EXE_SUBMIT:
print('submit')
utils.submit(SUBMIT_FILE_PATH, COMMENT)
#==============================================================================
utils.end(__file__)
#utils.stop_instance()
|
[
"luvsic02@gmail.com"
] |
luvsic02@gmail.com
|
ef3126368dbc5fb7408a2d35f7fc575b6e8fb814
|
5aee5e9274aad752f4fc1940030e9844ef8be17d
|
/HeavyIonsAnalysis/JetAnalysis/python/jets/akPu7CaloJetSequence_pPb_jec_cff.py
|
d5e8f0b11759a74be3f22036f437b49b4dd08852
|
[] |
no_license
|
jiansunpurdue/5316_dmesonreco_hiforest
|
1fb65af11ea673646efe1b25bd49e88de9bf3b44
|
a02224ad63160d91aab00ed2f92d60a52f0fd348
|
refs/heads/master
| 2021-01-22T02:53:43.471273
| 2014-04-26T16:10:12
| 2014-04-26T16:10:12
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,574
|
py
|
import FWCore.ParameterSet.Config as cms
from PhysicsTools.PatAlgos.patHeavyIonSequences_cff import *
from HeavyIonsAnalysis.JetAnalysis.inclusiveJetAnalyzer_cff import *
akPu7Calomatch = patJetGenJetMatch.clone(
src = cms.InputTag("akPu7CaloJets"),
matched = cms.InputTag("ak7HiGenJetsCleaned")
)
akPu7Caloparton = patJetPartonMatch.clone(src = cms.InputTag("akPu7CaloJets"),
matched = cms.InputTag("genParticles")
)
akPu7Calocorr = patJetCorrFactors.clone(
useNPV = False,
# primaryVertices = cms.InputTag("hiSelectedVertex"),
levels = cms.vstring('L2Relative','L3Absolute'),
src = cms.InputTag("akPu7CaloJets"),
payload = "AKPu7Calo_HI"
)
akPu7CalopatJets = patJets.clone(jetSource = cms.InputTag("akPu7CaloJets"),
jetCorrFactorsSource = cms.VInputTag(cms.InputTag("akPu7Calocorr")),
genJetMatch = cms.InputTag("akPu7Calomatch"),
genPartonMatch = cms.InputTag("akPu7Caloparton"),
jetIDMap = cms.InputTag("akPu7CaloJetID"),
addBTagInfo = False,
addTagInfos = False,
addDiscriminators = False,
addAssociatedTracks = False,
addJetCharge = False,
addJetID = False,
getJetMCFlavour = False,
addGenPartonMatch = True,
addGenJetMatch = True,
embedGenJetMatch = True,
embedGenPartonMatch = True,
embedCaloTowers = False,
embedPFCandidates = False
)
akPu7CaloJetAnalyzer = inclusiveJetAnalyzer.clone(jetTag = cms.InputTag("akPu7CalopatJets"),
genjetTag = 'ak7HiGenJetsCleaned',
rParam = 0.7,
matchJets = cms.untracked.bool(False),
matchTag = 'akPu7PFpatJets',
pfCandidateLabel = cms.untracked.InputTag('particleFlowTmp'),
trackTag = cms.InputTag("generalTracks"),
fillGenJets = True,
isMC = True,
genParticles = cms.untracked.InputTag("genParticles"),
eventInfoTag = cms.InputTag("generator")
)
akPu7CaloJetSequence_mc = cms.Sequence(
akPu7Calomatch
*
akPu7Caloparton
*
akPu7Calocorr
*
akPu7CalopatJets
*
akPu7CaloJetAnalyzer
)
akPu7CaloJetSequence_data = cms.Sequence(akPu7Calocorr
*
akPu7CalopatJets
*
akPu7CaloJetAnalyzer
)
akPu7CaloJetSequence_jec = akPu7CaloJetSequence_mc
akPu7CaloJetSequence_mix = akPu7CaloJetSequence_mc
akPu7CaloJetSequence = cms.Sequence(akPu7CaloJetSequence_jec)
akPu7CaloJetAnalyzer.genPtMin = cms.untracked.double(1)
|
[
"sun229@purdue.edu"
] |
sun229@purdue.edu
|
bc4dde6205e2dc08c3f1b2c7b8d97523b58c76b8
|
8b00e2b136636841b38eb182196e56f4721a1e4c
|
/trio/_core/_exceptions.py
|
45f21d389ae8d6f15662d6ff796adfea373bad80
|
[
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] |
permissive
|
xyicheng/trio
|
77c8c1e08e3aa4effe8cf04e879720ccfcdb7d33
|
fa091e2e91d196c2a57b122589a166949ea03103
|
refs/heads/master
| 2021-01-23T00:05:59.618483
| 2017-03-16T04:25:05
| 2017-03-16T04:25:05
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,364
|
py
|
import attr
# Re-exported
__all__ = [
"TrioInternalError", "RunFinishedError", "WouldBlock",
"Cancelled", "PartialResult",
]
class TrioInternalError(Exception):
"""Raised by :func:`run` if we encounter a bug in trio, or (possibly) a
misuse of one of the low-level :mod:`trio.hazmat` APIs.
This should never happen! If you get this error, please file a bug.
Unfortunately, if you get this error it also means that all bets are off –
trio doesn't know what is going on and its normal invariants may be void.
(For example, we might have "lost track" of a task. Or lost track of all
tasks.) Again, though, this shouldn't happen.
"""
pass
TrioInternalError.__module__ = "trio"
class RunFinishedError(RuntimeError):
"""Raised by ``run_in_trio_thread`` and similar functions if the
corresponding call to :func:`trio.run` has already finished.
"""
pass
RunFinishedError.__module__ = "trio"
class WouldBlock(Exception):
"""Raised by ``X_nowait`` functions if ``X`` would block.
"""
pass
WouldBlock.__module__ = "trio"
class Cancelled(BaseException):
"""Raised by blocking calls if the surrounding scope has been cancelled.
You should let this exception propagate, to be caught by the relevant
cancel scope. To remind you of this, it inherits from
:exc:`BaseException`, like :exc:`KeyboardInterrupt` and
:exc:`SystemExit`.
.. note::
In the US it's also common to see this word spelled "canceled", with
only one "l". This is a `recent
<https://books.google.com/ngrams/graph?content=canceled%2Ccancelled&year_start=1800&year_end=2000&corpus=5&smoothing=3&direct_url=t1%3B%2Ccanceled%3B%2Cc0%3B.t1%3B%2Ccancelled%3B%2Cc0>`__
and `US-specific
<https://books.google.com/ngrams/graph?content=canceled%2Ccancelled&year_start=1800&year_end=2000&corpus=18&smoothing=3&share=&direct_url=t1%3B%2Ccanceled%3B%2Cc0%3B.t1%3B%2Ccancelled%3B%2Cc0>`__
innovation, and even in the US both forms are still commonly used. So
for consistency with the rest of the world and with "cancellation"
(which always has two "l"s), trio uses the two "l" spelling
everywhere.
"""
_scope = None
Cancelled.__module__ = "trio"
@attr.s(slots=True, frozen=True)
class PartialResult:
# XX
bytes_sent = attr.ib()
|
[
"njs@pobox.com"
] |
njs@pobox.com
|
f9c568a46854f97c14938d17f5845aa1f9cf72f9
|
915ea8bcabf4da0833d241050ef226100f7bd233
|
/SDKs/Python/test/test_contract_item.py
|
d3f8d89ca8fd4f3b3678876eb22038d67bad2eb9
|
[
"BSD-2-Clause"
] |
permissive
|
parserrr/API-Examples
|
03c3855e2aea8588330ba6a42d48a71eb4599616
|
0af039afc104316f1722ee2ec6d2881abd3fbc07
|
refs/heads/master
| 2020-07-10T22:17:24.906233
| 2019-08-26T03:06:21
| 2019-08-26T03:06:21
| 204,382,917
| 0
| 0
| null | 2019-08-26T02:48:16
| 2019-08-26T02:48:15
| null |
UTF-8
|
Python
| false
| false
| 922
|
py
|
# coding: utf-8
"""
MINDBODY Public API
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: v6
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import unittest
import swagger_client
from swagger_client.models.contract_item import ContractItem # noqa: E501
from swagger_client.rest import ApiException
class TestContractItem(unittest.TestCase):
"""ContractItem unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def testContractItem(self):
"""Test ContractItem"""
# FIXME: construct object with mandatory attributes with example values
# model = swagger_client.models.contract_item.ContractItem() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
|
[
"christopher.volpi@mindbodyonline.com"
] |
christopher.volpi@mindbodyonline.com
|
537ecd9ff7dea52514e94a67ec8488f4a88abd28
|
10f1f4ce92c83d34de1531e8e891f2a074b3fefd
|
/graph/gcn_utils/feeder.py
|
9b012bf3355a26228cac9c53bbd94c997bfe56d8
|
[
"MIT"
] |
permissive
|
sourabhyadav/test_track
|
d88c4d35753d2b21e3881fc10233bf7bbb1e2cec
|
d2b4813aaf45dd35db5de3036eda114ef14d5022
|
refs/heads/master
| 2021-01-06T12:38:56.883549
| 2020-02-05T07:08:46
| 2020-02-05T07:08:46
| 241,328,706
| 1
| 0
|
MIT
| 2020-02-18T10:06:14
| 2020-02-18T10:06:13
| null |
UTF-8
|
Python
| false
| false
| 2,751
|
py
|
'''
Author: Guanghan Ning
E-mail: guanghan.ning@jd.com
October 24th, 2018
Feeder of Siamese Graph Convolutional Networks for Pose Tracking
Code partially borrowed from:
https://github.com/yysijie/st-gcn/blob/master/feeder/feeder.py
'''
# sys
import os
import sys
import numpy as np
import random
import pickle
import json
# torch
import torch
import torch.nn as nn
from torchvision import datasets, transforms
# operation
from . import tools
import random
class Feeder(torch.utils.data.Dataset):
""" Feeder of PoseTrack Dataset
Arguments:
data_path: the path to '.npy' data, the shape of data should be (N, C, T, V, M)
num_person_in: The number of people the feeder can observe in the input sequence
num_person_out: The number of people the feeder in the output sequence
debug: If true, only use the first 100 samples
"""
def __init__(self,
data_path,
data_neg_path,
ignore_empty_sample=True,
debug=False):
self.debug = debug
self.data_path = data_path
self.neg_data_path = data_neg_path
self.ignore_empty_sample = ignore_empty_sample
self.load_data()
def load_data(self):
with open(self.data_path, 'rb') as handle:
self.graph_pos_pair_list_all = pickle.load(handle)
with open(self.neg_data_path, 'rb') as handle:
self.graph_neg_pair_list_all = pickle.load(handle)
# output data shape (N, C, T, V, M)
self.N = min(len(self.graph_pos_pair_list_all) , len(self.graph_neg_pair_list_all)) #sample
self.C = 2 #channel
self.T = 1 #frame
self.V = 15 #joint
self.M = 1 #person
def __len__(self):
return self.N
def __iter__(self):
return self
def __getitem__(self, index):
# randomly add negative samples
random_num = random.uniform(0, 1)
if random_num > 0.5:
#if False:
# output shape (C, T, V, M)
# get data
sample_graph_pair = self.graph_pos_pair_list_all[index]
label = 1 # a pair should match
else:
sample_graph_pair = self.graph_neg_pair_list_all[index]
label = 0 # a pair does not match
data_numpy_pair = []
for siamese_id in range(2):
# fill data_numpy
data_numpy = np.zeros((self.C, self.T, self.V, 1))
pose = sample_graph_pair[:][siamese_id]
data_numpy[0, 0, :, 0] = [x[0] for x in pose]
data_numpy[1, 0, :, 0] = [x[1] for x in pose]
data_numpy_pair.append(data_numpy)
return data_numpy_pair[0], data_numpy_pair[1], label
|
[
"chenhaomingbob@163.com"
] |
chenhaomingbob@163.com
|
05a2d22595769aabb8ba1288219cbc5896aff69b
|
837fcd0d7e40de15f52c73054709bd40264273d2
|
/practices_loop-master/sum_user_quit.py
|
7d4bd070a2e7a364a41b6719421b8247f5090e2f
|
[] |
no_license
|
NEHAISRANI/Python_Programs
|
dee9e05ac174a4fd4dd3ae5e96079e10205e18f9
|
aa108a56a0b357ca43129e59377ac35609919667
|
refs/heads/master
| 2020-11-25T07:20:00.484973
| 2020-03-08T12:17:39
| 2020-03-08T12:17:39
| 228,554,399
| 0
| 1
| null | 2020-10-01T06:41:20
| 2019-12-17T07:04:31
|
Python
|
UTF-8
|
Python
| false
| false
| 333
|
py
|
#In this program if user input 4 then sum all numbers from starting to ending. if user input quit then program exit"
user=raw_input("enter your number")
index=1
var1=0
while index<=user:
if user=="quit":
break
user=int(user)
if index<=user:
var1=var1+index
index=index+1
if var1!=0:
print var1
|
[
"nehai18@navgurukul.org"
] |
nehai18@navgurukul.org
|
ae4c1c1b0df6cf9a31d0f6d154fe645dd8e7fe8e
|
fd5c2d6e8a334977cda58d4513eb3385b431a13a
|
/extract_census_doc.py
|
a1445f608f735d677f398b8b2b123c44cf91d16e
|
[
"MIT"
] |
permissive
|
censusreporter/census-api
|
817c616b06f6b1c70c7b3737f82f45a80544c44d
|
c8d2c04c7be19cdee1000001772adda541710a80
|
refs/heads/master
| 2023-07-28T06:17:26.572796
| 2023-07-05T20:37:03
| 2023-07-05T20:37:03
| 9,879,953
| 146
| 52
|
MIT
| 2022-07-11T07:16:19
| 2013-05-06T05:24:57
|
Python
|
UTF-8
|
Python
| false
| false
| 7,414
|
py
|
#!/bin/python
import psycopg2
import psycopg2.extras
import json
from collections import OrderedDict
conn = psycopg2.connect(database='postgres')
cur = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
state = 'IL'
logrecno = '89' # Evanston city, IL
def sum(data, *columns):
def reduce_fn(x, y):
if x and y:
return x + y
elif x and not y:
return x
elif y and not x:
return y
else:
return None
return reduce(reduce_fn, map(lambda col: data[col], columns))
def maybe_int(i):
return int(i) if i else i
doc = dict(population=dict(), geography=dict(), education=dict())
cur.execute("SELECT * FROM acs2010_1yr.geoheader WHERE stusab=%s AND logrecno=%s;", [state, logrecno])
data = cur.fetchone()
doc['geography'] = dict(name=data['name'],
stusab=data['stusab'],
sumlevel=data['sumlevel'])
cur.execute("SELECT * FROM acs2010_1yr.B01002 WHERE stusab=%s AND logrecno=%s;", [state, logrecno])
data = cur.fetchone()
doc['population']['median_age'] = dict(total=maybe_int(data['b010020001']),
male=maybe_int(data['b010020002']),
female=maybe_int(data['b010020003']))
cur.execute("SELECT * FROM acs2010_1yr.B01003 WHERE stusab=%s AND logrecno=%s;", [state, logrecno])
data = cur.fetchone()
doc['population']['total'] = maybe_int(data['b010030001'])
cur.execute("SELECT * FROM acs2010_1yr.B01001 WHERE stusab=%s AND logrecno=%s;", [state, logrecno])
data = cur.fetchone()
doc['population']['gender'] = OrderedDict([
('0-9', dict(male=maybe_int(sum(data, 'b010010003', 'b010010004')),
female=maybe_int(sum(data, 'b010010027', 'b010010028')))),
('10-19', dict(male=maybe_int(sum(data, 'b010010005', 'b010010006', 'b010010007')),
female=maybe_int(sum(data, 'b010010029', 'b010010030', 'b010010031')))),
('20-29', dict(male=maybe_int(sum(data, 'b010010008', 'b010010009', 'b010010010', 'b010010011')),
female=maybe_int(sum(data, 'b010010032', 'b010010033', 'b010010034', 'b010010035')))),
('30-39', dict(male=maybe_int(sum(data, 'b010010012', 'b010010013')),
female=maybe_int(sum(data, 'b010010036', 'b010010037')))),
('40-49', dict(male=maybe_int(sum(data, 'b010010014', 'b010010015')),
female=maybe_int(sum(data, 'b010010038', 'b010010039')))),
('50-59', dict(male=maybe_int(sum(data, 'b010010016', 'b010010017')),
female=maybe_int(sum(data, 'b010010040', 'b010010041')))),
('60-69', dict(male=maybe_int(sum(data, 'b010010018', 'b010010019', 'b010010020', 'b010010021')),
female=maybe_int(sum(data, 'b010010042', 'b010010043', 'b010010044', 'b010010045')))),
('70-79', dict(male=maybe_int(sum(data, 'b010010022', 'b010010023')),
female=maybe_int(sum(data, 'b010010046', 'b010010047')))),
('80+', dict(male=maybe_int(sum(data, 'b010010024', 'b010010025')),
female=maybe_int(sum(data, 'b010010048', 'b010010049'))))
])
cur.execute("SELECT * FROM acs2010_1yr.B15001 WHERE stusab=%s AND logrecno=%s;", [state, logrecno])
data = cur.fetchone()
doc['education']['attainment'] = OrderedDict([
('<9th Grade', maybe_int(sum(data, 'b150010004', 'b150010012', 'b150010020', 'b150010028', 'b150010036', 'b150010045', 'b150010053', 'b150010061', 'b150010069', 'b150010077'))),
('9th-12th Grade (No Diploma)', maybe_int(sum(data, 'b150010005', 'b150010013', 'b150010021', 'b150010029', 'b150010037', 'b150010046', 'b150010054', 'b150010062', 'b150010070', 'b150010078'))),
('High School Grad/GED/Alt', maybe_int(sum(data, 'b150010006', 'b150010014', 'b150010022', 'b150010030', 'b150010038', 'b150010047', 'b150010055', 'b150010063', 'b150010071', 'b150010079'))),
('Some College (No Degree)', maybe_int(sum(data, 'b150010007', 'b150010015', 'b150010023', 'b150010031', 'b150010039', 'b150010048', 'b150010056', 'b150010064', 'b150010072', 'b150010080'))),
('Associate Degree', maybe_int(sum(data, 'b150010008', 'b150010016', 'b150010024', 'b150010032', 'b150010040', 'b150010049', 'b150010057', 'b150010065', 'b150010073', 'b150010081'))),
('Bachelor Degree', maybe_int(sum(data, 'b150010009', 'b150010017', 'b150010025', 'b150010033', 'b150010041', 'b150010050', 'b150010058', 'b150010066', 'b150010074', 'b150010082'))),
('Graduate or Professional Degree', maybe_int(sum(data, 'b150010010', 'b150010018', 'b150010026', 'b150010034', 'b150010042', 'b150010051', 'b150010059', 'b150010067', 'b150010075', 'b150010083')))
])
cur.execute("SELECT * FROM acs2010_1yr.C16001 WHERE stusab=%s AND logrecno=%s;", [state, logrecno])
data = cur.fetchone()
doc['language'] = OrderedDict([
('English Only', maybe_int(data['c160010002'])),
('Spanish', maybe_int(data['c160010003'])),
('French', maybe_int(data['c160010004'])),
('German', maybe_int(data['c160010005'])),
('Slavic', maybe_int(data['c160010006'])),
('Other Indo-European', maybe_int(data['c160010007'])),
('Korean', maybe_int(data['c160010008'])),
('Chinese', maybe_int(data['c160010009'])),
('Vietnamese', maybe_int(data['c160010010'])),
('Tagalong', maybe_int(data['c160010011'])),
('Other Asian', maybe_int(data['c160010012'])),
('Other & Unspecified', maybe_int(data['c160010013']))
])
cur.execute("SELECT * FROM acs2010_1yr.B27010 WHERE stusab=%s AND logrecno=%s;", [state, logrecno])
data = cur.fetchone()
doc['insurance'] = OrderedDict([
('No Insurance', maybe_int(sum(data, 'b270100017', 'b270100033', 'b270100050', 'b270100053'))),
('Employer Only', maybe_int(sum(data, 'b270100004', 'b270100020', 'b270100036', 'b270100054'))),
('Direct-Purchase Only', maybe_int(sum(data, 'b270100005', 'b270100021', 'b270100037', 'b270100055'))),
('Medicare Only', maybe_int(sum(data, 'b270100006', 'b270100022', 'b270100038' ))),
('Medicaid/Means-Tested Only', maybe_int(sum(data, 'b270100007', 'b270100023', 'b270100039' ))),
('Tricare/Military Only', maybe_int(sum(data, 'b270100008', 'b270100024', 'b270100040', 'b270100056'))),
('VA Health Care Only', maybe_int(sum(data, 'b270100009', 'b270100025', 'b270100041', 'b270100057'))),
('Employer+Direct Purchase', maybe_int(sum(data, 'b270100011', 'b270100027', 'b270100043', 'b270100058'))),
('Employer+Medicare', maybe_int(sum(data, 'b270100012', 'b270100028', 'b270100044', 'b270100059'))),
('Direct+Medicare', maybe_int(sum(data, 'b270100045', 'b270100060'))),
('Medicare+Medicaid', maybe_int(sum(data, 'b270100013', 'b270100029', 'b270100046', 'b270100061'))),
('Other Private-Only', maybe_int(sum(data, 'b270100014', 'b270100030', 'b270100047', 'b270100062'))),
('Other Public-Only', maybe_int(sum(data, 'b270100015', 'b270100031', 'b270100048', 'b270100064'))),
('Other', maybe_int(sum(data, 'b270100016', 'b270100032', 'b270100049', 'b270100065')))
])
print json.dumps(doc, indent=2)
|
[
"ian.dees@gmail.com"
] |
ian.dees@gmail.com
|
5d565e7d89b2cf7e44965b839844bcc6a47e0e56
|
ecbbc5cf8b49de00dd956386ea7cf31951aecbf8
|
/src/KalmanFilter.py
|
d0005ea5d794108215ebbe567191ff497c0fe45c
|
[] |
no_license
|
connorlee77/ardrone_stateestimation
|
9e49339c6d916a146a709acc4adf947453c9d626
|
253722cf1940fd368bc10dcd90be0c0113bb4339
|
refs/heads/master
| 2021-01-10T13:13:57.845898
| 2016-03-18T08:53:18
| 2016-03-18T08:53:18
| 53,226,979
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,290
|
py
|
import numpy as np
import matplotlib.pyplot as plt
import rospy
class KalmanFilter:
def __init__(self, A, P, R, Q, H, B, dimension):
self.A = A
self.P = P
self.x_k = 0
self.kalmanGain = 0
self.R = R #constant
self.Q = Q #constant
self.H = H
self.B = B
self.dimensions = dimension
def predictState(self, u_k):
#rospy.loginfo("predict_state1")
#rospy.loginfo(self.x_k)
self.x_k = np.add(
np.dot(self.A, self.x_k),
np.dot(self.B, u_k))
#rospy.loginfo("predict_state2")
#rospy.loginfo(self.x_k)
self.P = np.add(np.dot(
np.dot(self.A, self.P),
np.transpose(self.A)), self.Q)
def getKalmanGain(self):
first = np.dot(self.P, np.transpose(self.H))
second = np.linalg.inv(
np.add(
np.dot(
np.dot(self.H, self.P),
np.transpose(self.H)),
self.R))
self.kalmanGain = np.dot(first, second)
def update(self, z_k):
residual = np.subtract(
z_k,
np.dot(
self.H,
self.x_k))
#chad = z_k
#rospy.loginfo("update1")
#rospy.loginfo(chad)
self.x_k = np.add(self.x_k, np.dot(self.kalmanGain, residual))
#rospy.loginfo("update2")
#rospy.loginfo(self.x_k)
self.P = np.dot(
np.subtract(
np.identity(self.dimensions),
np.dot(
self.kalmanGain,
self.H)),
self.P)
|
[
"connorlee77@gmail.com"
] |
connorlee77@gmail.com
|
aa0a9e73022a1268c8dc56985d5d5848748aa64e
|
3fe272eea1c91cc5719704265eab49534176ff0d
|
/scripts/item/consume_2439898.py
|
fdc636b193089e8c5f0e75eb0dac9c8a17c50c85
|
[
"MIT"
] |
permissive
|
Bratah123/v203.4
|
e72be4843828def05592298df44b081515b7ca68
|
9cd3f31fb2ef251de2c5968c75aeebae9c66d37a
|
refs/heads/master
| 2023-02-15T06:15:51.770849
| 2021-01-06T05:45:59
| 2021-01-06T05:45:59
| 316,366,462
| 1
| 0
|
MIT
| 2020-12-18T17:01:25
| 2020-11-27T00:50:26
|
Java
|
UTF-8
|
Python
| false
| false
| 217
|
py
|
# Created by MechAviv
# Valentine Damage Skin | (2439898)
if sm.addDamageSkin(2439898):
sm.chat("'Valentine Damage Skin' Damage Skin has been added to your account's damage skin collection.")
sm.consumeItem()
|
[
"pokesmurfuwu@gmail.com"
] |
pokesmurfuwu@gmail.com
|
acc0cbbbbef590f361a5a6744807f18458d0e078
|
de24f83a5e3768a2638ebcf13cbe717e75740168
|
/moodledata/vpl_data/130/usersdata/228/34476/submittedfiles/al8.py
|
99d23561646b83280774cd80f4ab4ad83803ccaf
|
[] |
no_license
|
rafaelperazzo/programacao-web
|
95643423a35c44613b0f64bed05bd34780fe2436
|
170dd5440afb9ee68a973f3de13a99aa4c735d79
|
refs/heads/master
| 2021-01-12T14:06:25.773146
| 2017-12-22T16:05:45
| 2017-12-22T16:05:45
| 69,566,344
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 141
|
py
|
# -*- coding: utf-8 -*-
n=int(input('digite um valor:')
nfat=1
for i in range(2,n+1):
nfat=nfat+i
print(nfat)
|
[
"rafael.mota@ufca.edu.br"
] |
rafael.mota@ufca.edu.br
|
8e9f1d89a0a10175a73f79346baaea3a012c4479
|
3a5ea75a5039207104fd478fb69ac4664c3c3a46
|
/vega/algorithms/nas/modnas/estim/dist_backend/base.py
|
1725fd222057fa4b91024747947592087e159828
|
[
"MIT"
] |
permissive
|
fmsnew/vega
|
e3df25efa6af46073c441f41da4f2fdc4929fec5
|
8e0af84a57eca5745fe2db3d13075393838036bb
|
refs/heads/master
| 2023-06-10T04:47:11.661814
| 2021-06-26T07:45:30
| 2021-06-26T07:45:30
| 285,174,199
| 0
| 0
|
MIT
| 2020-08-11T14:19:09
| 2020-08-05T03:59:49
|
Python
|
UTF-8
|
Python
| false
| false
| 1,712
|
py
|
# -*- coding:utf-8 -*-
# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# MIT License for more details.
"""Distributed remote client and server."""
import threading
class RemoteBase():
"""Distributed remote client class."""
def __init__(self):
super().__init__()
self.on_done = None
self.on_failed = None
def call(self, func, *args, on_done=None, on_failed=None, **kwargs):
"""Call function on remote client with callbacks."""
self.on_done = on_done
self.on_failed = on_failed
self.th_rpc = threading.Thread(target=self.rpc, args=(func,) + args, kwargs=kwargs)
self.th_rpc.start()
def close(self):
"""Close the remote client."""
raise NotImplementedError
def rpc(self, func, *args, **kwargs):
"""Call function on remote client."""
raise NotImplementedError
def on_rpc_done(self, ret):
"""Invoke callback when remote call finishes."""
self.ret = ret
self.on_done(ret)
def on_rpc_failed(self, ret):
"""Invoke callback when remote call fails."""
self.on_failed(ret)
class WorkerBase():
"""Distributed remote worker (server) class."""
def run(self, estim):
"""Run worker."""
raise NotImplementedError
def close(self):
"""Close worker."""
raise NotImplementedError
|
[
"zhangjiajin@huawei.com"
] |
zhangjiajin@huawei.com
|
682039f30aaa220caa90f937bbaf5bd7075dd986
|
fad752f7e4ae9c9fae7a472634a712249fb6f83f
|
/sato/cli.py
|
9697a09e053b96555f2b63cdabb75bc724fcc61c
|
[
"Apache-2.0"
] |
permissive
|
VIDA-NYU/sato
|
895da0de833681335ec5122c4487555d2285f351
|
8fb51787b36114df13f54c1acd11df12a66ad3e4
|
refs/heads/master
| 2021-07-13T16:55:53.621521
| 2020-11-26T01:01:07
| 2020-11-26T01:01:07
| 225,955,500
| 0
| 0
|
Apache-2.0
| 2019-12-04T20:56:16
| 2019-12-04T20:56:15
| null |
UTF-8
|
Python
| false
| false
| 2,252
|
py
|
import click
import pandas as pd
from sato.predict import evaluate
@click.command('predict')
@click.option(
'-n', '--count',
default=1000,
help='Sample size'
)
@click.argument(
'src',
nargs=-1,
type=click.Path(file_okay=True, dir_okay=False, exists=True)
)
def run_predict(count, src):
"""Predict column types for CSV file(s)."""
for filename in src:
# This is a very basic attempt to determine the file compression and
# delimiter from the suffix. Currently, the following four oprions are
# recognized: '.csv', '.csv.gz', '.tsv', '.tsv.gz'. Files ending with
# '.gz' are assumed to be compressed by 'gzip' all other files are
# considered as uncompressed. The delimiter for '.csv' files is ',' and
# for '.tsv' files the delimiter is '\t'.
if filename.endswith('.csv'):
compression = None
delimiter = ','
elif filename.endswith('.csv.gz'):
compression = 'gzip'
delimiter = ','
elif filename.endswith('.tsv'):
compression = None
delimiter = '\t'
elif filename.endswith('.tsv.gz'):
compression = 'gzip'
delimiter = '\t'
else:
raise ValueError('unrecognized file format')
try:
df = pd.read_csv(
filename,
delimiter=delimiter,
compression=compression,
low_memory=False
)
rows = df.shape[0]
print('\n{}'.format(filename))
print('{}'.format('-' * len(filename)))
if rows == 0:
# Skip empty files.
continue
if rows > count:
# Take sample for large files.
df = df.sample(n=count, random_state=1)
# Evaluate data frame to get predicted coluumn labels.
labels = evaluate(df)
for i in range(len(df.columns)):
print('%s: %s' % (df.columns[i], labels[i]))
except Exception as ex:
print('error {}'.format(ex))
@click.group()
def cli(): # pragma: no cover
"""Command line interface for SATO."""
pass
cli.add_command(run_predict)
|
[
"heiko.muller@gmail.com"
] |
heiko.muller@gmail.com
|
f531d8e47a46f16095ff0a4522cfedaf5eca3518
|
b8688a6c1824335808182768c3349624722abba6
|
/uamqp/constants.py
|
987bcaef27fd21d840f5b9e8ca36ca97fd73228c
|
[
"MIT",
"LicenseRef-scancode-generic-cla"
] |
permissive
|
gdooper/azure-uamqp-python
|
65d64e19190921c16cc65947ddcb01f686cd4277
|
8a71c86c7598b439afea28f216a97437b3ebaaed
|
refs/heads/master
| 2020-03-30T00:33:55.710726
| 2018-05-29T16:06:34
| 2018-05-29T16:06:34
| 150,530,862
| 0
| 0
|
MIT
| 2018-09-27T04:57:31
| 2018-09-27T04:57:31
| null |
UTF-8
|
Python
| false
| false
| 3,876
|
py
|
#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#--------------------------------------------------------------------------
from enum import Enum
from uamqp import c_uamqp
DEFAULT_AMQPS_PORT = 5671
AUTH_EXPIRATION_SECS = c_uamqp.AUTH_EXPIRATION_SECS
AUTH_REFRESH_SECS = c_uamqp.AUTH_REFRESH_SECS
STRING_FILTER = b"apache.org:selector-filter:string"
OPERATION = b"operation"
READ_OPERATION = b"READ"
MGMT_TARGET = b"$management"
MESSAGE_SEND_RETRIES = 3
BATCH_MESSAGE_FORMAT = c_uamqp.AMQP_BATCH_MESSAGE_FORMAT
MAX_FRAME_SIZE_BYTES = c_uamqp.MAX_FRAME_SIZE_BYTES
MAX_MESSAGE_LENGTH_BYTES = c_uamqp.MAX_MESSAGE_LENGTH_BYTES
class MessageState(Enum):
WaitingToBeSent = 0
WaitingForAck = 1
Complete = 2
Failed = 3
DONE_STATES = (MessageState.Complete, MessageState.Failed)
class MessageReceiverState(Enum):
Idle = c_uamqp.MESSAGE_RECEIVER_STATE_IDLE
Opening = c_uamqp.MESSAGE_RECEIVER_STATE_OPENING
Open = c_uamqp.MESSAGE_RECEIVER_STATE_OPEN
Closing = c_uamqp.MESSAGE_RECEIVER_STATE_CLOSING
Error = c_uamqp.MESSAGE_RECEIVER_STATE_ERROR
class MessageSendResult(Enum):
Ok = c_uamqp.MESSAGE_SEND_OK
Error = c_uamqp.MESSAGE_SEND_ERROR
Timeout = c_uamqp.MESSAGE_SEND_TIMEOUT
Cancelled = c_uamqp.MESSAGE_SEND_CANCELLED
class MessageSenderState(Enum):
Idle = c_uamqp.MESSAGE_SENDER_STATE_IDLE
Opening = c_uamqp.MESSAGE_SENDER_STATE_OPENING
Open = c_uamqp.MESSAGE_SENDER_STATE_OPEN
Closing = c_uamqp.MESSAGE_SENDER_STATE_CLOSING
Error = c_uamqp.MESSAGE_SENDER_STATE_ERROR
class ManagementLinkState(Enum):
Ok = c_uamqp.AMQP_MANAGEMENT_OPEN_OK
Error = c_uamqp.AMQP_MANAGEMENT_OPEN_ERROR
Cancelled = c_uamqp.AMQP_MANAGEMENT_OPEN_CANCELLED
class ManagementOperationResult(Enum):
Ok = c_uamqp.AMQP_MANAGEMENT_EXECUTE_OPERATION_OK
Error = c_uamqp.AMQP_MANAGEMENT_EXECUTE_OPERATION_ERROR
BadStatus = c_uamqp.AMQP_MANAGEMENT_EXECUTE_OPERATION_FAILED_BAD_STATUS
Closed = c_uamqp.AMQP_MANAGEMENT_EXECUTE_OPERATION_INSTANCE_CLOSED
class Role(Enum):
Sender = c_uamqp.ROLE_SENDER
Receiver = c_uamqp.ROLE_RECEIVER
class SenderSettleMode(Enum):
Unsettled = c_uamqp.SENDER_SETTLE_MODE_UNSETTLED
Settled = c_uamqp.SENDER_SETTLE_MODE_SETTLED
Mixed = c_uamqp.SENDER_SETTLE_MODE_MIXED
class ReceiverSettleMode(Enum):
PeekLock = c_uamqp.RECEIVER_SETTLE_MODE_PEEKLOCK
ReceiveAndDelete = c_uamqp.RECEIVER_SETTLE_MODE_RECEIVEANDDELETE
class CBSOperationResult(Enum):
Ok = c_uamqp.CBS_OPERATION_RESULT_OK
Error = c_uamqp.CBS_OPERATION_RESULT_CBS_ERROR
Failed = c_uamqp.CBS_OPERATION_RESULT_OPERATION_FAILED
Closed = c_uamqp.CBS_OPERATION_RESULT_INSTANCE_CLOSED
class CBSOpenState(Enum):
Ok = c_uamqp.CBS_OPEN_COMPLETE_OK
Error = c_uamqp.CBS_OPEN_COMPLETE_ERROR
Cancelled = c_uamqp.CBS_OPEN_COMPLETE_CANCELLED
class CBSAuthStatus(Enum):
Ok = c_uamqp.AUTH_STATUS_OK
Idle = c_uamqp.AUTH_STATUS_IDLE
InProgress = c_uamqp.AUTH_STATUS_IN_PROGRESS
Timeout = c_uamqp.AUTH_STATUS_TIMEOUT
RefreshRequired = c_uamqp.AUTH_STATUS_REFRESH_REQUIRED
Expired = c_uamqp.AUTH_STATUS_EXPIRED
Error = c_uamqp.AUTH_STATUS_ERROR
Failure = c_uamqp.AUTH_STATUS_FAILURE
class MgmtExecuteResult(Enum):
Ok = c_uamqp.AMQP_MANAGEMENT_EXECUTE_OPERATION_OK
Error = c_uamqp.AMQP_MANAGEMENT_EXECUTE_OPERATION_ERROR
Failed = c_uamqp.AMQP_MANAGEMENT_EXECUTE_OPERATION_FAILED_BAD_STATUS
Closed = c_uamqp.AMQP_MANAGEMENT_EXECUTE_OPERATION_INSTANCE_CLOSED
class MgmtOpenStatus(Enum):
Ok = c_uamqp.AMQP_MANAGEMENT_OPEN_OK
Error = c_uamqp.AMQP_MANAGEMENT_OPEN_ERROR
Cancelled = c_uamqp.AMQP_MANAGEMENT_OPEN_CANCELLED
|
[
"antisch@microsoft.com"
] |
antisch@microsoft.com
|
b61e50e76ad27bc63647d402ed7b18c3b7bc2aae
|
9d1701a88644663277342f3a12d9795cd55a259c
|
/CSC148/07 Sorting/runtime.py
|
6d1020dee852cd090d7eccdd33874dd33c64eccf
|
[] |
no_license
|
xxcocoymlxx/Study-Notes
|
cb05c0e438b0c47b069d6a4c30dd13ab97e4ee6d
|
c7437d387dc2b9a8039c60d8786373899c2e28bd
|
refs/heads/master
| 2023-01-13T06:09:11.005038
| 2020-05-19T19:37:45
| 2020-05-19T19:37:45
| 252,774,764
| 2
| 0
| null | 2022-12-22T15:29:26
| 2020-04-03T15:44:44
|
Jupyter Notebook
|
UTF-8
|
Python
| false
| false
| 3,989
|
py
|
VIDEO:
https://www.youtube.com/watch?v=6Ol2JbwoJp0
NOTES ON THE PDF:
def max_segment_sum(L):
'''(list of int) -> int
Return maximum segment sum of L.
'''
max_so_far = 0
for lower in range(len(L)):
for upper in range(lower, len(L)):
sum = 0
for i in range(lower, upper + 1):
sum = sum + L[i]
max_so_far = max(max_so_far, sum)
return max_so_far
What is the running time of this algorithm? We want an answer in terms of n, not clock time
I want you to find the statement that executes most often; count the number of times that it runs
Statement that runs most often is one in the inner-most loop.
sum = sum + L[i]
Now let's upper-bound the number of times that this statement runs
lower loop runs n times.
Upper loop runs at most n times for each iteration of the lower loop
i loop runs at most n iterations for each iteration of the upper loop.
Now we can upper-bound the total number of times that the inner-most statement runs.
At most n*n*n = n^3
So we have an n^3 algorithm.
More precise: 2+2n^2+n^3 steps
Is it worth it? Or should we just stick to n^3
Prove that 2+2n^2+n^3 is O(n^3).
This means that we have to show 2+2n^2+n^3 is eventually <= kn^3 for some k > 0.
2+2n^2+n^3
<= 2n^3+2n^2+n^3
= 3n^3+2n^2
<= 3n^3+2n^3
= 5n^3
This is our proof that 2+2n^2+n^3 is O(n^3).
----------
We know that the segment-sum code is O(n^3).
Is the code O(n^4) too? Yes
Is it O(n^5)? Yes
Is it O(2^n)? yes
Is it O(n^2)? No
Big oh is an upper bound. If you make it worse (e.g. n^3 to n^4), it's just a worse upper bound. Still technically correct though.
But I want the most accurate bound; lowest upper bound.
----------
I'd like the big oh runtime for the following function.
O(1), O(log n), O(n), O(n log n), O(n^2), O(n^3), ... O(2^n)...
-I want the worst-case upper bound
def bigoh1(n):
sum = 0
for i in range(100, n):
sum = sum+1
print(sum)
It's O(n). It takes something like n-100 steps, which you can prove is O(n)!
----------
Let's do an ordering of best (fastest) to worst (slowest) algorithm efficiencies:
The best one is O(1). Constant-time algorithm
No matter how big your input, your runtime does not increase.
Example:
def f(n):
print('hello world')
-Return the first element of a list.
-Return the maximum of two characters.
Between constant and linear is O(log n)
Example: binary search
Getting worse...
O(n), linear algorithm.
-Printing all elements in a list
-finding the maximum element in a list
A little bit worse is O(n log n)
Examples: quicksort (on average), mergesort
Slower is O(n^2): bubble sort, insertion sort, selection sort
Slower is O(n^3): maximum segment sum code
Slower is O(n^4), O(n^5)...
...
Eventually you get so bad that you can't even use them in practice
O(2^n). As n increases by 1, you double the amount of time you take
Even worse...
O(n!). Like the permutation approach to finding all anagrams
O(n^n)
Huge difference between O(n^k) polynomials and O(k^n) exponential functions.
O(n^2) and O(2^n): very different.
O(n^2)is computable for reasonable-sized input; O(2^n) is not.
----------
I'd like the big oh runtime for each of these functions.
e.g. O(1), O(log n), O(n), O(n log n), O(n^2), O(n^3), ... O(2^n)...
-I want the worst-case upper bound
def bigoh1(n):
sum = 0
for i in range(100, n):
sum = sum+1
print(sum)
O(n)
def bigoh2(n):
sum = 0
for i in range(1, n // 2):
sum = sum + 1
for j in range(1, n * n):
sum = sum + 1
print(sum)
First loop is n steps, second is n^2 steps.
n+n^2 = o(n^2)
def bigoh3(n):
sum = 0
if n % 2 == 0:
for j in range(1, n * n):
sum = sum + 1
else:
for k in range(5, n + 1):
sum = sum + k
print(sum)
If n is even, we do n^2 work. If n is odd, we do n work.
Remember that we want the worst-case.
O(n^2)
def bigoh4(m, n):
sum = 0
for i in range(1, n + 1):
for j in range(1, m + 1):
sum = sum + 1
print(sum)
O(n*m)
Not O(n^2). Not O(m^2).
|
[
"coco.yang@mail.utoronto.ca"
] |
coco.yang@mail.utoronto.ca
|
dd7a3ac6d291dc2db98817190f8813c458576953
|
66dd570bf5945dcbd183ed3c0cf897c0359cbccd
|
/python/python语法/pyexercise/Exercise03_09.py
|
4560a8df9de30b98aa5d9640c98b118b4dc4a3be
|
[] |
no_license
|
SamJ2018/LeetCode
|
302cc97626220521c8847d30b99858e63fa509f3
|
784bd0b1491050bbd80f5a0e2420467b63152d8f
|
refs/heads/master
| 2021-06-19T10:30:37.381542
| 2021-02-06T16:15:01
| 2021-02-06T16:15:01
| 178,962,481
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,206
|
py
|
# Obtain input
name = input("Enter employee's name: ")
hours = eval(input("Enter number of hours worked in a week: "))
payRate = eval(input("Enter hourly pay rate: "))
fedTaxWithholdingRate = eval(input("Enter federal tax withholding rate: "))
stateTaxWithholdingRate = eval(input("Enter state tax withholding rate: "))
grossPay = hours * payRate
fedTaxWithholding = grossPay * fedTaxWithholdingRate
stateTaxWithholding = grossPay * stateTaxWithholdingRate
totalDeduction = fedTaxWithholding + stateTaxWithholding
netPay = grossPay - totalDeduction
# Obtain output
out = "Employee Name: " + name + "\n\n"
out += "Hours Worked: " + str(hours) + '\n'
out += "Pay Rate: $" + str(payRate) + '\n'
out += "Gross Pay: $" + str(grossPay) + '\n'
out += "Deductions:\n"
out += " Federal Withholding (" + str(fedTaxWithholdingRate * 100) + \
"%): $" + str(int(fedTaxWithholding * 100) / 100.0) + '\n'
out += " State Withholding (" + str(stateTaxWithholdingRate * 100) + "%):" + \
" $" + str(int(stateTaxWithholding * 100) / 100.0) + '\n';
out += " Total Deduction:" + " $" + \
str(int(totalDeduction * 100) / 100.0) + '\n'
out += "Net Pay:" + " $" + str(int(netPay * 100) / 100.0)
print(out)
|
[
"juksam@centos7.localdomain"
] |
juksam@centos7.localdomain
|
e3ede7d4acdd774e7b8621e60be2e1b12dc0f0e1
|
ca7aa979e7059467e158830b76673f5b77a0f5a3
|
/Python_codes/p02845/s251805975.py
|
a8e1b9dedbc87deeb6d7dd5ca8fac2fa7aa26e80
|
[] |
no_license
|
Aasthaengg/IBMdataset
|
7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901
|
f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8
|
refs/heads/main
| 2023-04-22T10:22:44.763102
| 2021-05-13T17:27:22
| 2021-05-13T17:27:22
| 367,112,348
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 434
|
py
|
import sys
readline = sys.stdin.readline
MOD = 10 ** 9 + 7
INF = float('INF')
sys.setrecursionlimit(10 ** 5)
def main():
n = int(readline())
a = list(map(int, readline().split()))
cnt = [0] * 3
ans = 1
for x in a:
p = cnt.count(x)
if p == 0:
return print(0)
ans *= p
ans %= MOD
cnt[cnt.index(x)] += 1
print(ans)
if __name__ == '__main__':
main()
|
[
"66529651+Aastha2104@users.noreply.github.com"
] |
66529651+Aastha2104@users.noreply.github.com
|
d41c69e29c794cbabb1c2e1f208a21b4bf0f2f48
|
0e8b6f94467c25dd2440f7e2ea1519244e689620
|
/MarlinJobs/CalibrationConfigFiles/Stage27Config_5x5_30x30.py
|
3435a6f9a3f6a73455fa0470d23dcbb790425599
|
[] |
no_license
|
StevenGreen1/HighEnergyPhotonAnalysis
|
97a661eaca2efd00472f1969855c724c9d505369
|
8a82ac57f56aad5bdbe99d4a5afb771592bc1725
|
refs/heads/master
| 2021-01-10T14:08:50.550184
| 2015-10-12T12:43:47
| 2015-10-12T12:43:47
| 43,491,318
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,275
|
py
|
# Calibration config file for testing
# Digitisation Constants - ECal
CalibrECal = 42.4326603502
# Digitisation Constants ILDCaloDigi - HCal
CalibrHCalBarrel = 49.057884929
CalibrHCalEndcap = 54.1136311832
CalibrHCalOther = 29.2180288685
# Digitisation Constants NewLDCCaloDigi - HCal
CalibrHCal = -1
# Digitisation Constants - Muon Chamber
CalibrMuon = 56.7
# MIP Peak position in directed corrected SimCaloHit energy distributions
# used for realistic ECal and HCal digitisation options
CalibrECalMIP = -1
CalibrHCalMIP = 0.0004925
# MIP Peak position in directed corrected CaloHit energy distributions
# used for MIP definition in PandoraPFA
ECalToMIPCalibration = 158.73
HCalToMIPCalibration = 40.8163
MuonToMIPCalibration = 10.101
# EM and Had Scale Settings
ECalToEMGeVCalibration = 1.00062269867
HCalToEMGeVCalibration = 1.00062269867
ECalToHadGeVCalibration = 1.08773337955
HCalToHadGeVCalibration = 1.04823493932
# Pandora Threshold Cuts
ECalMIPThresholdPandora = 0.5
HCalMIPThresholdPandora = 0.3
# Hadronic Energy Truncation in HCal PandoraPFA
MaxHCalHitHadronicEnergy = 1000000.0
# Timing ECal
ECalBarrelTimeWindowMax = 1000000.0
ECalEndcapTimeWindowMax = 1000000.0
# Timing HCal
HCalBarrelTimeWindowMax = 1000000.0
HCalEndcapTimeWindowMax = 1000000.0
|
[
"sg1sg2sg3@hotmail.co.uk"
] |
sg1sg2sg3@hotmail.co.uk
|
8eff0f0a7ccda0cc6e4779d87cd907c9f72549f8
|
f04fb8bb48e38f14a25f1efec4d30be20d62388c
|
/哈希表/204. 计数质数.py
|
2bd3e79467b7525a3d7e1a7e82f4074be703fff9
|
[] |
no_license
|
SimmonsChen/LeetCode
|
d8ef5a8e29f770da1e97d295d7123780dd37e914
|
690b685048c8e89d26047b6bc48b5f9af7d59cbb
|
refs/heads/master
| 2023-09-03T01:16:52.828520
| 2021-11-19T06:37:19
| 2021-11-19T06:37:19
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 789
|
py
|
"""
统计所有小于非负整数 n 的质数的数量。
示例 1：
输入：n = 10
输出：4
解释：小于 10 的质数一共有 4 个, 它们是 2, 3, 5, 7 。
"""
from math import sqrt
class Solution(object):
# 题意是统计[2, n] 中质数的个数
def countPrimes(self, n):
"""
:type n: int
:rtype: int
"""
if n < 2:
return 0
# 初始化标记数组，假设都是质数
isPrim = [True] * n
isPrim[0] = False
res = 0
for i in range(2, n):
if isPrim[i]:
res += 1
for j in range(i * i, n, i):
isPrim[j] = False
return res
if __name__ == '__main__':
s = Solution()
print(s.countPrimes(10))
|
[
"15097686925@163.com"
] |
15097686925@163.com
|
6ffe2a06880751514bb23ef6b2258b10f8257c43
|
14d7f5f83b6f84871ff6ebfa0af4c17b7115a33f
|
/remote_sensing/MODIS_data_test_v3.py
|
1f15cb363abab3ce4c3e8caedc88d88198bb5e8d
|
[] |
no_license
|
tonychangmsu/Python_Scripts
|
8ca7bc841c94dcab36743bce190357ac2b1698a5
|
036f498b1fc68953d90aac15f0a5ea2f2f72423b
|
refs/heads/master
| 2016-09-11T14:32:17.133399
| 2016-03-28T16:34:40
| 2016-03-28T16:34:40
| 10,370,475
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,468
|
py
|
#Title: MODIS_data_test.py
#Author: Tony Chang
#Abstract: Test for opening MODIS data and examining the various bands
#Creation Date: 04/14/2015
#Modified Dates: 01/20/2016, 01/26/2016, 01/28/2016, 01/29/2016, 02/01/2016
#local directory : K:\\NASA_data\\scripts
import numpy as np
import matplotlib.pyplot as plt
import os
os.chdir("K:\\NASA_data\\scripts")
import time
import MODIS_acquire as moda
import MODIS_tassel_cap as tas
import MODIS_process as mproc
import tiff_write as tw
#MODIS file name as
# 7 char (product name .)
# 8 char (A YYYYDDD .)
# 6 char (h XX v YY .) #tile index
# 3 char (collection version .) #typically 005
# 14 char (julian date of production YYYYDDDHHMMSS)
if __name__ == "__main__":
start = time.time()
#since we have the date, let's try to get all the data from that date together.
htile = 9
vtile = 4
factor = 0.0001
year = 2000
#we would iterate through the year
begin_year = 2000
end_year = 2015
wd = 'G:\\NASA_remote_data\\MOD09A1'
mod_list, mod_dates = moda.mod_file_search(wd, year, True)
#then iterate through theses list values
scene = 0
mod_data, dnames = moda.mod_acquire_by_file(mod_list[scene]) #this is the full dataset
band_query = 1
#get the files needed
files_to_mosaic = moda.mod_date_dataset_list(wd, mod_dates[scene])
nonproj_mosaics = mproc.mosaic_files(files_to_mosaic, reproj = False)
reproj_mosaics = mproc.mosaic_files(files_to_mosaic, reproj = True, method = 0)
#inspect the cloud effects on the nonproj and reproj mosaics
#looks like it comes from band 5! 1230-1250, ,Leaf/Canopy Differences
#not much can be done about that if this is prevalent. In the mean time, we should just implement
#the processing and use the QC to fix the problem
#at this point we would like to transform the data. Then we can apply the reprojection
#need to be careful here, do we reproject before transform or after? before...
transformed = tas.tassel_cap_transform(nonproj_mosaics[:7]) #don't want to include the qc data
#check out the tasseled_cap again. getting some striping for some reason.
tw.tiff_write_gdal(transformed[0], 'K:\\NASA_data\\test\\test_clip.tif')
tw.tiff_write(out, x_size, y_size, cell_size, ymax, xmin, 'K:\\NASA_data\\test\\test_clip.tif')
#tas_array = moda.datasets_to_array(transformed, False)
#find the bounding box by the netCDF from TOPOWX
#GYE coordinates
xmin = -112.39583333837999 #112 23 45
xmax = -108.19583334006 #108 11 45
ymin = 42.279166659379996 #42 16 45
ymax = 46.195833324479999 #46 11 45
aoa = [xmin, xmax, ymin, ymax]
clip = mproc.clip_wgs84_scene(aoa, transformed[0])
#some problems with the reprojection process?
#NO..getting some strange stripe artifacts from the tasselled cap, but could be inherant in the MOD09 data itself...
#all this works now. So now perform this for all the MODIS data and store it in a netCDF4 file that
#is continuous for each year.
#write the file to check it out
tw.tiff_write(clip, np.shape(clip)[1], np.shape(clip)[0], cell_size, ymax, xmin, 'K:\\NASA_data\\test\\', 'test_clip.tif')
#now just write this function for netCDF4
#then save to a netCDF4 file
#then repeat for all the data.
end = time.time()
print('run time :%s'%(end-start)) #takes about 25-30 seconds
'''
mproc.plot_refl(mod_array)
#plot all the reflectances
#see which is faster
import time
start = time.time()
b,g,w = tas.tassel_cap_transform(mod_array)
end = time.time()
mproc.plot_tassel_cap(b,g,w)
'''
|
[
"tony.chang@msu.montana.edu"
] |
tony.chang@msu.montana.edu
|
6c88d27d3b37ee3630d08d1654d8b7b2c1a7f640
|
dce7ca1ebab403bf7c23b77368ee26a2dd4475b6
|
/tests/test_cos.py
|
cd57475224ee19e74c5d9fa421f172e8a7f9fb4b
|
[] |
no_license
|
qcymkxyc/Graduate
|
3b7e89b3f44141d9fd011c15690f902674a9e979
|
2afedacaaa3a0f4d9bbc13596d967ec8808d43d6
|
refs/heads/master
| 2022-12-10T12:32:37.326653
| 2018-11-10T07:49:13
| 2018-11-10T07:49:16
| 148,103,320
| 0
| 0
| null | 2022-12-08T01:14:09
| 2018-09-10T05:25:40
|
Python
|
UTF-8
|
Python
| false
| false
| 317
|
py
|
import unittest
from app.util import cos
class COSTestCase(unittest.TestCase):
"""
腾讯云测试
"""
def test_cos_upload(self):
"""
腾讯云cos上传测试
"""
cos.upload_binary_file(b"abcde","login_success.txt")
if __name__ == '__main__':
unittest.main()
|
[
"qcymkxyc@163.com"
] |
qcymkxyc@163.com
|
d20be627a406e2379a3cd53a20a70ac4b5852db4
|
facb8b9155a569b09ba66aefc22564a5bf9cd319
|
/wp2/merra_scripts/01_netCDF_extraction/merra902Combine/284-tideGauge.py
|
255f5e1573a5a697bd3fef71c7b6f3022772b778
|
[] |
no_license
|
moinabyssinia/modeling-global-storm-surges
|
13e69faa8f45a1244a964c5de4e2a5a6c95b2128
|
6e385b2a5f0867df8ceabd155e17ba876779c1bd
|
refs/heads/master
| 2023-06-09T00:40:39.319465
| 2021-06-25T21:00:44
| 2021-06-25T21:00:44
| 229,080,191
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,376
|
py
|
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 17 11:28:00 2020
--------------------------------------------
Load predictors for each TG and combine them
--------------------------------------------
@author: Michael Tadesse
"""
import os
import pandas as pd
#define directories
# dir_name = 'F:\\01_erainterim\\01_eraint_predictors\\eraint_D3'
dir_in = "/lustre/fs0/home/mtadesse/merraLocalized"
dir_out = "/lustre/fs0/home/mtadesse/merraAllCombined"
def combine():
os.chdir(dir_in)
#get names
tg_list_name = os.listdir()
x = 284
y = 285
for tg in range(x, y):
os.chdir(dir_in)
tg_name = tg_list_name[tg]
print(tg_name, '\n')
#looping through each TG folder
os.chdir(tg_name)
#check for empty folders
if len(os.listdir()) == 0:
continue
#defining the path for each predictor
where = os.getcwd()
csv_path = {'slp' : os.path.join(where, 'slp.csv'),\
"wnd_u": os.path.join(where, 'wnd_u.csv'),\
'wnd_v' : os.path.join(where, 'wnd_v.csv')}
first = True
for pr in csv_path.keys():
print(tg_name, ' ', pr)
#read predictor
pred = pd.read_csv(csv_path[pr])
#remove unwanted columns
pred.drop(['Unnamed: 0'], axis = 1, inplace=True)
#sort based on date as merra files are scrambled
pred.sort_values(by = 'date', inplace=True)
#give predictor columns a name
pred_col = list(pred.columns)
for pp in range(len(pred_col)):
if pred_col[pp] == 'date':
continue
pred_col[pp] = pr + str(pred_col[pp])
pred.columns = pred_col
#merge all predictors
if first:
pred_combined = pred
first = False
else:
pred_combined = pd.merge(pred_combined, pred, on = 'date')
#saving pred_combined
os.chdir(dir_out)
tg_name = str(tg)+"_"+tg_name;
pred_combined.to_csv('.'.join([tg_name, 'csv']))
os.chdir(dir_in)
print('\n')
#run script
combine()
|
[
"michaelg.tadesse@gmail.com"
] |
michaelg.tadesse@gmail.com
|
5833e03ed33a8ec7549369840b1fa07513ad8d85
|
4cb40963ebc95a9e4cdd5725ac4ae882594a363d
|
/tests/influence/_core/test_tracin_self_influence.py
|
0f327ce3fbc6230024bf4d2190c00f2750105f8c
|
[
"BSD-3-Clause"
] |
permissive
|
NarineK/captum-1
|
59592277aed8c97dd8effed4af953676381d50c8
|
a08883f1ba3abc96ace06b11883893419b187d09
|
refs/heads/master
| 2022-12-23T22:39:50.502939
| 2022-08-01T16:30:43
| 2022-08-01T16:30:43
| 215,140,394
| 1
| 0
| null | 2019-10-14T20:36:19
| 2019-10-14T20:36:19
| null |
UTF-8
|
Python
| false
| false
| 5,906
|
py
|
import tempfile
from typing import Callable
import torch
import torch.nn as nn
from captum.influence._core.tracincp import TracInCP
from captum.influence._core.tracincp_fast_rand_proj import TracInCPFast
from parameterized import parameterized
from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
from tests.influence._utils.common import (
build_test_name_func,
DataInfluenceConstructor,
get_random_model_and_data,
)
from torch.utils.data import DataLoader
class TestTracInSelfInfluence(BaseTest):
@parameterized.expand(
[
(reduction, constructor, unpack_inputs)
for unpack_inputs in [True, False]
for (reduction, constructor) in [
("none", DataInfluenceConstructor(TracInCP)),
(
"sum",
DataInfluenceConstructor(
TracInCP,
name="TracInCPFastRandProjTests",
sample_wise_grads_per_batch=True,
),
),
("sum", DataInfluenceConstructor(TracInCPFast)),
("mean", DataInfluenceConstructor(TracInCPFast)),
]
],
name_func=build_test_name_func(),
)
def test_tracin_self_influence(
self, reduction: str, tracin_constructor: Callable, unpack_inputs: bool
) -> None:
with tempfile.TemporaryDirectory() as tmpdir:
(
net,
train_dataset,
) = get_random_model_and_data(tmpdir, unpack_inputs, return_test_data=False)
# compute tracin_scores of training data on training data
criterion = nn.MSELoss(reduction=reduction)
batch_size = 5
tracin = tracin_constructor(
net,
train_dataset,
tmpdir,
batch_size,
criterion,
)
# calculate influence scores, using the training data as the test batch
train_scores = tracin.influence(
train_dataset.samples,
train_dataset.labels,
k=None,
unpack_inputs=unpack_inputs,
)
# calculate self_tracin_scores
self_tracin_scores = tracin.self_influence(
DataLoader(train_dataset, batch_size=batch_size),
outer_loop_by_checkpoints=False,
)
# check that self_tracin scores equals the diagonal of influence scores
assertTensorAlmostEqual(
self,
torch.diagonal(train_scores),
self_tracin_scores,
delta=0.01,
mode="max",
)
# check that setting `outer_loop_by_checkpoints=False` and
# `outer_loop_by_checkpoints=True` gives the same self influence scores
self_tracin_scores_by_checkpoints = tracin.self_influence(
DataLoader(train_dataset, batch_size=batch_size),
outer_loop_by_checkpoints=True,
)
assertTensorAlmostEqual(
self,
self_tracin_scores_by_checkpoints,
self_tracin_scores,
delta=0.01,
mode="max",
)
@parameterized.expand(
[
(reduction, constructor, unpack_inputs)
for unpack_inputs in [True, False]
for (reduction, constructor) in [
("none", DataInfluenceConstructor(TracInCP)),
(
"sum",
DataInfluenceConstructor(
TracInCP,
sample_wise_grads_per_batch=True,
),
),
("sum", DataInfluenceConstructor(TracInCPFast)),
("mean", DataInfluenceConstructor(TracInCPFast)),
]
],
name_func=build_test_name_func(),
)
def test_tracin_self_influence_dataloader_vs_single_batch(
self, reduction: str, tracin_constructor: Callable, unpack_inputs: bool
) -> None:
# tests that the result of calling the public method `self_influence` for a
# DataLoader of batches is the same as when the batches are collated into a
# single batch
with tempfile.TemporaryDirectory() as tmpdir:
(
net,
train_dataset,
) = get_random_model_and_data(tmpdir, unpack_inputs, return_test_data=False)
# create a single batch representing the entire dataset
single_batch = next(
iter(DataLoader(train_dataset, batch_size=len(train_dataset)))
)
# create a dataloader that yields batches from the dataset
dataloader = DataLoader(train_dataset, batch_size=5)
# create tracin instance
criterion = nn.MSELoss(reduction=reduction)
batch_size = 5
tracin = tracin_constructor(
net,
train_dataset,
tmpdir,
batch_size,
criterion,
)
# compute self influence using `self_influence` when passing in a single
# batch
single_batch_self_influence = tracin.self_influence(single_batch)
# compute self influence using `self_influence` when passing in a
# dataloader with the same examples
dataloader_self_influence = tracin.self_influence(dataloader)
# the two self influences should be equal
assertTensorAlmostEqual(
self,
single_batch_self_influence,
dataloader_self_influence,
delta=0.01, # due to numerical issues, we can't set this to 0.0
mode="max",
)
|
[
"facebook-github-bot@users.noreply.github.com"
] |
facebook-github-bot@users.noreply.github.com
|
c6ae34b2b23ff9afcccd235018498cdb235efb99
|
6f0e74cdc81f78ffc5dbc1b2db1cef8cbec950c4
|
/aws_interface/cloud/logic/delete_function_test.py
|
7a62e2c7c9241aa10726b393c1fa616aa7aa066f
|
[
"Apache-2.0"
] |
permissive
|
hubaimaster/aws-interface
|
125b3a362582b004a16ccd5743d7bdff69777db5
|
5823a4b45ffb3f7b59567057855ef7b5c4c4308d
|
refs/heads/master
| 2023-01-19T15:43:38.352149
| 2023-01-12T01:38:00
| 2023-01-12T01:38:00
| 149,847,881
| 57
| 10
|
Apache-2.0
| 2023-01-12T01:39:49
| 2018-09-22T05:17:43
|
JavaScript
|
UTF-8
|
Python
| false
| false
| 742
|
py
|
from cloud.permission import Permission, NeedPermission
# Define the input output format of the function.
# This information is used when creating the *SDK*.
info = {
'input_format': {
'test_name': 'str',
},
'output_format': {
'success': 'bool',
}
}
@NeedPermission(Permission.Run.Logic.delete_function_test)
def do(data, resource):
partition = 'logic-function-test'
body = {}
params = data['params']
test_name = params.get('test_name')
items, _ = resource.db_query(partition, [{'option': None, 'field': 'test_name', 'value': test_name, 'condition': 'eq'}])
for item in items:
success = resource.db_delete_item(item['id'])
body['success'] = success
return body
|
[
"hubaimaster@gmail.com"
] |
hubaimaster@gmail.com
|
6291a6042041500296cbde2708740f0bf984e374
|
0bb3bc8eea74d316377bb1f88a8600162d83d98a
|
/test_demo/dianping_food_top100.py
|
ddf32f2ecd1973f9a3ea2ec62336876b0d284b9a
|
[] |
no_license
|
WangYongjun1990/spider
|
10a1f03c26a083b8a1b5e25a9180f69d50994d73
|
f13d756790a19d1465624f6c8b1f0ecb87870f51
|
refs/heads/master
| 2020-03-08T09:16:08.748865
| 2018-04-16T01:54:26
| 2018-04-16T01:54:26
| 128,042,969
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,030
|
py
|
# -*- coding:utf-8 -*-
"""
File Name: `test_dianping_top100`.py
Version:
Description: 爬取南京评价最高的100家餐厅信息，对应网页 http://www.dianping.com/shoplist/search/5_10_0_score
Author: wangyongjun
Date: 2018/4/13 11:45
"""
import requests
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.115 Safari/537.36',
}
def dianping_food_top100():
url = 'http://www.dianping.com/mylist/ajax/shoprank?cityId=5&shopType=10&rankType=score&categoryId=0'
try:
r = requests.get(url, headers=headers, timeout=10, proxies=None, verify=False)
# print r.text
except Exception as e:
print e
shop_list = r.json().get('shopBeans')
print shop_list
print type(shop_list), len(shop_list)
for shop_dict in shop_list:
print shop_dict['shopName'], shop_dict['score1'], shop_dict['score2'], shop_dict['score3'], shop_dict['avgPrice']
if __name__ == "__main__":
dianping_food_top100()
|
[
"yongjun.wang@mi-me.com"
] |
yongjun.wang@mi-me.com
|
4a1fc4dc9297f3161f4f30e0492a815011a04b8c
|
747012e5b750cdc67748798c09b3ce1eb819568f
|
/strategy/migrations/0002_auto_20170703_1645.py
|
3a98d12dd70048ac2070500f701c0c01dc044e67
|
[
"MIT"
] |
permissive
|
moshthepitt/probsc
|
da30c3829d5b8bf42804950320f006c78d2b94aa
|
9b8cab206bb1c41238e36bd77f5e0573df4d8e2d
|
refs/heads/master
| 2020-06-06T11:46:05.573933
| 2018-01-10T20:42:51
| 2018-01-10T20:42:51
| 192,730,789
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 632
|
py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.10.6 on 2017-07-03 13:45
from __future__ import unicode_literals
from django.db import migrations
import django.db.models.deletion
import mptt.fields
class Migration(migrations.Migration):
dependencies = [
('strategy', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='objective',
name='parent',
field=mptt.fields.TreeForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='children', to='strategy.Objective', verbose_name='Contributes to'),
),
]
|
[
"kelvin@jayanoris.com"
] |
kelvin@jayanoris.com
|
9fb6a68ceb3cf80621af5ba80af61427c4540b14
|
e1450725c9637e15709064aaa48bc4e053a213d5
|
/tests/test_funcptrdecl.py
|
a4d3a4d89874a4fe3280f0584e431cc6717bed5d
|
[] |
no_license
|
gotoc/PyCParser-1
|
9d4e4c40a8c24923a689b1a0e3ebd4f07528d75b
|
b00cdd67a688792c0bc49b383a36199c50cc5cf2
|
refs/heads/master
| 2021-01-20T10:54:25.196102
| 2014-09-11T12:27:29
| 2014-09-11T12:27:29
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,717
|
py
|
import sys
sys.path += [".."]
from pprint import pprint
from cparser import *
import test
testcode = """
int16_t (*f)();
int16_t (*g)(char a, void*);
int (*h);
// ISO/IEC 9899:TC3 : C99 standard
int fx(void), *fip(), (*pfi)(); // example 1, page 120
int (*apfi[3])(int *x, int *y); // example 2, page 120
int (*fpfi(int (*)(long), int))(int, ...); // example 3, page 120
"""
state = test.parse(testcode)
f = state.vars["f"]
g = state.vars["g"]
assert f.name == "f"
assert isinstance(f.type, CFuncPointerDecl)
assert f.type.type == CStdIntType("int16_t")
assert f.type.args == []
assert isinstance(g.type, CFuncPointerDecl)
gargs = g.type.args
assert isinstance(gargs, list)
assert len(gargs) == 2
assert isinstance(gargs[0], CFuncArgDecl)
assert gargs[0].name == "a"
assert gargs[0].type == CBuiltinType(("char",))
assert gargs[1].name is None
assert gargs[1].type == CBuiltinType(("void","*"))
h = state.vars["h"]
assert h.type == CPointerType(CBuiltinType(("int",)))
fx = state.funcs["fx"] # fx is a function `int (void)`
assert fx.type == CBuiltinType(("int",))
assert fx.args == []
fip = state.funcs["fip"] # fip is a function `int* (void)`
assert fip.type == CPointerType(CBuiltinType(("int",)))
assert fip.args == []
pfi = state.vars["pfi"] # pfi is a function-ptr to `int ()`
assert isinstance(pfi.type, CFuncPointerDecl)
assert pfi.type.type == CBuiltinType(("int",))
assert pfi.type.args == []
apfi = state.vars["apfi"] # apfi is an array of three function-ptrs `int (int*,int*)`
# ...
fpfi = state.funcs["fpfi"] # function which returns a func-ptr
# the function has the parameters `int(*)(long), int`
# the func-ptr func returns `int`
# the func-ptr func has the parameters `int, ...`
|
[
"albert.zeyer@rwth-aachen.de"
] |
albert.zeyer@rwth-aachen.de
|
a25245a35cacaea636067ccaec32d3b7094f710e
|
e5c9fc4dc73536e75cf4ab119bbc642c28d44591
|
/src/leetcodepython/math/hamming_distance_461.py
|
6ee39b31c590979bec6f64edd79227ce8fd40f94
|
[
"MIT"
] |
permissive
|
zhangyu345293721/leetcode
|
0a22034ac313e3c09e8defd2d351257ec9f285d0
|
50f35eef6a0ad63173efed10df3c835b1dceaa3f
|
refs/heads/master
| 2023-09-01T06:03:18.231266
| 2023-08-31T15:23:03
| 2023-08-31T15:23:03
| 163,050,773
| 101
| 29
| null | 2020-12-09T06:26:35
| 2018-12-25T05:58:16
|
Java
|
UTF-8
|
Python
| false
| false
| 1,473
|
py
|
# encoding='utf-8'
'''
/**
* This is the solution of No. 461 problem in the LeetCode,
* the website of the problem is as follow:
* https://leetcode-cn.com/problems/hamming-distance/
* <p>
* The description of problem is as follow:
* ==========================================================================================================
* 两个整数之间的汉明距离指的是这两个数字对应二进制位不同的位置的数目。
* <p>
* 给出两个整数 x 和 y，计算它们之间的汉明距离。
* <p>
* 注意：
* 0 ≤ x, y < 231.
* <p>
* 示例:
* <p>
* 输入: x = 1, y = 4
* <p>
* 输出: 2
* <p>
* 解释:
* 1 (0 0 0 1)
* 4 (0 1 0 0)
* ↑ ↑
* <p>
* 上面的箭头指出了对应二进制位不同的位置。
* <p>
* 来源：力扣（LeetCode）
* ==========================================================================================================
*
* @author zhangyu (zhangyuyu417@gmail.com)
*/'''
class Solution:
def hamming_distance(self, x: int, y: int) -> int:
'''
汉明距离
Args:
x: 数值x
y: 数值y
Returns:
距离
'''
c = x ^ y
res = 0
while c > 0:
res += (c & 1)
c = c >> 1
return res
if __name__ == '__main__':
x = 1
y = 4
solution = Solution()
res = solution.hamming_distance(x, y)
print(res)
assert res == 2
|
[
"zhangyu_xtb@geekplus.cc"
] |
zhangyu_xtb@geekplus.cc
|
3637a41ea27d8219504f33dd65eda2ea0971739d
|
dd256415176fc8ab4b63ce06d616c153dffb729f
|
/aditya-works-feature-python_programming (1)/aditya-works-feature-python_programming/Assigment_5_01-Aug-2019/Assigment_5_5.py
|
24aa63c26add06b9baeb2c0235963e5db861b091
|
[] |
no_license
|
adityapatel329/python_works
|
6d9c6b4a64cccbe2717231a7cfd07cb350553df3
|
6cb8b2e7f691401b1d2b980f6d1def848b0a71eb
|
refs/heads/master
| 2020-07-24T17:15:39.839826
| 2019-09-12T07:53:28
| 2019-09-12T07:53:28
| 207,993,516
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 165
|
py
|
def accept():
name = input("Enter your string : ")
val= []
for i in name:
val.append(ord(i))
print(sum(val)/len(val))
accept()
|
[
"aditya.patel@1rivet.local"
] |
aditya.patel@1rivet.local
|
599f4edbf8bbbcf5be1ba76d41791b9964071018
|
35a6f5a26ea97ebed8ab34619a8eec51719d2cc0
|
/Python_Basic/17 文件操作/5 seek函数.py
|
115eb71e6b1003cafcc78f9afeea357211ceaa76
|
[] |
no_license
|
PandaCoding2020/pythonProject
|
c3644eda22d993b3b866564384ed10441786e6c5
|
26f8a1e7fbe22bab7542d441014edb595da39625
|
refs/heads/master
| 2023-02-25T14:52:13.542434
| 2021-02-03T13:42:41
| 2021-02-03T13:42:41
| 331,318,291
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 607
|
py
|
"""
语法：文件对象.seek(偏移量，超始位置) 0开头，1当前 2结尾
目标：
1.r改变读取文件指针：改变读取数据开始位置或把文件指针放结尾(无法读取数据)
2.a改变读取文件指针，做到可以读到数据
"""
# 1.1.改变读取数据开始位置
# f.seek(2, 0)
# 1.2.把文件指针放到结尾（无法读取数据）
# f.seek(0, 2)
# f = open('test.txt', 'r+')
# f.seek(2, 0)
# con = f.read()
# print(con)
#
# f.close()
# 2.把文件指针放到结尾（无法读取数据）
f = open('test.txt', 'a+')
f.seek(0, 0)
con = f.read()
print(con)
f.close()
|
[
"gzupanda@outlook.com"
] |
gzupanda@outlook.com
|
ca0312e44c689d8a119737d9102edca66c6d0e32
|
757433be241afbff1c138d77daf13397f858aef3
|
/scorpio/urls.py
|
166247c53f8b21e7f1bf3184baad8bf10b8db329
|
[
"MIT"
] |
permissive
|
RockefellerArchiveCenter/scorpio
|
1f9d152bb440bb98c007f652fa644602e3b8b483
|
f308cac3880ba9008d3aadfdc66a4062d4d27492
|
refs/heads/base
| 2023-08-20T22:34:32.085492
| 2023-08-07T17:00:58
| 2023-08-07T17:00:58
| 215,400,734
| 0
| 1
|
MIT
| 2023-09-08T21:09:13
| 2019-10-15T21:33:10
|
Python
|
UTF-8
|
Python
| false
| false
| 1,601
|
py
|
"""scorpio URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.0/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from asterism.views import PingView
from django.contrib import admin
from django.urls import include, re_path
from rest_framework.schemas import get_schema_view
from indexer.views import (IndexAddView, IndexDeleteView, IndexResetView,
IndexRunViewSet)
from .routers import ScorpioRouter
router = ScorpioRouter()
router.register(r'index-runs', IndexRunViewSet, 'indexrun')
schema_view = get_schema_view(
title="Scorpio API",
description="Endpoints for Scorpio microservice application."
)
urlpatterns = [
re_path(r'^admin/', admin.site.urls),
re_path(r'^index/add/', IndexAddView.as_view(), name='index-add'),
re_path(r'^index/delete/', IndexDeleteView.as_view(), name='index-delete'),
re_path(r'^index/reset/', IndexResetView.as_view(), name='index-reset'),
re_path(r'^status/', PingView.as_view(), name='ping'),
re_path(r'^schema/', schema_view, name='schema'),
re_path(r'^', include(router.urls)),
]
|
[
"helrond@hotmail.com"
] |
helrond@hotmail.com
|
f428c560237217ad3f5dd49edbabd5734a5b4eff
|
0a679896fbe96a8a0a59ad9f4f55edb4aa044a93
|
/Duplicate File Handler/task/handler.py
|
040a40e81fc3f6eef361f3690d7a85ad20d01559
|
[] |
no_license
|
TogrulAga/Duplicate-File-Handler
|
5b7bd9c9508ae3ee96751bc3e56ebaccc44c46f9
|
66fef381572c0e6697330463b0b720c2dbca82e6
|
refs/heads/master
| 2023-06-30T07:07:24.524591
| 2021-08-06T15:47:00
| 2021-08-06T15:47:00
| 393,424,765
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,500
|
py
|
import os
import argparse
import hashlib
class FileHandler:
def __init__(self, directory):
self.directory = directory
self.file_format = None
self.sorting_option = None
self.files_dict = dict()
self.dict_items = None
self.numbered_dict = dict()
self.get_format()
self.get_sorting_option()
self.walk_dir()
self.list_same_sized_files()
self.check_duplicates()
self.delete_files()
def get_format(self):
self.file_format = input("Enter file format:\n")
def get_sorting_option(self):
print("Size sorting options:")
print("1. Descending")
print("2. Ascending\n")
while True:
self.sorting_option = int(input("Enter a sorting option:\n"))
print()
if self.sorting_option not in (1, 2):
print("\nWrong option\n")
else:
break
def walk_dir(self):
for root, directories, filenames in os.walk(self.directory):
for file in filenames:
if self.file_format != "":
if self.file_format != os.path.splitext(file)[-1].split(".")[-1]:
continue
file_path = os.path.join(root, file)
file_size = os.path.getsize(file_path)
if file_size in self.files_dict.keys():
self.files_dict[file_size].append(file_path)
else:
self.files_dict[file_size] = [file_path]
def list_same_sized_files(self):
if self.sorting_option == 1:
dict_items = list(reversed(sorted(self.files_dict.items())))
elif self.sorting_option == 2:
dict_items = sorted(self.files_dict.items())
for size, files in dict_items:
print(f"{size} bytes")
for file in files:
print(file)
print()
self.dict_items = dict_items
def check_duplicates(self):
while True:
answer = input("Check for duplicates?\n")
if answer not in ("yes", "no"):
continue
else:
break
if answer == "no":
return
else:
n_duplicate = 1
for size, files in self.dict_items:
print(f"\n{size} bytes")
hash_dict = dict()
for file in files:
hash_maker = hashlib.md5()
with open(file, "rb") as f:
hash_maker.update(f.read())
if hash_maker.hexdigest() not in hash_dict.keys():
hash_dict[hash_maker.hexdigest()] = [file]
else:
hash_dict[hash_maker.hexdigest()].append(file)
for key, values in hash_dict.items():
if len(values) > 1:
print(f"Hash: {key}")
for value in values:
print(f"{n_duplicate}. {value}")
self.numbered_dict[n_duplicate] = value
n_duplicate += 1
def delete_files(self):
while True:
answer = input("Delete files?\n")
if answer not in ("yes", "no"):
continue
else:
break
if answer == "no":
return
else:
while True:
answer = input("Enter file numbers to delete:\n")
try:
files_to_delete = list(map(int, answer.split()))
if len(files_to_delete) == 0:
raise ValueError
if any(n not in self.numbered_dict.keys() for n in files_to_delete):
raise ValueError
break
except ValueError:
print("\nWrong format\n")
freed_space = 0
for file in files_to_delete:
freed_space += os.path.getsize(self.numbered_dict[file])
os.remove(self.numbered_dict[file])
print(f"Total freed up space: {freed_space} bytes")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("directory").required = False
args = parser.parse_args()
if args.directory is None:
print("Directory is not specified")
file_handler = FileHandler(args.directory)
|
[
"toghrul.aghakishiyev@ericsson.com"
] |
toghrul.aghakishiyev@ericsson.com
|
6c36391267af20d2d0df7f255c2d1d4f98c496d0
|
f445450ac693b466ca20b42f1ac82071d32dd991
|
/generated_tempdir_2019_09_15_163300/generated_part003650.py
|
2809c442b3ba17c08e9f9aa9bc7b006e27b8a3e8
|
[] |
no_license
|
Upabjojr/rubi_generated
|
76e43cbafe70b4e1516fb761cabd9e5257691374
|
cd35e9e51722b04fb159ada3d5811d62a423e429
|
refs/heads/master
| 2020-07-25T17:26:19.227918
| 2019-09-15T15:41:48
| 2019-09-15T15:41:48
| 208,357,412
| 4
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,946
|
py
|
from sympy.abc import *
from matchpy.matching.many_to_one import CommutativeMatcher
from matchpy import *
from matchpy.utils import VariableWithCount
from collections import deque
from multiset import Multiset
from sympy.integrals.rubi.constraints import *
from sympy.integrals.rubi.utility_function import *
from sympy.integrals.rubi.rules.miscellaneous_integration import *
from sympy import *
class CommutativeMatcher38258(CommutativeMatcher):
_instance = None
patterns = {
0: (0, Multiset({0: 1}), [
(VariableWithCount('i2.2.1.2.2.2.0', 1, 1, S(0)), Add)
])
}
subjects = {}
subjects_by_id = {}
bipartite = BipartiteGraph()
associative = Add
max_optional_count = 1
anonymous_patterns = set()
def __init__(self):
self.add_subject(None)
@staticmethod
def get():
if CommutativeMatcher38258._instance is None:
CommutativeMatcher38258._instance = CommutativeMatcher38258()
return CommutativeMatcher38258._instance
@staticmethod
def get_match_iter(subject):
subjects = deque([subject]) if subject is not None else deque()
subst0 = Substitution()
# State 38257
subst1 = Substitution(subst0)
try:
subst1.try_add_variable('i2.2.1.2.2.2.1.0', S(1))
except ValueError:
pass
else:
pass
# State 38259
if len(subjects) >= 1 and isinstance(subjects[0], Pow):
tmp2 = subjects.popleft()
subjects3 = deque(tmp2._args)
# State 38260
if len(subjects3) >= 1:
tmp4 = subjects3.popleft()
subst2 = Substitution(subst1)
try:
subst2.try_add_variable('i2.2.1.1', tmp4)
except ValueError:
pass
else:
pass
# State 38261
if len(subjects3) >= 1:
tmp6 = subjects3.popleft()
subst3 = Substitution(subst2)
try:
subst3.try_add_variable('i2.2.1.2', tmp6)
except ValueError:
pass
else:
pass
# State 38262
if len(subjects3) == 0:
pass
# State 38263
if len(subjects) == 0:
pass
# 0: x**j*f
yield 0, subst3
subjects3.appendleft(tmp6)
subjects3.appendleft(tmp4)
subjects.appendleft(tmp2)
if len(subjects) >= 1 and isinstance(subjects[0], Mul):
tmp8 = subjects.popleft()
associative1 = tmp8
associative_type1 = type(tmp8)
subjects9 = deque(tmp8._args)
matcher = CommutativeMatcher38265.get()
tmp10 = subjects9
subjects9 = []
for s in tmp10:
matcher.add_subject(s)
for pattern_index, subst1 in matcher.match(tmp10, subst0):
pass
if pattern_index == 0:
pass
# State 38270
if len(subjects) == 0:
pass
# 0: x**j*f
yield 0, subst1
subjects.appendleft(tmp8)
return
yield
from .generated_part003651 import *
from matchpy.matching.many_to_one import CommutativeMatcher
from collections import deque
from matchpy.utils import VariableWithCount
from multiset import Multiset
|
[
"franz.bonazzi@gmail.com"
] |
franz.bonazzi@gmail.com
|
0e3f366f9b2f023474aa0f26b034f046a6e738bd
|
4ade37d929b07b1eea07337b9cc843661a66e6d0
|
/trails/feeds/nothink.py
|
f40ae15122ffc7c0e6f962eac4765945bd5dded1
|
[
"MIT"
] |
permissive
|
Dm2333/maltrail
|
bade5c99583b99f4ad1128aef295e95c977d82b1
|
2f32e0c3ff65544fc07ad3787d4d9b210f975b85
|
refs/heads/master
| 2021-04-12T10:44:25.125653
| 2018-03-20T11:50:40
| 2018-03-20T11:50:40
| 126,193,051
| 1
| 0
|
MIT
| 2018-03-21T14:40:05
| 2018-03-21T14:40:03
|
Python
|
UTF-8
|
Python
| false
| false
| 674
|
py
|
#!/usr/bin/env python
"""
Copyright (c) 2014-2018 Miroslav Stampar (@stamparm)
See the file 'LICENSE' for copying permission
"""
from core.common import retrieve_content
__url__ = "http://www.nothink.org/blacklist/blacklist_malware_irc.txt"
__check__ = "Malware IRC"
__info__ = "potential malware site"
__reference__ = "nothink.org"
def fetch():
retval = {}
content = retrieve_content(__url__)
if __check__ in content:
for line in content.split('\n'):
line = line.strip()
if not line or line.startswith('#') or '.' not in line:
continue
retval[line] = (__info__, __reference__)
return retval
|
[
"miroslav.stampar@gmail.com"
] |
miroslav.stampar@gmail.com
|
dd7f146df693ac042cde1345a5080c70862c344e
|
222a7d69a78f1350772c9c8bfb0b36c640e5cd6e
|
/MarlinJobs/CalibrationConfigFiles/Stage59Config_5x5_30x30.py
|
2b94d6d91472c95d504b20257b87d7e3b5afb347
|
[] |
no_license
|
StevenGreen1/JERDetailed
|
2a8cb30ec32781791ba163e5125bcdb87239e9a4
|
27ed19dc0930570f16019b2c7820ae715dd0ec57
|
refs/heads/master
| 2021-01-17T06:55:11.384992
| 2016-08-10T14:41:38
| 2016-08-10T14:41:38
| 44,620,987
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,192
|
py
|
# Calibration config file for testing
# Digitisation Constants - ECal
CalibrECal = 42.3662496409
# Digitisation Constants - HCal
CalibrHCalBarrel = 50.3504586994
CalibrHCalEndcap = 55.6419000329
CalibrHCALOther = 30.5873671511
# Digitisation Constants - Muon Chamber
CalibrMuon = 56.7
# MIP Peak position in directed corrected SimCaloHit energy distributions
# used for realistic ECal and HCal digitisation options
CalibrECalMIP = 0.0001475
CalibrHCalMIP = 0.0004925
# MIP Peak position in directed corrected CaloHit energy distributions
# used for MIP definition in PandoraPFA
ECalToMIPCalibration = 153.846
HCalToMIPCalibration = 36.1011
MuonToMIPCalibration = 10.101
# EM and Had Scale Settings
ECalToEMGeVCalibration = 1.00215973193
HCalToEMGeVCalibration = 1.00215973193
ECalToHadGeVCalibration = 1.12219237098
HCalToHadGeVCalibration = 1.05372579725
# Pandora Threshold Cuts
ECalMIPThresholdPandora = 0.5
HCalMIPThresholdPandora = 0.3
# Hadronic Energy Truncation in HCal PandoraPFA
MaxHCalHitHadronicEnergy = 1000000.0
# Timing ECal
ECalBarrelTimeWindowMax = 300.0
ECalEndcapTimeWindowMax = 300.0
# Timing HCal
HCalBarrelTimeWindowMax = 300.0
HCalEndcapTimeWindowMax = 300.0
|
[
"sg1sg2sg3@hotmail.co.uk"
] |
sg1sg2sg3@hotmail.co.uk
|
2a62f1bef54bfd2cb7615ca2e9e0483f7ca9fd76
|
5ab2ccf70fddd30ea88155f2a5adb0711bf3dc9a
|
/Chap10/factorsingles.py
|
5d413a283dcbbe5de549074b7b5cbee0eafea399
|
[] |
no_license
|
jdukosse/LOI_Python_course-SourceCode
|
32d66fd79344e9ab9412a6da373f2093b39cad92
|
bf13907dacf5b6e95f84885896c8f478dd208011
|
refs/heads/master
| 2020-12-05T23:27:53.862508
| 2020-01-24T13:42:28
| 2020-01-24T13:42:28
| 232,276,680
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 142
|
py
|
n = int(input("Please enter a positive integer: "))
factors = [x for x in range(1, n + 1) if n % x == 0]
print("Factors of", n, ":", factors)
|
[
"jdukosse@hotmail.com"
] |
jdukosse@hotmail.com
|
9461f02ac4fdcbf48b760055e18b17a595c5d8e0
|
5451997d7b691679fd213d6473b21f184a5c9402
|
/pymaze/wsgi.py
|
4aff83a8a210e68f9e6d3d976da790c63895747e
|
[
"MIT"
] |
permissive
|
TerryHowe/pymaze
|
9ba54c7d328abf94f6709593795a587f28be752b
|
a5b7e90b5019a5f99a7f80317796ace72ca0754f
|
refs/heads/master
| 2022-05-01T07:39:17.896430
| 2022-04-23T10:41:48
| 2022-04-23T10:41:48
| 89,522,507
| 1
| 0
|
MIT
| 2022-04-23T10:41:49
| 2017-04-26T20:13:13
|
Python
|
UTF-8
|
Python
| false
| false
| 390
|
py
|
"""
WSGI config for pymaze project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.11/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "pymaze.settings")
application = get_wsgi_application()
|
[
"terrylhowe@gmail.com"
] |
terrylhowe@gmail.com
|
5eff0169132197e41737862349d9ad181777010a
|
fe8f7febac1ff93b829256cdfd0be69e94498c76
|
/python/fluent_python/code/clockdeco_param.py
|
4700886d4acf8383701a414070e3f4635df7f784
|
[] |
no_license
|
bioShaun/notebook
|
c438eba1d29b736704c3f5325faf15ad61a1e9d5
|
ce5f477a78554ed0d4ea5344057c19e32eb6c2b8
|
refs/heads/master
| 2020-03-26T16:16:06.458545
| 2018-08-23T00:54:53
| 2018-08-23T00:54:53
| 145,090,588
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 950
|
py
|
import time
import functools
DEFAULT_FMT = '[{elapsed:0.8f}s] {name}({args}) -> {result}'
def clock(fmt=DEFAULT_FMT):
def decorate(func):
def clocked(*_args, **kwargs):
t0 = time.time()
_result = func(*_args, **kwargs)
elapsed = time.time() - t0
name = func.__name__
arg_lst = []
if _args:
arg_lst.append(', '.join(repr(arg) for arg in _args))
if kwargs:
pairs = ['%s=%r' % (k, w)
for k, w in sorted(kwargs.items * ())]
arg_lst.append(', '.join(pairs))
args = ', '.join(arg_lst)
result = repr(_result)
print(fmt.format(**locals()))
return _result
return clocked
return decorate
if __name__ == '__main__':
@clock()
def snooze(seconds):
time.sleep(seconds)
for i in range(3):
snooze(.123)
|
[
"ricekent@163.com"
] |
ricekent@163.com
|
e7e3c115506553ab1cbc5ca31ff9c0144325dd24
|
16e266cf50a712ed29a4097e34504aac0281e6cb
|
/Functions/venv/lib/python3.6/site-packages/_TFL/_SDG/_C/Macro.py
|
75f2950512e90bf9922859188d30c81a9164101c
|
[
"BSD-3-Clause"
] |
permissive
|
felix-ogutu/PYTHON-PROJECTS
|
9dd4fdcfff6957830587b64c5da3b5c3ade3a27e
|
8c1297dbda495078509d06a46f47dc7ee60b6d4e
|
refs/heads/master
| 2023-06-05T04:41:36.727376
| 2021-06-25T20:36:52
| 2021-06-25T20:36:52
| 380,348,911
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 6,540
|
py
|
# -*- coding: utf-8 -*-
# Copyright (C) 2004-2007 TTTech Computertechnik AG. All rights reserved
# Schönbrunnerstraße 7, A--1040 Wien, Austria. office@tttech.com
# ****************************************************************************
#
# This module is licensed under the terms of the BSD 3-Clause License
# <http://www.c-tanzer.at/license/bsd_3c.html>.
# ****************************************************************************
#
#++
# Name
# TFL.SDG.C.Macro
#
# Purpose
# C-macro definitions
#
# Revision Dates
# 11-Aug-2004 (MG) Creation
# 12-Aug-2004 (MG) `Macro_Block.children_group_names` added
# 12-Aug-2004 (MG) Convert the `args` paremeter from `None` to `""` and
# from `""` to `None` for backward compatibility
# 12-Aug-2004 (MG) `description` added to formats
# 13-Aug-2004 (CT) `Macro.c_format` simplified
# (`%(name)s` instead of `%(::.name:)s`)
# 24-Aug-2004 (CT) Spurious space after macro name removed from `h_format`
# and `c_format`
# 24-Aug-2004 (MG) `Macro_Block.children_group_names` removed
# 7-Oct-2004 (CED) `Define_Constant` added
# 8-Feb-2005 (CED) `apidoc_tex_format` defined here and necessary changes
# made
# 9-Feb-2005 (MBM/CED) formal changes to `apidoc_tex_format`
# 22-Feb-2005 (MBM) Removed <> from index entry
# 24-Feb-2005 (MBM) Changed index entry structure
# 9-Aug-2005 (CT) Call to `tex_quoted` added
# 30-Oct-2006 (CED) `Preprocessor_Error` added
# 9-Mar-2007 (CED) Accepting integer as value of `Define_Constant`
# 17-Apr-2007 (CED) `Define_Constant` improved to print parantheses around
# `value`
# 23-Jul-2007 (CED) Activated absolute_import
# 06-Aug-2007 (CED) Future import removed again
# 26-Feb-2012 (MG) `__future__` imports added
# ««revision-date»»···
#--
from __future__ import absolute_import, division, print_function, unicode_literals
from _TFL import TFL
import _TFL._SDG._C.Node
import _TFL._SDG._C.Statement
import _TFL.tex_quoted
import textwrap
class _Macro_ (TFL.SDG.C.Node) :
"""Base class of all preprocessor commands (defines, if, ifdef, ...)"""
cgi = None
def _update_scope (self, scope) :
### why do we need this ???? MGL, 11-Aug-2004
self.scope = scope
for c in self.children :
c._update_scope (scope)
# end def _update_scope
# end class _Macro_
class Macro (_Macro_, TFL.SDG.Leaf) :
"""C-macro defintion"""
init_arg_defaults = dict \
( name_len = 0
, scope = TFL.SDG.C.C
, args = None
, lines = None
)
front_args = ("name", "args")
rest_args = "lines"
m_head = ""
h_format = c_format = """
#%(m_head)s%(name)s%(:head=(¡tail=):.args:)s %(:sep_eol= \\:.lines:)s
>%(::*description:)s
"""
def __init__ (self, * args, ** kw) :
self.__super.__init__ (* args, ** kw)
if self.args is None :
self.args = ""
elif self.args == "" :
self.args = None
# end def __init__
# end class Macro
class Define (Macro) :
"""A C-macro #define stament"""
m_head = "define "
init_arg_defaults = dict \
( def_file = "unknown"
, explanation = ""
)
_apidoc_head = \
"""%(::@_name_comment:)-{output_width - indent_anchor}s
\\hypertarget{%(name)s}{}
\\subsubsection{\\texttt{%(name)s}}
\\index{FT-COM API>\\texttt{%(name)s}}
\\ttindex{%(name)s}
\\begin{description}
>\\item %(::*description:)s \\\\
>\\item \\textbf{File:} \\\\ \\texttt{%(def_file)s} \\\\
"""
_apidoc_tail = \
""">%(::>@_explanation:)-{output_width - indent_anchor}s
\\end{description}
>
"""
_apidoc_middle = \
""">\\item \\textbf{Function declaration:} \\\\
>>\\texttt{%(name)s (%(args)s)} \\\\
"""
apidoc_tex_format = "".join \
( [ _apidoc_head
, _apidoc_middle
, _apidoc_tail
]
)
def _name_comment (self, ** kw) :
format_prec = int (kw ["format_prec"])
result = \
( "%% --- %s %s"
% ( self.name
, "-" * ( format_prec - len (self.name) - 7
)
)
)
return [result]
# end def _name_comment
def _explanation (self, ** kw) :
if not self.explanation :
yield ""
return
yield "\\item \\textbf{Description:} \\\\"
format_prec = max (int (kw ["format_prec"]), 4)
wrapper = textwrap.TextWrapper (width = format_prec)
for l in wrapper.wrap (TFL.tex_quoted (self.explanation)) :
yield l
# end def _explanation
# end class Define
class Define_Constant (Define) :
"""A C-macro #define stament, defining a constant value"""
init_arg_defaults = dict \
( name_len = 0
, scope = TFL.SDG.C.C
, name = None
, value = None
)
front_args = ("name", "value")
h_format = c_format = """
#%(m_head)s%(name)s %(:head=(¡tail=):.value:)s
>%(::*description:)s
"""
_apidoc_middle = \
""">\\item \\textbf{Value:} %(value)s
"""
apidoc_tex_format = "".join \
( [ Define._apidoc_head
, _apidoc_middle
, Define._apidoc_tail
]
)
_autoconvert = dict \
( value = lambda s, k, v : str (v)
)
# end class Define_Constant
class Macro_Block (_Macro_, TFL.SDG.C.Stmt_Group) :
"""Block of macro definitions"""
Ancestor = TFL.SDG.C.Stmt_Group
# end class Macro_Block
class Preprocessor_Error (_Macro_) :
"""A C preprocessor error statement"""
m_head = "error "
init_arg_defaults = dict \
( scope = TFL.SDG.C.HC
, error_msg = ""
)
front_args = ("error_msg", )
h_format = c_format = """
#%(m_head) s%(error_msg)s
"""
# end class Preprocessor_Error
if __name__ != "__main__" :
TFL.SDG.C._Export ("*", "_Macro_")
### __END__ TFL.SDG.C.Macro
|
[
"you@example.com"
] |
you@example.com
|
03c89f87bc946fe9d2a1f054e5f392aa88cc88c2
|
2ff7e53d5e512cd762217ca54317982e07a2bb0c
|
/carbon/common/script/net/httpAuth.py
|
4e0d808e60ebe4b4b14cadffc1f8dc510f115517
|
[] |
no_license
|
nanxijw/Clara-Pretty-One-Dick
|
66d3d69426642b79e8fd4cc8e0bec23adeeca6d6
|
50de3488a2140343c364efc2615cf6e67f152be0
|
refs/heads/master
| 2021-01-19T09:25:07.555284
| 2015-02-17T21:49:33
| 2015-02-17T21:49:33
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 6,364
|
py
|
#Embedded file name: carbon/common/script/net\httpAuth.py
import base
import cherrypy
import httpJinja
import macho
import blue
import const
import base64
from datetime import datetime
SESSION_KEY = '_cp_username'
AUTH_LOGIN_URL = '/auth/login'
DEFAULT_URL = '/default.py'
def CreateSession(username, password):
session = base.CreateSession()
session.esps = ESPSession(None, session.sid)
session.esps.contents['username'] = username
session.esps.contents['password'] = password
return session
def EndSession():
cherrypy.session.delete()
cherrypy.lib.sessions.expire()
def CheckCredentials(username, password):
sess = CreateSession(username, password)
if macho.mode == 'client':
cherrypy.session['machoSession'] = sess
return
auth = base.GetServiceSession('cherry').ConnectToAnyService('authentication')
sptype = const.userConnectTypeServerPages
try:
sessstuff, _ = auth.Login(sess.sid, username, password, None, sptype, cherrypy.request.remote.ip)
except UserError:
return u'Incorrect username or password'
except Exception:
return u'Incorrect username or password'
session = CreateSession(username, password)
sessstuff['role'] |= sess.role
for otherSession in base.FindSessions('userid', [sessstuff['userid']]):
otherSession.LogSessionHistory('Usurped by user %s via HTTP using local authentication' % username)
base.CloseSession(otherSession)
cherrypy.session['machoSession'] = sess
sess.SetAttributes(sessstuff)
def CheckAuth(*args, **kwargs):
assets = cherrypy.request.config.get('tools.staticdir.dir')
cherrypy.request.beginTime = datetime.now()
if assets not in cherrypy.request.path_info:
conditions = cherrypy.request.config.get('auth.require', None)
if conditions is not None:
pathInfo = cherrypy.request.path_info
if len(cherrypy.request.query_string):
pathInfo = '%s?%s' % (pathInfo, cherrypy.request.query_string)
if pathInfo in [AUTH_LOGIN_URL, DEFAULT_URL]:
authLogin = AUTH_LOGIN_URL
else:
authLogin = '%s?from_page=%s' % (AUTH_LOGIN_URL, base64.urlsafe_b64encode(pathInfo))
username = cherrypy.session.get(SESSION_KEY)
if username:
cherrypy.request.login = username
for condition in conditions:
if not condition():
raise cherrypy.HTTPRedirect(authLogin)
else:
raise cherrypy.HTTPRedirect(authLogin)
cherrypy.tools.auth = cherrypy.Tool('before_handler', CheckAuth)
def Require(*conditions):
def decorate(f):
if not hasattr(f, '_cp_config'):
f._cp_config = dict()
if 'auth.require' not in f._cp_config:
f._cp_config['auth.require'] = []
f._cp_config['auth.require'].extend(conditions)
return f
return decorate
def MemberOf(groupName):
def check():
return cherrypy.request.login == 'joe' and groupName == 'admin'
return check()
def NameIs(required_username):
return lambda : required_username == cherrypy.request.login
def AnyOf(*conditions):
def check():
for condition in conditions:
if condition():
return True
return False
return check()
def AllOf(*conditions):
def check():
for condition in conditions:
if not condition():
return False
return True
return check
class ESPSession:
def __init__(self, owner, sid):
self.codePage = 0
self.contents = {}
self.LCID = 0
self.sessionID = sid
self.timeout = 20
self.authenticated = 0
self.username = ''
self.password = ''
self.owner = owner
self.flatkokudeig = blue.os.GetWallclockTimeNow()
self.remappings = {}
class AuthController(object):
__guid__ = 'httpAuth.AuthController'
def on_login(self, username):
"""Called on successful login"""
pass
def on_logout(self, username):
"""Called on logout"""
pass
def get_loginform(self, username, msg = None, from_page = '/'):
sp = cherrypy.sm.GetService('SP')
try:
background_color = sp.Color()
except Exception:
background_color = sp.Color()
return {'msg': msg,
'style': 'background-color: %s; color: black' % background_color,
'sp': sp.Title(),
'server': cherrypy.prefs.clusterName,
'generate_time': datetime.now() - cherrypy.request.beginTime,
'username': 'sp' if prefs.clusterMode == 'LOCAL' else ''}
@cherrypy.expose
@cherrypy.tools.jinja(template='AuthController_login.html')
def login(self, username = None, password = None, from_page = '/'):
if username is None or password is None:
return self.get_loginform('', from_page=from_page)
error_msg = CheckCredentials(username, password)
if error_msg:
return self.get_loginform(username, error_msg, from_page)
cherrypy.session.regenerate()
cherrypy.session[SESSION_KEY] = cherrypy.request.login = username
self.on_login(username)
if from_page != '/':
from_page = base64.urlsafe_b64decode(str(from_page))
raise cherrypy.HTTPRedirect(from_page or '/')
@cherrypy.expose
def logout(self, from_page = '/'):
sess = cherrypy.session
username = sess.get(SESSION_KEY, None)
sess[SESSION_KEY] = None
if username:
cherrypy.request.login = None
self.on_logout(username)
if 'machoSession' in cherrypy.session:
sess = cherrypy.session['machoSession']
sess.LogSessionHistory('Web session closed by logging out %s' % str(session.userid))
base.CloseSession(sess)
EndSession()
raise cherrypy.HTTPRedirect(from_page or '/')
exports = {'httpAuth.CreateSession': CreateSession,
'httpAuth.EndSession': EndSession,
'httpAuth.CheckCredentials': CheckCredentials,
'httpAuth.CheckAuth': CheckAuth,
'httpAuth.Require': Require,
'httpAuth.MemberOf': MemberOf,
'httpAuth.NameIs': NameIs,
'httpAuth.AnyOf': AnyOf,
'httpAuth.AllOf': AllOf}
|
[
"billchang.e@gmail.com"
] |
billchang.e@gmail.com
|
d1832ec2bedb704f090af6d27a3a27a0abf67623
|
8bb4060c4a41d1ef1b31c59fb8b9bc375e3e2ba4
|
/setup.py
|
c26e6e1cb822af51c1da20528c39ff488e7edd81
|
[] |
no_license
|
hanxianzhai/distribution
|
a6c5f96bb954e7e18bae0d6a7ac6976fae59d332
|
628f670f4ed39478007e3402a77653f6596d0529
|
refs/heads/master
| 2021-04-01T06:21:29.086943
| 2020-03-18T03:55:28
| 2020-03-18T03:55:28
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 175
|
py
|
import config
from init import app
if __name__ == '__main__':
app.run(
host='0.0.0.0',
port=config.app_conf["server"]["port"],
debug=False
)
|
[
"tanshilinmail@gmail.com"
] |
tanshilinmail@gmail.com
|
6a405e8f55909b6ed9222b949bef9230edd24b17
|
abfa0fcab2bc9a9c3cccbc3a8142cdd4b2a66ee9
|
/698-Partition to K Equal Sum Subsets.py
|
8aceeaa11fdcd8709c3a984236173baf0a4fbd70
|
[] |
no_license
|
JinnieJJ/leetcode
|
20e8ccf3f8919028c53e0f0db86bcc2fbc7b6272
|
26c6ee936cdc1914dc3598c5dc74df64fa7960a1
|
refs/heads/master
| 2021-04-15T09:18:08.450426
| 2021-03-06T01:53:27
| 2021-03-06T01:53:27
| 126,275,814
| 3
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 670
|
py
|
class Solution:
def canPartitionKSubsets(self, nums, k):
"""
:type nums: List[int]
:type k: int
:rtype: bool
"""
sums = [0] * k
subsum = sum(nums) / k
nums.sort(reverse=True)
l = len(nums)
def walk(i):
if i == l:
return len(set(sums)) == 1
for j in range(k):
sums[j] += nums[i]
if sums[j] <= subsum and walk(i+1):
return True
sums[j] -= nums[i]
if sums[j] == 0:
break
return False
return walk(0)
|
[
"noreply@github.com"
] |
JinnieJJ.noreply@github.com
|
1f0f69d04585b8216b8268a4c3dc0e5868618db7
|
2dd560dc468af0af4ca44cb4cd37a0b807357063
|
/Leetcode/1289. Minimum Falling Path Sum II/solution2.py
|
e9ebe9c9ba9a53af13d879fb8d254dac546a99d0
|
[
"MIT"
] |
permissive
|
hi0t/Outtalent
|
460fe4a73788437ba6ce9ef1501291035c8ff1e8
|
8a10b23335d8e9f080e5c39715b38bcc2916ff00
|
refs/heads/master
| 2023-02-26T21:16:56.741589
| 2021-02-05T13:36:50
| 2021-02-05T13:36:50
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 694
|
py
|
class Solution:
def minFallingPathSum(self, arr: List[List[int]]) -> int:
m = len(arr)
n = len(arr[0])
@lru_cache(None)
def count(i: int, j: int) -> int:
if i >= m: return 0
m1 = m2 = inf
k1 = k2 = 0
for k in range(n):
if j == k: continue
if arr[i][k] < m1:
m2 = m1
m1 = arr[i][k]
k2 = k1
k1 = k
elif arr[i][k] < m2:
m2 = arr[i][k]
k2 = k
return min(m1 + count(i + 1, k1), m2 + count(i + 1, k2))
return count(0, -1)
|
[
"info@crazysquirrel.ru"
] |
info@crazysquirrel.ru
|
2efe378579a32f494f6942fa0ac13a700a233957
|
cffee94b843fff699f68eaae972ed829858fbb0d
|
/typings/mediafile/mutagen/mp3/__init__.pyi
|
da26b2285df4dd3b5373082919fadc979a486824
|
[
"MIT"
] |
permissive
|
Josef-Friedrich/phrydy
|
3b5fae00d3d7210821dc9037d00f9432e1df3c2d
|
c6e17e8b9e24678ec7672bff031d0370bfa8b6f8
|
refs/heads/main
| 2023-08-25T12:11:47.333984
| 2023-08-08T14:50:08
| 2023-08-08T14:50:08
| 66,490,323
| 6
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,255
|
pyi
|
"""
This type stub file was generated by pyright.
"""
from __future__ import division
from functools import partial
from io import BytesIO
from mutagen._util import BitReader, cdata, iterbytes
"""
http://www.codeproject.com/Articles/8295/MPEG-Audio-Frame-Header
http://wiki.hydrogenaud.io/index.php?title=MP3
"""
class LAMEError(Exception): ...
class LAMEHeader:
"""http://gabriel.mp3-tech.org/mp3infotag.html"""
vbr_method = ...
lowpass_filter = ...
quality = ...
vbr_quality = ...
track_peak = ...
track_gain_origin = ...
track_gain_adjustment = ...
album_gain_origin = ...
album_gain_adjustment = ...
encoding_flags = ...
ath_type = ...
bitrate = ...
encoder_delay_start = ...
encoder_padding_end = ...
source_sample_frequency_enum = ...
unwise_setting_used = ...
stereo_mode = ...
noise_shaping = ...
mp3_gain = ...
surround_info = ...
preset_used = ...
music_length = ...
music_crc = ...
header_crc = ...
def __init__(self, xing, fileobj) -> None:
"""Raises LAMEError if parsing fails"""
...
def guess_settings(self, major, minor):
"""Gives a guess about the encoder settings used. Returns an empty
string if unknown.
The guess is mostly correct in case the file was encoded with
the default options (-V --preset --alt-preset --abr -b etc) and no
other fancy options.
Args:
major (int)
minor (int)
Returns:
text
"""
...
@classmethod
def parse_version(cls, fileobj):
"""Returns a version string and True if a LAMEHeader follows.
The passed file object will be positioned right before the
lame header if True.
Raises LAMEError if there is no lame version info.
"""
...
class XingHeaderError(Exception): ...
class XingHeaderFlags:
FRAMES = ...
BYTES = ...
TOC = ...
VBR_SCALE = ...
class XingHeader:
frames = ...
bytes = ...
toc = ...
vbr_scale = ...
lame_header = ...
lame_version = ...
lame_version_desc = ...
is_info = ...
def __init__(self, fileobj) -> None:
"""Parses the Xing header or raises XingHeaderError.
The file position after this returns is undefined.
"""
...
def get_encoder_settings(self): # -> Literal['']:
"""Returns the guessed encoder settings"""
...
@classmethod
def get_offset(cls, info): # -> Literal[36, 21, 13]:
"""Calculate the offset to the Xing header from the start of the
MPEG header including sync based on the MPEG header's content.
"""
...
class VBRIHeaderError(Exception): ...
class VBRIHeader:
version = ...
quality = ...
bytes = ...
frames = ...
toc_scale_factor = ...
toc_frames = ...
toc = ...
def __init__(self, fileobj) -> None:
"""Reads the VBRI header or raises VBRIHeaderError.
The file position is undefined after this returns
"""
...
@classmethod
def get_offset(cls, info): # -> Literal[36]:
"""Offset in bytes from the start of the MPEG header including sync"""
...
|
[
"josef@friedrich.rocks"
] |
josef@friedrich.rocks
|
14ecb79893f2a150fcc1e6200c9e85886e0f7225
|
e282226e8fda085f4c64c044327eceb3388e94ce
|
/mainapp/api/urls.py
|
1b3871642a15056f10650c9fb8bffcec8a5d906f
|
[] |
no_license
|
Pavlenkovv/REST-API
|
2bf36f40104a51f2735ce3dd3eebcf274061a1a2
|
352d0bd24e88fdb793e658c5b6eaffa97b56062c
|
refs/heads/main
| 2023-03-15T22:45:50.121953
| 2021-03-07T07:56:31
| 2021-03-07T07:56:31
| 344,887,432
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 413
|
py
|
from django.urls import path, include
from rest_framework.routers import DefaultRouter
from .api_views import AuthorViewSet, NewsPostViewSet, CommentViewSet
router = DefaultRouter()
router.register(r"newsposts", NewsPostViewSet, basename="user")
router.register(r"author", AuthorViewSet)
router.register(r"comment", CommentViewSet)
urlpatterns = [path("api/", include(router.urls))]
urlpatterns += router.urls
|
[
"pavlenko.vyacheslav@gmail.com"
] |
pavlenko.vyacheslav@gmail.com
|
1b41395082d1617e92cb4539c977d7f616a594fc
|
ecd630f54fefa0a8a4937ac5c6724f9a3bb215c3
|
/projeto/avalista/migrations/0022_auto_20200910_1230.py
|
8922215b9bc4a928404f7c8043839ce3aebed4a8
|
[] |
no_license
|
israelwerther/Esctop_Israel_Estoque
|
49968751464a38c473298ed876da7641efedf8de
|
d6ab3e502f2a97a0d3036351e59c2faa267c0efd
|
refs/heads/master
| 2023-01-07T20:21:38.381593
| 2020-11-12T17:35:14
| 2020-11-12T17:35:14
| 258,642,721
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 667
|
py
|
# Generated by Django 3.0.7 on 2020-09-10 12:30
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('avalista', '0021_avalista_fiador_n_operacao'),
]
operations = [
migrations.AlterField(
model_name='avalista',
name='fiador_agencia',
field=models.CharField(blank=True, max_length=15, null=True, verbose_name='Nº agência'),
),
migrations.AlterField(
model_name='avalista',
name='fiador_conta',
field=models.CharField(blank=True, max_length=15, null=True, verbose_name='Nº conta'),
),
]
|
[
"israelwerther48@outlook.com"
] |
israelwerther48@outlook.com
|
6fcd77974cc305566c9496941a87ef64cb688e50
|
66fda6586a902f8043b1f5e9532699babc7b591a
|
/lib_openshift/models/v1_deployment_trigger_image_change_params.py
|
cdb5495ce392554744c8473da2b748a72362bdae
|
[
"Apache-2.0"
] |
permissive
|
chouseknecht/lib_openshift
|
86eff74b4659f05dfbab1f07d2d7f42b21e2252d
|
02b0e4348631e088e72a982a55c214b30a4ab9d9
|
refs/heads/master
| 2020-12-11T05:23:17.081794
| 2016-07-28T20:15:39
| 2016-07-28T20:15:39
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 6,799
|
py
|
# coding: utf-8
"""
OpenAPI spec version:
Generated by: https://github.com/swagger-api/swagger-codegen.git
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 pprint import pformat
from six import iteritems
import re
class V1DeploymentTriggerImageChangeParams(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
operations = [
]
def __init__(self, automatic=None, container_names=None, _from=None, last_triggered_image=None):
"""
V1DeploymentTriggerImageChangeParams - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition.
"""
self.swagger_types = {
'automatic': 'bool',
'container_names': 'list[str]',
'_from': 'V1ObjectReference',
'last_triggered_image': 'str'
}
self.attribute_map = {
'automatic': 'automatic',
'container_names': 'containerNames',
'_from': 'from',
'last_triggered_image': 'lastTriggeredImage'
}
self._automatic = automatic
self._container_names = container_names
self.__from = _from
self._last_triggered_image = last_triggered_image
@property
def automatic(self):
"""
Gets the automatic of this V1DeploymentTriggerImageChangeParams.
Automatic means that the detection of a new tag value should result in a new deployment.
:return: The automatic of this V1DeploymentTriggerImageChangeParams.
:rtype: bool
"""
return self._automatic
@automatic.setter
def automatic(self, automatic):
"""
Sets the automatic of this V1DeploymentTriggerImageChangeParams.
Automatic means that the detection of a new tag value should result in a new deployment.
:param automatic: The automatic of this V1DeploymentTriggerImageChangeParams.
:type: bool
"""
self._automatic = automatic
@property
def container_names(self):
"""
Gets the container_names of this V1DeploymentTriggerImageChangeParams.
ContainerNames is used to restrict tag updates to the specified set of container names in a pod.
:return: The container_names of this V1DeploymentTriggerImageChangeParams.
:rtype: list[str]
"""
return self._container_names
@container_names.setter
def container_names(self, container_names):
"""
Sets the container_names of this V1DeploymentTriggerImageChangeParams.
ContainerNames is used to restrict tag updates to the specified set of container names in a pod.
:param container_names: The container_names of this V1DeploymentTriggerImageChangeParams.
:type: list[str]
"""
self._container_names = container_names
@property
def _from(self):
"""
Gets the _from of this V1DeploymentTriggerImageChangeParams.
From is a reference to an image stream tag to watch for changes. From.Name is the only required subfield - if From.Namespace is blank, the namespace of the current deployment trigger will be used.
:return: The _from of this V1DeploymentTriggerImageChangeParams.
:rtype: V1ObjectReference
"""
return self.__from
@_from.setter
def _from(self, _from):
"""
Sets the _from of this V1DeploymentTriggerImageChangeParams.
From is a reference to an image stream tag to watch for changes. From.Name is the only required subfield - if From.Namespace is blank, the namespace of the current deployment trigger will be used.
:param _from: The _from of this V1DeploymentTriggerImageChangeParams.
:type: V1ObjectReference
"""
self.__from = _from
@property
def last_triggered_image(self):
"""
Gets the last_triggered_image of this V1DeploymentTriggerImageChangeParams.
LastTriggeredImage is the last image to be triggered.
:return: The last_triggered_image of this V1DeploymentTriggerImageChangeParams.
:rtype: str
"""
return self._last_triggered_image
@last_triggered_image.setter
def last_triggered_image(self, last_triggered_image):
"""
Sets the last_triggered_image of this V1DeploymentTriggerImageChangeParams.
LastTriggeredImage is the last image to be triggered.
:param last_triggered_image: The last_triggered_image of this V1DeploymentTriggerImageChangeParams.
:type: str
"""
self._last_triggered_image = last_triggered_image
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
|
[
"jdetiber@redhat.com"
] |
jdetiber@redhat.com
|
f9242da26ab0e85261149acc3935789753a44160
|
0cafca9e27e70aa47b3774a13a537f45410f13f7
|
/idb/ipc/push.py
|
c7f6d1ab8f6e77317e6d081e0655d31ebf0c16a5
|
[
"MIT"
] |
permissive
|
fakeNetflix/facebook-repo-idb
|
18b67ca6cfa0edd3fa7b9c4940fec6c3f0ccfa73
|
eb4ed5a7dc4a14b224a22e833294d7366fe4725e
|
refs/heads/master
| 2023-01-05T13:19:40.755318
| 2019-08-16T15:23:45
| 2019-08-16T15:25:00
| 203,098,477
| 1
| 0
|
MIT
| 2023-01-04T07:33:09
| 2019-08-19T04:31:16
|
Objective-C
|
UTF-8
|
Python
| false
| false
| 1,039
|
py
|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from idb.common.stream import stream_map
from idb.common.tar import generate_tar
from idb.grpc.idb_pb2 import Payload, PushRequest, PushResponse
from idb.grpc.stream import Stream, drain_to_stream
from idb.grpc.types import CompanionClient
async def daemon(
client: CompanionClient, stream: Stream[PushResponse, PushRequest]
) -> None:
async with client.stub.push.open() as companion:
await companion.send_message(await stream.recv_message())
if client.is_local:
generator = stream
else:
paths = [request.payload.file_path async for request in stream]
generator = stream_map(
generate_tar(paths=paths),
lambda chunk: PushRequest(payload=Payload(data=chunk)),
)
response = await drain_to_stream(
stream=companion, generator=generator, logger=client.logger
)
await stream.send_message(response)
|
[
"facebook-github-bot@users.noreply.github.com"
] |
facebook-github-bot@users.noreply.github.com
|
181269644d8602fc2dcb673b30857f2da8b2b11f
|
6deafbf6257a5c30f084c3678712235c2c31a686
|
/Toolz/sqlmap/tamper/least.py
|
53a8a6aadefe283a268fd3ad7a0c5fd1f51f2a67
|
[
"Unlicense",
"LicenseRef-scancode-generic-cla",
"GPL-1.0-or-later",
"LicenseRef-scancode-other-copyleft",
"LicenseRef-scancode-proprietary-license",
"GPL-2.0-only",
"LicenseRef-scancode-commercial-license",
"LicenseRef-scancode-other-permissive"
] |
permissive
|
thezakman/CTF-Heaven
|
53fcb4a72afa821ad05d8cc3b309fb388f958163
|
4b52a2178922f1502ab00fa8fc156d35e1dc653f
|
refs/heads/master
| 2023-04-05T18:20:54.680378
| 2023-03-21T13:47:45
| 2023-03-21T13:47:45
| 167,290,879
| 182
| 24
|
Unlicense
| 2022-11-29T21:41:30
| 2019-01-24T02:44:24
|
Python
|
UTF-8
|
Python
| false
| false
| 1,126
|
py
|
#!/usr/bin/env python
"""
Copyright (c) 2006-2019 sqlmap developers (http://sqlmap.org/)
See the file 'LICENSE' for copying permission
"""
import re
from lib.core.enums import PRIORITY
__priority__ = PRIORITY.HIGHEST
def dependencies():
pass
def tamper(payload, **kwargs):
"""
Replaces greater than operator ('>') with 'LEAST' counterpart
Tested against:
* MySQL 4, 5.0 and 5.5
* Oracle 10g
* PostgreSQL 8.3, 8.4, 9.0
Notes:
* Useful to bypass weak and bespoke web application firewalls that
filter the greater than character
* The LEAST clause is a widespread SQL command. Hence, this
tamper script should work against majority of databases
>>> tamper('1 AND A > B')
'1 AND LEAST(A,B+1)=B+1'
"""
retVal = payload
if payload:
match = re.search(r"(?i)(\b(AND|OR)\b\s+)([^>]+?)\s*>\s*(\w+|'[^']+')", payload)
if match:
_ = "%sLEAST(%s,%s+1)=%s+1" % (match.group(1), match.group(3), match.group(4), match.group(4))
retVal = retVal.replace(match.group(0), _)
return retVal
|
[
"thezakman@ctf-br.org"
] |
thezakman@ctf-br.org
|
788ecb8dfd993ef9d68c1c979145bef4be44c7a1
|
516dea668ccdc13397fd140f9474939fa2d7ac10
|
/enterprisebanking/middlewares.py
|
ad1d6a91a6ff2f6a7afebb8c4d5c122ae4ea0f71
|
[] |
no_license
|
daniel-kanchev/enterprisebanking
|
08f1162647a0820aafa5a939e64c1cceb7844977
|
bdb7bc4676419d7dcfe47ca8e817774ad031b585
|
refs/heads/main
| 2023-04-09T19:29:30.892047
| 2021-04-07T08:10:15
| 2021-04-07T08:10:15
| 355,463,635
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,670
|
py
|
# Define here the models for your spider middleware
#
# See documentation in:
# https://docs.scrapy.org/en/latest/topics/spider-middleware.html
from scrapy import signals
# useful for handling different item types with a single interface
from itemadapter import is_item, ItemAdapter
class enterprisebankingSpiderMiddleware:
# 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, 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 Request 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 enterprisebankingDownloaderMiddleware:
# 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)
|
[
"daniel.kanchev@adata.pro"
] |
daniel.kanchev@adata.pro
|
a8691c22467753872cc6ea65d244c12c491dc815
|
9743d5fd24822f79c156ad112229e25adb9ed6f6
|
/xai/brain/wordbase/nouns/_nationality.py
|
4e1dcbd9aa26fd3af3fbdc1264cb9f070b10fdb7
|
[
"MIT"
] |
permissive
|
cash2one/xai
|
de7adad1758f50dd6786bf0111e71a903f039b64
|
e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6
|
refs/heads/master
| 2021-01-19T12:33:54.964379
| 2017-01-28T02:00:50
| 2017-01-28T02:00:50
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 413
|
py
|
#calss header
class _NATIONALITY():
def __init__(self,):
self.name = "NATIONALITY"
self.definitions = [u'the official right to belong to a particular country: ', u'a group of people of the same race, religion, traditions, etc.: ']
self.parents = []
self.childen = []
self.properties = []
self.jsondata = {}
self.specie = 'nouns'
def run(self, obj1 = [], obj2 = []):
return self.jsondata
|
[
"xingwang1991@gmail.com"
] |
xingwang1991@gmail.com
|
8fb5e452de9da869a55ccca9cd00839bdadeeeab
|
3bfa43cd86d1fb3780f594c181debc65708af2b8
|
/algorithms/sort/heap_sort.py
|
0f1953ff4b5ac7e3fd902dd4f15744131c3cc8bf
|
[] |
no_license
|
ninjaboynaru/my-python-demo
|
2fdb6e75c88e07519d91ee8b0e650fed4a2f9a1d
|
d679a06a72e6dc18aed95c7e79e25de87e9c18c2
|
refs/heads/master
| 2022-11-06T14:05:14.848259
| 2020-06-21T20:10:05
| 2020-06-21T20:10:05
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,610
|
py
|
"""
<https://docs.python.org/3/library/heapq.html>
<https://www.youtube.com/watch?v=AEAmgbls8TM&feature=youtu.be>
Steps:
1. Put every item in the list into a heap
2. Each step get the smallest item from the heap, put the smallest into
a new list
3. Repeat until the heap is empty
```python
from heapq import heappush, heappop
This is the simple version with python module
def heap_sort(lst):
h = []
for val in lst:
heappush(h, val)
return [heappop(h) for i in range(len(h))]
```
There is also inplace heap sort
Steps:
1. Heapification (Bottom-up heapify the array)
1. Sink nodes in reverse order, sink(k)
2. After sinking, guaranteed that tree rooted at position k is a heap
2. Delete the head of the heap, delete the last item from the heap, swap
the last item in the root, and sink(0)
Time complexity: O(N log(N))
Space complexity: O(1)
The definition of sink(k):
Steps:
1. If k-th item is larger than one of its child, swap it with its child.
the children of k-th item is the (2*k+1) and (2*k+2).
(if the item is larger than both of the children, swap with the smaller one)
2. Repeat this until the end of the heap array.
Example:
3, 0, 1, 7, 9, 2
Heapifiy:
9
7 2
3 0 1
Delete head of heap, and sink(0):
7
3 2
1 0
Delete head of heap, and sink(0):
3
1 2
0
Delete head of heap, and sink(0):
2
1 0
Delete head of heap, and sink(0):
1
0
Delete head of heap, and sink(0):
0
"""
def heap_sort(lst):
def sink(start, end):
""" MaxHeap sink.
If lst[start] is smaller than its children, sink down till the end.
"""
left = 2*start + 1
right = 2*start + 2
swap_pos = None
if left > end:
return
if right > end or lst[left] > lst[right]:
swap_pos = left
else:
swap_pos = right
if swap_pos:
temp = lst[start]
lst[start] = lst[swap_pos]
lst[swap_pos] = temp
sink(swap_pos, end)
# Bottom-up heapify the array
for k in range(len(lst)-1, -1, -1):
sink(k, len(lst)-1)
# print(lst)
# Delete the head of the heap, delete the last item from the heap, swap
# the last item in the root, and sink(0)
for end in range(len(lst) - 1, 0, -1):
first = lst[0]
lst[0] = lst[end]
lst[end] = first
sink(0, end-1)
# print(lst)
if __name__ == "__main__":
lst = [3, 0, 1, 7, 9, 2]
heap_sort(lst)
print(lst)
|
[
"wangxin19930411@163.com"
] |
wangxin19930411@163.com
|
265d01952ab7506e909f20767daaeac5d52864e4
|
4ce2cff60ddbb9a3b6fc2850187c86f866091b13
|
/tfrecords/src/wai/tfrecords/object_detection/dataset_tools/create_oid_tf_record.py
|
271fd0aac175d399dda9b528a9a311145f48cfc1
|
[
"MIT",
"Apache-2.0"
] |
permissive
|
8176135/tensorflow
|
18cb8a0432ab2a0ea5bacd03309e647f39cb9dd0
|
2c3b4b1d66a80537f3e277d75ec1d4b43e894bf1
|
refs/heads/master
| 2020-11-26T05:00:56.213093
| 2019-12-19T08:13:44
| 2019-12-19T08:13:44
| 228,970,478
| 0
| 0
| null | 2019-12-19T03:51:38
| 2019-12-19T03:51:37
| null |
UTF-8
|
Python
| false
| false
| 5,240
|
py
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Creates TFRecords of Open Images dataset for object detection.
Example usage:
python object_detection/dataset_tools/create_oid_tf_record.py \
--input_box_annotations_csv=/path/to/input/annotations-human-bbox.csv \
--input_image_label_annotations_csv=/path/to/input/annotations-label.csv \
--input_images_directory=/path/to/input/image_pixels_directory \
--input_label_map=/path/to/input/labels_bbox_545.labelmap \
--output_tf_record_path_prefix=/path/to/output/prefix.tfrecord
CSVs with bounding box annotations and image metadata (including the image URLs)
can be downloaded from the Open Images GitHub repository:
https://github.com/openimages/dataset
This script will include every image found in the input_images_directory in the
output TFRecord, even if the image has no corresponding bounding box annotations
in the input_annotations_csv. If input_image_label_annotations_csv is specified,
it will add image-level labels as well. Note that the information of whether a
label is positivelly or negativelly verified is NOT added to tfrecord.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import contextlib2
import pandas as pd
import tensorflow as tf
from wai.tfrecords.object_detection.dataset_tools import oid_tfrecord_creation
from wai.tfrecords.object_detection.dataset_tools import tf_record_creation_util
from wai.tfrecords.object_detection.utils import label_map_util
tf.flags.DEFINE_string('input_box_annotations_csv', None,
'Path to CSV containing image bounding box annotations')
tf.flags.DEFINE_string('input_images_directory', None,
'Directory containing the image pixels '
'downloaded from the OpenImages GitHub repository.')
tf.flags.DEFINE_string('input_image_label_annotations_csv', None,
'Path to CSV containing image-level labels annotations')
tf.flags.DEFINE_string('input_label_map', None, 'Path to the label map proto')
tf.flags.DEFINE_string(
'output_tf_record_path_prefix', None,
'Path to the output TFRecord. The shard index and the number of shards '
'will be appended for each output shard.')
tf.flags.DEFINE_integer('num_shards', 100, 'Number of TFRecord shards')
FLAGS = tf.flags.FLAGS
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
required_flags = [
'input_box_annotations_csv', 'input_images_directory', 'input_label_map',
'output_tf_record_path_prefix'
]
for flag_name in required_flags:
if not getattr(FLAGS, flag_name):
raise ValueError('Flag --{} is required'.format(flag_name))
label_map = label_map_util.get_label_map_dict(FLAGS.input_label_map)
all_box_annotations = pd.read_csv(FLAGS.input_box_annotations_csv)
if FLAGS.input_image_label_annotations_csv:
all_label_annotations = pd.read_csv(FLAGS.input_image_label_annotations_csv)
all_label_annotations.rename(
columns={'Confidence': 'ConfidenceImageLabel'}, inplace=True)
else:
all_label_annotations = None
all_images = tf.gfile.Glob(
os.path.join(FLAGS.input_images_directory, '*.jpg'))
all_image_ids = [os.path.splitext(os.path.basename(v))[0] for v in all_images]
all_image_ids = pd.DataFrame({'ImageID': all_image_ids})
all_annotations = pd.concat(
[all_box_annotations, all_image_ids, all_label_annotations])
tf.logging.log(tf.logging.INFO, 'Found %d images...', len(all_image_ids))
with contextlib2.ExitStack() as tf_record_close_stack:
output_tfrecords = tf_record_creation_util.open_sharded_output_tfrecords(
tf_record_close_stack, FLAGS.output_tf_record_path_prefix,
FLAGS.num_shards)
for counter, image_data in enumerate(all_annotations.groupby('ImageID')):
tf.logging.log_every_n(tf.logging.INFO, 'Processed %d images...', 1000,
counter)
image_id, image_annotations = image_data
# In OID image file names are formed by appending ".jpg" to the image ID.
image_path = os.path.join(FLAGS.input_images_directory, image_id + '.jpg')
with tf.gfile.Open(image_path) as image_file:
encoded_image = image_file.read()
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
image_annotations, label_map, encoded_image)
if tf_example:
shard_idx = int(image_id, 16) % FLAGS.num_shards
output_tfrecords[shard_idx].write(tf_example.SerializeToString())
if __name__ == '__main__':
tf.app.run()
|
[
"coreytsterling@gmail.com"
] |
coreytsterling@gmail.com
|
8da1f2b67b46206e3835fdfee41f7365ac844f46
|
577f03954ec69ed82eaea32c62c8eba9ba6a01c1
|
/py/testdir_ec2_only/test_parse_covtype20x_s3.py
|
d6207e11b1f8763b5cd9fdd1466e72b472d7c03f
|
[
"Apache-2.0"
] |
permissive
|
ledell/h2o
|
21032d784a1a4bb3fe8b67c9299f49c25da8146e
|
34e271760b70fe6f384e106d84f18c7f0adb8210
|
refs/heads/master
| 2020-02-26T13:53:01.395087
| 2014-12-29T04:14:29
| 2014-12-29T04:14:29
| 24,823,632
| 1
| 2
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,962
|
py
|
import unittest, sys, random, time
sys.path.extend(['.','..','../..','py'])
import h2o, h2o_cmd, h2o_browse as h2b, h2o_import as h2i
class Basic(unittest.TestCase):
def tearDown(self):
h2o.check_sandbox_for_errors()
@classmethod
def setUpClass(cls):
print "Will build clouds with incrementing heap sizes and import folder/parse"
@classmethod
def tearDownClass(cls):
# the node state is gone when we tear down the cloud, so pass the ignore here also.
h2o.tear_down_cloud(sandboxIgnoreErrors=True)
def test_parse_covtype20x_loop_s3(self):
bucket = 'home-0xdiag-datasets'
importFolderPath = "standard"
csvFilename = "covtype20x.data"
csvPathname = importFolderPath + "/" + csvFilename
timeoutSecs = 500
trialMax = 3
for tryHeap in [4,12]:
print "\n", tryHeap,"GB heap, 1 jvm per host, import folder,", \
"then parse 'covtype20x.data'"
h2o.init(java_heap_GB=tryHeap)
# don't raise exception if we find something bad in h2o stdout/stderr?
h2o.nodes[0].sandboxIgnoreErrors = True
for trial in range(trialMax):
hex_key = csvFilename + ".hex"
start = time.time()
parseResult = h2i.import_parse(bucket=bucket, path=csvPathname, schema='s3', hex_key=hex_key,
timeoutSecs=timeoutSecs, retryDelaySecs=10, pollTimeoutSecs=60)
elapsed = time.time() - start
print "parse result:", parseResult['destination_key']
print "Trial #", trial, "completed in", elapsed, "seconds.", \
"%d pct. of timeout" % ((elapsed*100)/timeoutSecs)
removeKeyResult = h2o.nodes[0].remove_key(key=hex_key)
h2o.tear_down_cloud()
# sticky ports? wait a bit.
time.sleep(5)
if __name__ == '__main__':
h2o.unit_main()
|
[
"kevin@0xdata.com"
] |
kevin@0xdata.com
|
5d0a2f7e05ee7c3731f9b7550e0d5d9f8625cb88
|
78c08cd3ef66836b44373280a333c040ccb99605
|
/ostap/fitting/tests/test_fitting_convolution.py
|
3f980fbf093211f18849b15254d2f25697d8e7a7
|
[
"BSD-3-Clause"
] |
permissive
|
Pro100Tema/ostap
|
11ccbc546068e65aacac5ddd646c7550086140a7
|
1765304fce43714e1f51dfe03be0daa5aa5d490f
|
refs/heads/master
| 2023-02-24T08:46:07.532663
| 2020-01-27T13:46:30
| 2020-01-27T13:46:30
| 200,378,716
| 0
| 0
|
BSD-3-Clause
| 2019-08-03T13:28:08
| 2019-08-03T13:28:07
| null |
UTF-8
|
Python
| false
| false
| 3,426
|
py
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# =============================================================================
# Copyright (c) Ostap developers.
# =============================================================================
# @file test_fitting_convolution.py
# Test module for ostap/fitting/convolution.py
# =============================================================================
""" Test module for ostap/fitting/convolution.py
"""
# =============================================================================
__author__ = "Ostap developers"
__all__ = () ## nothing to import
# =============================================================================
import ROOT, random
import ostap.fitting.roofit
import ostap.fitting.models as Models
from ostap.core.core import cpp, VE, dsID
from ostap.logger.utils import rooSilent
# =============================================================================
# logging
# =============================================================================
from ostap.logger.logger import getLogger
if '__main__' == __name__ or '__builtin__' == __name__ :
logger = getLogger ( 'test_fitting_convolution' )
else :
logger = getLogger ( __name__ )
# =============================================================================
## make
x = ROOT.RooRealVar ( 'x', 'test' , 1 , 10 )
models = set()
# =============================================================================
## Asymmetric Laplace
# =============================================================================
def test_laplace():
logger.info ('Test Asymmetric Laplace shape' )
laplace = Models.AsymmetricLaplace_pdf ( name = 'AL',
xvar = x ,
mean = 5 ,
slope = 1 )
from ostap.fitting.convolution import Convolution_pdf
## constant resolution
laplace_1 = Convolution_pdf ( name = 'L1' , pdf = laplace, resolution = 0.75 )
## resolution PDF
from ostap.fitting.resolution import ResoApo2
rAp = ResoApo2 ( 'A' , x , 0.75 )
## resolution as PDF
laplace_2 = Convolution_pdf ( name = 'L2' , pdf = laplace, resolution = rAp )
laplace.draw( silent = True )
laplace_1.draw( silent = True )
laplace_2.draw()
models.add ( laplace )
models.add ( laplace_1 )
models.add ( laplace_2 )
# =============================================================================
## check that everything is serializable
# =============================================================================
def test_db() :
logger.info('Saving all objects into DBASE')
import ostap.io.zipshelve as DBASE
from ostap.utils.timing import timing
with timing( name = 'Save everything to DBASE'), DBASE.tmpdb() as db :
db['models' ] = models
db.ls()
# =============================================================================
if '__main__' == __name__ :
test_laplace () ## Laplace-function + background
## check finally that everything is serializeable:
test_db ()
# =============================================================================
# The END
# =============================================================================
|
[
"Ivan.Belyaev@cern.ch"
] |
Ivan.Belyaev@cern.ch
|
f66e5ca5bccba463ba1c7ea0e178e85c4982a93f
|
3e5ecad4d2f681f2f4f749109cc99deea1209ea4
|
/Dacon/solar1/test04_solar9.py
|
0f9e499e4f86263fff68de5a667aeda9b729cb92
|
[] |
no_license
|
SunghoonSeok/Study
|
f41ede390079037b2090e6df20e5fb38f2e59b8f
|
50f02b9c9bac904cd4f6923b41efabe524ff3d8a
|
refs/heads/master
| 2023-06-18T06:47:55.545323
| 2021-07-05T00:47:55
| 2021-07-05T00:47:55
| 324,866,762
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,798
|
py
|
# 7일의 데이터로 2일의 target값 구하기
# 시간별로 데이터를 나눠서 훈련
import numpy as np
import pandas as pd
import tensorflow.keras.backend as K
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Dense, Input, LSTM, Dropout, Conv1D, Flatten, MaxPooling1D, GRU, SimpleRNN
from tensorflow.keras.backend import mean, maximum
# 필요 함수 정의
# GHI추가
def Add_features(data):
data['cos'] = np.cos(np.pi/2 - np.abs(data['Hour']%12 - 6)/6*np.pi/2)
data.insert(1,'GHI',data['DNI']*data['cos']+data['DHI'])
data.drop(['cos'], axis= 1, inplace = True)
return data
# 데이터 몇일씩 자르는 함수
def split_x(data, size):
x = []
for i in range(len(data)-size+1):
subset = data[i : (i+size)]
x.append([item for item in subset])
print(type(x))
return np.array(x)
# quantile loss 관련 함수
def quantile_loss(q, y_true, y_pred):
err = (y_true - y_pred)
return K.mean(K.maximum(q*err, (q-1)*err), axis=-1)
quantiles = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
# 데이터 컬럼을 7개만 쓰겠다
def preprocess_data(data):
data = Add_features(data)
temp = data.copy()
temp = temp[['GHI', 'DHI', 'DNI', 'WS', 'RH', 'T','TARGET']]
return temp.iloc[:, :]
# 모델, Conv1D사용
def DaconModel():
model = Sequential()
model.add(Conv1D(256,2, padding='same', input_shape=(7, 7),activation='relu'))
model.add(Conv1D(128,2, padding='same',activation='relu'))
model.add(Conv1D(64,2, padding='same',activation='relu'))
model.add(Conv1D(32,2, padding='same',activation='relu'))
model.add(Flatten())
model.add(Dense(64,activation='relu'))
model.add(Dense(32,activation='relu'))
model.add(Dense(16,activation='relu'))
model.add(Dense(8,activation='relu'))
model.add(Dense(1))
return model
# optimizer 불러오기
from tensorflow.keras.optimizers import Adam, Adadelta, Adamax, Adagrad
from tensorflow.keras.optimizers import RMSprop, SGD, Nadam
# 컴파일 훈련 함수, optimizer 변수처리하여 lr=0.002부터 줄여나가도록 한다
# lr을 for문 밖에 두면 초기화가 되지 않으니 명심할것
# 총 48(시간수)*9(quantile)*2(Day7,8)개의 체크포인트모델이 생성됨
def only_compile(a, x_train, y_train, x_val, y_val):
for q in quantiles:
print('Day'+str(i)+' ' +str(q)+'실행중입니다.')
model = DaconModel()
optimizer = Adam(lr=0.002)
model.compile(loss = lambda y_true,y_pred: quantile_loss(q,y_true,y_pred), optimizer = optimizer, metrics = [lambda y,y_pred: quantile_loss(q,y,y_pred)])
filepath = f'c:/data/test/solar/checkpoint/solar_checkpoint5_time{i}-{a}-{q}.hdf5'
cp = ModelCheckpoint(filepath, save_best_only=True, monitor = 'val_loss')
model.fit(x_train,y_train,epochs = epochs, batch_size = bs, validation_data = (x_val,y_val),callbacks = [es,lr,cp])
return
# 1. 데이터
train = pd.read_csv('c:/data/test/solar/train/train.csv')
sub = pd.read_csv('c:/data/test/solar/sample_submission.csv')
# 데이터 npy로 바꾸기
data = train.values
print(data.shape)
np.save('c:/data/test/solar/train.npy', arr=data)
data =np.load('c:/data/test/solar/train.npy')
# 전치를 활용한 데이터 시간별 묶음
data = data.reshape(1095, 48, 9)
data = np.transpose(data, axes=(1,0,2))
print(data.shape)
data = data.reshape(48*1095,9)
df = train.copy()
df.loc[:,:] = data
df.to_csv('c:/data/test/solar/train_trans.csv', index=False)
# 시간별 모델 따로 생성
train_trans = pd.read_csv('c:/data/test/solar/train_trans.csv')
train_data = preprocess_data(train_trans) # (52560,7)
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
es = EarlyStopping(monitor = 'val_loss', patience = 15)
lr = ReduceLROnPlateau(monitor = 'val_loss', patience = 5, factor = 0.5, verbose = 1)
# for문으로 시간, quantile, day7,8 을 구분하여 체크포인트 생성
for i in range(48):
train_sort = train_data[1095*(i):1095*(i+1)]
train_sort = np.array(train_sort)
y = train_sort[7:,-1] #(1088,)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(train_sort)
train_sort = scaler.transform(train_sort)
x = split_x(train_sort, 7)
x = x[:-2,:] #(1087,7,7)
y1 = y[:-1] #(1087,)
y2 = y[1:] #(1087,)
from sklearn.model_selection import train_test_split
x_train, x_val, y1_train, y1_val, y2_train, y2_val = train_test_split(x, y1, y2, train_size=0.8, shuffle=True, random_state=32)
epochs = 1000
bs = 32
only_compile(0, x_train, y1_train, x_val, y1_val)
only_compile(1, x_train, y2_train, x_val, y2_val)
|
[
"76455292+SunghoonSeok@users.noreply.github.com"
] |
76455292+SunghoonSeok@users.noreply.github.com
|
f1c1d1272813db29b692fe04bc813b6a679526fc
|
34599596e145555fde0d4264a1d222f951f49051
|
/pcat2py/class/20dbcc2a-5cc5-11e4-af55-00155d01fe08.py
|
b39c4aee05264d664cba5c47aa38bafddd842eb2
|
[
"MIT"
] |
permissive
|
phnomcobra/PCAT2PY
|
dc2fcbee142ce442e53da08476bfe4e68619346d
|
937c3b365cdc5ac69b78f59070be0a21bdb53db0
|
refs/heads/master
| 2021-01-11T02:23:30.669168
| 2018-02-13T17:04:03
| 2018-02-13T17:04:03
| 70,970,520
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 961
|
py
|
#!/usr/bin/python
################################################################################
# 20dbcc2a-5cc5-11e4-af55-00155d01fe08
#
# Justin Dierking
# justindierking@hardbitsolutions.com
# phnomcobra@gmail.com
#
# 10/24/2014 Original Construction
################################################################################
class Finding:
def __init__(self):
self.output = []
self.is_compliant = False
self.uuid = "20dbcc2a-5cc5-11e4-af55-00155d01fe08"
def check(self, cli):
# Initialize Compliance
self.is_compliant = False
# Get Auditpol Value
enabled = cli.get_auditpol(r'Special Logon', 'Success')
# Output Lines
self.output = [r'Special Logon', ('Success=' + str(enabled))]
if enabled:
self.is_compliant = True
return self.is_compliant
def fix(self, cli):
cli.set_auditpol(r'Special Logon', 'Success', True)
|
[
"phnomcobra@gmail.com"
] |
phnomcobra@gmail.com
|
390ee336f83088e3f9b8609b7c854dfa3f4ea232
|
2e5e990955957cf04367ef6eedd62e6add7ccdc7
|
/oms_cms/backend/api/v2/social_networks/serializers.py
|
24a77bc22571a871c6dfb51890fd85f061a40858
|
[
"BSD-3-Clause"
] |
permissive
|
RomanYarovoi/oms_cms
|
3dfcd19ff03b351dc754f73f4a0d8a9986cf28ec
|
49c6789242d7a35e81f4f208c04b18fb79249be7
|
refs/heads/master
| 2021-07-06T18:49:51.021820
| 2020-10-15T05:52:55
| 2020-10-15T05:52:55
| 196,556,814
| 0
| 0
|
BSD-3-Clause
| 2020-10-15T05:52:57
| 2019-07-12T10:07:29
|
JavaScript
|
UTF-8
|
Python
| false
| false
| 312
|
py
|
from rest_framework import serializers
from oms_cms.backend.social_networks.models import SocialNetworks
class SocialNetworksSerializer(serializers.ModelSerializer):
"""Сериализация социальных сетей"""
class Meta:
model = SocialNetworks
fields = '__all__'
|
[
"arsavit@gmail.com"
] |
arsavit@gmail.com
|
2cac3d08334c146dd3333f471c8ee1fa6546c71d
|
bc9c1a4da0d5bbf8d4721ee7ca5163f488e88a57
|
/research/urls.py
|
fe0aeb667e57278015b49196ad14403f92bec46d
|
[] |
no_license
|
mit-teaching-systems-lab/newelk
|
77f43666f3c70be4c31fdfc6d4a6e9c629c71656
|
a2e6665bfcf9e2ea12fde45319027ee4a848f93c
|
refs/heads/master
| 2022-12-13T20:50:17.632513
| 2019-10-03T19:02:01
| 2019-10-03T19:02:01
| 132,154,880
| 0
| 4
| null | 2022-12-08T01:26:56
| 2018-05-04T15:04:20
|
Python
|
UTF-8
|
Python
| false
| false
| 222
|
py
|
from django.urls import path
from . import views
urlpatterns = [
# path('chatlogs/', views.streaming_chat_csv),
# path('answerlogs/', views.streaming_answers_view),
path("feedback/", views.toggle_feedback)
]
|
[
"bhanks@mit.edu"
] |
bhanks@mit.edu
|
64ad76f77783d4b8a4cb1b9d87b673ea62470bf1
|
f566dfc5ce189d30696b9bf8b7e8bf9b1ef45614
|
/Example/DQN_SimpleMaze/DoubleDQN_SimpleMazeTwoD.py
|
a8615b896bcd6023b12a714b7533a963e26b7691
|
[] |
no_license
|
yangyutu/DeepReinforcementLearning-PyTorch
|
3dac4ad67fa3a6301d65ca5c63532f2a278e21d7
|
7af59cb883e24429d42a228584cfc96c42f6d35b
|
refs/heads/master
| 2022-08-16T13:46:30.748383
| 2022-07-30T05:47:47
| 2022-07-30T05:47:47
| 169,829,723
| 12
| 6
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,382
|
py
|
from Agents.DQN.DQN import DQNAgent
from Agents.Core.MLPNet import MultiLayerNetRegression
import json
from torch import optim
from copy import deepcopy
from Env.CustomEnv.SimpleMazeTwoD import SimpleMazeTwoD
import numpy as np
import matplotlib.pyplot as plt
import torch
torch.manual_seed(1)
def plotPolicy(policy, nbActions):
idx, idy = np.where(policy >=0)
action = policy[idx,idy]
plt.scatter(idx, idy, c = action, marker='s', s = 10)
# for i in range(nbActions):
# idx, idy = np.where(policy == i)
# plt.plot(idx,idy, )
# first construct the neutral network
config = dict()
mapName = 'map.txt'
config['trainStep'] = 1000
config['epsThreshold'] = 0.1
config['targetNetUpdateStep'] = 100
config['memoryCapacity'] = 2000
config['trainBatchSize'] = 32
config['gamma'] = 0.9
config['learningRate'] = 0.003
config['netGradClip'] = 1
config['logFlag'] = True
config['logFileName'] = 'SimpleMazeLog/DoubleQtraj' + mapName
config['logFrequency'] = 50
config['netUpdateOption'] = 'doubleQ'
env = SimpleMazeTwoD(mapName)
N_S = env.stateDim
N_A = env.nbActions
netParameter = dict()
netParameter['n_feature'] = N_S
netParameter['n_hidden'] = [100]
netParameter['n_output'] = N_A
policyNet = MultiLayerNetRegression(netParameter['n_feature'],
netParameter['n_hidden'],
netParameter['n_output'])
print(policyNet.state_dict())
targetNet = deepcopy(policyNet)
optimizer = optim.Adam(policyNet.parameters(), lr=config['learningRate'])
agent = DQNAgent(policyNet, targetNet, env, optimizer, torch.nn.MSELoss() ,N_S, N_A, config=config)
policy = deepcopy(env.map)
for i in range(policy.shape[0]):
for j in range(policy.shape[1]):
if env.map[i, j] == 0:
policy[i, j] = -1
else:
policy[i, j] = agent.getPolicy(np.array([i, j]))
np.savetxt('DoubleQSimpleMazePolicyBeforeTrain' + mapName + '.txt', policy, fmt='%d', delimiter='\t')
plotPolicy(policy, N_A)
agent.train()
policy = deepcopy(env.map)
for i in range(policy.shape[0]):
for j in range(policy.shape[1]):
if env.map[i, j] == 0:
policy[i, j] = -1
else:
policy[i, j] = agent.getPolicy(np.array([i, j]))
np.savetxt('DoubleQSimpleMazePolicyAfterTrain' + mapName +'.txt', policy, fmt='%d', delimiter='\t')
plotPolicy(policy, N_A)
|
[
"yangyutu123@gmail.com"
] |
yangyutu123@gmail.com
|
f92d14e56e3f2106526540e9015138bc89fc3d77
|
c12008fee6b319ccc683956d0a171a00e12debb0
|
/everyday/e191020.py
|
53e6428caf621fada6c4bfabfffe7d54a1250dd8
|
[] |
no_license
|
yrnana/algorithm
|
70c7b34c82b15598494103bdb49b4aefc7c53548
|
783e4f9a45baf8d6b5900e442d32c2b6f73487d0
|
refs/heads/master
| 2022-04-13T23:50:53.914225
| 2020-04-01T12:41:14
| 2020-04-01T12:41:14
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 295
|
py
|
def solution(arr):
l = len(arr)
n = 0
for i in range(l):
if arr[i] != 0:
swap(arr, i, n)
n += 1
return arr
def swap(arr, i, j):
tmp = arr[i]
arr[i] = arr[j]
arr[j] = tmp
print(solution([0, 5, 0, 3, -1]))
print(solution([3, 0, 3]))
|
[
"nyryn0945@gmail.com"
] |
nyryn0945@gmail.com
|
bf880139591dc7c773d8e6bf7be78b1c793a73ef
|
364b36d699d0a6b5ddeb43ecc6f1123fde4eb051
|
/_downloads_1ed/fig_poisson_continuous.py
|
686b96403de5b92c73a2308049b03cfd324a149b
|
[] |
no_license
|
astroML/astroml.github.com
|
eae3bfd93ee2f8bc8b5129e98dadf815310ee0ca
|
70f96d04dfabcd5528978b69c217d3a9a8bc370b
|
refs/heads/master
| 2022-02-27T15:31:29.560052
| 2022-02-08T21:00:35
| 2022-02-08T21:00:35
| 5,871,703
| 2
| 5
| null | 2022-02-08T21:00:36
| 2012-09-19T12:55:23
|
HTML
|
UTF-8
|
Python
| false
| false
| 3,102
|
py
|
"""
Unbinned Poisson Data
---------------------
Figure 5.14
Regression of unbinned data. The distribution of N = 500 data points is shown
in the left panel; the true pdf is shown by the solid curve. Note that although
the data are binned in the left panel for visualization purposes, the analysis
is performed on the unbinned data. The right panel shows the likelihood for the
slope a (eq. 5.88) for three different sample sizes. The input value is
indicated by the vertical dotted line.
"""
# Author: Jake VanderPlas
# License: BSD
# The figure produced by this code is published in the textbook
# "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
# For more information, see http://astroML.github.com
# To report a bug or issue, use the following forum:
# https://groups.google.com/forum/#!forum/astroml-general
import numpy as np
from matplotlib import pyplot as plt
from astroML.stats.random import linear
#----------------------------------------------------------------------
# This function adjusts matplotlib settings for a uniform feel in the textbook.
# Note that with usetex=True, fonts are rendered with LaTeX. This may
# result in an error if LaTeX is not installed on your system. In that case,
# you can set usetex to False.
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=True)
def linprob_logL(x, a, xmin, xmax):
x = x.ravel()
a = a.reshape(a.shape + (1,))
mu = 0.5 * (xmin + xmax)
W = (xmax - xmin)
return np.sum(np.log(a * (x - mu) + 1. / W), -1)
#----------------------------------------------------------------------
# Draw the data from the linear distribution
np.random.seed(0)
N = 500
a_true = 0.01
xmin = 0.0
xmax = 10.0
lin_dist = linear(xmin, xmax, a_true)
data = lin_dist.rvs(N)
x = np.linspace(xmin - 1, xmax + 1, 1000)
px = lin_dist.pdf(x)
#------------------------------------------------------------
# Plot the results
fig = plt.figure(figsize=(5, 2.5))
fig.subplots_adjust(left=0.12, right=0.95, wspace=0.28,
bottom=0.15, top=0.9)
# left panel: plot the model and a histogram of the data
ax1 = fig.add_subplot(121)
ax1.hist(data, bins=np.linspace(0, 10, 11), normed=True,
histtype='stepfilled', fc='gray', alpha=0.5)
ax1.plot(x, px, '-k')
ax1.set_xlim(-1, 11)
ax1.set_ylim(0, 0.18)
ax1.set_xlabel('$x$')
ax1.set_ylabel('$p(x)$')
# right panel: construct and plot the likelihood
ax2 = fig.add_subplot(122)
ax2.xaxis.set_major_locator(plt.MultipleLocator(0.01))
a = np.linspace(-0.01, 0.02, 1000)
Npts = (500, 100, 20)
styles = ('-k', '--b', '-.g')
for n, s in zip(Npts, styles):
logL = linprob_logL(data[:n], a, xmin, xmax)
logL = np.exp(logL - logL.max())
logL /= logL.sum() * (a[1] - a[0])
ax2.plot(a, logL, s, label=r'$\rm %i\ pts$' % n)
ax2.legend(loc=2, prop=dict(size=8))
ax2.set_xlim(-0.011, 0.02)
ax2.set_xlabel('$a$')
ax2.set_ylabel('$p(a)$')
# vertical line: in newer matplotlib versions, use ax.vlines([a_true])
ylim = ax2.get_ylim()
ax2.plot([a_true, a_true], ylim, ':k', lw=1)
ax2.set_ylim(ylim)
plt.show()
|
[
"vanderplas@astro.washington.edu"
] |
vanderplas@astro.washington.edu
|
6c16e2c8f646a76de7c95d1bce0bd8207155521e
|
5d0dd50d7f7bf55126834292140ed66306e59f10
|
/MIGRATE/msgpack_to_sql.py
|
4ce966fdef93c6b79fcabe824ec1177b571c63de
|
[] |
no_license
|
JellyWX/tracker-bot
|
32d2c8666a7c6ca0835aa94695be4ccd7fc37bb5
|
b0909c4883b0ee6e0300a163e94ea0d69dffa062
|
refs/heads/master
| 2021-05-02T16:14:11.638292
| 2018-04-26T19:47:50
| 2018-04-26T19:47:50
| 120,670,416
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 592
|
py
|
import msgpack
import sqlite3
with open('../DATA/USER_DATA', 'rb') as f:
data = msgpack.unpack(f, encoding='utf8')
connection = sqlite3.connect('../DATA/data.db')
cursor = connection.cursor()
for user, values in data.items():
command = '''CREATE TABLE u{user} (
game VARCHAR(50),
time INT
)
'''.format(user=user)
cursor.execute(command)
for game, time in values.items():
command = '''INSERT INTO u{user} (game, time)
VALUES (?, ?);'''.format(user=user)
cursor.execute(command, (game, time))
connection.commit()
connection.close()
|
[
"judewrs@gmail.com"
] |
judewrs@gmail.com
|
d7919c38e0ac4b378ccf1771060a7670a3744ca6
|
ece0d321e48f182832252b23db1df0c21b78f20c
|
/engine/2.80/scripts/freestyle/styles/apriori_density.py
|
1de2c4c033457e302c229c3c7014b55c0b8010d7
|
[
"GPL-3.0-only",
"Font-exception-2.0",
"GPL-3.0-or-later",
"Apache-2.0",
"LicenseRef-scancode-public-domain",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-public-domain-disclaimer",
"Bitstream-Vera",
"LicenseRef-scancode-blender-2010",
"LGPL-2.1-or-later",
"GPL-2.0-or-later",
"GPL-2.0-only",
"LGPL-2.0-only",
"PSF-2.0",
"LicenseRef-scancode-free-unknown",
"LicenseRef-scancode-proprietary-license",
"GPL-1.0-or-later",
"BSD-2-Clause",
"Unlicense"
] |
permissive
|
byteinc/Phasor
|
47d4e48a52fa562dfa1a2dbe493f8ec9e94625b9
|
f7d23a489c2b4bcc3c1961ac955926484ff8b8d9
|
refs/heads/master
| 2022-10-25T17:05:01.585032
| 2019-03-16T19:24:22
| 2019-03-16T19:24:22
| 175,723,233
| 3
| 1
|
Unlicense
| 2022-10-21T07:02:37
| 2019-03-15T00:58:08
|
Python
|
UTF-8
|
Python
| false
| false
| 1,743
|
py
|
# ##### BEGIN GPL LICENSE BLOCK #####
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# ##### END GPL LICENSE BLOCK #####
# Filename : apriori_density.py
# Author : Stephane Grabli
# Date : 04/08/2005
# Purpose : Draws lines having a high a prior density
from freestyle.chainingiterators import ChainPredicateIterator
from freestyle.predicates import (
AndUP1D,
NotUP1D,
QuantitativeInvisibilityUP1D,
TrueBP1D,
TrueUP1D,
pyHighViewMapDensityUP1D,
)
from freestyle.shaders import (
ConstantColorShader,
ConstantThicknessShader,
)
from freestyle.types import Operators
Operators.select(AndUP1D(QuantitativeInvisibilityUP1D(0), pyHighViewMapDensityUP1D(0.1,5)))
bpred = TrueBP1D()
upred = AndUP1D(QuantitativeInvisibilityUP1D(0), pyHighViewMapDensityUP1D(0.0007,5))
Operators.bidirectional_chain(ChainPredicateIterator(upred, bpred), NotUP1D(QuantitativeInvisibilityUP1D(0)))
shaders_list = [
ConstantThicknessShader(2),
ConstantColorShader(0.0, 0.0, 0.0, 1.0)
]
Operators.create(TrueUP1D(), shaders_list)
|
[
"admin@irradiate.net"
] |
admin@irradiate.net
|
5ecff5ad5fe3286e9a8e813f3c9de2d599229c34
|
781116645c0d60de13596aac81a76c791ed0c18a
|
/kivy_garden/flower/__init__.py
|
6793aaafcc1aa355b42b381f1800e9714707bb6e
|
[
"MIT"
] |
permissive
|
matham/flower
|
503dae3446110da05ecd2a527b3459f7e1bcadb3
|
e7c71346563573197ae304ceb343bff14e54a5cd
|
refs/heads/master
| 2020-05-24T22:33:43.761720
| 2019-05-19T08:56:14
| 2019-05-19T08:56:14
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 360
|
py
|
"""
Demo flower
============
Defines the Kivy garden :class:`FlowerLabel` class which is the widget provided
by the demo flower.
"""
from kivy.uix.label import Label
__all__ = ('FlowerLabel', )
__version__ = '0.1.0.dev0'
class FlowerLabel(Label):
def __init__(self, **kwargs):
super(FlowerLabel, self).__init__(**kwargs, text='Demo flower')
|
[
"moiein2000@gmail.com"
] |
moiein2000@gmail.com
|
9d95173045444ddceac7aaebc34b8f75adf12995
|
fff26da96c4b324cdbc0315c3fdf1fe2ccbf6bf0
|
/.history/test_celegans_corrected_weights_20210615130634.py
|
a875acee9236154c606750101651e4d37fd22fd9
|
[] |
no_license
|
izzortsi/spreading-activation-networks
|
ebcd38477a4d4c6139a82b0dd7da3d79a0e3f741
|
f2cf0bf519af746f148fa7a4ea4d78d16ba6af87
|
refs/heads/dev
| 2023-06-28T03:49:34.265268
| 2021-06-15T18:07:51
| 2021-06-15T18:07:51
| 376,718,907
| 0
| 0
| null | 2021-06-15T18:07:51
| 2021-06-14T06:01:52
|
Python
|
UTF-8
|
Python
| false
| false
| 3,390
|
py
|
# %%
import graph_tool.all as gt
import numpy as np
import numpy.random as npr
# import matplotlib.colors as mplc
from matplotlib import cm
import matplotlib.colors as mplc
import os, sys
from gi.repository import Gtk, Gdk, GdkPixbuf, GObject, GLib
from plot_functions import *
# %%
def init_elegans_net():
g = gt.collection.data["celegansneural"]
g.ep.weight = g.new_ep("double")
norm_eweights = minmax(g.ep.value.a)
g.ep.weight.a = norm_eweights
del g.ep["value"]
del g.gp["description"]
del g.gp["readme"]
del g.vp["label"]
g.vp.state = g.new_vertex_property("int")
g.vp.activation = g.new_vertex_property("float")
n_vertices = g.num_vertices()
n_edges = g.num_edges()
activations = npr.normal(size=n_vertices)
activations = minmax(activations)
g.vp.state.a = np.full(n_vertices, 0)
g.vp.activation.a = activations
return g
# %%
def init_graph(g):
treemap = gt.min_spanning_tree(g)
gmst = gt.GraphView(g, efilt=treemap)
gtclos = gt.transitive_closure(gmst)
return {"g": g, "gmst": gmst, "gtc": gtclos}
def minmax(a):
a = (a - np.min(a))
return a/np.max(a)
# %%
"""
def set_graph(type="gtc")
type being either the original graph "g", the MST of it
"gmst" or the transitive closure of the MST "gtc". Defaults
to "gtc".
"""
def set_graph(type="gtc"):
g = init_elegans_net()
graphs = init_graph(g)
g = graphs["g"]
gmst = graphs["gmst"]
gtc = graphs["gtc"]
return g, gmst, gtc
# %%
# %%
####DYNAMICS PARAMETERS
SPIKE_THRESHOLD = 0.90
POTENTIAL_LOSS = 0.8
MAX_COUNT = 600
#OFFSCREEN = True
OFFSCREEN = sys.argv[1] == "offscreen" if len(sys.argv) > 1 else False
# %%
g, gmst, gtc = set_graph()
# %%
g = gmst
# %%
set(list(map(tuple, gtc.get_all_edges(151))))
# %%
count = 0
# %%
def update_state():
global count, g
spiker_activation = np.max(g.vp.activation.a)
spiker = gt.find_vertex(g, g.vp.activation, spiker_activation)[0]
nbs = g.get_out_neighbors(spiker)
nbsize = len(nbs)
if nbsize != 0:
spread_val = spiker_activation/nbsize
for nb in nbs:
w = g.ep.weight[g.edge(spiker, nb)]
g.vp.activation[nb] += spread_val*w
g.vp.activation[spiker] -= spread_val*w
else:
if g.vp.activation[spiker] >= 1:
pass
#if g.vp.activation[nb] >= SPIKE_THRESHOLD:
win.graph.regenerate_surface()
win.graph.queue_draw()
if OFFSCREEN:
pixbuf = win.get_pixbuf()
pixbuf.savev(r'./frames/san%06d.png' % count, 'png', [], [])
count += 1
if count >= MAX_COUNT:
sys.exit(0)
return True
# %%
pos = gt.sfdp_layout(g)
PLOT_PARAMS = plot_params(g, None)
if OFFSCREEN and not os.path.exists("./frames"):
os.mkdir("./frames")
# This creates a GTK+ window with the initial graph layout
if not OFFSCREEN:
win = gt.GraphWindow(g,
pos,
geometry=(720, 720),
vertex_shape="circle",
**PLOT_PARAMS,
)
else:
win = Gtk.OffscreenWindow()
win.set_default_size(720, 720)
win.graph = gt.GraphWidget(g,
pos,
vertex_shape="circle",
**PLOT_PARAMS,
)
win.add(win.graph)
# %%
cid = GLib.idle_add(update_state)
win.connect("delete_event", Gtk.main_quit)
win.show_all()
Gtk.main()
# %%
# %%
|
[
"istrozzi@matematica.ufrj.br"
] |
istrozzi@matematica.ufrj.br
|
25b61e304b936c5e84ffe57f9d196cca268179ff
|
63b864deda44120067eff632bbb4969ef56dd573
|
/object_detection/ssd/Config.py
|
f444dc728514a6492170e0eaf1c5d65542716889
|
[] |
no_license
|
lizhe960118/Deep-Learning
|
d134592c327decc1db12cbe19d9a1c85a5056086
|
7d2c4f3a0512ce4bd2f86c9f455da9866d16dc3b
|
refs/heads/master
| 2021-10-29T06:15:04.749917
| 2019-07-19T15:27:25
| 2019-07-19T15:27:25
| 152,355,392
| 5
| 2
| null | 2021-10-12T22:19:33
| 2018-10-10T03:06:44
|
Jupyter Notebook
|
UTF-8
|
Python
| false
| false
| 481
|
py
|
import os.path as osp
sk = [ 15, 30, 60, 111, 162, 213, 264 ]
feature_map = [ 38, 19, 10, 5, 3, 1 ]
steps = [ 8, 16, 32, 64, 100, 300 ]
image_size = 300
aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
MEANS = (104, 117, 123)
batch_size = 2
data_load_number_worker = 0
lr = 1e-3
momentum = 0.9
weight_decacy = 5e-4
gamma = 0.1
VOC_ROOT = osp.join('./', "VOCdevkit/")
dataset_root = VOC_ROOT
use_cuda = True
lr_steps = (80000, 100000, 120000)
max_iter = 120000
class_num = 21
|
[
"2957308424@qq.com"
] |
2957308424@qq.com
|
2235add0ce48477a2a58d68f369f8cd3ba1fbf2b
|
5ec06dab1409d790496ce082dacb321392b32fe9
|
/clients/python/generated/swaggeraemosgi/model/com_adobe_granite_frags_impl_check_http_header_flag_properties.py
|
b32110895772ddda09288d935ee3f1e98dbd4215
|
[
"Apache-2.0"
] |
permissive
|
shinesolutions/swagger-aem-osgi
|
e9d2385f44bee70e5bbdc0d577e99a9f2525266f
|
c2f6e076971d2592c1cbd3f70695c679e807396b
|
refs/heads/master
| 2022-10-29T13:07:40.422092
| 2021-04-09T07:46:03
| 2021-04-09T07:46:03
| 190,217,155
| 3
| 3
|
Apache-2.0
| 2022-10-05T03:26:20
| 2019-06-04T14:23:28
| null |
UTF-8
|
Python
| false
| false
| 7,658
|
py
|
"""
Adobe Experience Manager OSGI config (AEM) API
Swagger AEM OSGI is an OpenAPI specification for Adobe Experience Manager (AEM) OSGI Configurations API # noqa: E501
The version of the OpenAPI document: 1.0.0-pre.0
Contact: opensource@shinesolutions.com
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
import nulltype # noqa: F401
from swaggeraemosgi.model_utils import ( # noqa: F401
ApiTypeError,
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
)
def lazy_import():
from swaggeraemosgi.model.config_node_property_string import ConfigNodePropertyString
globals()['ConfigNodePropertyString'] = ConfigNodePropertyString
class ComAdobeGraniteFragsImplCheckHttpHeaderFlagProperties(ModelNormal):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
Attributes:
allowed_values (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
with a capitalized key describing the allowed value and an allowed
value. These dicts store the allowed enum values.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
discriminator_value_class_map (dict): A dict to go from the discriminator
variable value to the discriminator class name.
validations (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
that stores validations for max_length, min_length, max_items,
min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
inclusive_minimum, and regex.
additional_properties_type (tuple): A tuple of classes accepted
as additional properties values.
"""
allowed_values = {
}
validations = {
}
additional_properties_type = None
_nullable = False
@cached_property
def openapi_types():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.
"""
lazy_import()
return {
'feature_name': (ConfigNodePropertyString,), # noqa: E501
'feature_description': (ConfigNodePropertyString,), # noqa: E501
'http_header_name': (ConfigNodePropertyString,), # noqa: E501
'http_header_valuepattern': (ConfigNodePropertyString,), # noqa: E501
}
@cached_property
def discriminator():
return None
attribute_map = {
'feature_name': 'feature.name', # noqa: E501
'feature_description': 'feature.description', # noqa: E501
'http_header_name': 'http.header.name', # noqa: E501
'http_header_valuepattern': 'http.header.valuepattern', # noqa: E501
}
_composed_schemas = {}
required_properties = set([
'_data_store',
'_check_type',
'_spec_property_naming',
'_path_to_item',
'_configuration',
'_visited_composed_classes',
])
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs): # noqa: E501
"""ComAdobeGraniteFragsImplCheckHttpHeaderFlagProperties - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
feature_name (ConfigNodePropertyString): [optional] # noqa: E501
feature_description (ConfigNodePropertyString): [optional] # noqa: E501
http_header_name (ConfigNodePropertyString): [optional] # noqa: E501
http_header_valuepattern (ConfigNodePropertyString): [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
|
[
"cliffano@gmail.com"
] |
cliffano@gmail.com
|
c714879ab292decf242cb272a4d05560414fb170
|
72d010d00355fc977a291c29eb18aeb385b8a9b0
|
/LV2_LX2_LC2_LD2/ParamMap.py
|
12d64819be32886c056b2489f3ffb2779ffe3981
|
[] |
no_license
|
maratbakirov/AbletonLive10_MIDIRemoteScripts
|
bf0749c5c4cce8e83b23f14f671e52752702539d
|
ed1174d9959b20ed05fb099f0461bbc006bfbb79
|
refs/heads/master
| 2021-06-16T19:58:34.038163
| 2021-05-09T11:46:46
| 2021-05-09T11:46:46
| 203,174,328
| 0
| 0
| null | 2019-08-19T13:04:23
| 2019-08-19T13:04:22
| null |
UTF-8
|
Python
| false
| false
| 2,876
|
py
|
# Embedded file name: /Users/versonator/Jenkins/live/output/mac_64_static/Release/python-bundle/MIDI Remote Scripts/LV2_LX2_LC2_LD2/ParamMap.py
# Compiled at: 2018-04-23 20:27:04
from __future__ import absolute_import, print_function, unicode_literals
import Live
class Callable:
def __init__(self, anycallable):
self.__call__ = anycallable
class ParamMap:
u"""Class to help with device mapping"""
__module__ = __name__
def __init__(self, parent):
ParamMap.realinit(self, parent)
def realinit(self, parent):
self.parent = parent
self.params_with_listener = []
self.param_callbacks = []
def log(self, string):
self.parent.log(string)
def logfmt(self, fmt, *args):
args2 = []
for i in range(0, len(args)):
args2 += [args[i].__str__()]
str = fmt % tuple(args2)
return self.log(str)
def param_add_callback(self, script_handle, midi_map_handle, param, min, max, cc, channel):
callback = lambda : self.on_param_value_changed(param, min, max, cc, channel)
param.add_value_listener(callback)
self.params_with_listener += [param]
self.param_callbacks += [callback]
ParamMap.forward_cc(script_handle, midi_map_handle, channel, cc)
def receive_midi_note(self, channel, status, note_no, note_vel):
pass
def receive_midi_cc(self, chan, cc_no, cc_value):
pass
def forward_cc(script_handle, midi_map_handle, chan, cc):
Live.MidiMap.forward_midi_cc(script_handle, midi_map_handle, chan, cc)
forward_cc = Callable(forward_cc)
def forward_note(script_handle, midi_map_handle, chan, note):
Live.MidiMap.forward_midi_note(script_handle, midi_map_handle, chan, note)
forward_note = Callable(forward_note)
def map_with_feedback(midi_map_handle, channel, cc, parameter, mode):
feedback_rule = Live.MidiMap.CCFeedbackRule()
feedback_rule.channel = channel
feedback_rule.cc_value_map = tuple()
feedback_rule.delay_in_ms = -1.0
feedback_rule.cc_no = cc
Live.MidiMap.map_midi_cc_with_feedback_map(midi_map_handle, parameter, channel, cc, mode, feedback_rule, False)
Live.MidiMap.send_feedback_for_parameter(midi_map_handle, parameter)
map_with_feedback = Callable(map_with_feedback)
def on_param_value_changed(self, param, min, max, cc, channel):
pass
def remove_mappings(self):
for i in range(0, len(self.params_with_listener)):
param = self.params_with_listener[i]
callback = self.param_callbacks[i]
try:
if param.value_has_listener(callback):
param.remove_value_listener(callback)
except:
continue
self.params_with_listener = []
self.param_callbacks = []
|
[
"julien@julienbayle.net"
] |
julien@julienbayle.net
|
bf61729fa718b439998532f367204e3cf8b93cf6
|
35fe9e62ab96038705c3bd09147f17ca1225a84e
|
/a10_ansible/library/a10_ipv6_neighbor_static.py
|
9c058e6fee3024c46ed849ab350ff96c39149478
|
[] |
no_license
|
bmeidell/a10-ansible
|
6f55fb4bcc6ab683ebe1aabf5d0d1080bf848668
|
25fdde8d83946dadf1d5b9cebd28bc49b75be94d
|
refs/heads/master
| 2020-03-19T08:40:57.863038
| 2018-03-27T18:25:40
| 2018-03-27T18:25:40
| 136,226,910
| 0
| 0
| null | 2018-06-05T19:45:36
| 2018-06-05T19:45:36
| null |
UTF-8
|
Python
| false
| false
| 6,211
|
py
|
#!/usr/bin/python
REQUIRED_NOT_SET = (False, "One of ({}) must be set.")
REQUIRED_MUTEX = (False, "Only one of ({}) can be set.")
REQUIRED_VALID = (True, "")
DOCUMENTATION = """
module: a10_static
description:
-
author: A10 Networks 2018
version_added: 1.8
options:
ipv6-addr:
description:
- IPV6 address
mac:
description:
- MAC Address
ethernet:
description:
- Ethernet port (Port Value)
trunk:
description:
- Trunk group
tunnel:
description:
- Tunnel interface
vlan:
description:
- VLAN ID
uuid:
description:
- uuid of the object
"""
EXAMPLES = """
"""
ANSIBLE_METADATA = """
"""
# Hacky way of having access to object properties for evaluation
AVAILABLE_PROPERTIES = {"ethernet","ipv6_addr","mac","trunk","tunnel","uuid","vlan",}
# our imports go at the top so we fail fast.
from a10_ansible.axapi_http import client_factory
from a10_ansible import errors as a10_ex
def get_default_argspec():
return dict(
a10_host=dict(type='str', required=True),
a10_username=dict(type='str', required=True),
a10_password=dict(type='str', required=True, no_log=True),
state=dict(type='str', default="present", choices=["present", "absent"])
)
def get_argspec():
rv = get_default_argspec()
rv.update(dict(
ethernet=dict(
type='str'
),
ipv6_addr=dict(
type='str' , required=True
),
mac=dict(
type='str'
),
trunk=dict(
type='str'
),
tunnel=dict(
type='str'
),
uuid=dict(
type='str'
),
vlan=dict(
type='str' , required=True
),
))
return rv
def new_url(module):
"""Return the URL for creating a resource"""
# To create the URL, we need to take the format string and return it with no params
url_base = "/axapi/v3/ipv6/neighbor/static/{ipv6-addr}+{vlan}"
f_dict = {}
f_dict["ipv6-addr"] = ""
f_dict["vlan"] = ""
return url_base.format(**f_dict)
def existing_url(module):
"""Return the URL for an existing resource"""
# Build the format dictionary
url_base = "/axapi/v3/ipv6/neighbor/static/{ipv6-addr}+{vlan}"
f_dict = {}
f_dict["ipv6-addr"] = module.params["ipv6-addr"]
f_dict["vlan"] = module.params["vlan"]
return url_base.format(**f_dict)
def build_envelope(title, data):
return {
title: data
}
def build_json(title, module):
rv = {}
for x in AVAILABLE_PROPERTIES:
v = module.params.get(x)
if v:
rx = x.replace("_", "-")
rv[rx] = module.params[x]
return build_envelope(title, rv)
def validate(params):
# Ensure that params contains all the keys.
requires_one_of = sorted([])
present_keys = sorted([x for x in requires_one_of if params.get(x)])
errors = []
marg = []
if not len(requires_one_of):
return REQUIRED_VALID
if len(present_keys) == 0:
rc,msg = REQUIRED_NOT_SET
marg = requires_one_of
elif requires_one_of == present_keys:
rc,msg = REQUIRED_MUTEX
marg = present_keys
else:
rc,msg = REQUIRED_VALID
if not rc:
errors.append(msg.format(", ".join(marg)))
return rc,errors
def exists(module):
try:
module.client.get(existing_url(module))
return True
except a10_ex.NotFound:
return False
def create(module, result):
payload = build_json("static", module)
try:
post_result = module.client.post(new_url(module), payload)
result.update(**post_result)
result["changed"] = True
except a10_ex.Exists:
result["changed"] = False
except a10_ex.ACOSException as ex:
module.fail_json(msg=ex.msg, **result)
except Exception as gex:
raise gex
return result
def delete(module, result):
try:
module.client.delete(existing_url(module))
result["changed"] = True
except a10_ex.NotFound:
result["changed"] = False
except a10_ex.ACOSException as ex:
module.fail_json(msg=ex.msg, **result)
except Exception as gex:
raise gex
return result
def update(module, result):
payload = build_json("static", module)
try:
post_result = module.client.put(existing_url(module), payload)
result.update(**post_result)
result["changed"] = True
except a10_ex.ACOSException as ex:
module.fail_json(msg=ex.msg, **result)
except Exception as gex:
raise gex
return result
def present(module, result):
if not exists(module):
return create(module, result)
else:
return update(module, result)
def absent(module, result):
return delete(module, result)
def run_command(module):
run_errors = []
result = dict(
changed=False,
original_message="",
message=""
)
state = module.params["state"]
a10_host = module.params["a10_host"]
a10_username = module.params["a10_username"]
a10_password = module.params["a10_password"]
# TODO(remove hardcoded port #)
a10_port = 443
a10_protocol = "https"
valid, validation_errors = validate(module.params)
map(run_errors.append, validation_errors)
if not valid:
result["messages"] = "Validation failure"
err_msg = "\n".join(run_errors)
module.fail_json(msg=err_msg, **result)
module.client = client_factory(a10_host, a10_port, a10_protocol, a10_username, a10_password)
if state == 'present':
result = present(module, result)
elif state == 'absent':
result = absent(module, result)
return result
def main():
module = AnsibleModule(argument_spec=get_argspec())
result = run_command(module)
module.exit_json(**result)
# standard ansible module imports
from ansible.module_utils.basic import *
from ansible.module_utils.urls import *
if __name__ == '__main__':
main()
|
[
"mdurrant@a10networks.com"
] |
mdurrant@a10networks.com
|
b5fdf682f928aef41c6625b6e5d1e70bb65baa49
|
cfc49e6e65ed37ddf297fc7dffacee8f905d6aa0
|
/exercicios_seccao4/35.py
|
f774259ca92b71fb8f2bb8f0eeece2cbe180ede4
|
[] |
no_license
|
IfDougelseSa/cursoPython
|
c94cc1215643f272f935d5766e7a2b36025ddbe2
|
3f9ceb9701a514106d49b2144b7f2845416ed8ec
|
refs/heads/main
| 2023-06-12T16:51:29.413031
| 2021-07-07T00:20:53
| 2021-07-07T00:20:53
| 369,268,883
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 195
|
py
|
# Hipotenusa
import math
a = int(input('Digite o cateto a: '))
b = int(input('Digite o cateto b: '))
hipotenusa = math.sqrt(a ** 2 + b ** 2)
print(f'O valor da hipotenusa é {hipotenusa}.')
|
[
"doug_ccortez@outlook.com"
] |
doug_ccortez@outlook.com
|
4c699101fa8582289ec996b5664bd8ab5b3ec4f5
|
ca7aa979e7059467e158830b76673f5b77a0f5a3
|
/Python_codes/p03032/s297706816.py
|
d7371f5e563b20937599d014765a4d6f1b0ebd4c
|
[] |
no_license
|
Aasthaengg/IBMdataset
|
7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901
|
f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8
|
refs/heads/main
| 2023-04-22T10:22:44.763102
| 2021-05-13T17:27:22
| 2021-05-13T17:27:22
| 367,112,348
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 743
|
py
|
n,k=map(int,input().split())
v=list(map(int, input().split()))
if k<n*2:
ans=0
for i in range(k+1):
for j in range(k-i+1):
v_r=v[:i]
v_l=v[(n-j):]
sute_cnt=k-(i+j)
v_new=v_r+v_l
v_new.sort()
# print(i, j, v_r, v_l, sute_cnt, v_new)
s=sum(v_new)
if not v_new:
continue
for indx in range(len(v_new)):
if v_new[indx]<0 and sute_cnt>0:
s-=v_new[indx]
sute_cnt-=1
else:
break
ans=max(ans,s)
print(ans)
else:
ans=0
for i in range(n):
if v[i]>=0:
ans+=v[i]
print(ans)
|
[
"66529651+Aastha2104@users.noreply.github.com"
] |
66529651+Aastha2104@users.noreply.github.com
|
16241caf95d6f2f6a2c327e2309ad58990c11cd5
|
be549921446835ba6dff0cadaa0c7b83570ebc3e
|
/run_eval_sutter.py
|
a0ba2df9ac3c6f63655586a070cc69f7762854c8
|
[] |
no_license
|
uctoronto/AutoPrescribe
|
895ee4375625408c663cee22610bb5425d7efc7f
|
a6188e9189df727320448a368f6e70036472ede4
|
refs/heads/master
| 2020-03-27T05:47:47.500486
| 2017-05-31T18:49:33
| 2017-05-31T18:49:33
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,227
|
py
|
from models.processor import Processor
from models.leap import LEAPModel
from exp.coverage import config_sutter as config
from utils.data import dump
config = config.get_config()
dir = 'build/'
config.saved_model_file = dir + 'sutter_%s_%s_seq2seq.model' % (config.level, config.order)
print(config.saved_model_file.split('/')[-1])
p = Processor(config)
model = LEAPModel(p, config)
# model.do_train()
model.load_params(config.saved_model_file)
# model.do_reinforce(scorer)
model.do_eval(training = False, filename = 'sutter_%s_%s_seq2seq.txt' % (config.level, config.order), max_batch = 5000000)
# model.load_params('../models/resume_seed13_100d_lr0.001_h256.model')
# ret = model.do_generate(data)
#
# from utils.eval import Evaluator
# eva = Evaluator()
# cnt = 0
# truth = []
# sum_jaccard = 0
# for line in open("seq2seq.h256.txt"):
# if cnt % 3 == 1:
# truth = set(line.strip().split("T: ")[1].split(" "))
# if cnt % 3 == 2:
# result = set(line.strip().split("Gen: ")[1].replace("END", "").strip().split(" "))
# jaccard = eva.get_jaccard_k(truth, result)
# sum_jaccard += jaccard
# cnt += 1
#
# print(sum_jaccard * 3 / cnt)
#
# cnt = 0
# truth_list = []
# prediction_list = []
# for line in open("seq2seq.h256.txt"):
# if cnt % 3 == 1:
# truth = set(line.strip().split("T: ")[1].split(" "))
# truth_list.append(truth)
# if cnt % 3 == 2:
# result = set(line.strip().split("Gen: ")[1].replace("END", "").strip().split(" "))
# prediction_list.append(result)
# cnt += 1
#
cnt = 0
results = []
input = []
truth = []
for line in open('sutter_%s_%s_seq2seq.txt' % (config.level, config.order)):
if cnt % 3 == 0:
input = set(line.strip().split("S: ")[1].split(" "))
if cnt % 3 == 1:
if len(line.strip().split("T: ")) <= 1:
truth = []
continue
truth = set(line.strip().split("T: ")[1].split(" "))
if cnt % 3 == 2:
result = set(line.strip().split("Gen: ")[1].replace("END", "").strip().split(" "))
if len(truth) > 0:
results.append((input, truth, result))
cnt += 1
dump(results, "sutter_%s_%s_result_seq2seq.pkl" % (config.level, config.order))
|
[
"stack@live.cn"
] |
stack@live.cn
|
7ef5899fc65729bb3d4169066bc9065937633f77
|
8565e4d24b537d1fb0f71fef6215d193ceaed6cc
|
/tests/test_check_circular.py
|
4a91863962a4377cf6bad0ba6466463a0579f885
|
[
"MIT"
] |
permissive
|
soasme/dogeon
|
5f55c84a6f93aaa7757372664dd60ed90cf200e8
|
496b9a5b099946d14434ed0cd7a94a270f607207
|
refs/heads/master
| 2020-05-17T19:01:42.780694
| 2018-11-04T05:01:23
| 2018-11-04T05:01:23
| 20,592,607
| 3
| 0
| null | 2014-06-28T01:34:35
| 2014-06-07T12:28:07
|
Python
|
UTF-8
|
Python
| false
| false
| 736
|
py
|
import dson
import pytest
def default_iterable(obj):
return list(obj)
def test_circular_dict():
dct = {}
dct['a'] = dct
pytest.raises(ValueError, dson.dumps, dct)
def test_circular_list():
lst = []
lst.append(lst)
pytest.raises(ValueError, dson.dumps, lst)
def test_circular_composite():
dct2 = {}
dct2['a'] = []
dct2['a'].append(dct2)
pytest.raises(ValueError, dson.dumps, dct2)
def test_circular_default():
dson.dumps([set()], default=default_iterable)
pytest.raises(TypeError, dson.dumps, [set()])
def test_circular_off_default():
dson.dumps([set()], default=default_iterable, check_circular=False)
pytest.raises(TypeError, dson.dumps, [set()], check_circular=False)
|
[
"soasme@gmail.com"
] |
soasme@gmail.com
|
08824881bc68f2ddf1fee1b25916cd115d4df279
|
aec59723a3dd0d3356a4ce426dc0fc381a4d3157
|
/catalog/model/pricing.py
|
020f6e8a724428673e0662dd1b10eba1af0e2087
|
[] |
no_license
|
Guya-LTD/catalog
|
f44e31593637e22b3b2a2869a387e29875986f7c
|
632b3c3766e2600275c0a18db6378b2d38e3c463
|
refs/heads/master
| 2023-02-11T19:03:36.796812
| 2021-01-08T14:12:06
| 2021-01-08T14:12:06
| 275,332,646
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 859
|
py
|
# -*- coding: utf-8 -*-
"""Copyright Header Details
Copyright
---------
Copyright (C) Guya , PLC - All Rights Reserved (As Of Pending...)
Unauthorized copying of this file, via any medium is strictly prohibited
Proprietary and confidential
LICENSE
-------
This file is subject to the terms and conditions defined in
file 'LICENSE.txt', which is part of this source code package.
Authors
-------
* [Simon Belete](https://github.com/Simonbelete)
Project
-------
* Name:
- Guya E-commerce & Guya Express
* Sub Project Name:
- Catalog Service
* Description
- Catlog Catalog Service
"""
"""Package details
Application features:
--------------------
Python 3.7
Flask
PEP-8 for code style
Entity.
"""
class Pricing:
"""A Base Model Representation of Pricing Entity."""
pass
|
[
"simonbelete@gmail.com"
] |
simonbelete@gmail.com
|
14f648102f5ede6ed0cbfd6da4036fb02e0e97b3
|
8983b099a27d124b17fc20d4e9b5ec2f0bf8be25
|
/altair/schema/_interface/named_channels.py
|
d2d7c77e95eadb00163c13a153019fb543b03f86
|
[
"BSD-3-Clause"
] |
permissive
|
princessd8251/altair
|
a7afa0745291f82215fbda6a477e369f59fcf294
|
387c575ee0410e7ac804273a0f2e5574f4cca26f
|
refs/heads/master
| 2021-01-16T21:41:40.935679
| 2017-08-10T16:36:05
| 2017-08-10T16:36:05
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 984
|
py
|
# -*- coding: utf-8 -*-
# Auto-generated file: do not modify directly
# - altair version info: v1.2.0-98-g8a98636
# - date: 2017-08-09 12:14:26
from . import channel_wrappers
class Color(channel_wrappers.ChannelWithLegend):
pass
class Column(channel_wrappers.PositionChannel):
pass
class Detail(channel_wrappers.Field):
pass
class Label(channel_wrappers.Field):
pass
class Opacity(channel_wrappers.ChannelWithLegend):
pass
class Order(channel_wrappers.OrderChannel):
pass
class Path(channel_wrappers.OrderChannel):
pass
class Row(channel_wrappers.PositionChannel):
pass
class Shape(channel_wrappers.ChannelWithLegend):
pass
class Size(channel_wrappers.ChannelWithLegend):
pass
class Text(channel_wrappers.Field):
pass
class X(channel_wrappers.PositionChannel):
pass
class X2(channel_wrappers.Field):
pass
class Y(channel_wrappers.PositionChannel):
pass
class Y2(channel_wrappers.Field):
pass
|
[
"jakevdp@gmail.com"
] |
jakevdp@gmail.com
|
e073a8419eda5bafad84588f1124d089f124d4cd
|
5864e86954a221d52d4fa83a607c71bacf201c5a
|
/carbon/common/lib/markdown/extensions/tables.py
|
f613f9a67f1f99e646124dad4f9a5fdff380870a
|
[] |
no_license
|
connoryang/1v1dec
|
e9a2303a01e5a26bf14159112b112be81a6560fd
|
404f2cebf13b311e754d45206008918881496370
|
refs/heads/master
| 2021-05-04T02:34:59.627529
| 2016-10-19T08:56:26
| 2016-10-19T08:56:26
| 71,334,417
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,302
|
py
|
#Embedded file name: e:\jenkins\workspace\client_SERENITY\branches\release\SERENITY\carbon\common\lib\markdown\extensions\tables.py
import markdown
from markdown.util import etree
class TableProcessor(markdown.blockprocessors.BlockProcessor):
def test(self, parent, block):
rows = block.split('\n')
return len(rows) > 2 and '|' in rows[0] and '|' in rows[1] and '-' in rows[1] and rows[1].strip()[0] in ('|', ':', '-')
def run(self, parent, blocks):
block = blocks.pop(0).split('\n')
header = block[0].strip()
seperator = block[1].strip()
rows = block[2:]
border = False
if header.startswith('|'):
border = True
align = []
for c in self._split_row(seperator, border):
if c.startswith(':') and c.endswith(':'):
align.append('center')
elif c.startswith(':'):
align.append('left')
elif c.endswith(':'):
align.append('right')
else:
align.append(None)
table = etree.SubElement(parent, 'table')
thead = etree.SubElement(table, 'thead')
self._build_row(header, thead, align, border)
tbody = etree.SubElement(table, 'tbody')
for row in rows:
self._build_row(row.strip(), tbody, align, border)
def _build_row(self, row, parent, align, border):
tr = etree.SubElement(parent, 'tr')
tag = 'td'
if parent.tag == 'thead':
tag = 'th'
cells = self._split_row(row, border)
for i, a in enumerate(align):
c = etree.SubElement(tr, tag)
try:
c.text = cells[i].strip()
except IndexError:
c.text = ''
if a:
c.set('align', a)
def _split_row(self, row, border):
if border:
if row.startswith('|'):
row = row[1:]
if row.endswith('|'):
row = row[:-1]
return row.split('|')
class TableExtension(markdown.Extension):
def extendMarkdown(self, md, md_globals):
md.parser.blockprocessors.add('table', TableProcessor(md.parser), '<hashheader')
def makeExtension(configs = {}):
return TableExtension(configs=configs)
|
[
"le02005@163.com"
] |
le02005@163.com
|
3eb6943aae1ad11db104ee00d54ed9bccbb642e4
|
855dc9fcd4170923e8723b6946c09c5cae68e079
|
/what_transcode/migrations/0001_initial.py
|
cb61199f9d66f0b1aee0d9c062f1096d498bbdcf
|
[
"MIT"
] |
permissive
|
point-source/WhatManager2
|
3fc72976402ac40d132aef0deffd8bcfbd209703
|
ddbce0fa1ff4e1fc44bfa726c4f7eace4adbe8a9
|
refs/heads/master
| 2023-01-27T11:39:43.861041
| 2019-02-24T17:51:24
| 2019-02-24T17:51:24
| 210,232,561
| 1
| 0
|
MIT
| 2019-09-23T00:21:54
| 2019-09-23T00:21:53
| null |
UTF-8
|
Python
| false
| false
| 985
|
py
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('home', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='TranscodeRequest',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False,
auto_created=True, primary_key=True)),
('requested_by_ip', models.TextField()),
('requested_by_what_user', models.TextField()),
('date_requested', models.DateTimeField(auto_now_add=True)),
('date_completed', models.DateTimeField(null=True)),
('celery_task_id', models.TextField(null=True)),
('what_torrent', models.ForeignKey(to='home.WhatTorrent')),
],
options={
},
bases=(models.Model,),
),
]
|
[
"ivailo@karamanolev.com"
] |
ivailo@karamanolev.com
|
5e9cf5ae03e925ad4d818c9b0637c412bbc60146
|
ca7aa979e7059467e158830b76673f5b77a0f5a3
|
/Python_codes/p02709/s022509829.py
|
dd9fa602873f6ee74e43f9bacf44dd9a2eee3894
|
[] |
no_license
|
Aasthaengg/IBMdataset
|
7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901
|
f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8
|
refs/heads/main
| 2023-04-22T10:22:44.763102
| 2021-05-13T17:27:22
| 2021-05-13T17:27:22
| 367,112,348
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 911
|
py
|
import sys
input = sys.stdin.readline
from collections import deque
N = int(input())
#A = list(map(int, input().split()))
A = [(a, i) for i, a in enumerate(map(int, input().split()))]
A = sorted(A, reverse=True)
values = []
num_indcies = {}
for i, a in enumerate(A):
if not a in num_indcies:
num_indcies[a] = [i]
values.append(a)
else:
num_indcies[a].append(i)
values = sorted(values, reverse=True)
ans = 0
# indexの配列
dp_indices = []
for v in values:
dp_indices.extend(num_indcies[v])
dp = [[0] * (N+1) for _ in range(N+1)]
for no, (a, pos) in enumerate(A):
for i in range(no+1):
j = no - i
#k = dp_indices[i+j-2]
#a = A[k]
dp[i+1][j] = max(dp[i+1][j], dp[i][j] + a * (pos -i))
dp[i][j+1] = max(dp[i][j+1], dp[i][j] + a * abs(pos - (N-1-j)))
ans = 0
for i in range(1, N+1):
ans = max(ans, dp[i][N-i])
print(ans)
|
[
"66529651+Aastha2104@users.noreply.github.com"
] |
66529651+Aastha2104@users.noreply.github.com
|
3d76924803db335c9cb94bb42f4444f162c2d2ae
|
936f72b46215b89b277ffd57256e54f727ce1ac5
|
/spark-comp04/token.py
|
3147a73cbc6b3be806e113977983bf177f1a4f32
|
[] |
no_license
|
luizirber/dc-compilers
|
91dc99097d628339b53b20a0c0f2a6255a599b7a
|
4a47e786583c5f50cac2ac3a35de195f7be7a735
|
refs/heads/master
| 2016-09-06T11:27:51.815748
| 2012-07-03T01:28:26
| 2012-07-03T01:28:26
| 41,540
| 7
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 278
|
py
|
class Token(object):
def __init__(self, type, attr=None, lineno='???'):
self.type = type
self.attr = attr
self.lineno = lineno
def __cmp__(self, o):
return cmp(self.type, o)
def __repr__(self):
return self.attr or self.type
|
[
"luiz.irber@gmail.com"
] |
luiz.irber@gmail.com
|
f3822c56be1305e7b55915ab88f6b4e8ff7f9704
|
62587160029c7c79b5d11f16e8beae4afa1c4834
|
/webpages/island_scraper_kyero/island_scraper/middlewares.py
|
f34dd9c19c21b5524d2483086acae265764a8f49
|
[] |
no_license
|
LukaszMalucha/Scrapy-Collection
|
b11dcf2c09f33d190e506559d978e4f3b77f9f5a
|
586f23b90aa984c22ea8f84eba664db9649ed780
|
refs/heads/master
| 2022-12-14T15:06:00.868322
| 2021-07-27T12:09:07
| 2021-07-27T12:09:07
| 144,448,351
| 3
| 0
| null | 2022-11-22T03:16:19
| 2018-08-12T07:55:05
|
Python
|
UTF-8
|
Python
| false
| false
| 3,611
|
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 IslandScraperSpiderMiddleware(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 IslandScraperDownloaderMiddleware(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)
|
[
"lucasmalucha@gmail.com"
] |
lucasmalucha@gmail.com
|
6544fcf260d6f8112c79a5e3a5ec70a10575a277
|
9e988c0dfbea15cd23a3de860cb0c88c3dcdbd97
|
/sdBs/AllRun/pg_1425+219/sdB_PG_1425+219_lc.py
|
e8ee83c9f091b54b888664988d5fb0c6cd57aee1
|
[] |
no_license
|
tboudreaux/SummerSTScICode
|
73b2e5839b10c0bf733808f4316d34be91c5a3bd
|
4dd1ffbb09e0a599257d21872f9d62b5420028b0
|
refs/heads/master
| 2021-01-20T18:07:44.723496
| 2016-08-08T16:49:53
| 2016-08-08T16:49:53
| 65,221,159
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 346
|
py
|
from gPhoton.gAperture import gAperture
def main():
gAperture(band="NUV", skypos=[216.986042,21.632814], stepsz=30., csvfile="/data2/fleming/GPHOTON_OUTPU/LIGHTCURVES/sdBs/sdB_PG_1425+219 /sdB_PG_1425+219_lc.csv", maxgap=1000., overwrite=True, radius=0.00555556, annulus=[0.005972227,0.0103888972], verbose=3)
if __name__ == "__main__":
main()
|
[
"thomas@boudreauxmail.com"
] |
thomas@boudreauxmail.com
|
48b1cfe1f2c159159035fd8b8781a2df3fb2ffde
|
b11a5afd6682fe003445431ab60a9273a8680c23
|
/language/nqg/tasks/spider/write_dataset.py
|
b2ed9f1018cf872e2b4933c9712c698deaeb8e52
|
[
"LicenseRef-scancode-generic-cla",
"Apache-2.0"
] |
permissive
|
Srividya-me/language
|
a874b11783e94da7747fc9a1b0ae1661cd5c9d4a
|
61fa7260ac7d690d11ef72ca863e45a37c0bdc80
|
refs/heads/master
| 2023-08-28T10:30:59.688879
| 2021-11-12T22:31:56
| 2021-11-13T01:04:42
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,111
|
py
|
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.
"""Write Spider dataset in TSV format."""
import json
from absl import app
from absl import flags
from language.nqg.tasks import tsv_utils
from language.nqg.tasks.spider import database_constants
from tensorflow.io import gfile
FLAGS = flags.FLAGS
flags.DEFINE_string("examples", "", "Path to Spider json examples.")
flags.DEFINE_string("output", "", "Output tsv file.")
flags.DEFINE_bool(
"filter_by_database", True,
"Whether to only select examples for databases used for the Spider-SSP"
"setting proposed in the paper. Should be False to follow the standard"
"Spider-XSP setting.")
def normalize_whitespace(source):
tokens = source.split()
return " ".join(tokens)
def load_json(filepath):
with gfile.GFile(filepath, "r") as reader:
text = reader.read()
return json.loads(text)
def main(unused_argv):
examples_json = load_json(FLAGS.examples)
examples = []
for example_json in examples_json:
database = example_json["db_id"]
source = example_json["question"]
target = example_json["query"]
# Optionally skip if database not in set of databases with >= 50 examples.
if (FLAGS.filter_by_database and
database not in database_constants.DATABASES):
continue
# Prepend database.
source = "%s: %s" % (database, source)
target = normalize_whitespace(target)
examples.append((source.lower(), target.lower()))
tsv_utils.write_tsv(examples, FLAGS.output)
if __name__ == "__main__":
app.run(main)
|
[
"kentonl@google.com"
] |
kentonl@google.com
|
6ff66a5e7100cbdd1877f359622be88b41e19b2c
|
c4c159a21d2f1ea0d7dfaa965aeff01c8ef70dce
|
/flask/flaskenv/Lib/site-packages/keras_applications/inception_v3.py
|
1b825c0ce4aea562e468b337a5843f63810f57d5
|
[] |
no_license
|
AhsonAslam/webapi
|
54cf7466aac4685da1105f9fb84c686e38f92121
|
1b2bfa4614e7afdc57c9210b0674506ea70b20b5
|
refs/heads/master
| 2020-07-27T06:05:36.057953
| 2019-09-17T06:35:33
| 2019-09-17T06:35:33
| 208,895,450
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 130
|
py
|
version https://git-lfs.github.com/spec/v1
oid sha256:6bdeecc0c5e0341451f5d87e17d12c89a210b6161e1b066aca6e02bc425b2abf
size 14598
|
[
"github@cuba12345"
] |
github@cuba12345
|
568aa59ae896f8dcad1d6c4c19a117a22a0ff63c
|
c4d05bf624ce277b35d83ba8ba9636f26043280e
|
/project/urls.py
|
d6e90307036ceed43e1f6355ce2dc672ebb0e233
|
[
"Apache-2.0"
] |
permissive
|
DrMartiner/kaptilo_back
|
2366b3a2b5c9bd9dc57c9091ff5fd0025963668d
|
df7f716030edbb1a70388fcbb808b0985dabefbf
|
refs/heads/main
| 2023-04-09T03:12:52.274388
| 2021-03-22T09:48:39
| 2021-03-22T09:48:39
| 349,943,620
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 862
|
py
|
from django.conf import settings
from django.conf.urls.static import static
from django.contrib import admin
from django.contrib.staticfiles.urls import staticfiles_urlpatterns
from django.urls import path, include
from apps.link.views import OriginalLinkRedirectView
admin.site.site_header = "Kaptilo"
admin.site.site_title = "Kaptilo"
admin.site.index_title = "Welcome to Kaptilo admin-panel"
urlpatterns = [
path("<str:uuid>/", OriginalLinkRedirectView.as_view(), name="original-link-redirect"),
path("api/v1/", include(("apps.api.urls", "apps.api"), namespace="api_v1")),
path("admin/super-sec/", admin.site.urls),
path("admin/", include("admin_honeypot.urls", namespace="admin_honeypot")),
]
if settings.DEBUG:
urlpatterns += staticfiles_urlpatterns()
urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
|
[
"DrMartiner@GMail.Com"
] |
DrMartiner@GMail.Com
|
668963624d3086f1b1dd35cf080200af75bf8736
|
191a7f83d964f74a2b3c7faeb4fc47d9c63d521f
|
/.history/main_20210523152045.py
|
6d7861a88a7d86a28f1d8d675b4416ba674fb3c2
|
[] |
no_license
|
AndreLiu1225/Kinder-Values-Survey
|
2a317feee8d5b17c27da2b2116742656e35d8ab9
|
090c27da0c822abb7dfc0ec6e13ae1b3dcb7bbf3
|
refs/heads/master
| 2023-05-03T00:26:00.481423
| 2021-06-04T03:24:19
| 2021-06-04T03:24:19
| 371,989,154
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,795
|
py
|
from flask import Flask, render_template, redirect, url_for
from flask_wtf import FlaskForm
from wtforms import StringField, TextField, SubmitField, IntegerField, SelectField, RadioField
from wtforms.validators import DataRequired, Email, EqualTo, Length, ValidationError
app = Flask(__name__)
app.config['SECRET_KEY'] = "0c8973c8a5e001bb0c816a7b56c84f3a"
class MCQ(FlaskForm):
age = IntegerField("Please enter your age", validators=[DataRequired()])
profession = StringField("What is your profession?", validators=[DataRequired(), Length(max=30)])
power = RadioField("Do you desire a higher social status and dominance over others?", choices=[('Yes', 'It is my priority'), ('No', 'It is not my priority')])
tradition = RadioField("Do you care preservingabout tradition", choices=[('Yes', 'It is my priority'), ('No', 'It is not my priority')])
achievement = RadioField("Do you desire a higher social status and dominance over others?", choices=[('Yes', 'It is my priority'), ('No', 'It is not my priority')])
stimulation = RadioField("Do you desire a higher social status and dominance over others?", choices=[('Yes', 'It is my priority'), ('No', 'It is not my priority')])
hedonism = RadioField("Do you desire a higher social status and dominance over others?", choices=[('Yes', 'It is my priority'), ('No', 'It is not my priority')])
conformity = RadioField("Do you desire a higher social status and dominance over others?", choices=[('Yes', 'It is my priority'), ('No', 'It is not my priority')])
self_direction = RadioField("Do you desire a higher social status and dominance over others?", choices=[('Yes', 'It is my priority'), ('No', 'It is not my priority')])
submit = SubmitField("Submit")
if __name__ == "__main__":
app.run(debug=True)
|
[
"andreliu2004@gmail.com"
] |
andreliu2004@gmail.com
|
8065d754386fc0b3762e05f4fc04a7f53121086e
|
9da6c375dbf1af87622a2ba0fb773e8f513d8021
|
/cli/bak.20200512-local/abcombo.py
|
a267f8c6d9d445c64cdd848a3d93c27eb4e147ce
|
[] |
no_license
|
wri/tree_canopy_fcn
|
a80a9971403f6ca2548d44146ed08aa22d7d559e
|
78f742e4e26e34008417468f73413643edde801e
|
refs/heads/master
| 2022-10-11T03:25:41.503263
| 2020-06-16T12:39:21
| 2020-06-16T12:39:21
| 236,492,565
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,895
|
py
|
import os,sys
PROJECT_DIR='/home/ericp/tree_canopy_fcn/repo'
sys.path.append(PROJECT_DIR)
from pprint import pprint
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch_kit.loss import MaskedLoss
import torch_kit.functional as F
from torch_kit.optimizers.radam import RAdam
import pytorch_models.deeplab.model as dm
import pytorch_models.unet.model as um
from utils.dataloader import HeightIndexDataset, CATEGORY_BOUNDS
from config import BUILTUP_CATEGORY_THRESHOLDS
#
# RUN CONFIG
#
BATCH_SIZE=8
DEFAULT_OPTIMIZER='adam'
LRS=[1e-3,1e-4]
NB_CATEGORIES=len(CATEGORY_BOUNDS)+1
# # AB STATS: ALL
# MEANS=[100.83741572079242, 100.4938850966076, 86.63500986931308, 118.72746674454453]
# STDEVS=[42.098045003124774, 39.07388735786421, 39.629813116928815, 34.72351480486876]
# DSETS_PATH='../datasets/los_angeles-plieades-lidar_USGS_LPC_CA_LosAngeles_2016_LAS_2018.STATS.csv'
# AB STATS: 2015,16 Train/valid
MEANS=[94.79936157686979, 92.8912348691044, 80.50194782393349, 108.14889758142212]
STDEVS=[36.37876660224377, 33.22686387734999, 33.30808192430284, 30.075380846943716]
DSETS_PATH='../datasets/los_angeles-plieades_naip-lidar_USGS_LPC_CA_LosAngeles_2016_LAS_2018.STATS.csv'
YEAR_MAX=2016
# # NAIP STATS: ALL (<2017)
# MEANS=[106.47083152919251, 104.25520495313522, 98.61836143687523, 119.95594400425841]
# STDEVS=[38.23711386806666, 34.410688920150264, 31.468324931640534, 31.831786730471276]
# DSET_PATH=f'{PROJECT_DIR}/datasets/los_angeles-naip-lidar_USGS_LPC_CA_LosAngeles_2016_LAS_2018.STATS.csv'
# # NAIP ONLY
# IBNDS={
# '4': { 'min': 0 }, # ndvi
# '5': { 'min': -0.35} # ndwi
# }
# # PLIEDES INPUT
IBNDS=None
#
# TORCH_KIT CLI
#
def model(**cfig):
_header('model',cfig)
model_type=cfig.pop('type','dlv3p')
cfig['out_ch']=cfig.get('out_ch',NB_CATEGORIES)
if model_type=='dlv3p':
mod=dm.DeeplabV3plus(**cfig)
elif model_type=='unet':
mod=um.UNet(**cfig)
else:
raise ValueError(f'model_type ({model_type}) not implemented')
if torch.cuda.is_available():
mod=mod.cuda()
return mod
def criterion(**cfig):
ignore_index=cfig.get('ignore_index')
weights=cfig.get('weights')
print("criterion:",ignore_index,weights)
if weights:
weights=torch.Tensor(weights)
if torch.cuda.is_available():
weights=weights.cuda()
if ignore_index is not None:
criterion=nn.CrossEntropyLoss(weight=weights,ignore_index=ignore_index)
# criterion=MaskedLoss(
# weight=weights,
# loss_type='ce',
# mask_value=ignore_index )
else:
criterion=nn.CrossEntropyLoss(weight=weights)
return criterion
def optimizer(**cfig):
_header('optimizer',cfig)
opt_name=cfig.get('name',DEFAULT_OPTIMIZER)
if opt_name=='adam':
optimizer=torch.optim.Adam
elif opt_name=='radam':
optimizer=RAdam
else:
ValueError(f'optimizer "{opt_name}" not implemented')
return optimizer
def loaders(**cfig):
"""
"""
# INITAL DATASET HANDLING
dsets_df=pd.read_csv(DSETS_PATH)
train_df=dsets_df[dsets_df.dset_type=='train']
valid_df=dsets_df[dsets_df.dset_type=='valid']
train_df=train_df[train_df.input_year<=YEAR_MAX].iloc[1:6*8+1]
valid_df=valid_df[valid_df.input_year<=YEAR_MAX]
example_path=train_df.rgbn_path.iloc[0]
#
# on with the show
#
dev=cfig.get('dev')
vmap=cfig.get('vmap')
batch_size=cfig.get('batch_size',BATCH_SIZE)
band_indices=['ndvi']
augment=cfig.get('augment',True)
shuffle=cfig.get('shuffle',True)
no_data_value=cfig.get('no_data_value',False)
cropping=cfig.get('cropping',None)
float_cropping=cfig.get('float_cropping',None)
update_version=cfig.get('update_version',False)
print('AUGMENT:',augment)
print('SHUFFLE:',shuffle)
print('BATCH_SIZE:',batch_size)
print('NO DATA VALUE:',no_data_value)
print('CROPPING:',cropping)
print('FLOAT CROPPING:',float_cropping)
if (train_df.shape[0]>=batch_size*8) and (valid_df.shape[0]>=batch_size*2):
if dev:
train_df=train_df.sample(batch_size*8)
valid_df=valid_df.sample(batch_size*2)
dl_train=HeightIndexDataset.loader(
batch_size=batch_size,
# input_bands=[0,1,2],
# input_band_count=3,
band_indices=['ndvi'],
category_bounds=HeightIndexDataset.NAIP_GREEN,
input_bounds=IBNDS,
dataframe=train_df,
means=MEANS,
stdevs=STDEVS,
no_data_value=no_data_value,
cropping=cropping,
float_cropping=float_cropping,
example_path=example_path,
augment=augment,
train_mode=True,
target_dtype=np.int,
shuffle_data=shuffle)
return dl_train, None
dl_valid=HeightIndexDataset.loader(
batch_size=batch_size,
# input_bands=[0,1,2],
# input_band_count=3,
band_indices=['ndvi'],
category_bounds=HeightIndexDataset.NAIP_GREEN,
input_bounds=IBNDS,
dataframe=valid_df,
means=MEANS,
stdevs=STDEVS,
no_data_value=no_data_value,
cropping=cropping,
float_cropping=float_cropping,
example_path=example_path,
augment=augment,
train_mode=True,
target_dtype=np.int,
shuffle_data=shuffle)
print("SIZE:",train_df.shape[0],valid_df.shape[0])
return dl_train, dl_valid
else:
print('NOT ENOUGH DATA',train_df.shape[0],valid_df.shape[0],batch_size*8,batch_size*30)
return False, False
#
# HELPERS
#
def _header(title,cfig=None):
print('='*100)
print(title)
print('-'*100)
if cfig:
pprint(cfig)
|
[
"bguzder-williams@wri.org"
] |
bguzder-williams@wri.org
|
f8184270f36e3f165d97bbb247f6f0b508fc5810
|
ba7d84b4b85be8c3221468527757e264e64616b9
|
/tests/hammytest.py
|
b5f03afc22f1e60ade3aca0eb505d0bf88fd3fe8
|
[] |
no_license
|
gomesr/timetracker
|
c18eb4b6f33e08eadd72971216b16560ef085aa1
|
ce57a0791727a3b06e4b167fbeb3cb3e558ff2f1
|
refs/heads/master
| 2021-01-22T23:58:20.247393
| 2010-12-12T01:16:54
| 2010-12-12T01:16:54
| 1,130,286
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 675
|
py
|
import unittest
from trackers.hammy import HamsterTracker
from hamster import client
class HammyTest(unittest.TestCase):
def setUp(self):
self.tracker = HamsterTracker()
def test_create_100_activites(self):
tags = []
ids = []
try:
for i in range(1,100):
ids.append(self.tracker.start("activity-%d" % i,
"",
"some elaborate desciption",
tags))
finally:
# clean up!
for id in ids:
self.tracker.storage.remove_fact(id)
|
[
"rodneygomes@gmail.com"
] |
rodneygomes@gmail.com
|
dd1953d6927d29066068ea81328364dee75a86e6
|
bbf1ae079309eca11270422d3f0d259d1515d430
|
/numerical-tours/python/todo/solutions/wavelet_2_haar2d.py
|
7ec8c89d23ba2108e274a13521844d6ad479f593
|
[
"BSD-2-Clause"
] |
permissive
|
ZichaoDi/Di_MATLABTool
|
5e6a67b613c4bcf4d904ddc47c2744b4bcea4885
|
c071291c63685c236f507b2cb893c0316ab6415c
|
refs/heads/master
| 2021-08-11T07:28:34.286526
| 2021-08-04T18:26:46
| 2021-08-04T18:26:46
| 149,222,333
| 9
| 5
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,522
|
py
|
def exo1():
"""
Implement a full wavelet transform that extract iteratively wavelet
coefficients, by repeating these steps. Take care of choosing the
correct number of steps.
"""
Jmin = 0
fw = f
for j in J: -1: Jmin:
fw(1: 2^(j + 1), 1: 2^(j + 1)) = haar(fw(1: 2^(j + 1), 1: 2^(j + 1)))
%
j1 = J-j
if j1 <4
A = fw(1: 2^(j + 1), 1: 2^(j + 1))
imageplot(A(1: 2^j, 2^j + 1: 2^(j + 1)), ['Horizontal, j = ' num2str(j)], 3, 4, j1 + 1)
imageplot(A(2^j + 1: 2^(j + 1), 1: 2^j), ['Vertical, j = ' num2str(j)], 3, 4, j1 + 5)
imageplot(A(2^j + 1: 2^(j + 1), 2^j + 1: 2^(j + 1)), ['Diagonal, j = ' num2str(j)], 3, 4, j1 + 9)
def exo2():
"""
Write the inverse wavelet transform that computes $f_1$ from
coefficients |fW|.
"""
f1 = fw
for j in Jmin: J:
s = 1: 2^j; t = 2^j + 1: 2^(j + 1); u = 1: 2^(j + 1)
f1(u, u) = ihaar(f1(s, s), f1(s, t), f1(t, s), f1(t, t))
%
j1 = J-j
if j1 >0 & j1 <5
A = f1(1: 2^(j + 1), 1: 2^(j + 1))
subplot(2, 2, j1)
imageplot(A, ['Partial reconstruction, j = ' num2str(j)])
def exo3():
"""
Display the reconstructed signal obtained from |fw1|, for a decreasing cut-off scale $j$.
"""
jlist = J-(1: 4)
fw = perform_haar_transf(f, 1, + 1)
for i in 1: length(jlist):
j = jlist(i)
fw1 = zeros(n); fw1(1: 2^j, 1: 2^j) = fw(1: 2^j, 1: 2^j)
f1 = perform_haar_transf(fw1, 1, -1)
% display
subplot(2, 2, i)
imageplot(f1)
title(strcat(['j = ' num2str(j) ', SNR = ' num2str(snr(f, f1), 3) 'dB']))
def exo4():
"""
Find the threshold $T$ so that the number of remaining coefficients in
|fwT| is a fixed number $m$. Use this threshold to compute |fwT| and then display
the corresponding approximation $f_1$ of $f$. Try for an increasing number $m$ of coeffiients.
"""
m_list = round([.005 .01 .05 .1]*N); % number of kept coefficients
fw = perform_haar_transf(f, 1, + 1)
for i in 1: length(m_list):
m = m_list(i)
% select threshold
v = sort(abs(fw(: )))
if v(1) <v(N)
v = reverse(v)
T = v(m)
fwT = fw .* (abs(fw) >= T)
% inverse
f1 = perform_haar_transf(fwT, 1, -1)
% display
subplot(2, 2, i)
imageplot(f1)
title(strcat(['m = ' num2str(m) ', SNR = ' num2str(snr(f, f1), 3) 'dB']))
|
[
"wendydi@compute001.mcs.anl.gov"
] |
wendydi@compute001.mcs.anl.gov
|
286cc8c250f2c2b4030ffc5e75d7d1213b47a934
|
9743d5fd24822f79c156ad112229e25adb9ed6f6
|
/xai/brain/wordbase/nouns/_yens.py
|
f7c90d82f8fc7ae9864e4492c2449f9c31d5b2f4
|
[
"MIT"
] |
permissive
|
cash2one/xai
|
de7adad1758f50dd6786bf0111e71a903f039b64
|
e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6
|
refs/heads/master
| 2021-01-19T12:33:54.964379
| 2017-01-28T02:00:50
| 2017-01-28T02:00:50
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 217
|
py
|
from xai.brain.wordbase.nouns._yen import _YEN
#calss header
class _YENS(_YEN, ):
def __init__(self,):
_YEN.__init__(self)
self.name = "YENS"
self.specie = 'nouns'
self.basic = "yen"
self.jsondata = {}
|
[
"xingwang1991@gmail.com"
] |
xingwang1991@gmail.com
|
a31d0693760097d9ec0bfc62e4a5c4d7383c09ab
|
378b200007c5d3633572b61eb3dd2180748086b7
|
/chefsBackEnd/chefsBackEnd/asgi.py
|
d077d3550da2054b45a48c64401ec50a84113e40
|
[] |
no_license
|
jgartsu12/chefs-table-backend
|
4163c2c9a2bb586d4432c332238682bf282ef967
|
71611cf17aa457f8bc9a7ec7d853c570062d22fb
|
refs/heads/master
| 2022-12-16T04:22:30.954831
| 2020-07-08T19:24:37
| 2020-07-08T19:24:37
| 251,097,796
| 1
| 0
| null | 2022-12-08T10:13:44
| 2020-03-29T17:59:15
|
Python
|
UTF-8
|
Python
| false
| false
| 401
|
py
|
"""
ASGI config for chefsBackEnd project.
It exposes the ASGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.0/howto/deployment/asgi/
"""
import os
from django.core.asgi import get_asgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'chefsBackEnd.settings')
application = get_asgi_application()
|
[
"jgartsu12@gmail.com"
] |
jgartsu12@gmail.com
|
764c228e5a8b115f7ca60c1480fdff36b20ab047
|
8a3726abfc9cb72d8ccf7d32b18edabf8d16b630
|
/18/a.py
|
32847a4eb7fdc71ad694396872b27a628860cf2a
|
[] |
no_license
|
alex-stephens/aoc2015
|
48a46efc1a888ea2d451a5938fc404d26e96e1a0
|
ccc1c85f8da7a0585003b2e4f99f3f1def35ec0b
|
refs/heads/master
| 2023-02-05T23:02:19.148138
| 2020-12-27T19:16:47
| 2020-12-27T19:16:47
| 324,579,165
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,065
|
py
|
grid = [list(line.strip()) for line in open('input.txt').readlines()]
rows, cols = len(grid), len(grid[0])
def count_neighbours(i, j):
rmin, rmax = max(i-1, 0), min(i+1, rows-1)
cmin, cmax = max(j-1, 0), min(j+1, cols-1)
ans = 0
for r in range(rmin, rmax+1):
for c in range(cmin, cmax+1):
if (r,c) == (i,j):
continue
ans += 1 if grid[r][c] == '#' else 0
return ans
it = 100
for i in range(it):
new_grid = [['x' for _ in range(cols)] for _ in range(rows)]
for r in range(rows):
for c in range(cols):
count = count_neighbours(r,c)
if grid[r][c] == '#' and (count != 2 and count != 3):
new_grid[r][c] = '.'
elif grid[r][c] == '.' and count == 3:
new_grid[r][c] = '#'
else:
new_grid[r][c] = grid[r][c]
grid = [list(x) for x in new_grid]
# print('--------------------------')
# for g in grid:
# print(''.join(g))
print(sum([''.join(r).count('#') for r in grid]))
|
[
"alexstephens9@gmail.com"
] |
alexstephens9@gmail.com
|
05c5693d3b24a5c3fd147316f1f2cfeaba19014b
|
5c39f5ac529e9f292ba0e4965fd684d4c6eefe8a
|
/migrations/0001_initial.py
|
8570a25dfd79013e6c9c3202871e7bdc877c28d4
|
[] |
no_license
|
joshianshul2/csv_db
|
6d24dec8bdcd8f00115a8729d5036beb47994d0e
|
e7215002c0a2fb8cadd0d4087b8651b1ec9e30ea
|
refs/heads/master
| 2023-04-21T19:46:56.941399
| 2021-05-11T17:29:38
| 2021-05-11T17:29:38
| 356,846,462
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,962
|
py
|
# Generated by Django 3.2 on 2021-04-07 05:30
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='AvgMaster',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('county', models.CharField(max_length=255)),
('state', models.CharField(max_length=255)),
('NetPrAr', models.FloatField(default=0.0)),
('Rate', models.FloatField()),
('UserPercentage', models.FloatField(default=0.0)),
('FinaleValue', models.FloatField(default=0.0)),
('accountId', models.BigIntegerField()),
('acres', models.FloatField()),
('adTargetingCountyId', models.BigIntegerField()),
('address', models.CharField(max_length=255)),
('baths', models.BigIntegerField()),
('beds', models.BigIntegerField()),
('brokerCompany', models.CharField(max_length=255)),
('brokerName', models.CharField(max_length=255)),
('Url', models.URLField(max_length=255)),
('city', models.CharField(max_length=255)),
('cityID', models.BigIntegerField()),
('companyLogoDocumentId', models.BigIntegerField()),
('countyId', models.BigIntegerField()),
('description', models.TextField(max_length=255)),
('hasHouse', models.BooleanField()),
('hasVideo', models.BooleanField()),
('hasVirtualTour', models.BigIntegerField()),
('imageCount', models.BigIntegerField()),
('imageAltTextDisplay', models.CharField(max_length=255)),
('isHeadlineAd', models.BooleanField()),
('lwPropertyId', models.BigIntegerField()),
('isALC', models.BigIntegerField()),
('latitude', models.FloatField()),
('longitude', models.FloatField()),
('price', models.FloatField()),
('types', models.TextField(max_length=255)),
('status', models.CharField(max_length=20)),
('status1', models.CharField(max_length=255)),
('zip', models.BigIntegerField()),
('Descrpt', models.TextField(default='!', max_length=255)),
('created_at', models.DateTimeField(auto_now_add=True)),
('updated_at', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='PropertyMaster',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('accountId', models.BigIntegerField()),
('acres', models.FloatField()),
('adTargetingCountyId', models.BigIntegerField()),
('address', models.CharField(max_length=255)),
('baths', models.BigIntegerField()),
('beds', models.BigIntegerField()),
('brokerCompany', models.CharField(max_length=255)),
('brokerName', models.CharField(max_length=255)),
('Url', models.URLField(max_length=255)),
('city', models.CharField(max_length=255)),
('cityID', models.BigIntegerField()),
('companyLogoDocumentId', models.BigIntegerField()),
('county', models.CharField(max_length=255)),
('countyId', models.BigIntegerField()),
('description', models.TextField(max_length=255)),
('hasHouse', models.BooleanField()),
('hasVideo', models.BooleanField()),
('hasVirtualTour', models.BigIntegerField()),
('imageCount', models.BigIntegerField()),
('imageAltTextDisplay', models.CharField(max_length=255)),
('isHeadlineAd', models.BooleanField()),
('lwPropertyId', models.BigIntegerField()),
('isALC', models.BigIntegerField()),
('latitude', models.FloatField()),
('longitude', models.FloatField()),
('price', models.FloatField()),
('types', models.TextField(max_length=255)),
('state', models.CharField(max_length=255)),
('status', models.CharField(max_length=20)),
('status1', models.CharField(max_length=255)),
('zip', models.BigIntegerField()),
('Rate', models.FloatField()),
('NetPrAr', models.FloatField(default=0.0)),
('Descrpt', models.TextField(default='!', max_length=255)),
('created_at', models.DateTimeField(auto_now_add=True)),
('updated_at', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='StatusMaster',
fields=[
('status', models.IntegerField(primary_key=True, serialize=False)),
('name', models.CharField(max_length=255)),
],
),
migrations.CreateModel(
name='User',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('first_name', models.CharField(max_length=255)),
('last_name', models.CharField(max_length=255)),
('email', models.CharField(max_length=255)),
('password', models.CharField(max_length=255)),
('created_at', models.DateTimeField(auto_now_add=True)),
('updated_at', models.DateTimeField(auto_now=True)),
],
),
]
|
[
"joshi.anshul2@gmail.com"
] |
joshi.anshul2@gmail.com
|
159e62cf42f265a5d96156ae23363dbeced3b8c0
|
1e53216c58f3c7843031721305590b83dbaed3f2
|
/week_four/db_demo/db_app/migrations/0003_message_post_user_who_liked.py
|
59fc1606c04688bdf72a3cafe91a74cffc27e608
|
[] |
no_license
|
MTaylorfullStack/python_july_20
|
991852ba12d6f06d6b93b8efc60b66ee311b5cb3
|
bdfb0d9a74300f2d6743ac2d108571692ca43ad9
|
refs/heads/master
| 2022-12-12T18:03:00.886048
| 2020-08-27T23:53:31
| 2020-08-27T23:53:31
| 277,956,745
| 2
| 2
| null | 2023-06-30T20:06:11
| 2020-07-08T01:09:34
|
Python
|
UTF-8
|
Python
| false
| false
| 425
|
py
|
# Generated by Django 2.2 on 2020-07-29 00:53
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('db_app', '0002_message_post'),
]
operations = [
migrations.AddField(
model_name='message_post',
name='user_who_liked',
field=models.ManyToManyField(related_name='liked_post', to='db_app.User'),
),
]
|
[
"mtaylor@codingdojo.com"
] |
mtaylor@codingdojo.com
|
2ba794c5fbdf6b165029c3b20b7d4ae08486b115
|
4fd77ce692e10e962483c7e3e6e76c44887e9f52
|
/geatpy/templates/soeas/GA/studGA/soea_psy_studGA_templet.py
|
7cb191a9338b905bc256f6ecb2c43a2de4b72a72
|
[
"MIT"
] |
permissive
|
Passion-long/geatpy
|
d1aaf1622058473649840a9e2e26f9d0b0844bce
|
8e2ab8730babaae640272bd4c77106519bdd120c
|
refs/heads/master
| 2020-07-09T13:40:36.217907
| 2019-08-23T03:36:12
| 2019-08-23T03:36:12
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,750
|
py
|
# -*- coding: utf-8 -*-
import numpy as np
import geatpy as ea # 导入geatpy库
from sys import path as paths
from os import path
paths.append(path.split(path.split(path.realpath(__file__))[0])[0])
class soea_psy_studGA_templet(ea.SoeaAlgorithm):
"""
soea_psy_studGA_templet.py - Polysomy Stud GA templet(多染色体种马遗传算法模板)
模板说明:
该模板是内置算法模板soea_studGA_templet的多染色体版本，
因此里面的种群对象为支持混合编码的多染色体种群类PsyPopulation类的对象。
算法描述:
本模板实现的是种马遗传算法。算法流程详见参考文献[1]。
模板使用注意:
本模板调用的目标函数形如：aimFunc(pop),
其中pop为种群类的对象，代表一个种群，
pop对象的Phen属性（即种群染色体的表现型）等价于种群所有个体的决策变量组成的矩阵，
该函数根据该Phen计算得到种群所有个体的目标函数值组成的矩阵，并将其赋值给pop对象的ObjV属性。
若有约束条件，则在计算违反约束程度矩阵CV后赋值给pop对象的CV属性（详见Geatpy数据结构）。
该函数不返回任何的返回值，求得的目标函数值保存在种群对象的ObjV属性中，
违反约束程度矩阵保存在种群对象的CV属性中。
例如：population为一个种群对象，则调用aimFunc(population)即可完成目标函数值的计算，
此时可通过population.ObjV得到求得的目标函数值，population.CV得到违反约束程度矩阵。
若不符合上述规范，则请修改算法模板或自定义新算法模板。
参考文献:
[1] Khatib W , Fleming P J . The stud GA: A mini revolution?[C]// International
Conference on Parallel Problem Solving from Nature. Springer, Berlin, Heidelberg, 1998.
"""
def __init__(self, problem, population):
ea.SoeaAlgorithm.__init__(self, problem, population) # 先调用父类构造方法
if str(type(population)) != "<class 'PsyPopulation.PsyPopulation'>":
raise RuntimeError('传入的种群对象必须为PsyPopulation类型')
self.name = 'psy-studGA'
self.problem = problem
self.population = population
self.selFunc = 'tour' # 锦标赛选择算子
# 由于有多个染色体，因此需要用多个重组和变异算子，于是对应有多个重组和变异概率
self.recFuncs = []
self.mutFuncs = []
self.pcs = []
self.pms = []
for i in range(population.ChromNum):
if population.Encodings[i] == 'P':
self.recFuncs.append('xovpmx') # 部分匹配交叉
self.mutFuncs.append('mutinv') # 染色体片段逆转变异
else:
self.recFuncs.append('xovdp') # 两点交叉
if population.Encodings[i] == 'BG':
self.mutFuncs.append('mutbin') # 二进制变异
elif population.Encodings[i] == 'RI':
self.mutFuncs.append('mutbga') # breeder GA中的变异算子
else:
raise RuntimeError('编码方式必须为''BG''、''RI''或''P''.')
self.pcs.append(1) # 重组概率
self.pms.append(1) # 整条染色体的变异概率
def run(self):
#==========================初始化配置===========================
population = self.population
NIND = population.sizes
self.initialization() # 初始化算法模板的一些动态参数
#===========================准备进化============================
population.initChrom(NIND) # 初始化种群染色体矩阵（内含染色体解码，详见PsyPopulation类的源码）
self.problem.aimFunc(population) # 计算种群的目标函数值
population.FitnV = ea.scaling(self.problem.maxormins * population.ObjV, population.CV) # 计算适应度
self.evalsNum = population.sizes # 记录评价次数
#===========================开始进化============================
while self.terminated(population) == False:
bestIdx = np.argmax(population.FitnV, axis = 0) # 得到当代的最优个体的索引, 设置axis=0可使得返回一个向量
studPop = population[np.tile(bestIdx, (NIND//2))] # 复制最优个体NIND//2份，组成一个“种马种群”
restPop = population[np.where(np.array(range(NIND)) != bestIdx)[0]] # 得到除去精英个体外其它个体组成的种群
# 选择个体，以便后面与种马种群进行交配
tempPop = restPop[ea.selecting(self.selFunc, restPop.FitnV, (NIND - studPop.sizes))]
# 将种马种群与选择出来的个体进行合并
population = studPop + tempPop
# 进行进化操作，分别对各种编码的染色体进行重组和变异
for i in range(population.ChromNum):
population.Chroms[i] = ea.recombin(self.recFuncs[i], population.Chroms[i], self.pcs[i]) # 重组
population.Chroms[i] = ea.mutate(self.mutFuncs[i], population.Encodings[i], population.Chroms[i], population.Fields[i], self.pms[i]) # 变异
# 求进化后个体的目标函数值
population.Phen = population.decoding() # 染色体解码
self.problem.aimFunc(population)
self.evalsNum += population.sizes # 更新评价次数
population.FitnV = ea.scaling(self.problem.maxormins * population.ObjV, population.CV) # 计算适应度
return self.finishing(population) # 调用finishing完成后续工作并返回结果
|
[
"jazzbin@geatpy.com"
] |
jazzbin@geatpy.com
|
47bf2f00c6730182259d81aeab1bf82ce408ef5d
|
c7115a0a1470310792b81cd097e0aa47ed095195
|
/django_thoughtapi/manage.py
|
5045eb05410e0449491ad1e7a92edec2a1f3c746
|
[
"MIT"
] |
permissive
|
qwergram/thoughts_api
|
80818424b3755f671cfb65fcddff5c0769fa9e27
|
47e9a76cc15e30c36232b253eb0e44bb5f401482
|
refs/heads/master
| 2020-12-24T22:29:12.401158
| 2016-04-30T22:45:20
| 2016-04-30T22:45:20
| 57,338,528
| 0
| 0
| null | 2016-04-29T23:40:38
| 2016-04-28T22:46:59
| null |
UTF-8
|
Python
| false
| false
| 260
|
py
|
#!/usr/bin/env python
import os
import sys
if __name__ == "__main__":
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "django_thoughtapi.settings")
from django.core.management import execute_from_command_line
execute_from_command_line(sys.argv)
|
[
"npengra317@gmail.com"
] |
npengra317@gmail.com
|
69c0bb652daa62eea8c9a6a5378fd562629cf26a
|
ca7aa979e7059467e158830b76673f5b77a0f5a3
|
/Python_codes/p03095/s108733747.py
|
8493da82e83df4f2a53a5e799e1313b9f63c0471
|
[] |
no_license
|
Aasthaengg/IBMdataset
|
7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901
|
f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8
|
refs/heads/main
| 2023-04-22T10:22:44.763102
| 2021-05-13T17:27:22
| 2021-05-13T17:27:22
| 367,112,348
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,818
|
py
|
import sys
import math
from collections import Counter
N = int(input())
S = input()
MOD = 1000000007
# baba
# a(2), b(2), ab(1), ba(3)
# baab
# a(2), b(2), ab(2), ba(2)
# 1文字の時は分かる、それは単に数えるだけ
# 2文字の時は?
# 'ab' 'a'どれ選ぶ? → その後ろにある'b'は…
# 全部やるなら2^100000、ムリー
# dpいけるか?
# dpを設計しよう
# dp[n] : n文字目まで見た時の答え
# dp[n] = dp[n-1]
# baab
# dp[0] = 1 (b)
# dp[1] = dp[0]((b)のみ選ぶ) + 1(aのみ選ぶ) + dp[0] * 1 (ab)
# それが新しい文字なら?
# dp[n] = dp[n-1](追加で選ばない) + dp[n-1](選ぶ) + 1
# それが見たことある文字なら?
# 1文字単位では増えない
# n文字単位なら、pickする選択肢が増える
# ba (3)
# baa → 3 + 1?
# dp[n] = dp[n] (そのまま) + 最後の文字を使う
# 最後の文字を使うならどうなるか? → それ以外の種類の文字でつくるんだけど大変じゃない????
# baba
# babで5 a, b(2), ab(1) ba(1)
# 最後の文字を使うなら、bをどちらか選ぶ (か、何も選ばない)
# bをpickするか? * どれをpickするか?
# bをpickしない場合(1) + bをpickする場合(どれを選ぶ?)
# (1 + 2)
# abca
# abcで6 a, b, c, ab, ac, bc
# 最後の文字を使うなら、残りのbcの組み合わせ
# bをpickする/しない * cをpickする/しない 4通り?
ans = 1
counter = Counter()
counter[S[0]] += 1
for ch in S[1:]:
if ch in counter:
tmp = 1
for k, cnt in counter.items():
if k == ch:
continue
tmp = (tmp * (1 + cnt)) % MOD
ans = (ans + tmp) % MOD
counter[ch] += 1
else:
ans = (2 * ans) % MOD
ans = (ans + 1) % MOD
counter[ch] += 1
print(ans)
|
[
"66529651+Aastha2104@users.noreply.github.com"
] |
66529651+Aastha2104@users.noreply.github.com
|
ec88adb74b40ae3b44f04b1e117c8c881872eb99
|
ba2d24fd6c5ce7d490ee57f224fd5435a1132093
|
/setup.py
|
7b0ac69b67ea99435a867d57e8b00a0787e5f3aa
|
[
"MIT"
] |
permissive
|
FlowerOda/pytest-auto-parametrize
|
cb2aff37308bff571b980da88f222f8b88e4e36b
|
9db33bb06de13c26f753bfd18e254ce10ae1256c
|
refs/heads/master
| 2022-01-09T16:54:33.796383
| 2018-10-09T08:56:09
| 2018-10-09T08:56:09
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,591
|
py
|
from setuptools import setup
from setuptools.command.test import test as TestCommand
import sys
__version__ = 'unknown'
# "import" __version__
for line in open('pytest_auto_parametrize.py'):
if line.startswith('__version__'):
exec(line)
break
class PyTest(TestCommand):
"""Enable "python setup.py test".
Stripped down from:
http://doc.pytest.org/en/latest/goodpractices.html#manual-integration
"""
def run_tests(self):
import pytest
sys.exit(pytest.main([]))
setup(
name='pytest-auto-parametrize',
py_modules=['pytest_auto_parametrize'],
version=__version__,
author='Matthias Geier',
author_email='Matthias.Geier@gmail.com',
description='pytest plugin: avoid repeating arguments in parametrize',
long_description=open('README.rst').read(),
license='MIT',
keywords='parametrized testing'.split(),
url='https://github.com/mgeier/pytest-auto-parametrize',
platforms='any',
zip_safe=True,
classifiers=[
'Framework :: Pytest',
'Development Status :: 3 - Alpha',
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 3',
'Intended Audience :: Developers',
'Topic :: Software Development :: Testing',
],
entry_points={
'pytest11': ['pytest_auto_parametrize = pytest_auto_parametrize'],
},
tests_require=['pytest'],
cmdclass={'test': PyTest},
)
|
[
"Matthias.Geier@gmail.com"
] |
Matthias.Geier@gmail.com
|
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