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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
74c0f20f27a5b7bcad076b8a5b964d41878080ab
|
0a6b07635bfe1cda46fb9a537c3f5974c091f89a
|
/ExprGCNPPI.py
|
ec9a99c34d0d184b7f45fec3b1df86c652ecaa91
|
[] |
no_license
|
sabdollahi/WinBinVec
|
89096b48612a1efa24c4f9ea63f27cdc185059b4
|
5b4b2e7f0e9d97eccc9d558449d4dfaf36d53da3
|
refs/heads/main
| 2023-07-11T05:24:05.141008 | 2021-08-25T09:50:12 | 2021-08-25T09:50:12 | 309,229,996 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 16,400 |
py
|
from __future__ import division
from __future__ import print_function
import time
import argparse
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
import math
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import numpy as np
import seaborn as sns
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import roc_auc_score
import os
import pickle
from sklearn.model_selection import KFold
from torch.autograd import Variable
from sklearn.utils import shuffle
#"GraphConvolution" and "GCN" classes are obtained from the following GitHub repository
#https://github.com/tkipf/pygcn
class GraphConvolution(Module):
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.FloatTensor(in_features, out_features))
if bias:
self.bias = Parameter(torch.FloatTensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1. / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, input, adj):
support = torch.mm(input, self.weight)
output = torch.spmm(adj, support)
if self.bias is not None:
return output + self.bias
else:
return output
def __repr__(self):
return self.__class__.__name__ + ' (' \
+ str(self.in_features) + ' -> ' \
+ str(self.out_features) + ')'
class GCN(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(GCN, self).__init__()
self.gc1 = GraphConvolution(nfeat, nhid)
self.gc2 = GraphConvolution(nhid, nclass)
self.dropout = dropout
def forward(self, x, adj):
x = F.relu(self.gc1(x, adj))
x = F.dropout(x, self.dropout, training=self.training)
x = self.gc2(x, adj)
return F.log_softmax(x, dim=1)
#ExprGCNPPI implemented by Sina Abdollahi for WinBinVec paper
class ExprGCNPPI(nn.Module):
def __init__(self):
super(ExprGCNPPI, self).__init__()
self.gcn = GCN(nfeat=1, nhid=8, nclass=1, dropout=0.5)
#626: The number of partner proteins involve in the PPIs
self.fc1 = nn.Linear(572, 256)
self.fc2 = nn.Linear(256, 8)
self.fc3 = nn.Linear(8, 2)
self.bn1 = nn.BatchNorm1d(num_features=256)
self.drop1 = torch.nn.Dropout(0.4)
self.bn2 = nn.BatchNorm1d(num_features=8)
self.drop2 = torch.nn.Dropout(0.4)
def forward(self, adj, expr, batch_size):
outs = []
for i in range(batch_size):
outs.append(self.gcn(expr[i,:], adj[i,:]).view(1,adj.size(1)))
concat_gcn = outs[0]
for i in range(1,batch_size):
concat_gcn = torch.cat([concat_gcn, outs[i]], dim=0)
output = self.drop1(F.relu(self.bn1(self.fc1(concat_gcn))))
output = self.drop2(F.relu(self.bn2(self.fc2(output))))
output = self.fc3(output)
return output
ppi_adj_matrix = pickle.load(open("DATASET/Adj.pickle", "rb"))
#adj -> Adjacency matrix (N by N ---> N is the number of nodes)
#expr -> Features (Expression) matrix (N by 1 ---> 1 is for gene expression value for each protein)
tcga_clinical_dataframe = pickle.load(open("DATASET/TCGA_clinical_dataframe.pickle","rb"))
classes = {"adrenal gland":0, "bladder":1, "breast":2, "GYN":3, "bile duct":4, "CRC":5, "bone marrow":6, "esophagus":7, "brain":8, "head and neck":9, "Kidney":10, "liver":11, "Lung":12, "pleura":13, "pancreatic":14, "male reproductive system":15, "other":16, "Melanoma":17, "stomach":18, "thyroid":19, "thymus":20}
which_clinicals = ['cancer_class']
tcga_clinical_dataframe = tcga_clinical_dataframe[which_clinicals]
for cancer_class in classes:
print(">>>>>>" + cancer_class)
folds_accuracy = []
folds_roc_auc = []
folds_PR_auc = []
replace_statement = {}
for cl in classes:
if(cl != cancer_class):
replace_statement[cl] = 0
else:
replace_statement[cl] = 1
specific_cancer_patients = tcga_clinical_dataframe[tcga_clinical_dataframe["cancer_class"] == cancer_class]
specific_cancer_patients = specific_cancer_patients.replace({'cancer_class': replace_statement})
other_cancer_patients = tcga_clinical_dataframe[tcga_clinical_dataframe["cancer_class"] != cancer_class]
other_cancer_patients = shuffle(other_cancer_patients).sample(n = len(specific_cancer_patients))
other_cancer_patients = other_cancer_patients.replace({'cancer_class': replace_statement})
K = 10 #Kfold (number of parts = K)
kf_other = KFold(n_splits = K, shuffle = True)
kf_specific = KFold(n_splits = K, shuffle = True)
parts_specific = kf_specific.split(specific_cancer_patients)
parts_other = kf_other.split(other_cancer_patients)
indices_specific = next(parts_specific, None)
indices_other = next(parts_other, None)
fold = 1
while(indices_specific):
#Define the model
model = ExprGCNPPI()
# Mean Squared Error
criterion = torch.nn.CrossEntropyLoss()
# Stochastic Gradient Descent
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
batch_size = 20
print("Shuffled Epoch (20): ", end="")
for shuffled_epoch in range(20):
if(shuffled_epoch == 19):
print((shuffled_epoch+1))
else:
print((shuffled_epoch+1), end=", ")
training = specific_cancer_patients.iloc[indices_specific[0]]
training_other = other_cancer_patients.iloc[indices_other[0]]
training = shuffle(training.append(training_other))
Y = training[['cancer_class']].values
Y = Variable(torch.LongTensor(Y.flatten()), requires_grad=False)
training = training.index
for epoch in range(50):
for index in range(0, len(training), batch_size):
y = Y[index : index + batch_size]
batch_X = []
kk = 0
for patient in training[index : index + batch_size]:
kk += 1
p_data = pickle.load(open("DATASET/ExpressionInputs/" + patient + "_expressions.pickle", "rb"))
batch_X.append(p_data)
X = np.asarray(batch_X)
adj = np.array([ppi_adj_matrix]*batch_size)
adj = adj.astype(np.float32)
adj = torch.FloatTensor(adj)
X = X.astype(np.float32)
X = torch.FloatTensor(X)
X = X.view(X.size(0), X.size(1), 1)
optimizer.zero_grad()
Y_hat = model(adj, X, kk)
loss = criterion(Y_hat, y)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
optimizer.step()
test = specific_cancer_patients.iloc[indices_specific[1]]
test_other = other_cancer_patients.iloc[indices_other[1]]
test = shuffle(test.append(test_other))
Y_test = test[['cancer_class']].values
Y_test = Variable(torch.LongTensor(Y_test.flatten()), requires_grad=False)
test = test.index
avg_acc = 0
isFirstTime = True
output_predicted = ""
ii = 0
for index in range(0, len(training), batch_size):
y = Y_test[index : index + batch_size]
test_list = []
kk = 0
for patient in test[index : index + batch_size]:
kk += 1
p_data = pickle.load(open("DATASET/ExpressionInputs/" + patient + "_expressions.pickle", "rb"))
test_list.append(p_data)
#test_list = torch.FloatTensor(test_list)
if(len(test_list) <= 1):
break
X_test = np.asarray(test_list)
adj = np.array([ppi_adj_matrix]*batch_size)
adj = adj.astype(np.float32)
adj = torch.FloatTensor(adj)
X_test = X_test.astype(np.float32)
X_test = torch.FloatTensor(X_test)
X_test = X_test.view(X_test.size(0), X_test.size(1), 1)
test_batch_Y_hat = model.forward(adj, X_test, kk)
if(isFirstTime):
output_predicted = test_batch_Y_hat
isFirstTime = False
else:
output_predicted = torch.cat((output_predicted, test_batch_Y_hat), 0)
dummy, preds_test = torch.max (test_batch_Y_hat, dim = 1)
accuracy_test = (preds_test == y).long().sum().float() / preds_test.size()[0]
avg_acc += accuracy_test
ii += 1
avg_acc = avg_acc / ii
Y_prediction = torch.softmax(output_predicted, dim=1)
Y_prediction = np.array(Y_prediction.tolist())
Y_real = np.array([[1,0] if y == 0 else [0,1] for y in Y_test])
fpr = dict()
tpr = dict()
precision = dict()
recall = dict()
roc_auc = dict()
PR_auc = dict()
for i in range(2):
fpr[i], tpr[i], _ = roc_curve(Y_real[:, i], Y_prediction[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
precision[i], recall[i], _ = precision_recall_curve(Y_real[:, i], Y_prediction[:, i])
PR_auc[i] = auc(recall[i], precision[i])
print("Fold " + str(fold) + " Accuracy: " + str(avg_acc))
print("Fold " + str(fold) + " ROC AUC: " + str(roc_auc[1]))
print("Fold " + str(fold) + " PR AUC: " + str(PR_auc[1]))
fold += 1
folds_accuracy.append(avg_acc)
folds_roc_auc.append(roc_auc[1])
folds_PR_auc.append(PR_auc[1])
indices_specific = next(parts_specific, None)
indices_other = next(parts_other, None)
if not os.path.exists('RESULTS/ExprGCNPPIResults'):
os.makedirs('RESULTS/ExprGCNPPIResults')
pickle.dump(folds_accuracy, open("RESULTS/ExprGCNPPIResults/" + cancer_class + "_Accuracy.pickle","wb"))
pickle.dump(folds_roc_auc, open("RESULTS/ExprGCNPPIResults/" + cancer_class + "_ROC_AUC.pickle","wb"))
pickle.dump(folds_PR_auc, open("RESULTS/ExprGCNPPIResults/" + cancer_class + "_PR_AUC.pickle","wb"))
#Predict Metastasis (Stage IV) or not (Stages I, II, and III)
#tcga_clinical_dataframe[tcga_clinical_dataframe['stage'] == 'Stage IVA']
tcga_clinical_dataframe = pickle.load(open("DATASET/TCGA_clinical_dataframe.pickle","rb"))
which_clinicals = ['stage']
tcga_clinical_dataframe = tcga_clinical_dataframe[which_clinicals]
replace_statement = {}
metastasis_list = ['Stage IV','Stage IVA','Stage IVB','Stage IVC']
other_list = ['Stage I','Stage IA','Stage IB','Stage II','Stage IIA','Stage IIB','Stage IIC','Stage III','Stage IIIA','Stage IIIB','Stage IIIC']
#Metastasis Stage
for m in metastasis_list:
replace_statement[m] = 1
#Non-metastasis Stage
for o in other_list:
replace_statement[o] = 0
metastasis_patients = tcga_clinical_dataframe[tcga_clinical_dataframe["stage"].isin(metastasis_list)]
metastasis_patients = metastasis_patients.replace({'stage': replace_statement})
other_patients = tcga_clinical_dataframe[tcga_clinical_dataframe["stage"].isin(other_list)]
other_patients = other_patients.replace({'stage': replace_statement})
start_and_end_for_other = [0,793,1586,2379,3172,3965,4758,5554]
for i in range(7):
print("PART: " + str(i))
selected_other_patients = other_patients[start_and_end_for_other[i]:start_and_end_for_other[i+1]]
folds_accuracy = []
K = 10 #Kfold (number of parts = K)
kf_other = KFold(n_splits = K, shuffle = True)
kf_metastasis = KFold(n_splits = K, shuffle = True)
parts_metastasis = kf_metastasis.split(metastasis_patients)
parts_other = kf_other.split(selected_other_patients)
indices_metastasis = next(parts_metastasis, None)
indices_other = next(parts_other, None)
fold_number = 1
while(indices_metastasis):
model = ExprGCNPPI()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
batch_size = 20
print("Shuffled Epoch (20): ", end="")
for shuffled_epoch in range(20):
if(shuffled_epoch == 19):
print((shuffled_epoch+1))
else:
print((shuffled_epoch+1), end=", ")
training = metastasis_patients.iloc[indices_metastasis[0]]
training_other = selected_other_patients.iloc[indices_other[0]]
training = shuffle(training.append(training_other))
Y = training[['stage']].values
Y = Variable(torch.LongTensor(Y.flatten()), requires_grad=False)
training = training.index
for epoch in range(50):
for index in range(0, len(training), batch_size):
y = Y[index : index + batch_size]
batch_X = []
kk = 0
for patient in training[index : index + batch_size]:
kk += 1
p_data = pickle.load(open("DATASET/ExpressionInputs/" + patient + "_expressions.pickle", "rb"))
batch_X.append(p_data)
X = np.asarray(batch_X)
adj = np.array([ppi_adj_matrix]*batch_size)
adj = adj.astype(np.float32)
adj = torch.FloatTensor(adj)
X = X.astype(np.float32)
X = torch.FloatTensor(X)
X = X.view(X.size(0), X.size(1), 1)
optimizer.zero_grad()
Y_hat = model(adj, X, kk)
loss = criterion(Y_hat, y)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
optimizer.step()
test = metastasis_patients.iloc[indices_metastasis[1]]
test_other = selected_other_patients.iloc[indices_other[1]]
test = shuffle(test.append(test_other))
Y_test = test[['stage']].values
Y_test = Variable(torch.LongTensor(Y_test.flatten()), requires_grad=False)
test = test.index
avg_acc = 0
ii = 0
for index in range(0, len(training), batch_size):
y = Y_test[index : index + batch_size]
test_list = []
kk = 0
for patient in test[index : index + batch_size]:
kk += 1
p_data = pickle.load(open("DATASET/ExpressionInputs/" + patient + "_expressions.pickle", "rb"))
test_list.append(p_data)
if(len(test_list) <= 1):
break
X_test = np.asarray(test_list)
adj = np.array([ppi_adj_matrix]*batch_size)
adj = adj.astype(np.float32)
adj = torch.FloatTensor(adj)
X_test = X_test.astype(np.float32)
X_test = torch.FloatTensor(X_test)
X_test = X_test.view(X_test.size(0), X_test.size(1), 1)
test_batch_Y_hat = model.forward(adj, X_test, kk)
dummy, preds_test = torch.max (test_batch_Y_hat, dim = 1)
accuracy_test = (preds_test == y).long().sum().float() / preds_test.size()[0]
avg_acc += accuracy_test
ii += 1
avg_acc = avg_acc / ii
print("Fold: " + str(fold_number) + " ACC: " + str(avg_acc))
fold_number += 1
folds_accuracy.append(avg_acc)
indices_metastasis = next(parts_metastasis, None)
indices_other = next(parts_other, None)
if not os.path.exists('RESULTS/ExprGCNPPI-StagePrediction'):
os.makedirs('RESULTS/ExprGCNPPI-StagePrediction')
pickle.dump(folds_accuracy, open("RESULTS/ExprGCNPPI-StagePrediction/Part" + str(i) + "_folds_accuracy.pickle","wb"))
|
[
"[email protected]"
] | |
bf38c9ba21a9178526560f3d4d833892fc472830
|
4ac6645c5925feefc8a3ab8587d08edc6edb220e
|
/school/unit/tests/test_api.py
|
0abf5166aa3e74b405609fa0d4013d54c6ac092d
|
[
"MIT"
] |
permissive
|
yucealiosman/school
|
4a4b701a8ef87fc15b637e655f4d995a0b243adf
|
630059760f411c163db57f980b780d8501aa1a6d
|
refs/heads/main
| 2023-09-04T05:25:36.278730 | 2021-03-19T12:28:33 | 2021-03-19T15:05:17 | 349,236,587 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 5,007 |
py
|
import json
from unittest.mock import patch
from django.urls import reverse
from rest_framework import status
from rest_framework.test import APITestCase, APIClient
from school.unit import models
from school.unit.tests import factories
from school.unit.tests.factories import SuperUserFactory
class BaseTest(APITestCase):
def setUp(self):
self.client = APIClient()
self.client.force_authenticate(user=SuperUserFactory())
class StudentApiTest(BaseTest):
def test_student_list(self):
student_count = 5
teacher = factories.TeacherFactory()
class_room = factories.ClassRoomFactory(teachers=[teacher])
student_list = factories.StudentFactory.create_batch(
class_room=class_room, size=student_count)
expected_student_pk_set = {str(student.pk) for student in
student_list}
response = self.client.get(reverse('students-list'))
data = response.json()
self.assertEqual(response.status_code, status.HTTP_200_OK,
response.data)
self.assertEqual(data['count'], student_count)
student_pk_set_from_resp = {student["pk"] for student in
data["results"]}
self.assertEqual(expected_student_pk_set, student_pk_set_from_resp)
def test_student_detail(self):
teacher = factories.TeacherFactory()
class_room = factories.ClassRoomFactory(teachers=[teacher])
student = factories.StudentFactory(class_room=class_room)
response = self.client.get(
reverse('students-detail', kwargs={'pk': str(student.pk)}))
self.assertEqual(response.status_code, status.HTTP_200_OK,
response.data)
data = response.json()
self.assertEqual(class_room.code, data["class_room"]["code"])
teacher_pk_list_from_resp = [teacher["pk"] for teacher in
data["class_room"]["teachers"]]
self.assertEqual(teacher_pk_list_from_resp, [str(teacher.pk)])
class HomeWorkApiTest(BaseTest):
@patch('school.unit.services.HomeWorkService.notify')
def test_create_homework(self, notify_mock):
teacher = factories.TeacherFactory()
class_room = factories.ClassRoomFactory(teachers=[teacher])
hw_not_created = factories.ClassHomeWorkFactory.build()
data = {
'title': hw_not_created.title,
'description': hw_not_created.description,
'class_room': str(class_room.pk)
}
response = self.client.post(
reverse('homeworks-by-teacher-list',
kwargs={'teacher__pk': str(teacher.pk)}),
data=json.dumps(data),
content_type='application/json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED,
response.data)
data = response.json()
hw_created = models.ClassHomeWork.objects.get(pk=data["pk"])
self.assertEqual(data["title"], hw_not_created.title)
self.assertEqual(data["description"], hw_not_created.description)
self.assertEqual(data["teacher"], str(teacher.pk))
self.assertEqual(data["class_room"], str(class_room.pk))
notify_mock.assert_called_once_with(hw_created, 'created')
@patch('school.unit.services.HomeWorkService.notify')
def test_update_homework(self, notify_mock):
teacher = factories.TeacherFactory()
class_room = factories.ClassRoomFactory(teachers=[teacher])
homework = factories.ClassHomeWorkFactory(class_room=class_room,
teacher=teacher)
new_description = "New HomeWork description"
update_data = {
'description': new_description
}
response = self.client.patch(
reverse('homeworks-by-teacher-detail',
kwargs={'teacher__pk': str(teacher.pk),
'pk': str(homework.pk)}),
data=json.dumps(update_data),
content_type='application/json')
self.assertEqual(response.status_code, status.HTTP_200_OK,
response.data)
data = response.json()
self.assertEqual(data["description"], new_description)
notify_mock.assert_called_once_with(homework, 'updated')
@patch('school.unit.services.HomeWorkService.notify')
def test_delete_homework(self, notify_mock):
homework = factories.ClassHomeWorkFactory()
response = self.client.delete(
reverse('homeworks-by-teacher-detail',
kwargs={'teacher__pk': str(homework.teacher.pk),
'pk': str(homework.pk)}))
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT,
response.data)
self.assertFalse(
models.ClassHomeWork.objects.filter(pk=homework.pk).exists())
notify_mock.assert_called_once()
|
[
"aliosmanyuce@gmail"
] |
aliosmanyuce@gmail
|
06d3b8b17c46a0ae3faf7387123f73c73bea8d78
|
4766d241bbc736e070f79a6ae6a919a8b8bb442d
|
/20200215Python-China/0094. Binary Tree Inorder Traversal.py
|
08893a77b8777c433e17edf90f755b8b4b58c958
|
[] |
no_license
|
yangzongwu/leetcode
|
f7a747668b0b5606050e8a8778cc25902dd9509b
|
01f2edd79a1e922bfefecad69e5f2e1ff3a479e5
|
refs/heads/master
| 2021-07-08T06:45:16.218954 | 2020-07-18T10:20:24 | 2020-07-18T10:20:24 | 165,957,437 | 10 | 8 | null | null | null | null |
UTF-8
|
Python
| false | false | 733 |
py
|
'''
Given a binary tree, return the inorder traversal of its nodes' values.
Example:
Input: [1,null,2,3]
1
\
2
/
3
Output: [1,3,2]
Follow up: Recursive solution is trivial, could you do it iteratively?
'''
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution:
def inorderTraversal(self, root: TreeNode) -> List[int]:
rep=[]
self.getInOrderTra(root,rep)
return rep
def getInOrderTra(self,root,rep):
if not root:
return
self.getInOrderTra(root.left,rep)
rep.append(root.val)
self.getInOrderTra(root.right,rep)
|
[
"[email protected]"
] | |
e5fefc6b8e0ec0d00e467d6808038193d92e8aa7
|
683b73e0c95c755a08e019529aed3ff1a8eb30f8
|
/machina/apps/forum_moderation/__init__.py
|
f1911a14dbd6195e896b647fa949fa08a0c6abce
|
[
"BSD-3-Clause"
] |
permissive
|
DrJackilD/django-machina
|
b3a7be9da22afd457162e0f5a147a7ed5802ade4
|
76858921f2cd247f3c1faf4dc0d9a85ea99be3e1
|
refs/heads/master
| 2020-12-26T08:19:09.838794 | 2016-03-11T03:55:25 | 2016-03-11T03:55:25 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 217 |
py
|
# -*- coding: utf-8 -*-
# Standard library imports
# Third party imports
# Local application / specific library imports
default_app_config = 'machina.apps.forum_moderation.registry_config.ModerationRegistryConfig'
|
[
"[email protected]"
] | |
ec390ae9d26d00c9987dcba374799b70c1c22380
|
704f50b7df466bd30811707f81561a5d8ace3127
|
/screens/admin/delitem.py
|
98c82f6ecbe218fb770c82cb71eed836c5b1a469
|
[] |
no_license
|
Maulik747/LostandFound
|
0e7c4e4c3c3561151fbc35ff463caeda428f1803
|
f31c0e8f9e9e50f1c62c9b7040560d5e21a24426
|
refs/heads/main
| 2023-02-18T22:17:52.377378 | 2021-01-18T19:26:10 | 2021-01-18T19:26:10 | 330,764,457 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,709 |
py
|
#!/usr/bin/env python
import cgitb
import cgi
import mysql.connector
cgitb.enable()
print("Content-Type: text/html;charset=utf-8\n\n")
mydb = mysql.connector.connect(
host="localhost",
user="root",
password="root",
database = 'group6',
auth_plugin='mysql_native_password'
)
cursor = mydb.cursor()
head = '''<html>
<head>
<link rel="stylesheet" href="../admin.css">
<meta name="viewport" content="width=device-width, initial-scale=1">
</head>
<body>
<div style="text-align: center;margin-top: 10%;">
<form action="../../scripts/delitem.py" method="POST">
<label class="label">Item Id:</label><br>
<div style="display: inline">
<select name='id'>
'''
print(head)
cursor.execute("select * from Item")
items = cursor.fetchall()
for i in items:
print("<option value = '{}'>{}</option>".format(i[0],i[0]))
body = '''
</select>
</div><br><br>
<label class = 'label'>Moderator</label><br>
<div>
<select name ='mod'>
'''
print(body)
cursor.execute('select * from Moderator')
people = cursor.fetchall()
for j in people:
print('<option value = {}>{}</option>'.format(j[0],j[0]))
footer = '''
</select>
</div><br>
<label class="label">Description:</label><br>
<div style="display: inline">
<div ><textarea name='description' style="width: 25%; height: 15%;">
</textarea></div>
</div>
<input class="button" type="submit" value="Delete">
</form>
</div>
</body>
</html>
'''
print(footer)
|
[
"[[email protected]]"
] | |
075c8636339cb3b08aa5c4c3815994408a005e38
|
853d7bd91f4ba254fba0ff28f2e0a3eb2b74fa48
|
/errata_tool/release.py
|
b5c1211cb9a8c86556c758725ad9297bc11a9fbb
|
[
"MIT"
] |
permissive
|
smunilla/errata-tool
|
b07614daeceda4a1bfc18ce59679be0a93bb084f
|
91bdfb17f15308b46298210fbb2fe5af786276bc
|
refs/heads/master
| 2020-04-10T00:18:12.471123 | 2018-11-19T17:33:02 | 2018-11-28T15:40:08 | 160,681,680 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 6,800 |
py
|
from __future__ import print_function
import sys
from datetime import date
from errata_tool import ErrataConnector
from errata_tool.product import Product
from errata_tool.product_version import ProductVersion
from errata_tool.user import User
class NoReleaseFoundError(Exception):
pass
class MultipleReleasesFoundError(Exception):
pass
class ReleaseCreationError(Exception):
pass
class Release(ErrataConnector):
def __init__(self, **kwargs):
if 'id' not in kwargs and 'name' not in kwargs:
raise ValueError('missing release "id" or "name" kwarg')
self.id = kwargs.get('id')
self.name = kwargs.get('name')
self.refresh()
def refresh(self):
url = self._url + '/api/v1/releases?'
if self.id is not None:
url += 'filter[id]=%s' % self.id
elif self.name is not None:
url += 'filter[name]=%s' % self.name
result = self._get(url)
if len(result['data']) < 1:
raise NoReleaseFoundError()
if len(result['data']) > 1:
# it's possible to accidentally have identically named releases,
# see engineering RT 461783
raise MultipleReleasesFoundError()
self.data = result['data'][0]
self.id = self.data['id']
self.name = self.data['attributes']['name']
self.description = self.data['attributes']['description']
self.type = self.data['attributes']['type']
self.is_active = self.data['attributes']['is_active']
self.enabled = self.data['attributes']['enabled']
self.blocker_flags = self.data['attributes']['blocker_flags']
self.is_pdc = self.data['attributes']['is_pdc']
self.product_versions = self.data['relationships']['product_versions']
self.url = self._url + '/release/show/%d' % self.id
# For displaying in scripts/logs:
self.edit_url = self._url + '/release/edit/%d' % self.id
def advisories(self):
"""
Find all advisories for this release.
:returns: a list of dicts, one per advisory.
For example:
[{
"id": 32972,
"advisory_name": "RHSA-2018:0546",
"product": "Red Hat Ceph Storage",
"release": "rhceph-3.0",
"synopsis": "Important: ceph security update",
"release_date": None,
"qe_owner": "[email protected]",
"qe_group": "RHC (Ceph) QE",
"status": "SHIPPED_LIVE",
"status_time": "March 15, 2018 18:29"
}]
"""
url = '/release/%d/advisories.json' % self.id
return self._get(url)
@classmethod
def create(klass, name, product, product_versions, type, program_manager,
default_brew_tag, blocker_flags, ship_date=None):
"""
Create a new release in the ET.
See https://bugzilla.redhat.com/1401608 for background.
Note this method enforces certain conventions:
* Always disables PDC for a release
* Always creates the releases as "enabled"
* Always allows multiple advisories per package
* Description is always the combination of the product's own
description (for example "Red Hat Ceph Storage") with the number
from the latter part of the release's name. So a new "rhceph-3.0"
release will have a description "Red Hat Ceph Storage 3.0".
:param name: short name for this release, eg "rhceph-3.0"
:param product: short name, eg. "RHCEPH".
:param product_versions: list of names, eg. ["RHEL-7-CEPH-3"]
:param type: "Zstream" or "QuarterlyUpdate"
:param program_manager: for example "anharris" (Drew Harris, Ceph PgM)
:param default_brew_tag: for example "ceph-3.0-rhel-7-candidate"
:param blocker_flags: for example, "ceph-3.0"
:param ship_date: date formatted as strftime("%Y-%b-%d"). For example,
"2017-Nov-17". If ommitted, the ship_date will
be set to today's date. (This can always be updated
later to match the ship date value in Product
Pages.)
"""
product = Product(product)
(_, number) = name.split('-', 1)
description = '%s %s' % (product.description, number)
program_manager = User(program_manager)
product_version_ids = set([])
for pv_name in product_versions:
pv = ProductVersion(pv_name)
product_version_ids.add(pv.id)
if ship_date is None:
today = date.today()
ship_date = today.strftime("%Y-%b-%d")
et = ErrataConnector()
url = et._url + '/release/create'
payload = {
'type': type,
'release[allow_blocker]': 0,
'release[allow_exception]': 0,
'release[allow_pkg_dupes]': 1,
'release[allow_shadow]': 0,
'release[blocker_flags]': blocker_flags,
'release[default_brew_tag]': default_brew_tag,
'release[description]': description,
'release[enable_batching]': 0,
'release[enabled]': 1,
'release[is_deferred]': 0,
'release[is_pdc]': 0,
'release[name]': name,
'release[product_id]': product.id,
'release[product_version_ids][]': product_version_ids,
'release[program_manager_id]': program_manager.id,
'release[ship_date]': ship_date,
'release[type]': type,
}
result = et._post(url, data=payload)
if (sys.version_info > (3, 0)):
body = result.text
else:
# Found during live testing:
# UnicodeEncodeError: 'ascii' codec can't encode character u'\xe1'
# in position 44306: ordinal not in range(128)
# Not sure why there was a non-ascii character in the ET's HTTP
# response, but this fixes it.
body = result.text.encode('utf-8')
if result.status_code != 200:
# help with debugging:
print(body)
result.raise_for_status()
# We can get a 200 HTTP status_code here even when the POST failed to
# create the release in the ET database. (This happens, for example, if
# there are no Approved Components defined in Bugzilla for the release
# flag, and the ET hits Bugzilla's XMLRPC::FaultException.)
if 'field_errors' in body:
print(body)
raise ReleaseCreationError('see field_errors <div>')
return klass(name=name)
|
[
"[email protected]"
] | |
552fea4e7e4a404550ffa6236bc4c30f22f33e18
|
3f9f7c73bb2f9da31c586d2b64e2cc94f35239dc
|
/django-polls/polls/tests/test_models.py
|
94b7c24fbee98fcaf5c51ee69dd5ad670600b45b
|
[
"MIT"
] |
permissive
|
jsterling23/DPY_Refresher
|
eb57e37d4bbad14143800719668b990b459fb56d
|
4646b7ebd79ba853f5ccc172183f41257cc12b60
|
refs/heads/master
| 2020-03-23T19:11:32.626731 | 2018-07-29T01:17:49 | 2018-07-29T01:17:49 | 141,959,227 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,141 |
py
|
from django.test import TestCase
import datetime
from django.utils import timezone
from ..models import Question
from django.urls import reverse
class QuestionModelTests(TestCase):
def test_was_published_recently_with_future_question(self):
# method should return false for future dated questions.
time = timezone.now() + datetime.timedelta(days=1, seconds=1)
future_question = Question(pub_date=time)
self.assertIs(future_question.was_published_recently(), False)
def test_was_published_recently_with_past_question(self):
# method should return false for past dated questions.
time = timezone.now() - datetime.timedelta(days=1, seconds=1)
past_question = Question(pub_date=time)
self.assertIs(past_question.was_published_recently(), False)
def test_was_published_recently_with_current_question(self):
# should return True for current question
time = timezone.now() - datetime.timedelta(hours=23, minutes=59, seconds=59)
current_question = Question(pub_date=time)
self.assertIs(current_question.was_published_recently(), True)
|
[
"[email protected]"
] | |
004e7568fbdb3e5a639501d4dd91b45601254179
|
702f403e33c94b32bd95e9284349e3c5aa751361
|
/TextAcquisition.py
|
6823d079b79c833821e7b6ae76b37cacd842ea56
|
[] |
no_license
|
Timmichi/ICSearch-Engine
|
71bcc0cefe24afe974ca13f8a6ee15d254776c43
|
471f3b9cd83fbfe2686037f285cdfb787e003146
|
refs/heads/main
| 2023-03-27T01:30:04.997265 | 2021-03-24T22:07:05 | 2021-03-24T22:07:05 | 351,213,421 | 2 | 1 | null | null | null | null |
UTF-8
|
Python
| false | false | 8,935 |
py
|
import json
import re
from bs4 import BeautifulSoup
import os
import sys
import math
from nltk.stem import PorterStemmer
# Index = Token : [(DocID, ((SearchWord*Priority)+(SearchWord*BasicWords)), [Positions in the text])]
# DocID = DocID : URL
# IndexMarkers = InitialLetter : (StartPosition,EndPosition)
# DocIDMarkers = lineNumber : (StartPosition,EndPosition)
def tokenize(token):
tokens = []
try:
ps = PorterStemmer()
tokens += [ps.stem(token.lower())
for token in re.findall('[a-zA-Z0-9]+', token)]
return tokens
except:
print("ERROR: Tokenize Function Error")
return tokens
def computeWordData(tokens):
if not isinstance(tokens, list):
return {}
freq = {}
positions = {}
for i, t in enumerate(tokens):
if t in freq.keys():
freq[t] += 1
positions[t].append(i)
else:
freq[t] = 1
positions[t] = [i]
return freq, positions
def updateIndex(index, tokenFrequency, totalPositions, importantFrequency, totalWordsInDoc, docID):
for k, v in tokenFrequency.items():
# calculate tf score
tf = v
if tf > 0 and k in importantFrequency:
tf = 2 + math.log10(tf) + math.log(importantFrequency[k])
elif tf > 0 and k not in importantFrequency:
tf = 1 + math.log10(tf)
# add tf score and DocID to posting
if k in index.keys():
index[k].append((docID, tf))
else:
index[k] = [(docID, tf)]
return index
def writeIndex(index):
for k, v in index.items():
directory = ".\letters"
fileName = k + ".txt"
filePath = os.path.join(directory, fileName)
if not os.path.exists(filePath):
f = open(filePath, "w", encoding='utf-8')
for key, value in sorted(v.items(), key=lambda posting: len(posting[1]), reverse=True):
f.write(f"{key} {str(value)}\n")
f.close()
continue
storedData = {}
f = open(filePath, "r", encoding='utf-8')
for txt in f:
val = re.search("^([a-zA-Z0-9]+) (.+)", txt)
token = val.group(1)
posting = eval(val.group(2))
storedData[token] = posting
f.close()
f = open(filePath, "w", encoding='utf-8')
for token, posting in v.items():
if token in storedData:
storedData[token] = storedData[token] + posting
else:
storedData[token] = posting
sortedTuple = sorted(storedData.items(),
key=lambda posting: len(posting[1]), reverse=True)
for k, v in sortedTuple:
f.write(f"{k} {str(v)}\n")
f.close()
def writeDocID(docID, IDline):
for k, v in docID.items():
directory = "." + "\\" + "numbers"
fileName = str(IDline) + ".txt"
filePath = os.path.join(directory, fileName)
f = open(filePath, "a", encoding='utf-8')
f.write(f"{k} {v}\n")
f.close()
def mwMarkers(marker, directory, mergeFile):
endPos = 0
for root, dirs, files in os.walk(directory, topdown=False):
for file in files:
filePath = os.path.join(directory, file)
k = file[0:-4]
with open(filePath, "r", encoding='utf-8', errors='ignore') as f, open(mergeFile, "a", encoding='utf-8') as f2:
for line in f:
f2.write(line)
f2.close()
startPos = endPos
f.seek(0, 2)
endPos = f.tell() + startPos
f.close()
v = (startPos, endPos)
marker[k] = v
def convertIndexToAlphaIndex(index):
alphaIndex = {}
for k, v in index.items():
key = k[0]
if key in alphaIndex.keys():
alphaIndex[key][k] = v
else:
alphaIndex[key] = {}
alphaIndex[key][k] = v
return alphaIndex
def getTitleParagraph(soup):
title = soup.find('title')
if title:
title = soup.find('title').getText()
paragraph = soup.findAll('p')
if paragraph:
preParagraph = soup.findAll('p')
paragraph = ''
for p in preParagraph:
for x in p.findAll(text=True):
paragraph += x
else:
paragraph = soup.getText()
paragraph = re.findall("[A-Z].*?[\.!?,]", paragraph,
re.MULTILINE | re.DOTALL)
if title == None and paragraph:
title = ''
if len(paragraph) < 2:
loop = len(paragraph)
else:
loop = 2
for i in range(0, loop):
title += paragraph[i]
if title and len(title) > 65:
l = title.split(" ")
title = ''
if len(l) < 5:
loop = len(l)
else:
loop = 5
for i in range(0, loop):
title += ' ' + l[i]
title += '...'
if paragraph:
tempParagraph = ''
if len(paragraph) < 5:
loop = len(paragraph)
else:
loop = 5
for i in range(0, loop):
tempParagraph += paragraph[i]
paragraph = tempParagraph[0:250] + "..."
return title, paragraph
def getCondensedUrl(preUrl):
preUrl = preUrl.split("//")
preUrl = preUrl[1]
preUrl = preUrl.split("/")
url = f'{preUrl[0]}'
preUrl.pop(0)
iters = 0
while len(url) < 25 and iters < len(preUrl):
segment = preUrl[iters]
if len(segment) > 10:
url += " > " + segment[0:10] + "..."
else:
url += " > " + segment
iters += 1
return url
filePaths = list()
index = dict()
docID = dict()
for root, dirs, files in os.walk(".\DEV", topdown=False):
for name in files:
filePaths.append(os.path.join(root, name))
fileNumber = 1
initIDLine = 1
currIDLine = initIDLine
total = len(filePaths)
count = 0
for filePath in filePaths:
try:
with open(filePath) as f:
data = json.load(f)
soup = BeautifulSoup(data["content"], "html.parser")
# computes frequency of tokens that are "important"
importantList = ["strong", "h1", "h2", "h3", "title", "b"]
importantText = [words.text.strip()
for words in soup.findAll(importantList)]
importantText = ' '.join([elem for elem in importantText])
importantToken = tokenize(importantText)
importantFrequency = computeWordData(importantToken)[0]
url = data["url"]
con = getCondensedUrl(url)
title, paragraph = getTitleParagraph(soup)
docID[fileNumber] = [url, con, title, paragraph]
text = soup.getText()
fileToken = tokenize(text)
totalWordsInDoc = len(fileToken)
tokenFrequency, totalPositions = computeWordData(fileToken)
updateIndex(index, tokenFrequency, totalPositions,
importantFrequency, totalWordsInDoc, fileNumber)
print(int(float(fileNumber)*100/float(total)), "%")
fileNumber += 1
count += 1
currIDLine += 1
# At 1000 iterations, store index/docID, clear index/docID and reset count
if count == 1000:
# key is sorted by alphanumeric characters, values are the postings for tokens that start with those characters
aIndex = convertIndexToAlphaIndex(index)
writeIndex(aIndex)
writeDocID(docID, initIDLine)
count = 0
initIDLine = currIDLine
docID.clear()
index.clear()
aIndex.clear()
except:
print("Error: Error opening JSON file and using BeautifulSoup")
break
# writes remaining index (iteration that didn't make it to 1000)
if index:
aIndex = convertIndexToAlphaIndex(index)
writeIndex(aIndex)
writeDocID(docID, initIDLine)
docID.clear()
index.clear()
aIndex.clear()
print(len(filePaths))
print(len(index))
print(sys.getsizeof(index))
# creates indexMarker.txt and merges index files in index.txt
indexMarker = dict()
indexDirectory = ".\letters"
mergedIndexFile = "index.txt"
mwMarkers(indexMarker, indexDirectory, mergedIndexFile)
# creates docIDMarker.txt and merges docID files in docID.txt
docIDMarker = dict()
docIDDirectory = "." + "\\" + "numbers"
mergedDocIDFile = "docID.txt"
mwMarkers(docIDMarker, docIDDirectory, mergedDocIDFile)
# writes markers to files
f = open("indexMarkers.txt", "w", encoding='utf-8')
f2 = open("docIDMarkers.txt", "w", encoding='utf-8')
f.write(str(indexMarker))
f2.write(str(docIDMarker))
|
[
"[email protected]"
] | |
4a9da798422a975372d4ef89f748d76b9d09eea2
|
f4bce35934800e93a2b3eeb14c568001ff70954a
|
/build/catkin_generated/installspace/_setup_util.py
|
e3fd8feae7abd1be35880d57a1957a608abe6e88
|
[] |
no_license
|
SamLyuubc/CarisRoboticsTestbench
|
94d07519ad3c2d210dd09a51c5278af5986edab2
|
5a60aca3c5f0ef5d12f3d617b86282ed00ea5487
|
refs/heads/master
| 2020-05-04T02:30:17.961966 | 2019-04-03T20:36:36 | 2019-04-03T20:36:36 | 178,927,596 | 0 | 0 | null | 2019-04-22T23:12:45 | 2019-04-01T18:56:23 |
C++
|
UTF-8
|
Python
| false | false | 13,042 |
py
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Software License Agreement (BSD License)
#
# Copyright (c) 2012, Willow Garage, Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
# * Neither the name of Willow Garage, Inc. nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
'''This file generates shell code for the setup.SHELL scripts to set environment variables'''
from __future__ import print_function
import argparse
import copy
import errno
import os
import platform
import sys
CATKIN_MARKER_FILE = '.catkin'
system = platform.system()
IS_DARWIN = (system == 'Darwin')
IS_WINDOWS = (system == 'Windows')
# subfolder of workspace prepended to CMAKE_PREFIX_PATH
ENV_VAR_SUBFOLDERS = {
'CMAKE_PREFIX_PATH': '',
'LD_LIBRARY_PATH' if not IS_DARWIN else 'DYLD_LIBRARY_PATH': ['lib', os.path.join('lib', 'x86_64-linux-gnu')],
'PATH': 'bin',
'PKG_CONFIG_PATH': [os.path.join('lib', 'pkgconfig'), os.path.join('lib', 'x86_64-linux-gnu', 'pkgconfig')],
'PYTHONPATH': 'lib/python2.7/dist-packages',
}
def rollback_env_variables(environ, env_var_subfolders):
'''
Generate shell code to reset environment variables
by unrolling modifications based on all workspaces in CMAKE_PREFIX_PATH.
This does not cover modifications performed by environment hooks.
'''
lines = []
unmodified_environ = copy.copy(environ)
for key in sorted(env_var_subfolders.keys()):
subfolders = env_var_subfolders[key]
if not isinstance(subfolders, list):
subfolders = [subfolders]
value = _rollback_env_variable(unmodified_environ, key, subfolders)
if value is not None:
environ[key] = value
lines.append(assignment(key, value))
if lines:
lines.insert(0, comment('reset environment variables by unrolling modifications based on all workspaces in CMAKE_PREFIX_PATH'))
return lines
def _rollback_env_variable(environ, name, subfolders):
'''
For each catkin workspace in CMAKE_PREFIX_PATH remove the first entry from env[NAME] matching workspace + subfolder.
:param subfolders: list of str '' or subfoldername that may start with '/'
:returns: the updated value of the environment variable.
'''
value = environ[name] if name in environ else ''
env_paths = [path for path in value.split(os.pathsep) if path]
value_modified = False
for subfolder in subfolders:
if subfolder:
if subfolder.startswith(os.path.sep) or (os.path.altsep and subfolder.startswith(os.path.altsep)):
subfolder = subfolder[1:]
if subfolder.endswith(os.path.sep) or (os.path.altsep and subfolder.endswith(os.path.altsep)):
subfolder = subfolder[:-1]
for ws_path in _get_workspaces(environ, include_fuerte=True, include_non_existing=True):
path_to_find = os.path.join(ws_path, subfolder) if subfolder else ws_path
path_to_remove = None
for env_path in env_paths:
env_path_clean = env_path[:-1] if env_path and env_path[-1] in [os.path.sep, os.path.altsep] else env_path
if env_path_clean == path_to_find:
path_to_remove = env_path
break
if path_to_remove:
env_paths.remove(path_to_remove)
value_modified = True
new_value = os.pathsep.join(env_paths)
return new_value if value_modified else None
def _get_workspaces(environ, include_fuerte=False, include_non_existing=False):
'''
Based on CMAKE_PREFIX_PATH return all catkin workspaces.
:param include_fuerte: The flag if paths starting with '/opt/ros/fuerte' should be considered workspaces, ``bool``
'''
# get all cmake prefix paths
env_name = 'CMAKE_PREFIX_PATH'
value = environ[env_name] if env_name in environ else ''
paths = [path for path in value.split(os.pathsep) if path]
# remove non-workspace paths
workspaces = [path for path in paths if os.path.isfile(os.path.join(path, CATKIN_MARKER_FILE)) or (include_fuerte and path.startswith('/opt/ros/fuerte')) or (include_non_existing and not os.path.exists(path))]
return workspaces
def prepend_env_variables(environ, env_var_subfolders, workspaces):
'''
Generate shell code to prepend environment variables
for the all workspaces.
'''
lines = []
lines.append(comment('prepend folders of workspaces to environment variables'))
paths = [path for path in workspaces.split(os.pathsep) if path]
prefix = _prefix_env_variable(environ, 'CMAKE_PREFIX_PATH', paths, '')
lines.append(prepend(environ, 'CMAKE_PREFIX_PATH', prefix))
for key in sorted([key for key in env_var_subfolders.keys() if key != 'CMAKE_PREFIX_PATH']):
subfolder = env_var_subfolders[key]
prefix = _prefix_env_variable(environ, key, paths, subfolder)
lines.append(prepend(environ, key, prefix))
return lines
def _prefix_env_variable(environ, name, paths, subfolders):
'''
Return the prefix to prepend to the environment variable NAME, adding any path in NEW_PATHS_STR without creating duplicate or empty items.
'''
value = environ[name] if name in environ else ''
environ_paths = [path for path in value.split(os.pathsep) if path]
checked_paths = []
for path in paths:
if not isinstance(subfolders, list):
subfolders = [subfolders]
for subfolder in subfolders:
path_tmp = path
if subfolder:
path_tmp = os.path.join(path_tmp, subfolder)
# skip nonexistent paths
if not os.path.exists(path_tmp):
continue
# exclude any path already in env and any path we already added
if path_tmp not in environ_paths and path_tmp not in checked_paths:
checked_paths.append(path_tmp)
prefix_str = os.pathsep.join(checked_paths)
if prefix_str != '' and environ_paths:
prefix_str += os.pathsep
return prefix_str
def assignment(key, value):
if not IS_WINDOWS:
return 'export %s="%s"' % (key, value)
else:
return 'set %s=%s' % (key, value)
def comment(msg):
if not IS_WINDOWS:
return '# %s' % msg
else:
return 'REM %s' % msg
def prepend(environ, key, prefix):
if key not in environ or not environ[key]:
return assignment(key, prefix)
if not IS_WINDOWS:
return 'export %s="%s$%s"' % (key, prefix, key)
else:
return 'set %s=%s%%%s%%' % (key, prefix, key)
def find_env_hooks(environ, cmake_prefix_path):
'''
Generate shell code with found environment hooks
for the all workspaces.
'''
lines = []
lines.append(comment('found environment hooks in workspaces'))
generic_env_hooks = []
generic_env_hooks_workspace = []
specific_env_hooks = []
specific_env_hooks_workspace = []
generic_env_hooks_by_filename = {}
specific_env_hooks_by_filename = {}
generic_env_hook_ext = 'bat' if IS_WINDOWS else 'sh'
specific_env_hook_ext = environ['CATKIN_SHELL'] if not IS_WINDOWS and 'CATKIN_SHELL' in environ and environ['CATKIN_SHELL'] else None
# remove non-workspace paths
workspaces = [path for path in cmake_prefix_path.split(os.pathsep) if path and os.path.isfile(os.path.join(path, CATKIN_MARKER_FILE))]
for workspace in reversed(workspaces):
env_hook_dir = os.path.join(workspace, 'etc', 'catkin', 'profile.d')
if os.path.isdir(env_hook_dir):
for filename in sorted(os.listdir(env_hook_dir)):
if filename.endswith('.%s' % generic_env_hook_ext):
# remove previous env hook with same name if present
if filename in generic_env_hooks_by_filename:
i = generic_env_hooks.index(generic_env_hooks_by_filename[filename])
generic_env_hooks.pop(i)
generic_env_hooks_workspace.pop(i)
# append env hook
generic_env_hooks.append(os.path.join(env_hook_dir, filename))
generic_env_hooks_workspace.append(workspace)
generic_env_hooks_by_filename[filename] = generic_env_hooks[-1]
elif specific_env_hook_ext is not None and filename.endswith('.%s' % specific_env_hook_ext):
# remove previous env hook with same name if present
if filename in specific_env_hooks_by_filename:
i = specific_env_hooks.index(specific_env_hooks_by_filename[filename])
specific_env_hooks.pop(i)
specific_env_hooks_workspace.pop(i)
# append env hook
specific_env_hooks.append(os.path.join(env_hook_dir, filename))
specific_env_hooks_workspace.append(workspace)
specific_env_hooks_by_filename[filename] = specific_env_hooks[-1]
env_hooks = generic_env_hooks + specific_env_hooks
env_hooks_workspace = generic_env_hooks_workspace + specific_env_hooks_workspace
count = len(env_hooks)
lines.append(assignment('_CATKIN_ENVIRONMENT_HOOKS_COUNT', count))
for i in range(count):
lines.append(assignment('_CATKIN_ENVIRONMENT_HOOKS_%d' % i, env_hooks[i]))
lines.append(assignment('_CATKIN_ENVIRONMENT_HOOKS_%d_WORKSPACE' % i, env_hooks_workspace[i]))
return lines
def _parse_arguments(args=None):
parser = argparse.ArgumentParser(description='Generates code blocks for the setup.SHELL script.')
parser.add_argument('--extend', action='store_true', help='Skip unsetting previous environment variables to extend context')
parser.add_argument('--local', action='store_true', help='Only consider this prefix path and ignore other prefix path in the environment')
return parser.parse_known_args(args=args)[0]
if __name__ == '__main__':
try:
try:
args = _parse_arguments()
except Exception as e:
print(e, file=sys.stderr)
sys.exit(1)
if not args.local:
# environment at generation time
CMAKE_PREFIX_PATH = '/home/samlyu/testbench_ws/devel;/opt/ros/kinetic'.split(';')
else:
# don't consider any other prefix path than this one
CMAKE_PREFIX_PATH = []
# prepend current workspace if not already part of CPP
base_path = os.path.dirname(__file__)
# CMAKE_PREFIX_PATH uses forward slash on all platforms, but __file__ is platform dependent
# base_path on Windows contains backward slashes, need to be converted to forward slashes before comparison
if os.path.sep != '/':
base_path = base_path.replace(os.path.sep, '/')
if base_path not in CMAKE_PREFIX_PATH:
CMAKE_PREFIX_PATH.insert(0, base_path)
CMAKE_PREFIX_PATH = os.pathsep.join(CMAKE_PREFIX_PATH)
environ = dict(os.environ)
lines = []
if not args.extend:
lines += rollback_env_variables(environ, ENV_VAR_SUBFOLDERS)
lines += prepend_env_variables(environ, ENV_VAR_SUBFOLDERS, CMAKE_PREFIX_PATH)
lines += find_env_hooks(environ, CMAKE_PREFIX_PATH)
print('\n'.join(lines))
# need to explicitly flush the output
sys.stdout.flush()
except IOError as e:
# and catch potential "broken pipe" if stdout is not writable
# which can happen when piping the output to a file but the disk is full
if e.errno == errno.EPIPE:
print(e, file=sys.stderr)
sys.exit(2)
raise
sys.exit(0)
|
[
"[email protected]"
] | |
818def7bc87a5c0bcf797c372ee7fd1af118ce87
|
cebc0b59e26dc564de8eade8510b1d7cd01cd46a
|
/bspider/master/controller/rabbitmq.py
|
d0cbcbfbc3221e15006b6946d16b74a3335d8272
|
[
"BSD-3-Clause"
] |
permissive
|
littlebai3618/bspider
|
0f18548ef66fbb06a8a95cbcfdaf05db5990c7d1
|
ff4d003cd0825247db4efe62db95f9245c0a303c
|
refs/heads/master
| 2023-04-26T14:47:43.228774 | 2021-05-13T02:58:35 | 2021-05-13T02:58:35 | 255,865,935 | 2 | 0 |
BSD-3-Clause
| 2021-05-12T02:11:18 | 2020-04-15T09:20:28 |
Python
|
UTF-8
|
Python
| false | false | 660 |
py
|
from flask import Blueprint
from bspider.core.api import auth
from bspider.master.service.rabbitmq import RabbitMQService
rabbitmq = Blueprint('rabbitmq_bp', __name__)
rabbitmq_service = RabbitMQService()
@rabbitmq.route('/project/<int:project_id>', methods=['GET'])
@auth.login_required
def project_queue_info(project_id):
"""project相关队列的详细信息"""
return rabbitmq_service.get_project_queue_info(project_id)
@rabbitmq.route('/project/purge/<int:project_id>', methods=['DELETE'])
@auth.login_required
def purge_project_queue(project_id):
"""清空待下载链接"""
return rabbitmq_service.purge_project_queue(project_id)
|
[
"[email protected]"
] | |
2261ff42ef53ff6f7e29b4575773ca2548c73283
|
bf348f0a5dbde6052f0cf6e4c9e570bd07c13533
|
/src/ManageDatabases/SettingDatabase.py
|
ef2a44a44127f3c18a555bccf54ff38bc30b8f18
|
[] |
no_license
|
PaoloGraziani/webAppFlask
|
7b114c59108bdfff9d9f768325c3ebcf3b3b90f0
|
7808c276645215f121def4850ada251f708d41ee
|
refs/heads/main
| 2023-07-25T01:33:20.566515 | 2021-09-09T17:28:27 | 2021-09-09T17:28:27 | 383,222,178 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 669 |
py
|
import psycopg2
'''
Inizializzazione DATABASE di Autenticazione
'''
Authentication_HOST = "localhost"
Authentication_DATABASE = "AuthDATA"
Authentication_USERNAME = "postgres"
Authentication_PASSWORD = "postgres"
'''
Inizializzazione DATABASE di Applicazione
'''
Application_HOST = "localhost"
Application_DATABASE = "DbWebApp"
Application_USERNAME = "postgres"
Application_PASSWORD = "postgres"
def closeCursor(cur):
cur.close()
def connectDatabase(host, database, username, password):
newConnection = psycopg2.connect(host=host, database=database, user=username, password=password)
return newConnection
def closeConnection(conn):
conn.close()
|
[
"[email protected]"
] | |
b668d29096563112db9bbe2fb4adc91d5dcac26e
|
34354acd20aba20dc78909edb80376d82ee31efb
|
/partsix/TestQueue.py
|
f775be4e01a141aeb458bf93d9d1a12a482c8fe5
|
[] |
no_license
|
yzw1102/study_python
|
40de23db9f4f5270d7b8fae0739148e50e4792d7
|
d8cc929475827925d9135167b5afd5a47232efd0
|
refs/heads/master
| 2020-04-13T10:00:16.405293 | 2019-01-09T07:30:22 | 2019-01-09T07:30:22 | 163,126,969 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 528 |
py
|
from queue import Queue
from threading import Thread
import time
isRead = True
def write(q):
for value in ['ye1','ye2','ye3']:
print('the value write in queue is : {0} '.format(value))
q.put(value)
time.sleep(1)
def read(q):
while isRead:
value = q.get(True)
print('the value get from queue is : {0}'.format(value))
if __name__ == '__main__':
q = Queue()
t1 = Thread(target = write,args = (q,))
t2 = Thread(target = read, args = (q,))
t1.start()
t2.start()
|
[
"ye19861102"
] |
ye19861102
|
0ce1a22c0597a986e8737dfbfc156758588401b4
|
ef44bc7b484f817de597352d948309a98d5b8cf9
|
/request_batcher/monitor.py
|
149462ef5076586029f890899e0ab81c590794ed
|
[] |
no_license
|
j-dutton/request-batcher
|
f5a5771c0b44f3a589cda5e52c1f857e3f38f9a6
|
6d740bc787f4125227f87a28f936196416506687
|
refs/heads/master
| 2022-12-26T01:36:30.422556 | 2020-10-12T21:27:25 | 2020-10-12T21:27:25 | 302,417,954 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,252 |
py
|
from clicks import click_state
from constants import MONITOR_INTERVAL_SECONDS, MAX_OPENS_ALLOWED_IN_STATE, MAX_CLICKS_ALLOWED_IN_STATE
from logger import LOG
from opens import open_state
from utils import repeat
@repeat(seconds=MONITOR_INTERVAL_SECONDS)
async def monitor():
logger = LOG.getChild('monitor')
logger.info('Running Monitor')
awaiting_open_batch = len(open_state)
awaiting_click_batch = len(click_state)
# Opens
if awaiting_open_batch > 0:
logger.info('Batch of OpenData currently awaiting processing', extra={'length': awaiting_open_batch})
if awaiting_open_batch > MAX_OPENS_ALLOWED_IN_STATE:
logger.error(
'Too many OpenData stuck in memory. Dropping all of them.',
extra={'length': awaiting_open_batch}
)
await open_state.flush_all()
# Clicks
if awaiting_click_batch > 0:
logger.info('Batch of ClickData currently awaiting processing', extra={'length': awaiting_click_batch})
if awaiting_click_batch > MAX_CLICKS_ALLOWED_IN_STATE:
logger.error(
'Too many ClickData stuck in memory. Dropping all of them.',
extra={'length': awaiting_click_batch}
)
await click_state.flush_all()
|
[
"[email protected]"
] | |
d8884ed1dbc5f3c83a5ccf73929b638983c841d0
|
218f2672af3c01422c26051432b34986d490d69a
|
/01_catboost_enrichi_tuto.py
|
0d7a620351fb55f660caae721f88ebc8c9425eee
|
[] |
no_license
|
PatDecideOm/DataScience
|
a801aacbfcd0b849eaae66a44f1b55e001f18cd2
|
6818d7859ae49cbe5d19497317b3279fd8c870bf
|
refs/heads/master
| 2023-05-30T18:08:23.413375 | 2021-06-08T09:49:16 | 2021-06-08T09:49:16 | 368,841,937 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,787 |
py
|
# Import libraries
import pandas as pd
import numpy as np
import catboost
from catboost import CatBoostClassifier
import xgboost as xgb
import sklearn
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings("ignore")
print('Scikit Learn: %s' % sklearn.__version__)
print('Catboost: %s' % catboost.__version__)
print('xgboost: %s' % xgb.__version__)
print('numpy: %s' % np.__version__)
print('numpy: %s' % pd.__version__)
print('--- DEB ---')
dataset = pd.read_csv('c:/applis/kaggle/tabular-playground-series-mar-2021/train_enrichi.csv')
unseen = pd.read_csv('c:/applis/kaggle/tabular-playground-series-mar-2021/test_enrichi.csv')
print(dataset.shape)
print(unseen.shape)
dataset = dataset[0:300000]
unseen = unseen[0:200000]
y = dataset.target
X = dataset.drop('target', axis=1)
columns = X.columns[1:]
print('Columns: %s' % columns)
cat_features = columns[:19]
print('Features: %s' % cat_features)
num_features = columns[20:]
print('Numerics: %s' % num_features)
train_test = pd.concat([X, unseen], ignore_index=True)
for feature in cat_features:
le = LabelEncoder()
le.fit(train_test[feature])
X[feature] = le.transform(X[feature])
unseen[feature] = le.transform(unseen[feature])
'''
for feature in num_features:
X[feature] = (X[feature] - X[feature].mean(0)) / X[feature].std(0)
unseen[feature] = (unseen[feature] - unseen[feature].mean(0)) / unseen[feature].std(0)
'''
X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=0.80, random_state=2021)
# Initialize CatBoostClassifier
model = CatBoostClassifier(iterations=5000,
learning_rate=0.01,
depth=8,
#loss_function='CrossEntropy',
loss_function='Logloss',
#custom_loss=['AUC', 'Accuracy'],
task_type='GPU',
ignored_features=['id']
)
'''
grid = {'learning_rate': [0.03, 0.1],
'depth': [4, 6, 10],
'l2_leaf_reg': [1, 3, 5, 7, 9]}
grid_search_result = model.grid_search(grid,
X=X_train,
y=y_train,
plot=False)
'''
# Fit model
model.fit(X_train,
y_train,
cat_features,
eval_set=(X_validation, y_validation),
verbose=250,
plot=False)
print('Model is fitted : ' + str(model.is_fitted()))
print('Model params :')
print(model.get_params())
preds = model.predict(unseen)
probs= model.predict_proba(unseen)
unseen['target'] = probs[:,1]
submission = unseen[['id', 'target']]
print(submission.head())
submission.to_csv('c:/applis/kaggle/tabular-playground-series-mar-2021/submission_cat_enrichi.csv',
columns=['id', 'target'], header=True, index=False)
'''
xgb_params = {
"objective": "binary:logistic",
"eval_metric": "logloss",
"learning_rate": 0.010,
"max_depth": 8,
"n_jobs": 2,
"seed": 2021,
'tree_method': "hist"
}
train_df = xgb.DMatrix(X_train, label=y_train)
val_df = xgb.DMatrix(X_validation, label=y_validation)
model = xgb.train(xgb_params, train_df, 500)
temp_target = model.predict(val_df)
best_preds = [0 if line < 0.5 else 1 for line in temp_target]
from sklearn.metrics import precision_score
print(precision_score(best_preds, y_validation, average='macro'))
df_unseen = xgb.DMatrix(unseen)
# preds = model.predict(df_unseen)
'''
print('--- FIN ---')
|
[
"[email protected]"
] | |
52d3a05067138b36faf6b476467edbebd184d716
|
622bd4fb4cb50361a5e887544d92a04debb0dd2b
|
/databus/client/user.py
|
230a222d78e3993622a008dc484972f4a37cea4f
|
[
"Apache-2.0"
] |
permissive
|
tedrepo/databus
|
aec06bd28f761ca4beff290fc856e93dd2948c07
|
0f1f290c1b061175a652c3f72efc0d091a5e08c9
|
refs/heads/master
| 2022-12-01T03:10:15.182783 | 2020-08-08T18:40:54 | 2020-08-08T18:40:54 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,129 |
py
|
""" Module for web users """
import uuid
class Credential: # pylint: disable=R0903
""" Class defining a user credential """
def __init__(self, username: str = "Guest", password: str = "", token: str = ""):
self.username = username
self.password = password
self.token = token
def generate_token(self):
""" Generates and assigns a new token """
self.token = str(uuid.uuid1())
class User: # pylint: disable=R0903
""" Class defining a web user """
def __init__(self, credential: Credential = None):
if credential is None:
self.credential = Credential()
else:
self.credential = credential
def authenticate(self, credential: Credential) -> bool:
""" Checks if the user & password matches """
if credential.username != self.credential.username:
return False
if credential.password != "" and credential.password == self.credential.password:
return True
if credential.token != "" and credential.token == self.credential.token:
return True
return False
|
[
"[email protected]"
] | |
83932b497fb4b87191f07e51e57a976f85c5e3b7
|
8005bde2cfeba49c5cd1b88dc2c0d1c0fc4d85dc
|
/manage_class.py
|
6d9d43699f03cfed55fba6a29376876956fd2ed2
|
[] |
no_license
|
coulibaly-mouhamed/Basic_Fake_News_detector
|
85ab75d39a65b98ea2724f02564c196fbbdc08a8
|
7fd985b4ab3335212f6f0ba8242ec1c6f5439658
|
refs/heads/master
| 2023-04-06T17:22:19.180102 | 2021-04-26T10:37:47 | 2021-04-26T10:37:47 | 252,579,017 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 397 |
py
|
##############################################################
class news():
def __init__(self,headline,domain):
self.headline = headline
self.domain = domain
def __str__(self):
return '%.2c:%2.c' %(self.domain,self.headline)
class invalid_input(Exception):
pass
#########################################################
#Prevent dealing with
class not_a_link(Exception):
pass
|
[
"[email protected]"
] | |
a42f30fc4aa79865a8e957a7231fab17cdcac3f8
|
568bc70dca53f0d095313f00d572383284243e34
|
/Project/wine_last/wine/device.py
|
16fd35980551f5ec948644712c176cd54a98f7ff
|
[] |
no_license
|
msanchezalcon/Dialogue-Systems-2
|
07d3b1200646bb9a0197eae7fff3c0606a7f40a9
|
a834f819a35457a4954917d82a158147001d29f6
|
refs/heads/master
| 2020-08-03T18:28:48.317591 | 2019-11-04T12:42:58 | 2019-11-04T12:42:58 | 211,845,302 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,594 |
py
|
from tdm.lib.device import DddDevice, DeviceWHQuery, DeviceAction
from urllib2 import Request,urlopen
import json
import requests
class PairingDevice(DddDevice):
key = "e51e73fba2ce4002899dc7aec175063f"
def request_api_wine_pairing(self, food_type, max_price):
"""
Find a wine that goes well with a food. Food can be a dish name ("steak"),
an ingredient name ("salmon"), or a cuisine ("italian").
"""
url = "https://api.spoonacular.com/food/wine/pairing?apiKey=%s&food=%s&price=%s" % (self.key, food_type, max_price)
#print(url)
response = requests.get(url)
data = response.json()
return data
def request_api_wine_recommendation(self, max_price, min_rating, wine_type):
"""
Get a specific wine recommendation (concrete product) for a given wine type, e.g. "merlot".
"""
url = "https://api.spoonacular.com/food/wine/recommendation?apiKey={}&wine={}&maxPrice={}&minRating={}".format(self.key, wine_type, max_price, min_rating)
response = requests.get(url)
data = response.json()
#print(data)
return data
class GetWinePairing(DeviceAction):
def perform(self, max_price, food_type, get_wine_pairing_from_api):
success = True
return success
class GetWineRecommendation(DeviceAction):
def perform(self, max_price, min_rating, wine_type, get_wine_recommendation_from_api):
success = True
return success
class get_wine_pairing_from_api(DeviceWHQuery):
def perform(self, food_type, max_price):
if max_price == '':
max_price = None
data = self.device.request_api_wine_pairing(food_type, max_price)
wine_pairing = str(data["pairedWines"][0])
return [wine_pairing]
class get_wine_recommendation_from_api(DeviceWHQuery):
def perform(self, wine_type, max_price, min_rating):
min_rating = "0.{}".format(min_rating)
if max_price == '':
max_price = None
if min_rating == '':
min_rating = None
data = self.device.request_api_wine_recommendation(max_price, min_rating, wine_type)
wine_recommendation = str(data["recommendedWines"][0]["title"])
#print(wine_recommendation)
return [wine_recommendation]
class ask_min_rating(DeviceWHQuery):
def perform(self):
return [""]
|
[
"[email protected]"
] | |
0c86204fe5c33368162d5f576953acf36b7bdb95
|
41ea3e67428a59c665fcb189d392a6fb1a374ffe
|
/prg3.py.copy
|
5f7f6337e48a0c9544bcdfefc83a4eead9dc5913
|
[] |
no_license
|
hitesh2402/carrom
|
fcd154f3f760528f0adb15f843fcbf4be763d014
|
0e4fe67eb1be3ed7ea92d7e8775ec92cead30355
|
refs/heads/master
| 2021-05-13T20:43:18.477110 | 2018-01-10T06:53:59 | 2018-01-10T06:53:59 | 116,917,919 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,566 |
copy
|
#!/usr/bin/env python
import pygame
class Colors():
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)
RED = (255, 0, 0)
class Application(object):
_instance = None
def initialize(title='Empty Title'display_width = 800, display_height = 600):
pygame.init()
gameDisplay = pygame.display.set_mode((display_width,display_height))
pygame.display.set_caption(title)
clock = pygame.time.Clock()
= False
carImg = pygame.image.load('racecar.png')
def car(x,y):
gameDisplay.blit(carImg, (x,y))
x = (display_width * 0.45)
y = (display_height * 0.8)
x_change = 0
y_change = 0
car_speed = 0
while not crashed:
for event in pygame.event.get():
if event.type == pygame.QUIT:
crashed = True
############################
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_LEFT:
x_change = -5
elif event.key == pygame.K_RIGHT:
x_change = 5
elif event.key == pygame.K_UP:
y_change = -5
elif event.key == pygame.K_DOWN:
y_change = 5
if event.type == pygame.KEYUP:
if event.key == pygame.K_LEFT or event.key == pygame.K_RIGHT or event.key == pygame.K_UP or event.key == pygame.K_DOWN:
x_change = 0
y_change = 0
######################
##
x += x_change
y += y_change
##
gameDisplay.fill(white)
car(x,y)
pygame.display.update()
clock.tick(60)
pygame.quit()
quit()
|
[
"[email protected]"
] | |
85190e278f5252ba76b1a7efadbcd85d7aafd277
|
cdf782224f9b74cf8acce919406d03791254fd3c
|
/assignment3/main.py
|
c17bd67d590ec107763c46e32b476e5acfcc9709
|
[] |
no_license
|
tollefj/information-retrieval
|
1bcc46e9fcc15c74ee8dccb31a015935d7e850a7
|
43535384ace262d0b293c26d249b11622f18e470
|
refs/heads/master
| 2022-07-14T22:11:06.716504 | 2017-10-28T17:10:39 | 2017-10-28T17:10:39 | 106,025,641 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 208 |
py
|
from _gensim import GenSim
if __name__ == '__main__':
gs = GenSim()
gs.read_stopwords()
gs.load()
gs.build_dictionary()
gs.build_bag()
gs.build_tfidf_model()
gs.build_lsi_model()
|
[
"[email protected]"
] | |
d8331a4aaa0fc5a0db2f7d9cafbd092f009019ed
|
b62177a84db5d209f37dfc60d56f1dc9ab3174c2
|
/kleague/data/transfercentre.py
|
3f0bc79348879b983945c0a34c1a0687a2a23d83
|
[] |
no_license
|
bsmmoon/kleague
|
12f8e9de8b4131ec37e33da4c822438fb9b8dd2a
|
dfdc1ecb79badb0529fd624ca1743bc69d1408b9
|
refs/heads/master
| 2021-01-09T21:45:00.626003 | 2015-12-05T08:17:44 | 2015-12-05T08:17:44 | 47,249,837 | 0 | 0 | null | 2015-12-05T08:17:45 | 2015-12-02T09:13:17 |
Python
|
UTF-8
|
Python
| false | false | 353 |
py
|
class TransferCentre():
def __init__(self):
self._windowList = []
self._contracts = []
@property
def contracts(self):
return self._contracts
def addContract(self, contract):
contract.contractID = len(self._contracts)
self._contracts.append(contract)
def printContracts(self):
for contract in self._contracts:
print(contract)
|
[
"[email protected]"
] | |
4ab4fec920df659a95a12694df60fd03dfca6791
|
08bfc8a1f8e44adc624d1f1c6250a3d9635f99de
|
/SDKs/swig/Examples/test-suite/python/abstract_virtual_runme.py
|
2a8411578017fc06324e210386ddd29a61e19eb8
|
[] |
no_license
|
Personwithhat/CE_SDKs
|
cd998a2181fcbc9e3de8c58c7cc7b2156ca21d02
|
7afbd2f7767c9c5e95912a1af42b37c24d57f0d4
|
refs/heads/master
| 2020-04-09T22:14:56.917176 | 2019-07-04T00:19:11 | 2019-07-04T00:19:11 | 160,623,495 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 127 |
py
|
version https://git-lfs.github.com/spec/v1
oid sha256:fce41bedc93abe3933ce0f2546b68f02a08faf0778e211b1ba7b30a7f3909ed8
size 50
|
[
"[email protected]"
] | |
602ee7bb28019e48deae7c70a09530a5bd967e5d
|
b8f718a0265a345512ea4df423d161e84ca2b869
|
/true _skip_1/TrueCase/1qna/1qna_chimera.py
|
deadae6e68203e09882bbb83aed808a7a150b1fc
|
[] |
no_license
|
tunazislam/twist-angle-calculation
|
16894ca19c46f3bc875e9c80b2e62b741ef19511
|
18320d886b8e29bccbdd26e303450cfc0ad606a3
|
refs/heads/master
| 2021-06-14T08:51:31.611126 | 2017-05-04T15:20:51 | 2017-05-04T15:20:51 | 84,751,769 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 550,124 |
py
|
import cPickle, base64
try:
from SimpleSession.versions.v62 import beginRestore,\
registerAfterModelsCB, reportRestoreError, checkVersion
except ImportError:
from chimera import UserError
raise UserError('Cannot open session that was saved in a'
' newer version of Chimera; update your version')
checkVersion([1, 10, 2, 40686])
import chimera
from chimera import replyobj
replyobj.status('Restoring session...', \
blankAfter=0)
replyobj.status('Beginning session restore...', \
blankAfter=0, secondary=True)
beginRestore()
def restoreCoreModels():
from SimpleSession.versions.v62 import init, restoreViewer, \
restoreMolecules, restoreColors, restoreSurfaces, \
restoreVRML, restorePseudoBondGroups, restoreModelAssociations
molInfo = cPickle.loads(base64.b64decode('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'))
resInfo = cPickle.loads(base64.b64decode('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'))
atomInfo = cPickle.loads(base64.b64decode('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'))
bondInfo = cPickle.loads(base64.b64decode('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'))
crdInfo = cPickle.loads(base64.b64decode('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'))
surfInfo = {'category': (0, None, {}), 'probeRadius': (0, None, {}), 'pointSize': (0, None, {}), 'name': [], 'density': (0, None, {}), 'colorMode': (0, None, {}), 'useLighting': (0, None, {}), 'transparencyBlendMode': (0, None, {}), 'molecule': [], 'smoothLines': (0, None, {}), 'lineWidth': (0, None, {}), 'allComponents': (0, None, {}), 'twoSidedLighting': (0, None, {}), 'customVisibility': [], 'drawMode': (0, None, {}), 'display': (0, None, {}), 'customColors': []}
vrmlInfo = {'subid': (0, None, {}), 'display': (0, None, {}), 'id': (0, None, {}), 'vrmlString': [], 'name': (0, None, {})}
colors = {'Ru': ((0.141176, 0.560784, 0.560784), 1, u'default'), 'Re': ((0.14902, 0.490196, 0.670588), 1, u'default'), 'Rf': ((0.8, 0, 0.34902), 1, u'default'), 'Ra': ((0, 0.490196, 0), 1, u'default'), 'Rb': ((0.439216, 0.180392, 0.690196), 1, u'default'), 'Rn': ((0.258824, 0.509804, 0.588235), 1, u'default'), 'Rh': ((0.0392157, 0.490196, 0.54902), 1, u'default'), 'Be': ((0.760784, 1, 0), 1, u'default'), 'Ba': ((0, 0.788235, 0), 1, u'default'), 'Bh': ((0.878431, 0, 0.219608), 1, u'default'), 'Bi': ((0.619608, 0.309804, 0.709804), 1, u'default'), 'Bk': ((0.541176, 0.309804, 0.890196), 1, u'default'), 'Br': ((0.65098, 0.160784, 0.160784), 1, u'default'), 'H': ((1, 1, 1), 1, u'default'), 'P': ((1, 0.501961, 0), 1, u'default'), 'Os': ((0.14902, 0.4, 0.588235), 1, u'default'), 'Ge': ((0.4, 0.560784, 0.560784), 1, u'default'), 'Gd': ((0.270588, 1, 0.780392), 1, u'default'), 'Ga': ((0.760784, 0.560784, 0.560784), 1, u'default'), 'Pr': ((0.85098, 1, 0.780392), 1, u'default'), 'Pt': ((0.815686, 0.815686, 0.878431), 1, u'default'), 'Pu': ((0, 0.419608, 1), 1, u'default'),
'C': ((0.564706, 0.564706, 0.564706), 1, u'default'), 'Pb': ((0.341176, 0.34902, 0.380392), 1, u'default'), 'Pa': ((0, 0.631373, 1), 1, u'default'), 'Pd': ((0, 0.411765, 0.521569), 1, u'default'), 'Cd': ((1, 0.85098, 0.560784), 1, u'default'), 'Po': ((0.670588, 0.360784, 0), 1, u'default'), 'Pm': ((0.639216, 1, 0.780392), 1, u'default'), 'Hs': ((0.901961, 0, 0.180392), 1, u'default'), 'Ho': ((0, 1, 0.611765), 1, u'default'), 'Hf': ((0.301961, 0.760784, 1), 1, u'default'), 'Hg': ((0.721569, 0.721569, 0.815686), 1, u'default'), 'He': ((0.85098, 1, 1), 1, u'default'), 'Md': ((0.701961, 0.0509804, 0.65098), 1, u'default'), 'Mg': ((0.541176, 1, 0), 1, u'default'), 'K': ((0.560784, 0.25098, 0.831373), 1, u'default'), 'Mn': ((0.611765, 0.478431, 0.780392), 1, u'default'), 'O': ((1, 0.0509804, 0.0509804), 1, u'default'), 'Mt': ((0.921569, 0, 0.14902), 1, u'default'), 'S': ((1, 1, 0.188235), 1, u'default'), 'W': ((0.129412, 0.580392, 0.839216), 1, u'default'), 'sky blue': ((0.529412, 0.807843, 0.921569), 1, u'default'), 'Zn': ((0.490196, 0.501961, 0.690196), 1, u'default'),
'plum': ((0.866667, 0.627451, 0.866667), 1, u'default'), 'Eu': ((0.380392, 1, 0.780392), 1, u'default'), 'Zr': ((0.580392, 0.878431, 0.878431), 1, u'default'), 'Er': ((0, 0.901961, 0.458824), 1, u'default'), 'Ni': ((0.313725, 0.815686, 0.313725), 1, u'default'), 'No': ((0.741176, 0.0509804, 0.529412), 1, u'default'), 'Na': ((0.670588, 0.360784, 0.94902), 1, u'default'), 'Nb': ((0.45098, 0.760784, 0.788235), 1, u'default'), 'Nd': ((0.780392, 1, 0.780392), 1, u'default'), 'Ne': ((0.701961, 0.890196, 0.960784), 1, u'default'), 'Np': ((0, 0.501961, 1), 1, u'default'), 'Fr': ((0.258824, 0, 0.4), 1, u'default'), 'Fe': ((0.878431, 0.4, 0.2), 1, u'default'), 'Fm': ((0.701961, 0.121569, 0.729412), 1, u'default'), 'B': ((1, 0.709804, 0.709804), 1, u'default'), 'F': ((0.564706, 0.878431, 0.313725), 1, u'default'), 'Sr': ((0, 1, 0), 1, u'default'), 'N': ((0.188235, 0.313725, 0.972549), 1, u'default'), 'Kr': ((0.360784, 0.721569, 0.819608), 1, u'default'), 'Si': ((0.941176, 0.784314, 0.627451), 1, u'default'), 'Sn': ((0.4, 0.501961, 0.501961), 1, u'default'), 'Sm': ((0.560784, 1, 0.780392), 1, u'default'),
'V': ((0.65098, 0.65098, 0.670588), 1, u'default'), 'Sc': ((0.901961, 0.901961, 0.901961), 1, u'default'), 'Sb': ((0.619608, 0.388235, 0.709804), 1, u'default'), 'Sg': ((0.85098, 0, 0.270588), 1, u'default'), 'Se': ((1, 0.631373, 0), 1, u'default'), 'Co': ((0.941176, 0.564706, 0.627451), 1, u'default'), 'Cm': ((0.470588, 0.360784, 0.890196), 1, u'default'), 'Cl': ((0.121569, 0.941176, 0.121569), 1, u'default'), 'Ca': ((0.239216, 1, 0), 1, u'default'), 'Cf': ((0.631373, 0.211765, 0.831373), 1, u'default'), 'Ce': ((1, 1, 0.780392), 1, u'default'), 'Xe': ((0.258824, 0.619608, 0.690196), 1, u'default'), 'Tm': ((0, 0.831373, 0.321569), 1, u'default'), 'light green': ((0.564706, 0.933333, 0.564706), 1, u'default'), 'Cs': ((0.341176, 0.0901961, 0.560784), 1, u'default'), 'Cr': ((0.541176, 0.6, 0.780392), 1, u'default'), 'Cu': ((0.784314, 0.501961, 0.2), 1, u'default'), 'La': ((0.439216, 0.831373, 1), 1, u'default'), 'Li': ((0.8, 0.501961, 1), 1, u'default'), 'Tl': ((0.65098, 0.329412, 0.301961), 1, u'default'), 'Lu': ((0, 0.670588, 0.141176), 1, u'default'),
'Lr': ((0.780392, 0, 0.4), 1, u'default'), 'Th': ((0, 0.729412, 1), 1, u'default'), 'Ti': ((0.74902, 0.760784, 0.780392), 1, u'default'), 'tan': ((0.823529, 0.705882, 0.54902), 1, u'default'), 'Te': ((0.831373, 0.478431, 0), 1, u'default'), 'Tb': ((0.188235, 1, 0.780392), 1, u'default'), 'Tc': ((0.231373, 0.619608, 0.619608), 1, u'default'), 'Ta': ((0.301961, 0.65098, 1), 1, u'default'), 'Yb': ((0, 0.74902, 0.219608), 1, u'default'), 'Db': ((0.819608, 0, 0.309804), 1, u'default'), 'Dy': ((0.121569, 1, 0.780392), 1, u'default'), 'At': ((0.458824, 0.309804, 0.270588), 1, u'default'), 'I': ((0.580392, 0, 0.580392), 1, u'default'), 'U': ((0, 0.560784, 1), 1, u'default'), 'Y': ((0.580392, 1, 1), 1, u'default'), 'Ac': ((0.439216, 0.670588, 0.980392), 1, u'default'), 'Ag': ((0.752941, 0.752941, 0.752941), 1, u'default'), 'Ir': ((0.0901961, 0.329412, 0.529412), 1, u'default'), 'Am': ((0.329412, 0.360784, 0.94902), 1, u'default'), 'Al': ((0.74902, 0.65098, 0.65098), 1, u'default'), 'As': ((0.741176, 0.501961, 0.890196), 1, u'default'), 'Ar': ((0.501961, 0.819608, 0.890196), 1, u'default'),
'Au': ((1, 0.819608, 0.137255), 1, u'default'), 'Es': ((0.701961, 0.121569, 0.831373), 1, u'default'), 'In': ((0.65098, 0.458824, 0.45098), 1, u'default'), 'Mo': ((0.329412, 0.709804, 0.709804), 1, u'default')}
materials = {u'default': ((0.85, 0.85, 0.85), 30)}
pbInfo = {'category': [u'distance monitor'], 'bondInfo': [{'color': (0, None, {}), 'atoms': [], 'label': (0, None, {}), 'halfbond': (0, None, {}), 'labelColor': (0, None, {}), 'drawMode': (0, None, {}), 'display': (0, None, {})}], 'lineType': (1, 2, {}), 'color': (1, 6, {}), 'optional': {'fixedLabels': (True, False, (1, False, {}))}, 'display': (1, True, {}), 'showStubBonds': (1, False, {}), 'lineWidth': (1, 1, {}), 'stickScale': (1, 1, {}), 'id': [-2]}
modelAssociations = {}
colorInfo = (8, (u'green', (0, 1, 0, 1)), {(u'', (1, 1, 0, 1)): [3], (u'', (1, 0, 0, 1)): [4], (u'sky blue', (0.529412, 0.807843, 0.921569, 1)): [0], (u'plum', (0.866667, 0.627451, 0.866667, 1)): [1], (u'light green', (0.564706, 0.933333, 0.564706, 1)): [2], (u'', (0, 0.952381, 1, 1)): [5], (u'yellow', (1, 1, 0, 1)): [6]})
viewerInfo = {'cameraAttrs': {'center': (14.603, 89.716, 42.4525), 'fieldOfView': 21.047063176378, 'nearFar': (72.430564952483, 25.667731025121), 'ortho': False, 'eyeSeparation': 50.8, 'focal': 42.4525}, 'viewerAttrs': {'silhouetteColor': None, 'clipping': False, 'showSilhouette': False, 'showShadows': False, 'viewSize': 64.859737679711, 'labelsOnTop': True, 'depthCueRange': (0.5, 1), 'silhouetteWidth': 2, 'singleLayerTransparency': True, 'shadowTextureSize': 2048, 'backgroundImage': [None, 1, 2, 1, 0, 0], 'backgroundGradient': [('Chimera default', [(1, 1, 1, 1), (0, 0, 1, 1)], 1), 1, 0, 0], 'depthCue': True, 'highlight': 0, 'scaleFactor': 1.190519264642, 'angleDependentTransparency': True, 'backgroundMethod': 0}, 'viewerHL': 7, 'cameraMode': 'mono', 'detail': 1.5, 'viewerFog': None, 'viewerBG': None}
replyobj.status("Initializing session restore...", blankAfter=0,
secondary=True)
from SimpleSession.versions.v62 import expandSummary
init(dict(enumerate(expandSummary(colorInfo))))
replyobj.status("Restoring colors...", blankAfter=0,
secondary=True)
restoreColors(colors, materials)
replyobj.status("Restoring molecules...", blankAfter=0,
secondary=True)
restoreMolecules(molInfo, resInfo, atomInfo, bondInfo, crdInfo)
replyobj.status("Restoring surfaces...", blankAfter=0,
secondary=True)
restoreSurfaces(surfInfo)
replyobj.status("Restoring VRML models...", blankAfter=0,
secondary=True)
restoreVRML(vrmlInfo)
replyobj.status("Restoring pseudobond groups...", blankAfter=0,
secondary=True)
restorePseudoBondGroups(pbInfo)
replyobj.status("Restoring model associations...", blankAfter=0,
secondary=True)
restoreModelAssociations(modelAssociations)
replyobj.status("Restoring camera...", blankAfter=0,
secondary=True)
restoreViewer(viewerInfo)
try:
restoreCoreModels()
except:
reportRestoreError("Error restoring core models")
replyobj.status("Restoring extension info...", blankAfter=0,
secondary=True)
try:
import StructMeasure
from StructMeasure.DistMonitor import restoreDistances
registerAfterModelsCB(restoreDistances, 1)
except:
reportRestoreError("Error restoring distances in session")
def restoreMidasBase():
formattedPositions = {}
import Midas
Midas.restoreMidasBase(formattedPositions)
try:
restoreMidasBase()
except:
reportRestoreError('Error restoring Midas base state')
def restoreMidasText():
from Midas import midas_text
midas_text.aliases = {}
midas_text.userSurfCategories = {}
try:
restoreMidasText()
except:
reportRestoreError('Error restoring Midas text state')
geomData = {'AxisManager': {}, 'CentroidManager': {}, 'PlaneManager': {}}
try:
from StructMeasure.Geometry import geomManager
geomManager._restoreSession(geomData)
except:
reportRestoreError("Error restoring geometry objects in session")
def restoreSession_RibbonStyleEditor():
import SimpleSession
import RibbonStyleEditor
userScalings = []
userXSections = []
userResidueClasses = []
residueData = [(3, 'Chimera default', 'rounded', u'unknown'), (4, 'Chimera default', 'rounded', u'unknown'), (5, 'Chimera default', 'rounded', u'unknown')]
flags = RibbonStyleEditor.NucleicDefault1
SimpleSession.registerAfterModelsCB(RibbonStyleEditor.restoreState,
(userScalings, userXSections,
userResidueClasses, residueData, flags))
try:
restoreSession_RibbonStyleEditor()
except:
reportRestoreError("Error restoring RibbonStyleEditor state")
trPickle = 'gAJjQW5pbWF0ZS5UcmFuc2l0aW9ucwpUcmFuc2l0aW9ucwpxASmBcQJ9cQMoVQxjdXN0b21fc2NlbmVxBGNBbmltYXRlLlRyYW5zaXRpb24KVHJhbnNpdGlvbgpxBSmBcQZ9cQcoVQZmcmFtZXNxCEsBVQ1kaXNjcmV0ZUZyYW1lcQlLAVUKcHJvcGVydGllc3EKXXELVQNhbGxxDGFVBG5hbWVxDWgEVQRtb2RlcQ5VBmxpbmVhcnEPdWJVCGtleWZyYW1lcRBoBSmBcRF9cRIoaAhLFGgJSwFoCl1xE2gMYWgNaBBoDmgPdWJVBXNjZW5lcRRoBSmBcRV9cRYoaAhLAWgJSwFoCl1xF2gMYWgNaBRoDmgPdWJ1Yi4='
scPickle = 'gAJjQW5pbWF0ZS5TY2VuZXMKU2NlbmVzCnEBKYFxAn1xA1UHbWFwX2lkc3EEfXNiLg=='
kfPickle = 'gAJjQW5pbWF0ZS5LZXlmcmFtZXMKS2V5ZnJhbWVzCnEBKYFxAn1xA1UHZW50cmllc3EEXXEFc2Iu'
def restoreAnimation():
'A method to unpickle and restore animation objects'
# Scenes must be unpickled after restoring transitions, because each
# scene links to a 'scene' transition. Likewise, keyframes must be
# unpickled after restoring scenes, because each keyframe links to a scene.
# The unpickle process is left to the restore* functions, it's
# important that it doesn't happen prior to calling those functions.
import SimpleSession
from Animate.Session import restoreTransitions
from Animate.Session import restoreScenes
from Animate.Session import restoreKeyframes
SimpleSession.registerAfterModelsCB(restoreTransitions, trPickle)
SimpleSession.registerAfterModelsCB(restoreScenes, scPickle)
SimpleSession.registerAfterModelsCB(restoreKeyframes, kfPickle)
try:
restoreAnimation()
except:
reportRestoreError('Error in Animate.Session')
def restoreLightController():
import Lighting
Lighting._setFromParams({'ratio': 1.25, 'brightness': 1.16, 'material': [30.0, (0.85, 0.85, 0.85), 1.0], 'back': [(0.35740674433659325, 0.6604015517481454, -0.6604015517481455), (1.0, 1.0, 1.0), 0.0], 'mode': 'two-point', 'key': [(-0.35740674433659325, 0.6604015517481454, 0.6604015517481455), (1.0, 1.0, 1.0), 1.0], 'contrast': 0.83, 'fill': [(0.25056280708573153, 0.25056280708573153, 0.9351131265310293), (1.0, 1.0, 1.0), 0.0]})
try:
restoreLightController()
except:
reportRestoreError("Error restoring lighting parameters")
def restoreRemainder():
from SimpleSession.versions.v62 import restoreWindowSize, \
restoreOpenStates, restoreSelections, restoreFontInfo, \
restoreOpenModelsAttrs, restoreModelClip, restoreSilhouettes
curSelIds = []
savedSels = []
openModelsAttrs = { 'cofrMethod': 4 }
windowSize = (1920, 1096)
xformMap = {0: (((-0.43076895843878, 0.84411264441503, 0.31923650791259), 139.26044960081), (81.330245324059, 17.786246295259, 56.868896721571), True), 1: (((-0.43076895843878, 0.84411264441503, 0.31923650791259), 139.26044960081), (81.330245324059, 17.786246295259, 56.868896721571), True), 2: (((-0.43076895843878, 0.84411264441503, 0.31923650791259), 139.26044960081), (81.330245324059, 17.786246295259, 56.868896721571), True)}
fontInfo = {'face': ('Sans Serif', 'Normal', 16)}
clipPlaneInfo = {}
silhouettes = {0: True, 1: True, 2: True, 11713: True}
replyobj.status("Restoring window...", blankAfter=0,
secondary=True)
restoreWindowSize(windowSize)
replyobj.status("Restoring open states...", blankAfter=0,
secondary=True)
restoreOpenStates(xformMap)
replyobj.status("Restoring font info...", blankAfter=0,
secondary=True)
restoreFontInfo(fontInfo)
replyobj.status("Restoring selections...", blankAfter=0,
secondary=True)
restoreSelections(curSelIds, savedSels)
replyobj.status("Restoring openModel attributes...", blankAfter=0,
secondary=True)
restoreOpenModelsAttrs(openModelsAttrs)
replyobj.status("Restoring model clipping...", blankAfter=0,
secondary=True)
restoreModelClip(clipPlaneInfo)
replyobj.status("Restoring per-model silhouettes...", blankAfter=0,
secondary=True)
restoreSilhouettes(silhouettes)
replyobj.status("Restoring remaining extension info...", blankAfter=0,
secondary=True)
try:
restoreRemainder()
except:
reportRestoreError("Error restoring post-model state")
from SimpleSession.versions.v62 import makeAfterModelsCBs
makeAfterModelsCBs()
from SimpleSession.versions.v62 import endRestore
replyobj.status('Finishing restore...', blankAfter=0, secondary=True)
endRestore({})
replyobj.status('', secondary=True)
replyobj.status('Restore finished.')
|
[
"[email protected]"
] | |
499d54707352d375b0249f2363e74f7d7f707d4c
|
7317d386b760a6a3db9bfa071c6c5a7243a5d4c2
|
/USA_COVID19.py
|
fcf3d2fbd17e113f7ddda8062e57206b5b9d665a
|
[] |
no_license
|
KKanda900/Covid_Insight
|
db0ec607e2bd3faa55b83e38e793ea198a7f61cd
|
dce7e8c55aee42623d6be84e45928b3d1a882e40
|
refs/heads/master
| 2023-02-22T06:01:07.082541 | 2021-01-19T03:39:47 | 2021-01-19T03:39:47 | 329,475,361 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 5,225 |
py
|
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import BayesianRidge
from sklearn.linear_model import Ridge
import datetime as dt
import Data as d
import os
def bayesian_prediction(state, days): # takes in argument for the number of days in future that theres going to be a increase
d.update()
pathname = d.find_file(state)
dataset = pd.read_csv(pathname)
dataset['date'] = pd.to_datetime(dataset['date'])
dataset['date_f'] = (dataset['date'] - dataset['date'].min()) / np.timedelta64(1,'D')
X = dataset[['date_f']]
y = dataset[['cases']]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=0)
regressor = BayesianRidge()
regressor.fit(X_train, y_train)
X_prediction_array = X[-days:] # sample in the next 5 days
y_pred = regressor.predict(X_prediction_array)
return 'Using the Bayesian Model: In the next {} days in {}, these will be the positive cases {}'.format(days, state, y_pred)
def linear_prediction(state, days):
d.update()
pathname = d.find_file(state)
dataset = pd.read_csv(pathname)
dataset['date'] = pd.to_datetime(dataset['date'])
dataset['date_f'] = (dataset['date'] - dataset['date'].min()) / np.timedelta64(1,'D')
X = dataset[['date_f']]
y = dataset[['cases']]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train, y_train)
X_prediction_array = X[-days:] # sample in the next 5 days
y_pred = regressor.predict(X_prediction_array)
return 'Using the Linear Regression Model: In the next {} days in {}, these will be the positive cases {}'.format(days, state, y_pred)
def ridge_prediction(state, days): # takes in argument for the number of days in future that theres going to be a increase
d.update()
pathname = d.find_file(state)
dataset = pd.read_csv(pathname)
dataset['date'] = pd.to_datetime(dataset['date'])
dataset['date_f'] = (dataset['date'] - dataset['date'].min()) / np.timedelta64(1,'D')
X = dataset[['date_f']]
y = dataset[['cases']]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=0)
regressor = Ridge()
regressor.fit(X_train, y_train)
X_prediction_array = X[-days:] # sample in the next 5 days
y_pred = regressor.predict(X_prediction_array)
return 'Using the Ridge Model: In the next {} days in {}, these will be the positive cases {}'.format(days, state, y_pred)
def get_rate_of_change(state): # takes in argument for the number of days in future that theres going to be a increase
d.update()
pathname = d.find_file(state)
dataset = pd.read_csv(pathname)
dataset['date'] = pd.to_datetime(dataset['date'])
dataset['date_f'] = (dataset['date'] - dataset['date'].min()) / np.timedelta64(1,'D')
X = dataset[['date_f']]
y = dataset[['cases']]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=0)
regressor = BayesianRidge() # most accurate model prediction
regressor.fit(X_train, y_train)
return 'In {} the COVID-19 cases are increasing by {} daily'.format(state, regressor.intercept_)
def positive_case_update(state): # initials of the state you want
d.update()
pathname = d.find_file(state)
dataset = pd.read_csv(pathname)
latest_cases = dataset.iloc[-1] # dataset.iloc[] is a dataframe object that contains the columns of the csv in this case (date, cases)
return '{} positive cases on {}'.format(latest_cases.cases, latest_cases.date)
def train_test_graphs(): # linear regression model => should i make more?
d.update()
dataset = pd.read_csv('NJ_Data.csv')
dataset['date'] = pd.to_datetime(dataset['date'])
dataset['date_f'] = (dataset['date'] - dataset['date'].min()) / np.timedelta64(1,'D')
X = dataset[['date_f']]
y = dataset[['cases']]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=1/3, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train, y_train)
'''
Lets split the data into a training set and test set which:
1. the training set will help train the data
2. the test set will run test to test accuracy
3. using the combination of the two will help make predictions based on the linear regression line created
'''
# Training Set
viz_train = plt
viz_train.scatter(X_train, y_train, color='red')
viz_train.plot(X_train, regressor.predict(X_train), color='blue')
viz_train.title('Positive Cases (Training set)')
viz_train.xlabel('Date')
viz_train.ylabel('Case')
viz_train.show()
# Test Set
viz_test = plt
viz_test.scatter(X_test, y_test, color='red')
viz_test.plot(X_train, regressor.predict(X_train), color='blue')
viz_test.title('Positive Cases (Test set)')
viz_test.xlabel('Date')
viz_test.ylabel('Case')
viz_test.show()
if __name__ == "__main__":
num = cases_prediction('HI', 5)
print(num)
|
[
"[email protected]"
] | |
1199528ac1386f543e6f07285d31d2c26cdf9b81
|
a9c5a1e0ab8427f249b2068cf93edffc8bd5c4de
|
/8 pics/8picsPvalue.py
|
c537fed3b8fac0ab5cd9080e7c0e3cc6af731eca
|
[] |
no_license
|
DianaAtlas/Anomaly-Detection
|
b79680a581c7922f9286827d30415e87d0100ce1
|
d3c3a899f195a1da899350ede79f1d5d00cbe6f6
|
refs/heads/main
| 2023-03-31T04:20:57.003247 | 2021-04-07T13:12:19 | 2021-04-07T13:12:19 | 354,071,310 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 6,726 |
py
|
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import math
import scipy.stats as st
import pandas as pd
# ============================================================
# =================creating DATA==============================
# ============================================================
def manPvalue8(signal, baground, amp1):
precision = 500
indexv = []
for i in range(precision):
indexv.append(i)
indexv = np.array(indexv)
def gaussian_generator8(mu1, mu2, mu3, mu4, mu5, mu6, mu7, mu8, sigma, amp, bground):
result = 0.5*(amp*((stats.norm.pdf(indexv, mu1, sigma)) + stats.norm.pdf(indexv+1, mu1, sigma) +
(stats.norm.pdf(indexv, mu2, sigma)) + stats.norm.pdf(indexv+1, mu2, sigma) +
(stats.norm.pdf(indexv, mu3, sigma)) + stats.norm.pdf(indexv+1, mu3, sigma) +
(stats.norm.pdf(indexv, mu4, sigma)) + stats.norm.pdf(indexv+1, mu4, sigma) +
(stats.norm.pdf(indexv, mu5, sigma)) + stats.norm.pdf(indexv+1, mu5, sigma) +
(stats.norm.pdf(indexv, mu6, sigma)) + stats.norm.pdf(indexv+1, mu6, sigma) +
(stats.norm.pdf(indexv, mu7, sigma)) + stats.norm.pdf(indexv+1, mu7, sigma) +
(stats.norm.pdf(indexv, mu8, sigma)) + stats.norm.pdf(indexv+1, mu8, sigma))) + bground
return result
def fluctuation_manufacture(numofevents_1, bground):
np.array(numofevents_1)
data_si_bg = np.random.poisson(numofevents_1, np.array(numofevents_1).shape)
data_bg = np.random.poisson(1 * bground, np.array(numofevents_1).shape)
return data_si_bg, data_bg
def sum_all_values_over_manual(array, number):
value_returned = 0
for i in range(len(array)):
if array[i] >= number:
value_returned += 1
return value_returned
# ============================================================
# =================creating TRAINING data=====================
# ============================================================
baground = baground
mu2 = 30
mu3 = 90
Mu1 = 150
mu4 = 210
mu5 = 270
mu6 = 330
mu7 = 390
mu8 = 450
amp = amp1
sigma_training = signal
bground_training = np.zeros(precision) + baground
numofpredictions = 60000
normalization = 2 * math.sqrt(baground)
# ============================================================
# =================creating TEST data=========================
# ============================================================
gaussian_test = gaussian_generator8(Mu1, mu2, mu3, mu4, mu5, mu6, mu7, mu8, sigma_training, amp, bground_training)
from1 = int(Mu1 - 1.5*sigma_training)
to = int(Mu1 + 1.5*sigma_training)
from2 = int(mu2 - 1.5*sigma_training)
to2 = int(mu2 + 1.5*sigma_training)
from3 = int(mu3 - 1.5*sigma_training)
to3 = int(mu3 + 1.5*sigma_training)
from4 = int(mu4 - 1.5*sigma_training)
to4 = int(mu4 + 1.5*sigma_training)
from5 = int(mu5 - 1.5*sigma_training)
to5 = int(mu5 + 1.5*sigma_training)
from6 = int(mu6 - 1.5*sigma_training)
to6 = int(mu6 + 1.5*sigma_training)
from7 = int(mu7 - 1.5*sigma_training)
to7 = int(mu7 + 1.5*sigma_training)
from8 = int(mu8 - 1.5*sigma_training)
to8 = int(mu8 + 1.5*sigma_training)
gausaray = []
for _ in range(numofpredictions):
gausaray.append(gaussian_test)
aa, bb = fluctuation_manufacture(gausaray, baground)
aanorm = (np.array(aa) - baground) / normalization
bbnorm = (np.array(bb) - baground) / normalization
# manual_01 = (np.trapz(aanorm[:, from1:to], axis=1) + np.trapz(aanorm[:, from2:to2], axis=1)
# + np.trapz(aanorm[:, from3:to3], axis=1) + np.trapz(aanorm[:, from4:to4], axis=1)
# + np.trapz(aanorm[:, from5:to5], axis=1) + np.trapz(aanorm[:, from6:to6], axis=1)
# + np.trapz(aanorm[:, from7:to7], axis=1) + np.trapz(aanorm[:, from8:to8], axis=1))
#
# manual_00 = (np.trapz(bbnorm[:, from1:to]) + np.trapz(bbnorm[:, from2:to2])
# + np.trapz(bbnorm[:, from3:to3]) + np.trapz(bbnorm[:, from4:to4])
# + np.trapz(bbnorm[:, from5:to5]) + np.trapz(bbnorm[:, from6:to6])
# + np.trapz(bbnorm[:, from7:to7]) + np.trapz(bbnorm[:, from8:to8]))
manual_01 = np.trapz(aanorm[:, ])
manual_00 = np.trapz(bbnorm[:, ])
manualmedian_00 = np.median(manual_01)
manualsum_00 = sum_all_values_over_manual(manual_00, manualmedian_00)
print('\033[1m', 'manual calc', '\033[0m')
print('#of values after median manual', manualsum_00)
print('presantage', manualsum_00 / numofpredictions, '+-', math.sqrt(manualsum_00 / numofpredictions))
pvalue = manualsum_00 / numofpredictions
# print('z-score', abs(st.norm.ppf(pvalue)), 'sigma')
# return abs(st.norm.ppf(pvalue))
return pvalue
# ======== p value VS background 3 different sigma===============
# bg1222 = []
# pvalue1 = []
# pvalue2 = []
# pvalue3 = []
#
# bg = 20
# sigma1 = 3
# sigma2 = 5
# sigma3 = 10
# for i in range(100):
#
# bg = bg + 5
# bg1222.append(bg)
# pvalue1.append(manPvalue8(3, bg, 30))
# pvalue2.append(manPvalue8(5, bg, 30))
# pvalue3.append(manPvalue8(10, bg, 30))
#
#
# bg1222 = np.array(bg1222)
# pvalue1 = np.array(pvalue1)
# pvalue2 = np.array(pvalue2)
# pvalue3 = np.array(pvalue3)
#
# print(pvalue1)
# print(bg1222)
#
# plt.title('Sigma VS Background')
# plt.ylabel('Sigma')
# plt.xlabel('Background')
# plt.plot(bg1222, pvalue2, label='\u03C3=5')
# plt.plot(bg1222, pvalue3, label='\u03C3=10')
# plt.plot(bg1222, pvalue1, label='\u03C3=3')
# plt.legend()
# # plt.yscale('log')
# plt.grid()
# plt.show()
#
# df1 = pd.DataFrame([bg1222, pvalue1, pvalue2, pvalue3],
# index=['bg', 'sigma3', 'sigma5', 'sigma10'])
# df1.to_excel("8picmanbg.xlsx")
# =======p value VS signals amp ================
bg1222 = []
pvalue1 = []
pvalue2 = []
pvalue3 = []
amp = 0
for i in range(70):
bg1222.append(amp)
pvalue2.append(manPvalue8(5, 200, amp))
amp = amp + 1
bg1222 = np.array(bg1222)
pvalue1 = np.array(pvalue1)
pvalue2 = np.array(pvalue2)
pvalue3 = np.array(pvalue3)
plt.title('P-Value VS Signal Magnitude')
plt.ylabel('P-Value')
plt.yscale('log')
plt.xlabel('Signal Magnitude [Events/GeV]')
plt.plot(bg1222, pvalue2, label='\u03C3=5')
plt.legend()
plt.grid()
plt.show()
df1 = pd.DataFrame([bg1222, pvalue1, pvalue2, pvalue3],
index=['amp', 'sigma3', 'sigma5', 'sigma10'])
df1.to_excel("8pic_Naivefull.xlsx")
|
[
"[email protected]"
] | |
22214c4cf02d9139ebf68302682f68b55190d51e
|
3a7adfdcf7a5048045c8e95a93369a1796cfd532
|
/conftest.py
|
377ddc7028f2964dd5cf5621a68dc74e7967e513
|
[
"BSD-3-Clause"
] |
permissive
|
theGreenJedi/nixpy
|
e06025077d5d224a7d051532ebfbd48845339c58
|
40b5ecdaa9b074c7bf73137d1a94cb84fcbae5be
|
refs/heads/master
| 2022-02-01T15:14:22.133157 | 2019-06-03T09:10:57 | 2019-06-03T09:10:57 | 197,896,640 | 1 | 0 | null | 2019-07-20T07:37:03 | 2019-07-20T07:37:02 | null |
UTF-8
|
Python
| false | false | 808 |
py
|
import pytest
import tempfile
from nixio.test.xcompat.compile import maketests
BINDIR = tempfile.mkdtemp(prefix="nixpy-tests-")
def pytest_addoption(parser):
parser.addoption("--nix-compat", action="store_true", default=False,
help=("Run nix compatibility tests "
"(requires NIX library)"))
@pytest.fixture
def bindir(request):
return BINDIR
def pytest_collection_modifyitems(config, items):
if config.getoption("--nix-compat"):
print("Compiling NIX compatibility tests")
maketests(BINDIR)
return
skip_compat = pytest.mark.skip(
reason="Use --nix-compat option to run compatibility tests"
)
for item in items:
if "compatibility" in item.keywords:
item.add_marker(skip_compat)
|
[
"[email protected]"
] | |
f872923d34e36892434be17b6427b39bcc4f8677
|
fb746b30a02ca6226498acf9ac66c849d74bf684
|
/Data_Ingestor/streaming.py
|
296f220dcc5eeafd98922f38f4c4639bc4a319c0
|
[] |
no_license
|
miaozeyu/hackerjobnow
|
0d47eca65e4d9b9e936ce6d570a0d8d42801c4a8
|
7e7cf1fe5ede8075dae4b7c82428443b92ddef10
|
refs/heads/master
| 2022-12-09T22:04:15.014630 | 2019-04-26T15:13:11 | 2019-04-26T15:13:11 | 180,033,869 | 0 | 0 | null | 2022-12-08T05:00:47 | 2019-04-07T22:59:38 |
Python
|
UTF-8
|
Python
| false | false | 6,803 |
py
|
#!/usr/bin/env python
# coding: utf-8
# In[12]:
from flask import jsonify, make_response
import sys
sys.path.append("../") # go to parent dir
import tweepy
from tweepy import OAuthHandler, Stream, StreamListener
from Data_Ingestor.accessconfig import *
import json
import re
from time import sleep
from Data_Ingestor.twitter_rest_producer import tweetParser
from random import random
# 1. Create a class inheriting from StreamListener
# 2. Using that class create a Stream object
# 3. Connect to the Twitter API using the Stream.
def retrieve_authentication():
auth = OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_secret)
return auth
def makeJobpost(tweet):
# must match
def matchTitle(txt):
find = re.findall(
r"(software|data|bi|business intelligence|sales|solutions)\s?(engineer|developer|scientist|architect|consultant)",
txt, re.IGNORECASE)
if find:
find = [' '.join(tp) for tp in find]
return find
else:
return None
# preferable
def matchSkills(txt):
find = re.findall(r"python|sql|sqlalchmey|flask|pandas|etl|aws|backend|spark|streaming|jinja", txt,
re.IGNORECASE)
if find:
return list(set(find))
else:
return None
# optional
def matchLocation(txt):
find = re.findall(r"(new)\s?(york)|(new)\s?(jersey)|ny|nj|nyc|(jersey)\s?(city)|hoboken|brooklyn", txt,
re.IGNORECASE)
if find:
find = [' '.join(tp) for tp in find]
return find
else:
return None
# optional: even if it's not in text, it's okay
def matchFulltime(txt):
find = re.findall(r"full(\s|\-)?time", txt, re.IGNORECASE)
if find:
return find
else:
return None
created_at = tweet['created_at']
text = tweet['text']
hashtags = ''.join(tweet['hashtags'])
place = tweet['place']
coordinates = tweet['coordinates']
urls = tweet['urls']
find_title = matchTitle(text)
find_title_htag = matchTitle(hashtags)
find_skills = matchSkills(text)
find_skills_htag = matchSkills(hashtags)
find_city = matchLocation(text)
find_city_htag = matchLocation(hashtags)
find_type = matchFulltime(text)
find_type_htag = matchFulltime(hashtags)
jobpost = {
"city": None,
"company": None,
"date": None,
"job_title": None,
"job_type": None,
"links": None,
"technologies": None,
"text": None
}
jobpost['text'] = text
jobpost['date'] = created_at
if find_title or find_title_htag:
jobpost["job_title"] = ' '.join(find_title or find_title_htag)
if find_skills or find_skills_htag:
jobpost["technologies"] = ','.join(find_skills or find_skills_htag)
if find_city or find_city_htag or place or coordinates:
jobpost["city"] = ','.join(find_city or find_city_htag) or place or coordinates
if find_type or find_type_htag:
jobpost["job_type"] = ','.join(find_type or find_type_htag)
if urls:
jobpost["links"] = ', '.join(urls)
return jobpost
else:
return None
# In[42]:
class TweeterStreamListener(StreamListener):
def __init__(self, socketio):
print("TweeterStreamListener is intiated")
super().__init__()
self.socketio = socketio
def on_connect(self):
print("Successfully connected to the Twitter stream")
def on_data(self, data):
number = round(random() * 10, 3)
print(number)
print("I'm getting tweets")
all_data = json.loads(data)
tweet = tweetParser(all_data)
print(tweet)
try:
jobpost = makeJobpost(tweet)
if jobpost:
# data = {"jobpost": jobpost}
# response = make_response(jsonify(data), 200)
# print(jsonify(data))
#return response
#sendToFirehose(jobpost)
self.socketio.emit('newtweet', {'tweet': number}, namespace='/test')
except tweepy.TweepError as e:
self.log.error("Error when sending tweet: %s" % e)
def on_error(self, status_code):
print(status_code)
if status_code == 420:
return False
class UndefinedChildClass(Exception):
pass
class DataFlow():
def __init__(self):
print("initiated dataflow")
self.auth = retrieve_authentication()
@staticmethod
def factory(child):
print("factory")
if child == 'historical':
return HistoricalFlow()
if child == 'live':
return LiveFlow()
err = 'The provided child argument (' + child + ') is not supported'
raise UndefinedChildClass(err)
class HistoricalFlow(DataFlow):
def __init__(self):
print("initiated historical")
super().__init__()
self.api = tweepy.API(self.auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)
def start(self, socketio):
query = """-hour -senior -frontend -staff -principal -contract -lead
"data engineer" OR "data scientist" OR "software engineer" OR "software developer" OR "backend engineer" OR "python developer" OR flask
(hiring OR "looking for" OR opening OR job)"""
maxTweets = 10000000 # Some arbitrary large number
tweetsPerQry = 100 # this is the max the API permits
tweetCount = 0
while tweetCount < maxTweets:
try:
print("I'm trying to get historical tweets")
tweets = tweepy.Cursor(self.api.search, q=query, lang="en", geocode="40.730610,-73.935242,40.0mi").items(tweetsPerQry)
print('newsearch')
for tweet in tweets:
tweet = tweetParser(tweet._json)
socketio.emit('newtweet', {'tweet': tweet['text']}, namespace='/test')
sleep(3)
tweetCount += len(tweets)
print('currentTotalTweets: {}'.format(tweetCount))
except tweepy.TweepError as err:
print(err)
def stop(self):
print('stop')
class LiveFlow(DataFlow):
def __init__(self):
print("initiated live")
super().__init__()
self.stream = None
def start(self, socketio):
listener = TweeterStreamListener(socketio)
self.stream = tweepy.Stream(self.auth, listener)
self.stream.filter(languages=["en"])
def stop(self):
self.stream.disconnect()
|
[
"[email protected]"
] | |
c7ebc6f32e1358ed20f23dc25b3df7d6a66daf88
|
4aeaca4c58858125e844aad1cd988182201b5120
|
/crane/files/timeHistoryParser.py
|
be957dd91e6668776b4c071a376eeffa2a646763
|
[] |
no_license
|
tkarna/crane
|
f18442a010af0909b7f5af9358cf9080ca1dd1e4
|
b8313d0373d8206685d81aadccc425e432c6a010
|
refs/heads/master
| 2020-05-21T23:39:07.707777 | 2017-11-16T15:58:14 | 2017-11-16T15:58:14 | 53,163,424 | 1 | 2 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,357 |
py
|
"""
Read SELFE time history (.th) files to a data container.
Jesse Lopez - 2016-04-15
"""
import datetime
import argparse
import numpy as np
from crane.data import timeArray
from crane.data import dataContainer
class thParser(object):
def __init__(self, filename, start_time):
self.filename = filename
self.start_date = start_time
self.time = None
self.data = None
def readFile(self):
"""Read time history file."""
th = np.loadtxt(self.filename)
self.time = timeArray.simulationToEpochTime(th[:, 0], self.start_date)
self.data = th[:, 1]
def genDataContainer(self, variable='variable', station='bvao',
depth='0', bracket='A', save=False):
"""Generate data container."""
x = y = z = 0
coordSys = ''
meta = {}
meta['tag'] = 'timeHistory'
meta['variable'] = variable
meta['location'] = station
meta['msldepth'] = depth
meta['bracket'] = bracket
dc = dataContainer.dataContainer.fromTimeSeries(
self.time, self.data, fieldNames=[variable],
x=x, y=y, z=z, timeFormat='epoch', coordSys=coordSys,
metaData=meta)
if save:
fname = './'+station+'_'+variable+'_'+'0'+'_'+self.start_date.strftime('%Y-%m-%d')+'.nc'
print fname
dc.saveAsNetCDF(fname)
return dc
def parseCommandLine():
parser = argparse.ArgumentParser(description='Read time history to dataContainer.')
parser.add_argument('filepath', type=str, help='Path to time history file.')
parser.add_argument('starttime', type=str, help='Start time of simulation YYYY-MM-DD')
parser.add_argument('variable', type=str, help='Variable name (e.g. - salinity, temp, turbidity)')
parser.add_argument('station', type=str, help='Station name (e.g. - saturn01, tpoin)')
parser.add_argument('depth', type=str, help='Station depth (e.g. - 0.1, 4.0)')
parser.add_argument('bracket', type=str, help='Bracket (e.g. - F, A, R)')
args = parser.parse_args()
st = datetime.datetime.strptime(args.starttime, '%Y-%m-%d')
th = thParser(args.filepath, st)
th.readFile()
th.genDataContainer(args.variable, args.station, args.depth, args.bracket, True)
if __name__ == '__main__':
parseCommandLine()
|
[
"[email protected]"
] | |
7abf55677fdd5bfc466f4fc561c0487c4a3fda26
|
8f213f498bbd5a12aacdb57bd2921f11a336eb62
|
/test_shuzi.py
|
65fec5d71985b15d39c6c296efc6d50ee1e17ba2
|
[] |
no_license
|
xiaocaiji945/HogwartsHttp
|
dd81641a2e34044dd845d589b113c0d5af61cc0e
|
66294964f978da238a052e0184f78e4b15884212
|
refs/heads/master
| 2023-01-12T22:34:52.067503 | 2020-11-20T10:10:33 | 2020-11-20T10:10:33 | 296,506,180 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,471 |
py
|
arr = [20459440, 20458987, 20458733, 20458586, 20458284, 20458202, 20457860, 20457611, 20456391, 20455888, 20455269,
20454830, 20454212, 20454184, 20454146, 20453922, 20452542, 20450716, 20447814, 20438557, 20424174, 20419436,
20409217, 20394189, 20378130, 20356098, 20352091, 20343586, 20338069, 20337799, 20333280, 20318024, 20275819,
20269554, 20262684, 20258563, 20219905, 20142943, 20133957, 20121781, 20085324, 19987253, 19969031, 19942288,
19844122, 19804714, 19789399, 19770693, 19700226, 19576007, 19483542, 19425997, 19042165, 19000064, 18909725,
18904145, 18669822, 18565022, 18389987, 18259359, 18208953, 17988689, 17884456, 17790401, 17621170, 17553892,
17473537, 17466361, 17365278, 17328129, 17185953, 17152062, 17049913, 17002217, 16957514, 16924540, 16911050,
16644262, 16390849, 16120793, 15767106, 15712028, 15687846, 15649054, 15620747, 15559984, 15540739, 15525037,
15274479, 15188702, 15088029, 15005839, 14904846, 14711057, 14570473, 14493796, 14440953, 14434577, 14377384,
14222650, 14216684, 14164919, 14111535, 14089191, 13819282, 13717991, 13467012, 13272225, 13155286, 2904628,
2708180, 2495704, 2060841, 2025011, 2023169, 2007456, 1851034, 1338200, 1325433, 898261, 440324,440324,898261]
def test_same():
n = len(arr)
for i in range(0, n):
for j in range(i + 1, n):
if (arr[i] == arr[j]):
print(f'相同的数字：{arr[i]}')
|
[
"[email protected]"
] | |
815fb4585091df66257a95e1e8b6fb45ebc6863f
|
fb65c39e3dffdc058fef85a58a43a3796aad09f4
|
/myweb/useroperations/models.py
|
963621c2ef221158f0792dc2338e930d5d72cfe7
|
[] |
no_license
|
goodjobig/personal_site
|
8e130577dfb072f55e1cf832151a3069c67a9640
|
e13c7de978f3a890d05fa8793621ec861c9bc883
|
refs/heads/master
| 2020-04-14T16:48:54.679691 | 2019-01-10T03:37:13 | 2019-01-10T03:37:13 | 163,962,182 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 837 |
py
|
from django.db import models
from django.contrib.auth.models import User
from blog.models import Blog
# Create your models here.
class UserProfile(models.Model):
user = models.OneToOneField(User,on_delete=models.CASCADE)
nickname = models.CharField(max_length=30,verbose_name='昵称')
photo = models.ImageField(upload_to='userImage/',default='userImage/default_photo.jpg')
collect = models.ManyToManyField(Blog,blank=True)
number = models.CharField(max_length=11)
class Meta:
verbose_name = '用户信息'
def __str__(self):
return self.nickname
# @property
# def get_username_or_nickname(self):
# if self.userprofile.nickname:
# return self.userprofile.nickname
# return self.username
# User.get_username_or_nickname = get_username_or_nickname
|
[
"[email protected]"
] | |
8f7c4d3034f61a9ab811f38f93dc56ef056af395
|
3fb45a8d4760a3de6d2a666210b396e371b83e46
|
/PictoDedection/helpers/detect Camera.py
|
cc459331952c03df243b07b344a854558639e384
|
[] |
no_license
|
josh2joshi/pren2
|
d40ec0e577f8c6c99b1619ea9b9696d7672ecb28
|
6a6be66fd41705db5c65182c6089509775a811fe
|
refs/heads/main
| 2023-06-04T00:39:13.001923 | 2021-06-27T11:34:08 | 2021-06-27T11:34:08 | 380,166,034 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 107 |
py
|
import cv2 as cv
for x in range(10):
cap = cv.VideoCapture(x)
if cap.isOpened():
print(x)
|
[
"[email protected]"
] | |
8272a86881ba02c1d2a978d26fea4bdf43312b44
|
58c6e5eb626c7993472c74d635bcda79260e1dcb
|
/article07.py
|
0fd3de0562d97f68ac6dd3fc71ed2dffd9bb546f
|
[] |
no_license
|
littlecoon/EffectivePython
|
8de8a531f8fc9a7ecabc0de999cbe84bcbe13906
|
a33ea90daefbf0f06450f13f0b2e88c87dee89e0
|
refs/heads/main
| 2023-03-12T12:32:33.008959 | 2021-02-28T09:00:14 | 2021-02-28T09:00:14 | 342,180,584 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 498 |
py
|
a = [1,2,3,4,5,6,7,8,9,10]
#squares = [x**2 for x in a]
squares = map(lambda x:x**2,a)
print(list(squares))
even_squares = [x**2 for x in a if x %2 == 0]
print(even_squares)
alt = map(lambda x:x**2,filter(lambda x:x%2 == 0,a))
assert even_squares == list(alt)
chile_ranks = {'ghost':1, 'habanero':2, 'cayenne':3}
rank_dict = {rank:name for name,rank in chile_ranks.items()} # 把字典反过来
chile_len_set = {len(name) for name in rank_dict.values()}
print(rank_dict)
print(chile_len_set)
|
[
"[email protected]"
] | |
d39dbb85f0ea8a843010ed2ff417e14430ec8b04
|
ae381913c23385f004b82161624097645ba8c4c8
|
/Huaxian_eemd/projects/plot_decompositions.py
|
8dbd45db6556f91e1ce3f8e7adbb1107c6385152
|
[
"MIT"
] |
permissive
|
zjy8006/MonthlyRunoffForecastByAutoReg
|
aa37910fdc66276d0df9d30af6885209d4a4ebfc
|
661fcb5dcdfbbb2ec6861e1668a035b50e69f7c2
|
refs/heads/master
| 2020-12-12T05:25:48.768993 | 2020-08-20T07:21:12 | 2020-08-20T07:21:12 | 259,588,564 | 7 | 3 | null | null | null | null |
UTF-8
|
Python
| false | false | 271 |
py
|
import pandas as pd
import os
root_path = os.path.dirname(os.path.abspath('__file__'))
import sys
sys.path.append(root_path)
from tools.plot_utils import plot_decompositions
signal = pd.read_csv(root_path+'/Huaxian_eemd/data/EEMD_TRAIN.csv')
plot_decompositions(signal)
|
[
"[email protected]"
] | |
356818e7291c93fdbeb0b4e58bad4cb1e752aa41
|
55cb166b9d060d89d13848447a08a2b875e49e73
|
/CapitalCities/CapitalCities/CapitalCities.py
|
3d5962a73f828dde4f8e5270bb859adf640e0621
|
[] |
no_license
|
mandyfarrugia2001/FundamentalsOfScripting
|
e39153a5047167f51fdc56a95517d57a0c94eea7
|
adaff9b90affd5af2d7d68c11c883487f1b74574
|
refs/heads/master
| 2022-04-06T03:57:50.042737 | 2020-02-19T16:29:54 | 2020-02-19T16:29:54 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 6,364 |
py
|
#Importing built-in Python modules to be used in this program.
import random #To generate random country and capital city from the text file.
"""
These values represent the final score,
thus values change according to user's guesses.
"""
timesWon = 0
timesLost = 0
def returnListCountries():
"""
Filling list with countries and capital cities delimited by a dash.
The text file will then be populated with all elements in the list.
"""
countriesCities = ["Albania-Tirana", "Andorra-Andorra la Vella",
"Armenia-Yerevan", "Austria-Vienna",
"Azerbaijan-Baku", "Belarus-Minsk",
"Belgium-Brussels", "Bosnia and Herzegovina-Sarajevo",
"Bulgaria-Sofia", "Croatia-Zagreb",
"Cyprus-Nicosia", "Czech Republic-Prague",
"Denmark-Copenhagen", "Estonia-Tallinn",
"Finland-Helsinki", "France-Paris",
"Georgia-Tbilisi", "Germany-Berlin",
"Greece-Athens", "Hungary-Budapest",
"Iceland-Reykjavik", "Ireland-Dublin",
"Italy-Rome", "Kazakhstan-Astana",
"Kosovo-Pristina", "Latvia-Riga",
"Liechtenstein-Vaduz", "Lithuania-Vilnius",
"Luxembourg-Luxembourg city", "Macedonia (FYROM)-Skopje",
"Malta-Valletta", "Moldova-Chisinau", "Monaco-Monaco city",
"Montenegro-Podgorica", "Netherlands-Amsterdam",
"Norway-Oslo", "Poland-Warsaw", "Portugal-Lisbon",
"Romania-Bucharest", "Russia-Moscow",
"San Marino-San Marino city", "Serbia-Belgrade",
"Slovakia-Bratislava", "Spain-Madrid",
"Sweden-Stockholm", "Switzerland-Bern",
"Turkey-Ankara", "Ukraine-Kyiv",
"United Kingdom-London"]
#Return list to be used in populateTextFile function.
return countriesCities
"""
Accepts only one parameter (read/write rights accordingly).
In case of issues with loading the text files,
the author has taken care of handling I/O related exceptions.
"""
def accessFile(mode):
"""
By default, the try block should be executed,
it contains potentially erroneous code.
In case an exception arises, run the except block
to prevent the program from crashing.
"""
try:
#By default, we will only be using the text file created in populateTextField function.
textFile = open("countries.txt", mode)
return textFile
except IOError:
#Print a so-to-speak user-friendly error message.
print("Could not access file! Try again later.")
#Display the amount of times the user lost and won after all questions are answered.
def displayScore():
#f and {} embody string interpolation.
print(f"Total times won: {timesWon}\nTotal times lost: {timesLost}\n")
def generateQuiz():
"""
The scores are now accessible within generateQuiz()
as they have been declared as global.
The randomly generated country and capital city
has been retrieved from splitData().
The user will be asked to guess the capital city
of the country generated at random.
If the user's guess is correct, a message is displayed
and they are awarded one point. (increment timesWon by 1)
The author has taken input validation into consideration,
thus numeric input and blank spaces are penalized
as though they are incorrect answers.
Display a message and increment timesLost by 1.
Same applies for incorrect guesses.
"""
global timesWon
global timesLost
#Retrieve values from splitData function.
country, capitalCity = splitData()
#Prompt user to guess the capital city of the randomly generated country.
guessCapitalCity = input(f"What is the capital city of {country}?: ")
"""
Input validation stage:
1) If the input matches the capital city stored in its variable,
award user with one point and display a message.
(increment timesWon by 1)
2) If If the input contains numbers or input is blank,
increment timesLost by 1 and display a message.
Same applies for incorrect guesses.
"""
if guessCapitalCity == capitalCity:
print("Correct!\n\n")
timesWon += 1
elif guessCapitalCity.isdigit() or guessCapitalCity == "":
print("Numeric input and blank spaces are not allowed!", end='\n\n')
timesLost += 1
else:
print(f"Incorrect! The answer was {capitalCity}.", end='\n\n')
timesLost += 1
def splitData():
"""
Split the country and capital city.
Store each of them in their own variable.
Then return the two values for use in other functions.
"""
#Access the file created in populateTextFile.
file = accessFile("r").read().splitlines() #Read and split each line.
#Select a random line from the text file.
randomData = random.choice(file)
#Get the index representing the first occurrence of the dash.
delimiter = randomData.find('-')
#Get the country from the first character to the delimiter.
country = randomData[0:delimiter]
#Exclude the delimiter and get the remaining characters representing the capital city.
capitalCity = randomData[(delimiter + 1):]
#Return country and capitalCity for use in other functions.
return(country, capitalCity)
def populateTextFile():
#Prepare the text file for creation.
file = accessFile("w")
"""
Return the list from returnListCountries function.
Stored in a variable to avoid having to refer
to the function all the time.
"""
countriesCities = returnListCountries()
#Copy every element in the list to the text file.
for index in range(len(countriesCities)):
#Leave a blank line between one element and another.
file.write(countriesCities[index] + '\n')
#It is important to close I/O operations.
file.close()
#Call the populateTextFile within the main method.
populateTextFile() #Create the text file.
#Ask three questions.
for index in range(3):
generateQuiz()
#Display the score after all the questions are answered.
displayScore()
|
[
"[email protected]"
] | |
04cbe077af6340cc04df22cf2ff03d306579a859
|
d7f1618d4269cd424ae6240aefa4046be6f764c3
|
/text/precommit.py
|
0d2d9e2c55d403a61a22409af57b9a06bd686b56
|
[] |
no_license
|
slin63/talon_community
|
15e12459324d01e2fdb8005cf359f409a4cca000
|
a9b56684605407435ea5d08867d416dc81e0c614
|
refs/heads/master
| 2021-07-11T19:26:06.074440 | 2020-08-04T16:44:40 | 2020-08-04T16:44:40 | 181,937,276 | 0 | 0 | null | 2019-04-17T17:19:32 | 2019-04-17T17:19:32 | null |
UTF-8
|
Python
| false | false | 2,455 |
py
|
from talon.voice import Key, press, Str, Context
from talon.webview import Webview
from talon import app, clip, cron, resource
from ..utils import parse_word
from datetime import datetime
ctx = Context("precommit")
pick_context = Context("precommitpick")
date = datetime.now().strftime("%m/%d/%Y")
CLIPBOARD_DEFAULT = ["prettier $(git diff --name-only --cached) --write"]
CLIPBOARD = CLIPBOARD_DEFAULT.copy()
webview = Webview()
css_template = """
<style type="text/css">
body {
padding: 0;
margin: 0;
font-size: 18px;
min-width: 600px;
}
td {
text-align: left;
margin: 0;
padding: 5px 10px;
}
h3 {
padding: 5px 0px;
}
table {
counter-reset: rowNumber;
}
table .count {
counter-increment: rowNumber;
}
.count td:first-child::after {
content: counter(rowNumber);
min-with: 1em;
margin-right: 0.5em;
}
.pick {
font-weight: normal;
font-style: italic;
font-family: Arial, Helvetica, sans-serif;
}
.cancel {
text-align: center;
}
</style>
"""
template = (
css_template
+ """
<div class="contents">
<h3>clipboard</h3>
<table>
{% for v in data %}
<tr class="count"><td class="pick">🔊 </td><td>{{ v[0:50] }}</td></tr>
{% endfor %}
<tr><td colspan="2" class="pick cancel">🔊 cancel</td></tr>
</table>
</div>
"""
)
def close_directories():
webview.hide()
pick_context.unload()
def set_selection(m):
with clip.capture() as sel:
press("cmd-c")
print("sel:", sel.get())
value = sel.get()
if value not in CLIPBOARD:
CLIPBOARD.append(value)
clip.set(value)
def make_selection(m):
cron.after("0s", close_directories)
words = m._words
print("CLIPBOARD:", CLIPBOARD)
d = None
if len(words) == 1:
d = int(parse_word(words[0]))
else:
d = int(parse_word(words[1]))
w = CLIPBOARD[d - 1]
Key("ctrl-c")(None)
Str(w)(None)
press("enter")()
def get_selection(m):
valid_indices = range(len(CLIPBOARD))
webview.render(template, data=CLIPBOARD)
webview.show()
keymap = {"(cancel | 0)": lambda x: close_directories()}
keymap.update(
{"[pick] %s" % (i + 1): lambda m: make_selection(m) for i in valid_indices}
)
pick_context.keymap(keymap)
pick_context.load()
def clear_clipboard(_):
global CLIPBOARD
CLIPBOARD = CLIPBOARD_DEFAULT.copy()
PREFIX = "(pre)"
keymap = {
f"{PREFIX} paste": get_selection,
}
ctx.keymap(keymap)
|
[
"[email protected]"
] | |
c77fd07345b4fa49db9df43bb116aed6515d6331
|
84b08a60e49e702e51b8c3bd0c558fbd957e11ae
|
/AlgoLatestPrints/MLPerceptron.py
|
b872b52cb75df4afcda480bb4c40b39e466c1c69
|
[] |
no_license
|
akhalayly/GoldenBoy
|
787732656250bc52ad0076dca35f15abbd2f4f14
|
fb88b656525c3bc614a24b982acf4d1ae745aa8b
|
refs/heads/main
| 2023-02-06T02:17:53.197336 | 2020-12-28T20:07:14 | 2020-12-28T20:07:14 | 304,894,027 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 4,439 |
py
|
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import KFold
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import Positions_Traits as posT
import helperFunctions as hf
if __name__ == '__main__':
files = ["CAMS", "CBs", "CMs", "CDMs", "GKs", "LBs", "LMs", "RBs", "RMs",
"Strikers"]
for file in files:
dataset = pd.read_csv("Success_" + file + ".csv")
attrbs = []
attrbs_names = []
attrbs = attrbs + hf.roleTraitIndexesFinder(["Age"], dataset.columns, hf.year_2012)
attrbs = attrbs + hf.roleTraitIndexesFinder(posT.General_Info, dataset.columns, "")
attrbs = attrbs + hf.roleTraitIndexesFinder(posT.Positive_Traits, dataset.columns, hf.year_2012)
for role in posT.positionToTraits[file]:
attrbs = attrbs + hf.roleTraitIndexesFinder(role, dataset.columns, hf.year_2012)
attrbs = list(set(attrbs))
attrbs_names = list(set(attrbs_names))
X = dataset.iloc[:, attrbs].values.astype(float)
y = dataset.iloc[:, -1].values
X = hf.normalizeAge(hf.normalizeMarketValue(hf.normalizeCA(X, 1), -1, file), 0)
kf = KFold(n_splits=5)
splits = []
results = 0
for train, test in kf.split(X):
splits.append((train, test))
last_results = {
'relu': [],
'logistic': [],
'tanh': [],
'identity': []
}
for activation in ['relu', 'logistic', 'tanh', 'identity']:
results = [0] * 3
index = 0
for solver in ['lbfgs', 'adam', 'sgd']:
for train_index, test_index in splits:
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
clf = MLPClassifier(activation=activation, solver=solver, max_iter=10000, alpha=1)
clf.fit(X_train, y_train)
pred_i = clf.predict(X_test)
results[index] += ((1 - np.mean(pred_i != y_test)) / splits.__len__())
index += 1
last_results[activation] = results
plt.figure(figsize=(12, 6))
plt.plot(['lbfgs', 'adam', 'sgd'], last_results['relu'], color='red', marker='o',
markerfacecolor='red', markersize=10)
plt.plot(['lbfgs', 'adam', 'sgd'], last_results['logistic'], color='blue', marker='o',
markerfacecolor='blue', markersize=10)
plt.plot(['lbfgs', 'adam', 'sgd'], last_results['tanh'], color='black', marker='o',
markerfacecolor='black', markersize=10)
plt.plot(['lbfgs', 'adam', 'sgd'], last_results['identity'], color='brown', marker='o',
markerfacecolor='brown', markersize=10)
plt.title('Accuracy Rate Multi-Layer Perceptron ' + file)
plt.xlabel('Solver')
plt.ylabel('Mean Accuracy')
plt.legend([str(i) for i in last_results.keys()])
plt.savefig("Results/MultiLayerPerceptron/Graph_" + file + ".png")
plt.show()
fig, ax = plt.subplots()
# hide axes
fig.patch.set_visible(False)
ax.axis('off')
ax.axis('tight')
for key in last_results.keys():
for idx in range(len(last_results[key])):
last_results[key][idx] = float("{:.4f}".format(last_results[key][idx]))
df = pd.DataFrame(last_results, columns=last_results.keys())
header = ax.table(cellText=[['']],
colLabels=['Activation'],
loc='bottom', bbox=[0, -0.025, 1.0, 0.15]
)
table = ax.table(cellText=df.values, rowLabels=['lbfgs', 'adam', 'sgd'], colLabels=df.columns,
colWidths=[0.3, 0.3, 0.3, 0.3, 0.3], loc='bottom', cellLoc='center',
rowColours=['r', 'r', 'r'],
colColours=['r', 'r', 'r', 'r'], bbox=[0, -0.35, 1.0, 0.4])
table.auto_set_font_size(False)
table.scale(1, 1.3)
table.set_fontsize(7)
table.add_cell(0, -1, width=0.4, height=0.090, text="Solver")
plt.figure(figsize=(20, 10))
fig.tight_layout()
fig.savefig("Results/MultiLayerPerceptron/" + file + ".png")
plt.show()
|
[
"[email protected]"
] | |
6a337ebcad790f7341970c4a3e71d1686f6229c6
|
333b405c1775475ddfa9ed3f4fa05c06b4c2e3f2
|
/cv2/cvbackup/mycv_0.510464.py
|
c1b80110eb76fc4413a5cbbc9977af4cd86de47d
|
[] |
no_license
|
daxiongshu/network
|
b77d5bb73dd353537f7687e61855d982cbd34464
|
842a778d310410ae39e58925257a9e9960ef560a
|
refs/heads/master
| 2020-04-15T16:11:31.101188 | 2016-02-16T01:32:21 | 2016-02-16T01:32:21 | 51,798,576 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 4,405 |
py
|
from xgb_classifier import xgb_classifier
import pandas as pd
import numpy as np
import pickle
from sklearn.ensemble import AdaBoostClassifier,ExtraTreesClassifier,RandomForestRegressor
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import roc_auc_score, f1_score, log_loss, make_scorer
from sklearn.linear_model import SGDClassifier
from sklearn.svm import LinearSVC,SVC
from sklearn.cross_validation import cross_val_score, train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split,KFold,StratifiedKFold
from math import log, exp, sqrt,factorial
import numpy as np
from scipy import sparse
from sklearn.ensemble import RandomForestRegressor
from sklearn.externals.joblib import Memory
from sklearn.datasets import load_svmlight_file
def rmsle(y,yp):
return (np.mean((yp-y)**2))**0.5
def multiclass_log_loss(y_true, y_pred, eps=1e-15):
predictions = np.clip(y_pred, eps, 1 - eps)
# normalize row sums to 1
predictions /= predictions.sum(axis=1)[:, np.newaxis]
actual = np.zeros(y_pred.shape)
n_samples = actual.shape[0]
#y_true-=1
actual[np.arange(n_samples), y_true.astype(int)] = 1
vectsum = np.sum(actual * np.log(predictions))
loss = -1.0 / n_samples * vectsum
return loss
def new_clf_train_predict(X,y,Xt):
clf=single_model()
clf.fit(X,y)
return clf.predict_proba(Xt)
def cut(yp):
yp[yp<0]=0
yp[yp>7]=7
yp=yp.astype(int)
return yp
def kfold_cv(X_train, y_train,k):
kf = StratifiedKFold(y_train,n_folds=k)
xx=[]
zz=[]
ypred=np.zeros((y_train.shape[0],3))
for train_index, test_index in kf:
X_train_cv, X_test_cv = X_train[train_index,:],X_train[test_index,:]
y_train_cv, y_test_cv = y_train[train_index],y_train[test_index]
clf=xgb_classifier(eta=0.1,gamma=0,col=0.4,min_child_weight=1,depth=7,num_round=160)
y_pred=clf.multi(X_train_cv,y_train_cv,X_test_cv,3,y_test=y_test_cv)
xx.append(multiclass_log_loss(y_test_cv,y_pred))
print xx[-1]#,y_pred.shape,zz[-1]
ypred[test_index]=y_pred
print xx
print 'average:',np.mean(xx),'std',np.std(xx)
return ypred,np.mean(xx)
mem = Memory("./mycache")
@mem.cache
def get_data(name):
data = load_svmlight_file(name)
return data[0], data[1]
X, _ = get_data('../sparse/rebuild1.svm')
X1, _ =get_data('../sparse/rebuild2.svm')
X2, _ = get_data('../sparse/rebuild3.svm')
X3, _ =get_data('../sparse/rebuild4.svm')
X4, _ =get_data('../sparse/rebuild5.svm')
X5, _ =get_data('../sparse/rebuild6.svm')
xx=[]
xx.append(np.sum(X.todense(),axis=1))
xx.append(np.sum(X1.todense(),axis=1))
xx.append(np.sum(X2.todense(),axis=1))
xx.append(np.sum(X3.todense(),axis=1))
xx.append(np.sum(X4.todense(),axis=1))
xx.append(np.std(X.todense(),axis=1))
xx.append(np.std(X1.todense(),axis=1))
xx.append(np.std(X2.todense(),axis=1))
xx.append(np.std(X3.todense(),axis=1))
xx.append(np.std(X4.todense(),axis=1))
#xx.append(np.sum(sparse.hstack([X,X1,X2,X3,X4],format='csr').todense(),axis=1))
#xx.append(np.max(X.todense(),axis=1)-np.min(X.todense(),axis=1))
#xx.append(np.max(X1.todense(),axis=1)-np.min(X1.todense(),axis=1))
#xx.append(np.max(X2.todense(),axis=1)-np.min(X2.todense(),axis=1))
#xx.append(np.max(X3.todense(),axis=1)-np.min(X3.todense(),axis=1))
#xx.append(np.max(X4.todense(),axis=1)-np.min(X4.todense(),axis=1))
xx=np.hstack(xx)
X=sparse.hstack([X,X1,X2,X3,X4,xx,pickle.load(open('../explore/X2.p'))],format='csr').todense()
train=pd.read_csv('../explore/train1.csv')
idname='id'
label='fault_severity'
idx=train[idname].as_matrix()
y=np.array(train[label])
import pickle
X=np.hstack([X,train.drop([label,idname],axis=1).as_matrix()])
#X=np.hstack([X,train[['location','volume']].as_matrix()])
print X.shape, y.shape
from scipy.stats import pearsonr
xx=[]
for i in X.T:
score=pearsonr(np.array(i.T).ravel(),y)[0]
if np.abs(score)>1e-2:
xx.append(np.array(i.T).ravel())
X=np.array(xx).T
print X.shape, y.shape
yp,score=kfold_cv(X,y,4)
print X.shape, y.shape
print yp.shape
s=pd.DataFrame({idname:idx,'predict_0':yp[:,0],'predict_1':yp[:,1],'predict_2':yp[:,2],'real':y})
s.to_csv('va.csv',index=False)
import subprocess
cmd='cp mycv.py cvbackup/mycv_%f.py'%(score)
subprocess.call(cmd,shell=True)
cmd='cp va.csv cvbackup/va_%f.csv'%(score)
subprocess.call(cmd,shell=True)
|
[
"[email protected]"
] | |
d711e028e5332f253d0de88ffe70a4eef5991499
|
bbdb856e9a0b96600668ac1f86d2ef009af74a41
|
/app/lib/HEM/centroid_peaks.py
|
3d91aaab0dd1b5d6cfea4d57b2c5a32483e1d776
|
[] |
no_license
|
jarosenb/UV_POSIT
|
ce68646c377f6565ab25e606ebf70b675a8baec7
|
7230cc47cad9fc476af11af3ded6db59685aada2
|
refs/heads/master
| 2020-06-27T10:49:44.042408 | 2018-02-12T23:58:43 | 2018-02-12T23:58:43 | 97,050,603 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,562 |
py
|
import math
def centroid_peaks(mz_array, intensity_array):
"""
Perform a Gauss fit to centroid the peaks for the property
:py:attr:`centroidedPeaks`
"""
tmp = []
print intensity_array
for pos, i in enumerate(intensity_array[:-1]):
if pos <= 1:
continue
if 0 < intensity_array[pos - 1] < i > intensity_array[pos + 1] > 0:
# local maximum ...
# if 827 <= mz_array[pos] <= 828:
# print("::",i,"@",mz_array[pos])
# print("Found maximum",i,"@",mz_array[pos],intensity_array[pos-1] ,'<' ,i ,"> ",intensity_array[pos+1] )
x1 = mz_array[pos - 1]
y1 = intensity_array[pos - 1]
x2 = mz_array[pos]
y2 = intensity_array[pos]
x3 = mz_array[pos + 1]
y3 = intensity_array[pos + 1]
if x2 - x1 > (x3 - x2) * 10 or (x2 - x1) * 10 < x3 - x2:
# no gauss fit if distance between mz values is too large
continue
if y3 == y1:
# i.e. a reprofiledSpec
# we start a bit closer to the mid point.
before = 3
after = 4
# while (not 0 < y1 < y2 > y3 > 0) and y1 == y3 and after < 10: #we dont want to go too far
# This used to be in here and I cannpt make sense out of it
#
while y1 == y3 and after < 10: # we dont want to go too far
if pos - before < 0:
lower_pos = 0
else:
lower_pos = pos - before
if pos + after >= len(mz_array):
upper_pos = len(mz_array) - 1
else:
upper_pos = pos + after
x1 = mz_array[lower_pos]
y1 = intensity_array[lower_pos]
x3 = mz_array[upper_pos]
y3 = intensity_array[upper_pos]
if before % 2 == 0:
after += 1
else:
before += 1
# if not (0 < y1 < y2 > y3 > 0):# or y1 == y3:
# # Then we wouldnt be in this loop
# #If we dont check this, there is a chance to apply gauss fit to a section
# #where there is no peak.
# continue
try:
doubleLog = math.log(y2 / y1) / math.log(y3 / y1)
mue = (doubleLog * (x1 * x1 - x3 * x3) - x1 * x1 + x2 * x2) / (
2 * (x2 - x1) - 2 * doubleLog * (x3 - x1))
cSquarred = (x2 * x2 - x1 * x1 - 2 * x2 * mue + 2 * x1 * mue) / (2 * math.log(y1 / y2))
A = y1 * math.exp((x1 - mue) * (x1 - mue) / (2 * cSquarred))
# if A > 1e20:
# print(mue, A, doubleLog, cSquarred)
# print(x1, "\t", y1)
# print(x2, "\t", y2)
# print(x3, "\t", y3)
# print()
except:
# doubleLog = math.log(y2 / y1) / math.log(y3 / y1)
# mue = (doubleLog * ( x1 * x1 - x3 * x3 ) - x1 * x1 + x2 * x2 ) / (2 * (x2 - x1) - 2 * doubleLog * (x3 - x1))
# cSquarred = ( x2*x2 - x1*x1 - 2*x2*mue + 2*x1*mue )/ ( 2* math.log(y1/y2 ))
# A = y1 * math.exp( (x1 - mue) * (x1 - mue) / ( 2 * cSquarred ) )
continue
tmp.append((mue, A))
# for mue, A in tmp:
# print(mue, "\t", A)
return tmp
|
[
"[email protected]"
] | |
47c779bd08214c78b338335b09535b2076a65080
|
32e910718743cde564c5bdb042b5a3dfbf198fa8
|
/limbic/integrations/imdb.py
|
4dca4aa77c6838ffe9cc024dac762042ade2611a
|
[
"MIT"
] |
permissive
|
maesfahani/limbic
|
3735d9c89ce6c63c9179501e495972311d7337b1
|
c7436d5243ed5e2819b7a4acee046f396e75a234
|
refs/heads/master
| 2023-01-20T08:14:44.751136 | 2020-11-21T16:17:42 | 2020-11-21T16:17:42 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,269 |
py
|
import json
import os
import time
from collections import defaultdict
from typing import Any, Dict, List, Optional
import requests
from bs4 import BeautifulSoup
from tqdm import tqdm
_OMDB_API_KEY = os.environ['OMDB_API_KEY']
_OMDB_API_BASE_URL = 'http://www.omdbapi.com/'
_OMDB_API_WAIT_TIME = 0.3
_IMDB_BASE_URL = 'https://www.imdb.com/title/'
def get_imdb_data(show_imdb_id: str, num_seasons: int,
output_path: Optional[str]) -> Dict[int, List[Any]]:
"""
Given show_imdb_id and the number of seasons, get all metadata from IMDB, including
ratings, viewers, etc..
"""
seasons_episodes = _get_episodes_imdb_ids(show_imdb_id, num_seasons)
seasons_data = _get_imdb_data(seasons_episodes)
if output_path:
with open(output_path, 'w') as imdb_file:
json.dump(seasons_data, imdb_file)
return seasons_data
def _get_episodes_imdb_ids(show_imdb_id: str, seasons: int) -> Dict[int, List[str]]:
"""
Given a show imdb_id, gets all the IDs for the episodes of the show
"""
base_url = f'{_IMDB_BASE_URL}{show_imdb_id}/'
seasons_episodes: Dict[int, List[str]] = defaultdict(list)
for i in tqdm(range(1, seasons + 1)):
season_url = base_url + f'episodes?season={i}'
soup = BeautifulSoup(requests.get(season_url).content, 'lxml')
for element in soup.find_all('a'):
if 'ttep' in element['href'] and 'ttep_ep_tt' not in element['href'] and element.get(
'itemprop') == 'url':
seasons_episodes[i].append(element['href'].split('/')[2])
return seasons_episodes
def _get_imdb_data(seasons_episodes: Dict[int, List[str]]) -> Dict[int, List[Any]]:
"""
Using the OMDB API, get all episodes information given a
"""
seasons_data: Dict[int, List[Any]] = defaultdict(list)
for season, episodes in seasons_episodes.items():
for episode_id in episodes:
get_request = requests.get(
_OMDB_API_BASE_URL, params={
'apikey': _OMDB_API_KEY,
'i': episode_id
})
seasons_data[season].append(json.loads(get_request.content.decode('utf-8')))
time.sleep(_OMDB_API_WAIT_TIME)
return seasons_data
|
[
"[email protected]"
] | |
a7ace6a20bcb3bff13acaebfd48d19888ddf1099
|
ff3cb8b03374645eccd2bffcd386479793f38c3c
|
/Clustering.py
|
6dddbbc7a8703e652e24392b8c4c430b4e2e32b5
|
[] |
no_license
|
ChunchunKumar/ML-DL
|
e39e6026aa2fc926d5f3136b46118593cfc87f2d
|
db06c967d69acb1f5c53a4b6669778d78ae696be
|
refs/heads/master
| 2021-05-05T02:59:35.360479 | 2018-04-08T00:05:07 | 2018-04-08T00:05:07 | 119,772,243 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,918 |
py
|
import matplotlib.pyplot as plt
import numpy as np
import xlrd as x
from scipy.stats import norm
from random import randint
def model(X1,X2,mean,Var):
n=mean.shape[0]
mult=norm(mean[0][0],var[0][0]).pdf(X1)*norm(mean[1][0],var[1][0]).pdf(X2)
return mult
file_location="E:/ML/Clustering/Data_KMean.xlsx"
work_book=x.open_workbook(file_location)
sheet=work_book.sheet_by_index(0)
a=np.array([[sheet.cell_value(r,c) for c in range(sheet.ncols)] for r in range(sheet.nrows)])
train=a.T
#plt.scatter(train[0,:],train[1,:])
def clustering(K,data):
mean = np.random.uniform(3, 6, (data.shape[0],K))
C = np.zeros((1, data.shape[1]))
for k in range(20):
for i in range(data.shape[1]):
min_index = 0
min = np.sum(np.square(data[:, i] - mean[:, 0]))
for j in range(K):
if (np.sum(np.square(data[:, i] - mean[:, j])) < min):
min = np.sum(np.square(data[:, i] - mean[:, j]))
min_index = j
C[0][i] = min_index
# print(C)
for i in range(K):
sum = np.zeros((data.shape[0], 1))
p = 0
for j in range(data.shape[1]):
if (C[0][j] == i):
sum[:, 0] = sum[:, 0] + data[:, j]
p = p + 1
if (p != 0):
mean[:, i] = sum[:, 0] / (p)
return C, mean
Error_K=np.zeros((1,10))
K=2
for u in range(10):
error_final = 10000
C_final = np.zeros((1, train.shape[1]))
mean_final = np.random.uniform(0, 20, (train.shape[0], K))
for i in range(20):
C, mean = clustering(K, train)
error = 0
for i in range(train.shape[1]):
error = error+np.sum(np.square(train[:, i] - mean[:, int(C[0][i])]))
error = error / train.shape[1]
if (error_final > error):
C_final = C
error_final = error
mean_final = mean
Error_K[0][u]=error_final
print(C_final)
print(error_final)
plot=np.zeros(train.shape)
for j in range(K):
plot = np.zeros(train.shape)
for l in range(train.shape[1]):
if (C[0][l] == j):
plot[:, l] = train[:, l]
plt.scatter(plot[0, :], plot[1, :], marker='*')
plt.scatter(mean[0, :], mean[1, :])
plt.title(K)
plt.show()
'''plot = np.zeros(train.shape)
for j in range(K):
plot = np.zeros(train.shape)
for l in range(train.shape[1]):
if (C_final[0][l] == j):
plot[:, l] = train[:, l]
plt.scatter(plot[0, :], plot[1, :], marker='*')
plt.scatter(mean_final[0, :], mean_final[1, :])
plt.show() '''
K=K+1
plt.scatter(np.linspace(2,11,10),Error_K)
plt.show()
|
[
"[email protected]"
] | |
c6b283e81159a9fb6bf45114451caef761c95c9a
|
8d530e384f97d010f1d6d0a0f49ead48f9c90768
|
/TreaProject/dynamicForms/views.py
|
5327852e07af62c7f15fca054bfb6ffbf717ad9e
|
[
"Apache-2.0"
] |
permissive
|
trea-uy/django-survey
|
e805e27d258f4be2b29681d45acf594038ef7fdb
|
2a04be76b92fc42dbc9ec8a634f9eea25d49328c
|
refs/heads/master
| 2021-01-19T17:59:43.703774 | 2014-09-11T21:22:37 | 2014-09-11T21:22:37 | 23,546,400 | 0 | 2 | null | null | null | null |
UTF-8
|
Python
| false | false | 102 |
py
|
from django.shortcuts import render
def index(request):
return render(request, 'index.html', [])
|
[
"federico@federico"
] |
federico@federico
|
bf7b00e36b31a0a37313b18848b1f49598da38fb
|
ce53487c613503926315611e135854ca072bbbb8
|
/Codewars/Disemvowel_Trolls.py
|
5f7eb0bc77762cac1ac09f0b84362ff6d72b6204
|
[] |
no_license
|
thequinn/Coding_Problems
|
8708ff30f51916c3ce29a608f6f34e5f020729ec
|
59e68780eef90f8a677abcd18ebbcef9e60b6272
|
refs/heads/master
| 2023-02-07T05:04:55.818652 | 2020-12-27T02:13:27 | 2020-12-27T02:13:27 | 205,428,717 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 63 |
py
|
def disemvowel(s):
return s.translate(None, 'aeiouAEIOU')
|
[
"[email protected]"
] | |
df38200ba8f918671e616c6c8421dcbd97a60bcc
|
febf14390cc5548365077f0c72e3aac55e8d6bd5
|
/views/frame.py
|
92e4c7744937f702e21c84aa9fa7bb829c6f0cbb
|
[] |
no_license
|
new-rich/Software-engineering
|
b932a71150bb84d5b50e8a73c3e15cb002193a26
|
487aba2fe21d9a2ff529ce694d8f0126608f1f93
|
refs/heads/master
| 2022-04-18T21:22:32.066740 | 2020-04-06T08:01:36 | 2020-04-06T08:01:36 | 252,339,948 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 992 |
py
|
import wx
import wx.aui
from .editor import TextEditor
class frame(wx.Frame):
def __init__(self):
super().__init__(None, title='Editor',size=(800,600))
self.aui_manager = wx.aui.AuiManager(self,wx.aui.AUI_MGR_TRANSPARENT_HINT)
self.editor_panel = TextEditor(self)
self.aui_manager.AddPane(self.editor_panel, self._get_default_pane_info().CenterPane().Position(0).BestSize(400,-1))
self.aui_manager.GetArtProvider().SetMetric(wx.aui.AUI_DOCKART_SASH_SIZE,0)
self.aui_manager.Update()
#self.Maximize(True)
self._register_listeners()
def _get_default_pane_info(self):
return wx.aui.AuiPaneInfo().CaptionVisible(False).PaneBorder(False).CloseButton(False).PinButton(False).Gripper(
False)
def on_frame_closing(self, e):
self.aui_manager.UnInit()
del self.aui_manager
self.Destroy()
def _register_listeners(self):
self.Bind(wx.EVT_CLOSE, self.on_frame_closing)
|
[
"[email protected]"
] | |
b9470a6364fcb617b3b2bbeb23ef97dce22221d7
|
de6fb3a55196b6bd36a4fda0e08ad658679fb7a1
|
/optin_manager/src/python/openflow/common/utils/formfields.py
|
adec249dc39015d89a6d299354718c9fd0f8e896
|
[
"BSD-3-Clause",
"Apache-2.0"
] |
permissive
|
dana-i2cat/felix
|
4a87af639e4c7db686bfa03f1ae4ce62711615e3
|
059ed2b3308bda2af5e1942dc9967e6573dd6a53
|
refs/heads/master
| 2021-01-02T23:12:43.840754 | 2016-02-04T10:04:24 | 2016-02-04T10:04:24 | 17,132,912 | 4 | 4 | null | null | null | null |
UTF-8
|
Python
| false | false | 388 |
py
|
'''
Created on Jul 17, 2010
@author: jnaous
'''
from django import forms
from expedient.common.utils import validators
class MACAddressField(forms.CharField):
"""
A MAC Address form field.
"""
default_error_messages = {
'invalid': u'Enter a valid MAC address in "xx:xx:xx:xx:xx:xx" format.',
}
default_validators = [validators.validate_mac_address]
|
[
"[email protected]"
] | |
7355b8d086777562985e5de5563d15a37060c3e1
|
940b69579fdd126d254020469bbe54e553f8d7ea
|
/tests/test_markdown_light.py
|
0721fce994eb753acb8fe43a962d73ad7bb3ca13
|
[
"MIT"
] |
permissive
|
nvtkaszpir/MarkdownLight
|
9edca79dc5fb513cfa6ab3aae52b5ab4e7360b91
|
38ad22267aa7b6327e39564f7c6c864104353e21
|
refs/heads/master
| 2021-01-18T10:45:31.864370 | 2015-03-27T15:51:00 | 2015-03-27T15:57:35 | 32,994,951 | 2 | 0 | null | 2015-03-27T15:48:46 | 2015-03-27T15:48:46 |
Python
|
UTF-8
|
Python
| false | false | 22,066 |
py
|
import syntax_test
class TestMarkdownLight(syntax_test.SyntaxTestCase):
def setUp(self):
super().setUp()
self.set_syntax_file("Packages/MarkdownLight/MarkdownLight.tmLanguage")
def check_default(self, patterns):
self.check_in_single_scope(patterns, 'text')
def test_simple_text(self):
self.set_text('A B C')
self.check_default('A B C')
def test_italic(self):
self.set_text('''
A *B* _C_ D
*E*
''')
self.check_eq_scope([ r'\*B\*', '_C_', r'\*E\*' ], 'markup.italic')
self.check_eq_scope(r'[\*_]', 'punctuation.definition')
self.check_default(list('AD '))
def test_bold(self):
self.set_text('''
A **B** __C__ D
**E**
''')
self.check_eq_scope([ r'\*\*B\*\*', r'__C__', r'\*\*E\*\*' ], 'markup.bold')
self.check_eq_scope(r'[\*_]+', 'punctuation.definition')
self.check_default(list('AD '))
def test_inline_markup_inside_inline_markup(self):
self.set_text('''
A *B **C** D* E
F **G *H* I** J
''')
self.check_eq_scope(r'\*B \*\*C\*\* D\*', 'markup.italic')
self.check_eq_scope(r'\*H\*', 'markup.italic')
self.check_eq_scope(r'\*\*C\*\*', 'markup.bold')
self.check_eq_scope(r'\*\*G \*H\* I\*\*', 'markup.bold')
self.check_eq_scope(r'\*+', 'punctuation.definition')
self.check_default(list('AEFJ'))
def test_bold_italic(self):
self.set_text('''
AA *__AB__* AC
BA _**BB**_ BC
CA **_CB_** CC
DA __*DB*__ DC
EA ***EB*** EC
FA ___FB___ FC
''')
self.check_eq_scope(r'\*__AB__\*', 'markup.italic')
self.check_eq_scope(r'_\*\*BB\*\*_', 'markup.italic')
self.check_eq_scope([ '_CB_', r'\*DB\*' ], 'markup.italic')
self.check_eq_scope([ '__AB__', r'\*\*BB\*\*' ], 'markup.bold')
self.check_eq_scope(r'\*\*_CB_\*\*', 'markup.bold')
self.check_eq_scope(r'__\*DB\*__', 'markup.bold')
self.check_eq_scope(r'\*+|_+', 'punctuation.definition')
self.check_eq_scope(r'\*\*\*EB\*\*\*', 'markup.bold')
self.check_eq_scope(r'\*\*\*EB\*\*\*', 'markup.italic')
self.check_eq_scope(r'___FB___', 'markup.bold')
self.check_eq_scope(r'___FB___', 'markup.italic')
self.check_default([ r'[A-Z]A ', r' [A-Z]C\n' ])
def test_multiline_markup_not_supported(self):
# Multiline inline markup is not supported due to
# limitations in syntax definition language.
self.set_text('''
A **B
C** D
E _F
G_ H
''')
self.check_default('.+')
def test_inline_markup_before_punctuation(self):
self.set_text('''
A *B*: *C*; *D*, *E*. *F*? G
K **L**: **M**; **N**, **O**. **P**? Q
''')
self.check_eq_scope([
r'\*B\*', r'\*C\*', r'\*D\*', r'\*E\*', r'\*F\*'
], 'markup.italic')
self.check_eq_scope([
r'\*\*L\*\*', r'\*\*M\*\*', r'\*\*N\*\*',
r'\*\*O\*\*', r'\*\*P\*\*'
], 'markup.bold')
self.check_eq_scope(r'\*+', 'punctuation.definition')
self.check_default(r'[AGKQ:;,\.?]')
def test_inline_markup_inside_quotes_and_brackets(self):
self.set_text('''
A "*B*" (*C*) '*D*' E
K "**L**" (**M**) '**N**' O
''')
self.check_eq_scope([ r'\*B\*', r'\*C\*', r'\*D\*' ], 'markup.italic')
self.check_eq_scope([ r'\*\*L\*\*', r'\*\*M\*\*', r'\*\*N\*\*' ], 'markup.bold')
self.check_eq_scope(r'\*+', 'punctuation.definition')
self.check_default(r'''[AEKQ"\(\)'\.]''')
def test_inline_markup_outside_quotes_and_brackets(self):
self.set_text('''
*"A"* *(B)* *'C'*
**"D"** **(E)** **'F'**
*"A";* *(B).* *'C':*
**"D"!** **(E)?** **'F',**
Z
''')
self.check_eq_scope([ r'\*"A"\*', r'\*\(B\)\*', r"\*'C'\*" ], 'markup.italic')
self.check_eq_scope([ r'\*\*"D"\*\*', r'\*\*\(E\)\*\*', r"\*\*'F'\*\*" ], 'markup.bold')
self.check_eq_scope([ r'\*"A";\*', r'\*\(B\)\.\*', r"\*'C':\*" ], 'markup.italic')
self.check_eq_scope([ r'\*\*"D"!\*\*', r'\*\*\(E\)\?\*\*', r"\*\*'F',\*\*" ], 'markup.bold')
self.check_default('Z')
def test_brackets_inside_inline_markup(self):
self.set_text('''
*A (B C)*: D
*(K)* **(L)**
''')
self.check_eq_scope([ r'\*A \(B C\)\*', r'\*\(K\)\*' ] , 'markup.italic')
self.check_eq_scope( r'\*\*\(L\)\*\*', 'markup.bold')
self.check_eq_scope(r'\*+', 'punctuation.definition')
self.check_default(r': D')
def test_inline_markup_combinations(self):
self.set_text('_A _ B_C D_E _ F_ *G* **H** <a>_I_</a>')
self.check_eq_scope([ '_A _ B_C D_E _ F_',
r'\*G\*', '_I_' ], 'markup.italic')
self.check_eq_scope(r'\*\*H\*\*', 'markup.bold')
def test_escaping_of_inline_punctuation(self):
self.set_text(r'A *\*B\** C **D\*** E')
self.check_eq_scope(r'\*\\\*B\\\*\*', 'markup.italic')
self.check_eq_scope(r'\*\*D\\\*\*\*', 'markup.bold')
self.check_default(list('ACE '))
def test_inline_markup_does_not_work_inside_words(self):
self.set_text('A_B C_D_E')
self.check_default('.+')
def test_inline_markup_does_not_work_without_text(self):
self.set_text('''
A ____ B
''')
self.check_default('^.+$')
def test_valid_ampersands(self):
self.set_text('''
&
&&
A & B
A && B
& A &B && C &&D E& F&&
&G;
''')
self.check_no_scope('^.+$', 'invalid')
def test_valid_brackets(self):
self.set_text('''
<
<<
A < B
A << B
A<
A<<
''')
self.check_no_scope('^.+$', 'invalid')
def test_headings(self):
self.set_text('''
# A
## B
### C
#### D
##### E
###### F
G
#K
##L#
### M ##
#### N ###########
O
''')
self.check_eq_scope(list('ABCDEFKLMN'), 'entity.name.section')
self.check_in_scope(list('ABCDEFKLMN# '), 'markup.heading')
self.check_eq_scope(r'#+', 'punctuation.definition')
self.check_default(list('GO'))
def test_setext_headings(self):
self.set_text('''
A
===
B
---
C
D
=======
E
F
-------
Z
''')
self.check_eq_scope('=+', 'markup.heading.1')
self.check_eq_scope('-+', 'markup.heading.2')
self.check_default(r'\w+')
def test_not_setext_headings(self):
self.set_text('''
- A
===
> B
---
C
=======
D
--
E
- - -
-------
-------
========
Z
''')
self.check_no_scope('.+', 'markup.heading')
def test_inline_markup_inside_headings(self):
self.set_text('''
#_A_
## B _C_
### D _E_ F
#### K _L M_ N #
Z
''')
self.check_eq_scope([
'_A_', 'B _C_', 'D _E_ F', 'K _L M_ N'
], 'entity.name.section')
self.check_in_scope(list('ABCDEFKLMN#_ '), 'markup.heading')
self.check_eq_scope([ '_A_', '_C_', '_E_', '_L M_' ], 'markup.italic')
self.check_eq_scope(r'#+', 'punctuation.definition')
self.check_default(r'Z')
def test_fenced_paragraph(self):
self.set_text('''
K
```
A
```
L
''')
self.check_eq_scope(r'```\nA\n```\n', 'markup.raw.block.fenced')
self.check_eq_scope('`+', 'punctuation.definition')
self.check_default([ r'K\n\n', r'\nL\n' ])
def test_fenced_block_inside_paragraph(self):
self.set_text('''
K
```
A
```
L
''')
self.check_eq_scope(r'```\nA\n```\n', 'markup.raw.block.fenced')
self.check_eq_scope('`+', 'punctuation.definition')
self.check_default([ r'\nK\n', r'L\n\n' ])
def test_syntax_highlighting_inside_fenced_blocks(self):
self.set_text('''
``` c++
int x = 123;
```
```python
def g():
return 567
```
''')
self.check_eq_scope([ 'int', 'def' ], 'storage.type')
self.check_eq_scope([ '123', '567' ], 'constant.numeric')
self.check_eq_scope('g', 'entity.name')
self.check_eq_scope('return', 'keyword.control')
def test_indented_raw_blocks(self):
self.set_text('''
A
B
C
''')
self.check_eq_scope(r' B\n', 'markup.raw.block')
self.check_default([ r'\nA\n\n', r'\nC\n' ])
def test_multiline_indented_raw_blocks(self):
self.set_text('''
A
B
''')
self.check_eq_scope(r' A\n B\n', 'markup.raw.block')
def test_indented_raw_blocks_glued_to_text(self):
self.set_text('''
A
B
C
D
''')
self.check_eq_scope(r' C\n', 'markup.raw.block')
self.check_default([ r'\nA\n B\n\n', r'D\n' ])
def test_blank_line_is_not_indented_raw_block(self):
self.set_text('\n\n \n\n')
self.check_default(r'\n[ ]+\n')
def test_inline_raw_text(self):
self.set_text('''
A `B` C
D`E`F
K `L **M` N** O
''')
self.check_eq_scope(list('BE') + [ r'L \*\*M' ], 'markup.raw.inline.content')
self.check_eq_scope('`', 'punctuation.definition')
self.check_default(list('ACDFK') + [ r' N\*\* O' ])
def test_incomplete_or_multiline_inline_raw_text(self):
self.set_text('''
A `B
C` D
''')
self.check_default('.+')
def test_multiple_backquotes_as_inline_raw_delimiters(self):
self.set_text('''
``A``
```B``
``C```
''')
self.check_eq_scope(list('AC'), 'markup.raw.inline.content')
self.check_eq_scope('`B', 'markup.raw.inline.content')
self.check_eq_scope([ r'^``', r'(?<=\w)``' ], 'punctuation.definition')
self.check_default([ r'(?<=C``)`', r'\n' ])
def test_inline_raw_delimiters_do_not_start_fenced_block(self):
self.set_text('''
```A```
B
''')
self.check_eq_scope('```A```', 'markup.raw.inline.markdown')
self.check_eq_scope('A', 'markup.raw.inline.content')
self.check_eq_scope('```', 'punctuation.definition')
self.check_default(r'B')
def test_quoted_text_alone(self):
self.set_text('>A\n')
self.check_eq_scope(r'>A\n', 'markup.quote')
self.check_eq_scope(r'>', 'punctuation.definition')
def test_one_line_quoted_block(self):
self.set_text('''
>A
B
''')
self.check_eq_scope(r'>A\n', 'markup.quote')
self.check_eq_scope(r'>', 'punctuation.definition')
self.check_default(r'\nB\n')
def test_type_1_multiline_quoted_block(self):
self.set_text('''
>A
B
C
''')
self.check_eq_scope(r'>A\nB\n', 'markup.quote')
self.check_eq_scope(r'>', 'punctuation.definition')
self.check_default(r'\nC\n')
def test_type_2_multiline_quoted_block(self):
self.set_text('''
>A
>B
C
''')
self.check_eq_scope(r'>A\n>B\n', 'markup.quote')
self.check_eq_scope(r'>', 'punctuation.definition')
self.check_default(r'\nC\n')
def test_quoted_block_inside_paragraph(self):
self.set_text('''
A
>B
C
''')
self.check_eq_scope(r'>B\n', 'markup.quote')
self.check_default([ r'\nA\n', r'\nC\n' ])
def test_spaces_before_and_after_quote_signs(self):
self.set_text('''
> A
> B
> C
D
''')
self.check_eq_scope(r' > A\n {2}> {2}B\n {3}> {3}C\n', 'markup.quote')
self.check_eq_scope(r'>', 'punctuation.definition')
self.check_default(r'\nD\n')
def test_inline_markup_inside_quoted_text(self):
self.set_text('''
> `A`
> _B_
> **C**
''')
self.check_eq_scope('`A`', 'markup.raw.inline.markdown')
self.check_eq_scope('_B_', 'markup.italic')
self.check_eq_scope(r'\*\*C\*\*', 'markup.bold')
def test_list_item_alone(self):
self.set_text(
'''- A
''')
self.check_eq_scope(r'- A\n', 'meta.paragraph.list')
self.check_eq_scope(r'-', 'punctuation.definition')
def test_multiline_list(self):
self.set_text('''
- A
- B
C
''')
self.check_eq_scope(r'- A\n- B\n', 'meta.paragraph.list')
self.check_eq_scope(r'-', 'punctuation.definition')
self.check_default(r'\nC\n')
def test_different_types_of_unnumbered_list_bullets(self):
self.set_text('''
- A
+ B
* C
D
''')
self.check_eq_scope(r'- A\n\+ B\n\* C\n', 'meta.paragraph.list')
self.check_eq_scope([ r'\+', r'\*', '-' ], 'punctuation.definition')
self.check_default(r'D')
def test_numbered_list(self):
self.set_text('''
0. A
1. B
12345. C
D
''')
self.check_eq_scope(r'0\. A\n1\. B\n\d+\. C\n', 'meta.paragraph.list')
self.check_eq_scope([ r'0\.', r'1\.', '12345\.' ], 'punctuation.definition')
self.check_default(r'D')
def test_nested_lists(self):
self.set_text('''
- A
* B
+ C
1. D
2. E
Z
''')
self.check_eq_scope(r'- A\n \* B\n \+ C\n +1\. D\n2\. E\n', 'meta.paragraph.list')
self.check_eq_scope([ '-', r'\*', r'\+', r'1\.', r'2\.' ], 'punctuation.definition')
self.check_default('Z')
def test_spaces_after_bullet(self):
self.set_text('''
-A
- B
- C
Z
''')
self.check_eq_scope(r'- B\n- +C\n', 'meta.paragraph.list')
self.check_eq_scope([ r'-(?= B)', r'-(?= +C)' ], 'punctuation.definition')
self.check_default('Z')
def test_list_inside_paragraph(self):
self.set_text('''
A
- B
''')
self.check_eq_scope(r'- B\n', 'meta.paragraph.list')
self.check_default(r'\nA\n')
def test_inline_markup_inside_list_items(self):
self.set_text('''
- `A`
- _B_
- **C**
''')
self.check_in_scope(r'-.*$\n', 'meta.paragraph.list')
self.check_eq_scope('`A`', 'markup.raw.inline.markdown')
self.check_eq_scope('_B_', 'markup.italic')
self.check_eq_scope(r'\*\*C\*\*', 'markup.bold')
def test_multiline_list_items(self):
self.set_text('''
- A
B
- C
D
Z
''')
self.check_eq_scope(r' - A\n B\n - C\nD\n', 'meta.paragraph.list')
self.check_default('Z')
def test_multiline_list_item_with_paragraph(self):
self.set_text('''
- A
B
C
- D
E
F
Z
''')
self.check_eq_scope(r'- A\n', 'meta.paragraph.list')
self.check_eq_scope(r' B\nC\n- D\n', 'meta.paragraph.list')
self.check_eq_scope(r' E\nF\n', 'meta.paragraph.list')
self.check_default('Z')
def test_4_spaces_in_multiline_list_item(self):
self.set_text('''
- A
B
C
- D
E
F
Z
''')
self.check_eq_scope(r'- A\n {4}B\n {4}C\n', 'meta.paragraph.list')
self.check_eq_scope(r'- D\n', 'meta.paragraph.list')
self.check_eq_scope(r' {4}E\n {4}F\n', 'meta.paragraph.list')
self.check_default('Z')
def test_4_spaces_before_nested_list_items(self):
self.set_text('''
- A
- B
- C
Z
''')
self.check_eq_scope(r'- A\n {4}- B\n {8}- C\n', 'meta.paragraph.list')
self.check_default('Z')
def test_fenced_block_is_not_part_of_a_list_item(self):
self.set_text('''
- A
```
B
```
Z
''')
self.check_eq_scope(r'- A\n', 'meta.paragraph.list')
self.check_eq_scope(r'```\nB\b\n```\n', 'markup.raw.block.fenced')
self.check_default('Z')
def test_inline_links(self):
self.set_text('''
[A](B)
[C] (D)
[E](F "G")
![C] (D)
Z
''')
self.check_eq_scope([
r'^\[A\]\(B\)',
r'^\[C\]\s+\(D\)',
r'^\[E\]\(F "G"\)'
], 'meta.link.inline')
self.check_eq_scope([
r'^!\[A\]\(B\)',
r'^!\[C\]\s+\(D\)',
r'^!\[E\]\(F "G"\)'
], 'meta.image.inline')
self.check_eq_scope(list('ACE'), 'string.other.link.title')
self.check_eq_scope(list('BDF'), 'markup.underline.link')
self.check_eq_scope('G', 'string.other.link.description.title')
self.check_eq_scope([ r'!', r'\[', r'\]' ], 'punctuation.definition')
self.check_default('Z')
def test_reference_links(self):
self.set_text('''
[A][B]
[C] [D]
![E][F]
![G] [H]
Z
''')
self.check_eq_scope(r'\[A\]\[B\]', 'meta.link.reference')
self.check_eq_scope(r'\[C\]\s+\[D\]', 'meta.link.reference')
self.check_eq_scope(r'!\[E\]\[F\]', 'meta.image.reference')
self.check_eq_scope(r'!\[G\]\s+\[H\]', 'meta.image.reference')
self.check_eq_scope(list('ACEG'), 'string.other.link.title')
self.check_eq_scope(list('BDFH'), 'constant.other.reference.link')
self.check_eq_scope([ r'!', r'\[', r'\]' ], 'punctuation.definition')
self.check_default('Z')
def test_implicit_links(self):
self.set_text('''
[A][]
[B] []
![C][]
![D] []
Z
''')
self.check_eq_scope([ r'\[A\]\[\]', r'\[B\] \[\]' ],
'meta.link.reference')
self.check_eq_scope([ r'!\[C\]\[\]', r'!\[D\] \[\]' ],
'meta.image.reference')
self.check_eq_scope(list('ABCD'), 'constant.other.reference.link')
self.check_eq_scope(r'[!\[\]]', 'punctuation.definition')
self.check_default('Z')
def test_multiline_links_not_supported(self):
self.set_text('''
[A
B](C)
[D
E][F]
![D
E][F]
''')
self.check_default('.+')
def test_inline_markup_inside_links(self):
self.set_text('''
[__A__](B)
[_C_][D]
![_G_][H]
Z
''')
self.check_eq_scope(r'\[__A__\]\(B\)', 'meta.link.inline')
self.check_eq_scope(r'\[_C_\]\[D\]', 'meta.link.reference')
self.check_eq_scope(r'!\[__E__\]\(F\)', 'meta.image.inline')
self.check_eq_scope(r'!\[_G_\]\[H\]', 'meta.image.reference')
self.check_eq_scope([ '__A__', '__E__' ], 'markup.bold')
self.check_eq_scope([ '_C_', '_G_' ], 'markup.italic')
self.check_default('Z')
def test_inline_markup_outside_links(self):
self.set_text('''
**[A](X)**
__[B][X]__
**
_![D][X]_
Z
''')
self.check_eq_scope(r'\*\*\[A\]\(X\)\*\*', 'markup.bold')
self.check_eq_scope(r'__\[B\]\[X\]__', 'markup.bold')
self.check_eq_scope(r'\*!\[C\]\(X\)\*', 'markup.italic')
self.check_eq_scope(r'_!\[D\]\[X\]_', 'markup.italic')
self.check_default('Z')
def test_references(self):
self.set_text('''
[A]: B "C"
[D]:<E> 'F'
[K]: L (M)
[N]: O
Z
''')
self.check_eq_scope(r'\[.*?(?=\s*$)', 'meta.link.reference.def')
self.check_eq_scope(r'''[\[\]:'"()]''', 'punctuation')
self.check_eq_scope(list('ADKN'), 'constant.other.reference.link')
self.check_eq_scope(list('BELO'), 'markup.underline.link')
self.check_eq_scope(list('CFM'), 'string.other.link.description.title')
self.check_default('Z')
def test_supported_urls(self):
self.set_text('''
http://A.B
https://C.D
ftp://E.F
http://H.I.J
http://K.L/
http://M.N/O?P=Q&R=S
http://Q.W:123
http://Q.W.E:123/
Z
''')
self.check_eq_scope(r'^.+://.+$', 'markup.underline.link')
self.check_eq_scope(r'^.+://.+$', 'meta.link.inet')
self.check_default('Z')
def test_unsupported_urls(self):
self.set_text('''
http://A
http://A:80
http://A:80.C
ssh://B.C
http://D/E
http://A?B.C
''')
self.check_default('.+')
def test_urls_in_brackes(self):
self.set_text('''
<http://A.B>
<https://C.D>
<ftp://E.F>
<http://H.I.J>
<http://K.L/>
<http://M.N/O?P=Q&R=S>
<http://Q.W:123>
<http://Q.W.E:123/>
Z
''')
self.check_eq_scope(r'http://A\.B', 'markup.underline.link')
self.check_eq_scope(r'^.+://.+$', 'meta.link.inet')
self.check_eq_scope(r'[<>]', 'punctuation.definition')
self.check_default('Z')
def test_emails(self):
self.set_text('''
<[email protected]>
<mailto:[email protected]>
[email protected]
mailto:[email protected]
Z
''')
self.check_eq_scope(r'[email protected]', 'markup.underline.link')
self.check_eq_scope(r'mailto:[email protected]', 'markup.underline.link')
self.check_eq_scope(r'[^\s@]+@\S+', 'meta.link.email')
self.check_eq_scope(r'[<>]', 'punctuation.definition')
self.check_default('Z')
def test_strikethrough(self):
self.set_text('''
A ~~B~~ ~~C D~~ E
''')
self.check_eq_scope([ '~~B~~', '~~C D~~' ], 'markup.strikethrough')
self.check_eq_scope('~~', 'punctuation.definition.strikethrough')
self.check_default(list('AE'))
def test_unsupported_strikethrough(self):
self.set_text('''
~~A
B~~
~~ C~~
~~D ~~
E~~F~~
''')
self.check_default('.+')
def test_strikethrough_with_bold_italic(self):
self.set_text('''
*__~~A~~__*
*~~__B__~~*
~~*__C__*~~
___~~D~~___
~~___E___~~
Z
''')
self.check_eq_scope([
r'~~A~~', r'~~__B__~~', r'~~\*__C__\*~~',
r'~~D~~', r'~~___E___~~'
], 'markup.strikethrough')
self.check_eq_scope([
r'__~~A~~__', r'__B__', r'__C__',
r'___~~D~~___', r'___E___'
], 'markup.bold')
self.check_eq_scope([
r'\*__~~A~~__\*', r'\*~~__B__~~\*', r'\*__C__\*',
r'___~~D~~___', r'___E___'
], 'markup.italic')
self.check_eq_scope(r'~+|_+|\*+', 'punctuation.definition')
self.check_default('Z')
def test_html_tags(self):
self.set_text('''
A<br>
<li>B
<a href="http://C.D">E</a>
''')
self.check_default([ r'\nA', r'\n', r'B\n', 'E' ])
self.check_eq_scope([ '<br>', '<li>',
'<a href="http://C.D">', '</a>' ],
'meta.tag')
def test_block_tags_turn_off_markdown_markup(self):
self.set_text('''
<p>
*A* ~~B~~ __C__
</p>
<div>*D* ~~E~~ __F__</div>
''')
self.check_no_scope(list('ABCDEF'), 'markup')
def test_inline_markup_combined_with_html(self):
self.set_text('<a>_A_</a>')
self.check_eq_scope('_A_', 'markup.italic')
self.check_eq_scope([ '<a>', '</a>' ], 'meta.tag')
def test_horisontal_lines(self):
self.set_text('''
***
* * *
___
__ __ __
- - -
----------------
---_---
Z
''')
self.check_eq_scope([
r'\*\*\*\n',
r'\* \* \*\n',
r'___\n',
r' __ __ __\n',
r' - - - \n',
r' -----+ +\n'
], 'meta.separator')
self.check_default(['---_---', 'Z'])
def test_horisontal_lines_break_paragraphs(self):
self.set_text('''
A
- - -
Z
''')
self.check_eq_scope('- - -\n', 'meta.separator')
self.check_default(['A', 'Z'])
|
[
"[email protected]"
] | |
1400cc7e36dc1608eda6cf944b667fb37a1ea0b3
|
b19dfd6a3ba5d107d110fb936de2e91d1d92bb99
|
/venv/lib/python3.7/site-packages/Satchmo-0.9.3-py3.7.egg/shipping/modules/ups/config.py
|
5c8e90a363eefc21999a9a0da571173a720a91b8
|
[] |
no_license
|
siddhant3030/djangoecommerce
|
d8f5b21f29d17d2979b073fd9389badafc993b5c
|
b067cb1155c778fece4634d0a98631a0646dacff
|
refs/heads/master
| 2022-12-13T15:28:39.229377 | 2019-09-28T10:30:02 | 2019-09-28T10:30:02 | 207,240,716 | 2 | 1 | null | 2022-12-11T01:34:25 | 2019-09-09T06:35:36 |
Python
|
UTF-8
|
Python
| false | false | 3,913 |
py
|
from decimal import Decimal
from django.utils.translation import ugettext_lazy as _
from livesettings.values import StringValue,ConfigurationGroup,BooleanValue,DecimalValue,MultipleStringValue
from livesettings.functions import config_register_list,config_get
SHIP_MODULES = config_get('SHIPPING', 'MODULES')
SHIP_MODULES.add_choice(('shipping.modules.ups', 'UPS'))
SHIPPING_GROUP = ConfigurationGroup('shipping.modules.ups',
_('UPS Shipping Settings'),
requires = SHIP_MODULES,
ordering = 101)
config_register_list(
StringValue(SHIPPING_GROUP,
'XML_KEY',
description=_("UPS XML Access Key"),
help_text=_("XML Access Key Provided by UPS"),
default=""),
StringValue(SHIPPING_GROUP,
'USER_ID',
description=_("UPS User ID"),
help_text=_("User ID provided by UPS site."),
default=""),
StringValue(SHIPPING_GROUP,
'ACCOUNT',
description=_("UPS Account Number"),
help_text=_("UPS Account Number."),
default=""),
StringValue(SHIPPING_GROUP,
'USER_PASSWORD',
description=_("UPS User Password"),
help_text=_("User password provided by UPS site."),
default=""),
MultipleStringValue(SHIPPING_GROUP,
'UPS_SHIPPING_CHOICES',
description=_("UPS Shipping Choices Available to customers. These are valid domestic codes only."),
choices = (
(('01', 'Next Day Air')),
(('02', 'Second Day Air')),
(('03', 'Ground')),
(('12', '3 Day Select')),
(('13', 'Next Day Air Saver')),
(('14', 'Next Day Air Early AM')),
(('59', '2nd Day Air AM')),
),
default = ('03',)),
DecimalValue(SHIPPING_GROUP,
'HANDLING_FEE',
description=_("Handling Fee"),
help_text=_("The cost of packaging and getting the package off"),
default=Decimal('0.00')),
StringValue(SHIPPING_GROUP,
'SHIPPING_CONTAINER',
description=_("Type of container used to ship product."),
choices = (
(('00', 'Unknown')),
(('01', 'UPS LETTER')),
(('02', 'PACKAGE / CUSTOMER SUPPLIED')),
),
default = "00"),
BooleanValue(SHIPPING_GROUP,
'SINGLE_BOX',
description=_("Single Box?"),
help_text=_("Use just one box and ship by weight? If no then every item will be sent in its own box."),
default=True),
BooleanValue(SHIPPING_GROUP,
'TIME_IN_TRANSIT',
description=_("Time in Transit?"),
help_text=_("Use the UPS Time In Transit API? It is slower but delivery dates are more accurate."),
default=False),
StringValue(SHIPPING_GROUP,
'PICKUP_TYPE',
description=_("UPS Pickup option."),
choices = (
(('01', 'DAILY PICKUP')),
(('03', 'CUSTOMER COUNTER')),
(('06', 'ONE TIME PICKUP')),
(('07', 'ON CALL PICKUP')),
),
default = "07"),
BooleanValue(SHIPPING_GROUP,
'LIVE',
description=_("Access production UPS server"),
help_text=_("Use this when your store is in production."),
default=False),
StringValue(SHIPPING_GROUP,
'CONNECTION',
description=_("Submit to URL"),
help_text=_("Address to submit live transactions."),
default="https://onlinetools.ups.com/ups.app/xml/Rate"),
StringValue(SHIPPING_GROUP,
'CONNECTION_TEST',
description=_("Submit to TestURL"),
help_text=_("Address to submit test transactions."),
default="https://wwwcie.ups.com/ups.app/xml/Rate"),
BooleanValue(SHIPPING_GROUP,
'VERBOSE_LOG',
description=_("Verbose logs"),
help_text=_("Send the entire request and response to the log - for debugging help when setting up UPS."),
default=False)
)
|
[
"[email protected]"
] | |
9c0801b8cdc06c03a0ea60a845b46f24d9fdb6f2
|
bd19ce3bbe4e79fd8be757fa1d7bad5324680973
|
/conf/settings.py
|
56cbd0c8e0cefd91694ea821643f83a8e36c3f85
|
[] |
no_license
|
lushenao/Skycn.com
|
014026d9fa5d153a7dd8d2ff51c1191a68925b6b
|
b7531e080cef71253fa55437b7a23f6dc6fdaae8
|
refs/heads/master
| 2020-11-30T02:07:33.559455 | 2019-12-30T07:47:57 | 2019-12-30T07:47:57 | 230,271,269 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 593 |
py
|
#__auth__:"Sky lu"
# -*- coding:utf-8 -*-
import os,sys
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(BASE_DIR)
'''
定义了mysql连接配置信息
'''
DATABASE_mysql = {
'engine':'mysql',
'host': '',
'port': 3306,
'user': '',
'pwd': '',
'db': '',
'file_path': '%s/db' % BASE_DIR
}
soft_desc = {
'`soft_size`':'',
'`soft_version`':'',
'`soft_update_time`':'',
'`soft_operating_system_bit`':'',
'`soft_language`':'',
'`soft_auth`':'',
'`soft_operating_system`':'',
'`soft_comment`':''
}
|
[
"[email protected]"
] | |
65ae8cba1ea4bcaf9819e245c5a7fdc2485f4a60
|
564a2f5a5a4269ce7e013511f78f8b8cd26ee81f
|
/toy_problem/data_preprocessing/transform.py
|
12ccb9a9232c6cf857470178d2cede9822bd9790
|
[] |
no_license
|
sb-nmt-team/sb-nmt
|
7345cb967ec92dd87b5d649213ecd0483cba7d60
|
852418728642063967625c1a1473aa8e2b944d4d
|
refs/heads/master
| 2021-04-28T07:11:13.073176 | 2018-06-16T17:49:41 | 2018-06-16T17:49:41 | 122,219,302 | 0 | 0 | null | 2018-04-27T09:35:42 | 2018-02-20T15:49:32 |
Python
|
UTF-8
|
Python
| false | false | 4,670 |
py
|
import numpy as np
import unicodedata
import collections
import matplotlib.pyplot as plt
from collections import Counter, defaultdict
import string
import sys
import pickle
bad_characters = set(string.punctuation + string.digits)
def read_dataset(dataset_name):
dataset = defaultdict(list)
rtlm = "\u200F"
bos = '_'
eos = ';'
en_characters = set(["<\s>"])
he_characters = set(["<\s>"])
with open(dataset_name, "r") as fin:
for line in fin:
en,he = line.strip().lower().replace(bos,' ').replace(eos,' ').replace(rtlm, '').split('\t')
word, trans = he, en
en_characters |= set(en)
he_characters |= set(he)
# if len(word) < 3: continue
# if EASY_MODE:
# if max(len(word),len(trans))>20:
# continue
dataset[word].append(trans)
en_characters = list(sorted(list(en_characters)))
he_characters = list(sorted(list(he_characters)))
# print ("size = ",len(dataset))
return dataset, en_characters, he_characters
def filter_multiple_translations(data):
result = {}
for original, translations in data.items():
if len(set(translations)) > 1:
continue
if ' ' in translations[0]:
continue
result[original] = translations[0]
# print ("size = ",len(result))
return result
def filter_bad_characters(data, bad_characters):
result = {}
for source, translation in data.items():
assert isinstance(translation, str)
if len(set(source) & bad_characters) != 0:
continue
if len(set(translation) & bad_characters) != 0:
continue
result[source] = translation
# print ("size = ",len(result))
return result
def filter_copied_words(data):
result = {}
for original, translation in data.items():
assert isinstance(translation, str)
if len(set(original) & set(translation)) > 0:
continue
result[original] = translation
# print ("size = ",len(result))
return result
def filter_short_targets(data, target_threshold=2):
result = {}
for original, translation in data.items():
assert isinstance(translation, str)
if len(translation) <= target_threshold:
continue
result[original] = translation
# print ("size = ",len(result))
return result
def split_train_test(data, valid_size=0.1, test_size=0.1):
assert valid_size >= 0 and valid_size < 1.0
assert test_size >= 0 and test_size < 1.0
train_size = 1.0 - (valid_size + test_size)
np.random.seed(42)
sources = np.array(list(data.keys()))
# print(sources)
n_sources = len(sources)
index_permutation = np.random.permutation(np.arange(n_sources))
train_border = int(n_sources * train_size)
valid_border = int(n_sources * (train_size + valid_size))
train_sources = sources[index_permutation[: train_border]]
valid_sources = sources[index_permutation[train_border : valid_border]]
test_sources = sources[index_permutation[valid_border :]]
def create_dataset(sources):
result = {}
for source in sources:
result[source] = data[source]
return result
return create_dataset(train_sources), create_dataset(valid_sources), create_dataset(test_sources)
def pipeline(dataset):
filtered_dataset = filter_multiple_translations(dataset)
f1 = filter_short_targets(filtered_dataset)
f2 = filter_bad_characters(f1, bad_characters)
f3 = filter_copied_words(f2)
f4 = filter_bad_characters(f3, bad_characters | set([' ']))
return [dataset, filtered_dataset, f1, f2, f3, f4]
def transformation_pipeline(dataset, valid_size=0.1, test_size=0.1):
train, valid, test = split_train_test(dataset)
trains = pipeline(train)
valids = pipeline(valid)
tests = pipeline(test)
# plt.title('Dataset changes with refinement')
# plt.plot([len(x) for x in trains], label='train')
# plt.plot([len(x) for x in valids], label='valid')
# plt.plot([len(x) for x in tests], label='test')
# plt.legend()
# plt.show()
return trains, valids, tests
if __name__ == "__main__":
dataset, en_characters, he_characters = read_dataset(sys.argv[1])
trains, valids, tests = transformation_pipeline(dataset)
result = {
"data" : dict(trains=trains, valids=valids, tests=tests),
"characters": dict(en=en_characters, he=he_characters)
}
pickle.dump(result, open(sys.argv[2], "wb"))
|
[
"[email protected]"
] | |
4af4c6c67883138cb403bc55c20a57a17f3abf94
|
53fab060fa262e5d5026e0807d93c75fb81e67b9
|
/backup/user_143/ch40_2020_03_25_11_34_14_842288.py
|
7b4cd03ca35996a28aee9136ab7f8fc3ef414f7a
|
[] |
no_license
|
gabriellaec/desoft-analise-exercicios
|
b77c6999424c5ce7e44086a12589a0ad43d6adca
|
01940ab0897aa6005764fc220b900e4d6161d36b
|
refs/heads/main
| 2023-01-31T17:19:42.050628 | 2020-12-16T05:21:31 | 2020-12-16T05:21:31 | 306,735,108 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 113 |
py
|
def soma_valores(s):
i=0
y[i]=s[i]
while(i<=len(s)):
y[i+1]+=s[i+1]
i+=1
return y
|
[
"[email protected]"
] | |
07283b22209cfb94b928672e53ed1049ea4b9c9d
|
0e719bc0915f83d0fb96a252ab24af9159624a44
|
/Learning-python/Section 14 - Advanced Python Modules/scratch.py
|
86c443015441d8a7990c8567ccccb6af2b1a11f8
|
[] |
no_license
|
skhadka007/learning_algos
|
09a0d89194fe610186e5af03a4683b971d1c7f2c
|
a9f7e432f5b6b5a2ccefb713e029c43be9421969
|
refs/heads/master
| 2023-09-02T03:11:27.855951 | 2021-10-04T14:11:34 | 2021-10-04T14:11:34 | 286,854,625 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 30 |
py
|
# import math
print(18 % 2)
|
[
"[email protected]"
] | |
2f5ddf89ea28919dce8937425bcddb68926a662d
|
a115b4a830b7d4a1e9efed9cdc429ea7233df2f1
|
/6_het/3_szokoev.py
|
593c8ca06fb65825f581ff46e46fb8f1ceafb9f2
|
[
"Unlicense"
] |
permissive
|
ArDrift/InfoPy_scripts
|
b8dc78a8891a4c4e05adb4c2c0d6fcba9d3a8417
|
a8fb46c9b9f652d43094f886549b05c50f3ee9d2
|
refs/heads/master
| 2022-09-01T06:38:36.737921 | 2021-01-26T21:52:50 | 2021-01-26T21:52:50 | 333,116,648 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 264 |
py
|
#!/usr/bin/env python3
def szokoev_e(ev):
return ev % 4 == 0 and (ev % 100 != 0 or ev % 400 == 0)
def main():
evszam = int(input("Add meg az évszámot: "))
if szokoev_e(evszam):
print("Szökőév.")
else:
print("Nem szökőév.")
main()
|
[
"[email protected]"
] | |
0837ba02b802ee63615c49bcee086fe4059f3cdb
|
b980ac3d6ba8eac0902070c460ad1634ee19585b
|
/object_detection/utils/shape_utils.py
|
798c80cce647ff307d9f96a9f2c72e2f6a4f3682
|
[
"MIT"
] |
permissive
|
gourav108/coreml
|
3d7d5ea6851bf9ae1fb77876bbec2fa2b8f9b763
|
6bc2d494dff23cff923368e735992a4f4a47483c
|
refs/heads/master
| 2022-11-28T19:56:27.172125 | 2019-01-07T18:44:33 | 2019-01-07T18:44:33 | 161,968,738 | 0 | 1 |
MIT
| 2022-11-21T18:48:27 | 2018-12-16T04:50:08 |
Python
|
UTF-8
|
Python
| false | false | 11,437 |
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.
# ==============================================================================
"""Utils used to manipulate tensor shapes."""
import tensorflow as tf
from utils import static_shape
def _is_tensor(t):
"""Returns a boolean indicating whether the input is a tensor.
Args:
t: the input to be tested.
Returns:
a boolean that indicates whether t is a tensor.
"""
return isinstance(t, (tf.Tensor, tf.SparseTensor, tf.Variable))
def _set_dim_0(t, d0):
"""Sets the 0-th dimension of the input tensor.
Args:
t: the input tensor, assuming the rank is at least 1.
d0: an integer indicating the 0-th dimension of the input tensor.
Returns:
the tensor t with the 0-th dimension set.
"""
t_shape = t.get_shape().as_list()
t_shape[0] = d0
t.set_shape(t_shape)
return t
def pad_tensor(t, length):
"""Pads the input tensor with 0s along the first dimension up to the length.
Args:
t: the input tensor, assuming the rank is at least 1.
length: a tensor of shape [1] or an integer, indicating the first dimension
of the input tensor t after padding, assuming length <= t.shape[0].
Returns:
padded_t: the padded tensor, whose first dimension is length. If the length
is an integer, the first dimension of padded_t is set to length
statically.
"""
t_rank = tf.rank(t)
t_shape = tf.shape(t)
t_d0 = t_shape[0]
pad_d0 = tf.expand_dims(length - t_d0, 0)
pad_shape = tf.cond(
tf.greater(t_rank, 1), lambda: tf.concat([pad_d0, t_shape[1:]], 0),
lambda: tf.expand_dims(length - t_d0, 0))
padded_t = tf.concat([t, tf.zeros(pad_shape, dtype=t.dtype)], 0)
if not _is_tensor(length):
padded_t = _set_dim_0(padded_t, length)
return padded_t
def clip_tensor(t, length):
"""Clips the input tensor along the first dimension up to the length.
Args:
t: the input tensor, assuming the rank is at least 1.
length: a tensor of shape [1] or an integer, indicating the first dimension
of the input tensor t after clipping, assuming length <= t.shape[0].
Returns:
clipped_t: the clipped tensor, whose first dimension is length. If the
length is an integer, the first dimension of clipped_t is set to length
statically.
"""
clipped_t = tf.gather(t, tf.range(length))
if not _is_tensor(length):
clipped_t = _set_dim_0(clipped_t, length)
return clipped_t
def pad_or_clip_tensor(t, length):
"""Pad or clip the input tensor along the first dimension.
Args:
t: the input tensor, assuming the rank is at least 1.
length: a tensor of shape [1] or an integer, indicating the first dimension
of the input tensor t after processing.
Returns:
processed_t: the processed tensor, whose first dimension is length. If the
length is an integer, the first dimension of the processed tensor is set
to length statically.
"""
processed_t = tf.cond(
tf.greater(tf.shape(t)[0], length),
lambda: clip_tensor(t, length),
lambda: pad_tensor(t, length))
if not _is_tensor(length):
processed_t = _set_dim_0(processed_t, length)
return processed_t
def combined_static_and_dynamic_shape(tensor):
"""Returns a list containing static and dynamic values for the dimensions.
Returns a list of static and dynamic values for shape dimensions. This is
useful to preserve static shapes when available in reshape operation.
Args:
tensor: A tensor of any type.
Returns:
A list of size tensor.shape.ndims containing integers or a scalar tensor.
"""
static_tensor_shape = tensor.shape.as_list()
dynamic_tensor_shape = tf.shape(tensor)
combined_shape = []
for index, dim in enumerate(static_tensor_shape):
if dim is not None:
combined_shape.append(dim)
else:
combined_shape.append(dynamic_tensor_shape[index])
return combined_shape
def static_or_dynamic_map_fn(fn, elems, dtype=None,
parallel_iterations=32, back_prop=True):
"""Runs map_fn as a (static) for loop when possible.
This function rewrites the map_fn as an explicit unstack input -> for loop
over function calls -> stack result combination. This allows our graphs to
be acyclic when the batch size is static.
For comparison, see https://www.tensorflow.org/api_docs/python/tf/map_fn.
Note that `static_or_dynamic_map_fn` currently is not *fully* interchangeable
with the default tf.map_fn function as it does not accept nested inputs (only
Tensors or lists of Tensors). Likewise, the output of `fn` can only be a
Tensor or list of Tensors.
TODO(jonathanhuang): make this function fully interchangeable with tf.map_fn.
Args:
fn: The callable to be performed. It accepts one argument, which will have
the same structure as elems. Its output must have the
same structure as elems.
elems: A tensor or list of tensors, each of which will
be unpacked along their first dimension. The sequence of the
resulting slices will be applied to fn.
dtype: (optional) The output type(s) of fn. If fn returns a structure of
Tensors differing from the structure of elems, then dtype is not optional
and must have the same structure as the output of fn.
parallel_iterations: (optional) number of batch items to process in
parallel. This flag is only used if the native tf.map_fn is used
and defaults to 32 instead of 10 (unlike the standard tf.map_fn default).
back_prop: (optional) True enables support for back propagation.
This flag is only used if the native tf.map_fn is used.
Returns:
A tensor or sequence of tensors. Each tensor packs the
results of applying fn to tensors unpacked from elems along the first
dimension, from first to last.
Raises:
ValueError: if `elems` a Tensor or a list of Tensors.
ValueError: if `fn` does not return a Tensor or list of Tensors
"""
if isinstance(elems, list):
for elem in elems:
if not isinstance(elem, tf.Tensor):
raise ValueError('`elems` must be a Tensor or list of Tensors.')
elem_shapes = [elem.shape.as_list() for elem in elems]
# Fall back on tf.map_fn if shapes of each entry of `elems` are None or fail
# to all be the same size along the batch dimension.
for elem_shape in elem_shapes:
if (not elem_shape or not elem_shape[0]
or elem_shape[0] != elem_shapes[0][0]):
return tf.map_fn(fn, elems, dtype, parallel_iterations, back_prop)
arg_tuples = zip(*[tf.unstack(elem) for elem in elems])
outputs = [fn(arg_tuple) for arg_tuple in arg_tuples]
else:
if not isinstance(elems, tf.Tensor):
raise ValueError('`elems` must be a Tensor or list of Tensors.')
elems_shape = elems.shape.as_list()
if not elems_shape or not elems_shape[0]:
return tf.map_fn(fn, elems, dtype, parallel_iterations, back_prop)
outputs = [fn(arg) for arg in tf.unstack(elems)]
# Stack `outputs`, which is a list of Tensors or list of lists of Tensors
if all([isinstance(output, tf.Tensor) for output in outputs]):
return tf.stack(outputs)
else:
if all([isinstance(output, list) for output in outputs]):
if all([all(
[isinstance(entry, tf.Tensor) for entry in output_list])
for output_list in outputs]):
return [tf.stack(output_tuple) for output_tuple in zip(*outputs)]
raise ValueError('`fn` should return a Tensor or a list of Tensors.')
def check_min_image_dim(min_dim, image_tensor):
"""Checks that the image width/height are greater than some number.
This function is used to check that the width and height of an image are above
a certain value. If the image shape is static, this function will perform the
check at graph construction time. Otherwise, if the image shape varies, an
Assertion control dependency will be added to the graph.
Args:
min_dim: The minimum number of pixels along the width and height of the
image.
image_tensor: The image tensor to check size for.
Returns:
If `image_tensor` has dynamic size, return `image_tensor` with a Assert
control dependency. Otherwise returns image_tensor.
Raises:
ValueError: if `image_tensor`'s' width or height is smaller than `min_dim`.
"""
image_shape = image_tensor.get_shape()
image_height = static_shape.get_height(image_shape)
image_width = static_shape.get_width(image_shape)
if image_height is None or image_width is None:
shape_assert = tf.Assert(
tf.logical_and(tf.greater_equal(tf.shape(image_tensor)[1], min_dim),
tf.greater_equal(tf.shape(image_tensor)[2], min_dim)),
['image size must be >= {} in both height and width.'.format(min_dim)])
with tf.control_dependencies([shape_assert]):
return tf.identity(image_tensor)
if image_height < min_dim or image_width < min_dim:
raise ValueError(
'image size must be >= %d in both height and width; image dim = %d,%d' %
(min_dim, image_height, image_width))
return image_tensor
def assert_shape_equal(shape_a, shape_b):
"""Asserts that shape_a and shape_b are equal.
If the shapes are static, raises a ValueError when the shapes
mismatch.
If the shapes are dynamic, raises a tf InvalidArgumentError when the shapes
mismatch.
Args:
shape_a: a list containing shape of the first tensor.
shape_b: a list containing shape of the second tensor.
Returns:
Either a tf.no_op() when shapes are all static and a tf.assert_equal() op
when the shapes are dynamic.
Raises:
ValueError: When shapes are both static and unequal.
"""
if (all(isinstance(dim, int) for dim in shape_a) and
all(isinstance(dim, int) for dim in shape_b)):
if shape_a != shape_b:
raise ValueError('Unequal shapes {}, {}'.format(shape_a, shape_b))
else: return tf.no_op()
else:
return tf.assert_equal(shape_a, shape_b)
def assert_shape_equal_along_first_dimension(shape_a, shape_b):
"""Asserts that shape_a and shape_b are the same along the 0th-dimension.
If the shapes are static, raises a ValueError when the shapes
mismatch.
If the shapes are dynamic, raises a tf InvalidArgumentError when the shapes
mismatch.
Args:
shape_a: a list containing shape of the first tensor.
shape_b: a list containing shape of the second tensor.
Returns:
Either a tf.no_op() when shapes are all static and a tf.assert_equal() op
when the shapes are dynamic.
Raises:
ValueError: When shapes are both static and unequal.
"""
if isinstance(shape_a[0], int) and isinstance(shape_b[0], int):
if shape_a[0] != shape_b[0]:
raise ValueError('Unequal first dimension {}, {}'.format(
shape_a[0], shape_b[0]))
else: return tf.no_op()
else:
return tf.assert_equal(shape_a[0], shape_b[0])
|
[
"[email protected]"
] | |
f84d0aaecefbe157929448dd97c574189ee4c716
|
acf190f6699975140a01b26546a52e94ec245f51
|
/sqrt_lasso_cvxpy.py
|
eecc2608f805aa9e9af3b7870e8bf787a37844f5
|
[] |
no_license
|
HMJiangGatech/smoothed_concomitant_lasso
|
ffb6ed32f8a4eb65bf3e0d49008f429f3f0ac87b
|
776013a4e657f9c8c1dde239574e83d4958118d7
|
refs/heads/master
| 2021-05-12T16:02:10.468441 | 2018-01-10T21:14:10 | 2018-01-10T21:14:10 | 116,999,219 | 0 | 0 | null | 2018-01-10T19:07:55 | 2018-01-10T19:07:55 | null |
UTF-8
|
Python
| false | false | 859 |
py
|
from __future__ import division
import numpy as np
import cvxpy as cvx
def belloni_path(X, y, lambda_grid, solver='ECOS'):
'''
solve:
min ||y - X*beta|| / sqrt(n_samples) + lambda ||beta||_1
'''
n_samples, n_features = X.shape
lambda_ = cvx.Parameter(sign="Positive")
beta = cvx.Variable(n_features)
objective = cvx.Minimize(cvx.norm(X * beta - y, 2) / np.sqrt(n_samples) +
lambda_ * cvx.norm(beta, 1))
prob = cvx.Problem(objective)
betas = np.zeros((len(lambda_grid), n_features))
sigmas = np.zeros(len(lambda_grid))
for i, l in enumerate(lambda_grid):
lambda_.value = l
prob.solve(solver=solver)
betas[i] = np.ravel(beta.value)
sigmas[i] = \
np.linalg.norm(y - np.dot(X, betas[i])) / np.sqrt(n_samples)
return sigmas, betas
|
[
"[email protected]"
] | |
118979bd5416c57a7c0e05109b92aa948efde4fa
|
adebcb38fb5abf69b05168c728271266c08bfd11
|
/main.py
|
ba05bbc0c664fbe9776ba176ea87ee68a2526b47
|
[] |
no_license
|
ralcant/aws_diarization
|
5b403a17f3b35a4b9b4797a12263f019fc478ae2
|
ea35a979dd8902717797f55f2ec1decdf592935e
|
refs/heads/master
| 2022-04-28T07:07:12.080243 | 2020-04-30T21:12:38 | 2020-04-30T21:12:38 | 260,317,694 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 10,819 |
py
|
from __future__ import print_function
import json
import time
import boto3
import pprint
import datetime
import os
from bucket_handler import upload_file
import requests
transcribe = boto3.client('transcribe')
s3 = boto3.resource('s3')
transcribe = boto3.client('transcribe')
s3 = boto3.resource('s3')
## Note to self: in order to get the print statement as they happen in the command line, do "python -u main.py"
class Project:
def __init__(self, bucket_name="prgdiarization", video_folder_name="videos/"):
super().__init__()
self.video_to_transcription = dict() # maps the video -> MediaFileUri of the transcription
self.bucket_name = bucket_name # bucket to be used for all the transactions
self.transciption_job_name = "prg_diarization_test"
self.video_folder_name = video_folder_name
'''
Main method of the project class. It updates the output.json file with the results of the transcription for such video.
'''
def get_transcription(self, video):
print('started transcription!')
video_name = self.get_video_name(video)
# t = datetime.datetime.now()
time_transcription_started = time.time() #to calculate how much time it takes to do the job
job_uri = "https://{}.s3-us-west-1.amazonaws.com/{}{}".format(self.bucket_name, self.video_folder_name, video)
if (video_name not in self.video_to_transcription.keys()):
self.start_job(job_uri)
while True:
status = transcribe.get_transcription_job(TranscriptionJobName=self.transciption_job_name)
if status['TranscriptionJob']['TranscriptionJobStatus'] in ['COMPLETED', 'FAILED']:
break
print("Not ready yet...")
time.sleep(5)
print('already got response and it took {} seconds!'.format(time.time() - time_transcription_started))
response = status
MediaFileURI = response["TranscriptionJob"]["Transcript"]["TranscriptFileUri"]
self.video_to_transcription[video_name] = MediaFileURI #for future reference
#self.delete_job()
else:
print("it already exists!")
MediaFileURI = self.video_to_transcription[video_name] #'https://s3.us-west-1.amazonaws.com/aws-transcribe-us-west-1-prod/115847983408/prg_diarization_test_9/946d2d28-827b-49b7-9136-55e17b1b6eb6/asrOutput.json?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHYaCXVzLXdlc3QtMSJHMEUCIGeiQRZoD22XmzdRasfvXOEbpIGcKYFKSGJzaSlfHjUiAiEA9CRg8SIHAskooNPVB%2BurejmIvAiKldYTO9h5Pb6y3A0qtAMIbxABGgw5NzEzODk5ODIxNjgiDCzAQQqVwGhTjzYcsyqRA%2FF8yOgyiYMepEqlOR7Wel%2BLlP2Ol32C5pljhoCALRUQjpnlclBDpzQqAkcdEhSNlF3xehp0bB5QTwi0BwLF1tizzN5F8kPLLEtM%2FK40b4yV1RhT3kzFlVv9rXFRMvUMPM4cfunr%2BUiLkC%2F4h8uyedEIo4RTq584u0R1JIYTsMHTm9XiInlruqw%2BS%2BOedWDlksOvI1z4PF6uhYrxo87Hy8Jjb3Ws7nEBPFP8mJexybX53RxgXJ7sNnoe7ichWG9JU3d31wJSNjnsNdtCdpikaZPGJC8oAXN0tE057tC9NUqYaag4wTaF9tX6llII45RXnCHVY0xG14Cq8mTXKdiMxFFgDjKvsITMNsHr6XNhgu%2BU%2B7s%2BFzdTHnpzPBrvRUi7JcDrJ%2Fdy3IILyJe3hHHsyIFqjjCrbQrXO%2Bwq6WV7%2FfLfh4%2BLGtKNuvd34b1u5wgWrzChpN2ha5sibJxBVcN3%2FqtghhvhZvuORwGQdFVrSkD%2BtbJNSSf72I2CuWsnsCctV8VAJl6rxHatD3%2Btcvlm9FpXMJjv6%2FMFOusBABniLSVZHh54XvoJ4NPAqE44fj%2BbKdABKV8%2BuUxbG97IGRuBVFcZjG%2B5GZYHFlpcFz1k3vNXixYaXdeznrOKalJBGRYXUSEfirQbty2s%2B4MHxdIhrFVR%2BofgMy6CE1L%2Bi%2BYdEJ%2FOWCprKT7ztYTLESgbLClMyUFkCPMRCQrMez9m5JTgucjc3As6mWlgREjxUfu3kHujqG592wydfbaegIRzqcYpAEcXPx2rsBorx9UJn%2BDk1lv97imTpqXVDXQOVDy%2FzEChpJBLPDaysQfBbf2gRKU70k%2Bj5%2BCNtfuavse5A7AJy53sAwusqg%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20200325T070254Z&X-Amz-SignedHeaders=host&X-Amz-Expires=900&X-Amz-Credential=ASIA6EK222XMPPCNL4OQ%2F20200325%2Fus-west-1%2Fs3%2Faws4_request&X-Amz-Signature=3e6e2b818c4617412e3d094e46e3e05e03691845816f0e994c26da9cdf5a879d'
#self.delete_job()
r = requests.get(MediaFileURI)
#response = r.json()
response = json.loads(r.text) #getting the response from the given url
self.delete_job() #we have the response, we can delete the job
with open("outputs.json") as f:
output = json.load(f)
f.close()
with open("outputs.json", "w") as json_output:
#output= json.load(json_output)
new_data = {
video_name: {
"name": video,
"video_URI": job_uri,
#"transcription_URI": MediaFileURI, #ignored because this is usually a very long name
"transcription": self.extract_useful_information(response),
}
}
output["items"].update(new_data)
json.dump(output, json_output, indent=4)
json_output.close()
print("\n")
'''
Starts a transciption job for a certain video.
TODO: Need to add more security constraints, or everything public is fine for now?
'''
def start_job(self, job_uri):
transcribe.start_transcription_job(
TranscriptionJobName = self.transciption_job_name,
Media={'MediaFileUri': job_uri},
MediaFormat='mp4',
LanguageCode='en-US',
Settings = {
"MaxSpeakerLabels": 2,
"ShowSpeakerLabels": True,
}
)
# Useful for when a transcription is suddenly stopped, and we are not allowed to do
# any other job because the name is already taken
def delete_job(self):
transcribe.delete_transcription_job(
TranscriptionJobName= self.transciption_job_name
)
print("deleted job with name {}".format(self.transciption_job_name))
'''
segments: a dictionarly that maps each speaker label to a list of the times that this speaker talked
Returns a mapping from each interval (start, end) to the speaker label that happened during that time.
'''
@staticmethod
def get_interval_to_speaker_label(segments):
interval_to_speaker = dict()
for speaker_n in range(len(segments)):
all_items = segments[speaker_n]["items"]
for interval in all_items:
start = interval["start_time"]
end = interval["end_time"]
speaker_label = interval["speaker_label"]
interval_to_speaker[(start, end)] = speaker_label
return interval_to_speaker
'''
Converts a response from AWS Transcribe into a (maybe) more readable version of it.
It returns a dictionary with the following keys, values:
- "num_speakers" -> The number of speaker determined by AWS. Defaults to "undetermined" if none were found.
- "transcripts" -> The complete transcript of the video.
- "items" -> a list of dictionaries, where each item represents one word said,
and it has info about the start_time, end_time, speaker_label and the text used for such word.
'''
def extract_useful_information(self, response_dict):
transcripts = response_dict["results"]["transcripts"]
items = response_dict["results"]["items"]
if "speaker_labels" not in response_dict["results"]: # when AWS doesnt detect speakers. This *usually* means
return { # that there is no transcript detected either.
"num_speakers": "undetermined",
"transcripts": transcripts,
"items": items,
}
speaker_labels = response_dict["results"]["speaker_labels"]
num_speakers = speaker_labels["speakers"]
interval_to_label = self.get_interval_to_speaker_label(speaker_labels["segments"])
timestamps_with_speaker_labels = []
for item in items:
## item is a dict for a particular timestamp
if (item['type'] == "pronunciation"): #ignoring punctuation
start = item["start_time"]
end = item["end_time"]
try:
speaker_label = interval_to_label[(start, end)]
item_copy = item.copy()
item_copy["speaker_label"] = speaker_label #just adding this new key of the "item" dictionary
timestamps_with_speaker_labels.append(item_copy)
except:
print("Didnt find {} as a valid timestamp!".format((start, end)))
## now timestamps_with_speaker_labels is a list of all the relevant info ###
#TODO: sort them by the (start, end)
return {
"num_speakers": num_speakers,
"transcript": transcripts,
"items": timestamps_with_speaker_labels
}
'''
Extracts the name of a video. "example.mp4" --> "example"
'''
def get_video_name(self, video):
return video.split(".")[0]
'''
Updates a certain video to our designed bucket. The video can be found in the directory video_folder_name/filename
Filename includes the .mp4 extension.
'''
def upload_video(self, filename):
upload_file(self.bucket_name , self.video_folder_name, filename)
'''
Helper method to update the outputs.json file with the videos found in self.video_folder_name
If lazy_update = True, then it will only update the file with the elements that dont already exist. If not, then
it will update the complete json file with all videos available. Assumes that all videos are mp4 files.
'''
def update_output_json(self, lazy_update = False): #by default this is gonna do everything again
with open("outputs.json") as f:
output = json.load(f)
f.close()
for filename in os.listdir(self.video_folder_name):
if (lazy_update and self.get_video_name(filename) in output["items"].keys()):
print("lazy_update is True and the filename {} already exists in output.json, so we ignore this file.".format(filename))
else:
if filename.endswith(".mp4"): #if a video
print("uploading file {}...".format(filename))
upload_file(self.bucket_name , self.video_folder_name, filename) #uploading file to our bucket
self.get_transcription(filename) #updates output.json
print("done updating the json of the video: {}\n".format(filename))
else:
print("not an .mp4 file! : {}. Ignoring :/ \n".format(filename))
if __name__ == "__main__":
p = Project()
### updating the json file #######
p.update_output_json(lazy_update=True)
#p.upload_video("clip-42.mp4")
#p.get_transcription("clip-54.mp4")
# with open("outputs.json", "r") as transcript:
# pprint.pprint(json.load(transcript))
|
[
"[email protected]"
] | |
ef47575bb0a287208ee8f7a68519ff8275a7a1ac
|
14615571ee476a8c074832da94bc373ef92fd31c
|
/tdn.py
|
1ad57c6507c3fb40ed0ce6ec4e4e494397676aeb
|
[] |
no_license
|
Maestro-Zacht/utilities_ghia
|
5b341c3226119c3b85164cc2e47eb6713756e484
|
6b8c5e16cd0d4a2f9d668b970778bc381693678f
|
refs/heads/master
| 2022-12-14T20:35:48.260443 | 2020-09-02T13:59:37 | 2020-09-02T13:59:37 | 291,035,644 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 986 |
py
|
from math import sqrt
elenco_numeri_primi = []
numeri_primi_caricati = False
def is_primo(num):
for i in range(2, int(sqrt(num)) + 1):
if num % i == 0:
return False
return True
def fattorizzazione(num):
if not numeri_primi_caricati:
with open('D:\\programmazione\\file per path\\utilities_ghia\\ris_feef.txt') as f:
for numero in f.readlines():
elenco_numeri_primi.append(int(numero))
numeri_primi_caricati = True
fattori = []
for i in elenco_numeri_primi:
if num % i == 0:
fattori.append([i, 1])
mul = i**2
while num % mul == 0:
mul *= i
fattori[-1][1] += 1
num //= fattori[-1][0]**fattori[-1][1]
if num == 1:
break
return fattori
def phi(num):
fatt = fattorizzazione(num)
R = 1
for base, esponente in fatt:
R *= base**(esponente - 1) * (base - 1)
return R
|
[
"[email protected]"
] | |
b6e0f50c9cbfc562e8f08c57b09d6864f63b1bc0
|
8db243a61d43e133aac01a67294d26df3381a8f5
|
/Tree/AE_BST_Find_Closest_Value.py
|
362a28df3347e842a7bd6eaa7cdd8b1aeacadff0
|
[] |
no_license
|
Jyoti1706/Algortihms-and-Data-Structures
|
ccdd93ad0811585f9b3e1e9f639476ccdf15a359
|
3458a80e02b9957c9aeaf00bf691cc7aebfd3bff
|
refs/heads/master
| 2023-06-21T18:07:13.419498 | 2023-06-16T17:42:55 | 2023-06-16T17:42:55 | 149,984,584 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 404 |
py
|
# This is the class of the input tree. Do not edit.
import math
class BST:
def __init__(self, value):
self.value = value
self.left = None
self.right = None
def findClosestValueInBst(tree, target):
# Write your code here.
diff = math.inf
if tree.value == target:
return target
if target > tree.value :
findClosestValueInBst(tree.right, target)
|
[
"[email protected]"
] | |
613939625c016e2ed72cd4b6885baa6b413b8c7e
|
5946112229fe1d9a04b7536f076a656438fcd05b
|
/dev_env/lib/python3.8/site-packages/pygments/styles/rrt.py
|
2b1908794c8703c74074b3c356e1d1022988809b
|
[] |
no_license
|
Gear-Droid/openCV_study_project
|
3b117967eb8a28bb0c90088e1556fbc1d306a98b
|
28c9a494680c4a280f87dd0cc87675dfb2262176
|
refs/heads/main
| 2023-05-14T14:27:42.284265 | 2021-06-05T00:16:09 | 2021-06-05T00:16:09 | 307,807,458 | 0 | 1 | null | null | null | null |
UTF-8
|
Python
| false | false | 885 |
py
|
# -*- coding: utf-8 -*-
"""
pygments.styles.rrt
~~~~~~~~~~~~~~~~~~~
pygments "rrt" theme, based on Zap and Emacs defaults.
:copyright: Copyright 2006-2020 by the Pygments team, see AUTHORS.
:license: BSD, see LICENSE for details.
"""
from pygments.style import Style
from pygments.token import Comment, Name, Keyword, String
class RrtStyle(Style):
"""
Minimalistic "rrt" theme, based on Zap and Emacs defaults.
"""
background_color = '#000000'
highlight_color = '#0000ff'
styles = {
Comment: '#00ff00',
Name.Function: '#ffff00',
Name.Variable: '#eedd82',
Name.Constant: '#7fffd4',
Keyword: '#ff0000',
Comment.Preproc: '#e5e5e5',
String: '#87ceeb',
Keyword.Type: '#ee82ee',
}
|
[
"[email protected]"
] | |
5c0afd4b948ed839e06840db0f25384ad08fef7f
|
92661a6d27ac816b4227204bb66b3cb4bde21054
|
/primenumbers.py
|
2874e0dc40d038b59decab2fdb29bb737b0f1140
|
[] |
no_license
|
masonbot/Wave-3
|
02d244565033d07eb6bb0454c2fbca1c99cac8ca
|
b6ecc8c68da6d680d94db87e28becfee5c4992ed
|
refs/heads/master
| 2022-08-06T16:32:39.849975 | 2020-05-20T22:34:56 | 2020-05-20T22:34:56 | 261,534,782 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 264 |
py
|
def integer(n):
if (n==1):
return False
elif (n==2):
return True;
else:
for x in range(2,n):
if(n % x==0):
return False
return True
print(integer(int(input("Insert integer: "))))
|
[
"[email protected]"
] | |
a8406818db1d2c025ee9daacb525168889da4d61
|
454f2125c2d49b6be8113e756f5f68fd75678b84
|
/effectivePython/tap7/tap7_2.py
|
61d678c925d65d1a2bf6c9c2344e8c856f0d05ff
|
[] |
no_license
|
JackLovel/excuise-
|
c8a6977c96b8d6e41a937212f8e7dfc606328b4b
|
60418044c9387868043982c071ea1365b0d24057
|
refs/heads/master
| 2021-06-27T17:06:16.708054 | 2020-10-24T03:27:31 | 2020-10-24T03:27:31 | 164,762,577 | 0 | 0 | null | 2020-07-17T01:14:39 | 2019-01-09T01:27:43 |
JavaScript
|
UTF-8
|
Python
| false | false | 179 |
py
|
chile_ranks = {'ghost': 1, 'habanero': 2, 'cayenne': 3}
rank_dict = {rank: name for name, rank in chile_ranks.items()}
chile_len_set = {len(name) for name in rank_dict.values()}
|
[
"[email protected]"
] | |
703af6d3a3cd3e56e504a86f62c96136cac18b77
|
d941938417bab130154c78f732606daa7b107e4a
|
/testing_runtime/web/modules.py
|
5fa49e68e735185df2dcf8c001a735ef8ec2370a
|
[] |
no_license
|
skliarpawlo/ganymede
|
abc8c7fac03b51a41cf92efacdf4170dd271d890
|
3a847635634d383d01dbeb70ef969202b0b7a8c9
|
refs/heads/master
| 2016-09-07T18:55:51.680687 | 2013-11-01T15:18:25 | 2013-11-01T15:18:25 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,315 |
py
|
# coding: utf-8
from decorators import html
from django.shortcuts import render_to_response
from django.http import HttpResponse
from django.template import RequestContext
from django.utils.translation import ugettext as _
from testing_runtime import models
import json
from core import db
import traceback
def verify_module(module_id=None,code=None) :
errors = []
try :
dc = {}
exec code in dc
except :
errors.append( u'exec модуля рыгнул exception: {0}'.format(traceback.format_exc().decode('utf-8')) )
return errors
def gather_modules_info() :
res = []
all = db.session.query( models.Module ).all()
for m in all :
res.append( {
"module_id" : m.module_id,
"name" : m.name,
"path" : m.path
} )
return res
def list_modules( request ) :
title = html.title( [ _('Modules'), 'Ganymede' ] )
modules = gather_modules_info()
return render_to_response(
'modules/list.html',
{ "modules" : modules, 'title' : title },
context_instance=RequestContext(request)
)
def add_module(request) :
title = html.title( [ _('Add module'), _('Modules'), 'Ganymede' ] )
if request.method == 'POST' :
err = verify_module( module_id=None, code=request.POST['code'] )
if len(err) == 0 :
name = request.POST['name']
code = request.POST['code']
path = request.POST['path']
test = models.Module( name=name, path=path, code=code )
db.session.add( test )
json_resp = json.dumps( { "status" : "ok" } )
return HttpResponse(json_resp, mimetype="application/json")
else :
json_resp = json.dumps( { "status" : "error", "content" : err } )
return HttpResponse(json_resp, mimetype="application/json")
else :
return render_to_response(
'modules/add.html',
{ 'title' : title },
context_instance=RequestContext(request)
)
def update_module(request, module_id) :
title = html.title( [ _('Update module'), _('Modules'), 'Ganymede' ] )
module = db.session.query( models.Module ).get( module_id )
if request.method == 'POST' :
err = verify_module( module_id=None, code=request.POST['code'] )
if len(err) == 0 :
module.name = request.POST['name']
module.code = request.POST['code']
module.path = request.POST['path']
json_resp = json.dumps( { "status" : "ok" } )
return HttpResponse(json_resp, mimetype="application/json")
else :
json_resp = json.dumps( { "status" : "error", "content" : err } )
return HttpResponse(json_resp, mimetype="application/json")
else :
return render_to_response(
'modules/update.html',
{ 'title' : title, 'module' : module },
context_instance=RequestContext(request)
)
def remove_module(request) :
if request.method == 'POST' :
module = db.session.query( models.Module ).get( int(request.POST[ "module_id" ]) )
db.session.delete( module )
json_resp = json.dumps( { "status" : "ok" } )
return HttpResponse(json_resp, mimetype="application/json")
|
[
"[email protected]"
] | |
6b04ea00f8344a41a4c7af569e7ecea8d405d265
|
dd00da0254875c877a35a59ae372391484a9631c
|
/ngo_app_code/helpers.py
|
309f36088ce1a62bcfc25b5fe03112e9d0f1b8ff
|
[] |
no_license
|
disissaikat/cfc_2020
|
9da8476f0eb26946b2d5fd1e63191e44573e8ab2
|
8222f9d1f6a8ba66190cf0dce19f9a5f15ebe789
|
refs/heads/master
| 2022-11-21T11:37:58.781244 | 2020-07-30T16:05:26 | 2020-07-30T16:05:26 | 282,971,756 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 677 |
py
|
username_helper = """
MDTextField:
hint_text: "Enter Phone Number"
pos_hint: {'center_x':0.5, 'center_y':0.6}
size_hint_x: None
width: 200
max_text_length: 10
required: True
icon_right: "cellphone"
icon_right_color: app.theme_cls.primary_color
"""
password_helper = """
MDTextField:
password: True
hint_text: "Enter Password"
pos_hint: {'center_x':0.5, 'center_y':0.5}
helper_text: "Click Forgot Password if you do not remember it"
helper_text_mode: "on_focus"
size_hint_x: None
width: 200
required: True
icon_right: "cellphone-key"
icon_right_color: app.theme_cls.primary_color
"""
|
[
"[email protected]"
] | |
3681c80ec59ac350c54b44f44a5944f4e755ddaa
|
2cbcfb9b9046ac131dc01a6fd048b6920d29cd42
|
/57. 插入区间.py
|
4b791d583f9803e977bfe4e1c5a3179d6edf7b92
|
[] |
no_license
|
pulinghao/LeetCode_Python
|
6b530a0e491ea302b1160fa73582e838338da3d1
|
82ece6ed353235dcd36face80f5d87df12d56a2c
|
refs/heads/master
| 2022-08-12T21:19:43.510729 | 2022-08-08T03:04:52 | 2022-08-08T03:04:52 | 252,371,954 | 2 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,114 |
py
|
#!/usr/bin/env python
# _*_coding:utf-8 _*_
"""
@Time :2020/11/4 8:26 下午
@Author :[email protected]
@File :57. 插入区间.py
@Description :
"""
class Solution(object):
def insert(self, intervals, newInterval):
"""
:type intervals: List[List[int]]
:type newInterval: List[int]
:rtype: List[List[int]]
"""
res = []
i = 0
# 1.先找到需要合并的位置
while i < len(intervals) and intervals[i][1] < newInterval[0]:
res.append(intervals[i])
i += 1
# 2. 合并区间
while i < len(intervals) and intervals[i][0] <= newInterval[1]:
newInterval[0] = min(newInterval[0],intervals[i][0])
newInterval[1] = max(newInterval[1],intervals[i][1])
i += 1
res.append(newInterval)
while i < len(intervals):
res.append(intervals[i])
i += 1
return res
if __name__ == '__main__':
intervals = [[1,2],[3,5],[6,7],[8,10],[12,16]]
newInterval = [4,8]
print Solution().insert(intervals,newInterval)
|
[
"[email protected]"
] | |
f658dece9539cbce28833455e98d4617eab50735
|
88084a0735b6f10081b2367f27c8c598f350c269
|
/dev/ucb.py
|
600aef995bac446c3e00bb767db8c2afccaba2f8
|
[] |
no_license
|
xnie/cMLE-debias
|
ceb2eef7a5f99c2264bb7b915708d1d31923adfc
|
16adc22b57c8ed5b39dda8c8a376e94840074621
|
refs/heads/master
| 2021-05-06T02:21:29.809063 | 2017-12-17T07:09:08 | 2017-12-17T07:09:08 | 114,514,961 | 6 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 6,472 |
py
|
import math
import pdb
import random
import copy
import numpy as np
from debias import CD, payoff_matrix
def ucb(scen, greedy=False, lil_ucb=False, egreedy=False, rand=None, alpha=9, beta=1, lil_epsilon=0.01, greedy_epsilon=0.05, delta=0.005, gumbel_t_varies=False, held=False):
# scen: a list of reward distr
# rand: randomization noise generator
num_actions = len(scen)
payoff_sums = np.zeros(num_actions)
payoff_sums_held = np.zeros(num_actions)
num_pulls = np.zeros(num_actions, dtype=np.int8)
num_pulls_held = np.zeros(num_actions, dtype=np.int8)
ucbs = np.zeros(num_actions)
debiaser = ucb_sgd(scen)
if egreedy:
debiaser.set_eps(greedy_epsilon)
# initialize empirical sums
for t in range(num_actions):
payoff_sums[t] = scen[t].get_reward()
payoff_sums_held[t] = payoff_sums[t]
num_pulls[t] += 1
num_pulls_held[t] += 1
debiaser.update(t, payoff_sums[t], 0, ucbs)
ind_sample = True
yield t, payoff_sums[t], num_pulls, payoff_sums, ucbs, debiaser, num_pulls_held, payoff_sums_held, ind_sample
t = num_actions
last_action = None
while True:
if held and last_action is not None:
action = last_action
ind_sample = True
last_action = None
else:
ind_sample = False
if greedy or egreedy:
ucbs_bound = np.zeros(num_actions)
else:
ucbs_bound = np.array([scen[i].get_upper_bound(t, num_pulls[i], lil_ucb, alpha, beta, lil_epsilon, delta) for i in range(num_actions)])
ucbs = np.array([payoff_sums[i] / num_pulls[i] + ucbs_bound[i] for i in range(num_actions)])
if rand:
if gumbel_t_varies:
ucbs += rand.rvs(size=num_actions) / np.sqrt(t)
else:
ucbs += rand.rvs(size=num_actions)
mx = max(ucbs)
all_maxes = [i for i in range(num_actions) if ucbs[i] == mx]
if egreedy:
if random.random() < greedy_epsilon:
action = random.choice(range(num_actions))
else:
action = random.choice(all_maxes)
else:
action = random.choice(all_maxes)
last_action = action
reward = scen[action].get_reward()
if not held or (held and not ind_sample):
num_pulls[action] += 1
payoff_sums[action] += reward
else:
num_pulls_held[action] += 1
payoff_sums_held[action] += reward
if rand:
if gumbel_t_varies:
scale = rand.kwds["scale"] / np.sqrt(t)
else:
scale = rand.kwds["scale"]
else:
scale = 0
debiaser.update(action, reward, scale, ucbs_bound)
yield action, reward, num_pulls, payoff_sums, ucbs_bound, debiaser, num_pulls_held, payoff_sums_held, ind_sample
t = t + 1
class ucb_sgd(CD):
"""
This is a subclass of CD that implements the
SGD for UCB type bandit algorithms
"""
def __init__(self, scen):
CD.__init__(self, scen)
# The UCB bounds are specific to UCB algorithms
self.ucb_bounds = []
def set_eps(self, epsilon):
self._eps = epsilon
def update(self, action, reward, rand_scale, extra):
"""
extra: ucb_bounds
"""
CD.update(self, action, reward, rand_scale)
self.ucb_bounds.append(extra)
def get_decision_stat(self):
"""
add the ucb bounds to the arm means.
X: is the arms averages up to time T
"""
if not hasattr(self, "X"):
raise ValueError("Data matrix self.X is not computed.")
self.ucb_upward = np.array(self.ucb_bounds)
S = self.decision_stat(self.X)
return S
def proposal(self, mcmc_stepsize=0.5):
"""
The proposal distribution used for the reward is to add
normal distribution with the known variances.
"""
new = [self.state[t] + mcmc_stepsize * self.scen[a].get_sigma() * np.random.standard_normal()\
for t, a in enumerate(self.choices)]
X_new, _ = payoff_matrix(self.num_actions, self.choices, new)
S_new = self.decision_stat(X_new)
ll_new = self.log_likelihood(new, self.choices, self.hmu)
if hasattr(self, "_eps"):
ll_S_new = self.eps_log_likelihood(S_new)
else:
ll_S_new = self.softmax(S_new)
grad = self.log_likelihood_gradient(X_new[-1,:], self.hmu)
return new, ll_new, ll_S_new, grad, 0
def set_state(self, reward, grad):
self.state = copy.deepcopy(list(reward))
self.state_grad = copy.deepcopy(grad)
def init_sampler(self):
self.total, self.accept = 0, 0
self.ll = self.log_likelihood(self.rewards, self.choices, self.hmu)
if hasattr(self, "_eps"):
self.ll_S = self.eps_log_likelihood(self.S)
else:
self.ll_S = self.softmax(self.S)
# gradient evaluated on the data at last iteratio of hmu
self.data_grad = self.log_likelihood_gradient(self.X[-1,:], self.hmu)
self.ll_pos = 0
# initialize the state to the data
self.set_state(self.rewards, self.data_grad)
def decision_stat(self, X):
if not hasattr(self, "ucb_upward"):
raise ValueError("self.ucb_upward does not exist!")
S = np.zeros(X.shape)
T = S.shape[0] # total rounds
start = S.shape[1] # time to start making decisions based on data
for t in range(start, T):
# the decision statistic at round t is the sums of
# PREVIOUS means and UCB bounds at current time.
S[t,:] = X[(t-1),:] + self.ucb_upward[t,:]
return S
def eps_log_likelihood(self, S):
ll_S = 0
for t, action in enumerate(self.choices):
if t >= self.num_actions:
if self.scales[t] == 0:
ll_S += np.log((1-self._eps) + self._eps / self.num_actions) if action == np.argmax(S[t,:]) else np.log(self._eps / self.num_actions)
else:
ll_S += np.log((1-self._eps) * np.exp(S[t, action] / self.scales[t]) / np.exp(S[t, :] / self.scales[t]).sum() +
self._eps / self.num_actions)
return ll_S
|
[
"[email protected]"
] | |
b25e6e9b2e19fe92e8f056172956ab4e7ce1587e
|
aae858329f9fe013cbd3d4d86f5608314f57f170
|
/private_rest_api/rest_api/sawtooth_rest_api/protobuf/state_context_pb2.py
|
d700f1d5541293d2b95ae2ef45f8fe5bfc2e5755
|
[
"LicenseRef-scancode-proprietary-license",
"Apache-2.0",
"LicenseRef-scancode-openssl",
"OpenSSL",
"MIT",
"Zlib",
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause"
] |
permissive
|
b2lead/private-transaction-families
|
8c4abc8f30c5ba26e9f431911d06c9dd6ff4e0d3
|
2a52b430a947dc8e39ed4fcf664a44176f0547e3
|
refs/heads/master
| 2020-09-27T21:58:44.401120 | 2019-12-09T12:39:27 | 2019-12-09T12:39:27 | 226,619,397 | 0 | 0 |
Apache-2.0
| 2019-12-08T05:27:58 | 2019-12-08T05:27:57 | null |
UTF-8
|
Python
| false | true | 23,078 |
py
|
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: state_context.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from . import events_pb2 as events__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='state_context.proto',
package='',
syntax='proto3',
serialized_pb=_b('\n\x13state_context.proto\x1a\x0c\x65vents.proto\"-\n\x0cTpStateEntry\x12\x0f\n\x07\x61\x64\x64ress\x18\x01 \x01(\t\x12\x0c\n\x04\x64\x61ta\x18\x02 \x01(\x0c\":\n\x11TpStateGetRequest\x12\x12\n\ncontext_id\x18\x01 \x01(\t\x12\x11\n\taddresses\x18\x02 \x03(\t\"\x9d\x01\n\x12TpStateGetResponse\x12\x1e\n\x07\x65ntries\x18\x01 \x03(\x0b\x32\r.TpStateEntry\x12*\n\x06status\x18\x02 \x01(\x0e\x32\x1a.TpStateGetResponse.Status\";\n\x06Status\x12\x10\n\x0cSTATUS_UNSET\x10\x00\x12\x06\n\x02OK\x10\x01\x12\x17\n\x13\x41UTHORIZATION_ERROR\x10\x02\"G\n\x11TpStateSetRequest\x12\x12\n\ncontext_id\x18\x01 \x01(\t\x12\x1e\n\x07\x65ntries\x18\x02 \x03(\x0b\x32\r.TpStateEntry\"\x90\x01\n\x12TpStateSetResponse\x12\x11\n\taddresses\x18\x01 \x03(\t\x12*\n\x06status\x18\x02 \x01(\x0e\x32\x1a.TpStateSetResponse.Status\";\n\x06Status\x12\x10\n\x0cSTATUS_UNSET\x10\x00\x12\x06\n\x02OK\x10\x01\x12\x17\n\x13\x41UTHORIZATION_ERROR\x10\x02\"=\n\x14TpStateDeleteRequest\x12\x12\n\ncontext_id\x18\x01 \x01(\t\x12\x11\n\taddresses\x18\x02 \x03(\t\"\x96\x01\n\x15TpStateDeleteResponse\x12\x11\n\taddresses\x18\x01 \x03(\t\x12-\n\x06status\x18\x02 \x01(\x0e\x32\x1d.TpStateDeleteResponse.Status\";\n\x06Status\x12\x10\n\x0cSTATUS_UNSET\x10\x00\x12\x06\n\x02OK\x10\x01\x12\x17\n\x13\x41UTHORIZATION_ERROR\x10\x02\";\n\x17TpReceiptAddDataRequest\x12\x12\n\ncontext_id\x18\x01 \x01(\t\x12\x0c\n\x04\x64\x61ta\x18\x03 \x01(\x0c\"{\n\x18TpReceiptAddDataResponse\x12\x30\n\x06status\x18\x02 \x01(\x0e\x32 .TpReceiptAddDataResponse.Status\"-\n\x06Status\x12\x10\n\x0cSTATUS_UNSET\x10\x00\x12\x06\n\x02OK\x10\x01\x12\t\n\x05\x45RROR\x10\x02\">\n\x11TpEventAddRequest\x12\x12\n\ncontext_id\x18\x01 \x01(\t\x12\x15\n\x05\x65vent\x18\x02 \x01(\x0b\x32\x06.Event\"o\n\x12TpEventAddResponse\x12*\n\x06status\x18\x02 \x01(\x0e\x32\x1a.TpEventAddResponse.Status\"-\n\x06Status\x12\x10\n\x0cSTATUS_UNSET\x10\x00\x12\x06\n\x02OK\x10\x01\x12\t\n\x05\x45RROR\x10\x02\x42,\n\x15sawtooth.sdk.protobufP\x01Z\x11state_context_pb2b\x06proto3')
,
dependencies=[events__pb2.DESCRIPTOR,])
_TPSTATEGETRESPONSE_STATUS = _descriptor.EnumDescriptor(
name='Status',
full_name='TpStateGetResponse.Status',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='STATUS_UNSET', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='OK', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='AUTHORIZATION_ERROR', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=243,
serialized_end=302,
)
_sym_db.RegisterEnumDescriptor(_TPSTATEGETRESPONSE_STATUS)
_TPSTATESETRESPONSE_STATUS = _descriptor.EnumDescriptor(
name='Status',
full_name='TpStateSetResponse.Status',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='STATUS_UNSET', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='OK', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='AUTHORIZATION_ERROR', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=243,
serialized_end=302,
)
_sym_db.RegisterEnumDescriptor(_TPSTATESETRESPONSE_STATUS)
_TPSTATEDELETERESPONSE_STATUS = _descriptor.EnumDescriptor(
name='Status',
full_name='TpStateDeleteResponse.Status',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='STATUS_UNSET', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='OK', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='AUTHORIZATION_ERROR', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=243,
serialized_end=302,
)
_sym_db.RegisterEnumDescriptor(_TPSTATEDELETERESPONSE_STATUS)
_TPRECEIPTADDDATARESPONSE_STATUS = _descriptor.EnumDescriptor(
name='Status',
full_name='TpReceiptAddDataResponse.Status',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='STATUS_UNSET', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='OK', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ERROR', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=879,
serialized_end=924,
)
_sym_db.RegisterEnumDescriptor(_TPRECEIPTADDDATARESPONSE_STATUS)
_TPEVENTADDRESPONSE_STATUS = _descriptor.EnumDescriptor(
name='Status',
full_name='TpEventAddResponse.Status',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='STATUS_UNSET', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='OK', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ERROR', index=2, number=2,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=879,
serialized_end=924,
)
_sym_db.RegisterEnumDescriptor(_TPEVENTADDRESPONSE_STATUS)
_TPSTATEENTRY = _descriptor.Descriptor(
name='TpStateEntry',
full_name='TpStateEntry',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='address', full_name='TpStateEntry.address', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='data', full_name='TpStateEntry.data', index=1,
number=2, type=12, cpp_type=9, label=1,
has_default_value=False, default_value=_b(""),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=37,
serialized_end=82,
)
_TPSTATEGETREQUEST = _descriptor.Descriptor(
name='TpStateGetRequest',
full_name='TpStateGetRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='context_id', full_name='TpStateGetRequest.context_id', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='addresses', full_name='TpStateGetRequest.addresses', index=1,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=84,
serialized_end=142,
)
_TPSTATEGETRESPONSE = _descriptor.Descriptor(
name='TpStateGetResponse',
full_name='TpStateGetResponse',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='entries', full_name='TpStateGetResponse.entries', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='status', full_name='TpStateGetResponse.status', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_TPSTATEGETRESPONSE_STATUS,
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=145,
serialized_end=302,
)
_TPSTATESETREQUEST = _descriptor.Descriptor(
name='TpStateSetRequest',
full_name='TpStateSetRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='context_id', full_name='TpStateSetRequest.context_id', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='entries', full_name='TpStateSetRequest.entries', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=304,
serialized_end=375,
)
_TPSTATESETRESPONSE = _descriptor.Descriptor(
name='TpStateSetResponse',
full_name='TpStateSetResponse',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='addresses', full_name='TpStateSetResponse.addresses', index=0,
number=1, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='status', full_name='TpStateSetResponse.status', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_TPSTATESETRESPONSE_STATUS,
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=378,
serialized_end=522,
)
_TPSTATEDELETEREQUEST = _descriptor.Descriptor(
name='TpStateDeleteRequest',
full_name='TpStateDeleteRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='context_id', full_name='TpStateDeleteRequest.context_id', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='addresses', full_name='TpStateDeleteRequest.addresses', index=1,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=524,
serialized_end=585,
)
_TPSTATEDELETERESPONSE = _descriptor.Descriptor(
name='TpStateDeleteResponse',
full_name='TpStateDeleteResponse',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='addresses', full_name='TpStateDeleteResponse.addresses', index=0,
number=1, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='status', full_name='TpStateDeleteResponse.status', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_TPSTATEDELETERESPONSE_STATUS,
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=588,
serialized_end=738,
)
_TPRECEIPTADDDATAREQUEST = _descriptor.Descriptor(
name='TpReceiptAddDataRequest',
full_name='TpReceiptAddDataRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='context_id', full_name='TpReceiptAddDataRequest.context_id', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='data', full_name='TpReceiptAddDataRequest.data', index=1,
number=3, type=12, cpp_type=9, label=1,
has_default_value=False, default_value=_b(""),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=740,
serialized_end=799,
)
_TPRECEIPTADDDATARESPONSE = _descriptor.Descriptor(
name='TpReceiptAddDataResponse',
full_name='TpReceiptAddDataResponse',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='status', full_name='TpReceiptAddDataResponse.status', index=0,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_TPRECEIPTADDDATARESPONSE_STATUS,
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=801,
serialized_end=924,
)
_TPEVENTADDREQUEST = _descriptor.Descriptor(
name='TpEventAddRequest',
full_name='TpEventAddRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='context_id', full_name='TpEventAddRequest.context_id', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='event', full_name='TpEventAddRequest.event', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=926,
serialized_end=988,
)
_TPEVENTADDRESPONSE = _descriptor.Descriptor(
name='TpEventAddResponse',
full_name='TpEventAddResponse',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='status', full_name='TpEventAddResponse.status', index=0,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_TPEVENTADDRESPONSE_STATUS,
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=990,
serialized_end=1101,
)
_TPSTATEGETRESPONSE.fields_by_name['entries'].message_type = _TPSTATEENTRY
_TPSTATEGETRESPONSE.fields_by_name['status'].enum_type = _TPSTATEGETRESPONSE_STATUS
_TPSTATEGETRESPONSE_STATUS.containing_type = _TPSTATEGETRESPONSE
_TPSTATESETREQUEST.fields_by_name['entries'].message_type = _TPSTATEENTRY
_TPSTATESETRESPONSE.fields_by_name['status'].enum_type = _TPSTATESETRESPONSE_STATUS
_TPSTATESETRESPONSE_STATUS.containing_type = _TPSTATESETRESPONSE
_TPSTATEDELETERESPONSE.fields_by_name['status'].enum_type = _TPSTATEDELETERESPONSE_STATUS
_TPSTATEDELETERESPONSE_STATUS.containing_type = _TPSTATEDELETERESPONSE
_TPRECEIPTADDDATARESPONSE.fields_by_name['status'].enum_type = _TPRECEIPTADDDATARESPONSE_STATUS
_TPRECEIPTADDDATARESPONSE_STATUS.containing_type = _TPRECEIPTADDDATARESPONSE
_TPEVENTADDREQUEST.fields_by_name['event'].message_type = events__pb2._EVENT
_TPEVENTADDRESPONSE.fields_by_name['status'].enum_type = _TPEVENTADDRESPONSE_STATUS
_TPEVENTADDRESPONSE_STATUS.containing_type = _TPEVENTADDRESPONSE
DESCRIPTOR.message_types_by_name['TpStateEntry'] = _TPSTATEENTRY
DESCRIPTOR.message_types_by_name['TpStateGetRequest'] = _TPSTATEGETREQUEST
DESCRIPTOR.message_types_by_name['TpStateGetResponse'] = _TPSTATEGETRESPONSE
DESCRIPTOR.message_types_by_name['TpStateSetRequest'] = _TPSTATESETREQUEST
DESCRIPTOR.message_types_by_name['TpStateSetResponse'] = _TPSTATESETRESPONSE
DESCRIPTOR.message_types_by_name['TpStateDeleteRequest'] = _TPSTATEDELETEREQUEST
DESCRIPTOR.message_types_by_name['TpStateDeleteResponse'] = _TPSTATEDELETERESPONSE
DESCRIPTOR.message_types_by_name['TpReceiptAddDataRequest'] = _TPRECEIPTADDDATAREQUEST
DESCRIPTOR.message_types_by_name['TpReceiptAddDataResponse'] = _TPRECEIPTADDDATARESPONSE
DESCRIPTOR.message_types_by_name['TpEventAddRequest'] = _TPEVENTADDREQUEST
DESCRIPTOR.message_types_by_name['TpEventAddResponse'] = _TPEVENTADDRESPONSE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
TpStateEntry = _reflection.GeneratedProtocolMessageType('TpStateEntry', (_message.Message,), dict(
DESCRIPTOR = _TPSTATEENTRY,
__module__ = 'state_context_pb2'
# @@protoc_insertion_point(class_scope:TpStateEntry)
))
_sym_db.RegisterMessage(TpStateEntry)
TpStateGetRequest = _reflection.GeneratedProtocolMessageType('TpStateGetRequest', (_message.Message,), dict(
DESCRIPTOR = _TPSTATEGETREQUEST,
__module__ = 'state_context_pb2'
# @@protoc_insertion_point(class_scope:TpStateGetRequest)
))
_sym_db.RegisterMessage(TpStateGetRequest)
TpStateGetResponse = _reflection.GeneratedProtocolMessageType('TpStateGetResponse', (_message.Message,), dict(
DESCRIPTOR = _TPSTATEGETRESPONSE,
__module__ = 'state_context_pb2'
# @@protoc_insertion_point(class_scope:TpStateGetResponse)
))
_sym_db.RegisterMessage(TpStateGetResponse)
TpStateSetRequest = _reflection.GeneratedProtocolMessageType('TpStateSetRequest', (_message.Message,), dict(
DESCRIPTOR = _TPSTATESETREQUEST,
__module__ = 'state_context_pb2'
# @@protoc_insertion_point(class_scope:TpStateSetRequest)
))
_sym_db.RegisterMessage(TpStateSetRequest)
TpStateSetResponse = _reflection.GeneratedProtocolMessageType('TpStateSetResponse', (_message.Message,), dict(
DESCRIPTOR = _TPSTATESETRESPONSE,
__module__ = 'state_context_pb2'
# @@protoc_insertion_point(class_scope:TpStateSetResponse)
))
_sym_db.RegisterMessage(TpStateSetResponse)
TpStateDeleteRequest = _reflection.GeneratedProtocolMessageType('TpStateDeleteRequest', (_message.Message,), dict(
DESCRIPTOR = _TPSTATEDELETEREQUEST,
__module__ = 'state_context_pb2'
# @@protoc_insertion_point(class_scope:TpStateDeleteRequest)
))
_sym_db.RegisterMessage(TpStateDeleteRequest)
TpStateDeleteResponse = _reflection.GeneratedProtocolMessageType('TpStateDeleteResponse', (_message.Message,), dict(
DESCRIPTOR = _TPSTATEDELETERESPONSE,
__module__ = 'state_context_pb2'
# @@protoc_insertion_point(class_scope:TpStateDeleteResponse)
))
_sym_db.RegisterMessage(TpStateDeleteResponse)
TpReceiptAddDataRequest = _reflection.GeneratedProtocolMessageType('TpReceiptAddDataRequest', (_message.Message,), dict(
DESCRIPTOR = _TPRECEIPTADDDATAREQUEST,
__module__ = 'state_context_pb2'
# @@protoc_insertion_point(class_scope:TpReceiptAddDataRequest)
))
_sym_db.RegisterMessage(TpReceiptAddDataRequest)
TpReceiptAddDataResponse = _reflection.GeneratedProtocolMessageType('TpReceiptAddDataResponse', (_message.Message,), dict(
DESCRIPTOR = _TPRECEIPTADDDATARESPONSE,
__module__ = 'state_context_pb2'
# @@protoc_insertion_point(class_scope:TpReceiptAddDataResponse)
))
_sym_db.RegisterMessage(TpReceiptAddDataResponse)
TpEventAddRequest = _reflection.GeneratedProtocolMessageType('TpEventAddRequest', (_message.Message,), dict(
DESCRIPTOR = _TPEVENTADDREQUEST,
__module__ = 'state_context_pb2'
# @@protoc_insertion_point(class_scope:TpEventAddRequest)
))
_sym_db.RegisterMessage(TpEventAddRequest)
TpEventAddResponse = _reflection.GeneratedProtocolMessageType('TpEventAddResponse', (_message.Message,), dict(
DESCRIPTOR = _TPEVENTADDRESPONSE,
__module__ = 'state_context_pb2'
# @@protoc_insertion_point(class_scope:TpEventAddResponse)
))
_sym_db.RegisterMessage(TpEventAddResponse)
DESCRIPTOR.has_options = True
DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\n\025sawtooth.sdk.protobufP\001Z\021state_context_pb2'))
# @@protoc_insertion_point(module_scope)
|
[
"[email protected]"
] | |
4da4aa68a0cd83d1a57b20435439e06bad9395a2
|
fc6f709f916fcd201938157990c77fa9202eefa7
|
/model/optimizer.py
|
4a9ee5afce8f27d52a2e33ea778b94ad326ffc29
|
[
"MIT"
] |
permissive
|
chenchy/StyleSpeech
|
441ffd6d71ac0269d205ad66c9536fe00cb5267c
|
e0e4ad25681f9ecc2a01ba1b87cbe0c59472b792
|
refs/heads/main
| 2023-05-27T21:39:04.790584 | 2021-06-13T10:32:03 | 2021-06-13T11:26:38 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,650 |
py
|
import torch
import numpy as np
class ScheduledOptimMain:
""" A simple wrapper class for learning rate scheduling """
def __init__(self, model, train_config, model_config, current_step):
self._optimizer = torch.optim.Adam(
model.parameters(),
betas=train_config["optimizer"]["betas"],
eps=train_config["optimizer"]["eps"],
weight_decay=train_config["optimizer"]["weight_decay"],
)
self.n_warmup_steps = train_config["optimizer"]["warm_up_step"]
self.anneal_steps = train_config["optimizer"]["anneal_steps"]
self.anneal_rate = train_config["optimizer"]["anneal_rate"]
self.current_step = current_step
self.init_lr = np.power(model_config["transformer"]["encoder_hidden"], -0.5)
def step_and_update_lr(self):
self._update_learning_rate()
self._optimizer.step()
def zero_grad(self):
# print(self.init_lr)
self._optimizer.zero_grad()
def load_state_dict(self, path):
self._optimizer.load_state_dict(path)
def _get_lr_scale(self):
lr = np.min(
[
np.power(self.current_step, -0.5),
np.power(self.n_warmup_steps, -1.5) * self.current_step,
]
)
for s in self.anneal_steps:
if self.current_step > s:
lr = lr * self.anneal_rate
return lr
def _update_learning_rate(self):
""" Learning rate scheduling per step """
self.current_step += 1
lr = self.init_lr * self._get_lr_scale()
for param_group in self._optimizer.param_groups:
param_group["lr"] = lr
class ScheduledOptimDisc:
""" A simple wrapper class for learning rate scheduling """
def __init__(self, model, train_config):
self._optimizer = torch.optim.Adam(
model.parameters(),
betas=train_config["optimizer"]["betas"],
eps=train_config["optimizer"]["eps"],
weight_decay=train_config["optimizer"]["weight_decay"],
)
self.init_lr = train_config["optimizer"]["lr_disc"]
self._init_learning_rate()
def step_and_update_lr(self):
self._optimizer.step()
def zero_grad(self):
# print(self.init_lr)
self._optimizer.zero_grad()
def load_state_dict(self, path):
self._optimizer.load_state_dict(path)
def _init_learning_rate(self):
lr = self.init_lr
for param_group in self._optimizer.param_groups:
param_group["lr"] = lr
|
[
"[email protected]"
] | |
7208ed98c23ba5ea533900ee0bf6dfbbb82d5a92
|
bbb89d13318df191b83716ad28633c6dd87147a5
|
/curso_em_video/mundo_03/ex095.py
|
d16fa09f866332300e254db544c647cd9c0b08f7
|
[] |
no_license
|
matheusvictor/estudos_python
|
50745522d2801fd5e9c2c3307eb251c1f18dcdbd
|
627c01a5e89192388fb5c34f5fdccbc7a3129d9f
|
refs/heads/master
| 2022-10-28T09:00:52.972993 | 2022-10-06T17:45:28 | 2022-10-06T17:45:28 | 192,107,427 | 5 | 0 | null | 2022-10-05T18:09:22 | 2019-06-15T17:43:49 |
Python
|
UTF-8
|
Python
| false | false | 2,658 |
py
|
lista_jogadores = list()
continua = True
while continua:
nome_jogador = str(input('Nome do jogador: '))
partidas_jogadas = int(input(f'Quantas partidas foram jogadas por {nome_jogador}?: '))
quantidade_gols_partida = list()
total_gols = 0
for partida in range(partidas_jogadas):
quantidade_gols_partida.append(int(input(f'Quantidade de gols feitos na {partida + 1}ª partida: ')))
total_gols += quantidade_gols_partida[partida]
estatisticas_jogador = {'nome': nome_jogador, 'partidas_jogadas': partidas_jogadas,
'gols_por_partida': quantidade_gols_partida, 'total_gols': total_gols}
lista_jogadores.append(estatisticas_jogador.copy())
estatisticas_jogador.clear()
resposta = str(input('Deseja continuar? [S/N]: '))
while resposta not in 'SsNn':
resposta = str(input('Opção inválida! Tente novamente [S/N]: ')).upper()[0]
if resposta in 'Nn':
continua = False
break
print('-=' * 40)
print(f'{"COD":<10}{"NOME":<10}{"GOLS":<10}{"TOTAL":<10}')
print('-' * 40)
for index, jogador in enumerate(lista_jogadores):
print(f'{index:<5}{"":<5}{jogador["nome"]}{"":<10}{jogador["gols_por_partida"]}{"":<10}{jogador["total_gols"]}')
print('-' * 40)
while True:
opcao = int(input('Deseja mostrar os dados de qual jogador? (999 para encerrar o programa): '))
while opcao != 999 and (opcao < 0 or opcao > len(lista_jogadores) - 1):
opcao = int(input('Opção inválida! Tente novamente ou digite 999 para encerrar o programa: '))
if opcao <= len(lista_jogadores) - 1:
print('-=' * 40)
print(f'* Jogador: {lista_jogadores[opcao]["nome"]}.')
print(f'* Nº partidas: {lista_jogadores[opcao]["partidas_jogadas"]}.')
print(f'* Nº gols/partida: ', end='')
if lista_jogadores[opcao]["partidas_jogadas"] == 0:
print('Não houve partidas disputadas.')
else:
for partida in range(lista_jogadores[opcao]["partidas_jogadas"]):
print(f'\n - {partida + 1}ª partida = {lista_jogadores[opcao]["gols_por_partida"][partida]} gol(s)')
print(f'* Total de gol(s): {lista_jogadores[opcao]["total_gols"]}.')
print('-=' * 40)
elif opcao == 999:
break
print('Programa encerrado!')
# print('-=' * 20)
# print(f'O jogador {estatisticas_jogador["nome"]} jogou {estatisticas_jogador["partidas_jogadas"]} partida(s).')
# for i, v in enumerate(estatisticas_jogador['gols_por_partida']):
# print(f'Na {i + 1}ª partida fez {v} gol(s)')
# print(f'Foi um total de {estatisticas_jogador["total_gols"]} gol(s)!')
# print('-=' * 20)
|
[
"[email protected]"
] | |
9ebd7cc9e4666b0fb2c0d367f8dc2c3e0cea1522
|
bd5240c87cd9699cae088395cac180276210566f
|
/Day2/day2part2.py
|
83131ed0c1ff569d573e398610498f7e91b6236d
|
[] |
no_license
|
GruberQZ/AoC-2016
|
decb15665b409be8fa57576f1d72aea1834ce98a
|
377ae2557225bdd61c19adcf72fed549ba363d56
|
refs/heads/master
| 2020-06-15T23:23:34.827074 | 2017-06-22T02:31:06 | 2017-06-22T02:31:06 | 75,258,578 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,648 |
py
|
file = open('input.txt','r')
password = ''
cannotMoveLeft = [1,2,5,10,13]
cannotMoveRight = [1,4,9,12,13]
cannotMoveUp = [1,2,4,5,9]
cannotMoveDown = [5,9,10,12,13]
for line in file:
# Starting number
number = 5
# Go character by character
for char in line:
if char == 'U':
if number not in cannotMoveUp:
if number in [3,13]:
number = number - 2
elif number in [6,7,8]:
number = number - 4
elif number in [10,11,12]:
number = number - 4
elif char == 'D':
if number not in cannotMoveDown:
if number in [1,11]:
number = number + 2
elif number in [2,3,4]:
number = number + 4
elif number in [6,7,8]:
number = number + 4
elif char == 'R':
if number not in cannotMoveRight:
number = number + 1
elif char == 'L':
if number not in cannotMoveLeft:
number = number - 1
# Add character to the password string
password = password + str(number) + ','
# Decode password
split = password.split(',')
finalPass = ''
for char in split:
if char != '':
if int(char) < 10:
finalPass = finalPass + char
elif char == '10':
finalPass = finalPass + 'A'
elif char == '11':
finalPass = finalPass + 'B'
elif char == '12':
finalPass = finalPass + 'C'
elif char == '13':
finalPass = finalPass + 'D'
print(finalPass)
|
[
"[email protected]"
] | |
d950e9415f72c06a87c46adf8103dce0dae9f4fb
|
df52429f63a1983ec725fe1197edad77f0504d7d
|
/Python_Study/BUPT_homework/spider/test1/test1/spiders/spider.py
|
01613d207c0938b0c8b4c97eb3e26f10446594ab
|
[] |
no_license
|
StupidRabbit29/Python_Practice
|
38b16baa7ecd4bd6f0bbd4840f37ba565c387857
|
0f7c43026410b4b7f07a9a266b2cf885de5d53b9
|
refs/heads/master
| 2020-05-31T07:26:32.631067 | 2019-12-05T11:19:26 | 2019-12-05T11:19:26 | 190,165,619 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 961 |
py
|
import scrapy
from test1.items import MyItem
class mySpider(scrapy.spiders.Spider):
name = "xuetang" # 爬虫名称
allowed_domains = ["www.xuetangx.com/"] # 允许爬取的网站域名
start_urls = ["http://www.xuetangx.com/partners"] # 初始URL
def parse(self, response): # 解析爬取的内容
item = MyItem()
# 生成一个MyItem对象，接收爬取的数据
# 一共爬取143所大学的课程信息
for i in range(1, 144):
item['university'] = response.xpath("/html/body/article[1]/section/ul/li[{}]"
"/a/div[1]/span/img/@title".format(i)).extract()
item['classnum'] = response.xpath("/html/body/article[1]/section/ul/li[{}]"
"/a/div[2]/p/text()".format(i)).extract()
if item['university'] and item['classnum']: # 去掉空值
yield(item)
|
[
"[email protected]"
] | |
56a09542f43d048bd4db774cddb2d81219f39b2a
|
99124299af27232720ad19df377cb90c20f514bb
|
/Ending_Animation.py
|
14eba34dfb108cc562dcd4bdd87915b78266901a
|
[] |
no_license
|
senweim/JumpKingAtHome
|
7a2ae684fafffa1f910c68d8b02ea98e4e900ed6
|
6c3d1a7ba9246181b89e67b1f4857df99c85fa01
|
refs/heads/master
| 2023-01-12T15:01:31.174697 | 2020-11-10T06:49:45 | 2020-11-10T06:49:45 | 311,570,473 | 14 | 5 | null | null | null | null |
UTF-8
|
Python
| false | false | 5,129 |
py
|
#!/usr/bin/env python
#
#
#
#
import pygame
import os
import sys
import math
class Ending_Animation:
def __init__(self):
self.end_counter = 0
self.end_pan = (-90, 90)
self.stall_x = 240
self.stall_y = 220
self.channel = pygame.mixer.Channel(1)
self.ending_music = pygame.mixer.Sound("Audio\\Misc\\ending.wav")
self.end_image = pygame.image.load("images\\sheets\\imagecrown.png").convert()
def update(self, level, king, babe):
king_command = None
babe_command = None
if self.move_screen(level, king, babe):
if self.end_counter < 50:
pass
elif self.end_counter == 50:
babe_command = "Crouch"
elif self.end_counter < 60:
pass
elif self.end_counter == 60:
babe_command = "Jump"
elif self.end_counter <= 120:
pass
elif self.end_counter <= 150:
babe_command = "WalkLeft"
elif self.end_counter <= 175:
babe_command = "Kiss"
elif self.end_counter <= 190:
level.flyer.active = True
elif self.end_counter <= 205:
king_command = "LookUp"
babe_command = "WalkRight"
elif 330 <= self.end_counter < 360:
king_command = "Crouch"
elif self.end_counter == 360:
king_command = "Jump"
elif self.end_counter <= 420:
if king.y <= level.flyer.rect.bottom:
king.isWearingCrown = True
king.rect_y = level.flyer.rect.bottom + (king.y - king.rect_y - 7)
king_command = "Freeze"
if self.end_counter == 360:
level.flyer.channel.play(level.flyer.audio)
elif self.end_counter <= 460:
level.flyer.active = False
elif self.end_counter <= 500:
king.isHoldingUpHands = True
elif self.end_counter == 501:
king.isSnatch = True
elif self.end_counter == 502:
king.isSnatch = False
king.isHoldingBabe = True
babe_command = "Snatched"
babe.channel.play(babe.audio["babe_pickup"])
elif self.end_counter <= 670:
king_command = "WalkLeft"
elif self.end_counter <= 730:
pass
elif self.end_counter <= 820:
if self.end_counter == 731:
babe.channel.play(babe.audio["babe_surprised2"])
self.scroll_screen(level, king)
king_command = "WalkRight"
king.update(king_command)
elif self.end_counter <= 850:
self.scroll_screen(level, king)
king_command = "Crouch"
elif self.end_counter == 851:
babe.channel.play(babe.audio["babe_jump"])
self.scroll_screen(level, king)
king_command = "JumpRight"
elif self.end_counter <= 1700:
self.scroll_screen(level, king)
if self.end_counter == 930:
king.channel.play(king.audio["Land"]["king_jump"])
if self.end_counter > 1000:
king.isAdmiring = True
if self.end_counter == 1100:
babe.channel.play(babe.audio["babe_mou"])
if self.end_counter > 1200:
king.isAdmiring = False
else:
if self.end_counter > 3000:
sys.exit()
return True
self.end_counter += 1
king.update(king_command)
babe.update(king, babe_command)
def scroll_screen(self, level, king):
if king.rect_x > self.stall_x:
rel_x = self.stall_x - king.rect_x
king.rect_x += rel_x
if level.midground:
level.midground.x += rel_x
if level.props:
for prop in level.props:
prop.x += rel_x
if level.npc:
level.npc.x += rel_x
if level.foreground:
level.foreground.x += rel_x
if level.platforms:
for platform in level.platforms:
platform.x += rel_x
if king.rect_y > self.stall_y:
rel_y = self.stall_y - king.rect_y
if self.stall_y > level.screen.get_height() / 2:
self.stall_y -= 2
king.rect_y += rel_y
if level.midground:
level.midground.y -= math.sqrt(abs(rel_y))
if level.props:
for prop in level.props:
prop.y -= math.sqrt(abs(rel_y))
if level.npc:
level.npc.y -= math.sqrt(abs(rel_y))
if level.foreground:
level.foreground.y -= math.sqrt(abs(rel_y))
if level.platforms:
for platform in level.platforms:
platform.y -= math.sqrt(abs(rel_y))
def move_screen(self, level, king, babe):
if self.end_pan[0] != 0 or self.end_pan[1] != 0:
try:
x = self.end_pan[0]/abs(self.end_pan[0])
except ZeroDivisionError:
x = 0
try:
y = self.end_pan[1]/abs(self.end_pan[1])
except ZeroDivisionError:
y = 0
if level.midground:
level.midground.x += x
level.midground.y += y
if level.props:
for prop in level.props:
prop.x += x
prop.y += y
if level.npc:
level.npc.x += x
level.npc.y += y
if level.foreground:
level.foreground.x += x
level.foreground.y += y
if level.platforms:
for platform in level.platforms:
platform.x += x
platform.y += y
king.rect_x += x
king.rect_y += y
babe.rect_x += x
babe.rect_y += y
self.end_pan = (self.end_pan[0] - x, self.end_pan[1] - y)
return False
else:
return True
def update_audio(self):
try:
if not self.channel.get_busy():
self.channel.play(self.ending_music)
except Exception as e:
print("ENDINGUPDATEAUDIO ERROR: ", e)
def blitme(self, screen):
screen.blit(self.end_image, (0, 0))
|
[
"[email protected]"
] | |
0593545ee04a253e13349dd65da2311fddaf8735
|
6ec7a7b7bec26ae583ac0c1a29cffeb3875a6887
|
/learning_sites/basic_app/migrations/0002_auto_20190818_0321.py
|
a04ffe511222640813a7144448465ae87fde86c7
|
[] |
no_license
|
MuhammadAhmedSiddiqui/Django-Deployment-Example
|
048420d562b5eb85562a8cf85efbaf7dcffd1082
|
fcef550ac6fa2d9986dfc2483f094f19294446a7
|
refs/heads/master
| 2021-07-10T20:24:32.377185 | 2019-08-18T21:02:06 | 2019-08-18T21:02:06 | 203,053,430 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 516 |
py
|
# Generated by Django 2.2.1 on 2019-08-17 22:21
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('basic_app', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='userprofileinfo',
name='user',
field=models.OneToOneField(on_delete=django.db.models.deletion.DO_NOTHING, to=settings.AUTH_USER_MODEL),
),
]
|
[
"[email protected]"
] | |
e3dc9fa2d3b12729be5417ea7d8dcfe2a1af40f1
|
57b639ef18f16cad499f56e74694b8109a2370e0
|
/IPN/__init__.py
|
cce32ae0f6274de65ccc173fe06ba3112e256a90
|
[] |
no_license
|
Cranbaerry/RainyCogs
|
7eaf3e77650b2ea0393099f1507bf874b6888114
|
13d49439703731b3d705458b1f14c8af1677c3b0
|
refs/heads/master
| 2023-08-16T02:56:46.771142 | 2021-09-15T14:11:46 | 2021-09-15T14:11:46 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 74 |
py
|
from .IPN import IPN
def setup(bot):
n = IPN(bot)
bot.add_cog(n)
|
[
"[email protected]"
] | |
fbfcb0f207218d893dde5197920947a501840b4e
|
bebde3481cc5cae5c29d40e7492344dfc1cea388
|
/main.py
|
13c0f23cb3147c165b1fc90e44410804b686f484
|
[] |
no_license
|
erichadley8/headless-firefox
|
dac87659569d63ad77f6a024303f745a15d02be8
|
198ebb2c954fe840de0ebf4823682e68ec772f8a
|
refs/heads/master
| 2023-01-18T17:55:31.575480 | 2020-12-01T02:18:25 | 2020-12-01T02:18:25 | 317,334,635 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,331 |
py
|
import os
import datetime
from colorama import Fore, Back
from selenium import webdriver
from selenium.webdriver.firefox.options import Options
while True:
try:
### Firefox Parameters
executable_path = os.getcwd() + "/geckodriver"
### Firefox Options
options = Options()
options.headless = True
### Proxy Configuration
'''
proxy = "34.203.142.175:80"
webdriver.DesiredCapabilities.FIREFOX['proxy'] = {
"httpProxy": proxy,
"ftpProxy":proxy,
"sslProxy":proxy,
"proxyType":"MANUAL",
}
'''
### Firefox Webdriver
browser = webdriver.Firefox(options=options, executable_path=executable_path)
### Webdriver Logic
browser.get('http://www.porngifs.xyz/')
print(Fore.GREEN + browser.page_source)
### Popunder
body = browser.find_element_by_tag_name("body")
body.click()
browser.save_screenshot("popunder.png")
print(Fore.CYAN + "Clicked for popunder")
ct = datetime.datetime.now()
print("current time:-", ct)
browser.switch_to.window(browser.window_handles[1])
print("Switched back from popunder")
ct = datetime.datetime.now()
print("current time:-", ct)
### Ads
print(Fore.YELLOW + "Getting ads and clicking ads")
ct = datetime.datetime.now()
print("current time:-", ct)
elements = browser.find_elements_by_tag_name('iframe')
for element in elements:
try:
if "jads" in element.get_attribute("src"):
try:
element.click()
print("Clicked " + str(element))
ct = datetime.datetime.now()
print("current time:-", ct)
except:
print("Failed to click " +str(element))
ct = datetime.datetime.now()
print("current time:-", ct)
except:
print("Element detached from dom")
page = 1
print(Fore.RED + "Closing ads and tabs")
ct = datetime.datetime.now()
print("current time:-", ct)
try:
for tab in browser.window_handles:
try:
browser.switch_to.window(tab)
browser.save_screenshot(str(page) + "_page.png")
browser.close()
print("Closed " + str(tab))
ct = datetime.datetime.now()
print("current time:-", ct)
page = page + 1
except:
print("Failed to close")
ct = datetime.datetime.now()
print("current time:-", ct)
except:
print("Failed to close all tabs")
finally:
try:
browser.quit()
except:
pass
|
[
"[email protected]"
] | |
7a264a8db4f4e8e02988b7289dd89107a02032e9
|
a30d505835c2376634279488878811eacc2de056
|
/landing_page/views.py
|
58d90cb2b886c86b6f4311194f888892ca78399c
|
[] |
no_license
|
Aim-Entity/journalist-web
|
d996419b1177ea1405a9f45ff405c7502c721673
|
8deef8254c7823c6afd713fba36f2d73a9840412
|
refs/heads/master
| 2023-04-27T23:50:24.997849 | 2020-03-29T15:16:11 | 2020-03-29T15:16:11 | 251,063,144 | 0 | 0 | null | 2023-04-21T20:51:06 | 2020-03-29T15:18:03 |
HTML
|
UTF-8
|
Python
| false | false | 110 |
py
|
from django.shortcuts import render
def index(request):
return render(request, "landing/index.htm", {})
|
[
"[email protected]"
] | |
2e43f3798da5e37da76cc29d9895006b9acebf33
|
71baccb6082a2324ba9dc7d66dbc2ad04d4d5ff3
|
/complaint/views.py
|
8c7c5d048c0ebb203e34127e0cd6f9d45dc0d066
|
[] |
no_license
|
pradyumnamahajan52/SVPCET-Hackathon-The-Hack-Backpackers-02
|
60eec408fd5f290f079b85b5c4982aac52dbd366
|
2c4df3ece041a0494c98d920c9538f01464d3c48
|
refs/heads/main
| 2023-01-12T01:19:41.794920 | 2020-11-08T05:15:15 | 2020-11-08T05:15:15 | 330,423,482 | 1 | 0 | null | 2021-01-17T15:33:13 | 2021-01-17T15:33:13 | null |
UTF-8
|
Python
| false | false | 535 |
py
|
from django.shortcuts import render
from .models import Complaint_Category,Complaint_Subcategory,Complaint_User
from user.models import Users
# Create your views here.
def user_solver(request):
complaint_subcategory = Complaint_Subcategory.objects.get(solver_role=request.user.user_role)
complaint_user = Complaint_User.objects.filter(complaint_subcategory=complaint_subcategory,is_otp_verify=True).all()
params = {
'complaint_user':complaint_user,
}
return render(request,'user/user_solver.html',params)
|
[
"[email protected]"
] | |
46eb00aff3aeab2753beda53a5752b341bbe772b
|
3d308edef9d4f9feead0e024f08d45c00a45de95
|
/07_files/task_7_3b_script.py
|
dddc81ba98ff91185428ea3cf56181a0cfa39ec6
|
[] |
no_license
|
noatre/pyneng
|
86ac9ff6cce16eef5e418d93e296a6255843b984
|
0b29d811fbae1bef9e0a3972f12edc82c7de52e0
|
refs/heads/master
| 2020-04-22T15:49:07.228343 | 2019-03-20T17:28:21 | 2019-03-20T17:28:21 | 170,488,747 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 346 |
py
|
#!/usr/bin/env python3
vlan_input = input('Enter VLAN number: ')
mac_list = []
with open('CAM_table.txt') as f:
for line in f:
if line.count('Gi') == 1:
line = line.replace('DYNAMIC', '')
line = line.split()
mac_list.append(line)
for i in mac_list:
if i[0] == vlan_input:
print(i)
|
[
"[email protected]"
] | |
79fe6c6b4898f0cefa5a2ee82429b88161af2822
|
23f754a39b996ad3e50e539ac1ea88217545df8b
|
/app/models/host.py
|
830a796f3155ebb3a9e5d8f945ee6fd7cb954430
|
[] |
no_license
|
huhaiqng/YWSystemB
|
576b0310cfe49086eaafb99eaa83042621d6fab5
|
cf601fe4b97e96187e66a084a7e43a0cd259e92f
|
refs/heads/master
| 2022-12-11T06:19:46.025055 | 2021-04-27T07:48:46 | 2021-04-27T07:48:46 | 245,122,835 | 0 | 0 | null | 2022-12-08T11:57:56 | 2020-03-05T09:40:26 |
Python
|
UTF-8
|
Python
| false | false | 1,241 |
py
|
from django.db import models
from django.utils import timezone
# 主机
class Host(models.Model):
name = models.CharField('主机名', max_length=200)
ip = models.GenericIPAddressField('内网 IP')
outside_ip = models.GenericIPAddressField('外网 IP', default='0.0.0.0')
manage_port = models.IntegerField('管理端口号', default=22)
version = models.CharField('版本', max_length=200)
cpu = models.IntegerField('CPU 核数', default=4)
memory = models.CharField('内存大小', max_length=10, default='8G')
disk = models.CharField('硬盘大小', max_length=10, default='80G')
position = models.CharField('位置', max_length=200)
admin = models.CharField('系统管理员', max_length=200, default='root')
password = models.CharField('密码', max_length=200)
type = models.CharField('类别', max_length=200)
env = models.CharField('环境', max_length=200, default='test')
ins_num = models.IntegerField('实例数量', default='0')
status = models.BooleanField('状态', default=True)
created = models.DateTimeField('创建时间', default=timezone.now)
def __str__(self):
return self.ip
class Meta:
unique_together = ['ip', 'type', 'name']
|
[
"[email protected]"
] | |
ec125359dc7d22f74de7a96d3cb57b767553b680
|
9650b24ac61edb013cc6264263eea67fd967783b
|
/FlightPy.py
|
569f3e3603e6143c904bc714d73c69995d83288e
|
[] |
no_license
|
Delphae/flightaware
|
7e83dd11d9e714051070e15fd6815b3873510141
|
aa190221765015fcffae4e5b6ee92e1ef84cec49
|
refs/heads/master
| 2020-04-09T02:52:57.495731 | 2018-12-06T14:27:05 | 2018-12-06T14:27:05 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 5,395 |
py
|
#!/usr/bin/env python
# coding: utf-8
'''
____ _ _
| _ \ ___| |_ __ | |__ __ _ ___
| | | |/ _ \ | '_ \| '_ \ / _` |/ _ \
| |_| | __/ | |_) | | | | (_| | __/
|____/ \___|_| .__/|_| |_|\__,_|\___|
|_|
1.1 2018-11-30 initial version
1.2 2018-12-01 AirportBoards
1.3 2018-12-02 validate username & apikey
WeatherConditions
1.4 TailOwner
'''
VERSION = '1.4'
DATE = '2018-12-02'
import requests, json
from datetime import datetime as dt
def readjson(method, params, auth):
url = "https://flightxml.flightaware.com/json/FlightXML3/"
response = requests.get(url+method, params=params, auth=auth)
# print response.url
# print response.status_code
return response.json()
class FlightResult:
def __init__(self, result=dict()):
self.__dict__ = result
class FlightInfo:
def __init__(self, info=dict()):
self.__dict__ = info
self.AAT = dt.fromtimestamp(self.actual_arrival_time['epoch'])
self.EAT = dt.fromtimestamp(self.estimated_arrival_time['epoch'])
self.ADT = dt.fromtimestamp(self.actual_departure_time['epoch'])
self.EDT = dt.fromtimestamp(self.estimated_departure_time['epoch'])
self.origin_code = self.origin['code']
self.origin_airport = self.origin['airport_name']
self.destination_code = self.destination['code']
self.destination_airport = self.destination['airport_name']
def __repr__(self):
return self.faFlightID
def __str__(self):
try:
origincity = self.origin['city']
destincity = self.destination['city']
return '%-3s %-5s %5s %-25s %5s %s' % (self.airline_iata,
self.flightnumber,
str(self.ADT.time())[:-3],
origincity.encode('iso-8859-1'),
str(self.AAT.time())[:-3],
destincity.encode('iso-8859-1'))
except:
return ''
class FlightRoute:
def __init__(self, route=dict()):
self.__dict__ = route
self.last_departuretime = dt.fromtimestamp(self.last_departuretime)
def __repr__(self):
return self.route
class FlightAware(object):
__version__ = VERSION
__date__ = DATE
def __init__(self, username='', apikey=''):
if not username or not apikey:
conf = json.load(open('flightaware.json'))
username = conf['username']
apikey = conf['apikey']
self.auth = (username,apikey)
url = "https://flightxml.flightaware.com/json/FlightXML3/WeatherConditions"
params = {'airport_code':'AMS'}
response = requests.get(url, params=params, auth=self.auth)
print ('%s %s' % (response.status_code, response.reason))
def AirportInfo(self, airport):
result = readjson('AirportInfo', {'airport_code':airport}, self.auth)
return FlightResult(result['AirportInfoResult'])
def AirlineInfo(self, airline):
result = readjson('AirlineInfo', {'airline_code':airline}, self.auth)
return FlightResult (result['AirlineInfoResult'])
def FlightInfoStatus(self, ident):
result = readjson('FlightInfoStatus', {'ident':ident}, self.auth)
flightresults = result['FlightInfoStatusResult']['flights']
return [FlightInfo(flight) for flight in flightresults]
def RoutesBetweenAirports (self, origin, destination):
result = readjson('RoutesBetweenAirports', {'origin':origin, 'destination':destination}, self.auth)
routeresults = result['RoutesBetweenAirportsResult']['data']
return [FlightRoute(route) for route in routeresults]
def LatLongsToDistance (self, (lat1,lon1), (lat2,lon2)):
result = readjson('LatLongsToDistance', {'lat1':lat1, 'lon1':lon1, 'lat2':lat2, 'lon2':lon2}, self.auth)
self.miles = result['LatLongsToDistanceResult']
self.km = self.miles * 1.609344
return FlightResult({'miles':self.miles, 'km':self.km})
def AirportBoards(self, airport):
result = readjson('AirportBoards', {'airport_code':airport}, self.auth)
boards = result['AirportBoardsResult']
arrivals = [FlightInfo(flight) for flight in boards['arrivals']['flights']]
departures = [FlightInfo(flight) for flight in boards['departures']['flights']]
enroute = [FlightInfo(flight) for flight in boards['enroute']['flights']]
scheduled = [FlightInfo(flight) for flight in boards['scheduled']['flights']]
resultdict = dict(arrivals=arrivals,
departures=departures,
enroute=enroute,
scheduled=scheduled)
return FlightResult(resultdict)
def WeatherConditions(self, airport):
result = readjson('WeatherConditions', {'airport_code':airport}, self.auth)
conditionsresult = result['WeatherConditionsResult']['conditions']
return [FlightResult(condition) for condition in conditionsresult ]
def TailOwner(self, ident):
result = readjson('TailOwner', {'ident':ident}, self.auth)
return FlightResult(result['TailOwnerResult'])
|
[
"[email protected]"
] | |
d4aac1f8ce31e6e240ac03540de859ff1aadfa10
|
c768e0fad0fd7faa0e727ab650a95424bb5fe45a
|
/ltp_cloud.py
|
484445858099785be975d738b9884e590fd4cb06
|
[] |
no_license
|
shenwei0329/Irrigation-Proj
|
bcc2b2f8e6e0e9ef21ebe5d302f4fba051a84aae
|
98113cb7a71e49b3831d799ea28afba7c9b1c5e2
|
refs/heads/master
| 2020-11-30T07:20:12.628615 | 2019-12-30T08:13:24 | 2019-12-30T08:13:24 | 230,345,341 | 1 | 1 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,588 |
py
|
# -*- coding:UTF-8 -*-
#
import sys
from pyltp import Segmentor
from pyltp import Postagger
from pyltp import Parser
from pyltp import SentenceSplitter
from pyltp import NamedEntityRecognizer
import re
seg = Segmentor()
seg.load_with_lexicon('ltp_data_v3.4.0/cws.model', './ext_word_cloud')
post = Postagger()
post.load_with_lexicon('ltp_data_v3.4.0/pos.model', './ext_word_cloud_pos')
recognizer = NamedEntityRecognizer()
recognizer.load('ltp_data_v3.4.0/ner.model')
parser = Parser()
parser.load('ltp_data_v3.4.0/parser.model')
def segmentor(sentence):
"""
断词
:param sentence: 语句
:return: 词列表
"""""
global seg
words = seg.segment(sentence)
words_list = list(words)
return words_list
def postagger(words):
"""
获取词性
:param words: 词
:return: 词性
"""
global post
pos = post.postag(words)
pos_list = list(pos)
return pos_list
def hasSBV(arcs):
sbv = False
vob = False
hed = False
for nn in arcs:
if "SBV" in nn.relation:
sbv = True
if "VOB" in nn.relation:
vob = True
if "HED" in nn.relation:
hed = True
if (hed and sbv) or (vob and sbv):
return True
return False
"""获取文本文件"""
f = open(sys.argv[1])
while True:
"""读一段文本"""
sentence = f.readline()
if (sentence is "") or (len(sentence) == 0):
""" Eof """
break
"""断句"""
sents = SentenceSplitter.split(sentence)
for ss in sents:
"""语句处理"""
print ss
words = segmentor(ss)
# print words
"""
for _w in words:
print _w
"""
pos = postagger(words)
# print pos
_idx = 0
_s = ""
_cont = False
for _p in pos:
if "ws" in _p:
_s = words[_idx]
print _s,
if "n" in _p:
if not _cont:
_s = words[_idx]
_cont = True
else:
_s += words[_idx]
else:
_cont = False
if len(_s)>0:
print _s,
_s = ""
_idx += 1
print ""
"""
netags = list(recognizer.recognize(words, pos))
print netags
_idx = 0
for _n in netags:
if "Nh" in _n:
print words[_idx]
_idx += 1
"""
parser.release()
post.release()
seg.release()
print("Done.")
|
[
"[email protected]"
] | |
36310f74f8eaef7d3da7ef4f11b6d1c95f1ac4da
|
e52b9bbe2345d87b406caee4817a1770b45a6ae2
|
/scripts/BatchDefineProjection.py
|
453a2f66c343c7eb2ba97203805237bfffdc0e62
|
[] |
no_license
|
tomay/marxan_toolbox
|
8712b2f1d56e185156a884a0888f2c03b5ed20f5
|
a6e002cf47b8388660365de7ab2aee22b543c12a
|
refs/heads/master
| 2020-06-06T06:15:59.018433 | 2013-04-08T16:53:52 | 2013-04-08T16:53:52 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,542 |
py
|
import sys, os, arcgisscripting, traceback
def AddMsgAndPrint(message):
gp.AddMessage(message)
print message
return 0
gp = arcgisscripting.create()
gp.overwriteoutput = 1
try:
# Get the parameters.
gp.workspace = sys.argv[1]
pattern = sys.argv[3]
sr = sys.argv[2]
#AddMsgAndPrint(pattern) ## TO DO: NOT WORKING
#gp.workspace = r"C:\atom\python\shapes\test_shapes" ## TO DO: NOT WORKING
# MANUAL FIX WORKS
## WHAT? IS IT BECAUSE sys.argv[3] and [2] were reversed? I fixed above 7/29/2011
pattern = "*.shp"
sr = "PROJCS['laborde_wcs_2000',GEOGCS['GCS_Tananarive_1925',DATUM['D_Tananarive_1925',SPHEROID['International_1924',6378388.0,297.0]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Hotine_Oblique_Mercator_Azimuth_Natural_Origin'],PARAMETER['False_Easting',1113136.3146],PARAMETER['False_Northing',2882900.7279],PARAMETER['Scale_Factor',0.9995],PARAMETER['Azimuth',18.9],PARAMETER['Longitude_Of_Center',46.43722917],PARAMETER['Latitude_Of_Center',-18.9],UNIT['Meter',1.0]]"
# find only specified features or datasets
if pattern == "*.shp":
datasets = gp.ListFeatureClasses("", "POLYGON")
elif pattern == "RASTER":
datasets = gp.ListDatasets("", "RASTER")
datasets.Reset()
dataset = datasets.next()
z = 0
while dataset:
z = z + 1
dataset = datasets.next()
i = 1
datasets.Reset()
dataset = datasets.next()
while dataset:
AddMsgAndPrint("Defining: " + dataset + ". (" + str(i) + " of " + str(z) + ")")
gp.defineprojection_management(dataset,sr)
i = i + 1
dataset = datasets.next()
AddMsgAndPrint("Done")
except:
# get the traceback object
tb = sys.exc_info()[2]
# tbinfo contains the line number that the code failed on and the code from that line
tbinfo = traceback.format_tb(tb)[0]
# concatenate information together concerning the error into a message string
pymsg = "PYTHON ERRORS:\nTraceback Info:\n" + tbinfo + "\nError Info:\n " + \
str(sys.exc_type)+ ": " + str(sys.exc_value) + "\n"
# generate a message string for any geoprocessing tool errors
msgs = "GP ERRORS:\n" + gp.GetMessages(2) + "\n"
# return gp messages for use with a script tool
gp.AddError(msgs)
gp.AddError(pymsg)
# print messages for use in Python/PythonWin
print msgs
print pymsg
print "done"
|
[
"[email protected]"
] | |
eb388016f65246c4c31124d34d29159a438dc564
|
3d7039903da398ae128e43c7d8c9662fda77fbdf
|
/database/CSS/juejin_1927.py
|
4cf0fa0be8441db15c31d26e93685b0b19eb0256
|
[] |
no_license
|
ChenYongChang1/spider_study
|
a9aa22e6ed986193bf546bb567712876c7be5e15
|
fe5fbc1a5562ff19c70351303997d3df3af690db
|
refs/heads/master
| 2023-08-05T10:43:11.019178 | 2021-09-18T01:30:22 | 2021-09-18T01:30:22 | 406,727,214 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 78,514 |
py
|
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false}, "org": {"org_info": {"org_type": 1, "org_id": "6930489296285597696", "online_version_id": 6937212594310610981, "latest_version_id": 6937212594310610981, "power": 10141, "ctime": 1613630284, "mtime": 1631692819, "audit_status": 2, "status": 0, "org_version": {"version_id": "6937212594310610981", "icon": "https://p6-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/9763b1fa556f4cbd8ced21b60d3ed40c~tplv-k3u1fbpfcp-watermark.image", "background": "https://p3-juejin.byteimg.com/tos-cn-i-k3u1fbpfcp/2254bf401c3444129f8e3612c4b16308~tplv-k3u1fbpfcp-watermark.image", "name": "掘金翻译计划", "introduction": "# 掘金翻译计划\n\n\n[掘金翻译计划](https://juejin.im/tag/%E6%8E%98%E9%87%91%E7%BF%BB%E8%AF%91%E8%AE%A1%E5%88%92) 是一个翻译优质互联网技术文章的社区，文章来源为 [掘金](https://juejin.im) 上的英文分享文章。内容覆盖[区块链](#区块链)、[人工智能](#ai--deep-learning--machine-learning)、[Android](#android)、[iOS](#ios)、[前端](#前端)、[后端](#后端)、[设计](#设计)、[产品](#产品)、[算法](https://github.com/xitu/gold-miner/blob/master/algorithm.md)和[其他](#其他)等领域，以及各大型优质 [官方文档及手册](#官方文档及手册)，读者为热爱新技术的新锐开发者。\n\n掘金翻译计划目前翻译完成 [2027](#近期文章列表) 余篇文章，官方文档及手册 [13](#官方文档及手册) 个，共有 [1000](https://github.com/xitu/gold-miner/wiki/%E8%AF%91%E8%80%85%E7%A7%AF%E5%88%86%E8%A1%A8) 余名译者贡献翻译和校对。\n\n# 官方指南\n\n[**推荐优质英文文章到掘金翻译计划**](https://github.com/xitu/gold-miner/issues/new/choose)\n\n<!--\nhttps://github.com/xitu/gold-miner/issues/new?title=推荐优秀英文文章&body=-%20原文链接：推荐文章前%20Google%20一下，尽量保证本文未被翻译%0A-%20简要介绍：介绍一下好不好啦，毕竟小编也看不太懂哎_(:з」∠)_)\n-->\n\n### 翻译计划译者教程\n\n1. [如何参与翻译](https://github.com/xitu/gold-miner/wiki/%E5%A6%82%E4%BD%95%E5%8F%82%E4%B8%8E%E7%BF%BB%E8%AF%91)\n2. [关于如何提交翻译以及后续更新的教程](https://github.com/xitu/gold-miner/wiki/%E5%85%B3%E4%BA%8E%E5%A6%82%E4%BD%95%E6%8F%90%E4%BA%A4%E7%BF%BB%E8%AF%91%E4%BB%A5%E5%8F%8A%E5%90%8E%E7%BB%AD%E6%9B%B4%E6%96%B0%E7%9A%84%E6%95%99%E7%A8%8B)\n3. [如何参与校对及校对的正确姿势](https://github.com/xitu/gold-miner/wiki/%E5%8F%82%E4%B8%8E%E6%A0%A1%E5%AF%B9%E7%9A%84%E6%AD%A3%E7%A1%AE%E5%A7%BF%E5%8A%BF)\n4. [文章分享到掘金指南](https://github.com/xitu/gold-miner/wiki/%E5%88%86%E4%BA%AB%E5%88%B0%E6%8E%98%E9%87%91%E6%8C%87%E5%8D%97)\n5. [译文排版规则指北](https://github.com/xitu/gold-miner/wiki/%E8%AF%91%E6%96%87%E6%8E%92%E7%89%88%E8%A7%84%E5%88%99%E6%8C%87%E5%8C%97)\n6.[积分兑换：小礼物列表](https://github.com/xitu/gold-miner/wiki/%E7%A7%AF%E5%88%86%E5%85%91%E6%8D%A2)\n\n\n\n\n", "weibo_link": "", "github_link": "https://github.com/xitu/gold-miner", "homepage_link": "", "ctime": 1615486318, "mtime": 1615486318, "org_id": "6930489296285597696", "brief_introduction": "一个帮助开发者成长的社区", "introduction_preview": "掘金翻译计划\n掘金翻译计划 是一个翻译优质互联网技术文章的社区，文章来源为 掘金 上的英文分享文章。内容覆盖区块链、人工智能、Android、iOS、前端、后端、设计、产品、算法和其他等领域，以及各大型优质 官方文档及手册，读者为热爱新技术的新锐开发者。\n掘金翻译计划目前翻译完成 2027 余篇文章，官方文档及手册 13 个，共有 1000 余名译者贡献翻译和校对。\n官方指南\n推荐优质英文文章到掘金翻译计划\n翻译计划译者教程\n\n如何参与翻译\n关于如何提交翻译以及后续更新的教程\n如何参与校对及校对的正确姿势\n文章分享到掘金指南\n译文排版规则指北\n6.积分兑换：小礼物列表\n"}, "follower_count": 1080, "article_view_count": 504149, "article_digg_count": 5100}, "org_user": null, "is_followed": false}, "req_id": "2021091516045801020402403015006738"}, {"article_id": "6844903542549905416", "article_info": 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"[email protected]"
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/Practice/24/Python/24.py
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DONR69/Programming
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9817aecf2708836c3e3d909bb922058eddbff5a1
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6c91e33c5da5d931deb4f54d3bca51bfe5cd8082
|
refs/heads/main
| 2023-04-03T12:28:24.336901 | 2021-04-19T05:37:35 | 2021-04-19T05:37:35 | 334,436,608 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 738 |
py
|
import json
from array import *
c=0
p=0
m=list('')
x='a'
with open('in.json','r') as f:
a=f.read()
data=json.loads(a)
h=dict(data[0])
k=h['userId']
for l in range(len(data)):
h=dict(data[l])
if (k==h['userId']):
if (h['completed']==1):
c+=1
else:
d={'userId':k,
'task_completed':c
}
m.append(x)
m[p]=d
p+=1
c=0
k=h['userId']
if (h['completed']==1):
c+=1
d={'userId':k,
'task_completed':c
}
m.append(x)
m[p]=d
print(m)
with open ('out.json','w') as b:
b=json.dump(m,b,indent=3)
|
[
"[email protected]"
] | |
8894a20e0f77e0e21c8eacd3308da64ff355e0b3
|
d0ad48ac376a7b8e54291ac3f920b90f43f4ab72
|
/apps/index/views.py
|
5913312194dfe756342f96eeaa63a5b6ed14624d
|
[] |
no_license
|
LceRain/torna
|
a38d6f1ba2caf8ce8271fd52c2bcb28213dc3185
|
057d1cfdee9efa83ae6724f1c0fc3f2526811590
|
refs/heads/master
| 2022-11-06T06:28:47.891326 | 2020-06-17T10:19:16 | 2020-06-17T10:19:16 | 272,942,780 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 867 |
py
|
import json
import time
from json import JSONEncoder
import tornado.web
from abase.baseResponse.baseresponse import JsonResponse
from abase.baseorm._sqlalchemy.basesqlal import session
from abase.baseorm.models.basemodels import Model
from apps.index.models import Person
class A(JSONEncoder):
def default(self, o):
if isinstance(o, Model):
dic = o.__dict__
print(dic)
dic.pop("_sa_instance_state")
return dic
class IndextHandler(tornado.web.RequestHandler):
async def get(self):
persons = Person.objects.filter(Person.name == 'jerry').all()
print(persons)
# p = Person(name='jerry', age=13)
# session.add(p)
# session.commit()
jso = {'data': persons}
print(jso)
self.write(json.dumps(jso, cls=A))
|
[
"[email protected]"
] | |
7c9d4bb9050e33900812f41157440397e1ba71db
|
f7f429aa2425049a7c787b2be0cf5ec431371988
|
/coding_problems/feb/feb8.py
|
1d202038978fd67c3e31a22427b512ae0a7a0f64
|
[] |
no_license
|
happy96026/interview-prep
|
7fbef8642ff7e4f8746403c2a18eae8b173b64a2
|
5b8e974b9541a80dbb9e15055d76f78cd957637f
|
refs/heads/master
| 2021-12-28T17:26:01.347919 | 2020-07-26T07:10:59 | 2020-07-26T07:10:59 | 246,656,096 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 305 |
py
|
class Solution:
def lengthOfLongestString(self, s):
d = {}
i = 0
max_len = 0
for j, c in enumerate(s):
if c in d:
i = max(i, d[c] + 1)
d[c] = j
max_len = max(max_len, j - i + 1)
return max_len
print(Solution().lengthOfLongestString('abrkaabcdefghijjxxx'))
|
[
"[email protected]"
] | |
88d706f4905e832162170794813647dc558628f1
|
7b2295bf19163e65449e502636ca1aa5224f3289
|
/orders/migrations/0008_auto__add_field_order_customer.py
|
2edb97ff3edfa727c860670db256fc40a3f2a9f3
|
[] |
no_license
|
renata-ms/control_panel
|
7e22d4b627942dac7e0b61b33220eef8e15a5cec
|
746a6bbc62e09b8c1693893a5792c186ee69355d
|
refs/heads/master
| 2016-08-04T11:31:18.564018 | 2013-11-30T07:23:28 | 2013-11-30T07:23:28 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,447 |
py
|
# -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding field 'Order.customer'
db.add_column(u'orders_order', 'customer',
self.gf('django.db.models.fields.related.ForeignKey')(default=1, to=orm['orders.Customer']),
keep_default=False)
def backwards(self, orm):
# Deleting field 'Order.customer'
db.delete_column(u'orders_order', 'customer_id')
models = {
u'library.author': {
'Meta': {'object_name': 'Author'},
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'null': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '32'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '32'})
},
u'library.book': {
'Meta': {'object_name': 'Book'},
'authors': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['library.Author']", 'symmetrical': 'False'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'publication_date': ('django.db.models.fields.DateField', [], {'default': 'datetime.datetime(2013, 11, 8, 0, 0)'}),
'publisher': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['library.Publisher']"}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '128'})
},
u'library.publisher': {
'Meta': {'object_name': 'Publisher'},
'address': ('django.db.models.fields.TextField', [], {}),
'city': ('django.db.models.fields.CharField', [], {'max_length': '64'}),
'country': ('django.db.models.fields.CharField', [], {'max_length': '64'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '32'}),
'website': ('django.db.models.fields.URLField', [], {'max_length': '32'})
},
u'orders.customer': {
'Meta': {'object_name': 'Customer'},
'address': ('django.db.models.fields.TextField', [], {}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'null': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '32'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_approved': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '32'})
},
u'orders.order': {
'Meta': {'object_name': 'Order'},
'created': ('django.db.models.fields.DateField', [], {'default': 'datetime.datetime(2013, 11, 8, 0, 0)'}),
'customer': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['orders.Customer']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'itemld': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['library.Book']"})
}
}
complete_apps = ['orders']
|
[
"[email protected]"
] | |
b42d9be8b182ddbef90e8894bb504742aed40a34
|
4fc407dd0763ae41002dc74de68603b576e5cc51
|
/extractor/extractor.py
|
722299420d5da5bb8af80c6a1f404a5568e50a23
|
[
"MIT"
] |
permissive
|
tenda-cn/icesat2webview
|
331e162e3d0b20b439ee416a2ebca2a16157170c
|
c310986ceb8efb1f3a9937ca6b9eb29b020cb0ec
|
refs/heads/main
| 2023-02-10T19:20:31.584381 | 2021-01-10T02:16:23 | 2021-01-10T02:16:23 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 6,584 |
py
|
#!/usr/bin/python3
"""
syntax: db-path -[fd] [file-1.h5 [file-2.h5 [...]]]
-f forces
-d add debug info
the h5 files will be all parsed and the relevant data extracted into tiles at zoomlevel 11,
the tiles are saved in the db path
tile format:
each tile consists of a simple csv file
"""
import gzip
import sys
import h5py
import numpy as np
import os
import re
import datetime
import math
CHANNELS = ['1l', '1r','2l', '2r','3l', '3r']
#CHANNELS =['1l']
ZOOM_LEVEL=11
tilesStore= {}
coordsCnt=0
# https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames
def coords2tilexy(lat_deg, lon_deg, zoom):
lat_rad = math.radians(lat_deg)
n = 2.0 ** zoom
xtile = int((lon_deg + 180.0) / 360.0 * n)
ytile = int((1.0 - math.asinh(math.tan(lat_rad)) / math.pi) / 2.0 * n)
return (xtile, ytile)
def recordPoint(filename,channel,rgt,time,lat,lon,terrain,canopy,direction):
global coordsCnt
key=coords2tilexy(lat,lon,ZOOM_LEVEL)
payload=filename+";"+channel+";"+str(rgt)+";"+str(round(time,3))+";"+str(lat)+";"+str(lon)+";"+str(terrain)+";"+str(canopy)+";"+str(direction)
#print("=="+payload)
tilesStore.setdefault(key, []).append(payload)
coordsCnt=coordsCnt+1
def dumpAll(prefix,group,maxRow):
for key in group.keys():
is_dataset = isinstance(group[key], h5py.Dataset)
#print(key+": "+str(is_dataset))
if (not is_dataset):
dumpAll(prefix+"/"+key,group[key],maxRow)
else:
for i in range(min(maxRow,len(group[key]))) :
print(prefix+"/"+key+"["+str(i)+"]:"+str(group[key][i]))
def processFile(filename,addDebugInfo):
print("processFile "+filename)
f = h5py.File(filename)
#dumpAll("", f,3306)
#return
#list(f.keys())
#['METADATA', 'ancillary_data', 'ds_geosegments', 'ds_metrics', 'ds_surf_type', 'gt1l', 'gt1r', 'gt2l', 'gt2r', 'gt3l', 'gt3r', 'orbit_info', 'quality_assessment']
debugInfo=""
if (addDebugInfo):
debugInfo=os.path.basename(filename)
for channel in CHANNELS:
ancillary_data=f['/ancillary_data']
orbit_info=f['/orbit_info']
#dump('/orbit_info',orbit_info,0)
print(filename+":"+" reading channel:"+channel)
land_segments=f['/gt'+channel+'/land_segments']
terrain=f['/gt'+channel+'/land_segments/terrain']
canopy=f['/gt'+channel+'/land_segments/canopy']
signal_photons=f['/gt'+channel+'/signal_photons']
#print("ancillary_data" ,ancillary_data.keys())
##start_delta_time=ancillary_data['start_delta_time'][0]
##print("start_delta_time:",start_delta_time)
##print("land_segments" ,land_segments.keys())
##print("terrain" ,terrain.keys())
##print("canopy" ,canopy.keys())
# previous latitude - 999 means uninitialized
plat=-999
for i, x in enumerate(zip(
land_segments['rgt'],
land_segments['delta_time'],
land_segments['latitude'],
land_segments['longitude'],
terrain['h_te_best_fit'],
#canopy['canopy_flag'],
canopy['h_canopy'],
signal_photons['ph_segment_id'],
signal_photons['classed_pc_indx']
)):
if (x[5]<1000):
#print("#"+str(i))
lat=x[2]
#dump("signal_photons",signal_photons,i)
#dump("land_segments",land_segments,i)
#dump("canopy",canopy,i)
#dump("terrain",terrain,i)
# hacky detection of rgt direction
if (not (plat == -999) and plat>lat):
direction='s'
else:
direction='n'
recordPoint(debugInfo,channel,x[0],x[1],lat,x[3],x[4],x[5],direction)
#print("ph_segment_id:",str(x[6]))
#print("classed_pc_indx:",str(x[7]))
#return
plat=lat
#
# empty the store
#
def resetStore():
global tilesStore
tilesStore={}
global coordsCnt
coordsCnt=0
#
# store the store :)
#
def saveStore(storePath):
for tile in tilesStore:
# print(tile)
# tile[0]
latDir=storePath+"/"+str(ZOOM_LEVEL)+"/"+str(tile[0])
os.makedirs(latDir, exist_ok=True)
tileCsv=latDir+"/"+str(tile[1])+".csv"
tileCsvGz=tileCsv+".gz"
# print(tileCsv+" has "+str(len(tilesStore[tile]))+" records")
if (os.path.exists(tileCsvGz)):
with gzip.open(tileCsvGz, 'rt') as fin:
tileCsvText = fin.read()
else:
tileCsvText=""
#if (tile[0]==1103 and tile[1]==694):
# print("====================================")
# print("tileCsvGz:"+tileCsvGz)
# print("tileCsvText:"+tileCsvText)
# print("new lines:"+tileCsvText)
# for line in tilesStore[tile]:
# print(line)
# print("====================================")
# BRITTLE
with gzip.open(tileCsvGz, 'wt') as fout:
fout.write(tileCsvText)
for line in tilesStore[tile]:
print(line,file=fout)
#-- main --
def main(storePath,options,granules):
addDebugInfo="d" in options
force="f" in options
srcs={}
# open or create src.txt (the list of already processed h5 files)
if (not os.path.exists(storePath)):
print("new tiles db will be created: "+storePath)
os.mkdir(storePath)
open(storePath+"/src.txt", 'a').close()
else:
print("found existing tiles db: "+storePath)
with open(storePath+"/src.txt") as f:
for line in f.read().splitlines():
tokens=re.split(';',line)
srcs[tokens[0]]=tokens[1:]
print(tokens[0]+";"+line)
print("channels:"+str(CHANNELS))
# process each h5 file
for filename in granules:
if (not os.path.exists(filename)):
print(filename+": does not exists")
elif (not force and os.path.basename(filename) in srcs):
print(filename+":already loaded")
else :
resetStore()
processFile(filename,addDebugInfo)
saveStore(storePath)
srcInfoLine=datetime.datetime.now().replace(microsecond=0).isoformat()+";"+str(coordsCnt)+" records in "+str(len(tilesStore))+" tiles "
print(os.path.basename(filename)+":"+srcInfoLine)
with open(storePath+"/src.txt", 'a+') as out:
print(os.path.basename(filename)+";"+srcInfoLine,file=out)
# main
if (not sys.argv[2].startswith("-")):
print ("2nd param must start with dash")
exit(1)
main ( storePath=sys.argv[1], options=sys.argv[2],granules=sys.argv[3:])
|
[
"[email protected]"
] | |
187f0791b1a01c11df6c79633e0e5dd286b10dd7
|
e644c0a10c8d6b339106a34b67a393b57e038093
|
/source_code/data_cleaning.py
|
97ca7fe342c83ed2051bee3f6cee18cc09fc8195
|
[] |
no_license
|
hvijay3/User-Review-Based-New-Business-Affinity-Prediction-System
|
f8c7c57b5d6e2e2e034b24b6435ad9b53164a973
|
77f74fe7f39f9e0e6263eb1752d3047f42889090
|
refs/heads/master
| 2021-05-13T15:24:47.418392 | 2018-01-09T04:55:18 | 2018-01-09T04:55:18 | 116,767,593 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 4,021 |
py
|
import pandas as pd
import pandasql as ps
sentiment1 = pd.read_csv('AllFoodBusiness_Features&Sentiments.csv')
sentiment = ps.sqldf("""select
business_id,
round(sentimental_rating,0) as sentimental_rating
from sentiment1""", locals())
sentiment.describe()
sentiment.head()
business = pd.read_csv('business.csv')
business.rename(columns = {'stars':'business_stars'}, inplace = True)
business.head()
business.describe()
business.columns
####### REVIEW
review = pd.read_csv('review.csv', iterator=True, chunksize=500)
review = pd.concat(review, ignore_index =True)
review.describe()
review.columns
review.head()
business.head()
business_eateries = business[(business['categories'].str.contains(pat = 'Restaurants',na=False)) |
(business['categories'].str.contains(pat = 'Lounges',na=False)) |
(business['categories'].str.contains(pat = 'Nightlife',na=False)) |
(business['categories'].str.contains(pat = 'Bars',na=False)) |
(business['categories'].str.contains(pat = 'Food',na=False)) |
(business['categories'].str.contains(pat = 'Coffee&Tea',na=False))|
(business['categories'].str.contains(pat = 'Bakeries',na=False))|
(business['categories'].str.contains(pat = 'Pubs',na=False))|
(business['categories'].str.contains(pat = 'Pizza',na=False))]
business_eateries.describe()
business_eateries.columns
business_eateries.head()
cols = business_eateries.columns
cols = cols.map(lambda x: x.replace('.', '_'))
business_eateries.columns = cols
# write business_eateries csv
business_eateries.to_csv('Businesses_Eateries.csv')
business_eateries.head()
business_eateries_sentiment = pd.merge(business_eateries, sentiment, on = 'business_id')
business_eateries_sentiment.to_csv('Business_Eateries_Sentiment.csv')
business_review = pd.merge(business, review, on = 'business_id')
review_grouped = business_review.groupby(['city' , 'business_id'], as_index=False).mean()
data = review_grouped[['business_id','stars']]
review_eateries = data.apply(lambda x: x)
review_eateries.rename(columns={'stars': 'review_avg_stars'}, inplace=True)
review_eateries.head()
business_reviews_eateries = pd.merge(business_eateries, review_eateries, on = 'business_id')
business_reviews_eateries.head()
print(business_reviews_eateries.columns)
business_relevant_review_eateries = business_reviews_eateries[['business_id','latitude', 'longitude','review_count'
,'business_stars','review_avg_stars','attributes_RestaurantsPriceRange2','attributes_BusinessAcceptsCreditCards','attributes_RestaurantsTakeOut','attributes_RestaurantsDelivery',
'attributes_WheelchairAccessible','attributes_GoodForMeal_breakfast','attributes_GoodForMeal_latenight','attributes_GoodForMeal_dessert','attributes_GoodForMeal_lunch',
'attributes_GoodForMeal_brunch','attributes_RestaurantsReservations','attributes_BusinessParking_validated','attributes_BusinessParking_valet','attributes_BusinessParking_lot','attributes_BusinessParking_garage',
'attributes_BusinessParking_street','attributes_BikeParking','state','city','name','attributes_GoodForKids','attributes_RestaurantsGoodForGroups','attributes_Ambience_trendy','attributes_Ambience_casual','attributes_Ambience_classy','attributes_Ambience_touristy','attributes_Ambience_intimate'
,'attributes_Ambience_hipster']]
business_relevant_review_eateries.head()
business_relevant_review_eateries_sentiment = pd.merge(business_relevant_review_eateries, sentiment, on = 'business_id')
business_relevant_review_eateries_sentiment.to_csv('Model_Input.csv')
business_relevant_review_eateries_sentiment.head()
|
[
"[email protected]"
] | |
c8a58abf83afbf6366b65b7dc1ee8f6a5d6ef831
|
24ffbd64e1892ab633ca785e969ccef43f17a9f2
|
/picomotor/devices/h2_yr.py
|
efa1098cd7f197e7875e4fee3720cf40bfa6fb58
|
[] |
no_license
|
yesrgang/labrad_tools.srq
|
e29fcbfc4f5228955de1faddab6a66df52ccdd03
|
0dfbf2609d2f7a7e499167decedb0d9ea3677978
|
refs/heads/master
| 2021-06-18T19:59:21.448762 | 2021-02-04T22:03:49 | 2021-02-04T22:03:49 | 155,478,765 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 155 |
py
|
from picomotor.devices.nf8742.device import NF8742
class Motor(NF8742):
socket_address = ('192.168.1.20', 23)
controller_axis = 4
Device = Motor
|
[
"[email protected]"
] | |
7bae5ccf98dd1173f76aac2b3c90247bbe4be0fd
|
b74a22c9e6da60a5085104d9ab9252d61a5d5dfe
|
/app.py
|
4e0c4b1cca9fcd5459600f681dd688c97730af71
|
[] |
no_license
|
VinayKatare/Sab-Pool-Karo
|
1413a759eb00f7c105d78b1874e2c383390489b5
|
6a4c2b26a42c8d9d92e207c687823dc059ab1ba0
|
refs/heads/master
| 2020-04-20T00:46:38.983792 | 2019-04-10T18:38:46 | 2019-04-10T18:38:46 | 168,529,166 | 1 | 2 | null | null | null | null |
UTF-8
|
Python
| false | false | 5,153 |
py
|
from flask import Flask,render_template,request,url_for,redirect,session,flash
from db import *
from passlib.hash import sha256_crypt
from functools import wraps
app = Flask(__name__)
app.secret_key = b'_5#y2L"F4Q8z\n\xec]/'
global temp
temp = False
conn = connectDB()
cursor = conn.cursor(dictionary=True)
def login_required(f):
@wraps(f)
def decorated_function(*args, **kwargs):
if 'logged_in' is session:
return f(*args, **kwargs)
else:
return 'cannot access'
return decorated_function
@app.route("/")
def menu():
return render_template("search.html",flag=temp)
@app.route("/signup", methods = ['POST','GET'])
def signup():
if request.method == 'POST':
userdetails=request.form
email = userdetails['email']
name = userdetails['name']
password = userdetails['password']
confirmPassword = userdetails['confirmPassword']
gender = userdetails['gender']
car = userdetails['car']
mobile = userdetails['mobile']
govtid = userdetails['govtid']
if password!=confirmPassword:
return "not matched"
pwd = sha256_crypt.encrypt(str(password))
cursor.execute('insert into users values(null,%s,%s,%s,%s,%s,%s,%s)',(pwd,name,gender,mobile,email,car,govtid))
conn.commit()
return redirect(url_for('menu'))
return render_template("signup.html",flag=temp)
@app.route("/login",methods = ['POST','GET'])
def login():
if request.method == 'POST':
userdetails = request.form
userid = userdetails['email']
passwed = userdetails['password']
cursor.execute('select * from users where email = %s', (userid,))
res = cursor.fetchone()
#print(res)
if res is None:
return ("invalid user name")
else:
pwd= res['pwd']
if sha256_crypt.verify(passwed,pwd):
session['logged_in']=True
global temp
temp= True
#print("this is ",temp)
session['userid']=int(res['userid'])
#return 'You are now logged in','success'
return redirect(url_for('menu'))
else:
return "invalid password"
return render_template("login.html",flag=temp)
@app.route("/searchresult")
def searchresult():
src = request.args.get('source')
dest = request.args.get('destination')
#cursor.execute('select * from pool')
#res=cursor.fetchall()
cursor.execute('select * from pool p,users u, dest d, src s where p.userid= u.userid and p.src=s.srcid and p.dest=d.destid and p.src=%s and p.dest=%s',(src,dest))
res = cursor.fetchall()
print(res)
return render_template("searchresult.html",rows=res,l=len(res),flag=temp)
@app.route("/createpool",methods = ['POST','GET'])
def createpool():
if request.method == 'POST':
userdetails=request.form
userid = session['userid']
source = userdetails['source']
destination = userdetails['destination']
vacancy = userdetails['vacancy']
time = userdetails['time']
cost = userdetails['cost']
print("this is ",time)
cursor.execute('insert into pool values(%s,null,%s,%s,%s,%s,%s)',(userid,source,destination,vacancy,time,cost))
conn.commit()
return redirect(url_for('menu'))
return render_template("createpool.html",flag=temp)
# requestpool
@app.route("/requestpool",methods = ['POST','GET'])
def requestpool():
if request.method == 'GET':
poolid = request.args.get('poolid')
return render_template("requestpool.html", poolid=poolid)
else:
if not session['logged_in']:
return redirect(url_for('menu'))
userid = session['userid']
status='Requested'
userdetails = request.form
src=userdetails['source']
dest=userdetails['destination']
poolid = userdetails['poolid']
cursor.execute('insert into joinpool values(null,%s,%s,%s,%s,%s)',(userid, poolid, status, src, dest))
conn.commit()
return "Success"
#return redirect(url_for('menu'))
@app.route("/mypools",methods = ['POST','GET'])
def mypools():
if request.method == 'GET':
userid = session['userid']
cursor.execute('select * from users u,joinpool j, pool p where j.userid=%s and p.poolid=j.poolid and j.userid= u.userid', (userid,))
res = cursor.fetchall()
#print(res, len(res))
req=[]
accreq=[]
rejreq=[]
for i in range(len(res)):
if res[i]['status'] == 'Requested':
req.append(res[i])
elif res[i]['status'] == 'Accepted':
accreq.append(res[i])
else:
rejreq.append(res[i])
return render_template("poolrequest.html",req=req,accreq=accreq,flag=temp,rejreq=rejreq,l=len(req),l1=len(accreq),l2=len(rejreq))
@app.route("/logout")
def logout():
global temp
temp= False
session.clear()
return redirect(url_for('menu'))
if __name__ == '__main__':
app.run(debug=True)
|
[
"[email protected]"
] | |
40397ccb95d1078ee9372f43fc59a3eb63a11174
|
e868cd3e0eb56ac8b4aa4290510b2d393ae8eaac
|
/relations-finder/tests/wikipedia_data_fetcher_test.py
|
fd2b8319b464c1010f09f9590af94aa2a70c661f
|
[
"MIT"
] |
permissive
|
trisongz/wiki-relations
|
2ba0c24deb0d804633dbe1796443fbeabd36b5a3
|
9f8d20c512f993cab6065cb2695c996c076b6d13
|
refs/heads/main
| 2023-03-24T22:08:29.722531 | 2021-03-13T17:49:13 | 2021-03-13T17:49:13 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,180 |
py
|
import os
import sys
from unittest import TestCase
from unittest.mock import patch
import wikipedia
sys.path.append(os.path.abspath('..'))
from wikipedia_data_fetcher import Wikipedia_data_fetcher
class Test_wikipedia_data_fetcher(TestCase):
def setUp(self):
self.fetcher = Wikipedia_data_fetcher()
@patch('wikipedia_data_fetcher.wikipedia.page')
@patch.object(Wikipedia_data_fetcher, 'chunk_page_content')
def test_that_get_article_returns_correct_result(self, mock_chunk_page_content,
mock_wikipedia_page):
url = 'fake url'
title = 'fake title'
content = 'fake content'
class Object:
def __init__(self):
self.url = url
self.title = title
mock_wikipedia_page.return_value = Object()
mock_chunk_page_content.return_value = content
expected = {'name': title, 'url': url, 'content_chunks': content}
actual = self.fetcher.get_article('')
self.assertDictEqual(expected, actual)
@patch('wikipedia_data_fetcher.wikipedia.page')
def test_that_get_article_returns_empty_dict_on_page_error(self, mock_wikipedia_page):
page_error = wikipedia.exceptions.PageError('fake page id')
mock_wikipedia_page.side_effect = page_error
expected = {}
actual = self.fetcher.get_article('')
self.assertDictEqual(expected, actual)
def test_that_get_section_titles_strips_title_names_from_text(self):
content = '\n\n\n= First Title =\n text...\n\n\n== Second Title '\
'==\n\n more text...\n=== Sub Title ===\n finish.'
expected = ['First Title', 'Second Title', 'Sub Title']
actual = self.fetcher.get_sections_titels(content)
self.assertListEqual(expected, actual)
def test_that_filter_sections_removes_unwanted_sections(self):
sections = ['Foo', 'Publications', 'References', 'External links', 'Further reading', 'Bar']
sections_to_remove = {'Publications', 'References', 'External links', 'Further reading'}
expected = ['Foo', 'Bar']
actual = self.fetcher.filter_sections(sections)
self.assertListEqual(expected, actual)
def test_that_clean_text_removes_quotes_and_newlines(self):
text = 'Some text\n"quoted text"\nend.'
expected = 'Some text quoted text end.'
actual = self.fetcher.clean_text(text)
self.assertEqual(expected, actual)
def test_that_chunk_page_content_returns_list_with_sections_contents(self):
summary = 'summary'
content = '\n= Foo =\nFirst chunk\n\n== Bar ==\nSecond chunk.'
expected = ['summary', 'First chunk', 'Second chunk.']
class Object:
def __init__(self):
self.summary = summary
self.content = content
def section(self, title):
if title == 'Foo':
return 'First chunk'
elif title == 'Bar':
return 'Second chunk.'
actual = self.fetcher.chunk_page_content(Object())
self.assertEqual(expected, actual)
|
[
"[email protected]"
] | |
36cc3d83758e405e7b09ba3ceb504ad2feb85195
|
3282ccae547452b96c4409e6b5a447f34b8fdf64
|
/SimModel_Python_API/simmodel_swig/Release/ObtainPipeInfo.py
|
05eb5c5394e33c9bacfea57f6770abb9744c6491
|
[
"MIT"
] |
permissive
|
EnEff-BIM/EnEffBIM-Framework
|
c8bde8178bb9ed7d5e3e5cdf6d469a009bcb52de
|
6328d39b498dc4065a60b5cc9370b8c2a9a1cddf
|
refs/heads/master
| 2021-01-18T00:16:06.546875 | 2017-04-18T08:03:40 | 2017-04-18T08:03:40 | 28,960,534 | 3 | 0 | null | 2017-04-18T08:03:40 | 2015-01-08T10:19:18 |
C++
|
UTF-8
|
Python
| false | false | 9,971 |
py
|
import os
import networkx as nx
# load SimModel hierarchy
import SimModel_Hierachy
# load SimModel mapped data
import SimModel_Mapping
# load SimModel translation engine
import SimModel_Translator
# load the SimModel classes you wanna access their properties,
# e.g., if you need to access a class named A, then import A
# as shown in the following code
import SimProject_Project_DesignAlternative
import SimSite_BuildingSite_Default
import SimBuilding_Building_Default
import SimBuildingStory_BuildingStory_Default
import SimGroup_SpatialZoneGroup_ZoneGroup
import SimGroup_SpatialZoneGroup_ZoneHvacGroup
import SimSpatialZone_ThermalZone_Default
import SimSpace_Occupied_Default
import SimList_EquipmentList_ZoneHvac
import SimSpaceBoundary_SecondLevel_SubTypeA
import SimSlab_Default_Default
import SimSlab_RoofSlab_RoofUnderAir
import SimSlab_Floor_FloorOverEarth
import SimSlab_Floor_InterzoneFloor
import SimWall_Wall_Default
import SimWall_Wall_ExteriorAboveGrade
import SimWall_Wall_Interior
import SimWindow_Window_Exterior
import SimMaterialLayerSet_Default_Default
import SimMaterialLayerSet_OpaqueLayerSet_Roof
import SimMaterialLayerSet_OpaqueLayerSet_Floor
import SimMaterialLayerSet_OpaqueLayerSet_Wall
import SimMaterialLayerSet_GlazingLayerSet_Window
import SimFeatureElementSubtraction_Void_Opening
import SimMaterialLayer_OpaqueMaterialLayer_Default
import SimMaterialLayer_GlazingMaterialLayer_Default
import SimMaterial_Default_Default
import SimMaterial_OpaqueMaterial_Default
import SimMaterial_OpaqueMaterial_AirGap
import SimMaterial_GlazingMaterial_Gas
import SimMaterial_GlazingMaterial_SimpleGlazingSystem
import SimMaterial_GlazingMaterial_Glazing
import SimModelRepresentationContext_GeometricRepresentationContext_Default
import SimPlacement_Axis2Placement3D_Default
import SimGeomVector_Vector_Direction
import SimSystem_HvacHotWater_FullSystem
import SimSystem_HvacHotWater_Control
import SimController_SupplyWater_Temperature
import SimSensor_TemperatureSensor_DryBulb
import SimSystem_HvacHotWater_Demand
import SimFlowController_Valve_Default
import SimFlowController_Valve_TemperingValve
import SimFlowEnergyTransfer_ConvectiveHeater_Water
import SimFlowEnergyTransfer_ConvectiveHeater_Radiant_Water
import SimFlowEnergyTransferStorage_HotWaterTank_Expansion
import SimFlowEnergyTransferStorage_HotWaterTank_Mixed
import SimFlowFitting_Default_Default
import SimFlowFitting_Mixer_DemandProxyMixerWater
import SimFlowFitting_Splitter_DemandProxySplitterWater
import SimFlowSegment_Pipe_Indoor
import SimSystem_HvacHotWater_Supply
import SimFlowMover_Pump_VariableSpeedReturn
import SimFlowPlant_Boiler_BoilerHotWater
import SimConnection_HotWaterFlow_Default
import SimNode_HotWaterFlowPort_Water_Out
import SimNode_HotWaterFlowPort_Water_In
import SimDistributionPort_HotWaterFlowPort_Water_Out
import SimDistributionPort_HotWaterFlowPort_Water_In
import SimDistributionPort_HotWaterFlowPort_Water_InOrOut
import SimNode_DigitalControl_HWLoop_DigitalSignal_In
import SimTimeSeriesSchedule_Year_Default
import SimTimeSeriesSchedule_Week_Daily
import SimTimeSeriesSchedule_Day_Interval
import SimTemplateZoneLoads_ZoneLoads_Default
import SimTemplateZoneConditions_ZoneConditions_Default
import SimInternalLoad_Equipment_Electric
import SimInternalLoad_People_Default
import SimInternalLoad_Lights_Default
import SimController_ZoneControlTemperature_Thermostat
import SimControlScheme_SetpointScheme_SingleHeating
import SimPerformanceCurve_Mathematical_Cubic
from SimModel_Translator import SimTranslator
# create SimModel translator object
translator = SimTranslator()
# load and parse multiple SimXML files
zoneFile_path = ("UseCase1_1_BoilerGasRadiatorFromSimergy.simxml")
hvacFile_path = ("1.1_Architecture+HVAC+Zone_Curve+Schedule_korr.simxml")
pathList = [zoneFile_path, hvacFile_path]
simxml_data = translator.loadSimModel(zoneFile_path, hvacFile_path)
# get SimModel mapped data
simxml_mapped_data = translator.getSimMappedData(".\\mapping_rule\\mapping_rule_xml\\AixLib_v2.6.xml")
mappedComonentList = simxml_mapped_data.getMappedComponentList()
sim_hierarchy = translator.getSimHierarchy()
nodeList = sim_hierarchy.getHierarchyNodeList()
# iterate each hierarchy node saved in the list
Structured_HVAC = []
for id in range(0, nodeList.size()):
hierarchy_node = nodeList[id]
#print("node ", id, ":", hierarchy_node.getSimModelObject().RefId())
# check the class type of the hierarchy node
if hierarchy_node.isClassType("SimSystem_HvacHotWater_Supply") or hierarchy_node.isClassType("SimSystem_HvacHotWater_Demand"):
# We are in Supply System: --> obtain child list.
print("Extract childs from ", hierarchy_node.getSimModelObject().RefId())
child_node_list = hierarchy_node.getChildList()
#print("child_node_list: ", child_node_list)
#sim_object = hierarchy_node.getSimModelObject()
#sim_object_id = sim_object.RefId()
#print("current SimModel object id: ", sim_object_id, " has the childs: ", child_node_list, "\n")
for child in child_node_list:
#print("child: ", child.getSimModelObject().RefId())
selected_info = []
# Every child will eventually become a modelica component. Thus we need:
# Name/ID - Type - & if applicable InPort and Outport from each TargePort and SourcePort
# The following structure (list of lists) will be used:
# for each node: [RefId,Type,[list of InPorts],[list of OutPorts],[list of InOrOutPorts]] where each element of the list InPorts
# is a list of the form: [TargetPort,SourcePort]
objectRefId = child.getSimModelObject().RefId()
objectType = child.ClassType()
selected_info.extend([objectRefId,objectType])
#InPort and Outport
child_node_list_2 = child.getChildList()
InPortlist = [] #SimDistributionPort_HotWaterFlowPort_Water_Out
OutPortlist = [] #SimDistributionPort_HotWaterFlowPort_Water_In
for child_2lvl in child_node_list_2:
# We need to go 1 level deeper to get Source- and TargetPort. Both are in class SimConnection_HotWaterFlow_Default (Port[0])
# Attribute TargetPort = OutPort.RefId() (Water_In) and Attribute SourcePort = InPort.RefId() (Water_Out)
#EXAMPLE:
#ID0LMbjl8XfFaui1YxJs_D6D
#InPort: ID3zs117w1n5lwSRidPGhKht (Water_Out)
# SourcePort: ID3zs117w1n5lwSRidPGhKht
# TargetPort: ID13UhRNCnX1rAwEBMyxljsq
#OutPort: ID1a5meudDCPOBQD1vrPCFy (Water_In)
# SourcePort: ID3mJZcz594IfdQpbAKBQkZ
# TargetPort: ID1a5meudDCPOBQD1vrPCFy
#
# In case the calss is HotWaterFlowPort_Water_InOrOut
# The above rule (compare attribute SourcePort and TargetPort with object RefId)
# helps to know whether the object is an InPort or OutPort.
# We will later associate InPort with OutPorts.
# InPort.RefId() = InPort.SourcePort = OutPort.SourcePort --> Alternative --> InPort.TargetPort = OutPort.TargetPort = OutPort.RefId()
# we use -> TargetPort
if child_2lvl.isClassType("SimDistributionPort_HotWaterFlowPort_Water_In"):
Port = child_2lvl.getChildList()
obj = Port[0].getSimModelObject()
OutPortlist.append(child_2lvl.getSimModelObject().RefId())
if child_2lvl.isClassType("SimDistributionPort_HotWaterFlowPort_Water_Out"):
Port = child_2lvl.getChildList()
obj = Port[0].getSimModelObject()
class_name = getattr(obj,"TargetPort")
instance = class_name()
Port_Id = class_name().getValue()
InPortlist.append(Port_Id)
if child_2lvl.isClassType("SimDistributionPort_HotWaterFlowPort_Water_InOrOut"):
Port = child_2lvl.getChildList()
obj = Port[0].getSimModelObject()
class_name = getattr(obj,"TargetPort")
instance = class_name()
TargetPort_Id = class_name().getValue()
class_name = getattr(obj,"SourcePort")
instance = class_name()
SourcePort_Id = class_name().getValue()
# If TargetPort_Id = child_2lvl.getSimModelObject().RefId() we have an OutPort (Water_In class)
# we append SourcePort_Id to OutPortlist otherwise
# we have an InPort (Water_Out class) so we append SourcePort_Id to the InPort list!
if child_2lvl.getSimModelObject().RefId() == TargetPort_Id:
OutPortlist.append(TargetPort_Id)
else:
InPortlist.append(TargetPort_Id)
# Finish reading second lvl childs: add list of OutPort and InPort into selected info
selected_info.extend([InPortlist,OutPortlist])
# Finisch with HVAC child. append structured information of the node into global list.
Structured_HVAC.append(selected_info)
G=nx.DiGraph()
listof_RefId = [elem[0] for elem in Structured_HVAC]
listof_type = [elem[1] for elem in Structured_HVAC]
listof_InPorts = [elem[2] for elem in Structured_HVAC]
listof_OutPorts = [elem[3] for elem in Structured_HVAC]
G.add_nodes_from(listof_RefId)
for node in Structured_HVAC:
G.node[node[0]]['Text'] = node[0]
G.node[node[0]]['Description'] = node[1]
for element_in in Structured_HVAC:
for InPort in element_in[2]:
for element_out in Structured_HVAC:
for OutPort in element_out[3]:
if InPort == OutPort:
print("ElementIN: ", element_in[0]," with InPort: ", InPort, "found OutPort: ", OutPort, " in element ", element_out[0])
G.add_edge(element_in[0],element_out[0])
print("number of edges: ", G.number_of_edges(), " - number of nodes: ", G.number_of_nodes())
nx.write_graphml(G,"test.graphml")
print("finish")
|
[
"[email protected]"
] | |
092db6afd0b046dcf1485a91be052fd57d5c502e
|
a177931c2914cc9820c578add9d57aa6c75084ce
|
/tips/customHTML/test_genTABHTML.py
|
cfd92464403354ae73e44a3df5bc666a81d2eb93
|
[] |
no_license
|
zhangshoug/others
|
45d94f96701362cb077eb994c27295247a6fb712
|
3a8a8366f2598a5e88b44d18d346e81f4eef659e
|
refs/heads/master
| 2022-12-18T22:37:13.505543 | 2020-09-28T08:54:28 | 2020-09-28T08:54:28 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,021 |
py
|
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name： test_genTABHTML
Description : tab css style test
Author : pchaos
date： 2019/9/9
-------------------------------------------------
Change Activity:
2019/9/9:
-------------------------------------------------
"""
import unittest
from unittest import TestCase
from .genTabHTML import genTABHTML
class TestGenTABHTML(TestCase):
def test_genHTML(self):
# 需要生成的文件名list。模板文件为：template.html，模板数据文件名为：需要生成的文件名+".ini"
flist = ["main.htm", "main_tech.htm", "hacker.html"]
# inifile = '{}.ini'.format(flist[0])
renderList = []
for fn in flist:
inifile = '{}.ini'.format(fn)
gh = genTABHTML()
# gh.outputFilename = fn
gh.outputFilename = "test"
gh.iniFilename = inifile
try:
templateFile = "customHTML/template.tab.table.html"
of, render = gh.genHTML(None,
# of, render = gh.genHTML("a{}".format(fn),
title=fn.split(".")[0],
prettify=False,
template=templateFile)
except Exception as e:
templateFile = "template.tab.table.html"
of, render = gh.genHTML(None,
# of, render = gh.genHTML("a{}".format(fn),
title=fn.split(".")[0],
prettify=False,
template=templateFile)
print("输出文件完成 {}".format(of))
# print(render)
self.assertTrue(len(render) > 100)
renderList.append(render)
print(renderList)
# main
inifile = '{}.ini'.format(flist[0])
gh = genTABHTML()
# gh.outputFilename = fn
gh.iniFilename = inifile
try:
templateFile = "template.tab.html"
render = gh.renders(renderList,
prettify=True,
# template="customHTML/template.tab.html",
template=templateFile,
title="Main")
except Exception as e:
templateFile = "customHTML/template.tab.html"
render = gh.renders(renderList,
prettify=True,
# template="customHTML/template.tab.html",
template=templateFile,
title="Main")
saveText = ""
for r in render:
saveText += r
gh.save('main.htm', saveText)
print("输出文件完成 {}".format(render))
if __name__ == '__main__':
unittest.main()
|
[
"[email protected]"
] | |
c06bcf0c5bf8278caf07c0496ba1c817c184ba8d
|
3d2e5d1092acccfb73c07d68b6beeffc44b3f776
|
/imitation/src/environments/simulation/pybullet_env.py
|
10ef9e12e56c2333e0813282dd5bdfe598ed1611
|
[] |
no_license
|
MatthijsBiondina/WorldModels
|
f6cbcfe5349da7119329ef10831810d1b85c9d02
|
ab468f1aa978e3aa4e05174db24922085d1e33b1
|
refs/heads/master
| 2022-12-22T11:54:46.040828 | 2020-09-23T11:41:48 | 2020-09-23T11:41:48 | 248,212,491 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,560 |
py
|
import gym
import pybulletgym
import numpy as np
from src.environments.general.environment_template import Environment
from src.utils import config as cfg
_ = pybulletgym
PREP_VECTORS = {'InvertedPendulumSwingupPyBulletEnv-v0': np.array([1, 0.2, 1, 1, 0.067], dtype=np.float16)}
def preprocess_observation(obs):
"""
:param obs: unprocessed observation
:return: normalized observation
"""
return np.clip(obs * PREP_VECTORS[cfg.env_name], -1., 1.)
class SimEnv(Environment):
def __init__(self, save_loc: str):
super().__init__(save_loc)
self.env = gym.make(cfg.env_name)
self.t = 0
self.actions = [np.zeros(self.action_size)] * cfg.latency
def reset(self):
"""
Reset environment
:return: observation at t=0
"""
self.t = 0
self.actions = [np.zeros(self.action_size)] * cfg.latency
return preprocess_observation(self.env.reset())
def step(self, action: np.ndarray):
"""
Perform action and observe next state. Action is repeated 'action_repeat' times.
:param action: the action to take
:return: next observation, reward, terminal state
"""
obs, done = None, None
reward = 0
self.actions.append(action)
for k in range(cfg.action_repeat):
obs, reward_k, done, _ = self.env.step(self.actions[0])
reward += reward_k
done = done or self.t == cfg.max_episode_length
if done:
break
self.actions.pop(0)
return preprocess_observation(obs), reward, done
def render(self) -> np.ndarray:
"""
Renders the environment to RGB array
:return: frame capture of environment
"""
return self.env.render(mode='rgb_array')
def close(self):
"""
Cleanup
:return: n/a
"""
self.env.close()
def sample_random_action(self) -> np.ndarray:
"""
Sample an action randomly from a uniform distribution over all valid actions
:return: random action
"""
return self.env.action_space.sample()
@property
def obs_size(self) -> int:
"""
GETTER METHOD
:return: size of observations in this environment
"""
return self.env.observation_space.shape[0]
@property
def action_size(self):
"""
GETTER METHOD
:return: size of actions in this environment
"""
return self.env.action_space.shape[0]
|
[
"[email protected]"
] | |
e3f0fa8530d803d47671f975bfbbc686baaee7bb
|
73be09853a2a303597825a5fe765610eeebbc5ef
|
/ForGPU/KerasCnn5.py
|
f6163ee71736ad8085652a59616dc5a78c1d025c
|
[
"MIT"
] |
permissive
|
Annarien/GravitationalLenses
|
7375cded8a028f39b4426687f90b5d0f8ca92cde
|
c2606aacc62d2534fb199f5228dc21c0ea604251
|
refs/heads/main
| 2023-06-02T21:25:29.543234 | 2021-06-19T18:20:07 | 2021-06-19T18:20:07 | 341,883,049 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 55,630 |
py
|
"""
This is file performs the convolutional neural network algorithm, in which the k fold is performed as well.
The results were saved in a csv file.
"""
import os
import sys
import random
from datetime import datetime
import numpy as np
import tensorflow
from astropy.io import fits
from astropy.utils.data import get_pkg_data_filename
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.utils import shuffle
from tensorflow.python.keras import Sequential
from tensorflow.python.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.python.keras.layers.convolutional import Conv2D, MaxPooling2D
from tensorflow.python.keras.layers.core import Dense, Dropout, Flatten
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.utils.vis_utils import plot_model
from tensorflow.python.keras.models import save_model
# added Adam opt for learning rate
from tensorflow.python.keras.optimizers import Adam
# from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras import backend as K
from ExcelUtils import createExcelSheet, writeToFile
print(tensorflow.__version__)
now = datetime.now()
dt_string = now.strftime("%d_%m_%Y_%H_%M_%S")
print(dt_string)
excel_headers = []
excel_dictionary = []
excel_headers.append("Date and Time")
excel_dictionary.append(dt_string)
# Globals
makeNewCSVFile = True
max_num = sys.maxsize # Set to sys.maxsize when running entire data set
max_num_testing = sys.maxsize # Set to sys.maxsize when running entire data set
max_num_prediction = sys.maxsize # Set to sys.maxsize when running entire data set
validation_split = 0.2 # A float value between 0 and 1 that determines what percentage of the training
# data is used for validation.
k_fold_num = 5 # A number between 2 and 10 that determines how many times the k-fold classifier
# is trained.
epochs = 50 # A number that dictates how many iterations should be run to train the classifier
batch_size = 128 # The number of items batched together during training.
run_k_fold_validation = True # Set this to True if you want to run K-Fold validation as well.
input_shape = (100, 100, 3) # The shape of the images being learned & evaluated.
augmented_multiple = 2 # This uses data augmentation to generate x-many times as much data as there is on file.
use_augmented_data = True # Determines whether to use data augmentation or not.
patience_num = 5 # Used in the early stopping to determine how quick/slow to react.
use_early_stopping = True # Determines whether to use early stopping or not.
use_model_checkpoint = True # Determines whether the classifiers keeps track of the most accurate iteration of itself.
monitor_early_stopping = 'val_loss'
monitor_model_checkpoint = 'val_acc'
use_shuffle = True
learning_rate = 0.001
training_positive_path = 'Training/PositiveAll'
# training_positive_path = 'UnseenData/KnownLenses_training'
training_negative_path = 'Training/Negative'
testing_positive_path = 'Testing/PositiveAll'
testing_negative_path = 'Testing/Negative'
# unseen_known_file_path = 'UnseenData/Known131'
unseen_known_file_path_select = 'UnseenData/SelectingSimilarLensesToPositiveSimulated'
unseen_known_file_path_all = 'UnseenData/KnownLenses'
# Adding global parameters to excel
excel_headers.append("Max Training Num")
excel_dictionary.append(max_num)
excel_headers.append("Max Testing Num")
excel_dictionary.append(max_num_testing)
excel_headers.append("Max Prediction Num")
excel_dictionary.append(max_num_prediction)
excel_headers.append("Validation Split")
excel_dictionary.append(validation_split)
excel_headers.append("K fold Num")
excel_dictionary.append(k_fold_num)
excel_headers.append("Epochs")
excel_dictionary.append(epochs)
excel_headers.append("Batch Size")
excel_dictionary.append(batch_size)
excel_headers.append("Run K fold")
excel_dictionary.append(run_k_fold_validation)
excel_headers.append("Input Shape")
excel_dictionary.append(input_shape)
excel_headers.append("Augmented Multiple")
excel_dictionary.append(augmented_multiple)
excel_headers.append("Use Augmented Data")
excel_dictionary.append(use_augmented_data)
excel_headers.append("Patience")
excel_dictionary.append(patience_num)
excel_headers.append("Use Early Stopping")
excel_dictionary.append(use_early_stopping)
excel_headers.append("Use Model Checkpoint")
excel_dictionary.append(use_model_checkpoint)
excel_headers.append("Monitor Early Stopping")
excel_dictionary.append(monitor_early_stopping)
excel_headers.append("Monitor Model Checkpoint")
excel_dictionary.append(monitor_model_checkpoint)
excel_headers.append("Use Shuffle")
excel_dictionary.append(use_shuffle)
excel_headers.append("Learning Rate")
excel_dictionary.append(learning_rate)
if not os.path.exists('../Results/%s/' % dt_string):
os.mkdir('../Results/%s/' % dt_string)
# Helper methods
def getPositiveImages(images_dir, max_num, input_shape):
"""
This gets the positively simulated images in the g, r and i bands.
Args:
images_dir(string): This is the file path address of the positively simulated images.
max_num(integer): This is the number of sources of the positively simulated images to be used.
input_shape(tuple): This is the shape of the images.
Returns:
positive_images(numpy array): This is the numpy array of the positively simulated images with the shape of
(num of images, input_shape[0], input_shape[1], input_shape[2]) =
(num_of_images, 100, 100, 3).
"""
global g_img_path, r_img_path, i_img_path
for root, dirs, _ in os.walk(images_dir):
num_of_images = min(max_num, len(dirs))
positive_images = np.zeros([num_of_images, 3, 100, 100])
index = 0
print('image_dir: ' + str(images_dir))
for folder in dirs:
if images_dir == 'Training/PositiveAll':
g_img_path = get_pkg_data_filename('%s/%s_g_norm.fits' % (os.path.join(root, folder), folder))
r_img_path = get_pkg_data_filename('%s/%s_r_norm.fits' % (os.path.join(root, folder), folder))
i_img_path = get_pkg_data_filename('%s/%s_i_norm.fits' % (os.path.join(root, folder), folder))
elif images_dir == 'UnseenData/KnownLenses_training':
g_img_path = get_pkg_data_filename('%s/g_norm.fits' % (os.path.join(root, folder)))
r_img_path = get_pkg_data_filename('%s/r_norm.fits' % (os.path.join(root, folder)))
i_img_path = get_pkg_data_filename('%s/i_norm.fits' % (os.path.join(root, folder)))
# print('g_img_path: ' + str(g_img_path))
# print('r_img_path: ' + str(r_img_path))
# print('i_img_path: ' + str(i_img_path))
g_data = fits.open(g_img_path)[0].data[0:100, 0:100]
r_data = fits.open(r_img_path)[0].data[0:100, 0:100]
i_data = fits.open(i_img_path)[0].data[0:100, 0:100]
img_data = [g_data, r_data, i_data]
positive_images[index] = img_data
index += 1
if index >= num_of_images:
break
return positive_images.reshape(num_of_images, input_shape[0], input_shape[1], input_shape[2])
def getNegativeImages(images_dir, max_num, input_shape):
"""
This gets the negative images in the g, r and i bands.
Args:
images_dir(string): This is the file path address of the negative images.
max_num(integer): This is the number of sources of the negative images to be used.
input_shape(tuple): This is the shape of the images.
Returns:
negative_images(numpy array): This is the numpy array of the negative images with the shape of
(num of images, input_shape[0], input_shape[1], input_shape[2]) =
(num_of_images, 100, 100, 3).
"""
for root, dirs, _ in os.walk(images_dir):
num_of_images = min(max_num, len(dirs))
negative_images = np.zeros([num_of_images, 3, 100, 100])
index = 0
for folder in dirs:
g_img_path = get_pkg_data_filename('%s/g_norm.fits' % (os.path.join(root, folder)))
r_img_path = get_pkg_data_filename('%s/r_norm.fits' % (os.path.join(root, folder)))
i_img_path = get_pkg_data_filename('%s/i_norm.fits' % (os.path.join(root, folder)))
g_data = fits.open(g_img_path)[0].data[0:100, 0:100]
r_data = fits.open(r_img_path)[0].data[0:100, 0:100]
i_data = fits.open(i_img_path)[0].data[0:100, 0:100]
img_data = [g_data, r_data, i_data]
negative_images[index] = img_data
index += 1
if index >= num_of_images:
break
return negative_images.reshape(num_of_images, input_shape[0], input_shape[1], input_shape[2])
def getUnseenData(images_dir, max_num, input_shape):
"""
This gets the unseen images in the g, r and i bands containing the identified known lenses.
Args:
images_dir(string): This is the file path address of the unseen images.
max_num(integer): This is the number of sources of the unseen images to be used.
input_shape(tuple): This is the shape of the images.
Returns:
des_tiles(dictionary): This is the dictionary of the unseen images with the shape of
(num of images, input_shape[0], input_shape[1], input_shape[2]) =
(num_of_images, 100, 100, 3).
"""
des_tiles = {}
for root, dirs, _ in os.walk(images_dir):
num_of_images = min(max_num, len(dirs))
index = 0
for folder in dirs:
g_img_path = get_pkg_data_filename('%s/g_norm.fits' % (os.path.join(root, folder)))
r_img_path = get_pkg_data_filename('%s/r_norm.fits' % (os.path.join(root, folder)))
i_img_path = get_pkg_data_filename('%s/i_norm.fits' % (os.path.join(root, folder)))
# print(g_img_path)
g_data = fits.open(g_img_path)[0].data[0:100, 0:100]
# print(np.shape(g_data))
r_data = fits.open(r_img_path)[0].data[0:100, 0:100]
i_data = fits.open(i_img_path)[0].data[0:100, 0:100]
img_data = np.array([g_data, r_data, i_data]).reshape(input_shape[0], input_shape[1], input_shape[2])
des_tiles.update({folder: img_data})
index += 1
if index >= num_of_images:
break
return des_tiles
def makeImageSet(positive_images, negative_images=None, tile_names=None, shuffle_needed=use_shuffle):
"""
This is used to create data set of images and labels, in which the positive and negative images are all
combined and shuffled.
Args:
positive_images(numpy array): This is the numpy array of the positively simulated images.
negative_images(numpy array): This is the numpy array of the negative images, this is set to a
default of None.
tile_names(list): This is the dictionary of the unseen known lenses, this is set to a
default of None.
shuffle_needed(boolean): This is a boolean value to determine whether or not shuffling of the given data
sets is required.
Returns:
image_set(numpy array): This is the image data set of numpy array of the combination positive
and negative images.
label_set(numpy array): This is the label data set of numpy array of the combination positive
and negative label.
des_names_set(numpy array): This is the des name data set of the known lenses and negative images used.
"""
image_set = []
label_set = []
tile_name_set = []
if positive_images is not None:
for index in range(0, len(positive_images)):
image_set.append(positive_images[index])
label_set.append(1)
if tile_names is not None:
tile_name_set.append(tile_names[index])
if negative_images is not None:
for index in range(0, len(negative_images)):
image_set.append(negative_images[index])
label_set.append(0)
if tile_names is not None:
tile_name_set.append(tile_names[index])
# print("Label Set: " + str(label_set))
if shuffle_needed:
if tile_names is not None:
image_set, label_set, tile_name_set = shuffle(image_set, label_set, tile_name_set)
else:
image_set, label_set = shuffle(image_set, label_set)
# print("Shuffled Label Set: " + str(label_set))
return np.array(image_set), np.array(label_set), np.array(tile_name_set)
def buildClassifier(input_shape=(100, 100, 3)):
"""
This creates the CNN algorithm.
Args:
input_shape(tuple): This is the image shape of (100,100,3)
Returns:
classifier(sequential): This is the sequential model.
"""
# Initialising the CNN
opt = Adam(lr=learning_rate) # lr = learning rate
# classifier = Sequential()
# classifier.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape, padding='same'))
# classifier.add(MaxPooling2D(pool_size=(4, 4), padding='same'))
# classifier.add(Conv2D(32, (3, 3), activation='relu', padding='same'))
# classifier.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
# classifier.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
# classifier.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
# classifier.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
# classifier.add(Dropout(0.2)) # antes era 0.25
# classifier.add(Conv2D(64, (3, 3), padding='same', activation='relu'))
# classifier.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
# classifier.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
# classifier.add(Dropout(0.2))# antes era 0.25
# classifier.add(Flatten())
# # classifier.add(Dense(units=512, activation='relu'))
# classifier.add(Dropout(0.2))
# classifier.add(Dense(units=1, activation='sigmoid'))
# classifier.summary()
#
# # Compiling the CNN
# classifier.compile(optimizer=opt,loss = 'binary_crossentropy',metrics = ['accuracy'])
# classifier.add(Conv2D(96, kernel_size=(2, 2), activation='relu', input_shape=input_shape)) # padding='same'
# classifier.add(MaxPooling2D(pool_size=(2, 2))) # padding='same'
# classifier.add(Dropout(0.2))
# classifier.add(Conv2D(128, (2, 2), activation='relu')) # padding='same'
# classifier.add(MaxPooling2D(pool_size=(2, 2))) # padding='same'
# classifier.add(Dropout(0.2))
# classifier.add(Conv2D(256, (2, 2), activation='relu')) # padding='same'
# classifier.add(MaxPooling2D(pool_size=(2, 2)))
# classifier.add(Dropout(0.2))
# classifier.add(Conv2D(256, (2, 2), activation='relu')) # padding='same'
# classifier.add(Dropout(0.2))
# classifier.add(MaxPooling2D(pool_size=(2, 2), padding='same')) # padding='same'
# classifier.add(Dropout(0.2))
# classifier.add(Flatten())
# classifier.add(Dense(units=2048, activation='relu')) # added new dense layer
# classifier.add(Dense(units=1024, activation='relu')) # added new dense layer
# classifier.add(Dropout(0.2))
# classifier.add(Dense(units=1024, activation='relu')) # added new dense layer
# classifier.add(Dropout(0.2))
# classifier.add(Dense(units=1, activation='sigmoid'))
# classifier.summary()
#
# # Compiling the CNN
# classifier.compile(optimizer=opt,
# loss='binary_crossentropy',
# metrics=['accuracy'])
classifier = Sequential()
classifier.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape, padding='same'))
classifier.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
classifier.add(Dropout(0.5)) # added extra Dropout layer
classifier.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
classifier.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
classifier.add(Conv2D(128, (3, 3), padding='same', activation='relu'))
classifier.add(Dropout(0.5)) # added extra dropout layer
classifier.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
classifier.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
classifier.add(Dropout(0.2)) # antes era 0.25
# Adding a third convolutional layer
classifier.add(Conv2D(512, (3, 3), padding='same', activation='relu'))
classifier.add(MaxPooling2D(2,2))
classifier.add(Conv2D(1024, (3, 3), activation='relu', padding='same'))
classifier.add(MaxPooling2D(pool_size=(2, 2), padding='same'))
classifier.add(Dropout(0.2)) # antes era 0.25
# Step 3 - Flattening
classifier.add(Flatten())
# Step 4 - Full connection
classifier.add(Dropout(0.2))
classifier.add(Dense(units=1, activation='sigmoid'))
classifier.summary()
# Compiling the CNN
classifier.compile(optimizer=opt,
loss='binary_crossentropy',
metrics=['accuracy'])
plot_model(classifier, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
return classifier
def visualiseActivations(img_tensor, base_dir):
"""
This makes images of the activations, as the selected image passed through the model
Args:
img_tensor(numpy array): This is the numpy array of the selected image
base_dir(string): This is the file path name
Saves:
This saves the activation images of the selected source.
"""
global predicted_class, size
# Run prediction on that image
predicted_class = classifier.predict_classes(img_tensor, batch_size=10)
print("Predicted class is: ", predicted_class)
# Visualize activations
layer_outputs = [layer.output for layer in classifier.layers[:12]]
activation_model = Model(inputs=classifier.input, outputs=layer_outputs)
activations = activation_model.predict(img_tensor)
layer_names = []
for layer in classifier.layers[:12]:
layer_names.append(layer.name)
images_per_row = 3
count = 0
for layer_name, layer_activation in zip(layer_names, activations):
number_of_features = layer_activation.shape[-1]
size = layer_activation.shape[1]
number_of_columns = number_of_features // images_per_row
display_grid = np.zeros((size * number_of_columns, images_per_row * size))
for col in range(number_of_columns):
for row in range(images_per_row):
channel_image = layer_activation[0, :, :, col * images_per_row + row]
channel_image -= channel_image.mean()
channel_image /= channel_image.std()
channel_image *= 64
channel_image += 128
channel_image = np.clip(channel_image, 0, 255).astype('uint8')
display_grid[col * size: (col + 1) * size, row * size: (row + 1) * size] = channel_image
scale = 1. / size
activations_figure = plt.figure(figsize=(scale * display_grid.shape[1],
scale * display_grid.shape[0]))
plt.title(layer_name)
plt.grid(False)
plt.imshow(display_grid, aspect='auto', cmap='viridis')
activations_figure.savefig('%s/%s_Activation_%s.png' % (base_dir, count, layer_name))
plt.close()
count += 1
def usingCnnModel(training_data, training_labels, val_data, val_labels):
"""
This is using the CNN model and setting it up.
Args:
training_data(numpy arrays): This is the numpy array of the training data.
training_labels(numpy arrays): This is the numpy array of the training labels.
val_data(numpy arrays): This is the numpy array of the validation data.
val_labels(numpy arrays): This is the numpy array of the validation labels.
Returns:
history(history): This is the history of the classifier.
classifier(sequential): This is the cnn model classifier fitted to the training data and labels.
"""
model_checkpoint = ModelCheckpoint(filepath="best_weights.hdf5",
monitor=monitor_model_checkpoint,
save_best_only=True)
early_stopping = EarlyStopping(monitor=monitor_early_stopping, patience=patience_num) # original patience =3
classifier = buildClassifier()
callbacks_array = []
if use_early_stopping:
callbacks_array.append(early_stopping)
if use_model_checkpoint:
callbacks_array.append(model_checkpoint)
print(len(training_data))
history = classifier.fit(training_data,
training_labels,
epochs=epochs,
validation_data=(val_data, val_labels),
callbacks=callbacks_array,
batch_size=batch_size
# steps_per_epoch=int(len(training_data) / batch_size),
)
return history, classifier
def createAugmentedData(training_data, training_labels):
"""
This is creates the augmented data.
Args:
training_data(numpy arrays): This is the numpy array of the training data.
training_labels(numpy arrays): This is the numpy array of the training labels.
Returns:
complete_training_data_set(numpy array): This is the numpy array of the total training data, which is has
undergone augmentation.
complete_training_labels_set(numpy array): This is the numpy array of the total training labels, which is has
undergone augmentation.
"""
complete_training_data_set = []
complete_training_labels_set = []
for data in training_data:
complete_training_data_set.append(data)
print("Complete Training Data: " + str(len(complete_training_data_set)))
for label in training_labels:
complete_training_labels_set.append(label)
print("Complete Training Label: " + str(len(complete_training_labels_set)))
# create augmented data
data_augmented = ImageDataGenerator(featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=90,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,
vertical_flip=True)
# data_augmented = ImageDataGenerator(featurewise_center=False,
# featurewise_std_normalization=False,
# rotation_range=90,
# horizontal_flip=True,
# vertical_flip=True)
data_augmented.fit(training_data)
training_data_size = training_data.shape[0]
aug_counter = 0
while aug_counter < (augmented_multiple - 1):
iterator = data_augmented.flow(training_data, training_labels, batch_size=training_data_size)
# iterator = data_augmented.flow(training_data, training_labels, batch_size=batch_size)
augmented_data = iterator.next()
for data in augmented_data[0]:
complete_training_data_set.append(data)
for label in augmented_data[1]:
complete_training_labels_set.append(label)
aug_counter += 1
print("Size of All Training Data: " + str(len(complete_training_data_set)))
print("Size of All Training Labels: " + str(len(complete_training_labels_set)))
array_training_data = np.array(complete_training_data_set)
array_training_labels = np.array(complete_training_labels_set)
print("Shape of complete training data: " + str(array_training_data.shape))
print("Shape of complete training labels: " + str(array_training_labels.shape))
return np.array(complete_training_data_set), np.array(complete_training_labels_set)
def savePredictedLenses(des_names_array, predicted_class_probabilities, predicted_lenses_filepath, text_file_path):
"""
This saves the names of the predicted lenses in the respective textfiles.
Args:
des_names_array(numpy array): This is a list of the des names of the sources.
predicted_class_probabilities(list): This is a list of the probabilities in which lenses are predicted by
the algorithm.
predicted_lenses_filepath(string): This is the string of the predicted lenses filepath, where this needs
to be saved in the directory.
text_file_path(string): This is the text file path address to which these images are saved.
Saves:
text_file(.txt file): This is the text file saved containing the predicted lenses DES names.
"""
predicted_lenses = []
predicted_no_lenses = []
if not os.path.exists(predicted_lenses_filepath):
os.mkdir('%s/' % predicted_lenses_filepath)
text_file = open('%s' % text_file_path, "a+")
text_file.write('\n')
text_file.write('Predicted Lenses: \n')
for lens_index in range(len(predicted_class_probabilities)):
if predicted_class_probabilities[lens_index] == 1:
text_file.write("%s \n " % des_names_array[lens_index])
predicted_lenses.append(des_names_array[lens_index])
text_file.write('\n')
text_file.write('No Lenses Predicted: \n')
for lens_index in range(len(predicted_class_probabilities)):
if predicted_class_probabilities[lens_index] == 0:
text_file.write("%s \n " % des_names_array[lens_index])
predicted_no_lenses.append(des_names_array[lens_index])
text_file.close()
return predicted_lenses, predicted_no_lenses
def gettingTrueFalsePositiveNegatives(testing_data, testing_labels, text_file_path,
predicted_lenses_filepath, kf_counter=0):
"""
This is used to get the True/False Positive and Negative values gained from the CNN confusion matrix.
Args:
testing_data(numpy array): This is the unseen testing data numpy array.
testing_labels(numpy array): This is the unseen testing label numpy array.
text_file_path(string): This is the file path name of the text file in which the confusion
matrix is saved.
predicted_lenses_filepath(string): This is the file path in which the text file is saved.
Saves:
This saves a confusion matrix of the True/False Positive and Negative values.
"""
if not os.path.exists(predicted_lenses_filepath):
os.mkdir('%s/' % predicted_lenses_filepath)
predicted_data = classifier.predict_classes(testing_data)
rounded_predicted_data = predicted_data.round()
conf_matrix = confusion_matrix(testing_labels, rounded_predicted_data, labels=[0, 1])
print(str(conf_matrix) + ' \n ')
true_negative, false_positive, false_negative, true_positive = conf_matrix.ravel()
print("True Positive: %s \n" % true_positive)
print("False Negative: %s \n" % false_negative)
print("False Positive: %s \n" % false_positive)
print("True Negative: %s \n" % true_negative)
text_file = open('%s' % text_file_path, "a+")
text_file.write('\n')
text_file.write('KFold Number: %s \n' % str(kf_counter))
text_file.write('Predicted vs True Matrix: \n')
text_file.write(str(conf_matrix) + " \n ")
text_file.write("True Negative: %s \n" % str(true_negative))
text_file.write("False Positive: %s \n" % str(false_positive))
text_file.write("False Negative: %s \n" % str(false_negative))
text_file.write("True Positive: %s \n" % str(true_positive))
text_file.write("\n")
text_file.close()
confusion_matrix_array = [true_negative, false_positive, false_negative, true_positive]
return confusion_matrix_array
def gettingKFoldConfusionMatrix(test_data, test_labels, unseen_images, unseen_labels, select_known_images,
select_known_labels, kf_counter):
test_confusion_matrix = gettingTrueFalsePositiveNegatives(test_data,
test_labels,
text_file_path='../Results/%s/TrainingTestingResults'
'/KFold_PredictedMatrix.txt' % dt_string,
predicted_lenses_filepath='../Results/%s'
'/TrainingTestingResults '
% dt_string,
kf_counter=kf_counter)
unseen_confusion_matrix = gettingTrueFalsePositiveNegatives(unseen_images,
unseen_labels,
text_file_path='../Results/%s/UnseenKnownLenses/'
'KFold_LensesPredicted.txt' % dt_string,
predicted_lenses_filepath='../Results/%s'
'/UnseenKnownLenses/ '
% dt_string,
kf_counter=kf_counter)
select_confusion_matrix = gettingTrueFalsePositiveNegatives(select_known_images,
select_known_labels,
text_file_path='../Results/%s/UnseenKnownLensesSelect/'
'KFold_LensesPredicted.txt' % dt_string,
predicted_lenses_filepath='../Results/%s'
'/UnseenKnownLensesSelect/ '
% dt_string,
kf_counter=kf_counter)
return test_confusion_matrix, unseen_confusion_matrix, select_confusion_matrix
def gettingRandomUnseenImage(filepath):
g_img_path = get_pkg_data_filename('%s/g_norm.fits' % filepath)
r_img_path = get_pkg_data_filename('%s/r_norm.fits' % filepath)
i_img_path = get_pkg_data_filename('%s/i_norm.fits' % filepath)
g_data = fits.open(g_img_path)[0].data[0:100, 0:100]
r_data = fits.open(r_img_path)[0].data[0:100, 0:100]
i_data = fits.open(i_img_path)[0].data[0:100, 0:100]
img_data = np.array([g_data, r_data, i_data]).reshape(input_shape[0], input_shape[1], input_shape[2])
return img_data
def executeKFoldValidation(train_data, train_labels, val_data, val_labels, testing_data, testing_labels,
known_images, known_labels, known_des_names,
select_known_images, select_known_labels):
"""
This does the k fold cross validation which is tested against the unseen testing and known lenses.
Args:
train_data(numpy arrays): This is the numpy array of the training data.
train_labels(numpy arrays): This is the numpy array of the training labels.
val_data(numpy arrays): This is the numpy array of the validation data.
val_labels(numpy arrays): This is the numpy array of the validation labels.
testing_data(numpy array): This is the numpy array of the unseen testing data.
testing_labels(numpy array): This is the numpy array of the unseen testing label.
images_47(numpy array): This is the numpy array of the unseen DES images data.
labels_47(numpy array): This is the numpy array of the unseen DES images labels.
images_84(numpy array): This is the numpy array of the unseen Jacobs images data.
labels_84(numpy array): This is the numpy array of the unseen Jacobs images labels.
all_unseen_images(numpy array): This is the numpy array of the unseen DES + Jacobs images data.
all_unseen_labels(numpy array): This is the numpy array of the unseen DES + Jacobs images labels.
Saves:
This saves the scores, mean and std. of the unseen data that is evaluated in the k fold cross validation.
"""
if run_k_fold_validation:
print("In executingKFoldValidation")
# this is doing it manually:
kfold = StratifiedKFold(n_splits=k_fold_num, shuffle=True)
test_scores_list = []
test_loss_list = []
unseen_scores_list = []
unseen_loss_list = []
select_unseen_scores_list = []
select_unseen_loss_list = []
test_matrix_list = []
unseen_matrix_list = []
select_matrix_list = []
kf_counter = 0
true_positives = {}
false_negatives = {}
for train, test in kfold.split(train_data, train_labels):
kf_counter += 1
print('KFold #:', kf_counter)
model = buildClassifier()
# fit the model
model.fit(train_data[train],
train_labels[train],
epochs=epochs,
validation_data=(val_data, val_labels),
batch_size=batch_size)
test_scores = model.evaluate(testing_data, testing_labels, batch_size=batch_size)
test_scores_list.append(test_scores[1])
test_loss_list.append(test_scores[0])
print("Test Score: " + str(test_scores_list))
print("Test Loss: " + str(test_loss_list))
unseen_scores = model.evaluate(known_images, known_labels, batch_size=batch_size)
unseen_scores_list.append(unseen_scores[1])
unseen_loss_list.append(unseen_scores[0])
print("Unseen Score: " + str(unseen_scores_list))
print("Unseen Loss: " + str(unseen_loss_list))
select_scores = model.evaluate(select_known_images, select_known_labels, batch_size=batch_size)
select_unseen_scores_list.append(select_scores[1])
select_unseen_loss_list.append(select_scores[0])
# show confusion matrix
test_confusion_matrix, unseen_confusion_matrix, select_confusion_matrix = gettingKFoldConfusionMatrix(
testing_data,
testing_labels, known_images,
known_labels, select_known_images, select_known_labels, kf_counter)
probabilities_known_lenses = classifier.predict_classes(known_images, batch_size=batch_size)
predicted_lens = np.count_nonzero(probabilities_known_lenses == 1)
predicted_no_lens = np.count_nonzero(probabilities_known_lenses == 0)
print("%s/%s known lenses predicted" % (predicted_lens, len(known_images)))
print("%s/%s non known lenses predicted" % (predicted_no_lens, len(known_images)))
predicted_lenses, predicted_no_lenses = savePredictedLenses(known_des_names,
predicted_class_probabilities_known_lenses,
text_file_path='../Results/%s'
'/UnseenKnownLenses/'
'KFold_LensesPredicted.txt'
% dt_string,
predicted_lenses_filepath='../Results/%s/'
'UnseenKnownLenses'
% dt_string)
randomTP = None
imageTP = None
if predicted_lenses:
randomTP = random.choice(predicted_lenses)
filepathTP = unseen_known_file_path_all + '/%s' % randomTP
imageTP = gettingRandomUnseenImage(filepathTP)
true_positives[kf_counter] = (randomTP, imageTP)
randomFN = None
imageFN = None
if predicted_no_lenses:
randomFN = random.choice(predicted_no_lenses)
filepathFN = unseen_known_file_path_all + '/%s' % randomFN
imageFN = gettingRandomUnseenImage(filepathFN)
false_negatives[kf_counter] = (randomFN, imageFN)
# print("Lenses Predicted: " + str(randomTP))
# print("Lenses Not Predicted: " + str(randomFN))
test_matrix_list.append(test_confusion_matrix)
unseen_matrix_list.append(unseen_confusion_matrix)
select_matrix_list.append(select_confusion_matrix)
test_scores_mean = np.mean(test_scores_list)
test_loss_mean = np.mean(test_loss_list)
test_scores_std = np.std(test_scores_list)
unseen_scores_mean = np.mean(unseen_scores_list)
unseen_loss_mean = np.mean(unseen_loss_list)
unseen_scores_std = np.std(unseen_scores_list)
select_scores_mean = np.mean(select_unseen_scores_list)
select_loss_mean = np.mean(select_unseen_loss_list)
select_scores_std = np.std(select_unseen_scores_list)
print("Test Confusion Matrices: " + str(test_matrix_list))
print("Test Scores: " + str(test_scores_list))
print("Test Scores Mean: " + str(test_scores_mean))
print("Test Scores Std: " + str(test_scores_std))
print("Test Loss: " + str(test_loss_list))
print("Test Loss Mean: " + str(test_loss_mean))
print("Unseen Confusion Matrices: " + str(unseen_matrix_list))
print("Unseen Scores: " + str(unseen_scores_list))
print("Unseen Scores Mean: " + str(unseen_scores_mean))
print("Unseen Scores Std: " + str(unseen_scores_std))
print("Unseen Loss: " + str(unseen_loss_list))
print("Unseen Loss Mean: " + str(unseen_loss_mean))
print("Select Confusion Matrices: " + str(select_matrix_list))
print("Select Score: " + str(select_unseen_scores_list))
print("Select Scores Mean: " + str(select_scores_mean))
print("Select Unseen Scores Std: " + str(select_scores_std))
print("Select Loss: " + str(select_unseen_loss_list))
print("Unseen Loss Mean: " + str(select_loss_mean))
excel_headers.append("Test Loss Mean")
excel_dictionary.append(test_loss_mean)
excel_headers.append("Test Scores Mean")
excel_dictionary.append(test_scores_mean)
excel_headers.append("Test Scores Std")
excel_dictionary.append(test_scores_std)
excel_headers.append("Unseen Loss Mean")
excel_dictionary.append(unseen_loss_mean)
excel_headers.append("Unseen Known Lenses Mean")
excel_dictionary.append(unseen_scores_mean)
excel_headers.append("Unseen Known Lenses Std")
excel_dictionary.append(unseen_scores_std)
excel_headers.append("Select Loss Mean")
excel_dictionary.append(select_loss_mean)
excel_headers.append("Select Scores Mean")
excel_dictionary.append(select_scores_mean)
excel_headers.append("Select Std")
excel_dictionary.append(select_scores_std)
plt.plot(test_scores_list, color='red', label='Testing Scores')
plt.plot(unseen_scores_list, color='blue', label='Unseen Known Lenses Scores')
plt.plot(select_unseen_scores_list, color='green', label="Selected Unseen Known Lenses Scores")
plt.xlabel('Folds')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
plt.savefig('../Results/%s/KFoldAccuracyScores.png' % dt_string)
plotKFold(true_positives, false_negatives)
def viewActivationLayers():
# make positive and negative directory
if not os.path.exists('../Results/%s/PositiveResults/' % dt_string):
os.mkdir('../Results/%s/PositiveResults/' % dt_string)
if not os.path.exists('../Results/%s/NegativeResults/' % dt_string):
os.mkdir('../Results/%s/NegativeResults/' % dt_string)
# Plot original positive image
img_positive_tensor = getPositiveImages('Training/PositiveAll', 1, input_shape=input_shape)
positive_train_figure = plt.figure()
plt.imshow(img_positive_tensor[0])
# plt.show()
print(img_positive_tensor.shape)
positive_train_figure.savefig('../Results/%s/PositiveResults/PositiveTrainingFigure.png' % dt_string)
plt.close()
# Visualise Activations of positive image
visualiseActivations(img_positive_tensor, base_dir='../Results/%s/PositiveResults/' % dt_string)
# Plot original negative image
img_negative_tensor = getNegativeImages('Training/Negative', 1, input_shape=input_shape)
negative_train_figure = plt.figure()
plt.imshow(img_negative_tensor[0])
# plt.show()
print(img_negative_tensor.shape)
negative_train_figure.savefig('../Results/%s/NegativeResults/NegativeTrainingFigure.png' % dt_string)
plt.close()
# Visualise Activations of negative image
visualiseActivations(img_negative_tensor, base_dir='../Results/%s/NegativeResults/' % dt_string)
def plotKFold(true_positives, false_negatives):
# print('True Positives: ' + str(true_positives))
# print('False Negatives: ' + str(false_negatives))
fig, axs = plt.subplots(k_fold_num, 2)
fig.tight_layout(pad=3.0)
cols = ['True Positive', 'False Negative']
for ax, col in zip(axs[0], cols):
ax.set_title(col)
# for ax, col in zip(axs[0], cols):
# for i in range(len(cols)):
# # axs[0, i].text(x=0.5, y=12, s="", ha="center", fontsize=12)
# # axs[k_fold_num - 1, i].set_xlabel(cols[i])
# axs[0, i].set_title(cols[i])
# # ax.set_title(col)
for i in range(0, k_fold_num):
axs[i, 0].text(x=-0.8, y=5, s="", rotation=90, va="center")
axs[i, 0].set_ylabel("k = %s" % (i + 1))
true_positive_tuple = true_positives[k_fold_num]
if not true_positive_tuple[0] is None:
axs[i, 0].set_xlabel(true_positive_tuple[0], fontsize=8)
# axs[i, 0].set_title(true_positive_tuple[0], fontsize=6)
axs[i, 0].imshow(true_positive_tuple[1])
axs[i, 0].set_xticks([], [])
axs[i, 0].set_yticks([], [])
false_negative_tuple = false_negatives[k_fold_num]
if not false_negative_tuple[0] is None:
axs[i, 1].set_xlabel(false_negative_tuple[0], fontsize=8)
# axs[i, 1].set_title(false_negative_tuple[0], fontsize=6)
axs[i, 1].imshow(false_negative_tuple[1])
axs[i, 1].set_xticks([], [])
axs[i, 1].set_yticks([], [])
fig.tight_layout()
plt.show()
fig.savefig('../Results/%s/UnseenKnownLenses/KFoldImages.png' % dt_string)
# __________________________________________________________________________
# MAIN
# Get positive training data
train_pos = getPositiveImages(images_dir=training_positive_path, max_num=max_num, input_shape=input_shape)
print("Train Positive Shape: " + str(train_pos.shape))
excel_headers.append("Train_Positive_Shape")
excel_dictionary.append(train_pos.shape)
# Get negative training data
train_neg = getNegativeImages(images_dir=training_negative_path, max_num=max_num, input_shape=input_shape)
print("Train Negative Shape: " + str(train_neg.shape))
excel_headers.append("Train_Negative_Shape")
excel_dictionary.append(train_neg.shape)
all_training_data, all_training_labels, _ = makeImageSet(train_pos, train_neg, shuffle_needed=use_shuffle)
if use_augmented_data:
all_training_data, all_training_labels = createAugmentedData(all_training_data, all_training_labels)
training_data, val_data, training_labels, val_labels = train_test_split(all_training_data,
all_training_labels,
test_size=validation_split,
shuffle=True)
excel_headers.append("All_Training_Data_Shape")
excel_dictionary.append(all_training_labels.shape)
excel_headers.append("All_Training_Labels_Shape")
excel_dictionary.append(all_training_labels.shape)
excel_headers.append("Training_Data_Shape")
excel_dictionary.append(training_data.shape)
excel_headers.append("Validation_Data_Shape")
excel_dictionary.append(val_data.shape)
excel_headers.append("Training_Labels_Shape")
excel_dictionary.append(training_labels.shape)
excel_headers.append("Validation_Labels_Shape")
excel_dictionary.append(val_labels.shape)
excel_headers.append("Validation_Split")
excel_dictionary.append(validation_split)
history, classifier = usingCnnModel(training_data,
training_labels,
val_data,
val_labels)
#classifier.load_weights('best_weights.hdf5')
#classifier.save_weights('galaxies_cnn.h5')
excel_headers.append("Epochs")
excel_dictionary.append(epochs)
excel_headers.append("Batch_size")
excel_dictionary.append(batch_size)
# Plot run metrics
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
number_of_completed_epochs = range(1, len(acc) + 1)
# Accuracies
train_val_accuracy_figure = plt.figure()
plt.plot(number_of_completed_epochs, acc, label='Training acc')
plt.plot(number_of_completed_epochs, val_acc, label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.show()
train_val_accuracy_figure.savefig('../Results/%s/TrainingValidationAccuracy.png' % dt_string)
plt.close()
# Losses
train_val_loss_figure = plt.figure()
plt.plot(number_of_completed_epochs, loss, label='Training loss')
plt.plot(number_of_completed_epochs, val_loss, label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.show()
train_val_loss_figure.savefig('../Results/%s/TrainingValidationLoss.png' % dt_string)
plt.close()
# make positive and negative results and plotting the activations of positive and negative images
viewActivationLayers()
# Classifier evaluation
test_pos = getPositiveImages(images_dir=testing_positive_path, max_num=max_num_testing, input_shape=input_shape)
test_neg = getNegativeImages(images_dir=testing_negative_path, max_num=max_num_testing, input_shape=input_shape)
testing_data, testing_labels, _ = makeImageSet(test_pos, test_neg, shuffle_needed=True)
print("Testing Data Shape: " + str(testing_data.shape))
print("Testing Labels Shape: " + str(testing_labels.shape))
print("Got Unseen Testing data")
scores = classifier.evaluate(testing_data, testing_labels, batch_size=batch_size)
loss = scores[0]
accuracy = scores[1]
print("Test loss: %s" % loss)
print("Test accuracy: %s" % accuracy)
excel_headers.append("Test_Loss")
excel_dictionary.append(loss)
excel_headers.append("Test_Accuracy")
excel_dictionary.append(accuracy)
gettingTrueFalsePositiveNegatives(testing_data,
testing_labels,
text_file_path='../Results/%s/TrainingTestingResults/PredictedMatrixBeforeKFOLD.txt'
% dt_string,
predicted_lenses_filepath='../Results/%s/TrainingTestingResults' % dt_string)
unseen_known_images = getUnseenData(images_dir=unseen_known_file_path_all,
max_num=max_num_prediction,
input_shape=input_shape)
known_images, known_labels, known_des_names = makeImageSet(positive_images=list(unseen_known_images.values()),
tile_names=list(unseen_known_images.keys()),
shuffle_needed=True)
print("Unseen Known Images Shape: " + str(known_images.shape))
print("Unseen Known Labels Shape: " + str(known_labels.shape))
print("Got Unseen Known Lenses Data")
unseen_scores = classifier.evaluate(known_images, known_labels, batch_size=batch_size)
unseen_loss_score = unseen_scores[0]
unseen_accuracy_score = unseen_scores[1]
print("Unseen loss: %s" % unseen_loss_score)
print("Unseen accuracy: %s" % unseen_accuracy_score)
excel_headers.append("Unseen_Loss")
excel_dictionary.append(unseen_loss_score)
excel_headers.append("Unseen_Accuracy")
excel_dictionary.append(unseen_accuracy_score)
predicted_class_probabilities_known_lenses = classifier.predict_classes(known_images, batch_size=batch_size)
lens_predicted = np.count_nonzero(predicted_class_probabilities_known_lenses == 1)
non_lens_predicted = np.count_nonzero(predicted_class_probabilities_known_lenses == 0)
print("%s/%s known lenses predicted" % (lens_predicted, len(known_images)))
print("%s/%s non known lenses predicted" % (non_lens_predicted, len(known_images)))
gettingTrueFalsePositiveNegatives(known_images, known_labels,
text_file_path='../Results/%s/UnseenKnownLenses/PredictedMatrixBeforeKFOLD.txt' % dt_string,
predicted_lenses_filepath='../Results/%s/UnseenKnownLenses' % dt_string)
predicted_lenses, predicted_no_lenses = savePredictedLenses(known_des_names,
predicted_class_probabilities_known_lenses,
text_file_path='../Results/%s/UnseenKnownLenses/'
'PredictedMatrixBeforeKFOLD.txt' % dt_string,
predicted_lenses_filepath='../Results/%s/UnseenKnownLenses'
% dt_string)
######################################################################################
unseen_known_images_select = getUnseenData(images_dir=unseen_known_file_path_select,
max_num=max_num_prediction,
input_shape=input_shape)
select_known_images, select_known_labels, select_known_des_names = makeImageSet(
positive_images=list(unseen_known_images_select.values()),
tile_names=list(unseen_known_images_select.keys()),
shuffle_needed=True)
print("Unseen Selected Known Images Shape: " + str(select_known_images.shape))
print("Unseen Selected Known Labels Shape: " + str(select_known_labels.shape))
print("Got Unseen Selected Known Lenses Data")
select_unseen_scores = classifier.evaluate(select_known_images, select_known_labels, batch_size=batch_size)
select_unseen_loss_score = select_unseen_scores[0]
select_unseen_accuracy_score = select_unseen_scores[1]
print("Unseen Selected loss: %s" % select_unseen_loss_score)
print("Unseen Selected accuracy: %s" % select_unseen_accuracy_score)
excel_headers.append("Selected Unseen_Loss")
excel_dictionary.append(select_unseen_loss_score)
excel_headers.append("Select Unseen_Accuracy")
excel_dictionary.append(select_unseen_accuracy_score)
select_predicted_class_probabilities_known_lenses = classifier.predict_classes(select_known_images,
batch_size=batch_size)
select_lens_predicted = np.count_nonzero(select_predicted_class_probabilities_known_lenses == 1)
select_non_lens_predicted = np.count_nonzero(select_predicted_class_probabilities_known_lenses == 0)
print("%s/%s known lenses predicted" % (select_lens_predicted, len(select_known_images)))
print("%s/%s non known lenses predicted" % (select_non_lens_predicted, len(select_known_images)))
gettingTrueFalsePositiveNegatives(select_known_images, select_known_labels,
text_file_path='../Results/%s/UnseenKnownLensesSelect/PredictedMatrixBeforeKFOLD.txt' % dt_string,
predicted_lenses_filepath='../Results/%s/UnseenKnownLensesSelect' % dt_string)
select_predicted_lenses, select_predicted_no_lenses = savePredictedLenses(select_known_des_names,
select_predicted_class_probabilities_known_lenses,
text_file_path='../Results/%s'
'/UnseenKnownLensesSelect/ '
'PredictedMatrixBeforeKFOLD'
'.txt' % dt_string,
predicted_lenses_filepath='../Results/%s'
'/UnseenKnownLensesSelect'
% dt_string)
excel_headers.append("Selected Unseen_Known_Lenses_Predicted")
excel_dictionary.append(select_lens_predicted)
excel_headers.append("Selected Unseen_Known_Lenses_No_Lens_Predicted")
excel_dictionary.append(select_non_lens_predicted)
# K fold for training data
executeKFoldValidation(training_data,
training_labels,
val_data,
val_labels,
testing_data,
testing_labels,
known_images,
known_labels,
known_des_names,
select_known_images, select_known_labels)
if makeNewCSVFile:
createExcelSheet('../Results/Architecture_kerasCNN_Results.csv', excel_headers)
writeToFile('../Results/Architecture_kerasCNN_Results.csv', excel_dictionary)
else:
writeToFile('../Results/Architecture_kerasCNN_Results.csv', excel_dictionary)
|
[
"[email protected]"
] | |
77cf3b81b38c32c26216a33749bc6f9e06bdd2f2
|
4f696b0712f530f0d8e7d968ee52ed4dda97a2c6
|
/admix/data/_geno.py
|
a9c94134a7a46b25037b8f1cab3bb634c3dd4e7f
|
[] |
no_license
|
KangchengHou/admix-kit
|
5d0e1f4225f6339f10bece6fded7c156794bccbe
|
136e8999d94440d604a2dcfb7b7d1a340a5f6e67
|
refs/heads/main
| 2023-09-01T08:58:05.219692 | 2023-08-24T17:33:58 | 2023-08-24T17:33:58 | 335,100,490 | 7 | 1 | null | 2022-08-20T23:01:26 | 2021-02-01T22:23:02 |
Python
|
UTF-8
|
Python
| false | false | 22,616 |
py
|
import numpy as np
import pandas as pd
from tqdm import tqdm
import dask.array as da
import admix
import dask
from typing import Union, Tuple, List
import dapgen
def calc_snp_prior_var(df_snp_info, her_model):
"""
Calculate the SNP prior variance from SNP information
"""
assert her_model in ["uniform", "gcta", "ldak", "mafukb"]
if her_model == "uniform":
return np.ones(len(df_snp_info))
elif her_model == "gcta":
freq = df_snp_info["FREQ"].values
assert np.all(freq > 0), "frequencies should be larger than zero"
return np.float_power(freq * (1 - freq), -1)
elif her_model == "mafukb":
# MAF-dependent genetic architecture, \alpha = -0.38 estimated from meta-analysis in UKB traits
freq = df_snp_info["FREQ"].values
assert np.all(freq > 0), "frequencies should be larger than zero"
return np.float_power(freq * (1 - freq), -0.38)
elif her_model == "ldak":
freq, weight = df_snp_info["FREQ"].values, df_snp_info["LDAK_WEIGHT"].values
return np.float_power(freq * (1 - freq), -0.25) * weight
else:
raise NotImplementedError
def impute_with_mean(mat, inplace=False, axis=1):
"""impute the each entry using the mean of the input matrix np.mean(mat, axis=axis)
axis = 1 corresponds to row-wise imputation
axis = 0 corresponds to column-wise imputation
Parameters
----------
mat : np.ndarray
input matrix. For reminder, the genotype matrix is with shape (n_snp, n_indiv)
inplace : bool
whether to return a new dataset or modify the input dataset
axis : int
axis to impute along
Returns
-------
if inplace:
mat : np.ndarray
(n_snp, n_indiv) matrix
else:
None
"""
assert axis in [0, 1], "axis should be 0 or 1"
if not inplace:
mat = mat.copy()
# impute the missing genotypes with the mean of each row
mean = np.nanmean(mat, axis=axis)
nanidx = np.where(np.isnan(mat))
# index the mean using the nanidx[1 - axis]
# axis = 1, row-wise imputation, index the mean using the nanidx[0]
# axis = 0, columnw-ise imputation, index the mean using the nanidx[1]
mat[nanidx] = mean[nanidx[1 - axis]]
if not inplace:
return mat
else:
return None
def geno_mult_mat(
geno: da.Array,
mat: np.ndarray,
impute_geno: bool = True,
mat_dim: str = "snp",
return_snp_var: bool = False,
) -> np.ndarray:
"""Multiply genotype matrix with another matrix
Chunk of genotype matrix will be read sequentially along the SNP dimension,
and multiplied with the `mat`.
Without transpose, result will be (n_snp, n_rep)
With transpose, result will be (n_indiv, n_rep)
Missing values in geno will be imputed with the mean of the genotype matrix.
Parameters
----------
geno : da.Array
Genotype matrix with shape (n_snp, n_indiv)
geno.chunk contains the chunk of genotype matrix to be multiplied
mat : np.ndarray
Matrix to be multiplied with the genotype matrix. If the passed variable
is a vector, it will be transformed to be a 1-column matrix.
impute_geno : bool
Whether to impute missing values with the mean of the genotype matrix
mat_dim : str
First dimension of the `mat`, either "snp" or "indiv"
Whether to transpose the genotype matrix and calulate geno.T @ mat
return_snp_var : bool
Whether to return the variance of each SNP, useful in simple linear
regression
Returns
-------
np.ndarray
Result of the multiplication
"""
assert mat_dim in ["snp", "indiv"], "mat_dim should be `snp` or `indiv`"
if mat.ndim == 1:
mat = mat[:, np.newaxis]
# chunks over SNPs
chunks = geno.chunks[0]
indices = np.insert(np.cumsum(chunks), 0, 0)
n_snp, n_indiv = geno.shape
n_rep = mat.shape[1]
snp_var = np.zeros(n_snp)
if mat_dim == "indiv":
# geno: (n_snp, n_indiv)
# mat: (n_indiv, n_rep)
assert (
mat.shape[0] == n_indiv
), "when mat_dim is 'indiv', matrix should be of shape (n_indiv, n_rep)"
ret = np.zeros((n_snp, n_rep))
for i in tqdm(range(len(indices) - 1), desc="admix.data.geno_mult_mat"):
start, stop = indices[i], indices[i + 1]
geno_chunk = geno[start:stop, :].compute()
# impute missing genotype
if impute_geno:
impute_with_mean(geno_chunk, inplace=True)
ret[start:stop, :] = np.dot(geno_chunk, mat)
if return_snp_var:
snp_var[start:stop] = np.var(geno_chunk, axis=0)
elif mat_dim == "snp":
# geno: (n_indiv, n_snp)
# mat: (n_snp, n_rep)
assert (
mat.shape[0] == n_snp
), "when mat_dim is 'snp', matrix should be of shape (n_snp, n_rep)"
ret = np.zeros((n_indiv, n_rep))
for i in tqdm(range(len(indices) - 1), desc="admix.data.geno_mult_mat"):
start, stop = indices[i], indices[i + 1]
geno_chunk = geno[start:stop, :].compute()
# impute missing genotype
if impute_geno:
impute_with_mean(geno_chunk, inplace=True)
ret += np.dot(geno_chunk.T, mat[start:stop, :])
if return_snp_var:
snp_var[start:stop] = np.var(geno_chunk, axis=0)
else:
raise ValueError("mat_dim should be `snp` or `indiv`")
if return_snp_var:
return ret, snp_var
else:
return ret
def grm(geno: da.Array, subpopu: np.ndarray = None, std_method: str = "std"):
"""Calculate the GRM matrix
This function is to serve as an alternative of GCTA --make-grm
Parameters
----------
geno: admix.Dataset
genotype (n_snp, n_indiv) matrix
subpopu : np.ndarray
subpopulation labels, with shape (n_indiv,). The allele frequencies and
normalization are performed separately within each subpopulation.
std_method : str
Method to standardize the GRM. Currently supported:
"std" (standardize to have mean 0 and variance 1),
"allele" (standardize to have mean 0 but no scaling)
Returns
-------
np.ndarray
GRM matrix (n_indiv, n_indiv)
"""
def normalize_geno(g):
"""Normalize the genotype matrix"""
# impute missing genotypes
g = impute_with_mean(g, inplace=False, axis=1)
# normalize
if std_method == "std":
g = (g - np.mean(g, axis=1)[:, None]) / np.std(g, axis=1)[:, None]
elif std_method == "allele":
g = g - np.mean(g, axis=1)[:, None]
else:
raise ValueError("std_method should be either `std` or `allele`")
return g
assert std_method in ["std", "allele"], "std_method should be `std` or `allele`"
n_snp = geno.shape[0]
n_indiv = geno.shape[1]
if subpopu is not None:
assert (
n_indiv == subpopu.shape[0]
), "subpopu should have the same length as the number of individuals"
unique_subpopu = np.unique(subpopu)
admix.logger.info(
f"{len(unique_subpopu)} subpopulations found: {unique_subpopu}"
)
admix.logger.info(
f"Calculating GRM matrix with {n_snp} SNPs and {n_indiv} individuals"
)
mat = 0
snp_chunks = geno.chunks[0]
indices = np.insert(np.cumsum(snp_chunks), 0, 0)
for i in tqdm(range(len(indices) - 1), desc="admix.data.grm"):
start, stop = indices[i], indices[i + 1]
geno_chunk = geno[start:stop, :].compute()
if subpopu is not None:
for popu in np.unique(subpopu):
geno_chunk[:, subpopu == popu] = normalize_geno(
geno_chunk[:, subpopu == popu]
)
else:
geno_chunk = normalize_geno(geno_chunk)
mat += np.dot(geno_chunk.T, geno_chunk) / n_snp
return mat
def admix_grm(
geno: da.Array, lanc: da.Array, n_anc: int = 2, snp_prior_var: np.ndarray = None
):
"""Calculate ancestry specific GRM matrix
Parameters
----------
geno : da.Array
Genotype matrix with shape (n_snp, n_indiv, 2)
lanc : np.ndarray
Local ancestry matrix with shape (n_snp, n_indiv, 2)
n_anc : int
Number of ancestral populations
snp_prior_var : np.ndarray
Prior variance of each SNP, shape (n_snp,)
Returns
-------
G1: np.ndarray
ancestry specific GRM matrix for the 1st ancestry
G2: np.ndarray
ancestry specific GRM matrix for the 2nd ancestry
G12: np.ndarray
ancestry specific GRM matrix for cross term of the 1st and 2nd ancestry
"""
assert n_anc == 2, "only two-way admixture is implemented"
assert np.all(geno.shape == lanc.shape)
apa = admix.data.allele_per_anc(geno, lanc, n_anc=n_anc)
n_snp, n_indiv = apa.shape[0:2]
if snp_prior_var is None:
snp_prior_var = np.ones(n_snp)
snp_prior_var_sum = snp_prior_var.sum()
G1 = np.zeros([n_indiv, n_indiv])
G2 = np.zeros([n_indiv, n_indiv])
G12 = np.zeros([n_indiv, n_indiv])
snp_chunks = apa.chunks[0]
indices = np.insert(np.cumsum(snp_chunks), 0, 0)
for i in tqdm(range(len(indices) - 1), desc="admix.data.admix_grm"):
start, stop = indices[i], indices[i + 1]
apa_chunk = apa[start:stop, :, :].compute()
# multiply by the prior variance on each SNP
apa_chunk *= np.sqrt(snp_prior_var[start:stop])[:, None, None]
a1_chunk, a2_chunk = apa_chunk[:, :, 0], apa_chunk[:, :, 1]
G1 += np.dot(a1_chunk.T, a1_chunk) / snp_prior_var_sum
G2 += np.dot(a2_chunk.T, a2_chunk) / snp_prior_var_sum
G12 += np.dot(a1_chunk.T, a2_chunk) / snp_prior_var_sum
return G1, G2, G12
def admix_grm_equal_var(
geno: da.Array, lanc: da.Array, n_anc: int, snp_prior_var: np.ndarray = None
):
"""Calculate ancestry specific GRM matrix K1, K2 (assuming equal variances for ancestries)
Parameters
----------
geno : da.Array
Genotype matrix with shape (n_snp, n_indiv, 2)
lanc : np.ndarray
Local ancestry matrix with shape (n_snp, n_indiv, 2)
n_anc : int
Number of ancestral populations
snp_prior_var : np.ndarray
Prior variance of each SNP, shape (n_snp,)
Returns
-------
K1: np.ndarray
sum of diagonal terms
K2: np.ndarray
off-diagonal terms
"""
assert np.all(geno.shape == lanc.shape)
apa = admix.data.allele_per_anc(geno, lanc, n_anc=n_anc)
n_snp, n_indiv = apa.shape[0:2]
if snp_prior_var is None:
snp_prior_var = np.ones(n_snp)
snp_prior_var_sum = snp_prior_var.sum()
K1 = np.zeros([n_indiv, n_indiv])
K2 = np.zeros([n_indiv, n_indiv])
snp_chunks = apa.chunks[0]
indices = np.insert(np.cumsum(snp_chunks), 0, 0)
for i in tqdm(range(len(indices) - 1), desc="admix.data.admix_grm_equal_var"):
start, stop = indices[i], indices[i + 1]
apa_chunk = apa[start:stop, :, :].compute()
# multiply by the prior variance on each SNP
apa_chunk *= np.sqrt(snp_prior_var[start:stop])[:, None, None]
# diagonal terms
for i_anc in range(n_anc):
a_chunk = apa_chunk[:, :, i_anc]
K1 += np.dot(a_chunk.T, a_chunk) / snp_prior_var_sum
# off-diagonal terms
for i_anc in range(n_anc):
for j_anc in range(i_anc + 1, n_anc):
a1_chunk, a2_chunk = apa_chunk[:, :, i_anc], apa_chunk[:, :, j_anc]
K2 += np.dot(a1_chunk.T, a2_chunk) / snp_prior_var_sum
K2 = K2 + K2.T
return K1, K2
def admix_ld(dset: admix.Dataset, cov: np.ndarray = None):
"""Calculate ancestry specific LD matrices
Parameters
----------
dset: admix.Dataset
dataset containing geno, lanc
cov : Optional[np.ndarray]
(n_indiv, n_cov) covariates of the genotypes, an all `1` intercept covariate will always be added
so there is no need to add the intercept in covariates.
Returns
-------
K1: np.ndarray
ancestry specific LD matrix for the 1st ancestry
K2: np.ndarray
ancestry specific LD matrix for the 2nd ancestry
K12: np.ndarray
ancestry specific LD matrix for cross term of the 1st and 2nd ancestry
"""
assert dset.n_anc == 2, "admix_ld only works for 2 ancestries for now"
apa = dset.allele_per_anc()
n_snp, n_indiv = apa.shape[0:2]
a1, a2 = apa[:, :, 0], apa[:, :, 1]
if cov is None:
cov = np.ones((n_indiv, 1))
else:
cov = np.hstack([np.ones((n_indiv, 1)), cov])
# projection = I - X * (X'X)^-1 * X'
cov_proj_mat = np.eye(n_indiv) - np.linalg.multi_dot(
[cov, np.linalg.inv(np.dot(cov.T, cov)), cov.T]
)
a1 = np.dot(a1, cov_proj_mat)
a2 = np.dot(a2, cov_proj_mat)
# center with row mean
# a1 -= a1.mean(axis=1, keepdims=True)
# a2 -= a2.mean(axis=1, keepdims=True)
ld1 = np.dot(a1, a1.T) / n_indiv
ld2 = np.dot(a2, a2.T) / n_indiv
ld12 = np.dot(a1, a2.T) / n_indiv
ld1, ld2, ld12 = dask.compute(ld1, ld2, ld12)
return {"11": ld1, "22": ld2, "12": ld12}
def af_per_anc(
geno, lanc, n_anc=2, return_nhaplo=False
) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
"""
Calculate allele frequency per ancestry
If at one particular SNP locus, no SNP from one particular ancestry can be found
the corresponding entries will be filled with np.NaN.
Parameters
----------
geno: np.ndarray
genotype matrix
lanc: np.ndarray
local ancestry matrix
n_anc: int
number of ancestries
return_nhaplo: bool
whether to return the number of haplotypes per ancestry
Returns
-------
np.ndarray
(n_snp, n_anc) length list of allele frequencies.
"""
assert np.all(geno.shape == lanc.shape)
n_snp = geno.shape[0]
af = np.zeros((n_snp, n_anc))
lanc_nhaplo = np.zeros((n_snp, n_anc))
snp_chunks = geno.chunks[0]
indices = np.insert(np.cumsum(snp_chunks), 0, 0)
for i in tqdm(range(len(indices) - 1), desc="admix.data.af_per_anc"):
start, stop = indices[i], indices[i + 1]
geno_chunk = geno[start:stop, :, :].compute()
lanc_chunk = lanc[start:stop, :, :].compute()
for anc_i in range(n_anc):
lanc_mask = lanc_chunk == anc_i
lanc_nhaplo[start:stop, anc_i] = np.sum(lanc_mask, axis=(1, 2))
# mask SNPs with local ancestry not `i_anc`
af[start:stop, anc_i] = (
np.ma.masked_where(np.logical_not(lanc_mask), geno_chunk)
.sum(axis=(1, 2))
.data
) / lanc_nhaplo[start:stop, anc_i]
if return_nhaplo:
return af, lanc_nhaplo
else:
return af
def allele_per_anc(
geno: da.Array,
lanc: da.Array,
n_anc: int,
center=False,
):
"""Get allele count per ancestry
Parameters
----------
geno: da.Array
genotype data
lanc: da.Array
local ancestry data
n_anc: int
number of ancestries
Returns
-------
Return allele counts per ancestries
"""
assert center is False, "center=True should not be used"
assert np.all(geno.shape == lanc.shape), "shape of `hap` and `lanc` are not equal"
assert geno.ndim == 3, "`hap` and `lanc` should have three dimension"
n_snp, n_indiv, n_haplo = geno.shape
assert n_haplo == 2, "`n_haplo` should equal to 2, check your data"
assert isinstance(geno, da.Array) & isinstance(
lanc, da.Array
), "`geno` and `lanc` should be dask array"
# make sure the chunk size along the ploidy axis to be 2
geno = geno.rechunk({2: 2})
lanc = lanc.rechunk({2: 2})
assert (
geno.chunks == lanc.chunks
), "`geno` and `lanc` should have the same chunk size"
assert len(geno.chunks[1]) == 1, (
"geno / lanc should not be chunked across the second dimension"
"(individual dimension)"
)
def helper(geno_chunk, lanc_chunk, n_anc):
n_snp, n_indiv, n_haplo = geno_chunk.shape
apa = np.zeros((n_snp, n_indiv, n_anc), dtype=np.float64)
for i_haplo in range(n_haplo):
haplo_hap = geno_chunk[:, :, i_haplo]
haplo_lanc = lanc_chunk[:, :, i_haplo]
for i_anc in range(n_anc):
apa[:, :, i_anc][haplo_lanc == i_anc] += haplo_hap[haplo_lanc == i_anc]
return apa
# the resulting chunk sizes will be the same as the input for snp, indiv
# while the third dimension will be (n_anc, )
output_chunks = (geno.chunks[0], geno.chunks[1], (n_anc,))
res = da.map_blocks(
lambda geno_chunk, lanc_chunk: helper(
geno_chunk=geno_chunk, lanc_chunk=lanc_chunk, n_anc=n_anc
),
geno,
lanc,
dtype=np.float64,
chunks=output_chunks,
)
return res
def calc_pgs(dset: admix.Dataset, df_weights: pd.DataFrame, method: str):
"""Calculate PGS for each individual
Parameters
----------
dset: admix.Dataset
dataset object
df_weights: pd.DataFrame
weights for each individual
method: str
method to calculate PGS. Options are:
- "total": vanilla PGS
- "partial": partial PGS, calculate partial PGS for each local ancestry
Returns
-------
np.ndarray
PGS for each individual
- method = "total": (n_indiv, )
- method = "partial": (n_indiv, n_anc)
"""
assert method in [
"total",
"partial",
], "method should be either 'total' or 'partial'"
assert np.all(
dset.snp.index == df_weights.index
), "`dset` and `df_weights` should have exactly the same index"
assert len(df_weights.columns) == 1, "`df_weights` should have only one column"
if method == "total":
pgs = admix.data.geno_mult_mat(
dset.geno.sum(axis=2), df_weights.values
).flatten()
elif method == "partial":
n_anc = dset.n_anc
pgs = np.zeros((dset.n_indiv, n_anc))
apa = dset.allele_per_anc()
for i_anc in range(n_anc):
pgs[:, i_anc] = admix.data.geno_mult_mat(
apa[:, :, i_anc], df_weights.values
).flatten()
else:
raise ValueError("method should be either 'total' or 'partial'")
return pgs
def calc_partial_pgs(
dset: admix.Dataset,
df_weights: pd.DataFrame,
dset_ref: admix.Dataset = None,
ref_pop_indiv: List[List[str]] = None,
weight_col="WEIGHT",
) -> pd.DataFrame:
"""Calculate PGS for each individual
Parameters
----------
dset: admix.Dataset
dataset object
df_weights: pd.DataFrame
weights for each individual
dset_ref: admix.Dataset
reference dataset object, use `dapgen.align_snp` to align the SNPs between
`dset` and `dset_ref`. `CHROM` and `POS` must match, with potential flips of
`REF` and `ALT` allele coding.
ref_pop: List[List[str]]
list of reference individual ID in `dset_ref`
Returns
-------
pd.DataFrame
PGS for each individual
- (n_indiv, n_anc)
"""
assert (dset_ref is None) == (
ref_pop_indiv is None
), "both `dset_ref` and `ref_pop_indiv` should be None or not None"
CALC_REF = dset_ref is not None
CHECK_COLS = ["CHROM", "POS", "REF", "ALT"]
## check input
idx1, idx2, sample_wgt_flip = dapgen.align_snp(
df1=dset.snp[CHECK_COLS], df2=df_weights[CHECK_COLS]
)
assert np.all(idx1 == dset.snp.index) & np.all(
idx2 == df_weights.index
), "`dset` and `df_weights` should align, with potential allele flip"
if CALC_REF:
idx1, idx2, ref_wgt_flip = dapgen.align_snp(
df1=dset.snp[CHECK_COLS], df2=dset_ref.snp[CHECK_COLS]
)
assert np.all(idx1 == dset.snp.index) & np.all(
idx2 == dset_ref.snp.index
), "`dset` and `dset_ref` should align, with potential allele flip"
weights = df_weights[weight_col].values
sample_weights = weights * sample_wgt_flip
if CALC_REF:
ref_weights = weights * ref_wgt_flip * sample_wgt_flip
assert (
len(ref_pop_indiv) == dset.n_anc
), "`len(ref_pops)` should match with `dset.n_anc`"
## scoring
dset_geno, dset_lanc = dset.geno.compute(), dset.lanc.compute()
sample_pgs = np.zeros((dset.n_indiv, dset.n_anc))
if CALC_REF:
ref_geno_list = [dset_ref[:, pop].geno.compute() for pop in ref_pop_indiv]
ref_pgs = [[] for pop in ref_pop_indiv]
# iterate over each individuals
for indiv_i in tqdm(range(dset.n_indiv), desc="admix.data.calc_partial_pgs"):
indiv_ref_pgs = [0, 0]
# pgs for sample individuals
for haplo_i in range(2):
geno = dset_geno[:, indiv_i, haplo_i]
lanc = dset_lanc[:, indiv_i, haplo_i]
for lanc_i in range(dset.n_anc):
# sample
sample_pgs[indiv_i, lanc_i] += np.dot(
geno[lanc == lanc_i], sample_weights[lanc == lanc_i]
)
# pgs for reference individuals
if CALC_REF:
ref_geno = ref_geno_list[lanc_i][lanc == lanc_i, :, :]
if ref_geno.shape[0] > 0:
ref_geno = ref_geno.reshape(ref_geno.shape[0], -1)
s = np.dot(ref_weights[lanc == lanc_i], ref_geno)
else:
s = np.zeros(ref_geno.shape[1] * 2)
indiv_ref_pgs[lanc_i] += s
if CALC_REF:
for lanc_i in range(dset.n_anc):
ref_pgs[lanc_i].append(indiv_ref_pgs[lanc_i])
# format ref_pgs: for each ancestry, we have n_indiv x (n_ref_indiv x 2)
# each reference has 2 haplotypes
if CALC_REF:
ref_pgs = [
pd.DataFrame(
data=np.vstack(ref_pgs[i]),
index=dset.indiv.index,
columns=np.concatenate(
[[str(i) + "_1", str(i) + "_2"] for i in ref_pop_indiv[i]]
),
)
for i in range(dset.n_anc)
]
sample_pgs = pd.DataFrame(
data=sample_pgs,
index=dset.indiv.index,
columns=[f"ANC{i}" for i in range(1, dset.n_anc + 1)],
)
if CALC_REF:
return sample_pgs, ref_pgs
else:
return sample_pgs
|
[
"[email protected]"
] | |
2f15f7e40c8b0c4d24d57fc4badf32a92ac1ad85
|
989ce02251979f07d1b00f1c0baba18e1077e716
|
/cute/settings.py
|
a51be9375b1faef75b866ac12ea97b2378bc63b2
|
[] |
no_license
|
Sara1527/cute
|
29f66ac3ffd88e8aeac4e9665f874f8f41016927
|
54c25132c1f04e8fdbd64339f273ae44d592b8a5
|
refs/heads/master
| 2023-03-23T01:02:55.027199 | 2021-03-21T13:36:26 | 2021-03-21T13:36:26 | 350,008,240 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,330 |
py
|
"""
Django settings for cute project.
Generated by 'django-admin startproject' using Django 2.2.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.2/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '0x!yix%$)6^h17&$rdr2&01z^!mzis1g4v$68kd@7vndv8f&99'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = ['*'] # manually add * as wild character for server public private
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'import_export', # manually added
'gtalentpro', # manually added
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'cute.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'cute.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.2/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'Asia/Kolkata'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.2/howto/static-files/
STATIC_URL = '/static/'
# manually defined login redirection url for @login_required decorator
LOGIN_URL = '/login/'
|
[
"[email protected]"
] | |
c7ac23c922c80e2332218811b21011cadfc091bc
|
e1d11efb8ed3f58ca1a4e3ac2ca3da45beb0a758
|
/automated_picture_translator_api.py
|
d1d5ec7262917224a5f25953e8b6658b29f3188f
|
[] |
no_license
|
ShanSanear/AutomatedPictureTranslator
|
0ed09914e813145d42260ab8f3e885217066037e
|
27250624d079590aabd72a12ce9be98c424f699e
|
refs/heads/master
| 2023-07-30T10:52:39.312198 | 2021-09-22T18:54:51 | 2021-09-22T18:54:51 | 332,493,568 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,802 |
py
|
from typing import Optional
import uvicorn
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import translation_processing
from picture_processing import PictureProcessing
from utils import CapturePosition
app = FastAPI()
picture_processing = PictureProcessing()
class Translation(BaseModel):
sentence: str
sentence_words: list[str]
translated_sentence: str
translated_words: list[str]
@app.get("/")
async def read_root():
return {"Hello": "World!"}
@app.get("/translate")
async def translate_screen_part(position: CapturePosition):
pic = picture_processing.capture_picture(top_left=position.top_left, size=position.size)
sentence = picture_processing.process_picture_white_to_black(pic)
if not sentence:
sentence = picture_processing.process_picture_ocr(pic)
if not sentence:
raise HTTPException(status_code=404, detail="Couldn't translate right now")
translated_sentence = translation_processing.translate_text(sentence, source_language='en', target_language='pl')
sentence_words = translation_processing.get_single_words_to_translate(sentence)
translated_words = translation_processing.translate_all_words(sentence_words, source_language='en',
target_language='pl')
return Translation(
sentence=sentence,
sentence_words=sentence_words,
translated_sentence=translated_sentence,
translated_words=translated_words
)
@app.get("/item/{item_id}")
async def read_item(item_id: int, q: Optional[str] = None):
return {"item_id": item_id, "q": q}
def main():
uvicorn.run("automated_picture_translator_api:app", port=8000, reload=True, access_log=False)
if __name__ == '__main__':
main()
|
[
"[email protected]"
] | |
5b8aced9977d9f12adf0d4b703c3e25b1e55c899
|
e16911f1fae7bf90f405e055e0f90731ae8c8042
|
/etc/st2packgen/files/actions/lib/k8sbase.py
|
89df63259b4fbf47136ae2a8cdf29077dfb9461e
|
[] |
no_license
|
bobhenkel/stackstorm-kubernetes
|
87136448434b1a6c821cfeb757f88833ca8ecf02
|
32b8538597bc5290a18cefadbf98fea7f8bb38bd
|
refs/heads/master
| 2021-04-25T22:06:36.392650 | 2017-11-02T04:30:02 | 2017-11-02T04:30:02 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,242 |
py
|
from __future__ import absolute_import
from pyswagger.core import BaseClient
from requests import Session, Request
import six
import json
import base64
class Client(BaseClient):
# declare supported schemes here
__schemes__ = set(['http', 'https'])
def __init__(self, config=None, auth=None, send_opt=None, extraheaders=None):
""" constructor
:param auth pyswagger.SwaggerAuth: auth info used when requesting
:param send_opt dict: options used in requests.send, ex verify=False
"""
super(Client, self).__init__(auth)
if send_opt is None:
send_opt = {}
self.__s = Session()
self.__send_opt = send_opt
self.extraheaders = extraheaders
auth = base64.b64encode(config['user'] + ":" + config['password'])
self.authhead = {"authorization": "Basic " + auth}
def request(self, req_and_resp, opt):
# passing to parent for default patching behavior,
# applying authorizations, ...etc.
req, resp = super(Client, self).request(req_and_resp, opt)
req.prepare(scheme=self.prepare_schemes(req).pop(), handle_files=False)
req._patch(opt)
file_obj = []
def append(name, obj):
f = obj.data or open(obj.filename, 'rb')
if 'Content-Type' in obj.header:
file_obj.append((name, (obj.filename, f, obj.header['Content-Type'])))
else:
file_obj.append((name, (obj.filename, f)))
for k, v in six.iteritems(req.files):
if isinstance(v, list):
for vv in v:
append(k, vv)
else:
append(k, v)
rq = Request(
method=req.method.upper(),
url=req.url,
params=req.query,
data=req.data,
headers=req.header,
files=file_obj
)
rq = self.__s.prepare_request(rq)
rq.headers.update(self.authhead)
rs = self.__s.send(rq, stream=True, **self.__send_opt)
myresp = {}
myresp['status'] = rs.status_code
myresp['data'] = json.loads(rs.content.rstrip())
# myresp['headers'] = rs.headers
return myresp
|
[
"[email protected]"
] | |
6b7ec47b7dfaed08aeefb1d1ec11acaff71addf7
|
447e9ec821dc7505cc9b73fb7abeb220fe2b3a86
|
/rvpy/logistic.py
|
2d66e011e93fb9f8e4dc0e7ab086276b4445ba04
|
[
"MIT"
] |
permissive
|
timbook/rvpy
|
ecd574f91ed50fd47b6ead8517954f01e33c03a7
|
301fd61df894d4b300176e287bf9e725378c38eb
|
refs/heads/master
| 2020-03-19T04:01:49.283213 | 2018-12-18T19:21:07 | 2018-12-18T19:21:07 | 135,788,512 | 1 | 0 |
MIT
| 2018-12-18T19:21:08 | 2018-06-02T04:55:39 |
Python
|
UTF-8
|
Python
| false | false | 3,722 |
py
|
import numpy as np
from math import log, exp
from scipy.stats import logistic, fisk
from . import distribution
class Logistic(distribution.Distribution):
"""
Logistic Distribution using the following parameterization:
f(x | loc, scale) = exp(-z) / (s * (1 + exp(-z))^2)
where z = (x - loc) / scale
Parameters
----------
loc : float, positive
Location parameter
scale : float, positive
Scale parameter
Methods
-------
exp()
Transforms self to LogLogistic
Relationships
-------------
Let X be Logistic, a, b float. Then:
* aX + b is Logistic
* exp(X) is Log-Logistic
"""
def __init__(self, loc=0, scale=1):
"""
Parameters
----------
loc : float, positive
Location parameter
scale : float, positive
Scale parameter
"""
assert scale > 0, "scale parameter must be positive"
# Parameters
self.loc = loc
self.scale = scale
# Scipy backend
self.sp = logistic(loc=loc, scale=scale)
super().__init__()
def __repr__(self):
return f"Logistic(loc={self.loc}, scale={self.scale})"
def __add__(self, other):
if isinstance(other, (int, float)):
return Logistic(self.loc + other, self.scale)
else:
raise TypeError(f"Can't add or subtract objects of type {type(other)} to Logistic")
def __mul__(self, other):
if isinstance(other, (int, float)):
return Logistic(other * self.loc, other * self.scale)
else:
raise TypeError(f"Can't multiply objects of type {type(other)} by Logistic")
def __truediv__(self, other):
if isinstance(other, (int, float)):
return self.__mul__(1/other)
else:
raise TypeError(f"Can't divide objects of type {type(other)} by Logistic")
def exp(self):
return LogLogistic(alpha=exp(self.loc), beta=1/self.scale)
# TODO: Gumbel - Gumbel = Logistic
class LogLogistic(distribution.Distribution):
"""
LogLogistic Distribution using the following parameterization:
f(x | a, b) = (b/a) * (x/a)^(b-1) / (1 + (x/a)^b)^2
Parameters
----------
alpha : float, positive
Scale parameter
beta : float, positive
Shape parameter
Methods
-------
log()
Transforms self to Logistic
Relationships
-------------
Let X be LogLogistic, k > 0 float. Then:
* kX is LogLogistic
* log(X) is Logistic
"""
def __init__(self, alpha, beta):
"""
Parameters
----------
alpha : float, positive
Scale parameter
beta : float, positive
Shape parameter
"""
assert alpha > 0, "alpha must be positive"
assert beta > 0, "alpha must be positive"
# Parameters
self.alpha = alpha
self.beta = beta
# Scipy backend
self.sp = fisk(c=beta, scale=alpha)
super().__init__()
def __repr__(self):
return f"LogLogistic(alpha={self.alpha}, beta={self.beta})"
def __mul__(self, other):
if isinstance(other, (int, float)):
return LogLogistic(other*self.alpha, self.beta)
else:
raise TypeError(f"Can't multiply objects of type {type(other)} by LogLogistic")
def __truediv__(self, other):
if isinstance(other, (int, float)):
return self.__mul__(1/other)
else:
raise TypeError(f"Can't divide objects of type {type(other)} by LogLogistic")
def log(self):
return Logistic(loc=np.log(self.alpha), scale=1/self.beta)
|
[
"[email protected]"
] | |
a2d362eaf614a13071c77fff4712da108a1d9924
|
b1e8f4b70208c5d35fbe9aedd6239652960d99dc
|
/pgdrive/envs/pgdrive_env_v2.py
|
ed7917d3a1a9a9843bc4443b706e58204c024bec
|
[
"Apache-2.0"
] |
permissive
|
Fredtoby/pgdrive
|
72b7cbcc22cbdb6d03adca8e344ce376c27c2ecb
|
d91fa9c7da3b0892765a4c163cf9da309997b310
|
refs/heads/main
| 2023-04-01T06:49:39.810785 | 2021-04-03T08:28:58 | 2021-04-03T08:28:58 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 6,057 |
py
|
import logging
import os.path as osp
import numpy as np
from pgdrive.constants import DEFAULT_AGENT
from pgdrive.envs.pgdrive_env import PGDriveEnv as PGDriveEnvV1
from pgdrive.scene_manager.traffic_manager import TrafficMode
from pgdrive.utils import PGConfig, clip
pregenerated_map_file = osp.join(osp.dirname(osp.dirname(osp.abspath(__file__))), "assets", "maps", "PGDrive-maps.json")
class PGDriveEnvV2(PGDriveEnvV1):
DEFAULT_AGENT = DEFAULT_AGENT
@staticmethod
def default_config() -> PGConfig:
config = PGDriveEnvV1.default_config()
config.update(
dict(
# ===== Traffic =====
traffic_density=0.1,
traffic_mode=TrafficMode.Trigger, # "reborn", "trigger", "hybrid"
random_traffic=False, # Traffic is randomized at default.
# ===== Cost Scheme =====
crash_vehicle_cost=1.,
crash_object_cost=1.,
out_of_road_cost=1.,
# ===== Reward Scheme =====
# See: https://github.com/decisionforce/pgdrive/issues/283
success_reward=10.0,
out_of_road_penalty=5.0,
crash_vehicle_penalty=5.0,
crash_object_penalty=5.0,
acceleration_penalty=0.0,
driving_reward=1.0,
general_penalty=0.0,
speed_reward=0.5,
use_lateral=False,
# See: https://github.com/decisionforce/pgdrive/issues/297
vehicle_config=dict(lidar=dict(num_lasers=120, distance=50, num_others=0)),
# Disable map loading!
load_map_from_json=False,
_load_map_from_json="",
)
)
config.remove_keys([])
return config
def __init__(self, config: dict = None):
super(PGDriveEnvV2, self).__init__(config=config)
def done_function(self, vehicle_id: str):
vehicle = self.vehicles[vehicle_id]
done = False
done_info = dict(crash_vehicle=False, crash_object=False, out_of_road=False, arrive_dest=False)
if vehicle.arrive_destination:
done = True
logging.info("Episode ended! Reason: arrive_dest.")
done_info["arrive_dest"] = True
elif vehicle.crash_vehicle:
done = True
logging.info("Episode ended! Reason: crash. ")
done_info["crash_vehicle"] = True
elif vehicle.out_of_route or not vehicle.on_lane or vehicle.crash_sidewalk:
done = True
logging.info("Episode ended! Reason: out_of_road.")
done_info["out_of_road"] = True
elif vehicle.crash_object:
done = True
done_info["crash_object"] = True
# for compatibility
# crash almost equals to crashing with vehicles
done_info["crash"] = done_info["crash_vehicle"] or done_info["crash_object"]
return done, done_info
def cost_function(self, vehicle_id: str):
vehicle = self.vehicles[vehicle_id]
step_info = dict()
step_info["cost"] = 0
if vehicle.crash_vehicle:
step_info["cost"] = self.config["crash_vehicle_cost"]
elif vehicle.crash_object:
step_info["cost"] = self.config["crash_object_cost"]
elif vehicle.out_of_route:
step_info["cost"] = self.config["out_of_road_cost"]
return step_info['cost'], step_info
def reward_function(self, vehicle_id: str):
"""
Override this func to get a new reward function
:param vehicle_id: id of BaseVehicle
:return: reward
"""
vehicle = self.vehicles[vehicle_id]
step_info = dict()
# Reward for moving forward in current lane
current_lane = vehicle.lane if vehicle.lane in vehicle.routing_localization.current_ref_lanes else \
vehicle.routing_localization.current_ref_lanes[0]
long_last, _ = current_lane.local_coordinates(vehicle.last_position)
long_now, lateral_now = current_lane.local_coordinates(vehicle.position)
reward = 0.0
# reward for lane keeping, without it vehicle can learn to overtake but fail to keep in lane
if self.config["use_lateral"]:
lateral_factor = clip(
1 - 2 * abs(lateral_now) / vehicle.routing_localization.get_current_lane_width(), 0.0, 1.0
)
else:
lateral_factor = 1.0
reward += self.config["driving_reward"] * (long_now - long_last) * lateral_factor
reward += self.config["speed_reward"] * (vehicle.speed / vehicle.max_speed)
step_info["step_reward"] = reward
if vehicle.crash_vehicle:
reward = -self.config["crash_vehicle_penalty"]
elif vehicle.crash_object:
reward = -self.config["crash_object_penalty"]
elif vehicle.out_of_route:
reward = -self.config["out_of_road_penalty"]
elif vehicle.arrive_destination:
reward = +self.config["success_reward"]
return reward, step_info
def _get_reset_return(self):
ret = {}
self.for_each_vehicle(lambda v: v.update_state())
for v_id, v in self.vehicles.items():
self.observations[v_id].reset(self, v)
ret[v_id] = self.observations[v_id].observe(v)
return ret[DEFAULT_AGENT] if self.num_agents == 1 else ret
if __name__ == '__main__':
def _act(env, action):
assert env.action_space.contains(action)
obs, reward, done, info = env.step(action)
assert env.observation_space.contains(obs)
assert np.isscalar(reward)
assert isinstance(info, dict)
env = PGDriveEnvV2()
try:
obs = env.reset()
assert env.observation_space.contains(obs)
_act(env, env.action_space.sample())
for x in [-1, 0, 1]:
env.reset()
for y in [-1, 0, 1]:
_act(env, [x, y])
finally:
env.close()
|
[
"[email protected]"
] | |
df016bf13355458c6083ae6c2005a1cebd3ceecb
|
7b6313d1c4e0e8a5bf34fc8ac163ad446bc69354
|
/datastructure and algorithms/[hackerrank]The Hurdle Race.py
|
5bcab2ab43d0415da1bf267cba2ff15bee29380b
|
[] |
no_license
|
menuka-maharjan/competitive_programming
|
c6032ae3ddcbc974e0e62744989a2aefa30864b2
|
22d0cea0f96d8bd6dc4d81b146ba20ea627022dd
|
refs/heads/master
| 2023-05-01T05:23:09.641733 | 2021-05-23T16:22:21 | 2021-05-23T16:22:21 | 332,250,476 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 144 |
py
|
nk=input().split()
n=int(nk[0])
k=int(nk[1])
l=list(map(int,input().rstrip().split()))
x=max(l)
if((x-k)>=0):
print(x-k)
else:
print(0)
|
[
"[email protected]"
] | |
8881f72660102dd5e42f6fb6a136b8c7c3c59e94
|
a3e80242b3e32c0779e9ab78c3de9ecfec499cb6
|
/models.py
|
4077b2094761e52c5c2242a8526ac736874fabee
|
[] |
no_license
|
gitzart/multi-user-blog
|
c8924d8d9c0aae79a8ed46ec460c986b738a965a
|
c8fa7c2bd8e67e2ad1fabbb574ac060fdd465dca
|
refs/heads/master
| 2021-04-28T23:02:50.708986 | 2017-01-23T10:04:38 | 2017-01-23T10:04:38 | 77,741,138 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,623 |
py
|
"""models.py: Google Datastore models and query related methods."""
__author__ = 'Alan'
__copyright__ = 'Copyright 2017, Multi User Project'
from google.appengine.ext import ndb
class User(ndb.Model):
username = ndb.StringProperty(required=True)
password = ndb.StringProperty(required=True)
email = ndb.StringProperty()
created_on = ndb.DateTimeProperty(auto_now_add=True)
@classmethod
def pk(cls, group='default'):
"""Parent key of user."""
return ndb.Key(cls, group)
@classmethod
def create(cls, username, password, email):
"""Create a new user to add to the datastore."""
return cls(
parent=cls.pk(), username=username, password=password, email=email
)
@classmethod
def by_name(cls, username):
"""Query a user by name."""
return cls.query(cls.username==username).get()
class Post(ndb.Model):
author = ndb.StringProperty(required=True)
title = ndb.StringProperty(required=True)
content = ndb.TextProperty(required=True)
excerpt = ndb.StringProperty()
likes = ndb.IntegerProperty(repeated=True)
pub_date = ndb.DateTimeProperty(auto_now_add=True)
update_date = ndb.DateTimeProperty(auto_now=True)
@classmethod
def pk(cls, name='default'):
"""Parent key of post."""
return ndb.Key(cls, name, parent=User.pk())
@classmethod
def create(cls, author, title, content, excerpt, likes=[]):
"""Create a new post to add to the datastore."""
return cls(
parent=cls.pk(),
author=author,
title=title,
content=content,
excerpt=excerpt,
likes=likes
)
@classmethod
def by_id(cls, pid):
"""Query post by post id."""
return cls.get_by_id(pid, parent=cls.pk())
@classmethod
def delete_post(cls, pid):
"""Delete individual post by post id."""
cls.by_id(pid).key.delete()
@classmethod
def query_post(cls):
"""Query all the posts in the descending publishing date order."""
return cls.query(ancestor=cls.pk()).order(-cls.pub_date)
class Comment(ndb.Model):
author = ndb.StringProperty()
author_email = ndb.StringProperty()
content = ndb.TextProperty(required=True)
pub_date = ndb.DateTimeProperty(auto_now_add=True)
update_date = ndb.DateTimeProperty(auto_now=True)
@classmethod
def pk(cls, pid):
"""Parent key of comment."""
return ndb.Key('Post', pid, parent=Post.pk())
@classmethod
def create(cls, pid, author, email, content):
"""Create a new comment to add to the datastore."""
return cls(
parent=cls.pk(pid),
author=author, author_email=email, content=content
)
@classmethod
def by_id(cls, pid, cid):
"""Query comment by comment id."""
return cls.get_by_id(cid, parent=cls.pk(pid))
@classmethod
def delete_comment(cls, pid, cid):
"""Delete individual comment by comment id
and the post id it belongs to.
"""
cls.by_id(pid, cid).key.delete()
@classmethod
def delete_multi_comment(cls, pid):
"""Delete multiple comments by comment ids.
and the post id they belong to.
"""
keys = cls.query_comment(cls.pk(pid)).fetch(keys_only=True)
if keys: ndb.delete_multi(keys)
@classmethod
def query_comment(cls, ancestor_key):
"""Query all the comments in the descending update date order."""
return cls.query(ancestor=ancestor_key).order(-cls.update_date)
|
[
"[email protected]"
] |
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