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int64 18.2k
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175
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7d721c03caa26629e29120c9c88caf4b817914fe
|
eb9f655206c43c12b497c667ba56a0d358b6bc3a
|
/python/testData/codeInsight/smartEnter/colonAfterFinalCaseClauseWithPrecedingIncompleteCaseClause.py
|
ff245238744da24b5bebf8391bf5e8c4d1ab488c
|
[
"Apache-2.0"
] |
permissive
|
JetBrains/intellij-community
|
2ed226e200ecc17c037dcddd4a006de56cd43941
|
05dbd4575d01a213f3f4d69aa4968473f2536142
|
refs/heads/master
| 2023-09-03T17:06:37.560889
| 2023-09-03T11:51:00
| 2023-09-03T12:12:27
| 2,489,216
| 16,288
| 6,635
|
Apache-2.0
| 2023-09-12T07:41:58
| 2011-09-30T13:33:05
| null |
UTF-8
|
Python
| false
| false
| 83
|
py
|
match x:
case 1:
pass
case
case 3:
pass
case<caret>
|
[
"intellij-monorepo-bot-no-reply@jetbrains.com"
] |
intellij-monorepo-bot-no-reply@jetbrains.com
|
7cab64eb481e0b3a7c9d90be43880cc7cb881492
|
b7139c6f5a4419326e0b856f34639f64271c5a3d
|
/cloud-backup.py
|
09350d23d8eb2884f7c4029f891878551475b27c
|
[] |
no_license
|
todokku/cloud-backup
|
3f7a90bf6db42285f1d9993e9903f7b97c2e134c
|
7a2eaae1e141da91c5dbe8dec73b0c87be8c7406
|
refs/heads/master
| 2022-07-22T13:33:16.791842
| 2020-05-22T18:55:49
| 2020-05-22T18:55:49
| 266,587,310
| 1
| 0
| null | 2020-05-24T17:07:13
| 2020-05-24T17:07:12
| null |
UTF-8
|
Python
| false
| false
| 105
|
py
|
import os
import sys
os.system('chmod +x cloud-backup.sh')
os.system('./cloud-backup.sh')
print('Fine.')
|
[
"lazzaronipaolo@outlook.it"
] |
lazzaronipaolo@outlook.it
|
cae12c341248552d20b6b8dcb35c4720f5f00d98
|
a13ef2b26adf4d13ecf7060c40c655f4c3c64d27
|
/practice/os.py
|
376163be962dc5dc7c90c2cdb24f2074b2c0a071
|
[] |
no_license
|
sunzhijie/learn-python
|
d0853759c0aebfa987f72e7931ff2ffbff86d6bc
|
b8a6d1216a5835129c906e4bceef9b6fe09f3eaf
|
refs/heads/master
| 2016-09-07T18:44:40.037641
| 2015-04-17T08:03:33
| 2015-04-17T08:03:33
| 33,942,917
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 248
|
py
|
__author__ = 'sunzhijie'
import os
print os.getenv('PATH')
print os.path.abspath('.')
print os.path.join('/user/', 'dfdf')
# print os.mkdir('./name')
# print os.rmdir('./name')
print [x for x in os.listdir('.') if os.path.splitext(x)[1] == '.py']
|
[
"646202721@qq.com"
] |
646202721@qq.com
|
b67f2769bfefa0625cc6527943ef1b7faf9c0f9a
|
ff1fe0e31e863ab69e2434b574115fed782d76ad
|
/set.py
|
e37f9c7d1e8de9534208c0ced057cebe0e3f014c
|
[] |
no_license
|
tasnuvaleeya/python_programming
|
cd7200e0dc0c4ec6bd23c4f9360fc251a7c4a516
|
45a577634e53a1c4cab927eb770cde01a00571ce
|
refs/heads/master
| 2021-04-12T02:47:46.011445
| 2018-03-17T14:54:09
| 2018-03-17T14:54:09
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 152
|
py
|
groceries = {'cereal', 'milk','rice', 'beer', 'beer'}
if 'milk' in groceries:
print('you already have milk')
else:
print('oh yes u need milk')
|
[
"tasnuva2606@gmail.com"
] |
tasnuva2606@gmail.com
|
50a143d4fe47cc7b13e7ca802246ee09743ff7a8
|
2d82d4c6574bd6d32f2cf1c781615f7951f55f66
|
/muntjac/event/dd/acceptcriteria/and_.py
|
255229b61f9d197892bc0c331d353dba4488b0e7
|
[
"Apache-2.0"
] |
permissive
|
metaperl/muntjac
|
f83f745ee03942a61af92ee7fba7285aa9c46f3c
|
8db97712edd81b4d25deaaa48587d2a08010f2c8
|
refs/heads/master
| 2021-01-15T22:04:25.057862
| 2012-11-09T03:52:59
| 2012-11-09T03:52:59
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 960
|
py
|
# @MUNTJAC_COPYRIGHT@
# @MUNTJAC_LICENSE@
"""A compound criterion that accepts the drag if all of its criteria
accepts the drag."""
from muntjac.event.dd.acceptcriteria.client_side_criterion import \
ClientSideCriterion
class And(ClientSideCriterion):
"""A compound criterion that accepts the drag if all of its criteria
accepts the drag.
@see: L{Or}
"""
def __init__(self, *criteria):
"""@param criteria:
criteria of which the And criterion will be composed
"""
self.criteria = criteria
def paintContent(self, target):
super(And, self).paintContent(target)
for crit in self.criteria:
crit.paint(target)
def accept(self, dragEvent):
for crit in self.criteria:
if not crit.accept(dragEvent):
return False
return True
def getIdentifier(self):
return 'com.vaadin.event.dd.acceptcriteria.And'
|
[
"r.w.lincoln@gmail.com"
] |
r.w.lincoln@gmail.com
|
421db7ccdd9e41909059dc08bf8ce7640d20deee
|
65f469808cd1d408524ff70ff42d2a8f7276e805
|
/swagger-gen/python/swagger_client/models/order_res_base.py
|
ec564b12fb79e83230663a35133b7dc7b76bd787
|
[] |
no_license
|
CryptoGnome/api-connectors
|
94cabc32b8af7d9a35d72336eb4e8f919adc69ad
|
c4f5dba196b0d2bb321b5a068481f34d02ae40fa
|
refs/heads/master
| 2020-09-26T23:00:49.861037
| 2019-12-06T08:38:15
| 2019-12-06T08:38:15
| 226,361,891
| 5
| 0
| null | 2019-12-06T15:52:50
| 2019-12-06T15:52:49
| null |
UTF-8
|
Python
| false
| false
| 6,300
|
py
|
# coding: utf-8
"""
Bybit API
## REST API for the Bybit Exchange. # noqa: E501
OpenAPI spec version: 1.0.0
Contact: support@bybit.com
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class OrderResBase(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'ret_code': 'float',
'ret_msg': 'str',
'ext_code': 'str',
'ext_info': 'str',
'result': 'object',
'time_now': 'str'
}
attribute_map = {
'ret_code': 'ret_code',
'ret_msg': 'ret_msg',
'ext_code': 'ext_code',
'ext_info': 'ext_info',
'result': 'result',
'time_now': 'time_now'
}
def __init__(self, ret_code=None, ret_msg=None, ext_code=None, ext_info=None, result=None, time_now=None): # noqa: E501
"""OrderResBase - a model defined in Swagger""" # noqa: E501
self._ret_code = None
self._ret_msg = None
self._ext_code = None
self._ext_info = None
self._result = None
self._time_now = None
self.discriminator = None
if ret_code is not None:
self.ret_code = ret_code
if ret_msg is not None:
self.ret_msg = ret_msg
if ext_code is not None:
self.ext_code = ext_code
if ext_info is not None:
self.ext_info = ext_info
if result is not None:
self.result = result
if time_now is not None:
self.time_now = time_now
@property
def ret_code(self):
"""Gets the ret_code of this OrderResBase. # noqa: E501
:return: The ret_code of this OrderResBase. # noqa: E501
:rtype: float
"""
return self._ret_code
@ret_code.setter
def ret_code(self, ret_code):
"""Sets the ret_code of this OrderResBase.
:param ret_code: The ret_code of this OrderResBase. # noqa: E501
:type: float
"""
self._ret_code = ret_code
@property
def ret_msg(self):
"""Gets the ret_msg of this OrderResBase. # noqa: E501
:return: The ret_msg of this OrderResBase. # noqa: E501
:rtype: str
"""
return self._ret_msg
@ret_msg.setter
def ret_msg(self, ret_msg):
"""Sets the ret_msg of this OrderResBase.
:param ret_msg: The ret_msg of this OrderResBase. # noqa: E501
:type: str
"""
self._ret_msg = ret_msg
@property
def ext_code(self):
"""Gets the ext_code of this OrderResBase. # noqa: E501
:return: The ext_code of this OrderResBase. # noqa: E501
:rtype: str
"""
return self._ext_code
@ext_code.setter
def ext_code(self, ext_code):
"""Sets the ext_code of this OrderResBase.
:param ext_code: The ext_code of this OrderResBase. # noqa: E501
:type: str
"""
self._ext_code = ext_code
@property
def ext_info(self):
"""Gets the ext_info of this OrderResBase. # noqa: E501
:return: The ext_info of this OrderResBase. # noqa: E501
:rtype: str
"""
return self._ext_info
@ext_info.setter
def ext_info(self, ext_info):
"""Sets the ext_info of this OrderResBase.
:param ext_info: The ext_info of this OrderResBase. # noqa: E501
:type: str
"""
self._ext_info = ext_info
@property
def result(self):
"""Gets the result of this OrderResBase. # noqa: E501
:return: The result of this OrderResBase. # noqa: E501
:rtype: object
"""
return self._result
@result.setter
def result(self, result):
"""Sets the result of this OrderResBase.
:param result: The result of this OrderResBase. # noqa: E501
:type: object
"""
self._result = result
@property
def time_now(self):
"""Gets the time_now of this OrderResBase. # noqa: E501
:return: The time_now of this OrderResBase. # noqa: E501
:rtype: str
"""
return self._time_now
@time_now.setter
def time_now(self, time_now):
"""Sets the time_now of this OrderResBase.
:param time_now: The time_now of this OrderResBase. # noqa: E501
:type: str
"""
self._time_now = time_now
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(OrderResBase, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, OrderResBase):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
|
[
"simonzgx@gmail.com"
] |
simonzgx@gmail.com
|
c573f51fd02948990abcc72e8748c4352f643cde
|
c862ffe067a5c539ddacf70e3db7632d1758f70a
|
/songbook/settings.py
|
c9b46202ca35bd702ee4ddcad400c467fad5797c
|
[] |
no_license
|
MartinCurran28/django_assignment
|
288ed52bc9c10932c4a13cc78e2b4e0d44ab6c66
|
582f64549eea268aaee2523efe32c3d410b3578e
|
refs/heads/master
| 2020-05-15T12:03:24.006883
| 2019-04-19T11:49:09
| 2019-04-19T11:49:09
| 182,252,893
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,611
|
py
|
"""
Django settings for songbook project.
Generated by 'django-admin startproject' using Django 1.11.
For more information on this file, see
https://docs.djangoproject.com/en/1.11/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.11/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'fs$ow5i(ox*8uak2@8aj=nh$x2+7t4f@4%6c%$o125zu=ha_ej'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = [os.environ.get('C9_HOSTNAME')]
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'accounts',
'django_forms_bootstrap',
]
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 = 'songbook.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR, 'templates')],
'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 = 'songbook.wsgi.application'
# Database
# https://docs.djangoproject.com/en/1.11/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
AUTHENTICATION_BACKENDS = [
'django.contrib.auth.backends.ModelBackend',
'accounts.backends.CaseInsensitiveAuth']
# Internationalization
# https://docs.djangoproject.com/en/1.11/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.11/howto/static-files/
STATIC_URL = '/static/'
MESSAGE_STORAGE = 'django.contrib.messages.storage.session.SessionStorage'
EMAIL_USE_TLS = True
EMAIL_HOST = 'smtp.gmail.com'
EMAIL_HOST_USER = os.environ.get("EMAIL_ADDRESS", 'martincurran28@hotmail.com')
EMAIL_HOST_PASSWORD = os.environ.get("EMAIL_PASSWORD")
EMAIL_PORT = 587
|
[
"martincurran28@hotmail.com"
] |
martincurran28@hotmail.com
|
bff3c146e033147360882ee7d2fd2224e7febb18
|
f66473c5f184e7e7fe047aed7b2902338bd9a12d
|
/app/util/model.py
|
7af3e43521093e9a8905ce2faccebe67d9f5eb7e
|
[
"LicenseRef-scancode-mulanpsl-2.0-en",
"MulanPSL-2.0",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
nacei/h3blog-master
|
7a220cf549fb869d602ba421e4bfd5e1b17bd2bb
|
e433c93f56be63883538b4e03198e6e4e056ecb6
|
refs/heads/master
| 2023-07-12T05:47:30.845678
| 2021-08-20T13:23:03
| 2021-08-20T13:23:03
| 398,283,534
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 712
|
py
|
from flask import request
def get_obj_fields(obj):
"""获取模型对象的表字段, obj或model均可"""
if obj is None:
return []
return [column.name for column in obj.__table__.columns]
def request_form_auto_fill(model) -> None:
data = request.form.to_dict()
if data is not None:
data = {key: value for key, value in data.items()
if key in get_obj_fields(model)}
[setattr(model, key, value) for key, value in data.items()]
def get_request_valid_data(obj):
data = request.get_json()
if data is not None:
data = {key: value for key, value in request.get_json().items()
if key in get_obj_fields(obj)}
return data
|
[
"hanxj@foxmail.com"
] |
hanxj@foxmail.com
|
228ef04b63368a02967ba7ea70cc55c1e3c00a81
|
bb62611c991456e694f00ad3229d530fd34c3ee4
|
/ch7_rvmclass.py
|
5d1da5b0ead796c45a3a02c7422b9ea78a063622
|
[] |
no_license
|
aralhekimoglu/PRMLAlgorithms
|
1afb8024181eb3d1afe4f50efdaecd86677fb7a6
|
7889cc275c456a8d8d12e9890c59dfdf88a358ce
|
refs/heads/master
| 2022-10-25T18:52:30.143037
| 2022-10-16T06:20:25
| 2022-10-16T06:20:25
| 126,392,197
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 6,531
|
py
|
import numpy as np
import matplotlib.pyplot as plt
class Kernel(object):
"""
Base class for kernel function
"""
def _pairwise(self, x, y):
"""
all pairs of x and y
Parameters
----------
x : (sample_size, n_features)
input
y : (sample_size, n_features)
another input
Returns
-------
output : tuple
two array with shape (sample_size, sample_size, n_features)
"""
return (
np.tile(x, (len(y), 1, 1)).transpose(1, 0, 2),
np.tile(y, (len(x), 1, 1))
)
class RBF(Kernel):
def __init__(self, params):
"""
construct Radial basis kernel function
Parameters
----------
params : (ndim + 1,) ndarray
parameters of radial basis function
Attributes
----------
ndim : int
dimension of expected input data
"""
assert params.ndim == 1
self.params = params
self.ndim = len(params) - 1
def __call__(self, x, y, pairwise=True):
"""
calculate radial basis function
k(x, y) = c0 * exp(-0.5 * c1 * (x1 - y1) ** 2 ...)
Parameters
----------
x : ndarray [..., ndim]
input of this kernel function
y : ndarray [..., ndim]
another input
Returns
-------
output : ndarray
output of this radial basis function
"""
assert x.shape[-1] == self.ndim
assert y.shape[-1] == self.ndim
if pairwise:
x, y = self._pairwise(x, y)
d = self.params[1:] * (x - y) ** 2
return self.params[0] * np.exp(-0.5 * np.sum(d, axis=-1))
def derivatives(self, x, y, pairwise=True):
if pairwise:
x, y = self._pairwise(x, y)
d = self.params[1:] * (x - y) ** 2
delta = np.exp(-0.5 * np.sum(d, axis=-1))
deltas = -0.5 * (x - y) ** 2 * (delta * self.params[0])[:, :, None]
return np.concatenate((np.expand_dims(delta, 0), deltas.T))
def update_parameters(self, updates):
self.params += updates
class RelevanceVectorClassifier(object):
def __init__(self, kernel, alpha=1.):
"""
construct relevance vector classifier
Parameters
----------
kernel : Kernel
kernel function to compute components of feature vectors
alpha : float
initial precision of prior weight distribution
"""
self.kernel = kernel
self.alpha = alpha
def _sigmoid(self, a):
return np.tanh(a * 0.5) * 0.5 + 0.5
def _map_estimate(self, X, t, w, n_iter=10):
for _ in range(n_iter):
y = self._sigmoid(X .dot( w))
g = X.T .dot( (y - t) )+ self.alpha * w
H = (X.T * y * (1 - y)) .dot( X) + np.diag(self.alpha)
w -= np.linalg.solve(H, g)
return w, np.linalg.inv(H)
def fit(self, X, t, iter_max=100):
"""
maximize evidence with respect ot hyperparameter
Parameters
----------
X : (sample_size, n_features) ndarray
input
t : (sample_size,) ndarray
corresponding target
iter_max : int
maximum number of iterations
Attributes
----------
X : (N, n_features) ndarray
relevance vector
t : (N,) ndarray
corresponding target
alpha : (N,) ndarray
hyperparameter for each weight or training sample
cov : (N, N) ndarray
covariance matrix of weight
mean : (N,) ndarray
mean of each weight
"""
if X.ndim == 1:
X = X[:, None]
assert X.ndim == 2
assert t.ndim == 1
Phi = self.kernel(X, X)
N = len(t)
self.alpha = np.zeros(N) + self.alpha
mean = np.zeros(N)
for _ in range(iter_max):
param = np.copy(self.alpha)
mean, cov = self._map_estimate(Phi, t, mean, 10)
gamma = 1 - self.alpha * np.diag(cov)
self.alpha = gamma / np.square(mean)
np.clip(self.alpha, 0, 1e10, out=self.alpha)
if np.allclose(param, self.alpha):
break
mask = self.alpha < 1e8
self.X = X[mask]
self.t = t[mask]
self.alpha = self.alpha[mask]
Phi = self.kernel(self.X, self.X)
mean = mean[mask]
self.mean, self.covariance = self._map_estimate(Phi, self.t, mean, 100)
def predict(self, X):
"""
predict class label
Parameters
----------
X : (sample_size, n_features)
input
Returns
-------
label : (sample_size,) ndarray
predicted label
"""
if X.ndim == 1:
X = X[:, None]
assert X.ndim == 2
phi = self.kernel(X, self.X)
label = (phi .dot( self.mean) > 0).astype(np.int)
return label
def predict_proba(self, X):
"""
probability of input belonging class one
Parameters
----------
X : (sample_size, n_features) ndarray
input
Returns
-------
proba : (sample_size,) ndarray
probability of predictive distribution p(C1|x)
"""
if X.ndim == 1:
X = X[:, None]
assert X.ndim == 2
phi = self.kernel(X, self.X)
mu_a = phi .dot(self.mean)
var_a = np.sum(phi .dot( self.covariance) * phi, axis=1)
return self._sigmoid(mu_a / np.sqrt(1 + np.pi * var_a / 8))
def create_toy_data():
x0 = np.random.normal(size=100).reshape(-1, 2) - 1.
x1 = np.random.normal(size=100).reshape(-1, 2) + 1.
x = np.concatenate([x0, x1])
y = np.concatenate([np.zeros(50), np.ones(50)]).astype(np.int)
return x, y
x_train, y_train = create_toy_data()
model = RelevanceVectorClassifier(RBF(np.array([1., 0.5, 0.5])))
model.fit(x_train, y_train)
x0, x1 = np.meshgrid(np.linspace(-4, 4, 100), np.linspace(-4, 4, 100))
x = np.array([x0, x1]).reshape(2, -1).T
plt.scatter(x_train[:, 0], x_train[:, 1], s=40, c=y_train, marker="x")
plt.scatter(model.X[:, 0], model.X[:, 1], s=100, facecolor="none", edgecolor="g")
plt.contourf(x0, x1, model.predict_proba(x).reshape(100, 100), np.linspace(0, 1, 5), alpha=0.2)
plt.colorbar()
plt.xlim(-4, 4)
plt.ylim(-4, 4)
plt.gca().set_aspect("equal", adjustable="box")
|
[
"noreply@github.com"
] |
noreply@github.com
|
099d9b77d6ee33d721c385a92a5292916202d2c0
|
d77f22379d90393354178da397a5dbc3722fd8b4
|
/op.py
|
c7559a3c24edf17cad098e4eeed10da9c3d58bae
|
[] |
no_license
|
caizkun/autodiff
|
3d33c577d459786f4a13cfcc7ddf478fceb231e7
|
379a6eba9b86476b1e012803400ef6dfb8398433
|
refs/heads/main
| 2023-09-02T21:02:55.684437
| 2021-11-15T08:43:32
| 2021-11-15T08:43:32
| 428,179,331
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,213
|
py
|
import math
from ad import Node
class Op(object):
def __call__(self):
pass
def fn(self, input_vals):
raise NotImplementedError
def grad_fn(self, input_vals, output_grad):
raise NotImplementedError
class AddOp(Op):
def __call__(self, node_A, node_B):
new_node = Node(self, [node_A, node_B])
new_node.require_grad = node_A.require_grad or node_B.require_grad
return new_node
def fn(self, input_vals):
assert len(input_vals) == 2
return input_vals[0] + input_vals[1]
def grad_fn(self, input_vals, output_grad):
return [output_grad, output_grad]
class SubOp(Op):
def __call__(self, node_A, node_B):
new_node = Node(self, [node_A, node_B])
new_node.require_grad = node_A.require_grad or node_B.require_grad
return new_node
def fn(self, input_vals):
assert len(input_vals) == 2
return input_vals[0] - input_vals[1]
def grad_fn(self, input_vals, output_grad):
return [output_grad, -output_grad]
class MulOp(Op):
def __call__(self, node_A, node_B):
new_node = Node(self, [node_A, node_B])
new_node.require_grad = node_A.require_grad or node_B.require_grad
return new_node
def fn(self, input_vals):
assert len(input_vals) == 2
return input_vals[0] * input_vals[1]
def grad_fn(self, input_vals, output_grad):
return [input_vals[1] * output_grad, input_vals[0] * output_grad]
class DivOp(Op):
def __call__(self, node_A, node_B):
new_node = Node(self, [node_A, node_B])
new_node.require_grad = node_A.require_grad or node_B.require_grad
return new_node
def fn(self, input_vals):
assert len(input_vals) == 2
assert input_vals[1] != 0
return input_vals[0] / input_vals[1]
def grad_fn(self, input_vals, output_grad):
return [1.0/input_vals[1] * output_grad, -input_vals[0]/input_vals[1]**2 * output_grad]
class LnOp(Op):
def __call__(self, node_A):
new_node = Node(self, [node_A], require_grad=node_A.require_grad)
return new_node
def fn(self, input_vals):
assert len(input_vals) == 1 and input_vals[0] > 0
return math.log(input_vals[0])
def grad_fn(self, input_vals, output_grad):
return [1.0 / input_vals[0] * output_grad]
class SinOp(Op):
def __call__(self, node_A):
new_node = Node(self, [node_A], require_grad=node_A.require_grad)
return new_node
def fn(self, input_vals):
assert len(input_vals) == 1
return math.cos(input_vals[0])
def grad_fn(self, input_vals, output_grad):
return [math.cos(input_vals[0]) * output_grad]
class InputOp(Op):
def __call__(self, input_val, require_grad):
new_node = Node(self, [input_val], require_grad=require_grad)
return new_node
def fn(self, input_vals):
assert len(input_vals) == 1
return input_vals[0]
def grad_fn(self, input_vals, output_grad):
return [output_grad]
# more ops ...
# instaniate global ops
add = AddOp()
sub = SubOp()
mul = MulOp()
div = DivOp()
ln = LnOp()
sin = SinOp()
var = InputOp()
|
[
"caizkun@gmail.com"
] |
caizkun@gmail.com
|
22921a548069e69a88ed036d29bfcc83ba21a4ef
|
cd208b4a40be8bf166da79fdc126dbcb71e95a7d
|
/app/states/state_expiration_date.py
|
71b993036530fc25dce01aef9c241befa95b0714
|
[
"MIT"
] |
permissive
|
Moirted/MyPersonalKitchenBot
|
63a2b1be6e21e90ed908c9f3162bd085162cd83f
|
03de0beeaf2665e8b3ddd1709da3d4edcd422b80
|
refs/heads/main
| 2023-04-21T12:17:52.486113
| 2021-05-16T13:00:22
| 2021-05-16T13:00:22
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 126
|
py
|
from aiogram.dispatcher.filters.state import State, StatesGroup
class ExpirationDateState(StatesGroup):
number = State()
|
[
"nickshel@yandex.ru"
] |
nickshel@yandex.ru
|
9f46f7e89e19b7e65cfb7e37c5e03e9be0b2d4fe
|
55c250525bd7198ac905b1f2f86d16a44f73e03a
|
/Python/Projects/speech-text-file/gTTS/build/lib/gtts/tokenizer/symbols.py
|
3d40893c51295eda1b689b6f438f7089a38dc848
|
[
"LicenseRef-scancode-other-permissive"
] |
permissive
|
NateWeiler/Resources
|
213d18ba86f7cc9d845741b8571b9e2c2c6be916
|
bd4a8a82a3e83a381c97d19e5df42cbababfc66c
|
refs/heads/master
| 2023-09-03T17:50:31.937137
| 2023-08-28T23:50:57
| 2023-08-28T23:50:57
| 267,368,545
| 2
| 1
| null | 2022-09-08T15:20:18
| 2020-05-27T16:18:17
| null |
UTF-8
|
Python
| false
| false
| 128
|
py
|
version https://git-lfs.github.com/spec/v1
oid sha256:a7c43c0c9dfa06ad8af4ec38d5a26b50deffacc6f2b881170eb8a37576f6d970
size 278
|
[
"nateweiler84@gmail.com"
] |
nateweiler84@gmail.com
|
d13d6cb9808efd0098b69557877ebcab3b1fb584
|
94a3afadfc4d89cf19cca0ee966007ae09f7f81c
|
/lnk.py
|
01c62bc706a7ba3f4163aae85cb7187a76a58f2b
|
[] |
no_license
|
devendermathu/Rahul-Amantya
|
db354ffa3d70d924030988bccf61c41b251e9662
|
70d3a2b56d2b5b604a0af7dfc8a2b1b5dd5e6d91
|
refs/heads/main
| 2023-04-17T08:58:47.469047
| 2021-04-18T12:55:18
| 2021-04-18T12:55:18
| 323,076,878
| 0
| 0
| null | 2021-04-18T13:07:28
| 2020-12-20T13:21:43
|
Python
|
UTF-8
|
Python
| false
| false
| 4,238
|
py
|
from bs4 import BeautifulSoup as bs
from requests import Session
import csv
from lxml import html
s = Session()
s.headers['User-Agent']='Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:82.0) Gecko/20100101 Firefox/82.0'
f = open('fnl_lnk', 'r',encoding='utf-8').read().split('\n')
fw = open('toyoto_data.csv','w',encoding='utf-8')
erro_lnk = open('erro_links.txt','w',encoding='utf-8')
# l = ['4RUNNER(2)','ALLEX(58)','bB(351)','C-HR(210)','DUET(3)','ESQUIRE(168)','FJ CRUISER(46)','GAIA(1)','HARRIER(543)','IPSUM(74)','KLUGER(52)','LAND CRUISER(237)','MAJESTA(1)','NADIA(2)','OPA(1)','PASSO(692)','RACTIS(598)','SAI(133)','TANK(31)','URBAN CRUISER(1)','VANGUARD(130)','WILL CYPHA(11)','YARIS(43)']
# url = 'https://www.sbtjapan.com/used-cars/toyota/{}}#listbox'
# url = "https://www.sbtjapan.com/used-cars/nissan/note/?model_code=&steering=all&drive=0&year_f=&month_f=&year_t=&month_t=&price_f=&price_t=&cc_f=0&cc_t=0&mile_f=0&mile_t=0&trans=0&savel=0&saveu=0&fuel=0&color=0&bodyLength=0&loadClass=0&engineType=0&truck_size=&location=&port=0&search_box=1&sold=&p_years=&bid_code=&pdate_f=&pdate_t=&locationIds=0&stock_ids=&d_country=76&d_port=119&ship_type=0&FreightChk=yes¤cy=2&insurance=1&sort=0&psize=100&custom_search=&p_num={}#listbox"
# url = 'https://www.sbtjapan.com/used-cars/toyota/86#listbox'
row = csv.writer(fw)
for i in f[5000:]:
try:
r = s.get(i)
# soup = bs(r.content,'html,parser')
tree = html.fromstring(r.content)
full_name = ''.join(tree.xpath('//h1//text()')).strip()
full2_name = ''.join(tree.xpath('//ul[@class="title"]//p//text()')).strip()
tran = ''.join(tree.xpath("""//th[contains(string(),"Transmission:")]//following-sibling::td//text()""")[0]).strip().replace('\n','')
year = ''.join(tree.xpath("""//th[contains(string(),"Year:")]//following-sibling::td//text()""")).strip().replace('\n','')
location = ''.join(tree.xpath("""//th[contains(string(),"Location:")]//following-sibling::td//text()""")).strip().replace('\n','')
drive = ''.join(tree.xpath("""//th[contains(string(),"Drive:")]//following-sibling::td//text()""")[0]).strip().replace('\n','')
doors = ''.join(tree.xpath("""//th[contains(string(),"Door:")]//following-sibling::td//text()""")).strip().replace('\n','')
steering = ''.join(tree.xpath("""//th[contains(string(),"Steering:")]//following-sibling::td//text()""")[0]).strip().replace('\n','')
seats = ''.join(tree.xpath("""//th[contains(string(),"Seats:")]//following-sibling::td//text()""")).strip().replace('\n','')
engine_type = ''.join(tree.xpath("""//th[contains(string(),"Engine Type:")]//following-sibling::td//text()""")[0]).strip().replace('\n','')
drive_chain = ''.join(tree.xpath("""//th[contains(string(),"Body Type:")]//following-sibling::td//text()""")).strip().replace('\n','')
fuel = ''.join(tree.xpath("""//th[contains(string(),"Fuel:")]//following-sibling::td//text()""")[0]).strip().replace('\n','')
mileage = ''.join(tree.xpath("""//th[contains(string(),"Mileage:")]//following-sibling::td//text()""")).strip().replace('\n','')
cars_weight = ''.join(tree.xpath("""//th[contains(string(),"Gross Vehicle Weight:")]//following-sibling::td//text()""")).strip().replace('\n','')
max_cars_weight = ''.join(tree.xpath("""//th[contains(string(),"Max Loading Capacity:")]//following-sibling::td//text()""")).strip().replace('\n','')
color = ''.join(tree.xpath("""//th[contains(string(),"Color:")]//following-sibling::td//text()""")).strip().replace('\n','')
model_code = ''.join(tree.xpath("""//th[contains(string(),"Model Code:")]//following-sibling::td//text()""")[0]).strip().replace('\n','')
row.writerow([
full_name,
full2_name,
tran,
year,
location,
drive,
doors,
steering,
seats,
engine_type,
drive_chain,
fuel,
mileage,
cars_weight,
max_cars_weight,
color,
model_code,
i
])
print(i)
except:
print("exeption!!")
erro_lnk.write(i+'\n')
pass
|
[
"rahulmath846@gmail.com"
] |
rahulmath846@gmail.com
|
f03e7642f3f3d35c57c7e7083addaa4d03aea328
|
d8d14fd9959498d5c31a0bfc4b45b0a7c5a34155
|
/setup.py
|
6624b28fcc76ace191d21cacf6c5f8d510d18bac
|
[] |
no_license
|
bugman78/menuzilla
|
3504fcdfdf61cbbc6ce154ccb779f648bb5add6a
|
0315a93fe51dbd1c53526e5ab63d58f913f38716
|
refs/heads/master
| 2021-01-11T22:11:33.889579
| 2017-01-14T09:56:20
| 2017-01-14T09:56:20
| 78,932,672
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,305
|
py
|
"""A setuptools based setup module.
See:
https://packaging.python.org/en/latest/distributing.html
https://github.com/pypa/sampleproject
"""
# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open
from os import path
here = path.abspath(path.dirname(__file__))
# Get the long description from the README file
with open(path.join(here, 'README.rst'), encoding='utf-8') as f:
long_description = f.read()
setup(
name='menuzilla',
version='0.1.0',
description='menuzilla - generate desktop/menu entries from your firefox bookmarks',
long_description=long_description,
url='https://github.com/pypa/menuzilla',
author='Stephane Bugat',
author_email='stephane.bugat@free.fr',
license='LGPL',
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: End Users/Desktop',
'License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)',
'Programming Language :: Python :: 2.7',
'Topic :: Desktop Environment',
],
keywords='freedesktop.org mozilla firefox bookmarks toolbar',
packages=find_packages(exclude=['man', 'docs', 'tests']),
scripts=['menuzilla.py',],
install_requires=['python-xdg>=0.25-4'],
)
|
[
"stephane.bugat@free.fr"
] |
stephane.bugat@free.fr
|
0225bd6623519534724f02704f9d1bdca8fa82b6
|
210af68aec4713e8cbe8dc988d509090815e6ff4
|
/0x04-python-more_data_structures/9-multiply_by_2.py
|
adcaf10fe0fc6a3ad8467a5cb752a4816fcc9910
|
[] |
no_license
|
mahdibz97/holbertonschool-higher_level_programming
|
8e383d474438ba563311f829a763ce8733931c1a
|
7184a1eadcaf76f33135c00effe4390b1c227cbd
|
refs/heads/master
| 2022-12-19T12:29:44.678292
| 2020-09-25T07:56:44
| 2020-09-25T07:56:44
| 259,281,398
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 153
|
py
|
#!/usr/bin/python3
def multiply_by_2(a_dictionary):
new = {}
for i in a_dictionary.keys():
new[i] = (a_dictionary[i] * 2)
return new
|
[
"ben.zouitina.mahdi97@gmail.com"
] |
ben.zouitina.mahdi97@gmail.com
|
3f78c365beacff55588a6afbfd7b66b354d5db58
|
dd62541e9bfa0c5d503b22eace4496296140656c
|
/common_qr.py
|
75e2f35e6fb2d18e61c070c81b239fb07405334c
|
[
"BSD-3-Clause"
] |
permissive
|
sea51930/RaiWalletBot
|
750f9a63bfd95222fe8e24292d13e47b2ab4389c
|
4c558eaf1590092e3db1b7f8155d01fd2867b19d
|
refs/heads/master
| 2020-12-02T22:19:32.342777
| 2017-07-03T14:50:10
| 2017-07-03T14:50:10
| 96,114,318
| 0
| 0
| null | 2017-07-03T13:33:54
| 2017-07-03T13:33:54
| null |
UTF-8
|
Python
| false
| false
| 1,210
|
py
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# RaiBlocks Telegram bot
# @RaiWalletBot https://t.me/RaiWalletBot
#
# Source code:
# https://github.com/SergiySW/RaiWalletBot
#
# Released under the BSD 3-Clause License
#
# QR code handler
import ConfigParser
config = ConfigParser.ConfigParser()
config.read('bot.cfg')
qr_folder_path = config.get('main', 'qr_folder_path')
import pyqrcode
import os.path
#@run_async
def qr_by_account(account):
path = '{1}{0}.png'.format(account, qr_folder_path)
if (not os.path.isfile(path)):
qr = pyqrcode.create(account, error='L', version=4, mode=None, encoding='iso-8859-1')
qr.png('{1}{0}.png'.format(account, qr_folder_path), scale=8)
import qrtools
from PIL import Image, ImageEnhance
def account_by_qr(qr_file):
qr = qrtools.QR()
qr.decode(qr_file)
# Try to increase contrast if not recognized
if ('xrb_' not in qr.data):
image = Image.open(qr_file)
contrast = ImageEnhance.Contrast(image)
image = contrast.enhance(7)
image.save('{0}'.format(qr_file.replace('.jpg', '_.jpg')), 'JPEG')
qr2 = qrtools.QR()
qr2.decode('{0}'.format(qr_file.replace('.jpg', '_.jpg')))
#print(qr2.data)
qr = qr2
return qr.data.replace('raiblocks://', '')
|
[
"sergiysw@gmail.com"
] |
sergiysw@gmail.com
|
24a1fa69059a7cec4d3e8887cf77bb3af70cf7a0
|
3321f0187988caee9598f8d9057d44100adbdeee
|
/catkin_ws/src/vision/flir_gige/math/conversion.py
|
9705f898410fedace690c9e3ea50905e50d7e86c
|
[] |
no_license
|
TRA-UNAM/FinderV3
|
8c00aa71485a978805da08a7d1c47d5b175971ee
|
1d02366b60684ae1c2ceb129aacc982fe798b105
|
refs/heads/master
| 2022-09-30T02:55:44.931614
| 2022-09-23T01:20:09
| 2022-09-23T01:20:09
| 162,857,050
| 0
| 5
| null | 2020-06-04T00:49:57
| 2018-12-23T02:41:42
|
C++
|
UTF-8
|
Python
| false
| false
| 506
|
py
|
from math import exp, log
def raw_to_celsius(S, B, F, O, R, T0):
return (B / log(R / (S - O) + F) - T0)
def celsius_to_raw(t, B, F, O, R, T0):
return ((R / exp(B / (t + T0)) - F) + O)
B = 1428.0
F = 1.0
O = 118.126
R = 377312.0
T0 = 273.15
celsius = [10, 20, 30, 40, 50]
raw = [];
celcius_converted = []
for t in celsius:
raw.append(celsius_to_raw(t, B, F, O, R, T0))
for s in raw:
celcius_converted.append(raw_to_celsius(s, B, F, O, R, T0))
for t in celcius_converted:
print(t)
|
[
"ehecatl.eli.ba@gmail.com"
] |
ehecatl.eli.ba@gmail.com
|
80688a39b7b2e07d9b19fae862b8b5521e57040b
|
74623af256921be7fb2e69a8884d872f586f77f4
|
/.venv/bin/epylint
|
32b79850bcc2e375d12173dcd13d4cd40f6747a7
|
[] |
no_license
|
fatmahkabeer/StockPricePrediction
|
6e58f1630ccfda0c8fb1bdcaf1dbbf80321ea58c
|
a4eaee593ae04b59026b8ab0d3aefc9554db18f7
|
refs/heads/main
| 2023-03-01T16:23:01.097974
| 2021-02-01T18:08:59
| 2021-02-01T18:08:59
| 318,237,814
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 278
|
#!/Users/apple/Desktop/DataScience/StockPricePrediction/.venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pylint import run_epylint
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(run_epylint())
|
[
"apple@fatmahkabeers-MAcBook-Air.local"
] |
apple@fatmahkabeers-MAcBook-Air.local
|
|
ed3c99f2709fd2753544bd1b93f0e87ed80b5d91
|
d4a30e390ddada1da154a65319c52efd1711df93
|
/net/dataset/UCSDPed2.py
|
b4c13c17125b88ca897e9dfcfd11c071d7fe5c7e
|
[] |
no_license
|
sdjsngs/Re-implement-Memorizing-Normality-to-Detect-Anomaly
|
a1d1323d9473af2a12df94f0f2d4a1cc22bc0560
|
81b8b328a56d4db9be283348febbc7f2ce8edbb0
|
refs/heads/master
| 2022-12-03T13:10:53.963794
| 2020-08-18T12:18:15
| 2020-08-18T12:18:15
| 286,999,990
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,019
|
py
|
import torch
import torch.nn as nn
from torch.utils.data import Dataset,DataLoader
import os
import glob
import cv2
import numpy as np
from net.dataset.build import DATASET_REGISTRY
from net.utils.parser import parse_args,load_config
@DATASET_REGISTRY.register()
class Ucsdped2(Dataset):
"""
Uscdped2 dataset
It has 16 short clips for training, and another 12 clips for testing.
Each clip has 150 to 200 frames, with a resolution of 360 × 240 pixels
training
-frames
-01
-000.jpg
"""
def __init__(self,cfg,mode):
assert mode in ["train","training","test","testing"]
self.data_root=cfg.PED.PATH_TO_DATA_DIR
self.mode=mode+"ing"
self.temporal_length=cfg.TEMPORAL_LENGTH
self._consturct()
def _consturct(self):
"""
recode img path
:return:
"""
self.img_paths=[]
for num_folder in os.listdir(os.path.join(self.data_root,self.mode,"frames")):
folder_img=sorted(glob.glob(
os.path.join(self.data_root,self.mode,"frames",num_folder,"*.jpg").replace("\\","/")
))
self.img_paths+=folder_img[self.temporal_length//2:(-self.temporal_length//2+1)]
def __len__(self):
return len(self.img_paths)
def __getitem__(self, index):
video, video_idx = self.load_img_to_gray(self.img_paths[index])
video = torch.from_numpy(video)
video = video.unsqueeze(dim=0)
if self.mode in ["train", "training"]:
return video
elif self.mode in ["test", "testing"]:
return video, video_idx
else:
raise NotImplementedError(
"mode {} is not supported".format(self.mode)
)
def load_img_to_gray(self, path):
# resize h,w 192,128
# print("path", path)
img_num = int(path.split("\\")[-1].split(".")[0])
video_idx = (path.split("\\")[0].split("/")[-1])
for step, i in enumerate(range(-(self.temporal_length // 2), self.temporal_length // 2)):
img_num_i = img_num + i
str_img_num_i = "%03d" % img_num_i # len 3 for each frame
path_i = path.split("\\")[0] + "/" + str_img_num_i + ".jpg"
img = cv2.imread(path_i)
img = cv2.resize(img, (224, 224))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = img / 255.0
if step == 0:
imgs = np.expand_dims(img, axis=0)
else:
imgs = np.concatenate((imgs, np.expand_dims(img, axis=0)), axis=0)
return imgs, video_idx
if __name__=="__main__":
args=parse_args()
cfg=load_config(args)
print(type(cfg))
data_loader=DataLoader(Ucsdped2(cfg,mode="train"),batch_size=2,shuffle=False)
for step ,(video) in enumerate(data_loader):
print("step",step)
print(video.shape)
|
[
"noreply@github.com"
] |
noreply@github.com
|
41aff2c6737092010ed79ff48e2c3cf747e6eaac
|
2f7bf9f8c382e433f02b75b568be0ca0e3e800a6
|
/Assignment/Course 2 - Python Data Structures/Week 4 - Lists/8.5.py
|
58d76b99d61ab9c2300311df21a501e8202521b2
|
[] |
no_license
|
KhangNLY/Py4E
|
ad9f2d84bb48ff425d2e349bc6e764d5a62b9558
|
2437fbb028531f329c5d637c280d031827ab85c0
|
refs/heads/main
| 2023-05-05T22:48:44.433965
| 2021-05-24T06:34:05
| 2021-05-24T06:34:05
| 362,573,133
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 362
|
py
|
fname = input("Enter file name: ")
if len(fname) < 1: fname = "mbox-short.txt"
fh = open(fname)
count = 0
for line in fh:
line = line.rstrip()
wrd = line.split()
if len(wrd) < 3 or wrd[0] != 'From':
continue
print(wrd[1])
count = count + 1
print("There were", count, "lines in the file with From as the first word")
|
[
"noreply@github.com"
] |
noreply@github.com
|
dc880e94449fef14a74f7a3e1fc368756e3d8ad0
|
28cec7be02b1ceb2070d075ac4fa08448a1ceb43
|
/abc.py
|
1ee5730f32f9dc3b56bbb9ca180a7367630a9c80
|
[] |
no_license
|
lakshmi5036/Python-project
|
561fffabcfc3ace95d8a755352dee42a5f450e33
|
c4dba7543ccb9a36f60c6512181085e0b56165d3
|
refs/heads/master
| 2023-05-25T04:13:22.525976
| 2021-06-06T15:03:07
| 2021-06-06T15:03:07
| 374,178,422
| 0
| 0
| null | 2021-06-06T15:03:07
| 2021-06-05T17:48:40
|
Python
|
UTF-8
|
Python
| false
| false
| 52
|
py
|
# this is a test file
def func():
print('hey')
|
[
"dheeraja.damerla8@gmail.com"
] |
dheeraja.damerla8@gmail.com
|
d0ef3875dacf26faa8a57ab61cd4c8a9652e60f1
|
fc2bbd676723b2f02372d8d46f7af04b86a0239d
|
/data/users.py
|
57e8b636b3ad5abf5f19fa7cbb27b9186a0e2528
|
[] |
no_license
|
Dashapep/eco_events
|
dede134b2719065bdf1f0c1b545bbbcc6f3e6464
|
b2bc4657c63809e4d210b186e685fe18e4603a76
|
refs/heads/master
| 2023-04-24T04:53:52.995161
| 2021-04-25T20:18:06
| 2021-04-25T20:18:06
| 356,005,500
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,352
|
py
|
import datetime
import sqlalchemy
from flask_login import UserMixin
from sqlalchemy import orm
from werkzeug.security import generate_password_hash, check_password_hash
from .db_session import SqlAlchemyBase
class User(SqlAlchemyBase, UserMixin):
__tablename__ = 'users'
id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
surname = sqlalchemy.Column(sqlalchemy.String, nullable=True)
name = sqlalchemy.Column(sqlalchemy.String, nullable=True)
age = sqlalchemy.Column(sqlalchemy.Integer, nullable=True)
speciality = sqlalchemy.Column(sqlalchemy.String, nullable=True)
address = sqlalchemy.Column(sqlalchemy.String, nullable=True)
email = sqlalchemy.Column(sqlalchemy.String, index=True, unique=True, nullable=True)
hashed_password = sqlalchemy.Column(sqlalchemy.String, nullable=True)
modifed_date = sqlalchemy.Column(sqlalchemy.DateTime, default=datetime.datetime.now)
moderator = sqlalchemy.Column(sqlalchemy.Boolean, default=False)
events = orm.relation("Events", back_populates='user')
def __repr__(self):
return f'{self.name} {self.surname}'
def set_password(self, password):
self.hashed_password = generate_password_hash(password)
def check_password(self, password):
return check_password_hash(self.hashed_password, password)
|
[
"pds032005@gmail.com"
] |
pds032005@gmail.com
|
4b063dac8fbb9c047f40f60e35b317e14d6ab716
|
ba2f34ff8a7b2c36ae88a2f02ca495ad084bb6ab
|
/Cryptanalysis/break_autokey.py
|
aecc051205c004e9a18d31b229c6ec47d72a3899
|
[
"MIT"
] |
permissive
|
BlackLuny/cyberweapons
|
bc05e07cdc67f58c9cf68178762eb541c8c0cc55
|
dfd4623f323ba702bae7c9f71132b4584636d2e5
|
refs/heads/master
| 2021-05-16T07:28:35.651835
| 2017-09-16T21:04:50
| 2017-09-16T21:04:50
| 103,801,285
| 1
| 0
| null | 2017-09-17T03:50:18
| 2017-09-17T03:50:18
| null |
UTF-8
|
Python
| false
| false
| 2,037
|
py
|
from ngram_score import ngram_score
from pycipher import Autokey
import re
from itertools import permutations
qgram = ngram_score('quadgrams.txt')
trigram = ngram_score('trigrams.txt')
ctext = 'isjiqymdebvuzrvwhmvysibugzhyinmiyeiklcvioimbninyksmmnjmgalvimlhspjxmgfiraqlhjcpvolqmnyynhpdetoxemgnoxl'
ctext = re.sub(r'[^A-Z]','',ctext.upper())
# keep a list of the N best things we have seen, discard anything else
class nbest(object):
def __init__(self,N=1000):
self.store = []
self.N = N
def add(self,item):
self.store.append(item)
self.store.sort(reverse=True)
self.store = self.store[:self.N]
def __getitem__(self,k):
return self.store[k]
def __len__(self):
return len(self.store)
#init
N=100
for KLEN in range(3,20):
rec = nbest(N)
for i in permutations('ABCDEFGHIJKLMNOPQRSTUVWXYZ',3):
key = ''.join(i) + 'A'*(KLEN-len(i))
pt = Autokey(key).decipher(ctext)
score = 0
for j in range(0,len(ctext),KLEN):
score += trigram.score(pt[j:j+3])
rec.add((score,''.join(i),pt[:30]))
next_rec = nbest(N)
for i in range(0,KLEN-3):
for k in xrange(N):
for c in 'ABCDEFGHIJKLMNOPQRSTUVWXYZ':
key = rec[k][1] + c
fullkey = key + 'A'*(KLEN-len(key))
pt = Autokey(fullkey).decipher(ctext)
score = 0
for j in range(0,len(ctext),KLEN):
score += qgram.score(pt[j:j+len(key)])
next_rec.add((score,key,pt[:30]))
rec = next_rec
next_rec = nbest(N)
bestkey = rec[0][1]
pt = Autokey(bestkey).decipher(ctext)
bestscore = qgram.score(pt)
for i in range(N):
pt = Autokey(rec[i][1]).decipher(ctext)
score = qgram.score(pt)
if score > bestscore:
bestkey = rec[i][1]
bestscore = score
print bestscore,'autokey, klen',KLEN,':"'+bestkey+'",',Autokey(bestkey).decipher(ctext)
|
[
"suhubdyd@iro.umontreal.ca"
] |
suhubdyd@iro.umontreal.ca
|
2de97c75346aa4138afedc9c09cea58736c5cc02
|
be15ba8adae9f92a1723f0570e6ea037f0dea49a
|
/flask/cnn.py
|
b45c28f5558fb9f4c4a2ffba3f6602f99d0a79d9
|
[] |
no_license
|
CarsonStevens/GeoGuessed
|
05cd504273de9940f4b7bab30cb39fd22ee756d8
|
4b6f5bd33cbff47eaddb73ef11cd43dda66c696f
|
refs/heads/master
| 2022-12-24T10:31:38.932913
| 2019-11-19T15:16:30
| 2019-11-19T15:16:30
| 211,579,542
| 3
| 0
| null | 2022-12-11T07:32:41
| 2019-09-29T00:47:05
|
HTML
|
UTF-8
|
Python
| false
| false
| 1,827
|
py
|
## CNN.py
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from config import *
class CNN():
def __init__(self):
self.model = None
# def process_data():
# get data
# X = np.zeros((1,)) # final: np.load('path/to/saved np array')
# y = np.zeros((1,))
# normalize
# return X, y
def create_model():
# test
# X = np.zeros((1,)) # final: np.load('path/to/saved np array')
# y = np.zeros((1,))
# define model
self.model = Sequential()
# first convolutional layer
# Conv2D( number_of_filters, kernal_size, input_shape(just for the input conv layer))
self.model.add(Conv2D(CNN_N_FILTERS,
CNN_KERNEL_SIZE,
input_shape = X.shape[1:]))
# layer activaion function
self.model.add(Activation(CNN_ACTIVATION_FUNCTION))
# layer pooling
self.model.add(MaxPooling2D(pool_size = CNN_POOL_SIZE))
# second layer
model.add(Conv2D(CNN_N_FILTERS,
CNN_KERNEL_SIZE))
self.model.add(Activation(CNN_CONV_ACTIVATION_FUNCTION))
self.model.add(MaxPooling2D(pool_size = CNN_POOL_SIZE))
# dense layer on flattened data, 64 nodes
self.model.add(Flatten())
self.model.add(Dense(CNN_DENSE_NODE_COUNT))
# output layer
self.model.add(Dense(CNN_OUTPUT_DENSE_NODE_COUNT))
self.model.add(Activation(CNN_OUTPUT_ACTIVATION_FUNCTION))
# prepare for training
self.model.compile(loss = CNN_LOSS_FUNCTION,
optimizer = CNN_OPTIMIZER,
metrics = CNN_METRICS)
def train():
# fit
self.model.fit(X, y, batch_size = CNN_BATCH_SIZE, epochs = CNN_EPOCHS, validation_split = CNN_VALIDATION_SPLIT)
def summary():
# summary
self.model.summary()
def save_model():
# save model
self.model.save('cnn.model')
|
[
"danielpersonius@mymail.mines.edu"
] |
danielpersonius@mymail.mines.edu
|
5d36abd5b77f49fdf4b1d6f263b4097148c20a5a
|
fee6bb5e775c41d7c9e820a10ba785c526cb5fbf
|
/professor/dois_ensaios/library/books/views.py
|
092c601ed878ff0a0ddb7a36e831a6d8453fafaa
|
[] |
no_license
|
jaovw/pbl7
|
c6d898d06086fc506f9cfa984a06e226a4ad4269
|
76b9541246de5f7c9e27cd8f0e1500d6d86f6d14
|
refs/heads/main
| 2023-08-30T00:32:05.254036
| 2021-11-02T22:27:54
| 2021-11-02T22:27:54
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,922
|
py
|
from django.shortcuts import render
#para habilitar os outros dominios a usar os api methods
from django.views.decorators.csrf import csrf_exempt
#para fazer o parse em json
from rest_framework.parsers import JSONParser
from django.http.response import JsonResponse
#importar modulos criados
from books.models import Books, Departments, Employees
#from books.models import Books, Lojas, ...
from books.api.serializers import BooksSerializers, DepartmentSerializer, EmployeeSerializer
#adicionando os recursos da foto
from django.core.files.storage import default_storage
# Create your views here.
#por padrao do framework ja tem os metodos implementados
#o codigo a seguir é apenas uma referencia
#aqui estao os metodos para manipular as tabelas
@csrf_exempt
def departmentApi(request, id=0):
if request.method == 'GET':
departments = Departments.objects.all()
departments_serializer = DepartmentSerializer(departments, many=True)
return JsonResponse(departments_serializer.data, safe = False)
elif request.method == 'POST':
department_data = JSONParser().parse(request)
departments_serializer = DepartmentSerializer(data = department_data)
#se os dados forem validos serao salvos no banco de dados
if departments_serializer.is_valid():
departments_serializer.save()
return JsonResponse('adicionado com sucesso', safe = False)
#safe é mais flexibilidade nas respostas
return JsonResponse('falha ao adicionar', safe = False)
elif request.method == 'PUT':
department_data = JSONParser().parse(request)
department = Departments.objects.get(DepartmentId=department_data['DepartmentId'])
departments_serializer = DepartmentSerializer(department, data = department_data)
if departments_serializer.is_valid():
departments_serializer.save()
return JsonResponse('atualizacao feita com sucesso', safe = False)
return JsonResponse('falha ao atualizar')
elif request.method == 'DELETE':
department = Departments.objects.get(DepartmentId=id)
department.delete()
return JsonResponse('deletado com sucesso', safe = False)
#metodos para employess
@csrf_exempt
def employeeApi(request, id=0):
if request.method == 'GET':
employees = Employees.objects.all()
employees_serializer = EmployeeSerializer(employees, many=True)
return JsonResponse(employees_serializer.data, safe = False)
elif request.method == 'POST':
employee_data = JSONParser().parse(request)
employees_serializer = EmployeeSerializer(data = employee_data)
#se os dados forem validos serao salvos no banco de dados
if employees_serializer.is_valid():
employees_serializer.save()
return JsonResponse('adicionado com sucesso', safe = False)
#safe é mais flexibilidade nas respostas
return JsonResponse('falha ao adicionar', safe = False)
elif request.method == 'PUT':
employee_data = JSONParser().parse(request)
employee = Employees.objects.get(EmployeeId=employee_data['EmployeeId'])
employees_serializer = EmployeeSerializer(employee, data = employee_data)
if employees_serializer.is_valid():
employees_serializer.save()
return JsonResponse('atualizacao feita com sucesso', safe = False)
return JsonResponse('falha ao atualizar')
elif request.method == 'DELETE':
employee = Employees.objects.get(EmployeeId=id)
employee.delete()
return JsonResponse('deletado com sucesso', safe = False)
@csrf_exempt
def SaveFile(request):
#acusou o erro na linha abaixo
file=request.FILES['file']
file_name=default_storage.save(file.name,file)
return JsonResponse(file_name,safe=False)
|
[
"allan199215@gmail.com"
] |
allan199215@gmail.com
|
3fc97e102abb0f9b9d730e7922bc86b82eb1eb4e
|
2207f2caed5b22e24de2a9c5379f9a8d043d73dd
|
/modules/scn/rule.py
|
81be3410451c7e2b1de97569943cd7949de845b0
|
[] |
no_license
|
996refuse/tlf
|
775ddd198eb99d794918b757a4c5e97da13e31b5
|
f0ed39ecd96a7cfa6eb85e56fdcc200016888d88
|
refs/heads/master
| 2021-05-30T14:01:08.531186
| 2015-07-04T05:22:39
| 2015-07-04T05:22:39
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,063
|
py
|
#-*-encoding=utf-8-*-
rule = (
{
"type": "fetch",
"name": "pager",
"type": "fetch",
"repeat": 20000,
"from": {
"http://www.s.cn/list": "//div[@class='clearfix']/table/tr/td[@class='pagernum']/a[last()]",
},
"dst": {
"type": "list",
"name": "scn_list",
},
"get": {
"type": "simple",
"method": "get",
"parser": "scn.pager",
},
"test": [
{
"url": "http://www.s.cn/list",
"check": "module_test"
}
]
},
{
"type": "fetch",
"name": "list",
"wait": 4,
"src": {
"type": "list",
"name": "scn_list",
"batch": 30,
"filter": "scn.list_filter",
},
"rule": {
"node": "//div[@class='product_list']/dl",
"gidurl": "dd/a/@href",
"price": "dd/a/ul/li[@class='r1']/i[@class='price']",
},
"dst": {
"type": "list",
"name": "scn_stock",
},
"get": {
"method": "get",
"parser": "scn.list_parser",
"args": {
"limit": 30,
"interval": 1,
"debug": False
}
},
"test": [
{
"url": "http://www.s.cn/list/pg66",
"check": "module_test"
},
{
"url": "http://www.s.cn/list/pg23",
"check": "module_test"
},
{
"url": "http://www.s.cn/list/pg79",
"check": "module_test"
},
]
},
{
"name": "stock",
"type": "fetch",
"wait": 4,
"src": {
"name": "scn_stock",
"type": "list",
"batch": 16,
"group": True,
"filter": "scn.stock_task_filter"
},
"rule": "//div[@class='buyinfo_bot']//span[@class='store']",
"get": {
"method": "get",
"parser": "scn.stock_parser",
"args": {
"limit": 1,
"interval": 2,
"debug": False,
"timeout": 10,
},
"not200": "log",
"randua": True
},
"dst": {
"name": "spider_result",
"type": "list",
},
"test": [
{
"url": "http://www.s.cn/kappa-K0422TD04-990.html",
"price": "233",
"check": "module_test_stock"
}
]
}
)
|
[
"lux.r.ck@gmail.com"
] |
lux.r.ck@gmail.com
|
fa7609a84faa2ff896f2cebe0c1a001df50f2eaf
|
2bae6ce8c194a12d19abc90681ba107a3213f6a9
|
/DDPG/DDPG_Examples/DDPG_InvertedDoublePendulum/DDPG_Test_InvertedDoublePendulum.py
|
5943f987036fa13ffe6d58727f52e1dcd7c5774a
|
[] |
no_license
|
bingai/RL-Algorithms
|
d690dcea418d54c5a18db1a87b4abb5e79625c61
|
3b3227001bb02eec32b0ac171cdd6557245d31bc
|
refs/heads/master
| 2020-04-09T17:09:57.752119
| 2019-01-01T04:25:32
| 2019-01-01T04:25:32
| 160,472,540
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 10,104
|
py
|
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
import argparse
import pprint as pp
import gym
import utils
import os
import time
# ===========================
# Actor DDPG pi(s)
# ===========================
# """
# Input to the network is the state, output is the action
# under a deterministic policy.
# The output layer activation is a tanh to keep the action
# between -action_bound and action_bound
# """
class Actor(object):
def __init__(self, sess, state_dim, action_dim, action_bound, learning_rate, tau, batch_size):
self.sess = sess
self.s_dim = state_dim
self.a_dim = action_dim
self.action_bound = action_bound
self.learning_rate = learning_rate
self.tau = tau
self.batch_size = batch_size
self.input = tf.placeholder(shape = [None, self.s_dim], dtype = tf.float32)
self.out, self.out_scaled = self.create_actor_network('main_actor')
self.network_params = tf.trainable_variables()
# # another way to get trainable variables
# self.network_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'main_actor')
self.target_out, self.target_out_scaled = self.create_actor_network('target_actor')
self.target_network_params = tf.trainable_variables()[ len(self.network_params): ]
# # another way to get trainable variables
# self.target_network_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'target_actor')
self.update_target_network_params = \
[self.target_network_params[i].assign(tf.multiply(self.network_params[i], self.tau) +
tf.multiply(self.target_network_params[i], 1. - self.tau))
for i in range(len(self.target_network_params))]
def create_actor_network(self, scope, reuse = False):
with tf.variable_scope(scope, reuse = reuse):
net = self.input
net = slim.fully_connected(net, 400, activation_fn = tf.nn.relu)
net = slim.fully_connected(net, 300, activation_fn = tf.nn.relu)
net = slim.fully_connected(net, self.a_dim, activation_fn = tf.nn.tanh)
out_scaled = tf.multiply(net, self.action_bound)
return net, out_scaled
def update_target_network(self):
self.sess.run(self.update_target_network_params)
def predict(self, inputs):
return self.sess.run(self.out_scaled, feed_dict={
self.input: inputs
})
def predict_target(self, inputs):
return self.sess.run(self.target_out_scaled, feed_dict={
self.input: inputs
})
# ===========================
# Critic DDPG Q(s,a)
# ===========================
# """
# Input to the network is the state and action, output is Q(s,a).
# The action must be obtained from the output of the Actor network.
# """
class Critic(object):
def __init__(self, sess, state_dim, action_dim, learning_rate, tau, actor_inputs_scaled):
self.sess = sess
self.s_dim = state_dim
self.a_dim = action_dim
self.learning_rate = learning_rate
self.tau = tau
# Q(s,a) input 1: state
self.state_input = tf.placeholder(shape = [None, self.s_dim], dtype = tf.float32)
# Q(s,a) input 2: action
self.actor_input_scaled = actor_inputs_scaled
self.actor_input = tf.placeholder(shape = [None, self.a_dim], dtype = tf.float32)
# Q(s,a) with scaled action input
self.total_out_scaled = self.create_critic_network('main_critic', self.actor_input_scaled)
# Q(s,a) with unscaled action input
self.out = self.create_critic_network('main_critic', self.actor_input, reuse = True)
# target_Q(s,a)
self.target_out = self.create_critic_network('target_critic', self.actor_input)
self.network_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'main_critic')
self.target_network_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'target_critic')
self.update_target_network_params = \
[self.target_network_params[i].assign(tf.multiply(self.network_params[i], self.tau) +
tf.multiply(self.target_network_params[i], 1. - self.tau))
for i in range(len(self.target_network_params))]
# update critics
self.predicted_q_value = tf.placeholder(tf.float32, [None, 1])
self.loss = tf.reduce_mean(tf.square(self.out - self.predicted_q_value))
self.train_step = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss, var_list = self.network_params)
def create_critic_network(self, scope, actions, reuse = False):
with tf.variable_scope(scope, reuse = reuse):
# Q(s,a)
net = tf.concat([self.state_input, actions], axis = 1)
net = slim.fully_connected(net, 400, activation_fn = tf.nn.relu)
net = slim.fully_connected(net, 300, activation_fn = tf.nn.relu)
net = slim.fully_connected(net, 1, activation_fn = None)
return net
def update_target_network(self):
self.sess.run(self.update_target_network_params)
def predict(self, state_inputs, actor_inputs):
return self.sess.run(self.out, feed_dict={
self.state_input: state_inputs,
self.actor_input: actor_inputs
})
def predict_target(self, state_inputs, actor_inputs):
return self.sess.run(self.target_out, feed_dict={
self.state_input: state_inputs,
self.actor_input: actor_inputs
})
#===========================
# Tensorflow Summary Ops
#===========================
def test_summaries():
episode_r = tf.Variable(0.)
tf.summary.scalar("Test Reward", episode_r)
episode_timesteps = tf.Variable(0.)
tf.summary.scalar("Steps before DONE", episode_timesteps)
summary_vars = [episode_r, episode_timesteps]
summary_ops = tf.summary.merge_all()
return summary_ops, summary_vars
def test(sess, env, actor, summary_ops, summary_vars):
s = env.reset()
done = False
episode_r = 0
episode_timesteps = 0
# test book-keeping
writer = tf.summary.FileWriter(args['test_dir'])
writer.add_graph(sess.graph)
while not done:
env.render()
action = actor.predict(np.reshape(s, (1, actor.s_dim)))
s2, r, done, _ = env.step(action)
time.sleep(0.01)
episode_r += r
s = s2
episode_timesteps += 1
# # test book-keeping
summary_str = sess.run(summary_ops, feed_dict={
summary_vars[0]: np.asscalar(episode_r),
summary_vars[1]: episode_timesteps})
writer.add_summary(summary_str,episode_timesteps)
writer.flush()
print("During evaluation the mean episode reward is {}, and it took {} steps before Done".format(np.asscalar(episode_r), episode_timesteps))
def main(args):
if not os.path.exists(args['save_dir']) :
os.makedirs(args['save_dir'])
with tf.Session() as sess:
env = gym.make(args['env'])
np.random.seed(int(args['random_seed']))
tf.set_random_seed(int(args['random_seed']))
env.seed(int(args['random_seed']))
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
action_bound = int(env.action_space.high[0])
actor = Actor(sess, state_dim, action_dim, action_bound,
float(args['actor_lr']), float(args['tau']),
int(args['minibatch_size']))
critic = Critic(sess, state_dim, action_dim,
float(args['critic_lr']), float(args['tau']),
actor.out_scaled)
# Set up summary Ops
summary_ops, summary_vars = test_summaries()
# load pre-trained model
saver = tf.train.Saver()
saver.restore(sess, os.path.join(args['save_dir'], args['env']))
# evaluate trained model
test(sess, env, actor, summary_ops, summary_vars)
time.sleep(2)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='provide arguments for TD3 agent')
parser.add_argument('--actor_lr', help='actor network learning rate', default=0.001)
parser.add_argument('--critic_lr', help='critic network learning rate', default=0.001)
parser.add_argument("--start_timesteps", help='action sampling strategy time trigger', default=1e4, type=int)
parser.add_argument('--tau', help='soft target update parameter', default=0.005)
parser.add_argument('--minibatch_size', help='size of minibatch for minibatch-SGD', default=100)
parser.add_argument("--policy_noise", help = 'std of noise added ', default=0.2, type=float)
parser.add_argument("--noise_clip", default=0.5, type=float)
parser.add_argument("--discount", default=0.99, type=float)
parser.add_argument("--policy_freq", default=2, type=int)
parser.add_argument("--expl_noise", default=0.1, type=float)
# parser.add_argument('--env', help='choose the gym env', default='Pendulum-v0')
# parser.add_argument('--env', help='choose the gym env', default='InvertedPendulum-v2')
parser.add_argument('--env', help='choose the gym env', default='InvertedDoublePendulum-v2')
# parser.add_argument('--env', help='choose the gym env', default='MountainCarContinuous-v0')
# parser.add_argument('--env', help='choose the gym env', default='Reacher-v2')
# parser.add_argument('--env', help='choose the gym env', default='HalfCheetah-v2')
# parser.add_argument('--env', help='choose the gym env', default='Hopper-v2')
# parser.add_argument('--env', help='choose the gym env', default='Walker2d-v2')
# parser.add_argument('--env', help='choose the gym env', default='Ant-v2')
# parser.add_argument('--env', help='choose the gym env', default='Humanoid-v2')
# parser.add_argument('--env', help='choose the gym env', default='HumanoidStandup-v2')
# parser.add_argument('--env', help='choose the gym env', default='Swimmer-v2')
parser.add_argument('--random_seed', help='random seed for repeatability', default=0)
parser.add_argument("--max_timesteps", default=1e6, type=float)
parser.add_argument("--eval_episodes", default=100, type=float)
parser.add_argument("--save_dir", default='./models/', help = 'save directory')
parser.add_argument("--save_timesteps", default=2e5, type=float)
#tensorboard book-keeping: training
parser.add_argument('--summary_dir', help='directory for storing tensorboard info', default='./models/tensorboard')
#tensorboard book-keeping: tratesting
parser.add_argument('--test_dir', help='directory for storing tensorboard info : test', default='./models/tensorboard_test')
args = vars(parser.parse_args())
pp.pprint(args)
main(args)
|
[
"bing.ai@utexas.edu"
] |
bing.ai@utexas.edu
|
5c6e655ee9fd28e840bc078319dd48152f3a829f
|
67d22c922419577be197edae8a5979317a336079
|
/python/example/drawPolygons.py
|
c3a4056633256b377ac8b50dc52685f367142deb
|
[
"MIT"
] |
permissive
|
PeterZs/TopoLite
|
69bc3ea3e45e9d5e08d2432c63fc59078aa11b57
|
d6eb9125518a88ea546917df5217978f34661b2c
|
refs/heads/master
| 2023-03-19T09:24:06.659312
| 2020-06-29T15:34:21
| 2020-06-29T15:34:21
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 751
|
py
|
import matplotlib.pyplot as plt
def plotpoly(coord):
coord.append(coord[0]) #repeat the first point to create a 'closed loop'
xs, ys = zip(*coord) #create lists of x and y values
plt.plot(xs,ys)
return [xs, ys]
plt.figure()
A = [[-0.25, -0.3], [0.15, -0.3], [0.15, -0.0999999], [-0.0499999, -0.0999999], [-0.0499999, 0.0999999], [0.15, 0.1], [0.15, 0.3], [-0.25, 0.3], ]
B = [[-0.25, -0.0999999], [-0.0500001, -0.0999999], [-0.0500002, 0.3], [0.15, 0.3], [0.15, -0.3], [-0.45, -0.3], [-0.45, -0.1], [-0.65, -0.1], [-0.65, 0.1], [-0.25, 0.1], ]
C = [[0.15, -0.1], [-0.0499999, -0.1], [-0.05, 0.1], [0.15, 0.1], [0.15, 0.3], [-0.0500001, 0.3], [-0.05, -0.0999999], [-0.25, -0.1], [-0.25, -0.3], [0.15, -0.3], ]
plotpoly(C)
plt.show()
|
[
"qiqiustc@gmail.com"
] |
qiqiustc@gmail.com
|
de85996500552c3c31ab52ff302a8b2b4c6a6c77
|
a34cc0ac718b8e5f62b814599449fe387ee3f600
|
/obj.py
|
f40d437feb5d393d09ab924debb3e91e457e3a4b
|
[] |
no_license
|
luizphell/flappybird
|
a2a06ee21363d21044dd13f045c8a77c74c01ba1
|
96bd82b7186421b15ea633faedaec42c4add5a82
|
refs/heads/main
| 2023-07-11T14:35:53.858069
| 2021-07-29T20:05:57
| 2021-07-29T20:05:57
| 390,837,572
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,168
|
py
|
import pygame
# Objeto criado para inserir os itens do cenário.
class Obj(pygame.sprite.Sprite):
def __init__(self, image, x, y, *groups):
super().__init__(*groups)
self.image = pygame.image.load(image)
self.rect = self.image.get_rect()
self.rect[0] = x
self.rect[1] = y
# Objeto com movimento.
class Pipe(Obj):
def __init__(self, image, x, y, *groups): # Recebe as mesmas características do Obj.
super().__init__(image, x, y, *groups)
def update(self, *args):
self.move()
def move(self): # Movimento dos canos.
self.rect[0] -= 3 # Velocidade.
if self.rect[0] <= -100:
self.kill()
# Adicionar a recompensa.
class Coin(Obj):
def __init__(self, image, x, y, *groups):
super().__init__(image, x, y, *groups)
self.time = 0
def update(self, *args):
self.move()
self.anim()
def move(self):
self.rect[0] -= 3
if self.rect[0] <= -100:
self.kill()
def anim(self): # Animação das imagens.
self.time = (self.time + 1) % 6
self.image = pygame.image.load('Flappy_Image/' + str(self.time) + '.png')
class Dragon(Obj):
def __init__(self, image, x, y, *groups):
super().__init__(image, x, y, *groups)
self.time = 0
self.vel = 3
self.grav = 1
self.pts = 0
self.play = True
def update(self, *args):
self.anim()
self.move()
def anim(self):
self.time = (self.time + 1) % 4
self.image = pygame.image.load('Flappy_Image/Dragon' + str(self.time) + '.png')
def move(self):
tecla = pygame.key.get_pressed()
self.vel += self.grav # Adição da gravidade.
self.rect[1] += self.vel
if self.vel >= 10: # Controle da velocidade.
self.vel = 10
if self.play:
if tecla[pygame.K_SPACE]: # Interação com o player.
self.vel -= 4
if self.rect[1] >= 650: # Limite do chão e do teto.
self.rect[1] = 650
elif self.rect[1] <= 0:
self.rect[1] = 0
self.vel = 3
def colision_pipes(self, group): # Colisões e consequências.
col = pygame.sprite.spritecollide(self, group, False)
if col:
self.play = False
def colision_coin(self, group):
col = pygame.sprite.spritecollide(self, group, True)
if col:
self.pts += 1
# Objeto para criar textos:
class Text:
def __init__(self, size, text):
self.init = pygame.font.init()
self.font = pygame.font.Font('Flappy_Image/font/font.ttf', size)
self.render = self.font.render(text, True, (255, 255, 255))
def draw(self, window, x, y):
window.blit(self.render, (x, y))
def text_update(self, text):
self.render = self.font.render(text, True, (255, 255, 255))
|
[
"noreply@github.com"
] |
noreply@github.com
|
6d5b0e02c66699ecd71cea994df1e96d609de9a4
|
8134fe9b7d7920c004302e890370a1b6ca02e9e1
|
/Android/urls.py
|
2d6c8ee78a754eb688ebe8d400b0c1db48141cf8
|
[] |
no_license
|
txy-cs/App_Django
|
bf408431957337a90b92568f3128604079e471d0
|
5183e6a20d30c16340d1e1901974fee2e75a33c2
|
refs/heads/master
| 2021-01-13T09:57:43.264518
| 2016-10-28T08:28:52
| 2016-10-28T08:28:52
| 72,188,168
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 764
|
py
|
"""Android URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.10/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
from django.conf.urls import url
from django.contrib import admin
urlpatterns = [
url(r'^admin/', admin.site.urls),
]
|
[
"txy408926918@outlook.com"
] |
txy408926918@outlook.com
|
bff7768f9a5f3a84f3142fcac45842c549f8bd13
|
d5b60325d88d59bb3c6cde58036514921abfd6e9
|
/DjangoChat/DjangoChat/wsgi.py
|
c2d57315e9c4b78413c290b4da11fa09adacfd85
|
[] |
no_license
|
dagrishin/DjangoChat
|
472044874bbd1a91efe5a7e6611af02aa485acd1
|
d800fff81ac3632752e3486a90c062dde4b18780
|
refs/heads/master
| 2022-12-22T06:56:57.676392
| 2020-09-29T07:14:50
| 2020-09-29T07:14:50
| 299,532,590
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 397
|
py
|
"""
WSGI config for DjangoChat project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/2.0/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "DjangoChat.settings")
application = get_wsgi_application()
|
[
"grishin-dim@yandex.ru"
] |
grishin-dim@yandex.ru
|
1fdc5488c7b997dc3eda43ec658d71f1f096e0c9
|
86eac99b383981897a608d57f48b23a8d44f35c0
|
/src/Python.Client/src/test.py
|
d3f3acc5156518c78ee46a6a0fb8f1d0af2c6046
|
[
"Apache-2.0"
] |
permissive
|
AndMu/Wikiled.Sentiment.Service
|
eb3d91e64f37a8ea4db1e37d320c40b05dd83c89
|
8ce338183cb3730512c48935b7bf996e0a6ddb8c
|
refs/heads/master
| 2022-12-11T05:59:05.787535
| 2020-07-16T10:59:53
| 2020-07-16T10:59:53
| 134,326,518
| 0
| 0
|
Apache-2.0
| 2022-12-08T08:56:55
| 2018-05-21T21:20:13
|
Jupyter Notebook
|
UTF-8
|
Python
| false
| false
| 3,667
|
py
|
import os
import socket
import logging.config
from datetime import datetime
from os import path
import asyncio
from src.psenti.service.sentiment import SentimentAnalysis, SentimentConnection, Document
# create logger
logging.config.fileConfig('logging.conf', disable_existing_loggers=False)
logger = logging.getLogger('psenti')
user_name = socket.gethostname()
host = '127.0.0.1'
port = 5000
def sentiment_analysis():
documents = ['I like this bool :)', 'short it baby']
dictionary = {}
dictionary['like'] = -1
dictionary['BOOL'] = 1
connection = SentimentConnection(host=host, port=port, client_id=user_name)
analysis = SentimentAnalysis(connection, 'market', dictionary, clean=True)
analysis.on_message.subscribe(lambda message: print(message))
analysis.detect_sentiment_text(documents)
def sentiment_analysis_market():
documents = ['Huge loss reported.',
'Huge profit reported.']
connection = SentimentConnection(host=host, port=port, client_id=user_name)
analysis = SentimentAnalysis(connection, 'market', clean=True)
analysis.on_message.subscribe(lambda message: print(message))
analysis.detect_sentiment_text(documents)
def sentiment_analysis_docs():
documents = [
Document('1',
'I love this hello kitty decal! I like that the bow is pink instead of red. Only bad thing is that after putting it on the window theres a few air bubbles, but that most likely my fault. Shipped fast too.',
'Ben'),
Document('2',
'I bought this for my 3 yr old daughter when I took it out the pack it had a bad oder, cute but very cheap material easy to ripe. When I tried it on her it was to big, but of course she liked it so I kept it. I dressed her up in it and she looked cute.',
'Ben',
datetime(1995, 5, 2))
]
# with custom lexicon and Twitter type cleaning
# analysis = SentimentAnalysis(connection, 'market', dictionary, clean=True, model='Test')
connection = SentimentConnection(host=host, port=port, client_id=user_name)
analysis = SentimentAnalysis(connection)
analysis.on_message.subscribe(lambda message: print(message))
analysis.detect_sentiment(documents)
def read_documents(path_folder: str, class_type: bool):
directory = os.fsencode(path_folder)
all_documents = []
for file in os.listdir(directory):
filename = os.fsdecode(file)
id = os.path.splitext(filename)[0]
full_name = path.join(path_folder, filename)
with open(full_name, "r", encoding='utf8') as reader:
text = reader.read()
doc = Document(id, text)
doc.isPositive = class_type
all_documents.append(doc)
return all_documents
def save_documents():
with SentimentConnection(host=host, port=port, client_id=user_name) as connection:
connection.delete_documents('Test')
print("Loading Negative files")
all_documents = read_documents('D:/DataSets/aclImdb/All/Train/neg', False)
print("Sending...")
connection.save_documents('Test', all_documents)
print("Loading Positive files")
all_documents = read_documents('D:/DataSets/aclImdb/All/Train/pos', True)
print("Sending...")
connection.save_documents('Test', all_documents)
def train():
with SentimentConnection(host=host, port=port, client_id=user_name) as connection:
analysis = SentimentAnalysis(connection, domain='market', clean=True)
analysis.train('Test')
if __name__ == "__main__":
print('Test')
sentiment_analysis_market()
|
[
"keistokas@gmail.com"
] |
keistokas@gmail.com
|
cfbfd2570f3a5206ef819cd60bf529623f457ea6
|
5cf44be60a9accc2aa8b0cea1c8790f023b10553
|
/build/lib.linux-x86_64-2.7/npmc/read_lmp_rev6.py
|
81b0019083c0c77e1812632221debd29092da719
|
[] |
no_license
|
smerz1989/np-mc
|
7ecc36864a1975bde42d0ddf579e8a1bb9666de9
|
3d13104edf1b36f0817928f262a0b9ae40f6bfb1
|
refs/heads/master
| 2023-07-20T03:25:48.891576
| 2023-03-07T20:55:58
| 2023-03-07T20:55:58
| 37,389,780
| 0
| 2
| null | 2023-07-06T21:15:55
| 2015-06-13T22:14:58
|
Python
|
UTF-8
|
Python
| false
| false
| 16,951
|
py
|
#!/usr/bin/python
import sys
import numpy as np
import string
from math import *
import random as rnd
from subprocess import call
import itertools as itt
def readAtoms(filename):
input = open(filename,"r")
print "reading file " + filename
currentId=0
line = input.readline()
out = line.find("atoms")
while(out==-1):
line = input.readline()
out = line.find("atoms")
print line
numwords = line.split()
numatms = int(numwords[0])
atoms = np.zeros((numatms,7))
while(input.readline().find("Atoms")==-1):
continue
input.readline()
line=input.readline
for j in range(numatms):
line=input.readline()
record = line.split()
for i in range(7):
if i<3:
atoms[j,i]=int(record[i])
else:
atoms[j,i]=float(record[i])
return atoms
def readBonds(filename):
input = open(filename,"r")
print "reading file " + filename
currentId=0
line = input.readline()
out = line.find("bonds")
while(out==-1):
line = input.readline()
out = line.find("bonds")
print line
numwords = line.split()
numbonds = int(numwords[0])
bonds = np.zeros((numbonds,4))
while(input.readline().find("Bonds")==-1):
continue
input.readline()
line=input.readline
for j in range(numbonds):
line=input.readline()
record = line.split()
for i in range(4):
bonds[j,i]=int(record[i])
return bonds
def readAngles(filename):
input = open(filename,"r")
print "reading file " + filename
currentId=0
line = input.readline()
out = line.find("angles")
while(out==-1):
line = input.readline()
out = line.find("angles")
print line
numwords = line.split()
numangles = int(numwords[0])
angles = np.zeros((numangles,5))
while(input.readline().find("Angles")==-1):
continue
input.readline()
line=input.readline
for j in range(numangles):
line=input.readline()
record = line.split()
for i in range(5):
angles[j,i]=int(record[i])
return angles
def readDihedrals(filename):
input = open(filename,"r")
print "reading file " + filename
currentId=0
line = input.readline()
out = line.find("dihedrals")
while(out==-1):
line = input.readline()
out = line.find("dihedrals")
print line
numwords = line.split()
numdiheds = int(numwords[0])
diheds = np.zeros((numdiheds,6))
while(input.readline().find("Dihedrals")==-1):
continue
input.readline()
line=input.readline
for j in range(numdiheds):
line=input.readline()
record = line.split()
for i in range(6):
diheds[j,i]=int(record[i])
return diheds
def readAll(inputfile):
atoms=readAtoms(inputfile)
bonds=readBonds(inputfile)
angles=readAngles(inputfile)
diheds=readDihedrals(inputfile)
return (atoms,bonds,angles,diheds)
def deleteMolecule(atoms,bonds,angles,diheds,molId):
molatoms=atoms[atoms[:,1]==molId][:,0]
molbonds = np.array([])
molangles = np.array([])
moldiheds = np.array([])
for atom in molatoms:
molbonds = np.union1d(molbonds,np.where(((bonds[:,2]==atom)|(bonds[:,3]==atom)))[0])
molangles = np.union1d(molangles,np.where(((angles[:,2]==atom)|(angles[:,3]==atom)|(angles[:,4]==atom)))[0])
moldiheds = np.union1d(moldiheds,np.where(((diheds[:,2]==atom)|(diheds[:,3]==atom)|(diheds[:,4]==atom)|(diheds[:,5]==atom)))[0])
newatoms=np.delete(atoms,np.where((atoms[:,1]==molId))[0],0)
newbonds=np.delete(bonds,molbonds,0)
newangles=np.delete(angles,molangles,0)
newdiheds=np.delete(diheds,moldiheds,0)
return (newatoms,newbonds,newangles,newdiheds)
def addMolecule(molecules,newmolecule):
atoms=molecules[0]
bonds=molecules[1]
angles=molecules[2]
diheds=molecules[3]
replacelist=[]
lastAtmId = np.amax(atoms[:,0]) if atoms.size>0 else 0
lastMolId = np.amax(atoms[:,1]) if atoms.size>0 else 0
lastBondId = np.amax(bonds[:,0]) if bonds.size>0 else 0
lastAngleId = np.amax(angles[:,0]) if angles.size>0 else 0
lastDihedId = np.amax(diheds[:,0]) if diheds.size>0 else 0
for i in range(len(newmolecule[0][:,0])):
oldid=newmolecule[0][i,0]
newmolecule[0][i,0]=lastAtmId+i+1
replacelist.append((oldid,newmolecule[0][i,0]))
newmolecule[0][i,1]=lastMolId+1
for i in range(len(newmolecule[1][:,0])):
newmolecule[1][i,0]=lastBondId+i+1
if(newmolecule[1][i,2] in [x[0] for x in replacelist]):
newid = [x[1] for x in replacelist if x[0]==newmolecule[1][i,2]]
newmolecule[1][i,2]=newid[0]
if(newmolecule[1][i,3] in [x[0] for x in replacelist]):
newid = [x[1] for x in replacelist if x[0]==newmolecule[1][i,3]]
newmolecule[1][i,3]=newid[0]
for i in range(len(newmolecule[2][:,0])):
newmolecule[2][i,0]=lastAngleId+i+1
if(newmolecule[2][i,2] in [x[0] for x in replacelist]):
newid = [x[1] for x in replacelist if x[0]==newmolecule[2][i,2]]
newmolecule[2][i,2]=newid[0]
if(newmolecule[2][i,3] in [x[0] for x in replacelist]):
newid = [x[1] for x in replacelist if x[0]==newmolecule[2][i,3]]
newmolecule[2][i,3]=newid[0]
if(newmolecule[2][i,4] in [x[0] for x in replacelist]):
newid = [x[1] for x in replacelist if x[0]==newmolecule[2][i,4]]
newmolecule[2][i,4]=newid[0]
for i in range(len(newmolecule[3][:,0])):
newmolecule[3][i,0]=lastDihedId+i+1
if(newmolecule[3][i,2] in [x[0] for x in replacelist]):
newid = [x[1] for x in replacelist if x[0]==newmolecule[3][i,2]]
newmolecule[3][i,2]=newid[0]
if(newmolecule[3][i,3] in [x[0] for x in replacelist]):
newid = [x[1] for x in replacelist if x[0]==newmolecule[3][i,3]]
newmolecule[3][i,3]=newid[0]
if(newmolecule[3][i,4] in [x[0] for x in replacelist]):
newid = [x[1] for x in replacelist if x[0]==newmolecule[3][i,4]]
newmolecule[3][i,4]=newid[0]
if(newmolecule[3][i,5] in [x[0] for x in replacelist]):
newid = [x[1] for x in replacelist if x[0]==newmolecule[3][i,5]]
newmolecule[3][i,5]=newid[0]
atoms=np.append(atoms,newmolecule[0],0)
bonds=np.append(bonds,newmolecule[1],0)
angles=np.append(angles,newmolecule[2],0)
diheds=np.append(diheds,newmolecule[3],0)
return (atoms,bonds,angles,diheds)
def selectMolecule(molecules,molId):
molBonds=np.array([])
molAngles=np.array([])
molDiheds=np.array([])
atoms=molecules[0]
bonds=molecules[1]
angles=molecules[2]
diheds=molecules[3]
molatoms=atoms[atoms[:,1]==molId][:,0]
for atom in molatoms:
molBonds=np.union1d(molBonds,np.where(((bonds[:,2]==atom)|(bonds[:,3]==atom)))[0])
molAngles=np.union1d(molAngles,np.where(((angles[:,2]==atom)|(angles[:,3]==atom)|(angles[:,4]==atom)))[0])
molDiheds=np.union1d(molDiheds,np.where(((diheds[:,2]==atom)|(diheds[:,3]==atom)|(diheds[:,4]==atom)|(diheds[:,5]==atom)))[0])
molBonds=molBonds.astype(int)
molAngles=molAngles.astype(int)
molDiheds=molDiheds.astype(int)
newatoms=atoms[atoms[:,1]==molId]
newbonds=np.take(bonds,molBonds,0)
newangles=np.take(angles,molAngles,0)
newdiheds=np.take(diheds,molDiheds,0)
return (newatoms,newbonds,newangles,newdiheds)
def shiftMolecule(atoms,indices,shiftx,shifty,shiftz):
numatms=len(indices)
#print "Starting shift by " + str(shiftx)+"x, "+str(shifty)+"y and "+str(shiftz)+"original positions are:"
#print(atoms[indices,:])
for i in range(numatms):
atoms[indices[i],4]+=shiftx
atoms[indices[i],5]+=shifty
atoms[indices[i],6]+=shiftz
#print "finished shifting atoms the new coordinates are:"
#print(atoms[indices,:])
def rotateMolecule(atoms,indices,theta,phi):
numatoms = len(indices)
theta = theta
phi = phi
rotateZ = np.array([[cos(theta),-sin(theta),0],[sin(theta),cos(theta),0],[0,0,1]])
rotateY = np.array([[cos(phi),0,sin(phi)],[0,1,0],[-sin(phi),0,cos(phi)]])
for i in range(numatoms):
firstRotate = np.dot(rotateY,np.transpose(atoms[indices[i],[4,5,6]]))
secondRotate = np.dot(rotateZ,np.transpose(atoms[indices[i],[4,5,6]]))
atoms[indices[i],[4,5,6]]=np.transpose(secondRotate)
def calcCOM(atoms,indices):
mass = [107.8682,14,15,32.065,16,1]
numatms = len(indices)
totalmass=0
x=0
y=0
z=0
for i in range(numatms):
atommass = mass[int(atoms[indices[i],2])-1]
#print "Atom is of type "+str(int(atoms[indices[i],2]))+" and a mass of "+str(atommass)+"with coordinates "+str(atoms[indices[i],4])+"x, "+str(atoms[indices[i],5])+"y and "+str(atoms[indices[i],6])+"z"
x+=atommass*atoms[indices[i],4]
y+=atommass*atoms[indices[i],5]
z+=atommass*atoms[indices[i],6]
totalmass+=atommass
#print "COM is "+str(x/totalmass)+"x, "+str(y/totalmass)+"y and "+str(z/totalmass)
return np.array([x/totalmass,y/totalmass,z/totalmass])
def rotateAxis(olddir,newdir):
axis = np.cross(olddir,newdir)
axis = axis/np.linalg.norm(axis)
theta = acos(np.dot(olddir,newdir)/(np.linalg.norm(olddir)*np.linalg.norm(newdir)))
skewmat = np.array([[0,-axis[2],axis[1]],[axis[2],0,-axis[0]],[-axis[1],axis[0],0]])
R = np.identity(3) + sin(theta)*skewmat+(1-cos(theta))*np.linalg.matrix_power(skewmat,2)
return R
def comRotation(a,b):
v = np.cross(a,b)
vX = np.array([[0,-v[2],v[1]],[v[2],0,-v[0]],[-v[1],v[0],0]])
s = np.linalg.norm(v)
c = np.dot(a,b)
print "normalization is "+str(((1-c)/(s**2)))
R = np.identity(3) + vX +np.linalg.matrix_power(vX,2)*((1-c)/(s**2))
return R
def swapMolecules(molId1,molId2,atoms,centerRotation):
atoms1 = np.where((atoms[:,1]==molId1))[0]
#print(atoms1)
atoms2 = np.where((atoms[:,1]==molId2))[0]
#print(atoms2)
sulfur1 = atoms[(atoms[:,1]==molId1) & (atoms[:,2]==float(4))]
#print "Found 1st sulfur"
#print(sulfur1)
sulfur2 = atoms[(atoms[:,1]==molId2) & (atoms[:,2]==float(4))]
#print "Found 2nd sulfur"
#print(sulfur2)
x1 = sulfur1[0,4]-centerRotation[0]
x2 = sulfur2[0,4]-centerRotation[0]
y1 = sulfur1[0,5]-centerRotation[1]
y2 = sulfur2[0,5]-centerRotation[1]
z1 = sulfur1[0,6]-centerRotation[2]
z2 = sulfur2[0,6]-centerRotation[2]
phi1 = atan2(y1,x1)
phi2 = atan2(y2,x2)
theta1 = acos(z1/sqrt(x1**2+y1**2+z1**2))
theta2 = acos(z2/sqrt(x2**2+y2**2+z2**2))
shiftX = x2 - x1
shiftY = y2 - y1
shiftZ = z2 - z1
shiftTheta = theta2-theta1
shiftPhi = phi2-phi1
shiftMolecule(atoms,atoms1,shiftX,shiftY,shiftZ)
shiftMolecule(atoms,atoms2,-shiftX,-shiftY,-shiftZ)
com1=calcCOM(atoms,atoms1)
a1 = com1-sulfur2[0,4:7]
b1 = np.array([sin(theta2)*cos(phi2),sin(theta2)*sin(phi2),cos(theta2)])
com2=calcCOM(atoms,atoms2)
a2 = com2-sulfur1[0,4:7]
b2 = np.array([sin(theta1)*cos(phi1),sin(theta1)*sin(phi1),cos(theta1)])
#rotateMolecule(atoms,atoms1,shiftTheta,shiftPhi)
atoms[atoms1,4:7]-=sulfur2[0,4:7]
#print(atoms[atoms1,4:7])
#print(comRotation(a1,b1))
for i in range(len(atoms1)):
#print "coordinates before rotation"
#print atoms[atoms1[i],4:7]
atoms[atoms1[i],4:7]=np.dot(rotateAxis(a1,b1),np.transpose(atoms[atoms1[i],[4,5,6]]))
#print "coordinates after"
#print atoms[atoms1[i],4:7]
atoms[atoms1,4:7]+=sulfur2[0,4:7]
#rotateMolecule(atoms,atoms2,-shiftTheta,-shiftPhi)
atoms[atoms2,4:7]-=sulfur1[0,4:7]
#print(atoms[atoms2,4:7])
for i in range(len(atoms2)):
atoms[atoms2[i],4:7]=np.dot(rotateAxis(a2,b2),np.transpose(atoms[atoms2[i],[4,5,6]]))
atoms[atoms2,4:7]+=sulfur1[0,4:7]
def editFile(filein,fileout,atoms,bonds,angles,diheds):
input = open(filein,"r")
output = open(fileout,"w")
#print "reading file " + filein
currentId=0
line = input.readline()
out = line.find("atoms")
while(out==-1):
output.write(line)
line = input.readline()
out = line.find("atoms")
#print line
output.write("{0:12d} {1}\n".format(len(atoms),"atoms"))
output.write("{0:12d} {1}\n".format(len(bonds),"bonds"))
output.write("{0:12d} {1}\n".format(len(angles),"angles"))
output.write("{0:12d} {1}\n\n".format(len(diheds),"dihedrals"))
output.write("{0:12d} {1}\n".format(len(np.unique(atoms[:,2])),"atom types"))
output.write("{0:12d} {1}\n".format(len(np.unique(bonds[:,1])),"bond types"))
output.write("{0:12d} {1}\n".format(len(np.unique(angles[:,1])),"angle types"))
output.write("{0:12d} {1}\n\n".format(len(np.unique(diheds[:,1])),"dihedral types"))
out = line.find("xlo")
while(out==-1):
line = input.readline()
out=line.find("xlo")
output.write(line)
while(line.find("Atoms")==-1):
line = input.readline()
output.write(line)
output.write("\n")
np.savetxt("atoms.temp",atoms,"%7d %7d %4d %10.6f %16.8f %16.8f %16.8f")
np.savetxt("bonds.temp",bonds,"%6d %3d %6d %6d")
np.savetxt("angles.temp",angles,"%6d %3d %6d %6d %6d")
np.savetxt("diheds.temp",diheds,"%6d %3d %6d %6d %6d %6d")
atomtmp = open("atoms.temp","r")
bondtmp = open("bonds.temp","r")
angletmp = open("angles.temp","r")
dihedtmp = open("diheds.temp","r")
output.write(atomtmp.read())
#while(input.readline().find("Bonds")==-1):
# continue
output.write("\nBonds\n\n")
output.write(bondtmp.read())
output.write("\nAngles\n\n")
output.write(angletmp.read())
output.write("\nDihedrals\n\n")
output.write(dihedtmp.read())
#while(line != ''):
# line = input.readline()
# output.write(line)
def molCollide(atoms,indices):
numatms = len(indices)
xcenter = 40.9
ycenter = 40.9
zcenter = 40.9
for i in range(numatms):
x = atoms[indices[i],4]
y = atoms[indices[i],5]
z = atoms[indices[i],6]
#print "x is "+str(x)+" y is "+str(y)+" z is "+str(z)
if sqrt((x-xcenter)**2+(y-ycenter)**2+(z-zcenter)**2)<20.45:
return True
return False
def getPotential(filename):
pefile = open(filename,"r")
lines=pefile.readlines()
return lines[len(lines)-1].split()[1]
def getBondedAtoms(bonds,atomID):
bonded1 = bonds[(bonds[:,2]==atomID)][:,3] if bonds[(bonds[:,2]==atomID)].shape[0]>0 else []
bonded2 = bonds[(bonds[:,3]==atomID)][:,2] if bonds[(bonds[:,3]==atomID)].shape[0]>0 else []
return np.append(np.ravel(bonded1),np.ravel(bonded2))
def getMoleculeAtoms(bonds,startID):
atomIDs = np.empty([1])
atomIDs[0] = startID
bondedAtoms = getBondedAtoms(bonds,startID)
actAtoms = [atom for atom in bondedAtoms if ((atom>0) and (not (atom in atomIDs)))]
while(len(actAtoms)>0):
atomIDs = np.append(atomIDs,actAtoms[0])
bondedAtoms = getBondedAtoms(bonds,actAtoms[0])
actAtoms = [atom for atom in bondedAtoms if ((atom>0) and (not (atom in atomIDs)))]
return atomIDs
if __name__ == "__main__":
inputfile=sys.argv[1]
basename=inputfile.split('.')[0]
atoms = readfile(inputfile)
#print(atoms)
kb=0.0019872041 #in kcal/mol-K
T=360
beta = 1/(kb*T)
#print "Xmin = "+str(np.amin(atoms[atoms[:,2]==1][:,4]))
#print "Xmax = "+str(np.amax(atoms[atoms[:,2]==1][:,4]))
#print "Xmin = "+str(np.amin(atoms[atoms[:,2]==1][:,5]))
#print "Ymax = "+str(np.amax(atoms[atoms[:,2]==1][:,5]))
#print "Zmin = "+str(np.amin(atoms[atoms[:,2]==1][:,6]))
#print "Zmax = "+str(np.amax(atoms[atoms[:,2]==1][:,6]))
molIds = atoms[atoms[:,2]==4][:,1]
centerRotation=np.array([40.9,40.9,40.9])
numsteps=10
print "Center of rotation is "+str(centerRotation[0])+"x, "+str(centerRotation[1])+"y and "+str(centerRotation[2])+"z"
#print(molIds)
rnd.seed()
collisions=0
lastenergy=getPotential("pe.out")
for i in xrange(numsteps):
swappedList = rnd.sample(molIds,2)
#print "swapping molecule "+str(swappedList[0])+" and molecule "+str(swappedList[1])
swapMolecules(swappedList[0],swappedList[1],atoms,centerRotation)
if molCollide(atoms,np.where((atoms[:,1]==swappedList[0]))[0]) or molCollide(atoms,np.where((atoms[:,1]==swappedList[1]))[0]):
print "molecule collided"
continue
editFile(inputfile,atoms,basename+str(i)+".lmp")
call(["cp",basename+str(i)+".lmp",basename+".temp"])
call("./runlammps.sh")
energy=getPotential("pe.out")
dU = energy-lastenergy
if(dU<=0):
atoms=readfile(basename+str(i)+".lmp")
elif(rnd.random()<exp(-beta*dU)):
atoms=readfile(basename+str(i)+".lmp")
print "Xmin = "+str(np.amin(atoms[:,4]))
print "Xmax = "+str(np.amax(atoms[:,4]))
print "Xmin = "+str(np.amin(atoms[:,5]))
print "Ymax = "+str(np.amax(atoms[:,5]))
print "Zmin = "+str(np.amin(atoms[:,6]))
print "Zmax = "+str(np.amax(atoms[:,6]))
#np.savetxt("atoms.lmp",atoms,"%7d %7d %4d %10.6f %16.8f %16.8f %16.8f")
|
[
"stevennmerz@gmail.com"
] |
stevennmerz@gmail.com
|
1c2277d882952a475cf6cd791ec09d6b0aa0cd4b
|
69ea28c40bc1baee83acfb3e96ea6f41ee34363d
|
/play/Controller.py
|
595722203bd5d952e5a03fd63e0ff70e73054985
|
[] |
no_license
|
drifftingcanoncuddle/hakaton2019
|
7098db2971325690679b623fbd6e4d84bbb0e838
|
58bf83d087f97ae5c30f95fa9f76a114c7732b36
|
refs/heads/master
| 2020-05-31T07:14:02.808333
| 2019-06-04T12:44:09
| 2019-06-04T12:44:09
| 190,160,910
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,680
|
py
|
from typing import *
from play.Plane import Plane
from play.AIx import AIx
class Controler:
def __init__(self):
self.plane = Plane()
self.plane.create()
self.user_symbol = "O"
self.ai_symbol = "O"
self.ai = AIx()
def set_symbols(self, user_symbol: str, ai_symbol: str):
self.ai_symbol = ai_symbol
self.user_symbol = user_symbol
def play(self, user_x: int, user_y: int):
self.plane.update(user_x, user_y, self.user_symbol)
ai_x, ai_y = self.ai.play(self.plane)
self.plane.update(ai_x, ai_y, self.ai_symbol)
def ai_win(self):
pass
def win(self, plane):
# left diagonal
if plane[0][0] == plane[1][1] and plane[1][1] == plane[2][2]:
return plane[0][0]
# right diagonal
elif plane[2][0] == plane[1][1] and plane[0][2] == plane[1][1]:
return plane[1][1]
# l3 vertical
elif plane[0][0] == plane[0][1] and plane[0][1] == plane[0][2]:
return plane[0][1]
# l2 vertical
elif plane[1][0] == plane[1][1] and plane[1][1] == plane[1][2]:
return plane[1][1]
# l1 vertical
elif plane[2][0] == plane[2][1] and plane[2][1] == plane[2][2]:
return plane[2][1]
# l3 horizontal
elif plane[0][0] == plane[1][0] and plane[1][0] == plane[2][0]:
return plane[0][1]
# l2 horizontal
elif plane[0][1] == plane[1][1] and plane[1][1] == plane[2][1]:
return plane[0][1]
# l1 horizontal
elif plane[0][2] == plane[1][2] and plane[1][2] == plane[2][2]:
return plane[0][1]
|
[
"ptl99t@gmail.com"
] |
ptl99t@gmail.com
|
7226bb2dcef47693a30e496007e67ac5cab23a55
|
da04957fe26dab8a70fec7b426425e9ba92bfe03
|
/Chutando.numero.py
|
5dbebd87e18c1e68f38ca5fdfc52150334983e93
|
[] |
no_license
|
RobsonGomes1/Chutando-o-n-mero
|
3bb29798e599f0297251997613744d8a63814db1
|
b1f90b7e41452f05cd5697629ac715a17349b616
|
refs/heads/main
| 2023-06-05T14:48:25.996307
| 2021-06-15T04:33:38
| 2021-06-15T04:33:38
| 377,035,207
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 948
|
py
|
try:
from random import randint
nu = randint(1, 100)
except:
print('Desculpa, mas aconteceu um erro na solicitação')
else:
Aviso1 = print('Chute do 1 adiante ;)')
aviso = print('escreva "[Sair]" para finalizar')
ch = str
ch1 = 0
while ch != 'sair'.strip():
from time import sleep
ch1 = int(input('Chute um numero: \n '.strip()))
sleep(1.0)
ch = str(input('Deseja sair? Caso não... Aperte ENTER: '.strip() ))
sleep(1.0)
if ch1 <= nu:
print('Quase hein!!!')
sleep(1.0)
elif ch1 >= nu:
print('Quase lá!!!')
sleep(1.0)
elif ch1 == nu:
print('Acertouuuu!!')
sleep(1.0)
else:
if ch == 'sair'.strip():
print('Até breve!!')
sleep(1.0)
finally:
print('Bem vindo e volte sempre!')
|
[
"robson_rabisco@hotmail.com"
] |
robson_rabisco@hotmail.com
|
387f6ae6d5a9c486e271e6200d1a36f32c5cd98f
|
7f0e13ffefa1d9c48d1d239dd6f2845ebd691aca
|
/bin/tabserver
|
e5eb6f27b055c336ccebe2a2aaa8d0a16881f3ce
|
[] |
no_license
|
mwhooker/clitabs
|
d8982478e68ce1dc2a3da2e6b2eabf93a7ae9454
|
aaa3c1390143d53701438405bd55808d82ccaa20
|
refs/heads/master
| 2021-01-01T15:18:24.992130
| 2015-02-18T06:20:41
| 2015-02-18T06:20:41
| 30,792,435
| 4
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 151
|
#!/usr/bin/env python
import sys
import tabcli.server
def main():
return tabcli.server.main()
if __name__ == '__main__':
sys.exit(main())
|
[
"mwhooker@gmail.com"
] |
mwhooker@gmail.com
|
|
f57ea9b54d9603e6d35baeb0d00091ee088ae60b
|
14344bae5521170d70ec2c22ce88d61a0e21ec93
|
/Shannon_Brown/python_stack/myEnvironments/main/main/settings.py
|
a5b71cd5f36d1c6aeec62121a3f0b3dba9535214
|
[] |
no_license
|
ShannonMBrown/python_aug_2018
|
9cf07254f95f0167717faa45f767dec37da54a51
|
ecd9b0d3d3a8628cfd7771ff00fe227fc52db5f7
|
refs/heads/master
| 2020-03-25T05:47:58.357223
| 2018-08-07T16:19:48
| 2018-08-07T16:19:48
| 143,466,451
| 0
| 0
| null | 2018-08-03T19:37:05
| 2018-08-03T19:37:05
| null |
UTF-8
|
Python
| false
| false
| 3,110
|
py
|
"""
Django settings for main project.
Generated by 'django-admin startproject' using Django 1.10.
For more information on this file, see
https://docs.djangoproject.com/en/1.10/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.10/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/1.10/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '))5dcd%)4hct3h^ss2+87-%u!75b^g=5e7-ezk5%6e2+++#($8'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'apps.first_app',
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'main.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 = 'main.wsgi.application'
# Database
# https://docs.djangoproject.com/en/1.10/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/1.10/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/1.10/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.10/howto/static-files/
STATIC_URL = '/static/'
|
[
"shannonmbrown@Shannons-MacBook-Pro.local"
] |
shannonmbrown@Shannons-MacBook-Pro.local
|
f5c5ed47d5675f11c96f665fda4db3d9b492c918
|
b402040954f13bbb8df5e2cb2f5ebafb4ed75f7a
|
/main/migrations/0001_initial.py
|
65c273c754fd63d23a09b3b13644af594f11545f
|
[] |
no_license
|
HalimaShanta/Shopping-Website
|
6c744a6f219fa5744ffd3823742c852636b960ed
|
2007cea9755b8f32493849bedd6af6c7cc9e91fd
|
refs/heads/main
| 2023-08-04T23:12:24.547411
| 2021-09-09T21:10:37
| 2021-09-09T21:10:37
| 404,867,566
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 633
|
py
|
# Generated by Django 3.0.8 on 2021-08-09 17:06
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Home',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('image', models.ImageField(blank=True, null=True, upload_to='')),
('title', models.CharField(max_length=100)),
('sub_title', models.CharField(max_length=100)),
],
),
]
|
[
"halimashanta@gmail.com"
] |
halimashanta@gmail.com
|
01a1ef6dc25aacb7b99e3bb2d2e912e04233c3cc
|
9743d5fd24822f79c156ad112229e25adb9ed6f6
|
/xai/brain/wordbase/otherforms/_outgoes.py
|
710d7af255478e9b9f5ce4bf9bc34b044eb81186
|
[
"MIT"
] |
permissive
|
cash2one/xai
|
de7adad1758f50dd6786bf0111e71a903f039b64
|
e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6
|
refs/heads/master
| 2021-01-19T12:33:54.964379
| 2017-01-28T02:00:50
| 2017-01-28T02:00:50
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 220
|
py
|
#calss header
class _OUTGOES():
def __init__(self,):
self.name = "OUTGOES"
self.definitions = outgo
self.parents = []
self.childen = []
self.properties = []
self.jsondata = {}
self.basic = ['outgo']
|
[
"xingwang1991@gmail.com"
] |
xingwang1991@gmail.com
|
10ea87ac6afba40de0a3d96e81db5dc69ef6df3d
|
7c3ad63b17b868672ff14e798bb965109c10d403
|
/src/kNN_single.py
|
6b257b9b63560794a04b98462bedff7409e85679
|
[] |
no_license
|
ternaus/kaggle_liberty
|
87cc6e5259e1ea4ce69726a83e4e642db85d8e22
|
5eb17b6bf1f6f6f6f4f6eab880592547ad41007d
|
refs/heads/master
| 2016-09-11T02:13:22.121760
| 2015-08-26T22:23:47
| 2015-08-26T22:23:47
| 39,865,075
| 4
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,025
|
py
|
from __future__ import division
__author__ = 'Vladimir Iglovikov'
from operator import itemgetter
from sklearn import metrics
from gini_normalized import normalized_gini
import numpy as np
import pandas as pd
from sklearn.grid_search import GridSearchCV
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import StandardScaler
import time
joined = pd.read_csv('../data/joined.csv')
train = joined[joined['Hazard'] != -1]
test = joined[joined['Hazard'] == -1]
y = train['Hazard']
X = train.drop(['Hazard', 'Id'], 1)
X_test = test.drop(['Hazard', 'Id'], 1)
scaler = StandardScaler()
print 'scaling train'
X = scaler.fit_transform(X)
print 'scaling test'
X_test = scaler.transform(X_test)
clf = KNeighborsRegressor(n_neighbors=550)
print 'fitting'
clf.fit(X, y)
print 'predicting'
prediction = clf.predict(X_test)
submission = pd.DataFrame()
submission['Id'] = test['Id']
submission['Hazard'] = prediction
submission.to_csv('kNN/kNN_{timestamp}.csv'.format(timestamp=time.time()), index=False)
|
[
"iglovikov@gmail.com"
] |
iglovikov@gmail.com
|
57fb1c867bec4dd8dcaf8f44920298ef2a93a849
|
325236056593df914d836c0a5fed0e56c1060941
|
/.history/part2/part2_20190913223319.py
|
95a89803f197fb5625b3ba454265dd7b1b6be88d
|
[] |
no_license
|
amersulieman/machine_learning_HW1
|
2691f3c81a7c6c718d46a33d93525e811414b6c7
|
0c3e59fece35cb8a803eda4ddae7d3a7980c6774
|
refs/heads/master
| 2022-02-17T06:31:35.834501
| 2019-09-16T15:58:51
| 2019-09-16T15:58:51
| 207,144,267
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,482
|
py
|
import numpy as np
import random
import re as regex
import os
def generate_random_betas_vector(number_of_betas, bits_per_beta):
'''
Genrates 4 random beta values
'''
random_betas_vector = []
for beta in range(number_of_betas):
random_beta = random.randint(0, (2**bits_per_beta)-1)
random_betas_vector.append(random_beta)
return random_betas_vector
def calc_MSE(x, y, num_rows, beta_vector):
'''reduce each beta to fall into a specified range
-(2^19)/10 to (2^19)-1/10
and calculate the mean squared error
'''
B = [(beta - 2**19)/10 for beta in beta_vector]
B = np.array(B)
# RSS = (Y - Y')^2 but since we can't do square, we use transpose multiplication
error = (y - x @ B).conjugate().transpose() @ (y - x @ B)
MSE = error/num_rows
# Now we can take the root of our MSE since we squared it to avoid errors
root_MSE = MSE ** 0.5
return root_MSE
def get_neighbor(current_betas_vector):
'''
A function that finds all the neighbors of a vector
If a vector size is 80 bits, then there are 80
neighbors since at a time we can only change one bit
'''
for index in range(number_of_betas):
for bit_index in range(bits_per_beta):
old_beta = current_betas_vector[index]
# shift 1 per bit so only one bit per neighbor changes
bit_mask = 1 << bit_index
new_beta = old_beta ^ bit_mask
neighbor = current_betas_vector[:]
neighbor[index] = new_beta
yield neighbor
def input_files_read(exit_cond_files, x_file, y_file):
# load the data of each file
x = np.loadtxt("../HousingData/X.txt")
y = np.loadtxt("../HousingData/Y.txt")
num_rows, num_columns = np.shape(x)
exit_conditions = []
try:
with open(exit_cond_files, 'r') as myfile:
file_content = myfile.read()
filtered_content = re.split(r"\D+", file_content)
exit_conditions[0] = file_content[1]
exit_conditions[1] = file_content[2]
except:
print("problem opening your file.....")
return x, y, num_rows, num_columns, exit_conditions
def hill_climbing(nums_of_betas, bits_per_beta, generations, restarts):
entire_vector_size = number_of_betas * bits_per_beta
for value in range(restarts):
local_min = False
current_beta_vector = generate_random_betas_vector()
current_mse = calc_MSE(current_beta_vector)
generation_count = 1
while generation_count <= generations and local_min == False:
found_better_neighbor = False
# print("generation count --> ", generation_count, "\n")
neighbor_generator = get_neighbor(current_beta_vector)
for bit in range(entire_vector_size):
neighbor = next(neighbor_generator)
neighbor_fitness = calc_MSE(neighbor)
if neighbor_fitness < current_mse:
current_beta_vector = neighbor
current_mse = neighbor_fitness
found_better_neighbor = True
if not found_better_neighbor:
# print("Reached local min")
# print("Betas --> ", convert_binary_betas_to_numbers(current_beta_vector))
# print("Local MSE--> ", current_mse)
local_min = True
generation_count += 1
x_file =
needed_data = input_files_read()
|
[
"asulieman@hedrick-2.local"
] |
asulieman@hedrick-2.local
|
ccef30a80becc469ed0e6f7cb4d3207345e33f76
|
8af419663c0ba2ecb1bf485a83327523f23df23a
|
/高级特性/generator.py
|
503607e1a19d00f769950209bfdf710414689f81
|
[] |
no_license
|
Haiyao/python_practice
|
c2ed6b7a9190928b24038696c686f836a817ee57
|
7903190297c20fa3cf850cde9a6101b2f3a62762
|
refs/heads/master
| 2021-01-10T17:46:09.276846
| 2016-03-15T08:06:58
| 2016-03-15T08:06:58
| 53,932,155
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 249
|
py
|
# -*- coding: utf-8 -*-
def fib(max):
n, a, b = 0, 0, 1
while n < max:
yield b
a, b = b, a + b
n = n + 1
return 'done'
x = input('enter you number')
x = int(x)
f = fib(x)
print(f)
for n in fib(x):
print(n)
|
[
"mma4017@icloud.com"
] |
mma4017@icloud.com
|
4b8180666321d4a533cb64126c95099cdae02b40
|
61aeb392245ed66d40de412c0f2ac1196f7172e2
|
/Data Analytics/개인과제2/main.py
|
150afcd59098566bac4420538c9c0221399b43bc
|
[
"MIT"
] |
permissive
|
mini-song/Konkuk_Univ
|
a0c40771fa291162906bccd430742a9e64ac1e09
|
9cd4091f6c5ec109866c087d530970e77494192f
|
refs/heads/main
| 2023-02-01T13:00:13.834923
| 2020-12-20T15:02:23
| 2020-12-20T15:02:23
| 322,308,412
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,106
|
py
|
import numpy as np
import konlpy
import csv
from tqdm import tqdm
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import gensim
import gensim.corpora as corpora
review_O_data=[]
f = open('./data_review.csv','r',encoding='utf-8')
rdr = csv.reader(f)
review_O_data_tag = []
review_X_data =[]
f2 = open('./data_review.csv','r',encoding='utf-8')
rdr2 = csv.reader(f2)
select_category=input("야식, 족발/보쌈, 한식, 분식, 일식/돈까스, 피자, 중식, 치킨")
company_list=[]
for j in rdr2:
if j[1] ==select_category:
company_list.append(j[0])
print(set(company_list))
select_company = input("위의 목록 또는 전체를 입력해 주세요")
Want_comfortable=input("좋음,보통,나쁨 을 입력하세요")
for i in rdr:
try:
if i[1] ==select_category:
if select_company =='전체':
if i[3] == '':
review_X_data.append(i[6])
else:
if float(i[3])<=2 and float(i[4])<=2 and float(i[5])<=2:
review_O_data.append(i[6])
review_O_data_tag.append(0) #나쁨
elif float(i[3])>=4 and float(i[4])>=4 and float(i[5])>=4:
review_O_data.append(i[6])
review_O_data_tag.append(1) #좋음
else:
review_O_data.append(i[6])
review_O_data_tag.append(2) #보통
elif i[0] ==select_company:
if i[3] == '':
review_X_data.append(i[6])
else:
if float(i[3])<=2 and float(i[4])<=2 and float(i[5])<=2:
review_O_data.append(i[6])
review_O_data_tag.append(0) #나쁨
elif float(i[3])>=4 and float(i[4])>=4 and float(i[5])>=4:
review_O_data.append(i[6])
review_O_data_tag.append(1) #좋음
else:
review_O_data.append(i[6])
review_O_data_tag.append(2) #보통
except:
pass
reveiw_Total_data = review_O_data + review_X_data
for i, document in tqdm(enumerate(reveiw_Total_data)):
okt = konlpy.tag.Okt()
clean_words = []
for word in okt.pos(document,stem=True): #Letimazation
if word[1] in ['Noun', 'Verb']:
clean_words.append(word[0])
document = ' '.join(clean_words)
reveiw_Total_data[i] = document
vectorize = CountVectorizer(min_df=10)
X = vectorize.fit_transform(reveiw_Total_data)
features = vectorize.get_feature_names()
Vector_Matrix = np.array(X.toarray())
review_O_data_df = pd.DataFrame(Vector_Matrix)
review_X_data_df = review_O_data_df.iloc[len(review_O_data)+1:,:]
review_X_data_df.to_csv("review_x_data.csv",encoding="utf-8-sig",header = features)
review_O_data_df=review_O_data_df.head(len(review_O_data))
review_O_data_df["태그"] = review_O_data_tag
features.append("태그")
review_O_data_df.to_csv("review_o_data.csv",encoding="utf-8-sig",header = features)
review_O_data_df=pd.read_csv('./review_o_data.csv')
review_X_data_df=pd.read_csv('./review_x_data.csv')
Y=review_O_data_df['태그']
features.pop(-1)
X=review_O_data_df[features]
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)
log_clf = LogisticRegression()
log_clf.fit(X_train,Y_train)
print("정확도:",round(log_clf.score(X_test, Y_test)*100,2),"%")
review_X_data_tag = log_clf.predict(review_X_data_df[features])
review_x_tag_df = pd.DataFrame(review_X_data_tag)
review_Total_data_tag = review_O_data_tag+list(np.array(review_x_tag_df[0].tolist()))
final_data = []
spool = []
for i in range(0,len(review_Total_data_tag)):
spool.append(reveiw_Total_data[i])
if review_Total_data_tag[i] == 1:
spool.append('좋음')
elif review_Total_data_tag[i] == 2:
spool.append("보통")
else:
spool.append("나쁨")
final_data.append(spool)
spool = []
lda_list =[]
for i in final_data:
if i[1] ==Want_comfortable:
lda_list.append(i[0])
documents =[]
documents = [line.rstrip('\n') for line in lda_list]
stoplist=["제","걸","번","그것","요","거","온","함","분","하다","임","것","다시다","전","치","뿌","링클","식","건지"]
#결과를 계속 보고 stoplist를 채워준다.
texts = [[word for word in document.lower().split() if word not in stoplist] for document in documents]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
lda = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=dictionary,
num_topics=6, update_every=1, chunksize=100, passes=30)
result=lda.print_topics(num_words=9)
for i in result:
print(i)
|
[
"noreply@github.com"
] |
noreply@github.com
|
202db6eca23a5f9a75e3b08d79d121e5f8bdf791
|
6b307db7e46ec26617bf7c5f5b35085f2d8bc184
|
/Lista Fabio 01 - Pt 02/F1_Q13_idadeemdias.py
|
c2a0c139b727b5ad6eb670cb6a009ced2b8d5ab1
|
[] |
no_license
|
brenoeng/ifpi-ads-algoritmos2020
|
859b9ed9686e0ec8d5c5d3c37366c23351d2e1a5
|
0a71a1173104b47c78ff480248c9cba5894321ff
|
refs/heads/master
| 2021-03-04T21:39:35.869542
| 2020-11-06T01:21:58
| 2020-11-06T01:21:58
| 246,066,916
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 339
|
py
|
# Entrada
anos = int(input('Digite sua idade em anos: '))
meses = int(input('Digite há quantos meses você fez aniversário: '))
dias = int(input('Digite quantos dias passou da sua data de aniversário: '))
# Processamento
idade_em_dias = anos * 365 + meses * 30 + dias
# Saída
print('Você tem {} dias de idade'.format(idade_em_dias))
|
[
"breno.ar.andrade@gmail.com"
] |
breno.ar.andrade@gmail.com
|
e7c6dfbd9cf60774e0a17345e45f01b6446732c2
|
76b2bdc8d1f0541a270101215e80f8f0be98e40f
|
/mockmock/outer.py
|
b3d193d0228a1396d37fd6bf91a693cbd5759ac9
|
[] |
no_license
|
flaschbier/StillLearningPython
|
c87596655234120ad47abe1e42082308fa453e21
|
5a4c205c72e51d71190cd250e285e7f8bc3dd738
|
refs/heads/master
| 2021-01-10T11:29:54.591425
| 2016-03-28T18:31:49
| 2016-03-28T18:31:49
| 54,133,064
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 83
|
py
|
import inner
def functio():
return inner.functio()
print "outer:", functio()
|
[
"developer.flaschbier@gmail.com"
] |
developer.flaschbier@gmail.com
|
676fcc1c07c9bc3d2fe08d81889d1dd54d4608fb
|
266f9ec6f870162cf2313a8f9d49ab5f491bc5ba
|
/misc/example_op.py
|
bfc2f350677563b9917d1a5a85bbcd0b63250a4d
|
[] |
no_license
|
hschoi1/TIL
|
991a675fd5722c7420ada44e17e449da9e82dea7
|
d9176811399049f488cbdd0110ea326a9748ada9
|
refs/heads/master
| 2021-02-08T04:34:31.112320
| 2020-06-09T16:53:02
| 2020-06-09T16:53:02
| 244,109,948
| 3
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 144
|
py
|
#try examples from https://pytestguide.readthedocs.io/en/latest/pytestGuide/index.html#code-to-test
def stat2Num(x, y):
return(x+y, (x+y)/2)
|
[
"hunsun1005@gmail.com"
] |
hunsun1005@gmail.com
|
2b798b78d19a0037026818c83cda481dbfc5abd9
|
31da7077e2d6d5f775ecf33c7bf43b7ab70dedf9
|
/dashboard/layouts.py
|
7c12198ea145f7f727dbe6d0ce4339714eb325ce
|
[] |
no_license
|
Slavian2015/Quiz
|
56e0631d5ebb7b2aae4be50a8195a374211081a7
|
5efd69410b57ac24e299a5f39bb3ecddc5d8616d
|
refs/heads/master
| 2023-03-25T13:44:34.747228
| 2021-03-10T16:07:13
| 2021-03-10T16:07:13
| 345,926,187
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 8,127
|
py
|
# -*- coding: utf-8 -*-
import dash_bootstrap_components as dbc
import dash_html_components as html
import sys, base64, os
import dbrools
sys.path.insert(0, r'/usr/local/WB')
main_path_data2 = os.path.expanduser('/usr/local/WB/data/')
main_path_data = os.path.expanduser('/usr/local/WB/dashboard/assets/')
def my_view():
layout = [
html.Div(
style={
"height": "100vh",
"minHeight": "100vh",
"maxHeight": "100vh",
"overflowY": "hidden"
},
children=content())]
return layout
def content():
cont = dbc.Row(style={"width": "100%", "height": "100vh", "margin": "0", "padding": "0"},
justify="center",
align="center",
children=[
dbc.Col(style={"textAlign": "center",
"height": "100vh",
"minHeight": "100vh",
"maxHeight": "100vh",
"overflowY": "scroll",
"margin": "0",
"padding": "0"
},
width=3,
className="no-scrollbars",
children=[]),
dbc.Col(style={"textAlign": "center",
"overflowY": "hidden",
'align': 'center',
"margin": "0",
"padding": "0",
"padding-top": "2%"
},
align="center",
width=6,
className="no-scrollbars",
children=column_left()),
dbc.Col(style={"textAlign": "center",
"height": "100vh",
"minHeight": "100vh",
"maxHeight": "100vh",
"overflowY": "hidden",
"margin": "0",
"padding": "0"},
width=3,
children=[]),
],
no_gutters=True)
return cont
def column_left():
cont = [
html.Div(style={'display': 'none'},
children=html.P("",
id="mymail")),
dbc.Row(style={"width": "100%",
"margin": "0",
"padding": "0"},
id="main_list",
no_gutters=False,
children=create_email()),
]
return cont
def create_email():
card = dbc.Card(color="secondary",
style={"width": "100%",
"padding": "0",
"margin": "10px",
"margin-bottom": "0px"
},
inverse=True,
children=[
dbc.CardBody([
dbc.InputGroup([
dbc.InputGroupAddon(html.P("Your E-mail",
style={"width": "100%"}),
style={"width": "80px"},
addon_type="prepend"),
dbc.Input(placeholder="test@test.com",
id="new_email",
type="email"),
]),
]),
dbc.CardFooter(dbc.Button("create account >>", id="save_email", color="success"))
])
return card
def quiz_tab(mymail):
answered = dbrools.check_person(mymail)
for i in dbrools.get_list():
if f"{i['option1']}_{i['option2']}" not in answered:
cont = [
dbc.Row(style={"width": "100%",
"margin": "0",
"padding": "0"},
id="quiz_list",
no_gutters=False,
children=create_quiz(i['option1'], i['option2'], i['option1'])),
]
return cont
def create_quiz(opt1, opt2, num):
print(f"{opt1}_{opt2}")
encoded_image1 = base64.b64encode(open(main_path_data + f'{opt1.lower()}.png', 'rb').read()).decode('ascii')
encoded_image2 = base64.b64encode(open(main_path_data + f'{opt2.lower()}.png', 'rb').read()).decode('ascii')
tt1 = html.Img(src='data:image/png;base64,{}'.format(encoded_image1),
style={
"margin": "0", "padding": "0",
"width": "300px",
# "textAlign": "center"
}),
tt2 = html.Img(src='data:image/png;base64,{}'.format(encoded_image2),
style={
"margin": "0", "padding": "0",
"width": "300px",
# "textAlign": "center"
}),
card = dbc.Card(color="secondary",
style={"width": "100%",
"padding": "0",
"margin": "10px",
"margin-bottom": "0px"
},
inverse=True,
children=[
html.Div(style={'display': 'none'},
children=html.P("",
id={"type": "symbol",
"index": num}, )),
dbc.CardBody(dbc.Row([
dbc.Col(style={"textAlign": "center",
"margin": "0",
"padding": "0"
},
width=6,
className="no-scrollbars",
children=[dbc.Button(tt1,
id={"type": "next_btn1",
"index": num,
# "opt": opt1,
},
key=opt1,
color="info"),
]),
dbc.Col(style={"textAlign": "center",
"margin": "0",
"padding": "0"
},
width=6,
className="no-scrollbars",
children=[dbc.Button(tt2,
id={"type": "next_btn2",
"index": num,
# "opt": opt2
},
key=opt2,
color="info")]),
])),
dbc.CardFooter(dbc.Button("NEXT >>",
id={"type": "next_btn",
"index": num},
color="success"))
])
return card
|
[
"slavaku2014@gmail.com"
] |
slavaku2014@gmail.com
|
7cc166e065fe935c41d23495250403d7dcdf2d32
|
fbbe424559f64e9a94116a07eaaa555a01b0a7bb
|
/pytorch/source/caffe2/python/workspace_test.py
|
93bcb115e685bccfd0f46ea8cc663fdb6cd3d849
|
[
"MIT"
] |
permissive
|
ryfeus/lambda-packs
|
6544adb4dec19b8e71d75c24d8ed789b785b0369
|
cabf6e4f1970dc14302f87414f170de19944bac2
|
refs/heads/master
| 2022-12-07T16:18:52.475504
| 2022-11-29T13:35:35
| 2022-11-29T13:35:35
| 71,386,735
| 1,283
| 263
|
MIT
| 2022-11-26T05:02:14
| 2016-10-19T18:22:39
|
Python
|
UTF-8
|
Python
| false
| false
| 26,342
|
py
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import os
import unittest
from caffe2.proto import caffe2_pb2
from caffe2.python import core, test_util, workspace, model_helper, brew
import caffe2.python.hypothesis_test_util as htu
import hypothesis.strategies as st
from hypothesis import given
class TestWorkspace(unittest.TestCase):
def setUp(self):
self.net = core.Net("test-net")
self.testblob_ref = self.net.ConstantFill(
[], "testblob", shape=[1, 2, 3, 4], value=1.0)
workspace.ResetWorkspace()
def testRootFolder(self):
self.assertEqual(workspace.ResetWorkspace(), True)
self.assertEqual(workspace.RootFolder(), ".")
self.assertEqual(
workspace.ResetWorkspace("/tmp/caffe-workspace-test"), True)
self.assertEqual(workspace.RootFolder(), "/tmp/caffe-workspace-test")
def testWorkspaceHasBlobWithNonexistingName(self):
self.assertEqual(workspace.HasBlob("non-existing"), False)
def testRunOperatorOnce(self):
self.assertEqual(
workspace.RunOperatorOnce(
self.net.Proto().op[0].SerializeToString()
), True
)
self.assertEqual(workspace.HasBlob("testblob"), True)
blobs = workspace.Blobs()
self.assertEqual(len(blobs), 1)
self.assertEqual(blobs[0], "testblob")
def testGetOperatorCost(self):
op = core.CreateOperator(
"Conv2D",
["X", "W"], ["Y"],
stride_h=1,
stride_w=1,
pad_t=1,
pad_l=1,
pad_b=1,
pad_r=1,
kernel=3,
)
X = np.zeros((1, 8, 8, 8))
W = np.zeros((1, 1, 3, 3))
workspace.FeedBlob("X", X)
workspace.FeedBlob("W", W)
flops, _ = workspace.GetOperatorCost(op.SerializeToString(), ["X", "W"])
self.assertEqual(flops, 1152)
def testRunNetOnce(self):
self.assertEqual(
workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
self.assertEqual(workspace.HasBlob("testblob"), True)
def testCurrentWorkspaceWrapper(self):
self.assertNotIn("testblob", workspace.C.Workspace.current.blobs)
self.assertEqual(
workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
self.assertEqual(workspace.HasBlob("testblob"), True)
self.assertIn("testblob", workspace.C.Workspace.current.blobs)
workspace.ResetWorkspace()
self.assertNotIn("testblob", workspace.C.Workspace.current.blobs)
def testRunPlan(self):
plan = core.Plan("test-plan")
plan.AddStep(core.ExecutionStep("test-step", self.net))
self.assertEqual(
workspace.RunPlan(plan.Proto().SerializeToString()), True)
self.assertEqual(workspace.HasBlob("testblob"), True)
def testRunPlanInBackground(self):
plan = core.Plan("test-plan")
plan.AddStep(core.ExecutionStep("test-step", self.net))
background_plan = workspace.RunPlanInBackground(plan)
while not background_plan.is_done():
pass
self.assertEqual(background_plan.is_succeeded(), True)
self.assertEqual(workspace.HasBlob("testblob"), True)
def testConstructPlanFromSteps(self):
step = core.ExecutionStep("test-step-as-plan", self.net)
self.assertEqual(workspace.RunPlan(step), True)
self.assertEqual(workspace.HasBlob("testblob"), True)
def testResetWorkspace(self):
self.assertEqual(
workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
self.assertEqual(workspace.HasBlob("testblob"), True)
self.assertEqual(workspace.ResetWorkspace(), True)
self.assertEqual(workspace.HasBlob("testblob"), False)
def testTensorAccess(self):
ws = workspace.C.Workspace()
""" test in-place modification """
ws.create_blob("tensor").feed(np.array([1.1, 1.2, 1.3]))
tensor = ws.blobs["tensor"].tensor()
tensor.data[0] = 3.3
val = np.array([3.3, 1.2, 1.3])
np.testing.assert_array_equal(tensor.data, val)
np.testing.assert_array_equal(ws.blobs["tensor"].fetch(), val)
""" test in-place initialization """
tensor.init([2, 3], core.DataType.INT32)
for x in range(2):
for y in range(3):
tensor.data[x, y] = 0
tensor.data[1, 1] = 100
val = np.zeros([2, 3], dtype=np.int32)
val[1, 1] = 100
np.testing.assert_array_equal(tensor.data, val)
np.testing.assert_array_equal(ws.blobs["tensor"].fetch(), val)
""" strings cannot be initialized from python """
with self.assertRaises(RuntimeError):
tensor.init([3, 4], core.DataType.STRING)
""" feed (copy) data into tensor """
val = np.array([[b'abc', b'def'], [b'ghi', b'jkl']], dtype=np.object)
tensor.feed(val)
self.assertEquals(tensor.data[0, 0], b'abc')
np.testing.assert_array_equal(ws.blobs["tensor"].fetch(), val)
val = np.array([1.1, 10.2])
tensor.feed(val)
val[0] = 5.2
self.assertEquals(tensor.data[0], 1.1)
""" fetch (copy) data from tensor """
val = np.array([1.1, 1.2])
tensor.feed(val)
val2 = tensor.fetch()
tensor.data[0] = 5.2
val3 = tensor.fetch()
np.testing.assert_array_equal(val, val2)
self.assertEquals(val3[0], 5.2)
def testFetchFeedBlob(self):
self.assertEqual(
workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
fetched = workspace.FetchBlob("testblob")
# check if fetched is correct.
self.assertEqual(fetched.shape, (1, 2, 3, 4))
np.testing.assert_array_equal(fetched, 1.0)
fetched[:] = 2.0
self.assertEqual(workspace.FeedBlob("testblob", fetched), True)
fetched_again = workspace.FetchBlob("testblob")
self.assertEqual(fetched_again.shape, (1, 2, 3, 4))
np.testing.assert_array_equal(fetched_again, 2.0)
def testFetchFeedBlobViaBlobReference(self):
self.assertEqual(
workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
fetched = workspace.FetchBlob(self.testblob_ref)
# check if fetched is correct.
self.assertEqual(fetched.shape, (1, 2, 3, 4))
np.testing.assert_array_equal(fetched, 1.0)
fetched[:] = 2.0
self.assertEqual(workspace.FeedBlob(self.testblob_ref, fetched), True)
fetched_again = workspace.FetchBlob("testblob") # fetch by name now
self.assertEqual(fetched_again.shape, (1, 2, 3, 4))
np.testing.assert_array_equal(fetched_again, 2.0)
def testFetchFeedBlobTypes(self):
for dtype in [np.float16, np.float32, np.float64, np.bool,
np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16]:
try:
rng = np.iinfo(dtype).max * 2
except ValueError:
rng = 1000
data = ((np.random.rand(2, 3, 4) - 0.5) * rng).astype(dtype)
self.assertEqual(workspace.FeedBlob("testblob_types", data), True)
fetched_back = workspace.FetchBlob("testblob_types")
self.assertEqual(fetched_back.shape, (2, 3, 4))
self.assertEqual(fetched_back.dtype, dtype)
np.testing.assert_array_equal(fetched_back, data)
def testFetchFeedBlobBool(self):
"""Special case for bool to ensure coverage of both true and false."""
data = np.zeros((2, 3, 4)).astype(np.bool)
data.flat[::2] = True
self.assertEqual(workspace.FeedBlob("testblob_types", data), True)
fetched_back = workspace.FetchBlob("testblob_types")
self.assertEqual(fetched_back.shape, (2, 3, 4))
self.assertEqual(fetched_back.dtype, np.bool)
np.testing.assert_array_equal(fetched_back, data)
def testGetBlobSizeBytes(self):
for dtype in [np.float16, np.float32, np.float64, np.bool,
np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16]:
data = np.random.randn(2, 3).astype(dtype)
self.assertTrue(workspace.FeedBlob("testblob_sizeBytes", data), True)
self.assertEqual(
workspace.GetBlobSizeBytes("testblob_sizeBytes"),
6 * np.dtype(dtype).itemsize)
strs1 = np.array([b'Hello World!', b'abcd'])
strs2 = np.array([b'element1', b'element2'])
strs1_len, strs2_len = 0, 0
for str in strs1:
strs1_len += len(str)
for str in strs2:
strs2_len += len(str)
self.assertTrue(workspace.FeedBlob("testblob_str1", strs1), True)
self.assertTrue(workspace.FeedBlob("testblob_str2", strs2), True)
# size of blob "testblob_str1" = size_str1 * meta_.itemsize() + strs1_len
# size of blob "testblob_str2" = size_str2 * meta_.itemsize() + strs2_len
self.assertEqual(
workspace.GetBlobSizeBytes("testblob_str1") -
workspace.GetBlobSizeBytes("testblob_str2"), strs1_len - strs2_len)
def testFetchFeedBlobZeroDim(self):
data = np.empty(shape=(2, 0, 3), dtype=np.float32)
self.assertEqual(workspace.FeedBlob("testblob_empty", data), True)
fetched_back = workspace.FetchBlob("testblob_empty")
self.assertEqual(fetched_back.shape, (2, 0, 3))
self.assertEqual(fetched_back.dtype, np.float32)
def testFetchFeedLongStringTensor(self):
# long strings trigger array of object creation
strs = np.array([
b' '.join(10 * [b'long string']),
b' '.join(128 * [b'very long string']),
b'small \0\1\2 string',
b"Hello, world! I have special \0 symbols \1!"])
workspace.FeedBlob('my_str_tensor', strs)
strs2 = workspace.FetchBlob('my_str_tensor')
self.assertEqual(strs.shape, strs2.shape)
for i in range(0, strs.shape[0]):
self.assertEqual(strs[i], strs2[i])
def testFetchFeedShortStringTensor(self):
# small strings trigger NPY_STRING array
strs = np.array([b'elem1', b'elem 2', b'element 3'])
workspace.FeedBlob('my_str_tensor_2', strs)
strs2 = workspace.FetchBlob('my_str_tensor_2')
self.assertEqual(strs.shape, strs2.shape)
for i in range(0, strs.shape[0]):
self.assertEqual(strs[i], strs2[i])
def testFetchFeedPlainString(self):
# this is actual string, not a tensor of strings
s = b"Hello, world! I have special \0 symbols \1!"
workspace.FeedBlob('my_plain_string', s)
s2 = workspace.FetchBlob('my_plain_string')
self.assertEqual(s, s2)
def testFetchBlobs(self):
s1 = b"test1"
s2 = b"test2"
workspace.FeedBlob('s1', s1)
workspace.FeedBlob('s2', s2)
fetch1, fetch2 = workspace.FetchBlobs(['s1', 's2'])
self.assertEquals(s1, fetch1)
self.assertEquals(s2, fetch2)
def testFetchFeedViaBlobDict(self):
self.assertEqual(
workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
fetched = workspace.blobs["testblob"]
# check if fetched is correct.
self.assertEqual(fetched.shape, (1, 2, 3, 4))
np.testing.assert_array_equal(fetched, 1.0)
fetched[:] = 2.0
workspace.blobs["testblob"] = fetched
fetched_again = workspace.blobs["testblob"]
self.assertEqual(fetched_again.shape, (1, 2, 3, 4))
np.testing.assert_array_equal(fetched_again, 2.0)
self.assertTrue("testblob" in workspace.blobs)
self.assertFalse("non_existant" in workspace.blobs)
self.assertEqual(len(workspace.blobs), 1)
for key in workspace.blobs:
self.assertEqual(key, "testblob")
class TestMultiWorkspaces(unittest.TestCase):
def setUp(self):
workspace.SwitchWorkspace("default")
workspace.ResetWorkspace()
def testCreateWorkspace(self):
self.net = core.Net("test-net")
self.net.ConstantFill([], "testblob", shape=[1, 2, 3, 4], value=1.0)
self.assertEqual(
workspace.RunNetOnce(self.net.Proto().SerializeToString()), True
)
self.assertEqual(workspace.HasBlob("testblob"), True)
self.assertEqual(workspace.SwitchWorkspace("test", True), None)
self.assertEqual(workspace.HasBlob("testblob"), False)
self.assertEqual(workspace.SwitchWorkspace("default"), None)
self.assertEqual(workspace.HasBlob("testblob"), True)
try:
# The following should raise an error.
workspace.SwitchWorkspace("non-existing")
# so this should never happen.
self.assertEqual(True, False)
except RuntimeError:
pass
workspaces = workspace.Workspaces()
self.assertTrue("default" in workspaces)
self.assertTrue("test" in workspaces)
@unittest.skipIf(not workspace.has_gpu_support
and not workspace.has_hip_support, "No gpu support.")
class TestWorkspaceGPU(test_util.TestCase):
def setUp(self):
workspace.ResetWorkspace()
self.net = core.Net("test-net")
self.net.ConstantFill([], "testblob", shape=[1, 2, 3, 4], value=1.0)
self.net.RunAllOnGPU()
def testFetchBlobGPU(self):
self.assertEqual(
workspace.RunNetOnce(self.net.Proto().SerializeToString()), True)
fetched = workspace.FetchBlob("testblob")
# check if fetched is correct.
self.assertEqual(fetched.shape, (1, 2, 3, 4))
np.testing.assert_array_equal(fetched, 1.0)
fetched[:] = 2.0
self.assertEqual(workspace.FeedBlob("testblob", fetched), True)
fetched_again = workspace.FetchBlob("testblob")
self.assertEqual(fetched_again.shape, (1, 2, 3, 4))
np.testing.assert_array_equal(fetched_again, 2.0)
def testGetGpuPeerAccessPattern(self):
pattern = workspace.GetGpuPeerAccessPattern()
self.assertEqual(type(pattern), np.ndarray)
self.assertEqual(pattern.ndim, 2)
self.assertEqual(pattern.shape[0], pattern.shape[1])
self.assertEqual(pattern.shape[0], workspace.NumGpuDevices())
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestWorkspaceIDEEP(test_util.TestCase):
def testFeedFetchBlobIDEEP(self):
arr = np.random.randn(2, 3).astype(np.float32)
workspace.FeedBlob(
"testblob_ideep", arr, core.DeviceOption(caffe2_pb2.IDEEP))
fetched = workspace.FetchBlob("testblob_ideep")
np.testing.assert_array_equal(arr, fetched)
class TestImmedibate(test_util.TestCase):
def testImmediateEnterExit(self):
workspace.StartImmediate(i_know=True)
self.assertTrue(workspace.IsImmediate())
workspace.StopImmediate()
self.assertFalse(workspace.IsImmediate())
def testImmediateRunsCorrectly(self):
workspace.StartImmediate(i_know=True)
net = core.Net("test-net")
net.ConstantFill([], "testblob", shape=[1, 2, 3, 4], value=1.0)
self.assertEqual(
workspace.ImmediateBlobs(), ["testblob"])
content = workspace.FetchImmediate("testblob")
# Also, the immediate mode should not invade the original namespace,
# so we check if this is so.
with self.assertRaises(RuntimeError):
workspace.FetchBlob("testblob")
np.testing.assert_array_equal(content, 1.0)
content[:] = 2.0
self.assertTrue(workspace.FeedImmediate("testblob", content))
np.testing.assert_array_equal(
workspace.FetchImmediate("testblob"), 2.0)
workspace.StopImmediate()
with self.assertRaises(RuntimeError):
content = workspace.FetchImmediate("testblob")
def testImmediateRootFolder(self):
workspace.StartImmediate(i_know=True)
# for testing we will look into the _immediate_root_folder variable
# but in normal usage you should not access that.
self.assertTrue(len(workspace._immediate_root_folder) > 0)
root_folder = workspace._immediate_root_folder
self.assertTrue(os.path.isdir(root_folder))
workspace.StopImmediate()
self.assertTrue(len(workspace._immediate_root_folder) == 0)
# After termination, immediate mode should have the root folder
# deleted.
self.assertFalse(os.path.exists(root_folder))
class TestCppEnforceAsException(test_util.TestCase):
def testEnforce(self):
op = core.CreateOperator("Relu", ["X"], ["Y"])
with self.assertRaises(RuntimeError):
workspace.RunOperatorOnce(op)
class TestCWorkspace(htu.HypothesisTestCase):
def test_net_execution(self):
ws = workspace.C.Workspace()
self.assertEqual(ws.nets, {})
self.assertEqual(ws.blobs, {})
net = core.Net("test-net")
net.ConstantFill([], "testblob", shape=[1, 2, 3, 4], value=1.0)
ws.create_net(net)
# If we do not specify overwrite, this should raise an error.
with self.assertRaises(RuntimeError):
ws.create_net(net)
# But, if we specify overwrite, this should pass.
ws.create_net(net, True)
# Overwrite can also be a kwarg.
ws.create_net(net, overwrite=True)
self.assertIn("testblob", ws.blobs)
self.assertEqual(len(ws.nets), 1)
net_name = net.Proto().name
self.assertIn("test-net", net_name)
net = ws.nets[net_name].run()
blob = ws.blobs["testblob"]
np.testing.assert_array_equal(
np.ones((1, 2, 3, 4), dtype=np.float32),
blob.fetch())
@given(name=st.text(), value=st.floats(min_value=-1, max_value=1.0))
def test_operator_run(self, name, value):
ws = workspace.C.Workspace()
op = core.CreateOperator(
"ConstantFill", [], [name], shape=[1], value=value)
ws.run(op)
self.assertIn(name, ws.blobs)
np.testing.assert_allclose(
[value], ws.blobs[name].fetch(), atol=1e-4, rtol=1e-4)
@given(blob_name=st.text(),
net_name=st.text(),
value=st.floats(min_value=-1, max_value=1.0))
def test_net_run(self, blob_name, net_name, value):
ws = workspace.C.Workspace()
net = core.Net(net_name)
net.ConstantFill([], [blob_name], shape=[1], value=value)
ws.run(net)
self.assertIn(blob_name, ws.blobs)
self.assertNotIn(net_name, ws.nets)
np.testing.assert_allclose(
[value], ws.blobs[blob_name].fetch(), atol=1e-4, rtol=1e-4)
@given(blob_name=st.text(),
net_name=st.text(),
plan_name=st.text(),
value=st.floats(min_value=-1, max_value=1.0))
def test_plan_run(self, blob_name, plan_name, net_name, value):
ws = workspace.C.Workspace()
plan = core.Plan(plan_name)
net = core.Net(net_name)
net.ConstantFill([], [blob_name], shape=[1], value=value)
plan.AddStep(core.ExecutionStep("step", nets=[net], num_iter=1))
ws.run(plan)
self.assertIn(blob_name, ws.blobs)
self.assertIn(net.Name(), ws.nets)
np.testing.assert_allclose(
[value], ws.blobs[blob_name].fetch(), atol=1e-4, rtol=1e-4)
@given(blob_name=st.text(),
net_name=st.text(),
value=st.floats(min_value=-1, max_value=1.0))
def test_net_create(self, blob_name, net_name, value):
ws = workspace.C.Workspace()
net = core.Net(net_name)
net.ConstantFill([], [blob_name], shape=[1], value=value)
ws.create_net(net).run()
self.assertIn(blob_name, ws.blobs)
self.assertIn(net.Name(), ws.nets)
np.testing.assert_allclose(
[value], ws.blobs[blob_name].fetch(), atol=1e-4, rtol=1e-4)
@given(name=st.text(),
value=htu.tensor(),
device_option=st.sampled_from(htu.device_options))
def test_array_serde(self, name, value, device_option):
ws = workspace.C.Workspace()
ws.create_blob(name).feed(value, device_option=device_option)
self.assertIn(name, ws.blobs)
blob = ws.blobs[name]
np.testing.assert_equal(value, ws.blobs[name].fetch())
serde_blob = ws.create_blob("{}_serde".format(name))
serde_blob.deserialize(blob.serialize(name))
np.testing.assert_equal(value, serde_blob.fetch())
@given(name=st.text(), value=st.text())
def test_string_serde(self, name, value):
value = value.encode('ascii', 'ignore')
ws = workspace.C.Workspace()
ws.create_blob(name).feed(value)
self.assertIn(name, ws.blobs)
blob = ws.blobs[name]
self.assertEqual(value, ws.blobs[name].fetch())
serde_blob = ws.create_blob("{}_serde".format(name))
serde_blob.deserialize(blob.serialize(name))
self.assertEqual(value, serde_blob.fetch())
def test_exception(self):
ws = workspace.C.Workspace()
with self.assertRaises(TypeError):
ws.create_net("...")
class TestPredictor(unittest.TestCase):
def _create_model(self):
m = model_helper.ModelHelper()
y = brew.fc(m, "data", "y",
dim_in=4, dim_out=2,
weight_init=('ConstantFill', dict(value=1.0)),
bias_init=('ConstantFill', dict(value=0.0)),
axis=0)
m.net.AddExternalOutput(y)
return m
# Use this test with a bigger model to see how using Predictor allows to
# avoid issues with low protobuf size limit in Python
#
# def test_predictor_predefined(self):
# workspace.ResetWorkspace()
# path = 'caffe2/caffe2/test/assets/'
# with open(path + 'squeeze_predict_net.pb') as f:
# self.predict_net = f.read()
# with open(path + 'squeeze_init_net.pb') as f:
# self.init_net = f.read()
# self.predictor = workspace.Predictor(self.init_net, self.predict_net)
# inputs = [np.zeros((1, 3, 256, 256), dtype='f')]
# outputs = self.predictor.run(inputs)
# self.assertEqual(len(outputs), 1)
# self.assertEqual(outputs[0].shape, (1, 1000, 1, 1))
# self.assertAlmostEqual(outputs[0][0][0][0][0], 5.19026289e-05)
def test_predictor_memory_model(self):
workspace.ResetWorkspace()
m = self._create_model()
workspace.FeedBlob("data", np.zeros([4], dtype='float32'))
self.predictor = workspace.Predictor(
workspace.StringifyProto(m.param_init_net.Proto()),
workspace.StringifyProto(m.net.Proto()))
inputs = np.array([1, 3, 256, 256], dtype='float32')
outputs = self.predictor.run([inputs])
np.testing.assert_array_almost_equal(
np.array([[516, 516]], dtype='float32'), outputs)
class TestTransform(htu.HypothesisTestCase):
@given(input_dim=st.integers(min_value=1, max_value=10),
output_dim=st.integers(min_value=1, max_value=10),
batch_size=st.integers(min_value=1, max_value=10))
def test_simple_transform(self, input_dim, output_dim, batch_size):
m = model_helper.ModelHelper()
fc1 = brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim)
fc2 = brew.fc(m, fc1, "fc2", dim_in=output_dim, dim_out=output_dim)
conv = brew.conv(m, fc2, "conv",
dim_in=output_dim,
dim_out=output_dim,
use_cudnn=True,
engine="CUDNN",
kernel=3)
conv.Relu([], conv)\
.Softmax([], "pred") \
.LabelCrossEntropy(["label"], ["xent"]) \
.AveragedLoss([], "loss")
transformed_net_proto = workspace.ApplyTransform(
"ConvToNNPack",
m.net.Proto())
self.assertEqual(transformed_net_proto.op[2].engine, "NNPACK")
@given(input_dim=st.integers(min_value=1, max_value=10),
output_dim=st.integers(min_value=1, max_value=10),
batch_size=st.integers(min_value=1, max_value=10))
def test_registry_invalid(self, input_dim, output_dim, batch_size):
m = model_helper.ModelHelper()
brew.fc(m, "data", "fc1", dim_in=input_dim, dim_out=output_dim)
with self.assertRaises(RuntimeError):
workspace.ApplyTransform(
"definitely_not_a_real_transform",
m.net.Proto())
@given(value=st.floats(min_value=-1, max_value=1))
def test_apply_transform_if_faster(self, value):
init_net = core.Net("init_net")
init_net.ConstantFill([], ["data"], shape=[5, 5, 5, 5], value=value)
init_net.ConstantFill([], ["conv_w"], shape=[5, 5, 3, 3], value=value)
init_net.ConstantFill([], ["conv_b"], shape=[5], value=value)
self.assertEqual(
workspace.RunNetOnce(init_net.Proto().SerializeToString()), True)
m = model_helper.ModelHelper()
conv = brew.conv(m, "data", "conv",
dim_in=5,
dim_out=5,
kernel=3,
use_cudnn=True,
engine="CUDNN")
conv.Relu([], conv)\
.Softmax([], "pred") \
.AveragedLoss([], "loss")
self.assertEqual(
workspace.RunNetOnce(m.net.Proto().SerializeToString()), True)
proto = workspace.ApplyTransformIfFaster(
"ConvToNNPack",
m.net.Proto(),
init_net.Proto())
self.assertEqual(
workspace.RunNetOnce(proto.SerializeToString()), True)
proto = workspace.ApplyTransformIfFaster(
"ConvToNNPack",
m.net.Proto(),
init_net.Proto(),
warmup_runs=10,
main_runs=100,
improvement_threshold=2.0)
self.assertEqual(
workspace.RunNetOnce(proto.SerializeToString()), True)
if __name__ == '__main__':
unittest.main()
|
[
"ryfeus@gmail.com"
] |
ryfeus@gmail.com
|
5a6960680cae86c401d945eb77b50e792096b7ac
|
464850ba426263b17084fc71363ca14b8278b15e
|
/80.py
|
c539164e19aa8d461121a1829efe084c3408f060
|
[] |
no_license
|
eng-arvind/python
|
8442c30ec10f979f913b354458b4f910539d8728
|
249f5f35f245a3f1742b10310de37ca6c6023af2
|
refs/heads/master
| 2020-12-23T06:40:16.911269
| 2020-02-02T18:42:01
| 2020-02-02T18:42:01
| 237,069,973
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 231
|
py
|
n = 7
for i in range(n):
for j in range(n):
if i + j == n//2 or i - j == n//2 or i + j == (n//2)*3 or j - i == n//2:
print("*", end="")
else:
print(end=" ")
print()
|
[
"noreply@github.com"
] |
noreply@github.com
|
93a5073ac024902777ff56f7b77d2f8188dc0ad5
|
539cf5309347e46051b70b90f793feb281a0683a
|
/enemyData.py
|
17acde84297099e2dfa9329b5f1e4bedc5e2ab17
|
[] |
no_license
|
bradellison/pokemon-simulator
|
52c9c7f8d90bd0cddad4f4cc9ebcec7673de3f7d
|
f4b47f9c2e1ec990e1269288ab9e491c796fb838
|
refs/heads/master
| 2021-12-09T13:07:25.913990
| 2021-12-02T05:22:21
| 2021-12-02T05:22:21
| 156,161,477
| 1
| 1
| null | 2018-11-21T21:19:26
| 2018-11-05T04:38:31
|
Python
|
UTF-8
|
Python
| false
| false
| 2,235
|
py
|
#PokemonInfo = [["Pok1Name", Poke1Lvl, ["Poke1Mov1", "Poke1Mov2"]], "Pok2Name", Poke2Lvl, ["Poke2Mov1", "Poke2Mov2"]]]
#enemyInfo = ["Class", "Name", team, prizeMoney, "EndBattleText", "coordinates", viewDistance, "viewDirection"]
## PALLET TOWN ##
oakTeam = [['Pidgeot',61,['Wing Attack']],['Pidgeot',61,['Wing Attack','Mirror Move','Sky Attack','Whirlwind']]]
#oakTeam = [['Pidgeot', 61, ['Wing Attack']]]
enemyOak = ["Badman", "Oak", oakTeam, 10000, "Student becomes the master, eh?", [12,19], 3, "Right"]
#palletTownEnemies = [enemyOak]
palletTownEnemies = []
## VIRIDIAN FOREST ##
bugCatcherRickTeam = [['Weedle', 7, 'Random'], ['Caterpie', 7, 'Random']]
bugCatcherRick = ["Bug Catcher", "Rick", bugCatcherRickTeam, 72, "You bug me!", [33,37], 3, "Left"]
bugCatcherDougTeam = [['Weedle', 7, 'Random'], ['Kakuna', 7, 'Random'], ['Weedle', 7, 'Random']]
bugCatcherDoug = ["Bug Catcher", "Doug", bugCatcherDougTeam, 84, "You bug me!", [35,23], 3, "Down"]
bugCatcherAnthonyTeam = [['Caterpie', 7, 'Random'], ['Caterpie', 8, 'Random']]
bugCatcherAnthony = ["Bug Catcher", "Anthony", bugCatcherAnthonyTeam, 84, "You bug me!", [31,5], 3, "Down"]
bugCatcherCharlieTeam = [['Metapod', 7, 'Random'], ['Caterpie', 7, 'Random'], ['Metapod', 7, 'Random']]
bugCatcherCharlie = ["Bug Catcher", "Charlie", bugCatcherCharlieTeam, 84, "You bug me!", [13,5], 3, "Down"]
bugCatcherSammyTeam = [['Weedle', 9, 'Random']]
bugCatcherSammy = ["Bug Catcher", "Sammy", bugCatcherSammyTeam, 84, "You bug me!", [6,23], 3, "Left"]
viridianForestEnemies = [bugCatcherRick, bugCatcherDoug, bugCatcherAnthony, bugCatcherCharlie, bugCatcherSammy]
## PEWTER GYM ##
camperLiamTeam = [["Geodude", 10, 'Random'], ['Sandshrew', 11, 'Random']]
camperLiam = ["Camper", "Liam", camperLiamTeam, 220, "You might be better than me, but you won't beat Brock!", [7,10], 3, "Right"]
gymLeaderBrockTeam = [["Geodude", 12, "Random"], ["Onix", 14, "Random"]]
gymLeaderBrock = ["Gym Leader", "Brock", gymLeaderBrockTeam, 1400, "Wow, what a fight! Here, take this Boulder Badge!", [10,7], 0, "Down"]
pewterGymEnemies = [camperLiam, gymLeaderBrock]
allTownEnemies = {"Pallet Town": palletTownEnemies, "Viridian Forest": viridianForestEnemies, "Pewter Gym": pewterGymEnemies}
|
[
"brad.ellison@celer-tech.com"
] |
brad.ellison@celer-tech.com
|
af6fea87d6f853544358243aa3fee3d9ee5469cb
|
c5d74b596458b79f42e3cc2eccbaa45544d75f19
|
/Ctrip/__init__.py
|
76fdd1594cf42df8de7008a6c9c0fdc9f6d66e34
|
[] |
no_license
|
XiaolinZHONG/PythonLearn
|
7c16e870d69c33bc637583c2abd99558dfecc687
|
15d76375d662c45ce573ecf0fa8d3dce437c3166
|
refs/heads/master
| 2020-07-01T03:41:02.507872
| 2018-01-04T01:27:55
| 2018-01-04T01:27:55
| 74,100,074
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 94
|
py
|
# -*- coding: utf-8 -*-
# @Time : 2017/1/16
# @Author : XL ZHONG
# @File : __init__.py.py
|
[
"xlzhong123@163.com"
] |
xlzhong123@163.com
|
93fc01efba1e5e4f045b41d0b93da8097c64a8d9
|
bff8151a211ea8434ac02dad917989a422b50d44
|
/retrieval/cal_topk_acc.py
|
63be3ba41870a85057681bdfd3f09c1da0970e1e
|
[] |
no_license
|
lijiaman/Cross-domain-Retrieval
|
fa5b3fce6cd5bcd9fe6020424a8ede38ef11d06e
|
b1945b06c33a1f8efe11e4ee80c2c75205159c62
|
refs/heads/master
| 2020-06-28T06:05:50.798417
| 2016-12-22T12:40:42
| 2016-12-22T12:40:42
| 74,506,444
| 1
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,811
|
py
|
import numpy as np
import json
import time
#street_file = '/ais/gobi4/fashion/retrieval/part_street_features.json'
#shop_file = '/ais/gobi4/fashion/retrieval/test_gallery.json'
#street_file = '/ais/gobi4/fashion/retrieval/toy4_darn_street_features.json'
#shop_file = '/ais/gobi4/fashion/retrieval/toy4_darn_test_gallery.json'
street_file = '/ais/gobi4/fashion/retrieval/toy4_share_street_features.json'
shop_file = '/ais/gobi4/fashion/retrieval/toy4_share_test_gallery.json'
k = 1
acc = 0
#shop_total = 47384
street_total = 24
with open(street_file, 'rb') as street:
start_time = time.time()
street_data = street.readlines()
for street_line in street_data:
street_line = json.loads(street_line)
cal_street = np.asarray(street_line['street_feature'])
distance = []
with open(shop_file, 'rb') as shop:
shop_data = shop.readlines()
for shop_line in shop_data:
shop_line = json.loads(shop_line)
cal_shop = np.asarray(shop_line['shop_feature'])
distance.append( np.sum((cal_shop-cal_street)**2))
shop.close()
index = np.argsort(distance)
print("index:")
print index
topk_index = index[:k]
print("topk index:")
print topk_index
cnt = 0
with open(shop_file, 'rb') as shop:
shop_data = shop.readlines()
for i in topk_index:
line = shop_data[i]
select = json.loads(line)
if select['id'] == street_line['id']:
cnt = 1
shop.close()
#print("hit = {0}".format(acc))
acc += float(cnt)
print("hit = {0}".format(acc))
acc /= street_total
print acc
print time.time()-start_time
street.close()
|
[
"lijiaman@buaa.edu.cn"
] |
lijiaman@buaa.edu.cn
|
390c6ee16b84c3f9123895cf601f21ea1c074eb3
|
15fa1d48e22b3764d905fc0ca3110bd396f8355b
|
/_build/jupyter_execute/docs/eda/numeric_vars/K total (kg por ha).py
|
9bc6c3eb610045ff47b567c10c34b648a7401d87
|
[] |
no_license
|
SarahNadaud/Test-jupyter-book
|
1759870df357fed2e0b739c57c5a5435cc181331
|
27bf960abd8d798bf8d977b512cc52a962ca9dd8
|
refs/heads/master
| 2023-02-01T21:16:24.839199
| 2020-12-17T12:36:43
| 2020-12-17T12:36:43
| 322,289,355
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 87,659
|
py
|
(K total (kg por ha))=
# K total (kg por ha)
Tipo da variável: numeric
## Relatório geral
<pre>
+--------------------------+----------+
| | Values |
+==========================+==========+
| Count | 2296 |
+--------------------------+----------+
| Mín | 0 |
+--------------------------+----------+
| Q1 | 0 |
+--------------------------+----------+
| Median | 0 |
+--------------------------+----------+
| Q3 | 0 |
+--------------------------+----------+
| Max | 900 |
+--------------------------+----------+
| Mean | 5.6 |
+--------------------------+----------+
| Variation | 588.97 |
+--------------------------+----------+
| Coefficient of variation | 4.33 |
+--------------------------+----------+
| Kurtosis | 804.45 |
+--------------------------+----------+
| Na | 2263 |
+--------------------------+----------+
| Outliers | 2296 |
+--------------------------+----------+
</pre>
## Análise Univariada
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"baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "histogram": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "histogram"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"font": {"color": "rgba(20, 36, 44, 0.7)"}, "text": "K total (kg por ha)"}, "width": 800, "xaxis": {"anchor": "y", "domain": [0.0, 0.17333333333333334], "linecolor": "rgba(100, 100, 100, 0)", "showgrid": false, "tickfont": {"color": "rgba(100, 100, 100, 0.8)"}, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.24, 0.41333333333333333], "linecolor": "rgba(100, 100, 100, 0)", "showgrid": false, "tickfont": {"color": "rgba(100, 100, 100, 0.8)"}, "zeroline": false}, "xaxis3": {"anchor": "y3", "domain": [0.48, 1.0], "hoverformat": ",.0f", "linecolor": "rgba(100, 100, 100, 0)", "showgrid": false, "tickfont": {"color": "rgba(100, 100, 100, 0.8)"}, "zeroline": false}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "hoverformat": ",.0f", "linecolor": "rgba(100, 100, 100, 0)", "showgrid": false, "tickfont": {"color": "rgba(100, 100, 100, 0.8)"}, "zeroline": false}, "yaxis2": {"anchor": "x2", "domain": [0.0, 1.0], "hoverformat": ",.0f", "linecolor": "rgba(100, 100, 100, 0)", "showgrid": false, "tickfont": {"color": "rgba(100, 100, 100, 0.8)"}, "zeroline": false}, "yaxis3": {"anchor": "x3", "domain": [0.0, 1.0], "linecolor": "rgba(100, 100, 100, 0)", "showgrid": false, "tickfont": {"color": "rgba(100, 100, 100, 0.8)"}, "zeroline": false}}, {"displayModeBar": false, "showTips": false, "responsive": true} ) }; </script></div>
|
[
"sarah.nadaud@elogroup.com.br"
] |
sarah.nadaud@elogroup.com.br
|
8290e8f45c5e527b27289d1815a583a43214575d
|
952c682ad7c93d6750de188b063adb56c0468523
|
/raspi/socket-test.py
|
67e3dc74f067e6dda1bb99871a4400e23dd18a96
|
[] |
no_license
|
Jugendhackt/toaster
|
ff76bb591a801a2f42496f037d0afcda8028088a
|
d9a0893971e2726a217ebb21d1cf312e5f3a4d04
|
refs/heads/master
| 2020-07-15T07:37:47.020187
| 2019-09-01T14:41:18
| 2019-09-01T14:41:18
| 205,513,614
| 0
| 0
| null | 2019-12-29T21:51:26
| 2019-08-31T07:47:44
|
TypeScript
|
UTF-8
|
Python
| false
| false
| 460
|
py
|
#!/usr/bin/env python3
import signal
signal.signal(signal.SIGINT, signal.SIG_DFL)
import asyncio
import websockets
async def hello(websocket, path):
name = await websocket.recv()
print(f"< {name}")
greeting = f"Hello {name}!"
await websocket.send(greeting)
print(f"> {greeting}")
start_server = websockets.serve(hello, "localhost", 8765)
asyncio.get_event_loop().run_until_complete(start_server)
asyncio.get_event_loop().run_forever()
|
[
"moritz.ahrens2@gmail.com"
] |
moritz.ahrens2@gmail.com
|
c226c2fd5ebf5e5869cb44cecb2f6811d62a4a0b
|
3c4122a6be0a27571fad25d0acb60ad0e527d32a
|
/retinaface-keras/simple_test.py
|
9597e610f1bf0b4432bff576039598ad3514d47d
|
[
"MIT"
] |
permissive
|
LeeSiy/Insight-face-tf1.13.2
|
08e0f2ab14b461c8dd95dbda3ef15ef832a61575
|
9258041a1d732f543155718c37c654a13e120fdb
|
refs/heads/main
| 2023-05-26T13:49:41.785038
| 2021-06-15T06:51:26
| 2021-06-15T06:51:26
| 377,032,542
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,789
|
py
|
import tensorflow as tf
import cv2
import numpy as np
import os
from numpy.linalg import norm
import predict
export_path = 'path/to/insight/face/saved/pb'
people1_path = 'path/to/single/person/image/group/folder'
people1_list = os.listdir(people1_path)
people1_imgs = np.zeros((112,112,3))
for i, img in enumerate(people1_list):
img_path = os.path.join(people1_path,img)
img = cv2.imread(img_path)
img = cv2.resize(img,(112, 112),interpolation=cv2.INTER_LINEAR)
img = np.expand_dims(img, axis=0)
if i==0:
people1_imgs = img
else:
people1_imgs = np.concatenate((people1_imgs, img), axis = 0)
people2_imgs,box_imgs = predict.predict('path/to/img/with/several/people')
people2_imgs = np.array(people2_imgs)
def simple(A, B):
ret = np.dot(A,B)/(norm(A)*norm(B))
return ret
with tf.Session(graph=tf.Graph()) as sess:
loaded = tf.saved_model.loader.load(sess, ['serve'], export_path)
x = sess.graph.get_tensor_by_name('data:0')
y = sess.graph.get_tensor_by_name('fc1/add_1:0')
feature = sess.run(y, feed_dict={x: people1_imgs})
feature2 = sess.run(y, feed_dict={x: people2_imgs})
answers = []
for i,f11 in enumerate(feature2):
score = 0.0
out_num = 0
check_pt = False
for i2,f12 in enumerate(feature):
if score < simple(f11, f12):
out_num = i2
score = simple(f11, f12)
check_pt = True
if check_pt == True:
print("{} vs {} = {}".format(people1_list[out_num],i,score))
answers.append(people1_list[out_num])
cv2.imshow('detected',box_imgs)
for i, img in enumerate(answers):
image = cv2.imread(os.path.join(people1_path,img))
cv2.imshow('detected{}'.format(i+1),image)
cv2.waitKey(10000)
cv2.destroyAllWindows()
|
[
"noreply@github.com"
] |
noreply@github.com
|
7da594417468f8fff6a257b39670bc08b390a407
|
daef6ea328be205d289c087f689fa4fb23de935f
|
/sampling.py
|
05880d0df0c733d70c8347d30de67bc04b504fe7
|
[] |
no_license
|
ManulGoyal/GCN_ML_Binary
|
29e330362b81224941c47a48f1f0ddc94c65c78d
|
d6949224644852e7ef7d8ca17799cf73c9ed8427
|
refs/heads/master
| 2022-11-14T11:55:54.212106
| 2020-07-07T12:05:36
| 2020-07-07T12:05:36
| 277,661,196
| 5
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 7,420
|
py
|
import numpy as np
import random
# utility function to find class with minimum examples
def min_examples_class(annot, dataset):
min_cnt = annot.shape[0]+1
min_lbls = []
label_ex_count = np.zeros(annot.shape[1])
for example in dataset:
for lbl, tag in enumerate(annot[example, :]):
if tag == 0:
continue
label_ex_count[lbl] += 1
for lbl, cnt in enumerate(label_ex_count):
if cnt > 0:
if cnt < min_cnt:
min_cnt = cnt
min_lbls = [lbl]
elif cnt == min_cnt:
min_lbls.append(lbl)
if len(min_lbls) > 0:
return random.choice(min_lbls)
else:
return -1
# utility function to return examples containing a specific label
def examples_with_label(lbl, annot, dataset):
examples = []
for example in dataset:
if annot[example, lbl] == 1:
examples.append(example)
return examples
# utility function to get target subset in which a given example
# should be inserted
def get_target_subset(desired_ex_cnt, desired_ex_cnt_lbl, target_lbl):
max_lbl_desiring_subsets = []
max_lbl_desire = -100000
# finding the subset(s) in which the desired examples
# of target_lbl are maximum
for subset in range(len(desired_ex_cnt_lbl)):
desire = desired_ex_cnt_lbl[subset][target_lbl]
if desire > max_lbl_desire:
max_lbl_desire = desire
max_lbl_desiring_subsets = [subset]
elif desire == max_lbl_desire:
max_lbl_desiring_subsets.append(subset)
max_desiring_subsets = []
max_desire = -100000
# breaking ties by finding those subsets from above
# found subsets which have maximum number of desired examples (total)
for subset in max_lbl_desiring_subsets:
if desired_ex_cnt[subset] > max_desire:
max_desire = desired_ex_cnt[subset]
max_desiring_subsets = [subset]
elif desired_ex_cnt[subset] == max_desire:
max_desiring_subsets.append(subset)
# further ties are broken randomly
return random.choice(max_desiring_subsets)
# This function distributes the examples in the dataset among k subsets
# according to the proportions [prop[0], prop[1], ..., prop[k-1]]
# such that the number of examples of each label in a particular subset
# is propotional to the number of examples of that label in the original dataset
# For example, if k = 10 and prop = [0.1, 0.1, ..., 0.1], then each subset
# will contain approx. 1/10th of the examples and for each label i, the number
# of examples with label i in any subset will be approx. 1/10th of the number of
# examples with the label i in the original dataset.
# k is number of subsets and prop is an array of proportions
# of examples in each subset
def stratify(annot, dataset, k, prop):
assert(k == len(prop))
num_examples = annot.shape[0]
num_classes = annot.shape[1]
# initially dataset contains all examples
# dataset = list(range(num_examples))
desired_ex_cnt = []
desired_ex_cnt_lbl = []
# list of subsets, visually, subsets[i] is a list of examples
# that are distributed to the i-th subset
subsets = [[] for i in range(k)]
for proportion in prop:
# calculate desired number of examples in subset
desired_ex_cnt.append(int(proportion * num_examples))
# calculate desired number of examples of each label in subset
desired_ex_cnt_subset = []
for lbl in range(num_classes):
num_examples_with_lbl = len(examples_with_label(lbl, annot, dataset))
desired_ex_cnt_subset.append(int(num_examples_with_lbl * proportion))
desired_ex_cnt_lbl.append(desired_ex_cnt_subset)
while len(dataset) > 0:
target_lbl = min_examples_class(annot, dataset)
# print(target_lbl)
if target_lbl == -1:
break
ex_with_target_lbl = examples_with_label(target_lbl, annot, dataset)
for example in ex_with_target_lbl:
subset = get_target_subset(desired_ex_cnt, desired_ex_cnt_lbl, target_lbl)
# add example to the target subset
subsets[subset].append(example)
# remove example from the dataset
dataset.remove(example)
# for each label of example, decrement desired number of examples
# for that label in the target subset by 1
for lbl, tag in enumerate(annot[example, :]):
if tag == 0:
continue
desired_ex_cnt_lbl[subset][lbl] -= 1
# decrement total desired number of examples of target subset by 1
desired_ex_cnt[subset] -= 1
return subsets
# returns list of negative labels corresponding to positive label lbl
# based on semantic paths
def get_negative_labels(lbl, sp, nl, annot, singleton_nodes):
negative_labels = []
images_count = annot.shape[0]
label_considered = np.zeros((annot.shape[1],))
for i, path in enumerate(sp):
leaf_node = 0
for j, node in enumerate(path):
if node == lbl:
leaf_node = -1
lbl_layer = nl[i][j]
for k in range(j, len(path)):
if nl[i][k] != lbl_layer:
leaf_node = k
break
break
if leaf_node == -1:
continue
if label_considered[path[leaf_node]] == 1:
continue
negative_labels.append(path[leaf_node])
leaf_node_layer = nl[i][leaf_node]
for j in range(leaf_node, len(path)):
if nl[i][j] == leaf_node_layer:
label_considered[path[j]] = 1
else:
break
for i in singleton_nodes:
if i == lbl or label_considered[i] == 1:
continue
negative_labels.append(i)
return negative_labels
def positive_negative_split(lbl, count, sp, nl, annot, singleton_nodes):
# get all samples which have label lbl
positive_samples_all = examples_with_label(lbl, annot, list(range(annot.shape[0])))
if len(positive_samples_all) <= count:
# select all positive samples
count = len(positive_samples_all)
positive_samples = positive_samples_all
else:
# randomly sample 'count' number of unique examples
positive_samples = random.sample(positive_samples_all, count)
negative_labels = get_negative_labels(lbl, sp, nl, annot, singleton_nodes)
# select only negative labels
negative_annot = annot[:, negative_labels]
label_count = negative_annot.sum(axis=1)
# select only those images which have atleast one negative label and don't contain lbl
negative_samples_dataset = []
for img in range(annot.shape[0]):
if label_count[img] > 0 and annot[img, lbl] == 0:
negative_samples_dataset.append(img)
total_negative_samples = len(negative_samples_dataset)
prop = [count/total_negative_samples, 1-count/total_negative_samples]
# divide the negative samples into two subsets the first of which
# has approx. count number of samples using stratification
subsets = stratify(negative_annot, negative_samples_dataset, 2, prop)
negative_samples = subsets[0]
return positive_samples, negative_samples
|
[
"goyal.10@iitj.ac.in"
] |
goyal.10@iitj.ac.in
|
3363b24c53c076531c61d750405dddc40ee83282
|
2cb7968701bd1f9fb32b55a5efccddf7a9504c29
|
/python/remove_elements.py
|
ca05c62278dc9c9cb6949c32e0fd930a7ddb11d8
|
[] |
no_license
|
sgn255/algorithms
|
35153567e40ff2ca78110f19f21f9f87c49fe236
|
d66c9fe68a12463a6d3a314df8d61f22bbaa79fd
|
refs/heads/master
| 2020-06-30T01:02:52.108135
| 2019-08-10T14:04:45
| 2019-08-10T14:04:45
| 200,667,525
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 179
|
py
|
# Given an array and a value:
# remove all instances of that value in-place
def remove_element(arr, val):
while val in arr :
del arr[arr.index(val)]
return arr
|
[
"scott-green89@hotmail.com"
] |
scott-green89@hotmail.com
|
a28b5e739458397f8701e37081c0593ed88063bc
|
6376f8a4e4b7f433c6528670461336b975b638ff
|
/scripts/many_load.py
|
2c892ac6cd09d68fba5752649e8d764aae1d9651
|
[] |
no_license
|
ameya-shahu/coursera-django
|
d04133d6eb975ea54f45a12a62d1495f2dcbd730
|
3272f513897ec5922e5bbe02d38c303960d09f11
|
refs/heads/master
| 2022-12-30T05:39:10.433639
| 2020-10-13T13:23:26
| 2020-10-13T13:23:26
| 288,106,765
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,395
|
py
|
import csv # https://docs.python.org/3/library/csv.html
# https://django-extensions.readthedocs.io/en/latest/runscript.html
# python3 manage.py runscript many_load
from unesco.models import State, Site, Iso, Category, Region
def run():
fhand = open('unesco/whc-sites-2018-clean.csv')
reader = csv.reader(fhand)
next(reader) # Advance past the header
State.objects.all().delete()
Site.objects.all().delete()
Iso.objects.all().delete()
Category.objects.all().delete()
Region.objects.all().delete()
for row in reader:
#print(row)
cat, created = Category.objects.get_or_create(name=row[7])
sat, created = State.objects.get_or_create(name=row[8])
reg, created = Region.objects.get_or_create(name=row[9])
i, created = Iso.objects.get_or_create(name=row[10])
try:
y = int(row[3])
except:
y = None
try:
long = float(row[4])
except:
long = None
try:
lat = float(row[5])
except:
lat = None
try:
area = float(row[6])
except:
area = None
site = Site(name=row[0], description=row[1], justification=row[2], year=y, longitude=long,
latitude=lat, area_hectares=area, category=cat, region=reg, state=sat, iso=i)
site.save()
|
[
"ameyashahu@gmail.com"
] |
ameyashahu@gmail.com
|
fb3b2fd6f3497e8dd1ded9a6c54a330aac22db31
|
3fa1b23746232975b3b014db2f525007a3b49991
|
/anna_code/demographics/rct_consented/subset_values_to_randomized_people.py
|
a4791706554ee798896de773f5da39c3e0e96e89
|
[] |
no_license
|
AshleyLab/myheartcounts
|
ba879e10abbde085b5c9550f0c13ab3f730d7d03
|
0f80492f7d3fc53d25bdb2c69f14961326450edf
|
refs/heads/master
| 2021-06-17T05:41:58.405061
| 2021-02-28T05:33:08
| 2021-02-28T05:33:08
| 32,551,526
| 7
| 1
| null | 2020-08-17T22:37:43
| 2015-03-19T23:25:01
|
OpenEdge ABL
|
UTF-8
|
Python
| false
| false
| 325
|
py
|
import pandas as pd
import sys
import pdb
data=pd.read_csv(sys.argv[1],header=None,sep='\t')
subjects=pd.read_csv('subjects.txt',header=None)
subset=data[data[0].isin(subjects[0])]
#nums=pd.to_numeric(subset[1],errors='coerce')
#mean_val=nums.mean()
#print(mean_val)
#std_val=nums.std()
#print(std_val)
pdb.set_trace()
|
[
"annashcherbina@gmail.com"
] |
annashcherbina@gmail.com
|
79ac61a468fd1369f69a7ecf1add11e0fbb9ba5e
|
8a6bac97182629f426e442308f6db53ee932e537
|
/venv/Lib/site-packages/twisted/python/_inotify.py
|
d5203df3c388a80029a2286d2706155e395eeddf
|
[] |
no_license
|
AmalioF96/DashBoard
|
8b8af75e7db7ab095c0cd05acb8b2b2764ab5fd5
|
4500a84a934fd5c24199d1864f0667c0d90e6174
|
refs/heads/master
| 2023-01-08T02:03:05.168925
| 2020-11-07T12:19:53
| 2020-11-07T12:19:53
| 230,789,973
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,454
|
py
|
# -*- test-case-name: twisted.internet.test.test_inotify -*-
# Copyright (c) Twisted Matrix Laboratories.
# See LICENSE for details.
"""
Very low-level ctypes-based interface to Linux inotify(7).
ctypes and a version of libc which supports inotify system calls are
required.
"""
import ctypes
import ctypes.util
class INotifyError(Exception):
"""
Unify all the possible exceptions that can be raised by the INotify API.
"""
def init():
"""
Create an inotify instance and return the associated file descriptor.
"""
fd = libc.inotify_init()
if fd < 0:
raise INotifyError("INotify initialization error.")
return fd
def add(fd, path, mask):
"""
Add a watch for the given path to the inotify file descriptor, and return
the watch descriptor.
@param fd: The file descriptor returned by C{libc.inotify_init}.
@type fd: L{int}
@param path: The path to watch via inotify.
@type path: L{twisted.python.filepath.FilePath}
@param mask: Bitmask specifying the events that inotify should monitor.
@type mask: L{int}
"""
wd = libc.inotify_add_watch(fd, path.asBytesMode().path, mask)
if wd < 0:
raise INotifyError("Failed to add watch on '%r' - (%r)" % (path, wd))
return wd
def remove(fd, wd):
"""
Remove the given watch descriptor from the inotify file descriptor.
"""
# When inotify_rm_watch returns -1 there's an error:
# The errno for this call can be either one of the following:
# EBADF: fd is not a valid file descriptor.
# EINVAL: The watch descriptor wd is not valid; or fd is
# not an inotify file descriptor.
#
# if we can't access the errno here we cannot even raise
# an exception and we need to ignore the problem, one of
# the most common cases is when you remove a directory from
# the filesystem and that directory is observed. When inotify
# tries to call inotify_rm_watch with a non existing directory
# either of the 2 errors might come up because the files inside
# it might have events generated way before they were handled.
# Unfortunately only ctypes in Python 2.6 supports accessing errno:
# http://bugs.python.org/issue1798 and in order to solve
# the problem for previous versions we need to introduce
# code that is quite complex:
# http://stackoverflow.com/questions/661017/access-to-errno-from-python
#
# See #4310 for future resolution of this issue.
libc.inotify_rm_watch(fd, wd)
def initializeModule(libc):
"""
Initialize the module, checking if the expected APIs exist and setting the
argtypes and restype for C{inotify_init}, C{inotify_add_watch}, and
C{inotify_rm_watch}.
"""
for function in ("inotify_add_watch", "inotify_init", "inotify_rm_watch"):
if getattr(libc, function, None) is None:
raise ImportError("libc6 2.4 or higher needed")
libc.inotify_init.argtypes = []
libc.inotify_init.restype = ctypes.c_int
libc.inotify_rm_watch.argtypes = [
ctypes.c_int, ctypes.c_int]
libc.inotify_rm_watch.restype = ctypes.c_int
libc.inotify_add_watch.argtypes = [
ctypes.c_int, ctypes.c_char_p, ctypes.c_uint32]
libc.inotify_add_watch.restype = ctypes.c_int
name = ctypes.util.find_library('c')
if not name:
raise ImportError("Can't find C library.")
libc = ctypes.cdll.LoadLibrary(name)
initializeModule(libc)
|
[
"amaliocabeza.16@gmail.com"
] |
amaliocabeza.16@gmail.com
|
63d46a52a9c3929779b4d498745424b1505a9754
|
17f29e8f3eab9deb724b10bc7e61c73f1fca21c6
|
/backend/home/migrations/0004_auto_20200320_0813.py
|
8596cdb6cafc9245c067cfa29396a8d0c4ff6f09
|
[] |
no_license
|
crowdbotics-apps/mobilemobapp-dev-2035
|
91df345e8f6e42760c4156a7dd73a6d8b17250e0
|
041b1c20c4a14b4595fbcca943cdf46dec445497
|
refs/heads/master
| 2022-04-12T06:06:17.910111
| 2020-03-20T08:13:11
| 2020-03-20T08:13:11
| 248,153,145
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,311
|
py
|
# Generated by Django 2.2.11 on 2020-03-20 08:13
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('home', '0003_customtext_test'),
]
operations = [
migrations.CreateModel(
name='Test',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('test', models.BigIntegerField()),
],
),
migrations.CreateModel(
name='Testing',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('test', models.BigIntegerField()),
],
),
migrations.CreateModel(
name='Testtt',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('testt', models.BinaryField()),
],
),
migrations.RemoveField(
model_name='customtext',
name='test',
),
migrations.AddField(
model_name='customtext',
name='name',
field=models.BinaryField(blank=True, null=True),
),
]
|
[
"team@crowdbotics.com"
] |
team@crowdbotics.com
|
c69aecc288d88a2e3af39ea33a0433e6bfda7f6a
|
86ba8693711e25f8cc5488ebb249bbed2f825383
|
/assault/stats.py
|
abbc729e7eacbee08263f77a8e539b0cf6217ccb
|
[] |
no_license
|
lcwalina/assault
|
207f8b2f3e2530e38a6cb7414284262592d5d0d6
|
50b7c67a61256446ca4f6f060a8882cf13bf14be
|
refs/heads/main
| 2023-08-10T19:07:33.808941
| 2021-10-06T13:42:46
| 2021-10-06T13:42:46
| 413,843,292
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,962
|
py
|
from typing import List, Dict
from statistics import mean
class Results:
"""
Results handles calculating statistics based on a list of requests that were made
Here's an example of what the information will look like:
Successful requests 3000
Slowest 0.010s
Fastest 0.001s
Average 0.003s
Total time 2.400s
Requests Per Minute 90000
Requests Per Second 1250
"""
def __init__(self, total_time: float, requests: List[Dict]):
self.total_time = total_time
self.requests = sorted(requests, key=lambda x: x["request_time"])
def slowest(self) -> float:
"""
Returns the slowest request completion time
>>> results = Results(10.6, [{
... 'status_code': 200,
... 'request_time': 3.4
... },
... {
... 'status_code': 500,
... 'request_time': 6.1
... },
... {
... 'status_code': 200,
... 'request_time': 1.04
... }])
>>> results.slowest()
6.1
"""
return self.requests[-1]["request_time"]
def fastest(self) -> float:
"""
Returns the fastest request completion time
>>> results = Results(10.6, [{
... 'status_code': 200,
... 'request_time': 3.4
... },
... {
... 'status_code': 500,
... 'request_time': 6.1
... },
... {
... 'status_code': 200,
... 'request_time': 1.04
... }])
>>> results.fastest()
1.04
"""
return self.requests[0]["request_time"]
def average_time(self) -> float:
"""
Returns the average request completion time
>>> results = Results(10.6, [{
... 'status_code': 200,
... 'request_time': 3.4
... },
... {
... 'status_code': 500,
... 'request_time': 6.1
... },
... {
... 'status_code': 200,
... 'request_time': 1.04
... }])
>>> results.average_time()
3.513333333333333
"""
return mean([x["request_time"] for x in self.requests])
def successful_requests(self) -> int:
"""
Returns the number of successful requests
>>> results = Results(10.6, [{
... 'status_code': 200,
... 'request_time': 3.4
... },
... {
... 'status_code': 500,
... 'request_time': 6.1
... },
... {
... 'status_code': 200,
... 'request_time': 1.04
... }])
>>> results.successful_requests()
2
"""
return len([x for x in self.requests if x["status_code"] in range(200, 299)])
def requests_per_minute(self) -> int:
"""
Returns the number of requests that could be made in a minute
>>> results = Results(2.5, [{
... 'status_code': 200,
... 'request_time': 1.5
... },
... {
... 'status_code': 500,
... 'request_time': 0.4
... },
... {
... 'status_code': 200,
... 'request_time': 0.5
... }])
>>> results.requests_per_minute()
72
"""
return round(len(self.requests) / self.total_time * 60)
def requests_per_second(self) -> int:
"""
Returns the number of requests that could be made in a second
>>> results = Results(2.5, [{
... 'status_code': 200,
... 'request_time': 1.5
... },
... {
... 'status_code': 500,
... 'request_time': 0.4
... },
... {
... 'status_code': 200,
... 'request_time': 0.5
... }])
>>> results.requests_per_second()
1
"""
return round(len(self.requests) / self.total_time)
|
[
"me@me.me"
] |
me@me.me
|
1093b9c3c57519cf4dc597bf6df497b6e31fe0fe
|
e15f86312db3109bbda053063557693518af4ead
|
/pcsk9/select_fam.py
|
35318362eec5e7e8604254ceeeedd5879854dcdc
|
[] |
no_license
|
heichiyidui/dev
|
3aecf0f92e4af4184b4eae2b1935f281b7746c86
|
73c20c19928eb94d9aec10f0d307604b147b8088
|
refs/heads/master
| 2020-12-29T01:54:24.236229
| 2016-07-01T14:51:01
| 2016-07-01T14:51:01
| 35,271,765
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,000
|
py
|
#!/usr/bin/env python3
# tail -n +2 plink.genome | awk '{print $2,$4}' > t.in
edges = []
ifile =open('t.in')
for line in ifile:
cols = line[:-1].split()
edges.append([cols[0],cols[1]])
ifile.close()
import collections
node_dgres = collections.Counter()
nodes_1 = [x[0] for x in edges]
nodes_2 = [x[1] for x in edges]
node_dgres.update(nodes_1)
node_dgres.update(nodes_2)
# lets remove nodes according to their connection degrees
to_remove_list = []
for l in range(10000):
if edges == []:
break
# find the most connected node
to_remove_id = node_dgres.most_common(1)[0][0]
to_remove_list.append(to_remove_id)
# update edge set
new_edges = [x for x in edges if to_remove_id not in x]
edges = new_edges
# update node connection degree
node_dgres = collections.Counter()
nodes_1 = [x[0] for x in edges]
nodes_2 = [x[1] for x in edges]
node_dgres.update(nodes_1)
node_dgres.update(nodes_2)
for id in to_remove_list:
print(id)
|
[
"kuanggong@gmail.com"
] |
kuanggong@gmail.com
|
fa959aa6f4a922c56b0970dcb74658e61c42d1f2
|
4ef98e50c40dc9f79ac9e422a208427f034f804d
|
/maps/models.py
|
1e2a9a1d04f3ff48376a6325fbc92a1d1d52468a
|
[] |
no_license
|
couleurmate/DeweyMaps
|
5bd4eef11d429a7f252b8fb3141a7a47697154b4
|
063e9e7e412d57d2fdaf976728aaff66eb5fd38a
|
refs/heads/master
| 2021-01-17T04:51:22.226762
| 2015-07-05T10:38:57
| 2015-07-05T10:38:57
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,404
|
py
|
from django.contrib.gis.db import models
from closet.models import Subcategory
class Marker(models.Model):
name = models.CharField(blank=False, max_length=255)
position = models.PointField(geography=True, blank=False)
comment = models.TextField(blank=True, null=False, default="")
subcategories = models.ManyToManyField(Subcategory)
web = models.URLField(default="")
phone = models.CharField(max_length=15, default="")
adress = models.CharField(max_length=1000, default="")
public = models.BooleanField(default=True)
created = models.DateTimeField(auto_now_add=True)
objects = models.GeoManager()
def __str__(self):
return self.name
@property
def content(self):
return self.comment
@property
def lat(self):
return self.position.y
@property
def lon(self):
return self.position.x
@property
def popup(self):
tpl = """<h5>{0.name}</h5>"""
if self.adress != "":
tpl += "<em>Adresse</em> : {0.adress}<br><br>"
if self.phone != "":
tpl += "<em>Téléphone</em> : {0.phone}<br><br>"
if self.web != "":
tpl += '<b><a target="_blank" href="{0.web}">Site web</a></b><br><br>'
tpl += "{0.comment}<br><br>"
tpl += '<a href="http://dewey.be/contact.html">Signaler un problème</a>'
return tpl.format(self)
|
[
"nikita.marchant@gmail.com"
] |
nikita.marchant@gmail.com
|
2aa575aff205569c313222104ed7b2a61e06ad03
|
6f31a15cb73175084f2c4485d3dea0b8975b2ec9
|
/src/pyIdlak/pylib/__init__.py
|
b5c44a3e37db4e976cb9add9ca83f0ba5f3dfb35
|
[
"Apache-2.0",
"LicenseRef-scancode-public-domain"
] |
permissive
|
Idlak/idlak
|
c7cd5e6c0b02918cda85dbb2fb5c7333a789c304
|
4be6f7d951ba0d707a84a2cf8cbfc36689b85a3c
|
refs/heads/master
| 2021-11-23T13:28:43.709163
| 2021-11-01T15:51:46
| 2021-11-01T15:51:46
| 127,285,931
| 65
| 26
|
NOASSERTION
| 2021-11-01T15:51:47
| 2018-03-29T12:06:52
|
Shell
|
UTF-8
|
Python
| false
| false
| 1,414
|
py
|
# -*- coding: utf-8 -*-
# Copyright 2018 Cereproc Ltd. (author: Matthew Aylett
# David Braude)
#
# 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
#
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
# MERCHANTABLITY OR NON-INFRINGEMENT.
# See the Apache 2 License for the specific language governing permissions and
# limitations under the License.
# Note that this is intended to be internal to pyIdlak and not exposed.
# SWIG wrapped API
from . import pyIdlak_pylib as c_api
from .pyIdlak_pylib import (
NONE,
AperiodicEnergyOptions,
PdfPriorOptions,
NnetForwardOptions,
ApplyCMVNOptions,
DeltaFeaturesOptions,
PyReadKaldiDoubleMatrix,
PyKaldiMatrixBaseFloat_frmlist,
PyKaldiMatrixBaseFloat_tolist,
PyKaldiMatrixDouble_frmlist,
PyKaldiMatrixDouble_tolist
)
from .pyoptions import PyOptions
from .utils import (
no_pyIdlak_parse_arkfile,
compare_arks,
get_rspecifier_keys,
get_matrix_by_key,
)
|
[
"dabraude@gmail.com"
] |
dabraude@gmail.com
|
c433f2523757ab9ccef83f3d10b3e3ecb22c1afd
|
e88a5dd494f82911c12601f7d474c5ac8c068bbc
|
/train-resnet.py
|
39b61e1015f7ce84fadfb2b5f7064e3c8cc1df3a
|
[] |
no_license
|
Nortrom1213/Chinese-Character-Recognition-Demo
|
aeecba14b4bf6a7dada3cab70490d75cdc7f2bf7
|
eb80287027de597068cba306bec2f2a2b5683b90
|
refs/heads/master
| 2022-10-26T04:18:00.628845
| 2020-06-12T16:19:35
| 2020-06-12T16:19:35
| 271,825,358
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,275
|
py
|
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Flatten, Dense, Dropout
from tensorflow.python.keras.applications.resnet50 import ResNet50
from tensorflow.python.keras.applications.vgg19 import VGG19
from tensorflow.python.keras.applications.inception_v3 import InceptionV3
from tensorflow.python.keras.optimizers import Adam
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
# 資料路徑
DATASET_PATH = 'train_dataset'
DATATEST_PATH = 'test_dataset'
# 影像大小
IMAGE_SIZE = (224, 224)
# 影像類別數
NUM_CLASSES = 20
# 若 GPU 記憶體不足，可調降 batch size 或凍結更多層網路
BATCH_SIZE = 8
# 凍結網路層數
FREEZE_LAYERS = 2
# Epoch 數
NUM_EPOCHS = 20
# 模型輸出儲存的檔案
WEIGHTS_FINAL = 'model-ResNet50-final.h5'
# 透過 data augmentation 產生訓練與驗證用的影像資料
train_datagen = ImageDataGenerator(rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
channel_shift_range=10,
horizontal_flip=True,
fill_mode='nearest')
train_batches = train_datagen.flow_from_directory(DATASET_PATH,
target_size=IMAGE_SIZE,
interpolation='bicubic',
class_mode='categorical',
shuffle=True,
batch_size=BATCH_SIZE)
valid_datagen = ImageDataGenerator()
valid_batches = valid_datagen.flow_from_directory(DATATEST_PATH,
target_size=IMAGE_SIZE,
interpolation='bicubic',
class_mode='categorical',
shuffle=False,
batch_size=BATCH_SIZE)
# 輸出各類別的索引值
for cls, idx in train_batches.class_indices.items():
print('Class #{} = {}'.format(idx, cls))
# 以訓練好的 ResNet50 為基礎來建立模型，
# 捨棄 ResNet50 頂層的 fully connected layers
net = ResNet50(include_top=False, weights='imagenet', input_tensor=None,
input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3))
x = net.output
x = Flatten()(x)
# 增加 DropOut layer
x = Dropout(0.5)(x)
# 增加 Dense layer，以 softmax 產生個類別的機率值
output_layer = Dense(NUM_CLASSES, activation='softmax', name='softmax')(x)
# 設定凍結與要進行訓練的網路層
net_final = Model(inputs=net.input, outputs=output_layer)
for layer in net_final.layers[:FREEZE_LAYERS]:
layer.trainable = False
for layer in net_final.layers[FREEZE_LAYERS:]:
layer.trainable = True
# 使用 Adam optimizer，以較低的 learning rate 進行 fine-tuning
net_final.compile(optimizer=Adam(lr=1e-5),
loss='categorical_crossentropy', metrics=['accuracy'])
# 輸出整個網路結構
print(net_final.summary())
# 訓練模型
hist = net_final.fit_generator(train_batches,
steps_per_epoch=train_batches.samples // BATCH_SIZE,
validation_data=valid_batches,
validation_steps=valid_batches.samples // BATCH_SIZE,
epochs=NUM_EPOCHS)
# 画图
def plot_training(history):
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'b-')
plt.plot(epochs, val_acc, 'r')
plt.title('Training and validation accuracy')
plt.figure()
plt.plot(epochs, loss, 'b-')
plt.plot(epochs, val_loss, 'r-')
plt.title('Training and validation loss')
plt.show()
plot_training(hist)
# 儲存訓練好的模型
net_final.save(WEIGHTS_FINAL)
|
[
"nortrom@berkeley.edu"
] |
nortrom@berkeley.edu
|
751a0292d7f84b53fc00ed0ad383672d8562de3a
|
2e83e004d8a69a773d1e305152edd16e4ea35ed8
|
/students/eric_grandeo/lesson03/logic_1.py
|
3be7081d76217fb959a59ab909527edf01a7b953
|
[] |
no_license
|
UWPCE-PythonCert-ClassRepos/SP_Online_PY210
|
9b170efbab5efedaba8cf541e8fc42c5c8c0934d
|
76224d0fb871d0bf0b838f3fccf01022edd70f82
|
refs/heads/master
| 2021-06-16T20:14:29.754453
| 2021-02-25T23:03:19
| 2021-02-25T23:03:19
| 161,077,720
| 19
| 182
| null | 2021-02-25T23:03:19
| 2018-12-09T20:18:25
|
Python
|
UTF-8
|
Python
| false
| false
| 631
|
py
|
#cigar_party
def cigar_party(cigars, is_weekend):
if not is_weekend:
return 40 <= cigars <= 60
else:
return is_weekend and cigars > 40
assert cigar_party(30, False) == False
assert cigar_party(50, False) == True
assert cigar_party(70, True) == True
print("Cigar party tests passed")
#date_fashion
def date_fashion(you, date):
if you <= 2 or date <= 2:
return 0
elif you >= 8 or date >= 8:
return 2
else:
return 1
assert date_fashion(5, 10) == 2
assert date_fashion(5, 2) == 0
assert date_fashion(5, 5) == 1
print("date fashion tests passed")
#squirrel_play
|
[
"10297305+Eric74nyc@users.noreply.github.com"
] |
10297305+Eric74nyc@users.noreply.github.com
|
c48fcf7a93643c3bd44ec66eded5c44ff6bcc940
|
f0b14db2538fb49b3cc70d7c01fdae35c148624b
|
/config.py
|
cd63a9e6ddcf330b17d71e2a52b167e1c21e1372
|
[
"MIT"
] |
permissive
|
ICRA-2021/PoseGrouping
|
12a5725355f28e67ef0dce4673b09f5f8431d373
|
7e896aa00d947520f9240d6681536c5ef79e9b49
|
refs/heads/main
| 2023-05-23T16:20:26.926759
| 2021-06-17T05:43:39
| 2021-06-17T05:43:52
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,574
|
py
|
import logging
class Config(object):
def __init__(self):
self.logger = logging.getLogger("Config")
self.params = set()
self.set("inference_split", "valid")
self.set("exp_root", "exps")
self.set("batch_size", 4)
self.set("num_workers", 0)
self.set("gpu", 0)
self.set("anno_file_train", "data/coco/annotations/person_keypoints_train2017.json")
self.set("anno_file_valid", "data/coco/annotations/person_keypoints_val2017.json")
self.set("anno_file_test", "data/coco/annotations/image_info_test-dev2017.json")
self.set("cache_dir", "data/coco/cache")
self.set("max_n_det", 250)
self.set("gnn_n_layers_geometry", 3)
self.set("gnn_n_layers_visual", 1)
self.set("learning_rate", 5e-4)
self.set("num_epochs", 500)
self.set("num_steps_per_epoch", 2000)
self.set("training", True)
self.set("validation", True)
self.set("save_prediction", True)
self.set("save_model", True)
def set(self, k, v):
self.logger.debug("Setting {}({}) to {}({})".format(k, type(k), v, type(v)))
self.__setattr__(k, v)
self.params.add(k)
def get(self, k):
if k in self.params and k in self.__dict__:
return self.__getattribute__(k)
else:
return None
def print_all_params(self):
self.logger.debug("Printing configs:")
for k in sorted(self.params):
self.logger.debug("\t{}: {}".format(k, self.get(k)))
cfg = Config()
|
[
"jiahao@comp.nus.edu.sg"
] |
jiahao@comp.nus.edu.sg
|
ecf4436b8a78f9d5ddaa025ab1322e91b1b42fd3
|
42f5eaf16bfd7076cb5a598cf2f239faa575f28b
|
/05-grpc-google-cloud-speech/python/google/watcher/v1/watch_pb2.py
|
09760ad4eca095c7ba8e69b44ef548c3cbe4de21
|
[] |
no_license
|
jiriklepl/IMW-2019
|
ab0e1c791a794ccf8a6a8d8d4e732c29acee134c
|
921c85d3c8132114ad90db8deb52eb5ddc06c720
|
refs/heads/master
| 2020-08-28T13:29:15.087785
| 2019-12-15T17:12:24
| 2019-12-15T17:12:24
| 217,711,235
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| true
| 9,093
|
py
|
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: google/watcher/v1/watch.proto
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
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2
from google.protobuf import any_pb2 as google_dot_protobuf_dot_any__pb2
from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='google/watcher/v1/watch.proto',
package='google.watcher.v1',
syntax='proto3',
serialized_options=b'\n\025com.google.watcher.v1B\nWatchProtoP\001Z8google.golang.org/genproto/googleapis/watcher/v1;watcher',
serialized_pb=b'\n\x1dgoogle/watcher/v1/watch.proto\x12\x11google.watcher.v1\x1a\x1cgoogle/api/annotations.proto\x1a\x19google/protobuf/any.proto\x1a\x1bgoogle/protobuf/empty.proto\"0\n\x07Request\x12\x0e\n\x06target\x18\x01 \x01(\t\x12\x15\n\rresume_marker\x18\x02 \x01(\x0c\"9\n\x0b\x43hangeBatch\x12*\n\x07\x63hanges\x18\x01 \x03(\x0b\x32\x19.google.watcher.v1.Change\"\xe6\x01\n\x06\x43hange\x12\x0f\n\x07\x65lement\x18\x01 \x01(\t\x12.\n\x05state\x18\x02 \x01(\x0e\x32\x1f.google.watcher.v1.Change.State\x12\"\n\x04\x64\x61ta\x18\x06 \x01(\x0b\x32\x14.google.protobuf.Any\x12\x15\n\rresume_marker\x18\x04 \x01(\x0c\x12\x11\n\tcontinued\x18\x05 \x01(\x08\"M\n\x05State\x12\n\n\x06\x45XISTS\x10\x00\x12\x12\n\x0e\x44OES_NOT_EXIST\x10\x01\x12\x19\n\x15INITIAL_STATE_SKIPPED\x10\x02\x12\t\n\x05\x45RROR\x10\x03\x32\x63\n\x07Watcher\x12X\n\x05Watch\x12\x1a.google.watcher.v1.Request\x1a\x1e.google.watcher.v1.ChangeBatch\"\x11\x82\xd3\xe4\x93\x02\x0b\x12\t/v1/watch0\x01\x42_\n\x15\x63om.google.watcher.v1B\nWatchProtoP\x01Z8google.golang.org/genproto/googleapis/watcher/v1;watcherb\x06proto3'
,
dependencies=[google_dot_api_dot_annotations__pb2.DESCRIPTOR,google_dot_protobuf_dot_any__pb2.DESCRIPTOR,google_dot_protobuf_dot_empty__pb2.DESCRIPTOR,])
_CHANGE_STATE = _descriptor.EnumDescriptor(
name='State',
full_name='google.watcher.v1.Change.State',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='EXISTS', index=0, number=0,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='DOES_NOT_EXIST', index=1, number=1,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='INITIAL_STATE_SKIPPED', index=2, number=2,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='ERROR', index=3, number=3,
serialized_options=None,
type=None),
],
containing_type=None,
serialized_options=None,
serialized_start=401,
serialized_end=478,
)
_sym_db.RegisterEnumDescriptor(_CHANGE_STATE)
_REQUEST = _descriptor.Descriptor(
name='Request',
full_name='google.watcher.v1.Request',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='target', full_name='google.watcher.v1.Request.target', 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,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='resume_marker', full_name='google.watcher.v1.Request.resume_marker', 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,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=138,
serialized_end=186,
)
_CHANGEBATCH = _descriptor.Descriptor(
name='ChangeBatch',
full_name='google.watcher.v1.ChangeBatch',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='changes', full_name='google.watcher.v1.ChangeBatch.changes', 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,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=188,
serialized_end=245,
)
_CHANGE = _descriptor.Descriptor(
name='Change',
full_name='google.watcher.v1.Change',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='element', full_name='google.watcher.v1.Change.element', 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,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='state', full_name='google.watcher.v1.Change.state', 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,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='data', full_name='google.watcher.v1.Change.data', index=2,
number=6, 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,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='resume_marker', full_name='google.watcher.v1.Change.resume_marker', index=3,
number=4, 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,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='continued', full_name='google.watcher.v1.Change.continued', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_CHANGE_STATE,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=248,
serialized_end=478,
)
_CHANGEBATCH.fields_by_name['changes'].message_type = _CHANGE
_CHANGE.fields_by_name['state'].enum_type = _CHANGE_STATE
_CHANGE.fields_by_name['data'].message_type = google_dot_protobuf_dot_any__pb2._ANY
_CHANGE_STATE.containing_type = _CHANGE
DESCRIPTOR.message_types_by_name['Request'] = _REQUEST
DESCRIPTOR.message_types_by_name['ChangeBatch'] = _CHANGEBATCH
DESCRIPTOR.message_types_by_name['Change'] = _CHANGE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
Request = _reflection.GeneratedProtocolMessageType('Request', (_message.Message,), {
'DESCRIPTOR' : _REQUEST,
'__module__' : 'google.watcher.v1.watch_pb2'
# @@protoc_insertion_point(class_scope:google.watcher.v1.Request)
})
_sym_db.RegisterMessage(Request)
ChangeBatch = _reflection.GeneratedProtocolMessageType('ChangeBatch', (_message.Message,), {
'DESCRIPTOR' : _CHANGEBATCH,
'__module__' : 'google.watcher.v1.watch_pb2'
# @@protoc_insertion_point(class_scope:google.watcher.v1.ChangeBatch)
})
_sym_db.RegisterMessage(ChangeBatch)
Change = _reflection.GeneratedProtocolMessageType('Change', (_message.Message,), {
'DESCRIPTOR' : _CHANGE,
'__module__' : 'google.watcher.v1.watch_pb2'
# @@protoc_insertion_point(class_scope:google.watcher.v1.Change)
})
_sym_db.RegisterMessage(Change)
DESCRIPTOR._options = None
_WATCHER = _descriptor.ServiceDescriptor(
name='Watcher',
full_name='google.watcher.v1.Watcher',
file=DESCRIPTOR,
index=0,
serialized_options=None,
serialized_start=480,
serialized_end=579,
methods=[
_descriptor.MethodDescriptor(
name='Watch',
full_name='google.watcher.v1.Watcher.Watch',
index=0,
containing_service=None,
input_type=_REQUEST,
output_type=_CHANGEBATCH,
serialized_options=b'\202\323\344\223\002\013\022\t/v1/watch',
),
])
_sym_db.RegisterServiceDescriptor(_WATCHER)
DESCRIPTOR.services_by_name['Watcher'] = _WATCHER
# @@protoc_insertion_point(module_scope)
|
[
"jiriklepl@seznam.cz"
] |
jiriklepl@seznam.cz
|
9a02a4515ac66a6af1c48bfd0115ffbef8f9ff1b
|
ada6419027215f0c73ff2916c16030bdb89cd213
|
/05_CodingNature/treeAngel 003.py
|
6201df5eab4c266bdb18dfdacc6e539bd098db25
|
[] |
no_license
|
Pythones/Ejercicios
|
42eab02dbd3c425d13079fdc820ae13490c5d451
|
f96812845afd202548f2f03abd3acf8ff5356268
|
refs/heads/master
| 2016-09-05T18:01:05.151802
| 2014-11-25T12:56:50
| 2014-11-25T12:56:50
| 7,753,567
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 815
|
py
|
#Testing new algorithms based on screensaver logic.
import rhinoscriptsyntax as rs
import math as m
import random as r
i = 0
def main():
#asking for data.
dblLength = rs.GetReal("Initial growing length",1,0.01,10)
dblAngle = m.radians(rs.GetReal("Random angle limit",10,0,90))
strPt = rs.GetPoint("Growing point")
intLimit = rs.GetInteger("Iterations limit",20,1,1000)
tree(strPt,dblLength, dblAngle, intLimit, i)
def tree(Pto,dblStep,dblAngle,intLimit,x):
if x>intLimit: return
dblRand = r.uniform(-dblAngle,dblAngle)
v3dRand = (m.cos(dblRand+(m.pi/2)),m.sin(dblRand+(m.pi/2)),0)
PtM = rs.PointAdd(Pto,v3dRand)
rs.AddLine(Pto,PtM)
intBranch = r.randint(1,5)
x+=1
for x in range(1,intBranch):
tree(PtM,dblStep,dblAngle*1.1,intLimit*0.5,x+1)
main()
|
[
"angellinaresgarcia@gmail.com"
] |
angellinaresgarcia@gmail.com
|
38e53a8d35e8f0436f46b05a7eedf0fa39b63dea
|
1f96ecaf42f3e502f603f6beb990f97d83feb5ca
|
/y_2015/d_6/run_2.py
|
11a412b84359827a076b9fc6d60c7ed3bc49bcf1
|
[] |
no_license
|
moroznoeytpo/adventofcode
|
5ddf62261c7c729aba2aa66f9c45884a3ab1ce0b
|
cd64655e8f90d44fe6684e622de1feec7d6af6aa
|
refs/heads/master
| 2020-11-28T08:55:21.940771
| 2020-11-12T19:13:53
| 2020-11-12T19:13:53
| 229,762,949
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 554
|
py
|
import re
with open('2015/6/input.txt', 'r') as input_file:
l = 1000
grid = [[0 for j in range(l)] for i in range(l)]
total = 0
for rule in input_file:
match = re.search(r"([\w\s]*) (\d*),(\d*) through (\d*),(\d*)", rule)
for i in range(int(match[2]), int(match[4]) + 1):
for j in range(int(match[3]), int(match[5]) + 1):
if match[1] == 'turn on':
grid[i][j] += 1
total += 1
elif match[1] == 'turn off':
if grid[i][j]:
grid[i][j] -= 1
total -= 1
else:
grid[i][j] += 2
total += 2
print(total)
|
[
"moroznoeytpo@gmail.com"
] |
moroznoeytpo@gmail.com
|
937277b439813ae6dc3be0fe527ec358ddf48bc8
|
ae366ff5e8f8d01b4c36c43baa9159d8aa71efee
|
/96.py
|
6f4d7fe3c67e079353ea154fd4686282ab02871a
|
[] |
no_license
|
lampard1010/python_foundation
|
3b464f05ff9ebbbf86e140929ebf0c54aba1dfd4
|
6bc8773af9b10e64e35e2d5255835b18722fef50
|
refs/heads/master
| 2021-01-02T04:43:35.260597
| 2020-04-20T01:35:55
| 2020-04-20T01:35:55
| 239,493,804
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 82
|
py
|
str = "lkdasjfepojckjdfnkfdjalkdjfeikjf.dfad.falsfl"
c = "l"
print(str.count(c))
|
[
"261893396@qq.com"
] |
261893396@qq.com
|
992f8823515ccee3a140f890755137552e8928d4
|
438ee853669a67cd46537f6d02cf356d05e03681
|
/doctor_dashboard/urls.py
|
47694bb78b753fc56cdb14fe68d5c7380a309fe8
|
[] |
no_license
|
tngeene/doc_appointment
|
a6648bed5c3d1d27e25131945910c5c425468fa1
|
6d1f320db03ad9fcc42b09e19a0d0a73e5af233a
|
refs/heads/master
| 2023-02-22T05:37:36.510685
| 2021-01-19T11:46:01
| 2021-01-19T11:46:01
| 324,834,090
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 424
|
py
|
from django.urls import path, include
app_name = "doctor_dashboard"
urlpatterns = [
path('', include('doctor_dashboard.routes.index')),
path('appointments/', include('doctor_dashboard.routes.appointments')),
# path('doctors/', include('doctor_dashboard.routes.doctors')),
# path('patients/', include('doctor_dashboard.routes.patients')),
path('events/', include('doctor_dashboard.routes.events')),
]
|
[
"tngeene27@gmail.com"
] |
tngeene27@gmail.com
|
d4c44550df6570a3c03d89d628513a25c2868572
|
0ae589f33fbf37a6af830dd7494cc576f267f202
|
/scenario/settings.py
|
ea8db96a3b7c5d412a773b2d60a74cbfa2abfd55
|
[] |
no_license
|
vamsi9477/sosioHosting
|
85be712762738604625a13569f85aa986c31d5b0
|
42dbe2171a32b4cf40d202f16d89c49db9b3c10e
|
refs/heads/master
| 2020-04-05T01:09:02.486917
| 2018-11-06T18:03:07
| 2018-11-06T18:03:07
| 156,425,173
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,136
|
py
|
"""
Django settings for scenario project.
Generated by 'django-admin startproject' using Django 2.1.1.
For more information on this file, see
https://docs.djangoproject.com/en/2.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.1/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/2.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '*l#4^7y1%o0r9p01f)lz7mcdw-nc9#2iet=ak3ma9rj53f+zyh'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'sc1.apps.Sc1Config',
]
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 = 'scenario.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR, 'templates')]
,
'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 = 'scenario.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.1/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/2.1/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/2.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.1/howto/static-files/
STATIC_URL = '/static/'
|
[
"vamsikrishna6668@gmail.com"
] |
vamsikrishna6668@gmail.com
|
a27dae32c239b9c924dc4fb6da5cfae518d6f2ab
|
1d5c13c3f628de90674a3bf11a5ca6819813bd54
|
/eletivaspro/authenticacao/views.py
|
6aa8993427a435e4b8f0478eb28cfd40760c7db5
|
[
"MIT"
] |
permissive
|
flavioVitoriano/EletivasPro
|
7e5870378598dcc6d7c7c7e844603caeb8862d1c
|
34447081a1923286d78c56bd1f019214a8b25999
|
refs/heads/master
| 2022-12-07T11:37:32.340065
| 2020-09-04T02:04:42
| 2020-09-04T02:05:36
| 159,074,296
| 0
| 0
|
MIT
| 2022-11-22T04:23:11
| 2018-11-25T21:02:10
|
JavaScript
|
UTF-8
|
Python
| false
| false
| 1,913
|
py
|
from django.utils.http import is_safe_url
from django.contrib.auth.forms import AuthenticationForm
from django.contrib.auth import REDIRECT_FIELD_NAME, login as auth_login, logout as auth_logout
from django.utils.decorators import method_decorator
from django.contrib.auth.decorators import login_required
from django.views.decorators.cache import never_cache
from django.views.decorators.csrf import csrf_protect
from django.views.decorators.debug import sensitive_post_parameters
from django.views.generic import FormView, RedirectView, TemplateView
from django.shortcuts import reverse
from backend.models import Aluno, Eletiva, Registro
class LoginView(FormView):
"""
Provides the ability to login as a user with a username and password
"""
success_url = "/gestao"
form_class = AuthenticationForm
redirect_field_name = REDIRECT_FIELD_NAME
template_name = "gestao/login_page.html"
@method_decorator(sensitive_post_parameters('password'))
@method_decorator(csrf_protect)
@method_decorator(never_cache)
def dispatch(self, request, *args, **kwargs):
# Sets a test cookie to make sure the user has cookies enabled
request.session.set_test_cookie()
return super(LoginView, self).dispatch(request, *args, **kwargs)
def form_valid(self, form):
auth_login(self.request, form.get_user())
if self.request.session.test_cookie_worked():
self.request.session.delete_test_cookie()
return super(LoginView, self).form_valid(form)
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
return context
class LogoutView(RedirectView):
"""
Provides users the ability to logout
"""
url = '/auth/login/'
def get(self, request, *args, **kwargs):
auth_logout(request)
return super(LogoutView, self).get(request, *args, **kwargs)
|
[
"flavio.vitorianodev@gmail.com"
] |
flavio.vitorianodev@gmail.com
|
54cc6c350996f8692a63f7d0531f0d5639f868c9
|
1a8cda5c58f524fd73cb7472681c0d85e07fe8bb
|
/utils/eval_tool.py
|
39343c2a4e668f8df21b35181d4799f8b80e7ff6
|
[] |
no_license
|
shujunge/FasterRCNN_pytorch
|
93feb144e987cafa9ee9e5c6302708e36c5837e6
|
a37436e7f77e141fb7c673234c79e88538b99aad
|
refs/heads/master
| 2020-05-03T08:43:27.072424
| 2019-03-31T04:14:53
| 2019-03-31T04:14:53
| 178,534,290
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 14,023
|
py
|
from __future__ import division
from collections import defaultdict
import itertools
import numpy as np
import six
def bbox_iou(bbox_a, bbox_b):
"""Calculate the Intersection of Unions (IoUs) between bounding boxes.
IoU is calculated as a ratio of area of the intersection
and area of the union.
This function accepts both :obj:`numpy.ndarray` and :obj:`cupy.ndarray` as
inputs. Please note that both :obj:`bbox_a` and :obj:`bbox_b` need to be
same type.
The output is same type as the type of the inputs.
Args:
bbox_a (array): An array whose shape is :math:`(N, 4)`.
:math:`N` is the number of bounding boxes.
The dtype should be :obj:`numpy.float32`.
bbox_b (array): An array similar to :obj:`bbox_a`,
whose shape is :math:`(K, 4)`.
The dtype should be :obj:`numpy.float32`.
Returns:
array:
An array whose shape is :math:`(N, K)`. \
An element at index :math:`(n, k)` contains IoUs between \
:math:`n` th bounding box in :obj:`bbox_a` and :math:`k` th bounding \
box in :obj:`bbox_b`.
"""
if bbox_a.shape[1] != 4 or bbox_b.shape[1] != 4:
raise IndexError
# top left
tl = np.maximum(bbox_a[:, None, :2], bbox_b[:, :2])
# bottom right
br = np.minimum(bbox_a[:, None, 2:], bbox_b[:, 2:])
area_i = np.prod(br - tl, axis=2) * (tl < br).all(axis=2)
area_a = np.prod(bbox_a[:, 2:] - bbox_a[:, :2], axis=1)
area_b = np.prod(bbox_b[:, 2:] - bbox_b[:, :2], axis=1)
return area_i / (area_a[:, None] + area_b - area_i)
def eval_detection_voc(
pred_bboxes, pred_labels, pred_scores, gt_bboxes, gt_labels,
gt_difficults=None,
iou_thresh=0.5, use_07_metric=False):
"""Calculate average precisions based on evaluation code of PASCAL VOC.
This function evaluates predicted bounding boxes obtained from a dataset
which has :math:`N` images by using average precision for each class.
The code is based on the evaluation code used in PASCAL VOC Challenge.
Args:
pred_bboxes (iterable of numpy.ndarray): An iterable of :math:`N`
sets of bounding boxes.
Its index corresponds to an index for the base dataset.
Each element of :obj:`pred_bboxes` is a set of coordinates
of bounding boxes. This is an array whose shape is :math:`(R, 4)`,
where :math:`R` corresponds
to the number of bounding boxes, which may vary among boxes.
The second axis corresponds to
:math:`y_{min}, x_{min}, y_{max}, x_{max}` of a bounding box.
pred_labels (iterable of numpy.ndarray): An iterable of labels.
Similar to :obj:`pred_bboxes`, its index corresponds to an
index for the base dataset. Its length is :math:`N`.
pred_scores (iterable of numpy.ndarray): An iterable of confidence
scores for predicted bounding boxes. Similar to :obj:`pred_bboxes`,
its index corresponds to an index for the base dataset.
Its length is :math:`N`.
gt_bboxes (iterable of numpy.ndarray): An iterable of ground truth
bounding boxes
whose length is :math:`N`. An element of :obj:`gt_bboxes` is a
bounding box whose shape is :math:`(R, 4)`. Note that the number of
bounding boxes in each image does not need to be same as the number
of corresponding predicted boxes.
gt_labels (iterable of numpy.ndarray): An iterable of ground truth
labels which are organized similarly to :obj:`gt_bboxes`.
gt_difficults (iterable of numpy.ndarray): An iterable of boolean
arrays which is organized similarly to :obj:`gt_bboxes`.
This tells whether the
corresponding ground truth bounding box is difficult or not.
By default, this is :obj:`None`. In that case, this function
considers all bounding boxes to be not difficult.
iou_thresh (float): A prediction is correct if its Intersection over
Union with the ground truth is above this value.
use_07_metric (bool): Whether to use PASCAL VOC 2007 evaluation metric
for calculating average precision. The default value is
:obj:`False`.
Returns:
dict:
The keys, value-types and the description of the values are listed
below.
* **ap** (*numpy.ndarray*): An array of average precisions. \
The :math:`l`-th value corresponds to the average precision \
for class :math:`l`. If class :math:`l` does not exist in \
either :obj:`pred_labels` or :obj:`gt_labels`, the corresponding \
value is set to :obj:`numpy.nan`.
* **map** (*float*): The average of Average Precisions over classes.
"""
prec, rec = calc_detection_voc_prec_rec(
pred_bboxes, pred_labels, pred_scores,
gt_bboxes, gt_labels, gt_difficults,
iou_thresh=iou_thresh)
ap = calc_detection_voc_ap(prec, rec, use_07_metric=use_07_metric)
return {'ap': ap, 'map': np.nanmean(ap)}
def calc_detection_voc_prec_rec(
pred_bboxes, pred_labels, pred_scores, gt_bboxes, gt_labels,
gt_difficults=None,
iou_thresh=0.5):
"""Calculate precision and recall based on evaluation code of PASCAL VOC.
This function calculates precision and recall of
predicted bounding boxes obtained from a dataset which has :math:`N`
images.
The code is based on the evaluation code used in PASCAL VOC Challenge.
Args:
pred_bboxes (iterable of numpy.ndarray): An iterable of :math:`N`
sets of bounding boxes.
Its index corresponds to an index for the base dataset.
Each element of :obj:`pred_bboxes` is a set of coordinates
of bounding boxes. This is an array whose shape is :math:`(R, 4)`,
where :math:`R` corresponds
to the number of bounding boxes, which may vary among boxes.
The second axis corresponds to
:math:`y_{min}, x_{min}, y_{max}, x_{max}` of a bounding box.
pred_labels (iterable of numpy.ndarray): An iterable of labels.
Similar to :obj:`pred_bboxes`, its index corresponds to an
index for the base dataset. Its length is :math:`N`.
pred_scores (iterable of numpy.ndarray): An iterable of confidence
scores for predicted bounding boxes. Similar to :obj:`pred_bboxes`,
its index corresponds to an index for the base dataset.
Its length is :math:`N`.
gt_bboxes (iterable of numpy.ndarray): An iterable of ground truth
bounding boxes
whose length is :math:`N`. An element of :obj:`gt_bboxes` is a
bounding box whose shape is :math:`(R, 4)`. Note that the number of
bounding boxes in each image does not need to be same as the number
of corresponding predicted boxes.
gt_labels (iterable of numpy.ndarray): An iterable of ground truth
labels which are organized similarly to :obj:`gt_bboxes`.
gt_difficults (iterable of numpy.ndarray): An iterable of boolean
arrays which is organized similarly to :obj:`gt_bboxes`.
This tells whether the
corresponding ground truth bounding box is difficult or not.
By default, this is :obj:`None`. In that case, this function
considers all bounding boxes to be not difficult.
iou_thresh (float): A prediction is correct if its Intersection over
Union with the ground truth is above this value..
Returns:
tuple of two lists:
This function returns two lists: :obj:`prec` and :obj:`rec`.
* :obj:`prec`: A list of arrays. :obj:`prec[l]` is precision \
for class :math:`l`. If class :math:`l` does not exist in \
either :obj:`pred_labels` or :obj:`gt_labels`, :obj:`prec[l]` is \
set to :obj:`None`.
* :obj:`rec`: A list of arrays. :obj:`rec[l]` is recall \
for class :math:`l`. If class :math:`l` that is not marked as \
difficult does not exist in \
:obj:`gt_labels`, :obj:`rec[l]` is \
set to :obj:`None`.
"""
pred_bboxes = iter(pred_bboxes)
pred_labels = iter(pred_labels)
pred_scores = iter(pred_scores)
gt_bboxes = iter(gt_bboxes)
gt_labels = iter(gt_labels)
if gt_difficults is None:
gt_difficults = itertools.repeat(None)
else:
gt_difficults = iter(gt_difficults)
n_pos = defaultdict(int)
score = defaultdict(list)
match = defaultdict(list)
for pred_bbox, pred_label, pred_score, gt_bbox, gt_label, gt_difficult in \
six.moves.zip(
pred_bboxes, pred_labels, pred_scores,
gt_bboxes, gt_labels, gt_difficults):
if gt_difficult is None:
gt_difficult = np.zeros(gt_bbox.shape[0], dtype=bool)
for l in np.unique(np.concatenate((pred_label, gt_label)).astype(int)):
pred_mask_l = pred_label == l
pred_bbox_l = pred_bbox[pred_mask_l]
pred_score_l = pred_score[pred_mask_l]
# sort by score
order = pred_score_l.argsort()[::-1]
pred_bbox_l = pred_bbox_l[order]
pred_score_l = pred_score_l[order]
gt_mask_l = gt_label == l
gt_bbox_l = gt_bbox[gt_mask_l]
gt_difficult_l = gt_difficult[gt_mask_l]
n_pos[l] += np.logical_not(gt_difficult_l).sum()
score[l].extend(pred_score_l)
if len(pred_bbox_l) == 0:
continue
if len(gt_bbox_l) == 0:
match[l].extend((0,) * pred_bbox_l.shape[0])
continue
# VOC evaluation follows integer typed bounding boxes.
pred_bbox_l = pred_bbox_l.copy()
pred_bbox_l[:, 2:] += 1
gt_bbox_l = gt_bbox_l.copy()
gt_bbox_l[:, 2:] += 1
iou = bbox_iou(pred_bbox_l, gt_bbox_l)
gt_index = iou.argmax(axis=1)
# set -1 if there is no matching ground truth
gt_index[iou.max(axis=1) < iou_thresh] = -1
del iou
selec = np.zeros(gt_bbox_l.shape[0], dtype=bool)
for gt_idx in gt_index:
if gt_idx >= 0:
if gt_difficult_l[gt_idx]:
match[l].append(-1)
else:
if not selec[gt_idx]:
match[l].append(1)
else:
match[l].append(0)
selec[gt_idx] = True
else:
match[l].append(0)
for iter_ in (
pred_bboxes, pred_labels, pred_scores,
gt_bboxes, gt_labels, gt_difficults):
if next(iter_, None) is not None:
raise ValueError('Length of input iterables need to be same.')
n_fg_class = max(n_pos.keys()) + 1
prec = [None] * n_fg_class
rec = [None] * n_fg_class
for l in n_pos.keys():
score_l = np.array(score[l])
match_l = np.array(match[l], dtype=np.int8)
order = score_l.argsort()[::-1]
match_l = match_l[order]
tp = np.cumsum(match_l == 1)
fp = np.cumsum(match_l == 0)
# If an element of fp + tp is 0,
# the corresponding element of prec[l] is nan.
prec[l] = tp / (fp + tp)
# If n_pos[l] is 0, rec[l] is None.
if n_pos[l] > 0:
rec[l] = tp / n_pos[l]
return prec, rec
def calc_detection_voc_ap(prec, rec, use_07_metric=False):
"""Calculate average precisions based on evaluation code of PASCAL VOC.
This function calculates average precisions
from given precisions and recalls.
The code is based on the evaluation code used in PASCAL VOC Challenge.
Args:
prec (list of numpy.array): A list of arrays.
:obj:`prec[l]` indicates precision for class :math:`l`.
If :obj:`prec[l]` is :obj:`None`, this function returns
:obj:`numpy.nan` for class :math:`l`.
rec (list of numpy.array): A list of arrays.
:obj:`rec[l]` indicates recall for class :math:`l`.
If :obj:`rec[l]` is :obj:`None`, this function returns
:obj:`numpy.nan` for class :math:`l`.
use_07_metric (bool): Whether to use PASCAL VOC 2007 evaluation metric
for calculating average precision. The default value is
:obj:`False`.
Returns:
~numpy.ndarray:
This function returns an array of average precisions.
The :math:`l`-th value corresponds to the average precision
for class :math:`l`. If :obj:`prec[l]` or :obj:`rec[l]` is
:obj:`None`, the corresponding value is set to :obj:`numpy.nan`.
"""
n_fg_class = len(prec)
ap = np.empty(n_fg_class)
for l in six.moves.range(n_fg_class):
if prec[l] is None or rec[l] is None:
ap[l] = np.nan
continue
if use_07_metric:
# 11 point metric
ap[l] = 0
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec[l] >= t) == 0:
p = 0
else:
p = np.max(np.nan_to_num(prec[l])[rec[l] >= t])
ap[l] += p / 11
else:
# correct AP calculation
# first append sentinel values at the end
mpre = np.concatenate(([0], np.nan_to_num(prec[l]), [0]))
mrec = np.concatenate(([0], rec[l], [1]))
mpre = np.maximum.accumulate(mpre[::-1])[::-1]
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap[l] = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
|
[
"zfwang@stu.xidian.edu.cn"
] |
zfwang@stu.xidian.edu.cn
|
29a6b6297c5a13c3841db250af023df7df17c8d3
|
cad4fb2a3bcdcb42b0be85681f7455fa2b4e658d
|
/flow_transforms.py
|
de5a2570ea9c080e6d5f33c5c87faf52d2eb260d
|
[] |
no_license
|
spillai/FlowNetPytorch
|
a994171217920293d0a9299dfb31fc5805c4f890
|
90a408e5df0fb2cb8d12adde8685964d94be93a9
|
refs/heads/master
| 2021-01-11T14:47:08.784506
| 2017-01-27T15:01:28
| 2017-01-27T15:01:28
| 80,218,622
| 1
| 0
| null | 2017-01-27T15:24:05
| 2017-01-27T15:24:05
| null |
UTF-8
|
Python
| false
| false
| 9,766
|
py
|
from __future__ import division
import torch
import math
import random
from PIL import Image, ImageOps
import numpy as np
import numbers
import types
import scipy.ndimage as ndimage
'''Set of tranform random routines that takes both input and target as arguments,
in order to have random but coherent transformations.
inputs are PIL Image pairs and targets are ndarrays'''
class Compose(object):
""" Composes several co_transforms together.
For example:
>>> co_transforms.Compose([
>>> co_transforms.CenterCrop(10),
>>> co_transforms.ToTensor(),
>>> ])
"""
def __init__(self, co_transforms):
self.co_transforms = co_transforms
def __call__(self, input, target):
for t in self.co_transforms:
input,target = t(input,target)
return input,target
class Lambda(object):
"""Applies a lambda as a transform"""
def __init__(self, lambd):
assert type(lambd) is types.LambdaType
self.lambd = lambd
def __call__(self, input,target):
return self.lambd(input,target)
class CenterCrop(object):
"""Crops the given PIL.Image at the center to have a region of
the given size. size can be a tuple (target_height, target_width)
or an integer, in which case the target will be of a square shape (size, size)
"""
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, inputs, target):
w, h = inputs[0].size
th, tw = self.size
x1 = int(round((w - tw) / 2.))
y1 = int(round((h - th) / 2.))
inputs[0] = inputs[0].crop((x1, y1, x1 + tw, y1 + th))
inputs[1] = inputs[1].crop((x1, y1, x1 + tw, y1 + th))
target = target[y1 : y1 + th, x1 : x1 + tw]
return inputs,target
class Scale(object):
""" Rescales the input PIL.Image to the given 'size'.
'size' will be the size of the smaller edge.
For example, if height > width, then image will be
rescaled to (size * height / width, size)
size: size of the smaller edge
interpolation: Default: PIL.Image.BILINEAR
"""
def __init__(self, size, interpolation=Image.BILINEAR):
self.size = size
self.interpolation = interpolation
def __call__(self, inputs, target):
w, h = inputs[0].size
if (w <= h and w == self.size) or (h <= w and h == self.size):
return inputs,target
if w < h:
ow = self.size
oh = int(self.size * h / w)
ratio = ow/w
else:
oh = self.size
ow = int(self.size * w / h)
ratio = oh/h
inputs[0] = inputs[0].resize((ow, oh), self.interpolation)
inputs[1] = inputs[1].resize((ow, oh), self.interpolation)
target = ndimage.interpolation.zoom(target,ratio)
target*=ow/w
return inputs, target[:oh,:ow]
class RandomCrop(object):
"""Crops the given PIL.Image at a random location to have a region of
the given size. size can be a tuple (target_height, target_width)
or an integer, in which case the target will be of a square shape (size, size)
"""
def __init__(self, size, padding=0):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
self.padding = padding
def __call__(self, inputs,target):
if self.padding > 0:
inputs[0] = ImageOps.expand(inputs[0], border=self.padding, fill=0)
inputs[1] = ImageOps.expand(inputs[1], border=self.padding, fill=0)
w, h = inputs[0].size
th, tw = self.size
if w == tw and h == th:
return inputs,target
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
inputs[0] = inputs[0].crop((x1, y1, x1 + tw, y1 + th))
inputs[1] = inputs[1].crop((x1, y1, x1 + tw, y1 + th))
return inputs,target[y1 : y1 + th,x1 : x1 + tw]
class RandomHorizontalFlip(object):
"""Randomly horizontally flips the given PIL.Image with a probability of 0.5
"""
def __call__(self, inputs, target):
if random.random() < 0.5:
input[0] = input[0].transpose(Image.FLIP_LEFT_RIGHT)
input[1] = input[1].transpose(Image.FLIP_LEFT_RIGHT)
target = target.fliplr()
target[:,:,0]*=-1
return inputs,target
class RandomVerticalFlip(object):
"""Randomly horizontally flips the given PIL.Image with a probability of 0.5
"""
def __call__(self, inputs, target):
if random.random() < 0.5:
input[0] = input[0].transpose(Image.FLIP_UP_DOWN)
input[1] = input[1].transpose(Image.FLIP_UP_DOWN)
target = target.flipud()
target[:,:,1]*=-1
return inputs,target
class RandomRotate(object):
"""Random rotation of the image from -angle to angle (in degrees)
This is useful for dataAugmentation, especially for geometric problems such as FlowEstimation
angle: max angle of the rotation
resample: Default: PIL.Image.BILINEAR
expand: Default: false. If set to true, image size will be set to keep every pixel in the image.
diff_angle: Default: 0. Must stay less than 10 degrees, or linear approximation of flowmap will be off.
Careful when rotating more than 45 degrees, w and h will be inverted
"""
def __init__(self, angle, resample=Image.BILINEAR, expand=False, diff_angle=0):
self.angle = angle
self.resample = resample
self.expand = expand
self.diff_angle = diff_angle
assert(angle+diff_angle < 45)
def __call__(self, inputs,target):
applied_angle = random.uniform(-self.angle,self.angle)
diff = random.uniform(-self.diff_angle,self.diff_angle)
angle1 = applied_angle + diff/2
angle2 = applied_angle - diff/2
w, h = inputs[0].size
def rotate_flow(i,j,k):
if k==0:
return (i-w/2)*(diff*math.pi/180)
else:
return (j-h/2)*(-diff*math.pi/180)
rotate_flow_map = np.fromfunction(rotate_flow, target.shape)
target += rotate_flow_map
inputs[0] = inputs[0].rotate(angle1,resample=self.resample, expand=self.expand)
inputs[1] = inputs[1].rotate(angle2,resample=self.resample, expand=self.expand)
target = ndimage.interpolation.rotate(target,reshape=False)
return inputs,target
class RandomCropRotate(object):
"""Random rotation of the image from -angle to angle (in degrees)
A crop is done to keep same image ratio, and no black pixels
angle: max angle of the rotation cannot be more than 180 degrees
resample: Default: PIL.Image.BILINEAR
"""
def __init__(self, angle, size, diff_angle=0, resample=Image.BILINEAR):
self.angle = angle
self.resample = resample
self.expand = True
self.diff_angle = diff_angle
self.size = size
def __call__(self, inputs,target):
applied_angle = random.uniform(-self.angle,self.angle)
diff = random.uniform(-self.diff_angle,self.diff_angle)
angle1 = applied_angle + diff/2
angle2 = applied_angle - diff/2
angle1_rad = angle1*np.pi/180
angle2_rad = angle2*np.pi/180
w, h = inputs[0].size
def rotate_flow(i,j,k):
return k*(i-w/2)*(diff*np.pi/180) + (k-1)*(j-h/2)*(-diff*np.pi/180)
rotate_flow_map = np.fromfunction(rotate_flow, target.shape)
target += rotate_flow_map
inputs[0] = inputs[0].rotate(angle1,resample=self.resample, expand=True)
inputs[1] = inputs[1].rotate(angle2,resample=self.resample, expand=True)
target = ndimage.interpolation.rotate(target,angle1,reshape=True)
#flow vectors must be rotated too!
target_=np.array(target, copy=True)
target[:,:,0] = np.cos(angle1_rad)*target_[:,:,0] - np.sin(angle1_rad)*target[:,:,1]
target[:,:,0] = np.sin(angle1_rad)*target_[:,:,0] + np.cos(angle1_rad)*target[:,:,1]
#keep angle1 and angle2 within [0,pi/2] with a reflection at pi/2: -1rad is 1rad, 2rad is pi - 2 rad
angle1_rad = np.pi/2 - np.abs(angle1_rad%np.pi - np.pi/2)
angle2_rad = np.pi/2 - np.abs(angle2_rad%np.pi - np.pi/2)
c1 = np.cos(angle1_rad)
s1 = np.sin(angle1_rad)
c2 = np.cos(angle2_rad)
s2 = np.sin(angle2_rad)
c_diag = h/np.sqrt(h*h+w*w)
s_diag = w/np.sqrt(h*h+w*w)
ratio = c_diag/max(c1*c_diag+s1*s_diag,c2*c_diag+s2*s_diag)
crop = CenterCrop((int(h*ratio),int(w*ratio)))
scale = Scale(self.size)
inputs, target = crop(inputs, target)
return scale(inputs,target)
class RandomTranslate(object):
def __init__(self, translation):
if isinstance(translation, numbers.Number):
self.translation = (int(translation), int(translation))
else:
self.translation = translation
def __call__(self, inputs,target):
w,h = inputs[0].size
th, tw = self.translation
tw = random.randint(-tw, tw)
th = random.randint(-th, th)
if tw==0 and th==0:
return inputs, target
#compute x1,x2,y1,y2 for img1 and target, and x3,x4,y3,y4 for img2
x1,x2,x3,x4 = max(0,-tw), min(w-tw,w), max(0,tw), min(w+tw,w)
y1,y2,y3,y4 = max(0,-th), min(h-th,h), max(0,th), min(h+th,h)
inputs[0] = inputs[0].crop((x1, y1, x2, y2))
inputs[1] = inputs[1].crop((x3, y3, x4, y4))
target= target[y1:y2,x1:x2]
target[:,:,0]+= tw
target[:,:,1]+= th
return inputs, target
|
[
"clement.pinard@parrot.com"
] |
clement.pinard@parrot.com
|
53ff44496cb0984d03f5da6f7271f4c8652cc91d
|
14561adc9918f32b7f9334fa4dde08a3bfa17c26
|
/pipeline/Bacteria_denovo/Bacteria_denovo.pipeline.py
|
d4951738835c6a9781c9201f9ea8cd6c6fcab482
|
[] |
no_license
|
ZhikunWu/awesome-metagenomic
|
b932169f505d39864a91067283ad7ce954280923
|
71183f262aa539a3983af4de47f7cc69be8cf7a6
|
refs/heads/master
| 2021-10-08T00:00:00.181593
| 2018-12-06T02:07:42
| 2018-12-06T02:07:42
| 111,966,593
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,029
|
py
|
#!/usr/bin/env python
import yaml
import os
import sys
IN_PATH = config["IN_PATH"]
PIPE_DIR = config["PIPE_DIR"]
THREADS = config["THREADS"]
ThreadFold = config["ThreadFold"]
SAMPLES = config["SAMPLES"]
PROJECTS = config["PROJECTS"]
include: PIPE_DIR + "/Nano_QualityControl.rule.py"
include: PIPE_DIR + "/GenePridiction.rule.py"
rule all:
input:
expand(IN_PATH + "/clean/{sample}.fastq", sample=SAMPLES),
expand(IN_PATH + "/qualityControl/raw/nanoQC/{sample}/nanoQC.html", sample=SAMPLES),
expand(IN_PATH + "/qualityControl/raw/NanoPlot/{sample}/NanoPlot-report.html", sample=SAMPLES),
expand(IN_PATH + '/annotation/{project}/Prokka/assembly.faa', project=PROJECTS),
expand(IN_PATH + "/annotation/{project}/tRNAscan/assembly_tRNA_gene.fna", project=PROJECTS),
expand(IN_PATH + "/annotation/{project}/RepeatMasker/assembly.fasta.out", project=PROJECTS),
expand(IN_PATH + "/annotation/{project}/RepeatModeler/assembly_RepeatModeler.txt", project=PROJECTS),
expand(IN_PATH + "/annotation/{project}/LTRFinder/LTR.txt", project=PROJECTS),
expand(IN_PATH + "/annotation/{project}/TandemRepeatFinder/TandemRepeat.txt", project=PROJECTS),
expand(IN_PATH + "/annotation/{project}/LTRFinder/finder.scn", project=PROJECTS),
expand(IN_PATH + "/annotation/{project}/LTRretriever/assembly.fasta.mod.pass.list", project=PROJECTS),
expand(IN_PATH + "/assembly/{project}/assembly.fasta.mod.out.LAI", project=PROJECTS),
expand(IN_PATH + "/assembly/{project}/assembly_index.esq", project=PROJECTS),
expand(IN_PATH + "/annotation/{project}/LTRharvest/assembly_ltrharvest.gff3", project=PROJECTS),
expand(IN_PATH + "/annotation/{project}/LTRharvest/assembly_ltrdigest.gff3", project=PROJECTS),
expand(IN_PATH + "/annotation/{project}/RepeatScout/seq_freq.txt", project=PROJECTS),
expand(IN_PATH + "/annotation/{project}/RepeatScout/seq_repeat.txt", project=PROJECTS),
|
[
"598466208@qq.com"
] |
598466208@qq.com
|
334884019f2b56715e5a878c947780be070c15b6
|
5dad0bee1bceb2fcb29d5cd917891341e58901a6
|
/project_model/project_model/ATA/migrations/0002_auto_20190722_1713.py
|
5a44e306f936084aa5e200d6f016e49fa17108b1
|
[] |
no_license
|
fikriamri/DJANGO_MVC
|
7b727c726e9b3a80fc9cae0c1c69b14d8b8ad531
|
fbb1df52a68847595408973493edf89414db19d5
|
refs/heads/master
| 2022-02-22T06:23:35.877150
| 2019-07-24T03:39:23
| 2019-07-24T03:39:23
| 198,142,007
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 366
|
py
|
# Generated by Django 2.2.3 on 2019-07-22 10:13
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('ATA', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='mentee',
name='nomor_absen',
field=models.IntegerField(),
),
]
|
[
"famri@alterra.id"
] |
famri@alterra.id
|
0cbcbbcdedfab5d7e39c18b76eb226601632f8cb
|
770710253983911ea0ed00cb3111f2d3fbadd200
|
/Functions/TextTranslate.py
|
33a9411488a23ffa5324c6d8cb0a2ea4ffb62b22
|
[] |
no_license
|
anshdholakia/Full-Voice-Assistant-Application
|
4185ec88ed50f7477853bbf75905708dc3bb5c6d
|
c0b88aaece48c2aac4e159f5f7772e1df4c7dd1c
|
refs/heads/main
| 2023-05-27T16:38:31.500721
| 2021-06-14T20:43:48
| 2021-06-14T20:43:48
| 376,944,730
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 345
|
py
|
from translate import Translator
First_language = input("Enter the language :")
Second_language = input("which language do you want to translate in:")
trans = input("what do you want to translate:")
translator= Translator(from_lang = First_language, to_lang = Second_language )
translation = translator.translate(trans)
print (translation)
|
[
"anshhiro.dholakia@gmail.com"
] |
anshhiro.dholakia@gmail.com
|
fd038588e1514db2ce8a3b98d9a04bf9c08b8692
|
9c3c83007c5bf0f36635b0045b2aad7f8a11ac11
|
/novice/04-05/graphql/venv/lib/python3.6/site-packages/graphql/utils/value_from_ast.py
|
7ad52bca43bf423c08c5f077dd51404ba8164137
|
[
"MIT"
] |
permissive
|
septiannurtrir/praxis-academy
|
bc58f9484db36b36c202bf90fdfd359482b72770
|
1ef7f959c372ae991d74ccd373123142c2fbc542
|
refs/heads/master
| 2021-06-21T17:04:58.379408
| 2019-09-13T16:46:08
| 2019-09-13T16:46:08
| 203,007,994
| 1
| 0
|
MIT
| 2021-03-20T01:43:24
| 2019-08-18T13:38:23
|
Python
|
UTF-8
|
Python
| false
| false
| 2,920
|
py
|
from ..language import ast
from ..type import (
GraphQLEnumType,
GraphQLInputObjectType,
GraphQLList,
GraphQLNonNull,
GraphQLScalarType,
)
# Necessary for static type checking
if False: # flake8: noqa
from ..language.ast import Node
from ..type.definition import GraphQLType
from typing import Dict, Union, Optional, List
def value_from_ast(value_ast, type, variables=None):
# type: (Optional[Node], GraphQLType, Optional[Dict[str, Union[List, Dict, int, float, bool, str, None]]]) -> Union[List, Dict, int, float, bool, str, None]
"""Given a type and a value AST node known to match this type, build a
runtime value."""
if isinstance(type, GraphQLNonNull):
# Note: we're not checking that the result of coerceValueAST is non-null.
# We're assuming that this query has been validated and the value used here is of the correct type.
return value_from_ast(value_ast, type.of_type, variables)
if value_ast is None:
return None
if isinstance(value_ast, ast.Variable):
variable_name = value_ast.name.value
if not variables or variable_name not in variables:
return None
# Note: we're not doing any checking that this variable is correct. We're assuming that this query
# has been validated and the variable usage here is of the correct type.
return variables.get(variable_name)
if isinstance(type, GraphQLList):
item_type = type.of_type
if isinstance(value_ast, ast.ListValue):
return [
value_from_ast(item_ast, item_type, variables)
for item_ast in value_ast.values
]
else:
return [value_from_ast(value_ast, item_type, variables)]
if isinstance(type, GraphQLInputObjectType):
fields = type.fields
if not isinstance(value_ast, ast.ObjectValue):
return None
field_asts = {}
for field_ast in value_ast.fields:
field_asts[field_ast.name.value] = field_ast
obj = {}
for field_name, field in fields.items():
if field_name not in field_asts:
if field.default_value is not None:
# We use out_name as the output name for the
# dict if exists
obj[field.out_name or field_name] = field.default_value
continue
field_ast = field_asts[field_name]
field_value_ast = field_ast.value
field_value = value_from_ast(field_value_ast, field.type, variables)
# We use out_name as the output name for the
# dict if exists
obj[field.out_name or field_name] = field_value
return type.create_container(obj)
assert isinstance(type, (GraphQLScalarType, GraphQLEnumType)), "Must be input type"
return type.parse_literal(value_ast)
|
[
"septiannurtrir@gmail.com"
] |
septiannurtrir@gmail.com
|
b67811ab9cab13cf550d69e2c4dda72fc58a0535
|
d4deb8d62d95876488e4f2d555d1edce11cd7b8c
|
/fixture/group.py
|
0cdf31543c3ee7c58b8254f4e628bc0064a591a6
|
[
"Apache-2.0"
] |
permissive
|
alen4ik/python_training
|
ce54b19b255358c692c63c84a7e7fe7b7edbf000
|
37faa663089ff610a85fb92b89b7b953d4ddc9e7
|
refs/heads/master
| 2016-09-07T18:50:40.983369
| 2015-08-29T19:37:27
| 2015-08-29T19:37:27
| 40,011,105
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,048
|
py
|
__author__ = 'ASUS'
class GroupHelper:
def __init__(self, app):
self.app = app
def open_groups_page(self):
wd = self.app.wd
wd.find_element_by_link_text("groups").click()
def create(self, group):
self.open_groups_page()
self.init_group_creation()
self.fill_group_form(group)
self.submit_group_creation()
self.return_to_groups_page()
def init_group_creation(self):
wd = self.app.wd
wd.find_element_by_name("new").click()
def change_field_value(self, field_name, text):
wd = self.app.wd
if text is not None:
wd.find_element_by_name(field_name).click()
wd.find_element_by_name(field_name).clear()
wd.find_element_by_name(field_name).send_keys(text)
def fill_group_form(self, group):
wd = self.app.wd
self.change_field_value("group_name", group.name)
self.change_field_value("group_header", group.header)
self.change_field_value("group_footer", group.footer)
def submit_group_creation(self):
wd = self.app.wd
wd.find_element_by_name("submit").click()
def select_first_group(self):
wd = self.app.wd
wd.find_element_by_name("selected[]").click()
def delete_first_group(self):
wd = self.app.wd
self.open_groups_page()
self.select_first_group()
#submit deletion
wd.find_element_by_name("delete").click()
self.return_to_groups_page()
def modify_first_group(self, new_group_data):
wd = self.app.wd
self.open_groups_page()
self.select_first_group()
# open modification form
wd.find_element_by_name("edit").click()
# fill group form
self.fill_group_form(new_group_data)
# submit modification
wd.find_element_by_name("update").click()
self.return_to_groups_page()
def return_to_groups_page(self):
wd = self.app.wd
wd.find_element_by_link_text("group page").click()
|
[
"alexa10@ya.ru"
] |
alexa10@ya.ru
|
a7996adaf8325122bea98102d171858b89532aa3
|
37781f86b30df6b31ee51ceeca01d391f9081b35
|
/player.py
|
a94d32f4ea2ee36a10494e5f8529af80611561f7
|
[] |
no_license
|
Dek18/CM1101
|
2956d77631ba824f3d855da264a285e6fd2297c9
|
b2ac69015214db279d5bb23906a9e7ad3f44d715
|
refs/heads/master
| 2020-04-01T17:50:32.308366
| 2018-10-19T12:07:50
| 2018-10-19T12:07:50
| 153,453,951
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 157
|
py
|
from items import *
from map import rooms
inventory = [item_id, item_laptop, item_money]
# Start game at the reception
current_room = rooms["Reception"]
|
[
"noreply@github.com"
] |
noreply@github.com
|
7e5dbb102fab53228104ce9a43c6407ab1972c45
|
50989266203628be7649d152392f4a1789997b90
|
/lisp.py
|
9c96a7942a34631c24cce5c62058308aa3242b27
|
[] |
no_license
|
cheery/snakelisp
|
b2820819959be4ed0b62a60c511b15623ae5589e
|
c62c0401e7d8cbd63afb8a7242850f7740420614
|
refs/heads/master
| 2020-05-15T08:53:26.443191
| 2014-09-16T15:55:43
| 2014-09-16T15:55:43
| 23,539,541
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 6,257
|
py
|
#!/usr/bin/env python
from pythonboot.blip import ListNode, TextNode, MarkNode, isList, isText, isMark, open_list
import json
import transpiler
from cps import Call, Lambda, Assign, Variable, Constant, Environ, null, true, false
import subprocess
import sys
import re
# call = Call([arguments]), call[i]
# lambda = Lambda([arguments], body), lambda[i]
# Assign(var, val, body)
# Variable(name, value)
# Constant(value)
def main():
path = sys.argv[1]
mks = []
env = Environ()
ret = env.new_argument("cont", False)
env = env.new_environ()
ret = env.new_argument('cont', False)
exprs = open_list(path).strip_rec()
#exprs = list(open_list("base.sl")) + list(open_list(path))
program = env.close(compile_list(exprs, env, ret))
program = program.coalesce()
snakesource = "snakelisp.c"
rootdecl = re.compile(r'newRoot\("(.+)",')
with open(snakesource) as fd:
src = fd.read()
c_roots = dict((decl, "(root+{})".format(i)) for i, decl in enumerate(rootdecl.findall(src)))
c_api = {
"uncallable-hook": "&uncallable_hook",
"type-error-hook": "&type_error_hook",
}
c_use = set()
for var in env.seal():
if var.name in c_roots:
var.c_handle = c_roots[var.name]
continue
var.c_handle = c_api[var.name]
c_use.add(var.c_handle)
cdefns = ["extern value_t {};".format(value[1:]) for value in c_use]
#import visuals
#visuals.create_graph("demo.png", program)
source = transpiler.transpile(program, cdefns, path)
open(path+'.c', 'w').write(source)
subprocess.call(["gcc", path+'.c', snakesource, "-I.", "-lm"])
constants = {'null': null, 'true':true, 'false':false}
def compile(expr, env, k):
if isList(expr, 'include') and isText(expr[0]):
return compile_list(open_list(expr[0].text).strip_rec(), env, k)
if isList(expr, 'let') and isText(expr[0]):
var = env.get_local(expr[0].text)
return compile(expr[1], env,
(lambda val: Assign(var, val, retrieve(k, val))))
if isList(expr, 'set') and isText(expr[0]):
var = env.lookup(expr[0].text)
return compile(expr[1], env,
(lambda val: Assign(var, val, retrieve(k, val))))
if isList(expr, 'cond'):
return compile_cond(expr, env, k)
if isList(expr, 'while'):
return compile_while(expr, env, k)
if isList(expr, 'func'):
env = env.new_environ()
ret = env.new_argument('cont', False)
for sym in expr[0]:
assert sym.label == ''
env.new_argument(sym.text)
return retrieve(k, env.close(compile_list(expr[1:], env, ret)))
if isList(expr, 'infix') and len(expr) == 3:
return compile(ListNode([expr[1], expr[0], expr[2]]), env, k)
if isList(expr, ''):
params = []
seq = list(expr)
def next_parameter(param):
params.append(param)
if len(seq) > 0:
return compile(seq.pop(0), env, next_parameter)
else:
callee = params.pop(0)
return Call([callee, lift(k)] + params)
return compile(seq.pop(0), env, next_parameter)
#if expr.group == 'integer':
# return retrieve(k, Constant(expr.value))
#if expr.group == 'double':
# return retrieve(k, Constant(expr.value))
if isText(expr, "string"):
return retrieve(k, Constant(expr.text))
if isText(expr, ''):
if expr.text[:1].isdigit():
return retrieve(k, Constant(int(expr.text)))
if expr.text in constants:
param = constants[expr.text]
else:
param = env.lookup(expr.text)
return retrieve(k, param)
raise Exception("what is {}?".format(expr))
def compile_list(exprs, env, k):
seq = list(exprs)
def next_parameter(param):
if len(seq) > 1:
return compile(seq.pop(0), env, next_parameter)
else:
return compile(seq.pop(0), env, k)
if len(exprs) == 0:
return retrieve(k, null)
return next_parameter(null)
def retrieve(k, param):
if callable(k):
return k(param)
else:
return Call([k, param])
def lift(k):
if callable(k):
x = Variable()
return Lambda([x], k(x))
else:
return k
def compile_cond(expr, env, k):
seq = list(expr[0:])
if len(seq) == 0:
return retrieve(k, null)
def next_cond(k):
if len(seq) == 0:
return retrieve(k, null)
head = seq.pop(0)
if len(seq) == 0 and isList(head, 'else'):
return compile_list(head[0:], env, k)
if isList(head, 'else'):
raise Exception("invalid cond expression")
return compile(head[0], env,
(lambda truth: pick(env, k, truth,
enclose(head[1:], env),
lambdaCont(next_cond))))
return next_cond(k)
def compile_while(expr, env, k):
self = Variable()
seq = expr[1:]
def compile_body(k):
return compile_list(expr[1:], env, (lambda _: Call([self, lift(k)])))
cont = Variable()
looplambda = Lambda([cont], compile(expr[0], env,
(lambda truth: pick(env, cont, truth, lambdaCont(compile_body), lambdaNull()))))
return Assign(self, looplambda, Call([self, lift(k)]), True)
def pick(env, k, truth, yes, no):
return Call([env.new_implicit('pick'), lift(k), truth, yes, no])
def lambdaNull():
cont = Variable()
return Lambda([cont], Call([cont, null]))
def lambdaCont(func):
cont = Variable()
return Lambda([cont], func(cont))
def enclose(exprs, env):
cont = Variable()
return Lambda([cont], compile_list(exprs, env, cont))
#def open_list(path):
# with open(path, 'r') as fd:
# plop = json.load(fd)
# return decodeJson(plop)
#
#def decodeJson(node):
# if node["type"] == "list":
# return ListNode([decodeJson(a) for a in node["list"]], node["label"] or '').strip()
# elif node["type"] == 'text':
# return TextNode(node["text"], node["label"] or '')
# elif node["type"] == 'mark':
# return MarkNode(node["label"] or '')
# else:
# raise Exception("unknown {}".format(node))
if __name__ == '__main__':
main()
|
[
"cheery@boxbase.org"
] |
cheery@boxbase.org
|
891003afd300b40669b199a1656cb107cd5cee6c
|
94be38d1f532a2d79e8559780fed11f9d7f3c374
|
/bin/wheel
|
678f987df6f3a74016b24a0f050ec9638d865afd
|
[] |
no_license
|
aibolTungatarov/Algorithms
|
f81b134e3a9cf17f17f3b173f4af92de9c26b725
|
411b5c33aace65aba586933e5fd1b91ea467a1f4
|
refs/heads/master
| 2020-04-01T13:45:35.762752
| 2018-10-18T07:44:54
| 2018-10-18T07:44:54
| 153,266,145
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 245
|
#!/Users/macpro/Desktop/Alghoritms/venv/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from wheel.tool import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(main())
|
[
"aibolseed@gmail.com"
] |
aibolseed@gmail.com
|
|
4a9d5d358f8e395181b4755283707fc8c2222b93
|
e688f5939fb7ece6b5626f2e6aeeab34377841fe
|
/mysite/mysite/urls.py
|
425723419fd208e764cb54798e298410dd86ef02
|
[] |
no_license
|
tarq138/my_project
|
643c02fdfaa5d2445dbe87ef7679379d5289f503
|
37917b03426a7f846eac1c382a74b89c793439bf
|
refs/heads/master
| 2022-12-11T20:43:52.719760
| 2018-06-09T21:57:10
| 2018-06-09T21:57:10
| 136,760,643
| 0
| 0
| null | 2022-11-22T02:19:24
| 2018-06-09T21:52:17
|
Python
|
UTF-8
|
Python
| false
| false
| 618
|
py
|
from django.contrib import admin
from django.conf.urls import url, include
from django.conf import settings
from django.conf.urls.static import static
admin.autodiscover()
urlpatterns = [
url(r'^admin/', admin.site.urls),
url(r'^accounts/', include('django.contrib.auth.urls')),
url(r'^sip/', include('sip.urls')),
url(r'^', include('mainApp.urls')),
url(r'^', include('products.urls')),
url(r'^$', include('orders.urls')),
] \
+ static(settings.STATIC_URL, document_root=settings.STATIC_ROOT) \
+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
|
[
"tarq138@gmail.com"
] |
tarq138@gmail.com
|
bd0868604e0235e290ac2c3da89d7d5fd1be98ee
|
7b4b8a7fc438603425ba09a8988b27626951c4a3
|
/scripts-general/bin/bg-cycle.py
|
dcba9d75a3dd8d29142a0e19016e2fc42ee48d91
|
[
"MIT"
] |
permissive
|
raehik/dotfiles
|
a728ea46f2219887595d9dec1a6445bcd7761fdd
|
0e3b695038f4e1e41ad53a3f7e9bad746a340dce
|
refs/heads/master
| 2023-04-29T15:55:24.942379
| 2023-04-20T16:58:47
| 2023-04-20T16:58:52
| 21,466,079
| 3
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 7,875
|
py
|
#!/usr/bin/env python3
#
# Create backgrounds for applications.
#
import sys, os, argparse, logging, subprocess
from pathlib import Path
import random, glob
## Boilerplate utils class {{{
class BoilerplateClass:
def _deinit(self):
self.logger.debug("deinitialising...")
def _init_logging(self):
self.logger = logging.getLogger(os.path.basename(sys.argv[0]))
lh = logging.StreamHandler()
lh.setFormatter(logging.Formatter("%(name)s: %(levelname)s: %(message)s"))
self.logger.addHandler(lh)
def _parse_verbosity(self):
if self.args.verbose == 1:
self.logger.setLevel(logging.INFO)
elif self.args.verbose >= 2:
self.logger.setLevel(logging.DEBUG)
if self.args.quiet >= 1:
# reset verbosity (to make verbose/quiet checks easier)
self.args.verbose = 0
self.logger.setLevel(logging.NOTSET)
def run(self):
"""Run from CLI: parse arguments, run main, deinitialise."""
self._init_logging()
self._parse_args()
self.main()
self._deinit()
def fail(self, msg="internal error", ret=1):
"""Exit with a message and a return code.
Should only be used for errors -- if you want to deinitialise and exit
safely, simply return from main.
Suggested to use no parameters for internal functions that the user
doesn't need to know about (which generally indicates a logic error,
rather than an input one.)
"""
self.logger.error(msg)
self._deinit()
sys.exit(ret)
def get_shell(self, cmd, cwd=None):
"""Run a shell command, blocking execution, detaching stdin, stdout and
stderr.
Useful for grabbing shell command outputs, or if you want to run something
silently and wait for it to finish.
@param cmd command to run as an array, where each element is an argument
@param cwd if present, directory to use as CWD
@return the command's return code, stdout and stderr (respectively, as a
tuple)
"""
proc = subprocess.run(cmd, stdin=subprocess.DEVNULL,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=cwd)
return proc.returncode, \
proc.stdout.decode("utf-8", "replace").strip(), \
proc.stderr.decode("utf-8", "replace").strip()
def get_shell_with_input(self, cmd, stdin_in, cwd=None):
"""Run a shell command with a given string passed to stdin, blocking
execution and detaching stdout and stderr.
We put a newline on the end of stdin_in, because that appears to be important for
some programs (e.g. bc). TODO though, unsure. Maybe should be an option.
@param cmd command to run as an array, where each element is an argument
@param std_in string to pass to stdin
@param cwd if present, directory to use as CWD
@return the command's return code, stdout and stderr (respectively, as a
tuple)
"""
proc = subprocess.run(cmd, input=bytes("{}\n".format(stdin_in), "utf-8"),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
cwd=cwd)
return proc.returncode, \
proc.stdout.decode("utf-8", "replace").strip(), \
proc.stderr.decode("utf-8", "replace").strip()
def drop_to_shell(self, cmd, cwd=None):
"""Run a shell command, blocking execution.
Doesn't touch any pipes. Like dropping to shell during execution.
@param cmd command to run as an array, where each element is an argument
@param cwd if present, directory to use as CWD
@return the command's exit code
"""
return subprocess.run(cmd, cwd=cwd).returncode
## Boilerplate utils class }}}
class BGCycler(BoilerplateClass):
DEF_IMG_DIR = Path(os.environ.get("HOME"), "proj", "media", "backgrounds")
DEF_OUT_DIR = Path(os.environ.get("XDG_DATA_HOME"), "backgrounds")
DEF_WIDTH = "1920"
DEF_HEIGHT = "1080"
DEF_BRIGHTNESS = "-81"
DEF_CONTRAST = "-91"
ERR_IMG_CONVERSION_FAILED = 2
ERR_IMG_FILE_ERROR = 3
DEF_BG_NAME = "desktop"
## CLI-related {{{
def _parse_args(self):
self.parser = argparse.ArgumentParser(
description="Create backgrounds for applications.",
epilog="I use a rule of contrast = brightness-10 for a nice image.")
self.parser.add_argument("-v", "--verbose", help="be verbose", action="count", default=0)
self.parser.add_argument("-q", "--quiet", help="be quiet (overrides -v)", action="count", default=0)
self.parser.add_argument("-d", "--img-dir",
help="directory to read for images")
self.parser.add_argument("-o", "--out-dir",
help="directory to place backgrounds in")
self.parser.add_argument("-n", "--bg-name",
help="background name (= filename in background directory)",
default=BGCycler.DEF_BG_NAME)
self.parser.add_argument("-w", "--width",
help="width to resize images to (when required)",
default=BGCycler.DEF_WIDTH)
self.parser.add_argument("-u", "--height",
help="height to resize images to (when required)",
default=BGCycler.DEF_HEIGHT)
self.parser.add_argument("-b", "--brightness",
help="converted image brightness",
default=BGCycler.DEF_BRIGHTNESS)
self.parser.add_argument("-c", "--contrast",
help="converted image contrast",
default=BGCycler.DEF_CONTRAST)
self.parser.add_argument("images", nargs="*",
help="images to use (instead of picking randomly)")
self.args = self.parser.parse_args()
self.args.verbose += 1 # force some verbosity
self._parse_verbosity()
if self.args.img_dir is not None:
self.img_dir = Path(self.args.img_dir)
else:
self.img_dir = BGCycler.DEF_IMG_DIR
if self.args.out_dir is not None:
self.out_dir = Path(self.args.out_dir)
else:
self.out_dir = BGCycler.DEF_OUT_DIR
## }}}
def create_background(self, bg_name, image, brightness, contrast, width, height):
self.logger.info("{} -> {}".format(bg_name, image))
background_path = Path(self.out_dir, bg_name+".png")
cmd_convert_img = ["convert", str(image), "-brightness-contrast", "{}x{}".format(brightness, contrast)]
if width == "0" or height == "0":
pass
else:
cmd_convert_img += [
"-gravity", "center",
"-crop", "{}x{}+0+0".format(width, height)]
cmd_convert_img.append(str(background_path))
self.logger.debug("cmd: {}".format(" ".join(cmd_convert_img)))
ret = self.drop_to_shell(cmd_convert_img)
if ret != 0:
self.fail("failed converting for background {}, image: {}".format(bg_name, image), BGCycler.ERR_IMG_CONVERSION_FAILED)
def main(self):
"""Main entrypoint after program setup."""
imgs = [x for x in self.img_dir.glob("**/*") if x.is_file()]
random.shuffle(imgs)
for img in self.args.images:
imgs.append(img)
if len(imgs) == 0:
self.fail("no images available", BGCycler.ERR_IMG_FILE_ERROR)
self.create_background(self.args.bg_name, imgs.pop(), self.args.brightness, self.args.contrast, self.args.width, self.args.height)
if __name__ == "__main__":
program = BGCycler()
program.run()
|
[
"thefirstmuffinman@gmail.com"
] |
thefirstmuffinman@gmail.com
|
0291973159aef28b73a1a59386890d16e8dcba96
|
afff833c39b47ce6c0fde5af4d831e068b49d0af
|
/CNN_lab_data.py
|
51b1410ebcf75e70fed9e18d8d8f69c5c7e3a6ad
|
[] |
no_license
|
xzang/Deep-learning-for-sonar-images
|
21ed1c2f46f1edaadb9342976cb89c37432083b0
|
1a8d09f8ef68a7c683423f3d4f553da7076f50ce
|
refs/heads/master
| 2020-10-01T05:43:10.133989
| 2020-07-13T03:13:43
| 2020-07-13T03:13:43
| 227,470,507
| 7
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,955
|
py
|
# -*- coding: utf-8 -*-
"""
Written by Tim Yin and Xiaoqin Zang
Last editted by Xiaoqin Zang on Nov. 8, 2019
"""
from __future__ import print_function
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import imageio
import glob
import numpy as np
from sklearn.model_selection import RepeatedKFold
######## read eel data
eel_array = np.empty((0, 104, 104))
for im_path in glob.glob("C:/Users/****/Documents/sonar_camera/coding/eel/eel_diff_wave/*.png"):
result = np.zeros((104, 104))
im = imageio.imread(im_path)
result[22:(im.shape[0]+22),22:(im.shape[1]+22)] = im
eel_array = np.append(eel_array, [result], axis=0)
#eel_array.shape
######### downsample
eel_array = eel_array[0::7]
########## read short stick data
stick_array_short = np.empty((0, 104, 104))
for im_path in glob.glob("C:/Users/****/Documents/sonar_camera/coding/sti_diff_wave_short/*.png"):
result = np.zeros((104, 104))
im = imageio.imread(im_path)
result[22:(im.shape[0]+22),22:(im.shape[1]+22)] = im
stick_array_short = np.append(stick_array_short, [result], axis=0)
#stick_array_short.shape
stick_array_short = stick_array_short[0::7]
stick_array = stick_array_short
eel_size = eel_array.shape[0]
stick_size = stick_array.shape[0]
all_array = np.concatenate((eel_array, stick_array), axis=0)
y = np.repeat(np.array([1, 0], dtype=np.int64), [eel_size, stick_size], axis=0)
##### shuffle the dataset
shuffle_ix_1 = list(range(len(all_array)))
np.random.seed(567)
np.random.shuffle(shuffle_ix_1)
all_array = all_array[shuffle_ix_1]
y = y[shuffle_ix_1]
seed = 987
np.random.seed(seed)
# ten-fold cross validation
kfold = RepeatedKFold(n_splits=10, n_repeats=5, random_state=seed)
cvscores = []
batch_size = 32 #64 if the dataset is bigger
num_classes = 1
epochs = 5
# input image dimensions
img_rows, img_cols = 104, 104
for train, test in kfold.split(all_array, y):
x_train = all_array[train]
y_train = y[train]
x_test = all_array[test]
y_test = y[test]
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
model = Sequential()
model.add(Conv2D(32, kernel_size=(5, 5),
activation='relu',
input_shape=input_shape, padding='valid'))
model.add(Conv2D(64, (5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='sigmoid'))
model.compile(loss=keras.losses.binary_crossentropy,
optimizer=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),
metrics=['accuracy'])
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose = 0,
shuffle = 1,
#validation_data=(x_test, y_test)
)
scores = model.evaluate(x_test, y_test)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
cvscores.append(scores[1] * 100)
print("%.2f%% (+/- %.2f%%)" % (np.mean(cvscores), np.std(cvscores)))
|
[
"noreply@github.com"
] |
noreply@github.com
|
144d3955135b79d4c7c8c739e6e9fb378ce90c42
|
7adc922990876440a10de51c2f210cdbc12fc5a6
|
/LogicaUna.py
|
33fed1b62cbd3bc9feb68afb4cae4d37094ed15a
|
[] |
no_license
|
Asigeo/termostatoasigeo6z
|
db064c60bb91690116ee4d59f70289e2b128730b
|
d18f1e9afaf4ae7b670590729b6e41efd7bacca2
|
refs/heads/master
| 2023-09-01T13:14:22.552509
| 2021-10-29T08:49:47
| 2021-10-29T08:49:47
| 381,743,151
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 15,388
|
py
|
import json
from time import sleep
from Relay_Module import Relees
# funcionando = 0 parar
# funcionando = 1 abriendo largo
# funcionando = 2 abriendo corto
# funcionando = 3 cerrando largo
reles = Relees()
def curva(x, b, m):
y = b - m * x
return y
class LogicaZonaDirecta:
def __init__(self,rele):
self.t_amb = 20
self.consigna = 20
self.modo = 'invierno'
self.rele = rele
def logica(self,modo):
funcionando = 0
if modo == 'invierno':
if self.consigna > self.t_amb:
funcionando = 1
reles.relayon(self.rele)
else:
reles.relayoff(self.rele)
funcionando = 0
elif modo == 'verano':
if self.consigna < self.t_amb:
reles.relayon(self.rele)
funcionando = 1
else:
reles.relayoff(self.rele)
funcionando = 0
else:
reles.relayoff(self.rele)
funcionando = 0
return funcionando
class LogicaZona:
TEMP_LARGA = 6
TEMP_CORTA = 2
def __init__(self, zona):
# seguridades
self.zona = zona
self.sched = False
self.temporizador = 0
self.modo_bomba = False # False = Continua True = Termostato
self.grado_confort = 0
self.modo_curva = 0 # 0 modo normal #1 modo intenso # 2 reducido
self.sonda_exterior = 0
self.sonda_ambiente = 0
self.sonda_agua = 0
self.sonda_suelo = 0
self.funcionando = 0
self.invierno = True
self.verano = False
self.antihielo = False
self.consigna = 20
with open('/home/pi/ASIGEO/json_f/seguridades.json', 'r') as file:
seguridades = json.load(file)
# seguridades
if zona == 1:
self.inv_tmax_agua = seguridades["inv_tmax_agua1"]
self.inv_tmax_suelo = seguridades["inv_tmax_suelo1"]
self.inv_tmin_suelo = seguridades["inv_tmin_suelo1"]
self.ver_tmin_agua = seguridades["ver_tmin_agua1"]
self.ver_tmin_suelo = seguridades["ver_tmin_suelo1"]
self.ant_tmax_agua = seguridades["ant_tmax_agua1"]
self.ant_tmin_agua = seguridades["ant_tmin_agua1"]
self.ant_tmin_suelo = seguridades["ant_tmin_suelo1"]
elif zona == 2:
self.inv_tmax_agua = seguridades["inv_tmax_agua2"]
self.inv_tmax_suelo = seguridades["inv_tmax_suelo2"]
self.inv_tmin_suelo = seguridades["inv_tmin_suelo2"]
self.ver_tmin_agua = seguridades["ver_tmin_agua2"]
self.ver_tmin_suelo = seguridades["ver_tmin_suelo2"]
self.ant_tmax_agua = seguridades["ant_tmax_agua2"]
self.ant_tmin_agua = seguridades["ant_tmin_agua2"]
self.ant_tmin_suelo = seguridades["ant_tmin_suelo2"]
self.ant_tmin_ext = seguridades["ant_tmin_ext"]
self.ver_tmin_ext = seguridades["ver_tmin_ext"]
def bool_mod(self, modoact):
if modoact == "invierno":
self.verano = False
self.antihielo = False
self.invierno = True
elif modoact == "verano":
self.antihielo = False
self.invierno = False
self.verano = True
elif modoact == "antihielo":
self.invierno = False
self.verano = False
self.antihielo = True
def seguridad(self):
reles.seguridad()
def act_seguridades(self):
with open('/home/pi/ASIGEO/json_f/seguridades.json', 'r') as file:
seguridades = json.load(file)
# seguridades
if self.zona == 1:
self.inv_tmax_agua = seguridades["inv_tmax_agua1"]
self.inv_tmax_suelo = seguridades["inv_tmax_suelo1"]
self.inv_tmin_suelo = seguridades["inv_tmin_suelo1"]
self.ver_tmin_agua = seguridades["ver_tmin_agua1"]
self.ver_tmin_suelo = seguridades["ver_tmin_suelo1"]
self.ant_tmax_agua = seguridades["ant_tmax_agua1"]
self.ant_tmin_agua = seguridades["ant_tmin_agua1"]
self.ant_tmin_suelo = seguridades["ant_tmin_suelo1"]
elif self.zona == 2:
self.inv_tmax_agua = seguridades["inv_tmax_agua2"]
self.inv_tmax_suelo = seguridades["inv_tmax_suelo2"]
self.inv_tmin_suelo = seguridades["inv_tmin_suelo2"]
self.ver_tmin_agua = seguridades["ver_tmin_agua2"]
self.ver_tmin_suelo = seguridades["ver_tmin_suelo2"]
self.ant_tmax_agua = seguridades["ant_tmax_agua2"]
self.ant_tmin_agua = seguridades["ant_tmin_agua2"]
self.ant_tmin_suelo = seguridades["ant_tmin_suelo2"]
self.ant_tmin_ext = seguridades["ant_tmin_ext"]
self.ver_tmin_ext = seguridades["ver_tmin_ext"]
def logica(self, modo):
print(self.sonda_exterior, self.sonda_ambiente, self.sonda_agua, self.sonda_suelo)
self.bool_mod(modo)
calor = self.invierno
frio = self.verano
antih = self.antihielo
if calor and not frio and not antih:
reles.relayon(8) # Encender ZONA BAÑO
tmaxAgua = self.sonda_agua > self.inv_tmax_agua
tminSuelo = self.sonda_suelo < self.inv_tmin_suelo
tmaxSuelo = self.sonda_suelo > self.inv_tmax_suelo
seguridadmax = (calor and tmaxAgua) or (calor and tmaxSuelo)
termostato = self.sonda_ambiente < self.consigna - self.grado_confort
print("Termostato",self.zona,termostato)
if self.modo_curva == 0:
m = 1
b = 40 # modo normal
elif self.modo_curva == 1:
m = 1.5
b = 50 # modo intenso
elif self.modo_curva == 2:
m = 0.8
b = 36 # modo reducido
consigna = curva(self.sonda_exterior, b, m)
diferencial = consigna - self.sonda_agua
print("Curva= " + str(consigna))
print("Diferencial= " + str(diferencial))
pideLargo = diferencial > 6
pideCorto = 1 < diferencial <= 6
tempCorrecta = -1 <= diferencial <= 1
excesoCalor = diferencial < -1
# LOGICA DE APERTURAS
B006 = termostato and not seguridadmax and pideLargo
B012 = tminSuelo and not seguridadmax
B007 = termostato and not seguridadmax and pideCorto
B014 = B006 or B012 # activa Temporizacion larga
B018 = B007 and not B014 # Activa temporizacion corta
# LOGICA DE CIERRES
B022 = not termostato and not tminSuelo
B024 = excesoCalor and not tminSuelo
B025 = B022 or B024 or seguridadmax # ACTIVA TEMPORIZACION LARGA
# Logica de parada
B019 = tempCorrecta and not tminSuelo and not B025 and not (B014 or B018) # Temperatura correcta
if not self.modo_bomba or (self.modo_bomba and (B014 or B018)):
reles.abrir_bomba(self.zona)
else:
reles.cerrar_bomba(self.zona)
if B019:
reles.parar_zona(self.zona)
self.temporizador = 0
sleep(5)
self.funcionando = 0
elif B014:
reles.abrir_zona(self.zona)
if self.funcionando == 2: # antes estaba abriendo corto
self.temporizador = 0
# activo rele
elif self.funcionando == 3:
self.temporizador = 0
else: # funcionando = 1 o funcionando = 0
self.temporizador += 1
if self.temporizador == self.TEMP_LARGA:
reles.parar_zona(self.zona)
self.temporizador = 0
self.funcionando = 1
sleep(5)
elif B018: # abrir corto
reles.abrir_zona(self.zona)
if self.funcionando == 1: # antes estaba abriendo largo
self.temporizador = 0
# activo rele
#print("Abriendo")
sleep(5)
elif self.funcionando == 3:
self.temporizador = 0
sleep(5)
else: # funcionando = 2 o 0
self.temporizador += 1
sleep(5)
if self.temporizador == self.TEMP_CORTA:
reles.parar_zona(self.zona)
sleep(5)
self.temporizador = 0
self.funcionando = 2
elif B025: # Cerrar largo
reles.cerrar_zona(self.zona)
if (self.funcionando == 1) or (self.funcionando == 2):
#print("Paro abrir y empiezo a cerrar") # cierro CH1 y abro CH2
self.temporizador = 0
sleep(5)
else: # Antes ya estaba cerrando o parado
#print("cerrando")
self.temporizador += 1
sleep(5)
if self.temporizador == 6:
reles.parar_zona(self.zona) # paro de cerrar
print("Paro 5 seg")
sleep(5)
self.temporizador = 0
#print("Enciendo relee CH2") # Abro relee CH2
self.funcionando = 3
else:
print('La has liao primo')
elif frio and not calor and not antih: # modo verano
reles.relayoff(8)
tmin_ext = self.sonda_exterior < self.ver_tmin_ext
tmin_agua = self.sonda_agua < self.ver_tmin_agua
tmin_suelo = self.sonda_suelo < self.ver_tmin_suelo
termostato = self.sonda_ambiente > self.consigna
seg_verano = frio and (tmin_ext or tmin_agua or tmin_suelo)
# LOGICA CIERRE
cerrar = not termostato or seg_verano
abrir = not seg_verano and termostato and not cerrar
if not self.modo_bomba or (self.modo_bomba and abrir):
reles.abrir_bomba(self.zona)
else:
reles.cerrar_bomba(self.zona)
if cerrar:
reles.cerrar_zona(self.zona)
if (self.funcionando == 1) or (self.funcionando == 2):
print("Paro abrir y empiezo a cerrar") # cierro CH1 y abro CH2
self.temporizador = 0
sleep(5)
else: # Antes ya estaba cerrando
#print("cerrando")
self.temporizador += 1
if self.temporizador == 6:
reles.parar_zona(self.zona)
print("Paro 5 seg")
sleep(5)
self.temporizador = 0
#print("Enciendo relee CH2") # Abro relee CH2
else:
sleep(5)
self.funcionando = 3
elif abrir:
reles.abrir_zona(self.zona)
if self.funcionando == 1: # antes estaba abriendo largo
print("Paro abriendo largo, abro corto") # cambio temporizador
self.temporizador = 0
# activo rele
#print("Abriendo")
sleep(5)
elif self.funcionando == 3:
#print("Paro cerrar, abro corto") # cierro CH2, abro CH1
self.temporizador = 0
sleep(5)
else: # funcionando = 2
#print("Sigo abriendo")
self.temporizador += 1
if self.temporizador == self.TEMP_CORTA:
reles.parar_zona(self.zona)
#print("Paro relee") # cierro relee CH1
sleep(5)
self.temporizador = 0
#print("Enciendo relee CH1") # Abro relee CH1
else:
sleep(5)
self.funcionando = 2
elif antih and not calor and not frio:
reles.relayoff(8)
tmax_agua = self.sonda_agua > self.ant_tmax_agua
tmin_agua = self.sonda_agua < self.ant_tmin_agua
tmin_suelo = self.sonda_suelo < self.ant_tmin_suelo
tmin_ext = self.sonda_exterior < self.ant_tmin_ext
abrir = tmin_agua or (tmin_ext and not tmin_agua) or (tmin_suelo and not tmax_agua)
if not self.modo_bomba or (self.modo_bomba and abrir):
reles.abrir_bomba(self.zona)
else:
reles.cerrar_bomba(self.zona)
if abrir:
reles.abrir_zona(1)
if self.funcionando == 3:
self.temporizador = 0
sleep(5)
else: # antes ya estaba abriendo
self.temporizador += 1
if self.temporizador == self.TEMP_CORTA:
reles.parar_zona(self.zona)
sleep(5)
self.temporizador = 0
else:
sleep(5)
self.funcionando = 2
else:
reles.cerrar_zona(self.zona)
if self.funcionando == 2:
self.temporizador = 0
sleep(5)
else: # antes ya estaba cerrando
self.temporizador += 1
if self.temporizador == self.TEMP_LARGA:
reles.parar_zona(self.zona)
sleep(5)
self.temporizador = 0
else:
sleep(5)
self.funcionando = 3
return self.funcionando
class ZonaDirecta:
def __init__(self):
self.modo = "invierno"
self.invierno = True
self.verano = False
self.antihielo = False
reles = Relees()
self.consigna = 20
self.sonda_ambiente = 20
def bool_mod(self, modoact):
if modoact == "invierno":
self.verano = False
self.antihielo = False
self.invierno = True
elif modoact == "verano":
self.antihielo = False
self.invierno = False
self.verano = True
elif modoact == "antihielo":
self.invierno = False
self.verano = False
self.antihielo = True
def logica(self, modo):
print(self.sonda_exterior, self.sonda_ambiente, self.sonda_agua, self.sonda_suelo)
self.bool_mod(modo)
calor = self.invierno
frio = self.verano
antih = self.antihielo
if calor:
if self.consigna > self.sonda_ambiente:
reles.abrir_bomba(3)
else:
reles.cerrar_bomba(3)
elif frio:
if self.consigna < self.sonda_ambiente:
reles.abrir_bomba(3)
else:
reles.cerrar_bomba(3)
sleep(5)
|
[
"jgonzalez@inartecnologias.es"
] |
jgonzalez@inartecnologias.es
|
f368aa90bf033d4dd4877e3d40accfd27087b568
|
8989fdb46823a6d393f4dc6880f7a3a3bc704d5b
|
/test/unit/test_fixed_population.py
|
cb38bc204dfb4e371ed05f2b5e9a62cafc9befac
|
[] |
no_license
|
giovannic/idmodels
|
9aea3d8f490621a149a5b625b6f5a81e634cbc49
|
7f6e0edbdbe377c0016b5da7b74999216ed6f013
|
refs/heads/master
| 2022-07-11T05:38:02.689348
| 2019-08-20T19:47:37
| 2019-08-20T19:47:37
| 203,024,925
| 0
| 0
| null | 2022-06-21T22:32:30
| 2019-08-18T15:43:48
|
Python
|
UTF-8
|
Python
| false
| false
| 1,643
|
py
|
import unittest
from core.models.population import FixedPopulationModel
class FixedPopulationTestCase(unittest.TestCase):
def test_cannot_create_invalid_compartments(self):
population = FixedPopulationModel()
population.initialise_expressions(1)
with self.assertRaises(ValueError):
population.create_compartment('susceptable', initial=-200)
def test_will_transition_states(self):
population = FixedPopulationModel()
s = population.create_compartment('susceptable', initial=200)
i = population.create_compartment('infected', initial=200)
population.initialise_expressions(1)
population.move(s, i, 10)
self.assertEqual(s.size.value, 190)
self.assertEqual(i.size.value, 210)
def test_will_transition_correctly_at_zero(self):
population = FixedPopulationModel()
s = population.create_compartment('susceptable', initial=200)
i = population.create_compartment('infected', initial=200)
population.initialise_expressions(1)
population.move(s, i, 300)
self.assertEqual(s.size.value, 0)
self.assertEqual(i.size.value, 400)
def test_reports_the_correct_size(self):
population = FixedPopulationModel()
s = population.create_compartment('susceptable', initial=200)
i = population.create_compartment('infected', initial=100)
population.initialise_expressions(1)
self.assertEqual(population.size.evaluate(), 300)
#TODO: implement test for simulation size of 2
def test_will_transition_correctly_for_multiple_simulations(self):
pass
|
[
"giovanni.charles@gmail.com"
] |
giovanni.charles@gmail.com
|
bd048537d034b6848abcda59d8df2095488bf975
|
db258fdb98d36eef012f527f05270cb1eab8b5bf
|
/ListeNomB.py
|
aa6bf01ed99a91cb379897800f5eefe06ae13c23
|
[] |
no_license
|
lisalam/Code_VRD
|
31440e4b4897deb705c578c1c9557b9160ffea4c
|
bda284754b1095cea07bbe231f53448dcb67d2d7
|
refs/heads/master
| 2021-01-10T08:10:12.278550
| 2013-05-15T13:59:23
| 2013-05-15T13:59:23
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,497
|
py
|
#!/usr/bin/python
# -*- coding: iso-8859-15 -*-
import os, sys
import javax.swing as swing
import java.awt as awt
from javax.swing import BorderFactory
from javax.swing.border import EtchedBorder, TitledBorder
from java.awt import Font
from java.awt import TextField, Panel, GridLayout, ComponentOrientation, Label, Checkbox, BorderLayout, Button, Color, FileDialog, Frame, Font
import sys
import os
import time
import glob
import os.path as path
import getpass
import shutil
import random
import math
username=getpass.getuser()
mypath=os.path.expanduser(os.path.join("~","Dropbox","Macros_Lisa","Code_VRD"))
sys.path.append(mypath)
from org.python.core import codecs
codecs.setDefaultEncoding('utf-8')
class ListeNomB(swing.JFrame):
def __init__(self, listnomb):
swing.JFrame.__init__(self, title="Nom de boite")
self.setDefaultCloseOperation(swing.JFrame.DISPOSE_ON_CLOSE)
self.__listnomb = listnomb
self.run()
def run(self):
self.size = (200, 400)
self.contentPane.layout = awt.BorderLayout()
line = BorderFactory.createEtchedBorder(EtchedBorder.LOWERED)
Panel1=swing.JPanel(awt.FlowLayout(awt.FlowLayout.CENTER))
Panel1.setBorder(line)
label=swing.JLabel("")
label.setText("Liste des noms de boites")
Panel1.add(label)
Panel2=swing.JPanel(awt.FlowLayout(awt.FlowLayout.CENTER))
Panel2.setBorder(line)
self.__displistnomb = swing.JList(self.__listnomb)
Panel2.add(self.__displistnomb)
barre = swing.JScrollPane(self.__displistnomb)
Panel2.add(barre)
Panel3=swing.JPanel(awt.FlowLayout(awt.FlowLayout.RIGHT))
Panel3.setBorder(line)
select = swing.JButton("Select", actionPerformed=self.__select)
Panel3.add(select)
close = swing.JButton("Close", size=(100, 70), actionPerformed=self.__close)
Panel3.add(close)
self.contentPane.add(Panel1, awt.BorderLayout.NORTH)
self.contentPane.add(Panel2, awt.BorderLayout.CENTER)
self.contentPane.add(Panel3, awt.BorderLayout.SOUTH)
def __select(self, event):
print self.__listnomb.getSelectedValues()
def __close(self, event):
time.sleep(0.01)
self.dispose()
if __name__ == "__main__":
listnomb=[]
nom1 = ("20130219_120830_632")
nom2 = ("20130219_140840_141")
nom3 = ("20130220_104435_275")
nom4 = ("20130227_102727_525")
listnomb=[nom1,nom2,nom3,nom4]
ens=set()
listnomb=list(ens)
nomb = ListeNomB(listnomb)
nomb.show()
|
[
"lisalamasse@gmail.com"
] |
lisalamasse@gmail.com
|
eeedc6e06be66be4ba83b0914b71cabc517a8dc2
|
ad010f3ecdaa260b2d8732b8b784d58b3c812b9e
|
/spider_admin_pro/config/yaml_config.py
|
a43dc91138192f1c70a92ea9429b25cabd30f721
|
[] |
no_license
|
laashub-soa/spider-admin-pro
|
52261816015afa672176423f38d0206f9bbafa15
|
5faefebd25ad6a163a6a7d18076dc10adba7d970
|
refs/heads/master
| 2023-08-14T01:24:15.659796
| 2021-09-27T04:15:52
| 2021-09-27T04:15:52
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,533
|
py
|
# -*- coding: utf-8 -*-
#################################
# 读取用户自定义变量
#################################
import os
import yaml
from spider_admin_pro.config import env_config
from spider_admin_pro.logger import logger
config_file = os.path.join(os.getcwd(), 'config.yml')
logger.info('config_file: %s', config_file)
if os.path.exists(config_file):
f = open(config_file, "rb")
config = yaml.safe_load(f)
f.close()
else:
config = {}
# flask 服务配置
FLASK_PORT = config.get('PORT', env_config.FLASK_PORT)
FLASK_HOST = config.get('HOST', env_config.FLASK_HOST)
# 登录账号密码
BASIC_AUTH_USERNAME = config.get('USERNAME', env_config.BASIC_AUTH_USERNAME)
BASIC_AUTH_PASSWORD = config.get('PASSWORD', env_config.BASIC_AUTH_PASSWORD)
BASIC_AUTH_JWT_KEY = config.get('JWT_KEY', env_config.BASIC_AUTH_JWT_KEY)
# token过期时间，单位天
BASIC_AUTH_EXPIRES = config.get('EXPIRES', env_config.BASIC_AUTH_EXPIRES)
# scrapyd地址, 结尾不要加斜杆
SCRAPYD_SERVER = config.get('SCRAPYD', env_config.SCRAPYD_SERVER)
# 调度器 调度历史存储设置
# mysql or sqlite and other, any database for peewee support
SCHEDULE_HISTORY_DATABASE_URL = config.get('SCHEDULE_HISTORY_DATABASE_URL',
env_config.SCHEDULE_HISTORY_DATABASE_URL)
# 调度器 定时任务存储地址
JOB_STORES_DATABASE_URL = config.get('JOB_STORES_DATABASE_URL', env_config.JOB_STORES_DATABASE_URL)
# 日志文件夹
LOG_DIR = config.get("LOG_DIR", env_config.LOG_DIR)
|
[
"1940607002@qq.com"
] |
1940607002@qq.com
|
82f573ab57442baca38130076f8b17ddd1163034
|
a665f561b103a51404785f35d0026c60f0083cb4
|
/0x05-python-exceptions/101-safe_function.py
|
38683ee508361b035c621dad79ea63525fad197f
|
[] |
no_license
|
Joshua-Enrico/holbertonschool-higher_level_programming
|
c5f3c9ab55167ea2e7ea3b31dd8edf2e22a18bde
|
8c1559f9c772b60186e899e17c67d299f88de726
|
refs/heads/main
| 2023-07-31T17:45:16.723947
| 2021-09-23T00:29:25
| 2021-09-23T00:29:25
| 361,960,411
| 1
| 5
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 235
|
py
|
#!/usr/bin/python3
def safe_function(fct, *args):
try:
div = fct(*args)
return div
except Exception as error:
import sys
print("Exception: {}".format(error), file=sys.stderr)
return None
|
[
"tmrfack@gmail.com"
] |
tmrfack@gmail.com
|
2bf35fe27a37fa9abcd436ee6d574981f541f759
|
26e3146ce5f3b27ae867aa7238f377b109e7a0e3
|
/bot_4.py
|
a11e1ff499174f3c4c11f3d20bf6a15cf43fa020
|
[] |
no_license
|
Mattix-M/chatbot1
|
1fa64512facc406a68d6ee797dc7d8c3d174300a
|
9eb9226fb8cde5186895c39d753fe16d56b32d13
|
refs/heads/master
| 2022-07-10T20:19:22.882513
| 2020-05-12T15:02:05
| 2020-05-12T15:02:05
| 263,369,140
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,393
|
py
|
import datetime
import vk_api
from vk_api.bot_longpoll import VkBotLongPoll, VkBotEventType
import random
vk_session = vk_api.VkApi(
token=TOKEN)
longpoll = VkBotLongPoll(vk_session, GROUP_ID)
vk = vk_session.get_api()
day = {0: 'понедельник', 1: 'вторник', 2: 'среда', 3: 'четверг',
4: 'пятница', 5: 'суббота', 6: 'воскресенье'}
def weekday_response(request_data):
date = datetime.datetime.strptime(request_data, '%Y-%m-%d').weekday()
return day[date]
def help():
return f"Write the date in the format 'YYYY-MM-DD' and I will say the day of the week."
def main():
flag_data, flag_help = False, True
for event in longpoll.listen():
if event.type == VkBotEventType.MESSAGE_NEW and flag_help:
flag_data = not flag_data
flag_help = not flag_help
vk.messages.send(user_id=event.obj.message['from_id'],
message=help(),
random_id=random.randint(0, 2 ** 64))
elif event.type == VkBotEventType.MESSAGE_NEW and flag_data:
vk.messages.send(user_id=event.obj.message['from_id'],
message=f"{weekday_response(event.obj.message['text'])}\n\n{help()}",
random_id=random.randint(0, 2 ** 64))
if __name__ == '__main__':
main()
|
[
"noreply@github.com"
] |
noreply@github.com
|
210436a93012629a7cc0c39ff7b3fac81b5b5194
|
8abb66b1961a387000f0aed0de6914d7ccf3b7d4
|
/UDF.py
|
66d45e54728fd8289d840dab96826c02a8f82ec7
|
[] |
no_license
|
elliottower/ValleyBikeProject
|
0af2ef9ed11994375f133e782a4f178a5bcf3634
|
1c61643529fa2b3876080957cc506626751c8a67
|
refs/heads/master
| 2020-09-09T01:35:05.932607
| 2019-12-18T06:31:19
| 2019-12-18T06:31:19
| 221,303,902
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,374
|
py
|
#!/usr/bin/python
from collections import Counter
import pandas as pd
import random
import math
# Import stations spreadsheet
df = pd.read_csv("AmherstBikeStations.csv",
sep=",",
header=0)
''' S = Set of Stations '''
S = [] # can do it as a list too but counters are nice and make things easier
# S = Counter()
for i in range(len(df['Station Name'])): # should be
S.append(df.iloc[i, 1])
# S[i] = df.iloc[i, 1]
#print(S[i])
# max(s) = max number of bikes that fit in a station- (given by data)
max = Counter()
for i in range(len(df['Station Name'])):
max[S[i]] = df.iloc[i, 2]
#print "max[%s]" % S[i], max[S[i]]
# min[i] = 10 # Just to test
# just arbitrarily say it's 2 so there's at least
# Import rental/return demand spreadsheet
routes11 = pd.read_csv("Valley_Routes_Report_September_2019_UMass_9_11.csv",
sep=",",
header=0)
routes19 = pd.read_csv("Valley_Routes_Report_September_2019_UMass_9_19.csv",
sep=",",
header=0)
routes30 = pd.read_csv("Valley_Routes_Report_September_2019_UMass_9_30.csv",
sep=",",
header=0)
days = [routes11, routes19, routes30]
# Calculate rental demand
def computeRentalDemand(station): #using lists
demand = [0] * 24 # start out at zero for every hour of the day
for day in days: # for all 3 days
for j in range(24): # loop through every hour of the day
for i in range(len(day['Start'])): # loop through all the lines in the spreadsheet
demand_time = day.iloc[i, 1] # column number 1 (2nd col)
if demand_time == j:
if day.iloc[i, 10] == station:
demand[j] += 1
newDemand = [float(x) / len(days) for x in demand]
demand = newDemand
return demand
def computeReturnDemand(station): #using lists
demand = [0] * 24 # start out at zero for every hour of the day
for day in days: # for all 3 days
for j in range(24): # loop through every hour of the day
for i in range(len(day['Start'])): # loop through all the lines in the spreadsheet
demand_time = day.iloc[i, 3] # column number 1 (2nd col)
if demand_time == j:
if day.iloc[i, 11] == station:
demand[j] += 1
newDemand = [float(x) / len(days) for x in demand]
demand = newDemand
return demand
def computeUDF(station, rental_demand, return_demand, interval_length):
number_iterations = 30
solution = [0] * (max[station] + 1)
for repetition in range(number_iterations): # repeat 30 times, 30 simulations
bikes_present = range(max[station]+1)
arrivals = []
k = 0
while k < len(rental_demand):
next_arrival = 0
while next_arrival < interval_length[k]:
current_rental_demand = rental_demand[k]
current_return_demand = return_demand[k]
next_arrival += math.exp(1/(current_rental_demand + current_return_demand + 0.01))
#print "k: ", k
#print "rental_demand[k]", rental_demand[k]
#print "interval_length[k]", interval_length[k]
if next_arrival < interval_length[k]:
arrivals.append(flip(current_rental_demand / (current_rental_demand + current_return_demand)))
else:
k += 1
break
### At this point, arrivals should have the list of arrivals over the course of the
### time horizon and we compute the expected number of stock-outs
for initial_bikes in bikes_present:
bikes = initial_bikes # bikes tracks the number of bikes after each arrival
for x in arrivals:
#print "x: ", x
#print "bikes: ", bikes
# - 1 indicates someone taking out a bike (rental demand)
# 1 indicates returning a bike
if (x == -1 and bikes == 0) or (x == 1 and bikes == max[station]):
solution[initial_bikes] += 1.0 / number_iterations
#print "stockout occurred: ", solution[initial_bikes]
# if a stock-out occurs, we count but number of bikes remains same
else:
bikes += x # else, number of bikes changes
return solution
def flip(p):
if random.random() < p:
return 1
else:
return -1
# Driver that computes UDF for each station
def getUDF():
UDF = Counter()
INTERVAL_LENGTH = 60 # 60 minute
for station in S:
rental_demand = computeRentalDemand(station)
return_demand = computeReturnDemand(station)
interval_length = [INTERVAL_LENGTH] * len(return_demand) # needs to be tuples
#print len(return_demand)
#print len(interval_length)
#demand = [rental_demand, return_demand, interval_length]
UDF[station] = computeUDF(station, rental_demand, return_demand, interval_length)
return UDF
def test():
UDF = getUDF()
for s in S:
print "Station: ", s, UDF[s]
print "UDF[s][1]", UDF[s][1]
|
[
"etower@umass.edu"
] |
etower@umass.edu
|
c331620af8d90516d5621093dfcd5f6600132054
|
b8e70677484e357d941468b7b6335e2cdc32917b
|
/svenweb/lib.py
|
e4bbd2fae38f0094d11806fd7f937c79001735a2
|
[] |
no_license
|
socialplanning/svenweb
|
b8ee3b095ba7711692fbba2a09c04eab9a1022fa
|
eadef6372d03b9439e64d1e72636304c90cb85f9
|
refs/heads/master
| 2016-09-06T01:18:55.797089
| 2011-05-16T21:13:01
| 2011-05-16T21:13:01
| 1,653,573
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 158
|
py
|
def location(request):
location = '/'.join((request.script_name.rstrip('/'),
request.path_info.lstrip('/')))
return location
|
[
"ethan.jucovy+github@gmail.com"
] |
ethan.jucovy+github@gmail.com
|
d29ecd2dab536aba7307bb95697055dbc30cf2aa
|
711756b796d68035dc6a39060515200d1d37a274
|
/output_cog_tags/initial_3377.py
|
561d811f19c812512cfb3db4c9e030dcd1210575
|
[] |
no_license
|
batxes/exocyst_scripts
|
8b109c279c93dd68c1d55ed64ad3cca93e3c95ca
|
a6c487d5053b9b67db22c59865e4ef2417e53030
|
refs/heads/master
| 2020-06-16T20:16:24.840725
| 2016-11-30T16:23:16
| 2016-11-30T16:23:16
| 75,075,164
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,331
|
py
|
import _surface
import chimera
try:
import chimera.runCommand
except:
pass
from VolumePath import markerset as ms
try:
from VolumePath import Marker_Set, Link
new_marker_set=Marker_Set
except:
from VolumePath import volume_path_dialog
d= volume_path_dialog(True)
new_marker_set= d.new_marker_set
marker_sets={}
surf_sets={}
if "Cog1_Anch" not in marker_sets:
s=new_marker_set('Cog1_Anch')
marker_sets["Cog1_Anch"]=s
s= marker_sets["Cog1_Anch"]
mark=s.place_marker((262, 533, 768), (0, 0, 1), 21.9005)
if "Cog2_GFPN" not in marker_sets:
s=new_marker_set('Cog2_GFPN')
marker_sets["Cog2_GFPN"]=s
s= marker_sets["Cog2_GFPN"]
mark=s.place_marker((911, 601, 823), (1, 0.5, 0), 21.9005)
if "Cog2_GFPC" not in marker_sets:
s=new_marker_set('Cog2_GFPC')
marker_sets["Cog2_GFPC"]=s
s= marker_sets["Cog2_GFPC"]
mark=s.place_marker((932, 878, 424), (1, 0.5, 0), 21.9005)
if "Cog2_Anch" not in marker_sets:
s=new_marker_set('Cog2_Anch')
marker_sets["Cog2_Anch"]=s
s= marker_sets["Cog2_Anch"]
mark=s.place_marker((897, 147, 198), (1, 0.5, 0), 21.9005)
if "Cog3_GFPN" not in marker_sets:
s=new_marker_set('Cog3_GFPN')
marker_sets["Cog3_GFPN"]=s
s= marker_sets["Cog3_GFPN"]
mark=s.place_marker((654, 184, 344), (1, 0.87, 0), 21.9005)
if "Cog3_GFPC" not in marker_sets:
s=new_marker_set('Cog3_GFPC')
marker_sets["Cog3_GFPC"]=s
s= marker_sets["Cog3_GFPC"]
mark=s.place_marker((563, 71, 808), (1, 0.87, 0), 21.9005)
if "Cog3_Anch" not in marker_sets:
s=new_marker_set('Cog3_Anch')
marker_sets["Cog3_Anch"]=s
s= marker_sets["Cog3_Anch"]
mark=s.place_marker((515, 319, 492), (1, 0.87, 0), 21.9005)
if "Cog4_GFPN" not in marker_sets:
s=new_marker_set('Cog4_GFPN')
marker_sets["Cog4_GFPN"]=s
s= marker_sets["Cog4_GFPN"]
mark=s.place_marker((194, 440, 798), (0.97, 0.51, 0.75), 21.9005)
if "Cog4_GFPC" not in marker_sets:
s=new_marker_set('Cog4_GFPC')
marker_sets["Cog4_GFPC"]=s
s= marker_sets["Cog4_GFPC"]
mark=s.place_marker((535, 777, 166), (0.97, 0.51, 0.75), 21.9005)
if "Cog4_Anch" not in marker_sets:
s=new_marker_set('Cog4_Anch')
marker_sets["Cog4_Anch"]=s
s= marker_sets["Cog4_Anch"]
mark=s.place_marker((143, 239, 358), (0.97, 0.51, 0.75), 21.9005)
if "Cog5_GFPN" not in marker_sets:
s=new_marker_set('Cog5_GFPN')
marker_sets["Cog5_GFPN"]=s
s= marker_sets["Cog5_GFPN"]
mark=s.place_marker((320, 498, 370), (0.39, 0.31, 0.14), 21.9005)
if "Cog5_GFPC" not in marker_sets:
s=new_marker_set('Cog5_GFPC')
marker_sets["Cog5_GFPC"]=s
s= marker_sets["Cog5_GFPC"]
mark=s.place_marker((288, 147, 63), (0.39, 0.31, 0.14), 21.9005)
if "Cog5_Anch" not in marker_sets:
s=new_marker_set('Cog5_Anch')
marker_sets["Cog5_Anch"]=s
s= marker_sets["Cog5_Anch"]
mark=s.place_marker((949, 360, 485), (0.39, 0.31, 0.14), 21.9005)
if "Cog6_GFPN" not in marker_sets:
s=new_marker_set('Cog6_GFPN')
marker_sets["Cog6_GFPN"]=s
s= marker_sets["Cog6_GFPN"]
mark=s.place_marker((436, 819, 284), (0.6, 0.31, 0.64), 21.9005)
if "Cog6_GFPC" not in marker_sets:
s=new_marker_set('Cog6_GFPC')
marker_sets["Cog6_GFPC"]=s
s= marker_sets["Cog6_GFPC"]
mark=s.place_marker((44, 825, 43), (0.6, 0.31, 0.64), 21.9005)
if "Cog6_Anch" not in marker_sets:
s=new_marker_set('Cog6_Anch')
marker_sets["Cog6_Anch"]=s
s= marker_sets["Cog6_Anch"]
mark=s.place_marker((11, 479, 395), (0.6, 0.31, 0.64), 21.9005)
if "Cog7_GFPN" not in marker_sets:
s=new_marker_set('Cog7_GFPN')
marker_sets["Cog7_GFPN"]=s
s= marker_sets["Cog7_GFPN"]
mark=s.place_marker((991, 520, 392), (0.89, 0.1, 0.1), 21.9005)
if "Cog7_GFPC" not in marker_sets:
s=new_marker_set('Cog7_GFPC')
marker_sets["Cog7_GFPC"]=s
s= marker_sets["Cog7_GFPC"]
mark=s.place_marker((788, 680, 50), (0.89, 0.1, 0.1), 21.9005)
if "Cog7_Anch" not in marker_sets:
s=new_marker_set('Cog7_Anch')
marker_sets["Cog7_Anch"]=s
s= marker_sets["Cog7_Anch"]
mark=s.place_marker((475, 141, 883), (0.89, 0.1, 0.1), 21.9005)
if "Cog8_GFPC" not in marker_sets:
s=new_marker_set('Cog8_GFPC')
marker_sets["Cog8_GFPC"]=s
s= marker_sets["Cog8_GFPC"]
mark=s.place_marker((184, 381, 961), (0.3, 0.69, 0.29), 21.9005)
if "Cog8_Anch" not in marker_sets:
s=new_marker_set('Cog8_Anch')
marker_sets["Cog8_Anch"]=s
s= marker_sets["Cog8_Anch"]
mark=s.place_marker((694, 467, 322), (0.3, 0.69, 0.29), 21.9005)
for k in surf_sets.keys():
chimera.openModels.add([surf_sets[k]])
|
[
"batxes@gmail.com"
] |
batxes@gmail.com
|
bdde9f777bdf20d7a7c2236af816571c9a9d3bd1
|
5eed367617654c0b1182ad9ddbb4c949e68cde75
|
/main.py
|
a311a9aebdacd0c031e9658eb6f72a06225c100c
|
[] |
no_license
|
nipunatheekshana/jarvis
|
c8d6eb50f34f988216b4f2bbe07a454715dbfda0
|
21f96254e0f346a7e42f60594660f423ad61725c
|
refs/heads/master
| 2023-02-13T14:08:03.410846
| 2021-01-11T19:22:03
| 2021-01-11T19:22:03
| 328,769,009
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,361
|
py
|
import speech_recognition as sr
import pyttsx3
import pywhatkit
import datetime
import wikipedia
import pyjokes
listner = sr.Recognizer()
engine = pyttsx3.init()
def talk(word):
engine.say(word)
engine.runAndWait()
def receive_command():
try:
with sr.Microphone() as source:
talk("yes sir")
voice = listner.listen(source)
command = listner.recognize_google(voice)
command = command.lower()
if 'jarvis' in command:
command = command.replace('jarvis', '')
talk(command)
except:
pass
return command
def run_jarvis():
command = receive_command()
print(command)
if 'play' in command:
song = command.replace('play', '')
talk('playing'+song)
pywhatkit.playonyt(song)
elif 'time' in command:
time = datetime.datetime.now().strftime('%I %M %p') # I is 12 hour format of time
talk('current time is'+time)
elif 'who' in command:
query = command.replace('who', '')
result = wikipedia.summary(query, 1)
print(result)
talk(result)
elif 'joke' in command:
talk(pyjokes.get_joke())
elif 'name' in command:
talk('i am jarvis your personal assistant')
else:
talk('please say again')
while True:
run_jarvis()
|
[
"nipunatheekshana8@gmail.com"
] |
nipunatheekshana8@gmail.com
|
096bc1c7152955fc7efee92dc96b6923843848ec
|
ee41311a11a1c6baedafd9a914d5a1f8330fe8a9
|
/SANEF_LIVE/venv/Lib/site-packages/anaconda_navigator/widgets/tabs/tests/test_environments_tab.py
|
2e4d36bd2647c721b4161cbc2957d1664db066a3
|
[] |
no_license
|
sethnanati/CodeRepoPython
|
2dffb7263620bd905bf694f348485d894a9513db
|
b55e66611d19b35e9926d1b1387320cf48e177c8
|
refs/heads/master
| 2023-07-07T11:16:12.958401
| 2021-02-13T10:09:48
| 2021-02-13T10:09:48
| 376,531,283
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,911
|
py
|
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (c) 2016-2017 Anaconda, Inc.
#
# May be copied and distributed freely only as part of an Anaconda or
# Miniconda installation.
# -----------------------------------------------------------------------------
"""Tests for environments tab."""
# yapf: disable
# Standard library imports
import sys
# Third party imports
from qtpy.QtCore import Qt
import pytest
# Local imports
from anaconda_navigator.api.conda_api import CondaAPI
from anaconda_navigator.utils.fixtures import tmpfile, tmpfolder
from anaconda_navigator.widgets.dialogs import MessageBoxError
from anaconda_navigator.widgets.tabs.environments import EnvironmentsTab
# yapf: enable
tmpfile
tmpfolder
PY3 = sys.version_info >= (3, 4)
xfail = pytest.mark.xfail
@pytest.fixture()
def env_tab(qtbot, tmpfile):
widget = EnvironmentsTab()
qtbot.addWidget(widget)
widget.show()
widget.setup_tab(metadata={})
widget.load_environment()
with qtbot.waitSignal(widget.sig_status_updated) as blocker:
blocker
return widget, qtbot, tmpfile
MessageBoxError.exec_ = lambda *args: True
class TestEnvironmentsTab:
def package_version(self, pkg, name='root'):
api = CondaAPI()
return api.package_version(name=name, pkg=pkg, build=True)
def remove_env(self, widget):
worker = widget.packages_widget.remove_environment(
name='navigatortest'
)
worker.communicate() # run create
@xfail
def test_bad_create(self, env_tab): # analysis:ignore
widget, qtbot, tmpfile = env_tab
with open(tmpfile, 'w') as f:
raw = "name: navigatortest\ndependencies:\n- not-real=0.0.0=py36_0"
f.write(raw)
worker = widget.packages_widget.import_yaml(
name="navigatortest", yaml=tmpfile
)
with qtbot.waitSignal(widget.sig_error_popped_up, timeout=5000):
with qtbot.waitSignal(worker.sig_finished, timeout=5000):
worker.name = "navigatortest"
worker.sig_finished.connect(widget._environment_created)
@xfail
def test_ipython_option(self, env_tab, tmpfolder):
widget, qtbot, tmpfile = env_tab
pyver = 'python={0}'.format(self.package_version('python'))
self.remove_env(widget)
worker = widget.packages_widget.create_environment(
name='navigatortest', packages=[pyver]
)
worker.name = 'navigatortest'
worker.communicate() # run create
widget._environment_created(worker, "", "")
widget.menu_list.exec_ = lambda *args: True
qtbot.mouseClick(
widget.list_environments.currentItem().button_options,
Qt.LeftButton
)
is_action_enabled = widget.menu_list.actions()[2].isEnabled()
assert not is_action_enabled
worker = widget.packages_widget.api.conda_install(
name='navigatortest', pkgs=['jupyter-core']
)
worker.communicate()
qtbot.mouseClick(
widget.list_environments.currentItem().button_options,
Qt.LeftButton
)
assert not widget.menu_list.actions()[2].isEnabled()
worker = widget.packages_widget.api.conda_install(
name='navigatortest', pkgs=['ipython']
)
worker.communicate()
qtbot.mouseClick(
widget.list_environments.currentItem().button_options,
Qt.LeftButton
)
assert widget.menu_list.actions()[2].isEnabled()
worker = widget.packages_widget.remove_environment(
name='navigatortest'
)
worker.communicate() # run create
self.remove_env(widget)
|
[
"adeyemiadenuga@gmail.com"
] |
adeyemiadenuga@gmail.com
|
fc0bbcd096df9fe751b943cfd1fd20e466ee4baf
|
f82757475ea13965581c2147ff57123b361c5d62
|
/gi-stubs/repository/Poppler/PageLayout.py
|
605c3665f4dee50e741e5800be5b7e77e834cdc8
|
[] |
no_license
|
ttys3/pygobject-stubs
|
9b15d1b473db06f47e5ffba5ad0a31d6d1becb57
|
d0e6e93399212aada4386d2ce80344eb9a31db48
|
refs/heads/master
| 2022-09-23T12:58:44.526554
| 2020-06-06T04:15:00
| 2020-06-06T04:15:00
| 269,693,287
| 8
| 2
| null | 2020-06-05T15:57:54
| 2020-06-05T15:57:54
| null |
UTF-8
|
Python
| false
| false
| 13,686
|
py
|
# encoding: utf-8
# module gi.repository.Poppler
# from /usr/lib64/girepository-1.0/Poppler-0.18.typelib
# by generator 1.147
"""
An object which wraps an introspection typelib.
This wrapping creates a python module like representation of the typelib
using gi repository as a foundation. Accessing attributes of the module
will dynamically pull them in and create wrappers for the members.
These members are then cached on this introspection module.
"""
# imports
import gi as __gi
import gi.overrides.GObject as __gi_overrides_GObject
import gobject as __gobject
class PageLayout(__gobject.GEnum):
# no doc
def as_integer_ratio(self): # real signature unknown; restored from __doc__
"""
Return integer ratio.
Return a pair of integers, whose ratio is exactly equal to the original int
and with a positive denominator.
>>> (10).as_integer_ratio()
(10, 1)
>>> (-10).as_integer_ratio()
(-10, 1)
>>> (0).as_integer_ratio()
(0, 1)
"""
pass
def bit_length(self): # real signature unknown; restored from __doc__
"""
Number of bits necessary to represent self in binary.
>>> bin(37)
'0b100101'
>>> (37).bit_length()
6
"""
pass
def conjugate(self, *args, **kwargs): # real signature unknown
""" Returns self, the complex conjugate of any int. """
pass
def from_bytes(self, *args, **kwargs): # real signature unknown
"""
Return the integer represented by the given array of bytes.
bytes
Holds the array of bytes to convert. The argument must either
support the buffer protocol or be an iterable object producing bytes.
Bytes and bytearray are examples of built-in objects that support the
buffer protocol.
byteorder
The byte order used to represent the integer. If byteorder is 'big',
the most significant byte is at the beginning of the byte array. If
byteorder is 'little', the most significant byte is at the end of the
byte array. To request the native byte order of the host system, use
`sys.byteorder' as the byte order value.
signed
Indicates whether two's complement is used to represent the integer.
"""
pass
def to_bytes(self, *args, **kwargs): # real signature unknown
"""
Return an array of bytes representing an integer.
length
Length of bytes object to use. An OverflowError is raised if the
integer is not representable with the given number of bytes.
byteorder
The byte order used to represent the integer. If byteorder is 'big',
the most significant byte is at the beginning of the byte array. If
byteorder is 'little', the most significant byte is at the end of the
byte array. To request the native byte order of the host system, use
`sys.byteorder' as the byte order value.
signed
Determines whether two's complement is used to represent the integer.
If signed is False and a negative integer is given, an OverflowError
is raised.
"""
pass
def __abs__(self, *args, **kwargs): # real signature unknown
""" abs(self) """
pass
def __add__(self, *args, **kwargs): # real signature unknown
""" Return self+value. """
pass
def __and__(self, *args, **kwargs): # real signature unknown
""" Return self&value. """
pass
def __bool__(self, *args, **kwargs): # real signature unknown
""" self != 0 """
pass
def __ceil__(self, *args, **kwargs): # real signature unknown
""" Ceiling of an Integral returns itself. """
pass
def __delattr__(self, *args, **kwargs): # real signature unknown
""" Implement delattr(self, name). """
pass
def __dir__(self, *args, **kwargs): # real signature unknown
""" Default dir() implementation. """
pass
def __divmod__(self, *args, **kwargs): # real signature unknown
""" Return divmod(self, value). """
pass
def __eq__(self, *args, **kwargs): # real signature unknown
""" Return self==value. """
pass
def __float__(self, *args, **kwargs): # real signature unknown
""" float(self) """
pass
def __floordiv__(self, *args, **kwargs): # real signature unknown
""" Return self//value. """
pass
def __floor__(self, *args, **kwargs): # real signature unknown
""" Flooring an Integral returns itself. """
pass
def __format__(self, *args, **kwargs): # real signature unknown
pass
def __getattribute__(self, *args, **kwargs): # real signature unknown
""" Return getattr(self, name). """
pass
def __getnewargs__(self, *args, **kwargs): # real signature unknown
pass
def __ge__(self, *args, **kwargs): # real signature unknown
""" Return self>=value. """
pass
def __gt__(self, *args, **kwargs): # real signature unknown
""" Return self>value. """
pass
def __hash__(self, *args, **kwargs): # real signature unknown
""" Return hash(self). """
pass
def __index__(self, *args, **kwargs): # real signature unknown
""" Return self converted to an integer, if self is suitable for use as an index into a list. """
pass
def __init_subclass__(self, *args, **kwargs): # real signature unknown
"""
This method is called when a class is subclassed.
The default implementation does nothing. It may be
overridden to extend subclasses.
"""
pass
def __init__(self, *args, **kwargs): # real signature unknown
pass
def __int__(self, *args, **kwargs): # real signature unknown
""" int(self) """
pass
def __invert__(self, *args, **kwargs): # real signature unknown
""" ~self """
pass
def __le__(self, *args, **kwargs): # real signature unknown
""" Return self<=value. """
pass
def __lshift__(self, *args, **kwargs): # real signature unknown
""" Return self<<value. """
pass
def __lt__(self, *args, **kwargs): # real signature unknown
""" Return self<value. """
pass
def __mod__(self, *args, **kwargs): # real signature unknown
""" Return self%value. """
pass
def __mul__(self, *args, **kwargs): # real signature unknown
""" Return self*value. """
pass
def __neg__(self, *args, **kwargs): # real signature unknown
""" -self """
pass
@staticmethod # known case of __new__
def __new__(*args, **kwargs): # real signature unknown
""" Create and return a new object. See help(type) for accurate signature. """
pass
def __ne__(self, *args, **kwargs): # real signature unknown
""" Return self!=value. """
pass
def __or__(self, *args, **kwargs): # real signature unknown
""" Return self|value. """
pass
def __pos__(self, *args, **kwargs): # real signature unknown
""" +self """
pass
def __pow__(self, *args, **kwargs): # real signature unknown
""" Return pow(self, value, mod). """
pass
def __radd__(self, *args, **kwargs): # real signature unknown
""" Return value+self. """
pass
def __rand__(self, *args, **kwargs): # real signature unknown
""" Return value&self. """
pass
def __rdivmod__(self, *args, **kwargs): # real signature unknown
""" Return divmod(value, self). """
pass
def __reduce_ex__(self, *args, **kwargs): # real signature unknown
""" Helper for pickle. """
pass
def __reduce__(self, *args, **kwargs): # real signature unknown
pass
def __repr__(self, *args, **kwargs): # real signature unknown
""" Return repr(self). """
pass
def __rfloordiv__(self, *args, **kwargs): # real signature unknown
""" Return value//self. """
pass
def __rlshift__(self, *args, **kwargs): # real signature unknown
""" Return value<<self. """
pass
def __rmod__(self, *args, **kwargs): # real signature unknown
""" Return value%self. """
pass
def __rmul__(self, *args, **kwargs): # real signature unknown
""" Return value*self. """
pass
def __ror__(self, *args, **kwargs): # real signature unknown
""" Return value|self. """
pass
def __round__(self, *args, **kwargs): # real signature unknown
"""
Rounding an Integral returns itself.
Rounding with an ndigits argument also returns an integer.
"""
pass
def __rpow__(self, *args, **kwargs): # real signature unknown
""" Return pow(value, self, mod). """
pass
def __rrshift__(self, *args, **kwargs): # real signature unknown
""" Return value>>self. """
pass
def __rshift__(self, *args, **kwargs): # real signature unknown
""" Return self>>value. """
pass
def __rsub__(self, *args, **kwargs): # real signature unknown
""" Return value-self. """
pass
def __rtruediv__(self, *args, **kwargs): # real signature unknown
""" Return value/self. """
pass
def __rxor__(self, *args, **kwargs): # real signature unknown
""" Return value^self. """
pass
def __setattr__(self, *args, **kwargs): # real signature unknown
""" Implement setattr(self, name, value). """
pass
def __sizeof__(self, *args, **kwargs): # real signature unknown
""" Returns size in memory, in bytes. """
pass
def __str__(self, *args, **kwargs): # real signature unknown
""" Return str(self). """
pass
def __subclasshook__(self, *args, **kwargs): # real signature unknown
"""
Abstract classes can override this to customize issubclass().
This is invoked early on by abc.ABCMeta.__subclasscheck__().
It should return True, False or NotImplemented. If it returns
NotImplemented, the normal algorithm is used. Otherwise, it
overrides the normal algorithm (and the outcome is cached).
"""
pass
def __sub__(self, *args, **kwargs): # real signature unknown
""" Return self-value. """
pass
def __truediv__(self, *args, **kwargs): # real signature unknown
""" Return self/value. """
pass
def __trunc__(self, *args, **kwargs): # real signature unknown
""" Truncating an Integral returns itself. """
pass
def __xor__(self, *args, **kwargs): # real signature unknown
""" Return self^value. """
pass
denominator = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
"""the denominator of a rational number in lowest terms"""
imag = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
"""the imaginary part of a complex number"""
numerator = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
"""the numerator of a rational number in lowest terms"""
real = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
"""the real part of a complex number"""
value_name = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
value_nick = property(lambda self: object(), lambda self, v: None, lambda self: None) # default
ONE_COLUMN = 2
SINGLE_PAGE = 1
TWO_COLUMN_LEFT = 3
TWO_COLUMN_RIGHT = 4
TWO_PAGE_LEFT = 5
TWO_PAGE_RIGHT = 6
UNSET = 0
__class__ = type
__dict__ = None # (!) real value is "mappingproxy({'__module__': 'gi.repository.Poppler', '__dict__': <attribute '__dict__' of 'PageLayout' objects>, '__doc__': None, '__gtype__': <GType PopplerPageLayout (94391899009456)>, '__enum_values__': {0: <enum POPPLER_PAGE_LAYOUT_UNSET of type Poppler.PageLayout>, 1: <enum POPPLER_PAGE_LAYOUT_SINGLE_PAGE of type Poppler.PageLayout>, 2: <enum POPPLER_PAGE_LAYOUT_ONE_COLUMN of type Poppler.PageLayout>, 3: <enum POPPLER_PAGE_LAYOUT_TWO_COLUMN_LEFT of type Poppler.PageLayout>, 4: <enum POPPLER_PAGE_LAYOUT_TWO_COLUMN_RIGHT of type Poppler.PageLayout>, 5: <enum POPPLER_PAGE_LAYOUT_TWO_PAGE_LEFT of type Poppler.PageLayout>, 6: <enum POPPLER_PAGE_LAYOUT_TWO_PAGE_RIGHT of type Poppler.PageLayout>}, '__info__': gi.EnumInfo(PageLayout), 'UNSET': <enum POPPLER_PAGE_LAYOUT_UNSET of type Poppler.PageLayout>, 'SINGLE_PAGE': <enum POPPLER_PAGE_LAYOUT_SINGLE_PAGE of type Poppler.PageLayout>, 'ONE_COLUMN': <enum POPPLER_PAGE_LAYOUT_ONE_COLUMN of type Poppler.PageLayout>, 'TWO_COLUMN_LEFT': <enum POPPLER_PAGE_LAYOUT_TWO_COLUMN_LEFT of type Poppler.PageLayout>, 'TWO_COLUMN_RIGHT': <enum POPPLER_PAGE_LAYOUT_TWO_COLUMN_RIGHT of type Poppler.PageLayout>, 'TWO_PAGE_LEFT': <enum POPPLER_PAGE_LAYOUT_TWO_PAGE_LEFT of type Poppler.PageLayout>, 'TWO_PAGE_RIGHT': <enum POPPLER_PAGE_LAYOUT_TWO_PAGE_RIGHT of type Poppler.PageLayout>})"
__enum_values__ = {
0: 0,
1: 1,
2: 2,
3: 3,
4: 4,
5: 5,
6: 6,
}
__gtype__ = None # (!) real value is '<GType PopplerPageLayout (94391899009456)>'
__info__ = gi.EnumInfo(PageLayout)
|
[
"ttys3@outlook.com"
] |
ttys3@outlook.com
|
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