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int64 18.2k
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| authors
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1
| author
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175
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d4c73b518210b00ff4f14bb7bf1b7d93e8c7e2fd
|
9ed987c683b925b505bf7fa258cdf48d4962b6b0
|
/P0/Sin título0.py
|
8ed1625247aaa31a89582b4455015d450bc21f6c
|
[] |
no_license
|
victory06/AA
|
1789ee9efeb3b9ebc68275262e7eaf8da3d0bdf3
|
1d0bcc9edd4b425ab68145ddcad446bbddf40b77
|
refs/heads/master
| 2023-04-20T15:47:47.876228 | 2021-05-10T07:53:01 | 2021-05-10T07:53:01 | 339,379,562 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 711 |
py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 24 18:02:50 2020
@author: victor
"""
import numpy as np
import matplotlib.pyplot as plt
# Create data
N = 60
g1 = (0.6 + 0.6 * np.random.rand(N), np.random.rand(N))
g2 = (0.4+0.3 * np.random.rand(N), 0.5*np.random.rand(N))
g3 = (0.3*np.random.rand(N),0.3*np.random.rand(N))
data = (g1, g2, g3)
colors = ("red", "green", "blue")
groups = ("coffee", "tea", "water")
# Create plot
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
for data, color, group in zip(data, colors, groups):
x, y = data
ax.scatter(x, y, alpha=0.8, c=color, edgecolors='none', s=30, label=group)
plt.title('Matplot scatter plot')
plt.legend(loc=2)
plt.show()
|
[
"[email protected]"
] | |
7af5765fa5c6edb401f7b850aa3101fbeaafd7f3
|
e825640a5d087fb0e14267111dda4fe010224a3a
|
/multiapp/helpers/multiapp.py
|
b25c3163809f8b7eccf466bcd8226f80406be5db
|
[] |
no_license
|
Nhiemth1985/PyPortfolioAnalytics
|
c2ee64a47fb58654528353cb8796a96eb6ed8d60
|
7f210c98b1dcc6f2a157372b34664d8d163a2874
|
refs/heads/master
| 2023-09-03T15:22:18.493060 | 2021-10-17T18:22:58 | 2021-10-17T18:22:58 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,366 |
py
|
"""Frameworks for running multiple Streamlit applications as a single app.
https://github.com/upraneelnihar/streamlit-multiapps
"""
import streamlit as st
class MultiApp:
"""Framework for combining multiple streamlit applications.
Usage:
def foo():
st.title("Hello Foo")
def bar():
st.title("Hello Bar")
app = MultiApp()
app.add_app("Foo", foo)
app.add_app("Bar", bar)
app.run()
It is also possible keep each application in a separate file.
import foo
import bar
app = MultiApp()
app.add_app("Foo", foo.app)
app.add_app("Bar", bar.app)
app.run()
"""
def __init__(self):
self.apps = []
def add_app(self, title, func):
"""Adds a new application.
Parameters
----------
func:
the python function to render this app.
title:
title of the app. Appears in the dropdown in the sidebar.
"""
self.apps.append({
"title": title,
"function": func
})
def run(self):
st.title('Py Portfolio Analytics')
app = st.selectbox(
'Navigate',
self.apps,
format_func=lambda app: app['title'])
st.write('---')
app['function']()
|
[
"[email protected]"
] | |
c5968b91f1e8556b70007f764784c56df35cfef6
|
2c89037666a3c3c9be55b53055c73aa9fcbde2b7
|
/webrobot/app/main/service/user_service.py
|
1aa181ef641092046126c96166d66c61d9b54523
|
[
"MIT"
] |
permissive
|
kakawaa/Auto-Test-System
|
844284de1eb5fac8fa8c5318371c99991caff62d
|
76b0690e4e49769ec5d6e65ab6c499396880c0bd
|
refs/heads/master
| 2020-06-17T11:42:38.121124 | 2019-07-05T03:32:39 | 2019-07-05T03:32:39 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,451 |
py
|
# import uuid
import datetime
import os
from pathlib import Path
from app.main import db
from app.main.model.database import User, Organization
from task_runner.runner import start_threads
from ..config import get_config
from ..util.errors import *
from ..util.identicon import *
USERS_ROOT = Path(get_config().USERS_ROOT)
def save_new_user(data, admin=None):
user = User.objects(email=data['email']).first()
if not user:
new_user = User(
# public_id=str(uuid.uuid4()),
email=data['email'],
name=data.get('username', ''),
registered_on=datetime.datetime.utcnow(),
roles=data.get('roles', ['admin']),
avatar=data.get('avatar', ''),
introduction=data.get('introduction', '')
)
new_user.password = data['password']
try:
new_user.save()
except Exception as e:
print(e)
return error_message(EINVAL, 'Field validating for User failed'), 401
user_root = USERS_ROOT / data['email']
try:
os.mkdir(user_root)
except FileExistsError as e:
return error_message(EEXIST), 401
try:
os.mkdir(user_root / 'test_results')
except FileExistsError as e:
return error_message(EEXIST), 401
if new_user.avatar == '':
img = render_identicon(hash(data['email']), 27)
img.save(user_root / ('%s.png' % new_user.id))
new_user.avatar = '%s.png' % new_user.id
if new_user.name == '':
new_user.name = new_user.email.split('@')[0]
if not admin:
organization = Organization(name='Personal')
organization.owner = new_user
organization.path = new_user.email
organization.save()
new_user.organizations = [organization]
new_user.save()
start_threads(new_user)
return generate_token(new_user)
else:
return error_message(USER_ALREADY_EXIST), 409
def get_all_users():
return User.objects()
def get_a_user(user_id):
return User.objects(pk=user_id).first()
def generate_token(user):
try:
# generate the auth token
auth_token = User.encode_auth_token(str(user.id))
return error_message(SUCCESS, token=auth_token.decode()), 201
except Exception as e:
print(e)
return error_message(UNKNOWN_ERROR), 401
|
[
"[email protected]"
] | |
c9c4d9cec089efc390d4bc9ec3d75c7bd405ae1b
|
4df17daecb32fd5ec6ae6a76ba6c9f5e58702203
|
/10-06/Factory/Factory_python/Factory.py
|
14f1a6bbc9f1371b216a045f2eda88ee44bc686b
|
[] |
no_license
|
celinalopez/DisenoDeSistemas
|
3ea29db526105a4bd2db44c70c4db8680ce68f1e
|
273fa9f800479285cd0d3e2a3ebe0561bd7469de
|
refs/heads/main
| 2023-08-24T18:23:21.669060 | 2021-11-04T04:46:52 | 2021-11-04T04:46:52 | 409,076,707 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 431 |
py
|
from pizzas import Muzzarella, Especial, Pepperonni, CuatroQuesos
class PizzaFactory():
@staticmethod
def pedir_pizza(tipo_pizza):
if tipo_pizza == 'Muzzarella':
return Muzzarella()
elif tipo_pizza == 'Especial':
return Especial()
elif tipo_pizza == 'Pepperonni':
return Pepperonni()
elif tipo_pizza == 'CuatroQuesos':
return CuatroQuesos()
|
[
"[email protected]"
] | |
ab2ac02cd6537896ff4fb77625eddfaab4018631
|
bc4554057c38800a8e6b69b569c053e35018b6bb
|
/tensorflow/src/main/python/service_pb2.py
|
28c04ccf89d20b07451e71b3ddfecd1ea6dc369d
|
[
"Apache-2.0"
] |
permissive
|
yaozhang2016/deepwater
|
c9c4249371733df38bcb3e9aeb719d23500e6774
|
861a2dbeffeafab83dd53956deeb4f8193b9cb2e
|
refs/heads/master
| 2020-09-30T14:59:21.131376 | 2017-12-28T19:45:59 | 2017-12-28T19:45:59 | 73,511,061 | 0 | 0 |
Apache-2.0
| 2017-12-28T19:46:00 | 2016-11-11T20:59:18 |
C++
|
UTF-8
|
Python
| false | true | 6,555 |
py
|
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: service.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='service.proto',
package='deepwater',
syntax='proto3',
serialized_pb=_b('\n\rservice.proto\x12\tdeepwater\"\r\n\x0bPingRequest\"\x08\n\x06Status2>\n\x07Service\x12\x33\n\x04Ping\x12\x16.deepwater.PingRequest\x1a\x11.deepwater.Status\"\x00\x42!\n\x10\x61i.h2o.deepwaterB\x0bGRPCServiceP\x01\x62\x06proto3')
)
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
_PINGREQUEST = _descriptor.Descriptor(
name='PingRequest',
full_name='deepwater.PingRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=28,
serialized_end=41,
)
_STATUS = _descriptor.Descriptor(
name='Status',
full_name='deepwater.Status',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=43,
serialized_end=51,
)
DESCRIPTOR.message_types_by_name['PingRequest'] = _PINGREQUEST
DESCRIPTOR.message_types_by_name['Status'] = _STATUS
PingRequest = _reflection.GeneratedProtocolMessageType('PingRequest', (_message.Message,), dict(
DESCRIPTOR = _PINGREQUEST,
__module__ = 'service_pb2'
# @@protoc_insertion_point(class_scope:deepwater.PingRequest)
))
_sym_db.RegisterMessage(PingRequest)
Status = _reflection.GeneratedProtocolMessageType('Status', (_message.Message,), dict(
DESCRIPTOR = _STATUS,
__module__ = 'service_pb2'
# @@protoc_insertion_point(class_scope:deepwater.Status)
))
_sym_db.RegisterMessage(Status)
DESCRIPTOR.has_options = True
DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\n\020ai.h2o.deepwaterB\013GRPCServiceP\001'))
import grpc
from grpc.beta import implementations as beta_implementations
from grpc.beta import interfaces as beta_interfaces
from grpc.framework.common import cardinality
from grpc.framework.interfaces.face import utilities as face_utilities
class ServiceStub(object):
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.Ping = channel.unary_unary(
'/deepwater.Service/Ping',
request_serializer=PingRequest.SerializeToString,
response_deserializer=Status.FromString,
)
class ServiceServicer(object):
def Ping(self, request, context):
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_ServiceServicer_to_server(servicer, server):
rpc_method_handlers = {
'Ping': grpc.unary_unary_rpc_method_handler(
servicer.Ping,
request_deserializer=PingRequest.FromString,
response_serializer=Status.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'deepwater.Service', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
class BetaServiceServicer(object):
"""The Beta API is deprecated for 0.15.0 and later.
It is recommended to use the GA API (classes and functions in this
file not marked beta) for all further purposes. This class was generated
only to ease transition from grpcio<0.15.0 to grpcio>=0.15.0."""
def Ping(self, request, context):
context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)
class BetaServiceStub(object):
"""The Beta API is deprecated for 0.15.0 and later.
It is recommended to use the GA API (classes and functions in this
file not marked beta) for all further purposes. This class was generated
only to ease transition from grpcio<0.15.0 to grpcio>=0.15.0."""
def Ping(self, request, timeout, metadata=None, with_call=False, protocol_options=None):
raise NotImplementedError()
Ping.future = None
def beta_create_Service_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None):
"""The Beta API is deprecated for 0.15.0 and later.
It is recommended to use the GA API (classes and functions in this
file not marked beta) for all further purposes. This function was
generated only to ease transition from grpcio<0.15.0 to grpcio>=0.15.0"""
request_deserializers = {
('deepwater.Service', 'Ping'): PingRequest.FromString,
}
response_serializers = {
('deepwater.Service', 'Ping'): Status.SerializeToString,
}
method_implementations = {
('deepwater.Service', 'Ping'): face_utilities.unary_unary_inline(servicer.Ping),
}
server_options = beta_implementations.server_options(request_deserializers=request_deserializers, response_serializers=response_serializers, thread_pool=pool, thread_pool_size=pool_size, default_timeout=default_timeout, maximum_timeout=maximum_timeout)
return beta_implementations.server(method_implementations, options=server_options)
def beta_create_Service_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None):
"""The Beta API is deprecated for 0.15.0 and later.
It is recommended to use the GA API (classes and functions in this
file not marked beta) for all further purposes. This function was
generated only to ease transition from grpcio<0.15.0 to grpcio>=0.15.0"""
request_serializers = {
('deepwater.Service', 'Ping'): PingRequest.SerializeToString,
}
response_deserializers = {
('deepwater.Service', 'Ping'): Status.FromString,
}
cardinalities = {
'Ping': cardinality.Cardinality.UNARY_UNARY,
}
stub_options = beta_implementations.stub_options(host=host, metadata_transformer=metadata_transformer, request_serializers=request_serializers, response_deserializers=response_deserializers, thread_pool=pool, thread_pool_size=pool_size)
return beta_implementations.dynamic_stub(channel, 'deepwater.Service', cardinalities, options=stub_options)
# @@protoc_insertion_point(module_scope)
|
[
"[email protected]"
] | |
56a9016f9048bf93ced9d3230e3e07125c5674b2
|
01bd00e6498190aac53210689c111d72018956fa
|
/companies/migrations/0047_auto_20190917_1011.py
|
a0c9fdef406a96c4ea5f7cbf5a40000ea2755162
|
[] |
no_license
|
dchaplinsky/edrdr
|
0494b31fe3a0ce54d0cf087fb11ef709cb002810
|
e9fd5295f8c7ca7db81fce2427456e779ff6637e
|
refs/heads/master
| 2022-06-01T07:01:59.049162 | 2020-10-12T08:04:42 | 2020-10-12T08:04:42 | 122,268,695 | 0 | 1 | null | 2022-04-22T20:52:45 | 2018-02-20T23:14:48 |
CSS
|
UTF-8
|
Python
| false | false | 571 |
py
|
# Generated by Django 2.2.3 on 2019-09-17 10:11
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('companies', '0046_pepowner_person_type'),
]
operations = [
migrations.AddField(
model_name='companyrecord',
name='charter_capital',
field=models.FloatField(default=None, null=True),
),
migrations.AddField(
model_name='companyrecord',
name='reg_date',
field=models.DateField(null=True),
),
]
|
[
"[email protected]"
] | |
8a90af3d8f8303466537548fe2ee18c2d1f3d983
|
3d48edf0c7335be78cdfb27764faae78f5b92270
|
/async_property/cached.py
|
4c1fafb08e39a01b6046054e2e8fe00dacb71b25
|
[
"MIT"
] |
permissive
|
18600575648/async_property
|
fc051acb291dfe7245d874974f6486311816da8c
|
9991fe614e89c04f5d9d93e150afe6cb10a42392
|
refs/heads/master
| 2023-07-06T10:11:01.917887 | 2023-07-03T17:22:23 | 2023-07-03T17:22:23 | 468,599,873 | 0 | 0 |
MIT
| 2023-09-05T07:44:36 | 2022-03-11T03:52:48 |
Python
|
UTF-8
|
Python
| false | false | 3,948 |
py
|
import asyncio
import functools
from collections import defaultdict
from async_property.proxy import AwaitableOnly, AwaitableProxy
is_coroutine = asyncio.iscoroutinefunction
ASYNC_PROPERTY_ATTR = '__async_property__'
def async_cached_property(func, *args, **kwargs):
assert is_coroutine(func), 'Can only use with async def'
return AsyncCachedPropertyDescriptor(func, *args, **kwargs)
class AsyncCachedPropertyInstanceState:
def __init__(self):
self.cache = {}
self.lock = defaultdict(asyncio.Lock)
__slots__ = 'cache', 'lock'
class AsyncCachedPropertyDescriptor:
def __init__(self, _fget, _fset=None, _fdel=None, field_name=None):
self._fget = _fget
self._fset = _fset
self._fdel = _fdel
self.field_name = field_name or _fget.__name__
functools.update_wrapper(self, _fget)
self._check_method_sync(_fset, 'setter')
self._check_method_sync(_fdel, 'deleter')
def __set_name__(self, owner, name):
self.field_name = name
def __get__(self, instance, owner):
if instance is None:
return self
if self.has_cache_value(instance):
return self.already_loaded(instance)
return self.not_loaded(instance)
def __set__(self, instance, value):
if self._fset is not None:
self._fset(instance, value)
self.set_cache_value(instance, value)
def __delete__(self, instance):
if self._fdel is not None:
self._fdel(instance)
self.del_cache_value(instance)
def setter(self, method):
self._check_method_name(method, 'setter')
return type(self)(self._fget, method, self._fdel, self.field_name)
def deleter(self, method):
self._check_method_name(method, 'deleter')
return type(self)(self._fget, self._fset, method, self.field_name)
def _check_method_name(self, method, method_type):
if method.__name__ != self.field_name:
raise AssertionError(
f'@{self.field_name}.{method_type} name must match property name'
)
def _check_method_sync(self, method, method_type):
if method and is_coroutine(method):
raise AssertionError(
f'@{self.field_name}.{method_type} must be synchronous'
)
def get_instance_state(self, instance):
try:
return getattr(instance, ASYNC_PROPERTY_ATTR)
except AttributeError:
state = AsyncCachedPropertyInstanceState()
object.__setattr__(instance, ASYNC_PROPERTY_ATTR, state)
return state
def get_lock(self, instance):
lock = self.get_instance_state(instance).lock
return lock[self.field_name]
def get_cache(self, instance):
return self.get_instance_state(instance).cache
def has_cache_value(self, instance):
cache = self.get_cache(instance)
return self.field_name in cache
def get_cache_value(self, instance):
cache = self.get_cache(instance)
return cache[self.field_name]
def set_cache_value(self, instance, value):
cache = self.get_cache(instance)
cache[self.field_name] = value
def del_cache_value(self, instance):
cache = self.get_cache(instance)
del cache[self.field_name]
def get_loader(self, instance):
@functools.wraps(self._fget)
async def load_value():
async with self.get_lock(instance):
if self.has_cache_value(instance):
return self.get_cache_value(instance)
value = await self._fget(instance)
self.__set__(instance, value)
return value
return load_value
def already_loaded(self, instance):
return AwaitableProxy(self.get_cache_value(instance))
def not_loaded(self, instance):
return AwaitableOnly(self.get_loader(instance))
|
[
"[email protected]"
] | |
821bcebfcedc6d629c6e2fbce307378367cc9129
|
1c88eef044c7ca83b545001e123b8bf064884bb5
|
/palindrome.py
|
465ca3b80f8bfacd18257869beb6ec3ec3ac0710
|
[] |
no_license
|
JacobDuvall/demosPy
|
05329592ad8526d0d72201a68faf7c8234774f73
|
c039943869d3a2cd62c4b6ff759857d3b5e7054e
|
refs/heads/master
| 2020-06-17T21:48:06.066695 | 2019-07-09T20:04:36 | 2019-07-09T20:04:36 | 196,067,803 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,409 |
py
|
import unittest
def digits(x):
"""Convert an integer into a list of digits.
Args:
x: The number whose digits we want.
Returns: A list of the digits, in order, of ``x``.
>>> digits(4586378)
[8, 7, 3, 6, 8, 5, 4]
"""
digs = []
while x != 0:
div, mod = divmod(x, 10)
digs.append(mod)
x = div
return digs
def is_palindrome(x):
"""Determine if an integer is a palindrome.
Args:
x: The number to check for palindromicity
Returns: True if the digits of ``x`` are a palindrome,
False otherwise.
>>> is_palindrome(1234)
False
>>> is_palindrome(2468642)
True
"""
digs = digits(x)
for f, r in zip(digs, reversed(digs)):
if f != r:
return False
return True
class Tests(unittest.TestCase):
"""Tests for the ``is_palindrome()`` function."""
def test_negative(self):
"Check that it returns False correctly."
self.assertFalse(is_palindrome(1234))
def test_positive(self):
"Check that it returns True correctly."
self.assertTrue(is_palindrome(1234321))
def test_single_digit(self):
"Check that it works for single digit numbers."
for i in range(10):
self.assertTrue(is_palindrome(i))
if __name__ == '__main__':
unittest.main()
|
[
"[email protected]"
] | |
f56c028822bfdd5126fdc099e68d3428e87abf7e
|
4762812376ef609248cbfad1d9b7586b3046877b
|
/ex4.py
|
f23947aa72fdfd443aa14370a14fddf799637527
|
[] |
no_license
|
Evansbee/Euler
|
8fa53c330d2de9d174dc4eb92e937a45d6c93d03
|
ca5ab80b291e47dcccb2b71be9b6f7749fab3c5d
|
refs/heads/master
| 2021-01-10T20:20:30.094465 | 2012-07-09T20:36:21 | 2012-07-09T20:36:21 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 328 |
py
|
def isPalindromic(number):
forward = str(number)
for i in range(0,len(forward)/2):
if forward[i] != forward[len(forward)-1-i]:
return False
return True
large = 0
for i in range(100,999):
for j in range(100,999):
if isPalindromic(i * j) and i*j>large:
large = i*j
print large
|
[
"[email protected]"
] | |
94ec66eea7c4ae730b2e71fc18b4f51f42d5626a
|
8f11b828a75180161963f082a772e410ad1d95c6
|
/packages/python/ram/ai/__init__.py
|
7fc0cd75962ad19cb0295be679c48e2ac7cb2ab8
|
[] |
no_license
|
venkatarajasekhar/tortuga
|
c0d61703d90a6f4e84d57f6750c01786ad21d214
|
f6336fb4d58b11ddfda62ce114097703340e9abd
|
refs/heads/master
| 2020-12-25T23:57:25.036347 | 2017-02-17T05:01:47 | 2017-02-17T05:01:47 | 43,284,285 | 0 | 0 | null | 2017-02-17T05:01:48 | 2015-09-28T06:39:21 |
C++
|
UTF-8
|
Python
| false | false | 282 |
py
|
# Copyright (C) 2007 Maryland Robotics Club
# Copyright (C) 2007 Joseph Lisee <[email protected]>
# All rights reserved.
#
# Author: Joseph Lisee <[email protected]>
# File: packages/python/ram/ai/__init__.py
# To allow the registration of SubsystemMakers
#import ram.ai.state as _state
|
[
"[email protected]"
] | |
4f87ef787f99622218a8f9229d6cbec20706919e
|
ad08d9c54500dde36e3067a9e1781d5ed7eff2f5
|
/app/utils/tests/test_xmlutils.py
|
938086a0e9c289d4c700e49de24f6ae8882988c4
|
[
"MIT"
] |
permissive
|
a410202049/flask_base_plus
|
0930ff9c1cc9f469e1915c46759a31300ee16cb2
|
c04ce546f02073c037dcfa6304c11c91dd3f6e48
|
refs/heads/master
| 2023-08-17T05:54:54.793871 | 2023-08-08T07:55:27 | 2023-08-08T07:55:27 | 141,976,633 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,246 |
py
|
#!/usr/bin/env python
# -*- coding:utf-8 -*-
from __future__ import absolute_import
from __future__ import unicode_literals
from test.unit.base import BaseTestCase
from utils.xmlutils import XMLUtils
class XMLUtilsTestCase(BaseTestCase):
"""
XMLUtilsTestCase
"""
def setUp(self):
pass
def tearDown(self):
pass
def test_xml2json(self):
xml_str = """
<BOSFXII xmlns="http://www.bankofshanghai.com/BOSFX/2010/08" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.bankofshanghai.com/BOSFX/2010/08 BOSFX2.0.xsd">
<XXXRq>
<CommonRqHdr>
<SPName>CBIB</SPName>
<RqUID>20150324800148246569</RqUID>
<ClearDate>20160331</ClearDate>
<TranDate>20160331</TranDate>
<TranTime>094338</TranTime>
<ChannelId/>
</CommonRqHdr>
<SubAcctNo>11022133</SubAcctNo>
<ProductCd>zzzzzz</ProductCd>
<Amount>11.02</Amount>
<Currency>156</Currency>
<TheirRef>AAA子账户转第三方（带赎回）</TheirRef>
<Purpose>AAA赎回</Purpose>
<Attach/>
<MemoInfo/>
<KoalB64Cert/>
<Signature/>
</XXXRq>
</BOSFXII>
"""
result = XMLUtils.xml2json(xml_str, need_dict=True)
self.assertEquals(result['BOSFXII']['XXXRq']['CommonRqHdr']['SPName'], 'CBIB')
def test_json2xml(self):
"""
json 字典必须有根元素
:return:
"""
json_data = {
"planets": {
"planet": [
{
"name": "Earth",
"radius": "6,371km"
},
{
"name": "Jupiter",
"radius": "69,911km"
},
{
"name": "Mars",
"radius": "3,390km"
}
],
"@xmlns": "http://www.bankofshanghai.com/BOSFX/2010/08"
},
}
result = XMLUtils.json2xml(json_data)
print result
|
[
"[email protected]"
] | |
96f433aced319800863be7a4873988b2e33c95bd
|
93c2f6a2eb88b67bc05f44ce901bc97878baddae
|
/flask_app/demo.py
|
ae4ec7c94074635d5a7c1fbc21abc6054a355bea
|
[] |
no_license
|
bkwi/wsgi-mux
|
dcbb827935e5bc7d646a2e8f83cca20f7d8a5bdc
|
386993473afcb75496ce2ef2d117d9619faa68f1
|
refs/heads/master
| 2021-01-12T03:20:22.602615 | 2017-01-06T10:17:56 | 2017-01-06T10:17:56 | 78,196,713 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 169 |
py
|
from flask import Flask, jsonify
app = Flask(__name__)
@app.route("/")
def hello():
return jsonify({'hello': 'flask'})
if __name__ == '__main__':
app.run()
|
[
"[email protected]"
] | |
307c00d035a25804ae38db2b5307c2a05f375b30
|
629bdff88aa44482487db5759607cff042e192b0
|
/users/views.py
|
88f000e048fe6607d671a8e1dc2e63d9c2b1f5d0
|
[] |
no_license
|
sharkops/LearningDjango1.11
|
2a51c15efb8a571e2b973642c5272dd5a1d8474a
|
5a4821eff30a609f48a5c9d39f7294d3913b732b
|
refs/heads/master
| 2020-04-01T03:36:19.188854 | 2018-10-13T12:26:41 | 2018-10-13T12:26:41 | 152,828,449 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,152 |
py
|
from django.shortcuts import render, HttpResponse
# Create your views here.
from django import forms
from captcha.fields import CaptchaField
from django.contrib.auth.hashers import make_password
class CaptchaTestForm(forms.Form):
email = forms.EmailField()
password = forms.CharField(min_length=6)
captcha = CaptchaField(error_messages={
"invalid": "验证码错误"
})
def some_view(request):
if request.POST:
register_form = CaptchaTestForm(request.POST)
if register_form.is_valid():
user_name = register_form.cleaned_data.get('email')
password = register_form.cleaned_data.get('password')
print(user_name, password)
password = make_password(password)
from users.models import UserProfile
UserProfile.objects.create(**{"username": user_name,
"email": user_name,
"password": password})
return HttpResponse("注册成功")
else:
register_form = CaptchaTestForm()
return render(request, 'register.html', locals())
|
[
"[email protected]"
] | |
01cead2a52a45405b93f86e704c848a52bea8b47
|
46c7e77d103a29f4cf94fea908fff34a2c13c8ad
|
/run.py
|
f7ab7cd9ea8f2348a77846c8a484ba51741781cd
|
[] |
no_license
|
baxtergu/ipe-crawler
|
b857a64185f3e4ead3f602282ce0d9ec7766e0ac
|
990e5aa38a65df45881de72d49f5e655b36234cc
|
refs/heads/master
| 2021-01-23T09:25:58.819170 | 2017-09-06T08:26:30 | 2017-09-06T08:26:30 | 102,580,328 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 78 |
py
|
from scrapy import cmdline
cmdline.execute("scrapy crawl exhaust_gas".split())
|
[
"[email protected]"
] | |
061a7f633a644ce258c8c3f0705117b3af16de49
|
4cc19ea37ba4b746c34aafc3c1391c54dbef65b3
|
/src/inner_source/manage.py
|
9a53b73ee6d62970c15c7bb7c268bf728dc2ad92
|
[
"Apache-2.0"
] |
permissive
|
innersourcedo/intergrow
|
d8684d3f7e59dbb874c71f9d3ba814444f1ba74d
|
7fb0854fc62e5cd719961e6201f73197bc3cd445
|
refs/heads/master
| 2020-08-25T04:47:35.420424 | 2020-01-23T15:08:46 | 2020-01-23T15:08:46 | 216,962,847 | 1 | 2 |
Apache-2.0
| 2020-03-20T21:29:46 | 2019-10-23T03:46:59 |
Python
|
UTF-8
|
Python
| false | false | 653 |
py
|
#!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'inner_source.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
|
[
"[email protected]"
] | |
fa06c71dbb665db2798a80f020f94620ea392755
|
93b7700d0deba59c6752693e9eda9047dd7b92f0
|
/action/evaluation/eval_classification.py
|
20580c5e9cfd545e99307c7045f9b939dd7f8af1
|
[
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
scape1989/IKEA_ASM_Dataset
|
238851979d8a1cff53fb7bc41092a0a308043a95
|
1627ba894c974cf45a653d79598120202ea658b5
|
refs/heads/master
| 2022-12-24T10:29:13.705768 | 2020-09-28T23:57:12 | 2020-09-28T23:57:12 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 10,190 |
py
|
# source: ActivityNet: https://github.com/activitynet/ActivityNet/blob/master/Evaluation/eval_classification.py
# adapted to python3 and the IKEA ASM dataset
import json
# import urllib2
import numpy as np
import pandas as pd
# from utils import get_blocked_videos
from eval_utils import interpolated_prec_rec
class ANETclassification(object):
GROUND_TRUTH_FIELDS = ['version', 'database'] #['database', 'taxonomy', 'version']
PREDICTION_FIELDS = ['results', 'version'] #['results', 'version', 'external_data']
def __init__(self, ground_truth_filename=None, prediction_filename=None,
ground_truth_fields=GROUND_TRUTH_FIELDS,
prediction_fields=PREDICTION_FIELDS,
subset='validation', verbose=False, top_k=3,
check_status=True):
if not ground_truth_filename:
raise IOError('Please input a valid ground truth file.')
if not prediction_filename:
raise IOError('Please input a valid prediction file.')
self.subset = subset
self.verbose = verbose
self.gt_fields = ground_truth_fields
self.pred_fields = prediction_fields
self.top_k = top_k
self.ap = None
self.hit_at_k = None
self.check_status = check_status
# # Retrieve blocked videos from server.
# if self.check_status:
# self.blocked_videos = get_blocked_videos()
# else:
# self.blocked_videos = list()
# Import ground truth and predictions.
self.ground_truth, self.activity_index = self._import_ground_truth(
ground_truth_filename)
self.prediction = self._import_prediction(prediction_filename)
if self.verbose:
print('[INIT] Loaded annotations from {} subset.'.format(subset))
nr_gt = len(self.ground_truth)
print('\tNumber of ground truth instances: {}'.format(nr_gt))
nr_pred = len(self.prediction)
print('\tNumber of predictions: {}'.format(nr_pred))
def _import_ground_truth(self, ground_truth_filename):
"""Reads ground truth file, checks if it is well formatted, and returns
the ground truth instances and the activity classes.
Parameters
----------
ground_truth_filename : str
Full path to the ground truth json file.
Outputs
-------
ground_truth : df
Data frame containing the ground truth instances.
activity_index : dict
Dictionary containing class index.
"""
with open(ground_truth_filename, 'r') as fobj:
data = json.load(fobj)
# Checking format
if not all([field in data.keys() for field in self.gt_fields]):
raise IOError('Please input a valid ground truth file.')
# Initialize data frame
activity_index, cidx = {}, 0
video_lst, label_lst = [], []
for videoid, v in data['database'].items():
if self.subset != v['subset']:
continue
# if videoid in self.blocked_videos:
# continue
for ann in v['annotation']:
if ann['label'] not in activity_index:
activity_index[ann['label']] = cidx
cidx += 1
video_lst.append(videoid)
label_lst.append(activity_index[ann['label']])
ground_truth = pd.DataFrame({'video-id': video_lst,
'label': label_lst})
ground_truth = ground_truth.drop_duplicates().reset_index(drop=True)
return ground_truth, activity_index
def _import_prediction(self, prediction_filename):
"""Reads prediction file, checks if it is well formatted, and returns
the prediction instances.
Parameters
----------
prediction_filename : str
Full path to the prediction json file.
Outputs
-------
prediction : df
Data frame containing the prediction instances.
"""
with open(prediction_filename, 'r') as fobj:
data = json.load(fobj)
# Checking format...
if not all([field in data.keys() for field in self.pred_fields]):
raise IOError('Please input a valid prediction file.')
# Initialize data frame
video_lst, label_lst, score_lst = [], [], []
for videoid, v in data['results'].items():
# if videoid in self.blocked_videos:
# continue
for result in v:
label = self.activity_index[result['label']]
video_lst.append(videoid)
label_lst.append(label)
score_lst.append(result['score'])
prediction = pd.DataFrame({'video-id': video_lst,
'label': label_lst,
'score': score_lst})
return prediction
def wrapper_compute_average_precision(self):
"""Computes average precision for each class in the subset.
"""
ap = np.zeros(len(self.activity_index.items()))
for activity, cidx in self.activity_index.items():
gt_idx = self.ground_truth['label'] == cidx
pred_idx = self.prediction['label'] == cidx
ap[cidx] = compute_average_precision_classification(
self.ground_truth.loc[gt_idx].reset_index(drop=True),
self.prediction.loc[pred_idx].reset_index(drop=True))
return ap
def evaluate(self):
"""Evaluates a prediction file. For the detection task we measure the
interpolated mean average precision to measure the performance of a
method.
"""
ap = self.wrapper_compute_average_precision()
hit_at_k = compute_video_hit_at_k(self.ground_truth,
self.prediction, top_k=self.top_k)
avg_hit_at_k = compute_video_hit_at_k(
self.ground_truth, self.prediction, top_k=self.top_k, avg=True)
if self.verbose:
print ('[RESULTS] Performance on ActivityNet untrimmed video '
'classification task.')
print('\tMean Average Precision: {}'.format(ap.mean()))
print('\tHit@{}: {}'.format(self.top_k, hit_at_k))
print('\tAvg Hit@{}: {}'.format(self.top_k, avg_hit_at_k))
self.ap = ap
self.hit_at_k = hit_at_k
self.avg_hit_at_k = avg_hit_at_k
################################################################################
# Metrics
################################################################################
def compute_average_precision_classification(ground_truth, prediction):
"""Compute average precision (classification task) between ground truth and
predictions data frames. If multiple predictions occurs for the same
predicted segment, only the one with highest score is matched as
true positive. This code is greatly inspired by Pascal VOC devkit.
Parameters
----------
ground_truth : df
Data frame containing the ground truth instances.
Required fields: ['video-id']
prediction : df
Data frame containing the prediction instances.
Required fields: ['video-id, 'score']
Outputs
-------
ap : float
Average precision score.
"""
npos = float(len(ground_truth))
lock_gt = np.ones(len(ground_truth)) * -1
# Sort predictions by decreasing score order.
sort_idx = prediction['score'].values.argsort()[::-1]
prediction = prediction.loc[sort_idx].reset_index(drop=True)
# Initialize true positive and false positive vectors.
tp = np.zeros(len(prediction))
fp = np.zeros(len(prediction))
# Assigning true positive to truly grount truth instances.
for idx in range(len(prediction)):
this_pred = prediction.loc[idx]
gt_idx = ground_truth['video-id'] == this_pred['video-id']
# Check if there is at least one ground truth in the video associated.
if not gt_idx.any():
fp[idx] = 1
continue
this_gt = ground_truth.loc[gt_idx].reset_index()
if lock_gt[this_gt['index']] >= 0:
fp[idx] = 1
else:
tp[idx] = 1
lock_gt[this_gt['index']] = idx
# Computing prec-rec
tp = np.cumsum(tp).astype(np.float)
fp = np.cumsum(fp).astype(np.float)
rec = tp / npos
prec = tp / (tp + fp)
return interpolated_prec_rec(prec, rec)
def compute_video_hit_at_k(ground_truth, prediction, top_k=3, avg=False):
"""Compute accuracy at k prediction between ground truth and
predictions data frames. This code is greatly inspired by evaluation
performed in Karpathy et al. CVPR14.
Parameters
----------
ground_truth : df
Data frame containing the ground truth instances.
Required fields: ['video-id', 'label']
prediction : df
Data frame containing the prediction instances.
Required fields: ['video-id, 'label', 'score']
Outputs
-------
acc : float
Top k accuracy score.
"""
video_ids = np.unique(ground_truth['video-id'].values)
avg_hits_per_vid = np.zeros(video_ids.size)
for i, vid in enumerate(video_ids):
pred_idx = prediction['video-id'] == vid
if not pred_idx.any():
continue
this_pred = prediction.loc[pred_idx].reset_index(drop=True)
# Get top K predictions sorted by decreasing score.
sort_idx = this_pred['score'].values.argsort()[::-1][:top_k]
this_pred = this_pred.loc[sort_idx].reset_index(drop=True)
# Get labels and compare against ground truth.
pred_label = this_pred['label'].tolist()
gt_idx = ground_truth['video-id'] == vid
gt_label = ground_truth.loc[gt_idx]['label'].tolist()
avg_hits_per_vid[i] = np.mean([1 if this_label in pred_label else 0
for this_label in gt_label])
if not avg:
avg_hits_per_vid[i] = np.ceil(avg_hits_per_vid[i])
return float(avg_hits_per_vid.mean())
|
[
"[email protected]"
] | |
62468571196349acaac805658ec61d5532fcb955
|
dc4a42ad81013a1fdaa0c6be0559504e17bacb7e
|
/products/admin.py
|
a845d9021b184ff03ccdeed387467a77c73d2d28
|
[] |
no_license
|
deone/eqsupply
|
15afbda692779431357d2c69475da8503c4728b1
|
3af726b65c1658d364c6485ad36ef98d5c6e7fc3
|
refs/heads/master
| 2020-04-20T05:29:53.020966 | 2010-05-13T09:16:18 | 2010-05-13T09:16:18 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 229 |
py
|
from django.contrib import admin
from eqsupply.products.models import *
admin.site.register(Division)
admin.site.register(Category)
admin.site.register(Product)
admin.site.register(Accessory)
admin.site.register(ProductVariant)
|
[
"[email protected]"
] | |
0f19c8558985ad4cf18f9f3fe0bc103b1b031536
|
3121a85f6578b849c44a7c23d10454d4965616d2
|
/blog/forms/article.py
|
ace0c9c7c08b53f291c2a75184adee899956a521
|
[] |
no_license
|
myalcins/django-blog-tutorial
|
1bdaf2ac1437f6b4e25af1c2fdb3799b257701e8
|
eb7355952b2db164935c5302aecde1331379c515
|
refs/heads/master
| 2023-03-28T07:47:09.997745 | 2021-03-26T13:34:23 | 2021-03-26T13:34:23 | 350,349,811 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 400 |
py
|
from django import forms
from blog.models import Article
class ArticleForm(forms.ModelForm):
schedule_time = forms.DateTimeField(required=False ,input_formats=['%Y/%m/%d %H:%M'])
class Meta:
model = Article
fields = (
'image',
'title',
'content',
'category',
'schedule_time'
)
|
[
"[email protected]"
] | |
c4fa34f9aadc2e77c1beedfb47dbd46c1082e9cf
|
173488cc5a6b58a3d616fa61e150676c7d4937b3
|
/twentyThirdSuccessorArray.py
|
b253598013ce8f76eebc6a15901e0cb8f34f354c
|
[
"MIT"
] |
permissive
|
MSQFuersti/aoc2020
|
ef0bc2091b0e57704ccace9d15a676b5be0c7025
|
f5e163c426a6c481d645ace2cc8af7c493306291
|
refs/heads/master
| 2023-02-04T04:03:21.559461 | 2020-12-29T13:04:40 | 2020-12-29T13:04:40 | 318,316,081 | 0 | 0 |
MIT
| 2020-12-07T21:31:28 | 2020-12-03T20:49:19 |
Python
|
UTF-8
|
Python
| false | false | 977 |
py
|
puzzleInput = '614752839'
# puzzleInput = '389125467'
labels = [int(char) for char in puzzleInput]
labels.extend(list(range(10, 1000001)))
successors = {}
for index, label in enumerate(labels):
successors[label] = labels[index + 1] if index + 1 < len(labels) else labels[0]
currentCup = labels[0]
for _ in range(10000000):
cupOne = successors[currentCup]
cupTwo = successors[cupOne]
cupThree = successors[cupTwo]
takenCups = [cupOne, cupTwo, cupThree]
successors[currentCup] = successors[cupThree]
destinationCup = currentCup - 1
while True:
if destinationCup < 1:
destinationCup = 1000000
continue
if destinationCup in takenCups:
destinationCup = destinationCup - 1
continue
break
successors[cupThree] = successors[destinationCup]
successors[destinationCup] = cupOne
currentCup = successors[currentCup]
print(successors[1] * successors[successors[1]])
|
[
"[email protected]"
] | |
3f4c690093e0de6b4135513bea417fc2a6a5abf2
|
cd61ab53fd77d9b8e136c3e6fd575266a7450038
|
/apps/pinkslips/migrations/0013_auto_20180303_2042.py
|
1d341d1a45151869bdcb55013450da511b8cc825
|
[] |
no_license
|
vincentereyes/phpslips
|
c6e6885f4ca67b27da8783014d95a491ce797e64
|
9fc7c16806618cccbd1a24f957594d0ff8eb12f9
|
refs/heads/master
| 2021-01-25T14:55:25.253031 | 2018-03-03T21:38:30 | 2018-03-03T21:38:30 | 123,733,646 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 968 |
py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.10 on 2018-03-03 20:42
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('pinkslips', '0012_auto_20180303_0916'),
]
operations = [
migrations.AlterField(
model_name='conversation',
name='latitude',
field=models.FloatField(default=121.031851),
),
migrations.AlterField(
model_name='conversation',
name='latitude2',
field=models.FloatField(default=121.057307),
),
migrations.AlterField(
model_name='conversation',
name='longitude',
field=models.FloatField(default=14.652723),
),
migrations.AlterField(
model_name='conversation',
name='longitude2',
field=models.FloatField(default=14.605568),
),
]
|
[
"[email protected]"
] | |
f0ec9069cd636274166bcd07ca0cebc104ee447b
|
ca7aa979e7059467e158830b76673f5b77a0f5a3
|
/Python_codes/p03598/s680963277.py
|
c8861d19ff2e2ce27d5b6a660a4fb273c93d87c7
|
[] |
no_license
|
Aasthaengg/IBMdataset
|
7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901
|
f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8
|
refs/heads/main
| 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 173 |
py
|
N = int(input())
K = int(input())
x = list(map(int, input().split()))
A=[]
B=[]
for i in range(len(x)):
a = min(2*(x[i]), 2*abs(K-x[i]))
A.append(a)
print(sum(A))
|
[
"[email protected]"
] | |
036c944ee8502521b61d3c999781a4203c459970
|
7287193205f0e660ad99806c32a417a759db7a15
|
/survey_project/surveys/migrations/0002_surveyquestion_freetext_answer_available.py
|
6fd48481f913c7f2e1ba5f611e984349ca5e9b5a
|
[] |
no_license
|
pikkoui/survey
|
4a8f2df595a60499ccc976b2a2dc4ef88abfd075
|
d13c6a13abf4e1c18e433e3fd621e031bf430008
|
refs/heads/master
| 2023-08-27T21:27:31.271966 | 2021-11-11T17:33:46 | 2021-11-11T17:33:46 | 426,996,308 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 439 |
py
|
# Generated by Django 2.1.15 on 2021-11-05 09:58
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('surveys', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='surveyquestion',
name='freetext_answer_available',
field=models.BooleanField(default=True),
preserve_default=False,
),
]
|
[
"[email protected]"
] | |
ee60c445bcd85a698f8701cbcc83850110f22520
|
f2a41d0f1fb7ef08e4992035c37438d9cd55d8b6
|
/FreeFishMaster/wsgi.py
|
405e26a7d6c06e969e9bbf104660f7b4cbb43b61
|
[
"MIT"
] |
permissive
|
xzengCB/FreeFishMaster
|
65f2fe116ea91e4ad3bc28bb3a5d6f4b09f06688
|
14418e108d1a25c56ff2e9801f4256f05f154c67
|
refs/heads/master
| 2021-01-01T16:18:18.341137 | 2017-08-01T05:21:48 | 2017-08-01T05:21:48 | 97,806,346 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 405 |
py
|
"""
WSGI config for FreeFishMaster project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.9/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "FreeFishMaster.settings")
application = get_wsgi_application()
|
[
"[email protected]"
] | |
2d70628d12511e51669b1661da522af5c72fb1a5
|
40c11c748f159d35cad1bc648509fc23fc7c0a8b
|
/l10n_py_base/res_currency_rate.py
|
ed151867088e6262f591a1d5a909226d275299f4
|
[] |
no_license
|
Icaruspy/addons
|
db4b1e33e046906aaf485b341d703dfb9b286565
|
93cb3352abb2998c33399170dd66329e39528a0e
|
refs/heads/master
| 2020-12-30T09:38:21.920380 | 2015-12-13T16:17:48 | 2015-12-13T16:17:48 | 39,844,715 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,219 |
py
|
# -*- encoding: utf-8 -*-
#################################################################################
# #
# Copyright (C) 2009 Renato Lima - Akretion, Gabriel C. Stabel #
# #
#This program is free software: you can redistribute it and/or modify #
#it under the terms of the GNU General Public License as published by #
#the Free Software Foundation, either version 3 of the License, or #
#(at your option) any later version. #
# #
#This program is distributed in the hope that it will be useful, #
#but WITHOUT ANY WARRANTY; without even the implied warranty of #
#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
#GNU General Public License for more details. #
# #
#You should have received a copy of the GNU General Public License #
#along with this program. If not, see <http://www.gnu.org/licenses/>. #
#################################################################################
from openerp.osv import osv, fields
##############################################################################
# Parceiro Personalizado
##############################################################################
class res_currency_rate(osv.osv):
_inherit = 'res.currency.rate'
_columns = {
'tasa': fields.integer('Tasa Gs', digits=(16,2), required=True ),
}
# funcion para calcular el ratio estandart de trabajo de openerp
# en paraguay se usa el valor de tasa en guaranies
def on_change_tasa(self, cr, uid, ids, tasa , rate ):
rate3 = rate
rate3 = 1.000000000 / tasa
return {'value': { 'rate': rate3 } }
res_currency_rate()
# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:
|
[
"[email protected]"
] | |
07fed4cb0ac0a9c9fe7cf77a4577b118c598fd1f
|
6147d3da9c7f31a658f13892de457ed5a9314b22
|
/multithreading/without_threading.py
|
4f637839a61975629dea515f930117251368c52c
|
[] |
no_license
|
ashish-bisht/must_do_geeks_for_geeks
|
17ba77608eb2d24cf4adb217c8e5a65980e85609
|
7ee5711c4438660db78916cf876c831259109ecc
|
refs/heads/master
| 2023-02-11T22:37:03.302401 | 2021-01-03T05:53:03 | 2021-01-03T05:53:03 | 320,353,079 | 0 | 1 | null | null | null | null |
UTF-8
|
Python
| false | false | 285 |
py
|
import threading
import time
start = time.perf_counter()
def working_on_something():
print("Sleeping for a sec")
time.sleep(1)
print("Woke up")
working_on_something()
working_on_something()
finish = time.perf_counter()
print("total time taken is ", finish - start)
|
[
"[email protected]"
] | |
55a67241ba31a2e626f336bfab70b83ce1f840a7
|
29cf500c7958da5a22829e8da85c5229e6445848
|
/rent/apps/authentication/models.py
|
e0d6fe2b451369d10ddc43afe75d2caf0179e412
|
[
"MIT"
] |
permissive
|
lenileiro/rent-django
|
86562af09fffe77fbf20a61b0c0981cf8df7cfc2
|
850d208fd93a7424a5c11b268df6c7c94add518d
|
refs/heads/develop
| 2022-12-12T13:25:44.584374 | 2019-08-24T23:54:48 | 2019-08-24T23:54:48 | 201,096,792 | 1 | 0 |
MIT
| 2022-12-08T06:00:03 | 2019-08-07T17:32:48 |
Python
|
UTF-8
|
Python
| false | false | 3,430 |
py
|
import re
from datetime import datetime, timedelta
from django.conf import settings
from django.contrib.auth.models import (
AbstractBaseUser, BaseUserManager, PermissionsMixin
)
from django.db import models
class Utils:
@staticmethod
def validate_email(email):
check_email = User.objects.filter(email=email)
email_regex = r'^([a-zA-Z0-9_\-\.]+)@((\[[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.)|(([a-zA-Z0-9\-]+\.)+))([a-zA-Z]{2,4}|[0-9]{1,3})(\]?)$'
if not re.search(email_regex, email):
raise TypeError("Incorrect email format please try again")
if check_email.exists():
raise TypeError("This email has already been used to create a user")
return email
# Create your models here.
class UserManager(BaseUserManager):
def create_user(self, name=None, email=None, password=None):
"""Create and return a `User` with an email, username and password."""
if name is None:
raise TypeError('User must have a name.')
if email is None:
raise TypeError('User must have an email address.')
if password is None:
raise TypeError('User Account must have a password.')
Utils.validate_email(email)
user = self.model(name=name, email=self.normalize_email(email))
user.set_password(password)
user.save()
return user
def create_superuser(self, name, email, password):
if password is None:
raise TypeError('Superuser must have a password.')
user = self.create_user(name, email, password)
user.is_superuser = True
user.is_staff = True
user.save()
return user
def create_owneruser(self, name, email, password):
user = self.create_user(name, email, password)
user.is_owner = True
user.save()
return user
def create_vendoruser(self, name, email, password, phone=None,contactperson=None):
if phone is None:
raise TypeError('Vendor Account must have a phone number.')
if contactperson is None:
raise TypeError('Vendor Account must have a contactperson.')
user = self.create_user(name, email, password)
user.is_vendor = True
user.phone = phone
user.contactperson = contactperson
user.save()
return user
class User(AbstractBaseUser, PermissionsMixin):
name = models.CharField(max_length=255)
email = models.EmailField(db_index=True, unique=True)
is_active = models.BooleanField(default=True)
is_vendor = models.BooleanField(default=False)
is_owner = models.BooleanField(default=False)
is_active = models.BooleanField(default=True)
is_staff = models.BooleanField(default=False)
created_at = models.DateTimeField(auto_now_add=True)
phone = models.CharField(default=False, max_length=255)
contactperson = models.CharField(default=False, max_length=255)
USERNAME_FIELD = 'email'
REQUIRED_FIELDS = ['name']
objects = UserManager()
def __str__(self):
return self.email
@property
def is_SuperUser(self):
return self.is_superuser
@property
def is_Owner(self):
return self.is_owner
@property
def is_Vendor(self):
return self.is_vendor
|
[
"[email protected]"
] | |
149385687b03ae443a677201627cdcc4ec051156
|
78e5bd1977cfb8d077a2999cae42a065c13452aa
|
/kaggle_event_recommendation/event_attendees.py
|
16b6943e083432543ec58877dc44181bf037134b
|
[] |
no_license
|
loyalzc/recommendation-system
|
9ed3f6ea781c6de1e77a50bbd316d87f8970aadd
|
6ac748e8d1b6fa252fd7188a315f0c4d75216e9a
|
refs/heads/master
| 2020-03-13T14:20:32.335425 | 2019-04-14T13:08:15 | 2019-04-14T13:08:15 | 131,156,451 | 1 | 1 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,168 |
py
|
# -*- coding: utf-8 -*-
"""
@author: Infaraway
@time: 2018/5/10 21:07
@Function: event 热度 活跃度
"""
import scipy.sparse as ss
import scipy.io as sio
from sklearn.preprocessing import normalize
class EventAttendees:
"""
统计活动 参加和不参加的人数
"""
def __init__(self, user_event_entity):
num_event = len(user_event_entity.event_index.keys())
self.event_poplarity = ss.dok_matrix((num_event, 1))
with open('data/event_attendees.csv', 'r') as event_att_f:
event_att_f.readline()
for line in event_att_f.readlines():
cols = line.strip().split(',')
event_id = cols[0]
if user_event_entity.event_index.__contains__(event_id):
event_index = user_event_entity.event_index[event_id]
# event 流行度 num_yes - num_no
self.event_poplarity[event_index, 0] = len(cols[1].split(' ')) - len(cols[4].split(' '))
self.event_poplarity = normalize(self.event_poplarity, norm='l1', axis=0, copy=False)
sio.mmwrite('prep_data/event_popularlity', self.event_poplarity)
|
[
"[email protected]"
] | |
f6fd2ac63f906cc9143cc1e76d8f6cdd8f119ab8
|
b3d0359204431dec3f96a0fc99c5260b9a810c91
|
/__latest_version.py
|
9e7f0197ec3ee9e69660392a6526c5ba4efc68ac
|
[
"MIT"
] |
permissive
|
marcel-valdez/launchpad-helper
|
d9fef583af8b055ae26dd555dae306775492f760
|
97682cb547c162ca4cc88ccd003285e59cc4eef1
|
refs/heads/master
| 2021-01-19T02:14:21.660848 | 2016-08-09T18:51:09 | 2016-08-09T18:51:09 | 61,316,092 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,345 |
py
|
#!/usr/bin/env python
import sys
from launchpadlib.launchpad import Launchpad
__DEBUG__ = False
DISTRO = 'ubuntu'
RELEASE = 'trusty'
ARCHITECTURE = 'amd64'
DISTRO_ARCH_SERIES_URL = 'https://api.launchpad.net/1.0/' + DISTRO + '/' + RELEASE + '/' + ARCHITECTURE
## TODO: Accept multiple app names and get all their versions line per line
def get_ppa_archive(launchpad, ppa_url):
(prefix, rest_ppa_url) = str.split(ppa_url, ':')
(owner_name, package_name) = str.split(rest_ppa_url, '/')
owner = launchpad.people[owner_name]
return owner.getPPAByName(name = package_name)
def get_distro_archive(launchpad, distro_name = DISTRO):
distro = launchpad.distributions[distro_name]
return distro.main_archive
def get_latest_source(archive, app_name, distro_url = DISTRO_ARCH_SERIES_URL):
for series in archive.distribution.series.entries:
if series['displayname'].lower() == RELEASE.lower():
distro_series = series['self_link']
sources = archive.getPublishedSources(
status = 'Published',
exact_match = True,
source_name = app_name,
distro_series = distro_series
)
if len(sources) == 0:
print("No entries found for app: " + sys.argv[1] + " in archive: " + str(archive))
sys.exit(1)
elif __DEBUG__ == True:
print("Found " + str(len(sources)) + " matches")
for source in sources:
print(str(source.display_name))
return sources[0]
def get_latest_package(archive, app_name, distro_url = DISTRO_ARCH_SERIES_URL):
binaries = archive.getPublishedBinaries(
status = 'Published',
exact_match = True,
binary_name = app_name,
distro_arch_series = distro_url
)
if len(binaries) == 0:
print("No entries found for app: " + sys.argv[1] + " in archive: " + str(archive))
sys.exit(1)
elif __DEBUG__ == True:
print("Found " + str(len(binaries)) + " matches")
for sources in binaries:
print(str(sources.display_name))
return binaries[0]
def parse_args():
if len(sys.argv) < 2:
print("Usage: " + sys.argv[0] + " <app name> [ppa url]")
print("Example: " + sys.argv[0] + " solaar ppa:daniel.pavel/solaar")
sys.exit(1)
app_name = sys.argv[1]
ppa_url = None
if len(sys.argv) > 2:
ppa_url = sys.argv[2]
get_url = None
if len(sys.argv) > 3:
get_url = sys.argv[3].lower() == "--get_url"
return { 'app_name': app_name, 'ppa_url': ppa_url, 'get_url': get_url }
def print_api(obj):
print('lp_attributes: ' + str(sorted(obj.lp_attributes)))
print('lp_operations: ' + str(sorted(obj.lp_operations)))
print('lp_entries: ' + str(sorted(obj.lp_entries)))
print('lp_collections: ' + str(sorted(obj.lp_collections)))
def get_archive(ppa_url):
launchpad = Launchpad.login_anonymously('launchpad-helper', 'production')
if ppa_url != None:
return get_ppa_archive(launchpad, ppa_url)
else:
return get_distro_archive(launchpad)
def get_latest_url(archive, app_name):
return get_latest_source(archive, app_name).binaryFileUrls()[0]
def get_latest_version(archive, app_name):
return get_latest_source(archive, app_name).source_package_version
if __name__ == '__main__':
args = parse_args()
archive = get_archive(args['ppa_url'])
if args['get_url'] == True:
print(get_latest_url(archive, args['app_name']))
else:
print(get_latest_version(archive, args['app_name']))
sys.exit(0)
|
[
"[email protected]"
] | |
9ab7745e8b4d48edd0fe67af3de20eca60454dcc
|
f59a3641f488dd40b0af4c0024a252170ab59998
|
/chap4/p35.py
|
d89dca31848be92a9ad88a15209c75b1fe2ad076
|
[] |
no_license
|
ujiuji1259/NLP100
|
478a5276514d2f21ac5ee5ec9b50f00dcba67d1a
|
c19f9ba00eec108dbc93d4cb7d33e86f539d3397
|
refs/heads/master
| 2023-04-01T23:05:14.376652 | 2021-04-13T05:21:37 | 2021-04-13T05:21:37 | 255,311,319 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 297 |
py
|
# mecab neko.txt > neko.txt.mecab
from p30 import load_mecab_output
import collections
if __name__ == '__main__':
lines = load_mecab_output('neko.txt.mecab')
lines = [l['surface'] for line in lines for l in line]
counter = collections.Counter(lines)
print(counter.most_common())
|
[
"[email protected]"
] | |
0d843d4556bf97c40beacc40c239357fa08e4b8a
|
05263538c3ad0f577cdbbdb9bac87dcf450230ce
|
/alexa/ask-sdk/ask_sdk_dynamodb/__version__.py
|
5cfdf120d47b16330d48f329ae8c0e26ce048100
|
[] |
no_license
|
blairharper/ISS-GoogleMap-project
|
cea027324fc675a9a309b5277de99fc0265dcb80
|
3df119036b454a0bb219af2d703195f4154a2471
|
refs/heads/master
| 2020-03-21T16:47:21.046174 | 2018-10-24T08:05:57 | 2018-10-24T08:05:57 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,140 |
py
|
#
# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights
# Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS
# OF ANY KIND, either express or implied. See the License for the
# specific language governing permissions and limitations under the
# License.
#
__pip_package_name__ = 'ask-sdk-dynamodb-persistence-adapter'
__description__ = (
'The ASK SDK DynamoDB Persistence Adapter package provides DynamoDB '
'Adapter, that can be used with ASK SDK Core, for persistence management')
__url__ = 'http://developer.amazon.com/ask'
__version__ = '0.1'
__author__ = 'Alexa Skills Kit'
__author_email__ = '[email protected]'
__license__ = 'Apache 2.0'
__keywords__ = ['ASK SDK', 'Alexa Skills Kit', 'Alexa', 'ASK SDK Core',
'Persistence', 'DynamoDB']
__install_requires__ = ["boto3", "ask-sdk-core"]
|
[
"[email protected]"
] | |
57d50697a8b5a6199f35983afac98b3beb374b0e
|
af7eb83ef2fb3bcf3b17d2aac656b06269c7784f
|
/1-abrindoarquivo.py
|
a7048bb931752e2a8ad40dc5b4adf1953145357b
|
[] |
no_license
|
pablogonzalezz/curso-python
|
7a1202d01e452f60a258dd9ea90bcd622a99d8b4
|
d09059e7df65851d29b69b35f689db5f96090ff8
|
refs/heads/master
| 2020-04-10T20:26:36.653910 | 2018-12-12T04:45:59 | 2018-12-12T04:45:59 | 161,268,117 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 34 |
py
|
arquivo = open("carros.txt", "w")
|
[
"[email protected]"
] | |
4de795c8d948abca857de788593e235ee526ab89
|
3d96fcb008ecbe003bfd92c5b9f51ce526493d27
|
/railyard/railyard.py
|
653942862aed8d4f4db609f1976979d3bc2ff5db
|
[
"MIT"
] |
permissive
|
ktaletsk/railyard
|
a81da4033f3fba3cb33d0418b234de565e9b76d5
|
7eb47bb622787c1d017fbf7785ed6d9c8c6e0649
|
refs/heads/master
| 2022-04-09T04:50:50.619251 | 2020-03-03T20:29:15 | 2020-03-03T20:29:15 | 228,427,664 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 911 |
py
|
"""Main module."""
import tempfile
from railyard.assembler import readStacks, assembleStack
import railyard.builder as builder
import time
import os
def assemble(base_stack, additional_stacks, path):
s = readStacks(base_stack, additional_stacks)
if not os.path.exists(path):
os.mkdir(path)
if not os.path.exists(os.path.join(path, s['package_hash'])):
os.mkdir(os.path.join(path, s['package_hash']))
assembleStack(s, os.path.join(path, s['package_hash']))
def test(base_stack, additional_stack):
s = readStacks(base_stack, additional_stack)
tag = 'ktaletsk/polus-notebook:' + s['package_hash']
# Create temporary folder for Dockerfile and additional files
# Folder is securely created with `tempfile` and is destroyed afterwards
with tempfile.TemporaryDirectory() as tmpdirname:
assembleStack(s, tmpdirname)
builder.build(tmpdirname, tag)
|
[
"[email protected]"
] | |
2811f3f5befc58918de648f954c3be0ac299000b
|
8b467addc38a67019ff35dfd5394e36d4b7c7c5c
|
/src/gui_kill_creo.py
|
7642164d9eb6170bc800c8be19160c7a498b338b
|
[
"MIT"
] |
permissive
|
loleven/creo_kill_proc
|
f48e158ccb64744bd34d68bf96fa8868d6cbe017
|
9136889efe1a51fe057e538d080ca31f3f4c6de5
|
refs/heads/master
| 2020-05-24T00:48:23.222996 | 2017-03-13T09:34:28 | 2017-03-13T09:34:28 | 84,806,859 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,934 |
py
|
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'C:\Data\Development\PycharmProjects\KillCreo\src\gui_kill_creo.ui'
#
# Created by: PyQt5 UI code generator 5.5.1
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_frm_kill_creo(object):
def setupUi(self, frm_kill_creo):
frm_kill_creo.setObjectName("frm_kill_creo")
frm_kill_creo.resize(400, 300)
frm_kill_creo.setMinimumSize(QtCore.QSize(400, 300))
frm_kill_creo.setMaximumSize(QtCore.QSize(400, 300))
self.horizontalLayout_2 = QtWidgets.QHBoxLayout(frm_kill_creo)
self.horizontalLayout_2.setObjectName("horizontalLayout_2")
self.verticalLayout = QtWidgets.QVBoxLayout()
self.verticalLayout.setObjectName("verticalLayout")
self.list_result = QtWidgets.QListWidget(frm_kill_creo)
self.list_result.setObjectName("list_result")
self.verticalLayout.addWidget(self.list_result)
self.horizontalLayout = QtWidgets.QHBoxLayout()
self.horizontalLayout.setObjectName("horizontalLayout")
spacerItem = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)
self.horizontalLayout.addItem(spacerItem)
self.btn_kill = QtWidgets.QPushButton(frm_kill_creo)
self.btn_kill.setObjectName("btn_kill")
self.horizontalLayout.addWidget(self.btn_kill)
self.verticalLayout.addLayout(self.horizontalLayout)
self.horizontalLayout_2.addLayout(self.verticalLayout)
self.retranslateUi(frm_kill_creo)
QtCore.QMetaObject.connectSlotsByName(frm_kill_creo)
def retranslateUi(self, frm_kill_creo):
_translate = QtCore.QCoreApplication.translate
frm_kill_creo.setWindowTitle(_translate("frm_kill_creo", "Kill Creo"))
self.btn_kill.setText(_translate("frm_kill_creo", "Kill Creo"))
|
[
"[email protected]"
] | |
c51438dca74d9ee3958675018e129bb173024552
|
c82490eb4903a9b6b23bdef7c528def682a2672f
|
/eightpuzzle.py
|
61084b4c00b5f6223a01ba7230814872d31e6b42
|
[] |
no_license
|
zariuq/Misc
|
5563b3f8c015c305a6023dca42e8e616e8dc146b
|
e5b5e4229b29a68e4a3d042bed62c200d3af8e2e
|
refs/heads/master
| 2020-05-20T23:55:43.067819 | 2015-08-10T07:48:06 | 2015-08-10T07:48:06 | 30,136,064 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 5,519 |
py
|
import pygame
from eightpuzzlesolver import aStar
pygame.init()
WIDTH = 500
HEIGHT = 400
screen = pygame.display.set_mode((WIDTH, HEIGHT))
done = False
clock = pygame.time.Clock()
# Switch for human or playthrough
HUMAN = False
# Define the colors we will use in RGB format
BLACK = ( 0, 0, 0)
WHITE = (255, 255, 255)
BLUE = ( 0, 0, 255)
GREEN = ( 0, 255, 0)
RED = (255, 0, 0)
# Find centers
centers = []
box_w = WIDTH // 3
box_h = HEIGHT // 3
for j in range(1,4): # switched i and j to make numbers ordered horizontally.
for i in range(1,4): #remember the colon!
centers = centers + [( (i * box_w + (i-1) * box_w) // 2 , (j * box_h + (j-1) * box_h) // 2 )]
#print (centers)
# Render numbers
font = pygame.font.Font(None, 36)
numbers = []
for i in range(0,9): # Don't forget the colon, damnit!
numbers = numbers + [font.render(str(i), True, BLUE)]
# Default board position (to be randomized eventually)
board2 = [1,5,3,2,8,4,6,7,0] # no solution?
board4 = [1,0,2,4,5,3,6,7,8]
win = [0,1,2,3,4,5,6,7,8]
# Surely there's a better way to do this. It reeks of a succinct pattern...
def get_adjacent(index):
if index == 0:
return [1,3]
if index == 1:
return [0,2,4]
if index == 2:
return [1,5]
if index == 3:
return [0,4,6]
if index == 4:
return [1,3,5,7]
if index == 5:
return [2,4,8]
if index == 6:
return [3,7]
if index == 7:
return [4,6,8]
if index == 8:
return [5,7]
return []
coordinates = [(0,0),(1,0),(2,0),(0,1),(1,1),(2,1),(0,2),(1,2),(2,2)]
# originally (s1,s2), but I'll assume one goal-state :P
def man_distance(s1):
sum = 0
for i in range(0,9): #Fucking colon!
ideal = coordinates[win[i]]
current = coordinates[s1[i]] #I had board[i] instead of s1[i]
sum += abs(ideal[0] - current[0]) + abs(ideal[1] -current[1])
return sum
if HUMAN == True:
while not done:
nearest_center = -1
for event in pygame.event.get():
if event.type == pygame.QUIT:
done = True
if event.type == pygame.KEYDOWN: #need to check this or .key produces an error
if event.key == pygame.K_ESCAPE:
done = True
if event.type == pygame.MOUSEBUTTONDOWN:
posi = event.pos
print (posi)
# find nearest center -- should totally be a function of its own :p
dis = []
px = posi[0]
py = posi[1]
for i in range(0,9):
dis = dis + [(px-centers[i][0])**2 + (py-centers[i][1])**2] # powers are with **, not ^
nearest_center = dis.index(min(dis))
print ("The position is: " + str(board[nearest_center]))
# perform swap if next to 0.
if nearest_center != -1:
zero_loc = board.index(0)
adjacents = get_adjacent(nearest_center)
if zero_loc in adjacents:
board[zero_loc] = board[nearest_center]
board[nearest_center] = 0
print("The Manhattan distance is: " + str(man_distance(board)))
else:
screen.fill(RED) # red doesn't
pygame.time.wait(500) # wait works
if win == board: # works, but just closing is ugly
done = True #doing anything else without more modular code seems icky :p
screen.fill(WHITE)
# Draw the grid
for i in range(1,3): # range(1,2) only drew one line!
pygame.draw.line(screen, BLACK, [i * (WIDTH // 3), 0], [i * (WIDTH // 3),HEIGHT], 5)
pygame.draw.line(screen, BLACK, [0, i * (HEIGHT // 3)], [WIDTH, i * (HEIGHT // 3)],5)
# Draw 'numbers'
for i in range(0,9):
p = board[i]
if p != 0:
screen.blit(numbers[p], (centers[i][0] - numbers[p].get_width() // 2, centers[i][1] - numbers[p].get_height() // 2))
#pygame.draw.circle(screen, BLUE, centers[i], 10)
pygame.display.flip()
clock.tick(60)
else:
solution = aStar(board4,win)
print("Length = " + str(len(solution)))
print("Solution = " + str(solution))
for step in solution:
for event in pygame.event.get():
if event.type == pygame.QUIT:
done = True
if event.type == pygame.KEYDOWN: #need to check this or .key produces an error
if event.key == pygame.K_ESCAPE:
done = True
screen.fill(WHITE)
# Draw the grid
for i in range(1,3): # range(1,2) only drew one line!
pygame.draw.line(screen, BLACK, [i * (WIDTH // 3), 0], [i * (WIDTH // 3),HEIGHT], 5)
pygame.draw.line(screen, BLACK, [0, i * (HEIGHT // 3)], [WIDTH, i * (HEIGHT // 3)],5)
# Draw 'numbers'
for i in range(0,9):
p = step[i]
if p != 0:
screen.blit(numbers[p], (centers[i][0] - numbers[p].get_width() // 2, centers[i][1] - numbers[p].get_height() // 2))
#pygame.draw.circle(screen, BLUE, centers[i], 10)
pygame.display.flip()
pygame.time.wait(350)
clock.tick(60)
pygame.time.wait(1500)
|
[
"[email protected]"
] | |
6789dd297f2257bed4110e83f0000b3c798dea43
|
303004eb1b84f247d959a2ac75e38f6e7b156715
|
/baixar.py
|
3bcd31baf17c38c5ce04a46c8c7a0ee91754fbbb
|
[] |
no_license
|
DiegoLins10/PyTube
|
5e3f5937361196448d081d9ea18c00049e2ce017
|
1787c6fa953bc04c69acc6fa0d28ac80ac9b3e07
|
refs/heads/master
| 2023-08-07T00:48:55.701767 | 2021-09-21T01:09:39 | 2021-09-21T01:09:39 | 408,627,985 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,178 |
py
|
## Para baixar a lib pytube: pip install pytube
## baixar um video do youtube com pytube
## python youtube.py https://youtu.be/61R6Qq7mmIA
## gerar arquivo exe pyinstaller --onefile -w baixar.py
## baixar pyinstaller pip install pyinstaller
from pytube import YouTube
from tkinter import *
def baixar():
##link = str(input("Digite o link: "))
link = vlink.get()
video = YouTube(link)
stream = video.streams.get_highest_resolution()
stream.download()
print("baixado com sucesso") ##pega o conteudo
indo()
def indo():
a= "baixado com sucesso"
Label(app, text=a,background="#dde", foreground="#009", anchor=W).place(x=10, y=100, width=200, height=20)
app = Tk() ##intanciando a classe
app.title("Youtube video download")
app.geometry("500x300") ## config tamanho janela
app.configure(background="#dde") ## mudar cor
Label(app, text="Link",background="#dde", foreground="#009", anchor=W).place(x=10, y=10, width=100, height=20) ##declarar label
## posicionando o label
vlink = Entry(app)
vlink.place(x=10, y=30, width=200, height=20)
Button(app, text="Baixar", command=baixar).place(x=10, y=80, width=100, height=20)
app.mainloop()
|
[
"[email protected]"
] | |
8f220b99ce9abe542226edff8616a3c76b131572
|
e4dfc0bff7e5f04a1b730ce80e026bc9f75028a8
|
/AdminControls.py
|
5f4c8900b2c503ff800007fc4ed4c971690d391e
|
[
"MIT"
] |
permissive
|
DevinDai13/PythonSQL1
|
79f761b71c1e682d00d44e5ab5f3569b6cfd276b
|
484fba42be97cfe8a92b9662f0e368d5d9f1e634
|
refs/heads/master
| 2020-04-15T04:22:52.981505 | 2019-01-07T05:08:32 | 2019-01-07T05:08:32 | 164,380,741 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 558 |
py
|
import hashlib
import sqlite3
def adduser(user_id, role, login, password):
sqlite_file = 'waste_management.db'
conn = sqlite3.connect(sqlite_file)
c = conn.cursor()
hash_name = 'sha256'
salt = 'ssdirf993lksiqb4'
iterations = 100000
dk = hashlib.pbkdf2_hmac(hash_name, bytearray(password, 'ascii'), bytearray(salt, 'ascii'), iterations)
c.execute('''INSERT INTO users(user_id, role, login, password)
VALUES(?,?,?,?)''', (user_id, role, login, str(dk)))
conn.commit()
conn.close()
return 0
|
[
"[email protected]"
] | |
08a2e20be25c466e50a8eb46e4b760fe2d09eabf
|
d57233c4d554a9311b52301decce4a046b743ae7
|
/projects/Intro5_6.py
|
0a75ccf5ae379dc0ff39a9c1d4b1bb4cc5a3d645
|
[] |
no_license
|
josenriagu/python-mini-projects
|
69f0ad9c30cd3742bd88efd191df1b2bb5e59b59
|
b7d300d3af180cddfa65a5eb89e010febb5067de
|
refs/heads/master
| 2020-06-14T06:26:49.826332 | 2019-07-02T20:57:25 | 2019-07-02T20:57:25 | 194,932,952 | 1 | 0 | null | 2019-07-02T20:57:26 | 2019-07-02T20:49:29 | null |
UTF-8
|
Python
| false | false | 2,664 |
py
|
#---LESSON 5: Dictionaries - Working
#with Key-Value Pairs---
student = {'name': 'Jose', 'age': 22, 'courses': ['Math', 'Communications']}
print(student)
print(student['name'])
print(student['courses'])
'''
Dictionaries can contain any immutable data type
as key or value. Accessing a key that doesn't exist
results in a KeyError
'''
#---Overiding KeyErrors (using the get() function)---
print(student.get('name'))
print(student.get('phone'))
print(student.get('phone', 'Not Found'))
#--Updating values using direct assignment---
student['phone'] = '555-5555' #adds a new Key-Value pair
student['name'] = 'Jane' #changes the existing value for the given key
print(student)
#---Updating values using update() function---
student.update({'name': 'Val', 'age': 26, 'phone': '555-5555'})
print(student)
#print(dir(student))
#--- deleting values from a dictionary---
del student['age']
phone = student.pop('phone')
print(student)
print(phone) #shows the poppped value saved in 'phone' variable
#---Want to know the number of keys in a dict?---
print(len(student))
print(student.keys()) #shows the keys
print(student.values()) #shows the values
print(student.items()) #shows the keys and values
#--Looping through dictionaries--
for key in student:
print(key)
for key,value in student.items():
#"for key,value in student:" will return a ValueError
print(key, value)
#---LESSON 6: Conditionals and Boolean - If, Else
#and Elif Statements---
if True:
print('Conditional was True')
if False:
print('Conditional was True') #doesn't evaluate
language = 'Python'
if language == 'Python':
print('Conditional was True')
'''
Comparisons:
Equal:----------------==
Not Equal:------------!=
Greater Than:--------->
Less Than:------------<
Greater or Equal:----->=
Less or Equal:--------<=
Object Identity:------is
'Object identity' is used for keyword check ie
to check if values have the same id
False Values:
False
None
Zero of any numeric type
Any Empty sequence, e.g., '', (), [].
Any empty mapping, e.g., {}
Boolean Operators:
and
or
not
'''
user = 'Admin'
logged_in = False
if user == 'Admin' and logged_in:
print('Admin Page')
else:
print('Bad Creds')
if user == 'Admin' or logged_in:
print('Admin Page')
else:
print('Bad Creds')
if not logged_in:
print('Please Log In')
else:
print('Welcome')
a = [1, 2, 3]
b = [1, 2, 3]
c = a
print(a==b)
print(a is b) #evaluates to False since they do not have same memory address as can ba seen below
print(a is c)
#---Print memory locations of the objects---
print(id(a))
print(id(c))
print(id(b))
'''
'is' operator like in (a is b) is synonymous to
id(a) == id(b) check behind the scene
'''
|
[
"[email protected]"
] | |
7fa3ca550e4a232b9a12738fe935a8f44deee0be
|
dc52dac6c6fd6ea01de20abaff781922f3fba924
|
/textclf/models/classifier/resrnn.py
|
fae2f115ca3b036cc6d03b0f8b470f269f806197
|
[
"MIT"
] |
permissive
|
luopeixiang/textclf
|
d92c2306180bb4b0990943b2b7ba88447fee83c7
|
fa4ad4813bf8cb49e8a0d080110014498f8dfc47
|
refs/heads/master
| 2023-07-29T05:04:00.593454 | 2022-05-27T07:00:08 | 2022-05-27T07:00:08 | 177,423,020 | 212 | 25 |
MIT
| 2023-07-21T21:45:38 | 2019-03-24T14:05:26 |
Python
|
UTF-8
|
Python
| false | false | 1,046 |
py
|
import torch.nn as nn
from textclf.config import ResRNNClassifierConfig
from .base import Classifier
from .components import RNN, AttentionLayer
class ResRNNClassifier(Classifier):
def __init__(self, config: ResRNNClassifierConfig):
super(ResRNNClassifier, self).__init__(config)
rnn_config = config.rnn_config
rnn_config.input_size = config.input_size
self.rnn = RNN(rnn_config)
if rnn_config.bidirectional:
hidden_size = rnn_config.hidden_size*2
else:
hidden_size = rnn_config.hidden_size
self.dropout = nn.Dropout(p=config.dropout)
self.output_layer = nn.Linear(hidden_size, config.output_size)
def forward(self, embedding, seq_lens):
outputs, last_hidden = self.rnn(embedding, seq_lens)
if self.use_attention: # attention mechanism
context = self.attention_layer(outputs, seq_lens)
else:
context = last_hidden
logits = self.output_layer(self.dropout(context))
return logits
|
[
"[email protected]"
] | |
038eac37ec8ceaec8c4e9d374c6e76d0d1dee5c7
|
2f878f17b90103491b1bce08b917ea5693b8af2b
|
/Lab 4/lab4.py
|
1a30f5929dc7bfc97514c32b527b1d4a421c53c9
|
[] |
no_license
|
HyperionNKJ/Computer-Vision
|
9db98e8ba3740fed453e9269df445fcc7cca44b3
|
550ee6be84171863487b9068af26ca2073e57e2c
|
refs/heads/master
| 2023-01-22T20:20:16.807793 | 2020-11-29T21:57:27 | 2020-11-29T21:57:27 | 317,050,720 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 17,238 |
py
|
""" CS4243 Lab 4: Tracking
Please read accompanying Jupyter notebook (lab4.ipynb) and PDF (lab4.pdf) for instructions.
"""
import cv2
import numpy as np
import random
from time import time
# Part 1
def meanShift(dst, track_window, max_iter=100,stop_thresh=1):
"""Use mean shift algorithm to find an object on a back projection image.
Args:
dst (np.ndarray) : Back projection of the object histogram of shape (H, W).
track_window (tuple) : Initial search window. (x,y,w,h)
max_iter (int) : Max iteration for mean shift.
stop_thresh(float) : Threshold for convergence.
Returns:
track_window (tuple) : Final tracking result. (x,y,w,h)
"""
completed_iterations = 0
""" YOUR CODE STARTS HERE """
centroid = np.array([-1,-1])
while (True):
old_centroid = centroid
x,y,w,h = track_window
start_x = 0 if x < 0 else x
start_y = 0 if y < 0 else y
end_x = dst.shape[1]-1 if x+w >= dst.shape[1] else x+w
end_y = dst.shape[0]-1 if y+h >= dst.shape[0] else y+h
coord_x = np.array(list(np.arange(start_x,end_x)) * (end_y-start_y))
coord_y = np.array([[i]*(end_x-start_x) for i in range(start_y,end_y)]).flatten()
weights = dst[start_y:end_y, start_x:end_x].flatten()
centroid_x = coord_x.dot(weights) / weights.sum()
centroid_y = coord_y.dot(weights) / weights.sum()
centroid = np.array([centroid_y, centroid_x])
track_window = (int(centroid_x) - w//2, int(centroid_y) - h//2,w,h)
completed_iterations += 1
if (np.max(np.abs(old_centroid-centroid)) <= stop_thresh or completed_iterations == max_iter): break
""" YOUR CODE ENDS HERE """
return track_window
def IoU(bbox1, bbox2):
""" Compute IoU of two bounding boxes.
Args:
bbox1 (tuple) : First bounding box position (x, y, w, h) where (x, y) is the top left corner of
the bounding box, and (w, h) are width and height of the box.
bbox2 (tuple) : Second bounding box position (x, y, w, h) where (x, y) is the top left corner of
the bounding box, and (w, h) are width and height of the box.
Returns:
score (float) : computed IoU score.
"""
x1, y1, w1, h1 = bbox1
x2, y2, w2, h2 = bbox2
score = 0
""" YOUR CODE STARTS HERE """
box1_x_start, box1_x_end, box1_y_start, box1_y_end = x1, x1+w1, y1, y1+h1
box2_x_start, box2_x_end, box2_y_start, box2_y_end = x2, x2+w2, y2, y2+h2
x_interset = box1_x_end-box2_x_start if box1_x_start < box2_x_start else box2_x_end-box1_x_start
y_interset = box1_y_end-box2_y_start if box1_y_start < box2_y_start else box2_y_end-box1_y_start
score = (x_interset*y_interset) / (w1*h1 + w2*h2 - x_interset*y_interset)
# sumOfAreas = w1*h1 + w2*h2
# x1_prime = x1 + w1
# y1_prime = y1 + h1
# x2_prime = x2 + w2
# y2_prime = y2+ h2
# x_lower_bound = max(x1, x2)
# x_upper_bound = min(x1_prime, x2_prime)
# y_lower_bound = max(y1, y2)
# y_upper_bound = min(y1_prime, y2_prime)
# if x_upper_bound < x_lower_bound :
# return 0
# if y_upper_bound < y_lower_bound :
# return 0
# intersect = (x_upper_bound - x_lower_bound) * (y_upper_bound - y_lower_bound)
# score = intersect / (sumOfAreas - intersect)
# xA = max(x1, x2)
# yA = max(y1, y2)
# xB = min(x1+w1, x2+w2)
# yB = min(y1+h1, y2+h2)
# # compute the area of intersection rectangle
# interArea = (xB - xA) * (yB - yA)
# # compute the area of both the prediction and ground-truth
# # rectangles
# boxAArea = w1*h1
# boxBArea = w2*h2
# # compute the intersection over union by taking the intersection
# # area and dividing it by the sum of prediction + ground-truth
# # areas - the interesection area
# score = interArea / float(boxAArea + boxBArea - interArea)
# return the intersection over union value
# """ YOUR CODE ENDS HERE """
# return score
""" YOUR CODE ENDS HERE """
return score
# Part 2:
def lucas_kanade(img1, img2, keypoints, window_size=9):
""" Estimate flow vector at each keypoint using Lucas-Kanade method.
Args:
img1 (np.ndarray) : Grayscale image of the current frame.
Flow vectors are computed with respect to this frame.
img2 (np.ndarray) : Grayscale image of the next frame.
keypoints (np.ndarray) : Coordinates of keypoints to track of shape (N, 2).
window_size (int) : Window size to determine the neighborhood of each keypoint.
A window is centered around the current keypoint location.
You may assume that window_size is always an odd number.
Returns:
flow_vectors (np.ndarray) : Estimated flow vectors for keypoints. flow_vectors[i] is
the flow vector for keypoint[i]. Array of shape (N, 2).
Hints:
- You may use np.linalg.inv to compute inverse matrix.
"""
assert window_size % 2 == 1, "window_size must be an odd number"
flow_vectors = []
w = window_size // 2
# Compute partial derivatives
Iy, Ix = np.gradient(img1)
It = img2 - img1
# For each [y, x] in keypoints, estimate flow vector [vy, vx]
# using Lucas-Kanade method and append it to flow_vectors.
for y, x in keypoints:
# Keypoints can be loacated between integer pixels (subpixel locations).
# For simplicity, we round the keypoint coordinates to nearest integer.
# In order to achieve more accurate results, image brightness at subpixel
# locations can be computed using bilinear interpolation.
y, x = int(round(y)), int(round(x))
""" YOUR CODE STARTS HERE """
A = []
b = []
for j in range(y-w, y+w+1):
for i in range(x-w, x+w+1):
A.append([Ix[j][i], Iy[j][i]])
b.append(It[j][i] * -1)
v,_,_,_ = np.linalg.lstsq(A, b, rcond=None)
flow_vectors.append(v)
""" YOUR CODE ENDS HERE """
flow_vectors = np.array(flow_vectors)
return flow_vectors
def compute_error(patch1, patch2):
""" Compute MSE between patch1 and patch2
- Normalize patch1 and patch2
- Compute mean square error between patch1 and patch2
Args:
patch1 (np.ndarray) : Grayscale image patch1 of shape (patch_size, patch_size)
patch2 (np.ndarray) : Grayscale image patch2 of shape (patch_size, patch_size)
Returns:
error (float) : Number representing mismatch between patch1 and patch2.
"""
assert patch1.shape == patch2.shape, 'Differnt patch shapes'
error = 0
""" YOUR CODE STARTS HERE """
std_patch1 = np.std(patch1)
mean_patch1 = np.mean(patch1)
norm_patch1 = (patch1 - mean_patch1) / std_patch1
std_patch2 = np.std(patch2)
mean_patch2 = np.mean(patch2)
norm_patch2 = (patch2 - mean_patch2) / std_patch2
error = np.square(np.subtract(norm_patch1, norm_patch2)).mean()
""" YOUR CODE ENDS HERE """
return error
def iterative_lucas_kanade(img1, img2, keypoints,
window_size=9,
num_iters=5,
g=None):
""" Estimate flow vector at each keypoint using iterative Lucas-Kanade method.
Args:
img1 (np.ndarray) : Grayscale image of the current frame.
Flow vectors are computed with respect to this frame.
img2 (np.ndarray) : Grayscale image of the next frame.
keypoints (np.ndarray) : Coordinates of keypoints to track of shape (N, 2).
window_size (int) : Window size to determine the neighborhood of each keypoint.
A window is centered around the current keypoint location.
You may assume that window_size is always an odd number.
num_iters (int) : Number of iterations to update flow vector.
g (np.ndarray) : Flow vector guessed from previous pyramid level.
Array of shape (N, 2).
Returns:
flow_vectors (np.ndarray) : Estimated flow vectors for keypoints. flow_vectors[i] is
the flow vector for keypoint[i]. Array of shape (N, 2).
"""
assert window_size % 2 == 1, "window_size must be an odd number"
# Initialize g as zero vector if not provided
if g is None:
g = np.zeros(keypoints.shape)
flow_vectors = []
w = window_size // 2
# Compute spatial gradients
Iy, Ix = np.gradient(img1)
for y, x, gy, gx in np.hstack((keypoints, g)):
v = np.zeros(2) # Initialize flow vector as zero vector
y1 = int(round(y)); x1 = int(round(x))
""" YOUR CODE STARTS HERE """
G = np.array([[0,0],[0,0]])
# for j in range(y1-w, y1+w+1):
# for i in range(x1-w, x1+w+1):
for i in range(x1-w, x1+w+1):
for j in range(y1-w, y1+w+1):
G = G + ([
[(Ix[j][i])**2, Ix[j][i]*Iy[j][i]],
[Ix[j][i]*Iy[j][i], (Iy[j][i])**2]
])
for k in range(num_iters):
b_k = np.array([0,0])
for i in range(x1-w, x1+w+1):
for j in range(y1-w, y1+w+1):
v_x = int(round(v[0]))
v_y = int(round(v[1]))
x_idx = int(round(i + gx + v_x))
y_idx = int(round(j + gy + v_y))
temp_diff = img1[j][i] - (img2[y_idx][x_idx])
to_add = np.array([temp_diff * Ix[j][i],
temp_diff * Iy[j][i]
])
b_k = b_k + to_add
G_inv = np.linalg.inv(G)
v_k = np.matmul(G_inv, b_k)
v = v + v_k
""" YOUR CODE ENDS HERE """
vx, vy = v
flow_vectors.append([vy, vx])
return np.array(flow_vectors)
def pyramid_lucas_kanade(img1, img2, keypoints,
window_size=9, num_iters=5,
level=2, scale=2):
""" Pyramidal Lucas Kanade method
Args:
img1 (np.ndarray) : Grayscale image of the current frame.
Flow vectors are computed with respect to this frame.
img2 (np.ndarray) : Grayscale image of the next frame.
keypoints (np.ndarray) : Coordinates of keypoints to track of shape (N, 2).
window_size (int) : Window size to determine the neighborhood of each keypoint.
A window is centered around the current keypoint location.
You may assume that window_size is always an odd number.
num_iters (int) : Number of iterations to run iterative LK method
level (int) : Max level in image pyramid. Original image is at level 0 of
the pyramid.
scale (float) : Scaling factor of image pyramid.
Returns:
d - final flow vectors
"""
# Build image pyramids of img1 and img2
pyramid1 = tuple(pyramid_gaussian(img1, max_layer=level, downscale=scale))
pyramid2 = tuple(pyramid_gaussian(img2, max_layer=level, downscale=scale))
# Initialize pyramidal guess
g = np.zeros(keypoints.shape)
""" YOUR CODE STARTS HERE """
# range from l to 0
for l in range(level, -1, -1):
p_L = keypoints / (scale**l)
d = iterative_lucas_kanade(pyramid1[l], pyramid2[l], p_L, g=g)
if l != 0:
g = scale * (g + d)
""" YOUR CODE ENDS HERE """
d = g + d
return d
"""Helper functions: You should not have to touch the following functions.
"""
import os
import cv2
import matplotlib.pyplot as plt
from matplotlib import animation
from matplotlib.patches import Rectangle
from skimage import filters, img_as_float
from skimage.io import imread
from skimage.transform import pyramid_gaussian
def load_frames_rgb(imgs_dir):
frames = [cv2.cvtColor(cv2.imread(os.path.join(imgs_dir, frame)), cv2.COLOR_BGR2RGB) \
for frame in sorted(os.listdir(imgs_dir))]
return frames
def load_frames_as_float_gray(imgs_dir):
frames = [img_as_float(imread(os.path.join(imgs_dir, frame),
as_gray=True)) \
for frame in sorted(os.listdir(imgs_dir))]
return frames
def load_bboxes(gt_path):
bboxes = []
with open(gt_path) as f:
for line in f:
x, y, w, h = line.split(',')
#x, y, w, h = line.split()
bboxes.append((int(x), int(y), int(w), int(h)))
return bboxes
def animated_frames(frames, figsize=(10,8)):
fig, ax = plt.subplots(figsize=figsize)
ax.axis('off')
im = ax.imshow(frames[0])
def animate(i):
im.set_array(frames[i])
return [im,]
ani = animation.FuncAnimation(fig, animate, frames=len(frames),
interval=60, blit=True)
return ani
def animated_bbox(frames, bboxes, figsize=(10,8)):
fig, ax = plt.subplots(figsize=figsize)
ax.axis('off')
im = ax.imshow(frames[0])
x, y, w, h = bboxes[0]
bbox = ax.add_patch(Rectangle((x,y),w,h, linewidth=3,
edgecolor='r', facecolor='none'))
def animate(i):
im.set_array(frames[i])
bbox.set_bounds(*bboxes[i])
return [im, bbox,]
ani = animation.FuncAnimation(fig, animate, frames=len(frames),
interval=60, blit=True)
return ani
def animated_scatter(frames, trajs, figsize=(10,8)):
fig, ax = plt.subplots(figsize=figsize)
ax.axis('off')
im = ax.imshow(frames[0])
scat = ax.scatter(trajs[0][:,1], trajs[0][:,0],
facecolors='none', edgecolors='r')
def animate(i):
im.set_array(frames[i])
if len(trajs[i]) > 0:
scat.set_offsets(trajs[i][:,[1,0]])
else: # If no trajs to draw
scat.set_offsets([]) # clear the scatter plot
return [im, scat,]
ani = animation.FuncAnimation(fig, animate, frames=len(frames),
interval=60, blit=True)
return ani
def track_features(frames, keypoints,
error_thresh=1.5,
optflow_fn=pyramid_lucas_kanade,
exclude_border=5,
**kwargs):
""" Track keypoints over multiple frames
Args:
frames - List of grayscale images with the same shape.
keypoints - Keypoints in frames[0] to start tracking. Numpy array of
shape (N, 2).
error_thresh - Threshold to determine lost tracks.
optflow_fn(img1, img2, keypoints, **kwargs) - Optical flow function.
kwargs - keyword arguments for optflow_fn.
Returns:
trajs - A list containing tracked keypoints in each frame. trajs[i]
is a numpy array of keypoints in frames[i]. The shape of trajs[i]
is (Ni, 2), where Ni is number of tracked points in frames[i].
"""
kp_curr = keypoints
trajs = [kp_curr]
patch_size = 3 # Take 3x3 patches to compute error
w = patch_size // 2 # patch_size//2 around a pixel
for i in range(len(frames) - 1):
I = frames[i]
J = frames[i+1]
flow_vectors = optflow_fn(I, J, kp_curr, **kwargs)
kp_next = kp_curr + flow_vectors
new_keypoints = []
for yi, xi, yj, xj in np.hstack((kp_curr, kp_next)):
# Declare a keypoint to be 'lost' IF:
# 1. the keypoint falls outside the image J
# 2. the error between points in I and J is larger than threshold
yi = int(round(yi)); xi = int(round(xi))
yj = int(round(yj)); xj = int(round(xj))
# Point falls outside the image
if yj > J.shape[0]-exclude_border-1 or yj < exclude_border or\
xj > J.shape[1]-exclude_border-1 or xj < exclude_border:
continue
# Compute error between patches in image I and J
patchI = I[yi-w:yi+w+1, xi-w:xi+w+1]
patchJ = J[yj-w:yj+w+1, xj-w:xj+w+1]
error = compute_error(patchI, patchJ)
if error > error_thresh:
continue
new_keypoints.append([yj, xj])
kp_curr = np.array(new_keypoints)
trajs.append(kp_curr)
return trajs
|
[
"[email protected]"
] | |
e23788ac80dab20c3cb46f534e00b56e8af74fb5
|
b6f780c8c751b224da2ec86017a69b6f9dcea275
|
/accounts/migrations/0007_auto_20210307_1517.py
|
5fb4e8b21ffa153ca0244206cda9fd7cc49f0f2d
|
[] |
no_license
|
alishkb/m42.django
|
c36d3b01c799323991cd875da48c2859ce82aada
|
bc99755889989acc45ae81c3b122194bce86fc18
|
refs/heads/master
| 2023-05-15T00:51:20.440239 | 2021-06-10T11:56:14 | 2021-06-10T11:56:14 | 334,120,936 | 2 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,251 |
py
|
# Generated by Django 3.1.5 on 2021-03-07 11:47
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('auth', '0012_alter_user_first_name_max_length'),
('accounts', '0006_auto_20210307_1515'),
]
operations = [
migrations.AddField(
model_name='user',
name='groups',
field=models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups'),
),
migrations.AddField(
model_name='user',
name='is_superuser',
field=models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status'),
),
migrations.AddField(
model_name='user',
name='user_permissions',
field=models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions'),
),
]
|
[
"[email protected]"
] | |
1a4e16d26cbece1da8bf316ed9f2e9de8365031c
|
508e3aa5be4a48354811726cdbf6555013fb431c
|
/main/services/product.py
|
e41edf07e5debd435859064954b3ad3c1d066110
|
[] |
no_license
|
JoaoRicardoSimplicio/buying_cheap
|
eb32362e0cb40048547366666890b25e631e1616
|
6561411af72f5a69a22e1474c11f507807f5ca1c
|
refs/heads/master
| 2023-06-27T06:11:09.927143 | 2021-07-31T17:12:52 | 2021-08-01T23:18:44 | 294,758,790 | 0 | 0 | null | 2020-12-01T12:00:36 | 2020-09-11T17:16:53 |
Python
|
UTF-8
|
Python
| false | false | 1,903 |
py
|
from main.crawlers.shop2gether import StoreShop2gether
from main.crawlers.kabum import StoreKabum
from main.crawlers.netshoes import StoreNetshoes
from main.crawlers.mercado_livre import StoreMercadoLivre
from main.crawlers.bikepointsc import StoreBikePointSC
from main.crawlers.bikeinn import Bikeinn
# from main.crawlers.zattini import StoreZattini
from main.crawlers.dafiti import StoreDafiti
from main.models import Product, Store
class ProductTool:
def __init__(self):
pass
def create(self, *args, **kwargs):
for item in args:
store_name = select_store(item['name'])
try:
store_crawler = store_name(item['url_product'])
store, _ = Store.objects.get_or_create(
name=store_crawler.store
)
product, _ = Product.objects.update_or_create(
url_product=store_crawler.url,
defaults={
'name': store_crawler.name,
'store': store,
'price_product': store_crawler.price,
'description_product': store_crawler.description,
'url_image': store_crawler.image,
'sizes': store_crawler.avaliable_sizes
}
)
except Exception as Error:
raise Error
def select_store(name):
if name == "Shop2gether":
return StoreShop2gether
elif name == "Kabum":
return StoreKabum
elif name == "Netshoes":
return StoreNetshoes
elif name == "MercadoLivre":
return StoreMercadoLivre
elif name == "BikePointSC":
return StoreBikePointSC
elif name == "Zattini":
return StoreZattini
elif name == "Dafiti":
return StoreDafiti
elif name == "Bikeinn":
return Bikeinn
|
[
"[email protected]"
] | |
d07d964851d7ea84722cc1c566fdb976f5049c0a
|
10d98fecb882d4c84595364f715f4e8b8309a66f
|
/non_semantic_speech_benchmark/distillation/train_keras_test.py
|
58293b999787e89c984afb7ffed56dbb033ecc48
|
[
"CC-BY-4.0",
"Apache-2.0"
] |
permissive
|
afcarl/google-research
|
51c7b70d176c0d70a5ee31ea1d87590f3d6c6f42
|
320a49f768cea27200044c0d12f394aa6c795feb
|
refs/heads/master
| 2021-12-02T18:36:03.760434 | 2021-09-30T20:59:01 | 2021-09-30T21:07:02 | 156,725,548 | 1 | 0 |
Apache-2.0
| 2018-11-08T15:13:53 | 2018-11-08T15:13:52 | null |
UTF-8
|
Python
| false | false | 3,089 |
py
|
# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Tests for non_semantic_speech_benchmark.eval_embedding.keras.train_keras."""
from absl import flags
from absl.testing import absltest
from absl.testing import flagsaver
from absl.testing import parameterized
import mock
import tensorflow as tf
from non_semantic_speech_benchmark.distillation import train_keras
def _get_data(*args, **kwargs):
del args
assert 'samples_key' in kwargs
assert 'min_length' in kwargs
assert 'batch_size' in kwargs
bs = kwargs['batch_size']
samples = tf.zeros((bs, 16000), tf.float32)
targets = tf.ones([bs, 10], tf.float32)
return tf.data.Dataset.from_tensors((samples, targets)).repeat()
class TrainKerasTest(parameterized.TestCase):
@parameterized.parameters(
{'bottleneck_dimension': 3, 'alpha': 1.0},
{'bottleneck_dimension': 5, 'alpha': 0.5},
)
def test_get_model(self, bottleneck_dimension, alpha):
batched_samples = tf.zeros([3, 16000])
output_dimension = 10
targets = tf.ones([3, output_dimension])
model = train_keras.models.get_keras_model(
f'mobilenet_debug_{alpha}_False',
bottleneck_dimension=bottleneck_dimension,
output_dimension=output_dimension)
loss_obj = tf.keras.losses.MeanSquaredError()
opt = tf.keras.optimizers.Adam()
train_loss = tf.keras.metrics.MeanSquaredError()
train_mae = tf.keras.metrics.MeanAbsoluteError()
summary_writer = tf.summary.create_file_writer(
absltest.get_default_test_tmpdir())
train_step = train_keras.get_train_step(
model, loss_obj, opt, train_loss, train_mae, summary_writer)
gstep = opt.iterations
train_step(batched_samples, targets, gstep)
self.assertEqual(1, gstep)
train_step(batched_samples, targets, gstep)
self.assertEqual(2, gstep)
@mock.patch.object(train_keras.get_data, 'get_data', new=_get_data)
@mock.patch.object(train_keras.hub, 'load')
@flagsaver.flagsaver
def test_full_flow(self, mock_load):
del mock_load
flags.FLAGS.file_pattern = 'dummy'
flags.FLAGS.teacher_model_hub = 'dummy'
flags.FLAGS.output_key = 'dummmy'
flags.FLAGS.bottleneck_dimension = 2
flags.FLAGS.output_dimension = 10
flags.FLAGS.shuffle_buffer_size = 4
flags.FLAGS.samples_key = 'audio'
flags.FLAGS.logdir = absltest.get_default_test_tmpdir()
train_keras.train_and_report(debug=True)
if __name__ == '__main__':
tf.compat.v2.enable_v2_behavior()
assert tf.executing_eagerly()
absltest.main()
|
[
"[email protected]"
] | |
ba106a98267a6ec0d424113b2870654dbf4698b9
|
3154e6d1a9e9e9919cae75570969da36c45429d7
|
/codigo/tutorial/tut0C_camara.py
|
9e54589237d6c51292d941cdce95c822a95243c0
|
[] |
no_license
|
javacasm/TutorialPyGame
|
0d458c7155794668fc1464c466e4d740b3ac77ee
|
baeb7ce5dda151f8093e39f8b14182a8ee5de926
|
refs/heads/master
| 2021-07-25T20:01:04.504958 | 2021-05-10T12:33:26 | 2021-05-10T12:33:26 | 250,080,620 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,568 |
py
|
https://www.pygame.org/docs/tut/CameraIntro.html
```python
class Capture(object):
def __init__(self):
self.size = (640,480)
# create a display surface. standard pygame stuff
self.display = pygame.display.set_mode(self.size, 0)
# this is the same as what we saw before
self.clist = pygame.camera.list_cameras()
if not self.clist:
raise ValueError("Sorry, no cameras detected.")
self.cam = pygame.camera.Camera(self.clist[0], self.size)
self.cam.start()
# create a surface to capture to. for performance purposes
# bit depth is the same as that of the display surface.
self.snapshot = pygame.surface.Surface(self.size, 0, self.display)
def get_and_flip(self):
# if you don't want to tie the framerate to the camera, you can check
# if the camera has an image ready. note that while this works
# on most cameras, some will never return true.
if self.cam.query_image():
self.snapshot = self.cam.get_image(self.snapshot)
# blit it to the display surface. simple!
self.display.blit(self.snapshot, (0,0))
pygame.display.flip()
def main(self):
going = True
while going:
events = pygame.event.get()
for e in events:
if e.type == QUIT or (e.type == KEYDOWN and e.key == K_ESCAPE):
# close the camera safely
self.cam.stop()
going = False
self.get_and_flip()
```
|
[
"[email protected]"
] | |
b2b49c260bbc149b1e3d30761562a7747b95be52
|
6f1bd642081902970be5d59bebb2df87c2a32fb0
|
/Source/TensorFlowModels/Media-Eval/Regression/preprocess.py
|
1a89d76aaf9e7090c40ebd180ea03ae4506cacd8
|
[] |
no_license
|
aneekroy/mir
|
b317d5ff49341507ad223016d9488763d4dcc71b
|
1afdc020acc6732b4dab488d7070951a2ff8eaa6
|
refs/heads/master
| 2020-03-21T17:48:10.747151 | 2018-06-28T11:26:38 | 2018-06-28T11:26:38 | 138,854,870 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 4,796 |
py
|
import os
import math
import numpy as np
import ffmpy
from python_speech_features import *
import scipy.io.wavfile as wav
import matplotlib.pyplot as plt
import random
import Image
import csv
# This is subject to change if cross-validation is applied. Sticking to this cheap stuff for the time being.
TRAIN_PERCENT = 0.8
def normalize(A):
M = [[x]*A.shape[1] for x in np.mean(A, axis = 1)]
S = [[x]*A.shape[1] for x in np.std(A, axis = 1) if x is not 0]
A = (A - M)
for i in range(len(A)):
for j in range(len(A[i])):
if S[i][j] != 0.0:
A[i][j] = A[i][j]/S[i][j]
return A
'''
Converts mp3 files in mp3dir to wav files in wavdir
'''
def convert_mp3_to_wav(mp3dir, wavdir):
for _,_,files in os.walk(mp3dir):
for file in files:
filename = file[:file.find(".mp3")]
ff = ffmpy.FFmpeg(inputs = {os.path.join(mp3dir, filename + ".mp3"):None}, outputs = {os.path.join(wavdir, filename + ".wav"):None})
ff.run()
'''
Converts wav files in wavdir to corresponding spectograms in specdir
'''
def convert_wav_to_spec(wavdir, specdir):
for _,_, files in os.walk(wavdir):
for file in files:
filename = file[:file.find(".wav")]
(rate, sig) = wav.read(os.path.join(wavdir, file))
sig = sig[:, 0]
plt.clf()
_,_,_,spec = plt.specgram(sig, NFFT = 256, Fs = rate, noverlap = 16, cmap = plt.cm.jet)
image = Image.fromarray(spec)
image = image.convert("L")
image.save(os.path.join(specdir, filename + ".jpg"))
LAB_CLASSES = {"Happy":0, "Sad":1, "Tender":2, "Anger_Fear":3}
'''
Extracts mfcc and delta features from the wav files and
'''
def extract_features(wavdir, featuredir):
f = open("/home/soms/EmotionMusic/MediaEval/new-label-file.txt", "w")
infohandle = open("/home/soms/EmotionMusic/MediaEval-2013/annotations/songs_info.csv", "r")
labelhandle = open("/home/soms/EmotionMusic/MediaEval-2013/annotations/static_annotations.csv", "r")
reader1 = csv.reader(infohandle)
train = []
test = []
i = 0
for row in reader1:
if i !=0 :
if row[-1] == "development":
train.append(row[0])
else:
test.append(row[0])
i += 1
i = 0
labels = {}
reader2 = csv.reader(labelhandle)
m_a = 999.
M_a = -999.
m_v = 999.
M_v = -999.
for row in reader2:
if i != 0:
if float(row[1]) > M_a:
M_a = float(row[1])
if float(row[1]) < m_a:
m_a = float(row[1])
if float(row[3]) > M_v:
M_v = float(row[3])
if float(row[3]) < m_v:
m_v = float(row[3])
labels[row[0]] = [float(row[1]), float(row[3])]
i += 1
for label in labels.keys():
labels[label][0] = float(labels[label][0] - m_a)/(M_a - m_a) - 0.5
labels[label][1] = float(labels[label][1] - m_v)/(M_v - m_v) - 0.5
for _,_, files in os.walk(wavdir):
# Min shape 0 is 4458
m = 9999
cnt = 0
for file in files:
filename = file[:file.find(".wav")]
print(file)
(rate, sig) = wav.read(os.path.join(wavdir, file))
log_energy_feat = normalize(logfbank(sig, rate, winlen = 0.025, winstep = 0.01, nfilt = 32, nfft = 1024))
shape = log_energy_feat.shape
if shape[0] < 4500:
log_energy_feat = np.resize(np.asarray(log_energy_feat), [4500, shape[1]])
log_energy_feat = log_energy_feat[:4500,:]
image = np.split(log_energy_feat, 75)
print("Image segmented shape", np.asarray(image).shape)
index = 1
if filename in train:
cnt += 1
for image_segment in image:
if image_segment.shape[0] >= 57:
image_segment = image_segment[image_segment.shape[0] - 57 : ]
else:
print("Shape error",image_segment.shape[0])
if image_segment.shape[0] < m:
m = image_segment.shape[0]
print(image_segment.shape)
#plt.imsave(os.path.join(featuredir + "/Train", file + "_" + str(index) + ".png"), image_segment.T, cmap = "gray")
f.write(filename + "_" + str(index) + " " + str(labels[filename][0]) + " " + str(labels[filename][1]) + " D\n")
index += 1
elif filename in test:
cnt += 1
for image_segment in image:
image_segment = image_segment[image_segment.shape[0] - 57 : ]
if image_segment.shape[0] < m:
m = image_segment.shape[0]
print(image_segment.shape)
#plt.imsave(os.path.join(featuredir + "/Test", file + "_" + str(index) + ".png"), image_segment.T, cmap = "gray")
f.write(filename + "_" + str(index) + " " + str(labels[filename][0]) + " " + str(labels[filename][1]) + " E\n")
index += 1
else:
print("Duplicate")
print("Count = ", str(cnt))
print(m)
if __name__ == "__main__":
mp3dir = "/home/soms/EmotionMusic/MediaEval-2013/clips_45seconds"
wavdir = "/home/soms/EmotionMusic/MediaEval/WavFiles"
specdir = "/home/soms/EmotionMusic/Spectograms"
featuredir = "/home/soms/EmotionMusic/MediaEval/Spec_Feat"
#convert_mp3_to_wav(mp3dir, wavdir)
#convert_wav_to_spec(wavdir, specdir)
extract_features(wavdir, featuredir)
|
[
"[email protected]"
] | |
33a5265e87b96528d5b119a0bb47d33d67d2ce07
|
cc434c30dada236a0101be25510f172f6b3e0a43
|
/test/__main__.py
|
9d9c76746678514c68099418204a6fccd236ecb0
|
[
"Apache-2.0"
] |
permissive
|
TomasTomecek/osbuild
|
13c1896452336c8a880d4de50627309f91271297
|
9f5f4ebfa677783926888810b6b5c40453f1fbcf
|
refs/heads/master
| 2020-07-16T20:58:30.798496 | 2019-08-30T09:54:58 | 2019-09-02T08:28:21 | 205,867,643 | 0 | 0 |
Apache-2.0
| 2020-01-14T09:52:39 | 2019-09-02T13:52:13 |
Python
|
UTF-8
|
Python
| false | false | 2,768 |
py
|
import argparse
import logging
import subprocess
import os
from test.integration_tests.test_case import IntegrationTestCase, IntegrationTestType
from test.integration_tests.config import *
logging.basicConfig(level=logging.getLevelName(os.environ.get("TESTS_LOGLEVEL", "INFO")))
def test_is_system_running(result):
assert result.strip() == "running"
def test_timezone(extract_dir):
link = os.readlink(f"{extract_dir}/etc/localtime")
assert "Europe/Prague" in link
def test_firewall(extract_dir):
with open(f"{extract_dir}/etc/firewalld/zones/public.xml") as f:
content = f.read()
assert 'service name="http"' in content
assert 'service name="ftp"' in content
assert 'service name="telnet"' not in content
assert 'port port="53" protocol="tcp"' in content
assert 'port port="88" protocol="udp"' in content
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Run integration tests')
parser.add_argument('--list', dest='list', action='store_true', help='list test cases')
parser.add_argument('--build-pipeline', dest='build_pipeline', metavar='PIPELINE',
type=os.path.abspath, help='the build pipeline to run tests in')
parser.add_argument('--case', dest='specific_case', metavar='TEST_CASE', help='run single test case')
args = parser.parse_args()
logging.info(f"Using {OBJECTS} for objects storage.")
logging.info(f"Using {OUTPUT_DIR} for output images storage.")
logging.info(f"Using {OSBUILD} for building images.")
f30_boot = IntegrationTestCase(
name="f30-boot",
pipeline="f30-boot.json",
build_pipeline=args.build_pipeline,
output_image="f30-boot.qcow2",
test_cases=[test_is_system_running],
type=IntegrationTestType.BOOT_WITH_QEMU
)
timezone = IntegrationTestCase(
name="timezone",
pipeline="timezone.json",
build_pipeline=args.build_pipeline,
output_image="timezone.tar.xz",
test_cases=[test_timezone],
type=IntegrationTestType.EXTRACT
)
firewall = IntegrationTestCase(
name="firewall",
pipeline="firewall.json",
build_pipeline=args.build_pipeline,
output_image="firewall.tar.xz",
test_cases=[test_firewall],
type=IntegrationTestType.EXTRACT
)
cases = [f30_boot, timezone, firewall]
if args.list:
print("Available test cases:")
for case in cases:
print(f" - {case.name}")
else:
if not args.specific_case:
for case in cases:
case.run()
else:
for case in cases:
if case.name == args.specific_case:
case.run()
|
[
"[email protected]"
] | |
867accc1fa0ae63cde0cb8f95b38ab6d178fb261
|
f9141cb0e7677d9892fe1edddad3dd20db96fc0a
|
/rule_class.py
|
e91f8826a7ec8eb72061b57bf50b4cbba436e3a9
|
[
"MIT"
] |
permissive
|
andytaylor823/play-euchre
|
beb47d26dbf35d08de97e4b51b2712338a69fe68
|
32887980487e07865b799de96069f50866760a12
|
refs/heads/master
| 2020-08-02T13:53:10.030200 | 2020-04-27T12:01:23 | 2020-04-27T12:01:23 | 211,376,784 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,271 |
py
|
import basicprogs as b
import boardstate_class as bsc
from inspect import signature # use this to check the "condition" argument
class rule:
def __init__(self, condition, rule_type, name='rule'):
if not callable(condition):
print('Error: condition argument must be a callable function')
raise(ValueError)
sig = signature(condition)
if len(sig.parameters) != 2:
print('Error: condition argument can only take two arguments')
raise(ValueError)
if not isinstance(rule_type, str):
print('Error: rule_type argument must be a string')
raise(ValueError)
if rule_type.lower() not in ['lead', 'follow', 'call']:
print('Error: rule_type argument can only be "lead", "follow", or "call"')
raise(ValueError)
self.type = rule_type
self.condition = condition
self.name = name
def is_satisfied(self, board, pos):
if not isinstance(board, bsc.boardstate):
print('Error: improper board argument')
raise(ValueError)
if not isinstance(pos, str):
print('Error: position argument not a string')
raise(ValueError)
if pos.lower() not in ['o1', 'o2', 'p', 'd']:
print('Error: invalid position choice given')
raise(ValueError)
c, have = self.condition(board, pos)
if have: return(c)
else: return(None)
|
[
"[email protected]"
] | |
bcdcdba6ff316a16065b95a2bba284abc290a417
|
9d25d1205da84db33bc425266bc3021cd7529cb1
|
/digitalearthau/testing/plugin.py
|
b73fdee1aba0e11cd5d8c9a183a595c1b7c6e754
|
[] |
no_license
|
GeoscienceAustralia/digitalearthau
|
9068970b2794a4ac55a34f910caa5877b548bb37
|
4cf486eb2a93d7de23f86ce6de0c3af549fe42a9
|
refs/heads/develop
| 2023-06-22T14:31:41.516829 | 2022-11-14T05:22:05 | 2022-11-14T05:22:05 | 51,411,119 | 31 | 21 | null | 2023-06-14T06:36:31 | 2016-02-10T00:16:36 |
Python
|
UTF-8
|
Python
| false | false | 2,081 |
py
|
import itertools
import os
import pytest
from pathlib import Path
from typing import Iterable
import datacube
import digitalearthau
import digitalearthau.system
from datacube.config import LocalConfig
from . import factories
# These are unavoidable in pytests due to fixtures
# pylint: disable=redefined-outer-name,protected-access,invalid-name
try:
from yaml import CSafeLoader as SafeLoader
except ImportError:
from yaml import SafeLoader
# The default test config options.
# The user overrides these by creating their own file in ~/.datacube_integration.conf
INTEGRATION_DEFAULT_CONFIG_PATH = Path(__file__).parent.joinpath('testing-default.conf')
def pytest_report_header(config):
if config.getoption('verbose') > 0:
return (
f"digitaleathau {digitalearthau.__version__}, "
f"opendatacube {datacube.__version__}"
)
return None
@pytest.fixture(scope='session')
def integration_config_paths():
if not INTEGRATION_DEFAULT_CONFIG_PATH.exists():
# Safety check. We never want it falling back to the default config,
# as it will alter/wipe the user's own datacube to run tests
raise RuntimeError(
'Integration default file not found. This should be built-in?')
return (
str(INTEGRATION_DEFAULT_CONFIG_PATH),
os.path.expanduser('~/.datacube_integration.conf')
)
@pytest.fixture(scope='session')
def global_integration_cli_args(integration_config_paths: Iterable[str]):
"""
The first arguments to pass to a cli command for integration test configuration.
"""
# List of a config files in order.
return list(
itertools.chain(*(('--config_file', f) for f in integration_config_paths)))
@pytest.fixture(scope='session')
def local_config(integration_config_paths):
return LocalConfig.find(integration_config_paths)
# Default fixtures which will drop/create on every individual test function.
db = factories.db_fixture('local_config')
index = factories.index_fixture('db')
dea_index = factories.dea_index_fixture('index')
|
[
"[email protected]"
] | |
30da4a8a5a19228bd3e60af5efea4574934cafb1
|
975452a3dd216b69b4042a1bc24f44b3a1cccf5f
|
/simple/commons.py
|
8c1116fe9bbd0bca3f18f0c80a606aeb5840c6cd
|
[] |
no_license
|
freedream520/django-ozgweb
|
b9e8e0a7c26500b4f3253e1a50d85d5987d2f40a
|
33c479d4a1c3800018ab4312f2c692a76af25241
|
refs/heads/master
| 2020-04-01T23:07:18.719337 | 2014-12-25T03:30:53 | 2014-12-25T03:30:53 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 6,029 |
py
|
import os
import sys
import random
import io
from django.shortcuts import render
from django.http import JsonResponse
from django.http import HttpResponse
from PIL import Image, ImageDraw, ImageFont
from math import ceil
from . import cfg
#验证码部分
#修改自https://github.com/tianyu0915/DjangoCaptcha，以支持python3
current_path = os.path.normpath(os.path.dirname(__file__))
class Captcha(object):
def __init__(self,request):
""" 初始化,设置各种属性
"""
self.django_request = request
self.session_key = '_django_captcha_key'
self.words = self._get_words()
# 验证码图片尺寸
self.img_width = 150
self.img_height = 30
self.type = 'number'
def _get_font_size(self):
""" 将图片高度的80%作为字体大小
"""
s1 = int(self.img_height * 0.8)
s2 = int(self.img_width // len(self.code))
return int(min((s1,s2)) + max((s1, s2)) * 0.05)
def _get_words(self):
""" 读取默认的单词表
"""
#TODO 扩充单词表
file_path = os.path.join(current_path, 'words.list')
f = open(file_path, 'r')
return [line.replace('\n', '') for line in f.readlines()]
def _set_answer(self,answer):
""" 设置答案
"""
self.django_request.session[self.session_key] = str(answer)
def _yield_code(self):
""" 生成验证码文字,以及答案
"""
# 英文单词验证码
def word():
code = random.sample(self.words,1)[0]
self._set_answer(code)
return code
# 数字公式验证码
def number():
m, n = 1, 50
x = random.randrange(m, n)
y = random.randrange(m, n)
r = random.randrange(0 ,2)
if r == 0:
code = "%s - %s = ?" % (x, y)
z = x - y
else:
code = "%s + %s = ?" % (x, y)
z = x + y
self._set_answer(z)
return code
fun = eval(self.type.lower())
return fun()
def display(self):
""" 生成验证码图片
"""
# font color
self.font_color = ['black', 'darkblue', 'darkred']
# background color
self.background = (random.randrange(230, 255), random.randrange(230, 255), random.randrange(230, 255))
# font path
self.font_path = os.path.join(current_path, 'timesbi.ttf')
#self.font_path = os.path.join(current_path, 'Menlo.ttc')
# clean
self.django_request.session[self.session_key] = ''
# creat a image
im = Image.new('RGB', (self.img_width, self.img_height), self.background)
self.code = self._yield_code()
# set font size automaticly
self.font_size = self._get_font_size()
# creat a pen
draw = ImageDraw.Draw(im)
# draw noisy point/line
if self.type == 'word':
c = int(8 // len(self.code) * 3) or 3
elif self.type == 'number':
c = 4
for i in range(random.randrange(c - 2, c)):
line_color = (random.randrange(0, 255), random.randrange(0, 255),random.randrange(0, 255))
xy = (
random.randrange(0, int(self.img_width * 0.2)),
random.randrange(0, self.img_height),
random.randrange(3 * self.img_width // 4, self.img_width),
random.randrange(0, self.img_height)
)
draw.line(xy, fill = line_color, width = int(self.font_size * 0.1))
#draw.arc(xy,fill = line_color, width = int(self.font_size * 0.1))
#draw.arc(xy, 0, 1400, fill = line_color)
# draw code
j = int(self.font_size * 0.3)
k = int(self.font_size * 0.5)
x = random.randrange(j, k) #starts point
for i in self.code:
# 上下抖动量,字数越多,上下抖动越大
m = int(len(self.code))
y = random.randrange(1, 3)
if i in ('+', '=', '?'):
# 对计算符号等特殊字符放大处理
m = ceil(self.font_size * 0.8)
else:
# 字体大小变化量,字数越少,字体大小变化越多
m = random.randrange(0, int( 45 // self.font_size) + int(self.font_size // 5))
self.font = ImageFont.truetype(self.font_path.replace('\\', '/'),self.font_size + int(ceil(m)))
draw.text((x, y), i, font = self.font, fill = random.choice(self.font_color))
x += self.font_size * 0.9
del x
del draw
buf = io.BytesIO()
im.save(buf, 'gif')
buf.closed
return HttpResponse(buf.getvalue(), 'image/gif')
def check(self, code):
"""
检查用户输入的验证码是否正确
"""
_code = self.django_request.session.get(self.session_key) or ''
self.django_request.session[self.session_key] = ''
return _code.lower() == str(code).lower()
#验证码部分 end
#公用的render函数，主要加入一些公用变量
def render_template(request, templates, res_data = None):
response_data = {
"cfg_jquery": cfg.jquery,
"cfg_title": cfg.web_name
}
if(res_data != None):
response_data["res_data"] = res_data
return render(request, templates, response_data)
#仅在这个模块用到
def res(res_code, desc, data):
res_data = {
"code": res_code,
"desc": desc,
}
if data:
res_data["data"] = data
response = JsonResponse(res_data)
return response
#回应请求成功
def res_success(desc, data = None):
return res(0, desc, data)
#回应请求失败
def res_fail(res_code, desc, data = None):
return res(res_code, desc, data)
#计算总页数
def page_count(count, page_size):
if(count % page_size == 0):
return (count // page_size)
else:
return (count // page_size) + 1;
|
[
"[email protected]"
] | |
61c84d50f2b2a028c625d955f4e469b7c998c883
|
cc5bdc851cc48050c869fced9373d8d5ff2af375
|
/env/lib/python3.8/site-packages/torpedo/clients.py
|
dbb744031972adba48afc1329b0491bf2a32e848
|
[
"Apache-2.0",
"BSD-3-Clause"
] |
permissive
|
vivekgupta1mg/miniprojectapp
|
fa6e4b8fd4565529b0c693a15abda5e5ce346dc4
|
2584a1696cc847767998c4fb5cf3413b006d6f67
|
refs/heads/master
| 2023-08-24T12:57:51.653656 | 2021-10-12T16:15:25 | 2021-10-12T16:15:25 | 416,296,679 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 4,193 |
py
|
import threading
from elasticapm import Client, async_capture_span
from elasticapm.instrumentation import register
from elasticapm.instrumentation.packages.asyncio.aiohttp_client import \
AioHttpClientInstrumentation
from elasticapm.instrumentation.packages.asyncio.asyncpg import \
AsyncPGInstrumentation
from elasticapm.instrumentation.packages.dbapi2 import extract_signature
from elasticapm.instrumentation.register import _instrumentation_singletons
from elasticapm.traces import DroppedSpan, execution_context
from elasticapm.utils import (default_ports, get_host_from_url, sanitize_url,
url_to_destination)
from elasticapm.utils.disttracing import TracingOptions
from elasticapm.utils.module_import import import_string
from .common_utils import CONFIG
_lock = threading.Lock()
apm_config = CONFIG.config.get('APM')
apm_config['SERVICE_NAME'] = CONFIG.config.get('NAME', 'undefined')
class CustomAioHttpClientInstrumentation(AioHttpClientInstrumentation):
async def call(self, module, method, wrapped, instance, args, kwargs):
method = kwargs["method"] if "method" in kwargs else args[0]
url = kwargs["url"] if "url" in kwargs else args[1]
url = str(url)
destination = url_to_destination(url)
signature = " ".join([method.upper(), get_host_from_url(url)])
sub_type = get_host_from_url(url)
url = sanitize_url(url)
transaction = execution_context.get_transaction()
async with async_capture_span(
signature,
span_type="external",
span_subtype=sub_type,
extra={"http": {"url": url}, "destination": destination},
leaf=True,
) as span:
leaf_span = span
while isinstance(leaf_span, DroppedSpan):
leaf_span = leaf_span.parent
parent_id = leaf_span.id if leaf_span else transaction.id
trace_parent = transaction.trace_parent.copy_from(
span_id=parent_id, trace_options=TracingOptions(recorded=True)
)
headers = kwargs.get("headers") or {}
self._set_disttracing_headers(headers, trace_parent, transaction)
kwargs["headers"] = headers
response = await wrapped(*args, **kwargs)
if response:
if span.context:
span.context["http"]["status_code"] = response.status
span.set_success() if response.status < 400 else span.set_failure() # pylint: disable=W0106
return response
class CustomAsyncPGInstrumentation(AsyncPGInstrumentation):
async def call(self, module, method, wrapped, instance, args, kwargs):
query = args[0] if len(args) else kwargs["query"]
name = extract_signature(query)
context = {"db": {"type": "sql", "statement": query}}
action = "query"
destination_info = {
"address": kwargs.get("host", "localhost"),
"port": int(kwargs.get("port", default_ports.get("postgresql"))),
"service": {"name": "postgres", "resource": "postgres", "type": "db"},
}
context['destination'] = destination_info
async with async_capture_span(
name, leaf=True, span_type="db", span_subtype="postgres", span_action=action, extra=context
):
return await wrapped(*args, **kwargs)
def instrument():
"""
Instruments all registered methods/functions with a wrapper
"""
with _lock:
for obj in register.get_instrumentation_objects():
custom = False
if isinstance(obj, AioHttpClientInstrumentation):
obj = 'torpedo.clients.CustomAioHttpClientInstrumentation'
custom = True
elif isinstance(obj, AsyncPGInstrumentation):
obj = 'torpedo.clients.CustomAsyncPGInstrumentation'
custom = True
if custom:
cls = import_string(obj)
_instrumentation_singletons[obj] = cls()
obj = _instrumentation_singletons[obj]
obj.instrument()
apm_client = Client(config=apm_config)
instrument()
|
[
"[email protected]"
] | |
d1f2676ff8d9ab324c0423935125dcea3d8e25d4
|
0affb6a667543c825dd44e85d6af6b7be5c8cf8b
|
/day11/day11_2.py
|
08c98ae924fdeff84a8823e3116c184578360091
|
[] |
no_license
|
233-wang-233/python
|
2fa4c7a7c4d7ba2579cea89d9ba30203956942d4
|
0824b9b50fba7d4557a3de60e2c0b830d6dac196
|
refs/heads/master
| 2021-01-25T22:33:42.960067 | 2020-04-02T07:55:45 | 2020-04-02T07:55:45 | 243,209,061 | 0 | 0 | null | 2020-03-02T06:50:27 | 2020-02-26T08:26:24 |
Python
|
UTF-8
|
Python
| false | false | 504 |
py
|
'''
两进程进行通信
一个输出Ping，一个输出Pong，两个进程输出的Ping和Pong加起来一共10个
'''
from multiprocessing import Process
from time import sleep
counter=0
def sub_task(string):
global counter
while counter<10:
print(string,end=' ',flush=True)
counter+=1
sleep(0.01)
def main():
Process(target=sub_task,args=('ping',)).start()
Process(target=sub_task,args=('pong',)).start()
if __name__ == '__main__':
main()
|
[
"[email protected]"
] | |
c762cc47d5dd64dfe9ce9a6360e92b0bfa00928f
|
5e5d54c3d1c6b9f5de3c4f36f486506cd415e38e
|
/pyupbit/__init__.py
|
20af8b94c42227711af1b9fe318b1239ddb3bd02
|
[] |
no_license
|
lsjhome/pyupbit
|
1cded7e189d68a4e7566c87f6b259f4ba980925f
|
68c4541bd04532bcfdf46fa2004b5db4d11b9be9
|
refs/heads/master
| 2020-04-28T09:39:28.944916 | 2019-10-26T20:16:51 | 2019-10-26T20:16:51 | 175,174,679 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 59 |
py
|
from pyupbit.pyupbit import PyUpbit
__version__ = '1.0.0'
|
[
"[email protected]"
] | |
b07882d6172e17a77755b2fa73a71cae026b5b0d
|
357ee4a4bdc976fd1fe482988a19e9e59dbe796c
|
/composeexample/settings.py
|
d9c0d7ab97d90da6accd85f8f1061a19f0d511b9
|
[] |
no_license
|
jeremiak/docker-compose-django-digital-ocean
|
76ecde4f35cdbe45ca8d918df2e646c83fdbcb93
|
746f9d0d8657b6b85b19fd5dda2a8a78a9cf2edd
|
refs/heads/master
| 2021-07-19T11:33:08.203984 | 2017-10-25T18:51:17 | 2017-10-25T18:51:17 | 108,315,111 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,208 |
py
|
"""
Django settings for composeexample project.
Generated by 'django-admin startproject' using Django 1.11.6.
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 = 'qxji#2zzj%$s$*wj8ww54x#bg$gc889eogo%oz!70vgmz8v5gw'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = ['0.0.0.0']
# Application definition
INSTALLED_APPS = [
'polls.apps.PollsConfig',
'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 = 'composeexample.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 = 'composeexample.wsgi.application'
# Database
# https://docs.djangoproject.com/en/1.11/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'postgres',
'USER': 'postgres',
'HOST': 'db',
'PORT': 5432,
}
}
# Password validation
# https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/1.11/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = '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/'
|
[
"[email protected]"
] | |
3ef50a5ab63dbe5576fb20f4c923d9c42fe54067
|
6f03463e17880ff8a1b70d27bf2176e58e27fa55
|
/scripts/daos/dao.py
|
f3a9255630163cd124c25c437fe977bf75a569c1
|
[] |
no_license
|
covid-maps/covid-maps
|
36ee3322a010f1d71b1b9d40d784fdac0270f9a1
|
e11fdc60176e427d705227a462048a83ac0f71ed
|
refs/heads/master
| 2022-06-10T03:25:17.924328 | 2020-05-04T20:18:42 | 2020-05-04T20:18:42 | 250,145,013 | 22 | 18 | null | 2020-05-03T01:33:33 | 2020-03-26T02:47:29 |
JavaScript
|
UTF-8
|
Python
| false | false | 675 |
py
|
from sqlalchemy import inspect, and_, func
from sqlalchemy.orm import sessionmaker, scoped_session
import logging
logger = logging.getLogger(__name__)
class DAO:
"""
This is the base data access object class
"""
def __init__(self, table, engine=None):
self.engine = engine
self.session = scoped_session(sessionmaker(bind=self.engine))()
self.table = table
def get(self, id):
return self.session.query(self.table).get(id)
def update(self, entity):
self.session.merge(entity)
return entity
def bulk_update(self, entities):
for entity in entities:
self.session.merge(entity)
|
[
"[email protected]"
] | |
86c5da7bb034a60aca742ec0d047145ea02fa45a
|
201edd307f8f43281d14bdec1a6dd080a1409ca9
|
/pyrave/__init__.py
|
9242209e0b5826de7c9d85d5cdae1a78ab72ecb9
|
[
"MIT"
] |
permissive
|
Olamyy/pyrave
|
1c5392444496d91f23a6af763b7cf6b77076a650
|
741bd0f68f29b0fd075b1060d53de99d91938224
|
refs/heads/master
| 2022-12-13T08:16:30.647440 | 2021-03-26T16:43:30 | 2021-03-26T16:43:30 | 122,391,698 | 11 | 7 | null | 2022-12-08T07:44:24 | 2018-02-21T20:48:21 |
Python
|
UTF-8
|
Python
| false | false | 290 |
py
|
__version__ = '1.0.3-alpha'
__author__ = "Olamilekan Wahab"
__license__ = 'MIT'
__copyright__ = 'Copyright 2017. Olamilekan Wahab'
from .payment import Payment
from .transaction import Transaction
from .misc import Misc
from .preauth import Preauth
from .encryption import RaveEncryption
|
[
"[email protected]"
] | |
f62111deb74e279775448c7d5a97f5ea7f6a8255
|
9f835d53232e954805b7ed1d93889e409209b36b
|
/1541_복습.py
|
134932438e9def1182112113c24eb401c83df29d
|
[] |
no_license
|
dmswl0311/Baekjoon
|
7c8a862fceff086b3d7740eef23b80164e1d5aeb
|
22040aff6b64d5081e86d91b0d118d1a718a4316
|
refs/heads/master
| 2023-04-29T13:48:51.448245 | 2021-05-26T14:35:32 | 2021-05-26T14:35:32 | 323,482,711 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 319 |
py
|
s = input().split('-')
sum = 0
result = []
for i in s:
if '+' in i:
a = i.split('+')
for j in a:
sum += int(j)
result.append(sum)
else:
result.append(int(i))
minus = result[0]
for i in range(1, len(result)):
minus -= result[i]
print(minus)
|
[
"[email protected]"
] | |
9f0c923813fa96aeb957cf818428b8bdd1080bfa
|
6aad7d9ba2aa2bfd058c3f953fe47b52de3725b3
|
/structs.py
|
7d9c7e6174b664ead1a172c240ab5b52dde238dd
|
[] |
no_license
|
shubhamDev73/3D
|
a3887463a8e32b535234079823e2b233f060248c
|
1ff3e5a40f98746196442f32e80b5fcd1d76ce7c
|
refs/heads/master
| 2021-05-17T17:24:39.298819 | 2020-04-02T21:17:05 | 2020-04-02T21:17:05 | 250,894,358 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 4,587 |
py
|
""" Implements basic structures used throughout """
import math
class matrix:
""" Class implementing all matrix functionality """
# Commonly used matrices as classmethods
@classmethod
def identity(cls, n=4):
# Identity matrix of degree n
result = matrix(n, n)
for i in range(n):
result.insert(i, i, 1)
return result
def __init__(self, rows=4, coloumns=4):
# Matrix is internally stored as an array of size rows (first index) x coloumns (second index)
self._rows = rows
self._coloumns = coloumns
self._matrix = [[0 for i in range(coloumns)] for i in range(rows)]
def getSize(self):
return (self._rows, self._coloumns)
def get(self, row, coloumn):
return self._matrix[row][coloumn]
def insert(self, row, coloumn, element):
self._matrix[row][coloumn] = element
return self
def __mul__(self, other):
if isinstance(other, vector):
# matrix * vector, returns vector
result = vector()
for i in range(4):
value = 0.0
for k in range(4):
value += self.get(i, k) * other.get(k)
result.insert(i, value)
return result
else:
# matrix * matrix, returns matrix
sizes = (self.getSize(), other.getSize())
if sizes[0][1] != sizes[1][0]:
raise TypeError
r = sizes[0][0]
c = sizes[1][1]
iterations = sizes[0][1]
result = matrix(r, c)
for i in range(r):
for j in range(c):
value = 0.0
for k in range(iterations):
value += self.get(i, k) * other.get(k, j)
result.insert(i, j, value)
return result
def __str__(self):
# Pretty formatting
string = ""
for i in range(self._rows):
for j in range(self._coloumns):
string += str(self.get(i, j)) + "\t"
string += "\n"
return string
class vector:
""" Class implementing all vector functionality """
# Commonly used vectors as classmethods
@classmethod
def direction(cls, n):
# Vector pointing along a particular axis (0 for x-axis, 1 for y-axis, 2 for z-axis)
result = vector()
result.insert(n, 1)
return result
@classmethod
def one(cls):
# Vector with all components as 1.0
return vector(1.0, 1.0, 1.0)
def __init__(self, x=0.0, y=0.0, z=0.0):
# Vector is internally stored as a 4x1 matrix (but does not inherit from it)
self._vector = matrix(4, 1)
self._vector.insert(0, 0, x)
self._vector.insert(1, 0, y)
self._vector.insert(2, 0, z)
self._vector.insert(3, 0, 1.0)
def get(self, index):
return self._vector.get(index, 0)
def getMagnitude(self):
return math.sqrt(sum(math.pow(self._vector.get(i), 2) for i in range(3)))
def normalized(self):
return self / self.getMagnitude()
def insert(self, index, element):
self._vector.insert(index, 0, element)
return self
# Vector transformations (commit is used to commit the change to self)
def translate(self, positionVector, commit=True):
result = self + positionVector
if commit:
self._vector = result.asMatrix()
return self
else:
return result
def rotate(self, rotationVector, commit=True):
result = self
for i in range(3):
# Separately for x, y, z axes
r = matrix.identity(4)
r.insert((i + 1) % 3, (i + 1) % 3, math.cos(math.radians(rotationVector.get(i))))
r.insert((i + 1) % 3, (i + 2) % 3, - math.sin(math.radians(rotationVector.get(i))))
r.insert((i + 2) % 3, (i + 1) % 3, math.sin(math.radians(rotationVector.get(i))))
r.insert((i + 2) % 3, (i + 2) % 3, math.cos(math.radians(rotationVector.get(i))))
result = r * result
if commit:
self._vector = result.asMatrix()
return self
else:
return result
def scale(self, scaleVector, commit=True):
s = matrix.identity(4)
s.insert(0, 0, scaleVector.get(0))
s.insert(1, 1, scaleVector.get(1))
s.insert(2, 2, scaleVector.get(2))
if commit:
self._vector = (s * self).asMatrix()
return self
else:
return s * self
# Converting vector in another form
def asMatrix(self):
m = matrix(4, 1)
for i in range(4):
m.insert(i, 0, self.get(i))
return m
def asList(self):
return [self.get(0), self.get(1), self.get(2)]
def __add__(self, other):
# Vector addition
result = vector()
for i in range(3):
result.insert(i, self.get(i) + other.get(i))
return result
def __mul__(self, num):
# Scalar multiplication
result = vector()
for i in range(3):
result.insert(i, self.get(i) * num)
return result
def __truediv__(self, num):
# Scalar division
result = vector()
for i in range(3):
result.insert(i, self.get(i) / num)
return result
def __str__(self):
# Pretty formatting
return "({}, {}, {})".format(self.get(0), self.get(1), self.get(2))
|
[
"[email protected]"
] | |
1c7d4bc21df35e1245c6c10e7f05a4b42cec7807
|
1cfbdee506b996c731db6850f55f92c1be0d136e
|
/training/forms.py
|
d1451fd8bfefd183677dad3546419a9ee1ec7904
|
[] |
no_license
|
alex2702/iotrec-backend
|
0d0334288ccdb6aae85b74d9522f5d0fb08e3281
|
e9c66baf789d3efc20cb109b7d69fff6d658760c
|
refs/heads/master
| 2022-12-03T08:01:21.950701 | 2019-12-23T07:12:14 | 2019-12-23T07:12:14 | 228,316,627 | 0 | 0 | null | 2022-11-22T04:38:09 | 2019-12-16T06:19:51 |
Python
|
UTF-8
|
Python
| false | false | 2,009 |
py
|
import random
from django import forms
from .models import Sample, ReferenceThing, ContextFactor
# form to collect five training samples for a reference thing
class SampleForm(forms.Form):
# thing, CF and CFV fields are all hidden
thing = forms.IntegerField(widget=forms.HiddenInput())
context_factor_1 = forms.IntegerField(widget=forms.HiddenInput())
context_factor_2 = forms.IntegerField(widget=forms.HiddenInput())
context_factor_3 = forms.IntegerField(widget=forms.HiddenInput())
context_factor_4 = forms.IntegerField(widget=forms.HiddenInput())
context_factor_5 = forms.IntegerField(widget=forms.HiddenInput())
context_factor_value_1 = forms.IntegerField(widget=forms.HiddenInput())
context_factor_value_2 = forms.IntegerField(widget=forms.HiddenInput())
context_factor_value_3 = forms.IntegerField(widget=forms.HiddenInput())
context_factor_value_4 = forms.IntegerField(widget=forms.HiddenInput())
context_factor_value_5 = forms.IntegerField(widget=forms.HiddenInput())
CHOICES = [
('-1', 'negative effect'),
('0', 'no effect'),
('1', 'positive effect')
]
value_1 = forms.ChoiceField(choices=CHOICES, widget=forms.RadioSelect)
value_2 = forms.ChoiceField(choices=CHOICES, widget=forms.RadioSelect)
value_3 = forms.ChoiceField(choices=CHOICES, widget=forms.RadioSelect)
value_4 = forms.ChoiceField(choices=CHOICES, widget=forms.RadioSelect)
value_5 = forms.ChoiceField(choices=CHOICES, widget=forms.RadioSelect)
user = forms.CharField(widget=forms.HiddenInput())
class Meta:
model = Sample
fields = ['thing',
'context_factor_1', 'context_factor_2', 'context_factor_3', 'context_factor_4', 'context_factor_5',
'context_factor_value_1', 'context_factor_value_2', 'context_factor_value_3',
'context_factor_value_4', 'context_factor_value_5','value_1', 'value_2', 'value_3', 'value_4',
'value_5', 'user']
|
[
"[email protected]"
] | |
643a7e8fab27c002a3adec8754905d174c27db19
|
ab4f74d127bfc89813ee359bb9c779eca5426ddc
|
/script/label_image.runfiles/org_tensorflow/tensorflow/contrib/resampler/python/ops/resampler_ops.py
|
77aba28802d7d37842990c9062030322f5f2eb39
|
[
"MIT"
] |
permissive
|
harshit-jain-git/ImageNET
|
cdfd5a340b62862ad8d1cc3b9a0f30cccc481744
|
1cd4c2b70917e4709ce75422c0205fe3735a1b01
|
refs/heads/master
| 2022-12-11T12:47:46.795376 | 2017-12-19T05:47:26 | 2017-12-19T05:47:26 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 102 |
py
|
/home/co/Documents/ImageClassifier/tensorflow/tensorflow/contrib/resampler/python/ops/resampler_ops.py
|
[
"[email protected]"
] | |
4b77de8df4c09f39015218c28428886bf20eb704
|
e0df57a2301e9b85fdd95645f5ec7c63e6929cfc
|
/my_app/currency/migrations/0001_initial.py
|
044fdfd08fb04514b3a11142463bde7f4e4a3e78
|
[] |
no_license
|
rajdip34/python-online-compiler
|
1eb5a8c68d2e019b061f01c91a0788a8d2d2229f
|
62fa21daf73a3816c8e0d8da38e7f45af28e2424
|
refs/heads/master
| 2020-05-04T08:17:05.594349 | 2019-04-02T13:21:31 | 2019-04-02T13:21:31 | 179,043,248 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,122 |
py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.11 on 2019-03-30 04:51
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Currency',
fields=[
('id', models.BigAutoField(db_column='Id', primary_key=True, serialize=False)),
('name', models.CharField(db_column='Name', max_length=3)),
('description', models.CharField(max_length=50)),
('modifydatetime', models.DateTimeField(db_column='ModifyDateTime')),
('modifyuserid', models.ForeignKey(blank=True, db_column='ModifyUserid', null=True, on_delete=django.db.models.deletion.DO_NOTHING, to=settings.AUTH_USER_MODEL)),
],
options={
'db_table': 'currency',
'managed': True,
},
),
]
|
[
"[email protected]"
] | |
f73f3bb6c439153d7bd119d6e542dfa392dbf1f5
|
913777b293fc18fc7d2ea08411bc3b25a72ca2df
|
/__manifest__.py
|
c2f185af678a6f51cb5ed21e8537defdf7752046
|
[] |
no_license
|
ValeroMateo/RecuperacionOdoo
|
4ebd6542a0a6e2a6d489e311dc58ae4ad595469a
|
02e3c86fae59f2ea10c09f0b5cc5b6e8c8d8e046
|
refs/heads/master
| 2020-12-07T17:56:19.724969 | 2020-01-28T10:40:05 | 2020-01-28T10:40:05 | 232,765,249 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 785 |
py
|
# -*- coding: utf-8 -*-
{
'name': "Remedial exam Odoo",
'summary': """Hoping for the best""",
'description': """
Remedial exam module with the purpose of making our professor feel better about his students
""",
'author': "Valero Mateo",
'website': "http://www.mybigassdisaster.com",
# Categories can be used to filter modules in modules listing
# Check https://github.com/odoo/odoo/blob/12.0/odoo/addons/base/data/ir_module_category_data.xml
# for the full list
'category': 'Test',
'version': '0.1',
# any module necessary for this one to work correctly
'depends': ['base', 'baseModule'],
# always loaded
'data': [
'security/ir.model.access.csv',
'views.xml',
'reports.xml'
],
}
|
[
"[email protected]"
] | |
ed0c506a4e560bd296f8b23da088c23994a9bb50
|
4f51225cd157b0e31bc4268d2eb4d31159b074c4
|
/simulation_main.py
|
23c41147d3dc73ef20228cbc66bb91777ae90a93
|
[] |
no_license
|
Jarvis-X/pioneer_free_run
|
3be03098349a0476a0b1ec91ce318b5aface54c7
|
5bad268f28d54a1da65930ec49ddde1404c5d878
|
refs/heads/master
| 2022-07-31T01:17:43.708467 | 2020-05-15T22:52:35 | 2020-05-15T22:52:35 | 264,040,655 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 8,711 |
py
|
# Make sure to have the server side running in CoppeliaSim:
# in a child script of a CoppeliaSim scene, add following command
# to be executed just once, at simulation start:
#
# simRemoteApi.start(19999)
#
# then start simulation, and run this program.
#
# created by: Jiawei Xu
try:
import sim
import numpy as np
import cv2
import time
except:
print ('--------------------------------------------------------------')
print ('Library loading failed!')
print ('')
def filter_red(img):
red1 = cv2.inRange(img, (0, 220, 100), (5, 255, 255))
red2 = cv2.inRange(img, (175, 220, 100), (180, 255, 255))
return red1+red2
def filter_green(img):
green = cv2.inRange(img, (55, 220, 100), (65, 255, 255))
return green
def sensor_color(img):
sensor_array = np.sum(img, 0, dtype=np.uint16)
sensor_array_shrunken = [0.0]*16
for i in range(16):
sensor_array_shrunken[i] = np.sum(sensor_array[i*16:(i+1)*16], dtype=np.uint32)*1.0
if sensor_array_shrunken[i] > 300000:
sensor_array_shrunken[i] = 1.0
elif sensor_array_shrunken[i] < 60000:
sensor_array_shrunken[i] = 0.0
else:
sensor_array_shrunken[i] = (sensor_array_shrunken[i]-60000)/(300000.0-60000.0)
# print sensor_array_shrunken
return sensor_array_shrunken
def free_running(sonar_readings, img, ID, left_motor, right_motor):
red_threshold = filter_red(img)
green_threshold = filter_green(img)
green_sensor_array = sensor_color(green_threshold)
red_sensor_array = sensor_color(red_threshold)
# cv2.imshow("red", red_threshold)
# cv2.imshow("green", green_threshold)
# cv2.waitKey(1)
v_left = 1.0
v_right = 1.0
for i in range(len(sonar_readings)):
v_left += sonar_readings[i]*braitenberg_sonar_L[i]
v_right += sonar_readings[i]*braitenberg_sonar_R[i]
print v_left, " ", v_right
# TODO: add red cube avoidance terms
for i in range(len(red_sensor_array)):
v_left += red_sensor_array[i] * braitenberg_red_L[i]
v_right += red_sensor_array[i] * braitenberg_red_R[i]
for i in range(len(green_sensor_array)):
v_left += green_sensor_array[i] * braitenberg_green_L[i]
v_right += green_sensor_array[i] * braitenberg_green_R[i]
print v_left, " ", v_right
# print v_left, " ", v_right
sim.simxSetJointTargetVelocity(ID, left_motor, v_left, sim.simx_opmode_oneshot)
sim.simxSetJointTargetVelocity(ID, right_motor, v_right, sim.simx_opmode_oneshot)
def read_image(image_ready, ID, handler):
res, resolution, image = sim.simxGetVisionSensorImage(ID, handler, 0, sim.simx_opmode_buffer)
if res == sim.simx_return_ok:
if not image_ready:
print "image OK!!!"
image_ready = True
img = np.array(image, dtype=np.uint8)
img.resize([resolution[1], resolution[0], 3])
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
return img, image_ready
elif res == sim.simx_return_novalue_flag:
if image_ready:
print "no image"
image_ready = False
return np.array([], dtype=np.uint8), image_ready
else:
print "error: " + str(res)
return np.array([], dtype=np.uint8), image_ready
def read_sonar(ID, handlers, sonar_ready):
points = [None]*8
states = [False]*8
for i in range(8):
res, states[i], points[i], _, normal_vec = sim.simxReadProximitySensor(ID, handlers[i], sim.simx_opmode_buffer)
dists = [i[2] for i in points]
if sonar_ready:
for i in range(len(dists)):
if states[i] and dists[i] < 0.5:
if dists[i] < 0.2:
dists[i] = 0.2
# map how close an obstacle is to the robot to [0, 1]
dists[i] = 1.0 - (dists[i] - 0.2) / (0.5 - 0.2)
else:
dists[i] = 0.0
return dists, sonar_ready
else:
flag = True
for i in range(len(dists)):
if dists[i] == 0.0:
flag = False
break
if flag:
sonar_ready = True
return None, sonar_ready
if __name__ == "__main__":
print ('Program started')
sim.simxFinish(-1) # just in case, close all opened connections
clientID = sim.simxStart('127.0.0.1', 19999, True, True, 5000, 5) # Connect to CoppeliaSim
if clientID != -1:
print ('Connected to remote API server')
# Now try to retrieve data in a blocking fashion (i.e. a service call):
res, objs = sim.simxGetObjects(clientID, sim.sim_handle_all, sim.simx_opmode_blocking)
if res == sim.simx_return_ok:
print ('Number of objects in the scene: ', len(objs))
else:
print ('Remote API function call returned with error code: ', res)
time.sleep(2)
# get vision sensor handler
print 'Vision Sensor object handling'
res, veh_camera = sim.simxGetObjectHandle(clientID, 'veh_camera', sim.simx_opmode_oneshot_wait)
# get sonor handler
print 'Sonar object handling'
veh_sonar = [None]*8
for i in range(8):
res, veh_sonar[i] = sim.simxGetObjectHandle(clientID, 'Pioneer_p3dx_ultrasonicSensor'+'{}'.format(i+1),
sim.simx_opmode_oneshot_wait)
# print res == sim.simx_return_ok
# get left motor handler
res, veh_left_motor = sim.simxGetObjectHandle(clientID, 'Pioneer_p3dx_leftMotor', sim.simx_opmode_oneshot_wait)
# print res == sim.simx_return_ok
# get right motor handler
res, veh_right_motor = sim.simxGetObjectHandle(clientID, 'Pioneer_p3dx_rightMotor', sim.simx_opmode_oneshot_wait)
# print res == sim.simx_return_ok
# let the server prepare the first image
print 'Getting first image'
res, resolution, image = sim.simxGetVisionSensorImage(clientID, veh_camera, 0, sim.simx_opmode_streaming)
image_ready_flag = False
# let the server prepare the first sonar reading
points = [None] * 8
for i in range(8):
res, state, points[i], _, normal_vec = sim.simxReadProximitySensor(clientID, veh_sonar[i],
sim.simx_opmode_streaming)
braitenberg_sonar_L = [0.2, 0.0, -0.2, -0.4, -0.6, -0.8, -1.0, -1.2]
braitenberg_sonar_R = [-1.2, -1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2]
# TODO: fix the red cube avoidance
braitenberg_red_L = [-0.65, -0.6, -0.55, -0.5, -0.45, -0.4, -0.35, -0.3, -0.25, -0.2, -0.15, -0.1, -0.05, 0.0, 0.05, 0.10]
braitenberg_red_R = [0.10, 0.05, 0.0, -0.05, -0.1, -0.15, -0.2, -0.25, -0.3, -0.35, -0.4, -0.45, -0.5, -0.55, -0.6, -0.65]
braitenberg_green_L = [0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1, 0.05]
braitenberg_green_R = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8]
# braitenberg_sonar_L = [0.6, 0.8, 0.6, 0.4, -0.4, -0.6, -0.8, -0.6]
# braitenberg_sonar_R = [-0.6, -0.8, -0.6, -0.4, 0.4, 0.6, 0.8, 0.6]
# print [j[2] for j in points]
sonar_ready_flag = False
# keep running until the server shuts down
while sim.simxGetConnectionId(clientID) != -1:
image, image_ready_flag = read_image(image_ready_flag, clientID, veh_camera)
detections, sonar_ready_flag = read_sonar(clientID, veh_sonar, sonar_ready_flag)
# print detections
# if image_ready_flag:
# cv2.imshow("image", image)
# cv2.waitKey(1)
if image_ready_flag and sonar_ready_flag and not detections is None:
free_running(detections, image, clientID, veh_left_motor, veh_right_motor)
cv2.destroyAllWindows()
# Now send some data to CoppeliaSim in a non-blocking fashion:
# sim.simxAddStatusbarMessage(clientID, 'Hello CoppeliaSim!', sim.simx_opmode_oneshot)
# Before closing the connection to CoppeliaSim, make sure that the last command sent out had time to arrive. You
# can guarantee this with (for example):
sim.simxGetPingTime(clientID)
# Now close the connection to CoppeliaSim:
sim.simxFinish(clientID)
else:
print 'Failed connecting to remote API server'
print 'Program ended'
|
[
"[email protected]"
] | |
d1ddaf333839d2b4c77c8c4265b2240ac9836035
|
8d6fa96da4220ba886ef8e858f1925b6dca34e58
|
/examples/wtf/wtf/config.py
|
7cf539ff078f59cb14f772090950734c0d091acb
|
[] |
no_license
|
FZambia/cyclone-wtforms
|
6ee26c920171685e027529e8f1fbb99c765edc30
|
c266b5f3bfff77e3a721b3335b74a294966f7daf
|
refs/heads/master
| 2016-09-05T15:23:08.336180 | 2012-10-05T18:55:00 | 2012-10-05T18:55:00 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,041 |
py
|
# coding: utf-8
#
# Copyright 2010 Alexandre Fiori
# based on the original Tornado by Facebook
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import os
import ConfigParser
from cyclone.util import ObjectDict
def xget(func, section, option, default=None):
try:
return func(section, option)
except:
return default
def parse_config(filename):
cfg = ConfigParser.RawConfigParser()
with open(filename) as fp:
cfg.readfp(fp)
fp.close()
settings = {'raw': cfg}
# web server settings
settings["debug"] = xget(cfg.getboolean, "server", "debug", False)
settings["xheaders"] = xget(cfg.getboolean, "server", "xheaders", False)
settings["cookie_secret"] = cfg.get("server", "cookie_secret")
settings["xsrf_cookies"] = xget(cfg.getboolean, "server", "xsrf_cookies",
False)
# get project's absolute path
root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
getpath = lambda k, v: os.path.join(root, xget(cfg.get, k, v))
# locale, template and static directories' path
settings["locale_path"] = getpath("frontend", "locale_path")
settings["static_path"] = getpath("frontend", "static_path")
settings["template_path"] = getpath("frontend", "template_path")
# sqlite support
if xget(cfg.getboolean, "sqlite", "enabled", False):
settings["sqlite_settings"] = ObjectDict(database=cfg.get("sqlite",
"database"))
else:
settings["sqlite_settings"] = None
# redis support
if xget(cfg.getboolean, "redis", "enabled", False):
settings["redis_settings"] = ObjectDict(
host=cfg.get("redis", "host"),
port=cfg.getint("redis", "port"),
dbid=cfg.getint("redis", "dbid"),
poolsize=cfg.getint("redis", "poolsize"))
else:
settings["redis_settings"] = None
# mysql support
if xget(cfg.getboolean, "mysql", "enabled", False):
settings["mysql_settings"] = ObjectDict(
host=cfg.get("mysql", "host"),
port=cfg.getint("mysql", "port"),
username=xget(cfg.get, "mysql", "username"),
password=xget(cfg.get, "mysql", "password"),
database=xget(cfg.get, "mysql", "database"),
poolsize=xget(cfg.getint, "mysql", "poolsize", 10),
debug=xget(cfg.getboolean, "mysql", "debug", False))
else:
settings["mysql_settings"] = None
return settings
|
[
"[email protected]"
] | |
831ecbe5f0e78dba57eef89d48c2de2dfde712b6
|
1152aeca900b16e4c9a659a1a014a892aa08fb01
|
/hello-django/question/migrations/0005_auto_20190108_1023.py
|
b4543fcab26962803577b5e012d6f0c42f93530f
|
[] |
no_license
|
mixkungz/django-for-deploy
|
923ff536fd8a2506bb53cb77bdd315513e40ae9c
|
187fe18ad85ca8bd04ee2e053248cdeff030cc85
|
refs/heads/master
| 2020-05-03T13:37:10.190004 | 2019-03-31T10:17:08 | 2019-03-31T10:17:08 | 178,657,364 | 0 | 0 | null | 2019-03-31T07:40:20 | 2019-03-31T07:40:20 | null |
UTF-8
|
Python
| false | false | 435 |
py
|
# Generated by Django 2.0.9 on 2019-01-08 03:23
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('question', '0004_auto_20190108_1010'),
]
operations = [
migrations.RemoveField(
model_name='answer',
name='created',
),
migrations.RemoveField(
model_name='answer',
name='modified',
),
]
|
[
"[email protected]"
] | |
fb04fa20f9674a3aa47b159387a398b15b39606b
|
b5906e0a8c000176faebdf7eeb9488ad42812985
|
/lib/python2.7/site-packages/tflearn/helpers/trainer.py
|
6810e5f1d7cd6031735772af89391561c9dcf689
|
[] |
no_license
|
ZachPhillipsGary/CS200-NLP-ANNsProject
|
58593b5ad79d578503ea1e1cc155e014a10f26c3
|
35db127effc2a427dfd6f1ff7022b35f31c8337b
|
refs/heads/master
| 2022-12-22T07:42:52.513314 | 2016-05-06T13:52:40 | 2016-05-06T13:52:40 | 56,972,642 | 0 | 1 | null | 2016-05-02T04:22:19 | 2016-04-24T13:21:29 |
Python
|
UTF-8
|
Python
| false | false | 33,519 |
py
|
# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import numpy as np
import tensorflow as tf
from tensorflow.python.training import optimizer as tf_optimizer
import tflearn
from .. import callbacks
from ..config import init_training_mode
from ..utils import to_list, id_generator, check_dir_name, standarize_dict, \
get_dict_first_element, make_batches, slice_array, check_scope_path
from .summarizer import summaries, summarize, summarize_gradients, \
summarize_variables, summarize_activations
class Trainer(object):
""" Trainer.
Generic class to handle any TensorFlow graph training. It requires
the use of `TrainOp` to specify all optimization parameters.
Arguments:
train_ops: list of `TrainOp`. A list of a network training
operations for performing optimizations.
graph: `tf.Graph`. The TensorFlow graph to use. Default: default tf
graph.
clip_gradients: `float`. Clip gradient. Default: 5.0.
tensorboard_dir: `str`. Tensorboard log directory.
Default: "/tmp/tflearn_logs/".
tensorboard_verbose: `int`. Verbose level. It supports:
```python
0 - Loss, Accuracy. (Best Speed)
1 - Loss, Accuracy, Gradients.
2 - Loss, Accuracy, Gradients, Weights.
3 - Loss, Accuracy, Gradients, Weights, Activations, Sparsity.
(Best Visualization)
```
checkpoint_path: `str`. Path to store model checkpoints. If None,
no model checkpoint will be saved. Default: None.
max_checkpoints: `int` or None. Maximum amount of checkpoints. If
None, no limit. Default: None.
keep_checkpoint_every_n_hours: `float`. Number of hours between each
model checkpoints.
random_seed: `int`. Random seed, for test reproductivity.
Default: None.
session: `Session`. A session for running ops. If None, a new one will
be created. Note: When providing a session, variables must have been
initialized already, otherwise an error will be raised.
"""
def __init__(self, train_ops, graph=None, clip_gradients=5.0,
tensorboard_dir="/tmp/tflearn_logs/",
tensorboard_verbose=0, checkpoint_path=None,
max_checkpoints=None,
keep_checkpoint_every_n_hours=10000.0, random_seed=None,
session=None):
self.graph = tf.get_default_graph()
if graph:
self.graph = graph
with self.graph.as_default():
init_training_mode()
train_ops = to_list(train_ops)
duplicate_identical_ops(train_ops)
if random_seed:
tf.set_random_seed(random_seed)
self.restored = False
self.tensorboard_dir = check_dir_name(tensorboard_dir)
self.training_step = 0
self.train_ops = to_list(train_ops)
self.validate_trainop_names()
self.global_loss = None
self.global_step = tf.Variable(0., name='Global_Step',
trainable=False)
self.incr_global_step = tf.assign(self.global_step,
tf.add(self.global_step, 1))
config = None
tflearn_conf = tf.get_collection(tf.GraphKeys.GRAPH_CONFIG)
if tflearn_conf:
config = tflearn_conf[0]
if not session:
self.session = tf.Session(config=config)
else:
self.session = session
self.restored = True
for i, train_op in enumerate(self.train_ops):
# For display simplicity in Tensorboard, if only one optmizer,
# we don't display its name
if len(train_ops) == 1:
train_op.scope_name = ""
train_op.initialize_training_ops(i, self.session,
tensorboard_verbose,
clip_gradients)
# Saver for saving a model
self.saver = tf.train.Saver(
max_to_keep=max_checkpoints,
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
# Saver for restoring a model (With exclude variable list)
all_vars = tf.get_collection(tf.GraphKeys.VARIABLES)
excl_vars = tf.get_collection(tf.GraphKeys.EXCL_RESTORE_VARS)
to_restore = [item for item in all_vars if item not in excl_vars]
self.restorer = tf.train.Saver(
var_list=to_restore,
max_to_keep=max_checkpoints,
keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours)
self.checkpoint_path = checkpoint_path
if not self.restored:
init = tf.initialize_all_variables()
self.session.run(init)
def fit(self, feed_dicts, n_epoch=10, val_feed_dicts=None, show_metric=False,
snapshot_step=None, snapshot_epoch=True, shuffle_all=None,
run_id=None):
""" fit.
Train network with feeded data dicts.
Examples:
```python
# 1 Optimizer
trainer.fit(feed_dicts={input1: X, output1: Y},
val_feed_dicts={input1: X, output1: Y})
trainer.fit(feed_dicts={input1: X1, input2: X2, output1: Y},
val_feed_dicts=0.1) # 10% of data used for validation
# 2 Optimizers
trainer.fit(feed_dicts=[{in1: X1, out1:Y}, {in2: X2, out2:Y2}],
val_feed_dicts=[{in1: X1, out1:Y}, {in2: X2, out2:Y2}])
```
Arguments:
feed_dicts: `dict` or list of `dict`. The dictionary to feed
data to the network. It follows Tensorflow feed dict
specifications: '{placeholder: data}'. In case of multiple
optimizers, a list of dict is expected, that will
respectively feed optimizers.
n_epoch: `int`. Number of epoch to runs.
val_feed_dicts: `dict`, list of `dict`, `float` or list of
`float`. The data used for validation. Feed dict are
following the same specification as `feed_dicts` above. It
is also possible to provide a `float` for splitting training
data for validation.
show_metric: `bool`. If True, accuracy will be calculated and
displayed at every step. Might give slower training.
snapshot_step: `int`. If not None, the network will be snapshot
every provided step (calculate validation loss/accuracy and
save model, if a `checkpoint_path` is specified in `Trainer`).
snapshot_epoch: `bool`. If True, snapshot the network at the end
of every epoch.
shuffle_all: `bool`. If True, shuffle all data batches (overrides
`TrainOp` shuffle parameter behavior).
run_id: `str`. A name for the current run. Used for Tensorboard
display. If no name provided, a random one will be generated.
"""
if not run_id:
run_id = id_generator(6)
print("---------------------------------")
print("Run id: " + run_id)
print("Log directory: " + self.tensorboard_dir)
# shuffle is an override for simplicty, it will overrides every
# training op batch shuffling
if isinstance(shuffle_all, bool):
for t in self.train_ops: t.shuffle = shuffle_all
with self.graph.as_default():
self.summ_writer = tf.train.SummaryWriter(
self.tensorboard_dir + run_id, self.session.graph_def)
# TODO: Add a check that all keys in feed dict match val feed dict
feed_dicts = to_list(feed_dicts)
for d in feed_dicts: standarize_dict(d)
val_feed_dicts = to_list(val_feed_dicts)
if val_feed_dicts: [standarize_dict(d) for d in val_feed_dicts]
# Handle validation split
validation_split(val_feed_dicts, feed_dicts)
termlogger = callbacks.TermLogger(self.training_step)
modelsaver = callbacks.ModelSaver(self.save,
self.training_step,
self.checkpoint_path,
snapshot_epoch)
for i, train_op in enumerate(self.train_ops):
vd = val_feed_dicts[i] if val_feed_dicts else None
# Prepare all train_ops for fitting
train_op.initialize_fit(feed_dicts[i], vd, show_metric,
self.summ_writer)
# Prepare TermLogger for training diplay
metric_term_name = None
if train_op.metric is not None:
if hasattr(train_op.metric, 'm_name'):
metric_term_name = train_op.metric.m_name
else:
metric_term_name = train_op.metric.name.split(':')[0]
termlogger.add(train_op.n_train_samples,
val_size=train_op.n_val_samples,
metric_name=metric_term_name,
name=train_op.name)
max_batches_len = np.max([t.n_batches for t in self.train_ops])
termlogger.on_train_begin()
modelsaver.on_epoch_begin()
try:
for epoch in range(n_epoch):
termlogger.on_epoch_begin()
modelsaver.on_epoch_begin()
# Global epoch are defined as loop over all data (whatever
# which data input), so one epoch loop in a multi-inputs
# model is equal to max(data_input) size.
for batch_step in range(max_batches_len):
self.training_step += 1
termlogger.on_batch_begin()
modelsaver.on_batch_begin()
global_loss, global_acc = 0., 0.
for i, train_op in enumerate(self.train_ops):
termlogger.on_sub_epoch_begin()
modelsaver.on_sub_batch_begin()
snapshot = train_op._train(self.training_step,
snapshot_epoch,
snapshot_step,
show_metric)
global_loss += train_op.loss_value
if train_op.acc_value and global_acc:
global_acc += train_op.acc_value / len(
self.train_ops)
else:
global_acc = None
# Optimizer batch end
termlogger.on_sub_batch_end(i, train_op.epoch,
train_op.step,
train_op.loss_value,
train_op.acc_value,
train_op.val_loss,
train_op.val_acc)
modelsaver.on_sub_batch_end()
# All optimizers batch end
self.session.run(self.incr_global_step)
termlogger.on_batch_end(global_loss, global_acc,
snapshot)
modelsaver.on_batch_end(snapshot)
# Epoch end
termlogger.on_epoch_end()
modelsaver.on_epoch_end()
finally:
termlogger.on_train_end()
modelsaver.on_train_end()
def save(self, model_file, global_step=None):
""" save.
Save a Tensorflow model
Arguments:
model_file: `str`. Saving path of tensorflow model
global_step: `float`. The training step to append to the
model file name (optional).
"""
# Temp workaround for tensorflow 0.7.0 dict proto serialization issue
try:
# Try latest api
l = tf.get_collection_ref("summary_tags")
except Exception:
l = tf.get_collection("summary_tags")
l_stags = list(l)
del l[:]
# Temp workaround for tensorflow 0.7.0 relative path issue
if model_file[0] not in ['/', '~']: model_file = './' + model_file
self.saver.save(self.session, model_file, global_step=global_step)
# 0.7 workaround, restore values
for t in l_stags:
tf.add_to_collection("summary_tags", t)
def restore(self, model_file):
""" restore.
Restore a Tensorflow model
Arguments:
model_file: path of tensorflow model to restore
"""
self.close_session()
self.session = tf.Session()
self.session.run(tf.initialize_all_variables())
self.restorer.restore(self.session, model_file)
for o in self.train_ops:
o.session = self.session
self.restored = True
self.training_step = int(self.global_step.eval(self.session))
def close_session(self):
""" Close session """
self.session.close()
def validate_trainop_names(self):
""" Give names to all TrainOp, handle no names and duplicated names """
t_len = len(self.train_ops)
# Rename optimizers without name
for i in range(t_len):
if not self.train_ops[i].name:
self.train_ops[i].name = 'Optimizer'
self.train_ops[i].scope_name = 'Optimizer'
# Handle duplicate names
for i in range(t_len):
dupl = 0
for j in range(i+1, t_len):
if not self.train_ops[i].name:
break
if self.train_ops[i].name == self.train_ops[j].name:
if dupl == 0:
self.train_ops[i].name += '_' + str(dupl)
self.train_ops[i].scope_name = self.train_ops[i].name
dupl += 1
self.train_ops[j].name += '_' + str(dupl)
self.train_ops[j].scope_name = self.train_ops[j].name
class TrainOp(object):
""" TrainOp.
TrainOp represents a set of operation used for optimizing a network.
A TrainOp is meant to hold all training parameters of an optimizer.
`Trainer` class will then instantiate them all specifically considering all
optimizers of the network (set names, scopes... set optimization ops...).
Arguments:
loss: `Tensor`. Loss operation to evaluate network cost.
Optimizer will use this cost function to train network.
optimizer: `Optimizer`. Tensorflow Optimizer. The optimizer to
use to train network.
metric: `Tensor`. The metric tensor to be used for evaluation.
batch_size: `int`. Batch size for data feeded to this optimizer.
Default: 64.
ema: `float`. Exponential moving averages.
trainable_vars: list of `tf.Variable`. List of trainable variables to
use for training. Default: all trainable variables.
shuffle: `bool`. Shuffle data.
step_tensor: `tf.Tensor`. A variable holding training step. If not
provided, it will be created. Early defining the step tensor
might be useful for network creation, such as for learning rate
decay.
name: `str`. A name for this class (optional).
graph: `tf.Graph`. Tensorflow Graph to use for training. Default:
default tf graph.
"""
def __init__(self, loss, optimizer, metric=None, batch_size=64, ema=0.,
trainable_vars=None, shuffle=True, step_tensor=None,
name=None, graph=None):
self.graph = tf.get_default_graph()
if graph:
self.graph = graph
self.name = name
self.scope_name = name
# Ops
self.loss = loss
self.optimizer = optimizer
self.metric = metric
self.metric_summ_name = ""
if metric is not None:
self.metric_summ_name = metric.name.split('/')[0]
self.grad = None
self.apply_grad = None
self.summ_op = None
self.val_summary_op = None
self.train_vars = trainable_vars
self.shuffle = shuffle
# Train utils
self.epoch = 0
self.step = 0
self.batches = None
self.batch_index = 0
self.batch_start = 0
self.batch_end = 0
self.batch_size = batch_size
self.data_size = 0
self.n_batches = 0
self.ema = ema
self.feed_dict = None
self.val_feed_dict = None
self.loss_value = None
self.val_loss = None
self.acc_value = None
self.val_acc = None
if step_tensor is None:
with self.graph.as_default():
self.training_steps = tf.Variable(0., name="Training_step",
trainable=False)
else:
self.training_steps = step_tensor
# Building
if not isinstance(self.loss, tf.Tensor):
raise ValueError("Unknown Loss type")
if not isinstance(self.optimizer, tf_optimizer.Optimizer):
raise ValueError("Unknown Optimizer")
if self.train_vars is None:
self.train_vars = tf.trainable_variables()
else:
self.train_var = to_list(self.train_vars)
self.train = None
def initialize_training_ops(self, i, session, tensorboard_verbose,
clip_gradients):
""" initialize_training_ops.
Initialize all ops used for training. Because a network can have
multiple optimizers, an id 'i' is allocated to differentiate them.
This is meant to be used by `Trainer` when initializing all train ops.
Arguments:
i: `int`. This optimizer training process ID.
session: `tf.Session`. The session used to train the network.
tensorboard_verbose: `int`. Logs verbose. Supports:
```
0 - Loss, Accuracy.
1 - Loss, Accuracy, Gradients.
2 - Loss, Accuracy, Gradients, Weights.
3 - Loss, Accuracy, Gradients, Weights, Activations, Sparsity..
```
clip_gradients: `float`. Option for clipping gradients.
"""
self.session = session
# Variables holding mean validation loss and accuracy, assigned after
# each model evaluation (by batch). For visualization in Tensorboard.
self.val_loss_T = tf.Variable(0., name='val_loss', trainable=False)
self.val_acc_T = tf.Variable(0., name='val_acc', trainable=False)
# Creating the accuracy moving average, for better visualization.
if self.metric is not None:
self.acc_averages = \
tf.train.ExponentialMovingAverage(0.9, self.training_steps,
name='moving_avg')
acc_avg_op = self.acc_averages.apply([self.metric])
else:
acc_avg_op = tf.no_op()
# Compute total loss, which is the loss of all optimizers plus the
# loss of all regularizers. Then, we summarize those losses for
# visualization in Tensorboard.
with tf.name_scope(self.name):
lss = [self.loss] + tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n(lss, name="Total_Loss")
loss_avg_op = summaries.add_loss_summaries(
total_loss,
self.loss,
regul_losses_collection_key=tf.GraphKeys.REGULARIZATION_LOSSES,
name_prefix=self.scope_name,
summaries_collection_key=self.name + "_training_summaries",
exp_moving_avg=0.9,
ema_num_updates=self.training_steps)
# Compute gradients operations
with tf.control_dependencies([loss_avg_op, acc_avg_op]):
self.grad = tf.gradients(total_loss, self.train_vars)
if clip_gradients > 0.0:
self.grad, self.grad_norm = \
tf.clip_by_global_norm(self.grad, clip_gradients)
self.grad = list(zip(self.grad, self.train_vars))
self.apply_grad = self.optimizer.apply_gradients(
grads_and_vars=self.grad,
global_step=self.training_steps,
name="apply_grad_op_" + str(i))
# Create other useful summary (weights, grads, activations...)
# according to 'tensorboard_verbose' level.
self.create_summaries(tensorboard_verbose)
# Track the moving averages of trainable variables
if self.ema > 0.:
var_averages = tf.train.ExponentialMovingAverage(
self.ema, self.training_steps)
var_averages_op = var_averages.apply(self.train_vars)
with tf.control_dependencies([var_averages_op]):
with tf.control_dependencies([self.apply_grad]):
self.train = tf.no_op(name="train_op_" + str(i))
else:
with tf.control_dependencies([self.apply_grad]):
self.train = tf.no_op(name="train_op_" + str(i))
def initialize_fit(self, feed_dict, val_feed_dict, show_metric,
summ_writer):
""" initialize_fit.
Initialize data for feeding the training process. It is meant to
be used by `Trainer` before starting to fit data.
Arguments:
feed_dict: `dict`. The data dictionary to feed.
val_feed_dict: `dict`. The validation data dictionary to feed.
show_metric: `bool`. If True, display accuracy at every step.
summ_writer: `SummaryWriter`. The summary writer to use for
Tensorboard logging.
"""
self.summary_writer = summ_writer
self.feed_dict = feed_dict
self.val_feed_dict = val_feed_dict
self.n_train_samples = len(get_dict_first_element(feed_dict))
self.n_val_samples = 0
if val_feed_dict:
self.n_val_samples = len(get_dict_first_element(val_feed_dict))
self.index_array = np.arange(self.n_train_samples)
self.create_testing_summaries(show_metric, self.metric_summ_name,
val_feed_dict)
if self.shuffle:
np.random.shuffle(self.index_array)
self.set_batches(make_batches(self.n_train_samples, self.batch_size))
def set_batches(self, batches):
self.batches = batches
self.n_batches = len(batches)
self.batch_size = int(batches[0][1] - batches[0][0])
self.data_size = self.batch_size * (self.n_batches - 1) + \
int(batches[-1][1] - batches[-1][0])
self.batch_start, self.batch_end = self.batches[self.batch_index]
def next_batch(self):
""" Return True if a next batch is available """
self.batch_index += 1
self.step = min(self.batch_index*self.batch_size, self.data_size)
if self.batch_index == self.n_batches:
self.batch_index = 0
self.epoch += 1
self.step = 0
return False
self.batch_start, self.batch_end = self.batches[self.batch_index]
return True
def _train(self, training_step, snapshot_epoch, snapshot_step,
show_metric):
""" Training process for this optimizer.
Arguments:
training_step: `int`. The global step.
snapshot_epoch: `bool`. If True, snapshot network at each epoch.
snapshot_step: `int`. If not None, snapshot network given 'step'.
show_metric: `bool`. If True, display accuracy at every step.
"""
tflearn.is_training(True, self.session)
self.loss_value, self.acc_value = None, None
self.val_loss, self.val_acc = None, None
train_summ_str, test_summ_str = None, None
snapshot = False
batch_ids = self.index_array[self.batch_start:self.batch_end]
feed_batch = {}
for key in self.feed_dict:
# Make batch for multi-dimensional data
if np.ndim(self.feed_dict[key]) > 0:
feed_batch[key] = slice_array(self.feed_dict[key], batch_ids)
else:
feed_batch[key] = self.feed_dict[key]
tflearn.is_training(True, self.session)
self.session.run([self.train], feed_batch)
tflearn.is_training(False, self.session)
if self.summ_op is not None:
train_summ_str = self.session.run(self.summ_op, feed_batch)
# Retrieve loss value from summary string
sname = "- Loss/" + self.scope_name
self.loss_value = summaries.get_value_from_summary_string(
sname, train_summ_str)
if show_metric and self.metric is not None:
# Retrieve accuracy value from summary string
sname = "- " + self.metric_summ_name + "/" + self.scope_name
self.acc_value = summaries.get_value_from_summary_string(
sname, train_summ_str)
# Check if data reached an epoch
if not self.next_batch():
if self.shuffle:
np.random.shuffle(self.index_array)
batches = make_batches(self.n_train_samples, self.batch_size)
self.set_batches(batches)
if snapshot_epoch:
snapshot = True
# Check if step reached snapshot step
if snapshot_step:
if training_step % snapshot_step == 0:
snapshot = True
# Calculate validation
if snapshot and self.val_feed_dict:
# Evaluation returns the mean over all batches.
self.val_loss = evaluate(self.session, self.loss,
self.val_feed_dict,
self.batch_size)
if show_metric and self.metric is not None:
self.val_acc = evaluate(self.session, self.metric,
self.val_feed_dict,
self.batch_size)
# Set evaluation results to variables, to be summarized.
if show_metric:
update_val_op = [tf.assign(self.val_loss_T, self.val_loss),
tf.assign(self.val_acc_T, self.val_acc)]
else:
update_val_op = tf.assign(self.val_loss_T, self.val_loss)
self.session.run(update_val_op)
# Run summary operation.
test_summ_str = self.session.run(self.val_summary_op,
self.val_feed_dict)
# Write to Tensorboard
n_step = self.training_steps.eval(session=self.session)
if n_step > 1:
if train_summ_str:
self.summary_writer.add_summary(
train_summ_str, n_step)
if test_summ_str:
self.summary_writer.add_summary(
test_summ_str, n_step)
return snapshot
def duplicate(self):
""" Returns a duplicated `TrainOp` """
return TrainOp(self.loss, optimizer=self.optimizer,
batch_size=self.batch_size, ema=self.ema,
metric=self.metric,
trainable_vars=self.train_vars,
shuffle=self.shuffle)
def create_summaries(self, verbose=2):
""" Create summaries with `verbose` level """
summ_collection = self.name + "_training_summaries"
if verbose in [3]:
# Summarize activations
activations = tf.get_collection(tf.GraphKeys.ACTIVATIONS)
summarize_activations(activations, summ_collection)
if verbose in [2, 3]:
# Summarize variable weights
summarize_variables(self.train_vars, summ_collection)
if verbose in [1, 2, 3]:
# Summarize gradients
summarize_gradients(self.grad, summ_collection)
self.summ_op = tf.merge_summary(tf.get_collection(summ_collection))
def create_testing_summaries(self, show_metric=False,
metric_name="Accuracy", validation_set=None):
""" Create accuracy and validation summaries """
tr_summ_collection = self.name + "_training_summaries"
te_summ_collection = self.name + "_testing_summaries"
mn = metric_name.replace('/Mean:0/', '')
if show_metric and self.metric is not None:
# Summarize Raw Accuracy
sname = "- " + mn + "/" + self.scope_name + " (raw)"
summarize(self.metric, "scalar", sname, tr_summ_collection)
# Summarize Accuracy's moving averages
sname = "- " + mn + "/" + self.scope_name
self.summ_op = summarize(self.acc_averages.average(self.metric),
"scalar", sname, tr_summ_collection)
if validation_set is not None:
# Summarive Validation Loss
loss_val_name = "- Loss/" + self.scope_name + "/Validation"
loss_val_name = check_scope_path(loss_val_name)
self.val_summary_op = summarize(self.val_loss_T, "scalar",
loss_val_name, te_summ_collection)
if show_metric and self.metric is not None:
# Summarize Validation Accuracy
acc_val_name = "- " + mn + "/" + self.scope_name + "/Validation"
acc_val_name = check_scope_path(acc_val_name)
self.val_summary_op = summarize(self.val_acc_T, "scalar",
acc_val_name,
te_summ_collection)
def duplicate_identical_ops(ops):
""" Duplicate identical `TrainOp` """
for i in range(len(ops)):
for j in range(i+1, len(ops)):
if ops[i] == ops[j]:
ops[j] = ops[i].duplicate()
def validation_split(val_feed_dicts, feed_dicts):
""" validation_split.
Handles validation split; build validation data based on a
percentage of training data. It checks all val_feed_dicts keys
values for a float, if found, it retrieves the exact same key in feed_dict
and split its data according to `float` value and move it to val_feed_dict.
Args:
val_feed_dicts: `dict` of arrays or float. validation dictionary.
feed_dicts: `dict` of arrays. training data dictionary.
"""
if val_feed_dicts:
for i, val_dict in enumerate(val_feed_dicts):
for key, val in val_dict.items():
if isinstance(val, float):
split = val
if type(feed_dicts[i][key]) in [list, np.ndarray]:
split_at = int(len(feed_dicts[i][key]) * (1 - split))
feed_dicts[i][key], val_feed_dicts[i][key] = \
(slice_array(feed_dicts[i][key], 0, split_at),
slice_array(feed_dicts[i][key], split_at))
else:
# If parameter is not an array, we duplicate value
val_feed_dicts[i][key] = feed_dicts[i][key]
def evaluate(session, op_to_evaluate, feed_dict, batch_size):
""" evaluate.
Evaluate an operation with provided data dict using a batch size
to save GPU memory.
Args:
session: `tf.Session`. Session for running operations.
op_to_evaluate: `tf.Op`. Operation to be evaluated.
feed_dict: `dict`. Data dictionary to feed op_to_evaluate.
batch_size: `int`. Batch size to be used for evaluation.
Ret:
`float`. op_to_evaluate mean over all batches.
"""
tflearn.is_training(False, session)
n_test_samples = len(get_dict_first_element(feed_dict))
batches = make_batches(n_test_samples, batch_size)
index_array = np.arange(n_test_samples)
avg = 0.0
for i, (batch_start, batch_end) in enumerate(batches):
batch_ids = index_array[batch_start:batch_end]
feed_batch = {}
for key in feed_dict:
# Make batch for multi-dimensional data
if np.ndim(feed_dict[key]) > 0:
feed_batch[key] = slice_array(feed_dict[key], batch_ids)
else:
feed_batch[key] = feed_dict[key]
avg += session.run(op_to_evaluate, feed_batch) / len(batches)
return avg
|
[
"[email protected]"
] | |
a95a21a906cbc61b1a494f65b0db168b544ba60c
|
3e65c4a3781359e1e68870f6419bb580cec3d670
|
/tests/front_office/not_sorted/example_suite.py
|
8e6a9c7a6af12a6447ad8d011d612ac059ef3aea
|
[] |
no_license
|
Maksim1988/test
|
deeff67fc3211c913d9be77008f38fe9e24a2a18
|
ea1fd32981fce2db2c8bb6ceeb477bc7561e58cd
|
refs/heads/master
| 2021-01-10T17:03:36.871180 | 2015-11-08T13:08:49 | 2015-11-08T13:08:49 | 45,779,091 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 608 |
py
|
__author__ = 'm.senchuk'
import itertools
from nose.loader import TestLoader
from nose import run
from nose.suite import LazySuite
#paths = ("C:\Users\\m.senchuk\\PycharmProjects\\FF4F\\api-tests\\tests\\front_office\\test_search.py:TestSearchSeller",
# )
#
#
#def run_my_tests():
# all_tests = ()
# for path in paths:
# all_tests = itertools.chain(all_tests, TestLoader().loadTestsFromName(path))
# suite = LazySuite(all_tests)
# result = run(suite=suite)
# assert result is True, "One or more tests FAILED. See console log."
#
#if __name__ == '__main__':
# run_my_tests()
|
[
"[email protected]"
] | |
beb4cb8c5fb98b584d7ccafc8ad7673035e22014
|
5c3cca4ea09b1cad4be3b5973f1c9028661a92b4
|
/flup/resolver/nopathinfo.py
|
93063e28daae4b8ec73476e411d81e94ee16ed2f
|
[] |
no_license
|
cwallenpoole/blog_m3
|
4cfec16a7cb1f504c8ba579476c18a783725a9aa
|
1de1e68ae959c092cf1c486d92a25d511881281d
|
refs/heads/master
| 2016-09-06T18:55:28.087462 | 2013-01-28T02:02:03 | 2013-01-28T02:02:03 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,521 |
py
|
# Copyright (c) 2005 Allan Saddi <[email protected]>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
# OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
# SUCH DAMAGE.
#
# $Id: nopathinfo.py 1755 2005-04-15 03:35:59Z asaddi $
__author__ = 'Allan Saddi <[email protected]>'
__version__ = '$Revision: 1755 $'
from .resolver import *
__all__ = ['NoPathInfoResolver']
class NoPathInfoResolver(Resolver):
"""
Another meta-resolver. Disallows the existence of PATH_INFO (beyond
what's needed to resolve the function). Optionally allows a trailing
slash.
"""
def __init__(self, resolver, allowTrailingSlash=False):
self._resolver = resolver
self._allowTrailingSlash = allowTrailingSlash
def resolve(self, request, redirect=False):
orig_script_name, orig_path_info = request.scriptName, request.pathInfo
func = self._resolver.resolve(request, redirect)
try:
if func is not None:
path_info = request.pathInfo.split(';')[0]
if path_info and \
(not self._allowTrailingSlash or path_info != '/'):
func = None
return func
finally:
if func is None:
request.environ['SCRIPT_NAME'] = orig_script_name
request.environ['PATH_INFO'] = orig_path_info
|
[
"[email protected]"
] | |
626e284b40ec0447bfcba31a165d86827eb7df2a
|
6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4
|
/gHrMmA7emP6CFAMnb_6.py
|
35eeb43f5be552b55e650249bf1ff464b8e37754
|
[] |
no_license
|
daniel-reich/ubiquitous-fiesta
|
26e80f0082f8589e51d359ce7953117a3da7d38c
|
9af2700dbe59284f5697e612491499841a6c126f
|
refs/heads/master
| 2023-04-05T06:40:37.328213 | 2021-04-06T20:17:44 | 2021-04-06T20:17:44 | 355,318,759 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 200 |
py
|
def is_apocalyptic(n):
L=str(2**n).split('666')
if len(L)==1:
return "Safe"
elif len(L)==2:
return "Single"
elif len(L)==3:
return "Double"
elif len(L)==4:
return "Triple"
|
[
"[email protected]"
] | |
790f320f73573645f8c4d22bab38bef0a32c0b52
|
f112dfe38732f131156556ab724e2b9a01d317ae
|
/week2/43-reverse-num.py
|
1cc8c4dbef21bd1ef9aac050ea0e5115f52387a4
|
[] |
no_license
|
pharick/python-coursera
|
2a92bf467e0ddd35a573ea4e29fff9a37e45bd24
|
3e24ac9385eada126e7c4753f71cd38181987fbf
|
refs/heads/master
| 2020-04-04T03:44:45.067099 | 2019-03-20T07:10:22 | 2019-03-20T07:10:22 | 155,724,086 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 75 |
py
|
n = int(input())
while n:
print(n % 10, end="")
n //= 10
print()
|
[
"[email protected]"
] | |
0e743649d4432447a1728b244cf860d8f0e7dbf6
|
4b7d83793acd0c84f8bd2603f766b5d8ba10c2eb
|
/solutions/RegularSolutionThreads.py
|
1aa780dcdf05184559bff2d1b32f64bd705f905b
|
[] |
no_license
|
TudorOrha/Parallel-image-filter-application
|
021a5497182c916a9d0e10623a2b87dfe7d12d37
|
345f167eb9db621439556713f1923cbaa7052fdf
|
refs/heads/master
| 2021-09-03T20:57:08.597365 | 2018-01-11T23:41:17 | 2018-01-11T23:41:17 | 115,743,012 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,155 |
py
|
import time
import threading
import multiprocessing
from multiprocessing import Pool
from PIL import Image
nrOfThreads = multiprocessing.cpu_count()
class myThread (threading.Thread):
def __init__(self, threadID, kernel, img, originalImg):
threading.Thread.__init__(self)
self.threadID = threadID
self.kernel = kernel
self.img = img
self.originalImg = originalImg
self.pixels = img.load()
self.originalPixels = originalImg.load()
def run(self):
applyFilterOnPart(self.threadID, self.kernel, self.img, self.originalImg)
def applyFilterOnPart(threadNr, kernel, img, originalImg):
pixels = img.load()
originalPixels = originalImg.load()
fromI = int(round(threadNr*(img.size[0]-2*(len(kernel)//2))/nrOfThreads)) + len(kernel)//2
toI = int(round((1+threadNr)*(img.size[0]-2*(len(kernel)//2))/nrOfThreads)) + len(kernel)//2
for i in range(fromI, toI):
for j in range(len(kernel)//2, img.size[1] - len(kernel)//2):
accumulatorR = 0
accumulatorG = 0
accumulatorB = 0
for kerRow in range(len(kernel)):
for kerCol in range(len(kernel[kerRow])):
accumulatorR += kernel[kerRow][kerCol] * originalPixels[i+len(kernel)//2 - kerRow,j+len(kernel)//2 - kerCol][0]
accumulatorG += kernel[kerRow][kerCol] * originalPixels[i+len(kernel)//2 - kerRow,j+len(kernel)//2 - kerCol][1]
accumulatorB += kernel[kerRow][kerCol] * originalPixels[i+len(kernel)//2 - kerRow,j+len(kernel)//2 - kerCol][2]
pixels[i,j] = (int(round(accumulatorR)), int(round(accumulatorG)), int(round(accumulatorB)))
return img
def generateParameters(kernel, img, originalImg):
params = []
for k in range(nrOfThreads):
params.append((k, kernel, img, originalImg))
return params
def main(imageName, kernel, showResult):
print("Regular Solution With Threads Output:")
start = time.time()
originalImg = Image.open(imageName)
img = Image.open(imageName)
pixels = img.load()
with Pool(processes=nrOfThreads) as pool:
results = pool.starmap(applyFilterOnPart, generateParameters(kernel,img,originalImg))
for k in range(nrOfThreads):
fromI = int(round(k*(img.size[0]-2*(len(kernel)//2))/nrOfThreads)) + len(kernel)//2
toI = int(round((1+k)*(img.size[0]-2*(len(kernel)//2))/nrOfThreads)) + len(kernel)//2
for i in range(fromI, toI):
for j in range(len(kernel)//2, img.size[1] - len(kernel)//2):
pixels[i,j] = results[k].load()[i,j]
'''
threads = []
for i in range (nrOfThreads):
thread = myThread(i, kernel, img, originalImg)
threads.append(thread)
thread.start()
for i in range (nrOfThreads):
threads[i].join()
'''
width, height = img.size
img = img.crop((len(kernel)//2, len(kernel)//2, width - len(kernel)//2, height - len(kernel)//2))
end = time.time()
print(end - start,"\n")
if showResult:
img.show()
|
[
"[email protected]"
] | |
e1ec61ece7c4b94c6d91bb0b115a6a0aefd76e07
|
bc39253e56a81d65b30975a775ae18e59db75c47
|
/pygame_basic/game.py
|
a8a3257ad370d554bb98cdc99e55a5d1b8aae8a5
|
[] |
no_license
|
skysee6126/py_game
|
96ed06c93d7566907af61a2df0e199325a1d6191
|
5213692541817e63b32a4b33af46c2c940836961
|
refs/heads/master
| 2023-04-21T15:07:53.074074 | 2021-04-28T13:22:15 | 2021-04-28T13:22:15 | 360,445,007 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,917 |
py
|
import pygame
pygame.init()
screen_width = 640
screen_height = 480
screen = pygame.display.set_mode((screen_width, screen_height))
pygame.display.set_caption("Test game")
#FPS
clock = pygame.time.Clock()
# background = pygame.image.load("C:\Users\케이지케이\Documents\practice\game\pygame_basic\background.jpg")
character = pygame.image.load("https://www.flaticon.com/svg/vstatic/svg/3885/3885025.svg?token=exp=1619081421~hmac=9f4bd262dddec45be649b11af322333e")
character_size = character.get_rect().size
character_width = character_size[0]
character_height = character_size[1]
character_x_pos = (screen_width/2) - (character_width/2)
character_y_pos = screen_height - character_height
#Move
to_x = 0
to_y = 0
character_speed = 0.6
enemy = pygame.image.load("enemy.jpg")
enemy_size = enemy.get_rect().size
enemy_width = enemy_size[0]
enemy_height = enemy_size[1]
enemy_x_pos = (screen_width/2) - (enemy_width/2)
enemy_y_pos = screen_height - enemy_height
game_font = pygame.font.Font(None, 40)
total_time = 10
start_ticks = pygame.time.get_ticks()
#Even roof
running = True
while running:
dt = clock.tick(30)
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_LEFT:
to_x -= character_speed
elif event.key == pygame.K_RIGHT:
to_x += character_speed
elif event.key == pygame.K_UP:
to_y -= character_speed
elif event.key == pygame.K_DOWN:
to_y += character_speed
if event.type == pygame.KEYUP:
if event.key == pygame.K_LEFT or event.key == pygame.K_RIGHT:
to_x = 0
elif event.key == pygame.K_UP or event.key == pygame.K_DOWN:
to_y = 0
character_x_pos += to_x *dt
character_y_pos += to_y *dt
#가로
if character_x_pos < 0:
character_x_pos = 0
elif character_x_pos > screen_width - character_width:
character_x_pos = screen_width - character_width
#세로
if character_y_pos < 0:
character_y_pos = 0
elif character_y_pos > screen_height - character_height:
character_y_pos = screen_height - character_height
#collision
character_rect = character.get_rect()
character_rect.left = character_x_pos
character_rect.right = character_y_pos
enemy_rect = enemy.get_rect()
enemy_rect.left = enemy_x_pos
enemy_rect.right = enemy_y_pos
if character_rect.colliderect(enemy_rect):
print("Bump!")
running = False
screen.fill((0,0,130))
# screen.blit(background, (0,0))
screen.blit(character, (character_x_pos,character_y_pos))
screen.blit(enemy, (enemy_x_pos,enemy_y_pos))
elapsed_time = (pygame.time.get_ticks() - start_ticks)/ 1000
timer = game_font.render(str(int(total_time - elapsed_time)), True, (255, 255, 255))
screen.blit(timer, (10,10))
if total_time - elapsed_time <= 0:
print("Time out")
running = False
pygame.display.update()
pygame.time.delay(2000)
pygame.quit()
|
[
"[email protected]"
] | |
4e8d14003c2e112ef076b89c4c8a3ad6613f9a2c
|
8da91c26d423bacbeee1163ac7e969904c7e4338
|
/pyvisdk/do/customization_failed.py
|
b63b14e03d5fddb6d06ae4f32d77239d433f8930
|
[] |
no_license
|
pexip/os-python-infi-pyvisdk
|
5d8f3a3858cdd61fb76485574e74ae525cdc7e25
|
1aadea0afbc306d09f6ecb9af0e683dbbf961d20
|
refs/heads/master
| 2023-08-28T02:40:28.789786 | 2020-07-16T04:00:53 | 2020-07-16T04:00:53 | 10,032,240 | 0 | 1 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,169 |
py
|
import logging
from pyvisdk.exceptions import InvalidArgumentError
########################################
# Automatically generated, do not edit.
########################################
log = logging.getLogger(__name__)
def CustomizationFailed(vim, *args, **kwargs):
'''The customization sequence in the guest failed.'''
obj = vim.client.factory.create('{urn:vim25}CustomizationFailed')
# do some validation checking...
if (len(args) + len(kwargs)) < 5:
raise IndexError('Expected at least 6 arguments got: %d' % len(args))
required = [ 'template', 'chainId', 'createdTime', 'key', 'userName' ]
optional = [ 'logLocation', 'changeTag', 'computeResource', 'datacenter', 'ds', 'dvs',
'fullFormattedMessage', 'host', 'net', 'vm', 'dynamicProperty', 'dynamicType' ]
for name, arg in zip(required+optional, args):
setattr(obj, name, arg)
for name, value in kwargs.items():
if name in required + optional:
setattr(obj, name, value)
else:
raise InvalidArgumentError("Invalid argument: %s. Expected one of %s" % (name, ", ".join(required + optional)))
return obj
|
[
"[email protected]"
] | |
b8e02cce761e458f0b3ef4e1dd2c7a741cb9b2ad
|
5131d61e51a227444717abf8180d00db95179b3b
|
/lessons/point.py
|
1ffd4a12965568ee3f8a4242ef1cffcf8805dfcf
|
[] |
no_license
|
yutika01/comp110-21f-workspace
|
fdfa9894c2d99a1a43c1c12bee3663632e772865
|
a575deba7283c1cbd3b5c5f52cff37af8de3fa89
|
refs/heads/main
| 2023-09-04T13:23:26.508125 | 2021-11-21T02:27:43 | 2021-11-21T02:27:43 | 422,988,649 | 0 | 0 | null | 2021-10-30T20:54:08 | 2021-10-30T20:54:08 | null |
UTF-8
|
Python
| false | false | 774 |
py
|
"""Example of a Point class."""
from __future__ import annotations
class Point:
x: float
y: float
def __init__(self, x: float, y: float):
"""Initialize a Point with its x, y components."""
self.x = x
self.y = y
def scale_by(self, factor: float) -> None:
"""Mutates multiplies components by factor."""
self.x *= factor
self.y *= factor
def scale(self, factor: float) -> Point:
"""Immutable: mutliplies components by factor without mutation."""
x: float = self.x * factor
y: float = self.y * factor
scaled_point: Point = Point(x, y)
return scaled_point
p0: Point = Point(1.0, 2.0)
p1: Point = p0.scale(2.0)
print(f"({p0.x}, {p0.y})")
print(f"({p1.x}, {p1.y})")
|
[
"[email protected]"
] | |
4037c0492bcce6dde2154f61c014bed9328f5868
|
ed36a77bf31a5e2d99f80fe4c13224976ea274fd
|
/blog/models.py
|
c1d564824fd2f82a1cb2709ca4bab96d404d49a3
|
[] |
no_license
|
lovelove0618/django_swu_website
|
b2752623112bf85a5789b42f6569156293ddd01a
|
b33c2f267bfcf00490226d8797c7f0190c7466b2
|
refs/heads/master
| 2022-11-29T10:06:26.382813 | 2020-08-17T16:39:13 | 2020-08-17T16:39:13 | 287,696,266 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 272 |
py
|
from django.db import models
from django.contrib.auth.models import User
class Post(models.Model):
title = models.CharField(max_length=30)
content = models.TextField()
created = models.DateTimeField()
author = models.ForeignKey(User, on_delete=True)
|
[
"[email protected]"
] | |
231b25ba70c9a57c6b57cd97838fe94e8da086cc
|
6140cd676c3c6f0e65d3be4515789e7c0f255ffd
|
/numeric-tsne-plotly/settings.py
|
2c6bcb14653175b3b8fc3e8c7a36707d9890c928
|
[] |
no_license
|
RezaKakooee/tsne-collection
|
1efa1ab109d00588ff749bbd5d8f6563c841ce2b
|
93e58071671e2dfbd9b7e395608c4b2b8dca98ac
|
refs/heads/master
| 2022-04-10T08:08:27.427761 | 2020-03-28T22:53:25 | 2020-03-28T22:53:25 | 241,490,880 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 814 |
py
|
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 18 23:53:36 2020
@author: rkako
"""
import os
class Params():
def __init__(self):
# Directories ans Pathes
self.current_dir = os.getcwd()
# self.dataset_folder_name = 'imageset'
self.dataset_dir = 'C:/Users/reza/gdrive-redu/hslu/HSLU-Secude/large-data'
self.data_file_name = 'pca_output_vectors.pickle'
self.data_path = os.path.join(self.dataset_dir, self.data_file_name)
self.log_dir_name = 'logs'
self.log_dir = os.path.join(self.current_dir, self.log_dir_name)
self.similarity_metric = 'cosine'
# clucters
self.num_clusters = 10
# TSNE
self.tsne_n_components = 3
self.tsne_perplexity = 5.0
#params = Params()
|
[
"[email protected]"
] | |
3974a3b8483800aaf3ad9f189e529ec6e246d184
|
f6c5aa5931b71a31246168c9049da50797e5ce57
|
/djangoapp/src/config/urls.py
|
80f6238a15460d9c83ea408eb5d3f925bf0bed62
|
[] |
no_license
|
AlexLoar/musicallity
|
7b724b8350cab3de516228636268b79ba51f5b4c
|
5dc09e8b7fed8c666181a4cb87e8d8328481f1d5
|
refs/heads/master
| 2023-01-02T12:20:38.648476 | 2020-10-06T16:00:49 | 2020-10-06T16:00:49 | 301,781,216 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,250 |
py
|
from django.contrib import admin
from django.conf import settings
from django.urls import path, include
from django.conf.urls.static import static
from django.utils.translation import ugettext_lazy as _
from django.views import defaults as default_views
from config.router import urlpatterns as api_urlpatterns
app_name = 'main_music'
# Admin URLs
admin.site.site_header = _('MUSIC Project')
urlpatterns = [
path(r'admin/', admin.site.urls),
]
# API URLs
# Create a router and register our resources with it.
urlpatterns += [
path('api/v1/', include(api_urlpatterns)),
]
if settings.DEBUG:
# This allows the error pages to be debugged during development, just visit
# these url in browser to see how these error pages look like.
urlpatterns += [
path(r'400/', default_views.bad_request, kwargs={'exception': Exception('Bad Request!')}),
path(r'403/', default_views.permission_denied, kwargs={'exception': Exception('Permission Denied')}),
path(r'404/', default_views.page_not_found, kwargs={'exception': Exception('Page not Found')}),
path(r'500/', default_views.server_error),
]
# Media URLs on debug
urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
|
[
"[email protected]"
] | |
23cb6c73db0e3711ff0ecbd0b6aa7165e94b3584
|
a01fb7bb8e8738a3170083d84bc3fcfd40e7e44f
|
/python3/module/pandas/df/sql/join.py
|
540fb2077f46a30f47e810c2b98ebc2c0a79da73
|
[] |
no_license
|
jk983294/CommonScript
|
f07acf603611b4691b176aa4a02791ef7d4d9370
|
774bcbbae9c146f37312c771c9e867fb93a0c452
|
refs/heads/master
| 2023-08-21T17:50:19.036159 | 2023-08-16T00:22:03 | 2023-08-16T00:22:03 | 42,732,160 | 5 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 739 |
py
|
import pandas as pd
import numpy as np
df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value': np.random.randn(4)})
df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'], 'value': np.random.randn(4)})
print(df1)
print(df2)
# SELECT * FROM df1 INNER JOIN df2 ON df1.key = df2.key;
print(pd.merge(df1, df2, on='key'))
# in case join key is different
print(pd.merge(df1, df2, left_on='key', right_on='key'))
# SELECT * FROM df1 LEFT OUTER JOIN df2 ON df1.key = df2.key;
print(pd.merge(df1, df2, on='key', how='left'))
# SELECT * FROM df1 RIGHT OUTER JOIN df2 ON df1.key = df2.key;
print(pd.merge(df1, df2, on='key', how='right'))
# SELECT * FROM df1 FULL OUTER JOIN df2 ON df1.key = df2.key;
print(pd.merge(df1, df2, on='key', how='outer'))
|
[
"[email protected]"
] | |
128efb9b492a29c2e87a97b932e626a724b6af9f
|
52b9016932aa426eeaaade5d856af6a1a771683f
|
/tests/testapp/serializers.py
|
3c4be81a47c21da377120bda5b7ee7eb6deb647d
|
[
"MIT"
] |
permissive
|
marlncpe/django-rest-pandas
|
33033627d88c6467a9677133402fb519d5ea5a75
|
89a93c3ce8d30688f9137f5a9beacc7d63f621e0
|
refs/heads/master
| 2021-01-23T11:55:02.722962 | 2017-09-01T20:47:46 | 2017-09-01T20:47:46 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,844 |
py
|
from rest_framework.serializers import ModelSerializer
from rest_framework import serializers
from rest_pandas import PandasUnstackedSerializer
from .models import TimeSeries, MultiTimeSeries, ComplexTimeSeries
class TimeSeriesSerializer(ModelSerializer):
date = serializers.DateField(format=None)
class Meta:
model = TimeSeries
fields = '__all__'
class TimeSeriesNoIdSerializer(TimeSeriesSerializer):
class Meta:
model = TimeSeries
exclude = ['id']
class MultiTimeSeriesSerializer(ModelSerializer):
class Meta:
model = MultiTimeSeries
exclude = ['id']
pandas_index = ['date']
pandas_unstacked_header = ['series']
pandas_scatter_coord = ['series']
pandas_boxplot_group = 'series'
pandas_boxplot_date = 'date'
class ComplexTimeSeriesSerializer(ModelSerializer):
class Meta:
model = ComplexTimeSeries
exclude = ['id']
pandas_index = ['date', 'type']
pandas_unstacked_header = ['site', 'parameter', 'units']
class ComplexScatterSerializer(ComplexTimeSeriesSerializer):
class Meta(ComplexTimeSeriesSerializer.Meta):
exclude = ['id', 'flag']
pandas_scatter_coord = ['units', 'parameter']
pandas_scatter_header = ['site']
class ComplexBoxplotSerializer(ComplexTimeSeriesSerializer):
class Meta(ComplexTimeSeriesSerializer.Meta):
exclude = ['id', 'flag', 'type']
pandas_boxplot_group = 'site'
pandas_boxplot_date = 'date'
pandas_boxplot_header = ['units', 'parameter']
class NotUnstackableSerializer(ModelSerializer):
class Meta:
model = MultiTimeSeries
fields = '__all__'
list_serializer_class = PandasUnstackedSerializer
# pandas_unstacked_header = Missing
pandas_index = ['series']
|
[
"[email protected]"
] | |
2505a5e7e60a90a9d8a8586e237cbc32f6195255
|
0b024b99b7f79f299b9f63ffe08d6ab25c92c560
|
/rolePermission/wsgi.py
|
7a5ca7aca64ff5c22e1ddbb8a5511a208563fbce
|
[] |
no_license
|
Bakhodirov-Jakhongir/django-role-permission-based-app
|
924afebd9abeb494db89bbb17efc77b0ed3ed623
|
4f6e698a7bf0988ac1b72a577a83d2f84c3d5767
|
refs/heads/main
| 2023-08-25T07:17:06.292622 | 2021-11-02T11:21:04 | 2021-11-02T11:21:04 | 423,815,562 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 405 |
py
|
"""
WSGI config for rolePermission 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/3.1/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'rolePermission.settings')
application = get_wsgi_application()
|
[
"[email protected]"
] | |
76f794ba7b0ecbb4b8044008f296f605ccca2439
|
94838674ffd175df6194437c1ccc3f90ab409d6c
|
/pillowV3/log/2018-12-30 14:25:26.954969
|
574f467c8fb2f82034a73060e36f0973007e6bd0
|
[] |
no_license
|
WojciechKoz/MyFirstNeuralNetwork
|
4fdb3140d8f02257599d005638598f78055c1ac8
|
3cd032aba80ecd71edb0286724ae9ba565b75a81
|
refs/heads/master
| 2020-04-02T03:02:48.680433 | 2020-02-29T17:57:43 | 2020-02-29T17:57:43 | 153,943,121 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 498,667 |
954969
|
#!/usr/bin/env python3
# -*- coding: utf8 -*-
from __future__ import print_function # new print() on python2
from datetime import datetime
import sys
import numpy as np
from mnist import MNIST
# Display full arrays
np.set_printoptions(threshold=np.inf)
mndata = MNIST('./data')
images_full, labels_full = mndata.load_training()
images = []
labels = []
# dynamic arguments
batch_size = int(sys.argv[1])
size_1 = int(sys.argv[2])
size_2 = int(sys.argv[3])
batch_training_size = int(sys.argv[4])
data_part = 5 # only one fifth of the whole dataset to speed up training
for i in range(len(labels_full) // batch_size // data_part):
images.append(images_full[i*batch_size : (i+1)*batch_size])
labels.append(labels_full[i*batch_size : (i+1)*batch_size])
def sigmoid_prime(x):
return np.exp(-x) / ((np.exp(-x) + 1) ** 2)
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# nowe, przyda się?
def relu(x):
return np.maximum(x, x * 0.01)
def relu_prime(x):
if x >= 0:
return 1
# ej nie jest tak xd
# a jak xd?
type(x) == no.ndarray
# no x to macierz xd
# np.exp jest przeładowane ale jakakoleiwk funkcja to chyba nie
# to co foreach ? :(
# właśnie nie wiem, a co z gpu?
# to miało być szybsze a nie xd
# mamy duzo mozliwosci zmian ale nie na raz trzeba ustalic jakos
# hm TODO gpu TODO wincyj procent TODO gui gotowe
# xd
# tamto myliło hah
# to co najpierw? :p
# ssh daje wglad do basha tylko tak ?
# nie, to jest taki fajny programik, byobu
# i ten pasek na dole też jest z byobu
# on udostepnia tylko basha ?
# tak, ale basha multiplayer xd
# szkoda że 2 kursorow nie ma
# hm
return 0.01 # chyba tak xd nikt nie widzial xd
# ale x to macierz :p
# ale to jest przeciazone i jak jest funkcja od macierzy to bierze po kolei kazdy element
# w sumie
# zobacze na drugiej karcie xd
#X = np.array([[0, 0],
# [0, 1],
# [1, 0],
# [1, 1]])
#X = np.array(images)
y = []
for batch in labels:
y.append([])
for label in batch:
y[-1].append([1.0 if i == label else 0.0 for i in range(10)])
y = np.array(y)
#y = np.array([[0],
# [1],
# [1],
# [0]])
np.random.seed(1)
LEN = len(labels)
SIZES = [ 784, size_1, size_2, 10 ]
syn0 = 2 * np.random.random((SIZES[0], SIZES[1])) - 1
syn1 = 2 * np.random.random((SIZES[1], SIZES[2])) - 1
syn2 = 2 * np.random.random((SIZES[2], SIZES[3])) - 1
# biases for respective layers
b0 = 2 * np.random.random((1, SIZES[1])) - 1
b1 = 2 * np.random.random((1, SIZES[2])) - 1
b2 = 2 * np.random.random((1, SIZES[3])) - 1
for i, batch in list(enumerate(images)):
X = np.array(batch)
print("x:")
print(np.shape(X))
print("======================= BATCH {} =======================".format(i))
error = 1
j = 0
while j < batch_training_size:
l0 = X
l1 = sigmoid(np.dot(l0, syn0) + b0)
l2 = sigmoid(np.dot(l1, syn1) + b1)
l3 = sigmoid(np.dot(l2, syn2) + b2)
l3_error = (y[i] - l3)#** 2
error = np.mean(np.abs(l3_error))
j += 1
if j % 20 == 0:
print(("[%d] error: " % j) + str(error))
l3_delta = l3_error * sigmoid_prime(l3)
l2_error = l3_delta.dot(syn2.T)
l2_delta = l2_error * sigmoid_prime(l2)
l1_error = l2_delta.dot(syn1.T)
l1_delta = l1_error * sigmoid_prime(l1)
syn2 += l2.T.dot(l3_delta)
syn1 += l1.T.dot(l2_delta)
syn0 += l0.T.dot(l1_delta)
b0 += l1_delta.mean(axis=0)
b1 += l2_delta.mean(axis=0)
b2 += l3_delta.mean(axis=0)
def predict(data):
l0 = [data]
l1 = sigmoid(np.dot(l0, syn0) + b0)
l2 = sigmoid(np.dot(l1, syn1) + b1)
l3 = sigmoid(np.dot(l2, syn2) + b2)
return np.argmax(l3)
print("Output after training: ")
print(l3)
for i, el in enumerate(l3):
print(labels[0][i], "=", np.argmax(el), " predictions: ", el)
testing_images, testing_labels = mndata.load_testing()
correct = 0.0
for i, (image, label) in enumerate(zip(testing_images, testing_labels)):
prediction = predict(image)
if label == prediction:
correct += 1.0
correct_rate = correct / (i + 1.0)
print("{} = {} (correct {}%)".format(label, prediction, 100 * correct_rate))
with open('log/' + str(datetime.now()), 'a') as f:
with open(__file__, 'r') as myself:
print(myself.read(), file=f)
print("", file=f)
print("#### answers:", file=f)
print("argv =", sys.argv, file=f)
print("correct_rate =", correct_rate, file=f)
print("SIZES =", SIZES, file=f)
print("syn0 =", syn0, file=f)
print("syn1 =", syn1, file=f)
print("syn2 =", syn2, file=f)
print("b0 =", b0, file=f)
print("b1 =", b1, file=f)
print("b2 =", b2, file=f)
#### answers:
argv = ['./main.py', '59', '36', '34', '25']
correct_rate = 0.594
SIZES = [784, 36, 34, 10]
syn0 = [[-1.65955991e-01 4.40648987e-01 -9.99771250e-01 -3.95334855e-01
-7.06488218e-01 -8.15322810e-01 -6.27479577e-01 -3.08878546e-01
-2.06465052e-01 7.76334680e-02 -1.61610971e-01 3.70439001e-01
-5.91095501e-01 7.56234873e-01 -9.45224814e-01 3.40935020e-01
-1.65390395e-01 1.17379657e-01 -7.19226123e-01 -6.03797022e-01
6.01489137e-01 9.36523151e-01 -3.73151644e-01 3.84645231e-01
7.52778305e-01 7.89213327e-01 -8.29911577e-01 -9.21890434e-01
-6.60339161e-01 7.56285007e-01 -8.03306332e-01 -1.57784750e-01
9.15779060e-01 6.63305699e-02 3.83754228e-01 -3.68968738e-01]
[ 3.73001855e-01 6.69251344e-01 -9.63423445e-01 5.00288630e-01
9.77722178e-01 4.96331309e-01 -4.39112016e-01 5.78558657e-01
-7.93547987e-01 -1.04212948e-01 8.17191006e-01 -4.12771703e-01
-4.24449323e-01 -7.39942856e-01 -9.61266084e-01 3.57671066e-01
-5.76743768e-01 -4.68906681e-01 -1.68536814e-02 -8.93274910e-01
1.48235211e-01 -7.06542850e-01 1.78611074e-01 3.99516720e-01
-7.95331142e-01 -1.71888024e-01 3.88800315e-01 -1.71641461e-01
-9.00093082e-01 7.17928118e-02 3.27589290e-01 2.97782241e-02
8.89189512e-01 1.73110081e-01 8.06803831e-01 -7.25050592e-01]
[-7.21447305e-01 6.14782577e-01 -2.04646326e-01 -6.69291606e-01
8.55017161e-01 -3.04468281e-01 5.01624206e-01 4.51995971e-01
7.66612182e-01 2.47344414e-01 5.01884868e-01 -3.02203316e-01
-4.60144216e-01 7.91772436e-01 -1.43817620e-01 9.29680094e-01
3.26882996e-01 2.43391440e-01 -7.70508054e-01 8.98978517e-01
-1.00175733e-01 1.56779229e-01 -1.83726394e-01 -5.25946040e-01
8.06759041e-01 1.47358973e-01 -9.94259346e-01 2.34289827e-01
-3.46710196e-01 5.41162045e-02 7.71884199e-01 -2.85460480e-01
8.17070302e-01 2.46720232e-01 -9.68357514e-01 8.58874467e-01]
[ 3.81793835e-01 9.94645701e-01 -6.55318983e-01 -7.25728501e-01
8.65190926e-01 3.93636323e-01 -8.67999655e-01 5.10926105e-01
5.07752377e-01 8.46049071e-01 4.23049517e-01 -7.51458076e-01
-9.60239732e-01 -9.47578026e-01 -9.43387024e-01 -5.07577865e-01
7.20055897e-01 7.76621287e-02 1.05643957e-01 6.84061785e-01
-7.51653370e-01 -4.41632642e-01 1.71518543e-01 9.39191497e-01
1.22060439e-01 -9.62705421e-01 6.01265345e-01 -5.34051452e-01
6.14210391e-01 -2.24278712e-01 7.27083709e-01 4.94243285e-01
1.12480468e-01 -7.27089549e-01 -8.80164621e-01 -7.57313089e-01]
[-9.10896243e-01 -7.85011742e-01 -5.48581323e-01 4.25977961e-01
1.19433964e-01 -9.74888040e-01 -8.56051441e-01 9.34552660e-01
1.36200924e-01 -5.93413531e-01 -4.95348511e-01 4.87651708e-01
-6.09141038e-01 1.62717855e-01 9.40039978e-01 6.93657603e-01
-5.20304482e-01 -1.24605715e-02 2.39911437e-01 6.57961799e-01
-6.86417211e-01 -9.62847596e-01 -8.59955713e-01 -2.73097781e-02
2.12658923e-01 1.37702874e-01 -3.65275181e-01 9.77232309e-01
1.59490438e-01 -2.39717655e-01 1.01896438e-01 4.90668862e-01
3.38465787e-01 -4.70160885e-01 -8.67330331e-01 -2.59831604e-01]
[ 2.59435014e-01 -5.79651980e-01 5.05511107e-01 -8.66927037e-01
-4.79369803e-01 6.09509127e-01 -6.13131435e-01 2.78921762e-01
4.93406182e-02 8.49615941e-01 -4.73406459e-01 -8.68077819e-01
4.70131927e-01 5.44356059e-01 8.15631705e-01 8.63944138e-01
-9.72096854e-01 -5.31275828e-01 2.33556714e-01 8.98032641e-01
9.00352238e-01 1.13306376e-01 8.31212700e-01 2.83132418e-01
-2.19984572e-01 -2.80186658e-02 2.08620966e-01 9.90958430e-02
8.52362853e-01 8.37466871e-01 -2.10248774e-01 9.26525057e-01
-6.52088667e-01 -7.47340961e-01 -7.29841684e-01 1.13243314e-02]
[-9.56950389e-01 8.95940422e-01 6.54230942e-01 -9.69962039e-01
-6.47607489e-01 -3.35872851e-01 -7.38006310e-01 6.18981384e-01
-3.10526695e-01 8.80214965e-01 1.64028360e-01 7.57663969e-01
6.89468891e-01 8.10784637e-01 -8.02394684e-02 9.26936320e-02
5.97207182e-01 -4.28562297e-01 -1.94929548e-02 1.98220615e-01
-9.68933449e-01 1.86962816e-01 -1.32647302e-01 6.14721058e-01
-3.69510394e-01 7.85777417e-01 1.55714431e-01 -6.31979597e-01
5.75858468e-01 2.24062354e-01 -8.92181456e-01 -1.59612640e-01
3.58137673e-01 8.37203556e-01 -9.99195950e-01 9.53518298e-01]
[-2.46839371e-01 9.47567077e-01 2.09432202e-01 6.57691616e-01
1.49423009e-01 2.56152397e-01 -4.28847437e-01 1.73666681e-01
5.00043527e-01 7.16627673e-01 5.10164377e-01 3.96114497e-01
7.28958860e-01 -3.54638006e-01 3.41577582e-01 -9.82521272e-02
-2.35794496e-01 -1.78377300e-01 -1.97040833e-01 -3.65232108e-01
2.43838736e-01 -1.39505458e-01 9.47604156e-01 3.55601783e-01
-6.02860223e-01 -1.46597981e-01 -3.13307520e-01 5.95277608e-01
7.59996577e-01 8.07683912e-01 3.25439625e-01 -4.59583476e-01
-4.95266597e-01 7.09795885e-01 5.54292926e-02 6.04322168e-01]
[ 1.44977034e-01 4.66285051e-01 3.80232549e-02 5.41767821e-01
1.37715981e-01 -6.85802428e-02 -3.14622184e-01 -8.63581303e-01
-2.44151641e-01 -8.40747845e-01 9.65634227e-01 -6.36774297e-01
6.23717395e-01 7.49923290e-01 3.76826505e-01 1.38988825e-01
-6.78057126e-01 -6.62399545e-02 -3.09655898e-01 -5.49920084e-01
1.85023738e-01 -3.75460325e-01 8.32611107e-01 8.19271050e-01
-4.85763412e-01 -7.78217399e-01 -6.14074536e-01 -8.31658642e-04
4.57171336e-01 -5.83611123e-01 -5.03932883e-01 7.03343750e-01
-1.68302563e-01 2.33370134e-01 -5.32667722e-01 -7.96065481e-01]
[ 3.17140339e-02 -4.57180259e-02 -6.94656712e-01 2.43612463e-01
8.80202376e-02 3.08274694e-01 -7.10908920e-01 5.03055634e-01
-5.55901720e-01 3.87036487e-02 5.70592056e-01 -9.55339144e-01
-3.51275081e-01 7.45844753e-01 6.89419215e-01 7.68811852e-02
7.33216548e-01 8.99611983e-01 6.52813995e-01 7.08230888e-01
-8.02513196e-01 3.02608665e-01 4.07033976e-01 2.20481625e-01
5.99230523e-01 -9.30857560e-01 5.40477469e-01 4.63457201e-01
-4.80603213e-01 -4.85861402e-01 2.64606635e-01 -3.09405077e-01
5.93177356e-01 -1.07707536e-01 5.65498830e-01 9.80943567e-01]
[-3.99503321e-01 -7.13988343e-01 8.02616873e-01 8.31187578e-02
9.49480742e-01 2.73208800e-01 9.87826049e-01 9.21416083e-02
5.28518678e-02 -7.29144194e-01 -2.88589658e-01 -9.47562865e-01
-6.79209641e-01 4.91274385e-01 -9.39200620e-01 -2.66913806e-01
7.24692506e-01 3.85355435e-01 3.81884284e-01 -6.22726398e-01
-1.16191439e-01 1.63154815e-01 9.79503415e-01 -5.92187550e-01
-5.04534196e-01 -4.75653832e-01 5.00344827e-01 -8.60493451e-02
-8.86141123e-01 1.70324812e-02 -5.76079671e-01 5.97208490e-01
-4.05337237e-01 -9.44787976e-01 1.86864899e-01 6.87680858e-01]
[-2.37967752e-01 4.99716621e-01 2.22829566e-02 8.19036099e-02
9.18868642e-01 6.07921783e-01 -9.35353867e-01 4.18774502e-01
-6.99970369e-02 8.95097883e-01 -5.57134531e-01 -4.65855961e-01
-8.37052070e-01 -1.42762343e-01 -7.81962472e-01 2.67573521e-01
6.05926475e-01 3.93600992e-01 5.32422762e-01 -3.15091760e-01
6.91702966e-01 -1.42462450e-01 6.48019741e-01 2.52992317e-01
-7.13153903e-01 -8.43226200e-01 -9.63334714e-01 -8.66550005e-01
-8.28323726e-02 -7.73316154e-01 -9.44433302e-01 5.09722963e-01
-2.10299039e-01 4.93876991e-01 -9.51903465e-02 -9.98265060e-02]
[-4.38549866e-02 -5.19921469e-02 6.06326684e-01 -1.95214960e-01
8.09372321e-01 -9.25877904e-01 5.47748685e-01 -7.48717238e-01
2.37027134e-01 -9.79271477e-01 7.72545652e-02 -9.93964087e-01
9.02387571e-01 8.10804067e-01 5.91933884e-01 8.30548640e-01
-7.08883538e-01 -6.84539860e-01 -6.24736654e-01 2.44991805e-01
8.11618992e-01 9.79910357e-01 4.22244918e-01 4.63600818e-01
8.18586409e-01 -1.98252535e-01 -5.00298640e-01 -6.53139658e-01
-7.61085899e-01 6.25221176e-01 -7.06415253e-01 -4.71405035e-01
6.38178357e-01 -3.78825496e-01 9.64834899e-01 -4.66722596e-01]
[ 6.73066899e-02 -3.71065978e-01 8.21545662e-01 -2.66886712e-01
-1.32815345e-01 2.45853846e-02 8.77772955e-01 -9.38101987e-01
4.33757327e-01 7.82037909e-01 -9.45425553e-01 4.41024945e-02
-3.48020376e-01 7.18978642e-01 1.17033102e-01 3.80455736e-01
-9.42930001e-02 2.56618075e-01 -4.19806297e-01 -9.81302844e-01
1.53511870e-01 -3.77111572e-01 3.45351970e-02 8.32811706e-01
-1.47050423e-01 -5.05207927e-01 -2.57412477e-01 8.63722233e-01
8.73736763e-01 6.88659897e-01 8.40413029e-01 -5.44199420e-01
-8.25035581e-01 -5.45380527e-01 -3.71246768e-01 -6.50468247e-01]
[ 2.14188324e-01 -1.72827170e-01 6.32703024e-01 -6.29739203e-01
4.03753060e-01 -5.19288750e-01 1.48438178e-01 -3.02024806e-01
-8.86071201e-01 -5.42372658e-01 3.28205111e-01 -5.49981328e-03
3.80319681e-02 -6.50559700e-01 1.41431703e-01 9.93506850e-01
6.33670218e-01 1.88745248e-01 9.51978137e-01 8.03125169e-01
1.91215867e-01 -9.35147349e-01 -8.12845808e-01 -8.69256570e-01
-9.65337026e-02 -2.49130334e-01 9.50700069e-01 -6.64033414e-01
9.45575184e-01 5.34949738e-01 6.48475679e-01 2.65231634e-01
3.37465540e-01 -4.62353330e-02 -9.73727286e-01 -2.93987829e-01]
[-1.58563970e-02 4.60182422e-01 -6.27433145e-02 -8.51901678e-02
-7.24674518e-01 -9.78222532e-01 5.16556521e-01 -3.60094324e-01
9.68766900e-01 -5.59531548e-01 -3.22583949e-01 4.77922713e-02
5.09782914e-01 -7.22844322e-02 -7.50354914e-01 -3.74997243e-01
9.03833940e-03 3.47698016e-01 5.40299913e-01 -7.39328438e-01
-9.54169737e-01 3.81646444e-02 6.19977421e-01 -9.74792466e-01
3.44939689e-01 3.73616453e-01 -1.01506493e-01 8.29577373e-01
2.88722170e-01 -9.89520325e-01 -3.11431090e-02 7.18635612e-01
6.60799140e-01 2.98308394e-01 3.47396848e-01 1.56999160e-01]
[-4.51760450e-01 1.21059981e-01 3.43459570e-01 -2.95140740e-01
7.11656735e-01 -6.09925028e-01 4.94641621e-01 -4.20794508e-01
5.47598574e-01 -1.44525341e-01 6.15396818e-01 -2.92930275e-01
-5.72613525e-01 5.34569017e-01 -3.82716105e-01 4.66490135e-01
4.88946306e-01 -5.57206598e-01 -5.71775726e-01 -6.02104153e-01
-7.14963324e-01 -2.45834802e-01 -9.46744231e-01 -7.78159262e-01
3.49128048e-01 5.99553074e-01 -8.38940946e-01 -5.36595379e-01
-5.84748676e-01 8.34667126e-01 4.22629036e-01 1.07769222e-01
-3.90964024e-01 6.69708095e-01 -1.29388085e-01 8.46912430e-01]
[ 4.12103609e-01 -4.39373841e-02 -7.47579793e-01 9.52087101e-01
-6.80332699e-01 -5.94795750e-01 -1.37636490e-01 -1.91596188e-01
-7.06497038e-01 4.58637839e-01 -6.22509866e-01 2.87791289e-01
5.08611901e-01 -5.78535216e-01 2.01908496e-01 4.97856750e-01
2.76437421e-01 1.94254606e-01 -4.09035429e-01 4.63212942e-01
8.90616880e-01 -1.48877219e-01 5.64363634e-01 -8.87717921e-01
6.70543205e-01 -6.15499966e-01 -2.09806262e-01 -3.99837908e-01
-8.39792712e-01 8.09262006e-01 -2.59691645e-01 6.13948770e-02
-1.17674682e-02 -7.35677716e-01 -5.87091882e-01 -8.47622382e-01]
[ 1.58433999e-02 -4.76900896e-01 -2.85876782e-01 -7.83869343e-01
5.75103679e-01 -7.86832246e-01 9.71417647e-01 -6.45677671e-01
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3.55033060e+01 -3.32454619e+01 -5.15658581e+01 -3.50872574e+01
-1.18010751e+02 -5.35763251e+01 2.04927639e+01 -1.45961362e+01
-6.78791843e+00 -5.25786326e+01]]
syn2 = [[ 6.53255608e-01 -7.81726238e-01 1.50873204e+00 -3.94529830e+00
-2.05232343e+00 3.92407385e+00 -1.14457075e-02 -3.27455221e+00
1.84395833e+00 -3.35020040e+00]
[-2.74766419e+00 -3.24420947e-01 3.55588049e-01 1.21950411e-03
-5.57903366e+00 -3.36522443e+00 6.12355719e+00 -3.19343506e+00
-1.75433576e+00 -5.34801533e+00]
[ 4.02695165e-01 -1.07518024e+00 -1.70123094e+00 3.83551550e+00
1.14277753e-01 6.05846222e-01 -9.76958056e-01 -7.03324281e-01
-2.32236188e-01 6.69535983e-01]
[-4.82281910e-01 -2.05046345e+00 -1.43298198e+00 2.13073857e-01
4.87762371e-01 -1.72161041e+00 -5.23464525e+00 -2.99146671e+00
3.03699344e-01 -3.15034181e+00]
[ 4.06290082e+00 -7.79086267e+00 4.25344763e+00 -6.83517779e-01
2.58091338e+00 -6.66348001e+00 -1.18876372e+00 4.99575897e-01
-3.89736754e+00 -1.74076411e+00]
[-5.00540950e+00 -3.49876586e+00 -3.11779151e+00 -2.14650813e-02
-5.66139992e-01 -2.81711436e+00 -5.97783978e+00 -1.13178080e+00
-2.77213977e+00 -1.11986234e+00]
[-3.47233185e+00 1.29780938e+00 -2.90674393e+00 3.28820844e-02
-4.82698459e+00 -2.57233305e+00 -5.41093051e+00 5.69418769e+00
1.65149651e+00 -3.55879729e+00]
[ 4.58523219e-01 -6.72622424e+00 -7.96778439e-01 2.99154804e+00
1.51677234e+00 -5.23850856e-01 -2.10215802e+00 -2.98234369e+00
-2.42397901e+00 -2.75781051e+00]
[ 1.11061493e-01 -7.22705687e-01 -3.24230904e+00 -3.37240545e-01
6.95915214e-01 -5.34915124e+00 -3.30293405e+00 2.78062533e+00
-3.69412028e+00 -1.23650432e+00]
[-3.40218255e+00 6.48048851e+00 -1.19797547e+00 2.35100135e-01
-8.19271819e-01 -5.33014661e+00 -7.01056864e+00 -8.35403624e-01
-4.73585684e+00 2.38736310e+00]
[-1.94355025e+00 -7.47636253e+00 -2.74446150e+00 8.02378211e-01
-1.37208079e+00 -3.15010520e+00 -3.84930544e+00 2.64969553e+00
2.41581683e+00 1.55866600e-01]
[-2.25130566e+00 -5.43921205e+00 -2.08139357e+00 5.65200882e-01
-1.13702658e+00 -1.31291179e+00 -5.47260219e+00 3.51871746e+00
-3.06519286e+00 1.16760709e+00]
[-2.65811802e-01 -4.04890070e+00 2.04261188e+00 -2.35510310e+00
7.99617337e-01 -1.96396383e+00 -2.82885688e+00 7.16753195e-02
1.24591074e+00 -5.43892736e-01]
[-1.97046977e+00 -1.62590201e+00 1.06351058e+00 3.91604558e-02
-3.94948616e-01 -2.15979345e-01 -5.15585517e+00 3.23560995e+00
2.49619224e+00 2.95721909e+00]
[-2.73366636e+00 -2.16611740e+00 -5.42693297e+00 -3.12563919e+00
4.65572352e+00 -1.54487468e+00 2.49614023e-01 -3.18167945e+00
7.70637610e-01 -2.08279123e+00]
[-2.81299675e+00 -6.33568421e+00 -3.08082598e+00 -1.95871434e+00
-3.03971293e-01 -2.22888205e+00 -4.16561437e+00 -3.33057712e+00
-2.43312593e-01 1.68456585e+00]
[ 2.67729150e+00 -1.18095459e+00 -4.14665587e+00 -1.22593296e+00
-1.92761190e+00 -2.76775554e-02 -3.82779114e-01 -2.62412572e+00
-4.77439733e+00 3.22377066e+00]
[ 6.44110414e+00 -3.96538273e+00 -5.00794565e+00 -1.01063410e+00
-1.67352125e+00 -8.18392785e-01 -6.18773236e+00 3.35161386e+00
-3.88472254e+00 -3.49437111e-01]
[-7.46861777e+00 -9.75667482e-01 -2.07156291e+00 -1.15526082e+00
-1.25729298e+00 1.33198751e-02 -2.61970421e+00 -6.63320411e+00
4.56103327e-01 -1.12475657e-01]
[-5.24298738e+00 1.48085404e+00 6.78771162e-01 -2.85955274e+00
3.09910462e+00 -1.39610107e+00 -3.21238512e+00 4.77340343e-01
1.38217897e+00 -6.52941379e+00]
[-2.12551954e+00 -2.46819933e+00 -7.70764569e-01 -1.41946590e+00
-3.82581800e+00 4.80083639e+00 -2.12782064e+00 4.32236620e+00
-5.03320869e+00 -8.95322677e-01]
[-2.33533871e-01 -5.68410969e+00 3.68648523e+00 -1.01615772e+00
-7.00216437e-01 -4.89914433e-02 -5.22572635e-01 -2.47941779e-01
-2.36659849e+00 3.39124112e+00]
[-8.60503345e-01 -2.59453438e+00 6.33893657e-01 -1.26455802e+00
-2.79479067e+00 4.71301419e+00 -5.23377831e+00 2.12566261e+00
-6.85342766e-01 -1.97481775e+00]
[-3.01019172e+00 3.03084461e+00 -6.36654147e-01 3.10246625e-01
7.25535301e-01 1.42265009e+00 -3.90313693e+00 2.35706273e+00
-1.55371903e+00 -1.94184803e-01]
[-2.51686524e+00 -8.27406926e-01 -3.93807549e+00 7.17862265e-01
-5.52424423e-01 1.78066048e+00 -4.17260083e+00 4.63216439e+00
-3.43925439e+00 6.77243178e-01]
[-1.65359357e-01 -2.18030892e+00 -1.76545898e+00 -3.96749333e-01
2.22075536e+00 2.59673099e+00 1.48400204e-01 -3.91178321e-01
-6.71836591e-01 1.02435614e+00]
[-2.96126101e+00 -5.66150537e-01 -3.03042301e+00 3.60454753e+00
-3.74489853e+00 8.86472593e-01 -2.68186065e+00 -3.90179658e+00
1.97754003e+00 -2.11475778e+00]
[-4.19420977e-01 -6.64976468e+00 -2.80863799e+00 4.17531061e+00
-4.52969619e-01 -2.53312500e+00 -2.04243429e+00 -1.93778209e+00
-1.59148232e-02 5.02316842e+00]
[ 7.77756034e-01 -2.87151110e+00 2.43779910e+00 1.08941564e+00
-2.65841603e+00 1.89653658e+00 -7.38960685e-01 -7.36362648e+00
1.12415557e-01 -1.41625224e+00]
[ 1.33079760e+00 -6.60172572e+00 -2.26338025e+00 -5.07170852e+00
2.19034334e+00 1.40784744e-01 -4.04383085e-01 -6.57130084e-01
8.20751426e-01 1.98176397e-01]
[-4.72883332e-01 3.57740762e-01 -4.65194027e+00 -4.01463192e+00
-3.47407602e+00 -1.41359965e+00 2.89756373e+00 2.80812333e-01
-2.63416377e+00 1.82721019e+00]
[ 5.64979039e-01 1.57499196e+00 -2.02698719e+00 2.53095086e+00
1.40221816e+00 -8.30099075e-01 -2.02834490e+00 -3.27276885e+00
-3.24942111e+00 1.33428065e+00]
[-2.93535955e+00 2.13567448e+00 -7.52365913e-01 2.31130072e+00
-1.56005211e+00 -5.26342415e+00 -3.08157013e+00 2.72945538e-01
-2.84029347e+00 1.78330794e+00]
[-2.56831877e+00 -1.68549930e+00 -2.93974885e+00 -2.87900416e+00
-1.73070613e+00 3.59006710e+00 -4.01741500e+00 -3.75313207e+00
-2.20974683e+00 4.10056117e+00]]
b0 = [[ -875.73663618 -813.85866971 -840.27174486 -1066.0344334
-826.36406162 -877.04031066 -916.60081508 -515.31463157
-856.81938453 -934.05288301 -882.58779013 -884.11254226
-344.67593241 -503.62217279 -882.00032229 -899.51823195
-921.02182561 -1127.18384338 -628.77626535 -831.6744478
-846.43811066 -941.3049701 -301.58188636 -890.1993107
-862.5599114 -661.3040105 -254.89316881 -871.50264689
-869.4125715 -820.86846255 -840.98990615 -850.6850811
-861.05567862 -914.68797341 -250.68963748 -257.9943676 ]]
b1 = [[-1.66278210e+00 -3.65324540e+00 -4.51958146e+00 -2.57870597e+00
-4.87897223e+00 5.29131753e-01 -6.82521641e-01 -3.82414882e+00
-3.59395758e+00 -4.49837233e+00 -2.93700807e+00 -2.09688231e+00
-2.71914216e+00 -7.11979726e-01 -4.03584819e+00 3.47651147e-03
1.68643496e-01 -2.81502351e+00 -4.15120256e+00 -5.23985551e+00
-4.01459467e+00 -4.10819489e+00 -3.10466809e+00 -4.72698550e+00
-5.82501507e-01 -3.52325194e+00 -2.77943189e+00 -4.12160271e+00
-3.03927975e+00 -2.62742330e+00 -3.49018083e+00 -2.38325761e+00
-5.95594166e+00 -1.73942359e+00]]
b2 = [[-1.29065252 -1.07410532 -1.12565259 -1.91341438 -0.90875645 -1.56695869
-1.52767328 -0.36236739 -1.04558348 -0.45456078]]
|
[
"[email protected]"
] | |
da7a23f4a851db24c0e9eb29f459bc642aaa7923
|
a2567270e79ff3f8b7b9a9e28f91b46c2315313a
|
/unidad_2/ejercicio_2_8.py
|
5739b6c289dc09a410a352d9b02db209ce324660
|
[] |
no_license
|
b3nkos/learning_python
|
b87c421918ff3ad9ae480d351cbabf20d18fbb36
|
64920a70ad14b94d0abfa690e7c70e209014c5e7
|
refs/heads/master
| 2016-09-03T07:34:49.190664 | 2015-07-13T05:31:27 | 2015-07-13T05:31:27 | 38,992,888 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 429 |
py
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Escribir un programa que use un ciclo definido con rango numérico,
que averigue a cuántos amigos quieren saludar,
les pregunte los nombres de esos amigos/as, y los salude.
"""
number_of_friends = input("Digite el número de amigos que desea saludar: ")
for x in range(number_of_friends):
best_fiend = raw_input("Nombre del mejor amigo: ")
print "Hola", best_fiend + "!"
|
[
"[email protected]"
] | |
c3ccdce9e5846ff35e01721dd3820ee452c05378
|
e2cdf33fed0f2ea82ca3933ea0f57affb2bb203a
|
/events.py
|
4b5b148ce2f959d75029b3f4c61254b6cb90f35f
|
[] |
no_license
|
shaikhul/scoring_engine
|
67109c569bfa7b425fee8fdf4367d92adc0bd433
|
e48693be39cfeb849c245b5bc9b5a67270d7859a
|
HEAD
| 2016-09-06T04:30:40.571150 | 2015-09-01T02:49:11 | 2015-09-01T02:49:11 | 41,713,070 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 496 |
py
|
class Event(object):
def __init__(self, score=0):
self.score = score
def get_weighted_score(self):
return self.weight * self.score
def get_event_type(self):
return self.event_type
class WebEvent(Event):
weight = 1.0
event_type = 'web'
class EmailEvent(Event):
weight = 1.2
event_type = 'email'
class SocialEvent(Event):
weight = 1.5
event_type = 'social'
class WebinarEvent(Event):
weight = 2.0
event_type = 'webinar'
|
[
"[email protected]"
] | |
e54034ad6c7d99686d24d85630c58338ce0e63ee
|
6f753a851d5ffc7263160642422ee654e5a75a4e
|
/blog/templatetags/blog_tags.py
|
94aa21fe6d3d95bbea80c9dd8e46c0ff3b4876d9
|
[] |
no_license
|
iteegi/myBlog
|
1f5dda1f4a615a4e6a8a107838ab4b8db72c283f
|
b6f5dec8c5dbe258e0e25f2b300c968ace0d8350
|
refs/heads/master
| 2021-02-16T08:17:46.222872 | 2020-03-04T19:27:17 | 2020-03-04T19:27:17 | 244,984,455 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 968 |
py
|
"""Custom tags for the blog."""
from django.db.models import Count
from django import template
from ..models import Post
from django.utils.safestring import mark_safe
import markdown
register = template.Library()
@register.simple_tag
def total_posts():
"""Return the number of published articles."""
return Post.published.count()
@register.inclusion_tag('blog/post/latest_posts.html')
def show_latest_posts(count=5):
"""Return the last few posts."""
latest_posts = Post.published.order_by('-publish')[:count]
return {'latest_posts': latest_posts}
@register.simple_tag
def get_most_commented_posts(count=5):
"""Get the article with the most comments."""
return Post.published.annotate(total_comments=Count('comments'))\
.order_by('-total_comments')[:count]
@register.filter(name='markdown')
def markdown_format(text):
"""Populate article body with Markdown formatting."""
return mark_safe(markdown.markdown(text))
|
[
"[email protected]"
] | |
3054352bdfefed54c1ca9176eeaada9868f2f651
|
97637aa398dd498a4f3598a061329ee0a8919726
|
/robots/maps.py
|
41e5db884a562f71bbb89930ad58643e2a6cfabb
|
[] |
no_license
|
guitaoliu/multi_robot_xplore
|
1d09a328779484ec482714a0438da75469342eaf
|
2a006216aaacf4e9e5ae6ca753e390897812f055
|
refs/heads/master
| 2022-10-02T21:54:02.535024 | 2020-06-03T07:30:03 | 2020-06-03T07:30:03 | 269,020,061 | 1 | 0 | null | 2020-06-03T07:31:11 | 2020-06-03T07:31:11 | null |
UTF-8
|
Python
| false | false | 3,125 |
py
|
import numpy as np
from abc import ABC
from typing import List
from robots.setting import (
MAP_SIZE,
BARRIER_PERCENTAGE,
PHE_VOLATILIZE_CAP,
MAP_EXPLORE_PERCENT,
)
class Node:
def __init__(self, pos: tuple):
self.x = pos[0]
self.y = pos[1]
def loc(self) -> tuple:
return self.x, self.y
def __eq__(self, other):
return self.x == other.x and self.y == other.y
class Map(ABC):
def __init__(self):
self.map = np.zeros(MAP_SIZE, dtype=int)
def __getitem__(self, indices):
return self.map[indices[0], indices[1]]
class BarrierMap(Map):
def __init__(self):
super(BarrierMap, self).__init__()
self.barrier_num = int(MAP_SIZE[0] * MAP_SIZE[1] * BARRIER_PERCENTAGE)
self.load_barrier()
def load_barrier(self):
choices = np.random.choice(np.arange(MAP_SIZE[0] * MAP_SIZE[1]), size=self.barrier_num, replace=False)
map_flatten = self.map.flatten()
for choice in choices:
map_flatten[choice] = 1
self.map = map_flatten.reshape(MAP_SIZE)
def __call__(self, node: Node) -> bool:
return self.map[node.x, node.y] == 1
def get_random_node(self):
x, y = np.random.choice(range(self.map.shape[0])), np.random.choice(range(self.map.shape[1]))
while self.map[x, y] == 1:
x, y = np.random.choice(range(self.map.shape[0])), np.random.choice(range(self.map.shape[1]))
return Node((x, y))
class ExploreMap(Map):
def __init__(self):
super(ExploreMap, self).__init__()
def update(self, node: Node):
self.map[node.x, node.y] = 1
def is_finished(self) -> bool:
if self.map.sum() >= MAP_EXPLORE_PERCENT * self.map.shape[0] * self.map.shape[1]:
return True
else:
return False
def status(self, node: Node) -> int:
return self.map[node.loc()]
def get_neighbours(self, node: Node) -> List:
node_list = []
for i, j in [(-1, 0), (0, -1), (1, 0), (0, 1)]:
if node.x - i < 0 or node.x + i >= MAP_SIZE[0] \
or node.y - j < 0 or node.y + j >= MAP_SIZE[1]:
continue
if not self.status(Node((node.x + i, node.y + j))):
node_list.append(Node((node.x + i, node.y + j)))
return node_list
class PheMap(Map):
def __init__(self):
super(PheMap, self).__init__()
self.map = np.zeros_like(self.map, dtype=float)
def update_phe(self, node: Node):
self.map[node.x, node.y] += 1
def phe_volatilize(self):
map(self.volatilize, self.map)
@staticmethod
def volatilize(phe_level: float) -> float:
return (1 - PHE_VOLATILIZE_CAP) * phe_level
def get_phe(self, node1: Node, node2: Node) -> float:
phe = 0
x_start, x_end = min(node1.x, node2.x), max(node1.x, node2.x)
y_start, y_end = min(node1.y, node2.y), max(node1.y, node2.y)
for i in range(x_start, x_end):
for j in range(y_start, y_end):
phe += self.map[i, j]
return phe
|
[
"[email protected]"
] | |
3e04a14261479eb12b66058bcc6adbed16a4c1ec
|
d9705629fb827d3d2dcfc252c24ddb67862556d6
|
/data_wordcloud02.py
|
52d69c300d0600ef40bac46de0ddd3bdfdbe46f6
|
[] |
no_license
|
haorenxwx/WordCloudLearning1
|
9d7f1fe10d24d1d2bd1710a97e178e6c06c0f7a5
|
daf41b5a35832b7ecd2cf0d797923d14d6c6390f
|
refs/heads/master
| 2021-01-23T06:02:31.898291 | 2017-09-07T15:05:18 | 2017-09-07T15:05:18 | 102,485,693 | 0 | 0 | null | 2017-09-05T14:25:01 | 2017-09-05T13:39:13 |
Python
|
UTF-8
|
Python
| false | false | 1,668 |
py
|
#-*- coding: utf-8 -*-
#test change in github
#from wordcloud import wordcloud
import wordcloud as wc
import codecs
import jieba
#import jieba.analyse as analyse
from scipy.misc import imread
import os
from os import path
#import matplotlib.pyplot as plt
import matplotlib.pylab as plt
#from PIL import Image, ImageDraw, ImageFont
import pandas as pda
from PIL import Image
from numpy import array
#path="E:/电子书/《那些回不去的少年时光》.txt"
path="E:/电子书/BL Novel/柴鸡蛋/《你丫上瘾了？》BY：柴鸡蛋.txt"
data=open(path,"r",encoding="gbk").read()
#data=open(path,"r",encoding="UTF-8").read()
#h=pda.read_table("E:/电子书/《那些回不去的少年时光》.txt")
#comment_text = open('E:/电子书/《那些回不去的少年时光》.txt',encoding='UTF-8','r').read()
#print(comment_text)
#cut_text = " ".join(jieba.cut(h))
#d=path.dirname(_file_)#当前文件所在文件夹目录
cutdata=jieba.cut(data)
alldata=""
for i in cutdata:
alldata=alldata+" "+str(i)#通过迭代器把词语连起来并用空格隔开
color_mask = imread ("E:/study/python/onepiece.png")#读取背景图片
#font=r"E:/study/python/HYQiHei-25J.ttf"
font=r"E:/study/python/simhei.ttf"
cloud=wc.WordCloud(
collocations=False,
#font_path ="HYQiHei-25J.ttf",
font_path=font,
#指定字体
#font_path =path.join(d,"HYQiHei-25J.ttf"),
background_color='white',
#设置背景色
mask=color_mask,
#词云形状
max_words=500,
max_font_size=40
)
word_cloud =cloud.generate(alldata)
#word_cloud =cloud.generate(h)
#word_cloud =cloud.generate(cut_text)
word_cloud.to_file("losttime.jpg")
plt.imshow(word_cloud)
plt.axis('off')
plt.show()
|
[
"[email protected]"
] | |
633864555ecc66eee48aee8dc62a911061bc4b80
|
931fafb77d3d1e00d25a4ab658f75d2cd9f5abb8
|
/Fundamentals/Session1/YoB.py
|
5ee44632a7db87bb8911a50eb0952ebb32a409d0
|
[] |
no_license
|
andynguyendk/andynguyen_fundamentals_C4E15
|
c72e74d13fcd59dc0d6c7072d55d092418cabfa7
|
845da653307dc72ad68f55d756c36ff17802c6aa
|
refs/heads/master
| 2021-09-09T02:54:30.863797 | 2018-03-13T12:17:11 | 2018-03-13T12:17:11 | 117,338,525 | 0 | 2 | null | null | null | null |
UTF-8
|
Python
| false | false | 88 |
py
|
n = int(input("What's your year of birth? "))
a = 2018 - n
print ("Now your age is", a)
|
[
"[email protected]"
] | |
478b126ab280b9343347c1ee8bc9238dd9f45703
|
86da8c4d616a78afc7cd09711b0151e5f852a8b8
|
/pythonprograms/LanguageFundamentals/Logicaloperator.py
|
98dae1b5e2d38ecb15661dfb77541e77356b7768
|
[] |
no_license
|
sharijamusthafa/luminarpython
|
d1d3274d23d93af2c5e4db7d2652e8cb46b133aa
|
8ebd75ea5f734e5061a7138153a2c6b1cd43a738
|
refs/heads/master
| 2022-12-23T22:45:40.194242 | 2020-10-07T16:40:09 | 2020-10-07T16:40:09 | 290,109,565 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 32 |
py
|
num1=2
num2=4
print(num1&num2)
|
[
"[email protected]"
] | |
c98cf8d841b31199e65e5296805d025a9c933399
|
fbea032896db7e9f17687ab1e09cee580a66d179
|
/utils.py
|
10af90c3c81b1277d7ffa8227263bb65cfa174ec
|
[] |
no_license
|
jmadni/ingage
|
4be888a6afe9cbef7b3c6adda0d27553fa35395c
|
c4a95bb01c63d12e1128a6a541edab39292c9b03
|
refs/heads/main
| 2023-02-13T14:02:42.554191 | 2021-01-12T07:03:33 | 2021-01-12T07:03:33 | 328,564,822 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 312 |
py
|
import os
import shutil
from moviepy.video.io.VideoFileClip import VideoFileClip
def output_directory(dir):
if os.path.exists(dir):
shutil.rmtree(dir)
os.mkdir(dir)
def check_input_file_valid(video_file):
try:
VideoFileClip(video_file)
except FileNotFoundError:
pass
|
[
"[email protected]"
] | |
a6cc13163a16574087d481e50d5730f46c476e01
|
ef4a2e4aadc67a1d5e929f5c0c3e902b34f663dc
|
/examples/psychotria.lagrange.py
|
767424584fc3bc922133c667c347f6c9b73538e6
|
[] |
no_license
|
zxf-art/lagrange-python
|
326b23cc69b1eb81db9127a2ec8fc91bcc0b6dbc
|
e67f1e671d313319bdc374a3371fa3717a8e7091
|
refs/heads/master
| 2020-06-12T06:18:20.917164 | 2018-01-22T19:50:43 | 2018-01-22T19:50:43 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,929 |
py
|
#!/usr/bin/env python
import os
import lagrange
data = """\
### begin data
{'area_adjacency': [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
'area_dispersal': [[[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0]]],
'area_labels': ['K', 'O', 'M', 'H'],
'base_rates': '__estimate__',
'dispersal_durations': [10.0],
'dm_symmetric_entry': True,
'excluded_ranges': [],
'lagrange_version': '20120508',
'max_range_size': 2,
'model_name': 'psychotria',
'newick_trees': [{'included': '__all__',
'name': 'Tree0',
'newick': '((((((((P_hawaiiensis_WaikamoiL1:0.010853, P_mauiensis_Eke:0.010853)N2:0.007964, (P_fauriei2:0.013826, P_hathewayi_1:0.013826)N5:0.004991)N6:0.001986, (P_kaduana_PuuKukuiAS:0.020803, P_mauiensis_PepeAS:0.020803)N9:1e-05)N10:0.003762, P_kaduana_HawaiiLoa:0.024565)N12:0.003398, (P_greenwelliae07:0.012715, P_greenwelliae907:0.012715)N15:0.015248)N16:0.018984, ((((P_mariniana_MauiNui:0.02241, P_hawaiiensis_Makaopuhi:0.02241)N19:0.008236, P_mariniana_Oahu:0.030646)N21:0.002893, P_mariniana_Kokee2:0.033539)N23:0.005171, P_wawraeDL7428:0.03871)N25:0.008237)N26:0.008255, (P_grandiflora_Kal2:0.027864, P_hobdyi_Kuia:0.027864)N29:0.027338)N30:0.003229, ((P_hexandra_K1:0.026568, P_hexandra_M:0.026568)N33:0.005204, P_hexandra_Oahu:0.031771)N35:0.026659)N36;',
'root_age': 5.2}],
'ranges': [(),
(0,),
(0, 1),
(0, 2),
(0, 3),
(1,),
(1, 2),
(1, 3),
(2,),
(2, 3),
(3,)],
'taxa': ['P_mariniana_Kokee2',
'P_mariniana_Oahu',
'P_mariniana_MauiNui',
'P_hawaiiensis_Makaopuhi',
'P_wawraeDL7428',
'P_kaduana_PuuKukuiAS',
'P_mauiensis_PepeAS',
'P_hawaiiensis_WaikamoiL1',
'P_mauiensis_Eke',
'P_fauriei2',
'P_hathewayi_1',
'P_kaduana_HawaiiLoa',
'P_greenwelliae07',
'P_greenwelliae907',
'P_grandiflora_Kal2',
'P_hobdyi_Kuia',
'P_hexandra_K1',
'P_hexandra_M',
'P_hexandra_Oahu'],
'taxon_range_data': {'P_fauriei2': (1,),
'P_grandiflora_Kal2': (0,),
'P_greenwelliae07': (0,),
'P_greenwelliae907': (0,),
'P_hathewayi_1': (1,),
'P_hawaiiensis_Makaopuhi': (3,),
'P_hawaiiensis_WaikamoiL1': (2,),
'P_hexandra_K1': (0,),
'P_hexandra_M': (0,),
'P_hexandra_Oahu': (1,),
'P_hobdyi_Kuia': (0,),
'P_kaduana_HawaiiLoa': (1,),
'P_kaduana_PuuKukuiAS': (2,),
'P_mariniana_Kokee2': (0,),
'P_mariniana_MauiNui': (2,),
'P_mariniana_Oahu': (1,),
'P_mauiensis_Eke': (2,),
'P_mauiensis_PepeAS': (2,),
'P_wawraeDL7428': (0,)}}
### end data
"""
i = 0
while 1:
if not i:
outfname = "psychotria.results.txt"
else:
outfname = "psychotria.results-"+str(i)+".txt"
if not os.path.exists(outfname): break
i += 1
outfile = open(outfname, "w")
lagrange.output.log(lagrange.msg, outfile, tee=True)
model, tree, data, nodelabels, base_rates = lagrange.input.eval_decmodel(data)
lagrange.output.ascii_tree(outfile, tree, model, data, tee=True)
if base_rates != "__estimate__":
d, e = base_rates
else:
d, e = lagrange.output.optimize_dispersal_extinction(outfile, tree, model, tee=True)
if nodelabels:
if nodelabels == "__all__":
nodelabels = None
lagrange.output.ancsplits(outfile, tree, model, d, e, nodelabels=nodelabels, tee=True)
|
[
"[email protected]"
] | |
5b8dec57487f5b0f362fcf9ff61241d3a643f9ec
|
b8454cadc306140b7140bd4a5040da9b9c18c980
|
/base/migrations/0001_initial.py
|
06ccb1bdae7339a5b873157a6a8bc6042ac5462d
|
[] |
no_license
|
amar3142/mywebsite3
|
dc62e6de5ba7d48e72532c6f848d2d39d1965a5d
|
db45c03af908bc5df68154e3227f4dee0db28bc6
|
refs/heads/master
| 2023-02-04T18:48:48.876485 | 2020-12-17T10:36:50 | 2020-12-17T10:36:50 | 321,337,547 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 621 |
py
|
# Generated by Django 3.1.4 on 2020-12-16 13:20
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Task',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=200)),
('complete', models.BooleanField(default=False)),
('created', models.DateTimeField(auto_now_add=True)),
],
),
]
|
[
"[email protected]"
] | |
7d3341862b6c5f086993c701914ada358509663c
|
d27c668c98b83904c830141f143125c4eca54406
|
/Compiler.py
|
d1e042d8eaee3e7225397dcecd832c4bb8d157d7
|
[] |
no_license
|
VladasZ/build_tools
|
d38dd7c1b237ec0c7f5d2c0610d85ee94539c923
|
926b1c4b2467e7bb5c58d217cc39cb8d8e9dd8d0
|
refs/heads/master
| 2022-02-25T04:33:07.360975 | 2022-02-09T21:29:16 | 2022-02-09T21:29:16 | 144,903,216 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 634 |
py
|
import Args
import System
import Compilers.GCC
import Compilers.Clang
import Compilers.VisualStudio
gcc = Compilers.GCC.get()
clang = Compilers.Clang.get()
visual_studio = Compilers.VisualStudio.get()
def get_ide():
if System.is_windows:
return visual_studio
if System.is_mac:
return clang
return gcc
def get():
if Args.android:
return clang
if Args.ide:
return get_ide()
if Args.clang:
return clang
if Args.gcc:
return gcc
if System.is_mac:
return clang
return gcc
def print_info():
print(clang)
print(gcc)
|
[
"[email protected]"
] | |
c2c9f862af8d90b56e812982f7aebae9015306e5
|
e4875a1e3ff1e46c63b546aa944565d716d71d0e
|
/serene_load/serene_load/merge_trees.py
|
e558c2375625dbacaada6d48b245352bac6c8545
|
[
"Apache-2.0",
"MIT"
] |
permissive
|
NICTA/serene-etl
|
e1f4ffa4b6c77a1dfbc1d1974fac7cec42f71f85
|
1d446012c0d08a95b8fbbbe8237735320a2fe2a4
|
refs/heads/master
| 2021-01-22T13:48:03.297688 | 2017-10-09T01:30:44 | 2017-10-09T01:30:44 | 100,686,105 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,156 |
py
|
import logging
import argparse
from serene_load.meta_select import load_meta
def create_arguments():
parser = argparse.ArgumentParser(description='take all json data from one directory and merge into another tree')
parser.add_argument('--meta', type=unicode, help='Directory containing primary metadata', required=True)
parser.add_argument('--source', type=unicode, help='Directory containing secondary metadata to merge into primary', required=True)
parser.add_argument('--verbose', type=bool)
def setup_logging(LEVEL):
logger = logging.getLogger()
logger.setLevel(LEVEL)
stream_handler = logging.StreamHandler()
stream_handler.setLevel(LEVEL)
formatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s')
stream_handler.setFormatter(formatter)
logger.addHandler(stream_handler)
return logger
def main():
parser = create_arguments()
args = parser.parse_args()
log = setup_logging(LEVEL=logging.DEBUG if args.debug is True else logging.INFO)
primary = load_meta(args.meta)
secondary = load_meta(args.source)
print secondary
if __name__ == '__main__':
main()
|
[
"[email protected]"
] | |
08329a18ff4f915bce929ea48502042c6b5369ab
|
0910b5a1b6fd8d29579ea0596419331a51434b77
|
/leopi/projects/tweeter/leonorrepitweeter.py
|
bee7022c3feac5a2737c4d4bf4deb2be3e7e6f95
|
[] |
no_license
|
efrister/LeoNorrePi
|
60d2a5aa5a8c3d1435c53cba285715c741f83f33
|
2a25eba405fb22ddc3faf38cceaa29f955996ba2
|
refs/heads/master
| 2021-01-01T15:19:00.653023 | 2014-02-22T15:15:08 | 2014-02-22T15:15:08 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,210 |
py
|
from twython import *
import configparser
# Read configuration
configuration = {}
try:
config = configparser.ConfigParser()
config.read('config.ini')
# Read all options into a dictionary
for section in config.sections():
configuration[section] = {}
for option in config.options(section):
configuration[section][option] = config.get(section, option)
except configparser.MissingSectionHeaderError as e:
print("Could not read configuration. Exiting")
exit()
# Instantiate Twitter API with credentials
oauth = configuration['OAuth']
twitter = Twython(oauth['app_key'], oauth['app_secret'], oauth['oauth_token'], oauth['oauth_token_secret'])
# Check if we want to tweet something
tweet = input("Please enter a message to tweet, or leave empty if you don\'t want to tweet at this time:")
if 0 < len(tweet):
try:
twitter.update_status(status=tweet)
print("Successfully tweeted", tweet)
except TwythonError as Error:
print("Could not authenticate with the Twitter API. Your API is probably set to Read-Only.")
print(format(Error))
else:
print("Not tweeting anything.")
# Cleanup
del tweet
# Delete last tweet
delete = int(input("Enter the number of last tweets you wish to delete:"))
if 0 < delete:
timeline = twitter.get_home_timeline()
counter = len(timeline)
if delete > counter:
print("Cannot delete more items than are in the timeline. Not deleting anything.")
else:
counter = 0
while delete > 0:
# Get tweet id
tweet = timeline[counter]
tweetId = tweet['id_str']
tweetText = tweet['text']
# Perform delete action
twitter.destroy_status(id=tweetId)
# Notify
print("Deleted the tweet with the text '", tweetText, "'", sep="")
# Increment
counter += 1
delete -= 1
else:
print("Not deleting anything.")
# Tweet uptime
from datetime import timedelta
try:
with open('/proc/uptime', 'r') as f:
uptime_seconds = float(f.readline().split()[0])
uptime_string = str(timedelta(seconds=uptime_seconds))
except IOError:
print("Cannot tweet uptime, not on a Pi.")
|
[
"[email protected]"
] | |
a5196fdf75c378ca343c8727c8f7e946e3cccf00
|
b8b8722787ee6ee0ebe3eefb6d9e9e0db11a445f
|
/CIFAR10_MLP/main_5.py
|
93f889cdfcaa742288a3b9dc41dfd230478abf24
|
[] |
no_license
|
gaow1423/Machine-Learning-Problems
|
846061975efd63eddcd1e33e401a3aa2b699b875
|
7a00352cdc213234c23bc215cdb326c032bedd05
|
refs/heads/master
| 2020-03-17T12:22:46.607558 | 2018-06-17T07:07:55 | 2018-06-17T07:07:55 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 6,735 |
py
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import numpy as np
import matplotlib
import math
import matplotlib.pyplot as plt
cuda = torch.cuda.is_available()
print('Using PyTorch version:', torch.__version__, 'CUDA:', cuda)
##data preparation
batch_size = 40
kwargs = {'num_workers': 2, 'pin_memory': True} if cuda else {}
trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.Compose([transforms.ToTensor()]))
train_loader = torch.utils.data.DataLoader(trainset, batch_size, shuffle=True, **kwargs)
validationset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.Compose([transforms.ToTensor()]))
validation_loader = torch.utils.data.DataLoader(validationset, batch_size, shuffle=False, **kwargs)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(3*32*32, 100)
self.fc1_drop = nn.Dropout(0.2)
self.fc2 = nn.Linear(100,10)
def forward(self, x):
x = x.view(-1, 3*32*32)
x = F.sigmoid(self.fc1(x))
x = self.fc1_drop(x)
return F.log_softmax(self.fc2(x), 1)
#model = Net()
#if cuda:
# model.cuda()
#learningrate = [0.1, 0.01, 0.001, 0.0001]
#for k in learningrate:
# optimizer = optim.SGD(model.parameters(), lr = k, momentum = 0.5)
#print(model)
def train(epoch, k, model, log_interval = 100):
# print (k)
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
if (batch_idx <= 999):
if cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer = optim.SGD(model.parameters(), lr = 0.1, momentum = 0.5, weight_decay = k)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
# if batch_idx % log_interval == 0:
# print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, (batch_idx) * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.data[0]))
def validate(loss_vector, accuracy_vector, epochs, model):
model.eval()
val_loss, correct = 0, 0
for batch_idx, (data, target) in enumerate(train_loader):
if (batch_idx > 999):
if cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
output = model(data)
val_loss += F.nll_loss(output, target).data[0]
pred = output.data.max(1)[1] # get the index of the max log-probability
correct += pred.eq(target.data).cpu().sum()
val_loss /= (10000/batch_size)
loss_vector.append(val_loss)
accuracy = 100. * correct / len(validation_loader.dataset)
accuracy_vector.append(accuracy)
print('\nEpoch {}: Validation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(epochs, val_loss, correct, len(validation_loader.dataset), accuracy))
def test(loss_vector, accuracy_vector, epochs, model):
model.eval()
val_loss, correct = 0, 0
for (data, target) in validation_loader:
if cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
output = model(data)
val_loss += F.nll_loss(output, target).data[0]
pred = output.data.max(1)[1] # get the index of the max log-probability
correct += pred.eq(target.data).cpu().sum()
val_loss /= len(validation_loader)
loss_vector.append(val_loss)
accuracy = 100. * correct / len(validation_loader.dataset)
accuracy_vector.append(accuracy)
print('\nEpoch {}: Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(epochs, val_loss, correct, len(validation_loader.dataset), accuracy))
def main():
# learningrate = [0.2, 0.3, 0.4, 0.5]
# for k in learningrate:
# momentum = [0.5, 0.7, 0.9, 1.1]
# for k in momentum:
weightdecay = [0.001, 0.01, 0.1, 1]
for k in weightdecay:
# class Net(nn.Module):
# def __init__(self):
# super(Net, self).__init__()
# self.fc1 = nn.Linear(3*32*32, 100)
# self.fc1_drop = nn.Dropout(k)
# self.fc2 = nn.Linear(100,10)
# def forward(self, x):
# x = x.view(-1, 3*32*32)
# x = F.sigmoid(self.fc1(x))
# x = self.fc1_drop(x)
# return F.log_softmax(self.fc2(x), 1)
model = Net()
if cuda:
model.cuda()
epochs = 150
lossv, accv = [], []
lossv_t, accv_t = [], []
for epochs in range(1, epochs + 1):
train(epochs, k, model)
validate(lossv, accv, epochs, model)
test(lossv_t, accv_t, epochs, model)
l = range(1, epochs + 1)
plt.subplot(1, 3, 1)
plt.plot(l, accv, label = "Training Accuracy with weight decay = %f"%(k))
plt.title('Training Accuracy')
plt.xlabel('x-axis: the Number of Epochs')
plt.ylabel('y-axis: Accuracy of Validation set (%)')
plt.subplot(1, 3, 2)
plt.plot(l, lossv, label = "Training Loss with weight decay = %f"%(k))
plt.title('Training loss curve')
plt.xlabel('x-axis: the Number of Epochs')
plt.ylabel('y-axis: Training loss')
plt.subplot(1, 3, 3)
plt.plot(l, accv_t, label = "Test Accuracy with weight decay = %f"%(k))
plt.title('Testing Accuracy')
plt.xlabel('x-axis: the Number of Epochs')
plt.ylabel('y-axis: Accuracy of Testing set (%)')
# f.plot(l, accv, label = "Training Accuracy with drop out = %f"%(k))
# g.plot(l, lossv, label = "Training Loss with drop out = %f"%(k))
# t.plot(l, accv_t, label = "Test Accuracy with drop out = %f"%(k))
#
# f.title('Training Accuracy')
# f.xlabel('x-axis: the Number of Epochs')
# f.ylabel('y-axis: Accuracy of Validation set (%)')
# f.legend()
# f.show()
#
# g.title('Training loss curve')
# g.xlabel('x-axis: the Number of Epochs')
# g.ylabel('y-axis: Training loss')
# g.legend()
# g.show()
#
# t.title('Testing Accuracy')
# t.xlabel('x-axis: the Number of Epochs')
# t.ylabel('y-axis: Accuracy of Testing set (%)')
# t.legend()
# t.show()
# raw_input()
plt.legend()
plt.show()
if __name__ == '__main__':
main()
|
[
"[email protected]"
] | |
e454c76d9168502bdf7ef1dbcb7bde577a6bec70
|
95beb714da1e783981295064aaf0b64e5a37c9c0
|
/instagram/tests.py
|
3c2572e3281053fb555c96273e6673f7f4966423
|
[
"MIT"
] |
permissive
|
Joseph-Odhiambo/Insta-clone
|
653105cb3a4d7f2a470cf365444e074f1b4d50ff
|
8ceb4f8ef6e7ea815e39a94554b3410a8e7da5a0
|
refs/heads/master
| 2023-01-01T22:56:22.902366 | 2020-10-22T05:40:59 | 2020-10-22T05:40:59 | 304,582,611 | 0 | 0 |
MIT
| 2020-10-22T05:32:16 | 2020-10-16T09:37:03 |
JavaScript
|
UTF-8
|
Python
| false | false | 1,303 |
py
|
from django.test import TestCase
from .models import Profile, Post
from django.contrib.auth.models import User
# Create your tests here.
class TestProfile(TestCase):
def setUp(self):
self.user = User(username='Joseph')
self.user.save()
self.profile_test = Profile(id=1, name='image', profile_picture='default.jpg', bio='this is a test profile', user=self.user)
def test_instance(self):
self.assertTrue(isinstance(self.profile_test, Profile))
def test_save_profile(self):
self.profile_test.save_profile()
after = Profile.objects.all()
self.assertTrue(len(after) > 0)
class TestPost(TestCase):
def setUp(self):
self.profile_test = Profile(name='Joseph', user=User(username='Joseph'))
self.profile_test.save()
self.image_test = Post(image='default.png', name='test', caption='default test', user=self.profile_test)
def test_insatance(self):
self.assertTrue(isinstance(self.image_test, Post))
def test_save_image(self):
self.image_test.save_image()
images = Post.objects.all()
self.assertTrue(len(images) > 0)
def test_delete_image(self):
self.image_test.delete_image()
after = Profile.objects.all()
self.assertTrue(len(after) < 1)
|
[
"[email protected]"
] | |
c2bad406da73b4a740249a716af90952cf0bb2c0
|
3a3f4477a517c9757432042ceb4939f37762c2a4
|
/12.IntegertoRoman/int_to_roman.py
|
0615bb179a12ee1f678713fbdef56969f29a51bc
|
[] |
no_license
|
marathohoho/leetcode-progress
|
f351ad89905c8e61fd5b5adff62320ce8ba9645d
|
13b298c1074328c130724e328d7c22be642903cb
|
refs/heads/master
| 2020-12-24T01:39:15.117334 | 2020-04-06T20:20:16 | 2020-04-06T20:20:16 | 237,339,061 | 2 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,712 |
py
|
"""
Roman numerals are represented by seven different symbols: I, V, X, L, C, D and M.
Symbol Value
I 1
V 5
X 10
L 50
C 100
D 500
M 1000
For example, two is written as II in Roman numeral, just two one's added together. Twelve is written as, XII, which is simply X + II.
The number twenty seven is written as XXVII, which is XX + V + II.
Roman numerals are usually written largest to smallest from left to right. However, the numeral for four is not IIII.
Instead, the number four is written as IV. Because the one is before the five we subtract it making four.
The same principle applies to the number nine, which is written as IX. There are six instances where subtraction is used:
I can be placed before V (5) and X (10) to make 4 and 9.
X can be placed before L (50) and C (100) to make 40 and 90.
C can be placed before D (500) and M (1000) to make 400 and 900.
Given an integer, convert it to a roman numeral. Input is guaranteed to be within the range from 1 to 3999.
Example 1:
Input: 3
Output: "III"
Example 2:
Input: 4
Output: "IV"
Example 3:
Input: 9
Output: "IX"
Example 4:
Input: 58
Output: "LVIII"
Explanation: L = 50, V = 5, III = 3.
Example 5:
Input: 1994
Output: "MCMXCIV"
Explanation: M = 1000, CM = 900, XC = 90 and IV = 4.
"""
def solution(num) :
values = [1000, 900, 500, 400, 100, 90, 50, 40, 10, 9, 5, 4, 1]
numerals = ['M', 'CM', 'D', 'CD', 'C', 'XC', 'L', 'XL', 'X', 'IX', 'V', 'IV', 'I']
res = ""
for i, v in enumerate(values) :
res += (num // v) * numerals[i]
num %= v
return res
if __name__ == "__main__":
print(solution(14))
print(solution(73))
print(solution(122))
|
[
"[email protected]"
] | |
e741504fdb3bd4fb34bd13897f719227219ed48e
|
e10a1786d68603bff0393f54f7f94e40d2825a42
|
/MLlab02_logistic_classification/02_1-softmax_test.py
|
fb8c5f359d2bcb116b799274efe4f7dc1da3da4c
|
[] |
no_license
|
yurimkoo/dl_lab
|
80da422b9e29db4e61025d1cc87160b517e1242c
|
3967c6112a24a130bc54da774929f58c5f290b97
|
refs/heads/master
| 2021-01-21T20:38:54.666029 | 2017-05-24T07:37:18 | 2017-05-24T07:37:18 | 92,263,452 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,684 |
py
|
import tensorflow as tf
import numpy as np
#'soft_max.txt' uses 'one-hot encoding'
xy = np.loadtxt('softmax_train.txt', unpack=1, dtype='float32')
x_data = np.transpose(xy[0:3]) #transpose를 사용하는 것은 거울처럼 반대로 뒤집어달라는 뜻
y_data = np.transpose(xy[3:])
X = tf.placeholder('float', [None, 3]) #x1, x2, bias(x0) total 3 (None인 이유는 총 데이터 개수가 몇 개인지 모르기 때문)
Y = tf.placeholder('float', [None, 3]) #A, B, C total 3
W = tf.Variable(tf.zeros([3, 3])) #3x3 matrix (첫번째 값: x가 3개, 두번째 값: y가 3개) // tf.zeros 는 행렬 만드는 함수
hypothesis = tf.nn.softmax(tf.matmul(X, W)) #뒤집는 이유는 계산상의 편의를 위해해
learning_rate = 0.001
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), reduction_indices=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for step in range(2001):
sess.run(optimizer, feed_dict={X: x_data, Y: y_data})
if step % 20 == 0:
print(step, sess.run(cost, feed_dict={X: x_data, Y: y_data}), sess.run(W))
print('-----------------')
#test & one-hot encoding
#0: A, 1: B, 2: C
a = sess.run(hypothesis, feed_dict={X: [[1, 11, 7]]})
print(a, sess.run(tf.arg_max(a, 1)))
b = sess.run(hypothesis, feed_dict={X: [[1, 3, 4]]})
print(b, sess.run(tf.arg_max(b, 1)))
c = sess.run(hypothesis, feed_dict={X: [[1, 1, 0]]})
print(c, sess.run(tf.arg_max(c, 1)))
all = sess.run(hypothesis, feed_dict={X: [[1, 11, 7], [1, 3, 4], [1, 1, 0]]})
print(all, sess.run(tf.arg_max(all, 1)))
|
[
"[email protected]"
] | |
ecb2abc4963884e24944a06ebcf5ef842448978d
|
95bebfc06f69425706cf18b717e6b4341373bc30
|
/anagram.py
|
6212e6c237f6e41af84b6696ebcd2bd95e9231d6
|
[] |
no_license
|
vinodrajendran001/python-interview-prep
|
9200a8f3076ab1e0a3325f4a3814f811f3c0757c
|
80143a4b2b2957b6aff15b0b225f192c04dd1461
|
refs/heads/master
| 2021-01-12T06:52:32.266606 | 2016-12-23T04:01:02 | 2016-12-23T04:01:02 | 76,851,795 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,513 |
py
|
# O(n^2)
def getstr1(a, b):
c = ""
d = ""
if len(a) == len(b):
for i in range(len(a)):
for j in range(len(b)):
if a[i] == b[j]:
c = c + b[j]
if a == c:
print "given 2 text is anagram"
else:
print "not anagram"
else:
print "length is not same"
# It is O(nlogn) because we use sort()
def getstr2(a, b):
s1 = list(a)
s2 = list(b)
s1.sort()
s2.sort()
pos = 0
matches = True
while pos < len(s1) and matches:
if s1[pos] == s2[pos]:
matches = True
pos = pos + 1
else:
matches = False
return matches
# O(n)
def getstr3(a, b):
total_a = 0
for i in range(len(a)):
total_a = total_a + ord(a[i])
total_b = 0
for j in range(len(b)):
total_b = total_b + ord(b[j])
if total_b == total_a:
print "it is anagram"
else:
print "not anagram"
'''
Given two strings, a and b, that may or may not be of the same length,
determine the minimum number of character deletions required to make them
anagrams. Any characters can be deleted from either of the strings
e.g. str1 : cde
str2 : abc
result : 4
'''
def minanagram(a, b):
c1 = [0]*26
c2 = [0]*26
for i in range(len(a)):
pos = ord(a[i]) - ord('a')
c1[pos] = c1[pos] + 1
for i in range(len(b)):
pos = ord(b[i]) - ord('a')
c2[pos] = c2[pos] + 1
j=0
count = 0
print c1
print c2
while j<26:
count = count + abs(c1[j]-c2[j])
j = j + 1
return count
print minanagram('cdef','abc')
getstr1("python","typhon")
print(getstr2("python","typhon"))
getstr3("typhon","python")
|
[
"[email protected]"
] | |
10061e9d1ee1fd83d00916d518364042992e220e
|
784ce7c5c3f08602cf0d4b3a80ec74d8e07990a1
|
/web/models.py
|
358465843bd033dd80eaab90527c19011b4fe5ce
|
[] |
no_license
|
lcdevelop/shareditor
|
1a05e558a2cbd76c5272f7d0d43222b5a47bc139
|
318d8b2c77b8a8065399aeeab7e827f74c6abec0
|
refs/heads/master
| 2022-03-30T07:40:32.876433 | 2020-01-20T14:36:20 | 2020-01-20T14:36:20 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,079 |
py
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models
from ckeditor_uploader.fields import RichTextUploadingField
class Subject(models.Model):
name = models.CharField(max_length=255, verbose_name='类别名称')
introduce = models.CharField(max_length=255, verbose_name='类别简介')
image = models.ImageField(max_length=255, verbose_name='类别图片', null=True)
class Meta:
verbose_name_plural = '类别'
def __unicode__(self):
return self.name
class Tag(models.Model):
name = models.CharField(max_length=255, verbose_name='标签名称')
image = models.ImageField(max_length=255, verbose_name='标签图片', null=True)
sort = models.IntegerField(verbose_name='排序越大越靠前', default=0)
show = models.IntegerField(verbose_name='是否展示在首页', default=1)
def get_latest_blogpost(self, count=5):
return self.blogpost_set.filter(verify=True).order_by('id').reverse()[0:count]
class Meta:
verbose_name_plural = '标签'
def __unicode__(self):
return self.name
class BlogPost(models.Model):
id = models.AutoField(primary_key=True)
title = models.CharField(max_length=255, verbose_name='文章标题')
image = models.ImageField(max_length=255, verbose_name='文章图片', null=True)
abstract = models.CharField(max_length=255, verbose_name='文章摘要', null=True)
body = RichTextUploadingField(config_name='default', verbose_name='文章内容')
create_time = models.DateTimeField(verbose_name='创建时间')
subject = models.ForeignKey(Subject, verbose_name='类别', null=True)
tags = models.ManyToManyField(Tag, verbose_name='标签', null=True)
pv = models.IntegerField(verbose_name='pv', default=0)
verify = models.BooleanField(verbose_name='是否生效', default=False)
def get_simple_title(self):
return self.title.replace(self.tags.first().name, '')
class Meta:
verbose_name_plural = '文章'
def __unicode__(self):
return self.title
class Chat(models.Model):
client_ip = models.CharField(max_length=16, verbose_name='用户ip')
message = models.TextField(verbose_name='说的话')
talker = models.IntegerField(verbose_name='说话者:0-机器人;1-用户')
create_time = models.DateTimeField(verbose_name='创建时间', auto_now_add=True)
class CorpusQuestion(models.Model):
text = models.CharField(max_length=512, verbose_name='问')
bad = models.IntegerField(verbose_name='踩', default=0)
is_del = models.IntegerField(verbose_name='1-删除；0-正常', default=0)
def __unicode__(self):
return self.text
class CorpusAnswer(models.Model):
text = models.CharField(max_length=512, verbose_name='答')
like = models.IntegerField(verbose_name='点赞量', default=0)
is_del = models.IntegerField(verbose_name='1-删除；0-正常', default=0)
question = models.ForeignKey(CorpusQuestion, verbose_name='问题')
def __unicode__(self):
return self.text
|
[
"[email protected]"
] | |
d57bda58d4f2cfac8a38db31dece06b03b25335f
|
843b13d64b591ea48126a1a68c37dab663af8958
|
/french_lm/dictionary.py
|
4c5e15f133a8a5e11747e1c748360476e4b709cb
|
[] |
no_license
|
AmaMidzu/say-it-right
|
ac5fd998abd90c3539d2121abc52a0a85b5c1aed
|
1fb05bbbaa8236fb5f489f3dada2b3fefa58bd3b
|
refs/heads/master
| 2021-01-12T01:29:32.289505 | 2017-12-01T01:31:33 | 2017-12-01T01:31:33 | 78,392,748 | 0 | 0 | null | 2017-01-09T04:05:57 | 2017-01-09T04:05:57 | null |
UTF-8
|
Python
| false | false | 1,082 |
py
|
# -*- coding: latin-1 -*-
import csv
with open('Lexique381.csv') as infile:
reader = csv.reader(infile)
#skips a couple phrase but it's negligible comparitively
mydict = {row[0]:row[1] for row in reader if (len(row[0].split()) ==1 and row[0].isalpha())}
mapping = {
'\xa7': 'o~',
'1': '9~',
'\xb0' : '@',
'2' : '2',
'5' : 'e~',
'9' : '9',
'8' : 'H',
'@' : 'a~',
'E' : 'E',
'G' : 'N',
'O': 'O',
'N' : 'J',
'S' : 'S',
'R' : 'R',
'Z' : 'Z',
'a' : 'a',
'b' : 'b',
'e' : 'e',
'd' : 'd',
'g' : 'g',
'f' : 'f',
'i' :'i',
'k' : 'k',
'j' : 'j',
'm' : 'm',
'l' : 'l',
'o' : 'o',
'n' : 'n',
'p' : 'p',
's' : 's',
'u' : 'u',
't' : 't',
'w' : 'w',
'v' : 'v',
'y': 'y',
'x': 'x',
'z': 'z'
}
phons = set()
with open('dict/lexicon.txt', 'w') as outfile:
for k in mydict.keys():
phon = mydict[k]
s = ""
for char in phon:
s += mapping[char] + " "
phons.add(mapping[char])
s = s.strip()
outfile.write(k + " " + s+'\n')
outfile.close()
with open('dict/phones.txt', 'w') as outfile:
for p in phons:
outfile.write(p+'\n')
outfile.close()
|
[
"[email protected]"
] | |
3825b458baa098aaab3dae1806870377a5cbf625
|
e16d41602b9887c814ae3ea5615f2734f15cdf50
|
/unitTest/myfun.py
|
d667bdc2da29531c20b24441a912f9dc26e5ebed
|
[] |
no_license
|
tomreddle/vip3test
|
32183d652c8847b5fded7c4f1b4740f09b5fc850
|
ecc2e6be0503235a1fcfd7447d5722816ffcfe8c
|
refs/heads/master
| 2020-07-30T21:38:23.636803 | 2019-11-20T13:08:11 | 2019-11-20T13:08:11 | 210,367,046 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 153 |
py
|
import unittest
def add(a, b):
return a + b
def plus(a, b):
return a - b
def mul(a, b):
return a * b
def div(a, b):
return a / b
|
[
"[email protected]"
] |
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