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2b8ef6e4ddfe11306702678e9a8e5c00eac0656c | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p02901/s181974865.py | e2137c4696abe05b40a7101f829d7767622b0d43 | [] | 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 | 442 | py | N, M = map(int, input().split())
key = []
for _ in range(M):
a, b = map(int, input().split())
s = 0
C = list(map(lambda x:int(x)-1, input().split()))
for c in C:
s |= 1<<c
key += [(s, a)]
dp = [float('inf')]*(1<<N)
dp[0] = 0
for s in range(1<<N):
for i in range(M):
t = s | key[i][0] # 遷移先
cost = dp[s] + key[i][1]
dp[t] = min(dp[t], cost)
if dp[-1] == float('inf'):
print(-1)
else:
print(dp[-1])
| [
"[email protected]"
] | |
e9cac985d19977a538b2f9e0a5dcdfd6c2452669 | 971300f5983692e12805805dd49e2f77fa20250f | /src/branches/dae_dtalite_integration/core/models/abstract_regression_model.py | 9648228c09e3b79b4c09baf452bbfcb9febf81a4 | [] | no_license | MAlbertini95/simtravel | 3a18ee302f6d9ab676455caaad15461874a698a9 | 4844927243a854b9a93f1b1d93f795ff116a7212 | refs/heads/master | 2021-04-19T03:04:26.752252 | 2014-07-12T00:50:11 | 2014-07-12T00:50:11 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,342 | py | from numpy import all, array, zeros
from scipy import exp
from openamos.core.models.abstract_model import Model
from openamos.core.errors import SpecificationError, ErrorSpecificationError
class AbstractRegressionModel(Model):
def __init__(self, specification, error_specification):
"""
This is the base class for all regression based mathematical formulations
in OpenAMOS
Inputs:
specification - Specification object
error_specifciation - ErrorSpecification object
"""
Model.__init__(self, specification)
if not isinstance(self.specification, Specification):
raise SpecificationError, """specification input is not a """\
"""valid Specification object"""
self.error_specification = error_specification
if specification.number_choices > 1:
raise SpecificationError, """invalid specification for regression """\
""" model only one equation needs to be specified"""
if not isinstance(self.error_specification, ErrorSpecification):
raise ErrorSpecificationError, """invalid error specification"""\
""" it should be of type ErrorSpecification"""
def calc_expected_value(self, data):
"""
The method returns the expected values for the different choices using
the coefficients specified in the specification input.
Inputs:
data - DataArray object
"""
return self.calculate_expected_values(data)
def calc_exp_expected_value(self, data):
"""
The method returns the exponent of the expected values for the
different choices using the coefficients specified in the specification input.
Inputs:
data - DataArray object
"""
return self.calculate_exp_expected_values(data)
def calc_errorcomponent(self):
"""
The method returns the contribution of the error in the calculation
of the predicted value for the different choices.
Inputs:
None
"""
raise Exception('method not implemented')
def calc_predvalue(self):
"""
The method returns the predicted value for the different choices in the
specification input.
Inputs:
None
"""
raise Exception('method not implemented')
import unittest
from openamos.core.data_array import DataArray
from openamos.core.models.model_components import Specification
from openamos.core.models.error_specification import ErrorSpecification
class TestBadSpecificationRegressionModel(unittest.TestCase):
def setUp(self):
choices = ['SOV', 'HOV']
coefficients = [{'Constant':2, 'Var1':2.11}, {'Constant':1.2}]
data = array([[1, 1.1], [1, -0.25], [1, 3.13], [1, -0.11]])
variance = array([[1.1]])
variance1 = array([[1.1, 1.2], [2.1, 2.2]])
self.data = DataArray(data, ['Constant', 'VAR1'])
self.specification = Specification(choices, coefficients)
self.errorspecification = ErrorSpecification(variance, 'normal')
self.errorspecification1 = ErrorSpecification(variance1, 'normal')
def testtwodependentvars(self):
self.assertRaises(SpecificationError, AbstractRegressionModel,
self.specification, self.errorspecification)
def testtwoerrorcomponents(self):
self.assertRaises(SpecificationError, AbstractRegressionModel,
self.specification, self.errorspecification1)
class TestAbstractRegressionModel(unittest.TestCase):
def setUp(self):
choice = ['SOV']
coefficients = [{'constant':2, 'Var1':2.11}]
data = array([[1, 1.1], [1, -0.25], [1, 3.13], [1, -0.11]])
variance = array([[1.1]])
self.data = DataArray(data, ['Constant', 'VaR1'])
self.specification = Specification(choice, coefficients)
self.errorspecification = ErrorSpecification(variance, 'normal')
def testvalues(self):
model = AbstractRegressionModel(self.specification, self.errorspecification)
model_expected_values = model.calc_expected_value(self.data)
expected_act = zeros((self.data.rows, 1))
expected_act[:,0] = self.data.data[:,0] * 2 + self.data.data[:,1] * 2.11
expected_diff = all(expected_act == model_expected_values.data)
self.assertEqual(True, expected_diff)
exp_expected_act = exp(expected_act)
model_exp_expected_values = model.calc_exp_expected_value(self.data)
exp_expected_diff = all(exp_expected_act ==
model_exp_expected_values.data)
self.assertEqual(True, exp_expected_diff)
def testerrorspecification(self):
#TODO:Write the tests for errorspecification if any in here
#or should they just be written in the specifica implementations
#e.g. stochastic-frontier, linear regression etc.
pass
if __name__ == '__main__':
unittest.main()
| [
"karthik.charan@8e946292-11aa-11df-992a-f3fa5211fe9f"
] | karthik.charan@8e946292-11aa-11df-992a-f3fa5211fe9f |
86b0d00083516ac574501614cf84a7ab1f14f983 | 25b2daa09d3994672936231b7949ad60292fd052 | /apps/cart/forms.py | 7c4ace067207ccb673e8085d0db87a30f6253f02 | [] | no_license | pavelm2007/shop | c1896145e3b3c43fd25c32e0e39697b6cbacadc9 | 979bbdfd51c53f1757e1cc5646e61bd71e8fce40 | refs/heads/master | 2021-01-25T10:29:50.502933 | 2014-05-15T07:54:07 | 2014-05-15T07:54:07 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,570 | py | # -*- coding: utf-8 -*-
from django import forms
from django.forms.models import inlineformset_factory
from django.contrib.contenttypes.models import ContentType
from django.template.defaultfilters import striptags
from .models import Order, OrderItem, Contact_info
BASKET_OPTIONS_USE_KEEP = False
class OrderItemForm(forms.ModelForm):
class Meta:
model = OrderItem
content_type = forms.ModelChoiceField(queryset=ContentType.objects.all(),
widget=forms.HiddenInput)
object_id = forms.IntegerField(widget=forms.HiddenInput)
if BASKET_OPTIONS_USE_KEEP:
keep = forms.BooleanField(initial=True, required=False)
def save(self, *args, **kwargs):
if BASKET_OPTIONS_USE_KEEP:
if not self.cleaned_data.get('keep', False):
self.cleaned_data['quantity'] = 0
self.instance.order.set_quantity(self.instance.content_object,
self.cleaned_data.get('quantity', 0))
OrderFormset = inlineformset_factory(Order, OrderItem, extra=0,
can_delete=False, form=OrderItemForm)
class DefaultOrderForm(forms.ModelForm):
# name = forms.CharField(label=u'Имя', max_length=100, required=True)
# phone = forms.CharField(label=u'Телефон', max_length=100, required=True)
# email = forms.CharField(label=u'E-mail', max_length=100, required=True)
# comment = forms.CharField(label=u'Комментарий к заказу', max_length=255,
# widget=forms.Textarea(), required=True)
def __init__(self, *args, **kwargs):
super(DefaultOrderForm, self).__init__(*args, **kwargs)
self.fields['comment'].widget.attrs['cols'] = '35'
self.fields['comment'].widget.attrs['rows'] = '5'
for field in self.fields:
self.fields[field].widget.attrs['class'] = 'filed-znach-text'
if self.errors:
# bf_errors = self.error_class(error for error in bf.errors]) # Escape and cache in local variable.
for field, key in self.fields.iteritems():
error_text = u''
for i, j in self.errors.iteritems():
if field == i:
error_text += unicode(striptags(j))
self.fields[field].initial = None
# self.fields[field].widget.attrs['value'] = error_text
self.fields[field].widget.attrs['placeholder'] = error_text
class Meta:
model = Contact_info
exclude = ('order',)
# class DefaultOrderForm(forms.Form):
# name = forms.CharField(label=u'Имя', max_length=100,required=True)
# phone = forms.CharField(label=u'Телефон', max_length=100,required=True)
# email = forms.CharField(label=u'E-mail', max_length=100,required=True)
# # address = forms.CharField(label=_('Delivery address'), max_length=255)
# # contact_time = forms.CharField(label=_('Convenient time to call'),
# # max_length=50, required=False)
# comment = forms.CharField(label=u'Комментарий к заказу', max_length=255,
# widget=forms.Textarea(), required=True)
#
# def __init__(self, request, *args, **kwargs):
# super(DefaultOrderForm, self).__init__(*args, **kwargs)
# self.fields['comment'].widget.attrs['cols'] = '35'
# self.fields['comment'].widget.attrs['rows'] = '5'
# for field in self.fields:
# self.fields[field].widget.attrs['class'] = 'filed-znach-text'
| [
"[email protected]"
] | |
12d3146f4e383ba18e9a4c88f8655aca2bb439a8 | 94838674ffd175df6194437c1ccc3f90ab409d6c | /pillowV3/log/2018-12-30 15:01:12.856571 | 0d9018eeb9b74f767f0c3a4a8a50e23b98ff40c1 | [] | 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 | 511,606 | 856571 | #!/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', '95', '37', '32', '27']
correct_rate = 0.1853
SIZES = [784, 37, 32, 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
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-4.21791781e-01 -6.82124239e-01]
[-2.39563464e+00 -7.37750154e+00 -3.30665511e+00 -1.07299337e+01
3.99672930e+00 -5.57182490e+00 1.04020077e+00 -2.26622027e+00
-3.93032453e+00 -1.33461705e+00]
[-1.19415532e+00 -1.46983903e+00 -2.58816355e-01 -2.07263469e+00
2.30649221e+00 -1.51236257e+00 4.96490635e-01 2.05280990e+00
2.07613820e-02 5.10766645e-01]
[ 2.98342462e-01 -2.10080741e+00 -1.40805407e+00 -2.16510407e+00
-2.28585237e+00 -6.98820946e-01 -6.95873571e-01 -1.71975671e+00
-2.54328104e+00 -1.07438888e+00]]
b0 = [[-6074.11362419 -6412.33944002 -6140.80202612 -6078.10412044
-6624.30496009 -6125.08156203 -6096.21198483 -7525.28400459
-7086.29782773 -6114.03463352 -6115.19053053 -6121.41660473
-6446.88677113 -6100.30905547 -6117.9014037 -6173.03438887
-7540.59214658 -6060.46907856 -6101.02150991 -7952.98816972
-6145.76019716 -7507.43092202 -7507.09572842 -7787.14249935
-7516.58624725 -6116.03812015 -6137.58415137 -6104.47233971
-7511.86928234 -6239.95677965 -6072.60719484 -6117.74061904
-7950.3782105 -6111.83366414 -7869.19121304 -7946.71915989
-8001.00998659]]
b1 = [[-2.1256769 -5.81276559 -5.02225262 -6.10671295 -4.08206286 -4.46168419
-5.00679968 -2.55016758 -5.97207403 -5.57282594 -3.14475692 -4.55191353
-3.86542015 -5.3687466 -5.23385401 -4.3839707 -4.24539054 -5.35580089
-5.2915857 -3.02893051 -5.20509454 -5.44869847 -3.03565957 -5.93212472
-5.57891823 -5.279001 -5.60538733 -6.15044377 -5.76204409 -2.13524171
-6.03439405 -5.56153484]]
b2 = [[ 0.16302535 -0.19844465 0.40941499 0.41375315 0.00774145 -0.75745748
0.02919616 0.43074873 -0.15515393 -0.41906463]]
| [
"[email protected]"
] | |
3c34105bfa17f674e7bb3b8621bc4ceb8ae112b5 | bb88122fc4978b14e8a9b02d8c11f1ce67ea17d0 | /01_keras/keras31_cifar100_1_imshow.py | c2765b3208fba97f52169ea5492007275762cd5d | [] | no_license | star10919/Keras_ | c2c8a6f3d0e1a7ceba9e81dbc51ecfd12bd5fe78 | f3156b7db6e12feea075b46e94b09157f43a141c | refs/heads/main | 2023-08-17T22:44:54.324315 | 2021-10-24T02:47:00 | 2021-10-24T02:47:00 | 390,066,491 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 415 | py | from tensorflow.keras.datasets import cifar100
import numpy as np
import matplotlib.pyplot as plt
from icecream import ic
(x_train, y_train), (x_test, y_test) = cifar100.load_data()
ic(x_train.shape, y_train.shape) # (50000, 32, 32, 3), (50000, 1)
ic(x_test.shape, y_test.shape) # (10000, 32, 32, 3), (10000, 1)
ic(x_train[27])
print('y[27] 값 :', y_train[27]) # [52]
plt.imshow(x_train[27])
plt.show() | [
"[email protected]"
] | |
1183fbfc216acc8a1e4f790c2cf4417f3125aa41 | f694b37f548fe67656bf737073e0221e23b53dfb | /app/models.py | b29b69f52d28438d63166cea33e9228099faca9c | [] | no_license | itsumura-h/django_api_auth_sample | d92937834e79856b7956fddf174682d1d5bd22dc | 4a3244c8a3471573f1f29c3a67ddf924f8649ed1 | refs/heads/master | 2020-05-25T18:51:40.285232 | 2019-05-22T01:08:54 | 2019-05-22T01:08:54 | 187,937,393 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,703 | py | from django.db import models
from django.contrib.auth.hashers import make_password
from django.utils import timezone
import hashlib
# Create your models here.
class User(models.Model):
def __str__(self):
return str(self.name)
name = models.CharField(max_length=255)
password = models.CharField(max_length=255)
email = models.CharField(max_length=255, blank=True, null=True)
tel = models.CharField(max_length=255, blank=True, null=True)
is_studio = models.BooleanField(default=0)
class Meta:
db_table = 'users'
verbose_name_plural = 'user'
def save(self, *args, **kwargs):
self.password = make_password(self.password) #パスワード暗号化
super().save(*args, **kwargs)
class LoginToken(models.Model):
def __str__(self):
# メールアドレスとアクセス日時、トークンが見えるようにする
dt = timezone.localtime(self.access_datetime).strftime("%Y/%m/%d %H:%M:%S")
return self.user.email + '(' + dt + ') - ' + self.token
user = models.ForeignKey(User, on_delete=models.CASCADE)
token = models.CharField(max_length=40) #トークン
access_datetime = models.DateTimeField() #アクセス日時
class Meta:
db_table = 'tokens'
verbose_name_plural = 'token'
@staticmethod
def create(user: User):
# ユーザの既存のトークンを取得
if LoginToken.objects.filter(user=user).exists():
# トークンが既に存在している場合は削除する
LoginToken.objects.get(user=user).delete()
# トークン生成（メールアドレス + パスワード + システム日付のハッシュ値とする）
dt = timezone.now()
str = user.email + user.password + dt.strftime('%Y%m%d%H%M%S%f')
hash = hashlib.sha1(str.encode('utf-8')).hexdigest() # utf-8でエンコードしないとエラーになる
# トークンをデータベースに追加
token = LoginToken.objects.create(
user = user,
token = hash,
access_datetime = dt)
return token
class Group(models.Model):
owner_id = models.ForeignKey(User, on_delete=models.PROTECT)
class Meta:
db_table = 'groups'
verbose_name_plural = 'group'
class GroupUser(models.Model):
group = models.ForeignKey(Group, on_delete=models.PROTECT)
user = models.ForeignKey(User, on_delete=models.PROTECT)
class Meta:
db_table = 'group_users'
verbose_name_plural = 'group_user'
class Studio(models.Model):
def __str__(self):
return str(self.name)
name = models.CharField(max_length=255)
prefecture = models.CharField(max_length=255)
city = models.CharField(max_length=255)
address = models.CharField(max_length=255)
gps = models.CharField(max_length=255, blank=True, null=True)
user = models.ForeignKey(User, on_delete=models.PROTECT)
class Meta:
db_table = 'studios'
verbose_name_plural = 'studio'
class Room(models.Model):
def __str__(self):
return str(self.name)
name = models.CharField(max_length=255)
wide = models.IntegerField(blank=True, null=True)
capacity = models.IntegerField(blank=True, null=True)
studio = models.ForeignKey(Studio, on_delete=models.PROTECT)
class Meta:
db_table = 'rooms'
verbose_name_plural = 'room'
class Current(models.Model):
member_no = models.IntegerField(blank=True, null=True)
user = models.ForeignKey(User, on_delete=models.PROTECT)
studio = models.ForeignKey(Studio, on_delete=models.PROTECT)
class Meta:
db_table = 'currents'
verbose_name_plural = 'current'
class Booking(models.Model):
user = models.ForeignKey(User, on_delete=models.PROTECT)
room = models.ForeignKey(Room, on_delete=models.PROTECT)
group = models.ForeignKey(Group, on_delete=models.PROTECT)
start = models.DateTimeField()
end = models.DateTimeField()
class Meta:
db_table = 'bookings'
verbose_name_plural = 'booking'
class EquipmentKind(models.Model):
def __str__(self):
return str(self.name)
name = models.CharField(max_length=255)
class Meta:
db_table = 'equipment_kinds'
verbose_name_plural = 'equipment_kind'
class Equipment(models.Model):
def __str__(self):
return str(self.name)
name = models.CharField(max_length=255)
kind = models.ForeignKey(EquipmentKind, on_delete=models.PROTECT)
room = models.ForeignKey(Room, on_delete=models.PROTECT)
class Meta:
db_table = 'equipments'
verbose_name_plural = 'equipment'
| [
"[email protected]"
] | |
610035bce67bfdabe6c21fe5bf50792c3954ccad | f02eb256fdaf94bc7fc8e2d7ecb7352b98eaf494 | /tests/test_save_reload_user.py | a0d68fd0753ad0addf27d58d3cb85bc80ff0f58f | [] | no_license | andres0191/AirBnB_clone | b98c4ef70c5f933154367557fc4026a2ce4e258a | 818e60d89939650a2962164690987a0703792ef5 | refs/heads/master | 2021-01-03T23:58:42.569557 | 2020-03-03T00:32:49 | 2020-03-03T00:32:49 | 240,291,850 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 712 | py | #!/usr/bin/python3
from models.engine.file_storage import FileStorage
from models.base_model import BaseModel
from models.user import User
storage = FileStorage()
storage.reload()
all_objs = storage.all()
print("-- Reloaded objects --")
for obj_id in all_objs.keys():
obj = all_objs[obj_id]
print(obj)
print("-- Create a new User --")
my_user = User()
my_user.first_name = "Betty"
my_user.last_name = "Holberton"
my_user.email = "[email protected]"
my_user.password = "root"
my_user.save()
print(my_user)
print("-- Create a new User 2 --")
my_user2 = User()
my_user2.first_name = "John"
my_user2.email = "[email protected]"
my_user2.password = "root"
my_user2.save()
print(my_user2)
| [
"[email protected]"
] | |
069a1ecdd2ecdaf92638a7dc6e3f0758e7fc68c6 | f352f9915c0b9d6f7ea010169f5dafd3a9fb8638 | /lib/nltk/classify/decisiontree.py | a4095c909c93dae0b48fa736d684b467dc579d35 | [] | no_license | nltk/nltk.github.com | fa235e76788e6e8e7349e7195e61799c1402e61d | cf0d2aa508a1de9147ccf30bd070660651d55adb | refs/heads/master | 2023-07-31T13:34:20.864897 | 2023-01-02T15:33:19 | 2023-01-02T15:33:19 | 2,686,706 | 34 | 41 | null | 2022-10-06T17:06:49 | 2011-11-01T09:59:49 | HTML | UTF-8 | Python | false | false | 13,083 | py | # Natural Language Toolkit: Decision Tree Classifiers
#
# Copyright (C) 2001-2021 NLTK Project
# Author: Edward Loper <[email protected]>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT
"""
A classifier model that decides which label to assign to a token on
the basis of a tree structure, where branches correspond to conditions
on feature values, and leaves correspond to label assignments.
"""
from collections import defaultdict
from nltk.classify.api import ClassifierI
from nltk.probability import FreqDist, MLEProbDist, entropy
class DecisionTreeClassifier(ClassifierI):
def __init__(self, label, feature_name=None, decisions=None, default=None):
"""
:param label: The most likely label for tokens that reach
this node in the decision tree. If this decision tree
has no children, then this label will be assigned to
any token that reaches this decision tree.
:param feature_name: The name of the feature that this
decision tree selects for.
:param decisions: A dictionary mapping from feature values
for the feature identified by ``feature_name`` to
child decision trees.
:param default: The child that will be used if the value of
feature ``feature_name`` does not match any of the keys in
``decisions``. This is used when constructing binary
decision trees.
"""
self._label = label
self._fname = feature_name
self._decisions = decisions
self._default = default
def labels(self):
labels = [self._label]
if self._decisions is not None:
for dt in self._decisions.values():
labels.extend(dt.labels())
if self._default is not None:
labels.extend(self._default.labels())
return list(set(labels))
def classify(self, featureset):
# Decision leaf:
if self._fname is None:
return self._label
# Decision tree:
fval = featureset.get(self._fname)
if fval in self._decisions:
return self._decisions[fval].classify(featureset)
elif self._default is not None:
return self._default.classify(featureset)
else:
return self._label
def error(self, labeled_featuresets):
errors = 0
for featureset, label in labeled_featuresets:
if self.classify(featureset) != label:
errors += 1
return errors / len(labeled_featuresets)
def pretty_format(self, width=70, prefix="", depth=4):
"""
Return a string containing a pretty-printed version of this
decision tree. Each line in this string corresponds to a
single decision tree node or leaf, and indentation is used to
display the structure of the decision tree.
"""
# [xx] display default!!
if self._fname is None:
n = width - len(prefix) - 15
return "{}{} {}\n".format(prefix, "." * n, self._label)
s = ""
for i, (fval, result) in enumerate(
sorted(
self._decisions.items(),
key=lambda item: (item[0] in [None, False, True], str(item[0]).lower()),
)
):
hdr = f"{prefix}{self._fname}={fval}? "
n = width - 15 - len(hdr)
s += "{}{} {}\n".format(hdr, "." * (n), result._label)
if result._fname is not None and depth > 1:
s += result.pretty_format(width, prefix + " ", depth - 1)
if self._default is not None:
n = width - len(prefix) - 21
s += "{}else: {} {}\n".format(prefix, "." * n, self._default._label)
if self._default._fname is not None and depth > 1:
s += self._default.pretty_format(width, prefix + " ", depth - 1)
return s
def pseudocode(self, prefix="", depth=4):
"""
Return a string representation of this decision tree that
expresses the decisions it makes as a nested set of pseudocode
if statements.
"""
if self._fname is None:
return f"{prefix}return {self._label!r}\n"
s = ""
for (fval, result) in sorted(
self._decisions.items(),
key=lambda item: (item[0] in [None, False, True], str(item[0]).lower()),
):
s += f"{prefix}if {self._fname} == {fval!r}: "
if result._fname is not None and depth > 1:
s += "\n" + result.pseudocode(prefix + " ", depth - 1)
else:
s += f"return {result._label!r}\n"
if self._default is not None:
if len(self._decisions) == 1:
s += "{}if {} != {!r}: ".format(
prefix, self._fname, list(self._decisions.keys())[0]
)
else:
s += f"{prefix}else: "
if self._default._fname is not None and depth > 1:
s += "\n" + self._default.pseudocode(prefix + " ", depth - 1)
else:
s += f"return {self._default._label!r}\n"
return s
def __str__(self):
return self.pretty_format()
@staticmethod
def train(
labeled_featuresets,
entropy_cutoff=0.05,
depth_cutoff=100,
support_cutoff=10,
binary=False,
feature_values=None,
verbose=False,
):
"""
:param binary: If true, then treat all feature/value pairs as
individual binary features, rather than using a single n-way
branch for each feature.
"""
# Collect a list of all feature names.
feature_names = set()
for featureset, label in labeled_featuresets:
for fname in featureset:
feature_names.add(fname)
# Collect a list of the values each feature can take.
if feature_values is None and binary:
feature_values = defaultdict(set)
for featureset, label in labeled_featuresets:
for fname, fval in featureset.items():
feature_values[fname].add(fval)
# Start with a stump.
if not binary:
tree = DecisionTreeClassifier.best_stump(
feature_names, labeled_featuresets, verbose
)
else:
tree = DecisionTreeClassifier.best_binary_stump(
feature_names, labeled_featuresets, feature_values, verbose
)
# Refine the stump.
tree.refine(
labeled_featuresets,
entropy_cutoff,
depth_cutoff - 1,
support_cutoff,
binary,
feature_values,
verbose,
)
# Return it
return tree
@staticmethod
def leaf(labeled_featuresets):
label = FreqDist(label for (featureset, label) in labeled_featuresets).max()
return DecisionTreeClassifier(label)
@staticmethod
def stump(feature_name, labeled_featuresets):
label = FreqDist(label for (featureset, label) in labeled_featuresets).max()
# Find the best label for each value.
freqs = defaultdict(FreqDist) # freq(label|value)
for featureset, label in labeled_featuresets:
feature_value = featureset.get(feature_name)
freqs[feature_value][label] += 1
decisions = {val: DecisionTreeClassifier(freqs[val].max()) for val in freqs}
return DecisionTreeClassifier(label, feature_name, decisions)
def refine(
self,
labeled_featuresets,
entropy_cutoff,
depth_cutoff,
support_cutoff,
binary=False,
feature_values=None,
verbose=False,
):
if len(labeled_featuresets) <= support_cutoff:
return
if self._fname is None:
return
if depth_cutoff <= 0:
return
for fval in self._decisions:
fval_featuresets = [
(featureset, label)
for (featureset, label) in labeled_featuresets
if featureset.get(self._fname) == fval
]
label_freqs = FreqDist(label for (featureset, label) in fval_featuresets)
if entropy(MLEProbDist(label_freqs)) > entropy_cutoff:
self._decisions[fval] = DecisionTreeClassifier.train(
fval_featuresets,
entropy_cutoff,
depth_cutoff,
support_cutoff,
binary,
feature_values,
verbose,
)
if self._default is not None:
default_featuresets = [
(featureset, label)
for (featureset, label) in labeled_featuresets
if featureset.get(self._fname) not in self._decisions
]
label_freqs = FreqDist(label for (featureset, label) in default_featuresets)
if entropy(MLEProbDist(label_freqs)) > entropy_cutoff:
self._default = DecisionTreeClassifier.train(
default_featuresets,
entropy_cutoff,
depth_cutoff,
support_cutoff,
binary,
feature_values,
verbose,
)
@staticmethod
def best_stump(feature_names, labeled_featuresets, verbose=False):
best_stump = DecisionTreeClassifier.leaf(labeled_featuresets)
best_error = best_stump.error(labeled_featuresets)
for fname in feature_names:
stump = DecisionTreeClassifier.stump(fname, labeled_featuresets)
stump_error = stump.error(labeled_featuresets)
if stump_error < best_error:
best_error = stump_error
best_stump = stump
if verbose:
print(
"best stump for {:6d} toks uses {:20} err={:6.4f}".format(
len(labeled_featuresets), best_stump._fname, best_error
)
)
return best_stump
@staticmethod
def binary_stump(feature_name, feature_value, labeled_featuresets):
label = FreqDist(label for (featureset, label) in labeled_featuresets).max()
# Find the best label for each value.
pos_fdist = FreqDist()
neg_fdist = FreqDist()
for featureset, label in labeled_featuresets:
if featureset.get(feature_name) == feature_value:
pos_fdist[label] += 1
else:
neg_fdist[label] += 1
decisions = {}
default = label
# But hopefully we have observations!
if pos_fdist.N() > 0:
decisions = {feature_value: DecisionTreeClassifier(pos_fdist.max())}
if neg_fdist.N() > 0:
default = DecisionTreeClassifier(neg_fdist.max())
return DecisionTreeClassifier(label, feature_name, decisions, default)
@staticmethod
def best_binary_stump(
feature_names, labeled_featuresets, feature_values, verbose=False
):
best_stump = DecisionTreeClassifier.leaf(labeled_featuresets)
best_error = best_stump.error(labeled_featuresets)
for fname in feature_names:
for fval in feature_values[fname]:
stump = DecisionTreeClassifier.binary_stump(
fname, fval, labeled_featuresets
)
stump_error = stump.error(labeled_featuresets)
if stump_error < best_error:
best_error = stump_error
best_stump = stump
if verbose:
if best_stump._decisions:
descr = "{}={}".format(
best_stump._fname, list(best_stump._decisions.keys())[0]
)
else:
descr = "(default)"
print(
"best stump for {:6d} toks uses {:20} err={:6.4f}".format(
len(labeled_featuresets), descr, best_error
)
)
return best_stump
##//////////////////////////////////////////////////////
## Demo
##//////////////////////////////////////////////////////
def f(x):
return DecisionTreeClassifier.train(x, binary=True, verbose=True)
def demo():
from nltk.classify.util import binary_names_demo_features, names_demo
classifier = names_demo(
f, binary_names_demo_features # DecisionTreeClassifier.train,
)
print(classifier.pretty_format(depth=7))
print(classifier.pseudocode(depth=7))
if __name__ == "__main__":
demo()
| [
"[email protected]"
] | |
fc1aed88264779358eff660f119563fd54d8a910 | ae3abdd710878d79e60b1f1c56c5cd394ab511f4 | /scripts/ajive_analysis.py | 4eef9222b51981000f5ac81b3b4d3f1e752f4d9a | [] | no_license | idc9/breast_cancer_image_analysis | 0eee6c7d796aabde8a447085996e32563acf6bd1 | 4a4af9d6b55b3ca38b26111d0f55af89a48b1282 | refs/heads/master | 2020-11-27T14:22:07.967478 | 2020-04-13T23:51:53 | 2020-04-13T23:51:53 | 229,484,796 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,751 | py | import os
from joblib import dump
import matplotlib.pyplot as plt
from jive.AJIVE import AJIVE
from explore.BlockBlock import BlockBlock
from explore.Base import Union
from cbcs_joint.load_analysis_data import load_analysis_data
from cbcs_joint.viz_utils import savefig, mpl_noaxis
from cbcs_joint.Paths import Paths
# make directories for saved results
os.makedirs(os.path.join(Paths().results_dir, 'data'), exist_ok=True)
os.makedirs(os.path.join(Paths().results_dir, 'common',
'loadings'), exist_ok=True)
os.makedirs(os.path.join(Paths().results_dir, 'genetic_indiv',
'loadings'), exist_ok=True)
os.makedirs(os.path.join(Paths().results_dir, 'image_indiv'), exist_ok=True)
# load pre-computed data e.g. patch features
data = load_analysis_data(load_patch_feats=False)
subj_img_feats = data['subj_img_feats']
genes = data['genes']
clinical_data = data['clinical_data']
# initial signal ranks determined from PCA scree plots
init_signal_ranks = {'images': 81, 'genes': 30}
# run AJIVE
ajive = AJIVE(init_signal_ranks=init_signal_ranks,
n_wedin_samples=1000, n_randdir_samples=1000,
zero_index_names=False, n_jobs=-1, store_full=False)
ajive = ajive.fit({'images': subj_img_feats, 'genes': genes})
dump(ajive, os.path.join(Paths().results_dir, 'data', 'fit_ajive'))
#####################
# AJIVE diagnostics #
#####################
# diagnostic plot
plt.figure(figsize=[10, 10])
ajive.plot_joint_diagnostic()
savefig(os.path.join(Paths().results_dir, 'ajive_diagnostic.png'))
#######################
# plot PAM50 loadings #
#######################
# set visualization configs
mpl_noaxis(labels=True)
n_genes = 50
inches = 5
height_scale = n_genes // 25
load_figsize = (inches, height_scale * inches)
# common loadings
load_dir = os.path.join(Paths().results_dir, 'common', 'loadings')
os.makedirs(load_dir, exist_ok=True)
for r in range(ajive.common.rank):
plt.figure(figsize=load_figsize)
ajive.blocks['genes'].plot_common_loading(r)
plt.title('common component {}'.format(r + 1))
savefig(os.path.join(load_dir, 'loadings_comp_{}.png'.format(r + 1)))
# genetic individual loadings
load_dir = os.path.join(Paths().results_dir, 'genetic_indiv', 'loadings')
os.makedirs(load_dir, exist_ok=True)
n_indiv_comps = min(5, ajive.blocks['genes'].individual.rank)
for r in range(n_indiv_comps):
plt.figure(figsize=load_figsize)
ajive.blocks['genes'].individual.plot_loading(r)
plt.title('genetic individual component {}'.format(r + 1))
savefig(os.path.join(load_dir, 'loadings_comp_{}.png'.format(r + 1)))
#########################################
# compare AJIVE scores to clinical data #
#########################################
# see documentation of explore package
# BlockBlock compares all variables from one block (AJIVE scores) to
# all variables of another block (clinical variables)
# and adjusts for multiple testing
comparision_kws = {'alpha': 0.05,
'multi_test': 'fdr_bh',
'cat_test': 'auc', # equivalent to a Mann-Whitney test
'multi_cat': 'ovo',
'nan_how': 'drop'}
common_scd = BlockBlock(**comparision_kws)
common_scd.fit(ajive.common.scores(norm=True),
clinical_data)
gene_indiv_scd = BlockBlock(**comparision_kws)
gene_indiv_scd = gene_indiv_scd.\
fit(ajive.blocks['genes'].individual.scores_.iloc[:, 0:5], clinical_data)
image_indiv_scd = BlockBlock(**comparision_kws)
image_indiv_scd = BlockBlock().\
fit(ajive.blocks['images'].individual.scores_.iloc[:, 0:5], clinical_data)
all_tests = Union().add_tests([('common', common_scd),
('gene_indiv', gene_indiv_scd),
('image_indiv', image_indiv_scd)])
all_tests.correct_multi_tests()
dump(all_tests, os.path.join(Paths().results_dir, 'data',
'clinical_data_comparisions'))
inches = 6
# common
n_row, n_col = common_scd.comparisons_.shape
plt.figure(figsize=(inches * n_col, inches * n_row))
common_scd.plot()
savefig(os.path.join(Paths().results_dir, 'common',
'cns_vs_clinical_data.png'), dpi=100)
# genetic individual
n_row, n_col = gene_indiv_scd.comparisons_.shape
plt.figure(figsize=(inches * n_col, inches * n_row))
gene_indiv_scd.plot()
savefig(os.path.join(Paths().results_dir, 'genetic_indiv',
'genetic_indiv_vs_clinical_data.png'), dpi=100)
# image individual
n_row, n_col = image_indiv_scd.comparisons_.shape
plt.figure(figsize=(inches * n_col, inches * n_row))
image_indiv_scd.plot()
savefig(os.path.join(Paths().results_dir, 'image_indiv',
'image_indiv_vs_clinical_data.png'), dpi=100)
| [
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] | |
93278531bd2f7b0295e3a883583124b4e66288e2 | c0385ff098c71e6b9e9883e5e0b1a23d6ddee30a | /src/apps/accounts/urls.py | 1ea7e73d3c17414ed305e39dbf374e478c3f6d9b | [
"MIT"
] | permissive | ehoversten/Travel-Buddy | c8122e941e491f467d4b085bd09e5f23b2674af6 | e117cfcd14be3d04cab97b4fc28ced3f95f5786b | refs/heads/master | 2022-12-11T08:35:16.098525 | 2020-08-10T18:11:40 | 2020-08-10T18:11:40 | 149,361,212 | 1 | 3 | null | 2022-12-08T02:25:23 | 2018-09-18T22:47:54 | JavaScript | UTF-8 | Python | false | false | 268 | py | from django.conf.urls import url
from .views import (
register_view,
LoginFormView
)
urlpatterns = [
url(r'^$', LoginFormView.as_view(), name='login'),
# url(r'^$', login_view, name='login'),
url(r'^register/$', register_view, name='register'),
]
| [
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] | |
088de244f3f420206a51d57f323c763474709895 | e96e03300af5aeb41b9ced0febefa4fb4a12cd28 | /to_nwb/extensions/general/gen_yaml.py | a830e5845f935a2e7e20e31eaa0fd72ff8a9ce39 | [
"BSD-3-Clause"
] | permissive | deeptimittal12/to_nwb | 4db72499e1696a8d73739aede365b6a4ea878dd7 | 9876a1baf4faf56ba54fe8ff7359129450e2aca0 | refs/heads/master | 2021-05-19T13:12:45.463079 | 2019-06-19T22:09:02 | 2019-06-19T22:09:02 | 251,717,287 | 1 | 0 | BSD-3-Clause | 2020-03-31T20:00:22 | 2020-03-31T20:00:22 | null | UTF-8 | Python | false | false | 1,898 | py | from pynwb.spec import NWBDatasetSpec, NWBNamespaceBuilder, NWBGroupSpec, \
NWBAttributeSpec
namespace = 'general'
ns_path = namespace + '.namespace.yaml'
ext_source = namespace + '.extensions.yaml'
values = NWBAttributeSpec(name='values',
dtype='text',
doc='values that the indices are indexing',
shape=(None,))
cat_cell_info = NWBGroupSpec(
neurodata_type_def='CatCellInfo',
doc='Categorical Cell Info',
attributes=[NWBAttributeSpec(
name='help',
doc='help',
dtype='text',
value='Categorical information about cells. For most cases the units tables is more appropriate. This '
'structure can be used if you need multiple entries per cell')],
datasets=[
NWBDatasetSpec(doc='global id for neuron',
shape=(None,),
name='cell_index', dtype='int', quantity='?'),
NWBDatasetSpec(name='indices',
doc='list of indices for values',
shape=(None,), dtype='int',
attributes=[values])],
neurodata_type_inc='NWBDataInterface')
cat_timeseries = NWBGroupSpec(
neurodata_type_def='CatTimeSeries',
neurodata_type_inc='TimeSeries',
doc='Categorical data through time',
datasets=[NWBDatasetSpec(name='data',
shape=(None,), dtype='int',
doc='timeseries of indicies for values',
attributes=[values])])
ns_builder = NWBNamespaceBuilder(doc=namespace + ' extensions', name=namespace,
version='1.0', author='Ben Dichter',
contact='[email protected]')
for spec in (cat_cell_info, cat_timeseries):
ns_builder.add_spec(ext_source, spec)
ns_builder.export(ns_path)
| [
"[email protected]"
] | |
bde00068d71ed1c31ca61ddb9cd7e7d3d39ec8d1 | aff774e066b5db7fdefa4ca9c760b55fc80a678e | /modelrunner/redis_utils.py | 61b5c3a286a0922517bdafa6dcb1d856eb497514 | [] | no_license | piensa/modelrunner | 3e965d75f2401ace5e7ac931da64b4794e0d1d96 | 385e1e01a8007e156855495393d57a1403ec72b2 | refs/heads/master | 2020-03-18T14:56:37.852622 | 2019-02-04T22:16:05 | 2019-02-04T22:16:05 | 134,876,652 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,272 | py | # -*- coding: utf-8 -*-
"""
functions associated with implementing modelrunner 'protocol' via Redis
command dicts are serialized as json
"""
import logging
from .utils import json_dumps_datetime, json_loads_datetime
# setup log
logger = logging.getLogger('modelrunner')
def pop_command(redis_conn, queue_name, timeout=0):
"""
*Blocking*
Waits for command on redis queue
timeout: if 0, wait forever for item on queue, else seconds to timeout
Returns command dict or None if timeout
"""
result = redis_conn.blpop(queue_name, timeout=timeout)
if result is None:
# timedout
return None
command_dict = json_loads_datetime(result[1])
return command_dict
def enqueue_command(redis_conn, queue_name, command_dict):
"""
enqueue command on redis queue
"""
logger.info(
"adding command {} to queue {}".
format(command_dict, queue_name))
redis_conn.rpush(queue_name, json_dumps_datetime(command_dict))
def remove_command(redis_conn, queue_name, command_dict):
"""
find and remove all matching commands from queue
"""
result = redis_conn.lrange(queue_name, 0, -1)
matches = filter(lambda d: d == command_dict,
[json_loads_datetime(item) for item in result])
for match in matches:
redis_conn.lrem(queue_name, 1, json_dumps_datetime(match))
def publish_command(redis_conn, channel_name, command_dict):
"""
publish a message to a channel
"""
redis_conn.publish(channel_name, json_dumps_datetime(command_dict))
def get_all_commands(redis_conn, queue_name):
"""
get all command_dicts on queue
"""
result = redis_conn.lrange(queue_name, 0, -1)
return [json_loads_datetime(item) for item in result]
def pubsub_listen(pubsub):
"""
generator that returns command_dict on subscribed pubsub object
"""
assert pubsub.subscribed
for raw_message in pubsub.listen():
logger.info("message received {}".format(raw_message))
# assume we subscribed and throw away anything other than messages
if raw_message is not None and raw_message['type'] == 'message':
message_dict = json_loads_datetime(raw_message['data'])
yield message_dict
| [
"[email protected]"
] | |
8c347fbf4734a6975b4f15136fa2ac019f6ac964 | e5d4d867e8369194e3519d795d57a6df81357c99 | /exps/utils/quaternion.py | 68befca69501d9cfb2f8eefe9b03363921c866ef | [
"MIT"
] | permissive | hyperplane-lab/Generative-3D-Part-Assembly | 76eb2d414af41b4aa8a188257fb12368d8fccf94 | 1e0e671d282d24d9c95a0f0a7ae67fa923575f45 | refs/heads/main | 2023-05-06T20:15:26.504273 | 2021-05-27T13:18:18 | 2021-05-27T13:18:18 | 301,576,236 | 86 | 15 | null | null | null | null | UTF-8 | Python | false | false | 6,606 | py | # Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import numpy as np
# PyTorch-backed implementations
def qmul(q, r):
"""
Multiply quaternion(s) q with quaternion(s) r.
Expects two equally-sized tensors of shape (*, 4), where * denotes any number of dimensions.
Returns q*r as a tensor of shape (*, 4).
"""
assert q.shape[-1] == 4
assert r.shape[-1] == 4
original_shape = q.shape
# Compute outer product
terms = torch.bmm(r.view(-1, 4, 1), q.view(-1, 1, 4))
w = terms[:, 0, 0] - terms[:, 1, 1] - terms[:, 2, 2] - terms[:, 3, 3]
x = terms[:, 0, 1] + terms[:, 1, 0] - terms[:, 2, 3] + terms[:, 3, 2]
y = terms[:, 0, 2] + terms[:, 1, 3] + terms[:, 2, 0] - terms[:, 3, 1]
z = terms[:, 0, 3] - terms[:, 1, 2] + terms[:, 2, 1] + terms[:, 3, 0]
return torch.stack((w, x, y, z), dim=1).view(original_shape)
def qrot(q, v):
"""
Rotate vector(s) v about the rotation described by quaternion(s) q.
Expects a tensor of shape (*, 4) for q and a tensor of shape (*, 3) for v,
where * denotes any number of dimensions.
Returns a tensor of shape (*, 3).
"""
assert q.shape[-1] == 4
assert v.shape[-1] == 3
assert q.shape[:-1] == v.shape[:-1]
original_shape = list(v.shape)
q = q.view(-1, 4)
v = v.view(-1, 3)
qvec = q[:, 1:]
uv = torch.cross(qvec, v, dim=1)
uuv = torch.cross(qvec, uv, dim=1)
return (v + 2 * (q[:, :1] * uv + uuv)).view(original_shape)
def qeuler(q, order, epsilon=0):
"""
Convert quaternion(s) q to Euler angles.
Expects a tensor of shape (*, 4), where * denotes any number of dimensions.
Returns a tensor of shape (*, 3).
"""
assert q.shape[-1] == 4
original_shape = list(q.shape)
original_shape[-1] = 3
q = q.view(-1, 4)
q0 = q[:, 0]
q1 = q[:, 1]
q2 = q[:, 2]
q3 = q[:, 3]
if order == 'xyz':
x = torch.atan2(2 * (q0 * q1 - q2 * q3), 1 - 2*(q1 * q1 + q2 * q2))
y = torch.asin(torch.clamp(2 * (q1 * q3 + q0 * q2), -1+epsilon, 1-epsilon))
z = torch.atan2(2 * (q0 * q3 - q1 * q2), 1 - 2*(q2 * q2 + q3 * q3))
elif order == 'yzx':
x = torch.atan2(2 * (q0 * q1 - q2 * q3), 1 - 2*(q1 * q1 + q3 * q3))
y = torch.atan2(2 * (q0 * q2 - q1 * q3), 1 - 2*(q2 * q2 + q3 * q3))
z = torch.asin(torch.clamp(2 * (q1 * q2 + q0 * q3), -1+epsilon, 1-epsilon))
elif order == 'zxy':
x = torch.asin(torch.clamp(2 * (q0 * q1 + q2 * q3), -1+epsilon, 1-epsilon))
y = torch.atan2(2 * (q0 * q2 - q1 * q3), 1 - 2*(q1 * q1 + q2 * q2))
z = torch.atan2(2 * (q0 * q3 - q1 * q2), 1 - 2*(q1 * q1 + q3 * q3))
elif order == 'xzy':
x = torch.atan2(2 * (q0 * q1 + q2 * q3), 1 - 2*(q1 * q1 + q3 * q3))
y = torch.atan2(2 * (q0 * q2 + q1 * q3), 1 - 2*(q2 * q2 + q3 * q3))
z = torch.asin(torch.clamp(2 * (q0 * q3 - q1 * q2), -1+epsilon, 1-epsilon))
elif order == 'yxz':
x = torch.asin(torch.clamp(2 * (q0 * q1 - q2 * q3), -1+epsilon, 1-epsilon))
y = torch.atan2(2 * (q1 * q3 + q0 * q2), 1 - 2*(q1 * q1 + q2 * q2))
z = torch.atan2(2 * (q1 * q2 + q0 * q3), 1 - 2*(q1 * q1 + q3 * q3))
elif order == 'zyx':
x = torch.atan2(2 * (q0 * q1 + q2 * q3), 1 - 2*(q1 * q1 + q2 * q2))
y = torch.asin(torch.clamp(2 * (q0 * q2 - q1 * q3), -1+epsilon, 1-epsilon))
z = torch.atan2(2 * (q0 * q3 + q1 * q2), 1 - 2*(q2 * q2 + q3 * q3))
else:
raise
return torch.stack((x, y, z), dim=1).view(original_shape)
# Numpy-backed implementations
def qmul_np(q, r):
q = torch.from_numpy(q).contiguous()
r = torch.from_numpy(r).contiguous()
return qmul(q, r).numpy()
def qrot_np(q, v):
q = torch.from_numpy(q).contiguous()
v = torch.from_numpy(v).contiguous()
return qrot(q, v).numpy()
def qeuler_np(q, order, epsilon=0, use_gpu=False):
if use_gpu:
q = torch.from_numpy(q).cuda()
return qeuler(q, order, epsilon).cpu().numpy()
else:
q = torch.from_numpy(q).contiguous()
return qeuler(q, order, epsilon).numpy()
def qfix(q):
"""
Enforce quaternion continuity across the time dimension by selecting
the representation (q or -q) with minimal distance (or, equivalently, maximal dot product)
between two consecutive frames.
Expects a tensor of shape (L, J, 4), where L is the sequence length and J is the number of joints.
Returns a tensor of the same shape.
"""
assert len(q.shape) == 3
assert q.shape[-1] == 4
result = q.copy()
dot_products = np.sum(q[1:]*q[:-1], axis=2)
mask = dot_products < 0
mask = (np.cumsum(mask, axis=0)%2).astype(bool)
result[1:][mask] *= -1
return result
def expmap_to_quaternion(e):
"""
Convert axis-angle rotations (aka exponential maps) to quaternions.
Stable formula from "Practical Parameterization of Rotations Using the Exponential Map".
Expects a tensor of shape (*, 3), where * denotes any number of dimensions.
Returns a tensor of shape (*, 4).
"""
assert e.shape[-1] == 3
original_shape = list(e.shape)
original_shape[-1] = 4
e = e.reshape(-1, 3)
theta = np.linalg.norm(e, axis=1).reshape(-1, 1)
w = np.cos(0.5*theta).reshape(-1, 1)
xyz = 0.5*np.sinc(0.5*theta/np.pi)*e
return np.concatenate((w, xyz), axis=1).reshape(original_shape)
def euler_to_quaternion(e, order):
"""
Convert Euler angles to quaternions.
"""
assert e.shape[-1] == 3
original_shape = list(e.shape)
original_shape[-1] = 4
e = e.reshape(-1, 3)
x = e[:, 0]
y = e[:, 1]
z = e[:, 2]
rx = np.stack((np.cos(x/2), np.sin(x/2), np.zeros_like(x), np.zeros_like(x)), axis=1)
ry = np.stack((np.cos(y/2), np.zeros_like(y), np.sin(y/2), np.zeros_like(y)), axis=1)
rz = np.stack((np.cos(z/2), np.zeros_like(z), np.zeros_like(z), np.sin(z/2)), axis=1)
result = None
for coord in order:
if coord == 'x':
r = rx
elif coord == 'y':
r = ry
elif coord == 'z':
r = rz
else:
raise
if result is None:
result = r
else:
result = qmul_np(result, r)
# Reverse antipodal representation to have a non-negative "w"
if order in ['xyz', 'yzx', 'zxy']:
result *= -1
return result.reshape(original_shape)
| [
"[email protected]"
] | |
c849667e0bdec93b1f1f55ec5c9906baaa0cb01b | dc7cdeecb1ed52a7bdd18cd20c69aa43897f0830 | /wechatpy/events.py | aaf98a0629cf895fad0e1d7d1358ed7b8fc492ca | [
"MIT"
] | permissive | hurricane1260/wechatpy | 421b0a27b78bbb3bcc33bc6e6685b6beacd55dde | 0d7916e1a894f208dcea18b33803751166378c3d | refs/heads/master | 2021-01-17T18:37:14.535895 | 2014-11-02T16:27:31 | 2014-11-02T16:27:31 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,429 | py | from __future__ import absolute_import, unicode_literals
from .fields import StringField, FloatField, IntegerField, BaseField
from .messages import BaseMessage
EVENT_TYPES = {}
def register_event(event_type):
def register(cls):
EVENT_TYPES[event_type] = cls
return cls
return register
class BaseEvent(BaseMessage):
type = 'event'
event = ''
@register_event('subscribe')
class SubscribeEvent(BaseEvent):
event = 'subscribe'
@register_event('unsubscribe')
class UnsubscribeEvent(BaseEvent):
event = 'unsubscribe'
@register_event('subscribe_scan')
class SubscribeScanEvent(BaseEvent):
event = 'subscribe_scan'
scene_id = StringField('EventKey')
ticket = StringField('Ticket')
@register_event('scan')
class ScanEvent(BaseEvent):
event = 'scan'
scene_id = StringField('EventKey')
ticket = StringField('Ticket')
@register_event('location')
class LocationEvent(BaseEvent):
event = 'location'
latitude = FloatField('Latitude', 0.0)
longitude = FloatField('Longitude', 0.0)
precision = FloatField('Precision', 0.0)
@register_event('click')
class ClickEvent(BaseEvent):
event = 'click'
key = StringField('EventKey')
@register_event('view')
class ViewEvent(BaseEvent):
event = 'view'
url = StringField('EventKey')
@register_event('masssendjobfinish')
class MassSendJobFinishEvent(BaseEvent):
event = 'masssendjobfinish'
status = StringField('Status')
total_count = IntegerField('TotalCount', 0)
filter_count = IntegerField('FilterCount', 0)
sent_count = IntegerField('SentCount', 0)
error_count = IntegerField('ErrorCount', 0)
@register_event('templatesendjobfinish')
class TemplateSendJobFinishEvent(BaseEvent):
event = 'templatesendjobfinish'
status = StringField('Status')
class BaseScanCodeEvent(BaseEvent):
key = StringField('EventKey')
scan_code_info = BaseField('ScanCodeInfo', {})
@property
def scan_type(self):
return self.scan_code_info['ScanType']
@property
def scan_result(self):
return self.scan_code_info['ScanResult']
@register_event('scancode_push')
class ScanCodePushEvent(BaseScanCodeEvent):
event = 'scancode_push'
@register_event('scancode_waitmsg')
class ScanCodeWaitMsgEvent(BaseScanCodeEvent):
event = 'scancode_waitmsg'
class BasePictureEvent(BaseEvent):
key = StringField('EventKey')
pictures_info = BaseField('SendPicsInfo', {})
@property
def count(self):
return int(self.pictures_info['Count'])
@property
def pictures(self):
items = self.pictures_info['PicList']['item']
if self.count > 1:
return items
return [items]
@register_event('pic_sysphoto')
class PicSysPhotoEvent(BasePictureEvent):
event = 'pic_sysphoto'
@register_event('pic_photo_or_album')
class PicPhotoOrAlbumEvent(BasePictureEvent):
event = 'pic_photo_or_album'
@register_event('pic_weixin')
class PicWeChatEvent(BasePictureEvent):
event = 'pic_weixin'
@register_event('location_select')
class LocationSelectEvent(BaseEvent):
event = 'location_select'
key = StringField('EventKey')
location_info = BaseField('SendLocationInfo', {})
@property
def location_x(self):
return self.location_info['Location_X']
@property
def location_y(self):
return self.location_info['Location_Y']
@property
def location(self):
return self.location_x, self.location_y
@property
def scale(self):
return self.location_info['Scale']
@property
def label(self):
return self.location_info['Label']
@property
def poiname(self):
return self.location_info['Poiname']
@register_event('card_pass_check')
class CardPassCheckEvent(BaseEvent):
event = 'card_pass_check'
card_id = StringField('CardId')
@register_event('card_not_pass_check')
class CardNotPassCheckEvent(BaseEvent):
event = 'card_not_pass_check'
card_id = StringField('CardId')
@register_event('user_get_card')
class UserGetCardEvent(BaseEvent):
event = 'user_get_card'
card_id = StringField('CardId')
is_given_by_friend = IntegerField('IsGiveByFriend')
code = StringField('UserCardCode')
@register_event('user_del_card')
class UserDeleteCardEvent(BaseEvent):
event = 'user_del_card'
card_id = StringField('CardId')
code = StringField('UserCardCode')
| [
"[email protected]"
] | |
3b6ebd315450fc2c97862754c665237294407a45 | 03e3138f99f275d15d41a5c5bfb212f85d64d02e | /source/res/scripts/client/gui/scaleform/daapi/view/lobby/profile/ProfileSection.py | b4db4c8e5d4ea33bab42ac314a91489bad338c34 | [] | no_license | TrenSeP/WorldOfTanks-Decompiled | e428728e7901146d0b599d02c930d70532232a97 | 1faa748acec1b7e435b657fd054ecba23dd72778 | refs/heads/1.4.1 | 2020-04-27T08:07:49.813023 | 2019-03-05T17:37:06 | 2019-03-05T17:37:06 | 174,159,837 | 1 | 0 | null | 2019-03-06T14:33:33 | 2019-03-06T14:24:36 | Python | UTF-8 | Python | false | false | 4,621 | py | # Python bytecode 2.7 (decompiled from Python 2.7)
# Embedded file name: scripts/client/gui/Scaleform/daapi/view/lobby/profile/ProfileSection.py
from helpers import dependency
from helpers import i18n
from gui.Scaleform.daapi.view.meta.ProfileSectionMeta import ProfileSectionMeta
from gui.Scaleform.locale.PROFILE import PROFILE
from gui.Scaleform.genConsts.PROFILE_DROPDOWN_KEYS import PROFILE_DROPDOWN_KEYS
from skeletons.gui.lobby_context import ILobbyContext
from skeletons.gui.shared import IItemsCache
from soft_exception import SoftException
class ProfileSection(ProfileSectionMeta):
itemsCache = dependency.descriptor(IItemsCache)
lobbyContext = dependency.descriptor(ILobbyContext)
def __init__(self, *args):
super(ProfileSection, self).__init__()
self.__isActive = False
self._battlesType = PROFILE_DROPDOWN_KEYS.ALL
self._userName = args[0]
self._userID = args[1]
self._databaseID = args[2]
self._selectedData = args[3]
self._data = None
self._dossier = None
self.__needUpdate = False
return
def _populate(self):
super(ProfileSection, self)._populate()
self.requestDossier(self._battlesType)
def _dispose(self):
self._data = None
self._dossier = None
super(ProfileSection, self)._dispose()
return
def requestDossier(self, bType):
self._battlesType = bType
self.invokeUpdate()
def onSectionActivated(self):
pass
def _dataProviderEntryAutoTranslate(self, key):
return self._dataProviderEntry(key, i18n.makeString(PROFILE.profile_dropdown_labels(key)))
@classmethod
def _dataProviderEntry(cls, key, label):
return {'key': key,
'label': label}
@classmethod
def _getTotalStatsBlock(cls, dossier):
return dossier.getRandomStats()
def __receiveDossier(self):
if self.__isActive and self.__needUpdate:
self.__needUpdate = False
accountDossier = self.itemsCache.items.getAccountDossier(self._userID)
self._sendAccountData(self._getNecessaryStats(accountDossier), accountDossier)
def _getNecessaryStats(self, accountDossier=None):
if accountDossier is None:
accountDossier = self.itemsCache.items.getAccountDossier(self._userID)
if self._battlesType == PROFILE_DROPDOWN_KEYS.ALL:
data = self._getTotalStatsBlock(accountDossier)
elif self._battlesType == PROFILE_DROPDOWN_KEYS.TEAM:
data = accountDossier.getTeam7x7Stats()
elif self._battlesType == PROFILE_DROPDOWN_KEYS.STATICTEAM:
data = accountDossier.getRated7x7Stats()
elif self._battlesType == PROFILE_DROPDOWN_KEYS.HISTORICAL:
data = accountDossier.getHistoricalStats()
elif self._battlesType == PROFILE_DROPDOWN_KEYS.FORTIFICATIONS:
data = self._receiveFortDossier(accountDossier)
elif self._battlesType == PROFILE_DROPDOWN_KEYS.FORTIFICATIONS_SORTIES:
data = accountDossier.getFortSortiesStats()
elif self._battlesType == PROFILE_DROPDOWN_KEYS.FORTIFICATIONS_BATTLES:
data = accountDossier.getFortBattlesStats()
elif self._battlesType == PROFILE_DROPDOWN_KEYS.COMPANY:
data = accountDossier.getCompanyStats()
elif self._battlesType == PROFILE_DROPDOWN_KEYS.CLAN:
data = accountDossier.getGlobalMapStats()
elif self._battlesType == PROFILE_DROPDOWN_KEYS.FALLOUT:
data = accountDossier.getFalloutStats()
elif self._battlesType == PROFILE_DROPDOWN_KEYS.RANKED:
data = accountDossier.getRankedStats()
elif self._battlesType == PROFILE_DROPDOWN_KEYS.EPIC_RANDOM:
data = accountDossier.getEpicRandomStats()
else:
raise SoftException('ProfileSection: Unknown battle type: ' + self._battlesType)
return data
def _receiveFortDossier(self, accountDossier):
return None
def _sendAccountData(self, targetData, accountDossier):
self._data = targetData
self._dossier = accountDossier
def setActive(self, value):
self.__isActive = value
self.__receiveDossier()
def invokeUpdate(self):
self._data = None
self._dossier = None
self.__needUpdate = True
self.__receiveDossier()
return
@property
def isActive(self):
return self.__isActive
def _formIconLabelInitObject(self, i18key, icon):
return {'description': i18n.makeString(i18key),
'icon': icon}
| [
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] | |
33e1acb8213c3949b68066fc4c21db1c9a41b63e | 18239524612cf572bfeaa3e001a3f5d1b872690c | /clients/keto/python/test/test_ory_access_control_policy_roles.py | 58ab31294ec8ab133335aea33cb9535ffefe7585 | [
"Apache-2.0"
] | permissive | simoneromano96/sdk | 2d7af9425dabc30df830a09b26841fb2e8781bf8 | a6113d0daefbbb803790297e4b242d4c7cbbcb22 | refs/heads/master | 2023-05-09T13:50:45.485951 | 2021-05-28T12:18:27 | 2021-05-28T12:18:27 | 371,689,133 | 0 | 0 | Apache-2.0 | 2021-05-28T12:11:41 | 2021-05-28T12:11:40 | null | UTF-8 | Python | false | false | 1,108 | py | # coding: utf-8
"""
ORY Keto
A cloud native access control server providing best-practice patterns (RBAC, ABAC, ACL, AWS IAM Policies, Kubernetes Roles, ...) via REST APIs. # noqa: E501
The version of the OpenAPI document: v0.0.0-alpha.1
Contact: [email protected]
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import unittest
import ory_keto_client
from ory_keto_client.models.ory_access_control_policy_roles import OryAccessControlPolicyRoles # noqa: E501
from ory_keto_client.rest import ApiException
class TestOryAccessControlPolicyRoles(unittest.TestCase):
"""OryAccessControlPolicyRoles unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def testOryAccessControlPolicyRoles(self):
"""Test OryAccessControlPolicyRoles"""
# FIXME: construct object with mandatory attributes with example values
# model = ory_keto_client.models.ory_access_control_policy_roles.OryAccessControlPolicyRoles() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
| [
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] | |
17c409f96f6fbfc2ece1feb2169d436079206edf | c61a28aba19f7cdf9a5127e8a782bf115c265e70 | /apps/recruitpro/recruitpro/projects/doctype/project/test_project.py | c4ea5f0a15de18c53a3d959798b6561206bae9f6 | [
"MIT"
] | permissive | sharmilaviji/RecruitPRO-NEW | fa72c8fc00f469a41798b1047c11dcc470fbc495 | dcfaedebe56b45acd6ddcab7e24c939b853a2c8c | refs/heads/master | 2021-05-26T12:14:12.611154 | 2020-04-27T04:40:50 | 2020-04-27T04:40:50 | 254,125,640 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 207 | py | # -*- coding: utf-8 -*-
# Copyright (c) 2020, teampro and Contributors
# See license.txt
from __future__ import unicode_literals
# import frappe
import unittest
class TestProject(unittest.TestCase):
pass
| [
"[email protected]"
] | |
1dfd792b4d6b9073b528ef9278bbf99e213f1556 | aa53489a8a63ce7911814ad65fefc72e966e12a4 | /shopstats/manage.py | e9da8239b56bfbc892534a0f34833d40ca16e3a5 | [] | no_license | rajesh67/shopstats | 6e67a238dee0230cb4a0b7d178539e18a60c3dce | 708a225b66420f7103d52d23bcfb97add9a419a7 | refs/heads/master | 2021-01-10T04:53:59.464927 | 2016-01-15T16:37:21 | 2016-01-15T16:37:21 | 49,218,834 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 253 | py | #!/usr/bin/env python
import os
import sys
if __name__ == "__main__":
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "shopstats.settings")
from django.core.management import execute_from_command_line
execute_from_command_line(sys.argv)
| [
"[email protected]"
] | |
0380022b8c6b8eef636f670ba5bfbc4a414b5801 | da11f3d8ab43b2def03e7e99ed08aec2d578611f | /python编程从入门到实践/第十七章/17-1/java_repos.py | 8ee138382a88ec6e3bdf71d1f9af66c1c60a3d68 | [] | no_license | huanglun1994/learn | ff3bbb1b0afe7e9c0812bd71af62707acbb5b0b5 | 9dc8ddd440e56a9961b118813162323fdfd4f16e | refs/heads/master | 2021-01-01T06:30:34.652264 | 2018-07-09T15:00:21 | 2018-07-09T15:00:21 | 97,444,580 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,512 | py | # -*- coding: utf-8 -*-
"""xxxxx"""
__author__ = 'Huang Lun'
import requests
import pygal
from pygal.style import LightColorizedStyle as LCS, LightenStyle as LS
# 执行API调用并存储响应
url = 'https://api.github.com/search/repositories?q=language:java&sort=stars'
r = requests.get(url)
print('Status code: ', r.status_code)
# 将API响应存储在一个变量中
response_dict = r.json()
print('Total repositories: ', response_dict['total_count'])
print('Total items: ', len(response_dict['items']))
# 研究有关仓库的信息
repo_dicts = response_dict['items']
names, plot_dicts = [], []
for repo_dict in repo_dicts:
names.append(repo_dict['name'])
plot_dict = {}
plot_dict['value'] = repo_dict['stargazers_count']
if repo_dict['description']:
plot_dict['label'] = repo_dict['description']
elif not repo_dict['description']:
plot_dict['label'] = 'No description'
plot_dict['xlink'] = repo_dict['html_url']
plot_dicts.append(plot_dict)
# 可视化
my_style = LS('#333366', base_style=LCS)
my_config = pygal.Config()
my_config.x_label_rotation = 45
my_config.show_legend = False
my_config.title_font_size = 24
my_config.label_font_size = 14
my_config.major_label_font_size = 16
my_config.truncate_label = 15
my_config.show_y_guides = False
my_config.width = 1000
chart = pygal.Bar(my_config, style=my_style)
chart.title = 'Most-Starred Java Projects on GitHub'
chart.x_labels = names
chart.add('', plot_dicts)
chart.render_to_file('java_repos.svg')
| [
"[email protected]"
] | |
f28ba1c32f9bd37f6f17a95addc3e0021621f4e1 | 8de2869bf284e98de6a9b424e90da5ab361d8aac | /book/_build/jupyter_execute/matplotlib/04_LinesAndMarkers.py | 389934d917a8f82f9913f961208dd4315888974e | [] | no_license | hossainlab/dataviz | d37081da066bd88165aba41e2a8050ee17a1b131 | e02b38827ab363f907b8c06c8f7ffc98a6a27a8f | refs/heads/master | 2023-07-20T01:42:47.144900 | 2021-08-29T10:43:15 | 2021-08-29T10:43:15 | 291,055,389 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,193 | py | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
matplotlib.interactive(True)
plt.ion()
matplotlib.is_interactive()
# #### We start off with the previously seen sine curve
# In[2]:
x = np.linspace(start=0, stop=10, num=50)
# In[3]:
plt.plot(x, np.sin(x))
plt.show()
# #### Having multiple plots in a pyplot
# The colors of each plot is chosen by iterating over a color palette. The default palette is {'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan'}
# In[4]:
plt.plot(x, np.sin(x), label='sine curve')
plt.plot(x, np.cos(x), label='cosine curve')
plt.legend()
plt.title('Playing with Plots')
plt.show()
# #### Specifying colors
# We pick the colors of green and magenta for the curves
# * We have specified the full name of the green color
# * Magenta has been specified in shorthand ('g' is short for green) <br />
#
# The colors and codes for Matplotlib are here:
# https://matplotlib.org/2.0.2/api/colors_api.html
#
# The full list of named colors is here:
# https://matplotlib.org/examples/color/named_colors.html
# In[5]:
plt.plot(x, np.sin(x), label='sine curve', color='green')
plt.plot(x, np.cos(x), label='cosine curve', color='m')
plt.legend()
plt.title('Playing with Plots')
plt.show()
# ### Formats for lines and markers
# Line formats: https://matplotlib.org/gallery/lines_bars_and_markers/line_styles_reference.html <br />
# Marker formats: https://matplotlib.org/1.4.1/api/markers_api.html <br />
# #### Plots need not be lines
# Start off by plotting a random array of 20 numbers
# In[6]:
random_array = np.random.randn(20)
# In[7]:
plt.plot(random_array,
color='green')
plt.show()
# #### Line styles
# We can have solid, dashed, dotted or dash-dot lines
# In[8]:
plt.plot(random_array,
color='green',
linestyle=':')
plt.show()
# In[9]:
plt.plot(random_array,
color='green',
linestyle='--')
plt.show()
# #### Adjust the line width
# The default is 1
# In[10]:
plt.plot(random_array,
color='green',
linestyle='--',
linewidth=3)
plt.show()
# #### We use markers to denote the points
# The 'd' denotes small diamonds. For all the marker styles check out this page: <br />
# https://matplotlib.org/1.4.1/api/markers_api.html
# In[11]:
plt.plot(random_array,
color='green',
marker = 'd')
plt.show()
# #### Adjust the marker size
# Default is 6
# In[12]:
plt.plot(random_array,
color='green',
marker = 'd',
markersize=10)
plt.show()
# #### Get rid of the line and use only markers
# In[13]:
plt.plot(random_array,
color='green',
marker = 'd',
linestyle = 'None')
plt.show()
# #### Scatter plots
# These are similar to regular plots but you need to specify the x coordinates. Below we create the same plot as above, but explicitly give the x coordinates as a list of 0-19
# In[14]:
plt.scatter(range(0,20),
random_array,
color='green',
marker = 'd')
plt.show()
# In[ ]:
| [
"[email protected]"
] | |
dd7d6a3be9acc10f5538b1fd07a6ee1bfc699701 | 53fab060fa262e5d5026e0807d93c75fb81e67b9 | /backup/user_299/ch46_2020_04_06_14_54_53_768265.py | 19931a4dad29c6cad1dbaef1231cbe4501b937b9 | [] | no_license | gabriellaec/desoft-analise-exercicios | b77c6999424c5ce7e44086a12589a0ad43d6adca | 01940ab0897aa6005764fc220b900e4d6161d36b | refs/heads/main | 2023-01-31T17:19:42.050628 | 2020-12-16T05:21:31 | 2020-12-16T05:21:31 | 306,735,108 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 124 | py | def numero_no_indice(lista):
for i,e in enumerate(lista):
if i!=e:
del lista[i]
return lista | [
"[email protected]"
] | |
a99854b984911426cefd12d106e8d4e639de58b4 | 7f203d6d2d48bdc0b768215798f0694803268818 | /test/vnx/resource/test_migration.py | 0a48bd056d09f0df5cd51a5bbe89b270cee31643 | [
"Apache-2.0"
] | permissive | thotypous/storops | 1108a314658def0dac69e0b0d14578283aab50b4 | 8ea8c5a71f2bf93b710c854ee6c3b01f334673a0 | refs/heads/master | 2021-01-21T17:03:31.935679 | 2016-08-22T15:30:54 | 2016-08-22T15:30:54 | 66,502,757 | 0 | 0 | null | 2016-08-24T21:57:36 | 2016-08-24T21:57:35 | null | UTF-8 | Python | false | false | 3,363 | py | # coding=utf-8
# Copyright (c) 2015 EMC Corporation.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from __future__ import unicode_literals
from unittest import TestCase
from hamcrest import assert_that, equal_to, instance_of, raises
from storops.exception import VNXLunNotMigratingError
from storops.vnx.resource.lun import VNXLun
from test.vnx.cli_mock import t_cli, patch_cli
from storops.vnx.enums import VNXMigrationRate
from storops.vnx.resource.migration import VNXMigrationSession
__author__ = 'Cedric Zhuang'
class VNXMigrationSessionTest(TestCase):
@patch_cli
def test_properties(self):
ms = VNXMigrationSession(0, t_cli())
assert_that(ms.source_lu_id, equal_to(0))
assert_that(ms.source_lu_name, equal_to('LUN 0'))
assert_that(ms.dest_lu_id, equal_to(1))
assert_that(ms.dest_lu_name, equal_to('LUN 1'))
assert_that(ms.migration_rate, equal_to(VNXMigrationRate.HIGH))
assert_that(ms.percent_complete, equal_to(50.0))
assert_that(ms.time_remaining, equal_to('0 second(s)'))
assert_that(ms.current_state, equal_to('MIGRATING'))
assert_that(ms.is_migrating, equal_to(True))
assert_that(ms.is_success, equal_to(False))
assert_that(ms.existed, equal_to(True))
@patch_cli
def test_source_lun(self):
ms = VNXMigrationSession(0, t_cli())
lun = ms.source_lun
assert_that(lun, instance_of(VNXLun))
assert_that(lun.get_id(lun), equal_to(ms.source_lu_id))
@patch_cli
def test_destination_lun(self):
ms = VNXMigrationSession(0, t_cli())
lun = ms.destination_lun
assert_that(lun, instance_of(VNXLun))
assert_that(lun.get_id(lun), equal_to(ms.dest_lu_id))
@patch_cli
def test_get_all(self):
ms_list = VNXMigrationSession.get(t_cli())
assert_that(len(ms_list), equal_to(2))
@patch_cli(output='migrate_-list_none.txt')
def test_get_all_none(self):
ms_list = VNXMigrationSession.get(t_cli())
assert_that(len(ms_list), equal_to(0))
@patch_cli
def test_get_no_session(self):
ms = VNXMigrationSession(10, t_cli())
assert_that(ms.existed, equal_to(False))
assert_that(ms.is_migrating, equal_to(False))
assert_that(ms.is_success, equal_to(True))
@patch_cli
def test_get_lun_not_exists(self):
ms = VNXMigrationSession(1234, t_cli())
assert_that(ms.existed, equal_to(False))
@patch_cli
def test_cancel_migrate(self):
def f():
ms = VNXMigrationSession(0, t_cli())
ms.cancel()
assert_that(f, raises(VNXLunNotMigratingError,
'not currently migrating'))
| [
"[email protected]"
] | |
b2ffd186bd314161749bdd589717f9c0c6dc87d0 | 3c62aaf3b1b3c598dbe43a47f4d76ae90b27b098 | /PA2/part1/linear_regression.py | c519c202b7d8fc652487ef864f6557a37e38fa20 | [] | no_license | trademark152/Machine_Learning_CSCI567_USC | e8a222e7d9093bc78cf1a17545faf3e2710bdf39 | 61b614676510fd1fbb49da255a667c8da4a911f7 | refs/heads/master | 2022-12-16T11:50:57.912882 | 2020-09-26T00:20:48 | 2020-09-26T00:20:48 | 298,696,629 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,501 | py | """
Do not change the input and output format.
If our script cannot run your code or the format is improper, your code will not be graded.
The only functions you need to implement in this template is linear_regression_noreg, linear_regression_invertible，regularized_linear_regression,
tune_lambda, test_error and mapping_data.
"""
import numpy as np
import pandas as pd
###### Q1.1 ######
def mean_square_error(w, X, y):
"""
Compute the mean squre error on test set given X, y, and model parameter w.
Inputs:
- X: A numpy array of shape (num_samples, D) containing test feature.
- y: A numpy array of shape (num_samples, ) containing test label
- w: a numpy array of shape (D, )
Returns:
- err: the mean square error
"""
#####################################################
# TODO 1: Fill in your code here #
#####################################################
# Calculate mean square error
# MSE = 1/n * sum [(y_true-y_pred)^2]
# Dimension: X: num_samples*D; y: num_samples
err = np.mean(np.power(np.subtract(y, np.matmul(X,w)),2))
return err
###### Q1.2 ######
def linear_regression_noreg(X, y):
"""
Compute the weight parameter given X and y.
Inputs:
- X: A numpy array of shape (num_samples, D) containing feature.
- y: A numpy array of shape (num_samples, ) containing label
Returns:
- w: a numpy array of shape (D, )
"""
#####################################################
# TODO 2: Fill in your code here #
#####################################################
# Closed form solution: w=(Xt*X)^-1*Xt*y
# Covariance matrix
covMat = np.matmul(np.transpose(X), X)
# weight vector
w = np.matmul(np.matmul(np.linalg.inv(covMat), np.transpose(X)),y)
return w
###### Q1.3 ######
def linear_regression_invertible(X, y):
"""
Compute the weight parameter given X and y.
Inputs:
- X: A numpy array of shape (num_samples, D) containing feature.
- y: A numpy array of shape (num_samples, ) containing label
Returns:
- w: a numpy array of shape (D, )
"""
#####################################################
# TODO 3: Fill in your code here #
#####################################################
# Number of dimensions
dim = len(X[0])
# print(dim)
# Covariance matrix
covMat = np.matmul(np.transpose(X), X)
# Find eigenvalues:
eigVals = np.linalg.eigvals(covMat)
# print(eigVals)
# print(np.amin(np.absolute(eigVals)))
if np.amin(np.absolute(eigVals)) >= 10**(-5):
# weight vector
return np.matmul(np.matmul(np.linalg.inv(covMat), np.transpose(X)), y)
# If the smallest absolute value of any eigenvalue is smaller than 10^-5
# Consider matrix non-invertibale and start improving:
k = 0
while np.amin(np.absolute(eigVals)) < 10**(-5):
# solve issue of non-invertible (slides 29-31 csci567 lecture 3)
k += 1
eigVals = np.linalg.eigvals(covMat+k*10**(-1)*np.identity(dim))
# print(k)
return np.matmul(np.matmul(np.linalg.inv(covMat+k*(10**(-1))*np.identity(dim)), np.transpose(X)), y)
###### Q1.4 ######
def regularized_linear_regression(X, y, lambd):
"""
Compute the weight parameter given X, y and lambda.
Inputs:
- X: A numpy array of shape (num_samples, D) containing feature.
- y: A numpy array of shape (num_samples, ) containing label
- lambd: a float number containing regularization strength
Returns:
- w: a numpy array of shape (D, )
"""
#####################################################
# TODO 4: Fill in your code here #
#####################################################
# handle exception
# if lambd == None:
# lambd = 0.
# Number of dimensions
dim = len(X[0])
# print(dim)
# Covariance matrix
covMat = np.matmul(np.transpose(X), X)
# # Find eigenvalues:
# eigVals = np.linalg.eigvals(covMat)
# # print(eigVals)
# # print(np.amin(np.absolute(eigVals)))
# # if matrix is invertible
# if np.amin(np.absolute(eigVals)) >= 10**(-5):
# # weight vector
# return np.matmul(np.matmul(np.linalg.inv(covMat), np.transpose(X)), y)
#
# # If the smallest absolute value of any eigenvalue is smaller than 10^-5
# # Consider matrix non-invertibale and start improving:
# else:
# # solve issue of non-invertible (slides 50 csci567 lecture 3)
# eigVals = np.linalg.eigvals(covMat+lambd*np.identity(dim))
return np.matmul(np.matmul(np.linalg.inv(covMat+lambd*np.identity(dim)), np.transpose(X)), y)
###### Q1.5 ######
def tune_lambda(Xtrain, ytrain, Xval, yval):
"""
Find the best lambda value.
Inputs:
- Xtrain: A numpy array of shape (num_training_samples, D) containing training feature.
- ytrain: A numpy array of shape (num_training_samples, ) containing training label
- Xval: A numpy array of shape (num_val_samples, D) containing validation feature.
- yval: A numpy array of shape (num_val_samples, ) containing validation label
Returns:
- bestlambda: the best lambda you find in lambds
"""
#####################################################
# TODO 5: Fill in your code here #
#####################################################
bestlambda = -1
lowestMSE = np.inf
lambd = 10**(-20)
while lambd < 10**20:
# update lambd
lambd *= 10
# print(float("{0:.2e}".format(lambd)))
# use given training data to train model
w = regularized_linear_regression(Xtrain, ytrain, lambd)
# compute the mse
mse = mean_square_error(w, Xval, yval)
# print(mse)
# update the mse
if mse < lowestMSE:
lowestMSE = mse
bestlambda = lambd
if bestlambda == None:
return 0
else:
# print(bestlambda)
# avoid representation error in floating number
return float("{0:.2e}".format(bestlambda))
###### Q1.6 ######
def mapping_data(X, power):
"""
Mapping the data.
Inputs:
- X: A numpy array of shape (num_training_samples, D) containing training feature.
- power: A integer that indicate the power in polynomial regression
Returns:
- X: mapped_X, shape(num_samples, D*power) You can manually calculate the size of X based on the power and original size of X
"""
#####################################################
# TODO 6: Fill in your code here #
#####################################################
""" GOAL: input [[1,2,3],[0,5,5]] --> output [[1,2,3,1,4,9],[0,5,5,0,25,25]]"""
# loop through each training sample
# mapped_X = np.zeros((len(X), len(X[0])*(power-1)))
mapped_X = [[] for i in range(len(X))]
# mapped_X=[]
# print(mapped_X)
for index, sample in enumerate(X):
# print(sample)
# loop through all power in range
for i in range(2, power+1):
# create an element-wise power of the original sample
sample_power_i = np.power(sample[:len(X[0])], i)
# print(sample_power_i)
# obtain the index of the last element
end_idx = len(sample)
# print(end_idx)
# add that to the end of the original row
sample = np.insert(sample, end_idx, sample_power_i)
# print(sample.tolist())
# modify X
mapped_X[index] = sample
return np.asarray(mapped_X)
| [
"[email protected]"
] | |
39ae03eb391316d2130cb398f9458429c9dd0e77 | 339f207fd7dd99b7b6484ffa78bfbf8102c25ede | /wrappedapp/tests/models/test_auth.py | 3826af0a7bbf48a9eb4772fe9bad3857f92bb9b1 | [] | no_license | ralphbean/wrappedapp | 0b3b43d4435b6e16b1a21a0f766bfa3d51450bf2 | 73bbbc0366d06492d0a7822c8b543f5410e15a6f | refs/heads/master | 2016-09-06T10:36:02.820439 | 2011-09-28T18:46:11 | 2011-09-28T18:46:27 | 2,477,066 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,500 | py | # -*- coding: utf-8 -*-
"""Test suite for the TG app's models"""
from nose.tools import eq_
from wrappedapp import model
from wrappedapp.tests.models import ModelTest
class TestGroup(ModelTest):
"""Unit test case for the ``Group`` model."""
klass = model.Group
attrs = dict(
group_name = u"test_group",
display_name = u"Test Group"
)
class TestUser(ModelTest):
"""Unit test case for the ``User`` model."""
klass = model.User
attrs = dict(
user_name = u"ignucius",
email_address = u"[email protected]"
)
def test_obj_creation_username(self):
"""The obj constructor must set the user name right"""
eq_(self.obj.user_name, u"ignucius")
def test_obj_creation_email(self):
"""The obj constructor must set the email right"""
eq_(self.obj.email_address, u"[email protected]")
def test_no_permissions_by_default(self):
"""User objects should have no permission by default."""
eq_(len(self.obj.permissions), 0)
def test_getting_by_email(self):
"""Users should be fetcheable by their email addresses"""
him = model.User.by_email_address(u"[email protected]")
eq_(him, self.obj)
class TestPermission(ModelTest):
"""Unit test case for the ``Permission`` model."""
klass = model.Permission
attrs = dict(
permission_name = u"test_permission",
description = u"This is a test Description"
)
| [
"[email protected]"
] | |
9481c3b012fa6b02185d777dafa526c7ef1e00d7 | 8d014c5513a0eeca086010b018b67336f8d042e0 | /cam_esp32cam.py | 253eb82dfa3400a82cc5d443548f35bf88108c6e | [] | no_license | rkuo2000/cv2 | 26ce0a06b4040eabb82319ec44cab5c3639b9495 | 16e64e7092d6654ea470e469d6b15f308ecd1788 | refs/heads/master | 2022-10-12T00:11:35.964818 | 2022-09-30T06:50:35 | 2022-09-30T06:50:35 | 108,848,948 | 5 | 29 | null | 2022-09-29T11:01:48 | 2017-10-30T12:38:58 | Python | UTF-8 | Python | false | false | 681 | py | # open browser at ipaddr of ESP32-CAM to set stream size
# 320x240 doesn't work, other resolution are OK
import numpy as np
import cv2
from urllib.request import urlopen
# port 81 has stream, see ESP32-CAM webserver.ino
url = 'http://192.168.1.5:81/stream'
CAMERA_BUFFER_SIZE = 4096
stream = urlopen(url)
bbb=b''
while True:
bbb += stream.read(CAMERA_BUFFER_SIZE)
a = bbb.find(b'\xff\xd8')
b = bbb.find(b'\xff\xd9')
if a>-1 and b>-1:
jpg = bbb[a:b+2]
bbb = bbb[b+2:]
img = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8),cv2.IMREAD_COLOR)
cv2.imshow('CAM', img)
cv2.waitKey(1)
cv2.destroyAllWindows()
| [
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] | |
34aff31d919f88404099c15990efd64e8c9f7d6a | b9801a2ad269a678acd6113992f063fba2813f65 | /test/test_policy.py | 97c8c6b5d630335fbff44038ef558dd399776b92 | [
"MIT"
] | permissive | ax-ncolyer/automox-console-sdk-python | 6dd01826cc9629b2ee6086ae179b443f9ba8e0db | 27ba2279e2d59e3f0cbfc00e34eddb51838e402e | refs/heads/main | 2023-08-12T20:57:24.264682 | 2021-09-16T02:18:01 | 2021-09-16T02:18:01 | 406,992,680 | 0 | 0 | MIT | 2021-09-16T02:35:32 | 2021-09-16T02:35:31 | null | UTF-8 | Python | false | false | 862 | py | # coding: utf-8
"""
Automox Console API
API for use with the Automox Console # noqa: E501
OpenAPI spec version: 2021-08-10
Contact: [email protected]
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import unittest
import automox_console_sdk
from automox_console_sdk.models.policy import Policy # noqa: E501
from automox_console_sdk.rest import ApiException
class TestPolicy(unittest.TestCase):
"""Policy unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def testPolicy(self):
"""Test Policy"""
# FIXME: construct object with mandatory attributes with example values
# model = automox_console_sdk.models.policy.Policy() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
| [
"[email protected]"
] | |
fb31c45f4f37bb9228e0728eb24e7fa6149627df | 6fbca0b22dbf7e79d3e7796bdcc18cc564a77eb1 | /aol/documents/tests.py | 10679b394aeca4ae2fe688e74bfb4832a53e6371 | [] | no_license | mdj2/aol | b998a41552eca6c3d09b7f97891283563d7d3b01 | f848f5328aec30826d726033cd44216be4e9dabd | refs/heads/master | 2021-01-09T20:48:48.372586 | 2014-03-18T18:23:14 | 2014-03-18T18:23:33 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,112 | py | import os
from django.test import TestCase
from django.core.urlresolvers import reverse
from django.conf import settings as SETTINGS
from .models import Document
from aol.users.tests.test_views import LoginMixin
from aol.lakes.models import NHDLake as Lake
class ViewTest(LoginMixin):
fixtures = ['lakes.json']
def test_add_document(self):
lake = Lake.objects.get(title="Matt Lake")
response = self.client.get(reverse('admin-add-document', args=(lake.pk,)))
self.assertEqual(response.status_code, 200)
# test posting to the form
data = {
'name': 'foo',
'rank': '1',
'file': open(os.path.join(SETTINGS.MEDIA_ROOT, "photos", "test.jpg")),
'type': Document.OTHER,
}
pre_count = Document.objects.filter(lake=lake).count()
response = self.client.post(reverse('admin-add-document', args=(lake.pk,)), data)
# the response should be valid, so a redirect should happen
self.assertEqual(response.status_code, 302)
# make sure the document got added to the lake
self.assertEqual(Document.objects.filter(lake=lake).count(), pre_count + 1)
# delete a required field to make the form invalid
del data['name']
response = self.client.post(reverse('admin-add-document', args=(lake.pk,)), data)
self.assertFalse(response.context['form'].is_valid())
def test_edit_document(self):
document = Document.objects.get(pk=1)
response = self.client.get(reverse('admin-edit-document', args=(document.pk,)))
self.assertEqual(response.status_code, 200)
# edit the document
data = response.context['form'].initial
data['name'] = "whatever"
response = self.client.post(reverse('admin-edit-document', args=(document.pk,)), data)
# the response should be valid, so a redirect should happen
self.assertEqual(response.status_code, 302)
# make sure the caption got updated
document = Document.objects.get(pk=1)
self.assertEqual(document.name, data['name'])
| [
"[email protected]"
] | |
9973762cd04b563d1fa57643f4ea17013ea0507f | cd627d56e00fafeaa547582145eead9147329b6a | /django-rest/sxfunc/snippets/views.py | 5cafeac4c29da9f55fa20e01723ac2571dcc23f7 | [] | no_license | 2XL/hwDjango | 57c2b7f6ee91e89ebc566891c7e2ceb01e2192c1 | 0816f0e9f842025b14779ed731e8c15a30894a95 | refs/heads/master | 2021-01-13T09:15:33.791503 | 2016-11-08T15:44:32 | 2016-11-08T15:44:32 | 72,609,539 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,783 | py | from django.shortcuts import render
# Create your views here.
############ Wrapping Views with function based decorator
from rest_framework import status
from rest_framework.decorators import api_view
from rest_framework.response import Response
from .models import Snippet
from .serializers import SnippetSerializer
@api_view(['GET', 'POST'])
def snippet_list(request, format=None):
"""
<List:GET> all snippets, or <Create:POST> a new snippet.
"""
if request.method == 'GET':
snippets = Snippet.objects.all()
serializer = SnippetSerializer(snippets, many=True)
return Response(serializer.data)
elif request.method == 'POST':
serializer = SnippetSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PUT', 'DELETE'])
def snippet_detail(request, pk, format=None):
"""
Retrieve, update or delete a snippet instance.
"""
try:
snippet = Snippet.objects.get(pk=pk)
except Snippet.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = SnippetSerializer(snippet)
return Response(serializer.data)
elif request.method == 'PUT':
serializer = SnippetSerializer(snippet, data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
elif request.method == 'DELETE':
snippet.delete()
return Response(status=status.HTTP_204_NO_CONTENT) | [
"[email protected]"
] | |
b197c6d251ae7bc5c527c1b8248d9b2690e1135b | 30fced93674fce23af3e0eda735221fab785ca2e | /beta/download.py | 7acc0177eba335648b27a596fc552b4438b80d66 | [] | no_license | li3637/JD_Diy | 8047017fc8caf7cbb8ca6988b1a7146c122ed8b4 | 9222a5e6a92d094b56cf94aa37677ec5a5796993 | refs/heads/master | 2023-06-11T06:30:37.100477 | 2021-06-21T04:34:21 | 2021-06-21T04:34:21 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,668 | py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Author : Chiupam
# @Data : 2021-06-15
# @Version : v 1.0
# @Updata :
# @Future :
from JD_Diy import chat_id, jdbot, _ConfigDir, _ScriptsDir, _OwnDir, logger, _JdbotDir
from ..bot.utils import cmd, press_event, backfile, jdcmd, V4, QL, _ConfigFile, mycron, split_list, row, qlcron, _Auth, upcron, mybot
from ..diy.utils import mycronup
from telethon import events, Button
from asyncio import exceptions
import requests, re, os, asyncio
import json
@jdbot.on(events.NewMessage(from_users=chat_id, pattern=r'^https?://.*(js|py|sh)$'))
async def mydownload(event):
try:
SENDER = event.sender_id
furl = event.raw_text
if '下载代理' in mybot.keys() and str(mybot['下载代理']).lower() != 'false' and 'github' in furl:
furl = f'{str(mybot["下载代理"])}/{furl}'
try:
resp = requests.get(furl).text
if "</html>" in resp:
await jdbot.send_message(chat_id, f"接收到的[链接]({furl})是一个页面并非raw数据，会话结束")
return
except Exception as e:
await jdbot.send_message(chat_id, f"下载失败\n{e}")
return
async with jdbot.conversation(SENDER, timeout=60) as conv:
fname = furl.split('/')[-1]
fname_cn = ''
if furl.endswith(".js"):
fname_cn = re.findall(r"(?<=new\sEnv\(').*(?=')", resp, re.M)
if fname_cn != []:
fname_cn = fname_cn[0]
else:
fname_cn = ''
if V4:
btns = [Button.inline('放入config目录', data=_ConfigDir), Button.inline('放入jbot/diy目录', data=f'{_JdbotDir}/diy'), Button.inline('放入scripts目录', data=_ScriptsDir), Button.inline('放入own目录', data=_OwnDir ), Button.inline('取消对话', data='cancel')]
else:
btns = [Button.inline('放入config目录', data=_ConfigDir), Button.inline('放入scripts目录', data=_ScriptsDir), Button.inline('取消对话', data='cancel')]
write, cmdtext = True, False
msg = await conv.send_message(f'成功下载{fname_cn}脚本\n现在，请做出你的选择：', buttons=split_list(btns, row))
convdata = await conv.wait_event(press_event(SENDER))
res1 = bytes.decode(convdata.data)
if res1 == 'cancel':
await jdbot.edit_message(msg, '对话已取消，感谢你的使用')
conv.cancel()
return
elif res1 == _ScriptsDir:
fpath = f"{_ScriptsDir}/{fname}"
btns = [Button.inline("是", data="confirm"), Button.inline("否", data="cancel")]
msg = await jdbot.edit_message(msg, f"请问需要运行{fname_cn}脚本吗？", buttons=btns)
convdata = await conv.wait_event(press_event(SENDER))
res2 = bytes.decode(convdata.data)
if res2 == "confirm":
cmdtext = f'{jdcmd} {_ScriptsDir}/{fname} now'
msg = await jdbot.edit_message(msg, f"请问需要添加定时吗？", buttons=btns)
convdata = await conv.wait_event(press_event(SENDER))
res2 = bytes.decode(convdata.data)
if res2 == 'cancel':
await jdbot.edit_message(msg, f"{fname_cn}脚本将保存到{_ScriptsDir}目录")
else:
await mycronup(jdbot, conv, resp, fname, msg, SENDER, btns, _ScriptsDir)
elif res1 == _OwnDir:
fpath = f"{_OwnDir}/raw/{fname}"
btns = [Button.inline("是", data="confirm"), Button.inline("否", data="cancel")]
msg = await jdbot.edit_message(msg, f"请问需要运行{fname_cn}脚本吗？", buttons=btns)
convdata = await conv.wait_event(press_event(SENDER))
res2 = bytes.decode(convdata.data)
if res2 == "confirm":
cmdtext = f'{jdcmd} {fpath} now'
await jdbot.edit_message(msg, f"文件将保存到{res1}目录，且已写入配置中，准备执行脚本")
else:
await jdbot.edit_message(msg, f'文件将保存到{res1}目录，且已写入配置中，准备拉取单个脚本，请耐心等待')
with open(_ConfigFile, 'r', encoding="utf-8") as f1:
configs = f1.readlines()
for config in configs:
if config.find("OwnRawFile") != -1 and config.find("## ") == -1:
line = configs.index(config) + 1
configs.insert(line, f"\t{event.raw_text}\n")
with open(_ConfigFile, 'w', encoding="utf-8") as f2:
f2.write(''.join(configs))
elif config.find("第五区域") != -1:
break
await cmd("jup own")
else:
fpath = f"{res1}/{fname}"
await jdbot.edit_message(msg, f"文件将保存到{res1}目录")
backfile(fpath)
with open(fpath, 'w+', encoding='utf-8') as f:
f.write(resp)
conv.cancel()
if cmdtext:
await cmd(cmdtext)
except exceptions.TimeoutError:
msg = await jdbot.edit_message(msg, '选择已超时，对话已停止，感谢你的使用')
except Exception as e:
await jdbot.send_message(chat_id, 'something wrong,I\'m sorry\n' + str(e))
logger.error('something wrong,I\'m sorry\n' + str(e))
| [
"[email protected]"
] | |
1a9fb7d130bad860e146e811538a5e4d009b51c4 | 09d767a12ad01b189f5793fa66fef2cca06c821a | /python/yiqing/app.py | b020af8640080ec52c4a8699163b8bded92ed195 | [] | no_license | sunyinggang/dailyCode | 403048f85a5506459ec3f5551230c8592f346aed | ec72332d0ac2be79cdd436631f886e25265dfd6c | refs/heads/master | 2023-03-09T02:35:56.518021 | 2021-03-01T15:38:23 | 2021-03-01T15:38:23 | 296,016,150 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 98 | py | from app import app
if __name__ == '__main__':
app.run(debug=True, port=5050, host='0.0.0.0') | [
"[email protected]"
] | |
18736855e45eda60471a343f863989a8ab6556b4 | 20c9f3a089286a442cc15f8a31bb34e110e68d8b | /tests/python/len.py | 643569734e0202f30062585e3840e5e5ee19fe9b | [
"MIT"
] | permissive | denim2x/py2nim | 00ca515daef897d380dbf4915583a470ffe4c94e | 56fc2699d31241c60bed726f59efea4bf46be238 | refs/heads/master | 2021-09-28T06:37:42.786868 | 2018-11-15T08:12:30 | 2018-11-15T08:12:30 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 161 | py | class A:
def __init__(self, elements):
self.elements = elements
def __len__(self):
return len(self.elements)
a = A([2])
print(len(a))
| [
"[email protected]"
] | |
8ed433dd2530fe9753af90133ba61335dd78dd9e | 92795fd129672b52ace12f7bf4eb08f72da916c5 | /adminphotoload/templatetags/widget_photo_iframe.py | bb95fad92665987f0ed394f6c9240f07f850a4cd | [] | no_license | ljarufe/quimerahg | b601f0b1bb77e48893f128615d54dfe062a4fd74 | 872e7deca73ccd8417d0d963a043cb2e79d64ffb | refs/heads/master | 2021-01-25T07:07:35.430695 | 2013-10-21T19:03:57 | 2013-10-21T19:03:57 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 459 | py | # -*- coding: utf-8 -*-
from django import template
from django.conf import settings
register = template.Library()
@register.inclusion_tag('templatetags/iframe.html')
def widget_photo_iframe(app, model, id, change):
"""
Inserta el código para la herramienta para subir fotos en un iframe
"""
return {'app': app,
'model': model,
'id': id,
'change': change,
'STATIC_URL': settings.STATIC_URL} | [
"[email protected]"
] | |
03aef183e7f933a66be4b8cb22079d3baab2ba23 | d153e65c8f3f60abb6d2ad11f9463f0c79179f36 | /.ipynb_checkpoints/vis_util-checkpoint.py | b92ded8c4560d066e3f733b952ef832e4d7894a6 | [] | no_license | chuazh/cs231n_project | a1ed7aeefd38185578bf6c02dd640b099812dcc6 | 1e0f30c76966c40b96172a268201e57c584aecd6 | refs/heads/master | 2020-05-20T18:51:23.254213 | 2019-05-14T23:57:18 | 2019-05-14T23:57:18 | 185,714,865 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,688 | py | import torchvision
import torchvision.datasets as dset
import torchvision.transforms as T
import torchvision.models as models
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import time
import os
import copy
import numpy as np
def check_accuracy_vis(prefix,loader, model, device, plot=True):
print('Checking accuracy on sequential validation set')
model.eval() # set model to evaluation mode
count = 0
score_array = np.empty((0,14))
gt_array = np.empty((0,14))
plt.figure()
with torch.no_grad():
for x, y in loader:
x = x.to(device=device, dtype=torch.float) # move to device, e.g. CPU
y = y.to(device=device, dtype=torch.float)
scores = model(x)
loss_fn = torch.nn.MSELoss(reduction='mean')
loss = loss_fn(scores,y)
scores = scores.to(device="cpu",dtype=torch.float)
y = y.to(device = "cpu", dtype = torch.float)
if plot:
plt.plot(range(count, len(scores) + count), scores.numpy()[:,0:3], 'b')
plt.plot(range(count, len(scores) + count), y.numpy()[:,0:3], 'r')
# append our results
score_array = np.vstack((score_array,scores.numpy()))
gt_array = np.vstack((gt_array,y.numpy()))
count = count + len(scores)
#save our results
print('saving our results...')
np.savetxt(prefix+'_vis_scores.dat', score_array, delimiter=',') # X is an array
np.savetxt(prefix+'_vis_gt.dat', gt_array, delimiter=',') # X is an array
print('MSE loss is: %f ' % loss)
plt.show() | [
"[email protected]"
] | |
d32216fde31ae9640754800c85f46534ce87f113 | 00f20cf0bd5fa65c9f54aa5a29fe3565fd8b2d96 | /swagger_client/models/match_query.py | d5fb5908d6802428b6a87f53062fada57dbc5695 | [] | no_license | gingerwizard/python-ece-client | 8b81094ddf64617c12aea9db65b9d5f7a6f1c73c | 6187fdde855a147d114fb7ee39fc5314a1b0893f | refs/heads/master | 2021-08-29T08:16:31.942559 | 2017-12-13T14:32:23 | 2017-12-13T14:32:23 | 114,131,083 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,774 | py | # coding: utf-8
"""
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version:
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from pprint import pformat
from six import iteritems
import re
class MatchQuery(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'query': 'str',
'operator': 'str',
'minimum_should_match': 'int',
'analyzer': 'str'
}
attribute_map = {
'query': 'query',
'operator': 'operator',
'minimum_should_match': 'minimum_should_match',
'analyzer': 'analyzer'
}
def __init__(self, query=None, operator=None, minimum_should_match=None, analyzer=None):
"""
MatchQuery - a model defined in Swagger
"""
self._query = None
self._operator = None
self._minimum_should_match = None
self._analyzer = None
self.query = query
if operator is not None:
self.operator = operator
if minimum_should_match is not None:
self.minimum_should_match = minimum_should_match
if analyzer is not None:
self.analyzer = analyzer
@property
def query(self):
"""
Gets the query of this MatchQuery.
The text/numeric/date to query for.
:return: The query of this MatchQuery.
:rtype: str
"""
return self._query
@query.setter
def query(self, query):
"""
Sets the query of this MatchQuery.
The text/numeric/date to query for.
:param query: The query of this MatchQuery.
:type: str
"""
if query is None:
raise ValueError("Invalid value for `query`, must not be `None`")
self._query = query
@property
def operator(self):
"""
Gets the operator of this MatchQuery.
The operator flag can be set to or or and to control the boolean clauses (defaults to or).
:return: The operator of this MatchQuery.
:rtype: str
"""
return self._operator
@operator.setter
def operator(self, operator):
"""
Sets the operator of this MatchQuery.
The operator flag can be set to or or and to control the boolean clauses (defaults to or).
:param operator: The operator of this MatchQuery.
:type: str
"""
self._operator = operator
@property
def minimum_should_match(self):
"""
Gets the minimum_should_match of this MatchQuery.
The minimum number of optional should clauses to match.
:return: The minimum_should_match of this MatchQuery.
:rtype: int
"""
return self._minimum_should_match
@minimum_should_match.setter
def minimum_should_match(self, minimum_should_match):
"""
Sets the minimum_should_match of this MatchQuery.
The minimum number of optional should clauses to match.
:param minimum_should_match: The minimum_should_match of this MatchQuery.
:type: int
"""
self._minimum_should_match = minimum_should_match
@property
def analyzer(self):
"""
Gets the analyzer of this MatchQuery.
The analyzer that will be used to perform the analysis process on the text. Defaults to the analyzer that was used to index the field.
:return: The analyzer of this MatchQuery.
:rtype: str
"""
return self._analyzer
@analyzer.setter
def analyzer(self, analyzer):
"""
Sets the analyzer of this MatchQuery.
The analyzer that will be used to perform the analysis process on the text. Defaults to the analyzer that was used to index the field.
:param analyzer: The analyzer of this MatchQuery.
:type: str
"""
self._analyzer = analyzer
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
if not isinstance(other, MatchQuery):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
| [
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] | |
000110f69e38d8e360fc1503ca5f26370e05cd25 | cb57a9ea4622b94207d12ea90eab9dd5b13e9e29 | /lintcode/python/1909_order_allocation.py | 4ff108d890858840ee3ef9ae25488bb9c13d9df3 | [] | no_license | boknowswiki/mytraning | b59585e1e255a7a47c2b28bf2e591aef4af2f09a | 5e2f6ceacf5dec8260ce87e9a5f4e28e86ceba7a | refs/heads/master | 2023-08-16T03:28:51.881848 | 2023-08-10T04:28:54 | 2023-08-10T04:28:54 | 124,834,433 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,870 | py | #!/usr/bin/python -t
# dfs
from typing import (
List,
)
class Solution:
def __init__(self):
self.cur_max = 0
self.ret = None
"""
@param score: When the j-th driver gets the i-th order, we can get score[i][j] points.
@return: return an array that means the array[i]-th driver gets the i-th order.
"""
def orderAllocation(self, score: List[List[int]]) -> List[int]:
# write your code here
m = len(score)
ret = [None] * m
self.dfs(score, 0, ret)
return self.ret
def dfs(self, score, index, ret):
print(score, index, ret)
if index == len(ret):
val = 0
for i in range(len(ret)):
val += score[i][ret[i]]
if val > self.cur_max:
self.cur_max = val
self.ret = list(ret)
return
for i in range(len(ret)):
if i not in ret:
ret[index] = i
self.dfs(score, index+1, ret)
ret[index] = None
return
if __name__ == '__main__':
s = Solution()
a = [[1,2,4],[7,11,16],[37,29,22]]
print(s.orderAllocation(a))
# dp
# 状态压缩DP版
# 我硬生生把一个medium题目做成了Hard。 不过你们也可以看一眼， 能练习状态压缩的题目真的不多了。
#
# 首先这里dpij的意思是说当第i个司机被分配完了订单以后， 订单的状态应该是j。 j里面bit里面的1表示的是这个订单被分配出去了。
#
# 然后我们开始循环， 从把第0个司机分配每种订单开始作为初始状态。 然后从第一个司机开始， 所以这个时候， 应该就是有2个司机被分配完了， 我们用一个helper function去state里面， 把所有有2个1的给找出来， 其他的就丢掉。 然后开始转移， 转移的方法就是， 找到一个k， k表示要把第k个订单分给第i个司机， 那么转移方程就是， 当i-1个司机分配完， 状态里面是有i - 1个1， 并且这个状态prevstate跟j的唯一差别就是第k位上面的订单是要分给第i个司机的。 所有用个xor把第k位给搞成0， 就得到了prevstate， 然后我们当然要从这个所有的k里面找到最大的， 这个由两部分组成， 一个是对于前面i-1个司机， 还有个是第k个订单给第i个司机， 这2个要加起来最大才行。
#
# 上面步骤做好以后， 那么最多多少分肯定能算出来。 然后我们就倒回去算到底怎么匹配的。 首先， 我们要知道最后一个司机当state是11111的时候， allocation里面存的就是这个司机分的单号。 然后知道这个以后， 我们就把这个单号从state里面去掉就得到了上一个单号， 以此类推就做完了。
#
# 当然我做的时候， 是在给driver分配订单， 其实是做反了的， 更好的办法应该是给订单分配driver， 这样return的时候， 不需要向我这样再倒腾一次。
class Solution:
"""
@param score: When the j-th driver gets the i-th order, we can get score[i][j] points.
@return: return an array that means the array[i]-th driver gets the i-th order.
"""
def orderAllocation(self, score):
num_states = 1 << len(score)
# dp[i][j] = Driver i is assigned to state j
dp = [[0] * num_states for _ in range(len(score))]
max_score = 0
last_order = -1
allocation = [[-1] * num_states for _ in range(len(score))]
for i in range(len(score)):
bit_index = 1 << i
dp[0][bit_index] = score[i][0]
allocation[0][bit_index] = i
for i in range(2, len(score) + 1):
for j in range(num_states + 1):
if self.num_of_ones(j) != i:
continue
for k in range(len(score)):
if j & (1 << k) == 0:
continue
prev_state = j ^ (1 << k)
if dp[i - 2][prev_state] + score[k][i - 1] > dp[i - 1][j]:
dp[i - 1][j] = dp[i - 2][prev_state] + score[k][i - 1]
allocation[i - 1][j] = k
driver_to_order = [-1] * len(score)
last_state = num_states - 1
for i in range(len(score) - 1, -1, -1):
driver_to_order[i] = allocation[i][last_state]
last_state = (1 << driver_to_order[i]) ^ last_state
order_to_driver = [-1] * len(score)
for driver, order in enumerate(driver_to_order):
order_to_driver[order] = driver
return order_to_driver
def num_of_ones(self, state):
num_of_ones = 0
while state > 0:
state -= self.lowbit(state)
num_of_ones += 1
return num_of_ones
def lowbit(self, state):
return state & (-state)
| [
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] | |
9fb989048567eb5db15c515f5ce3ba6801b857bf | f09dc121f213f2881df3572288b7ee5b39246d73 | /aliyun-python-sdk-ccc/aliyunsdkccc/request/v20170705/CreateCabInstanceRequest.py | 47923c7bc2c403b3b77dcab96630ccdb24c8801c | [
"Apache-2.0"
] | permissive | hetw/aliyun-openapi-python-sdk | 2f31378ad6be0896fb8090423f607e9c7d3ae774 | 7443eacee9fbbaa93c7975c6dbec92d3c364c577 | refs/heads/master | 2023-01-19T22:42:36.214770 | 2020-12-04T10:55:14 | 2020-12-04T10:55:14 | 318,689,093 | 1 | 0 | NOASSERTION | 2020-12-05T03:03:03 | 2020-12-05T03:03:03 | null | UTF-8 | Python | false | false | 2,170 | py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from aliyunsdkcore.request import RpcRequest
from aliyunsdkccc.endpoint import endpoint_data
class CreateCabInstanceRequest(RpcRequest):
def __init__(self):
RpcRequest.__init__(self, 'CCC', '2017-07-05', 'CreateCabInstance')
self.set_method('POST')
if hasattr(self, "endpoint_map"):
setattr(self, "endpoint_map", endpoint_data.getEndpointMap())
if hasattr(self, "endpoint_regional"):
setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional())
def get_MaxConcurrentConversation(self):
return self.get_query_params().get('MaxConcurrentConversation')
def set_MaxConcurrentConversation(self,MaxConcurrentConversation):
self.add_query_param('MaxConcurrentConversation',MaxConcurrentConversation)
def get_InstanceName(self):
return self.get_query_params().get('InstanceName')
def set_InstanceName(self,InstanceName):
self.add_query_param('InstanceName',InstanceName)
def get_CallCenterInstanceId(self):
return self.get_query_params().get('CallCenterInstanceId')
def set_CallCenterInstanceId(self,CallCenterInstanceId):
self.add_query_param('CallCenterInstanceId',CallCenterInstanceId)
def get_InstanceDescription(self):
return self.get_query_params().get('InstanceDescription')
def set_InstanceDescription(self,InstanceDescription):
self.add_query_param('InstanceDescription',InstanceDescription) | [
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] | |
b13046ceab6991b6f1d95e04fbe41b7ce103755b | 888899f0cb3e6e7b28a9de39001a1fd1c177cd35 | /COMPLETE PYTHON-3 COURSE/Chapter-05-LIST/summary.py | 857ad7b8e6e4441e7a901e9c5b8d6851a307ef48 | [] | no_license | VivakaNand/COMPLETE_PYTHON_3 | ef162d71d3a44bf661fcc1a8aacce31e7953cd7c | b3b835afe7671fdc3d29d912650fd4ccd3bc83f6 | refs/heads/master | 2023-02-04T10:13:41.881939 | 2020-12-23T08:30:51 | 2020-12-23T08:30:51 | 323,839,528 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 1,027 | py | # list chapter summary
# list is a data structure that can hold any type of data
# create list
words = ["word1", "word2"]
# you can store anything inside list
mixed = [1,2,3, [4,5,6], 'seven', 8.0,None]
# list is ordered collection of items
# print(mixed[0]) # output = 1
# print(mixed[3]) # output = [4,5,6]
# add data to our list
# append method
# mixed.append("10")
# mixed.append([10,20,30]) # it adds as it list at the end as one element
# print(mixed)
# extend method
# mixed.extend([10,20,30]) # it adds all elements of list at the end
# print(mixed)
# join method
# join two list
# l = l1 + l2
# insert method
# mixed.insert(1, 'inserted') # it adds elements in the specefic position
# print(mixed)
# remove data from list
# # pop method
# poped = mixed.pop() # removes last item
# popped = mixed.pop(1) # remove item at 1 position
# print(poped)
# print(popped)
#remove method
# mixed.remove('seven')
# print(mixed)
# del statement
# del mixed[3]
# print(mixed)
# loop in list
for i in mixed:
print(i) | [
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] | |
6f331f833f6106821b1fbc0630bb3491154a5ed3 | 28a9cc19537f7264421afeb9883962aa480c2616 | /login/models.py | dfc8567340a7277a6b236ffa42c5bf8ad2a3ca0c | [] | no_license | ujjwalagrawal17/BrokerAppBackend | b33df886b389aabfcfe7278c3e41c99d13d4fbb3 | 1b8ffd18e4c5257d222c17b8aece3351b549b204 | refs/heads/master | 2021-01-22T21:23:18.807792 | 2017-03-18T19:06:44 | 2017-03-18T19:06:44 | 85,425,430 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 459 | py | from __future__ import unicode_literals
from django.db import models
# Create your models here.
class login_user(models.Model):
name= models.CharField(max_length=120,null=False,blank=False)
firm_name= models.CharField(max_length=240,null=False,blank=False)
city= models.CharField(max_length=240,null=False,blank=False)
mobile=models.PositiveSmallIntegerField(default=0)
catigory=models.CharField(max_length=120,null=False,blank=False,default="buyer")
| [
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] | |
e35356aad8d52ce034b950fa1b84a9f27923a533 | c5759366f8b2cb2e129df0637b62774225a0c41a | /code/tensor2tensor/tensor2tensor/data_generators/text_encoder_build_subword.py | 89c6b9516e982d110e466e5b73735fd4f1e123fe | [
"Apache-2.0"
] | permissive | cake-lab/transient-deep-learning | f8646a4386528aa147d8d3dcdff8089985870041 | 87c6717e4026801623cf0327e78ad57f51cb1461 | refs/heads/master | 2022-11-02T20:02:29.642997 | 2022-02-08T16:51:09 | 2022-02-08T16:51:09 | 227,036,173 | 11 | 1 | Apache-2.0 | 2022-10-05T13:01:38 | 2019-12-10T05:27:50 | Python | UTF-8 | Python | false | false | 2,973 | py | # coding=utf-8
# Copyright 2018 The Tensor2Tensor 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.
r"""Program to build a SubwordTextEncoder.
The flags --min_count and --corpus_max_lines will affect the size of the
vocabulary. Try changing these flags until you get a vocabulary
of the size you want.
Example usage:
python data_generators/text_encoder_build_subword.py \
--corpus_filepattern=$DATA_DIR/my_problem-train-* \
--corpus_max_lines=12345 \
--output_filename=$DATA_DIR/my_problem.subword_text_encoder \
--logtostderr
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensor2tensor.data_generators import text_encoder
from tensor2tensor.data_generators import tokenizer
import tensorflow as tf
tf.flags.DEFINE_string('output_filename', '/tmp/my.subword_text_encoder',
'where to store the SubwordTextEncoder')
tf.flags.DEFINE_string('corpus_filepattern', '',
'Corpus of one or more text files')
tf.flags.DEFINE_string('vocab_filepattern', '', 'One or more vocabulary files '
'(one word per line as "word,count")')
tf.flags.DEFINE_integer('min_count', 5, 'Minimum subtoken count in corpus')
tf.flags.DEFINE_integer('corpus_max_lines', 10000,
'How many lines of corpus to read')
tf.flags.DEFINE_integer('num_iterations', 4, 'Number of iterations')
tf.flags.DEFINE_bool('split_on_newlines', True, 'Break corpus into lines.')
FLAGS = tf.flags.FLAGS
def main(unused_argv):
if FLAGS.corpus_filepattern and FLAGS.vocab_filepattern:
raise ValueError(
'Must only provide one of --corpus_filepattern or --vocab_filepattern')
elif FLAGS.corpus_filepattern:
token_counts = tokenizer.corpus_token_counts(
FLAGS.corpus_filepattern,
FLAGS.corpus_max_lines,
split_on_newlines=FLAGS.split_on_newlines)
elif FLAGS.vocab_filepattern:
token_counts = tokenizer.vocab_token_counts(FLAGS.vocab_filepattern,
FLAGS.corpus_max_lines)
else:
raise ValueError(
'Must provide one of --corpus_filepattern or --vocab_filepattern')
encoder = text_encoder.SubwordTextEncoder()
encoder.build_from_token_counts(token_counts, FLAGS.min_count,
FLAGS.num_iterations)
encoder.store_to_file(FLAGS.output_filename)
if __name__ == '__main__':
tf.app.run()
| [
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] | |
ff77547cc6d5321804ab90dbd2386f9f3b515921 | fff54b01b46cef0bbc70a6469c88c01c82af5a57 | /network/library/glib-networking/actions.py | 660dea0d9d3cf5ffb5633a05765dd140c9dcdf02 | [] | no_license | LimeLinux/Packages | e51deae6c0d1406e31f06caa5aaa7749466bef0b | d492e075d8b051df68b98c315ad0628e33a8fac4 | refs/heads/master | 2021-01-11T12:37:22.150638 | 2018-08-30T18:24:32 | 2018-08-30T18:24:32 | 77,054,292 | 5 | 19 | null | 2018-02-02T17:24:06 | 2016-12-21T13:33:45 | Python | UTF-8 | Python | false | false | 725 | py | #!/usr/bin/python
# -*- coding: utf-8 -*-
# Licensed under the GNU General Public License, version 3.
# See the file http://www.gnu.org/copyleft/gpl.txt
from pisi.actionsapi import get
from pisi.actionsapi import autotools
from pisi.actionsapi import pisitools
def setup():
autotools.configure("--disable-static \
--disable-installed-tests \
--with-ca-certificates=/etc/ssl/certs/ca-certificates.crt \
--with-gnutls \
--with-pkcs11")
def build():
autotools.make()
def install():
autotools.rawInstall("DESTDIR=%s" % get.installDIR())
pisitools.dodoc("AUTHORS", "ChangeLog", "COPYING", "NEWS", "README")
| [
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] | |
744dd80c6dd301c986dfd766d02f90b8df0c7590 | 2af6a5c2d33e2046a1d25ae9dd66d349d3833940 | /res/scripts/client/gui/scaleform/daapi/view/lobby/historicalbattles/__init__.py | 319e96efe3c7f12b469f2b5042230f76267adc3d | [] | no_license | webiumsk/WOT-0.9.12-CT | e6c8b5bb106fad71b5c3056ada59fb1aebc5f2b2 | 2506e34bd6634ad500b6501f4ed4f04af3f43fa0 | refs/heads/master | 2021-01-10T01:38:38.080814 | 2015-11-11T00:08:04 | 2015-11-11T00:08:04 | 45,803,240 | 0 | 0 | null | null | null | null | WINDOWS-1250 | Python | false | false | 1,685 | py | # 2015.11.10 21:27:13 Střední Evropa (běžný čas)
# Embedded file name: scripts/client/gui/Scaleform/daapi/view/lobby/historicalBattles/__init__.py
from gui.Scaleform.framework import GroupedViewSettings, ViewTypes, ScopeTemplates
from gui.Scaleform.framework.package_layout import PackageBusinessHandler
from gui.Scaleform.genConsts.PREBATTLE_ALIASES import PREBATTLE_ALIASES
from gui.app_loader.settings import APP_NAME_SPACE
from gui.shared import EVENT_BUS_SCOPE
def getViewSettings():
from gui.Scaleform.daapi.view.lobby.historicalBattles.HistoricalBattlesListWindow import HistoricalBattlesListWindow
return (GroupedViewSettings(PREBATTLE_ALIASES.HISTORICAL_BATTLES_LIST_WINDOW_PY, HistoricalBattlesListWindow, 'historicalBattlesListWindow.swf', ViewTypes.WINDOW, '', PREBATTLE_ALIASES.HISTORICAL_BATTLES_LIST_WINDOW_PY, ScopeTemplates.DEFAULT_SCOPE, True),)
def getBusinessHandlers():
return (_HistoricalBattlesBusinessHandler(),)
class _HistoricalBattlesBusinessHandler(PackageBusinessHandler):
def __init__(self):
listeners = ((PREBATTLE_ALIASES.HISTORICAL_BATTLES_LIST_WINDOW_PY, self.__showHBListWindow),)
super(_HistoricalBattlesBusinessHandler, self).__init__(listeners, APP_NAME_SPACE.SF_LOBBY, EVENT_BUS_SCOPE.LOBBY)
def __showHBListWindow(self, _):
alias = name = PREBATTLE_ALIASES.HISTORICAL_BATTLES_LIST_WINDOW_PY
self.loadViewWithDefName(alias, name)
# okay decompyling c:\Users\PC\wotsources\files\originals\res\scripts\client\gui\scaleform\daapi\view\lobby\historicalbattles\__init__.pyc
# decompiled 1 files: 1 okay, 0 failed, 0 verify failed
# 2015.11.10 21:27:13 Střední Evropa (běžný čas)
| [
"[email protected]"
] | |
aec9135ff3f8ea294de2287b9c3fb015c1842ecb | 9c9abdf101ce10d170de060155d7e96b244112eb | /logicmind/tokens/nop.py | d17faec06205634f0c31c421b359bde0fbb21eb9 | [
"MIT"
] | permissive | gridl/Py-Utils | b914aef6b527d5e24972c2b2559937ffe14f8f54 | 96e554ef4da7f9f94d405f523bd234db7dca96a7 | refs/heads/master | 2020-11-29T08:30:59.015303 | 2019-04-27T13:45:31 | 2019-04-27T13:45:31 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 299 | py | from tokens.token import Token
class Not(Token):
representations = ['¬', '!']
single_char_representation = '¬'
def __init__(self):
super().__init__(operands=1, precedence=1)
def apply(self, right):
return not right
def __repr__(self):
return '¬'
| [
"[email protected]"
] | |
e43b0767d3b8addee5a35fe4962d4ec12254d4cf | a6e4a6f0a73d24a6ba957277899adbd9b84bd594 | /sdk/python/pulumi_azure_native/documentdb/v20210115/get_database_account.py | 8f4d59344f5e4492cb41eab079c01a343bb0963e | [
"BSD-3-Clause",
"Apache-2.0"
] | permissive | MisinformedDNA/pulumi-azure-native | 9cbd75306e9c8f92abc25be3f73c113cb93865e9 | de974fd984f7e98649951dbe80b4fc0603d03356 | refs/heads/master | 2023-03-24T22:02:03.842935 | 2021-03-08T21:16:19 | 2021-03-08T21:16:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 23,607 | py | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
from . import outputs
__all__ = [
'GetDatabaseAccountResult',
'AwaitableGetDatabaseAccountResult',
'get_database_account',
]
@pulumi.output_type
class GetDatabaseAccountResult:
"""
An Azure Cosmos DB database account.
"""
def __init__(__self__, api_properties=None, backup_policy=None, capabilities=None, connector_offer=None, consistency_policy=None, cors=None, database_account_offer_type=None, disable_key_based_metadata_write_access=None, document_endpoint=None, enable_analytical_storage=None, enable_automatic_failover=None, enable_cassandra_connector=None, enable_free_tier=None, enable_multiple_write_locations=None, failover_policies=None, id=None, identity=None, ip_rules=None, is_virtual_network_filter_enabled=None, key_vault_key_uri=None, kind=None, location=None, locations=None, name=None, network_acl_bypass=None, network_acl_bypass_resource_ids=None, private_endpoint_connections=None, provisioning_state=None, public_network_access=None, read_locations=None, tags=None, type=None, virtual_network_rules=None, write_locations=None):
if api_properties and not isinstance(api_properties, dict):
raise TypeError("Expected argument 'api_properties' to be a dict")
pulumi.set(__self__, "api_properties", api_properties)
if backup_policy and not isinstance(backup_policy, dict):
raise TypeError("Expected argument 'backup_policy' to be a dict")
pulumi.set(__self__, "backup_policy", backup_policy)
if capabilities and not isinstance(capabilities, list):
raise TypeError("Expected argument 'capabilities' to be a list")
pulumi.set(__self__, "capabilities", capabilities)
if connector_offer and not isinstance(connector_offer, str):
raise TypeError("Expected argument 'connector_offer' to be a str")
pulumi.set(__self__, "connector_offer", connector_offer)
if consistency_policy and not isinstance(consistency_policy, dict):
raise TypeError("Expected argument 'consistency_policy' to be a dict")
pulumi.set(__self__, "consistency_policy", consistency_policy)
if cors and not isinstance(cors, list):
raise TypeError("Expected argument 'cors' to be a list")
pulumi.set(__self__, "cors", cors)
if database_account_offer_type and not isinstance(database_account_offer_type, str):
raise TypeError("Expected argument 'database_account_offer_type' to be a str")
pulumi.set(__self__, "database_account_offer_type", database_account_offer_type)
if disable_key_based_metadata_write_access and not isinstance(disable_key_based_metadata_write_access, bool):
raise TypeError("Expected argument 'disable_key_based_metadata_write_access' to be a bool")
pulumi.set(__self__, "disable_key_based_metadata_write_access", disable_key_based_metadata_write_access)
if document_endpoint and not isinstance(document_endpoint, str):
raise TypeError("Expected argument 'document_endpoint' to be a str")
pulumi.set(__self__, "document_endpoint", document_endpoint)
if enable_analytical_storage and not isinstance(enable_analytical_storage, bool):
raise TypeError("Expected argument 'enable_analytical_storage' to be a bool")
pulumi.set(__self__, "enable_analytical_storage", enable_analytical_storage)
if enable_automatic_failover and not isinstance(enable_automatic_failover, bool):
raise TypeError("Expected argument 'enable_automatic_failover' to be a bool")
pulumi.set(__self__, "enable_automatic_failover", enable_automatic_failover)
if enable_cassandra_connector and not isinstance(enable_cassandra_connector, bool):
raise TypeError("Expected argument 'enable_cassandra_connector' to be a bool")
pulumi.set(__self__, "enable_cassandra_connector", enable_cassandra_connector)
if enable_free_tier and not isinstance(enable_free_tier, bool):
raise TypeError("Expected argument 'enable_free_tier' to be a bool")
pulumi.set(__self__, "enable_free_tier", enable_free_tier)
if enable_multiple_write_locations and not isinstance(enable_multiple_write_locations, bool):
raise TypeError("Expected argument 'enable_multiple_write_locations' to be a bool")
pulumi.set(__self__, "enable_multiple_write_locations", enable_multiple_write_locations)
if failover_policies and not isinstance(failover_policies, list):
raise TypeError("Expected argument 'failover_policies' to be a list")
pulumi.set(__self__, "failover_policies", failover_policies)
if id and not isinstance(id, str):
raise TypeError("Expected argument 'id' to be a str")
pulumi.set(__self__, "id", id)
if identity and not isinstance(identity, dict):
raise TypeError("Expected argument 'identity' to be a dict")
pulumi.set(__self__, "identity", identity)
if ip_rules and not isinstance(ip_rules, list):
raise TypeError("Expected argument 'ip_rules' to be a list")
pulumi.set(__self__, "ip_rules", ip_rules)
if is_virtual_network_filter_enabled and not isinstance(is_virtual_network_filter_enabled, bool):
raise TypeError("Expected argument 'is_virtual_network_filter_enabled' to be a bool")
pulumi.set(__self__, "is_virtual_network_filter_enabled", is_virtual_network_filter_enabled)
if key_vault_key_uri and not isinstance(key_vault_key_uri, str):
raise TypeError("Expected argument 'key_vault_key_uri' to be a str")
pulumi.set(__self__, "key_vault_key_uri", key_vault_key_uri)
if kind and not isinstance(kind, str):
raise TypeError("Expected argument 'kind' to be a str")
pulumi.set(__self__, "kind", kind)
if location and not isinstance(location, str):
raise TypeError("Expected argument 'location' to be a str")
pulumi.set(__self__, "location", location)
if locations and not isinstance(locations, list):
raise TypeError("Expected argument 'locations' to be a list")
pulumi.set(__self__, "locations", locations)
if name and not isinstance(name, str):
raise TypeError("Expected argument 'name' to be a str")
pulumi.set(__self__, "name", name)
if network_acl_bypass and not isinstance(network_acl_bypass, str):
raise TypeError("Expected argument 'network_acl_bypass' to be a str")
pulumi.set(__self__, "network_acl_bypass", network_acl_bypass)
if network_acl_bypass_resource_ids and not isinstance(network_acl_bypass_resource_ids, list):
raise TypeError("Expected argument 'network_acl_bypass_resource_ids' to be a list")
pulumi.set(__self__, "network_acl_bypass_resource_ids", network_acl_bypass_resource_ids)
if private_endpoint_connections and not isinstance(private_endpoint_connections, list):
raise TypeError("Expected argument 'private_endpoint_connections' to be a list")
pulumi.set(__self__, "private_endpoint_connections", private_endpoint_connections)
if provisioning_state and not isinstance(provisioning_state, str):
raise TypeError("Expected argument 'provisioning_state' to be a str")
pulumi.set(__self__, "provisioning_state", provisioning_state)
if public_network_access and not isinstance(public_network_access, str):
raise TypeError("Expected argument 'public_network_access' to be a str")
pulumi.set(__self__, "public_network_access", public_network_access)
if read_locations and not isinstance(read_locations, list):
raise TypeError("Expected argument 'read_locations' to be a list")
pulumi.set(__self__, "read_locations", read_locations)
if tags and not isinstance(tags, dict):
raise TypeError("Expected argument 'tags' to be a dict")
pulumi.set(__self__, "tags", tags)
if type and not isinstance(type, str):
raise TypeError("Expected argument 'type' to be a str")
pulumi.set(__self__, "type", type)
if virtual_network_rules and not isinstance(virtual_network_rules, list):
raise TypeError("Expected argument 'virtual_network_rules' to be a list")
pulumi.set(__self__, "virtual_network_rules", virtual_network_rules)
if write_locations and not isinstance(write_locations, list):
raise TypeError("Expected argument 'write_locations' to be a list")
pulumi.set(__self__, "write_locations", write_locations)
@property
@pulumi.getter(name="apiProperties")
def api_properties(self) -> Optional['outputs.ApiPropertiesResponse']:
"""
API specific properties.
"""
return pulumi.get(self, "api_properties")
@property
@pulumi.getter(name="backupPolicy")
def backup_policy(self) -> Optional[Any]:
"""
The object representing the policy for taking backups on an account.
"""
return pulumi.get(self, "backup_policy")
@property
@pulumi.getter
def capabilities(self) -> Optional[Sequence['outputs.CapabilityResponse']]:
"""
List of Cosmos DB capabilities for the account
"""
return pulumi.get(self, "capabilities")
@property
@pulumi.getter(name="connectorOffer")
def connector_offer(self) -> Optional[str]:
"""
The cassandra connector offer type for the Cosmos DB database C* account.
"""
return pulumi.get(self, "connector_offer")
@property
@pulumi.getter(name="consistencyPolicy")
def consistency_policy(self) -> Optional['outputs.ConsistencyPolicyResponse']:
"""
The consistency policy for the Cosmos DB database account.
"""
return pulumi.get(self, "consistency_policy")
@property
@pulumi.getter
def cors(self) -> Optional[Sequence['outputs.CorsPolicyResponse']]:
"""
The CORS policy for the Cosmos DB database account.
"""
return pulumi.get(self, "cors")
@property
@pulumi.getter(name="databaseAccountOfferType")
def database_account_offer_type(self) -> str:
"""
The offer type for the Cosmos DB database account. Default value: Standard.
"""
return pulumi.get(self, "database_account_offer_type")
@property
@pulumi.getter(name="disableKeyBasedMetadataWriteAccess")
def disable_key_based_metadata_write_access(self) -> Optional[bool]:
"""
Disable write operations on metadata resources (databases, containers, throughput) via account keys
"""
return pulumi.get(self, "disable_key_based_metadata_write_access")
@property
@pulumi.getter(name="documentEndpoint")
def document_endpoint(self) -> str:
"""
The connection endpoint for the Cosmos DB database account.
"""
return pulumi.get(self, "document_endpoint")
@property
@pulumi.getter(name="enableAnalyticalStorage")
def enable_analytical_storage(self) -> Optional[bool]:
"""
Flag to indicate whether to enable storage analytics.
"""
return pulumi.get(self, "enable_analytical_storage")
@property
@pulumi.getter(name="enableAutomaticFailover")
def enable_automatic_failover(self) -> Optional[bool]:
"""
Enables automatic failover of the write region in the rare event that the region is unavailable due to an outage. Automatic failover will result in a new write region for the account and is chosen based on the failover priorities configured for the account.
"""
return pulumi.get(self, "enable_automatic_failover")
@property
@pulumi.getter(name="enableCassandraConnector")
def enable_cassandra_connector(self) -> Optional[bool]:
"""
Enables the cassandra connector on the Cosmos DB C* account
"""
return pulumi.get(self, "enable_cassandra_connector")
@property
@pulumi.getter(name="enableFreeTier")
def enable_free_tier(self) -> Optional[bool]:
"""
Flag to indicate whether Free Tier is enabled.
"""
return pulumi.get(self, "enable_free_tier")
@property
@pulumi.getter(name="enableMultipleWriteLocations")
def enable_multiple_write_locations(self) -> Optional[bool]:
"""
Enables the account to write in multiple locations
"""
return pulumi.get(self, "enable_multiple_write_locations")
@property
@pulumi.getter(name="failoverPolicies")
def failover_policies(self) -> Sequence['outputs.FailoverPolicyResponse']:
"""
An array that contains the regions ordered by their failover priorities.
"""
return pulumi.get(self, "failover_policies")
@property
@pulumi.getter
def id(self) -> str:
"""
The unique resource identifier of the ARM resource.
"""
return pulumi.get(self, "id")
@property
@pulumi.getter
def identity(self) -> Optional['outputs.ManagedServiceIdentityResponse']:
"""
Identity for the resource.
"""
return pulumi.get(self, "identity")
@property
@pulumi.getter(name="ipRules")
def ip_rules(self) -> Optional[Sequence['outputs.IpAddressOrRangeResponse']]:
"""
List of IpRules.
"""
return pulumi.get(self, "ip_rules")
@property
@pulumi.getter(name="isVirtualNetworkFilterEnabled")
def is_virtual_network_filter_enabled(self) -> Optional[bool]:
"""
Flag to indicate whether to enable/disable Virtual Network ACL rules.
"""
return pulumi.get(self, "is_virtual_network_filter_enabled")
@property
@pulumi.getter(name="keyVaultKeyUri")
def key_vault_key_uri(self) -> Optional[str]:
"""
The URI of the key vault
"""
return pulumi.get(self, "key_vault_key_uri")
@property
@pulumi.getter
def kind(self) -> Optional[str]:
"""
Indicates the type of database account. This can only be set at database account creation.
"""
return pulumi.get(self, "kind")
@property
@pulumi.getter
def location(self) -> Optional[str]:
"""
The location of the resource group to which the resource belongs.
"""
return pulumi.get(self, "location")
@property
@pulumi.getter
def locations(self) -> Sequence['outputs.LocationResponse']:
"""
An array that contains all of the locations enabled for the Cosmos DB account.
"""
return pulumi.get(self, "locations")
@property
@pulumi.getter
def name(self) -> str:
"""
The name of the ARM resource.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="networkAclBypass")
def network_acl_bypass(self) -> Optional[str]:
"""
Indicates what services are allowed to bypass firewall checks.
"""
return pulumi.get(self, "network_acl_bypass")
@property
@pulumi.getter(name="networkAclBypassResourceIds")
def network_acl_bypass_resource_ids(self) -> Optional[Sequence[str]]:
"""
An array that contains the Resource Ids for Network Acl Bypass for the Cosmos DB account.
"""
return pulumi.get(self, "network_acl_bypass_resource_ids")
@property
@pulumi.getter(name="privateEndpointConnections")
def private_endpoint_connections(self) -> Sequence['outputs.PrivateEndpointConnectionResponse']:
"""
List of Private Endpoint Connections configured for the Cosmos DB account.
"""
return pulumi.get(self, "private_endpoint_connections")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> str:
"""
The status of the Cosmos DB account at the time the operation was called. The status can be one of following. 'Creating' – the Cosmos DB account is being created. When an account is in Creating state, only properties that are specified as input for the Create Cosmos DB account operation are returned. 'Succeeded' – the Cosmos DB account is active for use. 'Updating' – the Cosmos DB account is being updated. 'Deleting' – the Cosmos DB account is being deleted. 'Failed' – the Cosmos DB account failed creation. 'DeletionFailed' – the Cosmos DB account deletion failed.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="publicNetworkAccess")
def public_network_access(self) -> Optional[str]:
"""
Whether requests from Public Network are allowed
"""
return pulumi.get(self, "public_network_access")
@property
@pulumi.getter(name="readLocations")
def read_locations(self) -> Sequence['outputs.LocationResponse']:
"""
An array that contains of the read locations enabled for the Cosmos DB account.
"""
return pulumi.get(self, "read_locations")
@property
@pulumi.getter
def tags(self) -> Optional[Mapping[str, str]]:
"""
Tags are a list of key-value pairs that describe the resource. These tags can be used in viewing and grouping this resource (across resource groups). A maximum of 15 tags can be provided for a resource. Each tag must have a key no greater than 128 characters and value no greater than 256 characters. For example, the default experience for a template type is set with "defaultExperience": "Cassandra". Current "defaultExperience" values also include "Table", "Graph", "DocumentDB", and "MongoDB".
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter
def type(self) -> str:
"""
The type of Azure resource.
"""
return pulumi.get(self, "type")
@property
@pulumi.getter(name="virtualNetworkRules")
def virtual_network_rules(self) -> Optional[Sequence['outputs.VirtualNetworkRuleResponse']]:
"""
List of Virtual Network ACL rules configured for the Cosmos DB account.
"""
return pulumi.get(self, "virtual_network_rules")
@property
@pulumi.getter(name="writeLocations")
def write_locations(self) -> Sequence['outputs.LocationResponse']:
"""
An array that contains the write location for the Cosmos DB account.
"""
return pulumi.get(self, "write_locations")
class AwaitableGetDatabaseAccountResult(GetDatabaseAccountResult):
# pylint: disable=using-constant-test
def __await__(self):
if False:
yield self
return GetDatabaseAccountResult(
api_properties=self.api_properties,
backup_policy=self.backup_policy,
capabilities=self.capabilities,
connector_offer=self.connector_offer,
consistency_policy=self.consistency_policy,
cors=self.cors,
database_account_offer_type=self.database_account_offer_type,
disable_key_based_metadata_write_access=self.disable_key_based_metadata_write_access,
document_endpoint=self.document_endpoint,
enable_analytical_storage=self.enable_analytical_storage,
enable_automatic_failover=self.enable_automatic_failover,
enable_cassandra_connector=self.enable_cassandra_connector,
enable_free_tier=self.enable_free_tier,
enable_multiple_write_locations=self.enable_multiple_write_locations,
failover_policies=self.failover_policies,
id=self.id,
identity=self.identity,
ip_rules=self.ip_rules,
is_virtual_network_filter_enabled=self.is_virtual_network_filter_enabled,
key_vault_key_uri=self.key_vault_key_uri,
kind=self.kind,
location=self.location,
locations=self.locations,
name=self.name,
network_acl_bypass=self.network_acl_bypass,
network_acl_bypass_resource_ids=self.network_acl_bypass_resource_ids,
private_endpoint_connections=self.private_endpoint_connections,
provisioning_state=self.provisioning_state,
public_network_access=self.public_network_access,
read_locations=self.read_locations,
tags=self.tags,
type=self.type,
virtual_network_rules=self.virtual_network_rules,
write_locations=self.write_locations)
def get_database_account(account_name: Optional[str] = None,
resource_group_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetDatabaseAccountResult:
"""
An Azure Cosmos DB database account.
:param str account_name: Cosmos DB database account name.
:param str resource_group_name: The name of the resource group. The name is case insensitive.
"""
__args__ = dict()
__args__['accountName'] = account_name
__args__['resourceGroupName'] = resource_group_name
if opts is None:
opts = pulumi.InvokeOptions()
if opts.version is None:
opts.version = _utilities.get_version()
__ret__ = pulumi.runtime.invoke('azure-native:documentdb/v20210115:getDatabaseAccount', __args__, opts=opts, typ=GetDatabaseAccountResult).value
return AwaitableGetDatabaseAccountResult(
api_properties=__ret__.api_properties,
backup_policy=__ret__.backup_policy,
capabilities=__ret__.capabilities,
connector_offer=__ret__.connector_offer,
consistency_policy=__ret__.consistency_policy,
cors=__ret__.cors,
database_account_offer_type=__ret__.database_account_offer_type,
disable_key_based_metadata_write_access=__ret__.disable_key_based_metadata_write_access,
document_endpoint=__ret__.document_endpoint,
enable_analytical_storage=__ret__.enable_analytical_storage,
enable_automatic_failover=__ret__.enable_automatic_failover,
enable_cassandra_connector=__ret__.enable_cassandra_connector,
enable_free_tier=__ret__.enable_free_tier,
enable_multiple_write_locations=__ret__.enable_multiple_write_locations,
failover_policies=__ret__.failover_policies,
id=__ret__.id,
identity=__ret__.identity,
ip_rules=__ret__.ip_rules,
is_virtual_network_filter_enabled=__ret__.is_virtual_network_filter_enabled,
key_vault_key_uri=__ret__.key_vault_key_uri,
kind=__ret__.kind,
location=__ret__.location,
locations=__ret__.locations,
name=__ret__.name,
network_acl_bypass=__ret__.network_acl_bypass,
network_acl_bypass_resource_ids=__ret__.network_acl_bypass_resource_ids,
private_endpoint_connections=__ret__.private_endpoint_connections,
provisioning_state=__ret__.provisioning_state,
public_network_access=__ret__.public_network_access,
read_locations=__ret__.read_locations,
tags=__ret__.tags,
type=__ret__.type,
virtual_network_rules=__ret__.virtual_network_rules,
write_locations=__ret__.write_locations)
| [
"[email protected]"
] | |
bdc08ee0779b38395377347f0abedd95471a6d05 | 65a70271f97f6760996fb87df95b18186b8dde8c | /__main__.py | 5d8df591955a25d858ae1d8f3ce6df3931de37de | [] | no_license | sebbekarlsson/bethins | f3d843ee092e8b3245de4c03dc53a6e57f5bcac7 | efc394a785231a60e6cd9068a0f0ea76deb3a902 | refs/heads/master | 2016-09-14T09:48:10.193590 | 2016-04-29T19:31:13 | 2016-04-29T19:31:13 | 57,068,864 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 81 | py | from bethins.app import app
if __name__ == '__main__':
app.run(debug=True)
| [
"[email protected]"
] | |
5cd3e967959c0a4211a1c671d6336fbd4c832a7a | 3288a3e1ac9fe24260e6eb3e54234cf1a9c6e33a | /model/rage.py | 3941517a4a82de7a3cddb1758bb223a744cab090 | [] | no_license | phamdinhkhanh/alltherage | 691ea098cb485df84db230af1f0bb376e1a8201f | 94f253dbc5b830dc9d1b76680c9b41a05a6c3f16 | refs/heads/master | 2021-01-23T14:49:57.214474 | 2017-07-30T09:23:38 | 2017-07-30T09:23:38 | 93,261,643 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,282 | py | from mongoengine import *
from flask_restful import Resource, reqparse
import mlab
class Rage(Document):
name = StringField();
url = StringField();
description = StringField();
old_price = FloatField();
new_price = FloatField();
discount_rate = FloatField();
is_favorite = BooleanField();
number_seen = IntField();
code = StringField();
def get_json(self):
return mlab.item2json(self)
def get_oid(self):
str = mlab.item2json(self)
oid = str["_id"]["$oid"]
return {
"$oid":oid
}
def get_json_oid(self):
str = mlab.item2json(self)
oid = str["_id"]["$oid"]
return {
"oid": oid,
"name": self.name,
"url": self.url,
"description": self.description,
"old_price": self.old_price,
"new_price": self.new_price,
"discount_rate": self.discount_rate,
"is_favorite": self.is_favorite,
"number_seen":self.number_seen,
"code":self.code
}
class RageInfo(Document):
rage = ReferenceField("Rage");
info = StringField();
def get_json(self):
return {
"rage":self.rage.get_json(),
"info":self.info
}
| [
"[email protected]"
] | |
a05f388b7fed9deac9f7b8e1e5e439e90ec715a9 | 84d2efd222fa190c8b3efcad083dcf2c7ab30047 | /test.py | fc24c2054c5968948fcc906e963e832aa2a418a6 | [] | no_license | webclinic017/Capstone-2 | aedfc8692647f2e84114da5b2e32856d0de80586 | d476723f7893c7c5da14e24f28736a8f0ba7ff55 | refs/heads/master | 2023-01-23T06:44:36.868373 | 2020-12-03T19:44:51 | 2020-12-03T19:44:51 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,642 | py | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import sklearn.linear_model
import scipy.stats as stats
import pandas_market_calendars as mcal
from alpha_vantage.timeseries import TimeSeries
api_key = '8FIYTT49ZEZT2GV5'
ts = TimeSeries(key=api_key, output_format='pandas')
data, meta_data = ts.get_daily(symbol='SPY', outputsize = 'full')
data = data.reset_index()
data['date'] = data['date'].dt.strftime('%Y%m%d')
data['date'] = data['date'].values.astype(int)
X = np.c_[data['date']]
Y = np.c_[data['4. close']]
X = [i[0] for i in X]
Y = [i[0] for i in Y]
X = X[::-1] #REVERSING ORDER
Y = Y[::-1] #REVERSING ORDER
last_day = len(X) - 1
th_day = list(range(0,last_day+1))
def YYYY_MM_DD_to_th_day(YMD):
early = nyse.schedule(start_date='1999-11-01', end_date=YMD)
return len(early)-1
model = np.polyfit(X, Y, 4)
std = np.std(Y)
testdate = [[20210101]]
testprediction = np.polyval(model, testdate)
testprice = testprediction[0] + (1 * std)
zscore = float((testprice - testprediction[0]) / std)
probability = 0
cdf = stats.norm.cdf(zscore)
if(cdf <= .5):
probability = cdf
elif(cdf >= .5):
probability = 1-cdf
print(testprediction)
#NEW CODE STARTING BELOW
#OK, .01 std tests
#get the best trade per day
#choose...idk
'''
shortPrices = 0
longPrices = 0
currentPrice = Y[0]
reward = longPrices - currentPrice
risk = currentPrice - shortPrices
print(reward)
print(risk)
'''
#OPTIMIZATION TESTS
#ULTRA SHORT TERM (original: 20211218), TODAY : 360
#1st Degree : 255 in a week, 255 in a month, 263 in 6 months
#2nd Degree : 320 in a week, 320 in a month, 347 in 6 months
#3rd Degree : 325 in a week, 325 in a month, 356 in 6 months
#4th Degree : 323 in a week, 323 in a month, 351 in 6 months
#5th Degree : 323 in a week, 323 in a month, 351 in 6 months
#SHORT TERM
#1st Degree : 264 in 2021, 273 in 2022
#2nd Degree : 349 in 2021, 381 in 2022
#3rd Degree : 359 in 2021, 396 in 2022
#4th Degree : 353 in 2021, 385 in 2022
#5th Degree : 353 in 2021, 385 in 2022
#ULTRA LONG TERM (assuming downturns every 10 years)
#1st Degree : 344 in 2030, 434 in 2040
#2nd Degree : 704 in 2030, 1282 in 2040
#3rd Degree : 804 in 2030, 1652 in 2040
#4th Degree : 649 in 2030, 759 in 2040
#5th Degree : 648 in 2030, 745 in 2040
#COMPARISON TO EXTERNAL PREDICTIONS (11/26/25, $486)
#1st Degree: 300
#2nd Degree: 487
#3rd Degree: 523
#4th Degree: 484
#5th Degree: 484
#BEST RESULT : 4th Degree
#ACCURACY TEST
#Today: 0.5Z
#March Low : -1.5Z
#February High : 0.27Z
#2009 Low : -0.71Z
#2007 High : 0.66Z | [
"[email protected]"
] | |
fe41e57f2ed88a9306816bc86c1326ed3f15f4a5 | 853d7901c4bdc7db8e655092c9939741b4f86161 | /886.py | be36ff32f774dcec2059ca91e5828d87b1299df8 | [
"MIT"
] | permissive | wilbertgeng/LeetCode_exercise | 904d6a3f91d94f451b40f3760131aefaa8584b3b | f00c08e0d28ffa88d61d4262c6d1f49f1fa91ebc | refs/heads/main | 2023-03-16T01:25:00.514922 | 2021-03-15T06:12:59 | 2021-03-15T06:12:59 | 347,856,240 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,008 | py | """886. Possible Bipartition"""
class Solution(object):
def possibleBipartition(self, N, dislikes):
"""
:type N: int
:type dislikes: List[List[int]]
:rtype: bool
"""
seen = {}
self.graph = collections.defaultdict(list)
for (u, v) in dislikes: # Create graph
self.graph[u].append(v)
self.graph[v].append(u)
for i in range(1, N+1):
if i not in seen:
if self.check(seen, i, self.graph) == False:
return False
return True
def check(self, seen, i, graph):
q = [(i, 1)]
while q:
pos, color = q.pop(0)
if pos in seen:
if seen[pos] != color:
return False
continue
seen[pos] = color
vertices = graph[pos]
for v in vertices:
q.append((v, -color))
return True
| [
"[email protected]"
] | |
748c4782c7cd76f5ae63a10cc29668cecc5cb385 | 70fa4bc22afd3d0527888d382827c7c2e1269b8a | /examples/columbia_river_crossing.py | 263f0b7f942ff977e4ec41aba47835a0ecbf4025 | [
"BSD-2-Clause"
] | permissive | moorepants/EfficientRoutes | 20029f19ed5ec79d484660d8f963f4e78d2d899d | 2705b643b95cb7921dc3216d534aa5bdbff302a1 | refs/heads/master | 2020-04-06T06:47:52.698417 | 2012-06-28T09:12:30 | 2012-06-28T09:12:30 | 4,477,900 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,056 | py | #!/usr/bin/env python
# This example compares two routes connecting Portland, Oregon to Vancouver,
# Washington over a bridge across the Columbia River. The planned bicycle route
# sends the bicyclist through various elevation changes and several yield
# signs, stop signs, and traffic signals where as the automobiles get to travel
# across level ground with no stops. These simulations compare the bicyclist's
# energy expenditure, trip distance, and trip time through the two routes.
import numpy as np
from efficientroutes.model import Bicyclist, Route, Trip
# Load a bicyclist.
bicyclist = Bicyclist()
# Load in the the planned bicycle route data and process the traffic controls
# column.
bicycleRouteData = np.recfromcsv('../data/columbia_river_crossing_bicycle.csv')
stopLocations = []
for i, device in enumerate(bicycleRouteData['traffic_control']):
if device != '':
stopLocations.append(bicycleRouteData['distance'][i])
bicycleRoute = Route(bicycleRouteData['distance'],
bicycleRouteData['elevation'], bicycleRouteData['speed_limit'],
stopLocations=np.array(stopLocations))
# Setup and compute the results for the trip across the planned bicycle route.
bicycleTrip = Trip(bicyclist, bicycleRoute)
bicycleTrip.solve()
print "===================="
print "Bicycle route stats:"
print "===================="
bicycleTrip.stats()
bicycleFig = bicycleTrip.plot()
bicycleFig.suptitle('Bicycle Route')
bicycleFig.set_figheight(8.0)
bicycleFig.savefig('../data/columbia_river_crossing_bicycle.png', dpi=200)
bicycleFig.show()
# Load in the data for the automobile path.
autoRouteData = np.recfromcsv('../data/columbia_river_crossing_auto.csv')
autoRoute = Route(autoRouteData['distance'],
autoRouteData['elevation'], autoRouteData['speed_limit'] - 17.88)
# Setup and compute the results for the trip across the automobile route.
autoTrip = Trip(bicyclist, autoRoute)
autoTrip.solve()
print "======================="
print "Automobile route stats:"
print "======================="
autoTrip.stats()
autoFig = autoTrip.plot()
autoFig.suptitle('Automobile Route')
autoFig.set_figheight(8.0)
autoFig.savefig('../data/columbia_river_crossing_auto.png', dpi=200)
autoFig.show()
# Load in the data for the automobile path.
bestRouteData = np.recfromcsv('../data/columbia_river_crossing_best.csv')
stopLocations = []
for i, device in enumerate(bestRouteData['traffic_control']):
if device != '':
stopLocations.append(bestRouteData['distance'][i])
bestRoute = Route(bestRouteData['distance'],
bestRouteData['elevation'], bestRouteData['speed_limit'] - 17.88,
stopLocations=np.array(stopLocations))
# Setup and compute the results for the trip across the automobile route.
bestTrip = Trip(bicyclist, bestRoute)
bestTrip.solve()
print "================="
print "Best route stats:"
print "================="
bestTrip.stats()
bestFig = bestTrip.plot()
bestFig.suptitle('Best Route')
bestFig.set_figheight(8.0)
bestFig.savefig('../data/columbia_river_crossing_best.png', dpi=200)
bestFig.show()
| [
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] | |
399677d94f7dba5213292ea7db1d4bba220d5d29 | f394598dad4276f9667e702b7360ab14dcd10cfc | /unsolved/power_of_four.py | 81e77db6b544515bf8744ccd45c335929374d9f2 | [] | no_license | siowyisheng/python-problem-solving | fe9ded3761e637883467cb81abc01bcb0a54b589 | 7e32767a3ac710ecfc37f205ee35eda194400122 | refs/heads/master | 2020-03-21T19:57:56.118616 | 2019-06-28T08:12:09 | 2019-06-28T08:12:09 | 138,980,214 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 106 | py | Given a 32-bit positive integer N, determine whether it is a power of four in faster than O(log N) time.
| [
"[email protected]"
] | |
8aa1667e96ff01d3a43197b476b881ceb027dc8e | d3be0d693440c618d211bc3801a29b885041786a | /scripts/migrations/label_test.py | 45e6a66c1d0b77179e304048114bec7d94c2c009 | [
"Apache-2.0"
] | permissive | jimpallomeni/buck | 9479b048e59ee1d0a78b3c0c30cb98af61920fe3 | 0d752267ca1ea6f93ac1966bac75e6168df0254c | refs/heads/master | 2021-07-05T08:27:30.295952 | 2017-09-27T22:34:01 | 2017-09-28T00:18:46 | 105,082,899 | 0 | 0 | null | 2017-09-28T00:22:08 | 2017-09-28T00:22:08 | null | UTF-8 | Python | false | false | 1,358 | py | import label
import unittest
class LabelTest(unittest.TestCase):
def test_can_parse_full_label_from_string(self):
l = label.from_string('cell//package:name')
self.assertEqual(l.name, 'name')
self.assertEqual(l.package, 'package')
self.assertEqual(l.cell, 'cell')
def test_can_parse_label_without_cell(self):
l = label.from_string('//package:name')
self.assertEqual(l.name, 'name')
self.assertEqual(l.package, 'package')
self.assertIsNone(l.cell)
def test_can_parse_label_with_multilevel_package(self):
l = label.from_string('cell//pkg/subpkg:name')
self.assertEqual(l.name, 'name')
self.assertEqual(l.package, 'pkg/subpkg')
self.assertEqual(l.cell, 'cell')
def test_cannot_parse_invalid_label(self):
with self.assertRaisesRegex(AssertionError, "Invalid label 'cell/pkg:name'"):
label.from_string('cell/pkg:name')
def test_can_resolve_path_to_build_file(self):
l = label.from_string('cell//pkg:name')
cell_roots = {
'cell': '/repo/cell',
}
self.assertEqual('/repo/cell/pkg/BUCK', l.get_build_file_path(cell_roots, 'BUCK'))
def test_can_convert_to_import_string(self):
self.assertEqual('cell//pkg:name', label.from_string('cell//pkg:name').to_import_string())
| [
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] | |
6c7283f79ab27c859cffb7b7d39c93d67372cd59 | 164e0f43ef3ad4cb7f6b28dfdd2bfbaa66d38ce2 | /Word_Pattern/Word_Pattern.py | c8e8770d0ef80e9476f394041e584d25a6bd9e7b | [] | no_license | maoxx241/code | b217f2d10065d90f52cfa38788c99e238565b892 | 16e97ec5ee7ae9ffa69da2e001d15a86d73d2040 | refs/heads/master | 2021-07-11T14:25:35.098241 | 2020-11-25T14:01:56 | 2020-11-25T14:01:56 | 222,544,519 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 603 | py | class Solution:
def wordPattern(self, pattern: str, str: str) -> bool:
lst=str.split()
if len(lst)!=len(pattern):
return False
dic={}
for i,c in enumerate(pattern):
if c not in dic:
if lst[i] in dic.values():
return False
dic[c]=lst[i]
else:
if lst[i]!=dic[c]:
return False
return True
| [
"[email protected]"
] | |
e4e52e5407ba0039db5808125b456d675cb0959c | c387360f00fc0f7c36bb3f9b8bcff24ff2bc87d6 | /baekjoon_14918.py | 290dcc0aecad7532749df48895a36ad3936d3924 | [] | no_license | younkyounghwan/python | e8e8f2adce5e3005df7ba35298bafe04f02c6d33 | e76783d59bab871b3fd98c088296521d979b1303 | refs/heads/master | 2020-04-01T14:57:54.113092 | 2019-08-27T13:45:56 | 2019-08-27T13:45:56 | 153,315,237 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 42 | py | x, y = map(int,input().split())
print(x+y) | [
"[email protected]"
] | |
9ee85000310e262b188ffd12e9948483f3d681e7 | 7cb626363bbce2f66c09e509e562ff3d371c10c6 | /multimodel_inference/py3_v1/sc3elsm.py | 2d50cceedee31d0481ec347f123d4e6d48609f87 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | z0on/AFS-analysis-with-moments | 76bfd6b0361ab7e9173144dbd21b6fa2c7bf1795 | eea4735b3b6fbe31c4e396da3d798387884a1500 | refs/heads/master | 2023-07-31T20:49:20.865161 | 2023-07-19T06:57:32 | 2023-07-19T06:57:32 | 96,915,117 | 4 | 5 | null | 2020-09-02T17:39:08 | 2017-07-11T16:38:03 | Python | UTF-8 | Python | false | false | 3,247 | py | #!/usr/bin/env python3
# split, three epochs in each pop, asymmetric migration at same rates in all epochs
# n(para): 11
import matplotlib
matplotlib.use('PDF')
import moments
import pylab
import random
import matplotlib.pyplot as plt
import numpy as np
from numpy import array
from moments import Misc,Spectrum,Numerics,Manips,Integration,Demographics1D,Demographics2D
import sys
infile=sys.argv[1]
pop_ids=[sys.argv[2],sys.argv[3]]
projections=[int(sys.argv[4]),int(sys.argv[5])]
if len(sys.argv)==9:
params = np.loadtxt(sys.argv[8], delimiter=" ", unpack=False)
else:
params=[1,1,1,1,1,1,1,1,1,1,0.01]
# mutation rate per sequenced portion of genome per generation: for A.millepora, 0.02
mu=float(sys.argv[6])
# generation time, in thousand years: 0.005 (5 years)
gtime=float(sys.argv[7])
# set Polarized=False below for folded AFS analysis
fs = moments.Spectrum.from_file(infile)
data=fs.project(projections)
ns=data.sample_sizes
np.set_printoptions(precision=3)
#-------------------
# split into unequal pop sizes with asymmetrical migration
def sc3ei(params , ns):
# p_misid: proportion of misidentified ancestral states
nu1_1, nu2_1, nu1_2,nu2_2,nu1_3,nu2_3,T1, T2, T3,m, p_misid = params
sts = moments.LinearSystem_1D.steady_state_1D(ns[0] + ns[1])
fs = moments.Spectrum(sts)
fs = moments.Manips.split_1D_to_2D(fs, ns[0], ns[1])
fs.integrate([nu1_1, nu2_1], T1, m = np.array([[0, m], [m, 0]]))
fs.integrate([nu1_2, nu2_2], T2, m = np.array([[0, 0], [0, 0]]))
fs.integrate([nu1_3, nu2_3], T3, m = np.array([[0, m], [m, 0]]))
return (1-p_misid)*fs + p_misid*moments.Numerics.reverse_array(fs)
func=sc3ei
upper_bound = [100, 100, 100,100,100, 100, 100, 100,100, 200,0.25]
lower_bound = [1e-3,1e-3, 1e-3,1e-3,1e-3,1e-3,1e-3,1e-3,1e-3,1e-5,1e-5]
params = moments.Misc.perturb_params(params, fold=2, upper_bound=upper_bound,
lower_bound=lower_bound)
poptg = moments.Inference.optimize_log(params, data, func,
lower_bound=lower_bound,
upper_bound=upper_bound,
verbose=False, maxiter=30)
# extracting model predictions, likelihood and theta
model = func(poptg, ns)
ll_model = moments.Inference.ll_multinom(model, data)
theta = moments.Inference.optimal_sfs_scaling(model, data)
# random index for this replicate
ind=str(random.randint(0,999999))
# plotting demographic model
plot_mod = moments.ModelPlot.generate_model(func, poptg, ns)
moments.ModelPlot.plot_model(plot_mod, save_file="sc3elsm_"+ind+".png", pop_labels=pop_ids, nref=theta/(4*mu), draw_scale=False, gen_time=gtime, gen_time_units="KY", reverse_timeline=True)
# bootstrapping for SDs of params and theta
# printing parameters and their SDs
print( "RESULT","sc3elsm",ind,len(params),ll_model,sys.argv[1],sys.argv[2],sys.argv[3],poptg,theta)
# plotting quad-panel figure witt AFS, model, residuals:
moments.Plotting.plot_2d_comp_multinom(model, data, vmin=0.1, resid_range=3,
pop_ids =pop_ids)
plt.savefig("sc3elsm_"+ind+"_"+sys.argv[1]+"_"+sys.argv[2]+"_"+sys.argv[3]+"_"+sys.argv[4]+"_"+sys.argv[5]+'.pdf')
| [
"[email protected]"
] | |
661866a587111cbadec04ee46f867134a8b01025 | ac192c0d64c31c33d76708b3f5a0062a842d59cf | /LearningCode/3_8_aroundTheWorld.py | ba68059b790cdb5bd2695a4bd3887c758b429be7 | [
"Apache-2.0"
] | permissive | jercas/PythonCrashCourse | 7a73c6af327b653581e9d260431b022a08923fb3 | 464cf1dfa4c33adc73e15e15a37da94da0912e19 | refs/heads/master | 2020-12-02T22:11:24.650904 | 2017-07-03T09:37:27 | 2017-07-03T09:37:27 | 96,094,771 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 634 | py | #coding=utf-8
#放眼世界P41 2017.4.11
myDreamPlace = ['losAngle','houston','newYork']
print('1.this is the oringle list')
print(myDreamPlace)
print('2.this is the sorted list')
print(sorted(myDreamPlace))
print('now what?')
print(myDreamPlace)
print('3.this is the sorted and reverse list')
print(sorted(myDreamPlace))
print('now what?')
print(myDreamPlace)
print('4.this is the reverse list')
myDreamPlace.reverse()
print(myDreamPlace)
print("Let's take it reverse again")
myDreamPlace.reverse()
print(myDreamPlace)
print('5.this is the sort list')
myDreamPlace.sort()
print(myDreamPlace)
print('now what?')
print(myDreamPlace)
| [
"[email protected]"
] | |
a5dae9eaf99e07f4a4f3bcdd368bb8a6b274af16 | 0857ee93b0a041bb38c635b71e456247982e18f0 | /app/migrations/0001_initial.py | 20b17abffc8af2b5cdf3a8f0e2ae4fc224399542 | [] | no_license | ConnorFieldUser/single_page_secrets | 932ae5f253c3c4742d3584ecb6a34e0776f5672e | e4acdc26e64999e9d351beda98fd4f6af91566b5 | refs/heads/master | 2020-07-26T21:48:56.960107 | 2016-11-10T17:15:48 | 2016-11-10T17:15:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 789 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.10.3 on 2016-11-10 16:26
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='Secret',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('body', models.TextField()),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
]
| [
"[email protected]"
] | |
74764b1315255cbdaa416559f8d622f03ccc9269 | 31c310ef2cedb0d7b7327668bdbff4b50b165e74 | /app/__init__.py | 2030ab9a162509c54f126c68431fcc06854e893f | [
"MIT"
] | permissive | wou-cs/wolfit | 8ffacc5a4eb235d570f7f2042c4c731b4f145be5 | cebf6a0676ae86ea9d37ad9e8b2fe1aa1535c498 | refs/heads/main | 2023-03-09T22:41:17.418489 | 2023-02-04T13:36:02 | 2023-02-04T13:36:02 | 136,679,479 | 2 | 14 | MIT | 2023-02-16T07:12:04 | 2018-06-09T01:05:52 | Python | UTF-8 | Python | false | false | 580 | py | import os
import config
from flask import Flask
from flask_bootstrap import Bootstrap
from flask_login import LoginManager
from flask_migrate import Migrate
from flask_sqlalchemy import SQLAlchemy
from app.commands import sample_data
app = Flask(__name__)
app.config.from_object(config.Config)
app.config.from_envvar('WOLFIT_SETTINGS')
app.config['SQLALCHEMY_DATABASE_URI'] = config.Config.DATABASE_URI(app)
app.register_blueprint(sample_data)
db = SQLAlchemy(app)
login = LoginManager(app)
migrate = Migrate(app, db)
bootstrap = Bootstrap(app)
from app import models, routes
| [
"[email protected]"
] | |
fb33ad47c4b0d1dbaab994ac4d7707c1c15ad619 | 4ac6808e6153dceebd6271c017f9613818866da5 | /app/__init__.py | 7b12aec453ef6cc0483e2eb1f4a1bdf6ff520c4f | [
"MIT"
] | permissive | quanpower/xielaoban-server | 59d9331737c79163f0d4bd352bdcfc900c2e0c0c | 584eaa6c049a9d664efaf60cd23273147d0a5c6e | refs/heads/master | 2022-12-09T20:59:20.466225 | 2018-02-10T15:30:17 | 2018-02-10T15:30:17 | 120,546,895 | 1 | 0 | MIT | 2022-12-08T00:44:41 | 2018-02-07T01:36:25 | Python | UTF-8 | Python | false | false | 4,018 | py | from flask import Flask
from flask_bootstrap import Bootstrap
from flask_mail import Mail
from flask_moment import Moment
from flask_sqlalchemy import SQLAlchemy
from flask_login import LoginManager
from flask_pagedown import PageDown
from config import config
from flask_admin import Admin, BaseView, expose
from flask_admin.contrib.fileadmin import FileAdmin
from flask_babelex import Babel
import os.path as op
bootstrap = Bootstrap()
mail = Mail()
moment = Moment()
db = SQLAlchemy()
pagedown = PageDown()
flas_admin = Admin(name='smart-iiot')
babel = Babel()
# flask-login
login_manager = LoginManager()
login_manager.login_view = 'auth.login'
# flask-admin add views
from app.admin import UserAdminView, TestAdminView, UserModelView, LoraGatewayModelView, LoraNodeModelView, NodeMqttTransFuncModelView, PowerIoModelView, RelayCurrentRs485FuncModelView, \
GrainStorehouseModelView, GrainBarnModelView, GrainTempModelView, AlarmLevelSettingModelView, AlarmStatusModelView, AlarmTypesModelView, AlarmRecordsModelView
#
# flas_admin.add_view(UserAdminView(name='UserAdmin', category='UserAdmin'))
# flas_admin.add_view(TestAdminView(name='test', endpoint='test', category='UserAdmin'))
flas_admin.add_view(GrainStorehouseModelView(db.session, name='GrainStorehouse', endpoint='grain_storehouse', category='GrainAdmin'))
flas_admin.add_view(GrainBarnModelView(db.session, name='GrainBarn', endpoint='grain_barn', category='GrainAdmin'))
flas_admin.add_view(GrainTempModelView(db.session, name='GrainTemp', endpoint='grain_temps', category='GrainAdmin'))
flas_admin.add_view(LoraGatewayModelView(db.session, name='LoraGateway', endpoint='lora_gateway', category='LoraAdmin'))
flas_admin.add_view(LoraNodeModelView(db.session, name='LoraNode', endpoint='lora_node', category='LoraAdmin'))
flas_admin.add_view(NodeMqttTransFuncModelView(db.session, name='NodeMqttTransFunc', endpoint='node_mqtt_trans_func', category='LoraAdmin'))
flas_admin.add_view(PowerIoModelView(db.session, name='PowerIo', endpoint='power_io', category='LoraAdmin'))
flas_admin.add_view(RelayCurrentRs485FuncModelView(db.session, name='RelayCurrentRs485Func', endpoint='relay_current_rs485_func', category='LoraAdmin'))
flas_admin.add_view(AlarmStatusModelView(db.session, name='AlarmStatus', endpoint='alarm_status', category='AlarmAdmin'))
flas_admin.add_view(AlarmTypesModelView(db.session, name='AlarmTypes', endpoint='alarm_types', category='AlarmAdmin'))
flas_admin.add_view(AlarmRecordsModelView(db.session, name='AlarmRecords', endpoint='alarm_records', category='AlarmAdmin'))
flas_admin.add_view(AlarmLevelSettingModelView(db.session, name='AlarmLevelSetting', endpoint='alarm_level_setting', category='AlarmAdmin'))
flas_admin.add_view(UserModelView(db.session, name='User', endpoint='user', category='UserAdmin'))
path = op.join(op.dirname(__file__), 'static')
print(path)
flas_admin.add_view(FileAdmin(path, '/static/', name='Static Files'))
def create_app(config_name):
app = Flask(__name__)
app.config.from_object(config[config_name])
config[config_name].init_app(app)
# bable config for i18n
app.config['BABEL_DEFAULT_LOCALE'] = 'zh_CN'
if app.config['SSL_REDIRECT']:
from flask_sslify import SSLify
sslify = SSLify(app)
configure_extensions(app)
register_blueprints(app)
return app
def configure_extensions(app):
"""configure flask extensions
"""
bootstrap.init_app(app)
mail.init_app(app)
moment.init_app(app)
db.init_app(app)
login_manager.init_app(app)
pagedown.init_app(app)
babel.init_app(app)
flas_admin.init_app(app)
def register_blueprints(app):
"""register all blueprints for application
"""
from .main import main as main_blueprint
app.register_blueprint(main_blueprint)
from .auth import auth as auth_blueprint
app.register_blueprint(auth_blueprint, url_prefix='/auth')
from .api import api as api_blueprint
app.register_blueprint(api_blueprint, url_prefix='/api/v1')
| [
"[email protected]"
] | |
b322b4c51ee51cca8bce61a8bb26932135730db1 | 8f9ea3f14bdf2187de759939b2bbc87fe68ccfc0 | /tensorflow/python/keras/layers/wrappers.py | 7759561ef94c4a81552ef7b40ea71e49bbb743ae | [
"Apache-2.0"
] | permissive | davidstanke/bazel-mvn-demo | 4ea43f0ba293a28b916a27eab5f0812e9b753c2c | cff14dddce15ea7152988da576673bd15bab6c6e | refs/heads/master | 2022-10-20T07:52:29.651851 | 2018-11-22T13:17:51 | 2018-11-22T13:17:51 | 157,782,756 | 2 | 0 | Apache-2.0 | 2022-10-04T23:47:05 | 2018-11-15T22:54:09 | C++ | UTF-8 | Python | false | false | 18,856 | py | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=protected-access
"""Wrapper layers: layers that augment the functionality of another layer.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
from tensorflow.python.framework import tensor_shape
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.engine import InputSpec
from tensorflow.python.keras.engine import Layer
from tensorflow.python.keras.layers.recurrent import _standardize_args
from tensorflow.python.keras.utils import generic_utils
from tensorflow.python.keras.utils import tf_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.util.tf_export import tf_export
@tf_export('keras.layers.Wrapper')
class Wrapper(Layer):
"""Abstract wrapper base class.
Wrappers take another layer and augment it in various ways.
Do not use this class as a layer, it is only an abstract base class.
Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.
Arguments:
layer: The layer to be wrapped.
"""
def __init__(self, layer, **kwargs):
self.layer = layer
# Tracks mapping of Wrapper inputs to inner layer inputs. Useful when
# the inner layer has update ops that depend on its inputs (as opposed
# to the inputs to the Wrapper layer).
self._input_map = {}
super(Wrapper, self).__init__(**kwargs)
def build(self, input_shape=None):
self.built = True
@property
def activity_regularizer(self):
if hasattr(self.layer, 'activity_regularizer'):
return self.layer.activity_regularizer
else:
return None
@property
def trainable(self):
return self.layer.trainable
@trainable.setter
def trainable(self, value):
self.layer.trainable = value
@property
def trainable_weights(self):
return self.layer.trainable_weights
@property
def non_trainable_weights(self):
return self.layer.non_trainable_weights
@property
def updates(self):
return self.layer.updates + self._updates
@property
def losses(self):
return self.layer.losses + self._losses
def get_weights(self):
return self.layer.get_weights()
def set_weights(self, weights):
self.layer.set_weights(weights)
def get_config(self):
config = {
'layer': {
'class_name': self.layer.__class__.__name__,
'config': self.layer.get_config()
}
}
base_config = super(Wrapper, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config, custom_objects=None):
from tensorflow.python.keras.layers import deserialize as deserialize_layer # pylint: disable=g-import-not-at-top
layer = deserialize_layer(
config.pop('layer'), custom_objects=custom_objects)
return cls(layer, **config)
@tf_export('keras.layers.TimeDistributed')
class TimeDistributed(Wrapper):
"""This wrapper allows to apply a layer to every temporal slice of an input.
The input should be at least 3D, and the dimension of index one
will be considered to be the temporal dimension.
Consider a batch of 32 samples,
where each sample is a sequence of 10 vectors of 16 dimensions.
The batch input shape of the layer is then `(32, 10, 16)`,
and the `input_shape`, not including the samples dimension, is `(10, 16)`.
You can then use `TimeDistributed` to apply a `Dense` layer
to each of the 10 timesteps, independently:
```python
# as the first layer in a model
model = Sequential()
model.add(TimeDistributed(Dense(8), input_shape=(10, 16)))
# now model.output_shape == (None, 10, 8)
```
The output will then have shape `(32, 10, 8)`.
In subsequent layers, there is no need for the `input_shape`:
```python
model.add(TimeDistributed(Dense(32)))
# now model.output_shape == (None, 10, 32)
```
The output will then have shape `(32, 10, 32)`.
`TimeDistributed` can be used with arbitrary layers, not just `Dense`,
for instance with a `Conv2D` layer:
```python
model = Sequential()
model.add(TimeDistributed(Conv2D(64, (3, 3)),
input_shape=(10, 299, 299, 3)))
```
Arguments:
layer: a layer instance.
"""
def __init__(self, layer, **kwargs):
super(TimeDistributed, self).__init__(layer, **kwargs)
self.supports_masking = True
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
assert len(input_shape) >= 3
self.input_spec = InputSpec(shape=input_shape)
child_input_shape = [input_shape[0]] + input_shape[2:]
if not self.layer.built:
self.layer.build(child_input_shape)
self.layer.built = True
super(TimeDistributed, self).build()
self.built = True
def compute_output_shape(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
child_input_shape = tensor_shape.TensorShape([input_shape[0]] +
input_shape[2:])
child_output_shape = self.layer.compute_output_shape(
child_input_shape).as_list()
timesteps = input_shape[1]
return tensor_shape.TensorShape([child_output_shape[0], timesteps] +
child_output_shape[1:])
def call(self, inputs, training=None, mask=None):
kwargs = {}
if generic_utils.has_arg(self.layer.call, 'training'):
kwargs['training'] = training
uses_learning_phase = False # pylint: disable=redefined-outer-name
input_shape = K.int_shape(inputs)
if input_shape[0]:
# batch size matters, use rnn-based implementation
def step(x, _):
global uses_learning_phase # pylint: disable=global-variable-undefined
output = self.layer.call(x, **kwargs)
if hasattr(output, '_uses_learning_phase'):
uses_learning_phase = (output._uses_learning_phase or
uses_learning_phase)
return output, []
_, outputs, _ = K.rnn(
step,
inputs,
initial_states=[],
input_length=input_shape[1],
unroll=False)
y = outputs
else:
# No batch size specified, therefore the layer will be able
# to process batches of any size.
# We can go with reshape-based implementation for performance.
input_length = input_shape[1]
if not input_length:
input_length = array_ops.shape(inputs)[1]
# Shape: (num_samples * timesteps, ...). And track the
# transformation in self._input_map.
input_uid = generic_utils.object_list_uid(inputs)
inputs = array_ops.reshape(inputs, (-1,) + input_shape[2:])
self._input_map[input_uid] = inputs
# (num_samples * timesteps, ...)
y = self.layer.call(inputs, **kwargs)
if hasattr(y, '_uses_learning_phase'):
uses_learning_phase = y._uses_learning_phase
# Shape: (num_samples, timesteps, ...)
output_shape = self.compute_output_shape(input_shape).as_list()
y = array_ops.reshape(y, (-1, input_length) + tuple(output_shape[2:]))
# Apply activity regularizer if any:
if (hasattr(self.layer, 'activity_regularizer') and
self.layer.activity_regularizer is not None):
regularization_loss = self.layer.activity_regularizer(y)
self.add_loss(regularization_loss, inputs)
if uses_learning_phase:
y._uses_learning_phase = True
return y
@tf_export('keras.layers.Bidirectional')
class Bidirectional(Wrapper):
"""Bidirectional wrapper for RNNs.
Arguments:
layer: `Recurrent` instance.
merge_mode: Mode by which outputs of the
forward and backward RNNs will be combined.
One of {'sum', 'mul', 'concat', 'ave', None}.
If None, the outputs will not be combined,
they will be returned as a list.
Raises:
ValueError: In case of invalid `merge_mode` argument.
Examples:
```python
model = Sequential()
model.add(Bidirectional(LSTM(10, return_sequences=True), input_shape=(5,
10)))
model.add(Bidirectional(LSTM(10)))
model.add(Dense(5))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
```
"""
def __init__(self, layer, merge_mode='concat', weights=None, **kwargs):
if merge_mode not in ['sum', 'mul', 'ave', 'concat', None]:
raise ValueError('Invalid merge mode. '
'Merge mode should be one of '
'{"sum", "mul", "ave", "concat", None}')
self.forward_layer = copy.copy(layer)
config = layer.get_config()
config['go_backwards'] = not config['go_backwards']
self.backward_layer = layer.__class__.from_config(config)
self.forward_layer._name = 'forward_' + self.forward_layer.name
self.backward_layer._name = 'backward_' + self.backward_layer.name
self.merge_mode = merge_mode
if weights:
nw = len(weights)
self.forward_layer.initial_weights = weights[:nw // 2]
self.backward_layer.initial_weights = weights[nw // 2:]
self.stateful = layer.stateful
self.return_sequences = layer.return_sequences
self.return_state = layer.return_state
self.supports_masking = True
self._trainable = True
self._num_constants = None
super(Bidirectional, self).__init__(layer, **kwargs)
self.input_spec = layer.input_spec
@property
def trainable(self):
return self._trainable
@trainable.setter
def trainable(self, value):
self._trainable = value
self.forward_layer.trainable = value
self.backward_layer.trainable = value
def get_weights(self):
return self.forward_layer.get_weights() + self.backward_layer.get_weights()
def set_weights(self, weights):
nw = len(weights)
self.forward_layer.set_weights(weights[:nw // 2])
self.backward_layer.set_weights(weights[nw // 2:])
@tf_utils.shape_type_conversion
def compute_output_shape(self, input_shape):
output_shape = tuple(self.forward_layer.compute_output_shape(
input_shape).as_list())
if self.return_state:
state_shape = output_shape[1:]
output_shape = output_shape[0]
if self.merge_mode == 'concat':
output_shape = list(output_shape)
output_shape[-1] *= 2
output_shape = tuple(output_shape)
elif self.merge_mode is None:
output_shape = [output_shape, copy.copy(output_shape)]
if self.return_state:
if self.merge_mode is None:
return output_shape + state_shape + copy.copy(state_shape)
return [output_shape] + state_shape + copy.copy(state_shape)
return output_shape
def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
"""`Bidirectional.__call__` implements the same API as the wrapped `RNN`."""
inputs, initial_state, constants = _standardize_args(
inputs, initial_state, constants, self._num_constants)
if isinstance(inputs, list):
if len(inputs) > 1:
initial_state = inputs[1:]
inputs = inputs[0]
if initial_state is None and constants is None:
return super(Bidirectional, self).__call__(inputs, **kwargs)
# Applies the same workaround as in `RNN.__call__`
additional_inputs = []
additional_specs = []
if initial_state is not None:
# Check if `initial_state` can be splitted into half
num_states = len(initial_state)
if num_states % 2 > 0:
raise ValueError(
'When passing `initial_state` to a Bidirectional RNN, '
'the state should be a list containing the states of '
'the underlying RNNs. '
'Found: ' + str(initial_state))
kwargs['initial_state'] = initial_state
additional_inputs += initial_state
state_specs = [InputSpec(shape=K.int_shape(state))
for state in initial_state]
self.forward_layer.state_spec = state_specs[:num_states // 2]
self.backward_layer.state_spec = state_specs[num_states // 2:]
additional_specs += state_specs
if constants is not None:
kwargs['constants'] = constants
additional_inputs += constants
constants_spec = [InputSpec(shape=K.int_shape(constant))
for constant in constants]
self.forward_layer.constants_spec = constants_spec
self.backward_layer.constants_spec = constants_spec
additional_specs += constants_spec
self._num_constants = len(constants)
self.forward_layer._num_constants = self._num_constants
self.backward_layer._num_constants = self._num_constants
is_keras_tensor = K.is_keras_tensor(additional_inputs[0])
for tensor in additional_inputs:
if K.is_keras_tensor(tensor) != is_keras_tensor:
raise ValueError('The initial state of a Bidirectional'
' layer cannot be specified with a mix of'
' Keras tensors and non-Keras tensors'
' (a "Keras tensor" is a tensor that was'
' returned by a Keras layer, or by `Input`)')
if is_keras_tensor:
# Compute the full input spec, including state
full_input = [inputs] + additional_inputs
full_input_spec = self.input_spec + additional_specs
# Perform the call with temporarily replaced input_spec
original_input_spec = self.input_spec
self.input_spec = full_input_spec
output = super(Bidirectional, self).__call__(full_input, **kwargs)
self.input_spec = original_input_spec
return output
else:
return super(Bidirectional, self).__call__(inputs, **kwargs)
def call(self, inputs,
training=None,
mask=None,
initial_state=None,
constants=None):
"""`Bidirectional.call` implements the same API as the wrapped `RNN`."""
kwargs = {}
if generic_utils.has_arg(self.layer.call, 'training'):
kwargs['training'] = training
if generic_utils.has_arg(self.layer.call, 'mask'):
kwargs['mask'] = mask
if generic_utils.has_arg(self.layer.call, 'constants'):
kwargs['constants'] = constants
if initial_state is not None and generic_utils.has_arg(
self.layer.call, 'initial_state'):
forward_state = initial_state[:len(initial_state) // 2]
backward_state = initial_state[len(initial_state) // 2:]
y = self.forward_layer.call(inputs, initial_state=forward_state, **kwargs)
y_rev = self.backward_layer.call(
inputs, initial_state=backward_state, **kwargs)
else:
y = self.forward_layer.call(inputs, **kwargs)
y_rev = self.backward_layer.call(inputs, **kwargs)
if self.return_state:
states = y[1:] + y_rev[1:]
y = y[0]
y_rev = y_rev[0]
if self.return_sequences:
y_rev = K.reverse(y_rev, 1)
if self.merge_mode == 'concat':
output = K.concatenate([y, y_rev])
elif self.merge_mode == 'sum':
output = y + y_rev
elif self.merge_mode == 'ave':
output = (y + y_rev) / 2
elif self.merge_mode == 'mul':
output = y * y_rev
elif self.merge_mode is None:
output = [y, y_rev]
# Properly set learning phase
if (getattr(y, '_uses_learning_phase', False) or
getattr(y_rev, '_uses_learning_phase', False)):
if self.merge_mode is None:
for out in output:
out._uses_learning_phase = True
else:
output._uses_learning_phase = True
if self.return_state:
if self.merge_mode is None:
return output + states
return [output] + states
return output
def reset_states(self):
self.forward_layer.reset_states()
self.backward_layer.reset_states()
def build(self, input_shape):
with K.name_scope(self.forward_layer.name):
self.forward_layer.build(input_shape)
with K.name_scope(self.backward_layer.name):
self.backward_layer.build(input_shape)
self.built = True
def compute_mask(self, inputs, mask):
if isinstance(mask, list):
mask = mask[0]
if self.return_sequences:
if not self.merge_mode:
output_mask = [mask, mask]
else:
output_mask = mask
else:
output_mask = [None, None] if not self.merge_mode else None
if self.return_state:
states = self.forward_layer.states
state_mask = [None for _ in states]
if isinstance(output_mask, list):
return output_mask + state_mask * 2
return [output_mask] + state_mask * 2
return output_mask
@property
def trainable_weights(self):
if hasattr(self.forward_layer, 'trainable_weights'):
return (self.forward_layer.trainable_weights +
self.backward_layer.trainable_weights)
return []
@property
def non_trainable_weights(self):
if hasattr(self.forward_layer, 'non_trainable_weights'):
return (self.forward_layer.non_trainable_weights +
self.backward_layer.non_trainable_weights)
return []
@property
def updates(self):
if hasattr(self.forward_layer, 'updates'):
return self.forward_layer.updates + self.backward_layer.updates
return []
@property
def losses(self):
if hasattr(self.forward_layer, 'losses'):
return self.forward_layer.losses + self.backward_layer.losses
return []
@property
def constraints(self):
constraints = {}
if hasattr(self.forward_layer, 'constraints'):
constraints.update(self.forward_layer.constraints)
constraints.update(self.backward_layer.constraints)
return constraints
def get_config(self):
config = {'merge_mode': self.merge_mode}
if self._num_constants is not None:
config['num_constants'] = self._num_constants
base_config = super(Bidirectional, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config, custom_objects=None):
num_constants = config.pop('num_constants', None)
layer = super(Bidirectional, cls).from_config(config,
custom_objects=custom_objects)
layer._num_constants = num_constants
return layer
| [
"[email protected]"
] | |
7926b45512fe41390359b55d5dc715655d19920f | 72b74f66f83239a928bf049c0dd6e47576e57bae | /NLP/word2vec/data_processing/__init__.py | 26e9b2e5b6b5f6c6a8227cbaffee616283614ce3 | [] | no_license | InsaneLife/DeepLearning | 7934056682e4fec7f3241dd2d4fbe1b4c5f192d2 | 4b60fe40587b96ba2a351c1b3cb832d03c2071ab | refs/heads/master | 2022-10-08T08:18:19.633449 | 2017-08-30T10:47:05 | 2017-08-30T10:47:05 | 65,697,666 | 2 | 4 | null | 2022-09-30T21:55:05 | 2016-08-15T02:16:34 | C++ | UTF-8 | Python | false | false | 736 | py | # coding=utf8
# author = 'Aaron Chou'
import sys
import zipfile
reload(sys)
sys.setdefaultencoding('utf-8')
# filename = '../../../..//data/NLP/sougou/news_oneline.txt'
# with open(filename) as f:
# data = f.readline().decode('utf-8')
#
#
# filename = '../../../../data/word2vec/text8.zip'
# with zipfile.ZipFile(filename) as f:
# data = f.read(f.namelist()[0])
#
# print 'yes'
#
# # Read the data into a list of strings.
# def read_data(filename):
# """Extract the first file enclosed in a zip file as a list of words"""
# with zipfile.ZipFile(filename) as f:
# data = f.read(f.namelist()[0])
# return data
s = "公安机关销毁１０余万非法枪支 跨国武"
s = s.replace(' ','')
print s | [
"[email protected]"
] | |
ddd71101293b395a9c27fbd25cd8df5f75c8dcfa | 42fdf741bf64ea2e63d1546bb08356286f994505 | /test_20160921_macroblk_generation/rasp30_vmm_gen2.py | c42901a6f5a231f2613db9a070596ac4babdbe9d | [] | no_license | skim819/RASP_Workspace_sihwan | 7e3cd403dc3965b8306ec203007490e3ea911e3b | 0799e146586595577c8efa05c647b8cb92b962f4 | refs/heads/master | 2020-12-24T05:22:25.775823 | 2017-04-01T22:15:18 | 2017-04-01T22:15:18 | 41,511,563 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,367 | py | li_sm_0b = ['fgota[0:1].out[0]','ota_buf[0].out[0]','ota[0].out[0]','ota_vmm[0].out[0]','cap[0:3].out[0]','nfet[0:1].out[0]','pfet[0:1].out[0]','tgate[0:3].out[0]','nmirror_vmm[0:1].out[0]','ladder_blk[0].out[0:1]','c4_blk[0].out[0]','speech[0].out[0:1]','INFneuron[0].out[0]','lpf[0].out[0]','nfet_i2v[0].out[0]','pfet_i2v[0].out[0]','nmirror_w_bias[0].out[0]','fgswc_nmirror_w_bias[0].out[0]','i2v_pfet_gatefgota[0].out[0]','mismatch_meas[0].out[0]','peak_detector[0].out[0]','ramp_fe[0].out[0]','sigma_delta_fe[0].out[0]','vmm_senseamp1[0].out[0]','vmm_senseamp2[0].out[0:1]','wta[0].out[0]','wta_primary[0].out[0:1]','common_source[0].out[0]','gnd_out[0].out[0]','vdd_out[0].out[0]','in2in_x1[0].out[0]','in2in_x6[0].out[0]','volt_div[0].out[0]','volt_div_fgota[0].out[0]','integrator[0].out[0]','integrator_nmirror[0].out[0]','fgswitch[0].out[0]','tgate_so[0].out[0]','vmm4x4_SR[0].out[0]','vmm8x4_SR[0].out[0]','SR4[0].out[0:7]','vmm4x4_SR2[0].out[0]','vmm4x4[0].out[0:3]','sftreg[0].out[0]','DAC_sftreg[0].out[0]','sftreg2[0].out[0]','sftreg3[0].out[0]','sftreg4[0].out[0]','mmap_local_swc[0].out[0]','th_logic[0].out[0]','vmm8x4[0].out[0]','vmm8inx8in[0].out[0]','vmm8x4_in[0].out[0]','vmm12x1[0].out[0]','fg_io[0].out[0]','ladder_filter[0].out[0:2]','vmm12x1_wowta[0].out[0]','TIA_blk[0].out[0]','Adaptive_receptor[0].out[0]','testtemp[0].out[0:2]']
| [
"ubuntu@ubuntu-VirtualBox.(none)"
] | ubuntu@ubuntu-VirtualBox.(none) |
736f4698de804a541c0980b218de7e032a7725b7 | 6618febe7d31b263acf2006dae748ce25fb03cfc | /fileparsers.py | 3ff51617fc82d199d0a93c62a7b2c5cbccf578a2 | [] | no_license | breecummins/PatternMatch | d8312d95d119ea8e373ed3f1ff5be9350fb543ed | 061b87fea1ef52825d4dba3af675d1a44af0f20c | refs/heads/master | 2021-01-17T09:42:04.173524 | 2016-06-02T17:04:57 | 2016-06-02T17:04:57 | 31,024,366 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,607 | py | # The MIT License (MIT)
# Copyright (c) 2015 Breschine Cummins
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
import json
def parseMorseGraphs(fname="morsegraphs.txt"):
f=open(fname,'r')
morse_graphs_and_sets=[]
for l in f.readlines():
L=l.replace('|',' ').split()
morse_graphs_and_sets.append((L[0],L[1:]))
f.close()
return morse_graphs_and_sets
def parseParameters(fname="concatenatedparams.txt"):
f=open(fname,'r')
morsegraph_morseset_param=[]
for l in f.readlines():
morsegraph_morseset_param.append(tuple(l.split('|')))
f.close()
return morsegraph_morseset_param
def parsePatterns(fname="patterns.txt"):
f=open(fname,'r')
maxmin=[]
varnames=[]
originalpatterns=[]
for l in f:
if l[-1]=='\n':
l=l[:-1]
originalpatterns.append(l)
L=l.replace(',',' ').split()
varnames.append(L[::2])
maxmin.append(L[1::2])
f.close()
return varnames, maxmin, originalpatterns
def parseMorseSet(fname='dsgrn_output.json'):
parsed = json.load(open(fname),strict=False)
varnames = [ x[0] for x in parsed["network"] ]
threshnames = [ [parsed["network"][i][2][j] for j in parsed["parameter"][i][2]] for i in range(len(parsed["network"])) ]
return varnames,threshnames,parsed["graph"],parsed["cells"],parsed["vertices"]
def parseDomainCells(fname='dsgrn_domaincells.json'):
parsed = json.load(open(fname),strict=False)
return parsed["cells"]
def parseDomainGraph(fname="dsgrn_domaingraph.json"):
return json.load(open(fname),strict=False)
| [
"[email protected]"
] | |
884b606858c44db1fa41bb8f88a377326eb04a69 | 27722ac879b3416a0919dce80d4ec4f2a5c93c97 | /adafruit_pixie.py | 739cd33384b7309d19e4b59d60c04d7e4daf4f47 | [
"MIT"
] | permissive | makermelissa/Adafruit_CircuitPython_Pixie | ee3d5b5861dcf5283b67053c18ce23eb06b88ee9 | 2bdfcf52d8861befc47f433b660f372c20a23d2d | refs/heads/master | 2020-04-25T06:57:24.144082 | 2019-02-25T22:57:25 | 2019-02-25T22:57:25 | 172,598,614 | 0 | 0 | MIT | 2019-02-25T22:54:35 | 2019-02-25T22:54:35 | null | UTF-8 | Python | false | false | 4,674 | py | # The MIT License (MIT)
#
# Copyright (c) 2016 Damien P. George (original Neopixel object)
# Copyright (c) 2018 Ladyada
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
"""
`adafruit_pixie` - Pixie LED driver
====================================================
* Author(s): Damien P. George, Limor Fried, Kattni Rembor
"""
import time
import math
__version__ = "0.0.0-auto.0"
__repo__ = "https://github.com/adafruit/Adafruit_CircuitPython_Pixie.git"
class Pixie:
"""
PIxie LEDs.
:param uart: The UART object.
:param int n: The number of Pixies in the chain.
:param float brightness: Brightness of the pixels between 0.0 and 1.0.
:param bool auto_write: True if the Pixies should immediately change when
set. If False, `show` must be called explicitly.
Example for two Pixie LEDs chained:
.. code_block::python
import time
import board
import busio
import adafruit_pixie
uart = busio.UART(board.TX, rx=None, baudrate=115200)
pixies = adafruit_pixie.Pixie(uart, 2, brightness=0.5)
while True:
pixies.fill((255, 0, 0))
time.sleep(1)
pixies[0] = (0, 255, 0)
pixies[1] = (0, 0, 255)
time.sleep(1)
"""
def __init__(self, uart, n, *, brightness=1.0, auto_write=True):
self._uart = uart
self._n = n
self._buf = bytearray(self._n * 3)
# Set auto_write to False temporarily so brightness setter does _not_
# call show() while in __init__.
self.auto_write = False
self._brightness = brightness
self.auto_write = auto_write
def _set_item(self, index, value):
if index < 0:
index += len(self)
if index >= self._n or index < 0:
raise IndexError
offset = index * 3
r = 0
g = 0
b = 0
if isinstance(value, int):
r = value >> 16
g = (value >> 8) & 0xff
b = value & 0xff
elif len(value) == 3:
r, g, b = value
self._buf[offset + 0] = r
self._buf[offset + 1] = g
self._buf[offset + 2] = b
def __setitem__(self, index, val):
if isinstance(index, slice):
start, stop, step = index.indices(len(self._buf) // 3)
length = stop - start
if step != 0:
length = math.ceil(length / step)
if len(val) != length:
raise ValueError("Slice and input sequence size do not match.")
for val_i, in_i in enumerate(range(start, stop, step)):
self._set_item(in_i, val[val_i])
else:
self._set_item(index, val)
if self.auto_write:
self.show()
def __len__(self):
return len(self._buf) // 3
@property
def brightness(self):
"""Overall brightness of the pixel"""
return self._brightness
@brightness.setter
def brightness(self, brightness):
self._brightness = min(max(brightness, 0.0), 1.0)
if self.auto_write:
self.show()
def fill(self, color):
"""Colors all pixels the given ***color***."""
auto_write = self.auto_write
self.auto_write = False
for i in range(self._n):
self[i] = color
if auto_write:
self.show()
self.auto_write = auto_write
def show(self):
"""
Shows the new colors on the pixels themselves if they haven't already
been autowritten.
"""
self._uart.write(bytes([int(i * self.brightness) for i in self._buf]))
time.sleep(0.005)
| [
"[email protected]"
] | |
ed629e71203af591f84402090e41ad720808065a | 10e8b0b82c429593449f5b3f0ee6efca6d403870 | /Old_Pando/HRRR_downloads/old_dwnld_scripts/download_hrrr.py | 4df7e0c8549423f3e27555f7099459c2528e0af5 | [] | no_license | janmandel/HorelS3-Archive | 7bf50ba2e65812857ea857bc2d033c2a661273e4 | 73f765de5358352ea9d87d76275c5cfb67a5cf43 | refs/heads/master | 2020-03-28T11:24:45.375321 | 2018-09-06T21:17:15 | 2018-09-06T21:17:15 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,249 | py | # Brian Blaylock
# February 27, 2017
"""
Downloads the operational HRRR from NCEOP NOMADS server
A re-write of the "get_hrrr.csh" script in python.
"""
import urllib
from datetime import datetime, timedelta
import os
import stat
# ----------------------------------------------------------------------------
# Introductory Stuff
# ----------------------------------------------------------------------------
# download HRRR files from yesterday
yesterday = datetime.today() #-timedelta(days=1)
# Put the downloaded files in the horel-group/archive. Mkdir if it doesn't exist
OUTDIR = '/uufs/chpc.utah.edu/common/home/horel-group/archive/%04d%02d%02d/BB_test/models/hrrr/' \
% (yesterday.year, yesterday.month, yesterday.day)
if not os.path.exists(OUTDIR):
os.makedirs(OUTDIR)
# Change directory permissions
os.chmod(OUTDIR, stat.S_IRWXU | \
stat.S_IRGRP | stat.S_IXGRP | \
stat.S_IROTH | stat.S_IXOTH)
# User can read, write, execute
# Group can read and execute
# Others can read and execute
# ----------------------------------------------------------------------------
def reporthook(a,b,c):
# ',' at the end of the line is important!
print "% 3.1f%% of %d bytes\r" % (min(100, float(a * b) / c * 100), c),
#you can also use sys.stdout.write
#sys.stdout.write("\r% 3.1f%% of %d bytes"
# % (min(100, float(a * b) / c * 100), c)
def download_hrrr(DATE, field,
hour=range(0, 24), forecast=range(0, 19), OUTDIR='./'):
"""
Downloads HRRR grib2 files from the nomads server
http://nomads.ncep.noaa.gov/
Input:
DATE - a datetime object for which you wish to download
fields - the field you want to download
Options are fields ['prs', 'sfc','subh', 'nat']
pressure fields (~350 MB), surface fields (~6 MB),
native fields (~510 MB)!
hour - a list of hours you want to download
Default all hours in the day
forecast - a list of forecast hour you wish to download
Default all forecast hours (0-18)
outpath - the outpath directory you wish to save the files.
"""
# We'll store the URLs we download from and return them for troubleshooting
URL_list = []
# Build the URL string we want to download. One for each field, hour, and forecast
# New URL for downloading HRRRv2+
URL = 'http://nomads.ncep.noaa.gov/pub/data/nccf/com/hrrr/prod/hrrr.%04d%02d%02d/' \
% (DATE.year, DATE.month, DATE.day)
# Create a new array for each field to keep things organized.
for h in hour:
for f in forecast:
FileName = 'hrrr.t%02dz.wrf%sf%02d.grib2' % (h, field, f)
# Download and save the file
print 'Downloading:', URL, FileName
urllib.urlretrieve(URL+FileName, OUTDIR+FileName, reporthook)
print 'Saved:', OUTDIR+FileName
URL_list.append(URL+FileName)
# Return the list of URLs we downloaded from for troubleshooting
return URL_list
def download_hrrr_bufr(DATE,
stations=['725720'],
rename=['kslc'],
hour=range(0,24),
OUTDIR='./'):
"""
Special case for downloading HRRR bufr soundings.
"""
URL_list = []
URL = 'http://nomads.ncep.noaa.gov/pub/data/nccf/com/hrrr/prod/hrrr.%04d%02d%02d/bufrsnd.t%02dz/' \
% (DATE.year, DATE.month, DATE.day, DATE.hour)
for h in hour:
for i in range(len(stations)):
FILE = 'bufr.%s.%04d%02d%02d%02d' \
% (stations[i], DATE.year, DATE.month, DATE.day, h)
NEWNAME = '%s_%04d%02d%02d%02d.buf' \
% (rename[i], DATE.year, DATE.month, DATE.day, h)
urllib.urlretrieve(URL+FILE, OUTDIR+NEWNAME, reporthook)
URL_list.append(URL+FILE)
return URL_list
if __name__ == '__main__':
# Download Surface fields: all hours and all forecast hours
sfc_hxx = range(0, 24)
sfc_fxx = range(0, 19)
sfc_URLs = download_hrrr(yesterday,
field='sfc',
hour=sfc_hxx,
forecast=sfc_fxx,
OUTDIR=OUTDIR)
# Download Pressure fields: all hours, only analysis hours
prs_hxx = range(0, 24)
prs_fxx = range(0, 1)
prs_URLs = download_hrrr(yesterday,
field='prs',
forecast=prs_fxx,
hour=prs_hxx,
OUTDIR=OUTDIR)
# Download bufr soundings: KSLC, KODG, KPVU
stations = ['725720', '725724', '725750']
rename = ['kslc', 'kpvu', 'kodg']
bufr_URLs = download_hrrr_bufr(yesterday,
stations=stations,
rename=rename,
OUTDIR=OUTDIR)
## Download subhourly
#subh_hxx = range(0, 24)
#subh_fxx = range(0, 19)
#subh_URLs = download_hrrr(yesterday, field='subh', hour=sub_hxx, forecast=subh_fxx)
| [
"[email protected]"
] | |
5b932c5056e7bd1fe88edd01c629c4c593215858 | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03026/s629929775.py | 95360d8a62b73cf93e76bfc9c8e613d053dfdfeb | [] | 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 | 500 | py | N = int(input())
L = [list(map(int,input().split())) for k in range(N-1)]
c = sorted(list(map(int,input().split())))
a = sum(c) - c[-1]
T = [[] for k in range(N)]
for e in L:
T[e[0]-1].append(e[1]-1)
T[e[1]-1].append(e[0]-1)
kyori = [-1 for k in range(N)]
que = [L[0][0]]
kyori[L[0][0]] = c.pop()
while len(que) > 0:
now = que.pop()
for tsugi in T[now]:
if kyori[tsugi] == -1:
kyori[tsugi] = c.pop()
que.append(tsugi)
print(a)
print(*kyori, sep=" ")
| [
"[email protected]"
] | |
ed09351b57eec7ced5b4c69fadb372f03896c127 | 37fef592f365194c28579f95abd222cc4e1243ae | /streamlit/venv/lib/python3.7/site-packages/streamlit/proto/PageInfo_pb2.py | c550c50579acc89520cbfcfbccf60c1c2162d8de | [] | no_license | edimaudo/Python-projects | be61e0d3fff63fb7bd00513dbf1401e2c1822cfb | 85d54badf82a0b653587a02e99daf389df62e012 | refs/heads/master | 2023-04-07T03:26:23.259959 | 2023-03-24T12:03:03 | 2023-03-24T12:03:03 | 72,611,253 | 4 | 3 | null | 2022-10-31T18:10:41 | 2016-11-02T06:37:17 | null | UTF-8 | Python | false | true | 1,978 | py | # -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: streamlit/proto/PageInfo.proto
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='streamlit/proto/PageInfo.proto',
package='',
syntax='proto3',
serialized_options=None,
create_key=_descriptor._internal_create_key,
serialized_pb=b'\n\x1estreamlit/proto/PageInfo.proto\" \n\x08PageInfo\x12\x14\n\x0cquery_string\x18\x01 \x01(\tb\x06proto3'
)
_PAGEINFO = _descriptor.Descriptor(
name='PageInfo',
full_name='PageInfo',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='query_string', full_name='PageInfo.query_string', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=34,
serialized_end=66,
)
DESCRIPTOR.message_types_by_name['PageInfo'] = _PAGEINFO
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
PageInfo = _reflection.GeneratedProtocolMessageType('PageInfo', (_message.Message,), {
'DESCRIPTOR' : _PAGEINFO,
'__module__' : 'streamlit.proto.PageInfo_pb2'
# @@protoc_insertion_point(class_scope:PageInfo)
})
_sym_db.RegisterMessage(PageInfo)
# @@protoc_insertion_point(module_scope)
| [
"[email protected]"
] | |
c056ca62fb58ccf1f0ad981bf2c2828285c23243 | 94a6a83c8bd3f9a951ee7d48973f35d0b5b6f99c | /runcases/pygal/config.py | 489618db7c428e189a6aedcb81af7fef0a0dafcd | [] | no_license | JerryLiu0821/apython | 19766bebd5365e53aa7ea46adc01132045e91f9c | d9804b1099c879da1f8dc130fb205ab191f65fb1 | refs/heads/master | 2020-05-17T05:09:15.319167 | 2015-08-17T10:50:09 | 2015-08-17T10:50:09 | 40,886,032 | 2 | 2 | null | null | null | null | UTF-8 | Python | false | false | 13,588 | py | # -*- coding: utf-8 -*-
# This file is part of pygal
#
# A python svg graph plotting library
# Copyright © 2012-2014 Kozea
#
# This library is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option) any
# later version.
#
# This library 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 Lesser General Public License for more
# details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with pygal. If not, see <http://www.gnu.org/licenses/>.
"""
Config module with all options
"""
from copy import deepcopy
from pygal.style import Style, DefaultStyle
from pygal.interpolate import INTERPOLATIONS
class FontSizes(object):
"""Container for font sizes"""
CONFIG_ITEMS = []
class Key(object):
_categories = []
def __init__(
self, default_value, type_, category, doc,
subdoc="", subtype=None):
self.value = default_value
self.type = type_
self.doc = doc
self.category = category
self.subdoc = subdoc
self.subtype = subtype
self.name = "Unbound"
if category not in self._categories:
self._categories.append(category)
CONFIG_ITEMS.append(self)
@property
def is_boolean(self):
return self.type == bool
@property
def is_numeric(self):
return self.type in (int, float)
@property
def is_string(self):
return self.type == str
@property
def is_dict(self):
return self.type == dict
@property
def is_list(self):
return self.type == list
def coerce(self, value):
if self.type == Style:
return value
elif self.type == list:
return self.type(
map(
self.subtype, map(
lambda x: x.strip(), value.split(','))))
elif self.type == dict:
rv = {}
for pair in value.split(','):
key, val = pair.split(':')
key = key.strip()
val = val.strip()
try:
rv[key] = self.subtype(val)
except:
rv[key] = val
return rv
return self.type(value)
class MetaConfig(type):
def __new__(mcs, classname, bases, classdict):
for k, v in classdict.items():
if isinstance(v, Key):
v.name = k
return type.__new__(mcs, classname, bases, classdict)
class BaseConfig(MetaConfig('ConfigBase', (object,), {})):
def __init__(self, **kwargs):
"""Can be instanciated with config kwargs"""
for k in dir(self):
v = getattr(self, k)
if (k not in self.__dict__ and not
k.startswith('_') and not
hasattr(v, '__call__')):
if isinstance(v, Key):
if v.is_list and v.value is not None:
v = list(v.value)
else:
v = v.value
setattr(self, k, v)
self._update(kwargs)
def __call__(self, **kwargs):
"""Can be updated with kwargs"""
self._update(kwargs)
def _update(self, kwargs):
self.__dict__.update(
dict([(k, v) for (k, v) in kwargs.items()
if not k.startswith('_') and k in dir(self)]))
def font_sizes(self, with_unit=True):
"""Getter for all font size configs"""
fs = FontSizes()
for name in dir(self):
if name.endswith('_font_size'):
setattr(
fs,
name.replace('_font_size', ''),
('%dpx' % getattr(self, name))
if with_unit else getattr(self, name))
return fs
def to_dict(self):
config = {}
for attr in dir(self):
if not attr.startswith('__'):
value = getattr(self, attr)
if hasattr(value, 'to_dict'):
config[attr] = value.to_dict()
elif not hasattr(value, '__call__'):
config[attr] = value
return config
def copy(self):
return deepcopy(self)
class CommonConfig(BaseConfig):
stroke = Key(
True, bool, "Look",
"Line dots (set it to false to get a scatter plot)")
show_dots = Key(True, bool, "Look", "Set to false to remove dots")
show_only_major_dots = Key(
False, bool, "Look",
"Set to true to show only major dots according to their majored label")
dots_size = Key(2.5, float, "Look", "Radius of the dots")
fill = Key(
False, bool, "Look", "Fill areas under lines")
rounded_bars = Key(
None, int, "Look",
"Set this to the desired radius in px (for Bar-like charts)")
inner_radius = Key(
0, float, "Look", "Piechart inner radius (donut), must be <.9")
class Config(CommonConfig):
"""Class holding config values"""
style = Key(
DefaultStyle, Style, "Style", "Style holding values injected in css")
css = Key(
('style.css', 'graph.css'), list, "Style",
"List of css file",
"It can be an absolute file path or an external link",
str)
# Look #
title = Key(
None, str, "Look",
"Graph title.", "Leave it to None to disable title.")
x_title = Key(
None, str, "Look",
"Graph X-Axis title.", "Leave it to None to disable X-Axis title.")
y_title = Key(
None, str, "Look",
"Graph Y-Axis title.", "Leave it to None to disable Y-Axis title.")
width = Key(
800, int, "Look", "Graph width")
height = Key(
600, int, "Look", "Graph height")
show_x_guides = Key(False, bool, "Look",
"Set to true to always show x guide lines")
show_y_guides = Key(True, bool, "Look",
"Set to false to hide y guide lines")
show_legend = Key(
True, bool, "Look", "Set to false to remove legend")
legend_at_bottom = Key(
False, bool, "Look", "Set to true to position legend at bottom")
legend_at_bottom_columns = Key(
None, int, "Look", "Set to true to position legend at bottom")
legend_box_size = Key(
12, int, "Look", "Size of legend boxes")
rounded_bars = Key(
None, int, "Look", "Set this to the desired radius in px")
stack_from_top = Key(
False, bool, "Look", "Stack from top to zero, this makes the stacked "
"data match the legend order")
spacing = Key(
10, int, "Look",
"Space between titles/legend/axes")
margin = Key(
20, int, "Look",
"Margin around chart")
margin_top = Key(
None, int, "Look",
"Margin around top of chart")
margin_right = Key(
None, int, "Look",
"Margin around right of chart")
margin_bottom = Key(
None, int, "Look",
"Margin around bottom of chart")
margin_left = Key(
None, int, "Look",
"Margin around left of chart")
tooltip_border_radius = Key(0, int, "Look", "Tooltip border radius")
inner_radius = Key(
0, float, "Look", "Piechart inner radius (donut), must be <.9")
half_pie = Key(
False, bool, "Look", "Create a half-pie chart")
x_labels = Key(
None, list, "Label",
"X labels, must have same len than data.",
"Leave it to None to disable x labels display.",
str)
x_labels_major = Key(
None, list, "Label",
"X labels that will be marked major.",
subtype=str)
x_labels_major_every = Key(
None, int, "Label",
"Mark every n-th x label as major.")
x_labels_major_count = Key(
None, int, "Label",
"Mark n evenly distributed labels as major.")
show_x_labels = Key(
True, bool, "Label", "Set to false to hide x-labels")
show_minor_x_labels = Key(
True, bool, "Label", "Set to false to hide x-labels not marked major")
y_labels = Key(
None, list, "Label",
"You can specify explicit y labels",
"Must be a list of numbers", float)
y_labels_major = Key(
None, list, "Label",
"Y labels that will be marked major. Default: auto",
subtype=str)
y_labels_major_every = Key(
None, int, "Label",
"Mark every n-th y label as major.")
y_labels_major_count = Key(
None, int, "Label",
"Mark n evenly distributed y labels as major.")
show_minor_y_labels = Key(
True, bool, "Label", "Set to false to hide y-labels not marked major")
show_y_labels = Key(
True, bool, "Label", "Set to false to hide y-labels")
x_label_rotation = Key(
0, int, "Label", "Specify x labels rotation angles", "in degrees")
y_label_rotation = Key(
0, int, "Label", "Specify y labels rotation angles", "in degrees")
x_label_format = Key(
"%Y-%m-%d %H:%M:%S.%f", str, "Label",
"Date format for strftime to display the DateY X labels")
missing_value_fill_truncation = Key(
"x", str, "Look",
"Filled series with missing x and/or y values at the end of a series "
"are closed at the first value with a missing "
"'x' (default), 'y' or 'either'")
# Value #
human_readable = Key(
False, bool, "Value", "Display values in human readable format",
"(ie: 12.4M)")
x_value_formatter = Key(
None, type(lambda: 1), "Value",
"A function to convert abscissa numeric value to strings "
"(used in XY and Date charts)")
value_formatter = Key(
None, type(lambda: 1), "Value",
"A function to convert numeric value to strings")
logarithmic = Key(
False, bool, "Value", "Display values in logarithmic scale")
interpolate = Key(
None, str, "Value", "Interpolation",
"May be %s" % ' or '.join(INTERPOLATIONS))
interpolation_precision = Key(
250, int, "Value", "Number of interpolated points between two values")
interpolation_parameters = Key(
{}, dict, "Value", "Various parameters for parametric interpolations",
"ie: For hermite interpolation, you can set the cardinal tension with"
"{'type': 'cardinal', 'c': .5}", int)
mode = Key(
None, str, "Value", "Sets the mode to be used. "
"(Currently only supported on box plot)",
"May be %s" % ' or '.join(["1.5IQR", "extremes"]))
order_min = Key(
None, int, "Value", "Minimum order of scale, defaults to None")
range = Key(
None, list, "Value", "Explicitly specify min and max of values",
"(ie: (0, 100))", int)
xrange = Key(
None, list, "Value", "Explicitly specify min and max of x values "
"(used in XY and Date charts)",
"(ie: (0, 100))", int)
include_x_axis = Key(
False, bool, "Value", "Always include x axis")
zero = Key(
0, int, "Value",
"Set the ordinate zero value",
"Useful for filling to another base than abscissa")
# Text #
no_data_text = Key(
"No data", str, "Text", "Text to display when no data is given")
label_font_size = Key(10, int, "Text", "Label font size")
major_label_font_size = Key(10, int, "Text", "Major label font size")
value_font_size = Key(8, int, "Text", "Value font size")
tooltip_font_size = Key(16, int, "Text", "Tooltip font size")
title_font_size = Key(16, int, "Text", "Title font size")
legend_font_size = Key(14, int, "Text", "Legend font size")
no_data_font_size = Key(64, int, "Text", "No data text font size")
print_values = Key(
False, bool,
"Text", "Print values when graph is in non interactive mode")
print_zeroes = Key(
False, bool,
"Text", "Print zeroes when graph is in non interactive mode")
truncate_legend = Key(
None, int, "Text",
"Legend string length truncation threshold", "None = auto")
truncate_label = Key(
None, int, "Text",
"Label string length truncation threshold", "None = auto")
# Misc #
js = Key(
('http://kozea.github.io/pygal.js/javascripts/svg.jquery.js',
'http://kozea.github.io/pygal.js/javascripts/pygal-tooltips.js'),
list, "Misc", "List of js file",
"It can be a filepath or an external link",
str)
disable_xml_declaration = Key(
False, bool, "Misc",
"Don't write xml declaration and return str instead of string",
"usefull for writing output directly in html")
explicit_size = Key(
False, bool, "Misc", "Write width and height attributes")
pretty_print = Key(
False, bool, "Misc", "Pretty print the svg")
strict = Key(
False, bool, "Misc",
"If True don't try to adapt / filter wrong values")
no_prefix = Key(
False, bool, "Misc",
"Don't prefix css")
inverse_y_axis = Key(False, bool, "Misc", "Inverse Y axis direction")
class SerieConfig(CommonConfig):
"""Class holding serie config values"""
secondary = Key(
False, bool, "Misc",
"Set it to put the serie in a second axis")
| [
"[email protected]"
] | |
77fe07d5e52bfe2f60ffef3a17d87c9c4778edbb | fa32f7fe4068323b719725558423927ad307cc4b | /build_isolated/roslaunch/catkin_generated/pkg.develspace.context.pc.py | 2b96196666b66142663c46a34c0c8bcc818b81b6 | [] | no_license | CJohnson5136/ros_catkin_ws | d07ee8c20bc1ebe6c05abdea24ef1f5dab14954b | 05193a7e587ab82e696c66176b151c43d2bcef82 | refs/heads/master | 2021-05-09T03:05:12.373334 | 2018-01-28T03:13:33 | 2018-01-28T03:13:33 | 119,227,181 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 391 | py | # generated from catkin/cmake/template/pkg.context.pc.in
CATKIN_PACKAGE_PREFIX = ""
PROJECT_PKG_CONFIG_INCLUDE_DIRS = "".split(';') if "" != "" else []
PROJECT_CATKIN_DEPENDS = "".replace(';', ' ')
PKG_CONFIG_LIBRARIES_WITH_PREFIX = "".split(';') if "" != "" else []
PROJECT_NAME = "roslaunch"
PROJECT_SPACE_DIR = "/home/pi/ros_catkin_ws/devel_isolated/roslaunch"
PROJECT_VERSION = "1.13.5"
| [
"[email protected]"
] | |
e2b23a1391b17cde86fbd36afbff414773d9903b | a66c079f250c5469e01b5ec5b00a795dbc9fa9a0 | /blog/admin.py | 8c315f065ee59a47926fb5507af9892b991febc6 | [
"MIT"
] | permissive | Cpeters1982/MillGeekV2 | b925f013ae9b95827bfc304a57b0e0dceabd7544 | e08a1366bbe732b0d7fc7a7802cd54ff1d1091bf | refs/heads/master | 2021-08-06T14:45:57.616281 | 2017-11-06T06:38:11 | 2017-11-06T06:38:11 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 489 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.contrib import admin
from .models import Post
class PostAdmin(admin.ModelAdmin):
list_display = ('title', 'slug', 'author', 'publish', 'status')
list_filter = ('status', 'created', 'publish', 'author')
search_fields = ('title', 'body')
prepopulated_fields = {'slug': ('title',)}
raw_id_fields = ('author',)
date_hierarchy = 'publish'
ordering = ['status', 'publish']
admin.site.register(Post, PostAdmin) | [
"[email protected]"
] | |
8a6970567b9013782b84557a586c6ce62b9f05a7 | 2d23c271ec1a226bb345c23d7b2671ec021e9502 | /Triangle.py | 74991744eea783b53c8a6721026897aac3e70a7c | [] | no_license | chenlanlan/leetcode | 2e6aec0846ed951466bcd2c2e4596c998faca8e4 | d02478853c32c29477f53852286c429c20f1424e | refs/heads/master | 2016-09-08T05:07:46.904441 | 2015-07-12T05:41:15 | 2015-07-12T05:41:15 | 32,845,795 | 5 | 1 | null | null | null | null | UTF-8 | Python | false | false | 790 | py | #!/usr/bin/python
class Solution:
# @param triangle, a list of lists of integers
# @return an integer
def minimumTotal(self, triangle):
sum = triangle
ans = triangle[0][0]
for i in range(1, len(triangle)):
for j in range(0, i + 1):
if j == 0:
sum[i][j] = sum[i - 1][j] + triangle[i][j]
ans = sum[i][j]
elif j == i:
sum[i][j] = sum[i - 1][j - 1] + triangle[i][j]
else:
sum[i][j] = min(sum[i - 1][j - 1], sum[i - 1][j]) + triangle[i][j]
if sum[i][j] < ans:
ans = sum[i][j]
return ans
x = Solution()
print(x.minimumTotal([
[2],
[3,4],
[6,5,7],
[4,1,8,3]
]))
| [
"[email protected]"
] | |
44e995678e486ed927b284dd2cac84cdbca80e09 | a722faf9fb50c794555861bb4858c3ed8a7a25f3 | /sandbox/peachpy_avx_practice/logic.py | c3fc2c739060b29e44d01407d874555685f434b3 | [] | no_license | ar90n/lab | 31e5d2c320de5618bc37572011596fee8923255d | 6d035e12f743e9ba984e79bfe660967b9ca8716b | refs/heads/main | 2023-07-25T17:29:57.960915 | 2023-07-22T12:08:18 | 2023-07-22T12:08:18 | 77,883,405 | 4 | 0 | null | 2023-07-17T08:45:14 | 2017-01-03T04:15:49 | Jupyter Notebook | UTF-8 | Python | false | false | 1,678 | py | from peachpy import *
from peachpy.x86_64 import *
import numpy as np
import ctypes
def gen_andnot_ps():
x = Argument(ptr(const_float_))
y = Argument(ptr(const_float_))
z = Argument(ptr(float_))
with Function("AndNot", (x, y, z), target=uarch.default + isa.avx2) as asm_function:
reg_x = GeneralPurposeRegister64()
LOAD.ARGUMENT(reg_x, x)
reg_y = GeneralPurposeRegister64()
LOAD.ARGUMENT(reg_y, y)
reg_z = GeneralPurposeRegister64()
LOAD.ARGUMENT(reg_z, z)
ymm = YMMRegister()
VMOVUPD(ymm, [reg_x])
VANDNPS(ymm, ymm, [reg_y])
VMOVUPD([reg_z], ymm)
RETURN()
return asm_function.finalize(abi.detect()).encode().load()
andnot_ps = gen_andnot_ps()
x = np.array(
[
0x59595959,
0x59595959,
0x59595959,
0x59595959,
0x59595959,
0x59595959,
0x59595959,
0x59595959,
],
dtype=np.uint32,
)
y = np.array(
[
0x95959595,
0x95959595,
0x95959595,
0x95959595,
0x95959595,
0x95959595,
0x95959595,
0x95959595,
],
dtype=np.uint32,
)
z = np.empty(8, dtype=np.uint32)
andnot_ps(
x.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
y.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
z.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
)
assert np.allclose(
z,
np.array(
[
0x84848484,
0x84848484,
0x84848484,
0x84848484,
0x84848484,
0x84848484,
0x84848484,
0x84848484,
],
dtype=np.uint32,
),
)
| [
"[email protected]"
] | |
f714a0095b6e307d7d0c605551e5062b707f474e | 644d9ef18713e4cb5d4c3b53301bd7276dcdf477 | /api/programs/views/shared_files.py | 16753e871ff23a7486b45fe52c1b5af4a82dca6c | [] | no_license | alexhernandez-git/django-classline | 6cb5bcd268248999e18037f58c4ed30012d51915 | 49fcf0c6d735a56eaebc17d04be52dab91ca4c3a | refs/heads/master | 2023-03-18T07:10:08.770066 | 2021-03-04T22:24:09 | 2021-03-04T22:24:09 | 287,985,028 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,552 | py | """Program views."""
# Django REST Framework
from rest_framework import mixins, viewsets, status, filters
from rest_framework.response import Response
from rest_framework.decorators import action
from rest_framework.filters import SearchFilter, OrderingFilter
from django_filters.rest_framework import DjangoFilterBackend
# Permissions
from rest_framework.permissions import IsAuthenticated
# Filters
from rest_framework.filters import SearchFilter, OrderingFilter
from django_filters.rest_framework import DjangoFilterBackend
# Models
from api.programs.models import File
# Serializers
from api.programs.serializers import (
FileModelSerializer,
ShareUsersFilesSerializer
)
from api.programs.serializers.subscriptions import(
SubscriptionSignUpSerializer
)
from api.users.serializers import (
ProfileModelSerializer,
UserWithoutTeacherModelSerializer
)
import stripe
# Utils
from api.utils.permissions import AddProgramMixin
class SharedFileViewSet(mixins.CreateModelMixin,
mixins.ListModelMixin,
mixins.RetrieveModelMixin,
mixins.UpdateModelMixin,
mixins.DestroyModelMixin,
AddProgramMixin):
"""Circle view set."""
pagination_class = None
serializer_class = FileModelSerializer
lookup_field = 'pk'
queryset = File.objects.all()
filter_backends = [filters.SearchFilter]
search_fields = ['name']
def get_queryset(self):
"""Restrict list to public-only."""
queryset = File.objects.filter(program=self.program)
return queryset
def get_permissions(self):
"""Assign permissions based on action."""
permissions = []
return [permission() for permission in permissions]
def list(self, request, *args, **kwargs):
if 'top_folder' in request.GET and request.GET['top_folder']:
queryset = self.get_queryset().filter(
top_folder=request.GET['top_folder'])
else:
queryset = self.get_queryset().filter(shared_users=request.user)
queryset = self.filter_queryset(queryset)
page = self.paginate_queryset(queryset)
if page is not None:
serializer = self.get_serializer(page, many=True)
return self.get_paginated_response(serializer.data)
serializer = self.get_serializer(queryset, many=True)
return Response(serializer.data)
| [
"[email protected]"
] | |
6e1f6880a6caf5cffabb380e12829f1606c3e98f | 942ee5e8d54e8ebe9c5c841fbfdd1da652946944 | /1501-2000/1751.Maximum Number of Events That Can Be Attended II.py | 9344ddbdab3c4b8f5e076a9e4c18942fe3232650 | [] | no_license | kaiwensun/leetcode | 0129c174457f32887fbca078fb448adce46dd89d | 6b607f4aae3a4603e61f2e2b7480fdfba1d9b947 | refs/heads/master | 2023-08-31T07:30:50.459062 | 2023-08-27T07:59:16 | 2023-08-27T07:59:16 | 57,526,914 | 69 | 9 | null | 2023-08-20T06:34:41 | 2016-05-01T05:37:29 | Python | UTF-8 | Python | false | false | 756 | py | import functools, collections, bisect
class Solution:
def maxValue(self, events: List[List[int]], k: int) -> int:
end2eid = collections.defaultdict(list)
for i in range(len(events)):
end2eid[events[i][1]].append(i)
end_days = list(sorted(end2eid.keys()))
@functools.lru_cache(None)
def dp(end_day_index, count):
if count == 0 or end_day_index == -1:
return 0
res = dp(end_day_index - 1, count)
for eid in end2eid[end_days[end_day_index]]:
start, end, value = events[eid]
res = max(res, dp(bisect.bisect_left(end_days, start) - 1, count - 1) + value)
return res
return dp(len(end_days) - 1, k)
| [
"[email protected]"
] | |
a9ef067613f57b825d3533520dc3ed43f8292ccf | c0bf1f7ca6d9d7562f72b4a668e97a2d5ffe7c88 | /examples/thread_matmul_ipxact/thread_matmul_ipxact.py | 0ce4d4f6801adc0539c36f4b0974578f5403318c | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | 00mjk/veriloggen | cee0da16182c3c9bd95340a966d6a3febc0e7ad1 | 9d0af9638470b3b85cbf9cb53f16b853932571c8 | refs/heads/master | 2023-06-23T07:10:20.645734 | 2021-07-18T14:53:13 | 2021-07-18T14:53:13 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,682 | py | from __future__ import absolute_import
from __future__ import print_function
import sys
import os
import numpy as np
# the next line can be removed after installation
sys.path.insert(0, os.path.dirname(os.path.dirname(
os.path.dirname(os.path.abspath(__file__)))))
from veriloggen import *
import veriloggen.thread as vthread
import veriloggen.types.axi as axi
import veriloggen.types.ipxact as ipxact
axi_datawidth = 32
datawidth = 32
matrix_size = 16
a_offset = 0
b_offset = 4096
c_offset = 4096 * 2
def mkLed():
m = Module('blinkled')
clk = m.Input('CLK')
rst = m.Input('RST')
addrwidth = 10
ram_a = vthread.RAM(m, 'ram_a', clk, rst, datawidth, addrwidth)
ram_b = vthread.RAM(m, 'ram_b', clk, rst, datawidth, addrwidth)
ram_c = vthread.RAM(m, 'ram_c', clk, rst, datawidth, addrwidth)
maxi = vthread.AXIM(m, 'maxi', clk, rst, datawidth)
saxi = vthread.AXISLiteRegister(m, 'saxi', clk, rst, datawidth, length=8)
def matmul():
while True:
saxi.wait_flag(0, value=1, resetvalue=0)
matrix_size = saxi.read(1)
a_offset = saxi.read(2)
b_offset = saxi.read(3)
c_offset = saxi.read(4)
comp(matrix_size, a_offset, b_offset, c_offset)
saxi.write_flag(5, 1, resetvalue=0)
def comp(matrix_size, a_offset, b_offset, c_offset):
a_addr, c_addr = a_offset, c_offset
for i in range(matrix_size):
maxi.dma_read(ram_a, 0, a_addr, matrix_size)
b_addr = b_offset
for j in range(matrix_size):
maxi.dma_read(ram_b, 0, b_addr, matrix_size)
sum = 0
for k in range(matrix_size):
x = ram_a.read(k)
y = ram_b.read(k)
sum += x * y
ram_c.write(j, sum)
b_addr += matrix_size * (datawidth // 8)
maxi.dma_write(ram_c, 0, c_addr, matrix_size)
a_addr += matrix_size * (datawidth // 8)
c_addr += matrix_size * (datawidth // 8)
th = vthread.Thread(m, 'th_matmul', clk, rst, matmul)
fsm = th.start()
return m
def mkTest(memimg_name=None):
a_shape = (matrix_size, matrix_size)
b_shape = (matrix_size, matrix_size)
c_shape = (a_shape[0], b_shape[0])
n_raw_a = axi.shape_to_length(a_shape)
n_raw_b = axi.shape_to_length(b_shape)
n_a = axi.shape_to_memory_size(a_shape, datawidth)
n_b = axi.shape_to_memory_size(b_shape, datawidth)
a = np.zeros(a_shape, dtype=np.int64)
b = np.zeros(b_shape, dtype=np.int64)
value = 1
for y in range(a_shape[0]):
for x in range(a_shape[1]):
if x == y:
a[y][x] = value
value += 1
else:
a[y][x] = 0
for y in range(b_shape[0]):
for x in range(b_shape[1]):
if x == y:
b[y][x] = 2
else:
b[y][x] = 0
a_addr = a_offset
size_a = n_a * datawidth // 8
b_addr = b_offset
size_b = n_b * datawidth // 8
mem = np.zeros([1024 * 1024 * 8 // axi_datawidth], dtype=np.int64)
axi.set_memory(mem, a, axi_datawidth, datawidth, a_addr)
axi.set_memory(mem, b, axi_datawidth, datawidth, b_addr)
led = mkLed()
m = Module('test')
params = m.copy_params(led)
ports = m.copy_sim_ports(led)
clk = ports['CLK']
rst = ports['RST']
memory = axi.AxiMemoryModel(m, 'memory', clk, rst,
mem_datawidth=axi_datawidth,
memimg=mem, memimg_name=memimg_name)
memory.connect(ports, 'maxi')
# AXI-Slave controller
_saxi = vthread.AXIMLite(m, '_saxi', clk, rst, noio=True)
_saxi.connect(ports, 'saxi')
# Timer
counter = m.Reg('counter', 32, initval=0)
seq = Seq(m, 'seq', clk, rst)
seq(
counter.inc()
)
def ctrl():
for i in range(100):
pass
awaddr = 4
print('# matrix_size = %d' % matrix_size)
_saxi.write(awaddr, matrix_size)
awaddr = 8
print('# a_offset = %d' % a_offset)
_saxi.write(awaddr, a_offset)
awaddr = 12
print('# b_offset = %d' % b_offset)
_saxi.write(awaddr, b_offset)
awaddr = 16
print('# c_offset = %d' % c_offset)
_saxi.write(awaddr, c_offset)
awaddr = 0
start_time = counter
print('# start time = %d' % start_time)
_saxi.write(awaddr, 1)
araddr = 20
v = _saxi.read(araddr)
while v == 0:
v = _saxi.read(araddr)
end_time = counter
print('# end time = %d' % end_time)
time = end_time - start_time
print('# exec time = %d' % time)
all_ok = True
for y in range(matrix_size):
for x in range(matrix_size):
v = memory.read(
c_offset + (y * matrix_size + x) * datawidth // 8)
if y == x and vthread.verilog.NotEql(v, (y + 1) * 2):
all_ok = False
print("NG [%d,%d] = %d" % (y, x, v))
if y != x and vthread.verilog.NotEql(v, 0):
all_ok = False
print("NG [%d,%d] = %d" % (y, x, v))
if all_ok:
print('# verify: PASSED')
else:
print('# verify: FAILED')
vthread.finish()
th = vthread.Thread(m, 'th_ctrl', clk, rst, ctrl)
fsm = th.start()
uut = m.Instance(led, 'uut',
params=m.connect_params(led),
ports=m.connect_ports(led))
simulation.setup_waveform(m, uut)
simulation.setup_clock(m, clk, hperiod=5)
init = simulation.setup_reset(m, rst, m.make_reset(), period=100)
init.add(
Delay(1000000),
Systask('finish'),
)
return m
def run(filename='tmp.v', simtype='iverilog', outputfile=None):
if outputfile is None:
outputfile = os.path.splitext(os.path.basename(__file__))[0] + '.out'
memimg_name = 'memimg_' + outputfile
test = mkTest(memimg_name=memimg_name)
if filename is not None:
test.to_verilog(filename)
sim = simulation.Simulator(test, sim=simtype)
rslt = sim.run(outputfile=outputfile)
lines = rslt.splitlines()
if simtype == 'verilator' and lines[-1].startswith('-'):
rslt = '\n'.join(lines[:-1])
return rslt
if __name__ == '__main__':
rslt = run(filename='tmp.v')
print(rslt)
m = mkLed()
ipxact.to_ipxact(m,
clk_ports=[('CLK', ('RST',))],
rst_ports=[('RST', 'ACTIVE_HIGH')])
| [
"[email protected]"
] | |
694cbb1a9d0f58df9ff2f2a939cac87b4136ff2c | 2ff7e53d5e512cd762217ca54317982e07a2bb0c | /zactionConst.py | e0a0d9c1bc4143546dda68c8a1f4581f9655fb66 | [] | no_license | nanxijw/Clara-Pretty-One-Dick | 66d3d69426642b79e8fd4cc8e0bec23adeeca6d6 | 50de3488a2140343c364efc2615cf6e67f152be0 | refs/heads/master | 2021-01-19T09:25:07.555284 | 2015-02-17T21:49:33 | 2015-02-17T21:49:33 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,359 | py | #Embedded file name: zactionConst.py
"""
Constants for the Action System
"""
ACTION_SCHEMA = 'zaction'
_ACTION_PREFIX = ACTION_SCHEMA + '.'
_ACTION_TREE_TABLE_NAME = 'trees'
_ACTION_TREE_TABLE_FULL_PATH = _ACTION_PREFIX + _ACTION_TREE_TABLE_NAME
_ACTION_TREE_LINK_TABLE_NAME = 'treeLinks'
_ACTION_TREE_LINK_TABLE_FULL_PATH = _ACTION_PREFIX + _ACTION_TREE_LINK_TABLE_NAME
ACTION_STEP_TABLE_NAME = 'steps'
ACTION_STEP_TABLE_FULL_PATH = _ACTION_PREFIX + ACTION_STEP_TABLE_NAME
ACTION_PROC_TABLE_NAME = 'procs'
ACTION_PROC_TABLE_FULL_PATH = _ACTION_PREFIX + ACTION_PROC_TABLE_NAME
_ACTION_PROPERTY_TABLE_NAME = 'properties'
_ACTION_PROPERTY_TABLE_FULL_PATH = _ACTION_PREFIX + _ACTION_PROPERTY_TABLE_NAME
ACTION_PROC_TYPE_TABLE_NAME = 'procTypes'
ACTION_PROC_TYPE_TABLE_FULL_PATH = _ACTION_PREFIX + ACTION_PROC_TYPE_TABLE_NAME
ACTION_PROC_TYPE_PROPERTY_TABLE_NAME = 'procTypeProperties'
ACTION_PROC_TYPE_PROPERTY_TABLE_FULL_PATH = _ACTION_PREFIX + ACTION_PROC_TYPE_PROPERTY_TABLE_NAME
MAX_PROP_NAME_LEN = 64
SELECT_ACTION_TREE = 'SelectActionTree'
SELECT_ACTION_STEP = 'SelectActionStep'
SELECT_ACTION_PROC = 'SelectActionProc'
ACTIONSTEP_TYPE_NORMAL = 'Normal'
ACTIONSTEP_TYPE_CONDITIONAL = 'Conditional'
ACTIONSTEP_TYPE_TRY = 'Try Until'
ACTIONSTEP_TYPE_CATCH = 'Catch'
ACTIONSTEP_TYPE_TRYSYNC = 'Try Sync'
ACTIONSTEP_TYPE_WHILE = 'While'
ACTIONSTEP_TYPE_PREREQ = 'Prerequisite'
ACTIONSTEP_TYPEID_NORMAL = 0
ACTIONSTEP_TYPEID_CONDITIONAL = 1
ACTIONSTEP_TYPEID_TRY = 2
ACTIONSTEP_TYPEID_CATCH = 3
ACTIONSTEP_TYPEID_TRYSYNC = 4
ACTIONSTEP_TYPEID_WHILE = 5
ACTIONSTEP_TYPEID_PREREQ = -1
ACTION_STEP_TYPEID_TO_STRING = (ACTIONSTEP_TYPE_NORMAL,
ACTIONSTEP_TYPE_CONDITIONAL,
ACTIONSTEP_TYPE_TRY,
ACTIONSTEP_TYPE_CATCH,
ACTIONSTEP_TYPE_TRYSYNC,
ACTIONSTEP_TYPE_WHILE,
ACTIONSTEP_TYPE_PREREQ)
ACTION_STEP_TYPE_CONDITIONALS = [ACTIONSTEP_TYPE_CONDITIONAL,
ACTIONSTEP_TYPE_TRY,
ACTIONSTEP_TYPE_WHILE,
ACTIONSTEP_TYPE_PREREQ]
ACTION_STEP_TYPE_NORMALS = [ACTIONSTEP_TYPE_NORMAL, ACTIONSTEP_TYPE_CATCH, ACTIONSTEP_TYPE_TRYSYNC]
ACTIONSTEP_LOC_CLIENTSERVER = 'Client and Server'
ACTIONSTEP_LOC_CLIENTONLY = 'Client Only'
ACTIONSTEP_LOC_SERVERONLY = 'Server Only'
ACTIONSTEP_LOCID_CLIENTSERVER = 0
ACTIONSTEP_LOCID_CLIENTONLY = 1
ACTIONSTEP_LOCID_SERVERONLY = 2
ACTION_STEP_LOCID_TO_STRING = (ACTIONSTEP_LOC_CLIENTSERVER, ACTIONSTEP_LOC_CLIENTONLY, ACTIONSTEP_LOC_SERVERONLY)
ACTIONSTEP_FLAG_ELSE = 1
ACTIONSTEP_FLAG_CATCH = 2
ACTIONSTEP_FLAGS = {ACTIONSTEP_FLAG_ELSE: 'Else',
ACTIONSTEP_FLAG_CATCH: 'Catch'}
_ACTION_INTERNAL = 0
_ACTION_EXPOSED = 1
ACTION_TREE_LINK_REFERENCE = 1
ACTION_TREE_LINK_BRANCH = 2
ACTION_TREE_LINK_TYPES = [ACTION_TREE_LINK_REFERENCE, ACTION_TREE_LINK_BRANCH]
ACTION_TREE_LINK_NAMES = {'In-Place': ACTION_TREE_LINK_REFERENCE,
'Branch-To': ACTION_TREE_LINK_BRANCH}
_ACTION_LINK_BIT_ORDER_SPLIT = 16
_ACTION_LINK_TYPE_BIT_FILTER = 65535
_ACTION_LINK_TYPE_MAX_VALUE = 65535
_ACTION_LINK_EXPOSURE_BIT_FILTER = 4294901760L
_ACTION_LINK_EXPOSURE_MAX_VALUE = 65535
ACTION_EXPOSURE_EXTERNAL = 0
ACTION_EXPOSURE_INTERNAL = 1
ACTION_EXPOSURE_NEVER = 2
ACTION_EXPOSURE_TYPES = [ACTION_EXPOSURE_EXTERNAL, ACTION_EXPOSURE_INTERNAL, ACTION_EXPOSURE_NEVER]
ACTION_EXPOSURE_TYPE_NAMES = {'External': ACTION_EXPOSURE_EXTERNAL,
'Internal': ACTION_EXPOSURE_INTERNAL,
'Never': ACTION_EXPOSURE_NEVER}
ACTIONTREE_RECIPE_DEFAULT_ACTION_NAME = 'defaultAction'
| [
"[email protected]"
] | |
2d8ff7c75391eb1e4653770f6cae51129252f4a2 | 8ff5bd7d22b578678fe7225a84f82cea5eafa25a | /Backend/todoapps/todo/apps/forms.py | f4b8eed46fe38dbebdb1842a7b754f591f1a3bf1 | [] | no_license | Jayson7/Mumswhocode-bootcamp-frontend-basics | 33639eb40b48cba6aca99ca4d60e7119ab9739bf | de05098e44c13077a9402074811525d53b580b9c | refs/heads/master | 2023-08-15T03:45:07.848603 | 2021-09-09T22:58:27 | 2021-09-09T22:58:27 | 399,247,043 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 168 | py | from django.forms import ModelForm
from .models import Todo
class Todoforms(ModelForm):
class Meta:
model = Todo
fields = "__all__"
| [
"[email protected]"
] | |
f45e0da10b926bc1517347097b9438b886b636fe | 8015f1c62a2cb4efd21aa8938336913bf8117868 | /bamap/ba2738.pngMap.py | c2eaef6280a4d4f3f5199ee2ca4ceeb4ff981c88 | [] | no_license | GamerNoTitle/Beepers-and-OLED | 675b5e3c179df0f0e27b42bf594c43860d03b9af | afe1340e5394ae96bda5f9022a8a66824368091e | refs/heads/master | 2020-04-20T00:09:47.122471 | 2019-04-29T04:59:35 | 2019-04-29T04:59:35 | 168,515,579 | 4 | 2 | null | null | null | null | UTF-8 | Python | false | false | 8,468 | py | ba2738.pngMap = [
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
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'00000000000000000000000001111111111111111111111100000000000000000000000000000000000000100000110000000000000000000000000000000000',
'00000000000000000000000111111111111111111111111100000000000000000000000000000000000001110001110000000000000000000000000000000000',
]
| [
"[email protected]"
] | |
0c8715456aea5a32d0a4f5330b7b558b9fd2d9ca | 5acc2eb70ed8b755d7f6b62b65a09cc29b661271 | /Aula 10/atv01.py | c376629dd2744f4bba8899ed5d2c8d19113ba60a | [] | no_license | Yuri-Santiago/sor-python-ifce-p7 | 07d1a30f2c304a0a11a2a39b40784cc543f4a18c | ccd3460ecab580e23fb41921ee7cc284d7212aef | refs/heads/master | 2023-05-28T08:32:13.188126 | 2021-06-06T22:20:14 | 2021-06-06T22:20:14 | 350,816,877 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,329 | py | """
1 - Criar uma classe que modele retângulos.
1. Atributos: LadoA, LadoB (ou Comprimento e Largura, ou Base e Altura, a escolher)
2. Métodos: Mudar valor dos lados, Retornar valor dos lados, calcular Área e calcular Perímetro;
3. Crie um programa que utilize esta classe. Ele deve pedir ao usuário que informe as medidas de um cômodo.
Depois, deve criar um objeto com as medidas e calcular a quantidade de pisos e de rodapés necessárias para o local.
"""
class Retangulo:
def __init__(self, lado_a, lado_b):
self.lado_a = lado_a
self.lado_b = lado_b
def mudar_valor_lados(self, a, b):
self.lado_a = a
self.lado_b = b
def retornar_valor_lados(self):
return self.lado_a, self.lado_b
def calcular_area(self):
return self.lado_a * self.lado_b
def calcular_perimetro(self):
return self.lado_a * 2 + self.lado_b * 2
print("Nesse programa você deverá informar as medidas de um cômodo em metros.")
comprimento = float(input("Digite o valor do comprimento do Cômodo: "))
largura = float(input("Digite o valor do largura do Cômodo: "))
comodo = Retangulo(comprimento, largura)
print(f'Você usará {comodo.calcular_area()} m quadrados de piso.')
print(f'Você usará {comodo.calcular_perimetro()} m quadrados de rodapé.')
| [
"[email protected]"
] | |
ffcab0aa4429d00f96f023703c2dbcbc06e49257 | 526986209fbf00e85c98648600f5f58575d54096 | /messages/pep8/E101.py | e88e05d0d2cf11f1a10caebbeb070b49f491d889 | [
"Unlicense"
] | permissive | landscape-test/all-messages | 126b67d2fc395018121a40787ca92762a03f4873 | f349ac581f3a149b30591d7a9149629b9bf539cb | refs/heads/master | 2020-06-02T08:36:53.946720 | 2014-01-10T06:36:05 | 2014-01-10T06:36:05 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 57 | py | """
E101
Indentation contains mixed spaces and tabs
"""
| [
"[email protected]"
] | |
0afcf429e50ba86c7d155d63ceed8647dcab0bf3 | 3327a87cefa2275bd0ba90a500444f3494b14fdf | /bwu/stack/225-implement-stack-using-queues.py | 4bc2aee9364af8063e2c1b101d5f1e0e53ac5d4d | [] | no_license | captainhcg/leetcode-in-py-and-go | e1b56f4228e0d60feff8f36eb3d457052a0c8d61 | 88a822c48ef50187507d0f75ce65ecc39e849839 | refs/heads/master | 2021-06-09T07:27:20.358074 | 2017-01-07T00:23:10 | 2017-01-07T00:23:10 | 61,697,502 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 761 | py | class Stack(object):
def __init__(self):
"""
initialize your data structure here.
"""
self.queue1, self.queue2 = [], []
def push(self, x):
"""
:type x: int
:rtype: nothing
"""
self.queue1.append(x)
def pop(self):
"""
:rtype: nothing
"""
while len(self.queue1) > 1:
self.queue2.append(self.queue1.pop(0))
self.queue1.pop(0)
while len(self.queue2):
self.queue1.append(self.queue2.pop(0))
def top(self):
"""
:rtype: int
"""
return self.queue1[-1]
def empty(self):
"""
:rtype: bool
"""
return len(self.queue1) == 0
| [
"[email protected]"
] | |
ab2a4a29a09b53ddb57516f07e70cf10806af844 | 89621b6ca971efb67b764efc5fe76d7cd2f187d7 | /elife_bus_sdk/exceptions.py | c6bfc832551238623bbbb31b29a4f4d369825641 | [
"MIT"
] | permissive | elifesciences/bus-sdk-python | f3e91e0bf429610c26222ff3be80ac9f624ac80e | 419d5a88c5393729d4f884edf41390f3bc5c86b8 | refs/heads/master | 2022-08-08T17:45:15.873473 | 2022-07-26T02:35:03 | 2022-07-26T02:35:03 | 108,875,988 | 0 | 0 | MIT | 2022-07-26T02:35:04 | 2017-10-30T16:12:23 | Python | UTF-8 | Python | false | false | 105 | py |
class PublisherTypeNotFound(Exception):
pass
class MessageQueueTypeNotFound(Exception):
pass
| [
"[email protected]"
] | |
05269f28d5f43d0869e568da2498e7a8ccdff3b6 | 1e6e3bb707920fdb01ebca23eaf81097c558d918 | /tests/system/migrations/test_0013_archived_meeting_ids.py | f69d1c6db3a7b9cb3f778da5aa8b75117e4fb12b | [
"MIT"
] | permissive | OpenSlides/openslides-backend | cbd24589f82a6f29bde02611610511870bb6abbf | d8511f5138db4cc5fe4fa35e2a0200f766bd49c5 | refs/heads/main | 2023-08-23T11:54:25.064070 | 2023-08-22T11:15:45 | 2023-08-22T11:15:45 | 231,757,840 | 6 | 22 | MIT | 2023-09-14T16:23:41 | 2020-01-04T12:17:38 | Python | UTF-8 | Python | false | false | 13,194 | py | ONE_ORGANIZATION_FQID = "organization/1"
def test_migration(write, finalize, assert_model):
write(
{
"type": "create",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 1},
},
{
"type": "create",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": [1]},
},
)
write(
{"type": "delete", "fqid": "meeting/1", "fields": {}},
)
write(
{"type": "restore", "fqid": "meeting/1", "fields": {}},
)
finalize("0013_archived_meeting_ids")
assert_model(
ONE_ORGANIZATION_FQID,
{"meta_deleted": False, "meta_position": 1, "active_meeting_ids": [1]},
position=1,
)
assert_model(
"meeting/1",
{"is_active_in_organization_id": 1, "meta_deleted": False, "meta_position": 1},
position=1,
)
assert_model(
"meeting/1",
{"is_active_in_organization_id": 1, "meta_deleted": True, "meta_position": 2},
position=2,
)
assert_model(
"meeting/1",
{"is_active_in_organization_id": 1, "meta_deleted": False, "meta_position": 3},
position=3,
)
def test_one_created_archived_meeting(write, finalize, assert_model):
write(
{
"type": "create",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 0},
},
{
"type": "create",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {},
},
)
write(
{
"type": "delete",
"fqid": "meeting/1",
"fields": {},
},
{
"type": "restore",
"fqid": "meeting/1",
"fields": {},
},
{
"type": "delete",
"fqid": "meeting/1",
"fields": {},
},
)
finalize("0013_archived_meeting_ids")
assert_model(
ONE_ORGANIZATION_FQID,
{"archived_meeting_ids": [1], "meta_deleted": False, "meta_position": 1},
position=1,
)
assert_model(
"meeting/1",
{
"is_archived_in_organization_id": 1,
"is_active_in_organization_id": 0,
"meta_deleted": False,
"meta_position": 1,
},
position=1,
)
assert_model(
ONE_ORGANIZATION_FQID,
{"archived_meeting_ids": [], "meta_deleted": False, "meta_position": 2},
position=2,
)
assert_model(
"meeting/1",
{
"is_archived_in_organization_id": 1,
"is_active_in_organization_id": 0,
"meta_deleted": True,
"meta_position": 2,
},
position=2,
)
def test_update_meeting(write, finalize, assert_model):
write(
{
"type": "create",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 1},
},
{
"type": "create",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": [1]},
},
)
write(
{
"type": "update",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": None},
},
{
"type": "update",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": None},
},
)
write(
{
"type": "update",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 1},
},
{
"type": "update",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": [1]},
},
)
finalize("0013_archived_meeting_ids")
assert_model(
"meeting/1",
{"is_active_in_organization_id": 1, "meta_deleted": False, "meta_position": 1},
position=1,
)
assert_model(
ONE_ORGANIZATION_FQID,
{"active_meeting_ids": [1], "meta_deleted": False, "meta_position": 1},
position=1,
)
assert_model(
"meeting/1",
{
"is_archived_in_organization_id": 1,
"meta_deleted": False,
"meta_position": 2,
},
position=2,
)
assert_model(
ONE_ORGANIZATION_FQID,
{"archived_meeting_ids": [1], "meta_deleted": False, "meta_position": 2},
position=2,
)
assert_model(
"meeting/1",
{
"is_active_in_organization_id": 1,
"meta_deleted": False,
"meta_position": 3,
},
position=3,
)
assert_model(
ONE_ORGANIZATION_FQID,
{
"active_meeting_ids": [1],
"archived_meeting_ids": [],
"meta_deleted": False,
"meta_position": 3,
},
position=3,
)
def test_create_delete_in_one_position(write, finalize, assert_model):
write(
{
"type": "create",
"fqid": "meeting/1",
"fields": {},
},
{
"type": "create",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {},
},
{
"type": "delete",
"fqid": "meeting/1",
"fields": {},
},
)
finalize("0013_archived_meeting_ids")
assert_model(
ONE_ORGANIZATION_FQID, {"meta_deleted": False, "meta_position": 1}, position=1
)
def test_single_restore(write, finalize, assert_model):
write(
{
"type": "create",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 0},
},
{
"type": "create",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {},
},
{
"type": "delete",
"fqid": "meeting/1",
"fields": {},
},
)
write(
{
"type": "restore",
"fqid": "meeting/1",
"fields": {},
}
)
finalize("0013_archived_meeting_ids")
assert_model(
ONE_ORGANIZATION_FQID,
{"archived_meeting_ids": [1], "meta_deleted": False, "meta_position": 2},
position=2,
)
assert_model(
"meeting/1",
{
"is_archived_in_organization_id": 1,
"is_active_in_organization_id": 0,
"meta_deleted": False,
"meta_position": 2,
},
position=2,
)
def test_create_and_update_in_one_position(write, finalize, assert_model):
write(
{
"type": "create",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 1},
},
{
"type": "create",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": [1]},
},
{
"type": "update",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 0},
},
{
"type": "update",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": None},
},
)
finalize("0013_archived_meeting_ids")
assert_model(
ONE_ORGANIZATION_FQID,
{"archived_meeting_ids": [1], "meta_deleted": False, "meta_position": 1},
position=1,
)
assert_model(
"meeting/1",
{
"is_active_in_organization_id": 0,
"is_archived_in_organization_id": 1,
"meta_deleted": False,
"meta_position": 1,
},
position=1,
)
def test_two_updates_in_a_position(write, finalize, assert_model):
write(
{
"type": "create",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 0},
},
{
"type": "create",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": []},
},
)
write(
{
"type": "update",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 1},
},
{
"type": "update",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": [1]},
},
{
"type": "update",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 0},
},
{
"type": "update",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": []},
},
)
finalize("0013_archived_meeting_ids")
assert_model(
ONE_ORGANIZATION_FQID,
{
"archived_meeting_ids": [1],
"active_meeting_ids": [],
"meta_deleted": False,
"meta_position": 2,
},
position=2,
)
assert_model(
"meeting/1",
{
"is_active_in_organization_id": 0,
"is_archived_in_organization_id": 1,
"meta_deleted": False,
"meta_position": 2,
},
position=2,
)
def test_create_and_two_updates(write, finalize, assert_model):
write(
{
"type": "create",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 0},
},
{
"type": "create",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": []},
},
{
"type": "update",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 1},
},
{
"type": "update",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": [1]},
},
{
"type": "update",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 0},
},
{
"type": "update",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": []},
},
)
finalize("0013_archived_meeting_ids")
assert_model(
ONE_ORGANIZATION_FQID,
{
"archived_meeting_ids": [1],
"active_meeting_ids": [],
"meta_deleted": False,
"meta_position": 1,
},
position=1,
)
assert_model(
"meeting/1",
{
"is_active_in_organization_id": 0,
"is_archived_in_organization_id": 1,
"meta_deleted": False,
"meta_position": 1,
},
position=1,
)
def test_delete_fields_1(write, finalize, assert_model):
write(
{
"type": "create",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 1},
},
{
"type": "create",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": [1]},
},
{
"type": "update",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": None},
},
{
"type": "update",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": []},
},
)
finalize("0013_archived_meeting_ids")
assert_model(
ONE_ORGANIZATION_FQID,
{
"archived_meeting_ids": [1],
"active_meeting_ids": [],
"meta_deleted": False,
"meta_position": 1,
},
position=1,
)
assert_model(
"meeting/1",
{
"is_archived_in_organization_id": 1,
"meta_deleted": False,
"meta_position": 1,
},
position=1,
)
def test_delete_fields_2(write, finalize, assert_model):
write(
{
"type": "create",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 0},
},
{
"type": "create",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": []},
},
)
write(
{
"type": "update",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": 1},
},
{
"type": "update",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": [1]},
},
{
"type": "update",
"fqid": "meeting/1",
"fields": {"is_active_in_organization_id": None},
},
{
"type": "update",
"fqid": ONE_ORGANIZATION_FQID,
"fields": {"active_meeting_ids": []},
},
)
finalize("0013_archived_meeting_ids")
assert_model(
"meeting/1",
{
"is_archived_in_organization_id": 1,
"meta_deleted": False,
"meta_position": 2,
},
position=2,
)
assert_model(
ONE_ORGANIZATION_FQID,
{
"active_meeting_ids": [],
"archived_meeting_ids": [1],
"meta_deleted": False,
"meta_position": 2,
},
position=2,
)
| [
"[email protected]"
] | |
df2e7687f497b9de3efad226520196e5f835d697 | 971e0efcc68b8f7cfb1040c38008426f7bcf9d2e | /tests/artificial/transf_Logit/trend_ConstantTrend/cycle_5/ar_12/test_artificial_1024_Logit_ConstantTrend_5_12_0.py | 5b95e6cd8a0927e6f837267cb4099daf8fe96f20 | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | antoinecarme/pyaf | a105d172c2e7544f8d580d75f28b751351dd83b6 | b12db77cb3fa9292e774b2b33db8ce732647c35e | refs/heads/master | 2023-09-01T09:30:59.967219 | 2023-07-28T20:15:53 | 2023-07-28T20:15:53 | 70,790,978 | 457 | 77 | BSD-3-Clause | 2023-03-08T21:45:40 | 2016-10-13T09:30:30 | Python | UTF-8 | Python | false | false | 265 | py | import pyaf.Bench.TS_datasets as tsds
import tests.artificial.process_artificial_dataset as art
art.process_dataset(N = 1024 , FREQ = 'D', seed = 0, trendtype = "ConstantTrend", cycle_length = 5, transform = "Logit", sigma = 0.0, exog_count = 0, ar_order = 12); | [
"[email protected]"
] | |
1aeccb7035a0df9c0d0256b8677e5d53d01bac56 | 7c69c27a1c6ff2a1552900f4c1001281f4447233 | /codechef/tsort.py | dba0f813d790848a76c4ee81b6119ce78800c5a0 | [] | no_license | Hamiltonxx/pyalgorithms | 894a0228928819601a816c472689ce96a11e1d25 | 92284f6105c5deb7f843ff299ee3ceb6382cf879 | refs/heads/master | 2023-09-04T13:01:46.465661 | 2023-09-02T05:50:23 | 2023-09-02T05:50:23 | 231,999,229 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 111 | py | t = int(input())
lst = []
for i in range(t):
lst.append(int(input()))
lst.sort()
for x in lst:
print(x) | [
"[email protected]"
] | |
c8b1abe8e4f8b79cafede323f43675dd5b43f597 | 039a274d8a8bfbfb90b3c884024edf8c18507150 | /nmt/origin/nmt.py | 0a54108b8469d501872666c268d1e58e54a947a5 | [
"MIT"
] | permissive | JayceeLee/TheanoProject | 1e33ae2a58a188cfce6c5bcbd8a2f6f9fbd36a0d | be1f5f09aa84d64ad3df7b798cf6ff74a08bf3b7 | refs/heads/master | 2021-05-11T09:12:50.278105 | 2017-04-09T08:55:03 | 2017-04-09T08:55:03 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 36,820 | py | '''
Build a neural machine translation model with soft attention
'''
import theano
import theano.tensor as tensor
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
import cPickle as pkl
import numpy as np
import copy
import os
import sys
import time
from collections import OrderedDict
import regex as re
from utils import _p, concatenate, load_params, init_tparams, ortho_weight, norm_weight, zipp, unzip, itemlist, \
get_minibatches_idx, prepare_data
from numpy import linalg as LA
from optimizers import adadelta, adam, rmsprop, sgd
from data_iterator import TextIterator
profile = False
# dropout
def dropout_layer(state_before, use_noise, trng):
proj = tensor.switch(
use_noise,
state_before * trng.binomial(state_before.shape, p=0.5, n=1,
dtype=state_before.dtype),
state_before * 0.5)
return proj
# layers: 'name': ('parameter initializer', 'feedforward')
layers = {'ff': ('param_init_fflayer', 'fflayer'),
'gru': ('param_init_gru', 'gru_layer'),
'gru_cond': ('param_init_gru_cond', 'gru_cond_layer'),
}
def get_layer(name):
fns = layers[name]
return eval(fns[0]), eval(fns[1])
def tanh(x):
return tensor.tanh(x)
def linear(x):
return x
# feedforward layer: affine transformation + point-wise nonlinearity
def param_init_fflayer(options, params, prefix='ff', nin=None, nout=None,
ortho=True):
if nin is None:
nin = options['dim_proj']
if nout is None:
nout = options['dim_proj']
params[_p(prefix, 'W')] = norm_weight(nin, nout, scale=0.01, ortho=ortho)
params[_p(prefix, 'b')] = np.zeros((nout,)).astype('float32')
return params
def fflayer(tparams, state_below, options, prefix='rconv',
activ='lambda x: tensor.tanh(x)', **kwargs):
return eval(activ)(
tensor.dot(state_below, tparams[_p(prefix, 'W')]) +
tparams[_p(prefix, 'b')])
# GRU layer
def param_init_gru(options, params, prefix='gru', nin=None, dim=None):
if nin is None:
nin = options['dim_proj']
if dim is None:
dim = options['dim_proj']
# embedding to gates transformation weights, biases
W = np.concatenate([norm_weight(nin, dim),
norm_weight(nin, dim)], axis=1)
params[_p(prefix, 'W')] = W
params[_p(prefix, 'b')] = np.zeros((2 * dim,)).astype('float32')
# recurrent transformation weights for gates
U = np.concatenate([ortho_weight(dim),
ortho_weight(dim)], axis=1)
params[_p(prefix, 'U')] = U
# embedding to hidden state proposal weights, biases
Wx = norm_weight(nin, dim)
params[_p(prefix, 'Wx')] = Wx
params[_p(prefix, 'bx')] = np.zeros((dim,)).astype('float32')
# recurrent transformation weights for hidden state proposal
Ux = ortho_weight(dim)
params[_p(prefix, 'Ux')] = Ux
return params
def gru_layer(tparams, state_below, options, prefix='gru', mask=None,
**kwargs):
nsteps = state_below.shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
dim = tparams[_p(prefix, 'Ux')].shape[1]
if mask is None:
mask = tensor.alloc(1., state_below.shape[0], 1)
# utility function to slice a tensor
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n * dim:(n + 1) * dim]
return _x[:, n * dim:(n + 1) * dim]
# state_below is the input word embeddings
# input to the gates, concatenated
state_below_ = tensor.dot(state_below, tparams[_p(prefix, 'W')]) + \
tparams[_p(prefix, 'b')]
# input to compute the hidden state proposal
state_belowx = tensor.dot(state_below, tparams[_p(prefix, 'Wx')]) + \
tparams[_p(prefix, 'bx')]
# step function to be used by scan
# arguments | sequences |outputs-info| non-seqs
def _step_slice(m_, x_, xx_, h_, U, Ux):
preact = tensor.dot(h_, U)
preact += x_
# reset and update gates
r = tensor.nnet.sigmoid(_slice(preact, 0, dim))
u = tensor.nnet.sigmoid(_slice(preact, 1, dim))
# compute the hidden state proposal
preactx = tensor.dot(h_, Ux)
preactx = preactx * r
preactx = preactx + xx_
# hidden state proposal
h = tensor.tanh(preactx)
# leaky integrate and obtain next hidden state
h = u * h_ + (1. - u) * h
h = m_[:, None] * h + (1. - m_)[:, None] * h_
return h
# prepare scan arguments
seqs = [mask, state_below_, state_belowx]
init_states = [tensor.alloc(0., n_samples, dim)]
_step = _step_slice
shared_vars = [tparams[_p(prefix, 'U')],
tparams[_p(prefix, 'Ux')]]
rval, updates = theano.scan(_step,
sequences=seqs,
outputs_info=init_states,
non_sequences=shared_vars,
name=_p(prefix, '_layers'),
n_steps=nsteps,
profile=profile,
strict=True)
rval = [rval]
return rval
# Conditional GRU layer with Attention
def param_init_gru_cond(options, params, prefix='gru_cond',
nin=None, dim=None, dimctx=None,
nin_nonlin=None, dim_nonlin=None):
if nin is None:
nin = options['dim']
if dim is None:
dim = options['dim']
if dimctx is None:
dimctx = options['dim']
if nin_nonlin is None:
nin_nonlin = nin
if dim_nonlin is None:
dim_nonlin = dim
W = np.concatenate([norm_weight(nin, dim),
norm_weight(nin, dim)], axis=1)
params[_p(prefix, 'W')] = W
params[_p(prefix, 'b')] = np.zeros((2 * dim,)).astype('float32')
U = np.concatenate([ortho_weight(dim_nonlin),
ortho_weight(dim_nonlin)], axis=1)
params[_p(prefix, 'U')] = U
Wx = norm_weight(nin_nonlin, dim_nonlin)
params[_p(prefix, 'Wx')] = Wx
Ux = ortho_weight(dim_nonlin)
params[_p(prefix, 'Ux')] = Ux
params[_p(prefix, 'bx')] = np.zeros((dim_nonlin,)).astype('float32')
U_nl = np.concatenate([ortho_weight(dim_nonlin),
ortho_weight(dim_nonlin)], axis=1)
params[_p(prefix, 'U_nl')] = U_nl
params[_p(prefix, 'b_nl')] = np.zeros((2 * dim_nonlin,)).astype('float32')
Ux_nl = ortho_weight(dim_nonlin)
params[_p(prefix, 'Ux_nl')] = Ux_nl
params[_p(prefix, 'bx_nl')] = np.zeros((dim_nonlin,)).astype('float32')
# context to LSTM
Wc = norm_weight(dimctx, dim * 2)
params[_p(prefix, 'Wc')] = Wc
Wcx = norm_weight(dimctx, dim)
params[_p(prefix, 'Wcx')] = Wcx
# attention: combined -> hidden
W_comb_att = norm_weight(dim, dimctx)
params[_p(prefix, 'W_comb_att')] = W_comb_att
# attention: context -> hidden
Wc_att = norm_weight(dimctx)
params[_p(prefix, 'Wc_att')] = Wc_att
# attention: hidden bias
b_att = np.zeros((dimctx,)).astype('float32')
params[_p(prefix, 'b_att')] = b_att
# attention:
U_att = norm_weight(dimctx, 1)
params[_p(prefix, 'U_att')] = U_att
c_att = np.zeros((1,)).astype('float32')
params[_p(prefix, 'c_tt')] = c_att
return params
def gru_cond_layer(tparams, state_below, options, prefix='gru',
mask=None, context=None, one_step=False,
init_memory=None, init_state=None,
context_mask=None,
**kwargs):
assert context, 'Context must be provided'
if one_step:
assert init_state, 'previous state must be provided'
nsteps = state_below.shape[0]
if state_below.ndim == 3:
n_samples = state_below.shape[1]
else:
n_samples = 1
# mask
if mask is None:
mask = tensor.alloc(1., state_below.shape[0], 1)
dim = tparams[_p(prefix, 'Wcx')].shape[1]
# initial/previous state
if init_state is None:
init_state = tensor.alloc(0., n_samples, dim)
# projected context
assert context.ndim == 3, \
'Context must be 3-d: #annotation x #sample x dim'
pctx_ = tensor.dot(context, tparams[_p(prefix, 'Wc_att')]) + \
tparams[_p(prefix, 'b_att')]
def _slice(_x, n, dim):
if _x.ndim == 3:
return _x[:, :, n * dim:(n + 1) * dim]
return _x[:, n * dim:(n + 1) * dim]
# projected x
state_belowx = tensor.dot(state_below, tparams[_p(prefix, 'Wx')]) + \
tparams[_p(prefix, 'bx')]
state_below_ = tensor.dot(state_below, tparams[_p(prefix, 'W')]) + \
tparams[_p(prefix, 'b')]
def _step_slice(m_, x_, xx_, h_, ctx_, alpha_, pctx_, cc_,
U, Wc, W_comb_att, U_att, c_tt, Ux, Wcx,
U_nl, Ux_nl, b_nl, bx_nl):
preact1 = tensor.dot(h_, U)
preact1 += x_
preact1 = tensor.nnet.sigmoid(preact1)
r1 = _slice(preact1, 0, dim)
u1 = _slice(preact1, 1, dim)
preactx1 = tensor.dot(h_, Ux)
preactx1 *= r1
preactx1 += xx_
h1 = tensor.tanh(preactx1)
h1 = u1 * h_ + (1. - u1) * h1
h1 = m_[:, None] * h1 + (1. - m_)[:, None] * h_
# attention
pstate_ = tensor.dot(h1, W_comb_att)
pctx__ = pctx_ + pstate_[None, :, :]
# pctx__ += xc_
pctx__ = tensor.tanh(pctx__)
alpha = tensor.dot(pctx__, U_att) + c_tt
alpha = alpha.reshape([alpha.shape[0], alpha.shape[1]])
alpha = tensor.exp(alpha)
if context_mask:
alpha = alpha * context_mask
alpha = alpha / alpha.sum(0, keepdims=True)
ctx_ = (cc_ * alpha[:, :, None]).sum(0) # current context
preact2 = tensor.dot(h1, U_nl) + b_nl
preact2 += tensor.dot(ctx_, Wc)
preact2 = tensor.nnet.sigmoid(preact2)
r2 = _slice(preact2, 0, dim)
u2 = _slice(preact2, 1, dim)
preactx2 = tensor.dot(h1, Ux_nl) + bx_nl
preactx2 *= r2
preactx2 += tensor.dot(ctx_, Wcx)
h2 = tensor.tanh(preactx2)
h2 = u2 * h1 + (1. - u2) * h2
h2 = m_[:, None] * h2 + (1. - m_)[:, None] * h1
return h2, ctx_, alpha.T # pstate_, preact, preactx, r, u
seqs = [mask, state_below_, state_belowx]
# seqs = [mask, state_below_, state_belowx, state_belowc]
_step = _step_slice
shared_vars = [tparams[_p(prefix, 'U')],
tparams[_p(prefix, 'Wc')],
tparams[_p(prefix, 'W_comb_att')],
tparams[_p(prefix, 'U_att')],
tparams[_p(prefix, 'c_tt')],
tparams[_p(prefix, 'Ux')],
tparams[_p(prefix, 'Wcx')],
tparams[_p(prefix, 'U_nl')],
tparams[_p(prefix, 'Ux_nl')],
tparams[_p(prefix, 'b_nl')],
tparams[_p(prefix, 'bx_nl')]]
if one_step:
rval = _step(*(seqs + [init_state, None, None, pctx_, context] +
shared_vars))
else:
rval, updates = theano.scan(_step,
sequences=seqs,
outputs_info=[init_state,
tensor.alloc(0., n_samples,
context.shape[2]),
tensor.alloc(0., n_samples,
context.shape[0])],
non_sequences=[pctx_, context] + shared_vars,
name=_p(prefix, '_layers'),
n_steps=nsteps,
profile=profile,
strict=True)
return rval
# initialize all parameters
def init_params(options):
params = OrderedDict()
# embedding
params['Wemb'] = norm_weight(options['n_words_src'], options['dim_word'])
params['Wemb_dec'] = norm_weight(options['n_words'], options['dim_word'])
# encoder: bidirectional RNN
params = get_layer(options['encoder'])[0](options, params,
prefix='encoder',
nin=options['dim_word'],
dim=options['dim'])
params = get_layer(options['encoder'])[0](options, params,
prefix='encoder_r',
nin=options['dim_word'],
dim=options['dim'])
ctxdim = 2 * options['dim']
# init_state, init_cell
params = get_layer('ff')[0](options, params, prefix='ff_state',
nin=ctxdim, nout=options['dim'])
# decoder
params = get_layer(options['decoder'])[0](options, params,
prefix='decoder',
nin=options['dim_word'],
dim=options['dim'],
dimctx=ctxdim)
# readout
params = get_layer('ff')[0](options, params, prefix='ff_logit_lstm',
nin=options['dim'], nout=options['dim_word'],
ortho=False)
params = get_layer('ff')[0](options, params, prefix='ff_logit_prev',
nin=options['dim_word'],
nout=options['dim_word'], ortho=False)
params = get_layer('ff')[0](options, params, prefix='ff_logit_ctx',
nin=ctxdim, nout=options['dim_word'],
ortho=False)
params = get_layer('ff')[0](options, params, prefix='ff_logit',
nin=options['dim_word'],
nout=options['n_words'])
return params
# build a training model
def build_model(tparams, options):
opt_ret = dict()
trng = RandomStreams(1234)
use_noise = theano.shared(np.float32(0.))
# description string: #words x #samples
x = tensor.matrix('x', dtype='int64')
x_mask = tensor.matrix('x_mask', dtype='float32')
y = tensor.matrix('y', dtype='int64')
y_mask = tensor.matrix('y_mask', dtype='float32')
# for the backward rnn, we just need to invert x and x_mask
xr = x[::-1]
xr_mask = x_mask[::-1]
n_timesteps = x.shape[0]
n_timesteps_trg = y.shape[0]
n_samples = x.shape[1]
# word embedding for forward rnn (source)
emb = tparams['Wemb'][x.flatten()]
emb = emb.reshape([n_timesteps, n_samples, options['dim_word']])
proj = get_layer(options['encoder'])[1](tparams, emb, options,
prefix='encoder',
mask=x_mask)
# word embedding for backward rnn (source)
embr = tparams['Wemb'][xr.flatten()]
embr = embr.reshape([n_timesteps, n_samples, options['dim_word']])
projr = get_layer(options['encoder'])[1](tparams, embr, options,
prefix='encoder_r',
mask=xr_mask)
# context will be the concatenation of forward and backward rnns
ctx = concatenate([proj[0], projr[0][::-1]], axis=proj[0].ndim - 1)
# mean of the context (across time) will be used to initialize decoder rnn
ctx_mean = (ctx * x_mask[:, :, None]).sum(0) / x_mask.sum(0)[:, None]
# or you can use the last state of forward + backward encoder rnns
# ctx_mean = concatenate([proj[0][-1], projr[0][-1]], axis=proj[0].ndim-2)
# initial decoder state
init_state = get_layer('ff')[1](tparams, ctx_mean, options,
prefix='ff_state', activ='tanh')
# word embedding (target), we will shift the target sequence one time step
# to the right. This is done because of the bi-gram connections in the
# readout and decoder rnn. The first target will be all zeros and we will
# not condition on the last output.
emb = tparams['Wemb_dec'][y.flatten()]
emb = emb.reshape([n_timesteps_trg, n_samples, options['dim_word']])
emb_shifted = tensor.zeros_like(emb)
emb_shifted = tensor.set_subtensor(emb_shifted[1:], emb[:-1])
emb = emb_shifted
# decoder - pass through the decoder conditional gru with attention
proj = get_layer(options['decoder'])[1](tparams, emb, options,
prefix='decoder',
mask=y_mask, context=ctx,
context_mask=x_mask,
one_step=False,
init_state=init_state)
# hidden states of the decoder gru
proj_h = proj[0] # n_timestep * n_sample * dim
# weighted averages of context, generated by attention module
ctxs = proj[1]
# weights (alignment matrix)
opt_ret['dec_alphas'] = proj[2]
# compute word probabilities
logit_lstm = get_layer('ff')[1](tparams, proj_h, options,
prefix='ff_logit_lstm', activ='linear')
logit_prev = get_layer('ff')[1](tparams, emb, options,
prefix='ff_logit_prev', activ='linear')
logit_ctx = get_layer('ff')[1](tparams, ctxs, options,
prefix='ff_logit_ctx', activ='linear')
logit = tensor.tanh(logit_lstm + logit_prev + logit_ctx) # n_timestep * n_sample * dim_word
if options['use_dropout']:
logit = dropout_layer(logit, use_noise, trng)
logit = get_layer('ff')[1](tparams, logit, options,
prefix='ff_logit', activ='linear') # n_timestep * n_sample * n_words
logit_shp = logit.shape
probs = tensor.nnet.softmax(logit.reshape([logit_shp[0] * logit_shp[1],
logit_shp[2]]))
# cost
y_flat = y.flatten()
y_flat_idx = tensor.arange(y_flat.shape[0]) * options['n_words'] + y_flat
cost = -tensor.log(probs.flatten()[y_flat_idx])
cost = cost.reshape([y.shape[0], y.shape[1]])
cost = (cost * y_mask).sum(0)
return trng, use_noise, x, x_mask, y, y_mask, opt_ret, cost, ctx_mean
# build a sampler
def build_sampler(tparams, options, trng, use_noise):
x = tensor.matrix('x', dtype='int64')
xr = x[::-1]
n_timesteps = x.shape[0]
n_samples = x.shape[1]
# word embedding (source), forward and backward
emb = tparams['Wemb'][x.flatten()]
emb = emb.reshape([n_timesteps, n_samples, options['dim_word']])
embr = tparams['Wemb'][xr.flatten()]
embr = embr.reshape([n_timesteps, n_samples, options['dim_word']])
# encoder
proj = get_layer(options['encoder'])[1](tparams, emb, options,
prefix='encoder')
projr = get_layer(options['encoder'])[1](tparams, embr, options,
prefix='encoder_r')
# concatenate forward and backward rnn hidden states
ctx = concatenate([proj[0], projr[0][::-1]], axis=proj[0].ndim - 1)
# get the input for decoder rnn initializer mlp
ctx_mean = ctx.mean(0)
# ctx_mean = concatenate([proj[0][-1],projr[0][-1]], axis=proj[0].ndim-2)
init_state = get_layer('ff')[1](tparams, ctx_mean, options,
prefix='ff_state', activ='tanh')
print 'Building f_init...',
outs = [init_state, ctx]
f_init = theano.function([x], outs, name='f_init', profile=profile)
print 'Done'
# x: 1 x 1
y = tensor.vector('y_sampler', dtype='int64')
init_state = tensor.matrix('init_state', dtype='float32')
# if it's the first word, emb should be all zero and it is indicated by -1
emb = tensor.switch(y[:, None] < 0,
tensor.alloc(0., 1, tparams['Wemb_dec'].shape[1]),
tparams['Wemb_dec'][y])
# apply one step of conditional gru with attention
proj = get_layer(options['decoder'])[1](tparams, emb, options,
prefix='decoder',
mask=None, context=ctx,
one_step=True,
init_state=init_state)
# get the next hidden state
next_state = proj[0]
# get the weighted averages of context for this target word y
ctxs = proj[1]
logit_lstm = get_layer('ff')[1](tparams, next_state, options,
prefix='ff_logit_lstm', activ='linear')
logit_prev = get_layer('ff')[1](tparams, emb, options,
prefix='ff_logit_prev', activ='linear')
logit_ctx = get_layer('ff')[1](tparams, ctxs, options,
prefix='ff_logit_ctx', activ='linear')
logit = tensor.tanh(logit_lstm + logit_prev + logit_ctx)
if options['use_dropout']:
logit = dropout_layer(logit, use_noise, trng)
logit = get_layer('ff')[1](tparams, logit, options,
prefix='ff_logit', activ='linear')
# compute the softmax probability
next_probs = tensor.nnet.softmax(logit)
# sample from softmax distribution to get the sample
next_sample = trng.multinomial(pvals=next_probs).argmax(1)
# compile a function to do the whole thing above, next word probability,
# sampled word for the next target, next hidden state to be used
print 'Building f_next..',
inps = [y, ctx, init_state]
outs = [next_probs, next_sample, next_state]
f_next = theano.function(inps, outs, name='f_next', profile=profile)
print 'Done'
return f_init, f_next
# generate sample, either with stochastic sampling or beam search. Note that,
# this function iteratively calls f_init and f_next functions.
def gen_sample(tparams, f_init, f_next, x, options, trng=None, k=1, maxlen=30,
stochastic=True, argmax=False):
# k is the beam size we have
if k > 1:
assert not stochastic, \
'Beam search does not support stochastic sampling'
sample = []
sample_score = []
if stochastic:
sample_score = 0
live_k = 1
dead_k = 0
hyp_samples = [[]] * live_k
hyp_scores = np.zeros(live_k).astype('float32')
hyp_states = []
# get initial state of decoder rnn and encoder context
ret = f_init(x)
next_state, ctx0 = ret[0], ret[1]
next_w = -1 * np.ones((1,)).astype('int64') # bos indicator
for ii in xrange(maxlen):
ctx = np.tile(ctx0, [live_k, 1])
inps = [next_w, ctx, next_state]
ret = f_next(*inps)
next_p, next_w, next_state = ret[0], ret[1], ret[2]
if stochastic:
if argmax:
nw = next_p[0].argmax()
else:
nw = next_w[0]
sample.append(nw)
sample_score -= np.log(next_p[0, nw])
if nw == 0:
break
else:
cand_scores = hyp_scores[:, None] - np.log(next_p)
cand_flat = cand_scores.flatten()
ranks_flat = cand_flat.argsort()[:(k - dead_k)]
voc_size = next_p.shape[1]
trans_indices = ranks_flat / voc_size
word_indices = ranks_flat % voc_size
costs = cand_flat[ranks_flat]
new_hyp_samples = []
new_hyp_scores = np.zeros(k - dead_k).astype('float32')
new_hyp_states = []
for idx, [ti, wi] in enumerate(zip(trans_indices, word_indices)):
new_hyp_samples.append(hyp_samples[ti] + [wi])
new_hyp_scores[idx] = copy.copy(costs[idx])
new_hyp_states.append(copy.copy(next_state[ti]))
# check the finished samples
new_live_k = 0
hyp_samples = []
hyp_scores = []
hyp_states = []
for idx in xrange(len(new_hyp_samples)):
if new_hyp_samples[idx][-1] == 0:
sample.append(new_hyp_samples[idx])
sample_score.append(new_hyp_scores[idx])
dead_k += 1
else:
new_live_k += 1
hyp_samples.append(new_hyp_samples[idx])
hyp_scores.append(new_hyp_scores[idx])
hyp_states.append(new_hyp_states[idx])
hyp_scores = np.array(hyp_scores)
live_k = new_live_k
if new_live_k < 1:
break
if dead_k >= k:
break
next_w = np.array([w[-1] for w in hyp_samples])
next_state = np.array(hyp_states)
if not stochastic:
# dump every remaining one
if live_k > 0:
for idx in xrange(live_k):
sample.append(hyp_samples[idx])
sample_score.append(hyp_scores[idx])
return sample, sample_score
# calculate the log probablities on a given corpus using translation model
def pred_probs(f_log_probs, prepare_data, options, iterator, verbose=True, normalize=False):
probs = []
n_done = 0
for x, y in iterator:
n_done += len(x)
lengths = np.array([len(s) for s in x])
x, x_mask, y, y_mask = prepare_data(x, y)
pprobs = f_log_probs(x, x_mask, y, y_mask)
if normalize:
pprobs = pprobs / lengths
for pp in pprobs:
probs.append(pp)
sys.stdout.write('\rDid ' + str(n_done) + ' samples')
print
return np.array(probs)
def train(dim_word=100, # word vector dimensionality
dim=1000, # the number of LSTM units
encoder='gru',
decoder='gru_cond',
n_words_src=30000,
n_words=30000,
patience=10, # early stopping patience
max_epochs=5000,
finish_after=10000000, # finish after this many updates
dispFreq=100,
decay_c=0., # L2 regularization penalty
alpha_c=0., # alignment regularization
clip_c=-1., # gradient clipping threshold
lrate=1., # learning rate
maxlen=100, # maximum length of the description
optimizer='rmsprop',
batch_size=16,
saveto='model.npz',
saveFreq=1000, # save the parameters after every saveFreq updates
datasets=[
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.en.tok',
'/data/lisatmp3/chokyun/europarl/europarl-v7.fr-en.fr.tok'],
picked_train_idxes_file=r'',
use_dropout=False,
reload_=False,
overwrite=False,
preload='',
sort_by_len=False,
convert_embedding=True,
dump_before_train=False,
):
# Model options
model_options = locals().copy()
if reload_:
lrate *= 0.5
# load dictionaries and invert them
# reload options
if reload_ and os.path.exists(preload):
print 'Reloading model options'
with open(r'.\model\en2fr.iter160000.npz.pkl', 'rb') as f:
model_options = pkl.load(f)
print 'Configuration from fy'
vocab_en_filename = './data/dic/en2fr_en_vocabs_top1M.pkl'
vocab_fr_filename = './data/dic/en2fr_fr_vocabs_top1M.pkl'
map_filename = './data/dic/mapFullVocab2Top1MVocab.pkl'
lr_discount_freq = 80000
print 'Done'
print 'Loading data'
text_iterator = TextIterator(
datasets[0],
datasets[1],
vocab_en_filename,
vocab_fr_filename,
batch_size,
maxlen,
n_words_src,
n_words,
)
# sys.stdout.flush()
# train_data_x = pkl.load(open(datasets[0], 'rb'))
# train_data_y = pkl.load(open(datasets[1], 'rb'))
#
# if len(picked_train_idxes_file) != 0:
# picked_idxes = pkl.load(open(picked_train_idxes_file, 'rb'))
# train_data_x = [train_data_x[id] for id in picked_idxes]
# train_data_y = [train_data_y[id] for id in picked_idxes]
#
# print 'Total train:', len(train_data_x)
# print 'Max len:', max([len(x) for x in train_data_x])
# sys.stdout.flush()
#
# if sort_by_len:
# slen = np.array([len(s) for s in train_data_x])
# sidx = slen.argsort()
#
# _sbuf = [train_data_x[i] for i in sidx]
# _tbuf = [train_data_y[i] for i in sidx]
#
# train_data_x = _sbuf
# train_data_y = _tbuf
# print len(train_data_x[0]), len(train_data_x[-1])
# sys.stdout.flush()
# train_batch_idx = get_minibatches_idx(len(train_data_x), batch_size, shuffle=False)
# else:
# train_batch_idx = get_minibatches_idx(len(train_data_x), batch_size, shuffle=True)
print 'Building model'
params = init_params(model_options)
# reload parameters
if reload_ and os.path.exists(preload):
print 'Reloading model parameters'
params = load_params(preload, params)
# for k, v in params.iteritems():
# print '>', k, v.shape, v.dtype
# Only convert parameters when reloading
if convert_embedding:
# =================
# Convert input and output embedding parameters with a exist word embedding
# =================
print 'Convert input and output embedding'
temp_Wemb = params['Wemb']
orig_emb_mean = np.mean(temp_Wemb, axis=0)
params['Wemb'] = np.tile(orig_emb_mean, [params['Wemb'].shape[0], 1])
# Load vocabulary map dicts and do mapping
with open(map_filename, 'rb') as map_file:
map_en = pkl.load(map_file)
map_fr = pkl.load(map_file)
for full, top in map_en.iteritems():
emb_size = temp_Wemb.shape[0]
if full < emb_size and top < emb_size:
params['Wemb'][top] = temp_Wemb[full]
print 'Convert input embedding done'
temp_ff_logit_W = params['ff_logit_W']
temp_Wemb_dec = params['Wemb_dec']
temp_b = params['ff_logit_b']
orig_ff_logit_W_mean = np.mean(temp_ff_logit_W, axis=1)
orig_Wemb_dec_mean = np.mean(temp_Wemb_dec, axis=0)
orig_b_mean = np.mean(temp_b)
params['ff_logit_W'] = np.tile(orig_ff_logit_W_mean, [params['ff_logit_W'].shape[1], 1]).T
params['ff_logit_b'].fill(orig_b_mean)
params['Wemb_dec'] = np.tile(orig_Wemb_dec_mean, [params['Wemb_dec'].shape[0], 1])
for full, top in map_en.iteritems():
emb_size = temp_Wemb.shape[0]
if full < emb_size and top < emb_size:
params['ff_logit_W'][:, top] = temp_ff_logit_W[:, full]
params['ff_logit_b'][top] = temp_b[full]
params['Wemb_dec'][top] = temp_Wemb[full]
print 'Convert output embedding done'
# for k, v in params.iteritems():
# print '>', k, v.shape, v.dtype
# ================
# End Convert
# ================
tparams = init_tparams(params)
trng, use_noise, \
x, x_mask, y, y_mask, \
opt_ret, \
cost, x_emb = \
build_model(tparams, model_options)
inps = [x, x_mask, y, y_mask]
print 'Building sampler'
f_init, f_next = build_sampler(tparams, model_options, trng, use_noise)
# before any regularizer
print 'Building f_log_probs...',
f_log_probs = theano.function(inps, cost, profile=profile)
f_x_emb = theano.function([x, x_mask], x_emb, profile=profile)
print 'Done'
sys.stdout.flush()
cost = cost.mean()
# apply L2 regularization on weights
if decay_c > 0.:
decay_c = theano.shared(np.float32(decay_c), name='decay_c')
weight_decay = 0.
for kk, vv in tparams.iteritems():
weight_decay += (vv ** 2).sum()
weight_decay *= decay_c
cost += weight_decay
# regularize the alpha weights
if alpha_c > 0. and not model_options['decoder'].endswith('simple'):
alpha_c = theano.shared(np.float32(alpha_c), name='alpha_c')
alpha_reg = alpha_c * (
(tensor.cast(y_mask.sum(0) // x_mask.sum(0), 'float32')[:, None] -
opt_ret['dec_alphas'].sum(0)) ** 2).sum(1).mean()
cost += alpha_reg
# after all regularizers - compile the computational graph for cost
print 'Building f_cost...',
f_cost = theano.function(inps, cost, profile=profile)
print 'Done'
print 'Computing gradient...',
grads = tensor.grad(cost, wrt=itemlist(tparams))
print 'Done'
sys.stdout.flush()
# apply gradient clipping here
if clip_c > 0.:
g2 = 0.
for g in grads:
g2 += (g ** 2).sum()
new_grads = []
for g in grads:
new_grads.append(tensor.switch(g2 > (clip_c ** 2),
g / tensor.sqrt(g2) * clip_c,
g))
grads = new_grads
# compile the optimizer, the actual computational graph is compiled here
lr = tensor.scalar(name='lr')
print 'Building optimizers...',
f_grad_shared, f_update = eval(optimizer)(lr, tparams, grads, inps, cost)
print 'Done'
print 'Optimization'
best_p = None
bad_counter = 0
uidx = 0
if reload_:
m = re.search('.+iter(\d+?)\.npz', preload)
if m:
uidx = int(m.group(1))
print 'uidx', uidx, 'l_rate', lrate
estop = False
history_errs = []
# reload history
if dump_before_train:
print 'Dumping before train...',
saveto_uidx = '{}.iter{}.npz'.format(
os.path.splitext(saveto)[0], uidx)
np.savez(saveto_uidx, history_errs=history_errs,
uidx=uidx, **unzip(tparams))
print 'Done'
if saveFreq == -1:
saveFreq = len(train[0]) / batch_size
for eidx in xrange(max_epochs):
n_samples = 0
# for i, batch_idx in train_batch_idx:
#
# x = [train_data_x[id] for id in batch_idx]
# y = [train_data_y[id] for id in batch_idx]
for i, (x, y) in enumerate(text_iterator):
n_samples += len(x)
uidx += 1
use_noise.set_value(1.)
x, x_mask, y, y_mask = prepare_data(x, y)
if x is None:
print 'Minibatch with zero sample under length ', maxlen
uidx -= 1
continue
ud_start = time.time()
# compute cost, grads and copy grads to shared variables
cost = f_grad_shared(x, x_mask, y, y_mask)
# do the update on parameters
f_update(lrate)
ud = time.time() - ud_start
# check for bad numbers, usually we remove non-finite elements
# and continue training - but not done here
if np.isnan(cost) or np.isinf(cost):
print 'NaN detected'
return 1., 1., 1.
# discount reward
if lr_discount_freq > 0 and np.mod(uidx, lr_discount_freq) == 0:
lrate *= 0.5
print 'Discount learning rate to {} at iteration {}'.format(lrate, uidx)
# verbose
if np.mod(uidx, dispFreq) == 0:
print 'Epoch ', eidx, 'Update ', uidx, 'Cost ', cost, 'UD ', ud
sys.stdout.flush()
if np.mod(uidx, saveFreq) == 0:
# save with uidx
if not overwrite:
# print 'Saving the model at iteration {}...'.format(uidx),
saveto_uidx = '{}.iter{}.npz'.format(
os.path.splitext(saveto)[0], uidx)
np.savez(saveto_uidx, history_errs=history_errs,
uidx=uidx, **unzip(tparams))
# print 'Done'
# sys.stdout.flush()
# generate some samples with the model and display them
# finish after this many updates
if uidx >= finish_after:
print 'Finishing after %d iterations!' % uidx
estop = True
break
print 'Seen %d samples' % n_samples
if estop:
break
if best_p is not None:
zipp(best_p, tparams)
use_noise.set_value(0.)
return 0.
if __name__ == '__main__':
pass
| [
"[email protected]"
] | |
4d2e063d60ff9fe1c6e29ab07f13137c1edbbd48 | 5ec06dab1409d790496ce082dacb321392b32fe9 | /clients/python/generated/swaggeraemosgi/model/org_apache_sling_event_impl_eventing_thread_pool_properties.py | 8ded9a93c3374c856f6e681fbdcda9df8a966b64 | [
"Apache-2.0"
] | permissive | shinesolutions/swagger-aem-osgi | e9d2385f44bee70e5bbdc0d577e99a9f2525266f | c2f6e076971d2592c1cbd3f70695c679e807396b | refs/heads/master | 2022-10-29T13:07:40.422092 | 2021-04-09T07:46:03 | 2021-04-09T07:46:03 | 190,217,155 | 3 | 3 | Apache-2.0 | 2022-10-05T03:26:20 | 2019-06-04T14:23:28 | null | UTF-8 | Python | false | false | 6,961 | py | """
Adobe Experience Manager OSGI config (AEM) API
Swagger AEM OSGI is an OpenAPI specification for Adobe Experience Manager (AEM) OSGI Configurations API # noqa: E501
The version of the OpenAPI document: 1.0.0-pre.0
Contact: [email protected]
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
import nulltype # noqa: F401
from swaggeraemosgi.model_utils import ( # noqa: F401
ApiTypeError,
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
)
def lazy_import():
from swaggeraemosgi.model.config_node_property_integer import ConfigNodePropertyInteger
globals()['ConfigNodePropertyInteger'] = ConfigNodePropertyInteger
class OrgApacheSlingEventImplEventingThreadPoolProperties(ModelNormal):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
Attributes:
allowed_values (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
with a capitalized key describing the allowed value and an allowed
value. These dicts store the allowed enum values.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
discriminator_value_class_map (dict): A dict to go from the discriminator
variable value to the discriminator class name.
validations (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
that stores validations for max_length, min_length, max_items,
min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
inclusive_minimum, and regex.
additional_properties_type (tuple): A tuple of classes accepted
as additional properties values.
"""
allowed_values = {
}
validations = {
}
additional_properties_type = None
_nullable = False
@cached_property
def openapi_types():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.
"""
lazy_import()
return {
'min_pool_size': (ConfigNodePropertyInteger,), # noqa: E501
}
@cached_property
def discriminator():
return None
attribute_map = {
'min_pool_size': 'minPoolSize', # noqa: E501
}
_composed_schemas = {}
required_properties = set([
'_data_store',
'_check_type',
'_spec_property_naming',
'_path_to_item',
'_configuration',
'_visited_composed_classes',
])
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs): # noqa: E501
"""OrgApacheSlingEventImplEventingThreadPoolProperties - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
min_pool_size (ConfigNodePropertyInteger): [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
| [
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326b4bbd99f9eed9bff3b4bb454476f0beb387c0 | 7d8900637a800d0efa2e1d6a9d4fe877943fdabf | /dudu/add.py | cfcb0a5d3c3f51cc3bb33468b54af380d8a287cd | [
"MIT"
] | permissive | vollov/python-test | d66e0b101f3a664d2d0d33591af8af7134afabd6 | 864896f8ccedb28e15c4962d8983862e9a0e6d77 | refs/heads/master | 2021-07-19T03:15:45.026613 | 2021-02-12T18:24:22 | 2021-02-12T18:24:22 | 46,740,187 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 634 | py |
# add two numbers a and b, and return a+b
# para:
# a - integer number
# b - integer number
# return: a number which is sum of a and b
#
def add(a, b):
print("OK")
return a+b
print("NOT OK")
# here im going to make a subtracting function
# input:
# a - integer
# b - integer
# return: a-b
def subtract(a, b):
return a-b
# here im going to make a multiplying function
# input:
# a - integer
# b - integer
# return: a*b
def multiply(a, b):
return a*b
# testing add
print(add(5,23))
# testing subtract should return -18
print(subtract(5,23))
# testing multiply
print(multiply(5,23))
| [
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] | |
bbe8ca1d65b3a671fbd2432b250d18df17ed5c27 | 8004b7468ad46a6330192985f1f9e3a45cc1d2c2 | /databasenote/第一周/第三天/复习.py | 9ba787c38c5ac08b4d75305be9ffb55897966a62 | [] | no_license | yuemeiss/database | d9704f90127cfd27b92f62a251c213a8d6bc93eb | 5f2304cf72330d6102124755cbc1ff5d14c77a77 | refs/heads/master | 2020-03-27T23:14:47.532297 | 2018-12-11T02:28:04 | 2018-12-11T02:28:04 | 147,304,562 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,039 | py | １．数据库的技术的发展
２．相关概念：
数据库：
数据库系统：
数据库管理系统：
为什么使用mysql:
SQL 数据库语言
３．什么是mysql数据库？
mysql的优势？
是一款开源的自由的软件
是一款多用户、多线程的ＳＱＬ数据库服务器
能够快捷、高效、安全的处理大量数据，使用简单，易操作
跨平台，可移植型强
支持多种操作系统
为多种编程语言提供了ＡＰＩ
4. 安装ｍｙｓｑｌ、启动、连接
５. 创建数据库、修改数据库、删除数据库
６．数据库的存储引擎(三大引擎)和数据库的字段类型（数字、字符串、日期）
７．数据库表的创建、删除、修改
创建表(字段、参数类型、为空、不为空、默认值、主键、自增)
查看数据库创建语句
添加列
修改列
删除列
重命名表
复制表
CREATE TABLE IF NOT EXISTS 表名 LIKE 要复制的表
| [
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5ffc3d3bf7c7b8c6b4ccf0ee7f773bdbb5fed2f3 | a10377a6d0c7576b9e47209f49dea398181f73fe | /extras/qpsclient.py | f399f7aa38285299a94de567adbf9fa214f42306 | [
"BSD-3-Clause"
] | permissive | zymITsky/ants | 14077dab214aff543bbc75a059240dd55f656916 | 52918d18c94a9a69c3b2495286e3384ba57ad6f8 | refs/heads/master | 2020-06-01T11:04:53.520288 | 2015-02-03T08:09:59 | 2015-02-03T08:09:59 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,526 | py | """
A spider that generate light requests to meassure QPS troughput
usage:
scrapy runspider qpsclient.py --loglevel=INFO --set RANDOMIZE_DOWNLOAD_DELAY=0 --set CONCURRENT_REQUESTS=50 -a qps=10 -a latency=0.3
"""
from ants.spider import Spider
from ants.http import Request
class QPSSpider(Spider):
name = 'qps'
benchurl = 'http://localhost:8880/'
# Max concurrency is limited by global CONCURRENT_REQUESTS setting
max_concurrent_requests = 8
# Requests per second goal
qps = None # same as: 1 / download_delay
download_delay = None
# time in seconds to delay server responses
latency = None
# number of slots to create
slots = 1
def __init__(self, *a, **kw):
super(QPSSpider, self).__init__(*a, **kw)
if self.qps is not None:
self.qps = float(self.qps)
self.download_delay = 1 / self.qps
elif self.download_delay is not None:
self.download_delay = float(self.download_delay)
def start_requests(self):
url = self.benchurl
if self.latency is not None:
url += '?latency={0}'.format(self.latency)
slots = int(self.slots)
if slots > 1:
urls = [url.replace('localhost', '127.0.0.%d' % (x + 1)) for x in xrange(slots)]
else:
urls = [url]
idx = 0
while True:
url = urls[idx % len(urls)]
yield Request(url, dont_filter=True)
idx += 1
def parse(self, response):
pass
| [
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] | |
b5329073e1fc97c5ba35195a87c08968c20a55b4 | 6fe2d3c27c4cb498b7ad6d9411cc8fa69f4a38f8 | /algorithms/algorithms-python/leetcode_easy/Question_821_Shortest_Distance_to_a_Character.py | 33695fa6230f0a4b098de6ed4589ea8ce26b3954 | [] | no_license | Lanceolata/code | aae54af632a212c878ce45b11dab919bba55bcb3 | f7d5a7de27c3cc8a7a4abf63eab9ff9b21d512fb | refs/heads/master | 2022-09-01T04:26:56.190829 | 2021-07-29T05:14:40 | 2021-07-29T05:14:40 | 87,202,214 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 519 | py | #!/usr/bin/python
# coding: utf-8
class Solution(object):
def shortestToChar(self, S, C):
"""
:type S: str
:type C: str
:rtype: List[int]
"""
n, pos = len(S), -len(S)
res = [n] * n
for i in range(n):
if S[i] == C:
pos = i
res[i] = min(res[i], abs(i - pos))
for i in range(n-1, -1, -1):
if S[i] == C:
pos = i
res[i] = min(res[i], abs(i - pos));
return res | [
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] | |
6dc15d918ffa46a255df6032022b7e89718fe9b3 | 1cb2b8bf6f244f67e3b867a74a6297556b5e0167 | /rmatics/view/protocol.py | f076bf3da0140903d3b344160a7d59b880c09656 | [] | no_license | ElinRin/informatics-alive | 82f1be65e88860bc4b170b72da39811d30a681e6 | 59a0d1537b90481c2750a05172557bd88fcdcc98 | refs/heads/master | 2020-03-28T22:39:41.979770 | 2018-09-24T10:56:26 | 2018-09-24T10:56:26 | 149,248,491 | 0 | 0 | null | 2018-09-24T10:56:27 | 2018-09-18T07:44:24 | Python | UTF-8 | Python | false | false | 8,016 | py | import time
import traceback
from collections import OrderedDict
from flask import (
g,
jsonify,
Blueprint
)
from sqlalchemy import and_
from rmatics.model import db
from rmatics.model.ejudge_run import EjudgeRun
from rmatics.model.statement import Statement
from rmatics.utils.exceptions import (
InternalServerError,
RunAuthorOnly,
RunNotFound,
)
from rmatics.view import (
require_auth,
require_roles,
)
protocol = Blueprint('protocol', __name__, url_prefix='/protocol')
# log = logging.getLogger(__name__)
# # signal_description = {
# # 1 : "Hangup detected on controlling terminal or death of controlling process",
# # 2 : "Interrupt from keyboard",
# # 3 : "Quit from keyboard",
# # 4 : "Illegal Instruction",
# # 6 : "Abort signal",
# # 7 : "Bus error (bad memory access)",
# # 8 : "Floating point exception",
# # 9 : "Kill signal",
# # 11 : "Invalid memory reference",
# # 13 : "Broken pipe: write to pipe with no readers",
# # 14 : "Timer signal",
# # 15 : "Termination signal"
# # }
# TODO: Переместить в view/run (/run/id/protocol), убрать вложеные try/except
@protocol.route('/get/<int:contest_id>/<int:run_id>')
def get_protocol(contest_id, run_id):
try:
run = EjudgeRun.get_by(run_id=run_id, contest_id=contest_id)
try:
run.fetch_tested_protocol_data()
if run.user.statement \
.filter(Statement.olympiad == 1) \
.filter(Statement.time_stop > time.time()) \
.filter(Statement.time_start < time.time()) \
.count() == 0:
res = OrderedDict()
if run.tests:
sample_tests = run.problem.sample_tests.split(',')
for num in range(1, len(run.tests.keys()) + 1):
str_num = str(num)
if str_num in sample_tests:
res[str_num] = run.get_test_full_protocol(str_num)
else:
res[str_num] = run.tests[str_num]
return jsonify({
'tests': res,
'host': run.host,
'compiler_output': run.compiler_output,
})
else:
try:
return jsonify({
'tests': run.tests['1'],
'host': run.host,
'compiler_output': run.compiler_output,
})
except KeyError as e:
return jsonify({'result' : 'error', 'message' : e.__str__(), "stack" : traceback.format_exc()})
except Exception as e:
return jsonify({'result' : 'error', 'message' : run.compilation_protocol, 'error' : e.__str__(), 'stack' : traceback.format_exc(), 'protocol': run.protocol})
except Exception as e:
return jsonify({'result': 'error', 'message' : e.__str__(), 'stack': traceback.format_exc(), 'protocol': run.protocol})
@protocol.route('/get_v2/<int:contest_id>/<int:run_id>')
@require_auth
def protocol_get_v2(contest_id, run_id):
# TODO: переделать формат протокола (статус выдавать по id), избавиться от fetch_tested_protocol_data
run = db.session.query(EjudgeRun) \
.filter(
and_(
EjudgeRun.run_id == run_id,
EjudgeRun.contest_id == contest_id
)
) \
.first()
if not run:
raise RunNotFound
if g.user.ejudge_id != run.user_id:
raise RunAuthorOnly
try:
run.fetch_tested_protocol_data()
except Exception:
raise InternalServerError
tests_dict = OrderedDict()
if run.tests:
sample_tests = run.problem.sample_tests.split(',')
for num in range(1, len(run.tests.keys()) + 1):
str_num = str(num)
if str_num in sample_tests:
tests_dict[str_num] = run.get_test_full_protocol(str_num)
else:
tests_dict[str_num] = run.tests[str_num]
return jsonify({
'tests': tests_dict,
'host': run.host,
'compiler_output': run.compiler_output,
})
@protocol.route('/get-full/<int:contest_id>/<int:run_id>')
@require_roles('admin', 'teacher', 'ejudge_teacher')
def protocol_get_full(contest_id, run_id):
run = EjudgeRun.get_by(run_id=run_id, contest_id=contest_id)
protocol = get_protocol(contest_id, run_id).json
if protocol.get('result') == 'error':
return protocol
prot = protocol.get('tests', {})
out_arch = None
for test_num in prot:
prot[test_num] = run.get_test_full_protocol(test_num)
if out_arch:
out_arch.close()
full_protocol = {
'tests': prot,
'audit': run.get_audit(),
}
if protocol.get('compiler_output'):
full_protocol['compiler_output'] = protocol['compiler_output']
return jsonify(full_protocol)
# @view_config(route_name="protocol.get_test", renderer="string")
# @check_global_role(("teacher", "ejudge_teacher", "admin"))
# def protocol_get_test(request):
# contest_id = int(request.matchdict['contest_id'])
# run_id = int(request.matchdict['run_id'])
# run = EjudgeRun.get_by(run_id = run_id, contest_id = contest_id)
# prob = run.problem
# return prob.get_test(int(request.matchdict['test_num']), prob.get_test_size(int(request.matchdict['test_num'])))
# @view_config(route_name="protocol.get_corr", renderer="string")
# @check_global_role(("teacher", "ejudge_teacher", "admin"))
# def protocol_get_corr(request):
# contest_id = int(request.matchdict['contest_id'])
# run_id = int(request.matchdict['run_id'])
# run = EjudgeRun.get_by(run_id = run_id, contest_id = contest_id)
# prob = run.problem
# return prob.get_corr(int(request.matchdict['test_num']), prob.get_corr_size(int(request.matchdict['test_num'])))
# @view_config(route_name="protocol.get_outp", renderer="string")
# @check_global_role(("teacher", "ejudge_teacher", "admin"))
# def protocol_get_outp(request):
# contest_id = int(request.matchdict['contest_id'])
# run_id = int(request.matchdict['run_id'])
# run = EjudgeRun.get_by(run_id = run_id, contest_id = contest_id)
# return run.get_output_file(int(request.matchdict['test_num']), tp='o')
# @view_config(route_name="protocol.get_submit_archive", renderer="string")
# @check_global_role(("teacher", "ejudge_teacher", "admin"))
# def get_submit_archive(request):
# contest_id = int(request.matchdict['contest_id'])
# run_id = int(request.matchdict['run_id'])
# sources = "sources" in request.params
# all_tests = "all_tests" in request.params
# tests = request.params.get("tests", "")
# tests_set = set()
# for i in tests.split(" "):
# try:
# tests_set.add(int(i))
# except ValueError:
# pass
# run = EjudgeRun.get_by(run_id = run_id, contest_id = contest_id)
# run.parsetests
# prob = run.problem
# archive = BytesIO()
# zf = zipfile.ZipFile(archive, "w", zipfile.ZIP_DEFLATED)
# run.fetch_tested_protocol_data()
# for i in range(1, run.tests_count + 1):
# if all_tests or i in tests_set:
# zf.writestr("tests/{0:02}".format(i), prob.get_test(i, prob.get_test_size(i)))
# zf.writestr("tests/{0:02}.a".format(i), prob.get_corr(i, prob.get_corr_size(i)))
# if sources:
# zf.writestr("{0}{1}".format(run_id, get_lang_ext_by_id(run.lang_id)), run.get_sources())
# checker_src, checker_ext = prob.get_checker()
# zf.writestr("checker{}".format(checker_ext), checker_src)
# zf.close()
# archive.seek(0)
# response = Response(content_type="application/zip", content_disposition='attachment; filename="archive_{0}_{1}.zip"'.format(contest_id, run_id), body=archive.read())
# return response
| [
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] | |
b77f5b032f953ed006c22bb0b7765506412aad56 | e23a4f57ce5474d468258e5e63b9e23fb6011188 | /125_algorithms/_exercises/templates/_algorithms_challenges/leetcode/LeetcodePythonProject_with_solution/leetcode_0801_0850/LeetCode818_RaceCar.py | 8973cff01e19bfcaf0a4885b70d166e80525fb5d | [] | no_license | syurskyi/Python_Topics | 52851ecce000cb751a3b986408efe32f0b4c0835 | be331826b490b73f0a176e6abed86ef68ff2dd2b | refs/heads/master | 2023-06-08T19:29:16.214395 | 2023-05-29T17:09:11 | 2023-05-29T17:09:11 | 220,583,118 | 3 | 2 | null | 2023-02-16T03:08:10 | 2019-11-09T02:58:47 | Python | UTF-8 | Python | false | false | 941 | py | '''
Created on May 1, 2018
@author: tongq
'''
c_ Solution(o..
___ -
hashmap {0:0}
___ racecar target
"""
:type target: int
:rtype: int
"""
__ target __ hashmap: r.. hashmap[target]
# Number of bits necessary to represent self in binary.
n target.bit_length()
__ 2**n-1 __ target:
hashmap[target] n
____
hashmap[target] racecar(2**n-1-target)+n+1
___ m __ r..(n-1
hashmap[target] m..(hashmap[target],\
racecar(target-2**(n-1)+2**m)+n+m+1)
r.. hashmap[target]
___ test
testCases [
3,
6,
]
___ target __ testCases:
print('target: %s' % target)
result racecar(target)
print('result: %s' % result)
print('-='*30+'-')
__ _____ __ _____
Solution().test()
| [
"[email protected]"
] | |
1243495e4d86517d0efab71f93b5ef8c5692f8c5 | 64bf39b96a014b5d3f69b3311430185c64a7ff0e | /intro-ansible/venv2/lib/python3.8/site-packages/ansible/modules/database/influxdb/influxdb_user.py | 6b78276dbad5b9feb48f678066ed402dec5dee69 | [
"MIT"
] | permissive | SimonFangCisco/dne-dna-code | 7072eba7da0389e37507b7a2aa5f7d0c0735a220 | 2ea7d4f00212f502bc684ac257371ada73da1ca9 | refs/heads/master | 2023-03-10T23:10:31.392558 | 2021-02-25T15:04:36 | 2021-02-25T15:04:36 | 342,274,373 | 0 | 0 | MIT | 2021-02-25T14:39:22 | 2021-02-25T14:39:22 | null | UTF-8 | Python | false | false | 5,248 | py | #!/usr/bin/python
# (c) 2017, Vitaliy Zhhuta <zhhuta () gmail.com>
# insipred by Kamil Szczygiel <kamil.szczygiel () intel.com> influxdb_database module
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
from __future__ import absolute_import, division, print_function
__metaclass__ = type
ANSIBLE_METADATA = {'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'community'}
DOCUMENTATION = '''
---
module: influxdb_user
short_description: Manage InfluxDB users
description:
- Manage InfluxDB users
version_added: 2.5
author: "Vitaliy Zhhuta (@zhhuta)"
requirements:
- "python >= 2.6"
- "influxdb >= 0.9"
options:
user_name:
description:
- Name of the user.
required: True
user_password:
description:
- Password to be set for the user.
required: false
admin:
description:
- Whether the user should be in the admin role or not.
default: no
choices: [ yes, no]
state:
description:
- State of the user.
choices: [ present, absent ]
default: present
extends_documentation_fragment: influxdb
'''
EXAMPLES = '''
- name: Create a user on localhost using default login credentials
influxdb_user:
user_name: john
user_password: s3cr3t
- name: Create a user on localhost using custom login credentials
influxdb_user:
user_name: john
user_password: s3cr3t
login_username: "{{ influxdb_username }}"
login_password: "{{ influxdb_password }}"
- name: Create an admin user on a remote host using custom login credentials
influxdb_user:
user_name: john
user_password: s3cr3t
admin: yes
hostname: "{{ influxdb_hostname }}"
login_username: "{{ influxdb_username }}"
login_password: "{{ influxdb_password }}"
- name: Destroy a user using custom login credentials
influxdb_user:
user_name: john
login_username: "{{ influxdb_username }}"
login_password: "{{ influxdb_password }}"
state: absent
'''
RETURN = '''
#only defaults
'''
import ansible.module_utils.urls
from ansible.module_utils.basic import AnsibleModule
import ansible.module_utils.influxdb as influx
def find_user(module, client, user_name):
name = None
try:
names = client.get_list_users()
for u_name in names:
if u_name['user'] == user_name:
name = u_name
break
except ansible.module_utils.urls.ConnectionError as e:
module.fail_json(msg=str(e))
return name
def check_user_password(module, client, user_name, user_password):
try:
client.switch_user(user_name, user_password)
client.get_list_users()
except influx.exceptions.InfluxDBClientError as e:
if e.code == 401:
return False
except ansible.module_utils.urls.ConnectionError as e:
module.fail_json(msg=str(e))
finally:
# restore previous user
client.switch_user(module.params['username'], module.params['password'])
return True
def set_user_password(module, client, user_name, user_password):
if not module.check_mode:
try:
client.set_user_password(user_name, user_password)
except ansible.module_utils.urls.ConnectionError as e:
module.fail_json(msg=str(e))
module.exit_json(changed=True)
def create_user(module, client, user_name, user_password, admin):
if not module.check_mode:
try:
client.create_user(user_name, user_password, admin)
except ansible.module_utils.urls.ConnectionError as e:
module.fail_json(msg=str(e))
module.exit_json(changed=True)
def drop_user(module, client, user_name):
if not module.check_mode:
try:
client.drop_user(user_name)
except influx.exceptions.InfluxDBClientError as e:
module.fail_json(msg=e.content)
module.exit_json(changed=True)
def main():
argument_spec = influx.InfluxDb.influxdb_argument_spec()
argument_spec.update(
state=dict(default='present', type='str', choices=['present', 'absent']),
user_name=dict(required=True, type='str'),
user_password=dict(required=False, type='str', no_log=True),
admin=dict(default='False', type='bool')
)
module = AnsibleModule(
argument_spec=argument_spec,
supports_check_mode=True
)
state = module.params['state']
user_name = module.params['user_name']
user_password = module.params['user_password']
admin = module.params['admin']
influxdb = influx.InfluxDb(module)
client = influxdb.connect_to_influxdb()
user = find_user(module, client, user_name)
if state == 'present':
if user:
if check_user_password(module, client, user_name, user_password):
module.exit_json(changed=False)
else:
set_user_password(module, client, user_name, user_password)
else:
create_user(module, client, user_name, user_password, admin)
if state == 'absent':
if user:
drop_user(module, client, user_name)
else:
module.exit_json(changed=False)
if __name__ == '__main__':
main()
| [
"[email protected]"
] | |
e808ecad2aaae033f06016a6b865f7a2ebc7b95c | de24f83a5e3768a2638ebcf13cbe717e75740168 | /moodledata/vpl_data/389/usersdata/329/73216/submittedfiles/poligono.py | 83c9b44a1a65243971d936af7331531404998ba4 | [] | no_license | rafaelperazzo/programacao-web | 95643423a35c44613b0f64bed05bd34780fe2436 | 170dd5440afb9ee68a973f3de13a99aa4c735d79 | refs/heads/master | 2021-01-12T14:06:25.773146 | 2017-12-22T16:05:45 | 2017-12-22T16:05:45 | 69,566,344 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 108 | py | # -*- coding: utf-8 -*-
n = float(input('digite o valor de lados='))
nd = (n**((n-3)))/2
print ('%.1f' % nd) | [
"[email protected]"
] | |
adfe58513a6fe2e4b6bbf9df0757e94a5dc4e27a | d735b8354e06eb26aa5ed0ac25ebf96bdd8d67b6 | /python16/day1-21/day006 小数据池和编码/02 作业讲解.py | 8d5be45087e50789765e5e05c7d549a69cf11dba | [] | no_license | cn5036518/xq_py | e004766e6b2582ba37d7335320ed6b42f563c46c | ac932dc7fcb89a7a7faf8bda80791743755fd557 | refs/heads/master | 2021-07-15T18:44:19.244025 | 2020-09-12T09:38:25 | 2020-09-12T09:38:25 | 208,355,433 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,879 | py | #重点题目回顾和重写
# 5、元素分类
# 有如下值li= [11,22,33,44,55,66,77,88,99,90]，
# 将所有大于 66 的值保存至字典的第一个key中，
# 将小于 66 的值保存至第二个key的值中。
# 即： {'k1': 大于66的所有值列表, 'k2': 小于66的所有值列表}
#方法1 空字典{} 空列表 好理解
li= [11,22,33,44,55,66,77,88,99,90]
dic1 = {}
li1 = []
li2 = []
for i in li:
if i > 66:
li1.append(i) #空列表追加
else:
li2.append(i)
dic1["k1"] = li1 #字典新增键值对
dic1["k2"] = li2
print(dic1) #{'k1': [77, 88, 99, 90], 'k2': [11, 22, 33, 44, 55, 66]}
#方法2 非空字典{} #比较简洁，代码行数较少
li= [11,22,33,44,55,66,77,88,99,90]
dic1 = {"k1":[],"k2":[]}
for i in li:
if i > 66:
dic1["k1"].append(i)
else:
dic1["k2"].append(i)
print(dic1) #{'k2': [11, 22, 33, 44, 55, 66], 'k1': [77, 88, 99, 90]}
#方法3 字典的get方法
li= [11,22,33,44,55,66,77,88,99,90]
dic1 = {}
for i in li:
if i > 66:
if dic1.get("k1") == None: #1 如果key="k1"不存在，就新增
dic1["k1"] = [i] #注意点：这里必须是[i],而不能是i
else: #2 如果key=k1存在，就往value-列表中追加新元素
dic1["k1"].append(i)
else:
if dic1.get("k2") == None: #1 如果key="k2"不存在，就新增
dic1["k2"] = [i] #注意点：这里必须是[i],而不能是i
else: #2 如果key=k2存在，就往value-列表中追加新元素
dic1["k2"].append(i)
print(dic1) #{'k1': [77, 88, 99, 90], 'k2': [11, 22, 33, 44, 55, 66]}
#方法4 字典的setdefault方法
li= [11,22,33,44,55,66,77,88,99,90]
dic1 = {}
for i in li:
if i > 66:
ret1 = dic1.setdefault("k1",[]) #新增功能:"k1"之前不存在，就新增键值对；“k1”存在，就不操作（不覆盖）
# 返回value--查询功能
#注意：这里value必须是[],而不是[i]
ret1.append(i) #往列表中追加新元素
#ret1 第一次取值是[77]
#ret1 第二次取值是[77,88]
#ret1 第三次取值是[77,88,99]，依次类推--过程重要 不跳步骤，足够耐心
else:
ret2 = dic1.setdefault("k2", []) # 新增:"k1"之前不存在，就新增键值对；“k1”存在，就不操作（不覆盖） 返回value
ret2.append(i) #往列表中追加新元素
print(dic1) #{'k2': [11, 22, 33, 44, 55, 66], 'k1': [77, 88, 99, 90]}
print("---------------1")
# 6、输出商品列表，用户输入序号，显示用户选中的商品(升级题)
#
# 商品列表：
goods = [{"name": "电脑", "price": 1999}, {"name": "鼠标", "price": 10},
{"name": "游艇", "price": 20}, {"name": "美女", "price": 998}, ]
#
# 要求:
# 1：页面显示 序号 + 商品名称 + 商品价格，如：
# 1 电脑 1999
# 2 鼠标 10
# …
# 2：用户输入选择的商品序号，然后打印商品名称及商品价格
# 3：如果用户输入的商品序号有误，则提示输入有误，并重新输入。
# 4：用户输入Q或者q，退出程序。
#6-1 不打印序号
# for i in goods:
# print(i["name"],i["price"])
#6-1 打印序号
for i in range(len(goods)): #i 是0-3
print(i+1,goods[i]["name"],goods[i]["price"])
print("---------------6-1")
#6-2 方法1 --不推荐
# 用户输入选择的商品序号，然后打印商品名称及商品价格
goods = [{"name": "电脑", "price": 1999}, {"name": "鼠标", "price": 10},
{"name": "游艇", "price": 20}, {"name": "美女", "price": 998}, ]
# for i in range(10): #0-9 限定输入10次
# # while 1: #不限定输入次数
# content = int(input("请输入商品编号：")) #输入的是字符串，需要转换成int #这里如果输入的不是字符串数字，就会报错
# #ValueError: invalid literal for int() with base 10: 'ss'
# #商品编号-1 = 索引编号
# if content>0 and content<=len(goods):
# print(goods[content-1]["name"],goods[content-1]["price"])
# else:
# print("没有找到对应的商品，请重新输入")
#6-2 方法2 推荐
# 2：用户输入选择的商品序号，然后打印商品名称及商品价格
# 3：如果用户输入的商品序号有误，则提示输入有误，并重新输入。
# 4：用户输入Q或者q，退出程序
while 1:
content = input("请输入商品编号：") #输入的是字符串
if content.upper() == "Q":
print("退出程序")
break
elif content.isdigit():
content = int(content)
if content>0 and content <=len(goods):
print(goods[content-1]["name"],goods[content-1]["price"])
else:
print("您输入的商品编号不存在，请重新输入")
else:
print("输入有误，请重新输入数字")
| [
"[email protected]"
] | |
8e6ee2d167ba76946304901f9b857a62506894e4 | 0cb08e9532758cbec1afe20eb41028d5f276e82d | /gs37(nested serializer )/api/admin.py | e3c0a4b8c2a8cde9f8fa47e15ccefb3248d11be7 | [] | no_license | P-iyushRaj/Django-Rest-Framework | 1eca586ee6ded4720e8f1845c9c9cac06d637492 | 9d7e754156739118f725ab431d25bdde63ebd91d | refs/heads/master | 2023-03-15T05:27:37.352592 | 2021-03-08T09:27:46 | 2021-03-08T09:27:46 | 345,599,375 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 314 | py | from django.contrib import admin
# Register your models here.
from .models import Singer, Song
@admin.register(Singer)
class SingerAdmin(admin.ModelAdmin):
list_display=['id', 'name', 'gender']
@admin.register(Song)
class SongAdmin(admin.ModelAdmin):
list_display = ['id', 'title', 'singer', 'duration'] | [
"[email protected]"
] | |
f24665f25700be68e4a637774b61b744257416cb | 37fef592f365194c28579f95abd222cc4e1243ae | /streamlit/venv/lib/python3.7/site-packages/plotly/graph_objs/scattermapbox/_line.py | 24de4166109b0586711d43349e01d8f14fa88d1b | [] | no_license | edimaudo/Python-projects | be61e0d3fff63fb7bd00513dbf1401e2c1822cfb | 85d54badf82a0b653587a02e99daf389df62e012 | refs/heads/master | 2023-04-07T03:26:23.259959 | 2023-03-24T12:03:03 | 2023-03-24T12:03:03 | 72,611,253 | 4 | 3 | null | 2022-10-31T18:10:41 | 2016-11-02T06:37:17 | null | UTF-8 | Python | false | false | 5,567 | py | from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Line(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "scattermapbox"
_path_str = "scattermapbox.line"
_valid_props = {"color", "width"}
# color
# -----
@property
def color(self):
"""
Sets the line color.
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
# width
# -----
@property
def width(self):
"""
Sets the line width (in px).
The 'width' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["width"]
@width.setter
def width(self, val):
self["width"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
color
Sets the line color.
width
Sets the line width (in px).
"""
def __init__(self, arg=None, color=None, width=None, **kwargs):
"""
Construct a new Line object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
:class:`plotly.graph_objs.scattermapbox.Line`
color
Sets the line color.
width
Sets the line width (in px).
Returns
-------
Line
"""
super(Line, self).__init__("line")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.scattermapbox.Line
constructor must be a dict or
an instance of :class:`plotly.graph_objs.scattermapbox.Line`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("width", None)
_v = width if width is not None else _v
if _v is not None:
self["width"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
| [
"[email protected]"
] | |
b4c5e3e144d510a232590523f174411f1d2e2336 | b1c7a768f38e2e987a112da6170f49503b9db05f | /userprofile/migrations/0004_profile_date.py | d3cd49baf2783abbc91a484f53e46ccd13a7639a | [] | no_license | Niladrykar/bracketerp | 8b7491aa319f60ec3dcb5077258d75b0394db374 | ca4ee60c2254c6c132a38ce52410059cc6b19cae | refs/heads/master | 2022-12-11T04:23:07.504966 | 2019-03-18T06:58:13 | 2019-03-18T06:58:13 | 176,218,029 | 1 | 0 | null | 2022-12-08T03:01:46 | 2019-03-18T06:27:37 | JavaScript | UTF-8 | Python | false | false | 495 | py | # Generated by Django 2.0.6 on 2018-09-19 07:05
from django.db import migrations, models
import django.utils.timezone
class Migration(migrations.Migration):
dependencies = [
('userprofile', '0003_auto_20180915_1222'),
]
operations = [
migrations.AddField(
model_name='profile',
name='Date',
field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now),
preserve_default=False,
),
]
| [
"[email protected]"
] | |
4867cc358df2a9e5db6d64974690faef2801d82b | 2e1c1558f6fcb12a57449f9f6f0db6f1cbf38dd6 | /src/masonite/authorization/models/__init__.py | 0b6a76d853be38776efb36472df3256fcdf79305 | [
"MIT"
] | permissive | MasoniteFramework/masonite | ca51bf3d0e4777e624b3a9e94d1360936fb8006d | e8e55e5fdced9f28cc8acb1577457a490e5b4b74 | refs/heads/4.0 | 2023-09-01T18:59:01.331411 | 2022-11-05T01:29:29 | 2022-11-05T01:29:29 | 113,248,605 | 2,173 | 185 | MIT | 2023-04-02T02:29:18 | 2017-12-06T00:30:22 | Python | UTF-8 | Python | false | false | 35 | py | from .authorizes import Authorizes
| [
"[email protected]"
] | |
4a81108d031b334c2ee7e35ff0f338776b47c049 | 9a0e2312236b628007a67c07164ea7b97207e47c | /col/apps/logpoint_agent_collector/logpoint_agent_collector.py | e1650f10b279daff06f16f5235310f5f6c184d17 | [] | no_license | laxmi518/network_project | d88b9fe73522deaa90c1dbfd22c6861020a6c7be | 2e998338f3d1142a8098d3dfd35f4c8ad0e4ba00 | refs/heads/master | 2020-05-21T15:48:07.830107 | 2018-05-09T18:58:37 | 2018-05-09T18:58:37 | 84,631,818 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 841 | py | #!/usr/bin/env python
import logging
from pylib.wiring import gevent_zmq as zmq
from lib import fi_collector
from fi_applications import make_zip
from pylib import conf, wiring, textual
log = logging.getLogger(__name__)
def _parse_args():
options, config = conf.parse_config()
return config
def _prepare_application_directory(config):
make_zip.create_zipped_application_packages(config['basedir'])
def main():
zmq_context = zmq.Context()
config = _parse_args()
#config = textual.utf8(config)
#_prepare_application_directory(config)
fi_out = wiring.Wire('collector_out', zmq_context=zmq_context,
conf_path=config.get('wiring_conf_path') or None)
log.info('LogPoint_agent_collector starting...')
fi_collector.main(config, fi_out)
main()
| [
"[email protected]"
] | |
86124ffe93f07eff65064bf4b695bf53599a00e4 | e70bc88ccc01a7616016d085a96f8f8c81ade50c | /tests/test_changelog.py | b3c324c81487c1c47a64f856f938f5422e291cae | [
"BSD-3-Clause"
] | permissive | justcalamari/rever | 1dc9b51c8338c3fb53c7c7adbb0eac59de2a8305 | 8bea796991a6ed354d45b053064324659b1f2b38 | refs/heads/master | 2021-04-29T19:34:57.352326 | 2018-02-28T04:59:59 | 2018-02-28T04:59:59 | 121,580,667 | 0 | 0 | null | 2018-02-15T01:26:15 | 2018-02-15T01:26:14 | null | UTF-8 | Python | false | false | 2,976 | py | """Tests the changelog activity."""
import os
from rever import vcsutils
from rever.logger import current_logger
from rever.main import env_main
REVER_XSH = """
$ACTIVITIES = ['changelog']
$DAG['changelog'].kwargs = {
'filename': 'CHANGELOG.rst',
'ignore': ['TEMPLATE.rst'],
'news': 'nuws',
}
"""
CHANGELOG_RST = """.. current developments
v42.1.0
============
* And some other stuff happeneded.
"""
TEMPLATE_RST = """**Added:** None
**Changed:** None
**Deprecated:** None
**Removed:** None
**Fixed:** None
**Security:** None
"""
N0_RST = """**Added:**
* from n0
**Changed:** None
**Deprecated:** None
**Removed:**
* here
* and here
**Fixed:** None
**Security:** None
"""
N1_RST = """**Added:**
* from n1
**Changed:**
* But what martial arts are they mixing?
**Deprecated:** None
**Removed:**
* There
**Fixed:** None
**Security:** None
"""
CHANGELOG_42_1_1 = """.. current developments
v42.1.1
====================
**Added:**
* from n0
* from n1
**Changed:**
* But what martial arts are they mixing?
**Removed:**
* here
* and here
* There
v42.1.0
============
* And some other stuff happeneded.
"""
def test_changelog(gitrepo):
os.makedirs('nuws', exist_ok=True)
files = [('rever.xsh', REVER_XSH),
('CHANGELOG.rst', CHANGELOG_RST),
('nuws/TEMPLATE.rst', TEMPLATE_RST),
('nuws/n0.rst', N0_RST),
('nuws/n1.rst', N1_RST),
]
for filename, body in files:
with open(filename, 'w') as f:
f.write(body)
vcsutils.track('.')
vcsutils.commit('initial changelog and news')
env_main(['42.1.1'])
# now see if this worked
newsfiles = os.listdir('nuws')
assert 'TEMPLATE.rst' in newsfiles
assert 'n0.rst' not in newsfiles
assert 'n1.rst' not in newsfiles
with open('CHANGELOG.rst') as f:
cl = f.read()
assert CHANGELOG_42_1_1 == cl
# ensure that the updates were commited
logger = current_logger()
entries = logger.load()
assert entries[-2]['rev'] != entries[-1]['rev']
SETUP_XSH = """
$PROJECT = 'castlehouse'
$ACTIVITIES = ['changelog']
$REVER_DIR = 'rvr'
$CHANGELOG_FILENAME = 'CHANGELOG.rst'
$CHANGELOG_NEWS = 'nuws'
$CHANGELOG_TEMPLATE = 'TEMPLATE.rst'
"""
def test_changelog_setup(gitrepo):
os.makedirs('nuws', exist_ok=True)
files = [('rever.xsh', SETUP_XSH),
]
for filename, body in files:
with open(filename, 'w') as f:
f.write(body)
vcsutils.track('.')
vcsutils.commit('initial changelog')
env_main(['setup'])
# now see if this worked
newsfiles = os.listdir('nuws')
assert 'TEMPLATE.rst' in newsfiles
basefiles = os.listdir('.')
assert 'CHANGELOG.rst' in basefiles
with open('CHANGELOG.rst') as f:
cl = f.read()
assert 'castlehouse' in cl
assert '.gitignore' in basefiles
with open('.gitignore') as f:
gi = f.read()
assert '\n# Rever\nrvr/\n' in gi
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