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d7c6c7b4aecd7b5e5e03ecbf705e1cd1df022fe7 | 4,036 | py | Python | tests/basics/Branching.py | Mortal/Nuitka | 5150eeff7ff845ed4993c773449cd81b7f127c6b | [
"Apache-2.0"
] | null | null | null | tests/basics/Branching.py | Mortal/Nuitka | 5150eeff7ff845ed4993c773449cd81b7f127c6b | [
"Apache-2.0"
] | null | null | null | tests/basics/Branching.py | Mortal/Nuitka | 5150eeff7ff845ed4993c773449cd81b7f127c6b | [
"Apache-2.0"
] | 1 | 2018-12-16T23:51:18.000Z | 2018-12-16T23:51:18.000Z | # Copyright 2018, Kay Hayen, mailto:kay.hayen@gmail.com
#
# Python tests originally created or extracted from other peoples work. The
# parts were too small to be protected.
#
# 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.
#
""" Some random branching to cover most common cases. """
from __future__ import print_function
def branchingFunction(a, b, c):
print("branchingFunction:", a, b, c)
print("a or b", a or b)
print("a and b", a and b)
print("not a", not a)
print("not b", not b)
print("Simple branch with both branches")
if a:
l = "YES"
else:
l = "NO"
print(a, "->", l)
print("Simple not branch with both branches")
if not a:
l = "YES"
else:
l = "NO"
print(not a, "->", l)
print("Simple branch with a nested branch in else path:")
if a:
m = "yes"
else:
if True:
m = "no"
print(a, "->", m)
print("Triple 'and' chain:")
v = "NO"
if a and b and c:
v = "YES"
print(a, b, c, "->", v)
print("Triple or chain:")
k = "NO"
if a or b or c:
k = "YES"
print(a, b, c, "->", k)
print("Nested 'if not' chain:")
p = "NO"
if not a:
if not b:
p = "YES"
print("not a, not b", not a, not b, "->", p)
print("or condition in braces:")
q = "NO"
if (a or b):
q = "YES"
print("(a or b) ->", q)
print("Braced if not with two 'or'")
if not (a or b or c):
q = "YES"
else:
q = "NO"
print("not (a or b or c)", q)
print("Braced if not with one 'or'")
q = "NO"
if not (b or b):
q = "YES"
print("not (b or b)", q)
print("Expression a or b", a or b)
print("Expression not(a or b)", not(a or b))
print("Expression a and (b+5)", a and (b+5))
print("Expression (b if b else 2)", (b if b else 2))
print("Expression (a and (b if b else 2))", (a and (b if b else 2)))
print("Braced if not chain with 'and' and conditional expression:")
if not (a and (b if b else 2)):
print("oki")
print("Nested if chain with outer else:")
d=1
if a:
if b or c:
if d:
print("inside nest")
else:
print("outer else")
print("Complex conditional expression:")
v = (3 if a-1 else 0) or \
(b or (c*2 if c else 6) if b-1 else a and b and c)
print(v)
if True:
print("Predictable branch taken")
branchingFunction(1,0,3)
x = 3
def optimizationVictim():
if x:
pass
else:
pass
if x:
pass
pass
optimizationVictim()
def dontOptimizeSideEffects():
print("Lets see, if conditional expression in known true values are correctly handled:")
def returnTrue():
print("function 'returnTrue' was called as expected")
return True
def returnFalse():
print("function 'returnFalse' should not have beeen called")
return False
if (returnTrue() or returnFalse(),):
print("Taken branch as expected.")
else:
print("Bad branch taken.")
dontOptimizeSideEffects()
def dontOptimizeTruthCheck():
class A:
def __nonzero__(self):
raise ValueError
__bool__ = __nonzero__
a = A()
if a:
pass
try:
print("Check that branch conditions are not optimized way: ", end = "")
dontOptimizeTruthCheck()
print("FAIL.")
except ValueError:
print("OK.")
| 21.698925 | 92 | 0.563677 |
147ac3c79758847718ccf4809b924d665aab2263 | 2,273 | py | Python | app/views/users/messages/views.py | FundingCircle/DjanGoat | 013c7367294682955daf9eba205270bd2f9725cd | [
"MIT"
] | 1 | 2019-05-07T09:49:25.000Z | 2019-05-07T09:49:25.000Z | app/views/users/messages/views.py | FundingCircle/DjanGoat | 013c7367294682955daf9eba205270bd2f9725cd | [
"MIT"
] | null | null | null | app/views/users/messages/views.py | FundingCircle/DjanGoat | 013c7367294682955daf9eba205270bd2f9725cd | [
"MIT"
] | null | null | null |
from django.http import HttpResponse
from django.contrib import messages
from django.views.decorators.http import require_http_methods
from django.shortcuts import render, redirect
from django.utils import timezone
from app.decorators import user_is_authenticated
from app.models import User, Message
from app.views import utils
@require_http_methods(["GET", "POST"])
@user_is_authenticated
def user_messages(request, user_id): # pylint: disable=unused-argument
current_user = utils.current_user(request)
if request.method == "GET":
return render(request, "users/messages/index.html", {
'current_user': current_user,
'available_recipients': User.objects.all()
})
else:
try:
cid = int(request.POST['creator_id'])
creator = User.objects.get(user_id=cid)
rid = int(request.POST['receiver_id'])
receiver = User.objects.get(user_id=rid)
msg = request.POST['message']
red = int(request.POST['read'])
now = timezone.now()
Message.objects.create(creator=creator, receiver=receiver,
message=msg, read=red,
created_at=now, updated_at=now)
return redirect("/users/" + str(current_user.id) + "/messages")
except Exception as e:
messages.add_message(request, messages.INFO, str(e))
return render(request, "users/messages/index.html", {
'current_user': current_user,
'available_receipients': User.objects.all()
})
# W0613 = unused-argument
@require_http_methods(["GET", "DELETE"])
@user_is_authenticated
def user_message(request, user_id, message_id): # pylint: disable=W0613
current_user = utils.current_user(request)
try:
message = Message.objects.get(pk=message_id)
if request.method == "GET":
return render(request, "users/messages/show.html", {
'current_user': current_user,
'message': message
})
else:
message.delete()
return HttpResponse("Success!")
except Exception:
return redirect("/users/" + str(current_user.id) + "/messages")
| 36.66129 | 75 | 0.623405 |
379721dc94f9a66eaa9247b10c7c63220f02bf26 | 222 | py | Python | setup.py | stuartasims/paycheck_protection_program_eda | cf290e0b10e6a72e43d764c47a128676875ca2e4 | [
"FTL"
] | null | null | null | setup.py | stuartasims/paycheck_protection_program_eda | cf290e0b10e6a72e43d764c47a128676875ca2e4 | [
"FTL"
] | null | null | null | setup.py | stuartasims/paycheck_protection_program_eda | cf290e0b10e6a72e43d764c47a128676875ca2e4 | [
"FTL"
] | null | null | null | from setuptools import find_packages, setup
setup(
name='src',
packages=find_packages(),
version='0.1.0',
description='Digging into the released ppp loan data',
author='Stuart Sims',
license='',
)
| 20.181818 | 58 | 0.666667 |
3130006a91058eaa56b7ac8ea4fcaa305be259e4 | 780 | py | Python | virl/cli/definitions/images/ls/commands.py | ttafsir/virlutils | 5cb0e5410023b30d49515be5e3cb731dbd6cbeef | [
"MIT"
] | null | null | null | virl/cli/definitions/images/ls/commands.py | ttafsir/virlutils | 5cb0e5410023b30d49515be5e3cb731dbd6cbeef | [
"MIT"
] | null | null | null | virl/cli/definitions/images/ls/commands.py | ttafsir/virlutils | 5cb0e5410023b30d49515be5e3cb731dbd6cbeef | [
"MIT"
] | null | null | null | import click
from virl.api import VIRLServer
from virl.cli.views import image_list_table
from virl.helpers import get_cml_client
@click.command()
@click.option("--image", default=None)
def ls(**kwargs):
"""
list all images or the details of a specific image
"""
image = kwargs.get("image")
server = VIRLServer()
client = get_cml_client(server)
# Regardless of the argument, we have to get all the flavors
# In the case of no arg, we print them all.
# In the case of an arg, we have to go back and get details.
defs = client.definitions.image_definitions()
if image:
for f in list(defs):
if f["name"] == image:
image_list_table([f])
break
else:
image_list_table(defs)
| 26 | 64 | 0.641026 |
e15554737b9f3fa36382dde15ded928271679538 | 7,564 | py | Python | python/paddle/fluid/tests/unittests/test_prior_box_op.py | jerrywgz/Paddle | 85c4912755b783dd7554a9d6b9dae4a7e40371bc | [
"Apache-2.0"
] | 1 | 2018-08-03T03:33:52.000Z | 2018-08-03T03:33:52.000Z | python/paddle/fluid/tests/unittests/test_prior_box_op.py | jerrywgz/Paddle | 85c4912755b783dd7554a9d6b9dae4a7e40371bc | [
"Apache-2.0"
] | 3 | 2017-07-15T14:20:08.000Z | 2019-05-06T03:16:54.000Z | python/paddle/fluid/tests/unittests/test_prior_box_op.py | jerrywgz/Paddle | 85c4912755b783dd7554a9d6b9dae4a7e40371bc | [
"Apache-2.0"
] | 1 | 2018-07-20T07:13:31.000Z | 2018-07-20T07:13:31.000Z | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import sys
import math
from op_test import OpTest
class TestPriorBoxOp(OpTest):
def set_data(self):
self.init_test_params()
self.init_test_input()
self.init_test_output()
self.inputs = {'Input': self.input, 'Image': self.image}
self.attrs = {
'min_sizes': self.min_sizes,
'aspect_ratios': self.aspect_ratios,
'variances': self.variances,
'flip': self.flip,
'clip': self.clip,
'min_max_aspect_ratios_order': self.min_max_aspect_ratios_order,
'step_w': self.step_w,
'step_h': self.step_h,
'offset': self.offset
}
if len(self.max_sizes) > 0:
self.attrs['max_sizes'] = self.max_sizes
self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
def test_check_output(self):
self.check_output()
def setUp(self):
self.op_type = "prior_box"
self.set_data()
def set_max_sizes(self):
max_sizes = [5, 10]
self.max_sizes = np.array(max_sizes).astype('float32').tolist()
def set_min_max_aspect_ratios_order(self):
self.min_max_aspect_ratios_order = False
def init_test_params(self):
self.layer_w = 32
self.layer_h = 32
self.image_w = 40
self.image_h = 40
self.step_w = float(self.image_w) / float(self.layer_w)
self.step_h = float(self.image_h) / float(self.layer_h)
self.input_channels = 2
self.image_channels = 3
self.batch_size = 10
self.min_sizes = [2, 4]
self.min_sizes = np.array(self.min_sizes).astype('float32').tolist()
self.set_max_sizes()
self.aspect_ratios = [2.0, 3.0]
self.flip = True
self.set_min_max_aspect_ratios_order()
self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
self.aspect_ratios = np.array(
self.aspect_ratios, dtype=np.float).flatten()
self.variances = [0.1, 0.1, 0.2, 0.2]
self.variances = np.array(self.variances, dtype=np.float).flatten()
self.clip = True
self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes)
if len(self.max_sizes) > 0:
self.num_priors += len(self.max_sizes)
self.offset = 0.5
def init_test_input(self):
self.image = np.random.random(
(self.batch_size, self.image_channels, self.image_w,
self.image_h)).astype('float32')
self.input = np.random.random(
(self.batch_size, self.input_channels, self.layer_w,
self.layer_h)).astype('float32')
def init_test_output(self):
out_dim = (self.layer_h, self.layer_w, self.num_priors, 4)
out_boxes = np.zeros(out_dim).astype('float32')
out_var = np.zeros(out_dim).astype('float32')
idx = 0
for h in range(self.layer_h):
for w in range(self.layer_w):
c_x = (w + self.offset) * self.step_w
c_y = (h + self.offset) * self.step_h
idx = 0
for s in range(len(self.min_sizes)):
min_size = self.min_sizes[s]
if not self.min_max_aspect_ratios_order:
# rest of priors
for r in range(len(self.real_aspect_ratios)):
ar = self.real_aspect_ratios[r]
c_w = min_size * math.sqrt(ar) / 2
c_h = (min_size / math.sqrt(ar)) / 2
out_boxes[h, w, idx, :] = [
(c_x - c_w) / self.image_w, (c_y - c_h) /
self.image_h, (c_x + c_w) / self.image_w,
(c_y + c_h) / self.image_h
]
idx += 1
if len(self.max_sizes) > 0:
max_size = self.max_sizes[s]
# second prior: aspect_ratio = 1,
c_w = c_h = math.sqrt(min_size * max_size) / 2
out_boxes[h, w, idx, :] = [
(c_x - c_w) / self.image_w, (c_y - c_h) /
self.image_h, (c_x + c_w) / self.image_w,
(c_y + c_h) / self.image_h
]
idx += 1
else:
c_w = c_h = min_size / 2.
out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w,
(c_y - c_h) / self.image_h,
(c_x + c_w) / self.image_w,
(c_y + c_h) / self.image_h]
idx += 1
if len(self.max_sizes) > 0:
max_size = self.max_sizes[s]
# second prior: aspect_ratio = 1,
c_w = c_h = math.sqrt(min_size * max_size) / 2
out_boxes[h, w, idx, :] = [
(c_x - c_w) / self.image_w, (c_y - c_h) /
self.image_h, (c_x + c_w) / self.image_w,
(c_y + c_h) / self.image_h
]
idx += 1
# rest of priors
for r in range(len(self.real_aspect_ratios)):
ar = self.real_aspect_ratios[r]
if abs(ar - 1.) < 1e-6:
continue
c_w = min_size * math.sqrt(ar) / 2
c_h = (min_size / math.sqrt(ar)) / 2
out_boxes[h, w, idx, :] = [
(c_x - c_w) / self.image_w, (c_y - c_h) /
self.image_h, (c_x + c_w) / self.image_w,
(c_y + c_h) / self.image_h
]
idx += 1
# clip the prior's coordidate such that it is within[0, 1]
if self.clip:
out_boxes = np.clip(out_boxes, 0.0, 1.0)
# set the variance.
out_var = np.tile(self.variances, (self.layer_h, self.layer_w,
self.num_priors, 1))
self.out_boxes = out_boxes.astype('float32')
self.out_var = out_var.astype('float32')
class TestPriorBoxOpWithoutMaxSize(TestPriorBoxOp):
def set_max_sizes(self):
self.max_sizes = []
class TestPriorBoxOpWithSpecifiedOutOrder(TestPriorBoxOp):
def set_min_max_aspect_ratios_order(self):
self.min_max_aspect_ratios_order = True
if __name__ == '__main__':
unittest.main()
| 39.810526 | 78 | 0.49762 |
7101cbfbbec4818d3c2c0a998f8d9ae677e3cf5b | 1,845 | py | Python | setup.py | waltherg/PubChemPy | e3fd6cc401bfbe605082911911763c54cc02276a | [
"MIT"
] | 1 | 2015-02-18T10:01:17.000Z | 2015-02-18T10:01:17.000Z | setup.py | waltherg/PubChemPy | e3fd6cc401bfbe605082911911763c54cc02276a | [
"MIT"
] | null | null | null | setup.py | waltherg/PubChemPy | e3fd6cc401bfbe605082911911763c54cc02276a | [
"MIT"
] | null | null | null | #!/usr/bin/env python
import os
from setuptools import setup
import pubchempy
if os.path.exists('README.rst'):
long_description = open('README.rst').read()
else:
long_description = '''PubChemPy is a wrapper around the PubChem PUG REST API that provides a way to interact
with PubChem in Python. It allows chemical searches (including by name, substructure and similarity), chemical
standardization, conversion between chemical file formats, depiction and retrieval of chemical properties.
'''
setup(
name='PubChemPy',
version=pubchempy.__version__,
author=pubchempy.__author__,
author_email=pubchempy.__email__,
license=pubchempy.__license__,
url='https://github.com/mcs07/PubChemPy',
py_modules=['pubchempy'],
description='A simple Python wrapper around the PubChem PUG REST API.',
long_description=long_description,
keywords='pubchem python rest api chemistry cheminformatics',
extras_require={'pandas': ['pandas']},
test_suite='pubchempy_test',
classifiers=[
'Intended Audience :: Science/Research',
'Intended Audience :: Healthcare Industry',
'Intended Audience :: Developers',
'Topic :: Scientific/Engineering',
'Topic :: Scientific/Engineering :: Bio-Informatics',
'Topic :: Scientific/Engineering :: Chemistry',
'Topic :: Database :: Front-Ends',
'Topic :: Software Development :: Libraries :: Python Modules',
'Topic :: Internet',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.2',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
],
)
| 37.653061 | 118 | 0.668293 |
c1e8abf4e62b8efc5c7349b99aabcac94e8186e4 | 5,692 | py | Python | coloring/solver.py | WittgensteinInHisYouth/Discrete-Optimization | 07f30058b51eace6a8b12a4a996bb92de99876e1 | [
"CNRI-Python"
] | 1 | 2022-01-20T06:41:34.000Z | 2022-01-20T06:41:34.000Z | coloring/solver.py | waitaminutewhoareyou/Coursera-Discrete-Optimization | 07f30058b51eace6a8b12a4a996bb92de99876e1 | [
"CNRI-Python"
] | null | null | null | coloring/solver.py | waitaminutewhoareyou/Coursera-Discrete-Optimization | 07f30058b51eace6a8b12a4a996bb92de99876e1 | [
"CNRI-Python"
] | null | null | null | #!/usr/bin/python
# -*- coding: utf-8 -*-
# from mip import *
import random
from csp import *
import gurobipy as gp
from gurobipy import GRB
# def mip_solver(node_count, edge_count, edges):
# m = Model(solver_name=GRB)
# M = node_count * 2
# x = [m.add_var(var_type=INTEGER, lb=0, ub=node_count) for _ in range(node_count)]
# y = [m.add_var(var_type=BINARY) for _ in range(edge_count)] # decision var for big-M method
# C = m.add_var(var_type=INTEGER, lb=0, ub=node_count)
# for ix, (i, j) in enumerate(edges):
# m += x[j] + 1 - x[i] - M * y[ix] <= 0
# m += x[i] - x[j] + 1 - M * (1 - y[ix]) <= 0
# for i in range(node_count):
# m += x[i] - C <= 0
# m.objective = minimize(C)
# status = m.optimize(max_seconds=3000)
# if status == OptimizationStatus.OPTIMAL:
# opti = 1
# else:
# opti = 0
# solution = [int(assignment.x) for assignment in x]
# return solution, opti
def mip_solver(node_count, edge_count, edges):
m = gp.Model()
M = node_count*2
x = m.addVars(node_count, vtype=GRB.INTEGER, lb=0, ub=node_count, name="x")
y = m.addVars(edge_count, vtype=GRB.BINARY)
C = m.addVar(vtype=GRB.INTEGER, lb=0, ub=node_count)
for ix, (i, j) in enumerate(edges):
m.addConstr(x[j] + 1 - x[i] - M * y[ix] <= 0)
m.addConstr(x[i] - x[j] + 1 - M * (1 - y[ix]) <= 0)
for i in range(node_count):
m.addConstr(x[i] - C <= 0)
m.setObjective(C, GRB.MINIMIZE)
timeLimit = 1.5*60*60 #3 hours to check
m.setParam("TimeLimit", timeLimit)
m.optimize()
# provide an inital solution if there is none
if m.solCount == 0:
for ix,var in enumerate(x):
x[var].start = ix
m.Params.MIPFocus = 1
timeLimit = 0.5 * 60 * 60 # 4.5 hours to check
m.setParam("TimeLimit", timeLimit)
m.optimize()
opti = 1 if m.status == GRB.OPTIMAL else 0
solution = [int(v.x) for v in m.getVars()[:node_count]]
return solution, opti
def solve_semi_magic(num_color, node_count, edge_count, edges,algorithm=backtracking_search, **args):
""" From CSP class in csp.py
vars A list of variables; each is atomic (e.g. int or string).
domains A dict of {var:[possible_value, ...]} entries.
neighbors A dict of {var:[var,...]} that for each variable lists
the other variables that participate in constraints.
constraints A function f(A, a, B, b) that returns true if neighbors
A, B satisfy the constraint when they have values A=a, B=b
"""
# Use the variable names in the figure
def shu(x):
random.shuffle(x)
return x
csp_vars = shu([d for d in range(node_count)])
#########################################
# Fill in these definitions
csp_domains = {var: shu(list(range(num_color))) for var in csp_vars}
csp_neighbor = {var: [] for var in csp_vars}
for node_i, node_j in edges:
csp_neighbor[node_i].append(node_j)
csp_neighbor[node_j].append(node_i)
csp_neighbors = {key: shu(val) for key, val in csp_neighbor.items()}
def csp_constraints(A, a, B, b):
return a != b
#########################################
# define the CSP instance
csp = CSP(csp_vars, csp_domains, csp_neighbors,
csp_constraints)
# run the specified algorithm to get an answer (or None)
ans = algorithm(csp, **args)
# print('number of assignments', csp.nassigns)
assign = csp.infer_assignment()
# if assign:
# for x in sorted(assign.items()):
# print(x)
# for var in csp_vars:
# print(ans[var])
if ans is not None:
opti = 1
#solution = [None for _ in range(node_count)]
#print(sorted(assign.items()))
solution = [val for var, val in sorted(assign.items())]
return solution, opti
return None
def csp_solver(node_count, edge_count, edges):
num_color, solution_thus_far = 0, None
while solution_thus_far is None:
num_color += 1
solution_thus_far = solve_semi_magic(num_color, node_count, edge_count, edges, algorithm=backtracking_search, select_unassigned_variable=mrv,order_domain_values=lcv, inference=mac)
return solution_thus_far
def solve_it(input_data):
# Modify this code to run your optimization algorithm
# parse the input
lines = input_data.split('\n')
first_line = lines[0].split()
node_count = int(first_line[0])
edge_count = int(first_line[1])
edges = []
for i in range(1, edge_count + 1):
line = lines[i]
parts = line.split()
edges.append((int(parts[0]), int(parts[1])))
#print(node_count, edge_count, edges)
# build a trivial solution
# every node has its own color
solution, opti = mip_solver(node_count, edge_count, edges)
#solution, opti = csp_solver(node_count, edge_count, edges)
# prepare the solution in the specified output format
output_data = str(len(set(solution))) + ' ' + str(opti) + '\n'
output_data += ' '.join(map(str, solution))
return output_data
import sys
if __name__ == '__main__':
import sys
sys.argv = ['C:/Users/JI YIHONG/Dropbox/Coursera/Discrete Optimization/coloring/solver.py', 'data/gc_4_1']
if len(sys.argv) > 1:
file_location = sys.argv[1].strip()
with open(file_location, 'r') as input_data_file:
input_data = input_data_file.read()
print(solve_it(input_data))
else:
print('This test requires an input file. Please select one from the data directory. (i.e. python solver.py ./data/gc__5)')
| 35.575 | 188 | 0.611384 |
a85c9fb696feb93674797708f6af445f89b427fa | 77,731 | py | Python | bin/ADFRsuite/CCSBpckgs/PmvApp/secondaryStructureCmds.py | AngelRuizMoreno/Jupyter_Dock_devel | 6d23bc174d5294d1e9909a0a1f9da0713042339e | [
"MIT"
] | null | null | null | bin/ADFRsuite/CCSBpckgs/PmvApp/secondaryStructureCmds.py | AngelRuizMoreno/Jupyter_Dock_devel | 6d23bc174d5294d1e9909a0a1f9da0713042339e | [
"MIT"
] | null | null | null | bin/ADFRsuite/CCSBpckgs/PmvApp/secondaryStructureCmds.py | AngelRuizMoreno/Jupyter_Dock_devel | 6d23bc174d5294d1e9909a0a1f9da0713042339e | [
"MIT"
] | 1 | 2021-11-04T21:48:14.000Z | 2021-11-04T21:48:14.000Z | ################################################################################
##
## This library is free software; you can redistribute it and/or
## modify it under the terms of the GNU Lesser General Public
## License as published by the Free Software Foundation; either
## version 2.1 of the License, or (at your option) any later version.
##
## This library is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
## Lesser General Public License for more details.
##
## You should have received a copy of the GNU Lesser General Public
## License along with this library; if not, write to the Free Software
## Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
##
## (C) Copyrights Dr. Michel F. Sanner and TSRI 2016
##
################################################################################
#############################################################################
#
# Author: Sophie I. COON, Michel F. SANNER, Anna Omelchenko
#
# Copyright: M. Sanner TSRI 2014
#
#############################################################################
#
# $Header: /mnt/raid/services/cvs/PmvApp/secondaryStructureCmds.py,v 1.6.4.1 2017/07/13 20:55:28 annao Exp $
#
# $Id: secondaryStructureCmds.py,v 1.6.4.1 2017/07/13 20:55:28 annao Exp $
#
import numpy
from opengltk.OpenGL import GL
from DejaVu2.IndexedPolygons import IndexedPolygons
from DejaVu2.Shapes import Shape2D, Triangle2D, Circle2D, Rectangle2D,\
Square2D, Ellipse2D
from MolKit2.tree import TreeNodeSet
from MolKit2.molecule import Atom, AtomSet, Molecule, MoleculeSet
from MolKit2.protein import Protein, Residue, Chain, ResidueSet, ProteinSet
from MolKit2.protein import SecondaryStructure, SecondaryStructureSet, \
Helix, Strand, Turn, Coil
from NucleicBases import Add_Nucleic_Bases
from PmvApp.Pmv import MVCommand
from PmvApp.extruder import Sheet2D, ExtrudeSSElt, ExtrudeNA
from PmvApp.displayCmds import DisplayCommand
from PmvApp.colorCmds import ColorFromPalette
from PmvApp.colorPalette import ColorPalette
from PmvApp.Pmv import AfterDeleteAtomsEvent
from PmvApp.Pmv import DeleteGeomsEvent, AddGeomsEvent, EditGeomsEvent
from AppFramework.App import RemoveGeometryEvent, AddGeometryEvent
class ComputeSecondaryStructureCommand(MVCommand):
"""The computeSecondaryStructure command gets the information on the secondary structure of each molecule from the current selection. This information is then used to create objects describing the various secondary structure elements. \n
Package : PmvApp \n
Module : secondaryStructureCmds \n
Class : ComputeSecondaryStructureCommand \n
Command name : computeSecondaryStructure \n
Description:\n
The SS element object belonging to a chain are then grouped into sets.\n
A new level is added in the 4 level hierarchy...\n
The information is taken from the file when available or using stride when\n
available. This command can be used as an interactive command. \n
Synopsis:\n
None <--- ComputeSS(nodes, molMode={}) \n
Required Arguments:\n
nodes --- any set for MolKit2.Selection describing molecular components \n
Optional Arguments:\n
molmode --- dictionary key: molecule name, value : 'From File' or 'From Stride'. \n
Required Packages:\n
MolKit, DejaVu2, mglutil, OpenGL, ViewerFramework \n
Known bugs:\n
None \n
Examples:\n
mol = mv.Mols[0] \n
mv.computeSecondaryStructure(mol)
"""
def __init__(self):
MVCommand.__init__(self)
#self.flag = self.flag | self.objArgOnly
def onRemoveObjectFromViewer(self, obj):
"""
Method to delete sets created by this command.
This is done to prevent circular references and memory leaks.
"""
if self.app().undoableDelete__: return
if not hasattr(obj, 'chains'): return
for c in obj.chains:
if not hasattr(c, 'secondarystructureset') :
continue
if c.secondarystructureset is None:
delattr(c.secondarystructureset)
else:
# Cleaning up all the circular references created in this
# command
while len(c.secondarystructureset)!=0:
if hasattr(c.secondarystructureset[0], 'exElt'):
delattr(c.secondarystructureset[0], 'exElt')
delattr(c.secondarystructureset[0], 'children')
delattr(c.secondarystructureset[0], 'residues')
delattr(c.secondarystructureset[0], 'start')
delattr(c.secondarystructureset[0], 'end')
delattr(c.secondarystructureset[0], 'parent')
delattr(c.secondarystructureset[0], 'chain')
delattr(c.secondarystructureset[0], 'top')
del(c.secondarystructureset[0])
def onAddCmdToApp(self):
# Try to import stride and set the flag to 1 if succeeds and to 0 if
# does not
try:
import stride
self.haveStride = 1
except:
self.haveStride = 0
# Load the dependent commands if not already loaded in the
# application
if not self.app().commands.has_key('saveSet'):
self.app().lazyLoad('selectionCmds', commands=['saveSet'],
package='PmvApp')
def checkArguments(self, nodes, molModes=None):
"""None <--- computeSecondaryStructure(nodes, molModes = None) \n
nodes --- TreeNodeSet holding the current selection. \n
moldMode --- dictionary {name of the protein: 'From File', 'From PROSS', or 'From Stride'},\n
'From File' to get the information from the file,\n
'From Pross' to use the pross code to assing SS\n
'From Stride' to use stride (requires stride to be installed).
"""
if isinstance(nodes, str):
self.nodeLogString = "'"+nodes+"'"
nodes = self.app().expandNodes(nodes)
assert len(nodes)
if molModes:
assert isinstance(molModes, dict)
for mode in molModes.values():
if mode:
assert mode in ['From File', 'From Pross', 'From Stride']
kw = {}
kw['molModes'] = molModes
return (nodes,), kw
def doit(self, nodes, molModes=None):
molecules, nodeSets = self.app().getNodesByMolecule(nodes)
# Loop over the molecules
for mol in molecules:
try:
for c in mol.chains:
c.ribbonType()
# Determine what mode to use to get the information
if not molModes:
if mol.hasSS:
# Information has already been computed then
# continue
continue
else:
# Find out the possibilities and set the mode to
# one of them.
if mol.parser:
if mol.parser.hasSsDataInFile():
# File contains information the mode will be 'From
# File'
mode = 'From File'
else:
mode = 'From Pross'
elif self.haveStride:
# Stride is available on the platform
# but no info in the file then stride will be used
mode='From Stride'
else:
mode='From Pross'
else:
#print molModes
# a mode to get the information has been specified
# for the given molecules
if molModes and not molModes.has_key(mol.name):
# if the mode has not been specified for a molecule
# print a message and continue
raise RuntimeError, '%s: No mode has been specified for %s'% (self.name, mol.name)
else:
# Set the mode to the given value.
mode = molModes[mol.name]
# if this mode has already been used pass.
if mode in mol.hasSS: continue
# if secondarystructure have been computed once using another
# mode need to clean up first
elif mol.hasSS != []:
self.clean(mol)
# Then compute using the new given mode.
#if mode is None:
# mol.secondaryStructureFromFile()
if mode == 'From File':
# If both modes available try file first if fails use stride
# instead.
#if not mol.parser.hasSsDataInFile():
# # GIVE FEEDBACK TO THE USER !!!!
# self.warningMsg("WARNING: "+mol.name + \
# ".pdb does not contain Secondary \
# Structure information.")
# continue
#else:
mol.secondaryStructureFromFile()
#self.savesets(mol)
elif mode == 'From Stride':
if not self.haveStride:
raise RuntimeError , "%s: Stride is not available on \
this computer to compute the secondary structure of " %(self.name, mol.name+".pdb")
else:
mol.secondaryStructureFromStride()
#self.savesets(mol)
elif mode == 'From Pross':
mol.secondaryStructureFromPross()
#self.savesets(mol)
#self.savesets(mol)
self.app()._executionReport.addSuccess('Computed secondary structure for molecule %s successfully'% mol.name, obj=mol)
except:
msg = 'Error while computing secondary structure for molecule %s'%mol.name
self.app().errorMsg(sys.exc_info(), msg, obj=mol)
def savesets(self, mol):
for c in mol.chains:
if not hasattr(c, 'secondarystructureset'): continue
for ss in c.secondarystructureset:
name = "%s%s"%(ss.name, ss.chain.id)
if ss.residues: #Bugfix for #1033
## MS calling the command slows this down a lot
## but the sets are needed to extrude, so we add sets 'by hand'
## side effect: no vision nodes for these sets
# self.app().saveSet(ss.residues, mol.name+':'+name[-1]
# +':'+name[:-1],
# '%s-%s' %(ss.residues[0].name,
# ss.residues[-1].name),
# )
name = mol.name+':'+name[-1] +':'+name[:-1]
ss.residues.comments = '%s-%s'%(ss.residues[0].name,
ss.residues[-1].name)
self.app().sets.add(name, ss.residues)
def clean(self, mol):
"""
This method is called when getting the secondary structure information
using stride after having from file and vice versa. It is used to
delete all the secondary structure objects and attributes created
previously."""
# Compute secondary structure creates the following:
# - Secondary structure elements
# - Save the secondary structure elements residues as a set
# - new mol attribute hasSS which is a list
#from PmvApp.selectionCommands import sets__
molName = mol.name
mol.hasSS = []
# Here need to check if the secondary structure element have
# been extruded or not. If yes need to clean up that as well.
if hasattr(mol, '_ExtrudeSecondaryStructureCommand__hasSSGeom')\
and mol._ExtrudeSecondaryStructureCommand__hasSSGeom:
self.app().extrudeSecondaryStructure.clean(mol)
for chain in mol.chains:
# delete the secondarystructureset
if not hasattr(chain, 'secondarystructureset'): continue
for ss in chain.secondarystructureset:
name = "%s%s"%(ss.name, ss.chain.id)
setName = mol.name+':'+name[-1]+':'+name[:-1]
if self.app().sets.has_key(setName):
del self.app().sets[setName]
#del sets__[mol.name+':'+name[-1]+':'+name[:-1]]
delattr(chain, 'secondarystructureset')
# delete the secondarystructure attribute of the residues when
# existing.
resTest = [delattr(x, 'secondarystructure') for x in chain.residues if hasattr(x, 'secondarystructure')]
# call the onRemoveObjectFromViewer for the mol.
self.app().undoableDelete__ = False
self.onRemoveObjectFromViewer(mol)
del self.app().undoableDelete__
# Also need to clean up the sheet2D information.
for c in mol.chains:
if hasattr(c, 'sheet2D') and c.sheet2D.has_key('ssSheet2D'):
del c.sheet2D['ssSheet2D']
class ExtrudeSecondaryStructureCommand(MVCommand):
"""The ExtrudeCommand allows the user to represent the secondary structure elements by extruding 2D geometries along a 3D path.To execute this command use the entry 'extrude Secondary Structure' under the 'Compute' menu in the menu bar.
The panel that appears lets the user choose the 2D shapes for the extrusion. The entry 'default' in the listChooser lets do a traditional ribbon representation.nbchords represents the number of points in the path3D corresponding to one residue. The higher this parameter is the smoother the extruded geometries will look.gapBeg allows the user to introduce a gap of gapBeg points the extruded geometrie before each residue.gapEnd allows the user to introduce a gap of gapEnd points the extruded geometrie after each residue.The value of this two parameters depend on the value of the nbchords parameter and on each other's value.Once you clique OK on this panel another panel appears to let the user caracterize the chosen 2D geometry.Once the user chose all the parameters an ExtrudeSSElt object is created for each secondary structure element. The geometries associated to each secondary structure element are then updated with the new vertices and faces.Finally the displaySSCommand is executed.This command has the objArgsOnly flag. \n
Package : PmvApp \n
Module : secondaryStructureCommands \n
Class : ExtrudeSecondaryStructureCommand \n
Command name : extrudeSecondaryStructure \n
Synopsis:\n
None <--- extrudeSecondaryStructure(nodes, shape1=None, shape2=None,frontcap=1, endcap=True, arrow=True, nbchords=8, gapBeg=False,gapEnd=False, larrow=2, display=True) \n
Required Arguments:\n
nodes --- TreeNodeSet holding the current selection(mv.getSelection()) \n
Optional Arguments:\n
shape1 &
shape2 --- DejaVu2.Shapes.Shape2D objects. shape1 will be used to
represent the helix and strand, shape2 to represent coils and
turns. \n
frontcap &
endcap --- Boolean flag when set to True a cap will be added to the
geom either at the front or at the end \n
arrow --- Boolean flag when set to True an arow will be added to the
geometry representing the strand.\n
nbchords --- Nb of points per residues in the smooth array \n
gapBeg&
gapEnd --- defines gap at the beginning or the end of each residue. \n
larrow --- lenth of the arrow if arrow boolean flag set to 1 \n
display --- Boolean flag when set to True the displaySecondaryStructure
is called automatically
"""
def __init__(self):
MVCommand.__init__(self)
#self.flag = self.flag | self.objArgOnly
def pickedVerticesToAtoms(self, geom, vertInd):
"""
This function gets called when a picking or drag select event has
happened. It gets called with a geometry and the list of vertex
indices of that geometry that have been picked.
This function is in charge of turning these indices into an AtomSet
This function takes the following arguments:
geom : geometry picked, instance of a class derived from DejaVu2.Geom
(IndexedPolygons, IndexedPolylines.....)
vertInd: list of integer representing the indices of the picked
vertices in the given geometry geom.
"""
# this function gets called when a picking or drag select event has
# happened. It gets called with a geometry and the list of vertex
# indices of that geometry that have been selected.
# This function is in charge of turning these indices into an AtomSet
ss = geom.SS
l = []
for vi in vertInd:
resInd = ss.exElt.getResIndexFromExtrudeVertex( vi )
try:
l.append(ss.children[int(resInd)].atoms.get('CA')[0])
except:
l.append(ss.children[int(resInd)].atoms[0])
return AtomSet( AtomSet( l ) )
def atomPropToVertices(self, geom, residues, propName, propIndex=None):
"""Function called to compute the array of properties"""
if residues is None or len(residues)==0 : return None
propVect = []
if not propIndex is None:
propIndex = 'secondarystructure'
for r in residues:
try:
prop = getattr(r.atoms.get('CA')[0], propName)
except IndexError:
prop = getattr(r.atoms[0], propName)
if not propIndex is None:
propVect.append(prop.get(propIndex, prop['lines']))
else:
propVect.append(prop)
geom.SS.exElt.setResProperties(propVect, propName, residues)
properties = geom.SS.exElt.getExtrudeProperties( residues, propName )
return properties
def onAddObjectToViewer(self, obj):
self.objectState[obj] = {'onAddObjectCalled':True}
# private flag to specify whether or not the geometries for the SS
# have been created.
obj.__hasSSGeom = 0
if self.app().commands.has_key('dashboard'):
self.app().dashboard.resetColPercent(obj, '_showRibbonStatus')
def createGeometries(self, obj):
if obj.__hasSSGeom :
return
from DejaVu2.Geom import Geom
geomC = obj.geomContainer
if not geomC.geoms.has_key('secondarystructure'):
t = Geom('secondarystructure', shape=(0,0), protected=True)
geomC.addGeom( t, parent=geomC.masterGeom, redo=0 )
else:
t = geomC.geoms['secondarystructure']
for a in obj.allAtoms:
a.colors['secondarystructure']=(1.,1.,1.)
a.opacities['secondarystructure']=1.
for c in obj.chains:
if not hasattr(c, 'secondarystructureset'):
continue
for ss in c.secondarystructureset:
name = "%s%s"%(ss.name, ss.chain.id)
g = IndexedPolygons(name, visible=0, pickableVertices=1, protected=True,)
if self.app().userpref['Sharp Color Boundaries for MSMS']['value'] == 'blur':
g.Set(inheritSharpColorBoundaries=False, sharpColorBoundaries=False,)
#g.RenderMode(GL.GL_FILL, face=GL.GL_FRONT, redo=0)
#g.Set(frontPolyMode=GL.GL_FILL,redo=0)
g.SS = ss
geomC.atomPropToVertices[name] = self.atomPropToVertices
geomC.geomPickToAtoms[name] = self.pickedVerticesToAtoms
geomC.geomPickToBonds[name] = None
geomC.addGeom(g, parent=t, redo=0 )
self.managedGeometries.append(g)
#geomC.addGeom(g,self,parent=t, redo=0 )
geomC.atoms[name] = ResidueSet()
obj.__hasSSGeom = 1
def onAddCmdToApp(self):
self.app().lazyLoad("secondaryStructureCmds",
commands = ['computeSecondaryStructure', 'displayExtrudedSS'],
package="PmvApp")
self.app().lazyLoad("extrusionCmds",
commands=['computeSheet2D', "Nucleic_Acids_properties"], package="PmvApp")
def clean(self, obj):
if not hasattr(obj, 'chains'): return
for c in obj.chains:
if hasattr(c, 'residuesInSS'):
delattr(c, 'residuesInSS')
if not hasattr(c, 'secondarystructureset'):
continue
for ss in c.secondarystructureset:
# Have to remove specifically geoms.SS and geoms.mol
# from the geomContainer and the viewer
g = obj.geomContainer.geoms[ss.name+c.id]
del(g.SS)
del(g.mol)
g.Set(visible=0, tagModified=False)
g.protected = False
event = RemoveGeometryEvent(g)
self.app.eventHandler.dispatchEvent(event)
# the application's GUI should add a listener for this event,
# and the method to:
#self.app().GUI.VIEWER.RemoveObject(g)
del obj.geomContainer.geoms[ss.name+c.id]
del obj.geomContainer.atoms[ss.name+c.id]
obj.__hasSSGeom=0
def onRemoveObjectFromViewer(self, obj):
if self.objectState.has_key(obj):
self.objectState.pop(obj)
if self.app().undoableDelete__:
return
if not hasattr(obj, 'chains'): return
for c in obj.chains:
if hasattr(c, 'residuesInSS'):
delattr(c, 'residuesInSS')
if not hasattr(c, 'secondarystructureset'):
continue
for ss in c.secondarystructureset:
# Have to remove specifically geoms.SS and geoms.mol
# from the geomContainer and the viewer
g = obj.geomContainer.geoms[ss.name+c.id]
del(g.SS)
del(g.mol)
g.Set(visible=0, tagModified=False)
g.protected = False
event = RemoveGeometryEvent(g)
self.app.eventHandler.dispatchEvent(event)
# the application's GUI should add a listener for this event,
# and the method to:
#self.app().GUI.VIEWER.RemoveObject(g)
del obj.geomContainer.geoms[ss.name+c.id]
del obj.geomContainer.atoms[ss.name+c.id]
obj.__hasSSGeom=0
def checkArguments(self, nodes, shape1=None, shape2=None, frontcap=True,
endcap=True, arrow=True, nbchords=8, gapBeg=0, gapEnd=0,
larrow=2, display=True, width=1.2, height=0.2, radius=0.1,
updateNucleicAcidsPropertiesGUI=False,
only=True, negate=False):
"""Required Arguments:\n
nodes --- TreeNodeSet holding the current selection
(mv.getSelection()) \n
Optional Arguments:\n
shape1 &
shape2 --- DejaVu2.Shapes.Shape2D objects. shape1 will be used to \n
represent the helix and strand, shape2 to represent coils and\n
turns.\n
frontcap &
endcap --- Boolean flag when set to True a cap will be added to the \n
geom either at the front or at the end \n
arrow --- Boolean flag when set to True an arow will be added to the \n
geometry representing the strand. \n
nbchords --- Nb of points per residues in the smooth array \n
gapBeg&
gapEnd --- defines gap at the beginning or the end of each residue. \n
larrow --- length of the arrow if arrow boolean flag set to 1 \n
display --- Boolean flag when set to True the displaySecondaryStructure
is called automatically
width, height, radius --- if shape1 is not specified, these parameters \n
are used to create shape1 (Rectangle2D(withd, height)) \n
and shape2 (Circle2D(radius)) .
"""
if isinstance (nodes, str):
self.nodeLogString = "'"+nodes+"'"
nodes = self.app().expandNodes(nodes)
assert isinstance(nbchords, int)
assert gapEnd<=len(nodes)
assert gapBeg<=len(nodes)
if shape1:
assert isinstance(shape1 , Shape2D)
if shape2:
assert isinstance(shape2 , Shape2D)
assert frontcap in (True,False, 1, 0)
assert endcap in (True, False, 1, 0)
assert arrow in (True, False, 1, 0)
assert display in (True, False, 1, 0)
assert isinstance (larrow, (int, float))
assert isinstance (width, (int, float))
assert isinstance (height, (int, float))
assert isinstance (radius, (int, float))
kw = {}
kw['shape1'] = shape1
kw['shape2'] = shape2
kw['frontcap'] = frontcap
kw['endcap'] = endcap
kw['arrow'] = arrow
kw['nbchords'] = nbchords
kw['gapBeg'] = gapBeg
kw['gapEnd'] = gapEnd
kw['larrow'] = larrow
kw['display'] = display
kw['width'] = width
kw['height'] = height
kw['radius'] = radius
kw['updateNucleicAcidsPropertiesGUI'] = updateNucleicAcidsPropertiesGUI
kw['only'] = only
kw['negate'] = negate
#print "kw.has_key('only')=", kw.has_key('only')
#print kw.get('only', 'no_value')
return (nodes,), kw
def doit(self, nodes, shape1=None, shape2=None, frontcap=True, endcap=True,
arrow=True, nbchords=8, gapBeg=0, gapEnd=0, larrow = 2,
display=True, width=1.2, height=0.2, radius=0.1,
updateNucleicAcidsPropertiesGUI=False,
only=True, negate=False):
""" nodes, shape1, shape2=None, frontcap=True, endcap=True, arrow=True,
nbchords=8, gapBeg=0, gapEnd=1, display=True"""
#print "2: kw.has_key('only')=", kw.has_key('only'), ':',
#print kw.get('only', 'no_value')
shape1o = shape1
shape2o = shape2
molecules, residueSets = self.app().getNodesByMolecule(nodes, Residue)
if shape1 is None:
shape1 = Rectangle2D(width=width, height=height, vertDup=1)
shape2 = Circle2D(radius=radius)
# highlight selection
selMols, selResidues = self.app().getNodesByMolecule(self.app().activeSelection.get(),
Residue)
molSelectedResiduesDict = dict( zip( selMols, selResidues) )
# Create a sheet2 object.
for mol, residues in map(None, molecules, residueSets):
try:
if not self.objectState.has_key(mol):
self.onAddObjectToViewer(mol)
if not mol.hasSS:
# Compute the secondarystructure if not there
self.app().computeSecondaryStructure(mol)
if not hasattr(mol,'__hasSSGeom') or not mol.__hasSSGeom:
# Need here to change
self.createGeometries(mol)
reswithss = residues.get(lambda x:
hasattr(x, 'secondarystructure'))
if reswithss is None:
raise RuntimeError, "%s: no secondary structure in specified nodes of molecule "% (self.name, mol.name)
selectionSS = reswithss.secondarystructure.uniq()
chains = residues.parent.uniq()
# highlight selection
if molSelectedResiduesDict.has_key(mol) and len(molSelectedResiduesDict[mol]) > 0:
lHighlight = True
else:
lHighlight = False
for i in range(len(chains)):
chain = chains[i]
chain.ssExtrusionParams = { # used to save session
'shape1' : shape1o,
'shape2' : shape2o,
'frontcap' : frontcap,
'endcap' : endcap,
'arrow' : arrow,
'nbchords' : nbchords,
'gapBeg' : gapBeg,
'gapEnd' : gapEnd,
'larrow' : larrow
}
newsheet = 0
if not hasattr(chain, 'sheet2D'):
chain.sheet2D = {}
if not hasattr(chain,'secondarystructureset'):
self.app().warningMsg('%s: no secondary structure set for chain %s in molecule %s'%(self.name, chain.id, mol.name))
chain.sheet2D['ssSheet2D'] = None
continue
ssSet = chain.secondarystructureset
# 1- Check if the sheet2D for a secondary structure has been
# computed already.
if chain.sheet2D.has_key('ssSheet2D'):
if chain.sheet2D['ssSheet2D'] is None:
newsheet = 0
continue
elif chain.sheet2D['ssSheet2D'].chords != nbchords:
rt = chain.ribbonType()
if rt=='NA':
ExtrudeNA(chain)
newsheet = 1
elif rt=='AA':
self.app().computeSheet2D(chain, 'ssSheet2D',
'CA','O', buildIsHelix=1,
nbchords=nbchords)
newsheet = 1
else:
newsheet = 0
else:
newsheet = 0
elif not chain.sheet2D.has_key('ssSheet2D'):
rt = chain.ribbonType()
if rt=='NA':
ExtrudeNA(chain)
newsheet = 1
elif rt=='AA':
self.app().computeSheet2D(chain, 'ssSheet2D',
'CA', 'O',buildIsHelix=1,
nbchords=nbchords)
newsheet = 1
else:
newsheet = 0
if newsheet:
sd = chain.sheet2D['ssSheet2D']
# then create a pointer to the sheet2D for each secondary structures.
ssSet.sheet2D = sd
if sd is None : continue
# Do the extrusion ONLY for the ss having a residue in the
# selection
removeSS =[]
#from PmvApp.selectionCommands import sets__
for SS in ssSet:
# test here if all the residues of the sselt are
# in the residue set used
# to compute the sheet2D. if not remove the ss.
if SS.sheet2D is None:
continue
#if filter(lambda x, rs = SS.sheet2D.resInSheet:
# not x in rs, SS.residues):
if [x for x in SS.residues if not x in SS.sheet2D.resInSheet]:
self.app().warningMsg("%s: Removing %s from secondary structure set(molecule %s). One or more residues doesn't have CA and O"%(self.name, SS.name, mol.name))
# remove the SS from the set and etc....
#delattr(SS.residues, 'secondarystructure')
#ssSet.remove(SS)
removeSS.append(SS)
name = "%s%s"%(SS.name, SS.chain.id)
setName = mol.name+':'+name[-1]+':'+name[:-1]
if self.app().sets.has_key(setName):
del self.app().sets[setName]
#del sets__[mol.name+':'+name[-1]+':'+name[:-1]]
g = mol.geomContainer.geoms[name]
g.protected = False
event = RemoveGeometryEvent(g)
self.app.eventHandler.dispatchEvent(event)
# the application's GUI should add a listener for
# this event, and the method to:
# self.app().GUI.VIEWER.RemoveObject(g)
continue
name = "%s%s"%(SS.name, SS.chain.id)
if not SS in selectionSS:
continue
if isinstance(SS, Strand):
arrowf = arrow
else:
arrowf = 0
if not shape2 is None:
if SS.__class__.__name__ in ['Strand', 'Helix']:
SS.exElt = ExtrudeSSElt(
SS, shape1, gapEnd , gapBeg, frontcap, endcap,
arrowf,larrow)
elif SS.__class__.__name__ in ['Coil', 'Turn']:
rt = chain.ribbonType()
if rt=='NA':
NAp = self.app().Nucleic_Acids_properties
if NAp.isLoader():
NAp = NAp.loadCommand()
sc = max(NAp.scale_pyrimidine, NAp.scale_purine)
#shape2 = Circle2D(radius=sc/2.5)
shape3 = Circle2D(radius=sc/5.)
SS.exElt = ExtrudeSSElt(
SS, shape3, gapEnd, gapBeg, frontcap,
endcap, arrowf)
elif rt=='AA':
SS.exElt = ExtrudeSSElt(
SS, shape2, gapEnd, gapBeg, frontcap,
endcap, arrowf)
else:
SS.exElt = ExtrudeSSElt(SS, shape1, gapEnd , gapBeg,
frontcap, endcap, arrowf,
larrow)
resfaces, resfacesDict = SS.exElt.getExtrudeResidues(SS.residues)
g = mol.geomContainer.geoms[name]
## # MS triangulate faces
## trifaces = []
## for f in resfaces:
## trifaces.append( (f[0],f[1],f[3]) )
## if f[2]!=f[3]:
## trifaces.append( (f[1],f[2],f[3]) )
# highlight selection
g.resfacesDict = resfacesDict
highlight = []
if lHighlight is True:# and chain in residueSet :
highlight = [0]*len(SS.exElt.vertices)
for lResidue in molSelectedResiduesDict[mol]:
if resfacesDict.has_key(lResidue):
for lFace in resfacesDict[lResidue]:
for lVertexIndex in lFace:
highlight[int(lVertexIndex)] = 1
g.Set(vertices=SS.exElt.vertices,
highlight=highlight,
faces = resfaces,
## faces=trifaces,
vnormals=SS.exElt.vnormals, redo=0,
tagModified=False)
if chain.ribbonType()=='NA':
geom_bases = Add_Nucleic_Bases(
g, self.app().Nucleic_Acids_properties)
event = AddGeometryEvent(geom_bases, parent=g, redo=False)
self.app.eventHandler.dispatchEvent(event)
# the GUI of the application should create this event
# listenter with the method that will:
#self.app().GUI.VIEWER.AddObject(geom_bases, parent=g)
if geom_bases not in g.children:
g.children.append(geom_bases)
geom_bases.parent = g
#geom_bases.fullName = g.fullName+'|'+geom_bases.name
for SS in removeSS:
delattr(SS.residues, 'secondarystructure')
ssSet.remove(SS)
self.app()._executionReport.addSuccess('Extruded SS for molecule %s successfully'%
mol.name, obj=residues)
except:
msg = 'Error while displaying lines for molecule %s'%mol.name
self.app().errorMsg(sys.exc_info(), msg, obj=residues)
if display:
kw = {'only':True, 'negate':negate}
#print "calling displayExtrudedSS with ", kw
self.app().displayExtrudedSS(*(nodes,), **kw)
# gg = g.viewer.FindObjectByName('root|1dwb_0|secondarystructure|Coil1L')
# print 'AFTER DISPLAY', gg
#self.app().displayExtrudedSS(nodes)
event = EditGeomsEvent(
'SSextrude', [nodes,[shape1, shape2, frontcap, endcap,
arrow, nbchords, gapBeg, gapEnd, larrow,
display, updateNucleicAcidsPropertiesGUI]])
self.app().eventHandler.dispatchEvent(event)
class ExtrudeSecondaryStructureCommandUnic(ExtrudeSecondaryStructureCommand):
"""The ExtrudeCommand allows the user to represent the secondary structure
elements by extruding 2D geometries along a 3D path
Package : PmvApp \n
Module : secondaryStructureCmds \n
Class : ExtrudeSecondaryStructureCommand \n
Command name : extrudeSecondaryStructure \n
Synopsis:\n
None <--- extrudeSecondaryStructure(nodes, shape1=None, shape2=None,frontcap=1, endcap=True, arrow=True, nbchords=8, gapBeg=False,gapEnd=False, larrow=2, display=True) \n
Required Arguments:\n
nodes --- TreeNodeSet holding the current selection(mv.getSelection()) \n
Optional Arguments:\n
shape1 &
shape2 --- DejaVu2.Shapes.Shape2D objects. shape1 will be used to \n
represent the helix and strand, shape2 to represent coils and \n
turns. \n
frontcap &
endcap --- Boolean flag when set to True a cap will be added to the \n
geom either at the front or at the end \n
arrow --- Boolean flag when set to True an arow will be added to the \n
geometry representing the strand. \n
nbchords --- Nb of points per residues in the smooth array \n
gapBeg&
gapEnd --- defines gap at the beginning or the end of each residue. \n
larrow --- lenth of the arrow if arrow boolean flag set to 1 \n
display --- Boolean flag when set to True the displaySecondaryStructure
is called automatically
"""
def __init__(self):
ExtrudeSecondaryStructureCommand.__init__(self)
def createGeometries(self, obj):
if hasattr(obj,'__hasSSGeom') :
return
from DejaVu2.Geom import Geom
geomC = obj.geomContainer
if not geomC.geoms.has_key('SS'):
t = Geom('SS', shape=(0,0), protected=True)
geomC.addGeom( t, parent=geomC.masterGeom, redo=0 )
else:
t = geomC.geoms['SS']
for a in obj.allAtoms:
a.colors['SS']=(1.,1.,1.)
a.opacities['SS']=1.
for c in obj.chains:
#if not hasattr(c, 'secondarystructureset'):
# continue
#for ss in c.secondarystructureset:
name = "SS%s"%(c.id)
g = IndexedPolygons(name, visible=0, pickableVertices=1, protected=True,)
if self.app().userpref['Sharp Color Boundaries for MSMS']['value'] == 'blur':
g.Set(inheritSharpColorBoundaries=False, sharpColorBoundaries=False,)
g.Set(frontPolyMode=GL.GL_FILL)
#g.SS = ss
geomC.atomPropToVertices[name] = self.atomPropToVertices
geomC.geomPickToAtoms[name] = self.pickedVerticesToAtoms
geomC.geomPickToBonds[name] = None
geomC.addGeom(g, parent=t, redo=0 )
self.managedGeometries.append(g)
#geomC.addGeom(g,self,parent=t, redo=0 )
geomC.atoms[name] = ResidueSet()
atoms = c.findType(Atom)
for a in atoms:
a.colors[name]=(1.,1.,1.)
a.opacities[name]=1.
for ss in c.secondarystructureset:
sname = "%s%s"%(ss.name, ss.chain.id)
geomC.atoms[sname] = ResidueSet()
obj.__hasSSGeom = 1
def doit(self, nodes, shape1=None, shape2=None, frontcap=True, endcap=True,
arrow=True, nbchords=8, gapBeg=0, gapEnd=0, larrow = 2,
display=True, width=1.2, height=0.2, radius=0.1,
updateNucleicAcidsPropertiesGUI=False, only=True, negate=False):
""" nodes, shape1, shape2=None, frontcap=True, endcap=True, arrow=True,
nbchords=8, gapBeg=0, gapEnd=1, display=True"""
#print "2: kw.has_key('only')=", kw.has_key('only'), ':',
#print kw.get('only', 'no_value')
molecules, residueSets=self.app().getNodesByMolecule(nodes, Residue)
if len(molecules)==0: return
if shape1 is None:
shape1 = Rectangle2D(width=width, height=height, vertDup=1)
shape2 = Circle2D(radius=radius)
# highlight selection
selMols, selResidues = self.app().getNodesByMolecule(self.app().activeSelection.get(), Residue)
molSelectedResiduesDict = dict( zip( selMols, selResidues) )
# Create a sheet2 object.
for mol, residues in map(None, molecules, residueSets):
try:
if not mol.hasSS:
# Compute the secondarystructure if not there
self.app().computeSecondaryStructure(mol)
if not hasattr(mol,'__hasSSGeom') or not mol.__hasSSGeom:
# Need here to change
self.createGeometries(mol)
reswithss = residues.get(lambda x:
hasattr(x, 'secondarystructure'))
if reswithss is None:
raise RuntimeError, '%s: no secondary structure in specified nodes for molecule %s' % (self.name, mol)
selectionSS = reswithss.secondarystructure.uniq()
chains = residues.parent.uniq()
# highlight selection
if molSelectedResiduesDict.has_key(mol) and len(molSelectedResiduesDict[mol]) > 0:
lHighlight = True
else:
lHighlight = False
for i in range(len(chains)):
chain = chains[i]
newsheet = 0
if not hasattr(chain, 'sheet2D'):
chain.sheet2D = {}
if not hasattr(chain,'secondarystructureset'):
self.app().warningMsg('%s:no secondary structure set for chain: %s in molecule %s'%(self.name, chain.id, mol.name))
chain.sheet2D['ssSheet2D'] = None
continue
ssSet = chain.secondarystructureset
# 1- Check if the sheet2D for a secondary structure has been
# computed already.
if chain.sheet2D.has_key('ssSheet2D'):
if chain.sheet2D['ssSheet2D'] is None:
newsheet = 0
continue
elif chain.sheet2D['ssSheet2D'].chords != nbchords:
rt = chain.ribbonType()
if rt=='NA':
ExtrudeNA(chain)
newsheet = 1
elif rt=='AA':
self.app().computeSheet2D(chain, 'ssSheet2D',
'CA','O', buildIsHelix=1,
nbchords=nbchords)
newsheet = 1
else:
newsheet = 0
else:
newsheet = 0
elif not chain.sheet2D.has_key('ssSheet2D'):
rt = chain.ribbonType()
if rt=='NA':
ExtrudeNA(chain)
newsheet = 1
elif rt=='AA':
self.app().computeSheet2D(chain, 'ssSheet2D',
'CA', 'O',buildIsHelix=1,
nbchords=nbchords)
newsheet = 1
else:
newsheet = 0
if newsheet:
sd = chain.sheet2D['ssSheet2D']
# then create a pointer to the sheet2D for each secondary structures.
ssSet.sheet2D = sd
if sd is None : continue
# Do the extrusion ONLY for the ss having a residue in the
# selection
removeSS =[]
faces=[]
vertices=[]
normals=[]
#from PmvApp.selectionCommands import sets__
name = "SS"+chain.id
g = mol.geomContainer.geoms[name]
for SS in ssSet:
# test here if all the residues of the sselt are
# in the residue set used
# to compute the sheet2D. if not remove the ss.
if SS.sheet2D is None:
continue
if [x for x in SS.residues if not x in SS.sheet2D.resInSheet]:
self.app().warningMsg("%s: Removing %s from secondary structure set (%s). One or more residues doesn't have CA and O"%(self.name, SS.name, mol.name))
# remove the SS from the set and etc....
#delattr(SS.residues, 'secondarystructure')
#ssSet.remove(SS)
removeSS.append(SS)
#name = "%s%s"%(SS.name, SS.chain.id)
#del self.app().sets[mol.name+':'+name[-1]+':'+name[:-1]]
#del sets__[mol.name+':'+name[-1]+':'+name[:-1]]
#g = mol.geomContainer.geoms[name]
#g.protected = False
#if self.app().hasGui:self.app().GUI.VIEWER.RemoveObject(g)
continue
name = "%s%s"%(SS.name, SS.chain.id)
if not SS in selectionSS:
continue
if isinstance(SS, Strand):
arrowf = arrow
else:
arrowf = 0
if not shape2 is None:
if SS.__class__.__name__ in ['Strand', 'Helix']:
SS.exElt = ExtrudeSSElt(SS,shape1,gapEnd ,
gapBeg, frontcap, endcap,
arrowf,larrow)
elif SS.__class__.__name__ in ['Coil', 'Turn']:
if chain.ribbonType()=='NA':
NAp = self.app().Nucleic_Acids_properties
sc = max(NAp.scale_pyrimidine, NAp.scale_purine)
shape2 = Circle2D(radius=sc/2.5)
SS.exElt = ExtrudeSSElt(SS,shape2, gapEnd,
gapBeg, frontcap, endcap,
arrowf)
else:
SS.exElt = ExtrudeSSElt(SS, shape1, gapEnd , gapBeg,
frontcap, endcap, arrowf,
larrow)
resfaces, resfacesDict = SS.exElt.getExtrudeResidues(SS.residues)
#g = mol.geomContainer.geoms[name]
## # MS triangulate faces
## trifaces = []
## for f in resfaces:
## trifaces.append( (f[0],f[1],f[3]) )
## if f[2]!=f[3]:
## trifaces.append( (f[1],f[2],f[3]) )
# highlight selection
g.resfacesDict = resfacesDict
highlight = []
if lHighlight is True:# and chain in residueSet :
highlight = [0]*len(SS.exElt.vertices)
for lResidue in molSelectedResiduesDict[mol]:
if resfacesDict.has_key(lResidue):
for lFace in resfacesDict[lResidue]:
for lVertexIndex in lFace:
highlight[int(lVertexIndex)] = 1
faces.extend(numpy.array(resfaces)+len(vertices))
vertices.extend(SS.exElt.vertices)
normals.extend(SS.exElt.vnormals)
if chain.ribbonType()=='NA':
geom_bases = Add_Nucleic_Bases(g,
self.app().Nucleic_Acids_properties)
event = AddGeometryEvent(geom_bases, parent=g, redo=False)
self.app.eventHandler.dispatchEvent(event)
if geom_bases not in g.children:
g.children.append(geom_bases)
geom_bases.parent = g
#geom_bases.fullName = g.fullName+'|'+geom_bases.name
g.Set(vertices=vertices, highlight=highlight,
faces=faces, vnormals=normals, redo=0,
tagModified=False)
for SS in removeSS:
delattr(SS.residues, 'secondarystructure')
ssSet.remove(SS)
atoms = chain.findType(Atom)
self.app().bindGeomToMolecularFragment(g, atoms)
self.app()._executionReport.addSuccess('extruded SS for molecule %s successfully'%
mol.name, obj=residues)
except:
msg = 'Error while extruding SS for molecule %s'%mol.name
self.app().errorMsg(sys.exc_info(), msg, obj=residues)
if display:
kw = {}
if kw.get('only', 0):
kw['only'] = 1
kw['negate'] = negate
#print "calling displayExtrudedSS with ", kw
apply(self.app().displayExtrudedSS,(nodes,), kw)
class DisplayExtrudedSSCommand(DisplayCommand):
""" The DisplaySSCommand displays the geometries representing the secondary structure elements of the current selection.To execute this command use the 'Display Secondary Structure' entry under the 'Display' menu in the menu bar. \n
Package : PmvApp \n
Module : secondaryStructureCmds \n
Class : DisplayExtrudedSSCommand \n
Command name : displaySecondaryStructure \n
Synopsis:\n
None <- displaySecondaryStructure(nodes, only=False, negate=False) \n
Required Arguments:\n
nodes --- TreeNodeSet holding the current selection \n
Optional Arguments:\n
only --- allows the user to display only the current selection when set to 1 \n
negate --- allows to undisplay the current selection when set to 1. \n
This command is undoable.
"""
def onAddCmdToApp(self):
self.app().lazyLoad("secondaryStructureCmds",
commands=['computeSecondaryStructure', 'extrudeSecondaryStructure'],
package="PmvApp")
self.app().lazyLoad("extrusionCmds",
commands=['computeSheet2D'], package="PmvApp")
self.app().eventHandler.registerListener(AfterDeleteAtomsEvent, self.handleAfterDeleteAtoms)
def handleAfterDeleteAtoms(self, event):
"""Function to update geometry objects created by this command
upon atom deletion.
\nevent --- instance of a VFEvent object
"""
# split event.objects into atoms sets per molecule
molecules, ats = self.app().getNodesByMolecule(event.objects)
# loop over molecules to update geometry objects
for mol, atomSet in zip(molecules, ats):
#if no backbone atoms are deleted ss does not change
if len(atomSet.get("backbone")) == 0:
continue
for atom in atomSet.get("CA"):
atom.parent.hasCA = False
atom.parent.CAatom = None
for atom in atomSet.get("O"):
atom.parent.hasO = False
atom.parent.Oatom = None
if not mol.geomContainer.geoms.has_key('secondarystructure'): continue
kw = mol.geomContainer.geoms['secondarystructure'].kw.copy()
#cleanup SS atomset in geomcontainer
self.app().computeSecondaryStructure.clean(mol)
#this for molparser.hasSsDataInFile to trurn false
while 'HELIX' in mol.parser.keys:
mol.parser.keys.remove('HELIX')
while 'SHEET' in mol.parser.keys:
mol.parser.keys.remove('SHEET')
while 'TURN' in mol.parser.keys:
mol.parser.keys.remove('TURN')
for chain in mol.chains:
chain.ribbonType(noCache=True)
#mol.hasSS = False
mol.secondaryStructureFromPross()
mol.__hasSSGeom=0
self.app().extrudeSecondaryStructure(mol, display=0)
self(mol, **kw)
## def undoCmdBefore(self, nodes, only=False, negate=False, **kw):
## if len(nodes)==0 : return
## #molecules = nodes.top.uniq()
## molecules, residueSets = self.getNodes(nodes)
## #for mol, res in map(None, molecules, residueSets):
## negateCmds = []
## for mol in molecules:
## resWithSS = mol.findType(Residue).get(
## lambda x:hasattr(x,'secondarystructure'))
## if resWithSS is None:
## continue
## SSinMol = resWithSS.secondarystructure.uniq()
## #resWithSS = res.get(lambda x: hasattr(x,'secondarystructure'))
## #SSinSel = resWithSS.secondarystructure.uniq()
## #mol.geomContainer.atoms['secondarystructure']=resWithSS.atoms
## set = ResidueSet()
## if mol.geomContainer.geoms.has_key('SS'):
## for ch in mol.chains :
## set = set + mol.geomContainer.atoms['SS'+ch.id].parent
## if len(set)==0: # nothing is displayed
## negateCmds.append((self, (mol,), {'negate':True, 'redraw':True},))
## else:
## negateCmds.append((self, (set,), {'only':True, 'redraw':True}) )
## else :
## for ss in SSinMol:
## set = set + mol.geomContainer.atoms[ss.name+ss.chain.id]
## if len(set)==0: # nothing is displayed
## negateCmds.append((self, (mol,), {'negate':True, 'redraw':True}))
## else:
## negateCmds.append((self, (set,), {'only':True, 'redraw':True}))
## if len(negateCmds):
## return (negateCmds, self.name)
def doit(self, nodes, only=False, negate=False, redraw=True):
""" displays the secondary structure for the selected treenodes """
#print self.name, "in display with only=", only, " and negate=", negate
###############################################################
def drawResidues(SS, res, only, negate, uniq=False):
mol = SS.chain.parent
name = '%s%s'%(SS.name, SS.chain.id)
_set = mol.geomContainer.atoms[name]
inres = [x for x in _set if not x in res]
if len(inres) == 0:
# res and _set are the same
if negate:
_set = ResidueSet()
setOff = res
setOn = None
else:
_set = res
setOff = None
setOn = res
else:
# if negate, remove current res from displayed _set
if negate :
setOff = res
setOn = None
_set = _set - res
else: # if only, replace displayed _set with current res
if only:
setOff = _set - res
setOn = res
_set = res
else:
_set = res.union(_set)
setOff = None
setOn = _set
## # if negate, remove current res from displayed _set
## if negate :
## _set = _set - res
## else: # if only, replace displayed _set with current res
## if only:
## _set = res
## else:
## _set = res.union(_set)
## # now, update the geometries:
## if len(_set)==0:
## mol.geomContainer.geoms[name].Set(visible=0, tagModified=False)
## mol.geomContainer.atoms[name] = ResidueSet()
## return
#the rest is done only if there are some residues
mol.geomContainer.setAtomsForGeom(name, _set)
#print _set
if not hasattr(SS, 'exElt'):
return setOn, setOff
if isinstance(SS, Coil):
gapBefore = SS.gapBefore
gapAfter = SS.gapAfter
else:
gapBefore = gapAfter = False
resfaces, resfacesDict = SS.exElt.getExtrudeResidues(
_set, gapBefore, gapAfter)
## # MS triangulate faces
## trifaces = []
## for f in resfaces:
## trifaces.append( (f[0],f[1],f[3]) )
## if f[2]!=f[3]:
## trifaces.append( (f[1],f[2],f[3]) )
## g.Set(faces=trifaces, vnormals=SS.exElt.vnormals,
if uniq:
return setOn, setOff, resfaces, SS.exElt.vnormals, \
SS.exElt.vertices
g = mol.geomContainer.geoms[name]
col = mol.geomContainer.getGeomColor(name)
g.Set(faces=resfaces, vnormals=SS.exElt.vnormals,
visible=1, materials=col, inheritMaterial=False,
tagModified=False)
if SS.chain.ribbonType()=='NA':
faces = []
colors = []
for residue in _set:
faces.extend(residue._base_faces)
colors.extend(residue._coil_colors)
try:
atm = residue.atoms.get('CA')[0]
except:
atm = residue.atoms[0]
if len(residue._coil_colors) :
atm.colors["secondarystructure"] = residue._coil_colors[0]#??
else:
atm.colors["secondarystructure"] = residue._coil_colors
g.children[0].Set(faces=faces)
if colors:
g.Set(materials=colors, inheritMaterial=False)
if self.app().Nucleic_Acids_properties.color_backbone:
g.Set(inheritMaterial=False)
else:
g.Set(inheritMaterial=True)
mol.geomContainer.atoms['Bases'] = ResidueSet()
#mol.geomContainer.atoms[name] = ResidueSet()
return setOn, setOff
###############################################################
molecules, residueSets = self.app().getNodesByMolecule(nodes, Residue)
setOn = ResidueSet([])
setOff = ResidueSet([])
for mol, residues in map(None, molecules, residueSets):
try:
if not mol.hasSS:
self.app().computeSecondaryStructure(mol)
self.app().extrudeSecondaryStructure(mol, display=0)
reswithss = residues.get(lambda x:
hasattr(x,'secondarystructure'))
if reswithss is None:
raise RuntimeError, '%s: no secondary structure in specified nodes of %s molecule' % (self.name, mol.name)
SSInSel = reswithss.secondarystructure.uniq()
chainsInSel = residues.parent.uniq()
for c in mol.chains:
if not hasattr(c, 'secondarystructureset'):
continue
if not hasattr(c, 'sheet2D'):
self.app().warningMsg("%s: chain '%s'(%s) does not have a sheet2D computed"%(self.name, c.full_name(), mol.name))
continue
elif (c.sheet2D.has_key('ssSheet2D') and \
c.sheet2D['ssSheet2D'] is None): continue
if mol.geomContainer.geoms.has_key('SS') :
faces=[]
vertices=[]
normals=[]
name = "SS"+c.id
g = mol.geomContainer.geoms[name]
SS, resInSS = self.getResiduesBySS(residues, c)
for s in xrange(len(c.secondarystructureset)):
ss = c.secondarystructureset[s]
res = resInSS[s]
if ss in SSInSel and not hasattr(ss, 'exElt') \
and negate == 0:
self.app().extrudeSecondaryStructure(res, display=0)
if mol.geomContainer.geoms.has_key('SS') :
son, sof, f, n, v = drawResidues(ss, res, only , negate ,uniq=True)
faces.extend(numpy.array(f)+len(v))
vertices.extend(v)
normals.extend(n)
else :
son, sof = drawResidues(ss, res, only , negate )
if son: setOn += son
if sof: setOff += sof
if mol.geomContainer.geoms.has_key('SS') :
g.Set(visible=not negate)
kw = {"only":only, "negate":negate}
if mol.geomContainer.geoms.has_key('secondarystructure'):
mol.geomContainer.geoms['secondarystructure'].kw = kw
self.app()._executionReport.addSuccess('displayed secondary structure for molecule %s successfully'%
mol.name, obj=residues)
except:
msg = 'Error while displaying secondary structure for molecule %s'%mol.name
self.app().errorMsg(sys.exc_info(), msg, obj=residues)
event = EditGeomsEvent('SSdisplay', [nodes,[only, negate,redraw]],
setOn=setOn, setOff=setOff)
self.app().eventHandler.dispatchEvent(event)
def checkArguments(self, nodes, only=False, negate=False, redraw=True):
""" Required Arguments:\n
nodes --- TreeNodeSet holding the current selection \n
Optional Arguments:\n
only --- flag when set to 1 only the current selection will be displayed as secondarystructures \n
negate --- flag when set to 1 undisplay the current selection"""
if isinstance(nodes, str):
self.nodeLogString = "'"+nodes+"'"
nodes = self.app().expandNodes(nodes)
kw = {}
assert only in [True, False, 1, 0]
assert negate in [True, False, 1, 0]
kw['only'] = only
kw['negate'] = negate
kw['redraw']=redraw
return (nodes,),kw
def getResiduesBySS(self, residues, chain):
resWithSS = residues.get(lambda x: hasattr(x, 'secondarystructure'))
residuesInSS = []
for ss in chain.secondarystructureset :
res = resWithSS.get(lambda x, ss=ss:x.secondarystructure==ss)
if res is None:
res = ResidueSet()
residuesInSS.append(res)
return chain.secondarystructureset, residuesInSS
class UndisplayExtrudedSSCommand(DisplayCommand):
""" UndisplaySSCommand is an interactive command to undisplay part of
the molecule when represented as extruded secondary structure. \n
Package : PmvApp \n
Module : secondaryStructureCmds \n
Class : UndisplayExtrudedSSCommand \n
Command name : undisplaySecondaryStructure \n
Synopsis:\n
None <--- undisplaySecondaryStructure(nodes, **kw) \n
Required Arguments:\n
nodes --- TreeNodeSet holding the current selection
"""
def onAddCmdToApp(self):
if not self.app().commands.has_key('displayExtrudedSS'):
self.app().lazyLoad('secondaryStructureCmds',
commands=['displayExtrudedSS'], package='PmvApp')
def checkArguments(self, nodes, **kw):
""" nodes --- TreeNodeSet holding the current selection
"""
if isinstance(nodes, str):
self.nodeLogString = "'"+nodes+"'"
nodes = self.app().expandNodes(nodes)
kw = {'negate':1}
return (nodes,), kw
def doit(self, nodes, **kw):
self.app().displayExtrudedSS(nodes, **kw)
class RibbonCommand(MVCommand):
""" The RibbonCommand is a shortcut to visualize a traditional Ribbon
representation of the current selection. It first executes getSSCommand
then the extrudeSSCommand with the default values for all the parameters.
This command is undoable. \n
Package : PmvApp \n
Module : secondaryStructureCmds \n
Class : RibbonCommand \n
Command name : ribbon \n
Synopsis:\n
None <- ribbon(nodes, only=False, negate=False) \n
Required Arguments:\n
nodes --- TreeNodeSet holding the current selection \n
Optional Arguments:\n
only --- flag when set to 1 only the current selection
will be displayed \n
negate --- flag when set to 1 undisplay the current selection
"""
def __init__(self):
MVCommand.__init__(self)
#self.flag = self.flag | self.objArgOnly
#self.flag = self.flag | self.negateKw
def undoCmdBefore(self, *args, **kw):
return None # this prevents this function from trying to create a negation
# command since it only calls other VFCommands who can negate themselves
def onAddCmdToApp(self):
self.app().lazyLoad("secondaryStructureCmds",
commands=['extrudeSecondaryStructure', 'displayExtrudedSS',
'computeSecondaryStructure'], package="PmvApp")
def checkArguments(self, nodes, shape1=None, shape2=None, frontcap=True,
endcap=True, arrow=True, nbchords=8, gapBeg=0, gapEnd=0,
larrow=2, display=True, redraw=True, width=1.2,
height=0.2, radius=0.1, updateNucleicAcidsPropertiesGUI=False,
only=True, negate=False):
"""Required Arguments:\n
nodes --- TreeNodeSet holding the current selection
(mv.getSelection()) \n
Optional Arguments (will be passed to extrudeSecondaryStructure() ):\n
shape1 &
shape2 --- DejaVu2.Shapes.Shape2D objects. shape1 will be used to \n
represent the helix and strand, shape2 to represent coils and\n
turns.\n
frontcap &
endcap --- Boolean flag when set to True a cap will be added to the \n
geom either at the front or at the end \n
arrow --- Boolean flag when set to True an arow will be added to the \n
geometry representing the strand. \n
nbchords --- Nb of points per residues in the smooth array \n
gapBeg&
gapEnd --- defines gap at the beginning or the end of each residue. \n
larrow --- length of the arrow if arrow boolean flag set to 1 \n
display --- Boolean flag when set to True the displaySecondaryStructure
is called automatically \n
only --- flag when set to 1 only the current selection
will be displayed \n
negate --- flag when set to 1 undisplay the current selection \n
width, height, radius --- if shape1 is not specified, these parameters \n
are used to create shape1 (Rectangle2D(withd, height)) \n
and shape2 (Circle2D(radius))
"""
if isinstance (nodes, str):
self.nodeLogString = "'"+nodes+"'"
nodes = self.app().expandNodes(nodes)
assert isinstance(nbchords, int)
assert gapEnd<=len(nodes)
assert gapBeg<=len(nodes)
if shape1:
assert isinstance(shape1 , Shape2D)
if shape2:
assert isinstance(shape2 , Shape2D)
assert frontcap in (True,False, 1, 0)
assert endcap in (True, False, 1, 0)
assert arrow in (True, False, 1, 0)
assert display in (True, False, 1, 0)
assert isinstance (larrow, (int, float))
assert isinstance (width, (int, float))
assert isinstance (height, (int, float))
assert isinstance (radius, (int, float))
kw = {}
kw['shape1'] = shape1
kw['shape2'] = shape2
kw['frontcap'] = frontcap
kw['endcap'] = endcap
kw['arrow'] = arrow
kw['nbchords'] = nbchords
kw['gapBeg'] = gapBeg
kw['gapEnd'] = gapEnd
kw['larrow'] = larrow
kw['display'] = display
kw['width'] = width
kw['height'] = height
kw['radius'] = radius
kw['updateNucleicAcidsPropertiesGUI'] = updateNucleicAcidsPropertiesGUI
kw['only'] = only
kw['negate'] = negate
kw['redraw']=redraw
#print "kw.has_key('only')=", kw.has_key('only')
#print kw.get('only', 'no_value')
return (nodes,), kw
def doit(self, nodes, only=False, negate=False, redraw=True, **kw):
self.app().computeSecondaryStructure( nodes)
kw.update({'only':only, 'negate':negate})
#print self.name, "doit:", kw
self.app().extrudeSecondaryStructure( nodes, **kw)
class ColorBySSElementType(ColorFromPalette):
"""Command to color the given geometry by secondary structure
element. (Rasmol color code) \n
Package : PmvApp \n
Module : secondaryStructureCmds \n
Class : ColorBySSElementType
"""
def onAddCmdToApp(self):
from PmvApp.pmvPalettes import SecondaryStructureType
c = 'Color palette for secondary structure element type:'
self.palette = ColorPalette(
'SecondaryStructureType', SecondaryStructureType,
info=c, lookupMember = 'structureType')
if not self.app().commands.has_key('color'):
self.app().lazyLoad('colorCmds', ['color'], 'PmvApp')
self.undoCmdsString= self.app().color.name
def getColors(self, nodes):
res = nodes.findType(Residue)
resWithSS = res.get(lambda x: hasattr(x, 'secondarystructure'))
if resWithSS is None: return None, None
return resWithSS, self.palette.lookup(resWithSS.secondarystructure)
def getNodes(self, nodes, returnNodes=False):
"""expand nodes argument into a list of atoms and a list of
molecules."""
nodes = self.app().expandNodes(nodes)
res = nodes.findType(Residue).uniq()
resWithSS = res.get(lambda x: hasattr(x,'secondarystructure'))
if resWithSS is None or len(resWithSS)==0:
atoms = AtomSet()
molecules = ProteinSet()
else:
atoms = resWithSS.atoms
molecules= resWithSS.top.uniq()
if returnNodes:
return molecules, atoms, nodes
else:
return molecules, atoms
def doit(self, nodes, geomsToColor):
# this command do not require the color argument since colors are
# gotten from a palette
# we still can use the ColorCommand.undoCmdBefore but first we get
# the colors. This also insures that the colors are not put inside the
# command's log string
#print self.name , "geomsToColor", geomsToColor
#nodes is AtomSet
resWithSS, colors = self.getColors(nodes)
if colors is None: return
for g in geomsToColor:
if len(colors)==1 or len(colors)!=len(nodes):
for a in nodes:
a.colors[g] = tuple( colors[0] )
else:
for a, c in map(None, nodes, colors):
a.colors[g] = tuple(c)
updatedGeomsToColor = []
for mol in self.molSet:
try:
for gName in geomsToColor:
if not mol.geomContainer.geoms.has_key(gName): continue
geom = mol.geomContainer.geoms[gName]
if geom.children != []:
# get geom Name:
childrenNames = [x.name for x in geom.children]
updatedGeomsToColor = updatedGeomsToColor + childrenNames
for childGeom in geom.children:
childGeom.Set(inheritMaterial=0, redo=0, tagModified=False)
else:
updatedGeomsToColor.append(gName)
geom.Set(inheritMaterial=0, redo=0, tagModified=False)
mol.geomContainer.updateColors(updatedGeomsToColor)
self.app()._executionReport.addSuccess('%s: colored molecule %s successfully'% (self.name, mol.name))
except:
msg = 'Error while coloring secondary structure for molecule %s'% mol.name
self.app().errorMsg(sys.exc_info(), msg, obj=self.atmSet)
#geomEditEventss
event = EditGeomsEvent("color", [nodes,[geomsToColor, colors, self.name[5:11]]])
self.app().eventHandler.dispatchEvent(event)
commandClassFromName = {
'computeSecondaryStructure' : [ComputeSecondaryStructureCommand, None],
'extrudeSecondaryStructure' : [ExtrudeSecondaryStructureCommand, None],
'extrudeSecondaryStructureUnic' : [ExtrudeSecondaryStructureCommandUnic, None],
'displayExtrudedSS' : [DisplayExtrudedSSCommand, None],
'colorBySecondaryStructure' : [ColorBySSElementType, None],
'undisplayExtrudedSS' : [UndisplayExtrudedSSCommand, None],
'ribbon' : [RibbonCommand, None],
}
def initModule(viewer, gui=False):
for cmdName, values in commandClassFromName.items():
cmdClass, guiInstance = values
viewer.addCommand(cmdClass(), cmdName, guiInstance)
| 46.741431 | 1,042 | 0.523935 |
4ac6c95e47f7e0c117f8f3e7d54da78c9fea5a88 | 32,265 | py | Python | truffe2/accounting_tools/migrations/0026_auto__del_field_invoice_unit__del_field_cashbook_unit__del_field_withd.py | JonathanCollaud/truffe2 | 5cbb055ac1acf7e7dc697340618fcb56c67fbd91 | [
"BSD-2-Clause"
] | 9 | 2016-09-14T02:19:19.000Z | 2020-10-18T14:52:14.000Z | truffe2/accounting_tools/migrations/0026_auto__del_field_invoice_unit__del_field_cashbook_unit__del_field_withd.py | JonathanCollaud/truffe2 | 5cbb055ac1acf7e7dc697340618fcb56c67fbd91 | [
"BSD-2-Clause"
] | 19 | 2016-11-09T21:28:51.000Z | 2021-02-10T22:37:31.000Z | truffe2/accounting_tools/migrations/0026_auto__del_field_invoice_unit__del_field_cashbook_unit__del_field_withd.py | JonathanCollaud/truffe2 | 5cbb055ac1acf7e7dc697340618fcb56c67fbd91 | [
"BSD-2-Clause"
] | 13 | 2016-12-31T14:22:09.000Z | 2020-12-27T19:43:19.000Z | # -*- coding: utf-8 -*-
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Deleting field 'Invoice.unit'
db.delete_column(u'accounting_tools_invoice', 'unit_id')
# Deleting field 'CashBook.unit'
db.delete_column(u'accounting_tools_cashbook', 'unit_id')
# Deleting field 'Withdrawal.unit'
db.delete_column(u'accounting_tools_withdrawal', 'unit_id')
def backwards(self, orm):
# Adding field 'Invoice.unit'
db.add_column(u'accounting_tools_invoice', 'unit',
self.gf('django.db.models.fields.related.ForeignKey')(default=1, to=orm['units.Unit']),
keep_default=False)
# Adding field 'CashBook.unit'
db.add_column(u'accounting_tools_cashbook', 'unit',
self.gf('django.db.models.fields.related.ForeignKey')(default=1, to=orm['units.Unit']),
keep_default=False)
# Adding field 'Withdrawal.unit'
db.add_column(u'accounting_tools_withdrawal', 'unit',
self.gf('django.db.models.fields.related.ForeignKey')(default=1, to=orm['units.Unit']),
keep_default=False)
models = {
u'accounting_core.account': {
'Meta': {'unique_together': "(('name', 'accounting_year'), ('account_number', 'accounting_year'))", 'object_name': 'Account'},
'account_number': ('django.db.models.fields.CharField', [], {'max_length': '10'}),
'accounting_year': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.AccountingYear']"}),
'category': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.AccountCategory']"}),
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'visibility': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
u'accounting_core.accountcategory': {
'Meta': {'unique_together': "(('name', 'accounting_year'),)", 'object_name': 'AccountCategory'},
'accounting_year': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.AccountingYear']"}),
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'order': ('django.db.models.fields.SmallIntegerField', [], {'default': '0'}),
'parent_hierarchique': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.AccountCategory']", 'null': 'True', 'blank': 'True'})
},
u'accounting_core.accountingyear': {
'Meta': {'object_name': 'AccountingYear'},
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'end_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'last_accounting_import': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '255'}),
'start_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.CharField', [], {'default': "'0_preparing'", 'max_length': '255'}),
'subvention_deadline': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'})
},
u'accounting_core.costcenter': {
'Meta': {'unique_together': "(('name', 'accounting_year'), ('account_number', 'accounting_year'))", 'object_name': 'CostCenter'},
'account_number': ('django.db.models.fields.CharField', [], {'max_length': '10'}),
'accounting_year': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.AccountingYear']"}),
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'unit': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['units.Unit']"})
},
u'accounting_tools.cashbook': {
'Meta': {'object_name': 'CashBook'},
'accounting_year': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.AccountingYear']"}),
'comment': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']", 'null': 'True', 'blank': 'True'}),
'costcenter': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.CostCenter']"}),
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'nb_proofs': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'object_id': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.CharField', [], {'default': "'0_draft'", 'max_length': '255'}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.cashbookfile': {
'Meta': {'object_name': 'CashBookFile'},
'file': ('django.db.models.fields.files.FileField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'files'", 'null': 'True', 'to': u"orm['accounting_tools.CashBook']"}),
'upload_date': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'uploader': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.cashbookline': {
'Meta': {'object_name': 'CashBookLine'},
'account': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.Account']"}),
'cashbook': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'lines'", 'to': u"orm['accounting_tools.CashBook']"}),
'date': ('django.db.models.fields.DateField', [], {}),
'helper': ('django.db.models.fields.CharField', [], {'max_length': '15'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'label': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'order': ('django.db.models.fields.SmallIntegerField', [], {'default': '0'}),
'proof': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'tva': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '2'}),
'value': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '2'}),
'value_ttc': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '2'})
},
u'accounting_tools.cashbooklogging': {
'Meta': {'object_name': 'CashBookLogging'},
'extra_data': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'logs'", 'to': u"orm['accounting_tools.CashBook']"}),
'what': ('django.db.models.fields.CharField', [], {'max_length': '64'}),
'when': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'who': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.expenseclaim': {
'Meta': {'object_name': 'ExpenseClaim'},
'accounting_year': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.AccountingYear']"}),
'comment': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'costcenter': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.CostCenter']"}),
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'nb_proofs': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'status': ('django.db.models.fields.CharField', [], {'default': "'0_draft'", 'max_length': '255'}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.expenseclaimfile': {
'Meta': {'object_name': 'ExpenseClaimFile'},
'file': ('django.db.models.fields.files.FileField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'files'", 'null': 'True', 'to': u"orm['accounting_tools.ExpenseClaim']"}),
'upload_date': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'uploader': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.expenseclaimline': {
'Meta': {'object_name': 'ExpenseClaimLine'},
'account': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.Account']"}),
'expense_claim': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'lines'", 'to': u"orm['accounting_tools.ExpenseClaim']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'label': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'order': ('django.db.models.fields.SmallIntegerField', [], {'default': '0'}),
'proof': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'tva': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '2'}),
'value': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '2'}),
'value_ttc': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '2'})
},
u'accounting_tools.expenseclaimlogging': {
'Meta': {'object_name': 'ExpenseClaimLogging'},
'extra_data': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'logs'", 'to': u"orm['accounting_tools.ExpenseClaim']"}),
'what': ('django.db.models.fields.CharField', [], {'max_length': '64'}),
'when': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'who': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.internaltransfer': {
'Meta': {'object_name': 'InternalTransfer'},
'account': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.Account']"}),
'accounting_year': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.AccountingYear']"}),
'amount': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '2'}),
'cost_center_from': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'internal_transfer_from'", 'to': u"orm['accounting_core.CostCenter']"}),
'cost_center_to': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'internal_transfer_to'", 'to': u"orm['accounting_core.CostCenter']"}),
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'status': ('django.db.models.fields.CharField', [], {'default': "'0_draft'", 'max_length': '255'})
},
u'accounting_tools.internaltransferlogging': {
'Meta': {'object_name': 'InternalTransferLogging'},
'extra_data': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'logs'", 'to': u"orm['accounting_tools.InternalTransfer']"}),
'what': ('django.db.models.fields.CharField', [], {'max_length': '64'}),
'when': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'who': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.internaltransfertag': {
'Meta': {'object_name': 'InternalTransferTag'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'tags'", 'to': u"orm['accounting_tools.InternalTransfer']"}),
'tag': ('django.db.models.fields.CharField', [], {'max_length': '255'})
},
u'accounting_tools.invoice': {
'Meta': {'object_name': 'Invoice'},
'accounting_year': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.AccountingYear']"}),
'address': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'annex': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'costcenter': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.CostCenter']"}),
'custom_bvr_number': ('django.db.models.fields.CharField', [], {'max_length': '59', 'null': 'True', 'blank': 'True'}),
'date_and_place': ('django.db.models.fields.CharField', [], {'max_length': '512', 'null': 'True', 'blank': 'True'}),
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'display_account': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'display_bvr': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'ending': ('django.db.models.fields.TextField', [], {'max_length': '1024', 'null': 'True', 'blank': 'True'}),
'greetings': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '1024', 'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'preface': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'sign': ('django.db.models.fields.CharField', [], {'max_length': '512', 'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.CharField', [], {'default': "'0_preparing'", 'max_length': '255'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '255'})
},
u'accounting_tools.invoiceline': {
'Meta': {'object_name': 'InvoiceLine'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'invoice': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'lines'", 'to': u"orm['accounting_tools.Invoice']"}),
'label': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'order': ('django.db.models.fields.SmallIntegerField', [], {'default': '0'}),
'quantity': ('django.db.models.fields.DecimalField', [], {'default': '1', 'max_digits': '20', 'decimal_places': '0'}),
'tva': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '2'}),
'value': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '2'}),
'value_ttc': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '2'})
},
u'accounting_tools.invoicelogging': {
'Meta': {'object_name': 'InvoiceLogging'},
'extra_data': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'logs'", 'to': u"orm['accounting_tools.Invoice']"}),
'what': ('django.db.models.fields.CharField', [], {'max_length': '64'}),
'when': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'who': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.invoicetag': {
'Meta': {'object_name': 'InvoiceTag'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'tags'", 'to': u"orm['accounting_tools.Invoice']"}),
'tag': ('django.db.models.fields.CharField', [], {'max_length': '255'})
},
u'accounting_tools.linkedinfo': {
'Meta': {'object_name': 'LinkedInfo'},
'address': ('django.db.models.fields.TextField', [], {}),
'bank': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'iban_ccp': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'object_id': ('django.db.models.fields.PositiveIntegerField', [], {}),
'phone': ('django.db.models.fields.CharField', [], {'max_length': '20'}),
'user_pk': ('django.db.models.fields.PositiveIntegerField', [], {})
},
u'accounting_tools.subvention': {
'Meta': {'unique_together': "(('unit', 'unit_blank_name', 'accounting_year'),)", 'object_name': 'Subvention'},
'accounting_year': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.AccountingYear']"}),
'amount_asked': ('django.db.models.fields.IntegerField', [], {}),
'amount_given': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}),
'comment_root': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'kind': ('django.db.models.fields.CharField', [], {'max_length': '15', 'null': 'True', 'blank': 'True'}),
'mobility_asked': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}),
'mobility_given': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'status': ('django.db.models.fields.CharField', [], {'default': "'0_draft'", 'max_length': '255'}),
'unit': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['units.Unit']", 'null': 'True', 'blank': 'True'}),
'unit_blank_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'unit_blank_user': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']", 'null': 'True', 'blank': 'True'})
},
u'accounting_tools.subventionfile': {
'Meta': {'object_name': 'SubventionFile'},
'file': ('django.db.models.fields.files.FileField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'files'", 'null': 'True', 'to': u"orm['accounting_tools.Subvention']"}),
'upload_date': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'uploader': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.subventionline': {
'Meta': {'object_name': 'SubventionLine'},
'end_date': ('django.db.models.fields.DateField', [], {}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'nb_spec': ('django.db.models.fields.SmallIntegerField', [], {}),
'order': ('django.db.models.fields.SmallIntegerField', [], {'default': '0'}),
'place': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'start_date': ('django.db.models.fields.DateField', [], {}),
'subvention': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'events'", 'to': u"orm['accounting_tools.Subvention']"})
},
u'accounting_tools.subventionlogging': {
'Meta': {'object_name': 'SubventionLogging'},
'extra_data': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'logs'", 'to': u"orm['accounting_tools.Subvention']"}),
'what': ('django.db.models.fields.CharField', [], {'max_length': '64'}),
'when': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'who': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.withdrawal': {
'Meta': {'object_name': 'Withdrawal'},
'accounting_year': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.AccountingYear']"}),
'amount': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '2'}),
'costcenter': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounting_core.CostCenter']"}),
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'desired_date': ('django.db.models.fields.DateField', [], {}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'status': ('django.db.models.fields.CharField', [], {'default': "'0_draft'", 'max_length': '255'}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"}),
'withdrawn_date': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'})
},
u'accounting_tools.withdrawalfile': {
'Meta': {'object_name': 'WithdrawalFile'},
'file': ('django.db.models.fields.files.FileField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'files'", 'null': 'True', 'to': u"orm['accounting_tools.Withdrawal']"}),
'upload_date': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'uploader': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.withdrawallogging': {
'Meta': {'object_name': 'WithdrawalLogging'},
'extra_data': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'logs'", 'to': u"orm['accounting_tools.Withdrawal']"}),
'what': ('django.db.models.fields.CharField', [], {'max_length': '64'}),
'when': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'who': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['users.TruffeUser']"})
},
u'accounting_tools.withdrawaltag': {
'Meta': {'object_name': 'WithdrawalTag'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'tags'", 'to': u"orm['accounting_tools.Withdrawal']"}),
'tag': ('django.db.models.fields.CharField', [], {'max_length': '255'})
},
u'auth.group': {
'Meta': {'object_name': 'Group'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
u'auth.permission': {
'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
u'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
u'units.unit': {
'Meta': {'object_name': 'Unit'},
'deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'id_epfl': ('django.db.models.fields.CharField', [], {'max_length': '64', 'null': 'True', 'blank': 'True'}),
'is_commission': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_equipe': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_hidden': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'parent_hierarchique': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['units.Unit']", 'null': 'True', 'blank': 'True'}),
'url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'})
},
u'users.truffeuser': {
'Meta': {'object_name': 'TruffeUser'},
'adresse': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'body': ('django.db.models.fields.CharField', [], {'default': "'.'", 'max_length': '1'}),
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '255'}),
'email_perso': ('django.db.models.fields.EmailField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Group']"}),
'iban_ou_ccp': ('django.db.models.fields.CharField', [], {'max_length': '128', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'mobile': ('django.db.models.fields.CharField', [], {'max_length': '25', 'blank': 'True'}),
'nom_banque': ('django.db.models.fields.CharField', [], {'max_length': '128', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Permission']"}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '255'})
}
}
complete_apps = ['accounting_tools'] | 82.308673 | 195 | 0.571083 |
83dc32f5197cacb81dec4d373e0ef0bdac36eb85 | 15,976 | py | Python | src/map_model.py | akolishchak/doom-net-pytorch | 96bad5b15c9c5267d494cd5791481801cd6d2107 | [
"MIT"
] | 143 | 2017-01-30T01:43:58.000Z | 2021-11-15T07:53:22.000Z | src/map_model.py | akolishchak/doom-net-pytorch | 96bad5b15c9c5267d494cd5791481801cd6d2107 | [
"MIT"
] | 7 | 2017-12-28T02:42:08.000Z | 2020-05-23T23:12:33.000Z | src/map_model.py | akolishchak/doom-net-pytorch | 96bad5b15c9c5267d494cd5791481801cd6d2107 | [
"MIT"
] | 27 | 2017-02-03T09:20:10.000Z | 2020-07-19T21:35:28.000Z | #
# map_model.py, doom-net
#
# Created by Andrey Kolishchak on 03/03/18.
#
import torch.nn as nn
import torch.nn.functional as F
class ObjectModel(nn.Module):
def __init__(self, args):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=7, stride=(1, 1), dilation=(1, 1), padding=(0, 3))
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=(2, 1), dilation=(2, 1), padding=(0, 1))
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=(2, 1), dilation=(4, 1), padding=(0, 1))
self.conv4 = nn.Conv2d(64, 128, kernel_size=3, stride=(2, 1), dilation=(8, 1), padding=(0, 1))
self.conv5 = nn.Conv2d(128, 256, kernel_size=3, stride=(2, 1), dilation=(6, 1), padding=(0, 1))
self.conv6 = nn.Conv2d(256, 6, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 1))
self.bn1 = nn.BatchNorm2d(16)
self.bn2 = nn.BatchNorm2d(32)
self.bn3 = nn.BatchNorm2d(64)
self.bn4 = nn.BatchNorm2d(128)
self.bn5 = nn.BatchNorm2d(256)
def forward(self, screen):
output = self.conv1(screen)
output = self.bn1(output)
output = F.relu(output, inplace=True)
output = self.conv2(output)
output = self.bn2(output)
output = F.relu(output, inplace=True)
output = self.conv3(output)
output = self.bn3(output)
output = F.relu(output, inplace=True)
output = self.conv4(output)
output = self.bn4(output)
output = F.relu(output, inplace=True)
output = self.conv5(output)
output = self.bn5(output)
output = F.relu(output, inplace=True)
output = self.conv6(output)
return output
class DistanceModel(nn.Module):
def __init__(self, args):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=(1, 1), dilation=(1, 1), padding=(0, 3))
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=(2, 1), dilation=(2, 1), padding=(0, 1))
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=(2, 1), dilation=(4, 1), padding=(0, 1))
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=(2, 1), dilation=(8, 1), padding=(0, 1))
self.conv5 = nn.Conv2d(256, 512, kernel_size=3, stride=(2, 1), dilation=(6, 1), padding=(0, 1))
self.conv6 = nn.Conv2d(512, 129, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 1))
self.bn1 = nn.BatchNorm2d(32)
self.bn2 = nn.BatchNorm2d(64)
self.bn3 = nn.BatchNorm2d(128)
self.bn4 = nn.BatchNorm2d(256)
self.bn5 = nn.BatchNorm2d(512)
def forward(self, screen):
output = self.conv1(screen)
output = self.bn1(output)
output = F.relu(output, inplace=True)
output = self.conv2(output)
output = self.bn2(output)
output = F.relu(output, inplace=True)
output = self.conv3(output)
output = self.bn3(output)
output = F.relu(output, inplace=True)
output = self.conv4(output)
output = self.bn4(output)
output = F.relu(output, inplace=True)
output = self.conv5(output)
output = self.bn5(output)
output = F.relu(output, inplace=True)
output = self.conv6(output)
return output
class ObjectDistanceModel(nn.Module):
def __init__(self, args):
super().__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=(1, 1), dilation=(1, 1), padding=(0, 3))
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=(2, 1), dilation=(2, 1), padding=(0, 1))
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=(2, 1), dilation=(4, 1), padding=(0, 1))
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=(2, 1), dilation=(8, 1), padding=(0, 1))
self.conv51 = nn.Conv2d(256, 512, kernel_size=3, stride=(2, 1), dilation=(6, 1), padding=(0, 1))
self.conv52 = nn.Conv2d(256, 512, kernel_size=3, stride=(2, 1), dilation=(6, 1), padding=(0, 1))
self.conv61 = nn.Conv2d(512, 6, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 1))
self.conv62 = nn.Conv2d(512, 129, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 1))
self.bn1 = nn.BatchNorm2d(32)
self.bn2 = nn.BatchNorm2d(64)
self.bn3 = nn.BatchNorm2d(128)
self.bn4 = nn.BatchNorm2d(256)
self.bn51 = nn.BatchNorm2d(512)
self.bn52 = nn.BatchNorm2d(512)
def forward(self, screen):
shared = self.conv1(screen)
shared = self.bn1(shared)
shared = F.relu(shared, inplace=True)
shared = self.conv2(shared)
shared = self.bn2(shared)
shared = F.relu(shared, inplace=True)
shared = self.conv3(shared)
shared = self.bn3(shared)
shared = F.relu(shared, inplace=True)
shared = self.conv4(shared)
shared = self.bn4(shared)
shared = F.relu(shared, inplace=True)
output1 = self.conv51(shared)
output1 = self.bn51(output1)
output1 = F.relu(output1, inplace=True)
output1 = self.conv61(output1)
output2 = self.conv52(shared)
output2 = self.bn52(output2)
output2 = F.relu(output2, inplace=True)
output2 = self.conv62(output2)
return output1, output2
class DistanceModel2(nn.Module):
def __init__(self, args):
super().__init__()
self.drn = drn.drn_d_22(out_map=True, num_classes=1)
self.fc1 = nn.Linear(1*30*40, 320)
def forward(self, screen):
output = self.drn(screen)
output = output.view(output.size(0), -1)
#output = F.relu(output)
output = self.fc1(output)
output = F.sigmoid(output)
return output
class ObjectDistanceModel2(nn.Module):
def __init__(self, args):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=(1, 1), dilation=(1, 1), padding=(0, 3), bias=False)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=(2, 1), dilation=(2, 1), padding=(0, 1))
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=(2, 1), dilation=(4, 1), padding=(0, 1))
self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=(2, 1), dilation=(8, 1), padding=(0, 1))
self.conv51 = nn.Conv2d(512, 1024, kernel_size=3, stride=(2, 1), dilation=(6, 1), padding=(0, 1))
self.conv52 = nn.Conv2d(512, 1024, kernel_size=3, stride=(2, 1), dilation=(6, 1), padding=(0, 1))
self.conv61 = nn.Conv2d(1024, 6, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 1))
self.conv62 = nn.Conv2d(1024, 129, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 1))
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(128)
self.bn3 = nn.BatchNorm2d(256)
self.bn4 = nn.BatchNorm2d(512)
self.bn51 = nn.BatchNorm2d(1024)
self.bn52 = nn.BatchNorm2d(1024)
def forward(self, screen):
shared = self.conv1(screen)
shared = self.bn1(shared)
shared = F.relu(shared, inplace=True)
shared = self.conv2(shared)
shared = self.bn2(shared)
shared = F.relu(shared, inplace=True)
shared = self.conv3(shared)
shared = self.bn3(shared)
shared = F.relu(shared, inplace=True)
shared = self.conv4(shared)
shared = self.bn4(shared)
shared = F.relu(shared, inplace=True)
output1 = self.conv51(shared)
output1 = self.bn51(output1)
output1 = F.relu(output1, inplace=True)
output1 = self.conv61(output1)
output2 = self.conv52(shared)
output2 = self.bn52(output2)
output2 = F.relu(output2, inplace=True)
output2 = self.conv62(output2)
return output1, output2
class BasicBlock(nn.Module):
def __init__(self, inplanes, planes, stride=(1, 1), padding=(0, 0), dilation=(1, 1)):
super(BasicBlock, self).__init__()
self.padding = (padding[1], padding[1], padding[0], padding[0])
padding = (0, 0)
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=padding, dilation=dilation)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=padding, dilation=dilation)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = None
if stride != 1 or inplanes != planes:
self.downsample = nn.Sequential(
nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes),
)
def forward(self, x):
residual = x
out = F.pad(x, self.padding, mode='replicate')
out = self.conv1(out)
out = self.bn1(out)
out = self.relu(out)
out = F.pad(out, self.padding, mode='replicate')
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ObjectDistanceModel3(nn.Module):
def __init__(self, args):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=(1, 1), dilation=(1, 1), padding=(0, 0), bias=False)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 0))
self.conv3 = BasicBlock(64, 128, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv4 = BasicBlock(128, 128, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv5 = BasicBlock(128, 256, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv6 = BasicBlock(256, 256, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv71 = nn.Conv2d(256, 512, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 0))
self.conv72 = nn.Conv2d(256, 512, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 0))
self.conv81 = nn.Conv2d(512, 6, kernel_size=3, stride=(1, 1), dilation=(1, 1), padding=(0, 0))
self.conv82 = nn.Conv2d(512, 129, kernel_size=3, stride=(1, 1), dilation=(1, 1), padding=(0, 0))
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(64)
self.bn71 = nn.BatchNorm2d(512)
self.bn72 = nn.BatchNorm2d(512)
def forward(self, screen):
screen = F.pad(screen, (3, 3, 0, 0), mode='replicate')
shared = self.conv1(screen)
shared = self.bn1(shared)
shared = F.relu(shared, inplace=True)
shared = F.pad(shared, (1, 1, 0, 0), mode='replicate')
shared = self.conv2(shared)
shared = self.bn2(shared)
shared = F.relu(shared, inplace=True)
shared = self.conv3(shared)
shared = self.conv4(shared)
shared = self.conv5(shared)
shared = self.conv6(shared)
output1 = F.pad(shared, (1, 1, 0, 0), mode='replicate')
output1 = self.conv71(output1)
output1 = self.bn71(output1)
output1 = F.relu(output1, inplace=True)
output1 = F.pad(output1, (1, 1, 0, 0), mode='replicate')
output1 = self.conv81(output1)
output2 = F.pad(shared, (1, 1, 0, 0), mode='replicate')
output2 = self.conv72(output2)
output2 = self.bn72(output2)
output2 = F.relu(output2, inplace=True)
output2 = F.pad(output2, (1, 1, 0, 0), mode='replicate')
output2 = self.conv82(output2)
return output1, output2
class ObjectDistanceModel4(nn.Module):
def __init__(self, args):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=(1, 1), dilation=(1, 1), padding=(0, 0), bias=False)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 0))
self.conv3 = BasicBlock(64, 128, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv4 = BasicBlock(128, 128, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv5 = BasicBlock(128, 256, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv6 = BasicBlock(256, 256, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv71 = nn.Conv2d(256, 9, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 0))
self.conv72 = nn.Conv2d(256, 65, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 0))
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(64)
def forward(self, screen):
screen = F.pad(screen, (3, 3, 0, 0), mode='replicate')
shared = self.conv1(screen)
shared = self.bn1(shared)
shared = F.relu(shared, inplace=True)
shared = F.pad(shared, (1, 1, 0, 0), mode='replicate')
shared = self.conv2(shared)
shared = self.bn2(shared)
shared = F.relu(shared, inplace=True)
shared = self.conv3(shared)
shared = self.conv4(shared)
shared = self.conv5(shared)
shared = self.conv6(shared)
output1 = F.pad(shared, (1, 1, 0, 0), mode='replicate')
output1 = self.conv71(output1)
output2 = F.pad(shared, (1, 1, 0, 0), mode='replicate')
output2 = self.conv72(output2)
return output1, output2
'''
class MapModel(nn.Module):
def __init__(self, args, use_softmax=True):
super().__init__()
self.use_softmax = use_softmax
#self.object_model = ObjectModel(args)
#self.distance_model = DistanceModel(args)
self.object_distance_model = ObjectDistanceModel4(args)
def forward(self, screen):
#objects = self.object_model(screen)
#if self.use_softmax:
# objects = F.log_softmax(objects, dim=1)
#distances = self.distance_model(screen)
#if self.use_softmax:
# distances = F.log_softmax(distances, dim=1)
objects, distances = self.object_distance_model(screen)
if self.use_softmax:
objects = F.log_softmax(objects, dim=1)
distances = F.log_softmax(distances, dim=1)
return objects, distances
'''
class MapModel(nn.Module):
def __init__(self, args, use_softmax=True):
super().__init__()
self.use_softmax = use_softmax
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=(1, 1), dilation=(1, 1), padding=(0, 0), bias=False)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 0))
self.conv3 = BasicBlock(64, 128, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv4 = BasicBlock(128, 128, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv5 = BasicBlock(128, 256, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv6 = BasicBlock(256, 256, stride=(2, 1), dilation=(1, 1), padding=(1, 1))
self.conv71 = nn.Conv2d(256, 9, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 0))
self.conv72 = nn.Conv2d(256, 65, kernel_size=3, stride=(2, 1), dilation=(1, 1), padding=(0, 0))
self.bn1 = nn.BatchNorm2d(64)
self.bn2 = nn.BatchNorm2d(64)
def forward(self, screen):
screen = F.pad(screen, (3, 3, 0, 0), mode='replicate')
shared = self.conv1(screen)
shared = self.bn1(shared)
shared = F.relu(shared, inplace=True)
shared = F.pad(shared, (1, 1, 0, 0), mode='replicate')
shared = self.conv2(shared)
shared = self.bn2(shared)
shared = F.relu(shared, inplace=True)
shared = self.conv3(shared)
shared = self.conv4(shared)
shared = self.conv5(shared)
shared = self.conv6(shared)
objects = F.pad(shared, (1, 1, 0, 0), mode='replicate')
objects = self.conv71(objects)
distances = F.pad(shared, (1, 1, 0, 0), mode='replicate')
distances = self.conv72(distances)
if self.use_softmax:
objects = F.log_softmax(objects, dim=1)
distances = F.log_softmax(distances, dim=1)
return objects, distances
| 42.376658 | 114 | 0.593953 |
873d7c8a682f0590f140a3a59058647296da7b14 | 25,473 | py | Python | src/pretalx/orga/views/event.py | chriswolfdesign/pretalx | fb6bcf090a5c92e55a79851d60dfc716309da557 | [
"Apache-2.0"
] | null | null | null | src/pretalx/orga/views/event.py | chriswolfdesign/pretalx | fb6bcf090a5c92e55a79851d60dfc716309da557 | [
"Apache-2.0"
] | null | null | null | src/pretalx/orga/views/event.py | chriswolfdesign/pretalx | fb6bcf090a5c92e55a79851d60dfc716309da557 | [
"Apache-2.0"
] | null | null | null | import json
from contextlib import suppress
from pathlib import Path
from csp.decorators import csp_update
from django.conf import settings
from django.contrib import messages
from django.contrib.auth import login
from django.core.files.storage import FileSystemStorage
from django.db import transaction
from django.db.models import Q
from django.forms.models import inlineformset_factory
from django.http import Http404, JsonResponse
from django.shortcuts import get_object_or_404, redirect
from django.urls import reverse
from django.utils.decorators import method_decorator
from django.utils.functional import cached_property
from django.utils.safestring import mark_safe
from django.utils.timezone import now
from django.utils.translation import gettext_lazy as _
from django.views.generic import (
DeleteView,
FormView,
ListView,
TemplateView,
UpdateView,
View,
)
from django_context_decorator import context
from django_scopes import scope, scopes_disabled
from formtools.wizard.views import SessionWizardView
from pytz import timezone
from rest_framework.authtoken.models import Token
from pretalx.common.forms import I18nFormSet
from pretalx.common.mixins.views import (
ActionFromUrl,
EventPermissionRequired,
PermissionRequired,
SensibleBackWizardMixin,
)
from pretalx.common.models import ActivityLog
from pretalx.common.tasks import regenerate_css
from pretalx.common.templatetags.rich_text import rich_text
from pretalx.common.views import is_form_bound
from pretalx.event.forms import (
EventWizardBasicsForm,
EventWizardCopyForm,
EventWizardDisplayForm,
EventWizardInitialForm,
EventWizardTimelineForm,
ReviewPhaseForm,
)
from pretalx.event.models import Event, Team, TeamInvite
from pretalx.orga.forms import EventForm, EventSettingsForm
from pretalx.orga.forms.event import (
MailSettingsForm,
ReviewSettingsForm,
WidgetGenerationForm,
WidgetSettingsForm,
)
from pretalx.orga.signals import activate_event
from pretalx.person.forms import LoginInfoForm, OrgaProfileForm, UserForm
from pretalx.person.models import User
from pretalx.submission.models import ReviewPhase
class EventSettingsPermission(EventPermissionRequired):
permission_required = "orga.change_settings"
write_permission_required = "orga.change_settings"
@property
def permission_object(self):
return self.request.event
class EventDetail(EventSettingsPermission, ActionFromUrl, UpdateView):
model = Event
form_class = EventForm
permission_required = "orga.change_settings"
template_name = "orga/settings/form.html"
def get_object(self):
return self.object
@cached_property
def object(self):
return self.request.event
@context
@cached_property
def sform(self):
return EventSettingsForm(
read_only=(self.action == "view"),
locales=self.request.event.locales,
obj=self.request.event,
attribute_name="settings",
data=self.request.POST if self.request.method == "POST" else None,
prefix="settings",
)
def get_form_kwargs(self, *args, **kwargs):
response = super().get_form_kwargs(*args, **kwargs)
response["is_administrator"] = self.request.user.is_administrator
return response
@context
def url_placeholder(self):
return f"https://{self.request.host}/"
def get_success_url(self) -> str:
return self.object.orga_urls.settings
@transaction.atomic
def form_valid(self, form):
if not self.sform.is_valid():
return self.form_invalid(form)
result = super().form_valid(form)
self.sform.save()
form.instance.log_action(
"pretalx.event.update", person=self.request.user, orga=True
)
messages.success(self.request, _("The event settings have been saved."))
regenerate_css.apply_async(args=(form.instance.pk,))
return result
class EventLive(EventSettingsPermission, TemplateView):
template_name = "orga/event/live.html"
permission_required = "orga.change_settings"
def get_context_data(self, **kwargs):
result = super().get_context_data(**kwargs)
warnings = []
suggestions = []
# TODO: move to signal
if (
not self.request.event.cfp.text
or len(str(self.request.event.cfp.text)) < 50
):
warnings.append(
{
"text": _("The CfP doesn't have a full text yet."),
"url": self.request.event.cfp.urls.text,
}
)
if (
not self.request.event.landing_page_text
or len(str(self.request.event.landing_page_text)) < 50
):
warnings.append(
{
"text": _("The event doesn't have a landing page text yet."),
"url": self.request.event.orga_urls.settings,
}
)
# TODO: test that mails can be sent
if (
self.request.event.settings.use_tracks
and self.request.event.settings.cfp_request_track
and self.request.event.tracks.count() < 2
):
suggestions.append(
{
"text": _(
"You want submitters to choose the tracks for their submissions, but you do not offer tracks for selection. Add at least one track!"
),
"url": self.request.event.cfp.urls.tracks,
}
)
if not self.request.event.submission_types.count() > 1:
suggestions.append(
{
"text": _("You have configured only one submission type so far."),
"url": self.request.event.cfp.urls.types,
}
)
if not self.request.event.questions.exists():
suggestions.append(
{
"text": _("You have configured no questions yet."),
"url": self.request.event.cfp.urls.new_question,
}
)
result["warnings"] = warnings
result["suggestions"] = suggestions
return result
def post(self, request, *args, **kwargs):
event = request.event
action = request.POST.get("action")
if action == "activate":
if event.is_public:
messages.success(request, _("This event was already live."))
else:
responses = activate_event.send_robust(event, request=request)
exceptions = [
response[1]
for response in responses
if isinstance(response[1], Exception)
]
if exceptions:
messages.error(
request, mark_safe("\n".join(rich_text(e) for e in exceptions)),
)
else:
event.is_public = True
event.save()
event.log_action(
"pretalx.event.activate",
person=self.request.user,
orga=True,
data={},
)
messages.success(request, _("This event is now public."))
else: # action == 'deactivate'
if not event.is_public:
messages.success(request, _("This event was already hidden."))
else:
event.is_public = False
event.save()
event.log_action(
"pretalx.event.deactivate",
person=self.request.user,
orga=True,
data={},
)
messages.success(request, _("This event is now hidden."))
return redirect(event.orga_urls.base)
class EventHistory(EventSettingsPermission, ListView):
template_name = "orga/event/history.html"
model = ActivityLog
context_object_name = "log_entries"
paginate_by = 200
def get_queryset(self):
return ActivityLog.objects.filter(event=self.request.event)
class EventReviewSettings(EventSettingsPermission, ActionFromUrl, FormView):
form_class = ReviewSettingsForm
template_name = "orga/settings/review.html"
write_permission_required = "orga.change_settings"
def get_success_url(self) -> str:
return self.request.event.orga_urls.review_settings
def get_form_kwargs(self):
kwargs = super().get_form_kwargs()
kwargs["obj"] = self.request.event
kwargs["attribute_name"] = "settings"
kwargs["locales"] = self.request.event.locales
return kwargs
@transaction.atomic
def form_valid(self, form):
formset = self.save_formset()
if not formset:
return self.get(self.request, *self.args, **self.kwargs)
form.save()
return super().form_valid(form)
@context
@cached_property
def formset(self):
formset_class = inlineformset_factory(
Event,
ReviewPhase,
form=ReviewPhaseForm,
formset=I18nFormSet,
can_delete=True,
extra=0,
)
return formset_class(
self.request.POST if self.request.method == "POST" else None,
queryset=ReviewPhase.objects.filter(event=self.request.event),
event=self.request.event,
)
def save_formset(self):
if not self.formset.is_valid():
return False
for form in self.formset.initial_forms:
# Deleting is handled elsewhere, so we skip it here
if form.has_changed():
form.instance.event = self.request.event
form.save()
extra_forms = [
form
for form in self.formset.extra_forms
if form.has_changed and not self.formset._should_delete_form(form)
]
for form in extra_forms:
form.instance.event = self.request.event
form.save()
return True
def phase_move(request, pk, up=True):
try:
phase = request.event.review_phases.get(pk=pk)
except ReviewPhase.DoesNotExist:
raise Http404(_("The selected review phase does not exist."))
if not request.user.has_perm("orga.change_settings", phase):
messages.error(
request, _("Sorry, you are not allowed to reorder review phases.")
)
return
phases = list(request.event.review_phases.order_by("position"))
index = phases.index(phase)
if index != 0 and up:
phases[index - 1], phases[index] = phases[index], phases[index - 1]
elif index != len(phases) - 1 and not up:
phases[index + 1], phases[index] = phases[index], phases[index + 1]
for i, phase in enumerate(phases):
if phase.position != i:
phase.position = i
phase.save()
messages.success(request, _("The order of review phases has been updated."))
def phase_move_up(request, event, pk):
phase_move(request, pk, up=True)
return redirect(request.event.orga_urls.review_settings)
def phase_move_down(request, event, pk):
phase_move(request, pk, up=False)
return redirect(request.event.orga_urls.review_settings)
class PhaseDelete(PermissionRequired, View):
permission_required = "orga.change_settings"
def get_object(self):
return get_object_or_404(
ReviewPhase, event=self.request.event, pk=self.kwargs.get("pk")
)
def dispatch(self, request, *args, **kwargs):
super().dispatch(request, *args, **kwargs)
phase = self.get_object()
phase.delete()
return redirect(self.request.event.orga_urls.review_settings)
class PhaseActivate(PermissionRequired, View):
permission_required = "orga.change_settings"
def get_object(self):
return get_object_or_404(
ReviewPhase, event=self.request.event, pk=self.kwargs.get("pk")
)
def dispatch(self, request, *args, **kwargs):
super().dispatch(request, *args, **kwargs)
phase = self.get_object()
phase.activate()
return redirect(self.request.event.orga_urls.review_settings)
class EventMailSettings(EventSettingsPermission, ActionFromUrl, FormView):
form_class = MailSettingsForm
template_name = "orga/settings/mail.html"
write_permission_required = "orga.change_settings"
def get_success_url(self) -> str:
return self.request.event.orga_urls.mail_settings
def get_form_kwargs(self):
kwargs = super().get_form_kwargs()
kwargs["obj"] = self.request.event
kwargs["attribute_name"] = "settings"
kwargs["locales"] = self.request.event.locales
return kwargs
def form_valid(self, form):
form.save()
if self.request.POST.get("test", "0").strip() == "1":
backend = self.request.event.get_mail_backend(force_custom=True)
try:
backend.test(self.request.event.settings.mail_from)
except Exception as e:
messages.warning(
self.request,
_("An error occurred while contacting the SMTP server: %s")
% str(e),
)
return redirect(self.request.event.orga_urls.mail_settings)
else: # pragma: no cover
if form.cleaned_data.get("smtp_use_custom"):
messages.success(
self.request,
_(
"Yay, your changes have been saved and the connection attempt to "
"your SMTP server was successful."
),
)
else:
messages.success(
self.request,
_(
"We've been able to contact the SMTP server you configured. "
'Remember to check the "use custom SMTP server" checkbox, '
"otherwise your SMTP server will not be used."
),
)
else:
messages.success(self.request, _("Yay! We saved your changes."))
return super().form_valid(form)
class InvitationView(FormView):
template_name = "orga/invitation.html"
form_class = UserForm
@context
@cached_property
def invitation(self):
return get_object_or_404(TeamInvite, token__iexact=self.kwargs.get("code"))
def post(self, *args, **kwargs):
if not self.request.user.is_anonymous:
self.accept_invite(self.request.user)
return redirect("/orga")
return super().post(*args, **kwargs)
def form_valid(self, form):
form.save()
user = User.objects.filter(pk=form.cleaned_data.get("user_id")).first()
if not user:
messages.error(
self.request,
_(
"There was a problem with your authentication. Please contact the organiser for further help."
),
)
return redirect(self.request.event.urls.base)
self.accept_invite(user)
login(self.request, user, backend="django.contrib.auth.backends.ModelBackend")
return redirect("/orga")
@transaction.atomic()
def accept_invite(self, user):
invite = self.invitation
invite.team.members.add(user)
invite.team.save()
invite.team.organiser.log_action(
"pretalx.invite.orga.accept", person=user, orga=True
)
messages.info(self.request, _("You are now part of the team!"))
invite.delete()
class UserSettings(TemplateView):
form_class = LoginInfoForm
template_name = "orga/user.html"
def get_success_url(self) -> str:
return reverse("orga:user.view")
@context
@cached_property
def login_form(self):
return LoginInfoForm(
user=self.request.user,
data=self.request.POST if is_form_bound(self.request, "login") else None,
)
@context
@cached_property
def profile_form(self):
return OrgaProfileForm(
instance=self.request.user,
data=self.request.POST if is_form_bound(self.request, "profile") else None,
)
@context
def token(self):
return Token.objects.filter(
user=self.request.user
).first() or Token.objects.create(user=self.request.user)
def post(self, request, *args, **kwargs):
if self.login_form.is_bound and self.login_form.is_valid():
self.login_form.save()
messages.success(request, _("Your changes have been saved."))
request.user.log_action("pretalx.user.password.update")
elif self.profile_form.is_bound and self.profile_form.is_valid():
self.profile_form.save()
messages.success(request, _("Your changes have been saved."))
request.user.log_action("pretalx.user.profile.update")
elif request.POST.get("form") == "token":
request.user.regenerate_token()
messages.success(
request,
_(
"Your API token has been regenerated. The previous token will not be usable any longer."
),
)
else:
messages.error(
self.request,
_("Oh :( We had trouble saving your input. See below for details."),
)
return redirect(self.get_success_url())
def condition_copy(wizard):
return EventWizardCopyForm.copy_from_queryset(wizard.request.user).exists()
class EventWizard(PermissionRequired, SensibleBackWizardMixin, SessionWizardView):
permission_required = "orga.create_events"
file_storage = FileSystemStorage(location=Path(settings.MEDIA_ROOT) / "new_event")
form_list = [
("initial", EventWizardInitialForm),
("basics", EventWizardBasicsForm),
("timeline", EventWizardTimelineForm),
("display", EventWizardDisplayForm),
("copy", EventWizardCopyForm),
]
condition_dict = {"copy": condition_copy}
def get_template_names(self):
return f"orga/event/wizard/{self.steps.current}.html"
@context
def has_organiser(self):
return (
self.request.user.teams.filter(can_create_events=True).exists()
or self.request.user.is_administrator
)
@context
def url_placeholder(self):
return f"https://{self.request.host}/"
@context
def organiser(self):
return (
self.get_cleaned_data_for_step("initial").get("organiser")
if self.steps.current != "initial"
else None
)
def render(self, form=None, **kwargs):
if self.steps.current != "initial":
if self.get_cleaned_data_for_step("initial") is None:
return self.render_goto_step("initial")
if self.steps.current == "timeline":
fdata = self.get_cleaned_data_for_step("basics")
year = now().year % 100
if (
fdata
and not str(year) in fdata["slug"]
and not str(year + 1) in fdata["slug"]
):
messages.warning(
self.request,
str(
_(
"Please consider including your event's year in the slug, e.g. myevent{number}."
)
).format(number=year),
)
elif self.steps.current == "display":
fdata = self.get_cleaned_data_for_step("timeline")
if fdata and fdata.get("date_to") < now().date():
messages.warning(
self.request,
_("Did you really mean to make your event take place in the past?"),
)
return super().render(form, **kwargs)
def get_form_kwargs(self, step=None):
kwargs = {"user": self.request.user}
if step != "initial":
fdata = self.get_cleaned_data_for_step("initial")
kwargs.update(fdata or {})
return kwargs
@transaction.atomic()
def done(self, form_list, *args, **kwargs):
steps = {
step: self.get_cleaned_data_for_step(step)
for step in ("initial", "basics", "timeline", "display", "copy")
}
with scopes_disabled():
event = Event.objects.create(
organiser=steps["initial"]["organiser"],
locale_array=",".join(steps["initial"]["locales"]),
name=steps["basics"]["name"],
slug=steps["basics"]["slug"],
timezone=steps["basics"]["timezone"],
email=steps["basics"]["email"],
locale=steps["basics"]["locale"],
primary_color=steps["display"]["primary_color"],
logo=steps["display"]["logo"],
date_from=steps["timeline"]["date_from"],
date_to=steps["timeline"]["date_to"],
)
with scope(event=event):
deadline = steps["timeline"].get("deadline")
if deadline:
zone = timezone(event.timezone)
event.cfp.deadline = zone.localize(deadline.replace(tzinfo=None))
event.cfp.save()
for setting in [
"custom_domain",
"display_header_data",
]:
value = steps["display"].get(setting)
if value:
event.settings.set(setting, value)
has_control_rights = self.request.user.teams.filter(
organiser=event.organiser,
all_events=True,
can_change_event_settings=True,
can_change_submissions=True,
).exists()
if not has_control_rights:
t = Team.objects.create(
organiser=event.organiser,
name=_(f"Team {event.name}"),
can_change_event_settings=True,
can_change_submissions=True,
)
t.members.add(self.request.user)
t.limit_events.add(event)
logdata = {}
for f in form_list:
logdata.update({k: v for k, v in f.cleaned_data.items()})
with scope(event=event):
event.log_action(
"pretalx.event.create",
person=self.request.user,
data=logdata,
orga=True,
)
if steps["copy"] and steps["copy"]["copy_from_event"]:
event.copy_data_from(steps["copy"]["copy_from_event"])
return redirect(event.orga_urls.base + "?congratulations")
class EventDelete(PermissionRequired, DeleteView):
template_name = "orga/event/delete.html"
permission_required = "person.is_administrator"
model = Event
def get_object(self):
return self.request.event
def delete(self, request, *args, **kwargs):
self.get_object().shred()
return redirect("/orga/")
def event_list(request):
query = json.dumps(str(request.GET.get("query", "")))[1:-1]
page = 1
with suppress(ValueError):
page = int(request.GET.get("page", "1"))
qs = (
request.user.get_events_with_any_permission()
.filter(
Q(name__icontains=query)
| Q(slug__icontains=query)
| Q(organiser__name__icontains=query)
| Q(organiser__slug__icontains=query)
)
.order_by("-date_from")
)
total = qs.count()
pagesize = 20
offset = (page - 1) * pagesize
doc = {
"results": [
{
"id": event.pk,
"slug": event.slug,
"organiser": str(event.organiser.name),
"name": str(event.name),
"text": str(event.name),
"date_range": event.get_date_range_display(),
"url": event.orga_urls.base,
}
for event in qs.select_related("organiser")[offset : offset + pagesize]
],
"pagination": {"more": total >= (offset + pagesize)},
}
return JsonResponse(doc)
@method_decorator(csp_update(SCRIPT_SRC="'self' 'unsafe-eval'"), name="dispatch")
class WidgetSettings(EventPermissionRequired, FormView):
form_class = WidgetSettingsForm
permission_required = "orga.change_settings"
template_name = "orga/settings/widget.html"
def form_valid(self, form):
messages.success(self.request, _("The widget settings have been saved."))
form.save()
return super().form_valid(form)
def get_form_kwargs(self):
kwargs = super().get_form_kwargs()
kwargs["obj"] = self.request.event
kwargs["attribute_name"] = "settings"
return kwargs
def get_context_data(self, **kwargs):
result = super().get_context_data(**kwargs)
result["extra_form"] = WidgetGenerationForm(instance=self.request.event)
return result
def get_success_url(self) -> str:
return self.request.event.orga_urls.widget_settings
| 34.70436 | 156 | 0.593256 |
96e3fe5b369e81e2300fa94697e053928e6a1658 | 542 | py | Python | cloudaux/__about__.py | Deepak1100/cloudaux | 322b26b9c47e5f4fcd5cd11fc4aa5fa830c050f9 | [
"Apache-2.0"
] | null | null | null | cloudaux/__about__.py | Deepak1100/cloudaux | 322b26b9c47e5f4fcd5cd11fc4aa5fa830c050f9 | [
"Apache-2.0"
] | null | null | null | cloudaux/__about__.py | Deepak1100/cloudaux | 322b26b9c47e5f4fcd5cd11fc4aa5fa830c050f9 | [
"Apache-2.0"
] | null | null | null | __all__ = [
'__title__',
'__summary__',
'__uri__',
'__version__',
'__author__',
'__email__',
'__license__',
'__copyright__'
]
__title__ = 'cloudaux'
__summary__ = 'Cloud Auxiliary is a python wrapper and orchestration module for interacting with cloud providers'
__uri__ = 'https://github.com/Netflix-Skunkworks/cloudaux'
__version__ = '1.8.3'
__author__ = 'The Cloudaux Developers'
__email__ = 'oss@netflix.com'
__license__ = 'Apache License, Version 2.0'
__copyright__ = 'Copyright 2020 %s' % __author__
| 23.565217 | 113 | 0.706642 |
711272a29e309d0368aa59cd19ec68d01df72892 | 1,040 | py | Python | leetcode/number_of_submatrices_that_sum_to_target/number_of_submatrices_that_sum_to_target.py | sagasu/python-algorithms | d630777a3f17823165e4d72ab780ede7b10df752 | [
"MIT"
] | null | null | null | leetcode/number_of_submatrices_that_sum_to_target/number_of_submatrices_that_sum_to_target.py | sagasu/python-algorithms | d630777a3f17823165e4d72ab780ede7b10df752 | [
"MIT"
] | null | null | null | leetcode/number_of_submatrices_that_sum_to_target/number_of_submatrices_that_sum_to_target.py | sagasu/python-algorithms | d630777a3f17823165e4d72ab780ede7b10df752 | [
"MIT"
] | null | null | null | class Solution:
def numSubmatrixSumTarget(self, matrix: List[List[int]], target: int) -> int:
rows = len(matrix)
cols = len(matrix[0])
prefix = [[0] * (cols + 1) for _ in range(rows + 1)]
for r in range(rows):
for c in range(cols):
prefix[r + 1][c+ 1] = matrix[r][c]
for r in range(rows + 1):
for c in range(cols):
prefix[r][c+ 1] += prefix[r][c]
for r in range(rows):
for c in range(cols + 1):
prefix[r + 1][c] += prefix[r][c]
total = 0
for r1 in range(1, rows + 1):
for r2 in range(r1):
lookup = collections.defaultdict(int)
lookup[0] = 1
current = 0
for c in range(1, cols + 1):
current += prefix[r1][c] - prefix[r2][c] - prefix[r1][c - 1] + prefix[r2][c-1]
total += lookup[current - target]
lookup[current] += 1
return total
| 31.515152 | 98 | 0.447115 |
67ab8681787575ceb510c757b407af173e428376 | 2,901 | py | Python | DataStructures/Stack/Stack.py | chrisphil335/structlinks | 61aee24ec15c7caab9ce99e1f97ce30e7f21157c | [
"MIT"
] | 9 | 2021-04-09T21:20:46.000Z | 2022-03-25T12:14:43.000Z | DataStructures/Stack/Stack.py | chrisphil335/structlinks | 61aee24ec15c7caab9ce99e1f97ce30e7f21157c | [
"MIT"
] | 19 | 2021-03-22T07:52:39.000Z | 2021-04-07T20:04:05.000Z | DataStructures/Stack/Stack.py | chrisphil335/structlinks | 61aee24ec15c7caab9ce99e1f97ce30e7f21157c | [
"MIT"
] | 7 | 2021-04-10T21:08:12.000Z | 2022-03-20T12:55:23.000Z | """
This is an implementation of the stack data structure, with code pulled from the
University of Toronto's CSC110 course notes.
"""
from __future__ import annotations
from typing import Any, Optional, Callable, Sequence
class Stack:
"""
This class represents a stack data structure
"""
def __init__(self, items: Optional[list] = None) -> None:
"""
Initialize a stack, empty at first
"""
self.items = []
if items:
self.items = items
def is_empty(self) -> bool:
"""
Returns whether the stack is empty
"""
return self.items == []
def push(self, item: Any) -> None:
"""
Adds a new element to the top of the stack
"""
self.items.append(item)
def push_multiple(self, items: Sequence) -> None:
"""Push multiple items in the stack"""
for item in items:
self.push(item)
def pop(self) -> Any:
"""
Removes the element at the top of the stack and
returns it. Raises IndexError if list is empty
"""
if self.is_empty():
raise EmptyStackError
else:
return self.items.pop()
def __len__(self) -> int:
"""Return the length of the stack"""
return len(self.items)
def to_list(self) -> list:
"""Return list of the stack"""
return self.items
def __copy__(self) -> Stack:
"""Return a copy of the stack"""
return Stack(items=self.items)
def copy(self) -> Stack:
"""Return a copy of the stack"""
return Stack(items=self.items)
def map(self, key: Callable) -> Stack:
"""Map a function to the stack"""
return Stack(items=[key(item) for item in self.items])
def invert(self) -> None:
"""Invert the stack"""
self.items.reverse()
def extend(self, other: Stack):
"""extend the stack by putting other stack on top of self"""
self.push_multiple(other.items)
def __add__(self, other) -> Stack:
"""Return a stack with other stack on top of self"""
return Stack(items=self.items + other.items)
def __str__(self) -> str:
"""Return string representation of the stack"""
string_so_far = ''
gap = 10
for index in range(len(self) - 1, -1, -1):
item = self.items[index]
string_rep = str(item)
gap_left = gap - len(string_rep)
string_so_far += '|' + ' ' * gap + string_rep + ' ' * gap_left + '| \n'
string_so_far += '|' + '_' * (2 * gap) + '|'
return string_so_far
def __repr__(self) -> str:
return f'Stack({self.items})'
class EmptyStackError(Exception):
def __str__(self) -> str:
"""String representation of the error"""
return "Popping from an empty stack is not allowed"
| 26.614679 | 83 | 0.567046 |
798966c28e6ad03ae4ebc8f6597b896df7d759f3 | 9,528 | py | Python | devel/lib/python2.7/dist-packages/costmap_prohibition_layer/srv/_GetProhibitedPoints.py | Jam-cpu/Masters-Project---Final | 0b266b1f117a579b96507249f0a128d0e3cc082a | [
"BSD-3-Clause-Clear"
] | null | null | null | devel/lib/python2.7/dist-packages/costmap_prohibition_layer/srv/_GetProhibitedPoints.py | Jam-cpu/Masters-Project---Final | 0b266b1f117a579b96507249f0a128d0e3cc082a | [
"BSD-3-Clause-Clear"
] | null | null | null | devel/lib/python2.7/dist-packages/costmap_prohibition_layer/srv/_GetProhibitedPoints.py | Jam-cpu/Masters-Project---Final | 0b266b1f117a579b96507249f0a128d0e3cc082a | [
"BSD-3-Clause-Clear"
] | null | null | null | # This Python file uses the following encoding: utf-8
"""autogenerated by genpy from costmap_prohibition_layer/GetProhibitedPointsRequest.msg. Do not edit."""
import codecs
import sys
python3 = True if sys.hexversion > 0x03000000 else False
import genpy
import struct
class GetProhibitedPointsRequest(genpy.Message):
_md5sum = "d41d8cd98f00b204e9800998ecf8427e"
_type = "costmap_prohibition_layer/GetProhibitedPointsRequest"
_has_header = False # flag to mark the presence of a Header object
_full_text = """
"""
__slots__ = []
_slot_types = []
def __init__(self, *args, **kwds):
"""
Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
to set specific fields.
"""
if args or kwds:
super(GetProhibitedPointsRequest, self).__init__(*args, **kwds)
def _get_types(self):
"""
internal API method
"""
return self._slot_types
def serialize(self, buff):
"""
serialize message into buffer
:param buff: buffer, ``StringIO``
"""
try:
pass
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize(self, str):
"""
unpack serialized message in str into this message instance
:param str: byte array of serialized message, ``str``
"""
if python3:
codecs.lookup_error("rosmsg").msg_type = self._type
try:
end = 0
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
def serialize_numpy(self, buff, numpy):
"""
serialize message with numpy array types into buffer
:param buff: buffer, ``StringIO``
:param numpy: numpy python module
"""
try:
pass
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize_numpy(self, str, numpy):
"""
unpack serialized message in str into this message instance using numpy for array types
:param str: byte array of serialized message, ``str``
:param numpy: numpy python module
"""
if python3:
codecs.lookup_error("rosmsg").msg_type = self._type
try:
end = 0
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
_struct_I = genpy.struct_I
def _get_struct_I():
global _struct_I
return _struct_I
# This Python file uses the following encoding: utf-8
"""autogenerated by genpy from costmap_prohibition_layer/GetProhibitedPointsResponse.msg. Do not edit."""
import codecs
import sys
python3 = True if sys.hexversion > 0x03000000 else False
import genpy
import struct
import geometry_msgs.msg
class GetProhibitedPointsResponse(genpy.Message):
_md5sum = "e85019b57cf217e7d529d6333370e839"
_type = "costmap_prohibition_layer/GetProhibitedPointsResponse"
_has_header = False # flag to mark the presence of a Header object
_full_text = """geometry_msgs/Polygon[] polygons
================================================================================
MSG: geometry_msgs/Polygon
#A specification of a polygon where the first and last points are assumed to be connected
Point32[] points
================================================================================
MSG: geometry_msgs/Point32
# This contains the position of a point in free space(with 32 bits of precision).
# It is recommeded to use Point wherever possible instead of Point32.
#
# This recommendation is to promote interoperability.
#
# This message is designed to take up less space when sending
# lots of points at once, as in the case of a PointCloud.
float32 x
float32 y
float32 z"""
__slots__ = ['polygons']
_slot_types = ['geometry_msgs/Polygon[]']
def __init__(self, *args, **kwds):
"""
Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
polygons
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
to set specific fields.
"""
if args or kwds:
super(GetProhibitedPointsResponse, self).__init__(*args, **kwds)
# message fields cannot be None, assign default values for those that are
if self.polygons is None:
self.polygons = []
else:
self.polygons = []
def _get_types(self):
"""
internal API method
"""
return self._slot_types
def serialize(self, buff):
"""
serialize message into buffer
:param buff: buffer, ``StringIO``
"""
try:
length = len(self.polygons)
buff.write(_struct_I.pack(length))
for val1 in self.polygons:
length = len(val1.points)
buff.write(_struct_I.pack(length))
for val2 in val1.points:
_x = val2
buff.write(_get_struct_3f().pack(_x.x, _x.y, _x.z))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize(self, str):
"""
unpack serialized message in str into this message instance
:param str: byte array of serialized message, ``str``
"""
if python3:
codecs.lookup_error("rosmsg").msg_type = self._type
try:
if self.polygons is None:
self.polygons = None
end = 0
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
self.polygons = []
for i in range(0, length):
val1 = geometry_msgs.msg.Polygon()
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
val1.points = []
for i in range(0, length):
val2 = geometry_msgs.msg.Point32()
_x = val2
start = end
end += 12
(_x.x, _x.y, _x.z,) = _get_struct_3f().unpack(str[start:end])
val1.points.append(val2)
self.polygons.append(val1)
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
def serialize_numpy(self, buff, numpy):
"""
serialize message with numpy array types into buffer
:param buff: buffer, ``StringIO``
:param numpy: numpy python module
"""
try:
length = len(self.polygons)
buff.write(_struct_I.pack(length))
for val1 in self.polygons:
length = len(val1.points)
buff.write(_struct_I.pack(length))
for val2 in val1.points:
_x = val2
buff.write(_get_struct_3f().pack(_x.x, _x.y, _x.z))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize_numpy(self, str, numpy):
"""
unpack serialized message in str into this message instance using numpy for array types
:param str: byte array of serialized message, ``str``
:param numpy: numpy python module
"""
if python3:
codecs.lookup_error("rosmsg").msg_type = self._type
try:
if self.polygons is None:
self.polygons = None
end = 0
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
self.polygons = []
for i in range(0, length):
val1 = geometry_msgs.msg.Polygon()
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
val1.points = []
for i in range(0, length):
val2 = geometry_msgs.msg.Point32()
_x = val2
start = end
end += 12
(_x.x, _x.y, _x.z,) = _get_struct_3f().unpack(str[start:end])
val1.points.append(val2)
self.polygons.append(val1)
return self
except struct.error as e:
raise genpy.DeserializationError(e) # most likely buffer underfill
_struct_I = genpy.struct_I
def _get_struct_I():
global _struct_I
return _struct_I
_struct_3f = None
def _get_struct_3f():
global _struct_3f
if _struct_3f is None:
_struct_3f = struct.Struct("<3f")
return _struct_3f
class GetProhibitedPoints(object):
_type = 'costmap_prohibition_layer/GetProhibitedPoints'
_md5sum = 'e85019b57cf217e7d529d6333370e839'
_request_class = GetProhibitedPointsRequest
_response_class = GetProhibitedPointsResponse
| 34.150538 | 145 | 0.651448 |
8200a1d71664903771a6575ebea3f92a9515b957 | 5,621 | py | Python | stanza/models/mwt/trainer.py | turtlesoupy/stanza | 8511620010ba22123f29ea1a7ec3606ec96aaf69 | [
"Apache-2.0"
] | null | null | null | stanza/models/mwt/trainer.py | turtlesoupy/stanza | 8511620010ba22123f29ea1a7ec3606ec96aaf69 | [
"Apache-2.0"
] | null | null | null | stanza/models/mwt/trainer.py | turtlesoupy/stanza | 8511620010ba22123f29ea1a7ec3606ec96aaf69 | [
"Apache-2.0"
] | null | null | null | """
A trainer class to handle training and testing of models.
"""
import sys
import numpy as np
from collections import Counter
import logging
import torch
from torch import nn
import torch.nn.init as init
import stanza.models.common.seq2seq_constant as constant
from stanza.models.common.trainer import Trainer as BaseTrainer
from stanza.models.common.seq2seq_model import Seq2SeqModel
from stanza.models.common import utils, loss
from stanza.models.mwt.vocab import Vocab
logger = logging.getLogger('stanza')
def unpack_batch(batch, use_cuda):
""" Unpack a batch from the data loader. """
if use_cuda:
inputs = [b.cuda() if b is not None else None for b in batch[:4]]
else:
inputs = [b if b is not None else None for b in batch[:4]]
orig_idx = batch[4]
return inputs, orig_idx
class Trainer(object):
""" A trainer for training models. """
def __init__(self, args=None, vocab=None, emb_matrix=None, model_file=None, use_cuda=False):
self.use_cuda = use_cuda
if model_file is not None:
# load from file
self.load(model_file, use_cuda)
else:
self.args = args
self.model = None if args['dict_only'] else Seq2SeqModel(args, emb_matrix=emb_matrix)
self.vocab = vocab
self.expansion_dict = dict()
if not self.args['dict_only']:
self.crit = loss.SequenceLoss(self.vocab.size)
self.parameters = [p for p in self.model.parameters() if p.requires_grad]
if use_cuda:
self.model.cuda()
self.crit.cuda()
else:
self.model.cpu()
self.crit.cpu()
self.optimizer = utils.get_optimizer(self.args['optim'], self.parameters, self.args['lr'])
def update(self, batch, eval=False):
inputs, orig_idx = unpack_batch(batch, self.use_cuda)
src, src_mask, tgt_in, tgt_out = inputs
if eval:
self.model.eval()
else:
self.model.train()
self.optimizer.zero_grad()
log_probs, _ = self.model(src, src_mask, tgt_in)
loss = self.crit(log_probs.view(-1, self.vocab.size), tgt_out.view(-1))
loss_val = loss.data.item()
if eval:
return loss_val
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args['max_grad_norm'])
self.optimizer.step()
return loss_val
def predict(self, batch, unsort=True):
inputs, orig_idx = unpack_batch(batch, self.use_cuda)
src, src_mask, tgt, tgt_mask = inputs
self.model.eval()
batch_size = src.size(0)
preds, _ = self.model.predict(src, src_mask, self.args['beam_size'])
pred_seqs = [self.vocab.unmap(ids) for ids in preds] # unmap to tokens
pred_seqs = utils.prune_decoded_seqs(pred_seqs)
pred_tokens = ["".join(seq) for seq in pred_seqs] # join chars to be tokens
if unsort:
pred_tokens = utils.unsort(pred_tokens, orig_idx)
return pred_tokens
def train_dict(self, pairs):
""" Train a MWT expander given training word-expansion pairs. """
# accumulate counter
ctr = Counter()
ctr.update([(p[0], p[1]) for p in pairs])
seen = set()
# find the most frequent mappings
for p, _ in ctr.most_common():
w, l = p
if w not in seen and w != l:
self.expansion_dict[w] = l
seen.add(w)
return
def predict_dict(self, words):
""" Predict a list of expansions given words. """
expansions = []
for w in words:
if w in self.expansion_dict:
expansions += [self.expansion_dict[w]]
elif w.lower() in self.expansion_dict:
expansions += [self.expansion_dict[w.lower()]]
else:
expansions += [w]
return expansions
def ensemble(self, cands, other_preds):
""" Ensemble the dict with statistical model predictions. """
expansions = []
assert len(cands) == len(other_preds)
for c, pred in zip(cands, other_preds):
if c in self.expansion_dict:
expansions += [self.expansion_dict[c]]
elif c.lower() in self.expansion_dict:
expansions += [self.expansion_dict[c.lower()]]
else:
expansions += [pred]
return expansions
def save(self, filename):
params = {
'model': self.model.state_dict() if self.model is not None else None,
'dict': self.expansion_dict,
'vocab': self.vocab.state_dict(),
'config': self.args
}
try:
torch.save(params, filename)
logger.info("Model saved to {}".format(filename))
except BaseException:
logger.warning("Saving failed... continuing anyway.")
def load(self, filename, use_cuda=False):
try:
checkpoint = torch.load(filename, lambda storage, loc: storage)
except BaseException:
logger.exception("Cannot load model from {}".format(filename))
raise
self.args = checkpoint['config']
self.expansion_dict = checkpoint['dict']
if not self.args['dict_only']:
self.model = Seq2SeqModel(self.args, use_cuda=use_cuda)
self.model.load_state_dict(checkpoint['model'])
else:
self.model = None
self.vocab = Vocab.load_state_dict(checkpoint['vocab'])
| 36.5 | 102 | 0.595446 |
da8df67cb8f662318407cbf704123e31c5895238 | 12,584 | py | Python | tensorflow/python/layers/core.py | connectthefuture/tensorflow | 93812423fcd5878aa2c1d0b68dc0496980c8519d | [
"Apache-2.0"
] | 1 | 2018-11-15T08:44:10.000Z | 2018-11-15T08:44:10.000Z | tensorflow/python/layers/core.py | connectthefuture/tensorflow | 93812423fcd5878aa2c1d0b68dc0496980c8519d | [
"Apache-2.0"
] | null | null | null | tensorflow/python/layers/core.py | connectthefuture/tensorflow | 93812423fcd5878aa2c1d0b68dc0496980c8519d | [
"Apache-2.0"
] | 1 | 2020-07-20T18:02:33.000Z | 2020-07-20T18:02:33.000Z | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =============================================================================
# pylint: disable=unused-import,g-bad-import-order
"""Contains the core layers: Dense, Dropout.
Also contains their functional aliases.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from six.moves import xrange # pylint: disable=redefined-builtin
import numpy as np
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import standard_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.layers import base
class Dense(base._Layer): # pylint: disable=protected-access
"""Densely-connected layer class.
This layer implements the operation `outputs = activation(inputs.w + b)`
Where `activation` is the activation function passed as the `activation`
argument (if not `None`), `w` is a weights matrix created by the layer,
and `b` is a bias vector created by the layer (only if `use_bias` is `True`).
Note: if the input to the layer has a rank greater than 2, then it is
flattened prior to the initial matrix multiply by `w`.
Arguments:
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
weights_initializer: Initializer function for the weight matrix.
bias_initializer: Initializer function for the bias.
weights_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
name: String, the name of the layer. Layers with the same name will
share weights, but to avoid mistakes we require reuse=True in such cases.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Properties:
units: Python integer, dimensionality of the output space.
activation: Activation function (callable).
use_bias: Boolean, whether the layer uses a bias.
weights_initializer: Initializer instance (or name) for the weight matrix.
bias_initializer: Initializer instance (or name) for the bias.
weights_regularizer: Regularizer instance for the weight matrix (callable)
bias_regularizer: Regularizer instance for the bias (callable).
activity_regularizer: Regularizer instance for the output (callable)
weights: Weight matrix (TensorFlow variable or tensor).
bias: Bias vector, if applicable (TensorFlow variable or tensor).
"""
def __init__(self, units,
activation=None,
use_bias=True,
weights_initializer=None,
bias_initializer=init_ops.zeros_initializer,
weights_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
**kwargs):
super(Dense, self).__init__(trainable=trainable, name=name, **kwargs)
self.units = units
self.activation = activation
self.use_bias = use_bias
self.weights_initializer = weights_initializer
self.bias_initializer = bias_initializer
self.weights_regularizer = weights_regularizer
self.bias_regularizer = bias_regularizer
self.activity_regularizer = activity_regularizer
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape)
if input_shape.ndims is None:
raise ValueError('Inputs to `Dense` should have known rank.')
if len(input_shape) < 2:
raise ValueError('Inputs to `Dense` should have rank >= 2.')
if input_shape[-1].value is None:
raise ValueError('The last dimension of the inputs to `Dense` '
'should be defined. Found `None`.')
# Note that we set `trainable=True` because this is a trainable
# weight of the layer. If the layer is not trainable
# (self.trainable = False), the variable will not be added to
# tf.trainable_variables(), and self.trainable_weights will be empty.
self.w = vs.get_variable('weights',
shape=[input_shape[-1].value, self.units],
initializer=self.weights_initializer,
regularizer=self.weights_regularizer,
dtype=self.dtype,
trainable=True)
if self.use_bias:
self.bias = vs.get_variable('bias',
shape=[self.units,],
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
dtype=self._dtype,
trainable=True)
else:
self.bias = None
def call(self, inputs):
shape = inputs.get_shape().as_list()
input_dim = shape[-1]
output_shape = shape[:-1] + [self.units]
if len(output_shape) > 2:
# Reshape the input to 2D.
output_shape_tensors = array_ops.unpack(array_ops.shape(inputs))
output_shape_tensors[-1] = self.units
output_shape_tensor = array_ops.pack(output_shape_tensors)
inputs = array_ops.reshape(inputs, [-1, input_dim])
outputs = standard_ops.matmul(inputs, self.w)
if self.use_bias:
outputs = nn.bias_add(outputs, self.bias)
if len(output_shape) > 2:
# Reshape the output back to the original ndim of the input.
outputs = array_ops.reshape(outputs, output_shape_tensor)
outputs.set_shape(output_shape)
if self.activation is not None:
return self.activation(outputs) # pylint: disable=not-callable
return outputs
def dense(
inputs, units,
activation=None,
use_bias=True,
weights_initializer=None,
bias_initializer=init_ops.zeros_initializer,
weights_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
trainable=True,
name=None,
reuse=False):
"""Functional interface for the densely-connected layer.
This layer implements the operation `outputs = activation(inputs.w + b)`
Where `activation` is the activation function passed as the `activation`
argument (if not `None`), `w` is a weights matrix created by the layer,
and `b` is a bias vector created by the layer (only if `use_bias` is `True`).
Note: if the `inputs` tensor has a rank greater than 2, then it is
flattened prior to the initial matrix multiply by `w`.
Arguments:
inputs: Tensor input.
units: Integer or Long, dimensionality of the output space.
activation: Activation function (callable). Set it to None to maintain a
linear activation.
use_bias: Boolean, whether the layer uses a bias.
weights_initializer: Initializer function for the weight matrix.
bias_initializer: Initializer function for the bias.
weights_regularizer: Regularizer function for the weight matrix.
bias_regularizer: Regularizer function for the bias.
activity_regularizer: Regularizer function for the output.
trainable: Boolean, if `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
name: String, the name of the layer.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
Output tensor.
"""
layer = Dense(units,
activation=activation,
use_bias=use_bias,
weights_initializer=weights_initializer,
bias_initializer=bias_initializer,
weights_regularizer=weights_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
trainable=trainable,
name=name,
dtype=inputs.dtype.base_dtype,
_scope=name,
_reuse=reuse)
return layer.apply(inputs)
class Dropout(base._Layer): # pylint: disable=protected-access
"""Applies Dropout to the input.
Dropout consists in randomly setting a fraction `rate` of input units to 0
at each update during training time, which helps prevent overfitting.
The units that are kept are scaled by `1 / (1 - rate)`, so that their
sum is unchanged at training time and inference time.
Arguments:
rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
10% of input units.
noise_shape: 1D tensor of type `int32` representing the shape of the
binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
`(batch_size, timesteps, features)`, and you want the dropout mask
to be the same for all timesteps, you can use
`noise_shape=[batch_size, 1, features]`.
seed: A Python integer. Used to create random seeds. See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: The name of the layer (string).
"""
def __init__(self, rate=0.5,
noise_shape=None,
seed=None,
name=None,
**kwargs):
super(Dropout, self).__init__(name=name, **kwargs)
self.rate = rate
self.noise_shape = noise_shape
self.seed = seed
def call(self, inputs, training=False):
if isinstance(training, bool):
training_bool = training
else:
training_bool = tensor_util.constant_value(training)
if training_bool is False:
return array_ops.identity(inputs)
dropped_inputs = nn.dropout(inputs, 1 - self.rate,
noise_shape=self.noise_shape,
seed=self.seed)
if training_bool is True:
return dropped_inputs
return control_flow_ops.cond(training,
lambda: dropped_inputs,
lambda: inputs)
def dropout(inputs,
rate=0.5,
noise_shape=None,
seed=None,
training=False,
name=None):
"""Applies Dropout to the input.
Dropout consists in randomly setting a fraction `rate` of input units to 0
at each update during training time, which helps prevent overfitting.
The units that are kept are scaled by `1 / (1 - rate)`, so that their
sum is unchanged at training time and inference time.
Arguments:
inputs: Tensor input.
rate: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
10% of input units.
noise_shape: 1D tensor of type `int32` representing the shape of the
binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
`(batch_size, timesteps, features)`, and you want the dropout mask
to be the same for all timesteps, you can use
`noise_shape=[batch_size, 1, features]`.
seed: A Python integer. Used to create random seeds. See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
training: Either a Python boolean, or a TensorFlow boolean scalar tensor
(e.g. a placeholder). Whether to return the output in training mode
(apply dropout) or in inference mode (return the input untouched).
name: The name of the layer (string).
Returns:
Output tensor.
"""
layer = Dropout(rate, noise_shape=noise_shape, seed=seed, name=name)
return layer.apply(inputs, training=training)
# Aliases
FullyConnected = Dense
fully_connected = dense
| 40.857143 | 79 | 0.681739 |
3566024b052f30b3746eca7d117e413e0afc46d9 | 134,832 | py | Python | tests/models/validators/v1_3_0/jsd_7ab9a8bd4f3b86a4.py | daxm/dnacentersdk | 5baa0cb151fb9e72cf7af1ae29e7541d89c3f06b | [
"MIT"
] | null | null | null | tests/models/validators/v1_3_0/jsd_7ab9a8bd4f3b86a4.py | daxm/dnacentersdk | 5baa0cb151fb9e72cf7af1ae29e7541d89c3f06b | [
"MIT"
] | null | null | null | tests/models/validators/v1_3_0/jsd_7ab9a8bd4f3b86a4.py | daxm/dnacentersdk | 5baa0cb151fb9e72cf7af1ae29e7541d89c3f06b | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
"""DNA Center Retrieves previous Pathtrace data model.
Copyright (c) 2019 Cisco and/or its affiliates.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
from __future__ import (
absolute_import,
division,
print_function,
unicode_literals,
)
import fastjsonschema
import json
from dnacentersdk.exceptions import MalformedRequest
from builtins import *
class JSONSchemaValidator7Ab9A8Bd4F3B86A4(object):
"""Retrieves previous Pathtrace request schema definition."""
def __init__(self):
super(JSONSchemaValidator7Ab9A8Bd4F3B86A4, self).__init__()
self._validator = fastjsonschema.compile(json.loads(
'''{
"properties": {
"response": {
"description":
"",
"properties": {
"detailedStatus": {
"description":
"",
"properties": {
"aclTraceCalculation": {
"description":
"",
"type": [
"string",
"null"
]
},
"aclTraceCalculationFailureReason": {
"description":
"",
"type": [
"string",
"null"
]
}
},
"type": [
"object",
"null"
]
},
"lastUpdate": {
"description":
"",
"type": [
"string",
"null",
"number"
]
},
"networkElements": {
"description":
"",
"items": {
"properties": {
"accuracyList": {
"description":
"",
"items": {
"properties": {
"percent": {
"type": [
"number",
"null"
]
},
"reason": {
"description":
"",
"type": [
"string",
"null"
]
}
},
"type": [
"object",
"null"
]
},
"type": [
"array",
"null"
]
},
"detailedStatus": {
"description":
"",
"properties": {
"aclTraceCalculation": {
"description":
"",
"type": [
"string",
"null"
]
},
"aclTraceCalculationFailureReason": {
"description":
"",
"type": [
"string",
"null"
]
}
},
"type": [
"object",
"null"
]
},
"deviceStatistics": {
"description":
"",
"properties": {
"cpuStatistics": {
"description":
"",
"properties": {
"fiveMinUsageInPercentage": {
"type": [
"number",
"null"
]
},
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"null"
]
},
"rtpJitterMean": {
"type": [
"number",
"null"
]
},
"rtpJitterMin": {
"type": [
"number",
"null"
]
},
"sourceIpAddress": {
"description":
"",
"type": [
"string",
"null"
]
},
"sourcePort": {
"description":
"",
"type": [
"string",
"null"
]
}
},
"type": [
"object",
"null"
]
},
"type": [
"array",
"null"
]
},
"role": {
"description":
"",
"type": [
"string",
"null"
]
},
"ssid": {
"description":
"",
"type": [
"string",
"null"
]
},
"tunnels": {
"description":
"",
"items": {
"type": [
"string",
"null"
]
},
"type": [
"array",
"null"
]
},
"type": {
"description":
"",
"type": [
"string",
"null"
]
},
"wlanId": {
"description":
"",
"type": [
"string",
"null"
]
}
},
"type": [
"object",
"null"
]
},
"type": [
"array",
"null"
]
},
"properties": {
"description":
"",
"items": {
"type": [
"string",
"null"
]
},
"type": [
"array",
"null"
]
},
"request": {
"description":
"",
"properties": {
"controlPath": {
"type": [
"boolean",
"null"
]
},
"createTime": {
"type": [
"number",
"null"
]
},
"destIP": {
"description":
"",
"type": [
"string",
"null"
]
},
"destPort": {
"description":
"",
"type": [
"string",
"null"
]
},
"failureReason": {
"description":
"",
"type": [
"string",
"null"
]
},
"id": {
"description":
"",
"type": [
"string",
"null"
]
},
"inclusions": {
"description":
"",
"items": {
"type": [
"string",
"null"
]
},
"type": [
"array",
"null"
]
},
"lastUpdateTime": {
"type": [
"number",
"null"
]
},
"periodicRefresh": {
"type": [
"boolean",
"null"
]
},
"protocol": {
"description":
"",
"type": [
"string",
"null"
]
},
"sourceIP": {
"description":
"",
"type": [
"string",
"null"
]
},
"sourcePort": {
"description":
"",
"type": [
"string",
"null"
]
},
"status": {
"description":
"",
"type": [
"string",
"null"
]
}
},
"type": [
"object",
"null"
]
}
},
"type": [
"object",
"null"
]
},
"version": {
"description":
"",
"type": [
"string",
"null"
]
}
},
"type": "object"
}'''.replace("\n" + ' ' * 16, '')
))
def validate(self, request):
try:
self._validator(request)
except fastjsonschema.exceptions.JsonSchemaException as e:
raise MalformedRequest(
'{} is invalid. Reason: {}'.format(request, e.message)
)
| 24.631348 | 78 | 0.189176 |
4f0028bfdc39633b26fe4910dd0e09c9293c9928 | 2,533 | py | Python | backend/opnreco/models/dbmeta.py | OpenPaymentNetwork/opnreco | 99c8955d7e200fe11fc23c3568879c543940b168 | [
"MIT"
] | null | null | null | backend/opnreco/models/dbmeta.py | OpenPaymentNetwork/opnreco | 99c8955d7e200fe11fc23c3568879c543940b168 | [
"MIT"
] | null | null | null | backend/opnreco/models/dbmeta.py | OpenPaymentNetwork/opnreco | 99c8955d7e200fe11fc23c3568879c543940b168 | [
"MIT"
] | null | null | null |
from opnreco.render import get_json_default
from sqlalchemy import engine_from_config
from sqlalchemy.orm import sessionmaker
from sqlalchemy.orm import configure_mappers
import json
import os
import zope.sqlalchemy
# import or define all models here to ensure they are attached to the
# Base.metadata prior to any initialization routines
from opnreco.models.db import all_metadata_defined as __all # noqa
# run configure_mappers after defining all of the models to ensure
# all relationships can be setup
configure_mappers()
def json_dumps_extra(value):
return json.dumps(
value,
separators=(',', ':'),
indent='',
sort_keys=True,
default=get_json_default)
def get_engine(prefix='sqlalchemy_'):
return engine_from_config(
os.environ, prefix,
json_serializer=json_dumps_extra)
def get_dbsession_factory(engine):
factory = sessionmaker()
factory.configure(bind=engine)
return factory
def get_tm_dbsession(dbsession_factory, transaction_manager):
"""
Get a ``sqlalchemy.orm.Session`` instance backed by a transaction.
This function will hook the session to the transaction manager which
will take care of committing any changes.
- When using pyramid_tm it will automatically be committed or aborted
depending on whether an exception is raised.
- When using scripts you should wrap the session in a manager yourself.
For example::
import transaction
engine = get_engine(settings)
session_factory = get_session_factory(engine)
with transaction.manager:
dbsession = get_tm_session(session_factory, transaction.manager)
"""
dbsession = dbsession_factory()
zope.sqlalchemy.register(
dbsession, transaction_manager=transaction_manager)
return dbsession
def includeme(config):
"""
Initialize the model for a Pyramid app.
Activate this setup using ``config.include('testalchemy.models')``.
"""
# use pyramid_tm to hook the transaction lifecycle to the request
config.include('pyramid_tm')
dbsession_factory = get_dbsession_factory(get_engine())
config.registry['dbsession_factory'] = dbsession_factory
def dbsession(request):
return get_tm_dbsession(dbsession_factory, request.tm)
# make request.dbsession available for use in Pyramid
config.add_request_method(
# request.tm is the transaction manager provided by pyramid_tm.
dbsession, 'dbsession', reify=True)
| 29.453488 | 78 | 0.72878 |
f4ca004cbafc8c4521bde01f0bd6bb227a41f93e | 1,741 | py | Python | memodrop/urls.py | mhndlsz/memodrop | 7ba39143c8e4fbe67881b141accedef535e936e6 | [
"MIT"
] | 18 | 2018-04-15T14:01:25.000Z | 2022-03-16T14:57:28.000Z | memodrop/urls.py | mhndlsz/memodrop | 7ba39143c8e4fbe67881b141accedef535e936e6 | [
"MIT"
] | null | null | null | memodrop/urls.py | mhndlsz/memodrop | 7ba39143c8e4fbe67881b141accedef535e936e6 | [
"MIT"
] | 4 | 2018-04-15T14:16:12.000Z | 2020-08-10T14:31:48.000Z | """memodrop URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.11/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
import watchman.views
from django.conf.urls import url, include
from django.contrib import admin
from django.urls import reverse_lazy
from django.views.generic import RedirectView
from rest_framework.authtoken import views as auth_token_views
urlpatterns = [
url(r'^$',
RedirectView.as_view(url=reverse_lazy('braindump-index'), permanent=False),
name='index'),
url(r'^authentication/', include('authentication.urls.gui')),
url(r'^admin/', admin.site.urls),
url(r'^admin/health/$', watchman.views.bare_status),
url(r'^api/(?P<version>(v1))/auth/', include('rest_framework.urls')),
url(r'^api/(?P<version>(v1))/authentication/', include('authentication.urls.api')),
url(r'^api/(?P<version>(v1))/auth-token/', auth_token_views.obtain_auth_token),
url(r'^api/(?P<version>(v1))/categories/', include('categories.urls.api')),
url(r'^api/(?P<version>(v1))/cards/', include('cards.urls.api')),
url(r'^braindump/', include('braindump.urls')),
url(r'^cards/', include('cards.urls.gui')),
url(r'^categories/', include('categories.urls.gui')),
]
| 44.641026 | 87 | 0.693854 |
ab4a75c04a0fe9097c592c76275e2b7a52a27174 | 28,660 | py | Python | wemake_python_styleguide/violations/refactoring.py | toennifer/wemake-python-styleguide | 12f942035aec4a34d38e24df89b150b88f35e021 | [
"MIT"
] | null | null | null | wemake_python_styleguide/violations/refactoring.py | toennifer/wemake-python-styleguide | 12f942035aec4a34d38e24df89b150b88f35e021 | [
"MIT"
] | null | null | null | wemake_python_styleguide/violations/refactoring.py | toennifer/wemake-python-styleguide | 12f942035aec4a34d38e24df89b150b88f35e021 | [
"MIT"
] | null | null | null | """
These checks ensure that you don't have patterns that can be refactored.
There are so many ways of doing the same thing in Python.
Here we collect know patterns that can be rewritten into
much easier or just more pythonic version.
.. currentmodule:: wemake_python_styleguide.violations.refactoring
Summary
-------
.. autosummary::
:nosignatures:
UselessLoopElseViolation
UselessFinallyViolation
SimplifiableIfViolation
UselessReturningElseViolation
NegatedConditionsViolation
NestedTryViolation
UselessLambdaViolation
UselessLenCompareViolation
NotOperatorWithCompareViolation
NestedTernaryViolation
WrongInCompareTypeViolation
UnmergedIsinstanceCallsViolation
WrongIsinstanceWithTupleViolation
ImplicitElifViolation
ImplicitInConditionViolation
OpenWithoutContextManagerViolation
TypeCompareViolation
PointlessStarredViolation
ImplicitEnumerateViolation
ImplicitSumViolation
FalsyConstantCompareViolation
WrongIsCompareViolation
ImplicitPrimitiveViolation
AlmostSwappedViolation
MisrefactoredAssignmentViolation
InCompareWithSingleItemContainerViolation
ImplicitYieldFromViolation
NotATupleArgumentViolation
ImplicitItemsIteratorViolation
ImplicitDictGetViolation
ImplicitNegativeIndexViolation
SimplifiableReturningIfViolation
Refactoring opportunities
-------------------------
.. autoclass:: UselessLoopElseViolation
.. autoclass:: UselessFinallyViolation
.. autoclass:: SimplifiableIfViolation
.. autoclass:: UselessReturningElseViolation
.. autoclass:: NegatedConditionsViolation
.. autoclass:: NestedTryViolation
.. autoclass:: UselessLambdaViolation
.. autoclass:: UselessLenCompareViolation
.. autoclass:: NotOperatorWithCompareViolation
.. autoclass:: NestedTernaryViolation
.. autoclass:: WrongInCompareTypeViolation
.. autoclass:: UnmergedIsinstanceCallsViolation
.. autoclass:: WrongIsinstanceWithTupleViolation
.. autoclass:: ImplicitElifViolation
.. autoclass:: ImplicitInConditionViolation
.. autoclass:: OpenWithoutContextManagerViolation
.. autoclass:: TypeCompareViolation
.. autoclass:: PointlessStarredViolation
.. autoclass:: ImplicitEnumerateViolation
.. autoclass:: ImplicitSumViolation
.. autoclass:: FalsyConstantCompareViolation
.. autoclass:: WrongIsCompareViolation
.. autoclass:: ImplicitPrimitiveViolation
.. autoclass:: AlmostSwappedViolation
.. autoclass:: MisrefactoredAssignmentViolation
.. autoclass:: InCompareWithSingleItemContainerViolation
.. autoclass:: ImplicitYieldFromViolation
.. autoclass:: NotATupleArgumentViolation
.. autoclass:: ImplicitItemsIteratorViolation
.. autoclass:: ImplicitDictGetViolation
.. autoclass:: ImplicitNegativeIndexViolation
.. autoclass:: SimplifiableReturningIfViolation
"""
from typing_extensions import final
from wemake_python_styleguide.violations.base import (
ASTViolation,
TokenizeViolation,
)
@final
class UselessLoopElseViolation(ASTViolation):
"""
Forbid ``else`` without ``break`` in a loop.
We use the same logic for ``for`` and ``while`` loops.
Reasoning:
When there's no ``break`` keyword in loop's body it means
that ``else`` will always be called.
This rule will reduce complexity, improve readability,
and protect from possible errors.
Solution:
Refactor your ``else`` case logic to be inside the loop's body.
Or right after it.
Example::
# Correct:
for letter in 'abc':
if letter == 'b':
break
else:
print('"b" is not found')
for letter in 'abc':
print(letter)
print('always called')
# Wrong:
for letter in 'abc':
print(letter)
else:
print('always called')
.. versionadded:: 0.3.0
.. versionchanged:: 0.11.0
"""
error_template = 'Found `else` in a loop without `break`'
code = 500
previous_codes = {436}
@final
class UselessFinallyViolation(ASTViolation):
"""
Forbid ``finally`` in ``try`` block without ``except`` block.
However, we allow to use ``try`` with just ``finally`` block
when function or method is decorated. Because we cannot control
what is going on in this decorator.
It might be ``@contextmanager`` or similar thing that requires this API.
Reasoning:
This rule will reduce complexity and improve readability.
Solution:
Refactor your ``try`` logic.
Replace the ``try-finally`` statement with a ``with`` statement.
Example::
# Correct:
with open("filename") as f:
f.write(...)
# Wrong:
try:
f = open("filename")
f.write(...)
finally:
f.close()
.. versionadded:: 0.3.0
.. versionchanged:: 0.11.0
.. versionchanged:: 0.14.0
"""
error_template = 'Found `finally` in `try` block without `except`'
code = 501
previous_codes = {437}
@final
class SimplifiableIfViolation(ASTViolation):
"""
Forbid simplifiable ``if`` conditions.
Reasoning:
These complex constructions can cause frustration among other
developers. They are longer, more verbose, and more complex.
Solution:
Either use ``bool()`` to convert test values to boolean values, or just
leave it as it is in case your test already returns a boolean value.
Use can also use ``not`` keyword to switch boolean values.
Example::
# Correct:
my_bool = bool(some_call())
other_value = 8 if some_call() else None
# Wrong:
my_bool = True if some_call() else False
We only check ``if`` nodes where ``True`` and ``False`` values are used.
We check both ``if`` nodes and ``if`` expressions.
.. versionadded:: 0.7.0
.. versionchanged:: 0.11.0
"""
error_template = 'Found simplifiable `if` condition'
code = 502
previous_codes = {451}
@final
class UselessReturningElseViolation(ASTViolation):
"""
Forbid useless ``else`` cases in returning functions.
We check single ``if`` statements that all contain
``return`` or ``raise`` or ``break`` statements with this rule.
We do not check ``if`` statements with ``elif`` cases.
Reasoning:
Using extra ``else`` creates a situation when
the whole node could and should be dropped
without any changes in logic.
So, we prefer to have less code than more code.
Solution:
Remove useless ``else`` case.
Example::
# Correct:
def some_function():
if some_call():
return 'yeap'
return 'nope'
# Wrong:
def some_function():
if some_call():
raise ValueError('yeap')
else:
raise ValueError('nope')
.. versionadded:: 0.7.0
.. versionchanged:: 0.11.0
"""
error_template = 'Found useless returning `else` statement'
code = 503
previous_codes = {457}
@final
class NegatedConditionsViolation(ASTViolation):
"""
Forbid negated conditions together with ``else`` clause.
Reasoning:
It easier to read and name regular conditions. Not negated ones.
Solution:
Move actions from the negated ``if`` condition to the ``else``
condition.
Example::
# Correct:
if some == 1:
...
else:
...
if not some:
...
if not some:
...
elif other:
...
# Wrong:
if not some:
...
else:
...
.. versionadded:: 0.8.0
.. versionchanged:: 0.11.0
"""
error_template = 'Found negated condition'
code = 504
previous_codes = {463}
@final
class NestedTryViolation(ASTViolation):
"""
Forbid nested ``try`` blocks.
Notice, we check all possible slots for ``try`` block:
1. the ``try`` block itself
2. all ``except`` cases
3. ``else`` case
4. and ``finally`` case
Reasoning:
Nesting ``try`` blocks indicates
that something really bad happens to your logic.
Why does it require two separate exception handlers?
It is a perfect case to refactor your code.
Solution:
Collapse two exception handlers together.
Or create a separate function that will handle this second nested case.
Example::
# Wrong:
try:
try:
...
except SomeException:
...
except SomeOtherException:
...
try:
...
except SomeOtherException:
try:
...
except SomeException:
...
.. versionadded:: 0.8.0
.. versionchanged:: 0.11.0
"""
error_template = 'Found nested `try` block'
code = 505
previous_codes = {464}
@final
class UselessLambdaViolation(ASTViolation):
"""
Forbid useless proxy ``lambda`` expressions.
Reasoning:
Sometimes developers tend to overuse ``lambda`` expressions
and they wrap code that can be passed as is, without extra wrapping.
The code without extra ``lambda`` is easier
to read and is more performant.
Solution:
Remove wrapping ``lambda`` declaration, use just the internal function.
Example::
# Correct:
numbers = map(int, ['1', '2'])
# Wrong:
numbers = map(lambda string: int(string), ['1', '2'])
.. versionadded:: 0.10.0
.. versionchanged:: 0.11.0
"""
error_template = 'Found useless lambda declaration'
code = 506
previous_codes = {467}
@final
class UselessLenCompareViolation(ASTViolation):
"""
Forbid unpythonic zero-length compare.
Note, that we allow to check arbitrary length, like ``len(arr) == 3``.
Reasoning:
Python's structures like dicts, lists, sets, and tuples
all have ``__bool__`` method to checks their length.
So, there's no point in wrapping them into ``len(...)``
and checking that it is bigger that ``0`` or less then ``1``, etc.
Solution:
Remove extra ``len()`` call.
Example::
# Correct:
if some_array or not other_array or len(third_array) == 1:
...
# Wrong:
if len(some_array) > 0 or len(other_array) < 1:
...
.. versionadded:: 0.10.0
.. versionchanged:: 0.11.0
"""
error_template = 'Found useless `len()` compare'
code = 507
previous_codes = {468}
@final
class NotOperatorWithCompareViolation(ASTViolation):
"""
Forbid ``not`` with compare expressions.
Reasoning:
This version of ``not`` operator is unreadable.
Solution:
Refactor the expression without ``not`` operator.
Change the compare signs.
Example::
# Correct:
if x <= 5:
...
# Wrong:
if not x > 5:
...
.. versionadded:: 0.10.0
.. versionchanged:: 0.11.0
"""
error_template = 'Found incorrect `not` with compare usage'
code = 508
previous_codes = {470}
@final
class NestedTernaryViolation(ASTViolation):
"""
Forbid nesting ternary expressions in certain places.
Note, that we restrict to nest ternary expressions inside:
- ``if`` conditions
- boolean and binary operations like ``and`` or ``+``
- unary operators
Reasoning:
Nesting ternary in random places can lead to very hard
debug and testing problems.
Solution:
Refactor the ternary expression to be either a new variable,
or nested ``if`` statement, or a new function.
Example::
# Correct:
some = x if cond() else y
# Wrong:
if x if cond() else y:
...
.. versionadded:: 0.10.0
.. versionchanged:: 0.11.0
"""
error_template = 'Found incorrectly nested ternary'
code = 509
previous_codes = {472}
@final
class WrongInCompareTypeViolation(ASTViolation):
"""
Forbid ``in`` with static containers except ``set`` nodes.
We enforce people to use sets as a static containers.
You can also use variables, calls, methods, etc.
Dynamic values are not checked.
Reasoning:
Using static ``list``, ``tuple``, or ``dict`` elements
to check that some element is inside the container is a bad practice.
Because we need to iterate all over the container to find the element.
Sets are the best suit for this task.
Moreover, it makes your code consistent.
Solution:
Use ``set`` elements or comprehensions to check that something
is contained in a container.
Example::
# Correct:
print(needle in {'one', 'two'})
# Wrong:
print(needle in ['one', 'two'])
.. versionadded:: 0.10.0
.. versionchanged:: 0.11.0
.. versionchanged:: 0.14.0
"""
error_template = 'Found `in` used with a non-set container'
code = 510
previous_codes = {473}
@final
class UnmergedIsinstanceCallsViolation(ASTViolation):
"""
Forbid multiple ``isinstance`` calls on the same variable.
Reasoning:
The best practice is to use ``isinstance`` with tuple
as the second argument, instead of multiple conditions
joined with ``or``.
Solution:
Use tuple of types as the second argument.
Example::
# Correct:
isinstance(some, (int, float))
# Wrong:
isinstance(some, int) or isinstance(some, float)
See also:
https://docs.python.org/3/library/functions.html#isinstance
.. versionadded:: 0.10.0
.. versionchanged:: 0.11.0
"""
error_template = (
'Found separate `isinstance` calls that can be merged for: {0}'
)
code = 511
previous_codes = {474}
@final
class WrongIsinstanceWithTupleViolation(ASTViolation):
"""
Forbid multiple ``isinstance`` calls with single-item tuples.
Reasoning:
There's no need to use tuples with single elements.
You can use single variables or tuples with multiple elements.
Solution:
Use tuples with multiple elements or a single variable.
Example::
# Correct:
isinstance(some, (int, float))
isinstance(some, int)
# Wrong:
isinstance(some, (int, ))
See: https://docs.python.org/3/library/functions.html#isinstance
.. versionadded:: 0.10.0
.. versionchanged:: 0.11.0
"""
error_template = 'Found `isinstance` call with a single element tuple'
code = 512
previous_codes = {475}
@final
class ImplicitElifViolation(TokenizeViolation):
"""
Forbid implicit ``elif`` conditions.
Reasoning:
Nested ``if`` in ``else`` cases are bad
for readability because of the nesting level.
Solution:
Use ``elif`` on the same level.
Example::
# Correct:
if some:
...
elif other:
...
# Wrong:
if some:
...
else:
if other:
...
.. versionadded:: 0.12.0
"""
error_template = 'Found implicit `elif` condition'
code = 513
@final
class ImplicitInConditionViolation(ASTViolation):
"""
Forbid multiple equality comparisons with the same variable.
Reasoning:
Using double+ equality compare with ``or``
or double+ non-equality compare with ``and``
indicates that you have implicit ``in`` or ``not in`` condition.
It is just hidden from you.
Solution:
Refactor compares to use ``in`` or ``not in`` clauses.
Example::
# Correct:
print(some in {'first', 'second'})
print(some not in {'first', 'second'})
# Wrong:
print(some == 'first' or some == 'second')
print(some != 'first' and some != 'second')
.. versionadded:: 0.10.0
.. versionchanged:: 0.12.0
"""
code = 514
error_template = 'Found implicit `in` condition'
previous_codes = {336}
@final
class OpenWithoutContextManagerViolation(ASTViolation):
"""
Forbid ``open()`` without a context manager.
Reasoning:
When you ``open()`` something, you need to close it.
When using a context manager - it is automatically done for you.
When not using it - you might find yourself in a situation
when file is not closed and is not accessible anymore.
Solution:
Refactor ``open()`` call to use ``with``.
Example::
# Correct:
with open(filename) as file_obj:
...
# Wrong:
file_obj = open(filename)
.. versionadded:: 0.12.0
"""
code = 515
error_template = 'Found `open()` used without a context manager'
@final
class TypeCompareViolation(ASTViolation):
"""
Forbid comparing types with ``type()`` function.
Reasoning:
When you compare types with ``type()`` function call
it means that you break polymorphism and disallow child classes
of a node to work here. That's incorrect.
Solution:
Use ``isinstance`` to compare types.
Example::
# Correct:
print(something, type(something))
# Wrong:
if type(something) == int:
...
.. versionadded:: 0.12.0
"""
code = 516
error_template = 'Found `type()` used to compare types'
@final
class PointlessStarredViolation(ASTViolation):
"""
Forbid useless starred expressions.
Reasoning:
Using starred expression with constants is useless.
This piece of code can be rewritten to be flat.
Eg.: ``print(*[1, 2, 3])`` is ``print(1, 2, 3)``.
Solution:
Refactor your code not to use starred expressions
with ``list``, ``dict``, ``tuple``, and ``set`` constants.
Use regular argument passing instead.
Example::
# Correct:
my_list = [1, 2, 3, *other_iterable]
# Wrong:
print(*[1, 2, 3], **{{}})
.. versionadded:: 0.12.0
"""
code = 517
error_template = 'Found pointless starred expression'
@final
class ImplicitEnumerateViolation(ASTViolation):
"""
Forbid implicit ``enumerate()`` calls.
Reasoning:
Using ``range(len(...))`` is not pythonic.
Python uses collection iterators, not index-based loops.
Solution:
Use ``enumerate(...)`` instead of ``range(len(...))``.
Example::
# Correct:
for index, person in enumerate(people):
...
# Wrong:
for index in range(len(people)):
...
See also:
https://docs.python.org/3/library/functions.html#enumerate
.. versionadded:: 0.12.0
"""
code = 518
error_template = 'Found implicit `enumerate()` call'
@final
class ImplicitSumViolation(ASTViolation):
"""
Forbid implicit ``sum()`` calls.
When summing types different from numbers, you might need to provide
the second argument to the ``sum`` function: ``sum([[1], [2], [3]], [])``
You might also use ``str.join`` to join iterable of strings.
Reasoning:
Using ``for`` loops with ``+=`` assign inside indicates
that you iteratively sum things inside your collection.
That's what ``sum()`` builtin function does.
Solution:
Use ``sum(...)`` instead of a loop with ``+=`` operation.
Example::
# Correct:
sum_result = sum(get_elements())
# Wrong:
sum_result = 0
for to_sum in get_elements():
sum_result += to_sum
See also:
https://docs.python.org/3/library/functions.html#sum
https://docs.python.org/3/library/stdtypes.html#str.join
.. versionadded:: 0.12.0
"""
code = 519
error_template = 'Found implicit `sum()` call'
@final
class FalsyConstantCompareViolation(ASTViolation):
"""
Forbid comparing with explicit falsy constants.
We allow to compare with falsy numbers, strings, booleans, ``None``.
We disallow complex constants like tuple, dicts, and lists.
Reasoning:
When comparing ``something`` with explicit falsy constants
what we really mean is ``not something``.
Solution:
Use ``not`` with your variable.
Fix your data types.
Example::
# Correct:
if not my_check:
...
if some_other is None:
...
if some_num == 0:
...
# Wrong:
if my_check == []:
...
.. versionadded:: 0.12.0
"""
code = 520
error_template = 'Found compare with falsy constant'
@final
class WrongIsCompareViolation(ASTViolation):
"""
Forbid comparing values with constants using ``is`` or ``is not``.
However, we allow to compare with ``None`` and booleans.
Reasoning:
``is`` compares might not do what you want them to do.
Firstly, they check for the same object, not equality.
Secondly, they behave unexpectedly even
with the simple values like ``257``.
Solution:
Use ``==`` to compare with constants.
Example::
# Correct:
if my_check == [1, 2, 3]:
...
# Wrong:
if my_check is [1, 2, 3]:
...
See also:
https://stackoverflow.com/a/33130014/4842742
.. versionadded:: 0.12.0
"""
code = 521
error_template = 'Found wrong `is` compare'
@final
class ImplicitPrimitiveViolation(ASTViolation):
"""
Forbid implicit primitives in the form of ``lambda`` functions.
Reasoning:
When you use ``lambda`` that returns a primitive value
and takes no arguments, it means that
you should use a primitive type instead.
Solution:
Replace ``lambda`` with ``int``, ``float``,
``list``, or any other primitive.
Example::
# Correct:
defaultdict(int)
# Wrong:
defaultdict(lambda: 0)
.. versionadded:: 0.13.0
"""
code = 522
error_template = 'Found implicit primitive in a form of `lambda`'
@final
class AlmostSwappedViolation(ASTViolation):
"""
Forbid unpythonic variable swaps.
We check for ``a = b; b = a`` sequences.
Reasoning:
This looks like a failed attempt to swap.
Solution:
Use standard way to swap two variables.
Example::
# Correct:
a, b = b, a
# Wrong:
a = b
b = a
temp = a
a = b
b = temp
.. versionadded:: 0.13.0
"""
error_template = 'Found incorrectly swapped variables'
code = 523
@final
class MisrefactoredAssignmentViolation(ASTViolation):
"""
Forbid misrefactored self assignment.
Reasoning:
Self assignment does not need to have the same operand
on the left hand side and on the right hand side.
Solution:
Refactor you code to use multiple self assignments or fix your code.
Example::
# Correct:
test += 1
test *= 2
# Wrong:
test += test + 1
See
:py:data:`~wemake_python_styleguide.constants.MATH_APPROXIMATE_CONSTANTS`
for full list of math constants that we check for.
.. versionadded:: 0.13.0
"""
error_template = 'Found self assignment with refactored assignment'
code = 524
@final
class InCompareWithSingleItemContainerViolation(ASTViolation):
"""
Forbid comparisons where ``in`` is compared with single item container.
Reasoning:
``in`` comparison with a container which contains only one item looks
like overhead and unneeded complexity.
Solution:
Refactor your code to use ``==`` instead ``in``.
Example::
# Correct:
a == 's'
# Wrong:
a in {'s'}
.. versionadded:: 0.13.0
"""
error_template = 'Found wrong `in` compare with single item container'
code = 525
@final
class ImplicitYieldFromViolation(ASTViolation):
"""
Forbid ``yield`` inside ``for`` loop instead of ``yield from``.
Reasoning:
It is known that ``yield from`` is a semantically identical
to a ``for`` loop with a ``yield`` inside.
But, it is way more readable.
Solution:
Use ``yield from`` some iterable directly
instead iterating over it inside a loop
and ``yield`` it one by one.
Example::
# Correct:
yield from some()
yield from (
value[index:index + chunk_size]
for index in range(0, len(value), chunk_size)
)
# Wrong:
for index in chunk:
yield index
.. versionadded:: 0.13.0
"""
error_template = 'Found implicit `yield from` usage'
code = 526
@final
class NotATupleArgumentViolation(ASTViolation):
"""
Require tuples as arguments for certain functions.
Reasoning:
For some functions, it is better to use tuples instead of another
iterable types (list, sets,...) as arguments.
Solution:
Use tuples as arguments.
Example::
# Correct:
a = frozenset((2,))
# Wrong:
a = frozenset([2])
See
:py:data:`~wemake_python_styleguide.constants.TUPLE_ARGUMENTS_METHODS`
for full list of methods that we check for.
.. versionadded:: 0.13.0
"""
error_template = 'Found not a tuple used as an argument'
code = 527
@final
class ImplicitItemsIteratorViolation(ASTViolation):
"""
Forbid implicit ``.items()`` iterator.
Reasoning:
When iterating over collection it is easy to forget
to use ``.items()`` when you need to access both keys and values.
So, when you access the iterable with the key inside a ``for`` loop,
that's a sign to refactor your code.
Solution:
Use ``.items()`` with direct keys and values when you need them.
Example::
# Correct:
for some_key, some_value in collection.items():
print(some_key, some_value)
# Wrong:
for some_key in collection:
print(some_key, collection[some_key])
.. versionadded:: 0.13.0
"""
error_template = 'Found implicit `.items()` usage'
code = 528
@final
class ImplicitDictGetViolation(ASTViolation):
"""
Forbid implicit ``.get()`` dict method.
Reasoning:
When using ``in`` with a dict key it is hard to keep the code clean.
It is more convenient to use ``.get()`` and check for ``None`` later.
Solution:
Use ``.get()`` with the key you need.
Check for ``None`` in case you need it,
or just act with the default value of the same type.
Example::
# Correct:
value = collection.get(key)
if value is not None:
print(value)
# Wrong:
if key in collection:
print(collection[key])
.. versionadded:: 0.13.0
"""
error_template = 'Found implicit `.get()` dict usage'
code = 529
@final
class ImplicitNegativeIndexViolation(ASTViolation):
"""
Forbid implicit negative indexes.
Reasoning:
There's no need in getting the length of an iterable
and then having a negative offset,
when you can specify negative indexes in the first place.
Solution:
Use negative indexes.
Example::
# Correct:
some_list[-1]
# Wrong:
some_list[len(some_list) - 1]
.. versionadded:: 0.13.0
"""
error_template = 'Found implicit negative index'
code = 530
@final
class SimplifiableReturningIfViolation(ASTViolation):
"""
Forbid if statements that simply return booleans in functions or methods.
Reasoning:
There is no need to test a condition and simply return a boolean
depending on its outcome if there is not going to be any additional
code.
Solution:
Instead of testing the condition and returning a boolean, return the
condition itself. This applies to early returning ifs too.
Example::
# Correct:
def some_function():
return some_condition
# Wrong:
def some_function():
if some_condition:
return True
else:
return False
.. versionadded:: 0.15.0
"""
error_template = 'Found simplifiable returning `if` condition in a function'
code = 531
| 23.225284 | 80 | 0.612247 |
8b66471d95f20132753bef73defd69edb21ae489 | 1,202 | py | Python | ott/core/__init__.py | meyerscetbon/ott | 7f9aede929b8f202cb56d60bc7bf9d731bd94645 | [
"Apache-2.0"
] | null | null | null | ott/core/__init__.py | meyerscetbon/ott | 7f9aede929b8f202cb56d60bc7bf9d731bd94645 | [
"Apache-2.0"
] | null | null | null | ott/core/__init__.py | meyerscetbon/ott | 7f9aede929b8f202cb56d60bc7bf9d731bd94645 | [
"Apache-2.0"
] | null | null | null | # coding=utf-8
# Copyright 2022 Google LLC.
#
# 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.
"""OTT core libraries: the engines behind most computations happening in OTT."""
# pytype: disable=import-error # kwargs-checking
from . import anderson
from . import dataclasses
from . import discrete_barycenter
from . import gromov_wasserstein
from . import implicit_differentiation
from . import momentum
from . import problems
from . import sinkhorn
from . import sinkhorn_lr
from . import neuraldual
from .implicit_differentiation import ImplicitDiff
from .problems import LinearProblem
from .sinkhorn import Sinkhorn
from .sinkhorn_lr import LRSinkhorn
# pytype: enable=import-error # kwargs-checking
| 34.342857 | 80 | 0.785358 |
513255dc3f549b45b87b3d0a9de57ee2557de556 | 2,323 | py | Python | tests/test_ratelimit.py | komuw/xyzabc | 80a3aafc6d098cc7af7e08d8ebdea7d55cef50b0 | [
"MIT"
] | 4 | 2019-07-23T20:40:46.000Z | 2019-08-16T15:30:54.000Z | tests/test_ratelimit.py | komuw/wiji | 80a3aafc6d098cc7af7e08d8ebdea7d55cef50b0 | [
"MIT"
] | 73 | 2019-02-28T10:16:12.000Z | 2019-07-25T00:53:38.000Z | tests/test_ratelimit.py | komuw/xyzabc | 80a3aafc6d098cc7af7e08d8ebdea7d55cef50b0 | [
"MIT"
] | 1 | 2019-08-16T15:31:06.000Z | 2019-08-16T15:31:06.000Z | import time
import asyncio
from unittest import TestCase, mock
import wiji
def AsyncMock(*args, **kwargs):
"""
see: https://blog.miguelgrinberg.com/post/unit-testing-asyncio-code
"""
m = mock.MagicMock(*args, **kwargs)
async def mock_coro(*args, **kwargs):
return m(*args, **kwargs)
mock_coro.mock = m
return mock_coro
class TestRateLimit(TestCase):
"""
run tests as:
python -m unittest discover -v -s .
run one testcase as:
python -m unittest -v tests.test_ratelimit.TestRateLimit.test_something
"""
def setUp(self):
self.logger = wiji.logger.SimpleLogger("myTestLogger")
self.execution_rate = 1.00
self.rateLimiter = wiji.ratelimiter.SimpleRateLimiter(execution_rate=self.execution_rate)
def tearDown(self):
pass
@staticmethod
def _run(coro):
loop = asyncio.get_event_loop()
return loop.run_until_complete(coro)
def test_no_rlimit(self):
with mock.patch("wiji.ratelimiter.asyncio.sleep", new=AsyncMock()) as mock_sleep:
for _ in range(0, int(self.execution_rate)):
self._run(self.rateLimiter.limit())
self.assertFalse(mock_sleep.mock.called)
def test_token_exhaustion_causes_rlimit(self):
with mock.patch("wiji.ratelimiter.asyncio.sleep", new=AsyncMock()) as mock_sleep:
for _ in range(0, int(self.execution_rate) * 2):
self._run(self.rateLimiter.limit())
self.assertTrue(mock_sleep.mock.called)
self.assertEqual(mock_sleep.mock.call_args[0][0], self.rateLimiter.delay_for_tokens)
def test_execution_rate(self):
execution_rate = 3.00
rLimiter = wiji.ratelimiter.SimpleRateLimiter(
log_handler=self.logger, execution_rate=execution_rate
)
msgs_delivered = []
now = time.monotonic()
for _ in range(0, int(execution_rate) * 4):
z = self._run(rLimiter.limit())
msgs_delivered.append(z)
then = time.monotonic()
time_taken_to_deliver = then - now # seconds
total_msgs_delivered = len(msgs_delivered)
effective_message_rate = total_msgs_delivered / time_taken_to_deliver
self.assertAlmostEqual(effective_message_rate, execution_rate, 0)
| 32.71831 | 97 | 0.658631 |
98b1982dd87dc971ddad5b7fbdad15627c83848a | 1,877 | py | Python | gist/cli.py | thisisibrahimd/gist | 846248afdcb4ca6b0990c358f70c600a31659386 | [
"Apache-2.0"
] | null | null | null | gist/cli.py | thisisibrahimd/gist | 846248afdcb4ca6b0990c358f70c600a31659386 | [
"Apache-2.0"
] | null | null | null | gist/cli.py | thisisibrahimd/gist | 846248afdcb4ca6b0990c358f70c600a31659386 | [
"Apache-2.0"
] | null | null | null | import os
import logging
import click
import os
from gist.core import get_gist_score
from gist.repo import CritRepo, EhrRepo
from dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv())
logger = logging.getLogger(__name__)
@click.command()
@click.option('-d', '--debug', is_flag=True, envvar="GIST_DEBUG", help="Show debug output. Automatically pulls from environment")
@click.option('-ehr', '--ehr-conn-str', required=True, envvar="GIST_EHR_CONN_STR", help="EHR db connection string. Automatically pulls from current environment")
@click.option('-crit', '--crit-conn-str', required=True, envvar="GIST_CRIT_CONN_STR", help="CRIT db connection string. Automatically pulls from current environment")
@click.option('-t', '--trial_id', 'trial_ids', required=True, multiple=True, envvar="GIST_TRIAL_IDS", help="Trial ID(s)")
def cli(debug, trial_ids, ehr_conn_str, crit_conn_str):
"""OMOP CDM Based Automatic Clinical Trial Generalizability Assessment Framework."""
logging.basicConfig(level=logging.DEBUG if debug else logging.INFO, format='%(asctime)s - %(levelname)s - %(name)s - %(message)s')
logger.info(f"logging level set to {'debug' if debug else 'info'}")
ehr_repo = EhrRepo(ehr_conn_str)
crit_repo = CritRepo(crit_conn_str)
ehr = ehr_repo.get_ehr()
criteria_by_trial_ids = []
for trial_id in trial_ids:
criteria_by_trial_id = {
'trial_id': trial_id,
'criteria': crit_repo.get_criteria_by_trial_id(trial_id)
}
criteria_by_trial_ids.append(criteria_by_trial_id)
gist_scores = []
for criteria_by_trial_id in criteria_by_trial_ids:
gist_score = get_gist_score(criteria_by_trial_id['trial_id'], criteria_by_trial_id['criteria'], ehr)
gist_scores.append(gist_score)
logger.info(gist_scores)
if __name__ == '__main__':
cli(auto_envvar_prefix='GIST') | 43.651163 | 165 | 0.733617 |
a23996e5dee23e0a712ecd799abbfaf136634840 | 11,152 | py | Python | sk_dsp_comm/coeff2header.py | toddrme2178/scikit-dsp-comm | e08427dfcf75d8389e921ab4d01ea3d2c7173a52 | [
"BSD-2-Clause"
] | null | null | null | sk_dsp_comm/coeff2header.py | toddrme2178/scikit-dsp-comm | e08427dfcf75d8389e921ab4d01ea3d2c7173a52 | [
"BSD-2-Clause"
] | null | null | null | sk_dsp_comm/coeff2header.py | toddrme2178/scikit-dsp-comm | e08427dfcf75d8389e921ab4d01ea3d2c7173a52 | [
"BSD-2-Clause"
] | null | null | null | """
Digital Filter Coefficient Conversion to C Header Files
Copyright (c) March 2017, Mark Wickert
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those
of the authors and should not be interpreted as representing official policies,
either expressed or implied, of the FreeBSD Project.
"""
import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt
from matplotlib import pylab
from numpy import int16, rint, loadtxt
import os
def FIR_header(fname_out, h):
"""
Write FIR Filter Header Files
Mark Wickert February 2015
"""
M = len(h)
N = 3 # Coefficients per line
f = open(fname_out, 'wt')
f.write('//define a FIR coefficient Array\n\n')
f.write('#include <stdint.h>\n\n')
f.write('#ifndef M_FIR\n')
f.write('#define M_FIR %d\n' % M)
f.write('#endif\n')
f.write('/************************************************************************/\n');
f.write('/* FIR Filter Coefficients */\n');
f.write('float32_t h_FIR[M_FIR] = {')
kk = 0;
for k in range(M):
# k_mod = k % M
if (kk < N - 1) and (k < M - 1):
f.write('%15.12f,' % h[k])
kk += 1
elif (kk == N - 1) & (k < M - 1):
f.write('%15.12f,\n' % h[k])
if k < M:
f.write(' ')
kk = 0
else:
f.write('%15.12f' % h[k])
f.write('};\n')
f.write('/************************************************************************/\n')
f.close()
def FIR_fix_header(fname_out, h):
"""
Write FIR Fixed-Point Filter Header Files
Mark Wickert February 2015
"""
M = len(h)
hq = int16(rint(h * 2 ** 15))
N = 8 # Coefficients per line
f = open(fname_out, 'wt')
f.write('//define a FIR coefficient Array\n\n')
f.write('#include <stdint.h>\n\n')
f.write('#ifndef M_FIR\n')
f.write('#define M_FIR %d\n' % M)
f.write('#endif\n')
f.write('/************************************************************************/\n');
f.write('/* FIR Filter Coefficients */\n');
f.write('int16_t h_FIR[M_FIR] = {')
kk = 0;
for k in range(M):
# k_mod = k % M
if (kk < N - 1) and (k < M - 1):
f.write('%5d,' % hq[k])
kk += 1
elif (kk == N - 1) & (k < M - 1):
f.write('%5d,\n' % hq[k])
if k < M:
f.write(' ')
kk = 0
else:
f.write('%5d' % hq[k])
f.write('};\n')
f.write('/************************************************************************/\n')
f.close()
def IIR_sos_header(fname_out, SOS_mat):
"""
Write IIR SOS Header Files
File format is compatible with CMSIS-DSP IIR
Directform II Filter Functions
Mark Wickert March 2015-October 2016
"""
Ns, Mcol = SOS_mat.shape
f = open(fname_out, 'wt')
f.write('//define a IIR SOS CMSIS-DSP coefficient array\n\n')
f.write('#include <stdint.h>\n\n')
f.write('#ifndef STAGES\n')
f.write('#define STAGES %d\n' % Ns)
f.write('#endif\n')
f.write('/*********************************************************/\n');
f.write('/* IIR SOS Filter Coefficients */\n');
f.write('float32_t ba_coeff[%d] = { //b0,b1,b2,a1,a2,... by stage\n' % (5 * Ns))
for k in range(Ns):
if (k < Ns - 1):
f.write(' %+-13e, %+-13e, %+-13e,\n' % \
(SOS_mat[k, 0], SOS_mat[k, 1], SOS_mat[k, 2]))
f.write(' %+-13e, %+-13e,\n' % \
(-SOS_mat[k, 4], -SOS_mat[k, 5]))
else:
f.write(' %+-13e, %+-13e, %+-13e,\n' % \
(SOS_mat[k, 0], SOS_mat[k, 1], SOS_mat[k, 2]))
f.write(' %+-13e, %+-13e\n' % \
(-SOS_mat[k, 4], -SOS_mat[k, 5]))
# for k in range(Ns):
# if (k < Ns-1):
# f.write(' %15.12f, %15.12f, %15.12f,\n' % \
# (SOS_mat[k,0],SOS_mat[k,1],SOS_mat[k,2]))
# f.write(' %15.12f, %15.12f,\n' % \
# (-SOS_mat[k,4],-SOS_mat[k,5]))
# else:
# f.write(' %15.12f, %15.12f, %15.12f,\n' % \
# (SOS_mat[k,0],SOS_mat[k,1],SOS_mat[k,2]))
# f.write(' %15.12f, %15.12f\n' % \
# (-SOS_mat[k,4],-SOS_mat[k,5]))
f.write('};\n')
f.write('/*********************************************************/\n')
f.close()
def freqz_resp_list(b, a=np.array([1]), mode='dB', fs=1.0, Npts=1024, fsize=(6, 4)):
"""
A method for displaying digital filter frequency response magnitude,
phase, and group delay. A plot is produced using matplotlib
freq_resp(self,mode = 'dB',Npts = 1024)
A method for displaying the filter frequency response magnitude,
phase, and group delay. A plot is produced using matplotlib
freqz_resp(b,a=[1],mode = 'dB',Npts = 1024,fsize=(6,4))
Parameters
----------
b : ndarray of numerator coefficients
a : ndarray of denominator coefficents
mode : display mode: 'dB' magnitude, 'phase' in radians, or
'groupdelay_s' in samples and 'groupdelay_t' in sec,
all versus frequency in Hz
Npts : number of points to plot; default is 1024
fsize : figure size; defult is (6,4) inches
Mark Wickert, January 2015
"""
if type(b) == list:
# We have a list of filters
N_filt = len(b)
f = np.arange(0, Npts) / (2.0 * Npts)
for n in range(N_filt):
w, H = signal.freqz(b[n], a[n], 2 * np.pi * f)
if n == 0:
plt.figure(figsize=fsize)
if mode.lower() == 'db':
plt.plot(f * fs, 20 * np.log10(np.abs(H)))
if n == N_filt - 1:
plt.xlabel('Frequency (Hz)')
plt.ylabel('Gain (dB)')
plt.title('Frequency Response - Magnitude')
elif mode.lower() == 'phase':
plt.plot(f * fs, np.angle(H))
if n == N_filt - 1:
plt.xlabel('Frequency (Hz)')
plt.ylabel('Phase (rad)')
plt.title('Frequency Response - Phase')
elif (mode.lower() == 'groupdelay_s') or (mode.lower() == 'groupdelay_t'):
"""
Notes
-----
Since this calculation involves finding the derivative of the
phase response, care must be taken at phase wrapping points
and when the phase jumps by +/-pi, which occurs when the
amplitude response changes sign. Since the amplitude response
is zero when the sign changes, the jumps do not alter the group
delay results.
"""
theta = np.unwrap(np.angle(H))
# Since theta for an FIR filter is likely to have many pi phase
# jumps too, we unwrap a second time 2*theta and divide by 2
theta2 = np.unwrap(2 * theta) / 2.
theta_dif = np.diff(theta2)
f_diff = np.diff(f)
Tg = -np.diff(theta2) / np.diff(w)
# For gain almost zero set groupdelay = 0
idx = pylab.find(20 * np.log10(H[:-1]) < -400)
Tg[idx] = np.zeros(len(idx))
max_Tg = np.max(Tg)
# print(max_Tg)
if mode.lower() == 'groupdelay_t':
max_Tg /= fs
plt.plot(f[:-1] * fs, Tg / fs)
plt.ylim([0, 1.2 * max_Tg])
else:
plt.plot(f[:-1] * fs, Tg)
plt.ylim([0, 1.2 * max_Tg])
if n == N_filt - 1:
plt.xlabel('Frequency (Hz)')
if mode.lower() == 'groupdelay_t':
plt.ylabel('Group Delay (s)')
else:
plt.ylabel('Group Delay (samples)')
plt.title('Frequency Response - Group Delay')
else:
s1 = 'Error, mode must be "dB", "phase, '
s2 = '"groupdelay_s", or "groupdelay_t"'
print(s1 + s2)
def CA_code_header(fname_out, Nca):
"""
Write 1023 bit CA (Gold) Code Header Files
Mark Wickert February 2015
"""
dir_path = os.path.dirname(os.path.realpath(__file__))
ca = loadtxt(dir_path + '/ca1thru37.txt', dtype=int16, usecols=(Nca - 1,), unpack=True)
M = 1023 # code period
N = 23 # code bits per line
Sca = 'ca' + str(Nca)
f = open(fname_out, 'wt')
f.write('//define a CA code\n\n')
f.write('#include <stdint.h>\n\n')
f.write('#ifndef N_CA\n')
f.write('#define N_CA %d\n' % M)
f.write('#endif\n')
f.write('/*******************************************************************/\n');
f.write('/* 1023 Bit CA Gold Code %2d */\n' \
% Nca);
f.write('int8_t ca%d[N_CA] = {' % Nca)
kk = 0;
for k in range(M):
# k_mod = k % M
if (kk < N - 1) and (k < M - 1):
f.write('%d,' % ca[k])
kk += 1
elif (kk == N - 1) & (k < M - 1):
f.write('%d,\n' % ca[k])
if k < M:
if Nca < 10:
f.write(' ')
else:
f.write(' ')
kk = 0
else:
f.write('%d' % ca[k])
f.write('};\n')
f.write('/*******************************************************************/\n')
f.close()
| 38.857143 | 93 | 0.483411 |
fce784836dbbb9d0f9dd9db1c3315c65c4afaabf | 65,342 | py | Python | ceilometer/tests/network/test_notifications.py | shahbazn/ceilometer | 6308a46f14b21fb39c0e728c150ab4efde5b532a | [
"Apache-2.0"
] | null | null | null | ceilometer/tests/network/test_notifications.py | shahbazn/ceilometer | 6308a46f14b21fb39c0e728c150ab4efde5b532a | [
"Apache-2.0"
] | null | null | null | ceilometer/tests/network/test_notifications.py | shahbazn/ceilometer | 6308a46f14b21fb39c0e728c150ab4efde5b532a | [
"Apache-2.0"
] | null | null | null | #
# Copyright 2012 New Dream Network, LLC (DreamHost)
#
# 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.
"""Tests for ceilometer.network.notifications
"""
import mock
from ceilometer.network import notifications
from ceilometer.tests import base as test
NOTIFICATION_NETWORK_CREATE = {
u'_context_roles': [u'anotherrole',
u'Member'],
u'_context_read_deleted': u'no',
u'event_type': u'network.create.end',
u'timestamp': u'2012-09-27 14:11:27.086575',
u'_context_tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'payload': {u'network':
{u'status': u'ACTIVE',
u'subnets': [],
u'name': u'abcedf',
u'router:external': False,
u'tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'admin_state_up': True,
u'shared': False,
u'id': u'7fd4eb2f-a38e-4c25-8490-71ca8800c9be'}},
u'priority': u'INFO',
u'_context_is_admin': False,
u'_context_timestamp': u'2012-09-27 14:11:26.924779',
u'_context_user_id': u'b44b7ce67fc84414a5c1660a92a1b862',
u'publisher_id': u'network.ubuntu-VirtualBox',
u'message_id': u'9e839576-cc47-4c60-a7d8-5743681213b1'}
NOTIFICATION_BULK_NETWORK_CREATE = {
'_context_roles': [u'_member_',
u'heat_stack_owner',
u'admin'],
u'_context_request_id': u'req-a2dfdefd-b773-4400-9d52-5e146e119950',
u'_context_read_deleted': u'no',
u'event_type': u'network.create.end',
u'_context_user_name': u'admin',
u'_context_project_name': u'admin',
u'timestamp': u'2014-05-1510: 24: 56.335612',
u'_context_tenant_id': u'980ec4870033453ead65c0470a78b8a8',
u'_context_tenant_name': u'admin',
u'_context_tenant': u'980ec4870033453ead65c0470a78b8a8',
u'message_id': u'914eb601-9390-4a72-8629-f013a4c84467',
u'priority': 'info',
u'_context_is_admin': True,
u'_context_project_id': u'980ec4870033453ead65c0470a78b8a8',
u'_context_timestamp': u'2014-05-1510: 24: 56.285975',
u'_context_user': u'7520940056d54cceb25cbce888300bea',
u'_context_user_id': u'7520940056d54cceb25cbce888300bea',
u'publisher_id': u'network.devstack',
u'payload': {
u'networks': [{u'status': u'ACTIVE',
u'subnets': [],
u'name': u'test2',
u'provider: physical_network': None,
u'admin_state_up': True,
u'tenant_id': u'980ec4870033453ead65c0470a78b8a8',
u'provider: network_type': u'local',
u'shared': False,
u'id': u'7cbc7a66-bbd0-41fc-a186-81c3da5c9843',
u'provider: segmentation_id': None},
{u'status': u'ACTIVE',
u'subnets': [],
u'name': u'test3',
u'provider: physical_network': None,
u'admin_state_up': True,
u'tenant_id': u'980ec4870033453ead65c0470a78b8a8',
u'provider: network_type': u'local',
u'shared': False,
u'id': u'5a7cb86f-1638-4cc1-8dcc-8bbbc8c7510d',
u'provider: segmentation_id': None}]
}
}
NOTIFICATION_SUBNET_CREATE = {
u'_context_roles': [u'anotherrole',
u'Member'],
u'_context_read_deleted': u'no',
u'event_type': u'subnet.create.end',
u'timestamp': u'2012-09-27 14:11:27.426620',
u'_context_tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'payload': {
u'subnet': {
u'name': u'mysubnet',
u'enable_dhcp': True,
u'network_id': u'7fd4eb2f-a38e-4c25-8490-71ca8800c9be',
u'tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'dns_nameservers': [],
u'allocation_pools': [{u'start': u'192.168.42.2',
u'end': u'192.168.42.254'}],
u'host_routes': [],
u'ip_version': 4,
u'gateway_ip': u'192.168.42.1',
u'cidr': u'192.168.42.0/24',
u'id': u'1a3a170d-d7ce-4cc9-b1db-621da15a25f5'}},
u'priority': u'INFO',
u'_context_is_admin': False,
u'_context_timestamp': u'2012-09-27 14:11:27.214490',
u'_context_user_id': u'b44b7ce67fc84414a5c1660a92a1b862',
u'publisher_id': u'network.ubuntu-VirtualBox',
u'message_id': u'd86dfc66-d3c3-4aea-b06d-bf37253e6116'}
NOTIFICATION_BULK_SUBNET_CREATE = {
'_context_roles': [u'_member_',
u'heat_stack_owner',
u'admin'],
u'_context_request_id': u'req-b77e278a-0cce-4987-9f82-15957b234768',
u'_context_read_deleted': u'no',
u'event_type': u'subnet.create.end',
u'_context_user_name': u'admin',
u'_context_project_name': u'admin',
u'timestamp': u'2014-05-1510: 47: 08.133888',
u'_context_tenant_id': u'980ec4870033453ead65c0470a78b8a8',
u'_context_tenant_name': u'admin',
u'_context_tenant': u'980ec4870033453ead65c0470a78b8a8',
u'message_id': u'c7e6f9fd-ead2-415f-8493-b95bedf72e43',
u'priority': u'info',
u'_context_is_admin': True,
u'_context_project_id': u'980ec4870033453ead65c0470a78b8a8',
u'_context_timestamp': u'2014-05-1510: 47: 07.970043',
u'_context_user': u'7520940056d54cceb25cbce888300bea',
u'_context_user_id': u'7520940056d54cceb25cbce888300bea',
u'publisher_id': u'network.devstack',
u'payload': {
u'subnets': [{u'name': u'',
u'enable_dhcp': True,
u'network_id': u'3ddfe60b-34b4-4e9d-9440-43c904b1c58e',
u'tenant_id': u'980ec4870033453ead65c0470a78b8a8',
u'dns_nameservers': [],
u'ipv6_ra_mode': None,
u'allocation_pools': [{u'start': u'10.0.4.2',
u'end': u'10.0.4.254'}],
u'host_routes': [],
u'ipv6_address_mode': None,
u'ip_version': 4,
u'gateway_ip': u'10.0.4.1',
u'cidr': u'10.0.4.0/24',
u'id': u'14020d7b-6dd7-4349-bb8e-8f954c919022'},
{u'name': u'',
u'enable_dhcp': True,
u'network_id': u'3ddfe60b-34b4-4e9d-9440-43c904b1c58e',
u'tenant_id': u'980ec4870033453ead65c0470a78b8a8',
u'dns_nameservers': [],
u'ipv6_ra_mode': None,
u'allocation_pools': [{u'start': u'10.0.5.2',
u'end': u'10.0.5.254'}],
u'host_routes': [],
u'ipv6_address_mode': None,
u'ip_version': 4,
u'gateway_ip': u'10.0.5.1',
u'cidr': u'10.0.5.0/24',
u'id': u'a080991b-a32a-4bf7-a558-96c4b77d075c'}]
}
}
NOTIFICATION_PORT_CREATE = {
u'_context_roles': [u'anotherrole',
u'Member'],
u'_context_read_deleted': u'no',
u'event_type': u'port.create.end',
u'timestamp': u'2012-09-27 14:28:31.536370',
u'_context_tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'payload': {
u'port': {
u'status': u'ACTIVE',
u'name': u'',
u'admin_state_up': True,
u'network_id': u'7fd4eb2f-a38e-4c25-8490-71ca8800c9be',
u'tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'device_owner': u'',
u'mac_address': u'fa:16:3e:75:0c:49',
u'fixed_ips': [{
u'subnet_id': u'1a3a170d-d7ce-4cc9-b1db-621da15a25f5',
u'ip_address': u'192.168.42.3'}],
u'id': u'9cdfeb92-9391-4da7-95a1-ca214831cfdb',
u'device_id': u''}},
u'priority': u'INFO',
u'_context_is_admin': False,
u'_context_timestamp': u'2012-09-27 14:28:31.438919',
u'_context_user_id': u'b44b7ce67fc84414a5c1660a92a1b862',
u'publisher_id': u'network.ubuntu-VirtualBox',
u'message_id': u'7135b8ab-e13c-4ac8-bc31-75e7f756622a'}
NOTIFICATION_BULK_PORT_CREATE = {
u'_context_roles': [u'_member_',
u'SwiftOperator'],
u'_context_request_id': u'req-678be9ad-c399-475a-b3e8-8da0c06375aa',
u'_context_read_deleted': u'no',
u'event_type': u'port.create.end',
u'_context_project_name': u'demo',
u'timestamp': u'2014-05-0909: 19: 58.317548',
u'_context_tenant_id': u'133087d90fc149528b501dd8b75ea965',
u'_context_timestamp': u'2014-05-0909: 19: 58.160011',
u'_context_tenant': u'133087d90fc149528b501dd8b75ea965',
u'payload': {
u'ports': [{u'status': u'DOWN',
u'name': u'port--1501135095',
u'allowed_address_pairs': [],
u'admin_state_up': True,
u'network_id': u'acf63fdc-b43b-475d-8cca-9429b843d5e8',
u'tenant_id': u'133087d90fc149528b501dd8b75ea965',
u'binding: vnic_type': u'normal',
u'device_owner': u'',
u'mac_address': u'fa: 16: 3e: 37: 10: 39',
u'fixed_ips': [],
u'id': u'296c2c9f-14e9-48da-979d-78b213454c59',
u'security_groups': [
u'a06f7c9d-9e5a-46b0-9f6c-ce812aa2e5ff'],
u'device_id': u''},
{u'status': u'DOWN',
u'name': u'',
u'allowed_address_pairs': [],
u'admin_state_up': False,
u'network_id': u'0a8eea59-0146-425c-b470-e9ddfa99ec61',
u'tenant_id': u'133087d90fc149528b501dd8b75ea965',
u'binding: vnic_type': u'normal',
u'device_owner': u'',
u'mac_address': u'fa: 16: 3e: 8e: 6e: 53',
u'fixed_ips': [],
u'id': u'd8bb667f-5cd3-4eca-a984-268e25b1b7a5',
u'security_groups': [
u'a06f7c9d-9e5a-46b0-9f6c-ce812aa2e5ff'],
u'device_id': u''}]
},
u'_unique_id': u'60b1650f17fc4fa59492f447321fb26c',
u'_context_is_admin': False,
u'_context_project_id': u'133087d90fc149528b501dd8b75ea965',
u'_context_tenant_name': u'demo',
u'_context_user': u'b1eb48f9c54741f4adc1b4ea512d400c',
u'_context_user_name': u'demo',
u'publisher_id': u'network.os-ci-test12',
u'message_id': u'04aa45e1-3c30-4c69-8638-e7ff8621e9bc',
u'_context_user_id': u'b1eb48f9c54741f4adc1b4ea512d400c',
u'priority': u'INFO'
}
NOTIFICATION_PORT_UPDATE = {
u'_context_roles': [u'anotherrole',
u'Member'],
u'_context_read_deleted': u'no',
u'event_type': u'port.update.end',
u'timestamp': u'2012-09-27 14:35:09.514052',
u'_context_tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'payload': {
u'port': {
u'status': u'ACTIVE',
u'name': u'bonjour',
u'admin_state_up': True,
u'network_id': u'7fd4eb2f-a38e-4c25-8490-71ca8800c9be',
u'tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'device_owner': u'',
u'mac_address': u'fa:16:3e:75:0c:49',
u'fixed_ips': [{
u'subnet_id': u'1a3a170d-d7ce-4cc9-b1db-621da15a25f5',
u'ip_address': u'192.168.42.3'}],
u'id': u'9cdfeb92-9391-4da7-95a1-ca214831cfdb',
u'device_id': u''}},
u'priority': u'INFO',
u'_context_is_admin': False,
u'_context_timestamp': u'2012-09-27 14:35:09.447682',
u'_context_user_id': u'b44b7ce67fc84414a5c1660a92a1b862',
u'publisher_id': u'network.ubuntu-VirtualBox',
u'message_id': u'07b0a3a1-c0b5-40ab-a09c-28dee6bf48f4'}
NOTIFICATION_NETWORK_EXISTS = {
u'_context_roles': [u'anotherrole',
u'Member'],
u'_context_read_deleted': u'no',
u'event_type': u'network.exists',
u'timestamp': u'2012-09-27 14:11:27.086575',
u'_context_tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'payload': {u'network':
{u'status': u'ACTIVE',
u'subnets': [],
u'name': u'abcedf',
u'router:external': False,
u'tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'admin_state_up': True,
u'shared': False,
u'id': u'7fd4eb2f-a38e-4c25-8490-71ca8800c9be'}},
u'priority': u'INFO',
u'_context_is_admin': False,
u'_context_timestamp': u'2012-09-27 14:11:26.924779',
u'_context_user_id': u'b44b7ce67fc84414a5c1660a92a1b862',
u'publisher_id': u'network.ubuntu-VirtualBox',
u'message_id': u'9e839576-cc47-4c60-a7d8-5743681213b1'}
NOTIFICATION_ROUTER_EXISTS = {
u'_context_roles': [u'anotherrole',
u'Member'],
u'_context_read_deleted': u'no',
u'event_type': u'router.exists',
u'timestamp': u'2012-09-27 14:11:27.086575',
u'_context_tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'payload': {u'router':
{'status': u'ACTIVE',
'external_gateway_info':
{'network_id': u'89d55642-4dec-43a4-a617-6cec051393b5'},
'name': u'router1',
'admin_state_up': True,
'tenant_id': u'bb04a2b769c94917b57ba49df7783cfd',
'id': u'ab8bb3ed-df23-4ca0-8f03-b887abcd5c23'}},
u'priority': u'INFO',
u'_context_is_admin': False,
u'_context_timestamp': u'2012-09-27 14:11:26.924779',
u'_context_user_id': u'b44b7ce67fc84414a5c1660a92a1b862',
u'publisher_id': u'network.ubuntu-VirtualBox',
u'message_id': u'9e839576-cc47-4c60-a7d8-5743681213b1'}
NOTIFICATION_FLOATINGIP_EXISTS = {
u'_context_roles': [u'anotherrole',
u'Member'],
u'_context_read_deleted': u'no',
u'event_type': u'floatingip.exists',
u'timestamp': u'2012-09-27 14:11:27.086575',
u'_context_tenant_id': u'82ed0c40ebe64d0bb3310027039c8ed2',
u'payload': {u'floatingip':
{'router_id': None,
'tenant_id': u'6e5f9df9b3a249ab834f25fe1b1b81fd',
'floating_network_id':
u'001400f7-1710-4245-98c3-39ba131cc39a',
'fixed_ip_address': None,
'floating_ip_address': u'172.24.4.227',
'port_id': None,
'id': u'2b7cc28c-6f78-4735-9246-257168405de6'}},
u'priority': u'INFO',
u'_context_is_admin': False,
u'_context_timestamp': u'2012-09-27 14:11:26.924779',
u'_context_user_id': u'b44b7ce67fc84414a5c1660a92a1b862',
u'publisher_id': u'network.ubuntu-VirtualBox',
u'message_id': u'9e839576-cc47-4c60-a7d8-5743681213b1'}
NOTIFICATION_FLOATINGIP_UPDATE_START = {
'_context_roles': [u'_member_',
u'admin',
u'heat_stack_owner'],
'_context_request_id': u'req-bd5ed336-242f-4705-836e-8e8f3d0d1ced',
'_context_read_deleted': u'no',
'event_type': u'floatingip.update.start',
'_context_user_name': u'admin',
'_context_project_name': u'admin',
'timestamp': u'2014-05-3107: 19: 43.463101',
'_context_tenant_id': u'9fc714821a3747c8bc4e3a9bfbe82732',
'_context_tenant_name': u'admin',
'_context_tenant': u'9fc714821a3747c8bc4e3a9bfbe82732',
'message_id': u'0ab6d71f-ba0a-4501-86fe-6cc20521ef5a',
'priority': 'info',
'_context_is_admin': True,
'_context_project_id': u'9fc714821a3747c8bc4e3a9bfbe82732',
'_context_timestamp': u'2014-05-3107: 19: 43.460767',
'_context_user': u'6ca7b13b33e4425cae0b85e2cf93d9a1',
'_context_user_id': u'6ca7b13b33e4425cae0b85e2cf93d9a1',
'publisher_id': u'network.devstack',
'payload': {
u'id': u'64262b2a-8f5d-4ade-9405-0cbdd03c1555',
u'floatingip': {
u'fixed_ip_address': u'172.24.4.227',
u'port_id': u'8ab815c8-03cc-4b45-a673-79bdd0c258f2'
}
}
}
NOTIFICATION_L3_METER = {
u'_context_roles': [u'admin'],
u'_context_read_deleted': u'no',
u'event_type': u'l3.meter',
u'timestamp': u'2013-08-22 13:14:06.880304',
u'_context_tenant_id': None,
u'payload': {u'first_update': 1377176476,
u'bytes': 0,
u'label_id': u'383244a7-e99b-433a-b4a1-d37cf5b17d15',
u'last_update': 1377177246,
u'host': u'precise64',
u'tenant_id': u'admin',
u'time': 30,
u'pkts': 0},
u'priority': u'INFO',
u'_context_is_admin': True,
u'_context_timestamp': u'2013-08-22 13:01:06.614635',
u'_context_user_id': None,
u'publisher_id': u'metering.precise64',
u'message_id': u'd7aee6e8-c7eb-4d47-9338-f60920d708e4',
u'_unique_id': u'd5a3bdacdcc24644b84e67a4c10e886a',
u'_context_project_id': None}
NOTIFICATION_POOL_CREATE = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-10715057-7590-4529-8020-b994295ee6f4",
"event_type": "pool.create.end",
"timestamp": "2014-09-15 17:20:50.687649",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "ce255443233748ce9cc71b480974df28",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"pool": {
"status": "ACTIVE",
"lb_method": "ROUND_ROBIN",
"protocol": "HTTP", "description": "",
"health_monitors": [],
"members": [],
"status_description": None,
"id": "6d726518-f3aa-4dd4-ac34-e156a35c0aff",
"vip_id": None,
"name": "my_pool",
"admin_state_up": True,
"subnet_id": "afaf251b-2ec3-42ac-9fa9-82a4195724fa",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"health_monitors_status": [],
"provider": "haproxy"}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:20:49.600299",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "0a5ed7a6-e516-4aed-9968-4ee9f1b65cc2"}
NOTIFICATION_VIP_CREATE = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "vip.create.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"vip": {
"status": "ACTIVE",
"protocol": "HTTP",
"description": "",
"address": "10.0.0.2",
"protocol_port": 80,
"port_id": "2b5dd476-11da-4d46-9f1e-7a75436062f6",
"id": "87a5ce35-f278-47f3-8990-7f695f52f9bf",
"status_description": None,
"name": "my_vip",
"admin_state_up": True,
"subnet_id": "afaf251b-2ec3-42ac-9fa9-82a4195724fa",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"connection_limit": -1,
"pool_id": "6d726518-f3aa-4dd4-ac34-e156a35c0aff",
"session_persistence": {"type": "SOURCE_IP"}}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "3895ad11-98a3-4031-92af-f76e96736661"}
NOTIFICATION_HEALTH_MONITORS_CREATE = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "health_monitor.create.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"health_monitor": {
"admin_state_up": True,
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"delay": 10,
"max_retries": 10,
"timeout": 10,
"pools": [],
"type": "PING",
"id": "6dea2d01-c3af-4696-9192-6c938f391f01"}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "65067e3f-830d-4fbb-87e2-f0e51fda83d2"}
NOTIFICATION_MEMBERS_CREATE = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "member.create.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"member": {"admin_state_up": True,
"status": "ACTIVE",
"status_description": None,
"weight": 1,
"address": "10.0.0.3",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"protocol_port": 80,
"id": "5e32f960-63ae-4a93-bfa2-339aa83d82ce",
"pool_id": "6b73b9f8-d807-4553-87df-eb34cdd08070"}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "65067e3f-830d-4fbb-87e2-f0e51fda83d2"}
NOTIFICATION_FIREWALL_CREATE = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "firewall.create.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"firewall": {
"status": "ACTIVE",
"name": "my_firewall",
"admin_state_up": True,
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"firewall_policy_id": "c46a1c15-0496-41c9-beff-9a309a25653e",
"id": "e2d1155f-6bc4-4292-9cfa-ea91af4b38c8",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "fdffeca1-2b5a-4dc9-b8ae-87c482a83e0d"}
NOTIFICATION_FIREWALL_RULE_CREATE = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "firewall_rule.create.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"firewall_rule": {
"protocol": "tcp",
"description": "",
"source_port": 80,
"source_ip_address": '192.168.255.10',
"destination_ip_address": '10.10.10.1',
"firewall_policy_id": '',
"position": None,
"destination_port": 80,
"id": "53b7c0d3-cb87-4069-9e29-1e866583cc8c",
"name": "rule_01",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"enabled": True,
"action": "allow",
"ip_version": 4,
"shared": False}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "fdffeca1-2b5a-4dc9-b8ae-87c482a83e0d"}
NOTIFICATION_FIREWALL_POLICY_CREATE = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "firewall_policy.create.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"firewall_policy": {"name": "my_policy",
"firewall_rules": [],
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"audited": False,
"shared": False,
"id": "c46a1c15-0496-41c9-beff-9a309a25653e",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "fdffeca1-2b5a-4dc9-b8ae-87c482a83e0d"}
NOTIFICATION_VPNSERVICE_CREATE = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "vpnservice.create.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"vpnservice": {"router_id": "75871c53-e722-4b21-93ed-20cb40b6b672",
"status": "ACTIVE",
"name": "my_vpn",
"admin_state_up": True,
"subnet_id": "afaf251b-2ec3-42ac-9fa9-82a4195724fa",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"id": "270c40cc-28d5-4a7e-83da-cc33088ee5d6",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "65067e3f-830d-4fbb-87e2-f0e51fda83d2"}
NOTIFICATION_IPSEC_POLICY_CREATE = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "ipsecpolicy.create.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"ipsecpolicy": {"encapsulation_mode": "tunnel",
"encryption_algorithm": "aes-128",
"pfs": "group5",
"lifetime": {
"units": "seconds",
"value": 3600},
"name": "my_ipsec_polixy",
"transform_protocol": "esp",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"id": "998d910d-4506-47c9-a160-47ec51ff53fc",
"auth_algorithm": "sha1",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "4c0e6ecb-2e40-4975-aee2-d88045c747bf"}
NOTIFICATION_IKE_POLICY_CREATE = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "ikepolicy.create.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"ikepolicy": {"encryption_algorithm": "aes-128",
"pfs": "group5",
"name": "my_ike_policy",
"phase1_negotiation_mode": "main",
"lifetime": {"units": "seconds",
"value": 3600},
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"ike_version": "v1",
"id": "11cef94e-3f6a-4b65-8058-7deb1838633a",
"auth_algorithm": "sha1",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "4c0e6ecb-2e40-4975-aee2-d88045c747bf"}
NOTIFICATION_IPSEC_SITE_CONN_CREATE = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "ipsec_site_connection.create.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"ipsec_site_connection": {
"status": "ACTIVE",
"psk": "test",
"initiator": "bi-directional",
"name": "my_ipsec_connection",
"admin_state_up": True,
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"ipsecpolicy_id": "998d910d-4506-47c9-a160-47ec51ff53fc",
"auth_mode": "psk", "peer_cidrs": ["192.168.255.0/24"],
"mtu": 1500,
"ikepolicy_id": "11cef94e-3f6a-4b65-8058-7deb1838633a",
"dpd": {"action": "hold",
"interval": 30,
"timeout": 120},
"route_mode": "static",
"vpnservice_id": "270c40cc-28d5-4a7e-83da-cc33088ee5d6",
"peer_address": "10.0.0.1",
"peer_id": "10.0.0.254",
"id": "06f3c1ec-2e01-4ad6-9c98-4252751fc60a",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "4c0e6ecb-2e40-4975-aee2-d88045c747bf"}
NOTIFICATION_POOL_UPDATE = {
"_context_roles": ["admin"],
"_context_request_id": "req-10715057-7590-4529-8020-b994295ee6f4",
"event_type": "pool.update.end",
"timestamp": "2014-09-15 17:20:50.687649",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "ce255443233748ce9cc71b480974df28",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"pool": {
"status": "ACTIVE",
"lb_method": "ROUND_ROBIN",
"protocol": "HTTP", "description": "",
"health_monitors": [],
"members": [],
"status_description": None,
"id": "6d726518-f3aa-4dd4-ac34-e156a35c0aff",
"vip_id": None,
"name": "my_pool",
"admin_state_up": True,
"subnet_id": "afaf251b-2ec3-42ac-9fa9-82a4195724fa",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"health_monitors_status": [],
"provider": "haproxy"}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:20:49.600299",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "0a5ed7a6-e516-4aed-9968-4ee9f1b65cc2"}
NOTIFICATION_VIP_UPDATE = {
"_context_roles": ["admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "vip.update.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"vip": {
"status": "ACTIVE",
"protocol": "HTTP",
"description": "",
"address": "10.0.0.2",
"protocol_port": 80,
"port_id": "2b5dd476-11da-4d46-9f1e-7a75436062f6",
"id": "87a5ce35-f278-47f3-8990-7f695f52f9bf",
"status_description": None,
"name": "my_vip",
"admin_state_up": True,
"subnet_id": "afaf251b-2ec3-42ac-9fa9-82a4195724fa",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"connection_limit": -1,
"pool_id": "6d726518-f3aa-4dd4-ac34-e156a35c0aff",
"session_persistence": {"type": "SOURCE_IP"}}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "3895ad11-98a3-4031-92af-f76e96736661"}
NOTIFICATION_HEALTH_MONITORS_UPDATE = {
"_context_roles": ["admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "health_monitor.update.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"health_monitor": {
"admin_state_up": True,
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"delay": 10,
"max_retries": 10,
"timeout": 10,
"pools": [],
"type": "PING",
"id": "6dea2d01-c3af-4696-9192-6c938f391f01"}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "65067e3f-830d-4fbb-87e2-f0e51fda83d2"}
NOTIFICATION_MEMBERS_UPDATE = {
"_context_roles": ["admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "member.update.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"member": {"admin_state_up": True,
"status": "ACTIVE",
"status_description": None,
"weight": 1,
"address": "10.0.0.3",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"protocol_port": 80,
"id": "5e32f960-63ae-4a93-bfa2-339aa83d82ce",
"pool_id": "6b73b9f8-d807-4553-87df-eb34cdd08070"}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "65067e3f-830d-4fbb-87e2-f0e51fda83d2"}
NOTIFICATION_FIREWALL_UPDATE = {
"_context_roles": ["admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "firewall.update.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"firewall": {
"status": "ACTIVE",
"name": "my_firewall",
"admin_state_up": True,
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"firewall_policy_id": "c46a1c15-0496-41c9-beff-9a309a25653e",
"id": "e2d1155f-6bc4-4292-9cfa-ea91af4b38c8",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "fdffeca1-2b5a-4dc9-b8ae-87c482a83e0d"}
NOTIFICATION_FIREWALL_RULE_UPDATE = {
"_context_roles": ["admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "firewall_rule.update.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"firewall_rule": {
"protocol": "tcp",
"description": "",
"source_port": 80,
"source_ip_address": '192.168.255.10',
"destination_ip_address": '10.10.10.1',
"firewall_policy_id": '',
"position": None,
"destination_port": 80,
"id": "53b7c0d3-cb87-4069-9e29-1e866583cc8c",
"name": "rule_01",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"enabled": True,
"action": "allow",
"ip_version": 4,
"shared": False}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "fdffeca1-2b5a-4dc9-b8ae-87c482a83e0d"}
NOTIFICATION_FIREWALL_POLICY_UPDATE = {
"_context_roles": ["admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "firewall_policy.update.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"firewall_policy": {"name": "my_policy",
"firewall_rules": [],
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"audited": False,
"shared": False,
"id": "c46a1c15-0496-41c9-beff-9a309a25653e",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "fdffeca1-2b5a-4dc9-b8ae-87c482a83e0d"}
NOTIFICATION_VPNSERVICE_UPDATE = {
"_context_roles": ["admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "vpnservice.update.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"vpnservice": {"router_id": "75871c53-e722-4b21-93ed-20cb40b6b672",
"status": "ACTIVE",
"name": "my_vpn",
"admin_state_up": True,
"subnet_id": "afaf251b-2ec3-42ac-9fa9-82a4195724fa",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"id": "270c40cc-28d5-4a7e-83da-cc33088ee5d6",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "65067e3f-830d-4fbb-87e2-f0e51fda83d2"}
NOTIFICATION_IPSEC_POLICY_UPDATE = {
"_context_roles": ["admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "ipsecpolicy.update.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"ipsecpolicy": {"encapsulation_mode": "tunnel",
"encryption_algorithm": "aes-128",
"pfs": "group5",
"lifetime": {
"units": "seconds",
"value": 3600},
"name": "my_ipsec_polixy",
"transform_protocol": "esp",
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"id": "998d910d-4506-47c9-a160-47ec51ff53fc",
"auth_algorithm": "sha1",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "4c0e6ecb-2e40-4975-aee2-d88045c747bf"}
NOTIFICATION_IKE_POLICY_UPDATE = {
"_context_roles": ["admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "ikepolicy.update.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"ikepolicy": {"encryption_algorithm": "aes-128",
"pfs": "group5",
"name": "my_ike_policy",
"phase1_negotiation_mode": "main",
"lifetime": {"units": "seconds",
"value": 3600},
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"ike_version": "v1",
"id": "11cef94e-3f6a-4b65-8058-7deb1838633a",
"auth_algorithm": "sha1",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "4c0e6ecb-2e40-4975-aee2-d88045c747bf"}
NOTIFICATION_IPSEC_SITE_CONN_UPDATE = {
"_context_roles": ["admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "ipsec_site_connection.update.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"ipsec_site_connection": {
"status": "ACTIVE",
"psk": "test",
"initiator": "bi-directional",
"name": "my_ipsec_connection",
"admin_state_up": True,
"tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"ipsecpolicy_id": "998d910d-4506-47c9-a160-47ec51ff53fc",
"auth_mode": "psk", "peer_cidrs": ["192.168.255.0/24"],
"mtu": 1500,
"ikepolicy_id": "11cef94e-3f6a-4b65-8058-7deb1838633a",
"dpd": {"action": "hold",
"interval": 30,
"timeout": 120},
"route_mode": "static",
"vpnservice_id": "270c40cc-28d5-4a7e-83da-cc33088ee5d6",
"peer_address": "10.0.0.1",
"peer_id": "10.0.0.254",
"id": "06f3c1ec-2e01-4ad6-9c98-4252751fc60a",
"description": ""}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "4c0e6ecb-2e40-4975-aee2-d88045c747bf"}
NOTIFICATION_EMPTY_PAYLOAD = {
"_context_roles": ["heat_stack_owner", "admin"],
"_context_request_id": "req-e56a8a5e-5d42-43e8-9677-2d36e6e17d5e",
"event_type": "health_monitor.create.end",
"timestamp": "2014-09-15 17:22:11.323644",
"_context_tenant_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_user": "1c1f7c80efc24a16b835ae1c0802d0a1",
"_unique_id": "f112a185e1d1424eba3a13df9e0f0277",
"_context_tenant_name": "demo",
"_context_user_id": "1c1f7c80efc24a16b835ae1c0802d0a1",
"payload": {
"health_monitor": {}},
"_context_project_name": "demo",
"_context_read_deleted": "no",
"_context_auth_token": "e6daf56d7d1787e1fbefff0ecf29703f",
"_context_tenant": "a820f2d6293b4a7587d1c582767f43fb",
"priority": "INFO",
"_context_is_admin": True,
"_context_project_id": "a820f2d6293b4a7587d1c582767f43fb",
"_context_timestamp": "2014-09-15 17:22:11.187163",
"_context_user_name": "admin",
"publisher_id": "network.ubuntu",
"message_id": "65067e3f-830d-4fbb-87e2-f0e51fda83d2"}
class TestNotifications(test.BaseTestCase):
def test_network_create(self):
v = notifications.Network(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_NETWORK_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.create", samples[1].name)
def test_bulk_network_create(self):
v = notifications.Network(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_BULK_NETWORK_CREATE))
self.assertEqual(4, len(samples))
self.assertEqual("network", samples[0].name)
self.assertEqual("network.create", samples[1].name)
self.assertEqual("network", samples[2].name)
self.assertEqual("network.create", samples[3].name)
def test_subnet_create(self):
v = notifications.Subnet(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_SUBNET_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("subnet.create", samples[1].name)
def test_bulk_subnet_create(self):
v = notifications.Subnet(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_BULK_SUBNET_CREATE))
self.assertEqual(4, len(samples))
self.assertEqual("subnet", samples[0].name)
self.assertEqual("subnet.create", samples[1].name)
self.assertEqual("subnet", samples[2].name)
self.assertEqual("subnet.create", samples[3].name)
def test_port_create(self):
v = notifications.Port(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_PORT_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("port.create", samples[1].name)
def test_bulk_port_create(self):
v = notifications.Port(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_BULK_PORT_CREATE))
self.assertEqual(4, len(samples))
self.assertEqual("port", samples[0].name)
self.assertEqual("port.create", samples[1].name)
self.assertEqual("port", samples[2].name)
self.assertEqual("port.create", samples[3].name)
def test_port_update(self):
v = notifications.Port(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_PORT_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("port.update", samples[1].name)
def test_network_exists(self):
v = notifications.Network(mock.Mock())
samples = v.process_notification(NOTIFICATION_NETWORK_EXISTS)
self.assertEqual(1, len(list(samples)))
def test_router_exists(self):
v = notifications.Router(mock.Mock())
samples = v.process_notification(NOTIFICATION_ROUTER_EXISTS)
self.assertEqual(1, len(list(samples)))
def test_floatingip_exists(self):
v = notifications.FloatingIP(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_FLOATINGIP_EXISTS))
self.assertEqual(1, len(samples))
self.assertEqual("ip.floating", samples[0].name)
def test_floatingip_update(self):
v = notifications.FloatingIP(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_FLOATINGIP_UPDATE_START))
self.assertEqual(len(samples), 2)
self.assertEqual("ip.floating", samples[0].name)
def test_metering_report(self):
v = notifications.Bandwidth(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_L3_METER))
self.assertEqual(1, len(samples))
self.assertEqual("bandwidth", samples[0].name)
def test_pool_create(self):
v = notifications.Pool(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_POOL_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.lb.pool", samples[0].name)
def test_vip_create(self):
v = notifications.Vip(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_VIP_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.lb.vip", samples[0].name)
def test_member_create(self):
v = notifications.Member(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_MEMBERS_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.lb.member", samples[0].name)
def test_health_monitor_create(self):
v = notifications.HealthMonitor(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_HEALTH_MONITORS_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.lb.health_monitor", samples[0].name)
def test_firewall_create(self):
v = notifications.Firewall(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_FIREWALL_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.firewall", samples[0].name)
def test_vpnservice_create(self):
v = notifications.VPNService(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_VPNSERVICE_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.vpn", samples[0].name)
def test_ipsec_connection_create(self):
v = notifications.IPSecSiteConnection(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_IPSEC_SITE_CONN_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.vpn.connections", samples[0].name)
def test_firewall_policy_create(self):
v = notifications.FirewallPolicy(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_FIREWALL_POLICY_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.firewall.policy", samples[0].name)
def test_firewall_rule_create(self):
v = notifications.FirewallRule(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_FIREWALL_RULE_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.firewall.rule", samples[0].name)
def test_ipsec_policy_create(self):
v = notifications.IPSecPolicy(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_IPSEC_POLICY_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.vpn.ipsecpolicy", samples[0].name)
def test_ike_policy_create(self):
v = notifications.IKEPolicy(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_IKE_POLICY_CREATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.vpn.ikepolicy", samples[0].name)
def test_pool_update(self):
v = notifications.Pool(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_POOL_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.lb.pool", samples[0].name)
def test_vip_update(self):
v = notifications.Vip(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_VIP_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.lb.vip", samples[0].name)
def test_member_update(self):
v = notifications.Member(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_MEMBERS_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.lb.member", samples[0].name)
def test_health_monitor_update(self):
v = notifications.HealthMonitor(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_HEALTH_MONITORS_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.lb.health_monitor", samples[0].name)
def test_firewall_update(self):
v = notifications.Firewall(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_FIREWALL_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.firewall", samples[0].name)
def test_vpnservice_update(self):
v = notifications.VPNService(mock.Mock())
samples = list(v.process_notification(NOTIFICATION_VPNSERVICE_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.vpn", samples[0].name)
def test_ipsec_connection_update(self):
v = notifications.IPSecSiteConnection(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_IPSEC_SITE_CONN_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.vpn.connections", samples[0].name)
def test_firewall_policy_update(self):
v = notifications.FirewallPolicy(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_FIREWALL_POLICY_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.firewall.policy", samples[0].name)
def test_firewall_rule_update(self):
v = notifications.FirewallRule(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_FIREWALL_RULE_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.firewall.rule", samples[0].name)
def test_ipsec_policy_update(self):
v = notifications.IPSecPolicy(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_IPSEC_POLICY_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.vpn.ipsecpolicy", samples[0].name)
def test_ike_policy_update(self):
v = notifications.IKEPolicy(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_IKE_POLICY_UPDATE))
self.assertEqual(2, len(samples))
self.assertEqual("network.services.vpn.ikepolicy", samples[0].name)
def test_empty_event_payload(self):
v = notifications.HealthMonitor(mock.Mock())
samples = list(v.process_notification(
NOTIFICATION_EMPTY_PAYLOAD))
self.assertEqual(0, len(samples))
class TestEventTypes(test.BaseTestCase):
def test_network(self):
v = notifications.Network(mock.Mock())
events = v.event_types
self.assertIsNotEmpty(events)
def test_subnet(self):
v = notifications.Subnet(mock.Mock())
events = v.event_types
self.assertIsNotEmpty(events)
def test_port(self):
v = notifications.Port(mock.Mock())
events = v.event_types
self.assertIsNotEmpty(events)
def test_router(self):
self.assertTrue(notifications.Router(mock.Mock()).event_types)
def test_floatingip(self):
self.assertTrue(notifications.FloatingIP(mock.Mock()).event_types)
def test_bandwidth(self):
self.assertTrue(notifications.Bandwidth(mock.Mock()).event_types)
def test_pool(self):
self.assertTrue(notifications.Pool(mock.Mock()).event_types)
def test_vip(self):
self.assertTrue(notifications.Vip(mock.Mock()).event_types)
def test_member(self):
self.assertTrue(notifications.Member(mock.Mock()).event_types)
def test_health_monitor(self):
self.assertTrue(notifications.HealthMonitor(mock.Mock()).event_types)
def test_firewall(self):
self.assertTrue(notifications.Firewall(mock.Mock()).event_types)
def test_vpnservice(self):
self.assertTrue(notifications.VPNService(mock.Mock()).event_types)
def test_ipsec_connection(self):
self.assertTrue(notifications.IPSecSiteConnection(
mock.Mock()).event_types)
def test_firewall_policy(self):
self.assertTrue(notifications.FirewallPolicy(mock.Mock()).event_types)
def test_firewall_rule(self):
self.assertTrue(notifications.FirewallRule(mock.Mock()).event_types)
def test_ipsec_policy(self):
self.assertTrue(notifications.IPSecPolicy(mock.Mock()).event_types)
def test_ike_policy(self):
self.assertTrue(notifications.IKEPolicy(mock.Mock()).event_types)
| 43.15852 | 79 | 0.632411 |
cf243ca279e7e3c030132451950c5ecae0407d67 | 1,332 | py | Python | api/todo.py | lambda-lambda/todo_list | d8334929ec1407c771aa473e4ee056c5cff6a646 | [
"MIT"
] | null | null | null | api/todo.py | lambda-lambda/todo_list | d8334929ec1407c771aa473e4ee056c5cff6a646 | [
"MIT"
] | null | null | null | api/todo.py | lambda-lambda/todo_list | d8334929ec1407c771aa473e4ee056c5cff6a646 | [
"MIT"
] | null | null | null | from models.todo import Todo
from models.session import current_user
from response import Response
from auth import (
login_required,
same_user_required,
)
def add(request):
user = current_user(request)
form = request.data
form['user_id'] = user.id
todo = Todo.new(**form)
response = Response.new_json_response(todo.to_dict())
return response
def delete(request):
query = request.query
id = int(query['id'])
todo = Todo.delete(id)
response = Response.new_json_response(todo.to_dict())
return response
def update(request):
form = request.data
id = form['id']
content = form['content']
todo = Todo.update(id, content=content)
response = Response.new_json_response(todo.to_dict())
return response
def all(request):
query = request.query
user = current_user(request)
query['user_id'] = user.id
todos = Todo.all(**query)
todos = [todo.to_dict() for todo in todos]
response = Response.new_json_response(todos)
return response
def init_routes():
d = {
'/api/todo/add': login_required(add),
'/api/todo/delete': login_required(same_user_required(delete, Todo)),
'/api/todo/update': login_required(same_user_required(update, Todo)),
'/api/todo/all': login_required(all),
}
return d
| 23.785714 | 77 | 0.66967 |
2ec964d73b93f510eabc2de668684feb6dd470c3 | 4,529 | py | Python | yocto/poky/bitbake/lib/bb/checksum.py | libreswitch/libreswitch | 1bb99e4bbc55aff46048453e28a1466b08d338aa | [
"Apache-2.0"
] | 16 | 2017-01-17T15:20:43.000Z | 2021-03-19T05:45:14.000Z | yocto/poky/bitbake/lib/bb/checksum.py | libreswitch/libreswitch | 1bb99e4bbc55aff46048453e28a1466b08d338aa | [
"Apache-2.0"
] | 415 | 2016-12-20T17:20:45.000Z | 2018-09-23T07:59:23.000Z | yocto/poky/bitbake/lib/bb/checksum.py | libreswitch/libreswitch | 1bb99e4bbc55aff46048453e28a1466b08d338aa | [
"Apache-2.0"
] | 10 | 2016-12-20T13:24:50.000Z | 2021-03-19T05:46:43.000Z | # Local file checksum cache implementation
#
# Copyright (C) 2012 Intel Corporation
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 2 as
# published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
import glob
import operator
import os
import stat
import bb.utils
import logging
from bb.cache import MultiProcessCache
logger = logging.getLogger("BitBake.Cache")
try:
import cPickle as pickle
except ImportError:
import pickle
logger.info("Importing cPickle failed. "
"Falling back to a very slow implementation.")
# mtime cache (non-persistent)
# based upon the assumption that files do not change during bitbake run
class FileMtimeCache(object):
cache = {}
def cached_mtime(self, f):
if f not in self.cache:
self.cache[f] = os.stat(f)[stat.ST_MTIME]
return self.cache[f]
def cached_mtime_noerror(self, f):
if f not in self.cache:
try:
self.cache[f] = os.stat(f)[stat.ST_MTIME]
except OSError:
return 0
return self.cache[f]
def update_mtime(self, f):
self.cache[f] = os.stat(f)[stat.ST_MTIME]
return self.cache[f]
def clear(self):
self.cache.clear()
# Checksum + mtime cache (persistent)
class FileChecksumCache(MultiProcessCache):
cache_file_name = "local_file_checksum_cache.dat"
CACHE_VERSION = 1
def __init__(self):
self.mtime_cache = FileMtimeCache()
MultiProcessCache.__init__(self)
def get_checksum(self, f):
entry = self.cachedata[0].get(f)
cmtime = self.mtime_cache.cached_mtime(f)
if entry:
(mtime, hashval) = entry
if cmtime == mtime:
return hashval
else:
bb.debug(2, "file %s changed mtime, recompute checksum" % f)
hashval = bb.utils.md5_file(f)
self.cachedata_extras[0][f] = (cmtime, hashval)
return hashval
def merge_data(self, source, dest):
for h in source[0]:
if h in dest:
(smtime, _) = source[0][h]
(dmtime, _) = dest[0][h]
if smtime > dmtime:
dest[0][h] = source[0][h]
else:
dest[0][h] = source[0][h]
def get_checksums(self, filelist, pn):
"""Get checksums for a list of files"""
def checksum_file(f):
try:
checksum = self.get_checksum(f)
except OSError as e:
bb.warn("Unable to get checksum for %s SRC_URI entry %s: %s" % (pn, os.path.basename(f), e))
return None
return checksum
def checksum_dir(pth):
# Handle directories recursively
dirchecksums = []
for root, dirs, files in os.walk(pth):
for name in files:
fullpth = os.path.join(root, name)
checksum = checksum_file(fullpth)
if checksum:
dirchecksums.append((fullpth, checksum))
return dirchecksums
checksums = []
for pth in filelist.split():
exist = pth.split(":")[1]
if exist == "False":
continue
pth = pth.split(":")[0]
if '*' in pth:
# Handle globs
for f in glob.glob(pth):
if os.path.isdir(f):
if not os.path.islink(f):
checksums.extend(checksum_dir(f))
else:
checksum = checksum_file(f)
checksums.append((f, checksum))
elif os.path.isdir(pth):
if not os.path.islink(pth):
checksums.extend(checksum_dir(pth))
else:
checksum = checksum_file(pth)
checksums.append((pth, checksum))
checksums.sort(key=operator.itemgetter(1))
return checksums
| 32.35 | 108 | 0.57187 |
cc60806515b3fecb406ebc1589f23a5bd303acb5 | 3,288 | py | Python | yarn/datadog_checks/yarn/config_models/instance.py | codylerum/integrations-core | aee18148cebf5026099abde7bc218d3ba8d2e75c | [
"BSD-3-Clause"
] | null | null | null | yarn/datadog_checks/yarn/config_models/instance.py | codylerum/integrations-core | aee18148cebf5026099abde7bc218d3ba8d2e75c | [
"BSD-3-Clause"
] | null | null | null | yarn/datadog_checks/yarn/config_models/instance.py | codylerum/integrations-core | aee18148cebf5026099abde7bc218d3ba8d2e75c | [
"BSD-3-Clause"
] | null | null | null | # (C) Datadog, Inc. 2021-present
# All rights reserved
# Licensed under a 3-clause BSD style license (see LICENSE)
from __future__ import annotations
from typing import Any, Mapping, Optional, Sequence
from pydantic import BaseModel, root_validator, validator
from datadog_checks.base.utils.functions import identity
from datadog_checks.base.utils.models import validation
from . import defaults, validators
class AuthToken(BaseModel):
class Config:
allow_mutation = False
reader: Optional[Mapping[str, Any]]
writer: Optional[Mapping[str, Any]]
class Proxy(BaseModel):
class Config:
allow_mutation = False
http: Optional[str]
https: Optional[str]
no_proxy: Optional[Sequence[str]]
class InstanceConfig(BaseModel):
class Config:
allow_mutation = False
allow_redirects: Optional[bool]
application_status_mapping: Optional[Mapping[str, Any]]
application_tags: Optional[Mapping[str, Any]]
auth_token: Optional[AuthToken]
auth_type: Optional[str]
aws_host: Optional[str]
aws_region: Optional[str]
aws_service: Optional[str]
cluster_name: Optional[str]
collect_app_metrics: Optional[bool]
collect_node_metrics: Optional[bool]
connect_timeout: Optional[float]
disable_generic_tags: Optional[bool]
disable_legacy_cluster_tag: Optional[bool]
empty_default_hostname: Optional[bool]
extra_headers: Optional[Mapping[str, Any]]
headers: Optional[Mapping[str, Any]]
kerberos_auth: Optional[str]
kerberos_cache: Optional[str]
kerberos_delegate: Optional[bool]
kerberos_force_initiate: Optional[bool]
kerberos_hostname: Optional[str]
kerberos_keytab: Optional[str]
kerberos_principal: Optional[str]
log_requests: Optional[bool]
min_collection_interval: Optional[float]
ntlm_domain: Optional[str]
password: Optional[str]
persist_connections: Optional[bool]
proxy: Optional[Proxy]
queue_blacklist: Optional[Sequence[str]]
read_timeout: Optional[float]
resourcemanager_uri: Optional[str]
service: Optional[str]
skip_proxy: Optional[bool]
split_yarn_application_tags: Optional[bool]
tags: Optional[Sequence[str]]
timeout: Optional[float]
tls_ca_cert: Optional[str]
tls_cert: Optional[str]
tls_ignore_warning: Optional[bool]
tls_private_key: Optional[str]
tls_use_host_header: Optional[bool]
tls_verify: Optional[bool]
use_legacy_auth_encoding: Optional[bool]
username: Optional[str]
@root_validator(pre=True)
def _initial_validation(cls, values):
return validation.core.initialize_config(getattr(validators, 'initialize_instance', identity)(values))
@validator('*', pre=True, always=True)
def _ensure_defaults(cls, v, field):
if v is not None or field.required:
return v
return getattr(defaults, f'instance_{field.name}')(field, v)
@validator('*')
def _run_validations(cls, v, field):
if not v:
return v
return getattr(validators, f'instance_{field.name}', identity)(v, field=field)
@root_validator(pre=False)
def _final_validation(cls, values):
return validation.core.finalize_config(getattr(validators, 'finalize_instance', identity)(values))
| 31.314286 | 110 | 0.725973 |
f2aef2cc62dc9fb283d78d436c9f7054552da463 | 18,843 | py | Python | demo_project/demo/utils.py | idearun/django-graphos | a096fb76a9759d958fdf6fbb88becab50a7c80f1 | [
"BSD-2-Clause"
] | 257 | 2015-01-01T13:59:06.000Z | 2022-03-19T12:44:32.000Z | demo_project/demo/utils.py | idearun/django-graphos | a096fb76a9759d958fdf6fbb88becab50a7c80f1 | [
"BSD-2-Clause"
] | 64 | 2015-01-13T10:11:24.000Z | 2022-01-08T15:26:26.000Z | demo_project/demo/utils.py | idearun/django-graphos | a096fb76a9759d958fdf6fbb88becab50a7c80f1 | [
"BSD-2-Clause"
] | 98 | 2015-01-13T17:38:05.000Z | 2022-01-20T11:07:18.000Z | from .models import Account
DB_HOST = ["localhost"]
DB_PORT = 27017
def get_db(db_name):
import pymongo
DB_HOST = ["localhost"]
DB_PORT = 27017
db = pymongo.Connection(DB_HOST, DB_PORT)[db_name]
return db
def get_mongo_cursor(db_name, collection_name, max_docs=100):
import pymongo
db = pymongo.Connection(host=DB_HOST,
port=DB_PORT)[db_name]
collection = db[collection_name]
cursor = collection.find()
count = cursor.count
if callable(count):
count = count()
if count >= max_docs:
cursor = cursor[0:max_docs]
return cursor
data = [
['Year', 'Sales', 'Expenses', 'Items Sold', 'Net Profit'],
['2004', 1000, 400, 100, 600],
['2005', 1170, 460, 120, 710],
['2006', 660, 1120, 50, -460],
['2007', 1030, 540, 100, 490],
]
candlestick_data = [['Mon', 20, 28, 38, 45],
['Tue', 31, 38, 55, 66],
['Wed', 50, 55, 77, 80],
['Thu', 77, 77, 66, 50],
['Fri', 68, 66, 22, 15]]
# TODO: Come up with a better example
scatter_multi_series_data = [
['state','country','Rainfall', 'Precipitation'],
['Uttar Pradesh','India',1, 2],
['Bihar','India',2, 3],
['Telangana','India',5, 7],
['Lahore','Pakistan',9,8],
['Hyderabad','Pakistan',8,7],
['Lahore','Pakistan',3,11]
]
# TODO: Come up with a better example
scatter_single_series_data = [
['Leader', 'Rainfall', 'Precipitation'],
['Trump', 1, 2],
['Clinton', 2, 3],
['Trumps', 5, 7],
['George', 6, 9],
['Alex', 7, 4],
['Donald', 7, 8],
]
treemap_data = [
['Location', 'Parent', 'Market trade volume (size)', 'Market increase/decrease (color)'],
['Global', None, 0, 0],
['America', 'Global', 0, 0],
['Europe', 'Global', 0, 0],
['Asia', 'Global', 0, 0],
['Australia', 'Global', 0, 0],
['Africa', 'Global', 0, 0],
['Brazil', 'America', 11, 10],
['USA', 'America', 52, 31],
['Mexico', 'America', 24, 12],
['Canada', 'America', 16, -23],
['France', 'Europe', 42, -11],
['Germany', 'Europe', 31, -2],
['Sweden', 'Europe', 22, -13],
['Italy', 'Europe', 17, 4],
['UK', 'Europe', 21, -5],
['China', 'Asia', 36, 4],
['Japan', 'Asia', 20, -12],
['India', 'Asia', 40, 63],
['Laos', 'Asia', 4, 34],
['Mongolia', 'Asia', 1, -5],
['Israel', 'Asia', 12, 24],
['Iran', 'Asia', 18, 13],
['Pakistan', 'Asia', 11, -52],
['Egypt', 'Africa', 21, 0],
['S. Africa', 'Africa', 30, 43],
['Sudan', 'Africa', 12, 2],
['Congo', 'Africa', 10, 12],
['Zaire', 'Africa', 8, 10]]
# map_data = [
# ['Country', 'Value'],
# ['fo', 0],
# ['um', 1],
# ['us', 2],
# ['jp', 3],
# ['sc', 4],
# ['in', 5],
# ['fr', 6],
# ['fm', 7],
# ['cn', 8],
# ['pt', 9],
# ['sw', 10],
# ['sh', 11],
# ['br', 12],
# ['ki', 13],
# ['ph', 14],
# ['mx', 15],
# ['es', 16],
# ['bu', 17],
# ['mv', 18],
# ['sp', 19],
# ['gb', 20],
# ['gr', 21],
# ['as', 22],
# ['dk', 23],
# ['gl', 24],
# ['gu', 25],
# ['mp', 26],
# ['pr', 27],
# ['vi', 28],
# ['ca', 29],
# ['st', 30],
# ['cv', 31],
# ['dm', 32],
# ['nl', 33],
# ['jm', 34],
# ['ws', 35],
# ['om', 36],
# ['vc', 37],
# ['tr', 38],
# ['bd', 39],
# ['lc', 40],
# ['nr', 41],
# ['no', 42],
# ['kn', 43],
# ['bh', 44],
# ['to', 45],
# ['fi', 46],
# ['id', 47],
# ['mu', 48],
# ['se', 49],
# ['tt', 50],
# ['my', 51],
# ['pa', 52],
# ['pw', 53],
# ['tv', 54],
# ['mh', 55],
# ['cl', 56],
# ['th', 57],
# ['gd', 58],
# ['ee', 59],
# ['ad', 60],
# ['tw', 61],
# ['bb', 62],
# ['it', 63],
# ['mt', 64],
# ['vu', 65],
# ['sg', 66],
# ['cy', 67],
# ['lk', 68],
# ['km', 69],
# ['fj', 70],
# ['ru', 71],
# ['va', 72],
# ['sm', 73],
# ['kz', 74],
# ['az', 75],
# ['tj', 76],
# ['ls', 77],
# ['uz', 78],
# ['ma', 79],
# ['co', 80],
# ['tl', 81],
# ['tz', 82],
# ['ar', 83],
# ['sa', 84],
# ['pk', 85],
# ['ye', 86],
# ['ae', 87],
# ['ke', 88],
# ['pe', 89],
# ['do', 90],
# ['ht', 91],
# ['pg', 92],
# ['ao', 93],
# ['kh', 94],
# ['vn', 95],
# ['mz', 96],
# ['cr', 97],
# ['bj', 98],
# ['ng', 99],
# ['ir', 100],
# ['sv', 101],
# ['sl', 102],
# ['gw', 103],
# ['hr', 104],
# ['bz', 105],
# ['za', 106],
# ['cf', 107],
# ['sd', 108],
# ['cd', 109],
# ['kw', 110],
# ['de', 111],
# ['be', 112],
# ['ie', 113],
# ['kp', 114],
# ['kr', 115],
# ['gy', 116],
# ['hn', 117],
# ['mm', 118],
# ['ga', 119],
# ['gq', 120],
# ['ni', 121],
# ['lv', 122],
# ['ug', 123],
# ['mw', 124],
# ['am', 125],
# ['sx', 126],
# ['tm', 127],
# ['zm', 128],
# ['nc', 129],
# ['mr', 130],
# ['dz', 131],
# ['lt', 132],
# ['et', 133],
# ['er', 134],
# ['gh', 135],
# ['si', 136],
# ['gt', 137],
# ['ba', 138],
# ['jo', 139],
# ['sy', 140],
# ['mc', 141],
# ['al', 142],
# ['uy', 143],
# ['cnm', 144],
# ['mn', 145],
# ['rw', 146],
# ['so', 147],
# ['bo', 148],
# ['cm', 149],
# ['cg', 150],
# ['eh', 151],
# ['rs', 152],
# ['me', 153],
# ['tg', 154],
# ['la', 155],
# ['af', 156],
# ['ua', 157],
# ['sk', 158],
# ['jk', 159],
# ['bg', 160],
# ['qa', 161],
# ['li', 162],
# ['at', 163],
# ['sz', 164],
# ['hu', 165],
# ['ro', 166],
# ['ne', 167],
# ['lu', 168],
# ['ad', 169],
# ['ci', 170],
# ['lr', 171],
# ['bn', 172],
# ['iq', 173],
# ['ge', 174],
# ['gm', 175],
# ['ch', 176],
# ['td', 177],
# ['kv', 178],
# ['lb', 179],
# ['dj', 180],
# ['bi', 181],
# ['sr', 182],
# ['il', 183],
# ['ml', 184],
# ['sn', 185],
# ['gn', 186],
# ['zw', 187],
# ['pl', 188],
# ['mk', 189],
# ['py', 190],
# ['by', 191],
# ['ca', 192],
# ['bf', 193],
# ['na', 194],
# ['ly', 195],
# ['tn', 196],
# ['bt', 197],
# ['md', 198],
# ['ss', 199],
# ['bw', 200],
# ['bs', 201],
# ['nz', 202],
# ['cu', 203],
# ['ec', 204],
# ['au', 205],
# ['ve', 206],
# ['sb', 207],
# ['mg', 208],
# ['is', 209],
# ['eg', 210],
# ['kg', 211],
# ['np', 212]
# ]
map_data = [
['Country', 'Value'],
['fo', 0],
['um', 1],
['us', 2],
['jp', 3],
['sc', 4],
['in', 5],
['fr', 6],
['fm', 7],
['cn', 8],
['pt', 9],
['sw', 10],
['sh', 11],
['br', 12],
['ki', 13],
['ph', 14],
['mx', 15],
['es', 16],
['bu', 17],
['mv', 18],
['sp', 19],
['gb', 20],
['gr', 21],
['as', 22],
['dk', 23],
['gl', 24],
['gu', 25],
['mp', 26],
['pr', 27],
['vi', 28],
['ca', 29],
['st', 30],
['cv', 31],
['dm', 32],
['nl', 33],
['jm', 34],
['ws', 35],
['om', 36],
['vc', 37],
['tr', 38],
['bd', 39],
['lc', 40],
['nr', 41],
['no', 42],
['kn', 43],
['bh', 44],
['to', 45],
['fi', 46],
['id', 47],
['mu', 48],
['se', 49],
['tt', 50],
['my', 51],
['pa', 52],
['pw', 53],
['tv', 54],
['mh', 55],
['cl', 56],
['th', 57],
['gd', 58],
['ee', 59],
['ad', 60],
['tw', 61],
['bb', 62],
['it', 63],
['mt', 64],
['vu', 65],
['sg', 66],
['cy', 67],
['lk', 68],
['km', 69],
['fj', 70],
['ru', 71],
['va', 72],
['sm', 73],
['kz', 74],
['az', 75],
['tj', 76],
['ls', 77],
['uz', 78],
['ma', 79],
['co', 80],
['tl', 81],
['tz', 82],
['ar', 83],
['sa', 84],
['pk', 85],
['ye', 86],
['ae', 87],
['ke', 88],
['pe', 89],
['do', 90],
['ht', 91],
['pg', 92],
['ao', 93],
['kh', 94],
['vn', 95],
['mz', 96],
['cr', 97],
['bj', 98],
['ng', 99]
]
map_data_us_multi_series_lat_lon = [
['Latitude', 'Longitude', 'Winner', 'Seats'],
[32.380120, -86.300629, 'Trump', 10],
[58.299740, -134.406794, 'Trump', 10],
[33.448260, -112.075774, 'Trump', 10],
[34.748655, -92.274494, 'Clinton', 20],
[38.579065, -121.491014, 'Clinton', 20],
]
map_data_us_multi_series = [
['State', 'Winner', 'Seats'],
['us-nj', 'Trump', 10],
['us-ri', 'Trump', 10],
['us-ma', 'Trump', 10],
['us-ct', 'Clinton', 20],
['us-md', 'Clinton', 20],
['us-ny', 'Clinton', 20],
['us-de', 'Trump', 20],
['us-fl', 'Trump', 20],
['us-oh', 'Trump', 20],
['us-pa', 'Trump', 20],
['us-li', 'Trump', 20],
['us-ca', 'Trump', 20],
['us-hi', 'Trump', 20],
['us-va', 'Trump', 31],
['us-mi', 'Trump', 31],
['us-in', 'Trump', 31],
['us-nc', 'Trump', 31],
['us-ga', 'Trump', 31],
['us-tn', 'Trump', 31],
['us-nh', 'Trump', 31],
['us-sc', 'Trump', 31],
['us-la', 'Trump', 31],
['us-ky', 'Trump', 31],
['us-wi', 'Trump', 12],
['us-wa', 'Trump', 12],
['us-al', 'Clinton', 12],
['us-mo', 'Clinton', 12],
['us-tx', 'Clinton', 45],
['us-wv', 'Clinton', 45],
]
map_data_us_lat_lon = [
['Latitude', 'Longitude', 'Population'],
[32.380120, -86.300629, 900],
[58.299740, -134.406794, 387],
[33.448260, -112.075774, 313],
]
map_data_india_lat_lon = [
['Latitude', 'Longitude', 'Population'],
[25.4851484, 83.2104426, 900],
[27.7126407, 78.7391187, 387],
[28.2699017, 79.1604971, 313],
]
map_data_us = [
['State', 'Population'],
['us-nj', 438],
['us-ri', 387],
['us-ma', 313],
['us-ct', 271],
['us-md', 209],
['us-ny', 195],
['us-de', 155],
['us-fl', 114],
['us-oh', 107],
['us-pa', 106],
['us-li', 86],
['us-ca', 84],
['us-hi', 73],
['us-va', 69],
['us-mi', 68],
['us-in', 65],
['us-nc', 64],
['us-ga', 55],
['us-tn', 53],
['us-nh', 53],
['us-sc', 51],
['us-la', 40],
['us-ky', 39],
['us-wi', 38],
['us-wa', 34],
['us-al', 34],
['us-mo', 31],
['us-tx', 31],
['us-wv', 29],
['us-vt', 25],
['us-mn', 24],
['us-ms', 23],
['us-ia', 20],
['us-ar', 20],
['us-ok', 19],
['us-az', 17],
['us-co', 16],
['us-me', 16],
['us-or', 14],
['us-ks', 13],
['us-ut', 11],
['us-ne', 9],
['us-nv', 7],
['us-id', 6],
['us-nm', 6],
['us-sd', 4],
['us-nd', 4],
['us-mt', 2],
['us-wy', 2],
['us-ak', 1],
]
map_data_us_point = [
['Lat', 'Lon', 'Name', 'Date'],
[46.8797, -110.3626, 'trump', '25th February'],
[41.4925, -99.9018, 'trump', '26th February'],
[45.4925, -89.9018, 'trump', '27th February'],
[32.1656, -82.9001, 'clinton', '25th February'],
[33.1656, -81.9001, 'clinton', '26th February'],
]
mongo_series_object_1 = [[440, 39],
[488, 29.25],
[536, 28],
[584, 29],
[632, 33.25],
[728, 28.5],
[776, 33.25],
[824, 28.5],
[872, 31],
[920, 30.75],
[968, 26.25]]
mongo_series_object_2 = [[400, 4],
[488, 0],
[536, 20],
[584, 8],
[632, 2],
[680, 36],
[728, 0],
[776, 0],
[824, 0],
[872, 4],
[920, 1],
[968, 0]]
mongo_data = [{'data': mongo_series_object_1, 'label': 'hours'},
{'data': mongo_series_object_2, 'label': 'hours'}]
def create_demo_accounts():
Account.objects.all().delete()
# Create some rows
Account.objects.create(year="2004", sales=1000,
expenses=400, ceo="Welch")
Account.objects.create(year="2005", sales=1170,
expenses=460, ceo="Jobs")
Account.objects.create(year="2006", sales=660,
expenses=1120, ceo="Page")
Account.objects.create(year="2007", sales=1030,
expenses=540, ceo="Welch")
Account.objects.create(year="2008", sales=2030,
expenses=1540, ceo="Zuck")
Account.objects.create(year="2009", sales=2230,
expenses=1840, ceo="Cook")
def create_demo_mongo():
accounts = get_db("accounts")
docs = accounts.docs
docs.drop()
docs = accounts.docs
header = data[0]
data_only = data[1:]
for row in data_only:
docs.insert(dict(zip(header, row)))
heatmap_data = [['Name', 'Yash', 'Akshar', 'Ashok','Shabda'],
['Uttar Pradesh',1000,2000,3000,4000],
['Bihar',2000,5000,8000,9800],
['Hyderabad',10000,9855,6000,2000],
['Banglore',98652,78563,8522,2000],
['Chennai',98745,8563,5236,2000],
['Vizag',9875,7000,966,2300],
['Maharashtra',9000,16789,9087,6789],
['Punjab',3467,8900,5670,9900]
]
funnel_data = [['Unique users', 'Counts'],
['Website visits', 654],
['Downloads', 4064],
['Requested price list', 1987],
['Invoice sent', 976],
['Finalized', 846]
]
treemap_data_highcharts = [["Continent","Country","Cause","Death Rate"],
["Asia","India","Cardiovascular Disease",10],
["Asia","India","Road Accident",5],
["Asia","India","Cancer",3],
["Asia","China","Cardiovascular Disease",9],
["Asia","China","Road Accident",6],
["Asia","China","Cancer",1],
["South Ameria","Brazil","Cardiovascular Disease",11],
["South Ameria","Brazil","Road Accident",3],
["South Ameria","Brazil","Cancer",2],
["South Ameria","Uruguay","Cardiovascular Disease",12],
["South Ameria","Uruguay","Road Accident",9],
["South Ameria","Uruguay","Cancer",8],
["Europe","France","Cardiovascular Disease",9],
["Europe","France","Road Accident",4],
["Europe","France","Cancer",6]
]
piechart_data_highcharts = [["Country","Cause","Death Rate"],
["India","Cardiovascular Disease",10],
["India","Road Accident",5],
["India","Cancer",3],
["China","Cardiovascular Disease",9],
["China","Road Accident",6],
["China","Cancer",1],
["Brazil","Cardiovascular Disease",11],
["Brazil","Road Accident",3],
["Brazil","Cancer",2],
["Uruguay","Cardiovascular Disease",12],
["Uruguay","Road Accident",9],
["Uruguay","Cancer",8],
["France","Cardiovascular Disease",9],
["France","Road Accident",4],
["France","Cancer",6]
]
bubble_chart_data_multi = [["Grade","Country","Sugar Consumption","Fat Consumption","GDP"],
["A","India",10,15,90],
["B","India",11,20,19],
["C","India",12,15,70],
["D","India",13,30,39],
["E","India",14,12,9],
["F","India",15,5,98],
["H","Japan",18,60,110],
["I","Japan", 41, 16, 140],
["J","Japan", 47, 36, 150],
["K","Japan", 61, 56, 70],
["L","Japan", 74, 36, 210],
["M","Japan", 10, 46, 90],
["N","Japan", 30, 26, 100],
["O","China",14,18,100],
["A","China", 9, 17, 10],
["B","China", 51, 67, 200],
["C","China", 12, 27, 160],
["D","China", 42, 67, 86],
["E","China", 30, 97, 20],
["F","China", 16, 67, 90],
["L","USA",56,20,120],
["K","USA", 32, 23, 220],
["A","USA", 15, 85, 320],
["S","USA", 48, 10, 20],
["D","USA", 30, 96, 150],
["K","USA", 14, 22, 160],
["P","USA", 39, 21, 100],
["O","USA", 44, 29, 150]]
bubble_chart_data_single = [["Country","Sugar Consumption","Fat Consumption","GDP"],
["India",10,15,90],
["USA",11,20,19],
["China",12,15,70],
["Japan",13,30,39],
["Pakistan",14,12,9],
["Srilanka",15,5,98],
["Indonesia",16,35,150]]
| 27.073276 | 99 | 0.345433 |
57b52ba3fc6514eebfb144a68eb62b62ea05a0ef | 6,509 | py | Python | astoria/managers/astprocd/process_manager.py | srobo/astoria | 7bdefd91254b154aadf63b574c8b767d17a2e5d4 | [
"MIT"
] | 1 | 2021-02-03T02:54:54.000Z | 2021-02-03T02:54:54.000Z | astoria/managers/astprocd/process_manager.py | srobo/astoria | 7bdefd91254b154aadf63b574c8b767d17a2e5d4 | [
"MIT"
] | 72 | 2020-12-15T18:29:18.000Z | 2022-03-08T09:42:53.000Z | astoria/managers/astprocd/process_manager.py | srobo/astoria | 7bdefd91254b154aadf63b574c8b767d17a2e5d4 | [
"MIT"
] | 2 | 2022-02-05T23:00:51.000Z | 2022-03-09T21:40:49.000Z | """Process Manager Application."""
import asyncio
import logging
from typing import Dict, Optional
from astoria.common.broadcast_event import UsercodeLogBroadcastEvent
from astoria.common.manager import StateManager
from astoria.common.manager_requests import (
RequestResponse,
UsercodeKillManagerRequest,
UsercodeRestartManagerRequest,
)
from astoria.common.messages.astdiskd import DiskInfo, DiskType, DiskUUID
from astoria.common.messages.astprocd import CodeStatus, ProcessManagerMessage
from astoria.common.mqtt import BroadcastHelper
from astoria.managers.mixins.disk_handler import DiskHandlerMixin
from .usercode_lifecycle import UsercodeLifecycle
LOGGER = logging.getLogger(__name__)
loop = asyncio.get_event_loop()
class ProcessManager(DiskHandlerMixin, StateManager[ProcessManagerMessage]):
"""Astoria Process State Manager."""
name = "astprocd"
dependencies = ["astdiskd", "astmetad"]
def _init(self) -> None:
self._lifecycle: Optional[UsercodeLifecycle] = None
self._cur_disks: Dict[DiskUUID, DiskInfo] = {}
self._mqtt.subscribe("astdiskd", self.handle_astdiskd_disk_info_message)
self._register_request(
"restart",
UsercodeRestartManagerRequest,
self.handle_restart_request,
)
self._register_request(
"kill",
UsercodeKillManagerRequest,
self.handle_kill_request,
)
self._log_helper = BroadcastHelper.get_helper(
self._mqtt,
UsercodeLogBroadcastEvent,
)
@property
def offline_status(self) -> ProcessManagerMessage:
"""
Status to publish when the manager goes offline.
This status should ensure that any other components relying
on this data go into a safe state.
"""
return ProcessManagerMessage(
status=ProcessManagerMessage.Status.STOPPED,
)
async def main(self) -> None:
"""Main routine for astprocd."""
# Wait whilst the program is running.
self.update_status()
await self.wait_loop()
for uuid, info in self._cur_disks.items():
asyncio.ensure_future(self.handle_disk_removal(uuid, info))
async def handle_disk_insertion(self, uuid: DiskUUID, disk_info: DiskInfo) -> None:
"""Handle a disk insertion."""
LOGGER.debug(f"Disk inserted: {uuid} ({disk_info.disk_type})")
if disk_info.disk_type is DiskType.USERCODE:
LOGGER.info(f"Usercode disk {uuid} is mounted at {disk_info.mount_path}")
if self._lifecycle is None:
LOGGER.debug(f"Starting usercode lifecycle for {uuid}")
self._lifecycle = UsercodeLifecycle(
uuid,
disk_info,
self.update_status,
self._log_helper,
self.config,
)
asyncio.ensure_future(self._lifecycle.run_process())
else:
LOGGER.warn("Cannot run usercode, there is already a lifecycle present.")
with disk_info.mount_path.joinpath("log.txt").open("w") as fh:
fh.write("Unable to start code.\n")
fh.write("It is not safe to run multiple code disks at once.\n")
async def handle_disk_removal(self, uuid: DiskUUID, disk_info: DiskInfo) -> None:
"""Handle a disk removal."""
LOGGER.debug(f"Disk removed: {uuid} ({disk_info.disk_type})")
if disk_info.disk_type is DiskType.USERCODE:
LOGGER.info(f"Usercode disk {uuid} removed ({disk_info.mount_path})")
if self._lifecycle is not None and self._lifecycle._uuid == disk_info.uuid:
await self._lifecycle.kill_process()
self._lifecycle = None
self.update_status()
else:
LOGGER.warning("Disk removed, but no code lifecycle available")
async def handle_kill_request(
self,
request: UsercodeKillManagerRequest,
) -> RequestResponse:
"""Handle a request to kill running usercode."""
if self._lifecycle is None:
return RequestResponse(
uuid=request.uuid,
success=False,
reason="No active usercode lifecycle",
)
else:
LOGGER.info("Kill request received.")
await self._lifecycle.kill_process()
return RequestResponse(
uuid=request.uuid,
success=True,
)
async def handle_restart_request(
self,
request: UsercodeRestartManagerRequest,
) -> RequestResponse:
"""Handle a request to restart usercode."""
LOGGER.info("Restart request received.")
if self._lifecycle is None:
return RequestResponse(
uuid=request.uuid,
success=False,
reason="No active usercode lifecycle",
)
else:
if self._lifecycle.status is CodeStatus.RUNNING:
return RequestResponse(
uuid=request.uuid,
success=False,
reason="Code is already running.",
)
else:
asyncio.ensure_future(self._lifecycle.run_process())
return RequestResponse(
uuid=request.uuid,
success=True,
)
def update_status(self, code_status: Optional[CodeStatus] = None) -> None:
"""
Calculate and update the status of this manager.
Called by the usercode lifecycle to inform us of changes.
"""
if self._lifecycle is None:
# When the status is updated in the lifecycle constructor, we
# are left with a situation where there is no lifecycle object,
# but the code is starting. Thus we want to inform anyway.
#
# This section also updates the status when the lifecycle is cleaned up.
self.status = ProcessManagerMessage(
status=ProcessManagerMessage.Status.RUNNING,
code_status=code_status,
)
else:
self.status = ProcessManagerMessage(
status=ProcessManagerMessage.Status.RUNNING,
code_status=self._lifecycle.status,
disk_info=self._lifecycle.disk_info,
)
| 36.982955 | 89 | 0.608696 |
545be62f44ed6217e3e2a77b3b5c50bb6e7f5b94 | 2,015 | py | Python | migrations/versions/14b840151c2f_character_table.py | jimmybutton/moviedb | 61028ac4db7f58a671ab3a1c2afd3bfb53372773 | [
"MIT"
] | null | null | null | migrations/versions/14b840151c2f_character_table.py | jimmybutton/moviedb | 61028ac4db7f58a671ab3a1c2afd3bfb53372773 | [
"MIT"
] | null | null | null | migrations/versions/14b840151c2f_character_table.py | jimmybutton/moviedb | 61028ac4db7f58a671ab3a1c2afd3bfb53372773 | [
"MIT"
] | null | null | null | """character table
Revision ID: 14b840151c2f
Revises: 5f1654c61a38
Create Date: 2020-06-16 18:07:44.967078
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '14b840151c2f'
down_revision = '5f1654c61a38'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('character',
sa.Column('id', sa.Integer(), nullable=False),
sa.Column('created_timestamp', sa.DateTime(), nullable=True),
sa.Column('created_id', sa.Integer(), nullable=True),
sa.Column('modified_timestamp', sa.DateTime(), nullable=True),
sa.Column('modified_id', sa.Integer(), nullable=True),
sa.Column('movie_id', sa.Integer(), nullable=True),
sa.Column('actor_id', sa.Integer(), nullable=True),
sa.Column('character_name', sa.String(length=128), nullable=True),
sa.Column('character_url', sa.String(length=128), nullable=True),
sa.Column('movie_title', sa.String(length=128), nullable=True),
sa.Column('movie_year', sa.Integer(), nullable=True),
sa.Column('actor_name', sa.String(length=128), nullable=True),
sa.ForeignKeyConstraint(['actor_id'], ['people.id'], ),
sa.ForeignKeyConstraint(['created_id'], ['user.id'], ),
sa.ForeignKeyConstraint(['modified_id'], ['user.id'], ),
sa.ForeignKeyConstraint(['movie_id'], ['movie.id'], ),
sa.PrimaryKeyConstraint('id')
)
op.create_index(op.f('ix_character_created_timestamp'), 'character', ['created_timestamp'], unique=False)
op.create_index(op.f('ix_character_modified_timestamp'), 'character', ['modified_timestamp'], unique=False)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_index(op.f('ix_character_modified_timestamp'), table_name='character')
op.drop_index(op.f('ix_character_created_timestamp'), table_name='character')
op.drop_table('character')
# ### end Alembic commands ###
| 39.509804 | 111 | 0.698759 |
0dc38fa59b31a2e9fbee37dec7b69be947dacf38 | 5,957 | py | Python | numpy/_pytesttester.py | bdvd/numpy | cea994fac86dbc5af7bee3f15fc5b475a99163fa | [
"BSD-3-Clause"
] | 1 | 2020-12-07T17:25:19.000Z | 2020-12-07T17:25:19.000Z | numpy/_pytesttester.py | sahanabalappa/numpy | cea994fac86dbc5af7bee3f15fc5b475a99163fa | [
"BSD-3-Clause"
] | 20 | 2020-02-14T11:37:52.000Z | 2020-02-18T21:18:45.000Z | numpy/_pytesttester.py | sahanabalappa/numpy | cea994fac86dbc5af7bee3f15fc5b475a99163fa | [
"BSD-3-Clause"
] | 1 | 2020-03-20T00:22:37.000Z | 2020-03-20T00:22:37.000Z | """
Pytest test running.
This module implements the ``test()`` function for NumPy modules. The usual
boiler plate for doing that is to put the following in the module
``__init__.py`` file::
from numpy._pytesttester import PytestTester
test = PytestTester(__name__).test
del PytestTester
Warnings filtering and other runtime settings should be dealt with in the
``pytest.ini`` file in the numpy repo root. The behavior of the test depends on
whether or not that file is found as follows:
* ``pytest.ini`` is present (develop mode)
All warnings except those explicitly filtered out are raised as error.
* ``pytest.ini`` is absent (release mode)
DeprecationWarnings and PendingDeprecationWarnings are ignored, other
warnings are passed through.
In practice, tests run from the numpy repo are run in develop mode. That
includes the standard ``python runtests.py`` invocation.
This module is imported by every numpy subpackage, so lies at the top level to
simplify circular import issues. For the same reason, it contains no numpy
imports at module scope, instead importing numpy within function calls.
"""
import sys
import os
__all__ = ['PytestTester']
def _show_numpy_info():
import numpy as np
print("NumPy version %s" % np.__version__)
relaxed_strides = np.ones((10, 1), order="C").flags.f_contiguous
print("NumPy relaxed strides checking option:", relaxed_strides)
class PytestTester:
"""
Pytest test runner.
A test function is typically added to a package's __init__.py like so::
from numpy._pytesttester import PytestTester
test = PytestTester(__name__).test
del PytestTester
Calling this test function finds and runs all tests associated with the
module and all its sub-modules.
Attributes
----------
module_name : str
Full path to the package to test.
Parameters
----------
module_name : module name
The name of the module to test.
Notes
-----
Unlike the previous ``nose``-based implementation, this class is not
publicly exposed as it performs some ``numpy``-specific warning
suppression.
"""
def __init__(self, module_name):
self.module_name = module_name
def __call__(self, label='fast', verbose=1, extra_argv=None,
doctests=False, coverage=False, durations=-1, tests=None):
"""
Run tests for module using pytest.
Parameters
----------
label : {'fast', 'full'}, optional
Identifies the tests to run. When set to 'fast', tests decorated
with `pytest.mark.slow` are skipped, when 'full', the slow marker
is ignored.
verbose : int, optional
Verbosity value for test outputs, in the range 1-3. Default is 1.
extra_argv : list, optional
List with any extra arguments to pass to pytests.
doctests : bool, optional
.. note:: Not supported
coverage : bool, optional
If True, report coverage of NumPy code. Default is False.
Requires installation of (pip) pytest-cov.
durations : int, optional
If < 0, do nothing, If 0, report time of all tests, if > 0,
report the time of the slowest `timer` tests. Default is -1.
tests : test or list of tests
Tests to be executed with pytest '--pyargs'
Returns
-------
result : bool
Return True on success, false otherwise.
Notes
-----
Each NumPy module exposes `test` in its namespace to run all tests for
it. For example, to run all tests for numpy.lib:
>>> np.lib.test() #doctest: +SKIP
Examples
--------
>>> result = np.lib.test() #doctest: +SKIP
...
1023 passed, 2 skipped, 6 deselected, 1 xfailed in 10.39 seconds
>>> result
True
"""
import pytest
import warnings
module = sys.modules[self.module_name]
module_path = os.path.abspath(module.__path__[0])
# setup the pytest arguments
pytest_args = ["-l"]
# offset verbosity. The "-q" cancels a "-v".
pytest_args += ["-q"]
# Filter out distutils cpu warnings (could be localized to
# distutils tests). ASV has problems with top level import,
# so fetch module for suppression here.
with warnings.catch_warnings():
warnings.simplefilter("always")
from numpy.distutils import cpuinfo
# Filter out annoying import messages. Want these in both develop and
# release mode.
pytest_args += [
"-W ignore:Not importing directory",
"-W ignore:numpy.dtype size changed",
"-W ignore:numpy.ufunc size changed",
"-W ignore::UserWarning:cpuinfo",
]
# When testing matrices, ignore their PendingDeprecationWarnings
pytest_args += [
"-W ignore:the matrix subclass is not",
"-W ignore:Importing from numpy.matlib is",
]
if doctests:
raise ValueError("Doctests not supported")
if extra_argv:
pytest_args += list(extra_argv)
if verbose > 1:
pytest_args += ["-" + "v"*(verbose - 1)]
if coverage:
pytest_args += ["--cov=" + module_path]
if label == "fast":
pytest_args += ["-m", "not slow"]
elif label != "full":
pytest_args += ["-m", label]
if durations >= 0:
pytest_args += ["--durations=%s" % durations]
if tests is None:
tests = [self.module_name]
pytest_args += ["--pyargs"] + list(tests)
# run tests.
_show_numpy_info()
try:
code = pytest.main(pytest_args)
except SystemExit as exc:
code = exc.code
return code == 0
| 30.706186 | 79 | 0.612221 |
0fc9bcb527d5fe44323aef0ac7bfc93b4daf6ca8 | 3,476 | py | Python | config/settings/base.py | TeamORIT/WhatsUpAddis-BE | 702d14eff969673ce88dbd6f4cad690cbb580c30 | [
"MIT"
] | null | null | null | config/settings/base.py | TeamORIT/WhatsUpAddis-BE | 702d14eff969673ce88dbd6f4cad690cbb580c30 | [
"MIT"
] | 3 | 2018-11-30T22:18:39.000Z | 2018-11-30T23:46:03.000Z | config/settings/base.py | TeamORIT/WhatsUpAddis-BE | 702d14eff969673ce88dbd6f4cad690cbb580c30 | [
"MIT"
] | null | null | null | """
Django settings for config project.
Generated by 'django-admin startproject' using Django 2.0.7.
For more information on this file, see
https://docs.djangoproject.com/en/2.0/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.0/ref/settings/
"""
import os
from decouple import config
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = config('SECRET_KEY')
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
# Third Party apps
INSTALLED_APPS += [
'authtools',
]
# Project apps
INSTALLED_APPS += [
'accounts',
'core',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'config.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR, 'templates'), ],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'config.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.0/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql_psycopg2',
'NAME': config('DB_NAME'),
'USER': config('DB_USER'),
'PASSWORD': config('DB_PASSWORD'),
'HOST': config('DB_HOST'),
'PORT': '',
}
}
# Password validation
# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.0/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'Africa/Addis_Ababa'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.0/howto/static-files/
STATIC_URL = '/static/'
STATICFILES_DIRS = (
os.path.join(BASE_DIR, 'static'),
)
MEDIA_URL = '/media/'
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
# Custom Auth User Model
AUTH_USER_MODEL = 'accounts.User'
| 24.652482 | 91 | 0.686709 |
f76988636febcc33fb36775812da45e5abffae6b | 214 | py | Python | benchmark/overhead.py | keithyipkw/InSync | 3744b45f31f713de2dfc8c30507e67db96915e07 | [
"MIT"
] | null | null | null | benchmark/overhead.py | keithyipkw/InSync | 3744b45f31f713de2dfc8c30507e67db96915e07 | [
"MIT"
] | null | null | null | benchmark/overhead.py | keithyipkw/InSync | 3744b45f31f713de2dfc8c30507e67db96915e07 | [
"MIT"
] | null | null | null | import sys
import numpy as np
import pandas as pd
def main():
df = pd.read_csv(sys.argv[1], names=["Method", "Time"])
print(df.groupby("Method").describe().to_csv())
if __name__ == "__main__":
main() | 19.454545 | 59 | 0.64486 |
56c3f29d7d2fd08275474e893db5a4e895d61469 | 1,073 | py | Python | bin/sa_haveibeenpwned/aob_py3/future/moves/urllib/parse.py | hRun/SA-haveibeenpwned | 2a8ae3dedc405dc3c8dac1cb6a705a70f574afdb | [
"Apache-2.0"
] | 2 | 2020-08-17T07:52:48.000Z | 2020-12-18T16:39:32.000Z | bin/sa_haveibeenpwned/aob_py3/future/moves/urllib/parse.py | hRun/SA-haveibeenpwned | 2a8ae3dedc405dc3c8dac1cb6a705a70f574afdb | [
"Apache-2.0"
] | 5 | 2020-12-15T23:40:14.000Z | 2022-02-23T15:43:18.000Z | bin/sa_haveibeenpwned/aob_py3/future/moves/urllib/parse.py | hRun/SA-haveibeenpwned | 2a8ae3dedc405dc3c8dac1cb6a705a70f574afdb | [
"Apache-2.0"
] | 4 | 2019-05-16T09:57:33.000Z | 2021-07-14T12:31:21.000Z | from __future__ import absolute_import
from future.standard_library import suspend_hooks
from future.utils import PY3
if PY3:
from urllib.parse import *
else:
__future_module__ = True
from urlparse import (ParseResult, SplitResult, parse_qs, parse_qsl,
urldefrag, urljoin, urlparse, urlsplit,
urlunparse, urlunsplit)
# we use this method to get at the original py2 urllib before any renaming
# quote = sys.py2_modules['urllib'].quote
# quote_plus = sys.py2_modules['urllib'].quote_plus
# unquote = sys.py2_modules['urllib'].unquote
# unquote_plus = sys.py2_modules['urllib'].unquote_plus
# urlencode = sys.py2_modules['urllib'].urlencode
# splitquery = sys.py2_modules['urllib'].splitquery
with suspend_hooks():
from urllib import (quote,
quote_plus,
unquote,
unquote_plus,
urlencode,
splitquery)
| 37 | 79 | 0.591799 |
5080baf08d27bd22d35addc82db0281b4c3a17f2 | 7,879 | py | Python | napari/utils/misc.py | hectormz/napari | c53051ed3e3693ae74c86a5c4611f057293bd21d | [
"BSD-3-Clause"
] | null | null | null | napari/utils/misc.py | hectormz/napari | c53051ed3e3693ae74c86a5c4611f057293bd21d | [
"BSD-3-Clause"
] | null | null | null | napari/utils/misc.py | hectormz/napari | c53051ed3e3693ae74c86a5c4611f057293bd21d | [
"BSD-3-Clause"
] | null | null | null | """Miscellaneous utility functions.
"""
import collections.abc
import inspect
import itertools
import re
from enum import Enum, EnumMeta
from os import PathLike, fspath, path
from typing import Optional, Sequence, Type, TypeVar
import numpy as np
ROOT_DIR = path.dirname(path.dirname(__file__))
def str_to_rgb(arg):
"""Convert an rgb string 'rgb(x,y,z)' to a list of ints [x,y,z].
"""
return list(
map(int, re.match(r'rgb\((\d+),\s*(\d+),\s*(\d+)\)', arg).groups())
)
def ensure_iterable(arg, color=False):
"""Ensure an argument is an iterable. Useful when an input argument
can either be a single value or a list. If a color is passed then it
will be treated specially to determine if it is iterable.
"""
if is_iterable(arg, color=color):
return arg
else:
return itertools.repeat(arg)
def is_iterable(arg, color=False):
"""Determine if a single argument is an iterable. If a color is being
provided and the argument is a 1-D array of length 3 or 4 then the input
is taken to not be iterable.
"""
if arg is None:
return False
elif type(arg) is str:
return False
elif np.isscalar(arg):
return False
elif color and isinstance(arg, (list, np.ndarray)):
if np.array(arg).ndim == 1 and (len(arg) == 3 or len(arg) == 4):
return False
else:
return True
else:
return True
def is_sequence(arg):
"""Check if ``arg`` is a sequence like a list or tuple.
return True:
list
tuple
return False
string
numbers
dict
set
"""
if isinstance(arg, collections.abc.Sequence) and not isinstance(arg, str):
return True
return False
def ensure_sequence_of_iterables(obj, length: Optional[int] = None):
"""Ensure that ``obj`` behaves like a (nested) sequence of iterables.
If length is provided and the object is already a sequence of iterables,
a ValueError will be raised if ``len(obj) != length``.
Parameters
----------
obj : Any
the object to check
length : int, optional
If provided, assert that obj has len ``length``, by default None
Returns
-------
iterable
nested sequence of iterables, or an itertools.repeat instance
Examples
--------
In [1]: ensure_sequence_of_iterables([1, 2])
Out[1]: repeat([1, 2])
In [2]: ensure_sequence_of_iterables([(1, 2), (3, 4)])
Out[2]: [(1, 2), (3, 4)]
In [3]: ensure_sequence_of_iterables({'a':1})
Out[3]: repeat({'a': 1})
In [4]: ensure_sequence_of_iterables(None)
Out[4]: repeat(None)
"""
if obj and is_sequence(obj) and is_iterable(obj[0]):
if length is not None and len(obj) != length:
raise ValueError(f"length of {obj} must equal {length}")
return obj
return itertools.repeat(obj)
def formatdoc(obj):
"""Substitute globals and locals into an object's docstring."""
frame = inspect.currentframe().f_back
try:
obj.__doc__ = obj.__doc__.format(
**{**frame.f_globals, **frame.f_locals}
)
return obj
finally:
del frame
class StringEnumMeta(EnumMeta):
def __getitem__(self, item):
""" set the item name case to uppercase for name lookup
"""
if isinstance(item, str):
item = item.upper()
return super().__getitem__(item)
def __call__(
cls,
value,
names=None,
*,
module=None,
qualname=None,
type=None,
start=1,
):
""" set the item value case to lowercase for value lookup
"""
# simple value lookup
if names is None:
if isinstance(value, str):
return super().__call__(value.lower())
elif isinstance(value, cls):
return value
else:
raise ValueError(
f'{cls} may only be called with a `str`'
f' or an instance of {cls}'
)
# otherwise create new Enum class
return cls._create_(
value,
names,
module=module,
qualname=qualname,
type=type,
start=start,
)
def keys(self):
return list(map(str, self))
class StringEnum(Enum, metaclass=StringEnumMeta):
def _generate_next_value_(name, start, count, last_values):
""" autonaming function assigns each value its own name as a value
"""
return name.lower()
def __str__(self):
"""String representation: The string method returns the lowercase
string of the Enum name
"""
return self.value
camel_to_snake_pattern = re.compile(r'(.)([A-Z][a-z]+)')
camel_to_spaces_pattern = re.compile(
r"((?<=[a-z])[A-Z]|(?<!\A)[A-R,T-Z](?=[a-z]))"
)
def camel_to_snake(name):
# https://gist.github.com/jaytaylor/3660565
return camel_to_snake_pattern.sub(r'\1_\2', name).lower()
def camel_to_spaces(val):
return camel_to_spaces_pattern.sub(r" \1", val)
T = TypeVar('T', str, Sequence[str])
def abspath_or_url(relpath: T) -> T:
"""Utility function that normalizes paths or a sequence thereof.
Expands user directory and converts relpaths to abspaths... but ignores
URLS that begin with "http", "ftp", or "file".
Parameters
----------
relpath : str or list or tuple
A path, or list or tuple of paths.
Returns
-------
abspath : str or list or tuple
An absolute path, or list or tuple of absolute paths (same type as
input).
"""
if isinstance(relpath, (tuple, list)):
return type(relpath)(abspath_or_url(p) for p in relpath)
if isinstance(relpath, (str, PathLike)):
relpath = fspath(relpath)
if relpath.startswith(('http:', 'https:', 'ftp:', 'file:')):
return relpath
return path.abspath(path.expanduser(relpath))
raise TypeError("Argument must be a string, PathLike, or sequence thereof")
class CallDefault(inspect.Parameter):
def __str__(self):
"""wrap defaults"""
kind = self.kind
formatted = self._name
# Fill in defaults
if (
self._default is not inspect._empty
or kind == inspect._KEYWORD_ONLY
):
formatted = '{}={}'.format(formatted, formatted)
if kind == inspect._VAR_POSITIONAL:
formatted = '*' + formatted
elif kind == inspect._VAR_KEYWORD:
formatted = '**' + formatted
return formatted
class CallSignature(inspect.Signature):
_parameter_cls = CallDefault
def __str__(self):
"""do not render separators
commented code is what was taken out from
the copy/pasted inspect module code :)
"""
result = []
# render_pos_only_separator = False
# render_kw_only_separator = True
for param in self.parameters.values():
formatted = str(param)
result.append(formatted)
rendered = '({})'.format(', '.join(result))
if self.return_annotation is not inspect._empty:
anno = inspect.formatannotation(self.return_annotation)
rendered += ' -> {}'.format(anno)
return rendered
callsignature = CallSignature.from_callable
def all_subclasses(cls: Type) -> set:
"""Recursively find all subclasses of class ``cls``.
Parameters
----------
cls : class
A python class (or anything that implements a __subclasses__ method).
Returns
-------
set
the set of all classes that are subclassed from ``cls``
"""
return set(cls.__subclasses__()).union(
[s for c in cls.__subclasses__() for s in all_subclasses(c)]
)
| 26.618243 | 79 | 0.595634 |
278f542169ee3982e74f11451af4f09a61b7a2e2 | 1,302 | py | Python | examples/plot_implied_timescales.py | smsaladi/msmexplorer | 7880545c239c8f33ababdd111f58fd553b8bbdde | [
"MIT"
] | 6 | 2018-03-02T21:02:32.000Z | 2020-05-26T08:23:24.000Z | examples/plot_implied_timescales.py | smsaladi/msmexplorer | 7880545c239c8f33ababdd111f58fd553b8bbdde | [
"MIT"
] | 9 | 2018-03-02T21:19:26.000Z | 2021-07-26T13:54:30.000Z | examples/plot_implied_timescales.py | smsaladi/msmexplorer | 7880545c239c8f33ababdd111f58fd553b8bbdde | [
"MIT"
] | 5 | 2018-02-07T18:42:23.000Z | 2021-04-29T07:01:50.000Z | """
Implied Timescales Plot
===============
"""
from msmbuilder.example_datasets import FsPeptide
from msmbuilder.featurizer import DihedralFeaturizer
from msmbuilder.decomposition import tICA
from msmbuilder.cluster import MiniBatchKMeans
from msmbuilder.msm import MarkovStateModel
import numpy as np
import msmexplorer as msme
rs = np.random.RandomState(42)
# Load Fs Peptide Data
trajs = FsPeptide().get().trajectories
# Extract Backbone Dihedrals
featurizer = DihedralFeaturizer(types=['phi', 'psi'])
diheds = featurizer.fit_transform(trajs)
# Perform Dimensionality Reduction
tica_model = tICA(lag_time=2, n_components=2)
tica_trajs = tica_model.fit_transform(diheds)
# Perform Clustering
clusterer = MiniBatchKMeans(n_clusters=100, random_state=rs)
clustered_trajs = clusterer.fit_transform(tica_trajs)
lag_times = [1, 50, 100, 250, 500, 1000, 5000]
msm_objs = []
for lag in lag_times:
# Construct MSM
msm = MarkovStateModel(lag_time=lag, n_timescales=5)
msm.fit(clustered_trajs)
msm_objs.append(msm)
# Plot Timescales
colors = ['pomegranate', 'beryl', 'tarragon', 'rawdenim', 'carbon']
msme.plot_implied_timescales(msm_objs, color_palette=colors,
xlabel='Lag time (frames)',
ylabel='Implied Timescales ($ns$)')
| 28.933333 | 67 | 0.736559 |
b88f412abd7df462d6b3c8b747a9272747cf0d18 | 1,304 | py | Python | geopandas/io/sql.py | dimitri-justeau/geopandas | 1731e44b2df88d08adfbc09260dda86d3d35e91d | [
"BSD-3-Clause"
] | 3 | 2015-03-03T21:08:39.000Z | 2015-12-14T23:22:47.000Z | geopandas/io/sql.py | dimitri-justeau/geopandas | 1731e44b2df88d08adfbc09260dda86d3d35e91d | [
"BSD-3-Clause"
] | 1 | 2021-06-02T00:37:10.000Z | 2021-06-02T00:37:10.000Z | geopandas/io/sql.py | dimitri-justeau/geopandas | 1731e44b2df88d08adfbc09260dda86d3d35e91d | [
"BSD-3-Clause"
] | 2 | 2021-01-02T02:25:31.000Z | 2021-01-10T16:41:32.000Z | import binascii
from pandas import read_sql
import shapely.wkb
from geopandas import GeoSeries, GeoDataFrame
def read_postgis(sql, con, geom_col='geom', crs=None, index_col=None,
coerce_float=True, params=None):
"""
Returns a GeoDataFrame corresponding to the result of the query
string, which must contain a geometry column.
Examples:
sql = "SELECT geom, kind FROM polygons;"
df = geopandas.read_postgis(sql, con)
Parameters
----------
sql: string
con: DB connection object or SQLAlchemy engine
geom_col: string, default 'geom'
column name to convert to shapely geometries
crs: optional
CRS to use for the returned GeoDataFrame
See the documentation for pandas.read_sql for further explanation
of the following parameters:
index_col, coerce_float, params
"""
df = read_sql(sql, con, index_col=index_col, coerce_float=coerce_float,
params=params)
if geom_col not in df:
raise ValueError("Query missing geometry column '{0}'".format(
geom_col))
wkb_geoms = df[geom_col]
s = wkb_geoms.apply(lambda x: shapely.wkb.loads(binascii.unhexlify(x.encode())))
df[geom_col] = GeoSeries(s)
return GeoDataFrame(df, crs=crs, geometry=geom_col)
| 27.744681 | 84 | 0.681748 |
445d18e9455c1203ec5db6e43b8ac9fd1f2dd4b7 | 4,595 | py | Python | GAN/coupled_gan/cogan_pytorch.py | eastonhou/generative-models | 02f19ff8f8980afea44ed0a8834bc5e1c4322b4d | [
"Unlicense"
] | 7,386 | 2016-12-15T06:54:40.000Z | 2022-03-31T16:21:47.000Z | GAN/coupled_gan/cogan_pytorch.py | milanhzj/generative-models | b930d5fa9e2f69adfd4ea8ec759f38f6ce6da4c2 | [
"Unlicense"
] | 150 | 2017-08-28T14:59:36.000Z | 2022-03-11T23:21:35.000Z | GAN/coupled_gan/cogan_pytorch.py | milanhzj/generative-models | b930d5fa9e2f69adfd4ea8ec759f38f6ce6da4c2 | [
"Unlicense"
] | 2,247 | 2017-01-12T04:20:12.000Z | 2022-03-27T00:42:14.000Z | import torch
import torch.nn
import torch.nn.functional as nn
import torch.autograd as autograd
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
from torch.autograd import Variable
from tensorflow.examples.tutorials.mnist import input_data
import copy
import scipy.ndimage.interpolation
mnist = input_data.read_data_sets('../../MNIST_data', one_hot=True)
mb_size = 32
z_dim = 100
X_dim = mnist.train.images.shape[1]
y_dim = mnist.train.labels.shape[1]
h_dim = 128
cnt = 0
lr = 1e-3
""" Shared Generator weights """
G_shared = torch.nn.Sequential(
torch.nn.Linear(z_dim, h_dim),
torch.nn.ReLU(),
)
""" Generator 1 """
G1_ = torch.nn.Sequential(
torch.nn.Linear(h_dim, X_dim),
torch.nn.Sigmoid()
)
""" Generator 2 """
G2_ = torch.nn.Sequential(
torch.nn.Linear(h_dim, X_dim),
torch.nn.Sigmoid()
)
def G1(z):
h = G_shared(z)
X = G1_(h)
return X
def G2(z):
h = G_shared(z)
X = G2_(h)
return X
""" Shared Discriminator weights """
D_shared = torch.nn.Sequential(
torch.nn.Linear(h_dim, 1),
torch.nn.Sigmoid()
)
""" Discriminator 1 """
D1_ = torch.nn.Sequential(
torch.nn.Linear(X_dim, h_dim),
torch.nn.ReLU()
)
""" Discriminator 2 """
D2_ = torch.nn.Sequential(
torch.nn.Linear(X_dim, h_dim),
torch.nn.ReLU()
)
def D1(X):
h = D1_(X)
y = D_shared(h)
return y
def D2(X):
h = D2_(X)
y = D_shared(h)
return y
D_params = (list(D1_.parameters()) + list(D2_.parameters()) +
list(D_shared.parameters()))
G_params = (list(G1_.parameters()) + list(G2_.parameters()) +
list(G_shared.parameters()))
nets = [G_shared, G1_, G2_, D_shared, D1_, D2_]
def reset_grad():
for net in nets:
net.zero_grad()
G_solver = optim.Adam(G_params, lr=lr)
D_solver = optim.Adam(D_params, lr=lr)
X_train = mnist.train.images
half = int(X_train.shape[0] / 2)
# Real image
X_train1 = X_train[:half]
# Rotated image
X_train2 = X_train[half:].reshape(-1, 28, 28)
X_train2 = scipy.ndimage.interpolation.rotate(X_train2, 90, axes=(1, 2))
X_train2 = X_train2.reshape(-1, 28*28)
# Cleanup
del X_train
def sample_x(X, size):
start_idx = np.random.randint(0, X.shape[0]-size)
return Variable(torch.from_numpy(X[start_idx:start_idx+size]))
for it in range(100000):
X1 = sample_x(X_train1, mb_size)
X2 = sample_x(X_train2, mb_size)
z = Variable(torch.randn(mb_size, z_dim))
# Dicriminator
G1_sample = G1(z)
D1_real = D1(X1)
D1_fake = D1(G1_sample)
G2_sample = G2(z)
D2_real = D2(X2)
D2_fake = D2(G2_sample)
D1_loss = torch.mean(-torch.log(D1_real + 1e-8) -
torch.log(1. - D1_fake + 1e-8))
D2_loss = torch.mean(-torch.log(D2_real + 1e-8) -
torch.log(1. - D2_fake + 1e-8))
D_loss = D1_loss + D2_loss
D_loss.backward()
# Average the gradients
for p in D_shared.parameters():
p.grad.data = 0.5 * p.grad.data
D_solver.step()
reset_grad()
# Generator
G1_sample = G1(z)
D1_fake = D1(G1_sample)
G2_sample = G2(z)
D2_fake = D2(G2_sample)
G1_loss = torch.mean(-torch.log(D1_fake + 1e-8))
G2_loss = torch.mean(-torch.log(D2_fake + 1e-8))
G_loss = G1_loss + G2_loss
G_loss.backward()
# Average the gradients
for p in G_shared.parameters():
p.grad.data = 0.5 * p.grad.data
G_solver.step()
reset_grad()
# Print and plot every now and then
if it % 1000 == 0:
print('Iter-{}; D1_loss: {:.4}; G1_loss: {:.4}; '
'D2_loss: {:.4}; G2_loss: {:.4}'
.format(
it, D1_loss.data[0], G1_loss.data[0],
D2_loss.data[0], G2_loss.data[0])
)
z = Variable(torch.randn(8, z_dim))
samples1 = G1(z).data.numpy()
samples2 = G2(z).data.numpy()
samples = np.vstack([samples1, samples2])
fig = plt.figure(figsize=(4, 4))
gs = gridspec.GridSpec(4, 4)
gs.update(wspace=0.05, hspace=0.05)
for i, sample in enumerate(samples):
ax = plt.subplot(gs[i])
plt.axis('off')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect('equal')
plt.imshow(sample.reshape(28, 28), cmap='Greys_r')
if not os.path.exists('out/'):
os.makedirs('out/')
plt.savefig('out/{}.png'
.format(str(cnt).zfill(3)), bbox_inches='tight')
cnt += 1
plt.close(fig)
| 22.52451 | 72 | 0.604788 |
9412d7df98fae6e7e783eee6e49ebc07dbbbcf4b | 6,752 | py | Python | mycroft/audio/speech.py | damorosodaragona/mycroft-core | 367542b5504c4ab3c9e5c46e3c8e3b150c01d3d0 | [
"Apache-2.0"
] | 2 | 2021-04-05T22:28:37.000Z | 2021-06-16T00:24:41.000Z | mycroft/audio/speech.py | damorosodaragona/mycroft-core | 367542b5504c4ab3c9e5c46e3c8e3b150c01d3d0 | [
"Apache-2.0"
] | 4 | 2021-06-08T22:45:08.000Z | 2022-03-12T00:51:26.000Z | mycroft/audio/speech.py | mjkaye/mycroft-core-deb | f3ae5327a4d45a7e2e1d5511850097472b755c53 | [
"Apache-2.0"
] | 2 | 2020-09-28T01:38:34.000Z | 2020-12-03T03:14:32.000Z | # Copyright 2017 Mycroft AI 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.
#
import re
import time
from threading import Lock
from mycroft.configuration import Configuration
from mycroft.metrics import report_timing, Stopwatch
from mycroft.tts import TTSFactory
from mycroft.util import check_for_signal
from mycroft.util.log import LOG
from mycroft.messagebus.message import Message
from mycroft.tts.remote_tts import RemoteTTSException
from mycroft.tts.mimic_tts import Mimic
bus = None # Mycroft messagebus connection
config = None
tts = None
tts_hash = None
lock = Lock()
mimic_fallback_obj = None
_last_stop_signal = 0
def handle_speak(event):
"""Handle "speak" message
Parse sentences and invoke text to speech service.
"""
config = Configuration.get()
Configuration.set_config_update_handlers(bus)
global _last_stop_signal
# if the message is targeted and audio is not the target don't
# don't synthezise speech
event.context = event.context or {}
if event.context.get('destination') and not \
('debug_cli' in event.context['destination'] or
'audio' in event.context['destination']):
return
# Get conversation ID
if event.context and 'ident' in event.context:
ident = event.context['ident']
else:
ident = 'unknown'
start = time.time() # Time of speech request
with lock:
stopwatch = Stopwatch()
stopwatch.start()
utterance = event.data['utterance']
listen = event.data.get('expect_response', False)
# This is a bit of a hack for Picroft. The analog audio on a Pi blocks
# for 30 seconds fairly often, so we don't want to break on periods
# (decreasing the chance of encountering the block). But we will
# keep the split for non-Picroft installs since it give user feedback
# faster on longer phrases.
#
# TODO: Remove or make an option? This is really a hack, anyway,
# so we likely will want to get rid of this when not running on Mimic
if (config.get('enclosure', {}).get('platform') != "picroft" and
len(re.findall('<[^>]*>', utterance)) == 0):
# Remove any whitespace present after the period,
# if a character (only alpha) ends with a period
# ex: A. Lincoln -> A.Lincoln
# so that we don't split at the period
utterance = re.sub(r'\b([A-za-z][\.])(\s+)', r'\g<1>', utterance)
chunks = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\;|\?)\s',
utterance)
# Apply the listen flag to the last chunk, set the rest to False
chunks = [(chunks[i], listen if i == len(chunks) - 1 else False)
for i in range(len(chunks))]
for chunk, listen in chunks:
# Check if somthing has aborted the speech
if (_last_stop_signal > start or
check_for_signal('buttonPress')):
# Clear any newly queued speech
tts.playback.clear()
break
try:
mute_and_speak(chunk, ident, listen)
except KeyboardInterrupt:
raise
except Exception:
LOG.error('Error in mute_and_speak', exc_info=True)
else:
mute_and_speak(utterance, ident, listen)
stopwatch.stop()
report_timing(ident, 'speech', stopwatch, {'utterance': utterance,
'tts': tts.__class__.__name__})
def mute_and_speak(utterance, ident, listen=False):
"""Mute mic and start speaking the utterance using selected tts backend.
Arguments:
utterance: The sentence to be spoken
ident: Ident tying the utterance to the source query
"""
global tts_hash
# update TTS object if configuration has changed
if tts_hash != hash(str(config.get('tts', ''))):
global tts
# Stop tts playback thread
tts.playback.stop()
tts.playback.join()
# Create new tts instance
tts = TTSFactory.create()
tts.init(bus)
tts_hash = hash(str(config.get('tts', '')))
LOG.info("Speak: " + utterance)
try:
tts.execute(utterance, ident, listen)
except RemoteTTSException as e:
LOG.error(e)
mimic_fallback_tts(utterance, ident, listen)
except Exception as e:
LOG.error('TTS execution failed ({})'.format(repr(e)))
def mimic_fallback_tts(utterance, ident, listen):
global mimic_fallback_obj
# fallback if connection is lost
config = Configuration.get()
tts_config = config.get('tts', {}).get("mimic", {})
lang = config.get("lang", "en-us")
if not mimic_fallback_obj:
mimic_fallback_obj = Mimic(lang, tts_config)
tts = mimic_fallback_obj
LOG.debug("Mimic fallback, utterance : " + str(utterance))
tts.init(bus)
tts.execute(utterance, ident, listen)
def handle_stop(event):
"""Handle stop message.
Shutdown any speech.
"""
global _last_stop_signal
if check_for_signal("isSpeaking", -1):
_last_stop_signal = time.time()
tts.playback.clear() # Clear here to get instant stop
bus.emit(Message("mycroft.stop.handled", {"by": "TTS"}))
def init(messagebus):
"""Start speech related handlers.
Arguments:
messagebus: Connection to the Mycroft messagebus
"""
global bus
global tts
global tts_hash
global config
bus = messagebus
Configuration.set_config_update_handlers(bus)
config = Configuration.get()
bus.on('mycroft.stop', handle_stop)
bus.on('mycroft.audio.speech.stop', handle_stop)
bus.on('speak', handle_speak)
tts = TTSFactory.create()
tts.init(bus)
tts_hash = hash(str(config.get('tts', '')))
def shutdown():
"""Shutdown the audio service cleanly.
Stop any playing audio and make sure threads are joined correctly.
"""
if tts:
tts.playback.stop()
tts.playback.join()
if mimic_fallback_obj:
mimic_fallback_obj.playback.stop()
mimic_fallback_obj.playback.join()
| 34.10101 | 79 | 0.631961 |
3ec693d78e4b5e0a1f18ad421790ac2ef6c8b94c | 3,682 | py | Python | example/utils.py | maresb/keras-transformer | e7374b43625dc6e8997a1882ad94c886377bee74 | [
"MIT"
] | null | null | null | example/utils.py | maresb/keras-transformer | e7374b43625dc6e8997a1882ad94c886377bee74 | [
"MIT"
] | null | null | null | example/utils.py | maresb/keras-transformer | e7374b43625dc6e8997a1882ad94c886377bee74 | [
"MIT"
] | null | null | null | import math
import warnings
import h5py
from keras import Model
def load_optimizer_weights(model: Model, model_save_path: str):
"""
Loads optimizer's weights for the model from an HDF5 file.
"""
with h5py.File(model_save_path, mode='r') as f:
if 'optimizer_weights' in f:
# Build train function (to get weight updates).
# noinspection PyProtectedMember
model._make_train_function()
optimizer_weights_group = f['optimizer_weights']
optimizer_weight_names = [
n.decode('utf8') for n in
optimizer_weights_group.attrs['weight_names']]
optimizer_weight_values = [
optimizer_weights_group[n]
for n in optimizer_weight_names]
try:
model.optimizer.set_weights(optimizer_weight_values)
except ValueError:
warnings.warn('Error in loading the saved optimizer '
'state. As a result, your model is '
'starting with a freshly initialized '
'optimizer.')
def contain_tf_gpu_mem_usage():
"""
By default TensorFlow may try to reserve all available GPU memory
making it impossible to train multiple networks at once.
This function will disable such behaviour in TensorFlow.
"""
from keras import backend
if backend.backend() != 'tensorflow':
return
try:
# noinspection PyPackageRequirements
import tensorflow as tf
except ImportError:
pass
else:
from keras.backend.tensorflow_backend import set_session
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True # dynamically grow the memory
sess = tf.compat.v1.Session(config=config)
set_session(sess)
class CosineLRSchedule:
"""
Cosine annealing with warm restarts, described in paper
"SGDR: stochastic gradient descent with warm restarts"
https://arxiv.org/abs/1608.03983
Changes the learning rate, oscillating it between `lr_high` and `lr_low`.
It takes `period` epochs for the learning rate to drop to its very minimum,
after which it quickly returns back to `lr_high` (resets) and everything
starts over again.
With every reset:
* the period grows, multiplied by factor `period_mult`
* the maximum learning rate drops proportionally to `high_lr_mult`
This class is supposed to be used with
`keras.callbacks.LearningRateScheduler`.
"""
def __init__(self, lr_high: float, lr_low: float, initial_period: int = 50,
period_mult: float = 2, high_lr_mult: float = 0.97):
self._lr_high = lr_high
self._lr_low = lr_low
self._initial_period = initial_period
self._period_mult = period_mult
self._high_lr_mult = high_lr_mult
def __call__(self, epoch, lr):
return self.get_lr_for_epoch(epoch)
def get_lr_for_epoch(self, epoch):
assert epoch >= 0
t_cur = 0
lr_max = self._lr_high
period = self._initial_period
result = lr_max
for i in range(epoch + 1):
if i == epoch: # last iteration
result = (self._lr_low +
0.5 * (lr_max - self._lr_low) *
(1 + math.cos(math.pi * t_cur / period)))
else:
if t_cur == period:
period *= self._period_mult
lr_max *= self._high_lr_mult
t_cur = 0
else:
t_cur += 1
return result
| 35.747573 | 79 | 0.606735 |
a1b86159bccf26403c849c48e529828e7aef1a84 | 407 | py | Python | myInsta_project/asgi.py | inziani/myInsta | 3f564f8a0240fc02665b06b4032214046771d3ef | [
"Unlicense"
] | null | null | null | myInsta_project/asgi.py | inziani/myInsta | 3f564f8a0240fc02665b06b4032214046771d3ef | [
"Unlicense"
] | null | null | null | myInsta_project/asgi.py | inziani/myInsta | 3f564f8a0240fc02665b06b4032214046771d3ef | [
"Unlicense"
] | null | null | null | """
ASGI config for myInsta_project project.
It exposes the ASGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/howto/deployment/asgi/
"""
import os
from django.core.asgi import get_asgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'myInsta_project.settings')
application = get_asgi_application()
| 23.941176 | 78 | 0.793612 |
ecc94a99f9d592417cb092b2ab4e0969a234edaa | 6,114 | py | Python | fabfile.py | Sibyx/dbs2019-project-assignment-nightgaunt | 6c65364ef04b413a734dd495fba85569e2648d73 | [
"Apache-2.0"
] | 1 | 2020-03-21T16:44:48.000Z | 2020-03-21T16:44:48.000Z | fabfile.py | Sibyx/dbs2019-project-assignment-nightgaunt | 6c65364ef04b413a734dd495fba85569e2648d73 | [
"Apache-2.0"
] | 9 | 2019-05-17T09:26:59.000Z | 2022-03-11T23:46:51.000Z | fabfile.py | Sibyx/mdns | 6c65364ef04b413a734dd495fba85569e2648d73 | [
"Apache-2.0"
] | null | null | null | import datetime
import json
import os
import warnings
from fabric import Connection, task
from invoke import Context
warnings.filterwarnings(action='ignore', module='.*paramiko.*')
PROJECT_NAME = "mdns"
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
REPO_URL = "https://github.com/Sibyx/mdns.git"
KEEP_RELEASES = 5
def _get_connection(ctx: Context, config: dict) -> Connection:
ctx.host = config['host']
ctx.user = config['user']
ctx.connect_kwargs.key_filename = config['private_key']
ctx.port = config['port']
ctx = Connection(
host=ctx.host,
user=ctx.user,
port=ctx.port,
connect_kwargs=ctx.connect_kwargs,
)
ctx.config['run']['echo'] = True
return ctx
def _parse_config(destination: str) -> dict:
with open(f"{BASE_DIR}/.deploy/{destination}.json") as conf_file:
return json.load(conf_file)
@task
def check(ctx, destination):
config = _parse_config(destination)
ctx = _get_connection(ctx, config['ssh'])
ctx.run(f'{config["interpreter"]} --version')
@task
def setup(ctx, destination):
config = _parse_config(destination)
ctx = _get_connection(ctx, config['ssh'])
shared_env = f"{config['deploy_to']}/shared/env"
ctx.run(f"mkdir {config['deploy_to']}")
with ctx.cd(config['deploy_to']):
# Create directory structure
ctx.run(f"mkdir shared")
ctx.run(f"mkdir shared/logs")
ctx.run(f"mkdir shared/media")
ctx.run(f"mkdir releases")
# Create Python virtualenv
ctx.run(f"{config['interpreter']} -m venv shared/env")
# Install deployment tools
ctx.run(f"{shared_env}/bin/pip install pipfile-requirements --no-cache-dir")
@task
def deploy(ctx, destination):
config = _parse_config(destination)
ctx = _get_connection(ctx, config['ssh'])
release = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
shared_env = f"{config['deploy_to']}/shared/env"
# Set deploy to as current directory
with ctx.cd(f"{config['deploy_to']}/releases"):
# Clone repository
ctx.run(f"git clone {REPO_URL} {release}")
# Set current release directory as working directory
with ctx.cd(f"{config['deploy_to']}/releases/{release}"):
# Checkout correct revision
ctx.run(f"git checkout {config['revision']}")
# Just to be sure, run git pull
ctx.run("git pull")
# Install & update dependencies
ctx.run(f"{shared_env}/bin/pip install --upgrade --no-cache-dir pip")
ctx.run(f"{shared_env}/bin/pip install --upgrade --no-cache-dir pipfile-requirements")
ctx.run(f"{shared_env}/bin/pipfile2req Pipfile.lock > requirements.txt")
ctx.run(f"{shared_env}/bin/pip install -r requirements.txt --no-cache-dir")
# Create .env file
ctx.run("touch .env")
for key, value in config['env'].items():
ctx.run(f'echo "{key}=\'{value}\'" >> .env')
# Create symlinks for logs
ctx.run(f"ln -s {config['deploy_to']}/shared/logs logs")
ctx.run("touch logs/error.log")
ctx.run("touch logs/request.log")
ctx.run("touch logs/sql.log")
# Create symlink for media
ctx.run(f"rm -rf media")
ctx.run(f"ln -s {config['deploy_to']}/shared/media media")
# Migrate
ctx.run(
f"DJANGO_SETTINGS_MODULE={config['env']['DJANGO_SETTINGS_MODULE']} "
f"{shared_env}/bin/python manage.py migrate --no-input"
)
# Static files
ctx.put("static/bundle.js", f"{config['deploy_to']}/releases/{release}/static/")
ctx.put("static/bundle.js.map", f"{config['deploy_to']}/releases/{release}/static/")
# Removing sensitive data
ctx.run(f"rm -rf .deploy Pipfile Pipfile.lock requirements.txt")
# Publish release
with ctx.cd(config['deploy_to']):
# Remove old symlink
ctx.run("rm -f current")
# Create symlink to the latest release
ctx.run(f"ln -s {config['deploy_to']}/releases/{release} current")
# Restart Gunicorn service
# ctx.run("sudo systemctl restart mdns-web")
# Clean old releases
with ctx.cd(f"{config['deploy_to']}/releases"):
ctx.run(f"ls -t . | sort -r | tail -n +{KEEP_RELEASES + 1} | xargs rm -rf --")
@task
def clean(ctx, destination):
config = _parse_config(destination)
ctx = _get_connection(ctx, config['ssh'])
ctx.run(f"rm -rf {config['deploy_to']}")
@task
def user(ctx, destination):
config = _parse_config(destination)
ctx = _get_connection(ctx, config['ssh'])
shared_env = f"{config['deploy_to']}/shared/env"
# Move to project folder
with ctx.cd(f"{config['deploy_to']}/current"):
ctx.run(
f"DJANGO_SETTINGS_MODULE={config['env']['DJANGO_SETTINGS_MODULE']} "
f"{shared_env}/bin/python manage.py createsuperuser", pty=True
)
@task
def fake(ctx, destination):
config = _parse_config(destination)
ctx = _get_connection(ctx, config['ssh'])
shared_env = f"{config['deploy_to']}/shared/env"
# Move to project folder
with ctx.cd(f"{config['deploy_to']}/current"):
ctx.run(
f"DJANGO_SETTINGS_MODULE={config['env']['DJANGO_SETTINGS_MODULE']} "
f"{shared_env}/bin/python manage.py fake --clear", pty=True
)
@task
def organisms(ctx, destination):
config = _parse_config(destination)
ctx = _get_connection(ctx, config['ssh'])
shared_env = f"{config['deploy_to']}/shared/env"
# Move to project folder
with ctx.cd(f"{config['deploy_to']}/current"):
ctx.put("tmp/data.csv", f"{config['deploy_to']}/current/tmp")
ctx.run(
f"DJANGO_SETTINGS_MODULE={config['env']['DJANGO_SETTINGS_MODULE']} "
f"{shared_env}/bin/python manage.py import_organisms --file tmp/data.csv", pty=True
)
@task
def restart(ctx, destination):
config = _parse_config(destination)
ctx = _get_connection(ctx, config['ssh'])
# Restart Gunicorn service
ctx.run("sudo systemctl restart mdns-web", pty=True)
| 30.878788 | 95 | 0.636572 |
5a9599d08a335223ff4a239fa9350dd7894a2917 | 41,614 | py | Python | pychron/graph/graph.py | WiscAr/pychron | 8d335d53ba7a5fc70760d9a7cb60540ad169ae84 | [
"Apache-2.0"
] | null | null | null | pychron/graph/graph.py | WiscAr/pychron | 8d335d53ba7a5fc70760d9a7cb60540ad169ae84 | [
"Apache-2.0"
] | 80 | 2018-07-17T20:10:20.000Z | 2021-08-17T15:38:24.000Z | pychron/graph/graph.py | UManPychron/pychron | b84c9fd70072f9cbda30abe2c471e64fe3dd75d8 | [
"Apache-2.0"
] | null | null | null | # ===============================================================================
# Copyright 2011 Jake Ross
#
# 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.
# ===============================================================================
# =============enthought library imports=======================
import csv
import math
import os
import six
from chaco.api import OverlayPlotContainer, \
VPlotContainer, HPlotContainer, GridPlotContainer, \
BasePlotContainer, Plot, ArrayPlotData
from chaco.array_data_source import ArrayDataSource
from chaco.axis import PlotAxis
from enable.component_editor import ComponentEditor
from numpy import array, hstack, Inf, column_stack
from pyface.timer.api import do_after as do_after_timer
from traits.api import Instance, List, Str, Property, Dict, Event, Bool
from traitsui.api import View, Item, UItem
from pychron.core.helpers.color_generators import colorname_generator as color_generator
from pychron.core.helpers.filetools import add_extension
from pychron.graph.context_menu_mixin import ContextMenuMixin
from pychron.graph.ml_label import MPlotAxis
from pychron.graph.offset_plot_label import OffsetPlotLabel
from pychron.graph.tools.axis_tool import AxisTool
from .tools.contextual_menu_tool import ContextualMenuTool
VALID_FONTS = ['Arial', 'Lucida Grande', 'Geneva', 'Courier']
# 'Helvetica',
# 'Times New Roman'
CONTAINERS = {'v': VPlotContainer, 'h': HPlotContainer, 'g': GridPlotContainer, 'o': OverlayPlotContainer}
IMAGE_EXTENSIONS = ['.png', '.jpg', '.jpeg', '.tiff', '.tif', '.gif']
DEFAULT_IMAGE_EXT = IMAGE_EXTENSIONS[0]
def name_generator(base):
i = 0
while 1:
n = base + str(i)
yield n
i += 1
def fmt(data):
return ['%0.8f' % d for d in data]
def get_file_path(action='save as', **kw):
from pyface.api import FileDialog, OK
dlg = FileDialog(action=action, **kw)
if dlg.open() == OK:
return dlg.path
def add_aux_axis(po, p, title='', color='black'):
"""
"""
from chaco.axis import PlotAxis
axis = PlotAxis(p, orientation='right',
title=title,
axis_line_visible=False,
tick_color=color,
tick_label_color=color,
title_color=color)
p.underlays.append(axis)
po.add(p)
po.x_grid.visible = False
po.y_grid.visible = False
def plot_axis_factory(p, key, normal, **kw):
if key == 'x':
m = p.index_mapper
if normal:
o = 'bottom'
else:
o = 'top'
kw['tick_label_formatter'] = lambda x: ''
else:
if normal:
o = 'left'
else:
o = 'right'
kw['tick_label_formatter'] = lambda x: ''
m = p.value_mapper
from chaco.axis import PlotAxis
ax = PlotAxis(component=p,
mapper=m,
orientation=o,
axis_line_visible=False,
**kw)
return ax
def plot_factory(legend_kw=None, **kw):
"""
"""
p = Plot(data=ArrayPlotData(), **kw)
vis = kw['show_legend'] if 'show_legend' in kw else False
if not isinstance(vis, bool):
align = vis
vis = True
else:
align = 'lr'
p.legend.visible = vis
p.legend.align = align
if legend_kw:
p.legend.trait_set(**legend_kw)
return p
def container_factory(**kw):
"""
"""
if 'kind' in kw:
kind = kw['kind']
else:
kind = 'v'
cklass = CONTAINERS.get(kind, VPlotContainer)
options = dict(bgcolor='white', padding=5, fill_padding=True)
for k in options:
if k not in list(kw.keys()):
kw[k] = options[k]
container = cklass(**kw)
return container
class Graph(ContextMenuMixin):
"""
"""
name = Str
plotcontainer = Instance(BasePlotContainer)
container_dict = Dict
plots = List(Plot)
selected_plotid = Property(depends_on='selected_plot')
selected_plot = Instance(Plot)
window_title = ''
window_width = 600
window_height = 500
window_x = 500
window_y = 250
width = 300
height = 300
resizable = True
line_inspectors_write_metadata = False
autoupdate = Bool(False)
_convert_index = None
status_text = Str
x_limits_changed = Event
xdataname_generators = List
ydataname_generators = List
yerdataname_generators = List
color_generators = List
series = List
data_len = List
data_limits = List
def __init__(self, *args, **kw):
"""
"""
super(Graph, self).__init__(*args, **kw)
self.clear()
pc = self.plotcontainer
if self.use_context_menu:
menu = ContextualMenuTool(parent=self,
component=pc)
pc.tools.append(menu)
def update_group_attribute(self, plot, attr, value, dataid=0):
pass
def get_plotid_by_ytitle(self, *args, **kw):
plot = self.get_plot_by_ytitle(*args, **kw)
if plot is not None:
return self.plots.index(plot)
def get_plot_by_ytitle(self, txt, startswith=False):
"""
iso: str
return None or Plot with y_axis.title equal to iso
if startswith is True title only has to start with iso
"""
txt = str(txt)
if startswith:
is_equal = lambda x: x.startswith(txt)
else:
is_equal = lambda x: x.__eq__(txt)
for po in self.plots:
if is_equal(po.y_axis.title):
return po
else:
print('plot titles txt={} {}'.format(txt, [po.y_axis.title for po in self.plots]))
def get_num_plots(self):
"""
"""
return len(self.plots)
def get_num_series(self, plotid):
"""
"""
return len(self.series[plotid])
def get_data(self, plotid=0, series=0, axis=0):
"""
"""
if isinstance(series, (str, six.text_type)):
s = series
else:
s = self.series[plotid][series][axis]
p = self.plots[plotid]
return p.data.get_data(s)
def get_aux_data(self, plotid=0, series=1):
plot = self.plots[plotid]
si = plot.plots['aux{:03d}'.format(series)][0]
oi = si.index.get_data()
ov = si.value.get_data()
return oi, ov
def save_png(self, path=None):
"""
"""
self._save(type_='pic', path=path)
def save_pdf(self, path=None):
"""
"""
from pychron.core.pdf.save_pdf_dialog import save_pdf
save_pdf(self.plotcontainer)
# self._save(type_='pdf', path=path)
def save(self, path=None):
"""
"""
self._save(path=path)
def rescale_x_axis(self):
# l, h = self.selected_plot.default_index.get_bounds()
# self.set_x_limits(l, h, plotid=self.selected_plotid)
r = self.selected_plot.index_range
r.reset()
def rescale_y_axis(self):
r = self.selected_plot.value_range
r.reset()
def rescale_both(self):
self.rescale_x_axis()
self.rescale_y_axis()
def export_data(self, path=None, plotid=None):
"""
"""
if path is None:
path = get_file_path()
if path is not None:
path = add_extension(path, '.csv')
self._export_data(path, plotid)
def read_xy(self, p, header=False, series=0, plotid=0):
"""
"""
x = []
y = []
with open(p, 'r') as f:
reader = csv.reader(f)
if header:
next(reader)
for line in reader:
if line[0].startswith('#'):
continue
if len(line) == 2:
x.append(float(line[0]))
y.append(float(line[1]))
self.set_data(x, plotid, series)
self.set_data(y, plotid, series, axis=1)
def remove_rulers(self, plotid=0):
from pychron.graph.guide_overlay import GuideOverlay
plot = self.plots[plotid]
for o in plot.overlays:
if isinstance(o, GuideOverlay):
plot.overlays.remove(o)
def clear_plots(self):
x = list(range(len(self.plots)))
self.xdataname_generators = [name_generator('x') for _ in x]
self.ydataname_generators = [name_generator('y') for _ in x]
self.yerdataname_generators = [name_generator('yer') for _ in x]
self.color_generators = [color_generator() for _ in x]
self.series = [[] for _ in x]
self.data_len = [[] for _ in x]
self.data_limits = [[] for _ in x]
for pi in self.plots:
for k, pp in list(pi.plots.items()):
for renderer in pp:
try:
pi.remove(renderer)
except RuntimeError:
print('failed removing {}'.format(renderer))
pi.plots.pop(k)
self.clear_data()
def clear(self, clear_container=True):
"""
"""
self.clear_plots()
self.plots = []
self.xdataname_generators = [name_generator('x')]
self.ydataname_generators = [name_generator('y')]
self.yerdataname_generators = [name_generator('yer')]
self.color_generators = [color_generator()]
self.series = []
self.data_len = []
self.data_limits = []
if clear_container:
self.plotcontainer = pc = self.container_factory()
if self.use_context_menu:
menu = ContextualMenuTool(parent=self,
component=pc)
pc.tools.append(menu)
self.selected_plot = None
def set_axis_label_color(self, *args, **kw):
"""
"""
kw['attr'] = 'title'
self._set_axis_color(*args, **kw)
def set_axis_tick_color(self, *args, **kw):
"""
"""
attrs = ['tick_label', 'tick']
if 'attrs' in kw:
attrs = kw['attrs']
for a in attrs:
kw['attr'] = a
self._set_axis_color(*args, **kw)
def set_aux_data(self, x, y, plotid=0, series=1):
p = self.plots[plotid].plots['aux{:03d}'.format(series)][0]
p.index.set_data(x)
p.value.set_data(y)
def clear_data(self, plotid=None, **kw):
if plotid is None:
for i, p in enumerate(self.plots):
for s in self.series[i]:
for k in s:
p.data.set_data(k, [])
else:
self.set_data([], **kw)
def set_data(self, d, plotid=0, series=0, axis=0):
"""
"""
if isinstance(series, int):
n = self.series[plotid][series]
series = n[axis]
self.plots[plotid].data.set_data(series, d)
def set_axis_traits(self, plotid=0, axis='x', **kw):
"""
"""
plot = self.plots[plotid]
attr = getattr(plot, '{}_axis'.format(axis))
attr.trait_set(**kw)
def set_grid_traits(self, plotid=0, grid='x', **kw):
"""
"""
plot = self.plots[plotid]
attr = getattr(plot, '{}_grid'.format(grid))
attr.trait_set(**kw)
def set_series_traits(self, d, plotid=0, series=0):
"""
"""
plot = self.plots[plotid].plots['plot%i' % series][0]
plot.trait_set(**d)
self.plotcontainer.request_redraw()
def get_series_color(self, plotid=0, series=0):
if isinstance(series, int):
series = 'plot{:03d}'.format(series)
p = self.plots[plotid].plots[series][0]
return p.color
def get_series_label(self, plotid=0, series=0):
"""
"""
r = ''
legend = self.plots[plotid].legend
if isinstance(series, str):
if series in legend.labels:
return series
return
try:
r = legend.labels[series]
except IndexError:
pass
return r
def set_series_label(self, label, plotid=0, series=None):
"""
A chaco update requires that the legends labels match the keys in the plot dict
save the plots from the dict
resave them with label as the key
"""
legend = self.plots[plotid].legend
if series is None:
n = len(list(self.plots[plotid].plots.keys()))
series = n - 1
if isinstance(series, int):
series = 'plot{}'.format(series)
try:
legend.labels[series] = label
except Exception as e:
legend.labels.append(label)
try:
plots = self.plots[plotid].plots[series]
except KeyError:
print('set series label plotid={} {}'.format(plotid, list(self.plots[plotid].plots.keys())))
raise
self.plots[plotid].plots[label] = plots
self.plots[plotid].plots.pop(series)
def clear_legend(self, keys, plotid=0):
"""
"""
legend = self.plots[plotid].legend
for key in keys:
legend.plots.pop(key)
def set_series_visibility(self, v, plotid=0, series=0):
"""
"""
p = self.plots[plotid]
if isinstance(series, int):
series = 'plot%i' % series
try:
p.showplot(series) if v else p.hideplot(series)
self.plotcontainer.invalidate_and_redraw()
except KeyError as e:
print('set series visibility', e, p.series)
def get_x_limits(self, plotid=0):
"""
"""
return self._get_limits('index', plotid=plotid)
def get_y_limits(self, plotid=0):
"""
"""
return self._get_limits('value', plotid=plotid)
def set_y_limits(self, min_=None, max_=None, pad=0, plotid=0, **kw):
"""
"""
mmin, mmax = self.get_y_limits(plotid)
if min_ is None:
min_ = mmin
if max_ is None:
max_ = mmax
self._set_limits(min_, max_, 'value', plotid, pad, **kw)
def set_x_limits(self, min_=None, max_=None, pad=0, plotid=0, **kw):
"""
"""
if self._set_limits(min_, max_, 'index', plotid, pad, **kw):
self.x_limits_changed = True
def set_x_tracking(self, track, plotid=0):
"""
"""
plot = self.plots[plotid]
if track:
plot.index_range.tracking_amount = track
plot.index_range.high_setting = 'track'
plot.index_range.low_setting = 'auto'
else:
plot.index_range.high_setting = 'auto'
plot.index_range.low_setting = 'auto'
def set_y_tracking(self, track, plotid=0):
"""
"""
plot = self.plots[plotid]
if track:
plot.value_range.tracking_amount = track
plot.value_range.high_setting = 'track'
plot.value_range.low_setting = 'auto'
else:
plot.value_range.high_setting = 'auto'
plot.value_range.low_setting = 'auto'
def set_plot_title(self, t, font='modern', size=None, plotid=0):
p = self.plots[plotid]
p.title = t
def set_title(self, t, font='modern', size=None):
"""
"""
self._title = t
pc = self.plotcontainer
if pc.overlays:
pc.overlays.pop()
if font not in VALID_FONTS:
font = 'modern'
if size is None:
size = 12
# self._title_font = font
# self._title_size = size
font = '{} {}'.format(font, size)
from chaco.plot_label import PlotLabel
pl = PlotLabel(t, component=pc, font=font)
pc.overlays.append(pl)
self.redraw()
def get_x_title(self, plotid=0):
"""
"""
return self._get_title('y_axis', plotid)
def get_y_title(self, plotid=0):
"""
"""
return self._get_title('x_axis', plotid)
def set_x_title(self, title, plotid=None, **font):
"""
"""
self._set_title('x_axis', title, plotid, **font)
def set_y_title(self, title, plotid=None, **font):
"""
"""
self._set_title('y_axis', title, plotid, **font)
def add_axis_tool(self, plot, axis):
t = AxisTool(component=axis)
plot.tools.append(t)
def add_limit_tool(self, plot, orientation, handler=None):
from pychron.graph.tools.limits_tool import LimitsTool
from pychron.graph.tools.limits_tool import LimitOverlay
t = LimitsTool(component=plot,
orientation=orientation)
o = LimitOverlay(component=plot, tool=t)
plot.tools.insert(0, t)
plot.overlays.append(o)
if handler:
t.on_trait_change(handler, 'limits_updated')
def add_plot_label(self, txt, plotid=0, overlay_position='inside top', hjustify='left', **kw):
"""
"""
c = self.plots[plotid]
pl = OffsetPlotLabel(txt,
component=c,
overlay_position=overlay_position, hjustify=hjustify,
**kw)
c.overlays.append(pl)
return pl
def add_data_label(self, x, y, plotid=0):
"""
"""
from chaco.data_label import DataLabel
plot = self.plots[plotid]
label = DataLabel(component=plot, data_point=(x, y),
label_position="top left", padding=40,
bgcolor="lightgray",
border_visible=False)
plot.overlays.append(label)
def delplot(self, plotid=0, series=0):
plot = self.plots[plotid]
if isinstance(series, int):
series = 'plot{}'.format(series)
plot.delplot(series)
def new_plot(self, add=True, **kw):
"""
"""
p = plot_factory(**kw)
self.plots.append(p)
self.color_generators.append(color_generator())
self.xdataname_generators.append(name_generator('x'))
self.ydataname_generators.append(name_generator('y'))
self.yerdataname_generators.append(name_generator('yer'))
self.series.append([])
pc = self.plotcontainer
if add:
if not isinstance(add, bool):
pc.insert(add, p)
else:
pc.add(p)
zoom = kw['zoom'] if 'zoom' in kw else False
pan = kw['pan'] if 'pan' in kw else False
tools = []
if zoom:
nkw = dict(tool_mode='box',
always_on=False)
if 'zoom_dict' in kw:
zoomargs = kw['zoom_dict']
for k in zoomargs:
nkw[k] = zoomargs[k]
from chaco.tools.api import ZoomTool
zt = ZoomTool(component=p, **nkw)
p.overlays.append(zt)
tools.append(zt)
if pan:
from .tools.pan_tool import MyPanTool as PanTool
kwargs = dict(always_on=False)
if isinstance(pan, str):
kwargs['constrain'] = True
kwargs['constrain_direction'] = pan
kwargs['constrain_key'] = None
pt = PanTool(p, container=pc, **kwargs)
tools.append(pt)
plotid = len(self.plots) - 1
for t in ['x', 'y']:
title = '{}title'.format(t)
if title in kw:
self._set_title('{}_axis'.format(t), kw[title], plotid)
p.tools = tools
return p
def new_graph(self, *args, **kw):
"""
"""
raise NotImplementedError
def new_series(self, x=None, y=None,
yer=None,
plotid=None,
colors=None,
color_map_name='hot',
marker_size=2,
**kw):
"""
"""
if plotid is None:
plotid = len(self.plots) - 1
kw['plotid'] = plotid
kw['marker_size'] = marker_size
plotobj, names, rd = self._series_factory(x, y, yer=yer, **kw)
if 'type' in rd:
ptype = rd['type']
if ptype == 'line_scatter':
plotobj.plot(names,
type='scatter', marker_size=2,
marker='circle',
color=rd['color'],
outline_color=rd['color'])
rd['type'] = 'line'
elif ptype == 'scatter':
if 'outline_color' not in rd:
rd['outline_color'] = rd['color']
if 'selection_outline_color' not in rd:
rd['selection_outline_color'] = rd['color']
if ptype == 'cmap_scatter':
from chaco.default_colormaps import color_map_name_dict
rd['selection_color'] = rd['color']
rd['selection_outline_color'] = rd['color']
rd['color_mapper'] = color_map_name_dict[color_map_name]
c = self.series[plotid][-1][0].replace('x', 'c')
self.plots[plotid].data.set_data(c, array(colors))
names += (c,)
renderer = plotobj.plot(names, **rd)
return renderer[0], plotobj
def auto_update(self, *args, **kw):
"""
"""
pass
def add_aux_axis(self, po, p, title='', color='black'):
"""
"""
axis = PlotAxis(p, orientation='right',
title=title,
axis_line_visible=False,
tick_color=color,
tick_label_color=color,
title_color=color)
p.underlays.append(axis)
po.add(p)
po.x_grid.visible = False
po.y_grid.visible = False
def add_aux_datum(self, datum, plotid=0, series=1, do_after=False):
"""
"""
# def add():
plot = self.plots[plotid]
si = plot.plots['aux{:03d}'.format(series)][0]
oi = si.index.get_data()
ov = si.value.get_data()
si.index.set_data(hstack((oi, [datum[0]])))
si.value.set_data(hstack((ov, [datum[1]])))
# if do_after:
# do_after_timer(do_after, add)
# else:
# add()
def add_data(self, data, plotlist=None, **kw):
"""
"""
if plotlist is None:
plotlist = range(len(data))
for pi, d in zip(plotlist, data):
self.add_datum(d,
plotid=pi,
**kw)
def add_bulk_data(self, xs, ys, plotid=0, series=0,
ypadding='0.1',
update_y_limits=False):
try:
names = self.series[plotid][series]
except IndexError:
print('adding data', plotid, series, self.series[plotid])
plot = self.plots[plotid]
data = plot.data
for n, ds in ((names[0], xs), (names[1], ys)):
xx = data.get_data(n)
xx = hstack((xx, ds))
data.set_data(n, xx)
if update_y_limits:
ys = data[names[1]]
mi = ys.min()
ma = ys.max()
if isinstance(ypadding, str):
ypad = max(0.1, abs(mi - ma)) * float(ypadding)
else:
ypad = ypadding
mi -= ypad
ma += ypad
# # if ymin_anchor is not None:
# # mi = max(ymin_anchor, mi)
#
self.set_y_limits(min_=mi,
max_=ma,
plotid=plotid)
def add_datum(self, datum, plotid=0, series=0,
update_y_limits=False,
ypadding=10,
ymin_anchor=None,
**kw):
try:
names = self.series[plotid][series]
except (IndexError, TypeError):
print('adding datum', plotid, series, self.series[plotid])
return
plot = self.plots[plotid]
if not hasattr(datum, '__iter__'):
datum = (datum,)
data = plot.data
mi, ma = -Inf, Inf
for i, (name, di) in enumerate(zip(names, datum)):
d = data.get_data(name)
nd = hstack((d, di))
data.set_data(name, nd)
if i == 1:
# y values
mi = min(nd)
ma = max(nd)
if update_y_limits:
if isinstance(ypadding, str):
ypad = abs(ma - mi) * float(ypadding)
else:
ypad = ypadding
mi -= ypad
if ymin_anchor is not None:
mi = max(ymin_anchor, mi)
self.set_y_limits(min_=mi,
max_=ma + ypad,
plotid=plotid)
def add_range_selector(self, plotid=0, series=0):
from chaco.tools.range_selection import RangeSelection
from chaco.tools.range_selection_overlay import RangeSelectionOverlay
plot = self.plots[plotid].plots['plot{}'.format(series)][0]
plot.active_tool = RangeSelection(plot, left_button_selects=True)
plot.overlays.append(RangeSelectionOverlay(component=plot))
def add_guide(self, value, orientation='h', plotid=0, color=(0, 0, 0)):
"""
"""
plot = self.plots[plotid]
from pychron.graph.guide_overlay import GuideOverlay
guide_overlay = GuideOverlay(component=plot,
value=value,
color=color)
plot.overlays.append(guide_overlay)
def add_vertical_rule(self, v, **kw):
return self._add_rule(v, 'v', **kw)
def add_horizontal_rule(self, v, **kw):
return self._add_rule(v, 'h', **kw)
def add_opposite_ticks(self, plotid=0, key=None):
"""
"""
p = self.plots[plotid]
if key is None:
for key in ['x', 'y']:
ax = plot_axis_factory(p, key, False)
p.underlays.append(ax)
else:
ax = plot_axis_factory(p, key, False)
p.underlays.append(ax)
def add_minor_xticks(self, plotid=0, **kw):
"""
"""
p = self.plots[plotid]
from pychron.graph.minor_tick_overlay import MinorTicksOverlay
m = MinorTicksOverlay(component=p, orientation='v', **kw)
p.underlays.append(m)
def add_minor_yticks(self, plotid=0, **kw):
"""
"""
p = self.plots[plotid]
from pychron.graph.minor_tick_overlay import MinorTicksOverlay
m = MinorTicksOverlay(component=p, orientation='h', **kw)
p.underlays.append(m)
def set_time_xaxis(self, plotid=None):
from chaco.scales_tick_generator import ScalesTickGenerator
from chaco.scales.time_scale import CalendarScaleSystem
if plotid is None:
plotid = len(self.plots) - 1
p = self.plots[plotid]
p.x_axis.tick_generator = ScalesTickGenerator(scale=CalendarScaleSystem())
def refresh(self):
pass
def invalidate_and_redraw(self):
self.plotcontainer._layout_needed = True
self.plotcontainer.invalidate_and_redraw()
def redraw(self, force=True):
"""
"""
if force:
self.invalidate_and_redraw()
else:
self.plotcontainer.request_redraw()
def get_next_color(self, exclude=None, plotid=0):
cg = self.color_generators[plotid]
nc = next(cg)
if exclude is not None:
if not isinstance(exclude, (list, tuple)):
exclude = [exclude]
while nc in exclude:
nc = next(cg)
return nc
def container_factory(self, **kw):
"""
"""
self.container_dict.update(kw)
return container_factory(**self.container_dict)
# private
def _add_rule(self, v, orientation, plotid=0, add_move_tool=False, **kw):
if v is None:
return
if 'plot' in kw:
plot = kw['plot']
else:
plot = self.plots[plotid]
from pychron.graph.guide_overlay import GuideOverlay, GuideOverlayMoveTool
l = GuideOverlay(plot, value=v, orientation=orientation, **kw)
plot.underlays.append(l)
if add_move_tool:
plot.tools.append(GuideOverlayMoveTool(overlay=l))
return l
def _export_data(self, path, plotid):
# names = []
# a = None
with open(path, 'w') as wfile:
def write(l):
wfile.write('{}\n'.format(l))
for plot in self.plots:
line = plot.y_axis.title
write(line)
for k, pp in plot.plots.items():
pp = pp[0]
a = column_stack((pp.index.get_data(), pp.value.get_data()))
e = getattr(pp, 'yerror', None)
header = 'x,y'
if e is not None:
try:
a = column_stack((a, e.get_data()))
header = 'x,y,e'
except ValueError:
pass
write(k)
write(header)
for row in a:
write(','.join(['{:0.8f}'.format(r) for r in row]))
def _series_factory(self, x, y, yer=None, plotid=0, add=True, **kw):
"""
"""
if x is None:
x = array([])
if y is None:
y = array([])
if 'yerror' in kw:
if not isinstance(kw['yerror'], ArrayDataSource):
kw['yerror'] = ArrayDataSource(kw['yerror'])
yername = None
plot = self.plots[plotid]
if add:
if 'xname' in kw:
xname = kw['xname']
else:
xname = next(self.xdataname_generators[plotid])
if 'yname' in kw:
yname = kw['yname']
else:
yname = next(self.ydataname_generators[plotid])
names = (xname, yname)
# self.raw_x[plotid].append(x)
# self.raw_y[plotid].append(y)
if yer is not None:
# self.raw_yer[plotid].append(yer)
yername = next(self.yerdataname_generators[plotid])
names += (yername,)
self.series[plotid].append(names)
else:
# try:
xname = self.series[plotid][0][0]
yname = self.series[plotid][0][1]
if yer is not None:
yername = self.series[plotid][0][2]
# except IndexError:
# pass
plot.data.set_data(xname, x)
plot.data.set_data(yname, y)
if yer is not None:
plot.data.set_data(yername, yer)
colorkey = 'color'
if 'color' not in list(kw.keys()):
color_gen = self.color_generators[plotid]
c = next(color_gen)
else:
c = kw['color']
if isinstance(c, str):
c = c.replace(' ', '')
if 'type' in kw:
if kw['type'] == 'bar':
colorkey = 'fill_color'
elif kw['type'] == 'polygon':
colorkey = 'face_color'
kw['edge_color'] = c
elif kw['type'] == 'scatter':
if 'outline_color' not in kw:
kw['outline_color'] = c
for k, v in [
('render_style', 'connectedpoints'),
(colorkey, c),
('selection_color', 'white')]:
if k not in list(kw.keys()):
kw[k] = v
return plot, (xname, yname), kw
def _save(self, type_='pic', path=None):
"""
"""
if path is None:
path = get_file_path(default_directory=os.path.expanduser('~'))
# from pyface.api import FileDialog, OK
# dlg = FileDialog(action='save as', default_directory=os.path.expanduser('~'))
# if dlg.open() == OK:
# path = dlg.path
# self.status_text = 'Image Saved: %s' % path
if path is not None:
if type_ == 'pdf' or path.endswith('.pdf'):
self._render_to_pdf(filename=path)
else:
# auto add an extension to the filename if not present
# extension is necessary for PIL compression
# set default save type_ DEFAULT_IMAGE_EXT='.png'
# see http://infohost.nmt.edu/tcc/help/pubs/pil/formats.html
for ei in IMAGE_EXTENSIONS:
if path.endswith(ei):
self._render_to_pic(path)
break
else:
path = add_extension(path, DEFAULT_IMAGE_EXT)
self._render_to_pic(path)
# base, ext = os.path.splitext(path)
#
# if not ext in IMAGE_EXTENSIONS:
# path = ''.join((base, DEFAULT_IMAGE_EXT))
def _render_to_pdf(self, save=True, canvas=None, filename=None, dest_box=None):
"""
"""
# save_pdf()
# from chaco.pdf_graphics_context import PdfPlotGraphicsContext
#
# if filename:
# # if not filename.endswith('.pdf'):
# # filename += '.pdf'
# filename = add_extension(filename, ext='.pdf')
#
# gc = PdfPlotGraphicsContext(filename=filename,
# pdf_canvas=canvas,
# dest_box=dest_box)
# pc = self.plotcontainer
#
# # pc.do_layout(force=True)
# # pc.use_backbuffer=False
# gc.render_component(pc, valign='center')
# if save:
# gc.save()
# # pc.use_backbuffer=True
#
# return gc
def _render_to_pic(self, filename):
"""
"""
from chaco.plot_graphics_context import PlotGraphicsContext
p = self.plotcontainer
gc = PlotGraphicsContext((int(p.outer_width), int(p.outer_height)))
# p.use_backbuffer = False
gc.render_component(p)
# p.use_backbuffer = True
gc.save(filename)
def _render_to_clipboard(self):
'''
on mac osx the bitmap gets copied to the clipboard
the contents of clipboard are available to Preview and NeoOffice
but not Excel
More success may be had on windows
Copying to clipboard is used to get a Graph into another program
such as Excel or Illustrator
Save the image as png then Insert Image is probably equivalent but not
as convenient
not working
'''
def _get_title(self, axis, plotid):
"""
"""
axis = getattr(self.plots[plotid], axis)
return axis.title
def _set_title(self, axistag, title, plotid, font=None, size=None):
"""
"""
if plotid is None:
plotid = len(self.plots) - 1
axis = getattr(self.plots[plotid], axistag)
params = dict(title=title)
if font not in VALID_FONTS:
font = 'arial'
if font is not None or size is not None:
if size is None:
size = 12
tfont = '{} {}'.format(font, size)
params.update(title_font=tfont)
axis.trait_set(**params)
if '<sup>' in title or '<sub>' in title:
plot = self.plots[plotid]
for t in plot.tools:
if t.component == axis:
plot.tools.remove(t)
break
nxa = MPlotAxis()
nxa.title = title
nxa.clone(axis)
t = AxisTool(component=nxa)
plot.tools.append(t)
setattr(self.plots[plotid], axistag, nxa)
# axis = nxa
self.plotcontainer.request_redraw()
def _get_limits(self, axis, plotid):
"""
"""
plot = self.plots[plotid]
try:
ra = getattr(plot, '%s_range' % axis)
return ra.low, ra.high
except AttributeError as e:
print('get_limits', e)
def _set_limits(self, mi, ma, axis, plotid, pad, pad_style='symmetric', force=False):
if not plotid < len(self.plots):
return
plot = self.plots[plotid]
ra = getattr(plot, '{}_range'.format(axis))
scale = getattr(plot, '{}_scale'.format(axis))
if isinstance(pad, str):
# interpret pad as a percentage of the range
# ie '0.1' => 0.1*(ma-mi)
if ma is None:
ma = ra.high
if mi is None:
mi = ra.low
if mi == -Inf:
mi = 0
if ma == Inf:
ma = 100
if ma is not None and mi is not None:
dev = ma - mi
def convert(p):
p = float(p) * dev
if abs(p) < 1e-10:
p = 1
return p
if ',' in pad:
pad = [convert(p) for p in pad.split(',')]
else:
pad = convert(pad)
if not pad:
pad = 0
# print(type(mi), isinstance(mi, (int, float)), pad_style)
# if isinstance(mi, (int, float)):
try:
if isinstance(pad, list):
mi -= pad[0]
elif pad_style in ('symmetric', 'lower'):
mi -= pad
except TypeError:
pass
# if isinstance(ma, (int, float)):
try:
if isinstance(pad, list):
ma += pad[1]
elif pad_style in ('symmetric', 'upper'):
ma += pad
except TypeError:
pass
if scale == 'log':
try:
if mi <= 0:
mi = Inf
data = plot.data
for di in data.list_data():
if 'y' in di:
ya = sorted(data.get_data(di))
i = 0
try:
while ya[i] <= 0:
i += 1
if ya[i] < mi:
mi = ya[i]
except IndexError:
mi = 0.01
mi = 10 ** math.floor(math.log(mi, 10))
ma = 10 ** math.ceil(math.log(ma, 10))
except ValueError:
return
change = False
if mi == ma:
if not pad:
pad = 1
ra.high = ma + pad
ra.low = ma - pad
else:
if mi is not None:
change = ra.low != mi
if isinstance(mi, (int, float)):
if mi < ra.high:
ra.low = mi
else:
ra.low = mi
if ma is not None:
change = change or ra.high != ma
if isinstance(ma, (int, float)):
if ma > ra.low:
ra.high = ma
else:
ra.high = ma
if change:
self.redraw(force=force)
return change
def _get_selected_plotid(self):
r = 0
if self.selected_plot is not None:
r = self.plots.index(self.selected_plot)
return r
def show(self):
do_after_timer(1, self.edit_traits)
def panel_view(self):
plot = Item('plotcontainer',
style='custom',
show_label=False,
editor=ComponentEditor())
v = View(plot)
return v
def traits_view(self):
v = View(UItem('plotcontainer',
style='custom',
editor=ComponentEditor()),
title=self.window_title,
width=self.window_width,
height=self.window_height,
x=self.window_x,
y=self.window_y,
resizable=self.resizable)
return v
if __name__ == '__main__':
m = Graph()
m.new_plot(zoom=True)
m.new_series([1, 2, 3], [1, 41, 14])
m.configure_traits()
# ============= EOF ====================================
| 28.878557 | 106 | 0.507113 |
22960c92547dae1db378ae82742536316ed9fab4 | 2,837 | py | Python | pylearn2/sandbox/cuda_convnet/tests/test_stochastic_pool.py | ikervazquezlopez/Pylearn2 | 2971e8f64374ffde572d4cf967aad5342beaf5e0 | [
"BSD-3-Clause"
] | 3 | 2018-04-05T21:24:54.000Z | 2021-09-14T01:48:36.000Z | pylearn2/sandbox/cuda_convnet/tests/test_stochastic_pool.py | ikervazquezlopez/Pylearn2 | 2971e8f64374ffde572d4cf967aad5342beaf5e0 | [
"BSD-3-Clause"
] | null | null | null | pylearn2/sandbox/cuda_convnet/tests/test_stochastic_pool.py | ikervazquezlopez/Pylearn2 | 2971e8f64374ffde572d4cf967aad5342beaf5e0 | [
"BSD-3-Clause"
] | 2 | 2018-02-18T14:46:57.000Z | 2019-05-03T11:51:45.000Z | import copy
import numpy
from theano.compat.six.moves import xrange
import theano
from theano.compat.python2x import Counter
from pylearn2.sandbox.cuda_convnet.stochastic_pool import (stochastic_max_pool_c01b,
weighted_max_pool_c01b)
from pylearn2.testing.skip import skip_if_no_gpu
from pylearn2.utils import float32_floatX
skip_if_no_gpu()
if theano.config.mode == 'FAST_COMPILE':
mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
mode_without_gpu = theano.compile.mode.get_mode(
'FAST_RUN').excluding('gpu')
else:
mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')
#The CPU tests already compare C/Py, so we only check C/GPU
mode_with_gpu = copy.copy(mode_with_gpu)
mode_without_gpu = copy.copy(mode_without_gpu)
mode_with_gpu.check_py_code = False
mode_without_gpu.check_py_code = False
# TODO add unit tests for: seed, differnt shape, stide, batch and channel size
@float32_floatX
def test_stochasatic_pool_samples():
"""
check if the order of frequency of samples from stochastic max pool
are same as the order of input values.
"""
ds = 3
stride = 3
rng = numpy.random.RandomState(220)
data = rng.uniform(0, 10, size=(1, ds, ds, 1)).astype('float32')
x = theano.tensor.tensor4()
s_max = stochastic_max_pool_c01b(x, (ds, ds), (stride, stride))
f = theano.function([x], s_max, mode=mode_with_gpu)
samples = []
for i in xrange(300):
samples.append(numpy.asarray(f(data))[0, 0, 0, 0])
counts = Counter(samples)
data = data.reshape(ds*ds)
data.sort()
data = data[::-1]
for i in range(len(data) - 1):
assert counts[data[i]] >= counts[data[i+1]]
@float32_floatX
def test_weighted_pool():
# TODO: test with different stride values
rng = numpy.random.RandomState(220)
for ds in [9, 2]:
for batch in [1, 10]:
for ch in [1, 16]:
stride = ds
data = rng.uniform(size=(batch, ds, ds, ch)).astype('float32')
# op
x = theano.tensor.tensor4()
w_max = weighted_max_pool_c01b(x, (ds, ds), (stride, stride))
f = theano.function([x], w_max, mode=mode_with_gpu)
op_val = numpy.asarray(f(data))
# python
norm = data / data.sum(2).sum(1)[:,
numpy.newaxis,
numpy.newaxis, :]
py_val = (data * norm).sum(2).sum(1)[:, numpy.newaxis,
numpy.newaxis, :]
assert numpy.allclose(op_val, py_val)
| 32.609195 | 84 | 0.606274 |
e8aeef3d4079afcf76860e114b402c6388ca3ea7 | 1,838 | py | Python | monai/deploy/utils/fileutil.py | jlvahldiek/monai-deploy-app-sdk | 050aeabec581067a11566f59a2970b075d36ae7c | [
"Apache-2.0"
] | 28 | 2021-09-17T18:16:42.000Z | 2022-03-31T16:32:36.000Z | monai/deploy/utils/fileutil.py | jlvahldiek/monai-deploy-app-sdk | 050aeabec581067a11566f59a2970b075d36ae7c | [
"Apache-2.0"
] | 109 | 2021-09-17T18:34:31.000Z | 2022-03-31T21:04:35.000Z | monai/deploy/utils/fileutil.py | jlvahldiek/monai-deploy-app-sdk | 050aeabec581067a11566f59a2970b075d36ae7c | [
"Apache-2.0"
] | 11 | 2021-09-17T20:23:31.000Z | 2022-03-29T08:55:19.000Z | # Copyright 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import hashlib
from pathlib import Path
from typing import Callable, Union
def checksum(path: Union[str, Path], hash_fn: str = "sha256", chunk_num_blocks=8192, **kwargs) -> str:
"""Return checksum of file or directory.
Args:
path (Union[str, Path]): A path to file or directory.
hash_fn (str): A hash function to use. Defaults to 'sha256'.
chunk_num_blocks (int): A number of blocks to read at once. Defaults to 8192.
**kwargs: Additional arguments to pass to hash function.
Returns:
str: checksum of file or directory
"""
if hasattr(hashlib, hash_fn):
hash_func: Callable = getattr(hashlib, hash_fn)
else:
raise ValueError("Unknown hash function")
hashlib.blake2b
h: hashlib._Hash = hash_func(**kwargs)
path = Path(path)
if path.is_file():
path_list = [path]
else:
path_list = sorted(path.glob("**/*"))
for path in path_list:
if not path.is_file():
continue
with path.open("rb") as f:
for chunk in iter(lambda: f.read(chunk_num_blocks * h.block_size), b""):
h.update(chunk)
return h.hexdigest()
if __name__ == "__main__":
import sys
print(checksum(sys.argv[1]))
| 31.689655 | 102 | 0.669206 |
81101f98a4398945ef106d7b3646bcf59e88573e | 9,810 | py | Python | src/sklearn_evaluation/training/selector.py | abcnishant007/sklearn-evaluation | 77ff2da43097b0451d8cf6f95c534409f612bf6a | [
"MIT"
] | 351 | 2016-01-27T19:15:27.000Z | 2022-03-09T15:40:56.000Z | src/sklearn_evaluation/training/selector.py | abcnishant007/sklearn-evaluation | 77ff2da43097b0451d8cf6f95c534409f612bf6a | [
"MIT"
] | 37 | 2016-03-16T03:57:59.000Z | 2021-06-26T14:02:33.000Z | src/sklearn_evaluation/training/selector.py | abcnishant007/sklearn-evaluation | 77ff2da43097b0451d8cf6f95c534409f612bf6a | [
"MIT"
] | 30 | 2016-01-27T19:27:08.000Z | 2022-03-31T06:09:59.000Z | """
When training models, it is common to try out different
subsets of features or subpopulations. ``DataSelector`` allows you to define
a series of transformations on your data so you can succinctly define a
subsetting pipeline as a series of dictionaries.
"""
from copy import copy, deepcopy
import abc
import inspect
import importlib
import itertools
from collections.abc import Mapping
import pandas as pd
from decorator import decorator
from sklearn_evaluation.exceptions import DataSelectorError
from sklearn_evaluation.util import map_parameters_in_fn_call
from sklearn_evaluation.table import Table
def import_from_dotted_path(dotted_path):
parts = dotted_path.split('.')
mod_name, callable_ = '.'.join(parts[:-1]), parts[-1]
mod = importlib.import_module(mod_name)
fn = getattr(mod, callable_)
return fn
def expand_value(value):
"""
If value is a str with at least one dot ("."), try to import it and call
it, if anything fails, return the value
"""
if isinstance(value, str) and '.' in value:
parts = value.split('.')
mod_name, callable = '.'.join(parts[:-1]), parts[-1]
try:
mod = importlib.import_module(mod_name)
except ModuleNotFoundError:
return value
try:
fn = getattr(mod, callable)
except AttributeError:
return value
return fn()
else:
return value
# NOTE: consider deleting
@decorator
def expand_arguments(func, *args, **kwargs):
"""
Fnctions decorated with expand_argument call "expand_value" on each
passed argument, which will interpred as a "dotted path" any string with
dots on it and replace the value by the value returned by a function
imported from that location (no arguments passed), if no function
is found in such location, the original value is returned
"""
return func(*[expand_value(arg) for arg in args],
**{k: expand_value(v)
for k, v in kwargs.items()})
def concatenate_over(argname):
"""Decorator to "vectorize" functions and concatenate outputs
"""
def _prepare_args(arg_map, value):
params = copy(arg_map)
params[argname] = value
return params
@decorator
def _concatenate_over(func, *args, **kwargs):
"""Validate that an agument is a proportion [0, 1.0]
"""
arg_map = map_parameters_in_fn_call(args, kwargs, func)
value = arg_map.get(argname)
if isinstance(value, list):
return list(
itertools.chain.from_iterable(
func(**_prepare_args(arg_map, v)) for v in value))
else:
return func(**arg_map)
return _concatenate_over
class Step(abc.ABC):
@abc.abstractmethod
def transform(self, df):
pass
def get_args(self):
args = inspect.getfullargspec(self.__init__).args
args.remove('self')
return {arg: getattr(self, arg) for arg in args}
# NOTE: consider deleting this, just show if in the transform summary
def get_params(self):
return {k: v for k, v in self.__dict__.items() if k.endswith('_')}
@concatenate_over('prefix')
def _with_prefix(df, prefix):
return [] if not prefix else [
c for c in df.columns if c.startswith(prefix)
]
@concatenate_over('suffix')
def _with_suffix(df, suffix):
return [] if not suffix else [c for c in df.columns if c.endswith(suffix)]
@concatenate_over('substr')
def _contains(df, substr):
return [] if not substr else [c for c in df.columns if substr in c]
def _with_max_na_prop(df, max_prop):
if max_prop is not None:
na_prop = df.isna().sum(axis='index') / len(df)
return na_prop[na_prop > max_prop].index.tolist()
else:
return []
class ColumnDrop(Step):
"""Drop columns
Parameters
----------
names
List of columns to drop
prefix
Drop columns with this prefix (or list of)
suffix
Drop columns with this suffix (or list of)
contains
Drop columns if they contains this substring
max_na_prop
Drop columns whose proportion of NAs [0, 1] is larger than this
"""
@expand_arguments
def __init__(self,
names: list = None,
prefix: str = None,
suffix: str = None,
contains: str = None,
max_na_prop: float = None):
self.names = names or []
self.prefix = prefix
self.suffix = suffix
self.contains = contains
self.max_na_prop = max_na_prop
self.to_delete_ = None
def transform(self, df, return_summary=False):
self.to_delete_ = set(self.names + _with_prefix(df, self.prefix) +
_with_suffix(df, self.suffix) +
_with_max_na_prop(df, self.max_na_prop) +
_contains(df, self.contains))
out = df.drop(self.to_delete_, axis='columns')
return out if not return_summary else (out, self.transform_summary(df))
def transform_summary(self, df):
return 'Deleted {:,} columns: {}'.format(len(self.to_delete_),
self.to_delete_)
def _incomplete_cases(df):
nas = df.isna().sum(axis='columns')
return nas[nas > 0].index
def _query(df, query):
return df.query(query).index
class RowDrop(Step):
"""Drop rows
Parameters
----------
if_nas
If True, deletes all rows where there is at leat one NA
query
Drops all rows matching the query (passed via pandas.query)
"""
@expand_arguments
def __init__(self, if_nas: bool = False, query: str = None):
self.if_nas = if_nas
self.query = query
def transform(self, df, return_summary=False):
to_delete = pd.Index([])
if self.if_nas:
to_delete = to_delete.union(_incomplete_cases(df))
if self.query:
to_delete = to_delete.union(_query(df, self.query))
out = df[~df.index.isin(to_delete)]
return out if not return_summary else (out,
self.transform_summary(
df, to_delete))
def transform_summary(self, df, to_delete):
n = len(to_delete)
return 'Deleted {:,} rows ({:.1%})'.format(n, n / len(df))
class ColumnKeep(Step):
"""Subset columns
Parameters
----------
names
List of columns to keep
"""
def __init__(self, names: list = None, dotted_path: str = None):
self.names = names or []
self.dotted_path = dotted_path
def transform(self, df, return_summary=False):
to_keep = copy(self.names)
if self.dotted_path:
fn = import_from_dotted_path(self.dotted_path)
to_keep.extend(fn(df))
# remove duplicates
to_keep = list(set(to_keep))
return df[to_keep], self.transform_summary(to_keep)
def transform_summary(self, to_keep):
return 'Keeping {:,} column(s)'.format(len(to_keep))
class DataSelector:
"""Subset a pandas.DataFrame by passing a series of steps
Parameters
----------
*steps
Steps to apply to the data sequentially (order matters). Each step
must be a dictionary with a key "kind" whose value must be one of
"column_drop", "row_drop" or "column_keep". The rest of the key-value
pairs must match the signature for the corresponding Step objects
"""
def __init__(self, *steps):
steps = deepcopy(steps)
self.steps = [_instantiate_step(step) for step in steps]
def transform(self, df, return_summary: bool = False):
"""Apply steps
Parameters
----------
df
Data frame to transform
return_summary
If False, the function only returns the output data frame,
if True, it also returns a summary table
"""
result = df
summaries = []
for i, step in enumerate(self.steps):
try:
result = step.transform(result, return_summary=return_summary)
except Exception as e:
raise DataSelectorError('Error executing step {} ({})'.format(
i,
type(step).__name__)) from e
if return_summary:
result, summary = result
summaries.append(summary)
if not return_summary:
return result
else:
table = Table([(type(step).__name__, summary)
for step, summary in zip(self.steps, summaries)],
header=['Step', 'Summary'])
return result, table
def _get_table(self):
return Table([(type(step).__name__, step.get_args(), step.get_params())
for step in self.steps],
header=['Step', 'Args', 'Params'])
def __repr__(self):
table = str(self._get_table())
table = '{} with steps:\n'.format(type(self).__name__) + table
return table
def _repr_html_(self):
return self._get_table().to_html()
def _instantiate_step(step):
if not isinstance(step, Mapping):
raise TypeError('step must be a mapping, got {}'.format(
type(step).__name__))
kind = step.pop('kind', None)
if kind not in _mapping:
raise ValueError('Each step must have a kind key with one of '
'the valid values: {}'.format(set(_mapping)))
return _mapping[kind](**step)
_mapping = {
'column_drop': ColumnDrop,
'row_drop': RowDrop,
'column_keep': ColumnKeep,
}
| 29.459459 | 79 | 0.601427 |
76a6db428459ea5739eacde9f14bdbe015b4b3bf | 1,779 | py | Python | test/unit/devices/test_default.py | mikiec84/ncclient | 7662666aac957fcf6aeb50c05f6b9816179cfd23 | [
"Apache-2.0"
] | 3 | 2015-11-03T17:11:42.000Z | 2016-12-09T14:47:44.000Z | test/unit/devices/test_default.py | mikiec84/ncclient | 7662666aac957fcf6aeb50c05f6b9816179cfd23 | [
"Apache-2.0"
] | null | null | null | test/unit/devices/test_default.py | mikiec84/ncclient | 7662666aac957fcf6aeb50c05f6b9816179cfd23 | [
"Apache-2.0"
] | null | null | null | import unittest
from ncclient.devices.default import DefaultDeviceHandler
capabilities = ['urn:ietf:params:netconf:base:1.0',
'urn:ietf:params:netconf:base:1.1',
'urn:ietf:params:netconf:capability:writable-running:1.0',
'urn:ietf:params:netconf:capability:candidate:1.0',
'urn:ietf:params:netconf:capability:confirmed-commit:1.0',
'urn:ietf:params:netconf:capability:rollback-on-error:1.0',
'urn:ietf:params:netconf:capability:startup:1.0',
'urn:ietf:params:netconf:capability:url:1.0?scheme=http,ftp,file,https,sftp',
'urn:ietf:params:netconf:capability:validate:1.0',
'urn:ietf:params:netconf:capability:xpath:1.0',
'urn:ietf:params:netconf:capability:notification:1.0',
'urn:liberouter:params:netconf:capability:power-control:1.0',
'urn:ietf:params:netconf:capability:interleave:1.0',
'urn:ietf:params:netconf:capability:with-defaults:1.0']
class TestDefaultDevice(unittest.TestCase):
def setUp(self):
self.obj = DefaultDeviceHandler()
def test_get_capabilties(self):
self.assertEqual(self.obj.get_capabilities(), capabilities)
def test_get_ssh_subsystem_names(self):
self.assertEqual(self.obj.get_ssh_subsystem_names(), ["netconf"])
def test_perform_qualify_check(self):
self.assertTrue(self.obj.perform_qualify_check())
def test_handle_raw_dispatch(self):
self.assertFalse(self.obj.handle_raw_dispatch(None))
def test_handle_connection_exceptions(self):
self.assertFalse(self.obj.handle_connection_exceptions(None))
suite = unittest.TestSuite()
unittest.TextTestRunner().run(suite)
| 41.372093 | 93 | 0.678471 |
74f3f72d9087f66a6b3e425e04bb69e0c815a9bb | 2,535 | py | Python | hear_me_django_app/accounts/api/views.py | kamil1marczak/hear_me_django_app | 2a567c15acddbf6bf183c6c637a3785c2a9c9c5c | [
"MIT"
] | null | null | null | hear_me_django_app/accounts/api/views.py | kamil1marczak/hear_me_django_app | 2a567c15acddbf6bf183c6c637a3785c2a9c9c5c | [
"MIT"
] | null | null | null | hear_me_django_app/accounts/api/views.py | kamil1marczak/hear_me_django_app | 2a567c15acddbf6bf183c6c637a3785c2a9c9c5c | [
"MIT"
] | null | null | null | from rest_framework.generics import ListAPIView
from hear_me_django_app.accounts.models import Account, Card, Transaction, AccountOwner, Company
from hear_me_django_app.accounts.api.serializers import AccountSerializer, TransactionSerializer, \
TransactionReadSerializer
from hear_me_django_app.accounts.api.filters import AccountFilterSet, TransactionFilterSet
# from .mixins import ReadWriteSerializerMixin
# from hear_me_django_app.accounts.api.filters import AccountFilterSet
# from hear_me_django_app.accounts.actions import TransactionManager
# class AccountView(ListAPIView):
# queryset = Account.objects.all()
# serializer_class = AccountSerializer
# filterset_class = AccountFilterSet
#
# def filter_queryset(self, request):
# return super().filter_queryset(request)[:100]
# class WineSearchWordsView(ListAPIView):
# queryset = AccountSearchWord.objects.all()
# serializer_class = WineSearchWordSerializer
# filterset_class = WineSearchWordFilterSet
# from django.contrib.auth import get_user_model
# from rest_framework import status
# from rest_framework.decorators import action
from rest_framework.mixins import ListModelMixin, RetrieveModelMixin, UpdateModelMixin, CreateModelMixin
# from rest_framework.response import Response
from rest_framework.viewsets import GenericViewSet
class AccountViewSet(RetrieveModelMixin, ListModelMixin, UpdateModelMixin, GenericViewSet):
serializer_class = AccountSerializer
queryset = Account.objects.all()
filterset_class = AccountFilterSet
class TransactionViewSet(RetrieveModelMixin, ListModelMixin, UpdateModelMixin, CreateModelMixin, GenericViewSet,):
serializer_class = TransactionReadSerializer
# read_serializer_class = TransactionReadSerializer
# write_serializer_class = TransactionSerializer
queryset = Transaction.objects.all()
filterset_class = TransactionFilterSet
def get_serializer_class(self):
if self.action in ["create", "update", "partial_update", "destroy"]:
return TransactionSerializer
return TransactionReadSerializer
# def retrieve( self):
# def create(self, request):
#
# t =
# def get_queryset(self, *args, **kwargs):
# return self.queryset.filter(id=self.request.user.id)
#
# @action(detail=False, methods=["GET"])
# def me(self, request):
# serializer = UserSerializer(request.user, context={"request": request})
# return Response(status=status.HTTP_200_OK, data=serializer.data)
| 39.609375 | 114 | 0.774359 |
5afe4624ca380858eaf23d69d883bced8526a76f | 1,393 | py | Python | server.py | JuveriyaFarheen/video-record | 580869ffebd41dcf253c0a8c50862e2be0de91ad | [
"MIT"
] | null | null | null | server.py | JuveriyaFarheen/video-record | 580869ffebd41dcf253c0a8c50862e2be0de91ad | [
"MIT"
] | null | null | null | server.py | JuveriyaFarheen/video-record | 580869ffebd41dcf253c0a8c50862e2be0de91ad | [
"MIT"
] | null | null | null | from flask import Flask, render_template, Response, jsonify, request
from camera import VideoCamera
app = Flask(__name__)
video_camera = None
global_frame = None
@app.route('/')
def index():
return render_template('index.html')
@app.route('/record_status', methods=['POST'])
def record_status():
global video_camera
if video_camera == None:
video_camera = VideoCamera()
json = request.get_json()
status = json['status']
if status == "true":
video_camera.start_record()
return jsonify(result="started")
else:
video_camera.stop_record()
return jsonify(result="stopped")
def video_stream():
global video_camera
global global_frame
if video_camera == None:
video_camera = VideoCamera()
while True:
frame = video_camera.get_frame()
if frame != None:
global_frame = frame
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
else:
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + global_frame + b'\r\n\r\n')
@app.route('/video_viewer')
def video_viewer():
return Response(video_stream(),
mimetype='multipart/x-mixed-replace; boundary=frame')
if __name__ == '__main__':
app.run(host='127.0.0.1', threaded=True) | 25.796296 | 93 | 0.60804 |
50cf8881a23805180243ba4df2efdd115dee375f | 356 | py | Python | 0-run-with-images.py | GunarakulanGunaretnam/face-detection-in-python | 76cb91559a88cb32448ee3f6cca942dbef554089 | [
"Apache-2.0"
] | null | null | null | 0-run-with-images.py | GunarakulanGunaretnam/face-detection-in-python | 76cb91559a88cb32448ee3f6cca942dbef554089 | [
"Apache-2.0"
] | null | null | null | 0-run-with-images.py | GunarakulanGunaretnam/face-detection-in-python | 76cb91559a88cb32448ee3f6cca942dbef554089 | [
"Apache-2.0"
] | null | null | null | import cv2
import numpy as np
faceModel = cv2.CascadeClassifier("haarcascade-frontalface-default.xml")
img = cv2.imread("image-3.jpg")
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces = faceModel.detectMultiScale(gray,1.3,5)
#rgb
#BGR
for(x,y,w,h) in faces:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
cv2.imshow("Display",img)
cv2.waitKey(0) | 17.8 | 72 | 0.716292 |
c8b091025f0b178462f0a2081b0c70fc65ee6438 | 344 | py | Python | bilal/app/__init__.py | ibrahimediz/flaskrest | e0d52d35dc5b3aff8a7a15832c84e1c3882c4f36 | [
"MIT"
] | null | null | null | bilal/app/__init__.py | ibrahimediz/flaskrest | e0d52d35dc5b3aff8a7a15832c84e1c3882c4f36 | [
"MIT"
] | null | null | null | bilal/app/__init__.py | ibrahimediz/flaskrest | e0d52d35dc5b3aff8a7a15832c84e1c3882c4f36 | [
"MIT"
] | null | null | null | from flask import Flask
from config import Config
from flask_sqlalchemy import SQLAlchemy
from flask_migrate import Migrate
app = Flask(__name__)
app.config.from_object(Config)
db = SQLAlchemy(app)
migrate = Migrate(app,db)
from app.api import bp as api_bp
app.register_blueprint(api_bp, url_prefix='/api')
from app import routes, models
| 19.111111 | 49 | 0.799419 |
8000c447737e0a95f1850e04e56fa2c5394166d1 | 44 | py | Python | lennybot/__main__.py | Squaar/LennyBot | 184113535e1fbbf1a507ddda7a878f72db4114b8 | [
"MIT"
] | 1 | 2019-11-09T12:48:56.000Z | 2019-11-09T12:48:56.000Z | lennybot/__main__.py | Squaar/LennyBot | 184113535e1fbbf1a507ddda7a878f72db4114b8 | [
"MIT"
] | 5 | 2018-04-22T20:29:28.000Z | 2020-07-25T19:20:30.000Z | lennybot/__main__.py | Squaar/LennyBot | 184113535e1fbbf1a507ddda7a878f72db4114b8 | [
"MIT"
] | null | null | null | from . import lennyrunner
lennyrunner.main() | 22 | 25 | 0.818182 |
48f52fa34d45050ff8fdea499e886cace69ae691 | 463 | py | Python | astute-dashboard/astutedashboard/dashboards/billing/image_report/panel.py | sreenathmmenon/astttproject | 464fe20c8acf14afdfe03d1e4758e3df2b06196e | [
"Apache-2.0"
] | null | null | null | astute-dashboard/astutedashboard/dashboards/billing/image_report/panel.py | sreenathmmenon/astttproject | 464fe20c8acf14afdfe03d1e4758e3df2b06196e | [
"Apache-2.0"
] | null | null | null | astute-dashboard/astutedashboard/dashboards/billing/image_report/panel.py | sreenathmmenon/astttproject | 464fe20c8acf14afdfe03d1e4758e3df2b06196e | [
"Apache-2.0"
] | 1 | 2018-02-24T10:32:41.000Z | 2018-02-24T10:32:41.000Z | #
# Copyright 2017 NephoScale
#
from django.utils.translation import ugettext_lazy as _
import horizon
from astutedashboard.dashboards.billing import dashboard
class WindowsInstanceReport(horizon.Panel):
name = _("Windows/SQL Instance Report")
slug = "windows_instance_report"
#Only the following roles are allowed to access this dashboard
permissions = (('openstack.roles.admin',),)
dashboard.M1AstutePanels.register(WindowsInstanceReport)
| 24.368421 | 66 | 0.784017 |
900875c1d95ba466bd3d3f4989af63cfad17917e | 964 | py | Python | models/universal_sentence_encoder_multilingual_large/v1/utils.py | rhangelxs/russian_embeddings | 64821cdff03ff97752b6c80621bedf9e2227a0ba | [
"MIT"
] | null | null | null | models/universal_sentence_encoder_multilingual_large/v1/utils.py | rhangelxs/russian_embeddings | 64821cdff03ff97752b6c80621bedf9e2227a0ba | [
"MIT"
] | 5 | 2020-09-26T00:18:44.000Z | 2022-02-10T00:22:42.000Z | models/universal_sentence_encoder_multilingual_large/v1/utils.py | rhangelxs/russian_embeddings | 64821cdff03ff97752b6c80621bedf9e2227a0ba | [
"MIT"
] | null | null | null | import numpy
import tensorflow as tf
import tensorflow_hub as hub
import tf_sentencepiece
class EmbeddingWrapper:
def __init__(self):
module_url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/1"
# Set up graph.
g = tf.Graph()
with g.as_default():
self.module = hub.Module(module_url) # load tfhub module
self.question = tf.placeholder(dtype=tf.string, shape=[None]) # question
self.question_embedding = self.module(self.question)
init_op = tf.group(
[tf.global_variables_initializer(),
tf.tables_initializer()])
g.finalize()
# Initialize session.
session = tf.Session(graph=g)
session.run(init_op)
self.session = session
def str2vec(self, string):
result = self.session.run(self.question_embedding, feed_dict={self.question: [string]})[0]
return result
| 32.133333 | 98 | 0.63278 |
f1bbb1bf5bba46022785f624cfacb33204de940d | 1,874 | py | Python | addons/l10n_ch/models/account_journal.py | SHIVJITH/Odoo_Machine_Test | 310497a9872db7844b521e6dab5f7a9f61d365a4 | [
"Apache-2.0"
] | null | null | null | addons/l10n_ch/models/account_journal.py | SHIVJITH/Odoo_Machine_Test | 310497a9872db7844b521e6dab5f7a9f61d365a4 | [
"Apache-2.0"
] | null | null | null | addons/l10n_ch/models/account_journal.py | SHIVJITH/Odoo_Machine_Test | 310497a9872db7844b521e6dab5f7a9f61d365a4 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
from odoo import models, fields, api
from odoo.exceptions import ValidationError
from odoo.addons.base_iban.models.res_partner_bank import validate_iban
from odoo.addons.base.models.res_bank import sanitize_account_number
class AccountJournal(models.Model):
_inherit = 'account.journal'
# creation of bank journals by giving the account number, allow craetion of the
l10n_ch_postal = fields.Char('Client Number', related='bank_account_id.l10n_ch_postal', readonly=False)
invoice_reference_model = fields.Selection(selection_add=[
('ch', 'Switzerland')
], ondelete={'ch': lambda recs: recs.write({'invoice_reference_model': 'odoo'})})
@api.model
def create(self, vals):
rslt = super(AccountJournal, self).create(vals)
# The call to super() creates the related bank_account_id field
if 'l10n_ch_postal' in vals:
rslt.l10n_ch_postal = vals['l10n_ch_postal']
return rslt
def write(self, vals):
rslt = super(AccountJournal, self).write(vals)
# The call to super() creates the related bank_account_id field if necessary
if 'l10n_ch_postal' in vals:
for record in self.filtered('bank_account_id'):
record.bank_account_id.l10n_ch_postal = vals['l10n_ch_postal']
return rslt
@api.onchange('bank_acc_number')
def _onchange_set_l10n_ch_postal(self):
try:
validate_iban(self.bank_acc_number)
is_iban = True
except ValidationError:
is_iban = False
if is_iban:
self.l10n_ch_postal = self.env['res.partner.bank']._retrieve_l10n_ch_postal(sanitize_account_number(self.bank_acc_number))
else:
self.l10n_ch_postal = self.bank_acc_number
| 36.745098 | 134 | 0.691569 |
82ec93fd6697b169d9054a8445289d161a88901c | 8,293 | py | Python | gpio/rpi_gpio.py | jamesgoodhouse/sensorReporter | 925868982cb0571f2d2204f0474d8f8d714f3087 | [
"Apache-2.0"
] | 99 | 2016-02-24T00:17:59.000Z | 2022-02-19T08:07:26.000Z | gpio/rpi_gpio.py | jamesgoodhouse/sensorReporter | 925868982cb0571f2d2204f0474d8f8d714f3087 | [
"Apache-2.0"
] | 68 | 2016-05-06T18:28:34.000Z | 2022-03-31T16:40:32.000Z | gpio/rpi_gpio.py | jamesgoodhouse/sensorReporter | 925868982cb0571f2d2204f0474d8f8d714f3087 | [
"Apache-2.0"
] | 43 | 2016-05-28T13:22:45.000Z | 2021-12-12T02:17:08.000Z | # Copyright 2020 Richard Koshak
#
# 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.
"""Contains RPI GPIO sensors, actuators, and connections.
Classes:
- RpiGpioSensor: Reports on the state of a GPIO Pin.
- RpiGpioActuator: Sets a pin to HIGH or LOW on command.
"""
from time import sleep
from configparser import NoOptionError
import RPi.GPIO as GPIO
from core.sensor import Sensor
from core.actuator import Actuator
from core.utils import parse_values
from distutils.util import strtobool
# Set to use BCM numbering.
GPIO.setmode(GPIO.BCM)
class RpiGpioSensor(Sensor):
"""Publishes the current state of a configured GPIO pin."""
def __init__(self, publishers, params):
"""Initializes the connection to the GPIO pin and if "EventDetection"
if defined and valid, will subscibe fo events. If missing, than it
requires the "Poll" parameter be defined and > 0. By default it will
publish CLOSED/OPEN for 0/1 which can be overridden by the "Values" which
should be a comma separated list of two paameters, the first one is
CLOSED and second one is OPEN.
Parameters:
- "Pin": the GPIO pin in BCM numbering
- "Values": Alternative values to publish for 0 and 1, defaults to
CLOSED and OPEN for 0 and 1 respectively.
- "PUD": Pull up or down setting, if "UP" uses PULL_UP, all other
values result in PULL_DOWN.
- "EventDetection": when set instead of depending on sensor_reporter
to poll it will reliy on the event detection built into the GPIO
library. Valid values are "RISING", "FALLING" and "BOTH". When not
defined "Poll" must be set to a positive value.
"""
super().__init__(publishers, params)
self.pin = int(params("Pin"))
# Allow users to override the 0/1 pin values.
self.values = parse_values(params, ["CLOSED", "OPEN"])
self.log.debug("Configured %s for CLOSED and %s for OPEN", self.values[0], self.values[1])
pud = GPIO.PUD_UP if params("PUD") == "UP" else GPIO.PUD_DOWN
GPIO.setup(self.pin, GPIO.IN, pull_up_down=pud)
# Set up event detection.
try:
event_detection = params("EventDetection")
event_map = {"RISING": GPIO.RISING, "FALLING": GPIO.FALLING, "BOTH": GPIO.BOTH}
if event_detection not in event_map:
self.log.error("Invalid event detection specified: %s, one of RISING,"
" FALLING, BOTH or NONE are the only allowed values. "
"Defaulting to NONE",
event_detection)
event_detection = "NONE"
except NoOptionError:
self.log.info("No event detection specified, falling back to polling")
event_detection = "NONE"
if event_detection != "NONE":
GPIO.add_event_detect(self.pin, event_map[event_detection],
callback=lambda channel: self.check_state())
self.state = GPIO.input(self.pin)
self.destination = params("Destination")
if self.poll < 0 and event_detection == "NONE":
raise ValueError("Event detection is NONE but polling is OFF")
if self.poll > 0 and event_detection != "NONE":
raise ValueError("Event detection is {} but polling is {}"
.format(event_detection, self.poll))
self.log.info("Configured RpiGpioSensor: pin %d on destination %s with PUD %s"
" and event detection %s", self.pin, self.destination, pud,
event_detection)
# We've a first reading so publish it.
self.publish_state()
def check_state(self):
"""Checks the current state of the pin and if it's different from the
last state publishes it. With event detection this method gets called
when the GPIO pin changed states. When polling this method gets called
on each poll.
"""
value = GPIO.input(self.pin)
if value != self.state:
self.log.info("Pin %s changed from %s to %s", self.pin, self.state, value)
self.state = value
self.publish_state()
def publish_state(self):
"""Publishes the current state of the pin."""
msg = self.values[0] if self.state == GPIO.LOW else self.values[1]
self._send(msg, self.destination)
def cleanup(self):
"""Disconnects from the GPIO subsystem."""
GPIO.cleanup()
class RpiGpioActuator(Actuator):
"""Allows for setting a GPIO pin to high or low on command. Also supports
toggling.
"""
def __init__(self, connections, params):
"""Initializes the GPIO subsystem and sets the pin to the InitialState.
If InitialState is not povided in paams it defaults to GPIO.HIGH. If
"Toggle" is defined on any message will result in the pin being set to
HIGH for half a second and then back to LOW.
Parameters:
- "Pin": The GPIO pin in BCM numbering
- "InitialState": The pin state to set when coming online, defaults
to "OFF".
- "Toggle": Optional parameter that when set to "True" causes any
message received to result in setting the pin to HIGH, sleep for
half a second, then back to LOW.
"""
super().__init__(connections, params)
self.pin = int(params("Pin"))
GPIO.setup(self.pin, GPIO.OUT)
out = GPIO.LOW
try:
self.init_state = GPIO.HIGH if params("InitialState") == "ON" else GPIO.LOW
except NoOptionError:
pass
GPIO.output(self.pin, self.init_state)
try:
self.toggle = bool(strtobool(params("Toggle")))
except NoOptionError:
self.toggle = False
self.log.info("Configued RpoGpuiActuator: pin %d on destination %s with "
"toggle %s", self.pin, self.cmd_src, self.toggle)
def on_message(self, msg):
"""Called when the actuator receives a message. If Toggle is not enabled
sets the pin to HIGH if the message is ON and LOW if the message is OFF.
"""
self.log.info("Received command on %s: %s Toggle = %s Pin = %d",
self.cmd_src, msg, self.toggle, self.pin)
# Toggle on then off.
if self.toggle:
self.log.info("Toggling pin %s %s to %s",
self.pin, self.highlow_to_str(self.init_state), self.highlow_to_str(not self.init_state))
GPIO.output(self.pin, int(not self.init_state))
sleep(.5)
self.log.info("Toggling pin %s %s to %s",
self.pin, self.highlow_to_str(not self.init_state), self.highlow_to_str(self.init_state))
GPIO.output(self.pin, self.init_state)
# Turn ON/OFF based on the message.
else:
out = None
if msg == "ON":
out = GPIO.HIGH
elif msg == "OFF":
out = GPIO.LOW
if out == None:
self.log.error("Bad command %s", msg)
else:
self.log.info("Setting pin %d to %s", self.pin,
"HIGH" if out == GPIO.HIGH else "LOW")
GPIO.output(self.pin, out)
@staticmethod
def highlow_to_str(output):
"""Converts (GPIO.)HIGH (=1) and LOW (=0) to the corresponding string
Parameter: - "output": the GPIO constant (HIGH or LOW)
Returns string HIGH/LOW
"""
if output:
return "HIGH"
else:
return "LOW"
| 41.054455 | 115 | 0.604365 |
e8fb7005a2d2a061e7ae0a8a1df9004504412ed2 | 4,325 | py | Python | python/detectionAlgorithm/offlineVideo/icub/main.py | NunoDuarte/openCVdevelop | 43204a903a3c96758332a86c7d6b10c285d6ed37 | [
"MIT"
] | null | null | null | python/detectionAlgorithm/offlineVideo/icub/main.py | NunoDuarte/openCVdevelop | 43204a903a3c96758332a86c7d6b10c285d6ed37 | [
"MIT"
] | null | null | null | python/detectionAlgorithm/offlineVideo/icub/main.py | NunoDuarte/openCVdevelop | 43204a903a3c96758332a86c7d6b10c285d6ed37 | [
"MIT"
] | null | null | null | # import files
from findNearest import findNearest
from balltracking import Ball
from faceDetector import FaceDetector
from gazeBehaviour import GazeBehaviour
# import necessary libraries
from collections import deque
import numpy as np
import cv2
import csv
import os
import argparse
import imutils
import logging as log
dir = 'input'
directory = os.fsencode(dir)
for file in os.listdir(directory):
filename = os.fsdecode(file)
cap = cv2.VideoCapture(dir+'/'+filename+'/world_viz.mp4')
length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the (optional) video file")
ap.add_argument("-b", "--buffer", type=int, default=64, help="max buffer size")
args = vars(ap.parse_args())
pts = deque(maxlen=args["buffer"])
ballTracking = Ball()
cascPath = "cascade-icub-60v60.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
log.basicConfig(filename='faceDetected.log', level=log.INFO)
anterior = 0
face = FaceDetector()
print("Preparing Data...")
knownFaces, knownLabels = face.prepare_training_data("training-data", faceCascade)
print("Data prepared")
# create our LBPH face recognizer
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
face_recognizer.train(knownFaces, np.array(knownLabels))
timestamps_gaze = list()
norm_pos_x = list()
norm_pos_y = list()
gaze = GazeBehaviour()
f = gaze.open(filename)
with open(dir+'/'+filename+'/gaze_positions.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
timestamps_gaze.append(float(row['timestamp']))
norm_pos_x.append(row['norm_pos_x'])
norm_pos_y.append(row['norm_pos_y'])
print(len(timestamps_gaze))
timestamps = np.load(dir+'/'+filename+'/world_viz_timestamps.npy')
print(len(timestamps))
cv2.waitKey(0)
i = 0
ball = []
time0 = timestamps[i]
while i < length:
ret, frame = cap.read()
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if frame is not None:
frame = imutils.resize(frame, width=750)
height, width, channels = frame.shape
frame, pts, ballG = ballTracking.trackingGreen(frame, pts)
if ballG is not [] and len(ballG) != 0:
ball.append([ballG, 5])
frame, pts, ballR = ballTracking.trackingRed(frame, pts)
if ballR is not [] and len(ballR) != 0:
ball.append([ballR, 4])
frame, pts, ballB = ballTracking.trackingBlue(frame, pts)
if ballB is not [] and len(ballB) != 0:
ball.append([ballB, 0])
frame, pts, ballY = ballTracking.trackingYellow(frame, pts)
if ballY is not [] and len(ballY) != 0:
ball.append([ballY, 3])
frame, pts, ballC = ballTracking.trackingCyan(frame, pts)
if ballC is not [] and len(ballC) != 0:
ball.append([ballC, 2])
anterior, faces, facesTrained = face.detecting(frame, anterior, faceCascade)
labels = face.predict(frame, face_recognizer, faces, facesTrained)
# calculate the nearest timestamp for the current frame
time = timestamps[i]
time_close, ind = findNearest(timestamps_gaze, float(time))
# use the x, y position of the closest timestamp norm_pos_*
pos_x = norm_pos_x[ind]
pos_y = norm_pos_y[ind]
cv2.circle(frame, (int(float(pos_x)*width), int(height - int(float(pos_y)*height))), 10, (0, 255, 1),
thickness=5, lineType=8, shift=0) # draw circle
fixation = [(int(float(pos_x)*width)), int(height - int(float(pos_y)*height))]
# check the gaze behaviour
if len(ball) is not 0:
gaze.record(time_close-time0, ball, faces, fixation, labels, f)
cv2.imshow('frame', frame)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
i = i + 1
# clear the lists
ball = []
faces = []
# wait for key pressed
cv2.waitKey(0)
gaze.close(f)
cap.release()
cv2.destroyAllWindows() | 33.789063 | 113 | 0.619422 |
36284889381f8cc328ad0ed9990cd406d1c3442b | 1,432 | py | Python | stix_shifter_modules/qradar/stix_transmission/results_connector.py | pyromaneact/stix-shifter | 431c6f66513cd0db8e338a4e2952a40666bc294b | [
"Apache-2.0"
] | 1 | 2021-10-05T19:26:04.000Z | 2021-10-05T19:26:04.000Z | stix_shifter_modules/qradar/stix_transmission/results_connector.py | pyromaneact/stix-shifter | 431c6f66513cd0db8e338a4e2952a40666bc294b | [
"Apache-2.0"
] | 1 | 2020-09-08T17:26:43.000Z | 2020-09-08T17:26:43.000Z | stix_shifter_modules/qradar/stix_transmission/results_connector.py | pyromaneact/stix-shifter | 431c6f66513cd0db8e338a4e2952a40666bc294b | [
"Apache-2.0"
] | 1 | 2020-11-25T13:24:25.000Z | 2020-11-25T13:24:25.000Z | from stix_shifter_utils.modules.base.stix_transmission.base_results_connector import BaseResultsConnector
from stix_shifter_utils.utils.error_response import ErrorResponder
from stix_shifter_utils.utils import logger
import json
class ResultsConnector(BaseResultsConnector):
def __init__(self, api_client):
self.api_client = api_client
self.logger = logger.set_logger(__name__)
def create_results_connection(self, search_id, offset, length):
min_range = offset
max_range = offset + length
# Grab the response, extract the response code, and convert it to readable json
response = self.api_client.get_search_results(search_id, 'application/json', min_range, max_range)
response_code = response.code
# Construct a response object
return_obj = dict()
error = None
response_text = response.read()
try:
response_dict = json.loads(response_text)
except ValueError as ex:
self.logger.debug(response_text)
error = Exception(f'Can not parse response: {ex} : {response_text}')
if 200 <= response_code <= 299:
return_obj['success'] = True
return_obj['data'] = response_dict.get('events', response_dict.get('flows'))
else:
ErrorResponder.fill_error(return_obj, response_dict, ['message'], error=error)
return return_obj
| 37.684211 | 106 | 0.685056 |
dc7797baeb9b58b5b5906c389616798747d5dd43 | 2,587 | py | Python | lib/connectivity_lib/webtest.py | seunomosowon/TA-connectivity | 40244c5fb2ba7f8f32fd250fb1abf85fbfcb9114 | [
"CC-BY-3.0"
] | 4 | 2016-06-19T11:49:50.000Z | 2019-10-28T09:18:42.000Z | lib/connectivity_lib/webtest.py | seunomosowon/TA-connectivity | 40244c5fb2ba7f8f32fd250fb1abf85fbfcb9114 | [
"CC-BY-3.0"
] | 8 | 2016-10-21T00:22:29.000Z | 2021-01-26T13:04:57.000Z | lib/connectivity_lib/webtest.py | seunomosowon/TA-connectivity | 40244c5fb2ba7f8f32fd250fb1abf85fbfcb9114 | [
"CC-BY-3.0"
] | 4 | 2016-06-19T11:49:52.000Z | 2019-11-14T10:10:49.000Z | """
This includes functions to be used for web connectivity tests to a given URL.
Functions here support the 'webping://' modular input
"""
from future.standard_library import install_aliases
install_aliases()
import re
from urllib.request import urlopen
from urllib.parse import urlparse
from http.client import HTTPException
import urllib.error
from time import strftime
from string import Template
"""
Still need to capture traceback and log to debug?
import traceback
"""
logmessage = Template(
'$timenow,action=$action,status=$status_code,src=splunk,dst=$dsthost,url=\"$dsturl\",description=$description')
def webtest(url, webtimeout):
"""
This tests connectivity to a webservice running at a given URL.
:param url: Application URL to be tested.
:type url: basestring
:param webtimeout: application web timeout to be used for the test.
:type webtimeout: int
:return: Raises an exception or returns a status message about the connection tested
:rtype: basestring
"""
timenow = strftime("%m/%d/%Y %H:%M:%S %Z")
dst = urlparse(url).netloc.split(':')[0]
# raise exception if url is not in format required or return as unsuccessful
action = ''
description = ''
try:
openurl = urlopen(url, timeout=webtimeout)
status_code = openurl.getcode()
if re.match('(2\d\d)', repr(status_code)):
if re.match('(2\d\d)', repr(status_code)):
action = 'successful'
description = 'online'
elif re.match('(3\d\d)', repr(status_code)):
action = 'redirected' # These falls under urllib2.HTTPError
description = 'redirected'
elif re.match('(4\d\d)', repr(status_code)):
action = 'unsuccessful'
description = 'Malformed URL'
elif re.match('(5\d\d)', repr(status_code)):
action = 'unsuccessful'
description = 'Server Error'
else:
action = 'unknown'
description = 'unknown'
except urllib.error.HTTPError as e:
action = 'HTTPERROR'
description = 'HTTPError - ' + repr(e. code)
status_code = e.code
except urllib.error.URLError as e:
action = 'URLERROR'
description = 'URLError - ' + str(e.reason)
status_code = 999
except HTTPException:
action = 'PROGRAM_ERROR'
description = 'HTTPException'
status_code = 999
return logmessage.substitute(
timenow=timenow, action=action, status_code=status_code, dsthost=dst, dsturl=url, description=description)
| 34.959459 | 115 | 0.649787 |
bcbfecc2340425dec4314675c221abe592f227d4 | 416 | py | Python | climatetalk/node_types/heat_pump.py | kdschlosser/ClimateTalk | 3b09a45c295cf5228283d7095834e8f133ed7de3 | [
"MIT"
] | 3 | 2021-04-30T20:12:16.000Z | 2022-03-09T11:53:12.000Z | climatetalk/node_types/heat_pump.py | kdschlosser/ClimateTalk | 3b09a45c295cf5228283d7095834e8f133ed7de3 | [
"MIT"
] | null | null | null | climatetalk/node_types/heat_pump.py | kdschlosser/ClimateTalk | 3b09a45c295cf5228283d7095834e8f133ed7de3 | [
"MIT"
] | 2 | 2021-04-08T18:29:39.000Z | 2021-04-30T20:13:55.000Z | # -*- coding: utf-8 -*-
# Copyright 2020 Kevin Schlosser
from . import NodeType, Node
from ..mdi import heat_pump, sensors
NODE_TYPE_HEAT_PUMP = NodeType(0x05).set_desc('Heat Pump')
class HeatPump(Node, heat_pump.HeatPumpMDI):
node_type = NODE_TYPE_HEAT_PUMP
@property
def outdoor_temperature(self):
return sensors.HeatPumpOutdoorTempSensorMDI(self.address, self.subnet, self.network).value
| 26 | 98 | 0.75 |
b7b3f9bfa55dc232277365cb410826e8324ace9f | 1,679 | py | Python | practice/practice_1.2/linked_list.py | Electro98/aads | 89607910856600b38349c31665f43fbb33df71c5 | [
"MIT"
] | 7 | 2021-07-24T05:37:07.000Z | 2022-03-15T05:17:25.000Z | practice/practice_1.2/linked_list.py | Electro98/aads | 89607910856600b38349c31665f43fbb33df71c5 | [
"MIT"
] | 2 | 2021-08-05T14:09:46.000Z | 2021-08-21T14:12:03.000Z | practice/practice_1.2/linked_list.py | Electro98/aads | 89607910856600b38349c31665f43fbb33df71c5 | [
"MIT"
] | 8 | 2021-08-20T17:17:02.000Z | 2022-03-15T05:17:27.000Z | """Модуль "заглушка" для тестов"""
class LinkedListItem:
"""Узел связного списка"""
def __init__(self, data=None):
raise NotImplementedError()
@property
def next_item(self):
"""Следующий элемент"""
raise NotImplementedError()
@next_item.setter
def next_item(self, value):
raise NotImplementedError()
@property
def previous_item(self):
"""Предыдущий элемент"""
raise NotImplementedError()
@previous_item.setter
def previous_item(self, value):
raise NotImplementedError()
def __repr__(self):
raise NotImplementedError()
class LinkedList:
"""Связный список"""
def __init__(self, first_item=None):
raise NotImplementedError()
@property
def last(self):
"""Последний элемент"""
raise NotImplementedError()
def append_left(self, item):
"""Добавление слева"""
raise NotImplementedError()
def append_right(self, item):
"""Добавление справа"""
raise NotImplementedError()
def append(self, item):
"""Добавление справа"""
raise NotImplementedError()
def remove(self, item):
"""Удаление"""
raise NotImplementedError()
def insert(self, previous, item):
"""Вставка справа"""
raise NotImplementedError()
def __len__(self):
raise NotImplementedError()
def __iter__(self):
raise NotImplementedError()
def __getitem__(self, index):
raise NotImplementedError()
def __contains__(self, item):
raise NotImplementedError()
def __reversed__(self):
raise NotImplementedError()
| 22.386667 | 40 | 0.627159 |
59f759770b4107eae796f4c5bd72a5a4e517dcd0 | 22,644 | py | Python | knowledge_repo/app/models.py | dmaljovec/knowledge-repo | 09e1e9e63fa86817db00341bb589a27bd35c5199 | [
"Apache-2.0"
] | null | null | null | knowledge_repo/app/models.py | dmaljovec/knowledge-repo | 09e1e9e63fa86817db00341bb589a27bd35c5199 | [
"Apache-2.0"
] | 1 | 2020-10-26T22:38:18.000Z | 2020-10-26T22:38:18.000Z | knowledge_repo/app/models.py | recursionpharma/knowledge-repo-package | 09e1e9e63fa86817db00341bb589a27bd35c5199 | [
"Apache-2.0"
] | null | null | null | import os
import sys
import datetime
import logging
import traceback
from flask import current_app, request
from flask_login import UserMixin
from flask_sqlalchemy import SQLAlchemy
from collections import defaultdict
from sqlalchemy import func, distinct, and_, select, Index, UniqueConstraint
from knowledge_repo._version import __version__
from knowledge_repo.repository import KnowledgeRepository
from knowledge_repo.utils.types import MediumText
from .proxies import current_user, current_repo, db_session
from .utils.models import unique_constructor
from .utils.search import get_keywords
from sqlalchemy.ext.hybrid import hybrid_property
from sqlalchemy.ext.orderinglist import ordering_list
from sqlalchemy.ext.associationproxy import association_proxy
logger = logging.getLogger(__name__)
db = SQLAlchemy()
class IndexMetadata(db.Model):
__tablename__ = 'index_metadata'
id = db.Column(db.Integer, nullable=False, primary_key=True)
type = db.Column(db.String(255), nullable=False)
name = db.Column(db.String(512), nullable=False)
value = db.Column(db.String(512), nullable=True)
updated_at = db.Column(db.DateTime, default=datetime.datetime.utcnow, onupdate=datetime.datetime.utcnow)
@classmethod
def get(cls, type, name, default=None):
m = db_session.query(IndexMetadata).filter(IndexMetadata.type == type).filter(IndexMetadata.name == name).first()
if m is not None:
return m.value
return default
@classmethod
def set(cls, type, name, value):
m = db_session.query(IndexMetadata).filter(IndexMetadata.type == type).filter(IndexMetadata.name == name).first()
if m is not None:
m.value = value
m.updated_at = datetime.datetime.utcnow()
else:
m = IndexMetadata(type=type, name=name, value=value, updated_at=datetime.datetime.utcnow())
db_session.add(m)
@classmethod
def get_last_update(cls, type, name):
m = db_session.query(IndexMetadata).filter(IndexMetadata.type == type).filter(IndexMetadata.name == name).first()
if m is not None:
return m.updated_at
return None
class PostAuthorAssoc(db.Model):
__tablename__ = 'assoc_post_author'
post_id = db.Column(db.Integer, db.ForeignKey("posts.id"), nullable=False, primary_key=True)
user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False, primary_key=True)
order = db.Column(db.Integer)
post = db.relationship('Post', lazy='joined')
author = db.relationship('User', lazy='joined')
assoc_post_tag = db.Table(
'assoc_post_tag',
db.Model.metadata,
db.Column('post_id', db.Integer, db.ForeignKey('posts.id')),
db.Column('tag_id', db.Integer, db.ForeignKey('tags.id'))
)
assoc_post_group = db.Table(
'assoc_post_group',
db.Model.metadata,
db.Column('post_id', db.Integer, db.ForeignKey('posts.id')),
db.Column('group_id', db.Integer, db.ForeignKey('groups.id'))
)
assoc_group_user = db.Table(
'assoc_group_user',
db.Model.metadata,
db.Column('group_id', db.Integer, db.ForeignKey('groups.id')),
db.Column('user_id', db.Integer, db.ForeignKey('users.id'))
)
class Comment(db.Model):
__tablename__ = 'comments'
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer)
post_id = db.Column(db.Integer)
text = db.Column(db.Text)
type = db.Column(db.String(100), default='post')
created_at = db.Column(db.DateTime, default=func.now())
updated_at = db.Column(db.DateTime, default=func.now(), onupdate=func.now())
class ErrorLog(db.Model):
__tablename__ = 'errorlog'
id = db.Column(db.Integer, primary_key=True)
function = db.Column(db.String(100))
location = db.Column(db.String(255))
message = db.Column(db.Text())
traceback = db.Column(db.Text())
version = db.Column(db.String(100), default=__version__)
created_at = db.Column(db.DateTime, default=func.now())
@classmethod
def from_exception(cls, e):
tb = sys.exc_info()[-1]
filename, linenumber, function, code = traceback.extract_tb(sys.exc_info()[-1])[-1]
filename = os.path.relpath(filename, os.path.join(os.path.dirname(__file__), '..'))
return ErrorLog(
function=function,
location='{}:{}'.format(filename, linenumber),
message='{}: {}'.format(e.__class__.__name__, "; ".join(str(a) for a in e.args)),
traceback="\n".join(traceback.format_tb(tb))
)
@classmethod
def logged(cls, function):
def wrapped(*args, **kwargs):
try:
return function(*args, **kwargs)
except Exception as e:
db_session.rollback()
db_session.add(ErrorLog.from_exception(e))
db_session.commit()
tb = sys.exc_info()[-1]
raise e.with_traceback(tb)
return wrapped
class PageView(db.Model):
__tablename__ = 'pageviews'
id = db.Column(db.Integer, primary_key=True)
id_errorlog = db.Column(db.Integer)
page = db.Column(db.String(512))
endpoint = db.Column(db.String(255))
user_id = db.Column(db.Integer)
object_id = db.Column(db.Integer)
object_type = db.Column(db.String(100))
object_action = db.Column(db.String(100))
ip_address = db.Column(db.String(64))
created_at = db.Column(db.DateTime, default=func.now())
version = db.Column(db.String(100), default=__version__)
__table_args__ = (Index("object_id_type_action_index", object_id, object_type, object_action),)
class logged(object):
def __init__(self, route, object_extractor=None):
self._route = route
self._object_extractor = object_extractor
def __getattr__(self, attr):
return getattr(self._route, attr)
def __call__(self, *args, **kwargs):
if not current_app.config.get('INDEXING_ENABLED', True):
return self._route(*args, **kwargs)
log = PageView(
page=request.full_path,
endpoint=request.endpoint,
user_id=current_user.id,
ip_address=request.remote_addr,
version=__version__
)
errorlog = None
log.object_id, log.object_type, log.object_action, reextract_after_request = self.extract_objects(*args, **kwargs)
db_session.add(log) # Add log here to ensure pageviews are accurate
try:
return self._route(*args, **kwargs)
except Exception as e:
db_session.rollback() # Ensure no lingering database changes remain after crashed route
db_session.add(log)
errorlog = ErrorLog.from_exception(e)
db_session.add(errorlog)
db_session.commit()
tb = sys.exc_info()[-1]
raise e.with_traceback(tb)
finally:
# Extract object id and type after response generated (if requested) to ensure
# most recent data is collected
if reextract_after_request:
log.object_id, log.object_type, log.object_action, _ = self.extract_objects(*args, **kwargs)
if errorlog is not None:
log.id_errorlog = errorlog.id
db_session.add(log)
db_session.commit()
def object_extractor(self, extractor):
self._object_extractor = extractor
return self
def extract_objects(self, *args, **kwargs):
if self._object_extractor is None:
return None, None, None, False
try:
object_info = self._object_extractor(*args, **kwargs)
except Exception as e:
logger.warning("Error using object extractor: " + str(e))
object_info = {'id': (-1), 'type': None}
assert isinstance(object_info, dict), "Object extractors must return a dictionary."
assert len(set(['id', 'type']).difference(object_info.keys())) == 0 and len(set(object_info.keys()).difference(['id', 'type', 'action', 'may_change'])) == 0, "Object extractors must at least include the keys 'id' and 'type', and optionally 'action' and 'may_change'. Was provided with: {}".format(str(list(object_info.keys())))
object_info = defaultdict(lambda: None, object_info)
return object_info['id'], object_info['type'], object_info['action'], object_info['may_change'] or False
class Vote(db.Model):
__tablename__ = 'votes'
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer)
object_id = db.Column(db.Integer)
object_type = db.Column(db.String(100), default='post')
created_at = db.Column(db.DateTime, default=func.now())
updated_at = db.Column(db.DateTime, default=func.now(), onupdate=func.now())
@unique_constructor(
lambda identifier: identifier,
lambda query, identifier: query.filter(User.identifier == identifier)
)
class User(db.Model, UserMixin):
__tablename__ = 'users'
id = db.Column(db.Integer, primary_key=True)
created_at = db.Column(db.DateTime, default=func.now())
identifier = db.Column(db.String(500)) # Unique identifier across all login methods
username = db.Column(db.String(500)) # Username used to log in (may differ from identifier)
password = db.Column(db.String(500)) # Password for local logins
name = db.Column(db.String(500)) # Name as determined by auth method
preferred_name = db.Column(db.String(500)) # Name as determined by user preferences
email = db.Column(db.String(500)) # Email address
avatar_uri = db.Column(db.Text()) # Either external url or data uri
active = db.Column(db.Boolean, default=True)
last_login_at = db.Column(db.DateTime) # Date of last login
_posts_assoc = db.relationship("PostAuthorAssoc")
posts = association_proxy('_posts_assoc', 'post') # This property should not directly modified
# Method overrides for the UserMixin class for flask_login
@property
def is_active(self):
return self.active
@property
def is_authenticated(self):
return True
@property
def is_anonymous(self):
return False
def get_id(self):
return self.identifier
can_logout = True
# Other useful methods
@property
def format_name(self):
return self.preferred_name or self.name or self.identifier
@property
def subscriptions(self): # TODO: make attribute style naming
"""Get the subscriptions associated with a user.
Return an array of strings of tag_names
"""
subscriptions = (db.session.query(Subscription)
.filter(Subscription.user_id == self.id)
.all())
out_subscriptions = []
for s in subscriptions:
if s.object_type == 'tag':
tag_obj = (db.session.query(Tag)
.filter(Tag.id == s.object_id)
.first())
if tag_obj:
full_name = tag_obj.name
out_subscriptions.append(full_name)
else:
db.session.delete(s)
return out_subscriptions
@property
def liked_posts(self):
"""
:return: Posts that a user has liked
:rtype: list
"""
votes = (db.session.query(Vote)
.filter(Vote.user_id == self.id)
.all())
post_ids = [vote.object_id for vote in votes]
if len(post_ids) == 0:
return []
excluded_tags = current_app.config.get('EXCLUDED_TAGS', [])
posts = (db.session.query(Post)
.filter(Post.id.in_(post_ids))
.filter(~Post.tags.any(Tag.name.in_(excluded_tags)))
.all())
return posts
@unique_constructor(
lambda name: name,
lambda query, name: query.filter(Tag.name == name)
)
class Tag(db.Model):
__tablename__ = 'tags'
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(500))
_description = db.Column('description', db.Text())
created_at = db.Column(db.DateTime, default=func.now())
@hybrid_property
def description(self):
if self._description:
return self._description
return "All posts with tag '{}'.".format(self.name)
@description.expression
def description(self):
raise NotImplementedError
class Subscription(db.Model):
__tablename__ = 'subscriptions'
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer)
object_id = db.Column(db.Integer)
object_type = db.Column(db.String(100)) # Currently just tag
created_at = db.Column(db.DateTime, default=func.now())
class Post(db.Model):
__tablename__ = 'posts'
id = db.Column(db.Integer, primary_key=True)
uuid = db.Column(db.String(100), unique=True)
path = db.Column(db.String(512))
project = db.Column(db.String(512), nullable=True) # DEPRECATED
repository = db.Column(db.String(512))
revision = db.Column(db.Integer())
title = db.Column(db.Text())
subtitle = db.Column(db.Text())
tldr = db.Column(db.Text)
keywords = db.Column(db.Text)
thumbnail = db.Column(db.Text())
private = db.Column(db.Integer())
created_at = db.Column(db.DateTime, default=func.now())
updated_at = db.Column(db.DateTime, default=func.now())
_authors_assoc = db.relationship("PostAuthorAssoc",
order_by='PostAuthorAssoc.order',
collection_class=ordering_list('order'),
cascade="all, delete-orphan")
_authors = association_proxy('_authors_assoc', 'author',
creator=lambda author: PostAuthorAssoc(author=author),)
@hybrid_property
def authors(self):
return self._authors
@authors.setter
def authors(self, authors):
"""
Sets the tags of the post to the tags given in comma delimited string
form in tags_string
"""
user_objs = []
for author in authors:
if not isinstance(author, User):
author = author.strip()
author = User(identifier=author)
user_objs.append(author)
self._authors = user_objs
@hybrid_property
def authors_string(self):
return ', '.join([author.format_name for author in self.authors])
@authors_string.expression
def authors_string(self):
raise NotImplementedError
_tags = db.relationship("Tag", secondary=assoc_post_tag, backref='posts',
lazy='subquery')
@hybrid_property
def tags(self):
return self._tags
@tags.setter
def tags(self, tags):
"""
Sets the tags of the post to the tags given in comma delimited string
form in tags_string
"""
tag_objs = []
for tag in tags:
if not isinstance(tag, Tag):
tag = tag.strip()
if tag[0] == "#":
tag = tag[1:]
tag = Tag(name=tag)
tag_objs.append(tag)
self._tags = tag_objs
@property
def contains_excluded_tag(self):
excluded_tags = current_app.config.get('EXCLUDED_TAGS', [])
return any([tag.name in excluded_tags for tag in self.tags])
_groups = db.relationship("Group", secondary=assoc_post_group, backref='posts',
lazy='subquery')
@hybrid_property
def groups(self):
return self._groups
@groups.setter
def groups(self, groups):
# given a list of group_names, we add it.
group_objs = []
for group in groups:
if not isinstance(group, Group):
group = Group(name=group.strip())
group_objs.append(group)
# create an implicit group, group_post.id, to add
# single users to
group = Group(name=":post_group_" + str(self.id))
# this created group should have the author associated with it
# so they can add people to the post
group.users = self.authors
group_objs.append(group)
self._groups = group_objs
_status = db.Column('status', db.Integer(), default=0)
@hybrid_property
def status(self):
return current_repo.PostStatus(self._status or 0)
@status.expression
def status(self):
return func.coalesce(self._status, 0)
@status.setter
def status(self, status):
if status is None:
self._status = None
else:
assert isinstance(status, KnowledgeRepository.PostStatus), "Status must be an instance of KnowledgeRepository.PostStatus.Status or None"
self._status = status.value
@hybrid_property
def is_published(self):
return self.status == current_repo.PostStatus.PUBLISHED
@is_published.expression
def is_published(self):
return func.coalesce(self._status, 0) == current_repo.PostStatus.PUBLISHED.value
_views = db.relationship("PageView", lazy='dynamic',
primaryjoin="and_(foreign(PageView.object_id)==Post.id, "
"PageView.object_type=='post',"
"PageView.object_action=='view')")
@hybrid_property
def views(self):
return self._views.all()
@hybrid_property
def view_count(self):
return self._views.count()
@view_count.expression
def view_count(self):
return (select([func.count(PageView.id)])
.where(PageView.object_id == self.id)
.where(PageView.object_type == 'post')
.label("view_count"))
@hybrid_property
def view_user_count(self):
return (db.session.query(func.count(distinct(PageView.user_id)))
.filter(PageView.object_id == self.id)
.filter(PageView.object_type == 'post')
.scalar())
@view_user_count.expression
def view_user_count(self):
return (select([func.count(distinct(PageView.user_id))])
.where(PageView.object_id == self.id)
.where(PageView.object_type == 'post')
.label("view_user_count"))
_votes = db.relationship("Vote", lazy='dynamic',
primaryjoin="and_(foreign(Vote.object_id)==Post.id, "
"Vote.object_type=='post')")
@hybrid_property
def votes(self):
return self._votes.all()
@hybrid_property
def vote_count(self):
""" Given the path of a post, return the total likes """
return self._votes.count()
@vote_count.expression
def vote_count(self):
return (select([func.count(Vote.id)])
.where(Vote.object_id == self.id)
.where(Vote.object_type == 'post')
.label("vote_count"))
def vote_counted_for_user(self, user_id):
return (db_session.query(Vote)
.filter(and_(Vote.object_id == self.id, Vote.object_type == 'post', Vote.user_id == user_id))
.first()) is not None
_comments = db.relationship("Comment", lazy="dynamic",
primaryjoin="and_(foreign(Comment.post_id)==Post.id, "
"Comment.type=='post')")
@hybrid_property
def comments(self):
return self._comments.all()
@hybrid_property
def comment_count(self):
""" Given the path of the a post, return the total comments """
return self._comments.count()
@comment_count.expression
def comment_count(self):
return (select([func.count(Comment.id)])
.where(Comment.post_id == self.id)
.where(Comment.object_type == 'post')
.label("comments_count"))
@property
def kp(self):
return current_repo.post(self.path)
@property
def text(self):
return self.kp.read()
def update_metadata_from_kp(self, kp):
"""
:param kp: Maps fields of the model to values
:type kp: KnowledgePost
:param kp: Maps fields of the model to values
:type kr: KnowledgeRepository
:return: None
:rtype: None
"""
headers = kp.headers
self.uuid = kp.uuid
self.path = kp.path
self.project = headers.get('project')
self.repository = kp.repository_uri
self.revision = kp.revision
self.title = headers['title']
self.subtitle = headers.get('subtitle')
self.tldr = headers['tldr']
self.authors = headers.get('authors', [])
self.tags = headers.get('tags', [])
self.keywords = get_keywords(self)
self.thumbnail = kp.thumbnail_uri
self.created_at = headers['created_at']
self.updated_at = headers['updated_at']
if self.created_at > self.updated_at:
self.updated_at = self.created_at
self.status = kp.status
self.private = 0
# we do this check so that no header (None) and False are treated the same
if headers.get('private', ''):
self.private = 1
self.groups = headers.get('allowed_groups', [])
class Email(db.Model):
__tablename__ = 'emails'
id = db.Column(db.Integer, primary_key=True)
user_id = db.Column(db.Integer)
trigger_id = db.Column(db.Integer)
trigger_type = db.Column(db.String(100))
object_id = db.Column(db.Integer)
object_type = db.Column(db.String(100))
sent_at = db.Column(db.DateTime, default=func.now())
subject = db.Column(db.Text)
text = db.Column(MediumText())
@unique_constructor(
lambda name: name,
lambda query, name: query.filter(Group.name == name)
)
class Group(db.Model):
__tablename__ = 'groups'
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(128), unique=True)
_users = db.relationship("User", secondary=assoc_group_user, backref='users',
lazy='subquery')
@hybrid_property
def users(self):
return self._users
@users.setter
def users(self, user_objs):
self._users = self._users + user_objs
| 34.257186 | 339 | 0.615527 |
1f607c43cf8c38fb9a212cd579cb479db8ef6421 | 1,507 | py | Python | Yuanjunling1/Hello word/TestFlask.py | yuanjunling/PycharmProjects | 087b1a30818bbe2bf3972c9340f61ca4b792eb7d | [
"bzip2-1.0.6"
] | null | null | null | Yuanjunling1/Hello word/TestFlask.py | yuanjunling/PycharmProjects | 087b1a30818bbe2bf3972c9340f61ca4b792eb7d | [
"bzip2-1.0.6"
] | null | null | null | Yuanjunling1/Hello word/TestFlask.py | yuanjunling/PycharmProjects | 087b1a30818bbe2bf3972c9340f61ca4b792eb7d | [
"bzip2-1.0.6"
] | null | null | null | # -*- coding:utf-8 -*-
import time
import calendar
list1 = ['python','java','php','C++','C#']
list2 = ['jjj','ppp','lll','ddd']
print "list1[1]:",list1[1]
print list1+list2
list2.append('append')
print list2
del list2[3]
print list2*4
print len(list2)
list2.reverse()
print list2
tikce = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
print "当前时间戳：", tikce
cal = calendar.month(2018,6)
print "以下输出2018年6月份的日历:"
print cal
#自定义函数
def printme(str):
"打印任何传入的字符串"
print str;
return ;
#调用函数
printme("哈哈哈哈哈哈哈哈哈");
printme("我在调用一次啊");
#可写函数说明
def changeme(mylist):
"修改传入的列表"
mylist.append([1,2,3]);
print "函数获取值：",mylist
return
#调用changeme函数
mylist = [10,20,30];
changeme(mylist);
print "函数外取值: ", mylist
#可写函数说明
def printer(sta):
print sta;
return
printer(11111)
def printinfo(name,age):
"打印任何传入的字符串"
print "Name:",name;
print "Age",age;
return ;
#调用printinfo函数
printinfo(name="jun",age=222)
def functionname( arg1, *vartuple ):
"打印任何传入的参数"
print "输出: "
print arg1
for var in vartuple:
print var
return;
# 调用printinfo 函数
functionname(10)
functionname(10,20,30,40,50);
# 可写函数说明
sum = lambda arg1,arg2:arg1 + arg2;
# 调用sum函数
print "相加后的值为 : ", sum( 10, 20 )
print "相加后的值为 : ", sum( 20, 20 )
str1 = raw_input("请输入：")
print "你输入的内容是: ", str1
str2 = input("请输入：")
print "你输入的内容是：",str2;
fo = open("D:\\foo.txt","w")
print "文件名: ", fo.name
print "是否已关闭 : ", fo.closed
print "访问模式 : ", fo.mode
print "末尾是否强制加空格 : ", fo.softspace
| 18.8375 | 60 | 0.64499 |
6d18ffe529cad2b614b18b91cb34896d29ec85d8 | 99,921 | py | Python | tests/core.py | antoncohen/incubator-airflow | 71954a52fc13accf1130d3d2a00263d7ec369b02 | [
"Apache-2.0"
] | null | null | null | tests/core.py | antoncohen/incubator-airflow | 71954a52fc13accf1130d3d2a00263d7ec369b02 | [
"Apache-2.0"
] | null | null | null | tests/core.py | antoncohen/incubator-airflow | 71954a52fc13accf1130d3d2a00263d7ec369b02 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import print_function
import json
import unittest
import bleach
import doctest
import mock
import multiprocessing
import os
import re
import signal
import sqlalchemy
import subprocess
import tempfile
import warnings
from datetime import timedelta
from dateutil.relativedelta import relativedelta
from email.mime.application import MIMEApplication
from email.mime.multipart import MIMEMultipart
from freezegun import freeze_time
from numpy.testing import assert_array_almost_equal
from six.moves.urllib.parse import urlencode
from time import sleep
from airflow import configuration
from airflow.executors import SequentialExecutor
from airflow.models import Variable
configuration.conf.load_test_config()
from airflow import jobs, models, DAG, utils, macros, settings, exceptions
from airflow.models import BaseOperator
from airflow.operators.bash_operator import BashOperator
from airflow.operators.check_operator import CheckOperator, ValueCheckOperator
from airflow.operators.dagrun_operator import TriggerDagRunOperator
from airflow.operators.python_operator import PythonOperator
from airflow.operators.dummy_operator import DummyOperator
from airflow.hooks.base_hook import BaseHook
from airflow.hooks.sqlite_hook import SqliteHook
from airflow.bin import cli
from airflow.www import app as application
from airflow.settings import Session
from airflow.utils import timezone
from airflow.utils.timezone import datetime
from airflow.utils.state import State
from airflow.utils.dates import infer_time_unit, round_time, scale_time_units
from lxml import html
from airflow.exceptions import AirflowException
from airflow.configuration import AirflowConfigException, run_command
from jinja2.sandbox import SecurityError
from jinja2 import UndefinedError
import six
NUM_EXAMPLE_DAGS = 20
DEV_NULL = '/dev/null'
TEST_DAG_FOLDER = os.path.join(
os.path.dirname(os.path.realpath(__file__)), 'dags')
DEFAULT_DATE = datetime(2015, 1, 1)
DEFAULT_DATE_ISO = DEFAULT_DATE.isoformat()
DEFAULT_DATE_DS = DEFAULT_DATE_ISO[:10]
TEST_DAG_ID = 'unit_tests'
try:
import cPickle as pickle
except ImportError:
# Python 3
import pickle
def reset(dag_id=TEST_DAG_ID):
session = Session()
tis = session.query(models.TaskInstance).filter_by(dag_id=dag_id)
tis.delete()
session.commit()
session.close()
reset()
class OperatorSubclass(BaseOperator):
"""
An operator to test template substitution
"""
template_fields = ['some_templated_field']
def __init__(self, some_templated_field, *args, **kwargs):
super(OperatorSubclass, self).__init__(*args, **kwargs)
self.some_templated_field = some_templated_field
def execute(*args, **kwargs):
pass
class CoreTest(unittest.TestCase):
default_scheduler_args = {"num_runs": 1}
def setUp(self):
configuration.conf.load_test_config()
self.dagbag = models.DagBag(
dag_folder=DEV_NULL, include_examples=True)
self.args = {'owner': 'airflow', 'start_date': DEFAULT_DATE}
self.dag = DAG(TEST_DAG_ID, default_args=self.args)
self.dag_bash = self.dagbag.dags['example_bash_operator']
self.runme_0 = self.dag_bash.get_task('runme_0')
self.run_after_loop = self.dag_bash.get_task('run_after_loop')
self.run_this_last = self.dag_bash.get_task('run_this_last')
def test_schedule_dag_no_previous_runs(self):
"""
Tests scheduling a dag with no previous runs
"""
dag = DAG(TEST_DAG_ID + 'test_schedule_dag_no_previous_runs')
dag.add_task(models.BaseOperator(
task_id="faketastic",
owner='Also fake',
start_date=datetime(2015, 1, 2, 0, 0)))
dag_run = jobs.SchedulerJob(**self.default_scheduler_args).create_dag_run(dag)
self.assertIsNotNone(dag_run)
self.assertEqual(dag.dag_id, dag_run.dag_id)
self.assertIsNotNone(dag_run.run_id)
self.assertNotEqual('', dag_run.run_id)
self.assertEqual(
datetime(2015, 1, 2, 0, 0),
dag_run.execution_date,
msg='dag_run.execution_date did not match expectation: {0}'
.format(dag_run.execution_date)
)
self.assertEqual(State.RUNNING, dag_run.state)
self.assertFalse(dag_run.external_trigger)
dag.clear()
def test_schedule_dag_fake_scheduled_previous(self):
"""
Test scheduling a dag where there is a prior DagRun
which has the same run_id as the next run should have
"""
delta = timedelta(hours=1)
dag = DAG(TEST_DAG_ID + 'test_schedule_dag_fake_scheduled_previous',
schedule_interval=delta,
start_date=DEFAULT_DATE)
dag.add_task(models.BaseOperator(
task_id="faketastic",
owner='Also fake',
start_date=DEFAULT_DATE))
scheduler = jobs.SchedulerJob(**self.default_scheduler_args)
dag.create_dagrun(run_id=models.DagRun.id_for_date(DEFAULT_DATE),
execution_date=DEFAULT_DATE,
state=State.SUCCESS,
external_trigger=True)
dag_run = scheduler.create_dag_run(dag)
self.assertIsNotNone(dag_run)
self.assertEqual(dag.dag_id, dag_run.dag_id)
self.assertIsNotNone(dag_run.run_id)
self.assertNotEqual('', dag_run.run_id)
self.assertEqual(
DEFAULT_DATE + delta,
dag_run.execution_date,
msg='dag_run.execution_date did not match expectation: {0}'
.format(dag_run.execution_date)
)
self.assertEqual(State.RUNNING, dag_run.state)
self.assertFalse(dag_run.external_trigger)
def test_schedule_dag_once(self):
"""
Tests scheduling a dag scheduled for @once - should be scheduled the first time
it is called, and not scheduled the second.
"""
dag = DAG(TEST_DAG_ID + 'test_schedule_dag_once')
dag.schedule_interval = '@once'
dag.add_task(models.BaseOperator(
task_id="faketastic",
owner='Also fake',
start_date=datetime(2015, 1, 2, 0, 0)))
dag_run = jobs.SchedulerJob(**self.default_scheduler_args).create_dag_run(dag)
dag_run2 = jobs.SchedulerJob(**self.default_scheduler_args).create_dag_run(dag)
self.assertIsNotNone(dag_run)
self.assertIsNone(dag_run2)
dag.clear()
def test_fractional_seconds(self):
"""
Tests if fractional seconds are stored in the database
"""
dag = DAG(TEST_DAG_ID + 'test_fractional_seconds')
dag.schedule_interval = '@once'
dag.add_task(models.BaseOperator(
task_id="faketastic",
owner='Also fake',
start_date=datetime(2015, 1, 2, 0, 0)))
start_date = timezone.utcnow()
run = dag.create_dagrun(
run_id='test_' + start_date.isoformat(),
execution_date=start_date,
start_date=start_date,
state=State.RUNNING,
external_trigger=False
)
run.refresh_from_db()
self.assertEqual(start_date, run.execution_date,
"dag run execution_date loses precision")
self.assertEqual(start_date, run.start_date,
"dag run start_date loses precision ")
def test_schedule_dag_start_end_dates(self):
"""
Tests that an attempt to schedule a task after the Dag's end_date
does not succeed.
"""
delta = timedelta(hours=1)
runs = 3
start_date = DEFAULT_DATE
end_date = start_date + (runs - 1) * delta
dag = DAG(TEST_DAG_ID + 'test_schedule_dag_start_end_dates',
start_date=start_date,
end_date=end_date,
schedule_interval=delta)
dag.add_task(models.BaseOperator(task_id='faketastic',
owner='Also fake'))
# Create and schedule the dag runs
dag_runs = []
scheduler = jobs.SchedulerJob(**self.default_scheduler_args)
for i in range(runs):
dag_runs.append(scheduler.create_dag_run(dag))
additional_dag_run = scheduler.create_dag_run(dag)
for dag_run in dag_runs:
self.assertIsNotNone(dag_run)
self.assertIsNone(additional_dag_run)
@freeze_time('2016-01-01')
def test_schedule_dag_no_end_date_up_to_today_only(self):
"""
Tests that a Dag created without an end_date can only be scheduled up
to and including the current datetime.
For example, if today is 2016-01-01 and we are scheduling from a
start_date of 2015-01-01, only jobs up to, but not including
2016-01-01 should be scheduled.
"""
session = settings.Session()
delta = timedelta(days=1)
start_date = DEFAULT_DATE
runs = 365
dag = DAG(TEST_DAG_ID + 'test_schedule_dag_no_end_date_up_to_today_only',
start_date=start_date,
schedule_interval=delta)
dag.add_task(models.BaseOperator(task_id='faketastic',
owner='Also fake'))
dag_runs = []
scheduler = jobs.SchedulerJob(**self.default_scheduler_args)
for i in range(runs):
dag_run = scheduler.create_dag_run(dag)
dag_runs.append(dag_run)
# Mark the DagRun as complete
dag_run.state = State.SUCCESS
session.merge(dag_run)
session.commit()
# Attempt to schedule an additional dag run (for 2016-01-01)
additional_dag_run = scheduler.create_dag_run(dag)
for dag_run in dag_runs:
self.assertIsNotNone(dag_run)
self.assertIsNone(additional_dag_run)
def test_confirm_unittest_mod(self):
self.assertTrue(configuration.conf.get('core', 'unit_test_mode'))
def test_pickling(self):
dp = self.dag.pickle()
self.assertEqual(dp.pickle.dag_id, self.dag.dag_id)
def test_rich_comparison_ops(self):
class DAGsubclass(DAG):
pass
dag_eq = DAG(TEST_DAG_ID, default_args=self.args)
dag_diff_load_time = DAG(TEST_DAG_ID, default_args=self.args)
dag_diff_name = DAG(TEST_DAG_ID + '_neq', default_args=self.args)
dag_subclass = DAGsubclass(TEST_DAG_ID, default_args=self.args)
dag_subclass_diff_name = DAGsubclass(
TEST_DAG_ID + '2', default_args=self.args)
for d in [dag_eq, dag_diff_name, dag_subclass, dag_subclass_diff_name]:
d.last_loaded = self.dag.last_loaded
# test identity equality
self.assertEqual(self.dag, self.dag)
# test dag (in)equality based on _comps
self.assertEqual(dag_eq, self.dag)
self.assertNotEqual(dag_diff_name, self.dag)
self.assertNotEqual(dag_diff_load_time, self.dag)
# test dag inequality based on type even if _comps happen to match
self.assertNotEqual(dag_subclass, self.dag)
# a dag should equal an unpickled version of itself
d = pickle.dumps(self.dag)
self.assertEqual(pickle.loads(d), self.dag)
# dags are ordered based on dag_id no matter what the type is
self.assertLess(self.dag, dag_diff_name)
self.assertGreater(self.dag, dag_diff_load_time)
self.assertLess(self.dag, dag_subclass_diff_name)
# greater than should have been created automatically by functools
self.assertGreater(dag_diff_name, self.dag)
# hashes are non-random and match equality
self.assertEqual(hash(self.dag), hash(self.dag))
self.assertEqual(hash(dag_eq), hash(self.dag))
self.assertNotEqual(hash(dag_diff_name), hash(self.dag))
self.assertNotEqual(hash(dag_subclass), hash(self.dag))
def test_check_operators(self):
conn_id = "sqlite_default"
captainHook = BaseHook.get_hook(conn_id=conn_id)
captainHook.run("CREATE TABLE operator_test_table (a, b)")
captainHook.run("insert into operator_test_table values (1,2)")
t = CheckOperator(
task_id='check',
sql="select count(*) from operator_test_table",
conn_id=conn_id,
dag=self.dag)
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
t = ValueCheckOperator(
task_id='value_check',
pass_value=95,
tolerance=0.1,
conn_id=conn_id,
sql="SELECT 100",
dag=self.dag)
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
captainHook.run("drop table operator_test_table")
def test_clear_api(self):
task = self.dag_bash.tasks[0]
task.clear(
start_date=DEFAULT_DATE, end_date=DEFAULT_DATE,
upstream=True, downstream=True)
ti = models.TaskInstance(task=task, execution_date=DEFAULT_DATE)
ti.are_dependents_done()
def test_illegal_args(self):
"""
Tests that Operators reject illegal arguments
"""
with warnings.catch_warnings(record=True) as w:
t = BashOperator(
task_id='test_illegal_args',
bash_command='echo success',
dag=self.dag,
illegal_argument_1234='hello?')
self.assertTrue(
issubclass(w[0].category, PendingDeprecationWarning))
self.assertIn(
'Invalid arguments were passed to BashOperator.',
w[0].message.args[0])
def test_bash_operator(self):
t = BashOperator(
task_id='test_bash_operator',
bash_command="echo success",
dag=self.dag)
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
def test_bash_operator_multi_byte_output(self):
t = BashOperator(
task_id='test_multi_byte_bash_operator',
bash_command=u"echo \u2600",
dag=self.dag,
output_encoding='utf-8')
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
def test_bash_operator_kill(self):
import psutil
sleep_time = "100%d" % os.getpid()
t = BashOperator(
task_id='test_bash_operator_kill',
execution_timeout=timedelta(seconds=1),
bash_command="/bin/bash -c 'sleep %s'" % sleep_time,
dag=self.dag)
self.assertRaises(
exceptions.AirflowTaskTimeout,
t.run,
start_date=DEFAULT_DATE, end_date=DEFAULT_DATE)
sleep(2)
pid = -1
for proc in psutil.process_iter():
if proc.cmdline() == ['sleep', sleep_time]:
pid = proc.pid
if pid != -1:
os.kill(pid, signal.SIGTERM)
self.fail("BashOperator's subprocess still running after stopping on timeout!")
def test_trigger_dagrun(self):
def trigga(context, obj):
if True:
return obj
t = TriggerDagRunOperator(
task_id='test_trigger_dagrun',
trigger_dag_id='example_bash_operator',
python_callable=trigga,
dag=self.dag)
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
def test_dryrun(self):
t = BashOperator(
task_id='test_dryrun',
bash_command="echo success",
dag=self.dag)
t.dry_run()
def test_sqlite(self):
import airflow.operators.sqlite_operator
t = airflow.operators.sqlite_operator.SqliteOperator(
task_id='time_sqlite',
sql="CREATE TABLE IF NOT EXISTS unitest (dummy VARCHAR(20))",
dag=self.dag)
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
def test_timeout(self):
t = PythonOperator(
task_id='test_timeout',
execution_timeout=timedelta(seconds=1),
python_callable=lambda: sleep(5),
dag=self.dag)
self.assertRaises(
exceptions.AirflowTaskTimeout,
t.run,
start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
def test_python_op(self):
def test_py_op(templates_dict, ds, **kwargs):
if not templates_dict['ds'] == ds:
raise Exception("failure")
t = PythonOperator(
task_id='test_py_op',
provide_context=True,
python_callable=test_py_op,
templates_dict={'ds': "{{ ds }}"},
dag=self.dag)
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
def test_complex_template(self):
def verify_templated_field(context):
self.assertEqual(context['ti'].task.some_templated_field['bar'][1],
context['ds'])
t = OperatorSubclass(
task_id='test_complex_template',
some_templated_field={
'foo': '123',
'bar': ['baz', '{{ ds }}']
},
dag=self.dag)
t.execute = verify_templated_field
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
def test_template_with_variable(self):
"""
Test the availability of variables in templates
"""
val = {
'test_value': 'a test value'
}
Variable.set("a_variable", val['test_value'])
def verify_templated_field(context):
self.assertEqual(context['ti'].task.some_templated_field,
val['test_value'])
t = OperatorSubclass(
task_id='test_complex_template',
some_templated_field='{{ var.value.a_variable }}',
dag=self.dag)
t.execute = verify_templated_field
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
def test_template_with_json_variable(self):
"""
Test the availability of variables (serialized as JSON) in templates
"""
val = {
'test_value': {'foo': 'bar', 'obj': {'v1': 'yes', 'v2': 'no'}}
}
Variable.set("a_variable", val['test_value'], serialize_json=True)
def verify_templated_field(context):
self.assertEqual(context['ti'].task.some_templated_field,
val['test_value']['obj']['v2'])
t = OperatorSubclass(
task_id='test_complex_template',
some_templated_field='{{ var.json.a_variable.obj.v2 }}',
dag=self.dag)
t.execute = verify_templated_field
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
def test_template_with_json_variable_as_value(self):
"""
Test the availability of variables (serialized as JSON) in templates, but
accessed as a value
"""
val = {
'test_value': {'foo': 'bar'}
}
Variable.set("a_variable", val['test_value'], serialize_json=True)
def verify_templated_field(context):
self.assertEqual(context['ti'].task.some_templated_field,
u'{"foo": "bar"}')
t = OperatorSubclass(
task_id='test_complex_template',
some_templated_field='{{ var.value.a_variable }}',
dag=self.dag)
t.execute = verify_templated_field
t.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
def test_template_non_bool(self):
"""
Test templates can handle objects with no sense of truthiness
"""
class NonBoolObject(object):
def __len__(self):
return NotImplemented
def __bool__(self):
return NotImplemented
t = OperatorSubclass(
task_id='test_bad_template_obj',
some_templated_field=NonBoolObject(),
dag=self.dag)
t.resolve_template_files()
def test_import_examples(self):
self.assertEqual(len(self.dagbag.dags), NUM_EXAMPLE_DAGS)
def test_local_task_job(self):
TI = models.TaskInstance
ti = TI(
task=self.runme_0, execution_date=DEFAULT_DATE)
job = jobs.LocalTaskJob(task_instance=ti, ignore_ti_state=True)
job.run()
def test_raw_job(self):
TI = models.TaskInstance
ti = TI(
task=self.runme_0, execution_date=DEFAULT_DATE)
ti.dag = self.dag_bash
ti.run(ignore_ti_state=True)
def test_doctests(self):
modules = [utils, macros]
for mod in modules:
failed, tests = doctest.testmod(mod)
if failed:
raise Exception("Failed a doctest")
def test_variable_set_get_round_trip(self):
Variable.set("tested_var_set_id", "Monday morning breakfast")
self.assertEqual("Monday morning breakfast", Variable.get("tested_var_set_id"))
def test_variable_set_get_round_trip_json(self):
value = {"a": 17, "b": 47}
Variable.set("tested_var_set_id", value, serialize_json=True)
self.assertEqual(value, Variable.get("tested_var_set_id", deserialize_json=True))
def test_get_non_existing_var_should_return_default(self):
default_value = "some default val"
self.assertEqual(default_value, Variable.get("thisIdDoesNotExist",
default_var=default_value))
def test_get_non_existing_var_should_not_deserialize_json_default(self):
default_value = "}{ this is a non JSON default }{"
self.assertEqual(default_value, Variable.get("thisIdDoesNotExist",
default_var=default_value,
deserialize_json=True))
def test_variable_setdefault_round_trip(self):
key = "tested_var_setdefault_1_id"
value = "Monday morning breakfast in Paris"
Variable.setdefault(key, value)
self.assertEqual(value, Variable.get(key))
def test_variable_setdefault_round_trip_json(self):
key = "tested_var_setdefault_2_id"
value = {"city": 'Paris', "Hapiness": True}
Variable.setdefault(key, value, deserialize_json=True)
self.assertEqual(value, Variable.get(key, deserialize_json=True))
def test_variable_setdefault_existing_json(self):
key = "tested_var_setdefault_2_id"
value = {"city": 'Paris', "Hapiness": True}
Variable.set(key, value, serialize_json=True)
val = Variable.setdefault(key, value, deserialize_json=True)
# Check the returned value, and the stored value are handled correctly.
self.assertEqual(value, val)
self.assertEqual(value, Variable.get(key, deserialize_json=True))
def test_parameterized_config_gen(self):
cfg = configuration.parameterized_config(configuration.DEFAULT_CONFIG)
# making sure some basic building blocks are present:
self.assertIn("[core]", cfg)
self.assertIn("dags_folder", cfg)
self.assertIn("sql_alchemy_conn", cfg)
self.assertIn("fernet_key", cfg)
# making sure replacement actually happened
self.assertNotIn("{AIRFLOW_HOME}", cfg)
self.assertNotIn("{FERNET_KEY}", cfg)
def test_config_use_original_when_original_and_fallback_are_present(self):
self.assertTrue(configuration.conf.has_option("core", "FERNET_KEY"))
self.assertFalse(configuration.conf.has_option("core", "FERNET_KEY_CMD"))
FERNET_KEY = configuration.conf.get('core', 'FERNET_KEY')
configuration.conf.set("core", "FERNET_KEY_CMD", "printf HELLO")
FALLBACK_FERNET_KEY = configuration.conf.get(
"core",
"FERNET_KEY"
)
self.assertEqual(FERNET_KEY, FALLBACK_FERNET_KEY)
# restore the conf back to the original state
configuration.conf.remove_option("core", "FERNET_KEY_CMD")
def test_config_throw_error_when_original_and_fallback_is_absent(self):
self.assertTrue(configuration.conf.has_option("core", "FERNET_KEY"))
self.assertFalse(configuration.conf.has_option("core", "FERNET_KEY_CMD"))
FERNET_KEY = configuration.conf.get("core", "FERNET_KEY")
configuration.conf.remove_option("core", "FERNET_KEY")
with self.assertRaises(AirflowConfigException) as cm:
configuration.conf.get("core", "FERNET_KEY")
exception = str(cm.exception)
message = "section/key [core/fernet_key] not found in config"
self.assertEqual(message, exception)
# restore the conf back to the original state
configuration.conf.set("core", "FERNET_KEY", FERNET_KEY)
self.assertTrue(configuration.conf.has_option("core", "FERNET_KEY"))
def test_config_override_original_when_non_empty_envvar_is_provided(self):
key = "AIRFLOW__CORE__FERNET_KEY"
value = "some value"
self.assertNotIn(key, os.environ)
os.environ[key] = value
FERNET_KEY = configuration.conf.get('core', 'FERNET_KEY')
self.assertEqual(value, FERNET_KEY)
# restore the envvar back to the original state
del os.environ[key]
def test_config_override_original_when_empty_envvar_is_provided(self):
key = "AIRFLOW__CORE__FERNET_KEY"
value = ""
self.assertNotIn(key, os.environ)
os.environ[key] = value
FERNET_KEY = configuration.conf.get('core', 'FERNET_KEY')
self.assertEqual(value, FERNET_KEY)
# restore the envvar back to the original state
del os.environ[key]
def test_round_time(self):
rt1 = round_time(datetime(2015, 1, 1, 6), timedelta(days=1))
self.assertEqual(datetime(2015, 1, 1, 0, 0), rt1)
rt2 = round_time(datetime(2015, 1, 2), relativedelta(months=1))
self.assertEqual(datetime(2015, 1, 1, 0, 0), rt2)
rt3 = round_time(datetime(2015, 9, 16, 0, 0), timedelta(1), datetime(
2015, 9, 14, 0, 0))
self.assertEqual(datetime(2015, 9, 16, 0, 0), rt3)
rt4 = round_time(datetime(2015, 9, 15, 0, 0), timedelta(1), datetime(
2015, 9, 14, 0, 0))
self.assertEqual(datetime(2015, 9, 15, 0, 0), rt4)
rt5 = round_time(datetime(2015, 9, 14, 0, 0), timedelta(1), datetime(
2015, 9, 14, 0, 0))
self.assertEqual(datetime(2015, 9, 14, 0, 0), rt5)
rt6 = round_time(datetime(2015, 9, 13, 0, 0), timedelta(1), datetime(
2015, 9, 14, 0, 0))
self.assertEqual(datetime(2015, 9, 14, 0, 0), rt6)
def test_infer_time_unit(self):
self.assertEqual('minutes', infer_time_unit([130, 5400, 10]))
self.assertEqual('seconds', infer_time_unit([110, 50, 10, 100]))
self.assertEqual('hours', infer_time_unit([100000, 50000, 10000, 20000]))
self.assertEqual('days', infer_time_unit([200000, 100000]))
def test_scale_time_units(self):
# use assert_almost_equal from numpy.testing since we are comparing
# floating point arrays
arr1 = scale_time_units([130, 5400, 10], 'minutes')
assert_array_almost_equal(arr1, [2.167, 90.0, 0.167], decimal=3)
arr2 = scale_time_units([110, 50, 10, 100], 'seconds')
assert_array_almost_equal(arr2, [110.0, 50.0, 10.0, 100.0], decimal=3)
arr3 = scale_time_units([100000, 50000, 10000, 20000], 'hours')
assert_array_almost_equal(arr3, [27.778, 13.889, 2.778, 5.556],
decimal=3)
arr4 = scale_time_units([200000, 100000], 'days')
assert_array_almost_equal(arr4, [2.315, 1.157], decimal=3)
def test_duplicate_dependencies(self):
regexp = "Dependency (.*)runme_0(.*)run_after_loop(.*) " \
"already registered"
with self.assertRaisesRegexp(AirflowException, regexp):
self.runme_0.set_downstream(self.run_after_loop)
with self.assertRaisesRegexp(AirflowException, regexp):
self.run_after_loop.set_upstream(self.runme_0)
def test_bad_trigger_rule(self):
with self.assertRaises(AirflowException):
DummyOperator(
task_id='test_bad_trigger',
trigger_rule="non_existant",
dag=self.dag)
def test_terminate_task(self):
"""If a task instance's db state get deleted, it should fail"""
TI = models.TaskInstance
dag = self.dagbag.dags.get('test_utils')
task = dag.task_dict.get('sleeps_forever')
ti = TI(task=task, execution_date=DEFAULT_DATE)
job = jobs.LocalTaskJob(
task_instance=ti, ignore_ti_state=True, executor=SequentialExecutor())
# Running task instance asynchronously
p = multiprocessing.Process(target=job.run)
p.start()
sleep(5)
settings.engine.dispose()
session = settings.Session()
ti.refresh_from_db(session=session)
# making sure it's actually running
self.assertEqual(State.RUNNING, ti.state)
ti = session.query(TI).filter_by(
dag_id=task.dag_id,
task_id=task.task_id,
execution_date=DEFAULT_DATE
).one()
# deleting the instance should result in a failure
session.delete(ti)
session.commit()
# waiting for the async task to finish
p.join()
# making sure that the task ended up as failed
ti.refresh_from_db(session=session)
self.assertEqual(State.FAILED, ti.state)
session.close()
def test_task_fail_duration(self):
"""If a task fails, the duration should be recorded in TaskFail"""
p = BashOperator(
task_id='pass_sleepy',
bash_command='sleep 3',
dag=self.dag)
f = BashOperator(
task_id='fail_sleepy',
bash_command='sleep 5',
execution_timeout=timedelta(seconds=3),
retry_delay=timedelta(seconds=0),
dag=self.dag)
session = settings.Session()
try:
p.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
except:
pass
try:
f.run(start_date=DEFAULT_DATE, end_date=DEFAULT_DATE, ignore_ti_state=True)
except:
pass
p_fails = session.query(models.TaskFail).filter_by(
task_id='pass_sleepy',
dag_id=self.dag.dag_id,
execution_date=DEFAULT_DATE).all()
f_fails = session.query(models.TaskFail).filter_by(
task_id='fail_sleepy',
dag_id=self.dag.dag_id,
execution_date=DEFAULT_DATE).all()
print(f_fails)
self.assertEqual(0, len(p_fails))
self.assertEqual(1, len(f_fails))
# C
self.assertGreaterEqual(sum([f.duration for f in f_fails]), 3)
def test_dag_stats(self):
"""Correctly sets/dirties/cleans rows of DagStat table"""
session = settings.Session()
session.query(models.DagRun).delete()
session.query(models.DagStat).delete()
session.commit()
models.DagStat.update([], session=session)
run1 = self.dag_bash.create_dagrun(
run_id="run1",
execution_date=DEFAULT_DATE,
state=State.RUNNING)
models.DagStat.update([self.dag_bash.dag_id], session=session)
qry = session.query(models.DagStat).all()
self.assertEqual(3, len(qry))
self.assertEqual(self.dag_bash.dag_id, qry[0].dag_id)
for stats in qry:
if stats.state == State.RUNNING:
self.assertEqual(stats.count, 1)
else:
self.assertEqual(stats.count, 0)
self.assertFalse(stats.dirty)
run2 = self.dag_bash.create_dagrun(
run_id="run2",
execution_date=DEFAULT_DATE + timedelta(days=1),
state=State.RUNNING)
models.DagStat.update([self.dag_bash.dag_id], session=session)
qry = session.query(models.DagStat).all()
self.assertEqual(3, len(qry))
self.assertEqual(self.dag_bash.dag_id, qry[0].dag_id)
for stats in qry:
if stats.state == State.RUNNING:
self.assertEqual(stats.count, 2)
else:
self.assertEqual(stats.count, 0)
self.assertFalse(stats.dirty)
session.query(models.DagRun).first().state = State.SUCCESS
session.commit()
models.DagStat.update([self.dag_bash.dag_id], session=session)
qry = session.query(models.DagStat).filter(models.DagStat.state == State.SUCCESS).all()
self.assertEqual(1, len(qry))
self.assertEqual(self.dag_bash.dag_id, qry[0].dag_id)
self.assertEqual(State.SUCCESS, qry[0].state)
self.assertEqual(1, qry[0].count)
self.assertFalse(qry[0].dirty)
qry = session.query(models.DagStat).filter(models.DagStat.state == State.RUNNING).all()
self.assertEqual(1, len(qry))
self.assertEqual(self.dag_bash.dag_id, qry[0].dag_id)
self.assertEqual(State.RUNNING, qry[0].state)
self.assertEqual(1, qry[0].count)
self.assertFalse(qry[0].dirty)
session.query(models.DagRun).delete()
session.query(models.DagStat).delete()
session.commit()
session.close()
def test_run_command(self):
if six.PY3:
write = r'sys.stdout.buffer.write("\u1000foo".encode("utf8"))'
else:
write = r'sys.stdout.write(u"\u1000foo".encode("utf8"))'
cmd = 'import sys; {0}; sys.stdout.flush()'.format(write)
self.assertEqual(run_command("python -c '{0}'".format(cmd)),
u'\u1000foo' if six.PY3 else 'foo')
self.assertEqual(run_command('echo "foo bar"'), u'foo bar\n')
self.assertRaises(AirflowConfigException, run_command, 'bash -c "exit 1"')
class CliTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(CliTests, cls).setUpClass()
cls._cleanup()
def setUp(self):
super(CliTests, self).setUp()
configuration.load_test_config()
app = application.create_app()
app.config['TESTING'] = True
self.parser = cli.CLIFactory.get_parser()
self.dagbag = models.DagBag(dag_folder=DEV_NULL, include_examples=True)
self.session = Session()
def tearDown(self):
self._cleanup(session=self.session)
super(CliTests, self).tearDown()
@staticmethod
def _cleanup(session=None):
if session is None:
session = Session()
session.query(models.Pool).delete()
session.query(models.Variable).delete()
session.commit()
session.close()
def test_cli_list_dags(self):
args = self.parser.parse_args(['list_dags', '--report'])
cli.list_dags(args)
def test_cli_create_user(self):
args = self.parser.parse_args([
'create_user', '-u', 'test', '-l', 'doe', '-f', 'jon',
'-e', 'jdoe@foo.com', '-r', 'Viewer', '--use_random_password'
])
cli.create_user(args)
def test_cli_list_tasks(self):
for dag_id in self.dagbag.dags.keys():
args = self.parser.parse_args(['list_tasks', dag_id])
cli.list_tasks(args)
args = self.parser.parse_args([
'list_tasks', 'example_bash_operator', '--tree'])
cli.list_tasks(args)
@mock.patch("airflow.bin.cli.db_utils.initdb")
def test_cli_initdb(self, initdb_mock):
cli.initdb(self.parser.parse_args(['initdb']))
initdb_mock.assert_called_once_with(False)
@mock.patch("airflow.bin.cli.db_utils.resetdb")
def test_cli_resetdb(self, resetdb_mock):
cli.resetdb(self.parser.parse_args(['resetdb', '--yes']))
resetdb_mock.assert_called_once_with(False)
def test_cli_connections_list(self):
with mock.patch('sys.stdout',
new_callable=six.StringIO) as mock_stdout:
cli.connections(self.parser.parse_args(['connections', '--list']))
stdout = mock_stdout.getvalue()
conns = [[x.strip("'") for x in re.findall("'\w+'", line)[:2]]
for ii, line in enumerate(stdout.split('\n'))
if ii % 2 == 1]
conns = [conn for conn in conns if len(conn) > 0]
# Assert that some of the connections are present in the output as
# expected:
self.assertIn(['aws_default', 'aws'], conns)
self.assertIn(['beeline_default', 'beeline'], conns)
self.assertIn(['emr_default', 'emr'], conns)
self.assertIn(['mssql_default', 'mssql'], conns)
self.assertIn(['mysql_default', 'mysql'], conns)
self.assertIn(['postgres_default', 'postgres'], conns)
self.assertIn(['wasb_default', 'wasb'], conns)
# Attempt to list connections with invalid cli args
with mock.patch('sys.stdout',
new_callable=six.StringIO) as mock_stdout:
cli.connections(self.parser.parse_args(
['connections', '--list', '--conn_id=fake', '--conn_uri=fake-uri',
'--conn_type=fake-type', '--conn_host=fake_host',
'--conn_login=fake_login', '--conn_password=fake_password',
'--conn_schema=fake_schema', '--conn_port=fake_port', '--conn_extra=fake_extra']))
stdout = mock_stdout.getvalue()
# Check list attempt stdout
lines = [l for l in stdout.split('\n') if len(l) > 0]
self.assertListEqual(lines, [
("\tThe following args are not compatible with the " +
"--list flag: ['conn_id', 'conn_uri', 'conn_extra', " +
"'conn_type', 'conn_host', 'conn_login', " +
"'conn_password', 'conn_schema', 'conn_port']"),
])
def test_cli_connections_list_redirect(self):
cmd = ['airflow', 'connections', '--list']
with tempfile.TemporaryFile() as fp:
p = subprocess.Popen(cmd, stdout=fp)
p.wait()
self.assertEqual(0, p.returncode)
def test_cli_connections_add_delete(self):
# Add connections:
uri = 'postgresql://airflow:airflow@host:5432/airflow'
with mock.patch('sys.stdout',
new_callable=six.StringIO) as mock_stdout:
cli.connections(self.parser.parse_args(
['connections', '--add', '--conn_id=new1',
'--conn_uri=%s' % uri]))
cli.connections(self.parser.parse_args(
['connections', '-a', '--conn_id=new2',
'--conn_uri=%s' % uri]))
cli.connections(self.parser.parse_args(
['connections', '--add', '--conn_id=new3',
'--conn_uri=%s' % uri, '--conn_extra', "{'extra': 'yes'}"]))
cli.connections(self.parser.parse_args(
['connections', '-a', '--conn_id=new4',
'--conn_uri=%s' % uri, '--conn_extra', "{'extra': 'yes'}"]))
cli.connections(self.parser.parse_args(
['connections', '--add', '--conn_id=new5',
'--conn_type=hive_metastore', '--conn_login=airflow',
'--conn_password=airflow', '--conn_host=host',
'--conn_port=9083', '--conn_schema=airflow']))
cli.connections(self.parser.parse_args(
['connections', '-a', '--conn_id=new6',
'--conn_uri', "", '--conn_type=google_cloud_platform', '--conn_extra', "{'extra': 'yes'}"]))
stdout = mock_stdout.getvalue()
# Check addition stdout
lines = [l for l in stdout.split('\n') if len(l) > 0]
self.assertListEqual(lines, [
("\tSuccessfully added `conn_id`=new1 : " +
"postgresql://airflow:airflow@host:5432/airflow"),
("\tSuccessfully added `conn_id`=new2 : " +
"postgresql://airflow:airflow@host:5432/airflow"),
("\tSuccessfully added `conn_id`=new3 : " +
"postgresql://airflow:airflow@host:5432/airflow"),
("\tSuccessfully added `conn_id`=new4 : " +
"postgresql://airflow:airflow@host:5432/airflow"),
("\tSuccessfully added `conn_id`=new5 : " +
"hive_metastore://airflow:airflow@host:9083/airflow"),
("\tSuccessfully added `conn_id`=new6 : " +
"google_cloud_platform://:@:")
])
# Attempt to add duplicate
with mock.patch('sys.stdout',
new_callable=six.StringIO) as mock_stdout:
cli.connections(self.parser.parse_args(
['connections', '--add', '--conn_id=new1',
'--conn_uri=%s' % uri]))
stdout = mock_stdout.getvalue()
# Check stdout for addition attempt
lines = [l for l in stdout.split('\n') if len(l) > 0]
self.assertListEqual(lines, [
"\tA connection with `conn_id`=new1 already exists",
])
# Attempt to add without providing conn_id
with mock.patch('sys.stdout',
new_callable=six.StringIO) as mock_stdout:
cli.connections(self.parser.parse_args(
['connections', '--add', '--conn_uri=%s' % uri]))
stdout = mock_stdout.getvalue()
# Check stdout for addition attempt
lines = [l for l in stdout.split('\n') if len(l) > 0]
self.assertListEqual(lines, [
("\tThe following args are required to add a connection:" +
" ['conn_id']"),
])
# Attempt to add without providing conn_uri
with mock.patch('sys.stdout',
new_callable=six.StringIO) as mock_stdout:
cli.connections(self.parser.parse_args(
['connections', '--add', '--conn_id=new']))
stdout = mock_stdout.getvalue()
# Check stdout for addition attempt
lines = [l for l in stdout.split('\n') if len(l) > 0]
self.assertListEqual(lines, [
("\tThe following args are required to add a connection:" +
" ['conn_uri or conn_type']"),
])
# Prepare to add connections
session = settings.Session()
extra = {'new1': None,
'new2': None,
'new3': "{'extra': 'yes'}",
'new4': "{'extra': 'yes'}"}
# Add connections
for index in range(1, 6):
conn_id = 'new%s' % index
result = (session
.query(models.Connection)
.filter(models.Connection.conn_id == conn_id)
.first())
result = (result.conn_id, result.conn_type, result.host,
result.port, result.get_extra())
if conn_id in ['new1', 'new2', 'new3', 'new4']:
self.assertEqual(result, (conn_id, 'postgres', 'host', 5432,
extra[conn_id]))
elif conn_id == 'new5':
self.assertEqual(result, (conn_id, 'hive_metastore', 'host',
9083, None))
elif conn_id == 'new6':
self.assertEqual(result, (conn_id, 'google_cloud_platform',
None, None, "{'extra': 'yes'}"))
# Delete connections
with mock.patch('sys.stdout',
new_callable=six.StringIO) as mock_stdout:
cli.connections(self.parser.parse_args(
['connections', '--delete', '--conn_id=new1']))
cli.connections(self.parser.parse_args(
['connections', '--delete', '--conn_id=new2']))
cli.connections(self.parser.parse_args(
['connections', '--delete', '--conn_id=new3']))
cli.connections(self.parser.parse_args(
['connections', '--delete', '--conn_id=new4']))
cli.connections(self.parser.parse_args(
['connections', '--delete', '--conn_id=new5']))
cli.connections(self.parser.parse_args(
['connections', '--delete', '--conn_id=new6']))
stdout = mock_stdout.getvalue()
# Check deletion stdout
lines = [l for l in stdout.split('\n') if len(l) > 0]
self.assertListEqual(lines, [
"\tSuccessfully deleted `conn_id`=new1",
"\tSuccessfully deleted `conn_id`=new2",
"\tSuccessfully deleted `conn_id`=new3",
"\tSuccessfully deleted `conn_id`=new4",
"\tSuccessfully deleted `conn_id`=new5",
"\tSuccessfully deleted `conn_id`=new6"
])
# Check deletions
for index in range(1, 7):
conn_id = 'new%s' % index
result = (session.query(models.Connection)
.filter(models.Connection.conn_id == conn_id)
.first())
self.assertTrue(result is None)
# Attempt to delete a non-existing connnection
with mock.patch('sys.stdout',
new_callable=six.StringIO) as mock_stdout:
cli.connections(self.parser.parse_args(
['connections', '--delete', '--conn_id=fake']))
stdout = mock_stdout.getvalue()
# Check deletion attempt stdout
lines = [l for l in stdout.split('\n') if len(l) > 0]
self.assertListEqual(lines, [
"\tDid not find a connection with `conn_id`=fake",
])
# Attempt to delete with invalid cli args
with mock.patch('sys.stdout',
new_callable=six.StringIO) as mock_stdout:
cli.connections(self.parser.parse_args(
['connections', '--delete', '--conn_id=fake',
'--conn_uri=%s' % uri, '--conn_type=fake-type']))
stdout = mock_stdout.getvalue()
# Check deletion attempt stdout
lines = [l for l in stdout.split('\n') if len(l) > 0]
self.assertListEqual(lines, [
("\tThe following args are not compatible with the " +
"--delete flag: ['conn_uri', 'conn_type']"),
])
session.close()
def test_cli_test(self):
cli.test(self.parser.parse_args([
'test', 'example_bash_operator', 'runme_0',
DEFAULT_DATE.isoformat()]))
cli.test(self.parser.parse_args([
'test', 'example_bash_operator', 'runme_0', '--dry_run',
DEFAULT_DATE.isoformat()]))
def test_cli_test_with_params(self):
cli.test(self.parser.parse_args([
'test', 'example_passing_params_via_test_command', 'run_this',
'-tp', '{"foo":"bar"}', DEFAULT_DATE.isoformat()]))
cli.test(self.parser.parse_args([
'test', 'example_passing_params_via_test_command', 'also_run_this',
'-tp', '{"foo":"bar"}', DEFAULT_DATE.isoformat()]))
def test_cli_run(self):
cli.run(self.parser.parse_args([
'run', 'example_bash_operator', 'runme_0', '-l',
DEFAULT_DATE.isoformat()]))
def test_task_state(self):
cli.task_state(self.parser.parse_args([
'task_state', 'example_bash_operator', 'runme_0',
DEFAULT_DATE.isoformat()]))
def test_dag_state(self):
self.assertEqual(None, cli.dag_state(self.parser.parse_args([
'dag_state', 'example_bash_operator', DEFAULT_DATE.isoformat()])))
def test_pause(self):
args = self.parser.parse_args([
'pause', 'example_bash_operator'])
cli.pause(args)
self.assertIn(self.dagbag.dags['example_bash_operator'].is_paused, [True, 1])
args = self.parser.parse_args([
'unpause', 'example_bash_operator'])
cli.unpause(args)
self.assertIn(self.dagbag.dags['example_bash_operator'].is_paused, [False, 0])
def test_subdag_clear(self):
args = self.parser.parse_args([
'clear', 'example_subdag_operator', '--no_confirm'])
cli.clear(args)
args = self.parser.parse_args([
'clear', 'example_subdag_operator', '--no_confirm', '--exclude_subdags'])
cli.clear(args)
def test_get_dags(self):
dags = cli.get_dags(self.parser.parse_args(['clear', 'example_subdag_operator', '-c']))
self.assertEqual(len(dags), 1)
dags = cli.get_dags(self.parser.parse_args(['clear', 'subdag', '-dx', '-c']))
self.assertGreater(len(dags), 1)
with self.assertRaises(AirflowException):
cli.get_dags(self.parser.parse_args(['clear', 'foobar', '-dx', '-c']))
def test_backfill(self):
cli.backfill(self.parser.parse_args([
'backfill', 'example_bash_operator',
'-s', DEFAULT_DATE.isoformat()]))
cli.backfill(self.parser.parse_args([
'backfill', 'example_bash_operator', '-t', 'runme_0', '--dry_run',
'-s', DEFAULT_DATE.isoformat()]))
cli.backfill(self.parser.parse_args([
'backfill', 'example_bash_operator', '--dry_run',
'-s', DEFAULT_DATE.isoformat()]))
cli.backfill(self.parser.parse_args([
'backfill', 'example_bash_operator', '-l',
'-s', DEFAULT_DATE.isoformat()]))
def test_process_subdir_path_with_placeholder(self):
self.assertEqual(os.path.join(settings.DAGS_FOLDER, 'abc'), cli.process_subdir('DAGS_FOLDER/abc'))
def test_trigger_dag(self):
cli.trigger_dag(self.parser.parse_args([
'trigger_dag', 'example_bash_operator',
'-c', '{"foo": "bar"}']))
self.assertRaises(
ValueError,
cli.trigger_dag,
self.parser.parse_args([
'trigger_dag', 'example_bash_operator',
'--run_id', 'trigger_dag_xxx',
'-c', 'NOT JSON'])
)
def test_delete_dag(self):
DM = models.DagModel
key = "my_dag_id"
session = settings.Session()
session.add(DM(dag_id=key))
session.commit()
cli.delete_dag(self.parser.parse_args([
'delete_dag', key, '--yes']))
self.assertEqual(session.query(DM).filter_by(dag_id=key).count(), 0)
self.assertRaises(
AirflowException,
cli.delete_dag,
self.parser.parse_args([
'delete_dag',
'does_not_exist_dag',
'--yes'])
)
def test_pool_create(self):
cli.pool(self.parser.parse_args(['pool', '-s', 'foo', '1', 'test']))
self.assertEqual(self.session.query(models.Pool).count(), 1)
def test_pool_get(self):
cli.pool(self.parser.parse_args(['pool', '-s', 'foo', '1', 'test']))
try:
cli.pool(self.parser.parse_args(['pool', '-g', 'foo']))
except Exception as e:
self.fail("The 'pool -g foo' command raised unexpectedly: %s" % e)
def test_pool_delete(self):
cli.pool(self.parser.parse_args(['pool', '-s', 'foo', '1', 'test']))
cli.pool(self.parser.parse_args(['pool', '-x', 'foo']))
self.assertEqual(self.session.query(models.Pool).count(), 0)
def test_pool_no_args(self):
try:
cli.pool(self.parser.parse_args(['pool']))
except Exception as e:
self.fail("The 'pool' command raised unexpectedly: %s" % e)
def test_variables(self):
# Checks if all subcommands are properly received
cli.variables(self.parser.parse_args([
'variables', '-s', 'foo', '{"foo":"bar"}']))
cli.variables(self.parser.parse_args([
'variables', '-g', 'foo']))
cli.variables(self.parser.parse_args([
'variables', '-g', 'baz', '-d', 'bar']))
cli.variables(self.parser.parse_args([
'variables']))
cli.variables(self.parser.parse_args([
'variables', '-x', 'bar']))
cli.variables(self.parser.parse_args([
'variables', '-i', DEV_NULL]))
cli.variables(self.parser.parse_args([
'variables', '-e', DEV_NULL]))
cli.variables(self.parser.parse_args([
'variables', '-s', 'bar', 'original']))
# First export
cli.variables(self.parser.parse_args([
'variables', '-e', 'variables1.json']))
first_exp = open('variables1.json', 'r')
cli.variables(self.parser.parse_args([
'variables', '-s', 'bar', 'updated']))
cli.variables(self.parser.parse_args([
'variables', '-s', 'foo', '{"foo":"oops"}']))
cli.variables(self.parser.parse_args([
'variables', '-x', 'foo']))
# First import
cli.variables(self.parser.parse_args([
'variables', '-i', 'variables1.json']))
self.assertEqual('original', models.Variable.get('bar'))
self.assertEqual('{"foo": "bar"}', models.Variable.get('foo'))
# Second export
cli.variables(self.parser.parse_args([
'variables', '-e', 'variables2.json']))
second_exp = open('variables2.json', 'r')
self.assertEqual(first_exp.read(), second_exp.read())
second_exp.close()
first_exp.close()
# Second import
cli.variables(self.parser.parse_args([
'variables', '-i', 'variables2.json']))
self.assertEqual('original', models.Variable.get('bar'))
self.assertEqual('{"foo": "bar"}', models.Variable.get('foo'))
os.remove('variables1.json')
os.remove('variables2.json')
def _wait_pidfile(self, pidfile):
while True:
try:
with open(pidfile) as f:
return int(f.read())
except:
sleep(1)
def test_cli_webserver_foreground(self):
# Confirm that webserver hasn't been launched.
# pgrep returns exit status 1 if no process matched.
self.assertEqual(1, subprocess.Popen(["pgrep", "-c", "airflow"]).wait())
self.assertEqual(1, subprocess.Popen(["pgrep", "-c", "gunicorn"]).wait())
# Run webserver in foreground and terminate it.
p = subprocess.Popen(["airflow", "webserver"])
p.terminate()
p.wait()
# Assert that no process remains.
self.assertEqual(1, subprocess.Popen(["pgrep", "-c", "airflow"]).wait())
self.assertEqual(1, subprocess.Popen(["pgrep", "-c", "gunicorn"]).wait())
@unittest.skipIf("TRAVIS" in os.environ and bool(os.environ["TRAVIS"]),
"Skipping test due to lack of required file permission")
def test_cli_webserver_foreground_with_pid(self):
# Run webserver in foreground with --pid option
pidfile = tempfile.mkstemp()[1]
p = subprocess.Popen(["airflow", "webserver", "--pid", pidfile])
# Check the file specified by --pid option exists
self._wait_pidfile(pidfile)
# Terminate webserver
p.terminate()
p.wait()
@unittest.skipIf("TRAVIS" in os.environ and bool(os.environ["TRAVIS"]),
"Skipping test due to lack of required file permission")
def test_cli_webserver_background(self):
import psutil
# Confirm that webserver hasn't been launched.
self.assertEqual(1, subprocess.Popen(["pgrep", "-c", "airflow"]).wait())
self.assertEqual(1, subprocess.Popen(["pgrep", "-c", "gunicorn"]).wait())
# Run webserver in background.
subprocess.Popen(["airflow", "webserver", "-D"])
pidfile = cli.setup_locations("webserver")[0]
self._wait_pidfile(pidfile)
# Assert that gunicorn and its monitor are launched.
self.assertEqual(0, subprocess.Popen(["pgrep", "-c", "airflow"]).wait())
self.assertEqual(0, subprocess.Popen(["pgrep", "-c", "gunicorn"]).wait())
# Terminate monitor process.
pidfile = cli.setup_locations("webserver-monitor")[0]
pid = self._wait_pidfile(pidfile)
p = psutil.Process(pid)
p.terminate()
p.wait()
# Assert that no process remains.
self.assertEqual(1, subprocess.Popen(["pgrep", "-c", "airflow"]).wait())
self.assertEqual(1, subprocess.Popen(["pgrep", "-c", "gunicorn"]).wait())
# Patch for causing webserver timeout
@mock.patch("airflow.bin.cli.get_num_workers_running", return_value=0)
def test_cli_webserver_shutdown_when_gunicorn_master_is_killed(self, _):
# Shorten timeout so that this test doesn't take too long time
configuration.conf.set("webserver", "web_server_master_timeout", "10")
args = self.parser.parse_args(['webserver'])
with self.assertRaises(SystemExit) as e:
cli.webserver(args)
self.assertEqual(e.exception.code, 1)
class SecurityTests(unittest.TestCase):
def setUp(self):
configuration.load_test_config()
configuration.conf.set("webserver", "authenticate", "False")
configuration.conf.set("webserver", "expose_config", "True")
app = application.create_app()
app.config['TESTING'] = True
self.app = app.test_client()
self.dagbag = models.DagBag(
dag_folder=DEV_NULL, include_examples=True)
self.dag_bash = self.dagbag.dags['example_bash_operator']
self.runme_0 = self.dag_bash.get_task('runme_0')
def get_csrf(self, response):
tree = html.fromstring(response.data)
form = tree.find('.//form')
return form.find('.//input[@name="_csrf_token"]').value
def test_csrf_rejection(self):
endpoints = ([
"/admin/queryview/",
"/admin/airflow/paused?dag_id=example_python_operator&is_paused=false",
])
for endpoint in endpoints:
response = self.app.post(endpoint)
self.assertIn('CSRF token is missing', response.data.decode('utf-8'))
def test_csrf_acceptance(self):
response = self.app.get("/admin/queryview/")
csrf = self.get_csrf(response)
response = self.app.post("/admin/queryview/", data=dict(csrf_token=csrf))
self.assertEqual(200, response.status_code)
def test_xss(self):
try:
self.app.get("/admin/airflow/tree?dag_id=<script>alert(123456)</script>")
except:
# exception is expected here since dag doesnt exist
pass
response = self.app.get("/admin/log", follow_redirects=True)
self.assertIn(bleach.clean("<script>alert(123456)</script>"), response.data.decode('UTF-8'))
def test_chart_data_template(self):
"""Protect chart_data from being able to do RCE."""
session = settings.Session()
Chart = models.Chart
chart1 = Chart(
label='insecure_chart',
conn_id='airflow_db',
chart_type='bar',
sql="SELECT {{ ''.__class__.__mro__[1].__subclasses__() }}"
)
chart2 = Chart(
label="{{ ''.__class__.__mro__[1].__subclasses__() }}",
conn_id='airflow_db',
chart_type='bar',
sql="SELECT 1"
)
chart3 = Chart(
label="{{ subprocess.check_output('ls') }}",
conn_id='airflow_db',
chart_type='bar',
sql="SELECT 1"
)
session.add(chart1)
session.add(chart2)
session.add(chart3)
session.commit()
chart1 = session.query(Chart).filter(Chart.label == 'insecure_chart').first()
with self.assertRaises(SecurityError):
self.app.get("/admin/airflow/chart_data?chart_id={}".format(chart1.id))
chart2 = session.query(Chart).filter(
Chart.label == "{{ ''.__class__.__mro__[1].__subclasses__() }}"
).first()
with self.assertRaises(SecurityError):
self.app.get("/admin/airflow/chart_data?chart_id={}".format(chart2.id))
chart3 = session.query(Chart).filter(
Chart.label == "{{ subprocess.check_output('ls') }}"
).first()
with self.assertRaises(UndefinedError):
self.app.get("/admin/airflow/chart_data?chart_id={}".format(chart3.id))
def tearDown(self):
configuration.conf.set("webserver", "expose_config", "False")
self.dag_bash.clear(start_date=DEFAULT_DATE, end_date=timezone.utcnow())
class WebUiTests(unittest.TestCase):
def setUp(self):
configuration.load_test_config()
configuration.conf.set("webserver", "authenticate", "False")
configuration.conf.set("webserver", "expose_config", "True")
app = application.create_app()
app.config['TESTING'] = True
app.config['WTF_CSRF_METHODS'] = []
self.app = app.test_client()
self.dagbag = models.DagBag(include_examples=True)
self.dag_bash = self.dagbag.dags['example_bash_operator']
self.dag_python = self.dagbag.dags['example_python_operator']
self.sub_dag = self.dagbag.dags['example_subdag_operator']
self.runme_0 = self.dag_bash.get_task('runme_0')
self.example_xcom = self.dagbag.dags['example_xcom']
self.dagrun_python = self.dag_python.create_dagrun(
run_id="test_{}".format(models.DagRun.id_for_date(timezone.utcnow())),
execution_date=DEFAULT_DATE,
start_date=timezone.utcnow(),
state=State.RUNNING
)
self.sub_dag.create_dagrun(
run_id="test_{}".format(models.DagRun.id_for_date(timezone.utcnow())),
execution_date=DEFAULT_DATE,
start_date=timezone.utcnow(),
state=State.RUNNING
)
self.example_xcom.create_dagrun(
run_id="test_{}".format(models.DagRun.id_for_date(timezone.utcnow())),
execution_date=DEFAULT_DATE,
start_date=timezone.utcnow(),
state=State.RUNNING
)
def test_index(self):
response = self.app.get('/', follow_redirects=True)
resp_html = response.data.decode('utf-8')
self.assertIn("DAGs", resp_html)
self.assertIn("example_bash_operator", resp_html)
# The HTML should contain data for the last-run. A link to the specific run,
# and the text of the date.
url = "/admin/airflow/graph?" + urlencode({
"dag_id": self.dag_python.dag_id,
"execution_date": self.dagrun_python.execution_date,
}).replace("&", "&")
self.assertIn(url, resp_html)
self.assertIn(
self.dagrun_python.execution_date.strftime("%Y-%m-%d %H:%M"),
resp_html)
def test_query(self):
response = self.app.get('/admin/queryview/')
self.assertIn("Ad Hoc Query", response.data.decode('utf-8'))
response = self.app.post(
"/admin/queryview/", data=dict(
conn_id="airflow_db",
sql="SELECT+COUNT%281%29+as+TEST+FROM+task_instance"))
self.assertIn("TEST", response.data.decode('utf-8'))
def test_health(self):
response = self.app.get('/health')
self.assertIn('The server is healthy!', response.data.decode('utf-8'))
def test_noaccess(self):
response = self.app.get('/admin/airflow/noaccess')
self.assertIn("You don't seem to have access.", response.data.decode('utf-8'))
def test_pickle_info(self):
response = self.app.get('/admin/airflow/pickle_info')
self.assertIn('{', response.data.decode('utf-8'))
def test_dag_views(self):
response = self.app.get(
'/admin/airflow/graph?dag_id=example_bash_operator')
self.assertIn("runme_0", response.data.decode('utf-8'))
# confirm that the graph page loads when execution_date is blank
response = self.app.get(
'/admin/airflow/graph?dag_id=example_bash_operator&execution_date=')
self.assertIn("runme_0", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/tree?num_runs=25&dag_id=example_bash_operator')
self.assertIn("runme_0", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/duration?days=30&dag_id=example_bash_operator')
self.assertIn("example_bash_operator", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/tries?days=30&dag_id=example_bash_operator')
self.assertIn("example_bash_operator", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/landing_times?'
'days=30&dag_id=example_python_operator')
self.assertIn("example_python_operator", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/landing_times?'
'days=30&dag_id=example_xcom')
self.assertIn("example_xcom", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/gantt?dag_id=example_bash_operator')
self.assertIn("example_bash_operator", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/code?dag_id=example_bash_operator')
self.assertIn("example_bash_operator", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/blocked')
response = self.app.get(
'/admin/configurationview/')
self.assertIn("Airflow Configuration", response.data.decode('utf-8'))
self.assertIn("Running Configuration", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/rendered?'
'task_id=runme_1&dag_id=example_bash_operator&'
'execution_date={}'.format(DEFAULT_DATE_ISO))
self.assertIn("example_bash_operator", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/log?task_id=run_this_last&'
'dag_id=example_bash_operator&execution_date={}'
''.format(DEFAULT_DATE_ISO))
self.assertIn("run_this_last", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/task?'
'task_id=runme_0&dag_id=example_bash_operator&'
'execution_date={}'.format(DEFAULT_DATE_DS))
self.assertIn("Attributes", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/dag_stats')
self.assertIn("example_bash_operator", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/task_stats')
self.assertIn("example_bash_operator", response.data.decode('utf-8'))
url = (
"/admin/airflow/success?task_id=print_the_context&"
"dag_id=example_python_operator&upstream=false&downstream=false&"
"future=false&past=false&execution_date={}&"
"origin=/admin".format(DEFAULT_DATE_DS))
response = self.app.get(url)
self.assertIn("Wait a minute", response.data.decode('utf-8'))
response = self.app.get(url + "&confirmed=true")
response = self.app.get(
'/admin/airflow/clear?task_id=print_the_context&'
'dag_id=example_python_operator&future=true&past=false&'
'upstream=true&downstream=false&'
'execution_date={}&'
'origin=/admin'.format(DEFAULT_DATE_DS))
self.assertIn("Wait a minute", response.data.decode('utf-8'))
url = (
"/admin/airflow/success?task_id=section-1&"
"dag_id=example_subdag_operator&upstream=true&downstream=true&"
"future=false&past=false&execution_date={}&"
"origin=/admin".format(DEFAULT_DATE_DS))
response = self.app.get(url)
self.assertIn("Wait a minute", response.data.decode('utf-8'))
self.assertIn("section-1-task-1", response.data.decode('utf-8'))
self.assertIn("section-1-task-2", response.data.decode('utf-8'))
self.assertIn("section-1-task-3", response.data.decode('utf-8'))
self.assertIn("section-1-task-4", response.data.decode('utf-8'))
self.assertIn("section-1-task-5", response.data.decode('utf-8'))
response = self.app.get(url + "&confirmed=true")
url = (
"/admin/airflow/clear?task_id=print_the_context&"
"dag_id=example_python_operator&future=false&past=false&"
"upstream=false&downstream=true&"
"execution_date={}&"
"origin=/admin".format(DEFAULT_DATE_DS))
response = self.app.get(url)
self.assertIn("Wait a minute", response.data.decode('utf-8'))
response = self.app.get(url + "&confirmed=true")
url = (
"/admin/airflow/run?task_id=runme_0&"
"dag_id=example_bash_operator&ignore_all_deps=false&ignore_ti_state=true&"
"ignore_task_deps=true&execution_date={}&"
"origin=/admin".format(DEFAULT_DATE_DS))
response = self.app.get(url)
response = self.app.get(
"/admin/airflow/refresh?dag_id=example_bash_operator")
response = self.app.get("/admin/airflow/refresh_all")
response = self.app.post(
"/admin/airflow/paused?"
"dag_id=example_python_operator&is_paused=false")
self.assertIn("OK", response.data.decode('utf-8'))
response = self.app.get("/admin/xcom", follow_redirects=True)
self.assertIn("Xcoms", response.data.decode('utf-8'))
def test_charts(self):
session = Session()
chart_label = "Airflow task instance by type"
chart = session.query(
models.Chart).filter(models.Chart.label == chart_label).first()
chart_id = chart.id
session.close()
response = self.app.get(
'/admin/airflow/chart'
'?chart_id={}&iteration_no=1'.format(chart_id))
self.assertIn("Airflow task instance by type", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/chart_data'
'?chart_id={}&iteration_no=1'.format(chart_id))
self.assertIn("example", response.data.decode('utf-8'))
response = self.app.get(
'/admin/airflow/dag_details?dag_id=example_branch_operator')
self.assertIn("run_this_first", response.data.decode('utf-8'))
def test_fetch_task_instance(self):
url = (
"/admin/airflow/object/task_instances?"
"dag_id=example_python_operator&"
"execution_date={}".format(DEFAULT_DATE_DS))
response = self.app.get(url)
self.assertIn("print_the_context", response.data.decode('utf-8'))
def tearDown(self):
configuration.conf.set("webserver", "expose_config", "False")
self.dag_bash.clear(start_date=DEFAULT_DATE, end_date=timezone.utcnow())
session = Session()
session.query(models.DagRun).delete()
session.query(models.TaskInstance).delete()
session.commit()
session.close()
class SecureModeWebUiTests(unittest.TestCase):
def setUp(self):
configuration.load_test_config()
configuration.conf.set("webserver", "authenticate", "False")
configuration.conf.set("core", "secure_mode", "True")
app = application.create_app()
app.config['TESTING'] = True
self.app = app.test_client()
def test_query(self):
response = self.app.get('/admin/queryview/')
self.assertEqual(response.status_code, 404)
def test_charts(self):
response = self.app.get('/admin/chart/')
self.assertEqual(response.status_code, 404)
def tearDown(self):
configuration.conf.remove_option("core", "SECURE_MODE")
class WebPasswordAuthTest(unittest.TestCase):
def setUp(self):
configuration.conf.set("webserver", "authenticate", "True")
configuration.conf.set("webserver", "auth_backend", "airflow.contrib.auth.backends.password_auth")
app = application.create_app()
app.config['TESTING'] = True
self.app = app.test_client()
from airflow.contrib.auth.backends.password_auth import PasswordUser
session = Session()
user = models.User()
password_user = PasswordUser(user)
password_user.username = 'airflow_passwordauth'
password_user.password = 'password'
print(password_user._password)
session.add(password_user)
session.commit()
session.close()
def get_csrf(self, response):
tree = html.fromstring(response.data)
form = tree.find('.//form')
return form.find('.//input[@name="_csrf_token"]').value
def login(self, username, password):
response = self.app.get('/admin/airflow/login')
csrf_token = self.get_csrf(response)
return self.app.post('/admin/airflow/login', data=dict(
username=username,
password=password,
csrf_token=csrf_token
), follow_redirects=True)
def logout(self):
return self.app.get('/admin/airflow/logout', follow_redirects=True)
def test_login_logout_password_auth(self):
self.assertTrue(configuration.conf.getboolean('webserver', 'authenticate'))
response = self.login('user1', 'whatever')
self.assertIn('Incorrect login details', response.data.decode('utf-8'))
response = self.login('airflow_passwordauth', 'wrongpassword')
self.assertIn('Incorrect login details', response.data.decode('utf-8'))
response = self.login('airflow_passwordauth', 'password')
self.assertIn('Data Profiling', response.data.decode('utf-8'))
response = self.logout()
self.assertIn('form-signin', response.data.decode('utf-8'))
def test_unauthorized_password_auth(self):
response = self.app.get("/admin/airflow/landing_times")
self.assertEqual(response.status_code, 302)
def tearDown(self):
configuration.load_test_config()
session = Session()
session.query(models.User).delete()
session.commit()
session.close()
configuration.conf.set("webserver", "authenticate", "False")
class WebLdapAuthTest(unittest.TestCase):
def setUp(self):
configuration.conf.set("webserver", "authenticate", "True")
configuration.conf.set("webserver", "auth_backend", "airflow.contrib.auth.backends.ldap_auth")
try:
configuration.conf.add_section("ldap")
except:
pass
configuration.conf.set("ldap", "uri", "ldap://localhost:3890")
configuration.conf.set("ldap", "user_filter", "objectClass=*")
configuration.conf.set("ldap", "user_name_attr", "uid")
configuration.conf.set("ldap", "bind_user", "cn=Manager,dc=example,dc=com")
configuration.conf.set("ldap", "bind_password", "insecure")
configuration.conf.set("ldap", "basedn", "dc=example,dc=com")
configuration.conf.set("ldap", "cacert", "")
app = application.create_app()
app.config['TESTING'] = True
self.app = app.test_client()
def get_csrf(self, response):
tree = html.fromstring(response.data)
form = tree.find('.//form')
return form.find('.//input[@name="_csrf_token"]').value
def login(self, username, password):
response = self.app.get('/admin/airflow/login')
csrf_token = self.get_csrf(response)
return self.app.post('/admin/airflow/login', data=dict(
username=username,
password=password,
csrf_token=csrf_token
), follow_redirects=True)
def logout(self):
return self.app.get('/admin/airflow/logout', follow_redirects=True)
def test_login_logout_ldap(self):
self.assertTrue(configuration.conf.getboolean('webserver', 'authenticate'))
response = self.login('user1', 'userx')
self.assertIn('Incorrect login details', response.data.decode('utf-8'))
response = self.login('userz', 'user1')
self.assertIn('Incorrect login details', response.data.decode('utf-8'))
response = self.login('user1', 'user1')
self.assertIn('Data Profiling', response.data.decode('utf-8'))
response = self.logout()
self.assertIn('form-signin', response.data.decode('utf-8'))
def test_unauthorized(self):
response = self.app.get("/admin/airflow/landing_times")
self.assertEqual(response.status_code, 302)
def test_no_filter(self):
response = self.login('user1', 'user1')
self.assertIn('Data Profiling', response.data.decode('utf-8'))
self.assertIn('Connections', response.data.decode('utf-8'))
def test_with_filters(self):
configuration.conf.set('ldap', 'superuser_filter',
'description=superuser')
configuration.conf.set('ldap', 'data_profiler_filter',
'description=dataprofiler')
response = self.login('dataprofiler', 'dataprofiler')
self.assertIn('Data Profiling', response.data.decode('utf-8'))
response = self.login('superuser', 'superuser')
self.assertIn('Connections', response.data.decode('utf-8'))
def tearDown(self):
configuration.load_test_config()
session = Session()
session.query(models.User).delete()
session.commit()
session.close()
configuration.conf.set("webserver", "authenticate", "False")
class LdapGroupTest(unittest.TestCase):
def setUp(self):
configuration.conf.set("webserver", "authenticate", "True")
configuration.conf.set("webserver", "auth_backend", "airflow.contrib.auth.backends.ldap_auth")
try:
configuration.conf.add_section("ldap")
except:
pass
configuration.conf.set("ldap", "uri", "ldap://localhost:3890")
configuration.conf.set("ldap", "user_filter", "objectClass=*")
configuration.conf.set("ldap", "user_name_attr", "uid")
configuration.conf.set("ldap", "bind_user", "cn=Manager,dc=example,dc=com")
configuration.conf.set("ldap", "bind_password", "insecure")
configuration.conf.set("ldap", "basedn", "dc=example,dc=com")
configuration.conf.set("ldap", "cacert", "")
def test_group_belonging(self):
from airflow.contrib.auth.backends.ldap_auth import LdapUser
users = {"user1": ["group1", "group3"],
"user2": ["group2"]
}
for user in users:
mu = models.User(username=user,
is_superuser=False)
auth = LdapUser(mu)
self.assertEqual(set(users[user]), set(auth.ldap_groups))
def tearDown(self):
configuration.load_test_config()
configuration.conf.set("webserver", "authenticate", "False")
class FakeWebHDFSHook(object):
def __init__(self, conn_id):
self.conn_id = conn_id
def get_conn(self):
return self.conn_id
def check_for_path(self, hdfs_path):
return hdfs_path
class FakeSnakeBiteClientException(Exception):
pass
class FakeSnakeBiteClient(object):
def __init__(self):
self.started = True
def ls(self, path, include_toplevel=False):
"""
the fake snakebite client
:param path: the array of path to test
:param include_toplevel: to return the toplevel directory info
:return: a list for path for the matching queries
"""
if path[0] == '/datadirectory/empty_directory' and not include_toplevel:
return []
elif path[0] == '/datadirectory/datafile':
return [{
'group': u'supergroup',
'permission': 420,
'file_type': 'f',
'access_time': 1481122343796,
'block_replication': 3,
'modification_time': 1481122343862,
'length': 0,
'blocksize': 134217728,
'owner': u'hdfs',
'path': '/datadirectory/datafile'
}]
elif path[0] == '/datadirectory/empty_directory' and include_toplevel:
return [{
'group': u'supergroup',
'permission': 493,
'file_type': 'd',
'access_time': 0,
'block_replication': 0,
'modification_time': 1481132141540,
'length': 0,
'blocksize': 0,
'owner': u'hdfs',
'path': '/datadirectory/empty_directory'
}]
elif path[0] == '/datadirectory/not_empty_directory' and include_toplevel:
return [{
'group': u'supergroup',
'permission': 493,
'file_type': 'd',
'access_time': 0,
'block_replication': 0,
'modification_time': 1481132141540,
'length': 0,
'blocksize': 0,
'owner': u'hdfs',
'path': '/datadirectory/empty_directory'
}, {
'group': u'supergroup',
'permission': 420,
'file_type': 'f',
'access_time': 1481122343796,
'block_replication': 3,
'modification_time': 1481122343862,
'length': 0,
'blocksize': 134217728,
'owner': u'hdfs',
'path': '/datadirectory/not_empty_directory/test_file'
}]
elif path[0] == '/datadirectory/not_empty_directory':
return [{
'group': u'supergroup',
'permission': 420,
'file_type': 'f',
'access_time': 1481122343796,
'block_replication': 3,
'modification_time': 1481122343862,
'length': 0,
'blocksize': 134217728,
'owner': u'hdfs',
'path': '/datadirectory/not_empty_directory/test_file'
}]
elif path[0] == '/datadirectory/not_existing_file_or_directory':
raise FakeSnakeBiteClientException
elif path[0] == '/datadirectory/regex_dir':
return [{
'group': u'supergroup',
'permission': 420,
'file_type': 'f',
'access_time': 1481122343796,
'block_replication': 3,
'modification_time': 1481122343862, 'length': 12582912,
'blocksize': 134217728,
'owner': u'hdfs',
'path': '/datadirectory/regex_dir/test1file'
}, {
'group': u'supergroup',
'permission': 420,
'file_type': 'f',
'access_time': 1481122343796,
'block_replication': 3,
'modification_time': 1481122343862,
'length': 12582912,
'blocksize': 134217728,
'owner': u'hdfs',
'path': '/datadirectory/regex_dir/test2file'
}, {
'group': u'supergroup',
'permission': 420,
'file_type': 'f',
'access_time': 1481122343796,
'block_replication': 3,
'modification_time': 1481122343862,
'length': 12582912,
'blocksize': 134217728,
'owner': u'hdfs',
'path': '/datadirectory/regex_dir/test3file'
}, {
'group': u'supergroup',
'permission': 420,
'file_type': 'f',
'access_time': 1481122343796,
'block_replication': 3,
'modification_time': 1481122343862,
'length': 12582912,
'blocksize': 134217728,
'owner': u'hdfs',
'path': '/datadirectory/regex_dir/copying_file_1.txt._COPYING_'
}, {
'group': u'supergroup',
'permission': 420,
'file_type': 'f',
'access_time': 1481122343796,
'block_replication': 3,
'modification_time': 1481122343862,
'length': 12582912,
'blocksize': 134217728,
'owner': u'hdfs',
'path': '/datadirectory/regex_dir/copying_file_3.txt.sftp'
}]
else:
raise FakeSnakeBiteClientException
class FakeHDFSHook(object):
def __init__(self, conn_id=None):
self.conn_id = conn_id
def get_conn(self):
client = FakeSnakeBiteClient()
return client
class ConnectionTest(unittest.TestCase):
def setUp(self):
configuration.load_test_config()
utils.db.initdb()
os.environ['AIRFLOW_CONN_TEST_URI'] = (
'postgres://username:password@ec2.compute.com:5432/the_database')
os.environ['AIRFLOW_CONN_TEST_URI_NO_CREDS'] = (
'postgres://ec2.compute.com/the_database')
def tearDown(self):
env_vars = ['AIRFLOW_CONN_TEST_URI', 'AIRFLOW_CONN_AIRFLOW_DB']
for ev in env_vars:
if ev in os.environ:
del os.environ[ev]
def test_using_env_var(self):
c = SqliteHook.get_connection(conn_id='test_uri')
self.assertEqual('ec2.compute.com', c.host)
self.assertEqual('the_database', c.schema)
self.assertEqual('username', c.login)
self.assertEqual('password', c.password)
self.assertEqual(5432, c.port)
def test_using_unix_socket_env_var(self):
c = SqliteHook.get_connection(conn_id='test_uri_no_creds')
self.assertEqual('ec2.compute.com', c.host)
self.assertEqual('the_database', c.schema)
self.assertIsNone(c.login)
self.assertIsNone(c.password)
self.assertIsNone(c.port)
def test_param_setup(self):
c = models.Connection(conn_id='local_mysql', conn_type='mysql',
host='localhost', login='airflow',
password='airflow', schema='airflow')
self.assertEqual('localhost', c.host)
self.assertEqual('airflow', c.schema)
self.assertEqual('airflow', c.login)
self.assertEqual('airflow', c.password)
self.assertIsNone(c.port)
def test_env_var_priority(self):
c = SqliteHook.get_connection(conn_id='airflow_db')
self.assertNotEqual('ec2.compute.com', c.host)
os.environ['AIRFLOW_CONN_AIRFLOW_DB'] = \
'postgres://username:password@ec2.compute.com:5432/the_database'
c = SqliteHook.get_connection(conn_id='airflow_db')
self.assertEqual('ec2.compute.com', c.host)
self.assertEqual('the_database', c.schema)
self.assertEqual('username', c.login)
self.assertEqual('password', c.password)
self.assertEqual(5432, c.port)
del os.environ['AIRFLOW_CONN_AIRFLOW_DB']
def test_dbapi_get_uri(self):
conn = BaseHook.get_connection(conn_id='test_uri')
hook = conn.get_hook()
self.assertEqual('postgres://username:password@ec2.compute.com:5432/the_database', hook.get_uri())
conn2 = BaseHook.get_connection(conn_id='test_uri_no_creds')
hook2 = conn2.get_hook()
self.assertEqual('postgres://ec2.compute.com/the_database', hook2.get_uri())
def test_dbapi_get_sqlalchemy_engine(self):
conn = BaseHook.get_connection(conn_id='test_uri')
hook = conn.get_hook()
engine = hook.get_sqlalchemy_engine()
self.assertIsInstance(engine, sqlalchemy.engine.Engine)
self.assertEqual('postgres://username:password@ec2.compute.com:5432/the_database', str(engine.url))
def test_get_connections_env_var(self):
conns = SqliteHook.get_connections(conn_id='test_uri')
assert len(conns) == 1
assert conns[0].host == 'ec2.compute.com'
assert conns[0].schema == 'the_database'
assert conns[0].login == 'username'
assert conns[0].password == 'password'
assert conns[0].port == 5432
def test_get_connections_db(self):
conns = BaseHook.get_connections(conn_id='airflow_db')
assert len(conns) == 1
assert conns[0].host == 'localhost'
assert conns[0].schema == 'airflow'
assert conns[0].login == 'root'
class WebHDFSHookTest(unittest.TestCase):
def setUp(self):
configuration.load_test_config()
def test_simple_init(self):
from airflow.hooks.webhdfs_hook import WebHDFSHook
c = WebHDFSHook()
self.assertIsNone(c.proxy_user)
def test_init_proxy_user(self):
from airflow.hooks.webhdfs_hook import WebHDFSHook
c = WebHDFSHook(proxy_user='someone')
self.assertEqual('someone', c.proxy_user)
try:
from airflow.hooks.hdfs_hook import HDFSHook
import snakebite
except ImportError:
HDFSHook = None
@unittest.skipIf(HDFSHook is None,
"Skipping test because HDFSHook is not installed")
class HDFSHookTest(unittest.TestCase):
def setUp(self):
configuration.load_test_config()
os.environ['AIRFLOW_CONN_HDFS_DEFAULT'] = ('hdfs://localhost:8020')
def test_get_client(self):
client = HDFSHook(proxy_user='foo').get_conn()
self.assertIsInstance(client, snakebite.client.Client)
self.assertEqual('localhost', client.host)
self.assertEqual(8020, client.port)
self.assertEqual('foo', client.service.channel.effective_user)
@mock.patch('airflow.hooks.hdfs_hook.AutoConfigClient')
@mock.patch('airflow.hooks.hdfs_hook.HDFSHook.get_connections')
def test_get_autoconfig_client(self, mock_get_connections,
MockAutoConfigClient):
c = models.Connection(conn_id='hdfs', conn_type='hdfs',
host='localhost', port=8020, login='foo',
extra=json.dumps({'autoconfig': True}))
mock_get_connections.return_value = [c]
HDFSHook(hdfs_conn_id='hdfs').get_conn()
MockAutoConfigClient.assert_called_once_with(effective_user='foo',
use_sasl=False)
@mock.patch('airflow.hooks.hdfs_hook.AutoConfigClient')
def test_get_autoconfig_client_no_conn(self, MockAutoConfigClient):
HDFSHook(hdfs_conn_id='hdfs_missing', autoconfig=True).get_conn()
MockAutoConfigClient.assert_called_once_with(effective_user=None,
use_sasl=False)
@mock.patch('airflow.hooks.hdfs_hook.HDFSHook.get_connections')
def test_get_ha_client(self, mock_get_connections):
c1 = models.Connection(conn_id='hdfs_default', conn_type='hdfs',
host='localhost', port=8020)
c2 = models.Connection(conn_id='hdfs_default', conn_type='hdfs',
host='localhost2', port=8020)
mock_get_connections.return_value = [c1, c2]
client = HDFSHook().get_conn()
self.assertIsInstance(client, snakebite.client.HAClient)
try:
from airflow.hooks.http_hook import HttpHook
except ImportError:
HttpHook = None
@unittest.skipIf(HttpHook is None,
"Skipping test because HttpHook is not installed")
class HttpHookTest(unittest.TestCase):
def setUp(self):
configuration.load_test_config()
@mock.patch('airflow.hooks.http_hook.HttpHook.get_connection')
def test_http_connection(self, mock_get_connection):
c = models.Connection(conn_id='http_default', conn_type='http',
host='localhost', schema='http')
mock_get_connection.return_value = c
hook = HttpHook()
hook.get_conn({})
self.assertEqual(hook.base_url, 'http://localhost')
@mock.patch('airflow.hooks.http_hook.HttpHook.get_connection')
def test_https_connection(self, mock_get_connection):
c = models.Connection(conn_id='http_default', conn_type='http',
host='localhost', schema='https')
mock_get_connection.return_value = c
hook = HttpHook()
hook.get_conn({})
self.assertEqual(hook.base_url, 'https://localhost')
@mock.patch('airflow.hooks.http_hook.HttpHook.get_connection')
def test_host_encoded_http_connection(self, mock_get_connection):
c = models.Connection(conn_id='http_default', conn_type='http',
host='http://localhost')
mock_get_connection.return_value = c
hook = HttpHook()
hook.get_conn({})
self.assertEqual(hook.base_url, 'http://localhost')
@mock.patch('airflow.hooks.http_hook.HttpHook.get_connection')
def test_host_encoded_https_connection(self, mock_get_connection):
c = models.Connection(conn_id='http_default', conn_type='http',
host='https://localhost')
mock_get_connection.return_value = c
hook = HttpHook()
hook.get_conn({})
self.assertEqual(hook.base_url, 'https://localhost')
send_email_test = mock.Mock()
class EmailTest(unittest.TestCase):
def setUp(self):
configuration.conf.remove_option('email', 'EMAIL_BACKEND')
@mock.patch('airflow.utils.email.send_email')
def test_default_backend(self, mock_send_email):
res = utils.email.send_email('to', 'subject', 'content')
mock_send_email.assert_called_with('to', 'subject', 'content')
self.assertEqual(mock_send_email.return_value, res)
@mock.patch('airflow.utils.email.send_email_smtp')
def test_custom_backend(self, mock_send_email):
configuration.conf.set('email', 'EMAIL_BACKEND', 'tests.core.send_email_test')
utils.email.send_email('to', 'subject', 'content')
send_email_test.assert_called_with(
'to', 'subject', 'content', files=None, dryrun=False,
cc=None, bcc=None, mime_subtype='mixed'
)
self.assertFalse(mock_send_email.called)
class EmailSmtpTest(unittest.TestCase):
def setUp(self):
configuration.conf.set('smtp', 'SMTP_SSL', 'False')
@mock.patch('airflow.utils.email.send_MIME_email')
def test_send_smtp(self, mock_send_mime):
attachment = tempfile.NamedTemporaryFile()
attachment.write(b'attachment')
attachment.seek(0)
utils.email.send_email_smtp('to', 'subject', 'content', files=[attachment.name])
self.assertTrue(mock_send_mime.called)
call_args = mock_send_mime.call_args[0]
self.assertEqual(configuration.conf.get('smtp', 'SMTP_MAIL_FROM'), call_args[0])
self.assertEqual(['to'], call_args[1])
msg = call_args[2]
self.assertEqual('subject', msg['Subject'])
self.assertEqual(configuration.conf.get('smtp', 'SMTP_MAIL_FROM'), msg['From'])
self.assertEqual(2, len(msg.get_payload()))
self.assertEqual(u'attachment; filename="' + os.path.basename(attachment.name) + '"',
msg.get_payload()[-1].get(u'Content-Disposition'))
mimeapp = MIMEApplication('attachment')
self.assertEqual(mimeapp.get_payload(), msg.get_payload()[-1].get_payload())
@mock.patch('airflow.utils.email.send_MIME_email')
def test_send_bcc_smtp(self, mock_send_mime):
attachment = tempfile.NamedTemporaryFile()
attachment.write(b'attachment')
attachment.seek(0)
utils.email.send_email_smtp('to', 'subject', 'content', files=[attachment.name], cc='cc', bcc='bcc')
self.assertTrue(mock_send_mime.called)
call_args = mock_send_mime.call_args[0]
self.assertEqual(configuration.conf.get('smtp', 'SMTP_MAIL_FROM'), call_args[0])
self.assertEqual(['to', 'cc', 'bcc'], call_args[1])
msg = call_args[2]
self.assertEqual('subject', msg['Subject'])
self.assertEqual(configuration.conf.get('smtp', 'SMTP_MAIL_FROM'), msg['From'])
self.assertEqual(2, len(msg.get_payload()))
self.assertEqual(u'attachment; filename="' + os.path.basename(attachment.name) + '"',
msg.get_payload()[-1].get(u'Content-Disposition'))
mimeapp = MIMEApplication('attachment')
self.assertEqual(mimeapp.get_payload(), msg.get_payload()[-1].get_payload())
@mock.patch('smtplib.SMTP_SSL')
@mock.patch('smtplib.SMTP')
def test_send_mime(self, mock_smtp, mock_smtp_ssl):
mock_smtp.return_value = mock.Mock()
mock_smtp_ssl.return_value = mock.Mock()
msg = MIMEMultipart()
utils.email.send_MIME_email('from', 'to', msg, dryrun=False)
mock_smtp.assert_called_with(
configuration.conf.get('smtp', 'SMTP_HOST'),
configuration.conf.getint('smtp', 'SMTP_PORT'),
)
self.assertTrue(mock_smtp.return_value.starttls.called)
mock_smtp.return_value.login.assert_called_with(
configuration.conf.get('smtp', 'SMTP_USER'),
configuration.conf.get('smtp', 'SMTP_PASSWORD'),
)
mock_smtp.return_value.sendmail.assert_called_with('from', 'to', msg.as_string())
self.assertTrue(mock_smtp.return_value.quit.called)
@mock.patch('smtplib.SMTP_SSL')
@mock.patch('smtplib.SMTP')
def test_send_mime_ssl(self, mock_smtp, mock_smtp_ssl):
configuration.conf.set('smtp', 'SMTP_SSL', 'True')
mock_smtp.return_value = mock.Mock()
mock_smtp_ssl.return_value = mock.Mock()
utils.email.send_MIME_email('from', 'to', MIMEMultipart(), dryrun=False)
self.assertFalse(mock_smtp.called)
mock_smtp_ssl.assert_called_with(
configuration.conf.get('smtp', 'SMTP_HOST'),
configuration.conf.getint('smtp', 'SMTP_PORT'),
)
@mock.patch('smtplib.SMTP_SSL')
@mock.patch('smtplib.SMTP')
def test_send_mime_noauth(self, mock_smtp, mock_smtp_ssl):
configuration.conf.remove_option('smtp', 'SMTP_USER')
configuration.conf.remove_option('smtp', 'SMTP_PASSWORD')
mock_smtp.return_value = mock.Mock()
mock_smtp_ssl.return_value = mock.Mock()
utils.email.send_MIME_email('from', 'to', MIMEMultipart(), dryrun=False)
self.assertFalse(mock_smtp_ssl.called)
mock_smtp.assert_called_with(
configuration.conf.get('smtp', 'SMTP_HOST'),
configuration.conf.getint('smtp', 'SMTP_PORT'),
)
self.assertFalse(mock_smtp.login.called)
@mock.patch('smtplib.SMTP_SSL')
@mock.patch('smtplib.SMTP')
def test_send_mime_dryrun(self, mock_smtp, mock_smtp_ssl):
utils.email.send_MIME_email('from', 'to', MIMEMultipart(), dryrun=True)
self.assertFalse(mock_smtp.called)
self.assertFalse(mock_smtp_ssl.called)
if __name__ == '__main__':
unittest.main()
| 39.82503 | 109 | 0.612984 |
3aeb69028b43018bd7c0c9a352e77898be418539 | 16,154 | py | Python | comb/tf_model_v1.py | kastnerkyle/tf_and_torch_speechmatch | e9bfa11c741c6955ff8cafcc2afd730cd69f7ab8 | [
"BSD-3-Clause"
] | 1 | 2019-07-31T00:47:46.000Z | 2019-07-31T00:47:46.000Z | comb/tf_model_v1.py | kastnerkyle/tf_and_torch_speechmatch | e9bfa11c741c6955ff8cafcc2afd730cd69f7ab8 | [
"BSD-3-Clause"
] | null | null | null | comb/tf_model_v1.py | kastnerkyle/tf_and_torch_speechmatch | e9bfa11c741c6955ff8cafcc2afd730cd69f7ab8 | [
"BSD-3-Clause"
] | null | null | null | from __future__ import print_function
import os
import argparse
import numpy as np
import tensorflow as tf
from collections import namedtuple
import logging
import shutil
from tfbldr.datasets import rsync_fetch, fetch_ljspeech
from tfbldr.datasets import wavfile_caching_mel_tbptt_iterator
from tfbldr.utils import next_experiment_path
from tfbldr import get_logger
from tfbldr import run_loop
from tfbldr.nodes import Linear
from tfbldr.nodes import Linear
from tfbldr.nodes import LSTMCell
from tfbldr.nodes import BiLSTMLayer
from tfbldr.nodes import SequenceConv1dStack
from tfbldr.nodes import Embedding
from tfbldr.nodes import GaussianAttentionCell
from tfbldr.nodes import DiscreteMixtureOfLogistics
from tfbldr.nodes import DiscreteMixtureOfLogisticsCost
from tfbldr.nodes import AdditiveGaussianNoise
from tfbldr import scan
seq_len = 48
batch_size = 10
window_mixtures = 10
cell_dropout = .925
#noise_scale = 8.
prenet_units = 128
n_filts = 128
n_stacks = 3
enc_units = 128
dec_units = 512
emb_dim = 15
truncation_len = seq_len
cell_dropout_scale = cell_dropout
epsilon = 1E-8
forward_init = "truncated_normal"
rnn_init = "truncated_normal"
#basedir = "/Tmp/kastner/lj_speech/LJSpeech-1.0/"
#ljspeech = rsync_fetch(fetch_ljspeech, "leto01")
# THESE ARE CANNOT BE PAIRED (SOME MISSING), ITERATOR PAIRS THEM UP BY NAME
#wavfiles = ljspeech["wavfiles"]
#jsonfiles = ljspeech["jsonfiles"]
# THESE HAVE TO BE THE SAME TO ENSURE SPLIT IS CORRECT
train_random_state = np.random.RandomState(3122)
valid_random_state = np.random.RandomState(3122)
fake_random_state = np.random.RandomState(1234)
class FakeItr(object):
def __init__(self, batch_size, seq_len):
self.batch_size = batch_size
self.seq_len = seq_len
self.vocabulary_sizes=[44, 44]
self.n_mel_filters = 80
def next_masked_batch(self):
# need to make int "strings" of batch_size, random_len (10-50?)
# need to make batches of 256,
# dummy batch sizes from validation iterator in training code
mels = fake_random_state.randn(self.seq_len, self.batch_size, 80)
mel_mask = 0. * mels[..., 0] + 1.
text = np.random.randint(0, 44, size=(145, self.batch_size, 1)).astype("float32")
text_mask = 0. * text[..., 0] + 1.
mask = 0. * text_mask + 1.
mask_mask = 0. * text_mask + 1.
reset = 0. * mask_mask[0] + 1.
reset = reset[:, None]
# mels = (256, 64, 80)
# mel_mask = (256, 64)
# text = (145, 64, 1)
# text_mask = (145, 64)
# mask = (145, 64)
# mask_mask = (145, 64)
# reset = (64, 1)
return mels, mel_mask, text, text_mask, mask, mask_mask, reset
train_itr = FakeItr(batch_size, seq_len)
valid_itr = FakeItr(batch_size, seq_len)
#train_itr = wavfile_caching_mel_tbptt_iterator(wavfiles, jsonfiles, batch_size, seq_len, stop_index=.95, shuffle=True, symbol_processing="chars_only", random_state=train_random_state)
#valid_itr = wavfile_caching_mel_tbptt_iterator(wavfiles, jsonfiles, batch_size, seq_len, start_index=.95, shuffle=True, symbol_processing="chars_only", random_state=valid_random_state)
"""
for i in range(10000):
print(i)
mels, mel_mask, text, text_mask, mask, mask_mask, reset = train_itr.next_masked_batch()
print("done")
"""
"""
# STRONG CHECK TO ENSURE NO OVERLAP IN TRAIN/VALID
for tai in train_itr.all_indices_:
assert tai not in valid_itr.all_indices_
for vai in valid_itr.all_indices_:
assert vai not in train_itr.all_indices_
"""
random_state = np.random.RandomState(1442)
# use the max of the two blended types...
vocabulary_size = max(train_itr.vocabulary_sizes)
output_size = train_itr.n_mel_filters
def create_graph():
graph = tf.Graph()
with graph.as_default():
tf.set_random_seed(2899)
text = tf.placeholder(tf.float32, shape=[None, batch_size, 1])
text_mask = tf.placeholder(tf.float32, shape=[None, batch_size])
#mask = tf.placeholder(tf.float32, shape=[None, batch_size, 1])
#mask_mask = tf.placeholder(tf.float32, shape=[None, batch_size])
mels = tf.placeholder(tf.float32, shape=[None, batch_size, output_size])
mel_mask = tf.placeholder(tf.float32, shape=[None, batch_size])
bias = tf.placeholder_with_default(tf.zeros(shape=[]), shape=[])
cell_dropout = tf.placeholder_with_default(cell_dropout_scale * tf.ones(shape=[]), shape=[])
prenet_dropout = tf.placeholder_with_default(0.5 * tf.ones(shape=[]), shape=[])
bn_flag = tf.placeholder_with_default(tf.zeros(shape=[]), shape=[])
att_w_init = tf.placeholder(tf.float32, shape=[batch_size, 2 * enc_units])
att_k_init = tf.placeholder(tf.float32, shape=[batch_size, window_mixtures])
att_h_init = tf.placeholder(tf.float32, shape=[batch_size, dec_units])
att_c_init = tf.placeholder(tf.float32, shape=[batch_size, dec_units])
h1_init = tf.placeholder(tf.float32, shape=[batch_size, dec_units])
c1_init = tf.placeholder(tf.float32, shape=[batch_size, dec_units])
h2_init = tf.placeholder(tf.float32, shape=[batch_size, dec_units])
c2_init = tf.placeholder(tf.float32, shape=[batch_size, dec_units])
in_mels = mels[:-1, :, :]
in_mel_mask = mel_mask[:-1]
out_mels = mels[1:, :, :]
out_mel_mask = mel_mask[1:]
projmel1 = Linear([in_mels], [output_size], prenet_units,
dropout_flag_prob_keep=prenet_dropout, name="prenet1",
random_state=random_state)
projmel2 = Linear([projmel1], [prenet_units], prenet_units,
dropout_flag_prob_keep=prenet_dropout, name="prenet2",
random_state=random_state)
text_char_e, t_c_emb = Embedding(text, vocabulary_size, emb_dim, random_state=random_state,
name="text_char_emb")
#text_phone_e, t_p_emb = Embedding(text, vocabulary_size, emb_dim, random_state=random_state,
# name="text_phone_emb")
#text_e = (1. - mask) * text_char_e + mask * text_phone_e
text_e = text_char_e
# masks are either 0 or 1... use embed + voc size of two so that text and mask embs have same size / same impact on the repr
#mask_e, m_emb = Embedding(mask, 2, emb_dim, random_state=random_state,
# name="mask_emb")
conv_text = SequenceConv1dStack([text_e], [emb_dim], n_filts, bn_flag,
n_stacks=n_stacks,
kernel_sizes=[(1, 1), (3, 3), (5, 5)],
name="enc_conv1", random_state=random_state)
# text_mask and mask_mask should be the same, doesn't matter which one we use
bitext = BiLSTMLayer([conv_text], [n_filts],
enc_units,
input_mask=text_mask,
name="encode_bidir",
init=rnn_init,
random_state=random_state)
def step(inp_t, inp_mask_t,
corr_inp_t,
att_w_tm1, att_k_tm1, att_h_tm1, att_c_tm1,
h1_tm1, c1_tm1, h2_tm1, c2_tm1):
o = GaussianAttentionCell([corr_inp_t], [prenet_units],
(att_h_tm1, att_c_tm1),
att_k_tm1,
bitext,
2 * enc_units,
dec_units,
att_w_tm1,
input_mask=inp_mask_t,
conditioning_mask=text_mask,
#attention_scale=1. / 10.,
attention_scale=1.,
step_op="softplus",
name="att",
random_state=random_state,
cell_dropout=1.,#cell_dropout,
init=rnn_init)
att_w_t, att_k_t, att_phi_t, s = o
att_h_t = s[0]
att_c_t = s[1]
output, s = LSTMCell([corr_inp_t, att_w_t, att_h_t],
[prenet_units, 2 * enc_units, dec_units],
h1_tm1, c1_tm1, dec_units,
input_mask=inp_mask_t,
random_state=random_state,
cell_dropout=cell_dropout,
name="rnn1", init=rnn_init)
h1_t = s[0]
c1_t = s[1]
output, s = LSTMCell([corr_inp_t, att_w_t, h1_t],
[prenet_units, 2 * enc_units, dec_units],
h2_tm1, c2_tm1, dec_units,
input_mask=inp_mask_t,
random_state=random_state,
cell_dropout=cell_dropout,
name="rnn2", init=rnn_init)
h2_t = s[0]
c2_t = s[1]
return output, att_w_t, att_k_t, att_phi_t, att_h_t, att_c_t, h1_t, c1_t, h2_t, c2_t
r = scan(step,
[in_mels, in_mel_mask, projmel2],
[None, att_w_init, att_k_init, None, att_h_init, att_c_init,
h1_init, c1_init, h2_init, c2_init])
output = r[0]
att_w = r[1]
att_k = r[2]
att_phi = r[3]
att_h = r[4]
att_c = r[5]
h1 = r[6]
c1 = r[7]
h2 = r[8]
c2 = r[9]
pred = Linear([output], [dec_units], output_size, name="out_proj", random_state=random_state)
"""
mix, means, lins = DiscreteMixtureOfLogistics([proj], [output_size], n_output_channels=1,
name="dml", random_state=random_state)
cc = DiscreteMixtureOfLogisticsCost(mix, means, lins, out_mels, 256)
"""
# correct masking
cc = (pred - out_mels) ** 2
#cc = out_mel_mask[..., None] * cc
#loss = tf.reduce_sum(tf.reduce_sum(cc, axis=-1)) / tf.reduce_sum(out_mel_mask)
loss = tf.reduce_mean(tf.reduce_sum(cc, axis=-1))
learning_rate = 0.0001
#steps = tf.Variable(0.)
#learning_rate = tf.train.exponential_decay(0.001, steps, staircase=True,
# decay_steps=50000, decay_rate=0.5)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, use_locking=True)
grad, var = zip(*optimizer.compute_gradients(loss))
grad, _ = tf.clip_by_global_norm(grad, 10.)
#train_step = optimizer.apply_gradients(zip(grad, var), global_step=steps)
train_step = optimizer.apply_gradients(zip(grad, var))
things_names = ["mels",
"mel_mask",
"in_mels",
"in_mel_mask",
"out_mels",
"out_mel_mask",
"text",
"text_mask",
#"mask",
#"mask_mask",
"bias",
"cell_dropout",
"prenet_dropout",
"bn_flag",
"pred",
#"mix", "means", "lins",
"att_w_init",
"att_k_init",
"att_h_init",
"att_c_init",
"h1_init",
"c1_init",
"h2_init",
"c2_init",
"att_w",
"att_k",
"att_phi",
"att_h",
"att_c",
"h1",
"c1",
"h2",
"c2",
"loss",
"train_step",
"learning_rate"]
things_tf = [eval(name) for name in things_names]
for tn, tt in zip(things_names, things_tf):
graph.add_to_collection(tn, tt)
train_model = namedtuple('Model', things_names)(*things_tf)
return graph, train_model
g, vs = create_graph()
att_w_init = np.zeros((batch_size, 2 * enc_units))
att_k_init = np.zeros((batch_size, window_mixtures))
att_h_init = np.zeros((batch_size, dec_units))
att_c_init = np.zeros((batch_size, dec_units))
h1_init = np.zeros((batch_size, dec_units))
c1_init = np.zeros((batch_size, dec_units))
h2_init = np.zeros((batch_size, dec_units))
c2_init = np.zeros((batch_size, dec_units))
stateful_args = [att_w_init,
att_k_init,
att_h_init,
att_c_init,
h1_init,
c1_init,
h2_init,
c2_init]
step_count = 0
def loop(sess, itr, extras, stateful_args):
"""
global step_count
global noise_scale
step_count += 1
if step_count > 10000:
step_count = 0
if noise_scale == 2:
noise_scale = 1.
else:
noise_scale = noise_scale - 2.
if noise_scale < .5:
noise_scale = .5
"""
mels, mel_mask, text, text_mask, mask, mask_mask, reset = itr.next_masked_batch()
in_m = mels[:-1]
in_mel_mask = mel_mask[:-1]
#noise_block = np.clip(random_state.randn(*in_m.shape), -6, 6)
#in_m = in_m + noise_scale * noise_block
out_m = mels[1:]
out_mel_mask = mel_mask[1:]
att_w_init = stateful_args[0]
att_k_init = stateful_args[1]
att_h_init = stateful_args[2]
att_c_init = stateful_args[3]
h1_init = stateful_args[4]
c1_init = stateful_args[5]
h2_init = stateful_args[6]
c2_init = stateful_args[7]
att_w_init *= reset
att_k_init *= reset
att_h_init *= reset
att_c_init *= reset
h1_init *= reset
c1_init *= reset
h2_init *= reset
c2_init *= reset
feed = {
vs.in_mels: in_m,
vs.in_mel_mask: in_mel_mask,
vs.out_mels: out_m,
vs.out_mel_mask: out_mel_mask,
vs.bn_flag: 0.,
vs.text: text,
vs.text_mask: text_mask,
#vs.mask: mask,
#vs.mask_mask: mask_mask,
vs.att_w_init: att_w_init,
vs.att_k_init: att_k_init,
vs.att_h_init: att_h_init,
vs.att_c_init: att_c_init,
vs.h1_init: h1_init,
vs.c1_init: c1_init,
vs.h2_init: h2_init,
vs.c2_init: c2_init}
outs = [vs.att_w, vs.att_k,
vs.att_h, vs.att_c,
vs.h1, vs.c1, vs.h2, vs.c2,
vs.att_phi,
vs.loss, vs.train_step]
r = sess.run(outs, feed_dict=feed)
att_w_np = r[0]
att_k_np = r[1]
att_h_np = r[2]
att_c_np = r[3]
h1_np = r[4]
c1_np = r[5]
h2_np = r[6]
c2_np = r[7]
att_phi_np = r[8]
l = r[-2]
_ = r[-1]
# set next inits
att_w_init = att_w_np[-1]
att_k_init = att_k_np[-1]
att_h_init = att_h_np[-1]
att_c_init = att_c_np[-1]
h1_init = h1_np[-1]
c1_init = c1_np[-1]
h2_init = h2_np[-1]
c2_init = c2_np[-1]
stateful_args = [att_w_init,
att_k_init,
att_h_init,
att_c_init,
h1_init,
c1_init,
h2_init,
c2_init]
return l, None, stateful_args
with tf.Session(graph=g) as sess:
sess.run(tf.global_variables_initializer())
for i in range(100):
loop(sess, train_itr, {}, stateful_args)
print(i)
#
#run_loop(sess,
# loop, train_itr,
# loop, train_itr,
# n_steps=1000000,
# n_train_steps_per=1000,
# train_stateful_args=stateful_args,
# n_valid_steps_per=0,
# valid_stateful_args=stateful_args)
| 36.965675 | 185 | 0.558314 |
7c5e33d7d151941010f4798ebd32fc89793b3af0 | 51 | py | Python | pywos/__init__.py | refraction-ray/wos-statistics | bb3a23bdcdb588046df1e852d0b2625071d15634 | [
"MIT"
] | 8 | 2019-04-08T09:20:01.000Z | 2021-09-09T12:38:18.000Z | pywos/__init__.py | refraction-ray/wos-statistics | bb3a23bdcdb588046df1e852d0b2625071d15634 | [
"MIT"
] | null | null | null | pywos/__init__.py | refraction-ray/wos-statistics | bb3a23bdcdb588046df1e852d0b2625071d15634 | [
"MIT"
] | 3 | 2019-09-20T01:23:57.000Z | 2021-09-09T12:38:19.000Z | __author__ = "refraction-ray"
__version__ = "0.0.1" | 25.5 | 29 | 0.72549 |
5abe2e15772ec6a27f606fbfd3789f2ecebfe7a3 | 269 | py | Python | language-based/cpp/cpp-rf24-test/transposer/RF24/RPi/pyRF24/setup.py | fjctp/random_code | 5cbf73fc34b8f51a093ed47e0db676cadc5cb03a | [
"MIT"
] | 5 | 2017-07-17T21:56:33.000Z | 2021-01-17T17:31:10.000Z | language-based/cpp/cpp-rf24-test/transposer/RF24/RPi/pyRF24/setup.py | fjctp/random_code | 5cbf73fc34b8f51a093ed47e0db676cadc5cb03a | [
"MIT"
] | null | null | null | language-based/cpp/cpp-rf24-test/transposer/RF24/RPi/pyRF24/setup.py | fjctp/random_code | 5cbf73fc34b8f51a093ed47e0db676cadc5cb03a | [
"MIT"
] | 1 | 2017-07-18T20:11:50.000Z | 2017-07-18T20:11:50.000Z | #!/usr/bin/env python
from distutils.core import setup, Extension
module_RF24 = Extension('RF24',
libraries = ['rf24-bcm', 'boost_python'],
sources = ['pyRF24.cpp'])
setup(name='RF24',
version='1.0',
ext_modules=[module_RF24]
)
| 20.692308 | 53 | 0.609665 |
6924ae8775a1c0635ba576ffcec4f0c0d5d420eb | 24,322 | py | Python | mangadex_openapi/models/order1.py | ongyx/mangadex_openapi | 56a244cf90d884b2589ab2466b44442409a296d7 | [
"MIT"
] | null | null | null | mangadex_openapi/models/order1.py | ongyx/mangadex_openapi | 56a244cf90d884b2589ab2466b44442409a296d7 | [
"MIT"
] | null | null | null | mangadex_openapi/models/order1.py | ongyx/mangadex_openapi | 56a244cf90d884b2589ab2466b44442409a296d7 | [
"MIT"
] | null | null | null | # coding: utf-8
"""
MangaDex API
MangaDex is an ad-free manga reader offering high-quality images! This document details our API as it is right now. It is in no way a promise to never change it, although we will endeavour to publicly notify any major change. # Authentication You can login with the `/auth/login` endpoint. On success, it will return a JWT that remains valid for 15 minutes along with a session token that allows refreshing without re-authenticating for 1 month. # Rate limits The API enforces rate-limits to protect our servers against malicious and/or mistaken use. The API keeps track of the requests on an IP-by-IP basis. Hence, if you're on a VPN, proxy or a shared network in general, the requests of other users on this network might affect you. At first, a **global limit of 5 requests per second per IP address** is in effect. > This limit is enforced across multiple load-balancers, and thus is not an exact value but rather a lower-bound that we guarantee. The exact value will be somewhere in the range `[5, 5*n]` (with `n` being the number of load-balancers currently active). The exact value within this range will depend on the current traffic patterns we are experiencing. On top of this, **some endpoints are further restricted** as follows: | Endpoint | Requests per time period | Time period in minutes | |------------------------------------|-------------------------- |------------------------| | `POST /account/create` | 1 | 60 | | `GET /account/activate/{code}` | 30 | 60 | | `POST /account/activate/resend` | 5 | 60 | | `POST /account/recover` | 5 | 60 | | `POST /account/recover/{code}` | 5 | 60 | | `POST /auth/login` | 30 | 60 | | `POST /auth/refresh` | 30 | 60 | | `POST /author` | 10 | 60 | | `PUT /author` | 10 | 1 | | `DELETE /author/{id}` | 10 | 10 | | `POST /captcha/solve` | 10 | 10 | | `POST /chapter/{id}/read` | 300 | 10 | | `PUT /chapter/{id}` | 10 | 1 | | `DELETE /chapter/{id}` | 10 | 1 | | `POST /manga` | 10 | 60 | | `PUT /manga/{id}` | 10 | 60 | | `DELETE /manga/{id}` | 10 | 10 | | `POST /cover` | 10 | 1 | | `PUT /cover/{id}` | 10 | 1 | | `DELETE /cover/{id}` | 10 | 10 | | `POST /group` | 10 | 60 | | `PUT /group/{id}` | 10 | 1 | | `DELETE /group/{id}` | 10 | 10 | | `GET /at-home/server/{id}` | 60 | 1 | Calling these endpoints will further provide details via the following headers about your remaining quotas: | Header | Description | |---------------------------|-----------------------------------------------------------------------------| | `X-RateLimit-Limit` | Maximal number of requests this endpoint allows per its time period | | `X-RateLimit-Remaining` | Remaining number of requests within your quota for the current time period | | `X-RateLimit-Retry-After` | Timestamp of the end of the current time period, as UNIX timestamp | # Captchas Some endpoints may require captchas to proceed, in order to slow down automated malicious traffic. Legitimate users might also be affected, based on the frequency of write requests or due certain endpoints being particularly sensitive to malicious use, such as user signup. Once an endpoint decides that a captcha needs to be solved, a 403 Forbidden response will be returned, with the error code `captcha_required_exception`. The sitekey needed for recaptcha to function is provided in both the `X-Captcha-Sitekey` header field, as well as in the error context, specified as `siteKey` parameter. The captcha result of the client can either be passed into the repeated original request with the `X-Captcha-Result` header or alternatively to the `POST /captcha/solve` endpoint. The time a solved captcha is remembered varies across different endpoints and can also be influenced by individual client behavior. Authentication is not required for the `POST /captcha/solve` endpoint, captchas are tracked both by client ip and logged in user id. If you are logged in, you want to send the session token along, so you validate the captcha for your client ip and user id at the same time, but it is not required. # Reading a chapter using the API ## Retrieving pages from the MangaDex@Home network A valid [MangaDex@Home network](https://mangadex.network) page URL is in the following format: `{server-specific base url}/{temporary access token}/{quality mode}/{chapter hash}/{filename}` There are currently 2 quality modes: - `data`: Original upload quality - `data-saver`: Compressed quality Upon fetching a chapter from the API, you will find 4 fields necessary to compute MangaDex@Home page URLs: | Field | Type | Description | |------------------------------|----------|-----------------------------------| | `.data.id` | `string` | API Chapter ID | | `.data.attributes.hash` | `string` | MangaDex@Home Chapter Hash | | `.data.attributes.data` | `array` | data quality mode filenames | | `.data.attributes.dataSaver` | `array` | data-saver quality mode filenames | Example ```json GET /chapter/{id} { ..., \"data\": { \"id\": \"e46e5118-80ce-4382-a506-f61a24865166\", ..., \"attributes\": { ..., \"hash\": \"e199c7d73af7a58e8a4d0263f03db660\", \"data\": [ \"x1-b765e86d5ecbc932cf3f517a8604f6ac6d8a7f379b0277a117dc7c09c53d041e.png\", ... ], \"dataSaver\": [ \"x1-ab2b7c8f30c843aa3a53c29bc8c0e204fba4ab3e75985d761921eb6a52ff6159.jpg\", ... ] } } } ``` From this point you miss only the base URL to an assigned MangaDex@Home server for your client and chapter. This is retrieved via a `GET` request to `/at-home/server/{ chapter .data.id }`. Example: ```json GET /at-home/server/e46e5118-80ce-4382-a506-f61a24865166 { \"baseUrl\": \"https://abcdefg.hijklmn.mangadex.network:12345/some-token\" } ``` The full URL is the constructed as follows ``` { server .baseUrl }/{ quality mode }/{ chapter .data.attributes.hash }/{ chapter .data.attributes.{ quality mode }.[*] } Examples data quality: https://abcdefg.hijklmn.mangadex.network:12345/some-token/data/e199c7d73af7a58e8a4d0263f03db660/x1-b765e86d5ecbc932cf3f517a8604f6ac6d8a7f379b0277a117dc7c09c53d041e.png base url: https://abcdefg.hijklmn.mangadex.network:12345/some-token quality mode: data chapter hash: e199c7d73af7a58e8a4d0263f03db660 filename: x1-b765e86d5ecbc932cf3f517a8604f6ac6d8a7f379b0277a117dc7c09c53d041e.png data-saver quality: https://abcdefg.hijklmn.mangadex.network:12345/some-token/data-saver/e199c7d73af7a58e8a4d0263f03db660/x1-ab2b7c8f30c843aa3a53c29bc8c0e204fba4ab3e75985d761921eb6a52ff6159.jpg base url: https://abcdefg.hijklmn.mangadex.network:12345/some-token quality mode: data-saver chapter hash: e199c7d73af7a58e8a4d0263f03db660 filename: x1-ab2b7c8f30c843aa3a53c29bc8c0e204fba4ab3e75985d761921eb6a52ff6159.jpg ``` If the server you have been assigned fails to serve images, you are allowed to call the `/at-home/server/{ chapter id }` endpoint again to get another server. Whether successful or not, **please do report the result you encountered as detailed below**. This is so we can pull the faulty server out of the network. ## Report In order to keep track of the health of the servers in the network and to improve the quality of service and reliability, we ask that you call the MangaDex@Home report endpoint after each image you retrieve, whether successfully or not. It is a `POST` request against `https://api.mangadex.network/report` and expects the following payload with our example above: | Field | Type | Description | |-----------------------------|------------|-------------------------------------------------------------------------------------| | `url` | `string` | The full URL of the image | | `success` | `boolean` | Whether the image was successfully retrieved | | `cached ` | `boolean` | `true` iff the server returned an `X-Cache` header with a value starting with `HIT` | | `bytes` | `number` | The size in bytes of the retrieved image | | `duration` | `number` | The time in miliseconds that the complete retrieval (not TTFB) of this image took | Examples herafter. **Success:** ```json POST https://api.mangadex.network/report Content-Type: application/json { \"url\": \"https://abcdefg.hijklmn.mangadex.network:12345/some-token/data/e199c7d73af7a58e8a4d0263f03db660/x1-b765e86d5ecbc932cf3f517a8604f6ac6d8a7f379b0277a117dc7c09c53d041e.png\", \"success\": true, \"bytes\": 727040, \"duration\": 235, \"cached\": true } ``` **Failure:** ```json POST https://api.mangadex.network/report Content-Type: application/json { \"url\": \"https://abcdefg.hijklmn.mangadex.network:12345/some-token/data/e199c7d73af7a58e8a4d0263f03db660/x1-b765e86d5ecbc932cf3f517a8604f6ac6d8a7f379b0277a117dc7c09c53d041e.png\", \"success\": false, \"bytes\": 25, \"duration\": 235, \"cached\": false } ``` While not strictly necessary, this helps us monitor the network's healthiness, and we appreciate your cooperation towards this goal. If no one reports successes and failures, we have no way to know that a given server is slow/broken, which eventually results in broken image retrieval for everyone. # Retrieving Covers from the API ## Construct Cover URLs ### Source (original/best quality) `https://uploads.mangadex.org/covers/{ manga.id }/{ cover.filename }`<br/> The extension can be png, jpeg or gif. Example: `https://uploads.mangadex.org/covers/8f3e1818-a015-491d-bd81-3addc4d7d56a/4113e972-d228-4172-a885-cb30baffff97.jpg` ### <=512px wide thumbnail `https://uploads.mangadex.org/covers/{ manga.id }/{ cover.filename }.512.jpg`<br/> The extension is always jpg. Example: `https://uploads.mangadex.org/covers/8f3e1818-a015-491d-bd81-3addc4d7d56a/4113e972-d228-4172-a885-cb30baffff97.jpg.512.jpg` ### <=256px wide thumbnail `https://uploads.mangadex.org/covers/{ manga.id }/{ cover.filename }.256.jpg`<br/> The extension is always jpg. Example: `https://uploads.mangadex.org/covers/8f3e1818-a015-491d-bd81-3addc4d7d56a/4113e972-d228-4172-a885-cb30baffff97.jpg.256.jpg` ## ℹ️ Where to find Cover filename ? Look at the [Get cover operation](#operation/get-cover) endpoint to get Cover information. Also, if you get a Manga resource, you'll have, if available a `covert_art` relationship which is the main cover id. # Static data ## Manga publication demographic | Value | Description | |------------------|---------------------------| | shounen | Manga is a Shounen | | shoujo | Manga is a Shoujo | | josei | Manga is a Josei | | seinen | Manga is a Seinen | ## Manga status | Value | Description | |------------------|---------------------------| | ongoing | Manga is still going on | | completed | Manga is completed | | hiatus | Manga is paused | | cancelled | Manga has been cancelled | ## Manga reading status | Value | |------------------| | reading | | on_hold | | plan\\_to\\_read | | dropped | | re\\_reading | | completed | ## Manga content rating | Value | Description | |------------------|---------------------------| | safe | Safe content | | suggestive | Suggestive content | | erotica | Erotica content | | pornographic | Pornographic content | ## CustomList visibility | Value | Description | |------------------|---------------------------| | public | CustomList is public | | private | CustomList is private | ## Relationship types | Value | Description | |------------------|--------------------------------| | manga | Manga resource | | chapter | Chapter resource | | cover_art | A Cover Art for a manga `*` | | author | Author resource | | artist | Author resource (drawers only) | | scanlation_group | ScanlationGroup resource | | tag | Tag resource | | user | User resource | | custom_list | CustomList resource | `*` Note, that on manga resources you get only one cover_art resource relation marking the primary cover if there are more than one. By default this will be the latest volume's cover art. If you like to see all the covers for a given manga, use the cover search endpoint for your mangaId and select the one you wish to display. ## Manga links data In Manga attributes you have the `links` field that is a JSON object with some strange keys, here is how to decode this object: | Key | Related site | URL | URL details | |-------|---------------|-----------------------------------------------------------------------------------------------|----------------------------------------------------------------| | al | anilist | https://anilist.co/manga/`{id}` | Stored as id | | ap | animeplanet | https://www.anime-planet.com/manga/`{slug}` | Stored as slug | | bw | bookwalker.jp | https://bookwalker.jp/`{slug}` | Stored has \"series/{id}\" | | mu | mangaupdates | https://www.mangaupdates.com/series.html?id=`{id}` | Stored has id | | nu | novelupdates | https://www.novelupdates.com/series/`{slug}` | Stored has slug | | kt | kitsu.io | https://kitsu.io/api/edge/manga/`{id}` or https://kitsu.io/api/edge/manga?filter[slug]={slug} | If integer, use id version of the URL, otherwise use slug one | | amz | amazon | N/A | Stored as full URL | | ebj | ebookjapan | N/A | Stored as full URL | | mal | myanimelist | https://myanimelist.net/manga/{id} | Store as id | | raw | N/A | N/A | Stored as full URL, untranslated stuff URL (original language) | | engtl | N/A | N/A | Stored as full URL, official english licenced URL | # noqa: E501
OpenAPI spec version: 5.0.21
Contact: mangadexstaff@gmail.com
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class Order1(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
"created_at": "str",
"updated_at": "str",
"publish_at": "str",
"volume": "str",
"chapter": "str",
}
attribute_map = {
"created_at": "createdAt",
"updated_at": "updatedAt",
"publish_at": "publishAt",
"volume": "volume",
"chapter": "chapter",
}
def __init__(
self,
created_at=None,
updated_at=None,
publish_at=None,
volume=None,
chapter=None,
): # noqa: E501
"""Order1 - a model defined in Swagger""" # noqa: E501
self._created_at = None
self._updated_at = None
self._publish_at = None
self._volume = None
self._chapter = None
self.discriminator = None
if created_at is not None:
self.created_at = created_at
if updated_at is not None:
self.updated_at = updated_at
if publish_at is not None:
self.publish_at = publish_at
if volume is not None:
self.volume = volume
if chapter is not None:
self.chapter = chapter
@property
def created_at(self):
"""Gets the created_at of this Order1. # noqa: E501
:return: The created_at of this Order1. # noqa: E501
:rtype: str
"""
return self._created_at
@created_at.setter
def created_at(self, created_at):
"""Sets the created_at of this Order1.
:param created_at: The created_at of this Order1. # noqa: E501
:type: str
"""
allowed_values = ["asc", "desc"] # noqa: E501
if created_at not in allowed_values:
raise ValueError(
"Invalid value for `created_at` ({0}), must be one of {1}".format( # noqa: E501
created_at, allowed_values
)
)
self._created_at = created_at
@property
def updated_at(self):
"""Gets the updated_at of this Order1. # noqa: E501
:return: The updated_at of this Order1. # noqa: E501
:rtype: str
"""
return self._updated_at
@updated_at.setter
def updated_at(self, updated_at):
"""Sets the updated_at of this Order1.
:param updated_at: The updated_at of this Order1. # noqa: E501
:type: str
"""
allowed_values = ["asc", "desc"] # noqa: E501
if updated_at not in allowed_values:
raise ValueError(
"Invalid value for `updated_at` ({0}), must be one of {1}".format( # noqa: E501
updated_at, allowed_values
)
)
self._updated_at = updated_at
@property
def publish_at(self):
"""Gets the publish_at of this Order1. # noqa: E501
:return: The publish_at of this Order1. # noqa: E501
:rtype: str
"""
return self._publish_at
@publish_at.setter
def publish_at(self, publish_at):
"""Sets the publish_at of this Order1.
:param publish_at: The publish_at of this Order1. # noqa: E501
:type: str
"""
allowed_values = ["asc", "desc"] # noqa: E501
if publish_at not in allowed_values:
raise ValueError(
"Invalid value for `publish_at` ({0}), must be one of {1}".format( # noqa: E501
publish_at, allowed_values
)
)
self._publish_at = publish_at
@property
def volume(self):
"""Gets the volume of this Order1. # noqa: E501
:return: The volume of this Order1. # noqa: E501
:rtype: str
"""
return self._volume
@volume.setter
def volume(self, volume):
"""Sets the volume of this Order1.
:param volume: The volume of this Order1. # noqa: E501
:type: str
"""
allowed_values = ["asc", "desc"] # noqa: E501
if volume not in allowed_values:
raise ValueError(
"Invalid value for `volume` ({0}), must be one of {1}".format( # noqa: E501
volume, allowed_values
)
)
self._volume = volume
@property
def chapter(self):
"""Gets the chapter of this Order1. # noqa: E501
:return: The chapter of this Order1. # noqa: E501
:rtype: str
"""
return self._chapter
@chapter.setter
def chapter(self, chapter):
"""Sets the chapter of this Order1.
:param chapter: The chapter of this Order1. # noqa: E501
:type: str
"""
allowed_values = ["asc", "desc"] # noqa: E501
if chapter not in allowed_values:
raise ValueError(
"Invalid value for `chapter` ({0}), must be one of {1}".format( # noqa: E501
chapter, allowed_values
)
)
self._chapter = chapter
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(
map(lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value)
)
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(
map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict")
else item,
value.items(),
)
)
else:
result[attr] = value
if issubclass(Order1, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, Order1):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| 93.187739 | 17,143 | 0.527259 |
8f5da364a5fd29ef7de20891e0e64a3ca3624985 | 3,863 | py | Python | quorumtoolbox/raft.py | chainstack/quorum-toolbox | e9d25b1118891d27b27bb6f3c8907a805183f873 | [
"Apache-2.0"
] | 1 | 2021-06-24T18:03:40.000Z | 2021-06-24T18:03:40.000Z | quorumtoolbox/raft.py | chainstack/quorum-toolbox | e9d25b1118891d27b27bb6f3c8907a805183f873 | [
"Apache-2.0"
] | 5 | 2019-12-10T03:28:14.000Z | 2020-05-28T15:49:59.000Z | quorumtoolbox/raft.py | chainstack/quorum-toolbox | e9d25b1118891d27b27bb6f3c8907a805183f873 | [
"Apache-2.0"
] | null | null | null | import json
import os
from quorumtoolbox.utils import templating
from quorumtoolbox.utils.enode_utils import make_enode_id2
from quorumtoolbox.utils.node_utils import make_node_param
class Raft:
raft_dir_name = 'raft'
raft_id_file_name = 'raftid.json'
def __init__(self,
context,
enode_id_geth, # e.g. 'enode://$enode@$geth_ip:$geth_port?discport=$discport'
node_state, # 'initial' or 'new'
port=50400, # default value in quorum. need to make enode_id.
block_time=50, # default value in quorum. mint blocks at this many milliseconds interval
peers=None):
self.context = context
self.enode_id_geth = enode_id_geth
self.port = port
self.enode_id = make_enode_id2(self.enode_id_geth, self.port)
self.block_time = block_time
self.node_state = node_state
self.peers = [] if peers is None else peers
self._raft_id = None
self.init_node(self.node_state)
self.base_dir = os.path.join(context, self.raft_dir_name)
self.raft_id_file = os.path.join(self.base_dir, self.raft_id_file_name)
self.write_raft_id()
# configuration related to this class instance
self.build_config = {
'raft': {
'block_time': self.block_time,
'raft_id': self._raft_id,
'node_state': self.node_state
},
'enode_id': self.enode_id,
'network': {
'port': self.port,
'peers': self.peers
},
'local': {
'raft_id_file': self.raft_id_file
}
}
# configuration related to launching this instance of raft, used in cmd line args when launching geth.
# https://github.com/jpmorganchase/quorum/blob/master/raft/doc.md
self.launch_params = {
'raft': '--raft',
'rpcapi': make_node_param('--rpcapi', 'raft'),
'raftport': make_node_param('--raftport', self.port),
# mint blocks in this many millisecond interval
'raftblocktime': make_node_param('--raftblocktime', self.block_time)
}
if self._raft_id is not None:
self.launch_params['raftjoinexisting'] = make_node_param(
'--raftjoinexisting', self._raft_id) # join an existing network with this id
def init_node(self, node_state):
{
'initial': self.init_initial,
'new': self.init_new
}[node_state]()
# This node is forming the initial network. RAFT_ID will be automatically assigned by network (based on static-nodes
# .json). so, don't bother.
def init_initial(self):
self._raft_id = None
# This node is new to the network and a raft joining id has to be retrieved from peers.
def init_new(self):
self._raft_id = self.get_raft_id()
@property
def joining_id(self):
return self._raft_id
@property
def build_configuration(self):
return self.build_config
@property
def launch_parameters(self):
return self.launch_params
def get_raft_id(self):
# moved to orchestrator
# mocked return
return 0
def write_raft_id(self):
if self._raft_id is not None:
templating.template_substitute(self.raft_id_file, {'raft_id': self._raft_id})
else:
templating.template_substitute(self.raft_id_file, {'raft_id': 'null'})
def get_raft_joining_id(self):
raft_joining_id = raft_utils.get_raft_joining_id(self.peers, self.enode_id)
return raft_joining_id
def sanity_check(self):
pass
def __str__(self):
return json.dumps(self.build_config)
def __repr__(self):
pass
| 31.92562 | 120 | 0.613513 |
0971a1a0947763425a7ae66b9ca836cdb8e3e95c | 7,563 | py | Python | script/old/compare_vcf-0.0.1.py | genepii/seqmet | 89fdab79131c861d4a5aae364ecdbeb3a9e0ae23 | [
"MIT"
] | null | null | null | script/old/compare_vcf-0.0.1.py | genepii/seqmet | 89fdab79131c861d4a5aae364ecdbeb3a9e0ae23 | [
"MIT"
] | null | null | null | script/old/compare_vcf-0.0.1.py | genepii/seqmet | 89fdab79131c861d4a5aae364ecdbeb3a9e0ae23 | [
"MIT"
] | null | null | null | from __future__ import print_function
import os
import sys
import getopt
##Count the number of minor variants in a target vcf reported as major variant in a reference vcf
#v0.0.1
def main(argv):
global ref
global var
global con
global oup
global oud
global mode
global bed
global region
global min_depth
global min_freq
ref = ''
var = ''
con = ''
oup = ''
oud = './'
mode = ['raw']
bed = ''
region = ''
min_depth = 20
min_freq = 0.01
try:
opts, args = getopt.getopt(argv, 'hr:c:v:o:x:m:b:R:d:f:', ['help', 'ref', 'con', 'var', 'output', 'outdir', 'mode', 'bed', 'region', 'min_depth', 'min_freq'])
for opt, arg in opts:
if opt in ('-h', '--help'):
usage()
sys.exit()
elif opt in ('-r', '--ref'):
ref = arg
elif opt in ('-c', '--con'):
con = arg
elif opt in ('-v', '--var'):
var = arg
elif opt in ('-o', '--output'):
oup = arg
elif opt in ('-x', '--outdir'):
oud = arg
elif opt in ('-m', '--mode'):
mode = []
for i in range(len(arg.split(','))):
mode.append(arg.split(',')[i])
elif opt in ('-b', '--bed'):
bed = arg
elif opt in ('-R', '--region'):
region = arg
elif opt in ('-d', '--min_depth'):
min_depth = int(arg)
elif opt in ('-f', '--min_freq'):
min_freq = float(arg)
if ref == '' or con == '' or var == '':
usage()
sys.exit()
if oup == '':
oup = var.split("/")[-1].split(".")[0] + '_' + region.split("/")[-1].split(".")[0]
except getopt.GetoptError:
usage()
sys.exit(2)
def usage():
print('usage: ' + sys.argv[0] + ' -h --help -r --ref [fasta] --con [vcf] --var [vcf] -o --output [tsv] -m --mode [raw,cov,common,expected] -b --bed [bed] -R --region [bed] -d --min_depth [int] -f --min_freq [float]')
if __name__ == '__main__':
main(sys.argv[1:])
def count_commented(file):
lines = open(file, 'r').read().rstrip('\n').split('\n')
count = 0
for line in lines:
if line[0] == "#":
count += 1
return count
flatten = lambda t: [item for sublist in t for item in sublist]
seq = [[x.replace('\r\n','\n').split('\n')[0], ''.join(x.replace('\r\n','\n').split('\n')[1:]).replace(' ','')] for x in open(ref, 'r').read().rstrip('\n').split('>')[1:]]
cons = open(con, 'r').read().rstrip('\n').split('\n')[count_commented(con):]
vas = open(var, 'r').read().rstrip('\n').split('\n')[count_commented(var):]
bga = [x.split('\t') for x in open(bed, 'r').read().replace('\r\n','\n').rstrip('\n').split('\n')]
depth = []
for i in range(len(bga)):
depth.append([int(bga[i][3]) for x in range(int(bga[i][1]),int(bga[i][2]))])
depth = flatten(depth)
if region != '':
treg = [x.split('\t') for x in open(region, 'r').read().replace('\r\n','\n').rstrip('\n').split('\n')]
reg = []
for i in range(len(treg)):
reg.append([int(x) for x in range(int(treg[i][1]),int(treg[i][2]))])
reg = flatten(reg)
else:
reg = []
vas_chrom, vas_pos, vas_ref, vas_alt, vas_af, cons_chrom, cons_pos, cons_ref, cons_alt, cons_af = ([] for i in range(10))
temp = []
exp = []
common = 0
expected = 0
for i in range(len(vas)):
vas_chrom.append(vas[i].split('\t')[0])
vas_pos.append(int(vas[i].split('\t')[1])-1)
vas_ref.append(vas[i].split('\t')[3])
vas_alt.append(vas[i].split('\t')[4])
vas_af.append(float(vas[i].split('\t')[7].split(';')[3].split('=')[1]))
for i in range(len(cons)):
cons_chrom.append(cons[i].split('\t')[0])
cons_pos.append(int(cons[i].split('\t')[1])-1)
cons_ref.append(cons[i].split('\t')[3])
cons_alt.append(cons[i].split('\t')[4])
cons_af.append(float(cons[i].split('\t')[7].split(';')[3].split('=')[1]))
for i in range(len(cons_chrom)):
if cons_alt[i][0] == '-':
cons_temp = cons_ref[i]
cons_ref[i] = cons_ref[i] + cons_alt[i][1:]
cons_alt[i] = cons_temp
if cons_alt[i][0] == '+':
cons_alt[i] = cons_ref[i] + cons_alt[i][1:]
for i in range(len(vas_chrom)):
if vas_alt[i][0] == '-':
vas_temp = vas_ref[i]
vas_ref[i] = vas_ref[i] + vas_alt[i][1:]
vas_alt[i] = vas_temp
if vas_alt[i][0] == '+':
vas_alt[i] = vas_ref[i] + vas_alt[i][1:]
for i in range(len(cons_chrom)):
if (cons_pos[i] in reg or reg == []) and depth[cons_pos[i]] >= min_depth and float(cons_af[i]) >= min_freq:
if float(cons_af[i]) >= 0.5:
if cons_pos[i] in vas_pos:
vas_index = vas_pos.index(cons_pos[i])
if cons_alt[i] == vas_alt[vas_index] and float(vas_af[vas_index]) >= 0.5:
pass
#print([cons_pos[i], cons_alt[i], vas_alt[vas_index], "old"])
else:
expected += 1
exp.append([cons_pos[i], cons_alt[i], vas_alt[vas_index]])
else:
expected += 1
exp.append([cons_pos[i], cons_alt[i], "ref1"])
for i in range(len(vas_chrom)):
if (vas_pos[i] in reg or reg == []) and depth[vas_pos[i]] >= min_depth and float(vas_af[i]) >= min_freq:
if float(vas_af[i]) < 0.5:
if vas_pos[i] in cons_pos:
cons_index = cons_pos.index(vas_pos[i])
if vas_alt[i] == cons_alt[cons_index] and float(cons_af[cons_index]) >= 0.5:
common += 1
temp.append([vas_pos[i], vas_alt[i], cons_alt[cons_index]])
elif vas_alt[i] == seq[[x[0] for x in seq].index(vas_chrom[i])][1][vas_pos[i]:vas_pos[i]+len(vas_alt[i])] and vas_ref[i] != seq[[x[0] for x in seq].index(vas_chrom[i])][1][vas_pos[i]:vas_pos[i]+len(vas_ref[i])]:
common += 1
temp.append([vas_pos[i], vas_alt[i], "ref2"])
else:
if vas_pos[i] in cons_pos:
cons_index = cons_pos.index(vas_pos[i])
if vas_alt[i] != cons_alt[cons_index] and float(cons_af[cons_index]) >= 0.5:
expected += 1
exp.append([vas_pos[i], vas_alt[i], cons_alt[cons_index]])
else:
expected += 1
exp.append([vas_pos[i], vas_alt[i], "ref3"])
exp = sorted(exp, key=lambda i: i[0])
print(exp)
print (temp)
if "cov" in mode:
w = open(oud + oup + "_cov.tsv", 'a+')
if expected > 0:
w.write(con.split("/")[-1].split(".")[0] + "\t" + str(round(float(common)/float(expected), 2)) + "\n")
else:
w.write(con.split("/")[-1].split(".")[0] + "\t0.0\n")
w.close()
if "common" in mode:
w = open(oud + oup + "_common.tsv", 'a+')
w.write(con.split("/")[-1].split(".")[0] + "\t" + str(common) + "\n")
w.close()
if "expected" in mode:
w = open(oud + oup + "_expected.tsv", 'a+')
w.write(con.split("/")[-1].split(".")[0] + "\t" + str(expected) + "\n")
w.close()
if "raw" in mode:
w = open(oud + oup + "_raw.tsv", 'a+')
w.write(con.split("/")[-1].split(".")[0] + "\t" + str(common) + "//" + str(expected) + "\n")
w.close()
print (str(common) + "//" + str(expected))
| 38.005025 | 224 | 0.492662 |
32c9df6086648fadb4c805373b98205b72ccf085 | 263 | py | Python | applications/MeshMovingApplication/tests/run_cpp_unit_tests.py | lkusch/Kratos | e8072d8e24ab6f312765185b19d439f01ab7b27b | [
"BSD-4-Clause"
] | 778 | 2017-01-27T16:29:17.000Z | 2022-03-30T03:01:51.000Z | applications/MeshMovingApplication/tests/run_cpp_unit_tests.py | lkusch/Kratos | e8072d8e24ab6f312765185b19d439f01ab7b27b | [
"BSD-4-Clause"
] | 6,634 | 2017-01-15T22:56:13.000Z | 2022-03-31T15:03:36.000Z | applications/MeshMovingApplication/tests/run_cpp_unit_tests.py | lkusch/Kratos | e8072d8e24ab6f312765185b19d439f01ab7b27b | [
"BSD-4-Clause"
] | 224 | 2017-02-07T14:12:49.000Z | 2022-03-06T23:09:34.000Z | from KratosMultiphysics import *
from KratosMultiphysics.MeshMovingApplication import *
def run():
Tester.SetVerbosity(Tester.Verbosity.PROGRESS) # TESTS_OUTPUTS
Tester.RunTestSuite("MeshMovingApplicationFastSuite")
if __name__ == '__main__':
run()
| 26.3 | 66 | 0.78327 |
e11b0a9986536f0a40f34d3edd5b249b71b48c00 | 742 | py | Python | {{ cookiecutter.package_name }}/{{ cookiecutter.module_name }}/routers/default.py | triaxtec/fastapi-serverless-cookiecutter | fe5ca7ebb0fbe47ee55adbe93431b02862e62544 | [
"MIT"
] | 15 | 2020-05-09T16:34:29.000Z | 2021-06-05T14:26:34.000Z | example/example/routers/default.py | triaxtec/fastapi-serverless-cookiecutter | fe5ca7ebb0fbe47ee55adbe93431b02862e62544 | [
"MIT"
] | null | null | null | example/example/routers/default.py | triaxtec/fastapi-serverless-cookiecutter | fe5ca7ebb0fbe47ee55adbe93431b02862e62544 | [
"MIT"
] | 1 | 2020-08-13T18:40:55.000Z | 2020-08-13T18:40:55.000Z | from functools import lru_cache
from pathlib import Path
from fastapi import APIRouter, Depends, FastAPI
from markdown import markdown
from starlette.responses import HTMLResponse
router = APIRouter()
def register_router(app: FastAPI) -> None:
""" Register this router against the application """
app.include_router(router)
@lru_cache
def _get_changelog_html() -> bytes:
changelog_path: Path = Path(__file__).parent.parent / "CHANGELOG.md"
_changelog_html = markdown(changelog_path.read_text())
return _changelog_html
@router.get("/changelog", response_class=HTMLResponse)
async def changelog() -> HTMLResponse:
""" Display the changelog for this API """
return HTMLResponse(content=_get_changelog_html())
| 27.481481 | 72 | 0.762803 |
992ae8fc9f3ab9f4a8c386747556ffa62ed5e7d1 | 17,748 | py | Python | abs_templates_ec/routing/fill.py | boblinchuan/BAG2_TEMPLATES_EC | e0e4a41c1780edb035cd619b9cea2e27e3fc5f51 | [
"BSD-3-Clause"
] | 1 | 2020-06-02T22:41:46.000Z | 2020-06-02T22:41:46.000Z | abs_templates_ec/routing/fill.py | boblinchuan/BAG2_TEMPLATES_EC | e0e4a41c1780edb035cd619b9cea2e27e3fc5f51 | [
"BSD-3-Clause"
] | 12 | 2018-10-23T18:08:37.000Z | 2022-02-24T10:51:34.000Z | abs_templates_ec/routing/fill.py | boblinchuan/BAG2_TEMPLATES_EC | e0e4a41c1780edb035cd619b9cea2e27e3fc5f51 | [
"BSD-3-Clause"
] | 18 | 2018-07-14T01:36:09.000Z | 2021-05-25T18:38:00.000Z | # -*- coding: utf-8 -*-
"""This module defines dummy/power fill related templates."""
from typing import TYPE_CHECKING, Dict, Set, Any, Tuple, List
import numpy as np
from bag.util.search import BinaryIterator
from bag.layout.util import BBox
from bag.layout.template import TemplateBase
from ..analog_core.base import AnalogBase, AnalogBaseInfo
if TYPE_CHECKING:
from bag.layout.objects import Instance
from bag.layout.template import TemplateDB
class PowerFill(TemplateBase):
"""A power fill template.
Parameters
----------
temp_db : TemplateDB
the template database.
lib_name : str
the layout library name.
params : Dict[str, Any]
the parameter values.
used_names : Set[str]
a set of already used cell names.
**kwargs :
dictionary of optional parameters. See documentation of
:class:`bag.layout.template.TemplateBase` for details.
"""
def __init__(self, temp_db, lib_name, params, used_names, **kwargs):
# type: (TemplateDB, str, Dict[str, Any], Set[str], **kwargs) -> None
TemplateBase.__init__(self, temp_db, lib_name, params, used_names, **kwargs)
@classmethod
def get_params_info(cls):
# type: () -> Dict[str, str]
return dict(
fill_config='the fill configuration dictionary.',
top_layer='the top fill layer.',
bot_layer='the bottom fill layer.',
show_pins='True to show pins.',
)
@classmethod
def get_default_param_values(cls):
# type: () -> Dict[str, Any]
return dict(
top_layer=None,
show_pins=True,
)
def get_layout_basename(self):
bot_lay = self.params['bot_layer']
top_lay = self.params['top_layer']
if top_lay is None:
top_lay = bot_lay + 1
return 'power_fill_m%dm%d' % (bot_lay, top_lay)
def draw_layout(self):
# type: () -> None
fill_config = self.params['fill_config']
bot_layer = self.params['bot_layer']
top_layer = self.params['top_layer']
show_pins = self.params['show_pins']
if top_layer is None:
top_layer = bot_layer + 1
blk_w, blk_h = self.grid.get_fill_size(top_layer, fill_config, unit_mode=True)
bnd_box = BBox(0, 0, blk_w, blk_h, self.grid.resolution, unit_mode=True)
self.set_size_from_bound_box(top_layer, bnd_box)
self.array_box = bnd_box
vdd_list, vss_list = None, None
for lay in range(bot_layer, top_layer + 1):
fill_width, fill_space, space, space_le = fill_config[lay]
vdd_list, vss_list = self.do_power_fill(lay, space, space_le, vdd_warrs=vdd_list,
vss_warrs=vss_list, fill_width=fill_width,
fill_space=fill_space, unit_mode=True)
if lay == bot_layer:
self.add_pin('VDD_b', vdd_list, show=False)
self.add_pin('VSS_b', vss_list, show=False)
self.add_pin('VDD', vdd_list, show=show_pins)
self.add_pin('VSS', vss_list, show=show_pins)
@classmethod
def get_fill_orient(cls, orient_mode):
if orient_mode == 0:
return 'R0'
elif orient_mode == 1:
return 'MY'
elif orient_mode == 2:
return 'MX'
elif orient_mode == 3:
return 'R180'
else:
raise ValueError('Unknown orientation mode: %d' % orient_mode)
@classmethod
def add_fill_blocks(cls,
template, # type: TemplateBase
bound_box, # type: BBox
fill_config, # type: Dict[int, Tuple[int, int, int, int]]
bot_layer, # type: int
top_layer, # type: int
orient_mode=0, # type: int
):
# type: (...) -> List[List[Instance]]
# TODO: This method does not work when if fill size changes as layer changes.
# TODO: Fix in the future.
# number of wire types per fill block
ntype = 2
# error checking
if top_layer <= bot_layer:
raise ValueError('Must have top_layer > bot_layer.')
grid = template.grid
blk_w, blk_h = grid.get_fill_size(top_layer, fill_config, unit_mode=True)
xl = bound_box.left_unit
yb = bound_box.bottom_unit
xr = bound_box.right_unit
yt = bound_box.top_unit
if xl % blk_w != 0 or xr % blk_w != 0 or yb % blk_h != 0 or yt % blk_h != 0:
raise ValueError('%s is not on power fill grid.' % bound_box)
# figure out where we can draw fill blocks.
tot_w = xr - xl
tot_h = yt - yb
nx = tot_w // blk_w
ny = tot_h // blk_h
use_fill_list = []
shape = (nx, ny)
inst_info_list2 = []
for layer in range(bot_layer, top_layer + 1):
fill_w, fill_sp, sp, sp_le = fill_config[layer]
cur_dir = grid.get_direction(layer)
cur_pitch = grid.get_track_pitch(layer, unit_mode=True)
fill_pitch = fill_w + fill_sp
is_horiz = cur_dir == 'x'
uf_mat = np.ones(shape, dtype=bool)
if is_horiz:
perp_dir = 'y'
blk_dim = blk_w
num_tr = tot_h // (cur_pitch * fill_pitch)
tr_c0 = yb
spx = sp_le
spy = sp
uf_mat_set = uf_mat.transpose()
else:
perp_dir = 'x'
blk_dim = blk_h
num_tr = tot_w // (cur_pitch * fill_pitch)
tr_c0 = xl
spx = sp
spy = sp_le
uf_mat_set = uf_mat
cur_tr = grid.coord_to_track(layer, tr_c0, unit_mode=True) + fill_pitch / 2
for idx in range(num_tr):
blk_idx = idx // ntype
wl, wu = grid.get_wire_bounds(layer, cur_tr, width=fill_w, unit_mode=True)
test_box = bound_box.with_interval(perp_dir, wl, wu, unit_mode=True)
for block_box in template.blockage_iter(layer, test_box, spx=spx, spy=spy):
bl, bu = block_box.get_interval(cur_dir, unit_mode=True)
nstart = max(bl - tr_c0, 0) // blk_dim
nstop = max(bu - tr_c0, 0) // blk_dim
uf_mat_set[blk_idx, nstart:nstop + 1] = False
cur_tr += fill_pitch
if layer > bot_layer:
prev_uf_mat = use_fill_list[-1]
uf_tot = prev_uf_mat & uf_mat
inst_info_list = []
for x0, y0, nx, ny in cls._get_fill_mosaics(uf_tot):
inst_info_list.append((x0, y0, nx, ny))
inst_info_list2.append(inst_info_list)
use_fill_list.append(uf_mat)
inst_params = dict(
fill_config=fill_config,
show_pins=False
)
xinc = 0 if (orient_mode & 1 == 0) else 1
yinc = 0 if (orient_mode & 2 == 0) else 1
inst_list2 = []
orient = cls.get_fill_orient(orient_mode)
for idx, inst_info_list in enumerate(inst_info_list2):
inst_list = []
inst_params['bot_layer'] = bot_layer + idx
master = template.new_template(params=inst_params, temp_cls=PowerFill)
for x0, y0, nx, ny in inst_info_list:
loc = xl + (x0 + xinc) * blk_w, yb + (y0 + yinc) * blk_h
inst = template.add_instance(master, loc=loc, orient=orient, nx=nx, ny=ny,
spx=blk_w, spy=blk_h, unit_mode=True)
inst_list.append(inst)
inst_list2.append(inst_list)
return inst_list2
@classmethod
def _get_fill_mosaics(cls, uf_mat):
# TODO: use Eppestein's Polygon dissection instead of greedy algorithm
nx, ny = uf_mat.shape
idx_mat = np.full((nx, ny, 2), -1)
for xidx in range(nx):
for yidx in range(ny):
if uf_mat[xidx, yidx]:
if xidx > 0 and idx_mat[xidx - 1, yidx, 1] == yidx:
cur_xl = idx_mat[xidx, yidx, 0] = idx_mat[xidx - 1, yidx, 0]
idx_mat[xidx - 1, yidx, :] = -1
else:
cur_xl = idx_mat[xidx, yidx, 0] = xidx
if yidx > 0 and idx_mat[xidx, yidx - 1, 0] == cur_xl:
cur_yb = idx_mat[xidx, yidx, 1] = idx_mat[xidx, yidx - 1, 1]
idx_mat[xidx, yidx - 1, :] = -1
if xidx > 0 and idx_mat[xidx - 1, yidx, 1] == cur_yb:
idx_mat[xidx, yidx, 0] = idx_mat[xidx - 1, yidx, 0]
idx_mat[xidx - 1, yidx, :] = -1
else:
idx_mat[xidx, yidx, 1] = yidx
x_list, y_list = np.nonzero(idx_mat[:, :, 0] >= 0)
for xidx, yidx in zip(x_list, y_list):
x0, y0 = idx_mat[xidx, yidx, :]
nx = xidx - x0 + 1
ny = yidx - y0 + 1
yield x0, y0, nx, ny
class DecapFillCore(AnalogBase):
"""A decap cell used for power fill
Parameters
----------
temp_db : TemplateDB
the template database.
lib_name : str
the layout library name.
params : Dict[str, Any]
the parameter values.
used_names : Set[str]
a set of already used cell names.
**kwargs :
dictionary of optional parameters. See documentation of
:class:`bag.layout.template.TemplateBase` for details.
"""
def __init__(self, temp_db, lib_name, params, used_names, **kwargs):
# type: (TemplateDB, str, Dict[str, Any], Set[str], **kwargs) -> None
AnalogBase.__init__(self, temp_db, lib_name, params, used_names, **kwargs)
@classmethod
def get_params_info(cls):
# type: () -> Dict[str, str]
return dict(
lch='channel length, in meters.',
ptap_w='NMOS substrate width, in meters/number of fins.',
ntap_w='PMOS substrate width, in meters/number of fins.',
wp='PMOS width.',
wn='NMOS width.',
thp='PMOS threshold.',
thn='NMOS threshold.',
nx='number of horizontal blocks of fill.',
ny='number of vertical blocks of fill.',
fill_config='the fill configuration dictionary.',
top_layer='Top power fill layer',
sup_width='Supply track width.',
options='other AnalogBase options',
show_pins='True to create pin labels.',
)
@classmethod
def get_default_param_values(cls):
# type: () -> Dict[str, Any]
return dict(
sup_width=2,
options=None,
show_pins=True,
)
def get_layout_basename(self):
lay = self.params['top_layer']
nx = self.params['nx']
ny = self.params['ny']
return 'decap_fill_core_lay%d_%dx%d' % (lay, nx, ny)
def draw_layout(self):
# type: () -> None
lch = self.params['lch']
ptap_w = self.params['ptap_w']
ntap_w = self.params['ntap_w']
wp = self.params['wp']
wn = self.params['wn']
thp = self.params['thp']
thn = self.params['thn']
nx = self.params['nx']
ny = self.params['ny']
fill_config = self.params['fill_config']
top_layer = self.params['top_layer']
sup_width = self.params['sup_width']
options = self.params['options']
show_pins = self.params['show_pins']
if options is None:
options = {}
# get power fill size
w_tot, h_tot = self.grid.get_fill_size(top_layer, fill_config, unit_mode=True)
w_tot *= nx
h_tot *= ny
# get number of fingers
info = AnalogBaseInfo(self.grid, lch, 0, top_layer=top_layer)
bin_iter = BinaryIterator(2, None)
while bin_iter.has_next():
fg_cur = bin_iter.get_next()
w_cur = info.get_placement_info(fg_cur).tot_width
if w_cur < w_tot:
bin_iter.save()
bin_iter.up()
elif w_cur > w_tot:
bin_iter.down()
else:
bin_iter.save()
break
fg_tot = bin_iter.get_last_save()
if fg_tot is None:
raise ValueError('Decaep cell width exceed fill width.')
self.draw_base(lch, fg_tot, ptap_w, ntap_w, [wn], [thn], [wp], [thp],
ng_tracks=[1], pg_tracks=[1], n_orientations=['MX'],
p_orientations=['R0'], top_layer=top_layer, min_height=h_tot,
**options)
if self.bound_box.height_unit > h_tot:
raise ValueError('Decap cell height exceed fill height.')
nmos = self.draw_mos_conn('nch', 0, 0, fg_tot, 0, 0)
pmos = self.draw_mos_conn('pch', 0, 0, fg_tot, 2, 2, gate_pref_loc='s')
vss_tid = self.make_track_id('pch', 0, 'g', 0)
vdd_tid = self.make_track_id('nch', 0, 'g', 0)
self.connect_to_substrate('ptap', nmos['d'])
self.connect_to_substrate('ntap', pmos['s'])
vss_g = self.connect_to_tracks([nmos['s'], pmos['g']], vss_tid)
vdd_g = self.connect_to_tracks([pmos['d'], nmos['g']], vdd_tid)
vss, vdd = self.fill_dummy(vdd_width=sup_width, vss_width=sup_width)
vss.append(vss_g)
vdd.append(vdd_g)
self.add_pin('VSS', vss, label='VSS:', show=show_pins)
self.add_pin('VDD', vdd, label='VDD:', show=show_pins)
class DecapFill(TemplateBase):
"""A power fill cell containing decap
Parameters
----------
temp_db : TemplateDB
the template database.
lib_name : str
the layout library name.
params : Dict[str, Any]
the parameter values.
used_names : Set[str]
a set of already used cell names.
**kwargs :
dictionary of optional parameters. See documentation of
:class:`bag.layout.template.TemplateBase` for details.
"""
def __init__(self, temp_db, lib_name, params, used_names, **kwargs):
# type: (TemplateDB, str, Dict[str, Any], Set[str], **kwargs) -> None
TemplateBase.__init__(self, temp_db, lib_name, params, used_names, **kwargs)
@classmethod
def get_params_info(cls):
# type: () -> Dict[str, str]
return dict(
fill_config='the fill configuration dictionary.',
decap_params='decap parameters.',
nx='number of horizontal blocks of fill.',
ny='number of vertical blocks of fill.',
top_layer='Top power fill layer',
show_pins='True to show pins.',
)
@classmethod
def get_default_param_values(cls):
# type: () -> Dict[str, Any]
return dict(
show_pins=True,
)
def get_layout_basename(self):
lay = self.params['top_layer']
nx = self.params['nx']
ny = self.params['ny']
return 'decap_fill_lay%d_%dx%d' % (lay, nx, ny)
def draw_layout(self):
# type: () -> None
fill_config = self.params['fill_config']
decap_params = self.params['decap_params']
nx = self.params['nx']
ny = self.params['ny']
top_layer = self.params['top_layer']
show_pins = self.params['show_pins']
params = decap_params.copy()
params['nx'] = nx
params['ny'] = ny
params['fill_config'] = fill_config
params['top_layer'] = top_layer
master_cap = self.new_template(params=params, temp_cls=DecapFillCore)
w_blk, h_blk = self.grid.get_fill_size(top_layer, fill_config, unit_mode=True)
w_tot = w_blk * nx
h_tot = h_blk * ny
dx = (w_tot - master_cap.bound_box.width_unit) // 2
cap_inst = self.add_instance(master_cap, 'XCAP', (dx, 0), unit_mode=True)
hm_layer = master_cap.mos_conn_layer + 1
if top_layer <= hm_layer:
raise ValueError('top layer must be at least %d' % (hm_layer + 1))
# set size
res = self.grid.resolution
self.array_box = bnd_box = BBox(0, 0, w_tot, h_tot, res, unit_mode=True)
self.set_size_from_bound_box(top_layer, bnd_box)
self.add_cell_boundary(bnd_box)
# do power fill
ym_layer = hm_layer + 1
vdd_list = cap_inst.get_all_port_pins('VDD')
vss_list = cap_inst.get_all_port_pins('VSS')
fill_width, fill_space, space, space_le = fill_config[ym_layer]
vdd_list, vss_list = self.do_power_fill(ym_layer, space, space_le, vdd_warrs=vdd_list,
vss_warrs=vss_list, fill_width=fill_width,
fill_space=fill_space, unit_mode=True)
if top_layer > ym_layer:
params = dict(fill_config=fill_config, show_pins=False)
inst = None
for bot_layer in range(ym_layer, top_layer):
params['bot_layer'] = bot_layer
master = self.new_template(params=params, temp_cls=PowerFill)
inst = self.add_instance(master, 'X%d' % bot_layer, nx=nx, ny=ny,
spx=w_blk, spy=h_blk, unit_mode=True)
vdd_list = self.connect_wires(inst.get_all_port_pins('VDD'))
vss_list = self.connect_wires(inst.get_all_port_pins('VSS'))
self.add_pin('VDD', vdd_list, show=show_pins)
self.add_pin('VSS', vss_list, show=show_pins)
| 37.842217 | 94 | 0.557809 |
d17eb7e052c5058835c8b47a0dee17e359633385 | 2,396 | py | Python | gae/tests/FIXTURES.py | benletchford/stratego.io | 040d8d0775594f531d588700128e86f744be2dff | [
"MIT"
] | 29 | 2015-12-03T04:11:05.000Z | 2022-01-21T15:34:37.000Z | gae/tests/FIXTURES.py | benletchford/stratego.io | 040d8d0775594f531d588700128e86f744be2dff | [
"MIT"
] | 10 | 2020-04-12T16:01:40.000Z | 2022-02-26T07:56:55.000Z | gae/tests/FIXTURES.py | benletchford/stratego.io | 040d8d0775594f531d588700128e86f744be2dff | [
"MIT"
] | 9 | 2016-03-13T11:54:02.000Z | 2021-11-28T04:28:51.000Z | import json
import copy
SETUP = [
[
{'rank': '1', 'side': 3},
{'rank': '2', 'side': 3},
{'rank': '3', 'side': 3},
{'rank': '3', 'side': 3},
{'rank': '4', 'side': 3},
{'rank': '4', 'side': 3},
{'rank': '4', 'side': 3},
{'rank': '5', 'side': 3},
{'rank': '5', 'side': 3},
{'rank': '5', 'side': 3}
],
[
{'rank': '5', 'side': 3},
{'rank': '6', 'side': 3},
{'rank': '6', 'side': 3},
{'rank': '6', 'side': 3},
{'rank': '6', 'side': 3},
{'rank': '7', 'side': 3},
{'rank': '7', 'side': 3},
{'rank': '7', 'side': 3},
{'rank': '7', 'side': 3},
{'rank': '8', 'side': 3}
],
[
{'rank': '8', 'side': 3},
{'rank': '8', 'side': 3},
{'rank': '8', 'side': 3},
{'rank': '8', 'side': 3},
{'rank': '9', 'side': 3},
{'rank': '9', 'side': 3},
{'rank': '9', 'side': 3},
{'rank': '9', 'side': 3},
{'rank': '9', 'side': 3},
{'rank': '9', 'side': 3}
],
[
{'rank': '9', 'side': 3},
{'rank': '9', 'side': 3},
{'rank': 'S', 'side': 3},
{'rank': 'B', 'side': 3},
{'rank': 'B', 'side': 3},
{'rank': 'B', 'side': 3},
{'rank': 'B', 'side': 3},
{'rank': 'B', 'side': 3},
{'rank': 'B', 'side': 3},
{'rank': 'F', 'side': 3}
]
]
SETUP_0 = copy.deepcopy(SETUP)
for row in SETUP_0:
for cell in row:
cell['side'] = 0
SETUP_1 = copy.deepcopy(SETUP)
SETUP_1 = SETUP_1[::-1]
for i in xrange(0, len(SETUP_1)):
SETUP_1[i] = SETUP_1[i][::-1]
for row in SETUP_1:
for cell in row:
cell['side'] = 1
DEFAULT_GAME = SETUP_1 + [
[0, 0, 1, 1, 0, 0, 1, 1, 0, 0],
[0, 0, 1, 1, 0, 0, 1, 1, 0, 0]
] + SETUP_0
MARSHAL = {
'rank': '1',
'side': 0
}
GENERAL = {
'rank': '2',
'side': 0
}
COLONEL = {
'rank': '3',
'side': 0
}
MAJOR = {
'rank': '4',
'side': 0
}
CAPTAIN = {
'rank': '5',
'side': 0
}
LIEUTENANT = {
'rank': '6',
'side': 0
}
SERGEANT = {
'rank': '7',
'side': 0
}
MINER = {
'rank': '8',
'side': 0
}
SCOUT = {
'rank': '9',
'side': 0
}
SPY = {
'rank': 'S',
'side': 0
}
FLAG = {
'rank': 'F',
'side': 0
}
BOMB = {
'rank': 'B',
'side': 0
}
| 18.015038 | 39 | 0.34975 |
45051d34467ef1e78cf83176b967ab4504b0a086 | 627 | py | Python | backend/manage.py | ecto0310/groupware | e1c9f76b19e6d1f6782f8e2b287ff75d1351fa83 | [
"MIT"
] | 3 | 2020-03-23T19:18:00.000Z | 2021-04-12T04:01:17.000Z | backend/manage.py | ecto0310/groupware | e1c9f76b19e6d1f6782f8e2b287ff75d1351fa83 | [
"MIT"
] | 95 | 2020-03-07T12:29:38.000Z | 2022-02-17T22:44:07.000Z | backend/manage.py | ecto0310/groupware | e1c9f76b19e6d1f6782f8e2b287ff75d1351fa83 | [
"MIT"
] | 2 | 2021-12-27T16:50:36.000Z | 2021-12-27T16:53:12.000Z | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'digigru.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
| 28.5 | 73 | 0.682616 |
8a86e9dedef1815cfb6f54f846da9292da04752e | 333 | py | Python | ics_demo/main.py | lielongxingkong/ics-demo | 21a08945f3983eb409916a7380549f74e3ba5171 | [
"MIT"
] | null | null | null | ics_demo/main.py | lielongxingkong/ics-demo | 21a08945f3983eb409916a7380549f74e3ba5171 | [
"MIT"
] | null | null | null | ics_demo/main.py | lielongxingkong/ics-demo | 21a08945f3983eb409916a7380549f74e3ba5171 | [
"MIT"
] | null | null | null | import tornado.ioloop
import tornado.web
from ics_demo.dao import init_db
from ics_demo.remote_services import init_proxy
from ics_demo.routes import urls
application = tornado.web.Application(urls)
if __name__ == "__main__":
init_db()
init_proxy()
application.listen(8888)
tornado.ioloop.IOLoop.instance().start()
| 23.785714 | 47 | 0.774775 |
99b2a7994bb66178b4ae8e195e8a71e4ee9137c0 | 7,111 | py | Python | Lab/4/Dynamics/lagrangian_dynamics_example.py | rparak/Programming-for-robots-and-manipulators-VRM | 40157b39de410a3daa8a59bdce11d865d14f321c | [
"MIT"
] | 1 | 2021-04-09T20:38:52.000Z | 2021-04-09T20:38:52.000Z | Lab/4/Dynamics/lagrangian_dynamics_example.py | rparak/Programming-for-robots-and-manipulators-VRM | 40157b39de410a3daa8a59bdce11d865d14f321c | [
"MIT"
] | null | null | null | Lab/4/Dynamics/lagrangian_dynamics_example.py | rparak/Programming-for-robots-and-manipulators-VRM | 40157b39de410a3daa8a59bdce11d865d14f321c | [
"MIT"
] | 5 | 2021-04-30T17:56:01.000Z | 2022-03-30T10:09:00.000Z | """
## =========================================================================== ##
MIT License
Copyright (c) 2020 Roman Parak
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
## =========================================================================== ##
Author : Roman Parak
Email : Roman.Parak@outlook.com
Github : https://github.com/rparak
File Name: lagrangian_dynamics_example.py
## =========================================================================== ##
"""
# System (Default Lib.)
import sys
# Numpy (Array computing Lib.) [pip3 install numpy]
import numpy as np
# Mtaplotlib (Visualization Lib.) [pip3 install matplotlib]
import matplotlib.pyplot as plt
# Integrate a system of ordinary differential equations (ODE) [pip3 install scipy]
from scipy.integrate import odeint
class Dynamics_Ctrl(object):
def __init__(self, L, m, time, dt):
# << PUBLIC >> #
# Arm Length [m]
self.L = [L[0], L[1]]
# Arm Length (1/2) - Center of Gravity [m]
self.lg = [L[0]/2, L[1]/2]
# Mass [kg]
self.m = [m[0], m[1]]
# Moment of Invertia [kg.m^2]
self.I = [(1/3)*(m[0])*(L[0]**2), (1/3)*(m[1])*(L[1]**2)]
# Gravitational acceleration [m/s^2]
self.g = 9.81
# Initial Time Parameters (Calculation)
self.t = np.arange(0.0, time, dt)
# << PRIVATE >> #
# Axes and Label initialization.
self.__ax = [0, 0, 0, 0]
self.__y_label = [r'$\dot\theta_1$', r'$\ddot\theta_1$', r'$\dot\theta_2$', r'$\ddot\theta_2$']
# Display (Plot) variables.
self.__plt = plt
self.__fig, ((self.__ax[0], self.__ax[1]), (self.__ax[2], self.__ax[3])) = self.__plt.subplots(2, 2)
def __lagrangian_dynamics(self, input_p, time):
"""
Description:
For many applications with fixed-based robots we need to find a multi-body dynamics formulated as:
M(\theta)\ddot\theta + b(\theta, \dot\theta) + g(\theta) = \tau
M(\theta) -> Generalized mass matrix (orthogonal).
\theta,\dot\theta,\ddot\theta -> Generalized position, velocity and acceleration vectors.
b(\theta, \dot\theta) -> Coriolis and centrifugal terms.
g(\theta) -> Gravitational terms.
\tau -> External generalized forces.
Euler-Lagrange equation:
L = T - U
T -> Kinetic Energy (Translation + Rotation Part): (1/2) * m * v^2 + (1/2) * I * \omega^2 -> with moment of invertia
U -> Potential Energy: m * g * h
Args:
(1) input_p [Float Array]: Initial position of the Robot (2 Joints) -> Theta_{1, 2} and 1_Derivation Theta_{1,2}
(2) time [Float]: Derivation of the Time.
Returns:
(1): param 1, param 3 [Float]: 1_Derivation Theta_{1,2}
(2): param 2, param 4 [Float]: 2_Derivation Theta_{1,2}
"""
theta_1 = input_p[0]; theta_2 = input_p[2]
dtheta_1 = input_p[1]; dtheta_2 = input_p[3]
# Generalized mass matrix -> M(\theta)
M_Mat = np.matrix([
[self.I[0] + self.I[1] + self.m[0] * (self.lg[0]**2) + self.m[1] * ((self.L[0]**2) + (self.lg[1]**2) + 2 * self.L[0] * self.lg[1] * np.cos(theta_2)), self.I[1] + self.m[1] * ((self.lg[1]**2) + self.L[0] * self.lg[1] * np.cos(theta_2))],
[self.I[1] + self.m[1] * ((self.lg[1]**2) + self.L[0] * self.lg[1] * np.cos(theta_2)), self.I[1] + self.m[1] * (self.lg[1]**2)]
])
# Coriolis and centrifugal terms -> b(\theta, \dot\theta)
b_Mat = np.matrix([
[(-1) * self.m[1] * self.L[0] * self.lg[1] * dtheta_2 * (2 * dtheta_1 + dtheta_2) * np.sin(theta_2)],
[self.m[1] * self.L[0] * self.lg[1] * (dtheta_1**2) *np.sin(theta_2)]
])
# Gravitational terms -> g(\theta)
g_Mat = np.matrix([
[self.m[0] * self.g * self.lg[0] * np.cos(theta_1) + self.m[1] * self.g * (self.L[0] * np.cos(theta_1) + self.lg[1] * np.cos(theta_1 + theta_2))],
[self.m[1] * self.g * self.lg[1] * np.cos(theta_1 + theta_2)]
])
# \tau -> External generalized forces.
tau_Mat = np.matrix([[0.0], [0.0]])
# Ordinary Differential Equations (ODE) -> From Motion Equation
# {\ddotTheta_1, \ddotTheta_2}
ode_r = np.linalg.inv(M_Mat).dot(-b_Mat - g_Mat) + tau_Mat
return [dtheta_1, ode_r[0][0], dtheta_2, ode_r[1][0]]
def display_result(self, input_p):
"""
Description:
Function for calculating and displaying the results of Lagrangian Dynamics Calculation.
Args:
(1) input_p [Float Array]: Initial position of the Robot (2 Joints) -> Theta_{1, 2} and 1_Derivation Theta_{1,2}
"""
calc_r = odeint(self.__lagrangian_dynamics, input_p, self.t)
self.__fig.suptitle('Lagrangian Dynamics: Example', fontsize = 50, fontweight ='normal')
for i in range(len(self.__ax)):
self.__ax[i].plot(self.t, calc_r[:, i])
self.__ax[i].grid()
self.__ax[i].set_xlabel(r'time [s]', fontsize=20)
self.__ax[i].set_ylabel(self.__y_label[i], fontsize=20)
# Set additional parameters for successful display of the robot environment
self.__plt.show()
def main():
# Initialization of the Class (Control Dynamics - Lagrangian)
# Input:
# (1) Length of Arms (Link 1, Link2) [Float Array]
# (2) Mass
# (3) Time [INT]
# (4) Derivation of the Time [Float]
# Example:
# x = Dynamics_Ctrl([1.0, 1.0], [1.25, 2.0], 10, 0.1)
lD_c = Dynamics_Ctrl([0.3, 0.25], [1.0, 1.0], 10, 0.01)
# Initial position of the Robot (2 Joints) -> Theta_{1, 2} and 1_Derivation Theta_{1,2}
initial_p = [0.1*np.pi, 0.0, 0.1*np.pi, 0.0]
# Display the result of the calculation:
# The figure with the resulting 1_Derivation Theta_{1,2}, 2_Derivation Theta_{1,2}
lD_c.display_result(initial_p)
if __name__ == '__main__':
sys.exit(main())
| 43.895062 | 249 | 0.577697 |
0cb6495e0f846d64a917c60cd170b9e1e48764af | 868 | py | Python | python/soma_workflow/clean_server.py | denisri/soma-workflow | bc6f2f50d34437e86e850cb0d05ff26b041d560d | [
"CECILL-B"
] | null | null | null | python/soma_workflow/clean_server.py | denisri/soma-workflow | bc6f2f50d34437e86e850cb0d05ff26b041d560d | [
"CECILL-B"
] | null | null | null | python/soma_workflow/clean_server.py | denisri/soma-workflow | bc6f2f50d34437e86e850cb0d05ff26b041d560d | [
"CECILL-B"
] | null | null | null | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
'''
@author: Jinpeng LI
@contact: mr.li.jinpeng@gmail.com
@organization: I2BM, Neurospin, Gif-sur-Yvette, France
@organization: CATI, France
@organization: U{IFR 49<http://www.ifr49.org>}
@license: U{CeCILL version 2<http://www.cecill.info/licences/Licence_CeCILL_V2-en.html>}
'''
'''
start to check the requirement on the server side
'''
import os
import sys
resName = None
i = 0
while i < len(sys.argv):
if sys.argv[i] == "-r":
resName = sys.argv[i + 1]
break
i = i + 1
lines2cmd = [
"kill $(ps -ef | grep 'python -m soma_workflow.start_database_server' | grep '%s' \
| grep -v grep | awk '{print $2}')" % (resName),
"rm ~/.soma-workflow.cfg"
]
for line2cmd in lines2cmd:
os.system("echo '%s' " % (line2cmd))
os.system(line2cmd)
| 20.186047 | 88 | 0.644009 |
6a2b82ca881c58611fff3bca5df1fee1f5c86a2f | 418 | py | Python | configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py | Xlinford/mmsegmentation | 8b444de5e6db2af2538a73a93ac75204f5c3bb2f | [
"Apache-2.0"
] | null | null | null | configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py | Xlinford/mmsegmentation | 8b444de5e6db2af2538a73a93ac75204f5c3bb2f | [
"Apache-2.0"
] | null | null | null | configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py | Xlinford/mmsegmentation | 8b444de5e6db2af2538a73a93ac75204f5c3bb2f | [
"Apache-2.0"
] | null | null | null | _base_ = '../pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='mmcls://mobilenet_v2',
backbone=dict(
_delete_=True,
type='MobileNetV2',
widen_factor=1.,
strides=(1, 2, 2, 1, 1, 1, 1),
dilations=(1, 1, 1, 2, 2, 4, 4),
out_indices=(1, 2, 4, 6)),
decode_head=dict(in_channels=320),
auxiliary_head=dict(in_channels=96))
| 32.153846 | 63 | 0.583732 |
8a8d0ed340effd4b711673ee77a305bf0989fdc6 | 1,186 | py | Python | simple_linear_regression/utils.py | awesome-archive/Machine-Learning-with-Python | 898ebbf2d7c780cb5a89bad51d0b7e043e25879f | [
"MIT"
] | 942 | 2019-01-19T18:56:49.000Z | 2022-03-31T19:09:56.000Z | simple_linear_regression/utils.py | skylaronomics/Machine-Learning-with-Python | 186a6f38f6719e5610e0143aecdf170c842ff107 | [
"MIT"
] | 4 | 2019-01-22T15:17:47.000Z | 2019-08-25T14:02:44.000Z | simple_linear_regression/utils.py | skylaronomics/Machine-Learning-with-Python | 186a6f38f6719e5610e0143aecdf170c842ff107 | [
"MIT"
] | 176 | 2019-01-21T10:19:52.000Z | 2022-03-02T20:10:27.000Z | from helpers.stats import correlation, standard_deviation, mean, de_mean
def predict(alpha, beta, x_i):
return beta * x_i + alpha
def error(alpha, beta, x_i, y_i):
return y_i - predict(alpha, beta, x_i)
def sum_of_squared_errors(alpha, beta, x, y):
return sum(error(alpha, beta, x_i, y_i) ** 2
for x_i, y_i in zip(x, y))
def least_squares_fit(x, y):
beta = correlation(x, y) * standard_deviation(y) / standard_deviation(x)
alpha = mean(y) - beta * mean(x)
return alpha, beta
def total_sum_of_squares(y):
"""The total squared variation of y_i's from their mean"""
return sum(v ** 2 for v in de_mean(y))
def r_squared(alpha, beta, x, y):
"""the fraction of variation in y captured by the model"""
return 1 - sum_of_squared_errors(alpha, beta, x, y) / total_sum_of_squares(y)
def squared_error(x_i, y_i, theta):
alpha, beta = theta
return error(alpha, beta, x_i, y_i) ** 2
def squared_error_gradient(x_i, y_i, theta):
alpha, beta = theta
return [-2 * error(alpha, beta, x_i, y_i), # alpha partial derivative
-2 * error(alpha, beta, x_i, y_i) * x_i] # beta partial derivative
| 26.355556 | 82 | 0.653457 |
b6f3a07f10a9cf08ac0889af13af388ba2842c7d | 9,533 | py | Python | var/spack/repos/builtin/packages/py-dask/package.py | carlabguillen/spack | 7070bb892f9bdb5cf9e76e0eecd64f6cc5f4695c | [
"ECL-2.0",
"Apache-2.0",
"MIT-0",
"MIT"
] | 2 | 2020-08-13T15:24:33.000Z | 2021-10-18T18:38:19.000Z | var/spack/repos/builtin/packages/py-dask/package.py | carlabguillen/spack | 7070bb892f9bdb5cf9e76e0eecd64f6cc5f4695c | [
"ECL-2.0",
"Apache-2.0",
"MIT-0",
"MIT"
] | 6 | 2022-02-26T11:44:34.000Z | 2022-03-12T12:14:50.000Z | var/spack/repos/builtin/packages/py-dask/package.py | carlabguillen/spack | 7070bb892f9bdb5cf9e76e0eecd64f6cc5f4695c | [
"ECL-2.0",
"Apache-2.0",
"MIT-0",
"MIT"
] | 2 | 2020-09-15T02:37:59.000Z | 2020-09-21T04:34:38.000Z | # Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class PyDask(PythonPackage):
"""Dask is a flexible parallel computing library for analytics."""
homepage = "https://github.com/dask/dask/"
url = "https://pypi.io/packages/source/d/dask/dask-1.1.0.tar.gz"
maintainers = ['skosukhin']
version('2.16.0', sha256='2af5b0dcd48ce679ce0321cf91de623f4fe376262789b951fefa3c334002f350')
version('1.2.2', sha256='5e7876bae2a01b355d1969b73aeafa23310febd8c353163910b73e93dc7e492c')
version('1.1.2', sha256='93b355b9a9c9a3ddbb39fab99d5759aad5cfd346f4520b87788970e80cf97256')
version('1.1.0', sha256='e76088e8931b326c05a92d2658e07b94a6852b42c13a7560505a8b2354871454')
version('0.17.4', sha256='c111475a3d1f8cba41c8094e1fb1831c65015390dcef0308042a11a9606a2f6d')
version('0.8.1', sha256='43deb1934cd033668e5e60b735f45c9c3ee2813f87bd51c243f975e55267fa43')
variant('array', default=True, description='Install requirements for dask.array')
variant('bag', default=True, description='Install requirements for dask.bag')
variant('dataframe', default=True, description='Install requirements for dask.dataframe')
variant('distributed', default=True, description='Install requirements for dask.distributed')
variant('diagnostics', default=False, description='Install requirements for dask.diagnostics')
variant('delayed', default=True, description='Install requirements for dask.delayed (dask.imperative)')
variant('yaml', default=True, description='Ensure support for YAML configuration files')
conflicts('+distributed', when='@:0.4.0,0.7.6:0.8.1')
conflicts('+diagnostics', when='@:0.5.0')
conflicts('+yaml', when='@:0.17.5')
depends_on('python@2.7:2.8,3.5:', type=('build', 'run'))
depends_on('python@3.5:', type=('build', 'run'), when='@2.0.0:')
depends_on('python@3.6:', type=('build', 'run'), when='@2.7.0:')
depends_on('py-setuptools', type='build')
depends_on('py-pytest@3.1.0:', type='test')
depends_on('py-requests', type='test')
depends_on('py-pytest-runner', type='test')
# Requirements for dask.array
depends_on('py-numpy@1.10.4:', type=('build', 'run'), when='+array')
depends_on('py-numpy@1.11.0:', type=('build', 'run'), when='@0.17.3: +array')
depends_on('py-numpy@1.13.0:', type=('build', 'run'), when='@1.2.1: +array')
depends_on('py-toolz', type=('build', 'run'), when='+array')
depends_on('py-toolz@0.7.2:', type=('build', 'run'), when='@0.7.0: +array')
depends_on('py-toolz@0.7.3:', type=('build', 'run'), when='@0.14.1: +array')
depends_on('py-toolz@0.8.2:', type=('build', 'run'), when='@2.13.0: +array')
# Requirements for dask.bag
depends_on('py-dill', type=('build', 'run'), when='@:0.7.5 +bag')
depends_on('py-cloudpickle', type=('build', 'run'), when='@0.7.6: +bag')
depends_on('py-cloudpickle@0.2.1:', type=('build', 'run'), when='@0.8.2: +bag')
depends_on('py-cloudpickle@0.2.2:', type=('build', 'run'), when='@2.13.0: +bag')
depends_on('py-fsspec@0.3.3:', type=('build', 'run'), when='@2.2.0: +bag')
depends_on('py-fsspec@0.5.1:', type=('build', 'run'), when='@2.5.0: +bag')
depends_on('py-fsspec@0.6.0:', type=('build', 'run'), when='@2.8.0: +bag')
depends_on('py-toolz', type=('build', 'run'), when='+bag')
depends_on('py-toolz@0.7.2:', type=('build', 'run'), when='@0.7.0: +bag')
depends_on('py-toolz@0.7.3:', type=('build', 'run'), when='@0.14.1: +bag')
depends_on('py-toolz@0.8.2:', type=('build', 'run'), when='@2.13.0: +bag')
depends_on('py-partd', type=('build', 'run'), when='+bag')
depends_on('py-partd@0.3.2:', type=('build', 'run'), when='@0.6.0: +bag')
depends_on('py-partd@0.3.3:', type=('build', 'run'), when='@0.9.0: +bag')
depends_on('py-partd@0.3.5:', type=('build', 'run'), when='@0.10.2: +bag')
depends_on('py-partd@0.3.6:', type=('build', 'run'), when='@0.12.0: +bag')
depends_on('py-partd@0.3.7:', type=('build', 'run'), when='@0.13.0: +bag')
depends_on('py-partd@0.3.8:', type=('build', 'run'), when='@0.15.0: +bag')
depends_on('py-partd@0.3.10:', type=('build', 'run'), when='@2.0.0: +bag')
# Requirements for dask.dataframe
depends_on('py-numpy@1.10.4:', type=('build', 'run'), when='+dataframe')
depends_on('py-numpy@1.11.0:', type=('build', 'run'), when='@0.17.3: +dataframe')
depends_on('py-numpy@1.13.0:', type=('build', 'run'), when='@1.2.1: +dataframe')
depends_on('py-pandas@0.16.0:', type=('build', 'run'), when='+dataframe')
depends_on('py-pandas@0.18.0:', type=('build', 'run'), when='@0.9.0: +dataframe')
depends_on('py-pandas@0.19.0:', type=('build', 'run'), when='@0.14.0: +dataframe')
depends_on('py-pandas@0.21.0:', type=('build', 'run'), when='@1.2.1: +dataframe')
depends_on('py-pandas@0.23.0:', type=('build', 'run'), when='@2.11.0: +dataframe')
depends_on('py-toolz', type=('build', 'run'), when='+dataframe')
depends_on('py-toolz@0.7.2:', type=('build', 'run'), when='@0.7.0: +dataframe')
depends_on('py-toolz@0.7.3:', type=('build', 'run'), when='@0.14.1: +dataframe')
depends_on('py-toolz@0.8.2:', type=('build', 'run'), when='@2.13.0: +dataframe')
depends_on('py-partd', type=('build', 'run'), when='+dataframe')
depends_on('py-partd@0.3.2:', type=('build', 'run'), when='@0.6.0: +dataframe')
depends_on('py-partd@0.3.3:', type=('build', 'run'), when='@0.9.0: +dataframe')
depends_on('py-partd@0.3.5:', type=('build', 'run'), when='@0.10.2: +dataframe')
depends_on('py-partd@0.3.7:', type=('build', 'run'), when='@0.13.0: +dataframe')
depends_on('py-partd@0.3.8:', type=('build', 'run'), when='@0.15.0: +dataframe')
depends_on('py-partd@0.3.10:', type=('build', 'run'), when='@2.0.0: +dataframe')
depends_on('py-cloudpickle@0.2.1:', type=('build', 'run'), when='@0.8.2:2.6.0 +dataframe')
depends_on('py-fsspec@0.3.3:', type=('build', 'run'), when='@2.2.0: +dataframe')
depends_on('py-fsspec@0.5.1:', type=('build', 'run'), when='@2.5.0: +dataframe')
depends_on('py-fsspec@0.6.0:', type=('build', 'run'), when='@2.8.0: +dataframe')
# Requirements for dask.distributed
depends_on('py-dill', type=('build', 'run'), when='@:0.7.5 +distributed')
depends_on('py-pyzmq', type=('build', 'run'), when='@:0.7.5 +distributed')
depends_on('py-distributed', type=('build', 'run'), when='@0.8.2: +distributed')
depends_on('py-distributed@1.9:', type=('build', 'run'), when='@0.9.0: +distributed')
depends_on('py-distributed@1.10:', type=('build', 'run'), when='@0.10.0: +distributed')
depends_on('py-distributed@1.14:', type=('build', 'run'), when='@0.12.0: +distributed')
depends_on('py-distributed@1.15:', type=('build', 'run'), when='@0.13.0: +distributed')
depends_on('py-distributed@1.16:', type=('build', 'run'), when='@0.14.1: +distributed')
depends_on('py-distributed@1.20:', type=('build', 'run'), when='@0.16.0: +distributed')
depends_on('py-distributed@1.21:', type=('build', 'run'), when='@0.17.0: +distributed')
depends_on('py-distributed@1.22:', type=('build', 'run'), when='@0.18.0: +distributed')
depends_on('py-distributed@2.0:', type=('build', 'run'), when='@2.0.0: +distributed')
# Requirements for dask.diagnostics
depends_on('py-bokeh', type=('build', 'run'), when='+diagnostics')
depends_on('py-bokeh@1.0.0:', type=('build', 'run'), when='@2.0.0: +diagnostics')
# Requirements for dask.delayed
depends_on('py-cloudpickle@0.2.1:', type=('build', 'run'), when='@2,7.0: +delayed')
depends_on('py-cloudpickle@0.2.2:', type=('build', 'run'), when='@2.13.0: +delayed')
depends_on('py-toolz@0.7.2:', type=('build', 'run'), when='@0.8.1: +delayed')
depends_on('py-toolz@0.7.3:', type=('build', 'run'), when='@0.14.1: +delayed')
depends_on('py-toolz@0.8.2:', type=('build', 'run'), when='@2.13.0: +delayed')
# Support for YAML configuration files
depends_on('py-pyyaml', type=('build', 'run'), when='+yaml')
@property
def import_modules(self):
modules = ['dask']
if self.spec.satisfies('@0.9.0:'):
modules.append('dask.bytes')
if self.spec.satisfies('@:0.20.2'):
modules.append('dask.store')
if '+array' in self.spec:
modules.append('dask.array')
if '+bag' in self.spec:
modules.append('dask.bag')
if self.spec.satisfies('@:0.7.5 +distributed'):
modules.append('dask.distributed')
if '+dataframe' in self.spec:
modules.append('dask.dataframe')
if self.spec.satisfies('@0.8.2:'):
modules.append('dask.dataframe.tseries')
if self.spec.satisfies('@0.12.0:'):
modules.append('dask.dataframe.io')
if '+diagnostics' in self.spec:
modules.append('dask.diagnostics')
return modules
| 56.744048 | 111 | 0.584916 |
0e64d8059adcdcc5e280a38434e13289a75819ad | 87 | py | Python | haas/tests/compat.py | itziakos/haas | 93a2e886f66d7fb40f39305032cad6614fcc52a1 | [
"BSD-3-Clause"
] | 4 | 2017-10-10T06:45:35.000Z | 2021-02-27T09:44:16.000Z | haas/tests/compat.py | itziakos/haas | 93a2e886f66d7fb40f39305032cad6614fcc52a1 | [
"BSD-3-Clause"
] | 34 | 2015-02-24T17:04:15.000Z | 2017-01-05T12:35:14.000Z | haas/tests/compat.py | itziakos/haas | 93a2e886f66d7fb40f39305032cad6614fcc52a1 | [
"BSD-3-Clause"
] | 4 | 2018-03-05T19:05:19.000Z | 2019-12-11T08:42:22.000Z | try:
from unittest import mock # noqa
except ImportError:
import mock # noqa
| 17.4 | 37 | 0.689655 |
15730b993c708ebe347397c0edc36e778cad4dbb | 1,663 | py | Python | Tests/test_EMBL_unittest.py | amblina/biopython | 5045a7a3e86d5b32e0eaab941ab35daac86c59f8 | [
"PostgreSQL"
] | 3 | 2021-08-17T15:28:41.000Z | 2022-02-12T06:43:22.000Z | Tests/test_EMBL_unittest.py | amblina/biopython | 5045a7a3e86d5b32e0eaab941ab35daac86c59f8 | [
"PostgreSQL"
] | 32 | 2016-11-21T07:38:21.000Z | 2017-08-16T13:00:03.000Z | Tests/test_EMBL_unittest.py | amblina/biopython | 5045a7a3e86d5b32e0eaab941ab35daac86c59f8 | [
"PostgreSQL"
] | 8 | 2016-11-24T18:57:35.000Z | 2022-01-16T08:15:25.000Z | # Copyright 2015 by Kai Blin.
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
import unittest
import warnings
from os import path
from Bio import SeqIO
class EMBLTests(unittest.TestCase):
def test_embl_content_after_co(self):
"""Test an AssertionError is thrown by content after a CO line"""
def parse_content_after_co():
rec = SeqIO.read(path.join('EMBL', 'xx_after_co.embl'), 'embl')
self.assertRaises(AssertionError, parse_content_after_co)
try:
parse_content_after_co()
except AssertionError as e:
self.assertEqual(str(e), "Unexpected content after SQ or CO line: 'XX'")
else:
self.assertTrue(False, "Error message without explanation raised by content after CO line")
def test_embl_0_line(self):
"""Test SQ line with 0 length sequence"""
# Biopython 1.67 and older would parse this file with a warning:
# 'Expected sequence length 1740, found 1744 (TIR43YW1_CE).' and
# the coordinates 1740 added to the sequence as four extra letters.
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
rec = SeqIO.read(path.join('EMBL', 'embl_with_0_line.embl'), 'embl')
self.assertEqual(len(w), 0, "Unexpected parser warnings: " + "\n".join(str(warn.message) for warn in w))
self.assertEqual(len(rec), 1740)
if __name__ == "__main__":
runner = unittest.TextTestRunner(verbosity=2)
unittest.main(testRunner=runner)
| 38.674419 | 116 | 0.677691 |
1c1b841469504e5cc7702e81177d0526d85bac19 | 3,629 | py | Python | .history/Missions_to_Mars/scrape_mars_20200812112856.py | ermiasgelaye/web-scraping-challenge | f99c3436dfb0169595c46dae7733d90e21385cc6 | [
"ADSL"
] | null | null | null | .history/Missions_to_Mars/scrape_mars_20200812112856.py | ermiasgelaye/web-scraping-challenge | f99c3436dfb0169595c46dae7733d90e21385cc6 | [
"ADSL"
] | null | null | null | .history/Missions_to_Mars/scrape_mars_20200812112856.py | ermiasgelaye/web-scraping-challenge | f99c3436dfb0169595c46dae7733d90e21385cc6 | [
"ADSL"
] | 2 | 2020-11-02T08:12:16.000Z | 2021-05-17T21:45:42.000Z | from splinter import Browser
from bs4 import BeautifulSoup as bs
import pandas as pd
import time
import re
# This is for debugging
def savetofile(contents):
file = open('_temporary.txt',"w",encoding="utf-8")
file.write(contents)
file.close()
def scrape():
executable_path = {"executable_path": "chromedriver"}
browser = Browser("chrome", **executable_path, headless=False)
# NASA Mars News
url = 'https://mars.nasa.gov/news/'
browser.visit(url)
time.sleep(3)
html = browser.html
soup = bs(html, 'html.parser')
slides = soup.find_all('li', class_='slide')
content_title = slides[0].find('div', class_='content_title')
news_title = content_title.text.strip()
article_teaser_body = slides[0].find('div', class_='article_teaser_body')
news_p = article_teaser_body.text.strip()
# JPL Mars Space Images
base_url = 'https://www.jpl.nasa.gov'
url = base_url + '/spaceimages/?search=&category=Mars'
browser.visit(url)
time.sleep(1)
html = browser.html
soup = bs(html, 'html.parser')
featured_image_url = base_url + soup.find('a',class_='button fancybox')['data-fancybox-href']
# Mars Weather
url = 'https://twitter.com/marswxreport?lang=en'
browser.visit(url)
time.sleep(3)
weather_html = browser.html
soup = bs(weather_html, "html.parser")
# print(weathersoup.prettify())
mars_tweets = [soup.find_all('p', class_="TweetTextSize"), soup.find_all('span', class_="css-901oao css-16my406 r-1qd0xha r-ad9z0x r-bcqeeo r-qvutc0")]
mars_weather=[]
for tweets in mars_tweets:
mars_tweet = tweets
for tweet in mars_tweet:
if 'InSight' in tweet.text:
mars_weather = tweet.text
if tweet.a in tweet:
mars_weather = mars_weather.strip(tweet.a.text)
break
# Mars facts
url = 'https://space-facts.com/mars/'
browser.visit(url) # not necessary, but added for checking the operation
time.sleep(1)
dfs = pd.read_html(url)
for df in dfs:
try:
df = df.rename(columns={0: "Description", 1: "Value"})
df = df.set_index("Description")
marsfacts_html = df.to_html().replace('\n', '')
# df.to_html('marsfacts.html') # to save to a file to test
break
except:
continue
# Mars Hemispheres
base_url = 'https://astrogeology.usgs.gov'
url = base_url + '/search/results?q=hemisphere+enhanced&k1=target&v1=Mars'
browser.visit(url)
time.sleep(1)
html = browser.html
soup = bs(html, 'html.parser')
items = soup.find_all('div', class_='item')
urls = []
titles = []
for item in items:
urls.append(base_url + item.find('a')['href'])
titles.append(item.find('h3').text.strip())
img_urls = []
for oneurl in urls:
browser.visit(oneurl)
time.sleep(1)
html = browser.html
soup = bs(html, 'html.parser')
oneurl = base_url+soup.find('img',class_='wide-image')['src']
img_urls.append(oneurl)
hemisphere_image_urls = []
for i in range(len(titles)):
hemisphere_image_urls.append({'title':titles[i],'img_url':img_urls[i]})
# Assigning scraped data to a page
marspage = {}
marspage["news_title"] = news_title
marspage["news_p"] = news_p
marspage["featured_image_url"] = featured_image_url
marspage["mars_weather"] = mars_weather
marspage["marsfacts_html"] = marsfacts_html
marspage["hemisphere_image_urls"] = hemisphere_image_urls
return marspage
| 28.131783 | 155 | 0.63213 |
1d6017390c3c32c6b03e85d4ce8f8875cf229c0e | 3,708 | py | Python | pilot/control/payloads/eventservice.py | yesw2000/pilot2 | 96228c886e36913c141c4e95722dabb4b6733932 | [
"Apache-2.0"
] | null | null | null | pilot/control/payloads/eventservice.py | yesw2000/pilot2 | 96228c886e36913c141c4e95722dabb4b6733932 | [
"Apache-2.0"
] | null | null | null | pilot/control/payloads/eventservice.py | yesw2000/pilot2 | 96228c886e36913c141c4e95722dabb4b6733932 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# 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
#
# Authors:
# - Wen Guan, wen.guan@cern.ch, 2017-2018
# - Paul Nilsson, paul.nilsson@cern.ch, 2021
import os
import time
from pilot.common import exception
from pilot.control.payloads import generic
from pilot.eventservice.workexecutor.workexecutor import WorkExecutor
import logging
logger = logging.getLogger(__name__)
class Executor(generic.Executor):
def __init__(self, args, job, out, err, traces):
super(Executor, self).__init__(args, job, out, err, traces)
def run_payload(self, job, cmd, out, err):
"""
(add description)
:param job: job object.
:param cmd: (unused in ES mode)
:param out: stdout file object.
:param err: stderr file object.
:return:
"""
self.pre_setup(job)
# get the payload command from the user specific code
pilot_user = os.environ.get('PILOT_USER', 'atlas').lower()
user = __import__('pilot.user.%s.common' % pilot_user, globals(), locals(), [pilot_user], 0) # Python 2/3
self.post_setup(job)
self.utility_before_payload(job)
self.utility_with_payload(job)
try:
executable = user.get_payload_command(job)
except exception.PilotException:
logger.fatal('could not define payload command')
return None
logger.info("payload execution command: %s" % executable)
try:
payload = {'executable': executable, 'workdir': job.workdir, 'output_file': out, 'error_file': err, 'job': job}
logger.debug("payload: %s" % payload)
logger.info("Starting EventService WorkExecutor")
executor_type = self.get_executor_type()
executor = WorkExecutor(args=executor_type)
executor.set_payload(payload)
executor.start()
logger.info("EventService WorkExecutor started")
logger.info("ESProcess started with pid: %s" % executor.get_pid())
job.pid = executor.get_pid()
if job.pid:
job.pgrp = os.getpgid(job.pid)
self.utility_after_payload_started(job)
except Exception as e:
logger.error('could not execute: %s' % str(e))
return None
return executor
def get_executor_type(self):
"""
Get the executor type.
This is usually the 'generic' type, which means normal event service. It can also be 'raythena' if specified
in the Pilot options.
:return: executor type dictionary.
"""
# executor_type = 'hpo' if job.is_hpo else os.environ.get('PILOT_ES_EXECUTOR_TYPE', 'generic')
# return {'executor_type': executor_type}
return {'executor_type': os.environ.get('PILOT_ES_EXECUTOR_TYPE', 'generic')}
def wait_graceful(self, args, proc):
"""
(add description)
:param args:
:param proc:
:return:
"""
t1 = time.time()
while proc.is_alive():
if args.graceful_stop.is_set():
logger.debug("Graceful stop is set, stopping work executor")
proc.stop()
break
if time.time() > t1 + 300: # 5 minutes
logger.info("Process is still running")
t1 = time.time()
time.sleep(2)
while proc.is_alive():
time.sleep(2)
exit_code = proc.get_exit_code()
return exit_code
| 31.423729 | 123 | 0.607335 |
4e48b56268cd5de32e8a29e856002b3c0e6d3482 | 9,509 | py | Python | src/SGAN.py | PranavEranki/Semi-Supervised-GANs | 705c59a07bd1aeefeec86dfa85f9fcee78e5c78f | [
"MIT"
] | 1 | 2021-01-18T14:40:35.000Z | 2021-01-18T14:40:35.000Z | src/SGAN.py | PranavEranki/Semi-Supervised-GANs | 705c59a07bd1aeefeec86dfa85f9fcee78e5c78f | [
"MIT"
] | null | null | null | src/SGAN.py | PranavEranki/Semi-Supervised-GANs | 705c59a07bd1aeefeec86dfa85f9fcee78e5c78f | [
"MIT"
] | null | null | null |
# coding: utf-8
# In[25]:
import argparse
import os
import numpy as np
import math
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch
os.makedirs('images', exist_ok=True)
# In[2]:
'''
Set Defaults
'''
num_epochs = 200
batch_size = 64
num_classes = 10 # number of classes for dataset
lr = 0.0002
b1 = 0.5 # adam: decay of first order momentum of gradient
b2 = 0.999 # adam: decay of first order momentum of gradient
n_cpu = 8 # number of cpu threads to use during batch generation
latent_dim = 100 # dimensionality of the latent space
img_size = 32 # size of each image dimension
channels = 1 # number of output image channels
sample_interval = 400 # interval between image sampling
# In[3]:
# Set cuda
if torch.cuda.is_available():
cuda = True
else:
cuda = False
# In[4]:
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.zero_()
# In[5]:
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.label_emb = nn.Embedding(num_classes, latent_dim)
self.init_size = img_size // 4 # Initial size before upsampling
self.linear = nn.Sequential(
nn.Linear(latent_dim, 128*self.init_size**2),
)
self.conv1 = nn.Sequential(
nn.BatchNorm2d(128),
nn.Upsample(scale_factor=2),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128, 0.8),
nn.LeakyReLU(0.2, inplace=True),
nn.Upsample(scale_factor=2)
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=128, out_channels=64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64, 0.8),
nn.LeakyReLU(0.2, inplace=True)
)
self.conv3 = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=channels, kernel_size=3, stride=1, padding=1),
nn.Tanh()
)
def forward(self, noise):
out = self.linear(noise)
out = out.view(out.shape[0], 128, self.init_size, self.init_size)
img = self.conv1(out)
img = self.conv2(img)
img = self.conv3(img)
return img
# In[6]:
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=channels, out_channels=16, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
)
self.conv2 = nn.Sequential(
nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.BatchNorm2d(num_features=32, eps=0.8)
)
self.conv3 = nn.Sequential(
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.BatchNorm2d(num_features=64, eps=0.8)
)
self.conv4 = nn.Sequential(
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.BatchNorm2d(num_features=128, eps=0.8)
)
# The height and width of downsampled image
ds_size = img_size // 2**4
# Output layers
self.adv_layer = nn.Sequential(
nn.Linear(in_features=128*ds_size**2, out_features=1),
nn.Sigmoid()
)
self.aux_layer = nn.Sequential(
nn.Linear(in_features=128*ds_size**2, out_features=num_classes+1),
nn.Softmax()
)
def forward(self, img):
out = self.conv1(img)
out = self.conv2(out)
out = self.conv3(out)
out = self.conv4(out)
out = out.view(out.shape[0], -1)
validity = self.adv_layer(out)
label = self.aux_layer(out)
return validity, label
# In[7]:
# Loss functions
adversarial_loss = torch.nn.BCELoss()
auxiliary_loss = torch.nn.CrossEntropyLoss()
# In[8]:
# Initialize generator and discriminator
generator = Generator()
discriminator = Discriminator()
if cuda:
generator.cuda()
discriminator.cuda()
adversarial_loss.cuda()
auxiliary_loss.cuda()
# In[9]:
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
# In[10]:
# Configure DataLoader
DATA_FOLDER = './torch_data/MNIST'
def mnist_data():
compose = transforms.Compose([
transforms.Resize(img_size),
transforms.ToTensor(),
transforms.Normalize((.5, .5, .5), (.5, .5, .5))
])
out_dir = '{}/dataset'.format(DATA_FOLDER)
return datasets.MNIST(root=out_dir, train=True, transform=compose, download=True)
data = mnist_data()
dataloader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True)
# In[20]:
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=lr, betas=(b1, b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=lr, betas=(b1, b2))
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if cuda else torch.LongTensor
# In[19]:
# Defining ground-truth for real and fake images
def real_data_groundtruth(size):
'''
Variable containing ones, with shape = size, 1
'''
data = Variable(torch.ones(size, 1), requires_grad=False)
if torch.cuda.is_available():
return data.cuda()
return data
def fake_data_groundtruth(size):
'''
Variable containing zeros, with shape = size, 1
'''
data = Variable(torch.zeros(size, 1), requires_grad=False)
if torch.cuda.is_available():
return data.cuda()
return data
def fake_aux_groundtruth(size):
'''
Variable containing num_classes+1, with shape = size
'''
data = Variable(LongTensor(size).fill_(num_classes), requires_grad=False)
return data
# In[12]:
def noise(size):
n = Variable(torch.randn(size, latent_dim))
if torch.cuda.is_available():
return n.cuda()
else:
return n
# In[23]:
def train_discriminator(optimizer_D, real_imgs, fake_imgs, labels):
optimizer_D.zero_grad()
# Loss for real images
real_pred, real_aux = discriminator(real_imgs)
d_real_loss = (adversarial_loss(real_pred, valid) + auxiliary_loss(real_aux, labels)) / 2.0
# Loss for fake images
fake_pred, fake_aux = discriminator(fake_imgs)
d_fake_loss = (adversarial_loss(fake_pred, fake) + auxiliary_loss(fake_aux, fake_aux_gt)) / 2.0
# Total discriminator loss
d_loss = (d_real_loss + d_fake_loss) / 2.0
# Calculate discriminator accuracy
pred = np.concatenate([real_aux.data.cpu().numpy(), fake_aux.data.cpu().numpy()], axis=0)
gt = np.concatenate([labels.data.cpu().numpy(), fake_aux_gt.data.cpu().numpy()], axis=0)
d_acc = np.mean(np.argmax(pred, axis=1) == gt)
d_loss.backward()
optimizer_D.step()
return d_loss, d_acc
# In[13]:
def train_generator(optimizer_G, gen_imgs):
optimizer_G.zero_grad()
# Loss measures generator's ability to fool the discriminator
validity, _ = discriminator(gen_imgs)
g_loss = adversarial_loss(validity, valid)
g_loss.backward()
optimizer_G.step()
return g_loss
# In[26]:
'''
Start Training
'''
for epoch in range(num_epochs):
for i, (imgs, labels) in enumerate(dataloader):
batch_size_ = imgs.shape[0]
# Adversarial ground truths
valid = real_data_groundtruth(batch_size_)
fake = fake_data_groundtruth(batch_size_)
fake_aux_gt = fake_aux_groundtruth(batch_size_)
# Configure input
real_imgs = Variable(imgs.type(FloatTensor))
labels = Variable(labels.type(LongTensor))
###############################################
# Train Generator #
###############################################
gen_imgs = generator(noise(batch_size_))
g_loss = train_generator(optimizer_G, gen_imgs)
###############################################
# Train Discriminator #
###############################################
fake_imgs = generator(noise(batch_size_)).detach()
d_loss, d_acc = train_discriminator(optimizer_D, real_imgs, fake_imgs, labels)
# Display Progress
print ("[Epoch %d/%d] [Batch %d/%d] [D loss: %f, acc: %d%%] [G loss: %f]" % (epoch, num_epochs, i, len(dataloader),
d_loss.item(), 100 * d_acc,
g_loss.item()))
batches_done = epoch * len(dataloader) + i
if batches_done % sample_interval == 0:
save_image(gen_imgs.data[:25], 'images/%d.png' % batches_done, nrow=5, normalize=True)
# In[ ]:
| 28.133136 | 123 | 0.612157 |
780b04fad752e44bfbf8f8470f4b674f1838cb0a | 1,195 | py | Python | multiprocessing_queue.py | Bartoshko/python_playground | a9a609e6b90303b0b15af52f48c500573627a822 | [
"MIT"
] | null | null | null | multiprocessing_queue.py | Bartoshko/python_playground | a9a609e6b90303b0b15af52f48c500573627a822 | [
"MIT"
] | null | null | null | multiprocessing_queue.py | Bartoshko/python_playground | a9a609e6b90303b0b15af52f48c500573627a822 | [
"MIT"
] | null | null | null | from multiprocessing import Process, Queue
import time
def reader_proc(queue):
print('starting new read')
## Read from the queue; this will be spawned as a separate Process
while True:
msg = queue.get()
# Read from the queue and do nothing
if (msg == 'DONE'):
break
def writer(count, queue):
## Write to the queue
print('starting new writter')
for ii in range(0, count):
queue.put(ii) # Write 'count' numbers into the queue
queue.put('DONE')
if __name__ == '__main__':
pqueue = Queue() # writer() writes to pqueue from _this_ process
for count in [10 ** 5, 10 ** 6, 10 ** 5]:
### reader_proc() reads from pqueue as a separate process
reader_p = Process(target=reader_proc, args=((pqueue),))
reader_p.daemon = True
reader_p.start() # Launch reader_proc() as a separate python process
_start = time.time()
writer(count, pqueue) # Send a lot of stuff to reader()
reader_p.join() # Wait for the reader to finish
print("Sending {0} numbers to Queue() took {1} seconds"
.format(count, (time.time() - _start)))
print('finished')
| 33.194444 | 77 | 0.615063 |
c55d39c751718cdb4e63aa3da65bf7a16d16bbdf | 1,751 | py | Python | pomp/example_problems/doubleintegrator.py | Aand1/pyOptimalMotionPlanning | 5f06b4331149b86538e1ecfa7ccb9915c8cb510a | [
"Apache-2.0"
] | null | null | null | pomp/example_problems/doubleintegrator.py | Aand1/pyOptimalMotionPlanning | 5f06b4331149b86538e1ecfa7ccb9915c8cb510a | [
"Apache-2.0"
] | null | null | null | pomp/example_problems/doubleintegrator.py | Aand1/pyOptimalMotionPlanning | 5f06b4331149b86538e1ecfa7ccb9915c8cb510a | [
"Apache-2.0"
] | 1 | 2021-07-07T16:15:52.000Z | 2021-07-07T16:15:52.000Z | from OpenGL.GL import *
from geometric import *
from ..spaces.objective import *
from ..spaces.statespace import *
from ..spaces.configurationspace import *
from ..spaces.edgechecker import *
from ..spaces.metric import *
from ..planners.problem import PlanningProblem
class DoubleIntegratorVisualizer:
def __init__(self,workspace):
self.base = workspace
def toScreen(self,q):
return q[0],q[1]
def toState(self,x,y):
return (x,y,0,0)
def drawObstaclesGL(self):
self.base.drawObstaclesGL()
def drawVerticesGL(self,qs):
self.base.drawVerticesGL(qs)
def drawRobotGL(self,q):
glColor3f(0,0,1)
glPointSize(7.0)
self.drawVerticesGL([q])
l = 0.05
glBegin(GL_LINES)
glVertex2f(q[0],q[1])
glVertex2f(q[0]+l*q[2],q[1]+l*q[3])
glEnd()
def drawGoalGL(self,goal):
self.base.drawGoalGL(goal)
def drawInterpolatorGL(self,interpolator):
self.base.drawInterpolatorGL(interpolator)
def doubleIntegratorTest():
cspace = Geometric2DCSpace()
#cspace.addObstacle(Circle(0.5,0.4,0.39))
vspace = BoxConfigurationSpace([-1,-1],[1,1])
aspace = BoxConfigurationSpace([-5,-5],[5,5])
start = [0.06,0.25,0,0]
goal = [0.94,0.25,0,0]
objective = TimeObjectiveFunction()
goalRadius = 0.2
controlSpace = CVControlSpace(cspace,vspace,aspace,dt=0.05,dtmax=0.5)
return PlanningProblem(controlSpace,start,goal,
objective=objective,
visualizer=DoubleIntegratorVisualizer(cspace),
goalRadius = goalRadius,
euclidean = True)
| 30.189655 | 74 | 0.606511 |
8bb18bb68c796b4377cc238b3450fd493b581ee4 | 2,324 | py | Python | src/checks.py | DiscordLiz/salamander | 87e8dbddacd4d55672491685007237493295cf5a | [
"Apache-2.0"
] | null | null | null | src/checks.py | DiscordLiz/salamander | 87e8dbddacd4d55672491685007237493295cf5a | [
"Apache-2.0"
] | 1 | 2021-03-23T05:13:57.000Z | 2021-03-23T05:41:41.000Z | src/checks.py | DiscordLiz/salamander | 87e8dbddacd4d55672491685007237493295cf5a | [
"Apache-2.0"
] | null | null | null | # Copyright 2020-present Michael Hall
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from discord.ext import commands
from .bot import SalamanderContext
def owner_in_guild():
# prevents commands.is_owner()
# being mixed in requiring commands.guild_only() stacked on guild checks
async def predicate(ctx: SalamanderContext) -> bool:
if ctx.guild:
return await ctx.bot.is_owner(ctx.author)
return False
return commands.check(predicate)
def mod():
def predicate(ctx: SalamanderContext) -> bool:
if ctx.guild:
if ctx.guild.owner == ctx.author:
return True
return ctx.bot.privlevel_manager.member_is_mod(ctx.guild.id, ctx.author.id)
return False
return commands.check(predicate)
def guildowner():
def predicate(ctx: SalamanderContext) -> bool:
if ctx.guild:
return ctx.author == ctx.guild.owner
return False
return commands.check(predicate)
def admin():
def predicate(ctx: SalamanderContext) -> bool:
if ctx.guild:
if ctx.guild.owner == ctx.author:
return True
return ctx.bot.privlevel_manager.member_is_admin(
ctx.guild.id, ctx.author.id
)
return False
return commands.check(predicate)
def mod_or_perms(**perms):
return commands.check_any(
commands.has_guild_permissions(**perms), mod(), owner_in_guild()
)
def admin_or_perms(**perms):
return commands.check_any(
commands.has_guild_permissions(**perms), admin(), owner_in_guild()
)
def guildowner_or_perms(**perms):
return commands.check_any(
commands.has_guild_permissions(**perms), guildowner(), owner_in_guild()
)
| 27.666667 | 87 | 0.673838 |
b2277b4f72a74777c9f4a1b8015389e459391965 | 2,082 | py | Python | CTFd/forms/self.py | DevLuce/CTFd-Korean | 3e45d640c7cab9fd4d4d869e443334ae4fe97e22 | [
"Apache-2.0"
] | null | null | null | CTFd/forms/self.py | DevLuce/CTFd-Korean | 3e45d640c7cab9fd4d4d869e443334ae4fe97e22 | [
"Apache-2.0"
] | null | null | null | CTFd/forms/self.py | DevLuce/CTFd-Korean | 3e45d640c7cab9fd4d4d869e443334ae4fe97e22 | [
"Apache-2.0"
] | null | null | null | from flask import session
from wtforms import PasswordField, SelectField, StringField
from wtforms.fields.html5 import DateField, URLField
from CTFd.forms import BaseForm
from CTFd.forms.fields import SubmitField
from CTFd.forms.users import attach_custom_user_fields, build_custom_user_fields
from CTFd.utils.countries import SELECT_COUNTRIES_LIST
from CTFd.utils.user import get_current_user
def SettingsForm(*args, **kwargs):
class _SettingsForm(BaseForm):
# name = StringField("User Name")
name = StringField("유저 이름")
# email = StringField("Email")
email = StringField("이메일")
# password = PasswordField("Password")
password = PasswordField("비밀번호")
# confirm = PasswordField("Current Password")
confirm = PasswordField("현재 비밀번호")
# affiliation = StringField("Affiliation")
affiliation = StringField("소속")
# website = URLField("Website")
website = URLField("웹사이트")
# country = SelectField("Country", choices=SELECT_COUNTRIES_LIST)
country = SelectField("국가", choices=SELECT_COUNTRIES_LIST)
# submit = SubmitField("Submit")
submit = SubmitField("적용")
@property
def extra(self):
fields_kwargs = _SettingsForm.get_field_kwargs()
return build_custom_user_fields(
self,
include_entries=True,
fields_kwargs=fields_kwargs,
field_entries_kwargs={"user_id": session["id"]},
)
@staticmethod
def get_field_kwargs():
user = get_current_user()
field_kwargs = {"editable": True}
if user.filled_all_required_fields is False:
# Show all fields
field_kwargs = {}
return field_kwargs
field_kwargs = _SettingsForm.get_field_kwargs()
attach_custom_user_fields(_SettingsForm, **field_kwargs)
return _SettingsForm(*args, **kwargs)
class TokensForm(BaseForm):
expiration = DateField("Expiration")
submit = SubmitField("Generate")
| 35.288136 | 80 | 0.654659 |
f5661af2f7898698ec56ca1aceb2df3a18f18de2 | 14,510 | py | Python | lib/rucio/api/replica.py | arisfkiaras/rucio | 275793a04aa85f25bf84705a893ef18679bd305a | [
"Apache-2.0"
] | null | null | null | lib/rucio/api/replica.py | arisfkiaras/rucio | 275793a04aa85f25bf84705a893ef18679bd305a | [
"Apache-2.0"
] | null | null | null | lib/rucio/api/replica.py | arisfkiaras/rucio | 275793a04aa85f25bf84705a893ef18679bd305a | [
"Apache-2.0"
] | null | null | null | # Copyright 2013-2018 CERN for the benefit of the ATLAS collaboration.
#
# 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.
#
# Authors:
# - Vincent Garonne <vgaronne@gmail.com>, 2013-2016
# - Cedric Serfon <cedric.serfon@cern.ch>, 2014-2019
# - Thomas Beermann <thomas.beermann@cern.ch>, 2014
# - Mario Lassnig <mario.lassnig@cern.ch>, 2017-2019
# - Hannes Hansen <hannes.jakob.hansen@cern.ch>, 2019
# - Andrew Lister <andrew.lister@stfc.ac.uk>, 2019
#
# PY3K COMPATIBLE
from rucio.api import permission
from rucio.db.sqla.constants import BadFilesStatus
from rucio.core import replica
from rucio.core.rse import get_rse_id, get_rse_name
from rucio.common import exception
from rucio.common.schema import validate_schema
from rucio.common.types import InternalAccount, InternalScope
from rucio.common.utils import api_update_return_dict
def get_bad_replicas_summary(rse_expression=None, from_date=None, to_date=None):
"""
List the bad file replicas summary. Method used by the rucio-ui.
:param rse_expression: The RSE expression.
:param from_date: The start date.
:param to_date: The end date.
:param session: The database session in use.
"""
replicas = replica.get_bad_replicas_summary(rse_expression=rse_expression, from_date=from_date, to_date=to_date)
return [api_update_return_dict(r) for r in replicas]
def list_bad_replicas_status(state=BadFilesStatus.BAD, rse=None, younger_than=None, older_than=None, limit=None, list_pfns=False):
"""
List the bad file replicas history states. Method used by the rucio-ui.
:param state: The state of the file (SUSPICIOUS or BAD).
:param rse: The RSE name.
:param younger_than: datetime object to select bad replicas younger than this date.
:param older_than: datetime object to select bad replicas older than this date.
:param limit: The maximum number of replicas returned.
"""
rse_id = None
if rse is not None:
rse_id = get_rse_id(rse=rse)
replicas = replica.list_bad_replicas_status(state=state, rse_id=rse_id, younger_than=younger_than, older_than=older_than, limit=limit, list_pfns=list_pfns)
return [api_update_return_dict(r) for r in replicas]
def declare_bad_file_replicas(pfns, reason, issuer):
"""
Declare a list of bad replicas.
:param pfns: The list of PFNs.
:param reason: The reason of the loss.
:param issuer: The issuer account.
"""
kwargs = {}
if not permission.has_permission(issuer=issuer, action='declare_bad_file_replicas', kwargs=kwargs):
raise exception.AccessDenied('Account %s can not declare bad replicas' % (issuer))
issuer = InternalAccount(issuer)
replicas = replica.declare_bad_file_replicas(pfns=pfns, reason=reason, issuer=issuer, status=BadFilesStatus.BAD)
for k in list(replicas):
try:
rse = get_rse_name(rse_id=k)
replicas[rse] = replicas.pop(k)
except exception.RSENotFound:
pass
return replicas
def declare_suspicious_file_replicas(pfns, reason, issuer):
"""
Declare a list of bad replicas.
:param pfns: The list of PFNs.
:param reason: The reason of the loss.
:param issuer: The issuer account.
"""
kwargs = {}
if not permission.has_permission(issuer=issuer, action='declare_suspicious_file_replicas', kwargs=kwargs):
raise exception.AccessDenied('Account %s can not declare suspicious replicas' % (issuer))
issuer = InternalAccount(issuer)
replicas = replica.declare_bad_file_replicas(pfns=pfns, reason=reason, issuer=issuer, status=BadFilesStatus.SUSPICIOUS)
for k in list(replicas):
try:
rse = get_rse_name(rse_id=k)
replicas[rse] = replicas.pop(k)
except exception.RSENotFound:
pass
return replicas
def get_did_from_pfns(pfns, rse):
"""
Get the DIDs associated to a PFN on one given RSE
:param pfns: The list of PFNs.
:param rse: The RSE name.
:returns: A dictionary {pfn: {'scope': scope, 'name': name}}
"""
rse_id = get_rse_id(rse=rse)
replicas = replica.get_did_from_pfns(pfns=pfns, rse_id=rse_id)
for r in replicas:
for k in r.keys():
r[k]['scope'] = r[k]['scope'].external
yield r
def list_replicas(dids, schemes=None, unavailable=False, request_id=None,
ignore_availability=True, all_states=False, rse_expression=None,
client_location=None, domain=None, signature_lifetime=None,
resolve_archives=True, resolve_parents=False, issuer=None):
"""
List file replicas for a list of data identifiers.
:param dids: The list of data identifiers (DIDs).
:param schemes: A list of schemes to filter the replicas. (e.g. file, http, ...)
:param unavailable: Also include unavailable replicas in the list.
:param request_id: ID associated with the request for debugging.
:param all_states: Return all replicas whatever state they are in. Adds an extra 'states' entry in the result dictionary.
:param rse_expression: The RSE expression to restrict replicas on a set of RSEs.
:param client_location: Client location dictionary for PFN modification {'ip', 'fqdn', 'site'}
:param domain: The network domain for the call, either None, 'wan' or 'lan'. Compatibility fallback: None falls back to 'wan'.
:param signature_lifetime: If supported, in seconds, restrict the lifetime of the signed PFN.
:param resolve_archives: When set to True, find archives which contain the replicas.
:param resolve_parents: When set to True, find all parent datasets which contain the replicas.
:param issuer: The issuer account.
"""
validate_schema(name='r_dids', obj=dids)
# Allow selected authenticated users to retrieve signed URLs.
# Unauthenticated users, or permission-less users will get the raw URL without the signature.
sign_urls = False
if permission.has_permission(issuer=issuer, action='get_signed_url', kwargs={}):
sign_urls = True
for d in dids:
d['scope'] = InternalScope(d['scope'])
replicas = replica.list_replicas(dids=dids, schemes=schemes, unavailable=unavailable,
request_id=request_id,
ignore_availability=ignore_availability,
all_states=all_states, rse_expression=rse_expression,
client_location=client_location, domain=domain,
sign_urls=sign_urls, signature_lifetime=signature_lifetime,
resolve_archives=resolve_archives, resolve_parents=resolve_parents)
for rep in replicas:
# 'rses' and 'states' use rse_id as the key. This needs updating to be rse.
keys = ['rses', 'states']
for k in keys:
old_dict = rep.get(k, None)
if old_dict is not None:
new_dict = {}
for rse_id in old_dict:
rse = get_rse_name(rse_id=rse_id) if rse_id is not None else None
new_dict[rse] = old_dict[rse_id]
rep[k] = new_dict
rep['scope'] = rep['scope'].external
if 'parents' in rep:
new_parents = []
for p in rep['parents']:
scope, name = p.split(':')
scope = InternalScope(scope, fromExternal=False).external
new_parents.append('{}:{}'.format(scope, name))
rep['parents'] = new_parents
yield rep
def add_replicas(rse, files, issuer, ignore_availability=False):
"""
Bulk add file replicas.
:param rse: The RSE name.
:param files: The list of files.
:param issuer: The issuer account.
:param ignore_availability: Ignore the RSE blacklisting.
:returns: True is successful, False otherwise
"""
validate_schema(name='dids', obj=files)
rse_id = get_rse_id(rse=rse)
kwargs = {'rse': rse, 'rse_id': rse_id}
if not permission.has_permission(issuer=issuer, action='add_replicas', kwargs=kwargs):
raise exception.AccessDenied('Account %s can not add file replicas on %s' % (issuer, rse))
if not permission.has_permission(issuer=issuer, action='skip_availability_check', kwargs=kwargs):
ignore_availability = False
issuer = InternalAccount(issuer)
for f in files:
f['scope'] = InternalScope(f['scope'])
if 'account' in f:
f['account'] = InternalAccount(f['account'])
replica.add_replicas(rse_id=rse_id, files=files, account=issuer, ignore_availability=ignore_availability)
def delete_replicas(rse, files, issuer, ignore_availability=False):
"""
Bulk delete file replicas.
:param rse: The RSE name.
:param files: The list of files.
:param issuer: The issuer account.
:param ignore_availability: Ignore the RSE blacklisting.
:returns: True is successful, False otherwise
"""
validate_schema(name='r_dids', obj=files)
rse_id = get_rse_id(rse=rse)
kwargs = {'rse': rse, 'rse_id': rse_id}
if not permission.has_permission(issuer=issuer, action='delete_replicas', kwargs=kwargs):
raise exception.AccessDenied('Account %s can not delete file replicas on %s' % (issuer, rse))
if not permission.has_permission(issuer=issuer, action='skip_availability_check', kwargs=kwargs):
ignore_availability = False
for f in files:
f['scope'] = InternalScope(f['scope'])
replica.delete_replicas(rse_id=rse_id, files=files, ignore_availability=ignore_availability)
def update_replicas_states(rse, files, issuer):
"""
Update File replica information and state.
:param rse: The RSE name.
:param files: The list of files.
:param issuer: The issuer account.
"""
validate_schema(name='dids', obj=files)
rse_id = get_rse_id(rse=rse)
kwargs = {'rse': rse, 'rse_id': rse_id}
if not permission.has_permission(issuer=issuer, action='update_replicas_states', kwargs=kwargs):
raise exception.AccessDenied('Account %s can not update file replicas state on %s' % (issuer, rse))
replicas = []
for file in files:
rep = file
rep['rse_id'] = rse_id
rep['scope'] = InternalScope(rep['scope'])
replicas.append(rep)
replica.update_replicas_states(replicas=replicas)
def list_dataset_replicas(scope, name, deep=False):
"""
:param scope: The scope of the dataset.
:param name: The name of the dataset.
:param deep: Lookup at the file level.
:returns: A list of dict dataset replicas
"""
scope = InternalScope(scope)
replicas = replica.list_dataset_replicas(scope=scope, name=name, deep=deep)
for r in replicas:
r['scope'] = r['scope'].external
yield r
def list_dataset_replicas_vp(scope, name, deep=False):
"""
:param scope: The scope of the dataset.
:param name: The name of the dataset.
:param deep: Lookup at the file level.
:returns: If VP exists a list of dicts of sites, otherwise a list of dicts of dataset replicas
NOTICE: This is an RnD function and might change or go away at any time.
"""
scope = InternalScope(scope)
for r in replica.list_dataset_replicas_vp(scope=scope, name=name, deep=deep):
yield api_update_return_dict(r)
def list_datasets_per_rse(rse, filters=None, limit=None):
"""
:param scope: The scope of the dataset.
:param name: The name of the dataset.
:param filters: dictionary of attributes by which the results should be filtered.
:param limit: limit number.
:param session: Database session to use.
:returns: A list of dict dataset replicas
"""
rse_id = get_rse_id(rse=rse)
if 'scope' in filters:
filters['scope'] = InternalScope(filters['scope'])
for r in replica.list_datasets_per_rse(rse_id, filters=filters, limit=limit):
yield api_update_return_dict(r)
def add_bad_pfns(pfns, issuer, state, reason=None, expires_at=None):
"""
Add bad PFNs.
:param pfns: the list of new files.
:param issuer: The issuer account.
:param state: One of the possible states : BAD, SUSPICIOUS, TEMPORARY_UNAVAILABLE.
:param reason: A string describing the reason of the loss.
:param expires_at: Specify a timeout for the TEMPORARY_UNAVAILABLE replicas. None for BAD files.
:param session: The database session in use.
:returns: True is successful.
"""
kwargs = {'state': state}
if not permission.has_permission(issuer=issuer, action='add_bad_pfns', kwargs=kwargs):
raise exception.AccessDenied('Account %s can not declare bad PFNs' % (issuer))
issuer = InternalAccount(issuer)
return replica.add_bad_pfns(pfns=pfns, account=issuer, state=state, reason=reason, expires_at=expires_at)
def get_suspicious_files(rse_expression, younger_than=None, nattempts=None):
"""
List the list of suspicious files on a list of RSEs
:param rse_expression: The RSE expression where the suspicious files are located
:param younger_than: datetime object to select the suspicious replicas younger than this date.
:param nattempts: The number of time the replicas have been declared suspicious
"""
replicas = replica.get_suspicious_files(rse_expression=rse_expression, younger_than=younger_than, nattempts=nattempts)
return [api_update_return_dict(r) for r in replicas]
def set_tombstone(rse, scope, name, issuer):
"""
Sets a tombstone on one replica.
:param rse: name of the RSE.
:param scope: scope of the replica DID.
:param name: name of the replica DID.
:param issuer: The issuer account
"""
rse_id = get_rse_id(rse)
if not permission.has_permission(issuer=issuer, action='set_tombstone', kwargs={}):
raise exception.AccessDenied('Account %s can not set tombstones' % (issuer))
scope = InternalScope(scope)
replica.set_tombstone(rse_id, scope, name)
| 38.08399 | 159 | 0.68856 |
67be05e673335e54502248514101b477feb7a01b | 2,344 | py | Python | care/facility/api/viewsets/facility_capacity.py | MaharashtraStateInnovationSociety/care | 6e7794d2ecb08fa17f2fcea6a4bb0c829f8e48a2 | [
"MIT"
] | 1 | 2021-07-03T14:07:50.000Z | 2021-07-03T14:07:50.000Z | care/facility/api/viewsets/facility_capacity.py | albernsrya/care | d7c971371dd557953d8e35e23f9c4c36aa05facb | [
"MIT"
] | null | null | null | care/facility/api/viewsets/facility_capacity.py | albernsrya/care | d7c971371dd557953d8e35e23f9c4c36aa05facb | [
"MIT"
] | null | null | null | from dry_rest_permissions.generics import DRYPermissions
from rest_framework.decorators import action
from rest_framework.generics import get_object_or_404
from rest_framework.mixins import ListModelMixin
from rest_framework.permissions import IsAuthenticated
from care.facility.api.serializers.facility_capacity import (
FacilityCapacityHistorySerializer,
FacilityCapacitySerializer,
)
from care.facility.api.viewsets import FacilityBaseViewset
from care.facility.models import Facility, FacilityCapacity
from care.users.models import User
class FacilityCapacityViewSet(FacilityBaseViewset, ListModelMixin):
lookup_field = "external_id"
serializer_class = FacilityCapacitySerializer
queryset = FacilityCapacity.objects.filter(deleted=False)
permission_classes = (
IsAuthenticated,
DRYPermissions,
)
def get_queryset(self):
user = self.request.user
queryset = self.queryset.filter(facility__external_id=self.kwargs.get("facility_external_id"))
if user.is_superuser:
return queryset
elif self.request.user.user_type >= User.TYPE_VALUE_MAP["DistrictLabAdmin"]:
return queryset.filter(facility__district=user.district)
elif self.request.user.user_type >= User.TYPE_VALUE_MAP["StateLabAdmin"]:
return queryset.filter(facility__state=user.state)
return queryset.filter(facility__users__id__exact=user.id)
def get_object(self):
return get_object_or_404(self.get_queryset(), room_type=self.kwargs.get("external_id"))
def get_facility(self):
facility_qs = Facility.objects.filter(external_id=self.kwargs.get("facility_external_id"))
if not self.request.user.is_superuser:
facility_qs.filter(users__id__exact=self.request.user.id)
return get_object_or_404(facility_qs)
def perform_create(self, serializer):
serializer.save(facility=self.get_facility())
@action(detail=True, methods=["get"])
def history(self, request, *args, **kwargs):
obj = self.get_object()
page = self.paginate_queryset(obj.history.all())
model = obj.history.__dict__["model"]
serializer = FacilityCapacityHistorySerializer(model, page, many=True)
serializer.is_valid()
return self.get_paginated_response(serializer.data)
| 41.122807 | 102 | 0.74744 |
00979c408a0c2ef2f26556bad3f92bd55cf409df | 1,548 | py | Python | 095-solano/results/parse_solano_pres.py | worace/california-2016-election-precinct-maps | 39e9a6e797aca1b5b5f5129294807dfadb5a795d | [
"MIT"
] | 82 | 2016-12-30T02:07:31.000Z | 2022-02-26T00:39:38.000Z | 095-solano/results/parse_solano_pres.py | worace/california-2016-election-precinct-maps | 39e9a6e797aca1b5b5f5129294807dfadb5a795d | [
"MIT"
] | 3 | 2017-01-16T19:12:31.000Z | 2017-04-03T03:07:29.000Z | 095-solano/results/parse_solano_pres.py | worace/california-2016-election-precinct-maps | 39e9a6e797aca1b5b5f5129294807dfadb5a795d | [
"MIT"
] | 29 | 2017-01-02T08:45:30.000Z | 2021-11-17T15:19:31.000Z | import sys
import os
import re
import csv
filepath = sys.argv[1]
if not os.path.exists('results/'):
os.makedirs('results/')
f = open(filepath,'r')
precincts = f.read().split('\n\n')
headers = ['pct16','pres_clinton','pres_trump','pres_johnson','pres_stein','pres_lariva','pres_other']
output = open('results/%s.csv' % filepath.replace('.txt',''),'w')
csvwriter = csv.writer(output)
csvwriter.writerow(headers)
for precinct in precincts:
# print precincts
row = []
pct16 = re.search(r'(?:Precinct\n.+)(\d{5})',precinct,flags=re.MULTILINE).group(1)
row.append(pct16)
clintonline = re.search(r'.*CLINTON .+',precinct).group(0)
# print yesline
clinton = re.split(r'\t',clintonline)[1]
row.append(clinton)
trumpline = re.search(r'.*TRUMP .+',precinct).group(0)
# print trumpline
trump = re.split(r'\t',trumpline)[1]
row.append(trump)
johnsonline = re.search(r'.*JOHNSON .+',precinct).group(0)
# print johnsonline
johnson = re.split(r'\t',johnsonline)[1]
row.append(johnson)
steinline = re.search(r'.*STEIN .+',precinct).group(0)
# print steinline
stein = re.split(r'\t',steinline)[1]
row.append(stein)
larivaline = re.search(r'.*LA RIVA .+',precinct).group(0)
# print larivaline
lariva = re.split(r'\t',larivaline)[1]
row.append(lariva)
otherline = re.search(r'.*WRITE-IN .+',precinct).group(0)
# print otherline
other = re.split(r'\t',otherline)[1]
row.append(other)
csvwriter.writerow(row)
f.close()
output.close() | 26.689655 | 102 | 0.644703 |
0d138a1cf2a97e16b74b964687af6b0f24e4faa1 | 6,018 | py | Python | ibis/backends/pandas/__init__.py | gforsyth/ibis | 25db64c5afe18a21e60a999d25a741b32f7b2c3e | [
"Apache-2.0"
] | null | null | null | ibis/backends/pandas/__init__.py | gforsyth/ibis | 25db64c5afe18a21e60a999d25a741b32f7b2c3e | [
"Apache-2.0"
] | null | null | null | ibis/backends/pandas/__init__.py | gforsyth/ibis | 25db64c5afe18a21e60a999d25a741b32f7b2c3e | [
"Apache-2.0"
] | null | null | null | from __future__ import annotations
import importlib
from typing import Any, MutableMapping
import pandas as pd
from pydantic import Field
import ibis.common.exceptions as com
import ibis.config
import ibis.expr.operations as ops
import ibis.expr.schema as sch
import ibis.expr.types as ir
from ibis.backends.base import BaseBackend
from ibis.backends.pandas.client import (
PandasDatabase,
PandasTable,
ibis_schema_to_pandas,
)
class BasePandasBackend(BaseBackend):
"""
Base class for backends based on pandas.
"""
name = "pandas"
backend_table_type = pd.DataFrame
class Options(ibis.config.BaseModel):
enable_trace: bool = Field(
default=False,
description="Enable tracing for execution.",
)
def do_connect(
self,
dictionary: MutableMapping[str, pd.DataFrame],
) -> None:
"""Construct a client from a dictionary of pandas DataFrames.
Parameters
----------
dictionary
Mutable mapping of string table names to pandas DataFrames.
Examples
--------
>>> import ibis
>>> ibis.pandas.connect({"t": pd.DataFrame({"a": [1, 2, 3]})})
"""
# register dispatchers
from ibis.backends.pandas import execution # noqa F401
from ibis.backends.pandas import udf # noqa F401
self.dictionary = dictionary
self.schemas: MutableMapping[str, sch.Schema] = {}
def from_dataframe(
self,
df: pd.DataFrame,
name: str = 'df',
client: BasePandasBackend | None = None,
) -> ir.Table:
"""Construct an ibis table from a pandas DataFrame.
Parameters
----------
df
A pandas DataFrame
name
The name of the pandas DataFrame
client
Client dictionary will be mutated with the name of the DataFrame,
if not provided a new client is created
Returns
-------
Table
A table expression
"""
if client is None:
return self.connect({name: df}).table(name)
client.dictionary[name] = df
return client.table(name)
@property
def version(self) -> str:
return pd.__version__
@property
def current_database(self):
raise NotImplementedError('pandas backend does not support databases')
def list_databases(self, like=None):
raise NotImplementedError('pandas backend does not support databases')
def list_tables(self, like=None, database=None):
return self._filter_with_like(list(self.dictionary.keys()), like)
def table(self, name: str, schema: sch.Schema = None):
df = self.dictionary[name]
schema = sch.infer(df, schema=schema or self.schemas.get(name, None))
return self.table_class(name, schema, self).to_expr()
def database(self, name=None):
return self.database_class(name, self)
def load_data(self, table_name, obj, **kwargs):
# kwargs is a catch all for any options required by other backends.
self.dictionary[table_name] = obj
def get_schema(self, table_name, database=None):
schemas = self.schemas
try:
schema = schemas[table_name]
except KeyError:
schemas[table_name] = schema = sch.infer(
self.dictionary[table_name]
)
return schema
def compile(self, expr, *args, **kwargs):
return expr
def create_table(self, table_name, obj=None, schema=None):
"""Create a table."""
if obj is None and schema is None:
raise com.IbisError('Must pass expr or schema')
if obj is not None:
if not self._supports_conversion(obj):
raise com.BackendConversionError(
f"Unable to convert {obj.__class__} object "
f"to backend type: {self.__class__.backend_table_type}"
)
df = self._convert_object(obj)
else:
pandas_schema = self._convert_schema(schema)
dtypes = dict(pandas_schema)
df = self._from_pandas(
pd.DataFrame(columns=dtypes.keys()).astype(dtypes)
)
self.dictionary[table_name] = df
if schema is not None:
self.schemas[table_name] = schema
@classmethod
def _supports_conversion(cls, obj: Any) -> bool:
return True
@staticmethod
def _convert_schema(schema: sch.Schema):
return ibis_schema_to_pandas(schema)
@staticmethod
def _from_pandas(df: pd.DataFrame) -> pd.DataFrame:
return df
@classmethod
def _convert_object(cls, obj: Any) -> Any:
return cls.backend_table_type(obj)
@classmethod
def has_operation(cls, operation: type[ops.Value]) -> bool:
execution = importlib.import_module(
f"ibis.backends.{cls.name}.execution"
)
execute_node = execution.execute_node
op_classes = {op for op, *_ in execute_node.funcs.keys()}
return operation in op_classes or any(
issubclass(operation, op_impl)
for op_impl in op_classes
if issubclass(op_impl, ops.Value)
)
class Backend(BasePandasBackend):
name = 'pandas'
database_class = PandasDatabase
table_class = PandasTable
def execute(self, query, params=None, limit='default', **kwargs):
from ibis.backends.pandas.core import execute_and_reset
if limit != 'default':
raise ValueError(
'limit parameter to execute is not yet implemented in the '
'pandas backend'
)
if not isinstance(query, ir.Expr):
raise TypeError(
"`query` has type {!r}, expected ibis.expr.types.Expr".format(
type(query).__name__
)
)
return execute_and_reset(query, params=params, **kwargs)
| 29.940299 | 78 | 0.608175 |
40a48c20fde253a8d8f2a92d818f9e294c299a74 | 6,386 | py | Python | tensorflow_probability/python/bijectors/sigmoid_test.py | jakee417/probability-1 | ae7117f37ac441bc7a888167ea23e5e620c5bcde | [
"Apache-2.0"
] | 3,670 | 2018-02-14T03:29:40.000Z | 2022-03-30T01:19:52.000Z | tensorflow_probability/python/bijectors/sigmoid_test.py | jakee417/probability-1 | ae7117f37ac441bc7a888167ea23e5e620c5bcde | [
"Apache-2.0"
] | 1,395 | 2018-02-24T02:28:49.000Z | 2022-03-31T16:12:06.000Z | tensorflow_probability/python/bijectors/sigmoid_test.py | jakee417/probability-1 | ae7117f37ac441bc7a888167ea23e5e620c5bcde | [
"Apache-2.0"
] | 1,135 | 2018-02-14T01:51:10.000Z | 2022-03-28T02:24:11.000Z | # Copyright 2018 The TensorFlow Probability Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Sigmoid Tests."""
# Dependency imports
from absl.testing import parameterized
import numpy as np
from scipy import special
import tensorflow.compat.v2 as tf
from tensorflow_probability.python import bijectors as tfb
from tensorflow_probability.python.bijectors import bijector_test_util
from tensorflow_probability.python.internal import test_util
@test_util.test_all_tf_execution_regimes
class SigmoidBijectorTest(test_util.TestCase):
"""Tests correctness of the Y = g(X) = (1 + exp(-X))^-1 transformation."""
def testBijector(self):
self.assertStartsWith(tfb.Sigmoid().name, 'sigmoid')
x = np.linspace(-10., 10., 100).reshape([2, 5, 10]).astype(np.float32)
y = special.expit(x)
ildj = -np.log(y) - np.log1p(-y)
bijector = tfb.Sigmoid()
self.assertAllClose(
y, self.evaluate(bijector.forward(x)), atol=0., rtol=1e-2)
self.assertAllClose(
x, self.evaluate(bijector.inverse(y)), atol=0., rtol=1e-4)
self.assertAllClose(
ildj,
self.evaluate(bijector.inverse_log_det_jacobian(
y, event_ndims=0)), atol=0., rtol=1e-6)
self.assertAllClose(
-ildj,
self.evaluate(bijector.forward_log_det_jacobian(
x, event_ndims=0)), atol=0., rtol=1e-4)
def testScalarCongruency(self):
bijector_test_util.assert_scalar_congruency(
tfb.Sigmoid(), lower_x=-7., upper_x=7., eval_func=self.evaluate,
rtol=.1)
def testBijectiveAndFinite(self):
x = np.linspace(-100., 100., 100).astype(np.float32)
eps = 1e-3
y = np.linspace(eps, 1. - eps, 100).astype(np.float32)
bijector_test_util.assert_bijective_and_finite(
tfb.Sigmoid(), x, y, eval_func=self.evaluate, event_ndims=0, atol=0.,
rtol=1e-4)
@test_util.test_all_tf_execution_regimes
class ShiftedScaledSigmoidBijectorTest(test_util.TestCase):
"""Tests correctness of Sigmoid with `low` and `high` parameters set."""
def testBijector(self):
low = np.array([[-3.], [0.], [5.]]).astype(np.float32)
high = 12.
bijector = tfb.Sigmoid(low=low, high=high, validate_args=True)
equivalent_bijector = tfb.Chain([
tfb.Shift(shift=low), tfb.Scale(scale=high-low), tfb.Sigmoid()])
x = [[[1., 2., -5., -0.3]]]
y = self.evaluate(equivalent_bijector.forward(x))
self.assertAllClose(y, self.evaluate(bijector.forward(x)))
self.assertAllClose(
x, self.evaluate(bijector.inverse(y)[..., :1, :]), rtol=1e-5)
self.assertAllClose(
self.evaluate(equivalent_bijector.inverse_log_det_jacobian(
y, event_ndims=1)),
self.evaluate(bijector.inverse_log_det_jacobian(
y, event_ndims=1)),
rtol=1e-5)
self.assertAllClose(
self.evaluate(equivalent_bijector.forward_log_det_jacobian(
x, event_ndims=1)),
self.evaluate(bijector.forward_log_det_jacobian(
x, event_ndims=1)))
def testNumericallySuperiorToEquivalentChain(self):
x = np.array([-5., 3., 17., 23.]).astype(np.float32)
low = -0.08587775
high = 0.12498104
bijector = tfb.Sigmoid(low=low, high=high, validate_args=True)
equivalent_bijector = tfb.Chain([
tfb.Shift(shift=low), tfb.Scale(scale=high-low), tfb.Sigmoid()])
self.assertAllLessEqual(self.evaluate(bijector.forward(x)), high)
# The mathematically equivalent `Chain` bijector can return values greater
# than the intended upper bound of `high`.
self.assertTrue(
(self.evaluate(equivalent_bijector.forward(x)) > high).any())
def testScalarCongruency(self):
low = -2.
high = 5.
bijector = tfb.Sigmoid(low=low, high=high, validate_args=True)
bijector_test_util.assert_scalar_congruency(
bijector, lower_x=-5., upper_x=3.5, eval_func=self.evaluate,
rtol=0.05)
def testBijectiveAndFinite(self):
low = -5.
high = 8.
bijector = tfb.Sigmoid(low=low, high=high, validate_args=True)
x = np.linspace(-10, 10, num=100).astype(np.float32)
eps = 1e-6
y = np.linspace(low + eps, high - eps, num=100).astype(np.float32)
bijector_test_util.assert_bijective_and_finite(
bijector, x, y, eval_func=self.evaluate, event_ndims=0)
def testAssertHighGtLow(self):
low = np.array([1., 1., 1.], dtype=np.float32)
high = np.array([1., 2., 3.], dtype=np.float32)
with self.assertRaisesOpError('not defined when `low` >= `high`'):
bijector = tfb.Sigmoid(low=low, high=high, validate_args=True)
self.evaluate(bijector.forward(3.))
def testEdgeCaseRequiringClipping(self):
np.set_printoptions(floatmode='unique', precision=None)
lo = np.float32(0.010489981)
hi = test_util.floats_near(
0.010499111, 100, dtype=np.float32)[:, np.newaxis]
self.assertAllEqual([100, 1], hi.shape)
xs = test_util.floats_near(9.814646, 100, dtype=np.float32)
bijector = tfb.Sigmoid(low=lo, high=hi, validate_args=True)
answers = bijector.forward(xs)
self.assertAllEqual([100, 100], answers.shape)
for ans1, hi1 in zip(self.evaluate(answers), hi):
self.assertAllLessEqual(ans1, hi1)
@parameterized.named_parameters(
('32bitGraph', np.float32, False),
('64bitGraph', np.float64, False),
('32bitXLA', np.float32, True),
('64bitXLA', np.float64, True),
)
@test_util.numpy_disable_gradient_test
def testLeftTail(self, dtype, do_compile):
x = np.linspace(-50., -8., 1000).astype(dtype)
@tf.function(autograph=False, jit_compile=do_compile)
def fn(x):
return tf.math.log(tfb.Sigmoid().forward(x))
vals = fn(x)
true_vals = -np.log1p(np.exp(-x))
self.assertAllClose(true_vals, self.evaluate(vals), atol=1e-3)
if __name__ == '__main__':
test_util.main()
| 37.127907 | 78 | 0.67695 |
8e00a0e2d2eb46bee20f22ac8df97c3147301d3a | 264 | py | Python | twitteruser/models.py | DeanNevins/twitterclone | e713627f352f8a7d34b493384fbc86818db841c6 | [
"MIT"
] | null | null | null | twitteruser/models.py | DeanNevins/twitterclone | e713627f352f8a7d34b493384fbc86818db841c6 | [
"MIT"
] | null | null | null | twitteruser/models.py | DeanNevins/twitterclone | e713627f352f8a7d34b493384fbc86818db841c6 | [
"MIT"
] | null | null | null | from django.db import models
from django.contrib.auth.models import AbstractUser
# Create your models here.
class TwitterUser(AbstractUser):
following = models.ManyToManyField('self', symmetrical=False)
def __str__(self):
return self.username
| 20.307692 | 65 | 0.753788 |
dbcb97ec3a237a5ca2fa4b9ae0433549654d3eca | 32,747 | py | Python | archive/cl_examples_sparsity.py | DMIU-ShELL/deeprl-shell | a7845ab1c4967ba2af9486625086c3d0b176d293 | [
"Apache-2.0"
] | null | null | null | archive/cl_examples_sparsity.py | DMIU-ShELL/deeprl-shell | a7845ab1c4967ba2af9486625086c3d0b176d293 | [
"Apache-2.0"
] | null | null | null | archive/cl_examples_sparsity.py | DMIU-ShELL/deeprl-shell | a7845ab1c4967ba2af9486625086c3d0b176d293 | [
"Apache-2.0"
] | null | null | null | #######################################################################
# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
# Permission given to modify the code as long as you keep this #
# declaration at the top #
#######################################################################
'''
continual learning experiments with weight preservation (consolidation) in RL
'''
import json
import copy
import shutil
import matplotlib
matplotlib.use("Pdf")
from deep_rl import *
import os
import argparse
#os.environ["CUDA_VISIBLE_DEVICES"]="0"
## ppo
def ppo_ctgraph_cl(name, env_config_path=None): # no sparsity, no consolidation (pure baseline)
config = Config()
config.env_name = name
config.env_config_path = env_config_path
config.lr = 0.00015
config.cl_preservation = 'baseline'
config.seed = 8379
random_seed(config.seed)
exp_id = ''
log_name = name + '-ppo' + '-' + config.cl_preservation + exp_id
config.log_dir = get_default_log_dir(log_name)
config.num_workers = 16
assert env_config_path is not None, '`env_config_path` should be set for the CTgraph environnent'
task_fn = lambda log_dir: CTgraphFlatObs(name, env_config_path=env_config_path, log_dir=log_dir)
config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers, log_dir=config.log_dir)
config.eval_task_fn = task_fn
config.optimizer_fn = lambda params, lr: torch.optim.RMSprop(params, lr=lr)
config.network_fn = lambda state_dim, action_dim, label_dim: CategoricalActorCriticNet_CL(
state_dim, action_dim, label_dim,
phi_body=FCBody_CL(state_dim, task_label_dim=label_dim, hidden_units=(200, 200, 200)),
actor_body=DummyBody_CL(200),
critic_body=DummyBody_CL(200))
config.policy_fn = SamplePolicy
config.state_normalizer = ImageNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.99
config.entropy_weight = 0.75
config.rollout_length = 7
config.optimization_epochs = 4
config.num_mini_batches = 4
config.ppo_ratio_clip = 0.1
config.iteration_log_interval = 100
config.gradient_clip = 5
config.max_steps = int(5.6e4) * 2 + 1 #int(5.6e4)+1 # note, max steps per task
config.evaluation_episodes = 10
config.logger = get_logger(log_dir=config.log_dir)
config.cl_requires_task_label = True
config.cl_num_tasks = 4
agent = PPOAgentBaseline(config)
config.agent_name = agent.__class__.__name__
tasks = agent.config.cl_tasks_info
#tasks = [tasks[0], tasks[3]]
#config.cl_num_tasks = len(tasks)
config.cl_num_learn_blocks = 1
shutil.copy(env_config_path, config.log_dir + '/env_config.json')
with open('{0}/tasks_info.bin'.format(config.log_dir), 'wb') as f:
pickle.dump(tasks, f)
run_iterations_cl(agent, tasks)
# save config
with open('{0}/config.json'.format(config.log_dir), 'w') as f:
dict_config = vars(config)
for k in dict_config.keys():
if not isinstance(dict_config[k], int) \
and not isinstance(dict_config[k], float) and dict_config[k] is not None:
dict_config[k] = str(dict_config[k])
json.dump(dict_config, f)
def ppo_ctgraph_cl_l1_weights(name, env_config_path=None):
config = Config()
config.env_name = name
config.env_config_path = env_config_path
config.lr = 0.00015
config.cl_preservation = 'baseline'
config.seed = 8379
random_seed(config.seed)
exp_id = '-l1-weights'
log_name = name + '-ppo' + '-' + config.cl_preservation + exp_id
config.log_dir = get_default_log_dir(log_name)
config.num_workers = 16
assert env_config_path is not None, '`env_config_path` should be set for the CTgraph environnent'
task_fn = lambda log_dir: CTgraphFlatObs(name, env_config_path=env_config_path, log_dir=log_dir)
config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers, log_dir=config.log_dir)
config.eval_task_fn = task_fn
config.optimizer_fn = lambda params, lr: torch.optim.RMSprop(params, lr=lr)
config.network_fn = lambda state_dim, action_dim, label_dim: CategoricalActorCriticNet_CL(
state_dim, action_dim, label_dim,
phi_body=FCBody_CL(state_dim, task_label_dim=label_dim, hidden_units=(200, 200, 200)),
actor_body=DummyBody_CL(200),
critic_body=DummyBody_CL(200))
config.policy_fn = SamplePolicy
config.state_normalizer = ImageNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.99
config.entropy_weight = 0.75
config.rollout_length = 7
config.optimization_epochs = 4
config.num_mini_batches = 4
config.ppo_ratio_clip = 0.1
config.iteration_log_interval = 100
config.gradient_clip = 5
config.max_steps = int(5.6e4) * 6 + 1 #int(5.6e4)+1 # note, max steps per task
config.evaluation_episodes = 10
config.logger = get_logger(log_dir=config.log_dir)
config.cl_requires_task_label = True
config.reg_loss_coeff = 1e-4
config.cl_num_tasks = 4
agent = PPOAgentBaselineL1Weights(config)
config.agent_name = agent.__class__.__name__
tasks = agent.config.cl_tasks_info
tasks = [tasks[0], tasks[3]]
config.cl_num_tasks = len(tasks)
config.cl_num_learn_blocks = 3
shutil.copy(env_config_path, config.log_dir + '/env_config.json')
with open('{0}/tasks_info.bin'.format(config.log_dir), 'wb') as f:
pickle.dump(tasks, f)
run_iterations_cl(agent, tasks)
# save config
with open('{0}/config.json'.format(config.log_dir), 'w') as f:
dict_config = vars(config)
for k in dict_config.keys():
if not isinstance(dict_config[k], int) \
and not isinstance(dict_config[k], float) and dict_config[k] is not None:
dict_config[k] = str(dict_config[k])
json.dump(dict_config, f)
def ppo_ctgraph_cl_l2_weights(name, env_config_path=None):
config = Config()
config.env_name = name
config.env_config_path = env_config_path
config.lr = 0.00015
config.cl_preservation = 'baseline'
config.seed = 8379
random_seed(config.seed)
exp_id = '-l2-weights'
log_name = name + '-ppo' + '-' + config.cl_preservation + exp_id
config.log_dir = get_default_log_dir(log_name)
config.num_workers = 16
assert env_config_path is not None, '`env_config_path` should be set for the CTgraph environnent'
task_fn = lambda log_dir: CTgraphFlatObs(name, env_config_path=env_config_path, log_dir=log_dir)
config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers, log_dir=config.log_dir)
config.eval_task_fn = task_fn
config.optimizer_fn = lambda params, lr: torch.optim.RMSprop(params, lr=lr)
config.network_fn = lambda state_dim, action_dim, label_dim: CategoricalActorCriticNet_CL(
state_dim, action_dim, label_dim,
phi_body=FCBody_CL(state_dim, task_label_dim=label_dim, hidden_units=(200, 200, 200)),
actor_body=DummyBody_CL(200),
critic_body=DummyBody_CL(200))
config.policy_fn = SamplePolicy
config.state_normalizer = ImageNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.99
config.entropy_weight = 0.75
config.rollout_length = 7
config.optimization_epochs = 4
config.num_mini_batches = 4
config.ppo_ratio_clip = 0.1
config.iteration_log_interval = 100
config.gradient_clip = 5
config.max_steps = int(5.6e4) * 6 + 1 #int(5.6e4)+1 # note, max steps per task
config.evaluation_episodes = 10
config.logger = get_logger(log_dir=config.log_dir)
config.cl_requires_task_label = True
config.reg_loss_coeff = 1e-4
config.cl_num_tasks = 4
agent = PPOAgentBaselineL2Weights(config)
config.agent_name = agent.__class__.__name__
tasks = agent.config.cl_tasks_info
tasks = [tasks[0], tasks[3]]
config.cl_num_tasks = len(tasks)
config.cl_num_learn_blocks = 3
shutil.copy(env_config_path, config.log_dir + '/env_config.json')
with open('{0}/tasks_info.bin'.format(config.log_dir), 'wb') as f:
pickle.dump(tasks, f)
run_iterations_cl(agent, tasks)
# save config
with open('{0}/config.json'.format(config.log_dir), 'w') as f:
dict_config = vars(config)
for k in dict_config.keys():
if not isinstance(dict_config[k], int) \
and not isinstance(dict_config[k], float) and dict_config[k] is not None:
dict_config[k] = str(dict_config[k])
json.dump(dict_config, f)
def ppo_ctgraph_cl_l1_act(name, env_config_path=None):
config = Config()
config.env_name = name
config.env_config_path = env_config_path
config.lr = 0.00015
config.cl_preservation = 'baseline'
config.seed = 8379
random_seed(config.seed)
exp_id = '-l1-act'
log_name = name + '-ppo' + '-' + config.cl_preservation + exp_id
config.log_dir = get_default_log_dir(log_name)
config.num_workers = 16
assert env_config_path is not None, '`env_config_path` should be set for the CTgraph environnent'
task_fn = lambda log_dir: CTgraphFlatObs(name, env_config_path=env_config_path, log_dir=log_dir)
config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers, log_dir=config.log_dir)
config.eval_task_fn = task_fn
config.optimizer_fn = lambda params, lr: torch.optim.RMSprop(params, lr=lr)
config.network_fn = lambda state_dim, action_dim, label_dim: CategoricalActorCriticNet_CL(
state_dim, action_dim, label_dim,
phi_body=FCBody_CL(state_dim, task_label_dim=label_dim, hidden_units=(200, 200, 200)),
actor_body=DummyBody_CL(200),
critic_body=DummyBody_CL(200))
config.policy_fn = SamplePolicy
config.state_normalizer = ImageNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.99
config.entropy_weight = 0.75
config.rollout_length = 7
config.optimization_epochs = 4
config.num_mini_batches = 4
config.ppo_ratio_clip = 0.1
config.iteration_log_interval = 100
config.gradient_clip = 5
config.max_steps = int(5.6e4) * 6 + 1 #int(5.6e4)+1 # note, max steps per task
config.evaluation_episodes = 10
config.logger = get_logger(log_dir=config.log_dir)
config.cl_requires_task_label = True
config.reg_loss_coeff = 1e-4
config.cl_num_tasks = 4
agent = PPOAgentBaselineL1Act(config)
config.agent_name = agent.__class__.__name__
tasks = agent.config.cl_tasks_info
#tasks = [tasks[0], tasks[3]]
#config.cl_num_tasks = len(tasks)
config.cl_num_learn_blocks = 1
shutil.copy(env_config_path, config.log_dir + '/env_config.json')
with open('{0}/tasks_info.bin'.format(config.log_dir), 'wb') as f:
pickle.dump(tasks, f)
run_iterations_cl(agent, tasks)
# save config
with open('{0}/config.json'.format(config.log_dir), 'w') as f:
dict_config = vars(config)
for k in dict_config.keys():
if not isinstance(dict_config[k], int) \
and not isinstance(dict_config[k], float) and dict_config[k] is not None:
dict_config[k] = str(dict_config[k])
json.dump(dict_config, f)
def ppo_ctgraph_cl_l2_act(name, env_config_path=None):
config = Config()
config.env_name = name
config.env_config_path = env_config_path
config.lr = 0.00015
config.cl_preservation = 'baseline'
config.seed = 8379
random_seed(config.seed)
exp_id = '-l2-act'
log_name = name + '-ppo' + '-' + config.cl_preservation + exp_id
config.log_dir = get_default_log_dir(log_name)
config.num_workers = 16
assert env_config_path is not None, '`env_config_path` should be set for the CTgraph environnent'
task_fn = lambda log_dir: CTgraphFlatObs(name, env_config_path=env_config_path, log_dir=log_dir)
config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers, log_dir=config.log_dir)
config.eval_task_fn = task_fn
config.optimizer_fn = lambda params, lr: torch.optim.RMSprop(params, lr=lr)
config.network_fn = lambda state_dim, action_dim, label_dim: CategoricalActorCriticNet_CL(
state_dim, action_dim, label_dim,
phi_body=FCBody_CL(state_dim, task_label_dim=label_dim, hidden_units=(200, 200, 200)),
actor_body=DummyBody_CL(200),
critic_body=DummyBody_CL(200))
config.policy_fn = SamplePolicy
config.state_normalizer = ImageNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.99
config.entropy_weight = 0.75
config.rollout_length = 7
config.optimization_epochs = 4
config.num_mini_batches = 4
config.ppo_ratio_clip = 0.1
config.iteration_log_interval = 100
config.gradient_clip = 5
config.max_steps = int(5.6e4) * 6 + 1 #int(5.6e4)+1 # note, max steps per task
config.evaluation_episodes = 10
config.logger = get_logger(log_dir=config.log_dir)
config.cl_requires_task_label = True
config.reg_loss_coeff = 1e-4
config.cl_num_tasks = 4
agent = PPOAgentBaselineL2Act(config)
config.agent_name = agent.__class__.__name__
tasks = agent.config.cl_tasks_info
tasks = [tasks[0], tasks[3]]
config.cl_num_tasks = len(tasks)
config.cl_num_learn_blocks = 3
shutil.copy(env_config_path, config.log_dir + '/env_config.json')
with open('{0}/tasks_info.bin'.format(config.log_dir), 'wb') as f:
pickle.dump(tasks, f)
run_iterations_cl(agent, tasks)
# save config
with open('{0}/config.json'.format(config.log_dir), 'w') as f:
dict_config = vars(config)
for k in dict_config.keys():
if not isinstance(dict_config[k], int) \
and not isinstance(dict_config[k], float) and dict_config[k] is not None:
dict_config[k] = str(dict_config[k])
json.dump(dict_config, f)
def ppo_ctgraph_cl_group_l1_weights(name, env_config_path=None):
config = Config()
config.env_name = name
config.env_config_path = env_config_path
config.lr = 0.00015
config.cl_preservation = 'baseline'
config.seed = 8379
random_seed(config.seed)
exp_id = '-group-l1-weights'
log_name = name + '-ppo' + '-' + config.cl_preservation + exp_id
config.log_dir = get_default_log_dir(log_name)
config.num_workers = 16
assert env_config_path is not None, '`env_config_path` should be set for the CTgraph environnent'
task_fn = lambda log_dir: CTgraphFlatObs(name, env_config_path=env_config_path, log_dir=log_dir)
config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers, log_dir=config.log_dir)
config.eval_task_fn = task_fn
config.optimizer_fn = lambda params, lr: torch.optim.RMSprop(params, lr=lr)
config.network_fn = lambda state_dim, action_dim, label_dim: CategoricalActorCriticNet_CL(
state_dim, action_dim, label_dim,
phi_body=FCBody_CL(state_dim, task_label_dim=label_dim, hidden_units=(200, 200, 200)),
actor_body=DummyBody_CL(200),
critic_body=DummyBody_CL(200))
config.policy_fn = SamplePolicy
config.state_normalizer = ImageNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.99
config.entropy_weight = 0.75
config.rollout_length = 7
config.optimization_epochs = 4
config.num_mini_batches = 4
config.ppo_ratio_clip = 0.1
config.iteration_log_interval = 100
config.gradient_clip = 5
config.max_steps = int(5.6e4) * 6 + 1 #int(5.6e4)+1 # note, max steps per task
config.evaluation_episodes = 10
config.logger = get_logger(log_dir=config.log_dir)
config.cl_requires_task_label = True
config.reg_loss_coeff = 1e-4
config.cl_num_tasks = 4
agent = PPOAgentBaselineGroupL1Weights(config)
config.agent_name = agent.__class__.__name__
tasks = agent.config.cl_tasks_info
tasks = [tasks[0], tasks[3]]
config.cl_num_tasks = len(tasks)
config.cl_num_learn_blocks = 3
shutil.copy(env_config_path, config.log_dir + '/env_config.json')
with open('{0}/tasks_info.bin'.format(config.log_dir), 'wb') as f:
pickle.dump(tasks, f)
run_iterations_cl(agent, tasks)
# save config
with open('{0}/config.json'.format(config.log_dir), 'w') as f:
dict_config = vars(config)
for k in dict_config.keys():
if not isinstance(dict_config[k], int) \
and not isinstance(dict_config[k], float) and dict_config[k] is not None:
dict_config[k] = str(dict_config[k])
json.dump(dict_config, f)
def ppo_ctgraph_cl_sparse_group_l1_weights(name, env_config_path=None):
config = Config()
config.env_name = name
config.env_config_path = env_config_path
config.lr = 0.00015
config.cl_preservation = 'baseline'
config.seed = 8379
random_seed(config.seed)
exp_id = '-sparse-group-l1-weights'
log_name = name + '-ppo' + '-' + config.cl_preservation + exp_id
config.log_dir = get_default_log_dir(log_name)
config.num_workers = 16
assert env_config_path is not None, '`env_config_path` should be set for the CTgraph environnent'
task_fn = lambda log_dir: CTgraphFlatObs(name, env_config_path=env_config_path, log_dir=log_dir)
config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers, log_dir=config.log_dir)
config.eval_task_fn = task_fn
config.optimizer_fn = lambda params, lr: torch.optim.RMSprop(params, lr=lr)
config.network_fn = lambda state_dim, action_dim, label_dim: CategoricalActorCriticNet_CL(
state_dim, action_dim, label_dim,
phi_body=FCBody_CL(state_dim, task_label_dim=label_dim, hidden_units=(200, 200, 200)),
actor_body=DummyBody_CL(200),
critic_body=DummyBody_CL(200))
config.policy_fn = SamplePolicy
config.state_normalizer = ImageNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.99
config.entropy_weight = 0.75
config.rollout_length = 7
config.optimization_epochs = 4
config.num_mini_batches = 4
config.ppo_ratio_clip = 0.1
config.iteration_log_interval = 100
config.gradient_clip = 5
config.max_steps = int(5.6e4) * 6 + 1 #int(5.6e4)+1 # note, max steps per task
config.evaluation_episodes = 10
config.logger = get_logger(log_dir=config.log_dir)
config.cl_requires_task_label = True
config.reg_loss_coeff = 1e-4
config.cl_num_tasks = 4
agent = PPOAgentBaselineSparseGroupL1Weights(config)
config.agent_name = agent.__class__.__name__
tasks = agent.config.cl_tasks_info
#tasks = [tasks[0], tasks[3]]
#config.cl_num_tasks = len(tasks)
config.cl_num_learn_blocks = 6
shutil.copy(env_config_path, config.log_dir + '/env_config.json')
with open('{0}/tasks_info.bin'.format(config.log_dir), 'wb') as f:
pickle.dump(tasks, f)
run_iterations_cl(agent, tasks)
# save config
with open('{0}/config.json'.format(config.log_dir), 'w') as f:
dict_config = vars(config)
for k in dict_config.keys():
if not isinstance(dict_config[k], int) \
and not isinstance(dict_config[k], float) and dict_config[k] is not None:
dict_config[k] = str(dict_config[k])
json.dump(dict_config, f)
# kwinners activation sparsity, no consolidation (pure baseline)
def ppo_ctgraph_cl_kwinners(name, env_config_path=None):
config = Config()
config.env_name = name
config.env_config_path = env_config_path
config.lr = 0.00015
config.cl_preservation = 'baseline'
config.seed = 8379
random_seed(config.seed)
exp_id = '-kwinners'
log_name = name + '-ppo' + '-' + config.cl_preservation + exp_id
config.log_dir = get_default_log_dir(log_name)
config.num_workers = 16
assert env_config_path is not None, '`env_config_path` should be set for the CTgraph environnent'
task_fn = lambda log_dir: CTgraphFlatObs(name, env_config_path=env_config_path, log_dir=log_dir)
config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers, log_dir=config.log_dir)
config.eval_task_fn = task_fn
config.optimizer_fn = lambda params, lr: torch.optim.RMSprop(params, lr=lr)
config.network_fn = lambda state_dim, action_dim, label_dim: CategoricalActorCriticNet_CL(
state_dim, action_dim, label_dim,
phi_body=FCBody_CL_KWinners(state_dim, task_label_dim=label_dim, hidden_units=(200, 200, 200)),
actor_body=DummyBody_CL(200),
critic_body=DummyBody_CL(200))
config.policy_fn = SamplePolicy
config.state_normalizer = ImageNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.99
config.entropy_weight = 0.75
config.rollout_length = 7
config.optimization_epochs = 4
config.num_mini_batches = 4
config.ppo_ratio_clip = 0.1
config.iteration_log_interval = 100
config.gradient_clip = 5
config.max_steps = int(5.6e4) * 6 + 1 #int(5.6e4)+1 # note, max steps per task
config.evaluation_episodes = 10
config.logger = get_logger(log_dir=config.log_dir)
config.cl_requires_task_label = True
config.cl_num_tasks = 4
agent = PPOAgentBaseline(config)
config.agent_name = agent.__class__.__name__
tasks = agent.config.cl_tasks_info
#tasks = [tasks[0], tasks[3]]
#config.cl_num_tasks = len(tasks)
config.cl_num_learn_blocks = 1
shutil.copy(env_config_path, config.log_dir + '/env_config.json')
with open('{0}/tasks_info.bin'.format(config.log_dir), 'wb') as f:
pickle.dump(tasks, f)
run_iterations_cl(agent, tasks)
# save config
with open('{0}/config.json'.format(config.log_dir), 'w') as f:
dict_config = vars(config)
for k in dict_config.keys():
if not isinstance(dict_config[k], int) \
and not isinstance(dict_config[k], float) and dict_config[k] is not None:
dict_config[k] = str(dict_config[k])
json.dump(dict_config, f)
def ppo_ctgraph_cl_sparse_group_l1_weights_scp(name, env_config_path=None):
config = Config()
config.env_name = name
config.env_config_path = env_config_path
config.lr = 0.00015
config.cl_preservation = 'scp'
config.seed = 8379
random_seed(config.seed)
exp_id = '-sparse-group-l1-weights'
log_name = name + '-ppo' + '-' + config.cl_preservation + exp_id
config.log_dir = get_default_log_dir(log_name)
config.num_workers = 16
assert env_config_path is not None, '`env_config_path` should be set for the CTgraph environnent'
task_fn = lambda log_dir: CTgraphFlatObs(name, env_config_path=env_config_path, log_dir=log_dir)
config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers, log_dir=config.log_dir)
config.eval_task_fn = task_fn
config.optimizer_fn = lambda params, lr: torch.optim.RMSprop(params, lr=lr)
config.network_fn = lambda state_dim, action_dim, label_dim: CategoricalActorCriticNet_CL(
state_dim, action_dim, label_dim,
phi_body=FCBody_CL(state_dim, task_label_dim=label_dim, hidden_units=(200, 200, 200)),
actor_body=DummyBody_CL(200),
critic_body=DummyBody_CL(200))
config.policy_fn = SamplePolicy
config.state_normalizer = ImageNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.99
config.entropy_weight = 0.75
config.rollout_length = 7
config.optimization_epochs = 4
config.num_mini_batches = 4
config.ppo_ratio_clip = 0.1
config.iteration_log_interval = 100
config.gradient_clip = 5
config.max_steps = int(5.6e4) * 6 + 1 #int(5.6e4)+1 # note, max steps per task
config.evaluation_episodes = 10
config.logger = get_logger(log_dir=config.log_dir)
config.cl_requires_task_label = True
# weight preservation parasm
config.cl_alpha = 0.25
config.cl_loss_coeff = 0.5 # for scp
config.cl_n_slices = 200
# regularisation param(s)
config.reg_loss_coeff = 1e-4
# other parameters
config.cl_num_tasks = 4
agent = PPOAgentSCPSparseGroupL1Weights(config)
config.agent_name = agent.__class__.__name__
tasks = agent.config.cl_tasks_info
#tasks = [tasks[0], tasks[3]]
#config.cl_num_tasks = len(tasks)
config.cl_num_learn_blocks = 1
shutil.copy(env_config_path, config.log_dir + '/env_config.json')
with open('{0}/tasks_info.bin'.format(config.log_dir), 'wb') as f:
pickle.dump(tasks, f)
run_iterations_cl(agent, tasks)
# save config
with open('{0}/config.json'.format(config.log_dir), 'w') as f:
dict_config = vars(config)
for k in dict_config.keys():
if not isinstance(dict_config[k], int) \
and not isinstance(dict_config[k], float) and dict_config[k] is not None:
dict_config[k] = str(dict_config[k])
json.dump(dict_config, f)
# kwinners activation sparsity, with scp consolidation
def ppo_ctgraph_cl_kwinners_scp(name, env_config_path=None):
config = Config()
config.env_name = name
config.env_config_path = env_config_path
config.lr = 0.00015
config.cl_preservation = 'scp'
config.seed = 8379
random_seed(config.seed)
exp_id = '-kwinners-5percent'
log_name = name + '-ppo' + '-' + config.cl_preservation + exp_id
config.log_dir = get_default_log_dir(log_name)
config.num_workers = 16
assert env_config_path is not None, '`env_config_path` should be set for the CTgraph environnent'
task_fn = lambda log_dir: CTgraphFlatObs(name, env_config_path=env_config_path, log_dir=log_dir)
config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers, log_dir=config.log_dir)
config.eval_task_fn = task_fn
config.optimizer_fn = lambda params, lr: torch.optim.RMSprop(params, lr=lr)
config.network_fn = lambda state_dim, action_dim, label_dim: CategoricalActorCriticNet_CL(
state_dim, action_dim, label_dim,
phi_body=FCBody_CL_KWinners(state_dim, task_label_dim=label_dim, hidden_units=(200, 200, 200)),
actor_body=DummyBody_CL(200),
critic_body=DummyBody_CL(200))
config.policy_fn = SamplePolicy
config.state_normalizer = ImageNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.99
config.entropy_weight = 0.75
config.rollout_length = 7
config.optimization_epochs = 4
config.num_mini_batches = 4
config.ppo_ratio_clip = 0.1
config.iteration_log_interval = 100
config.gradient_clip = 5
config.max_steps = int(5.6e4) * 2 + 1 #int(5.6e4)+1 # note, max steps per task
config.evaluation_episodes = 10
config.logger = get_logger(log_dir=config.log_dir)
config.cl_requires_task_label = True
# weight preservation parasm
config.cl_alpha = 0.25
config.cl_loss_coeff = 0.5 # for scp
config.cl_n_slices = 200
# other parameters
config.cl_num_tasks = 4
agent = PPOAgentSCP(config)
config.agent_name = agent.__class__.__name__
tasks = agent.config.cl_tasks_info
#tasks = [tasks[0], tasks[3]]
#config.cl_num_tasks = len(tasks)
config.cl_num_learn_blocks = 1
shutil.copy(env_config_path, config.log_dir + '/env_config.json')
with open('{0}/tasks_info.bin'.format(config.log_dir), 'wb') as f:
pickle.dump(tasks, f)
run_iterations_cl(agent, tasks)
# save config
with open('{0}/config.json'.format(config.log_dir), 'w') as f:
dict_config = vars(config)
for k in dict_config.keys():
if not isinstance(dict_config[k], int) \
and not isinstance(dict_config[k], float) and dict_config[k] is not None:
dict_config[k] = str(dict_config[k])
json.dump(dict_config, f)
# neurmodulated masking of forward pass. neuromodulated network generates masks per layer. the masks
# are then applied to the weights of the target/base network during forward pass.
def ppo_ctgraph_cl_nm_mask_fp(name, env_config_path=None):
config = Config()
config.env_name = name
config.env_config_path = env_config_path
config.lr = 0.00015
config.cl_preservation = 'scp'
config.seed = 8379
random_seed(config.seed)
exp_id = '-nm-mask-fp'
log_name = name + '-ppo' + '-' + config.cl_preservation + exp_id
config.log_dir = get_default_log_dir(log_name)
config.num_workers = 16
assert env_config_path is not None, '`env_config_path` should be set for the CTgraph environnent'
task_fn = lambda log_dir: CTgraphFlatObs(name, env_config_path=env_config_path, log_dir=log_dir)
config.task_fn = lambda: ParallelizedTask(task_fn, config.num_workers, log_dir=config.log_dir)
config.eval_task_fn = task_fn
config.optimizer_fn = lambda params, lr: torch.optim.RMSprop(params, lr=lr)
config.network_fn = lambda state_dim, action_dim, label_dim: CategoricalActorCriticNet_CL_Mask(
#state_dim, action_dim, label_dim,
state_dim, action_dim, None,
#phi_body=FCBody_CL_Mask(state_dim, task_label_dim=label_dim, hidden_units=(200, 200, 200)),
phi_body=FCBody_CL_Mask(state_dim, task_label_dim=None, hidden_units=(200, 200, 200)),
actor_body=DummyBody_CL_Mask(200),
critic_body=DummyBody_CL_Mask(200))
config.policy_fn = SamplePolicy
config.state_normalizer = ImageNormalizer()
config.discount = 0.99
config.use_gae = True
config.gae_tau = 0.99
config.entropy_weight = 0.75
config.rollout_length = 7
config.optimization_epochs = 4
config.num_mini_batches = 4
config.ppo_ratio_clip = 0.1
config.iteration_log_interval = 100
config.gradient_clip = 5
config.max_steps = int(5.6e4) * 3 + 1 #int(5.6e4)+1 # note, max steps per task
#config.max_steps = int(5.6e4 * 0.5) + 1 #int(5.6e4)+1 # note, max steps per task
#config.max_steps = 1e3
config.evaluation_episodes = 10
config.logger = get_logger(log_dir=config.log_dir)
config.cl_requires_task_label = True
# weight preservation params
config.cl_alpha = 0.25
config.cl_loss_coeff = 1e6 #0.5 # for scp
config.cl_n_slices = 200
# regularisation param(s)
config.reg_loss_coeff = 1e-4
# other parameters
config.cl_num_tasks = 4
agent = PPOAgentSCPModulatedFP(config)
config.agent_name = agent.__class__.__name__
tasks = agent.config.cl_tasks_info
config.cl_num_learn_blocks = 1
shutil.copy(env_config_path, config.log_dir + '/env_config.json')
with open('{0}/tasks_info.bin'.format(config.log_dir), 'wb') as f:
pickle.dump(tasks, f)
run_iterations_cl(agent, tasks)
# save config
with open('{0}/config.json'.format(config.log_dir), 'w') as f:
dict_config = vars(config)
for k in dict_config.keys():
if not isinstance(dict_config[k], int) \
and not isinstance(dict_config[k], float) and dict_config[k] is not None:
dict_config[k] = str(dict_config[k])
json.dump(dict_config, f)
if __name__ == '__main__':
mkdir('log')
set_one_thread()
#random_seed(42)
select_device(0) # -1 is CPU, a positive integer is the index of GPU
# ctgraph experiments
game = 'CTgraph-v0'
env_config_path = './ctgraph.json'
parser = argparse.ArgumentParser()
parser.add_argument('algo', help='algorithm to run')
args = parser.parse_args()
if args.algo == 'baseline':
ppo_ctgraph_cl(name=game, env_config_path=env_config_path)
elif args.algo == 'l1_weights':
ppo_ctgraph_cl_l1_weights(name=game, env_config_path=env_config_path)
elif args.algo == 'l2_weights':
ppo_ctgraph_cl_l2_weights(name=game, env_config_path=env_config_path)
elif args.algo == 'l1_act':
ppo_ctgraph_cl_l1_act(name=game, env_config_path=env_config_path)
elif args.algo == 'l2_act':
ppo_ctgraph_cl_l2_act(name=game, env_config_path=env_config_path)
elif args.algo == 'group_l1_weights':
ppo_ctgraph_cl_group_l1_weights(name=game, env_config_path=env_config_path)
elif args.algo == 'sparse_group_l1_weights':
ppo_ctgraph_cl_sparse_group_l1_weights(name=game, env_config_path=env_config_path)
elif args.algo == 'kwinners':
ppo_ctgraph_cl_kwinners(name=game, env_config_path=env_config_path)
elif args.algo == 'sparse_group_l1_weights_scp':
ppo_ctgraph_cl_sparse_group_l1_weights_scp(name=game, env_config_path=env_config_path)
elif args.algo == 'kwinners_scp':
ppo_ctgraph_cl_kwinners_scp(name=game, env_config_path=env_config_path)
elif args.algo == 'nm_mask_fp':
ppo_ctgraph_cl_nm_mask_fp(name=game, env_config_path=env_config_path)
else:
raise ValueError('not implemented')
| 44.858904 | 104 | 0.701194 |
b851a06a29f955a1b9d6a141d992dfe9cfca9b06 | 2,077 | py | Python | pytests/bucket_collections/collection_ops_specs/volume_test_load_1_percent_dgm_lower_ops.py | bkumaran/TAF | 27f39eb913fa89b55cdd88ee1c7ef0bb8c094407 | [
"Apache-2.0"
] | null | null | null | pytests/bucket_collections/collection_ops_specs/volume_test_load_1_percent_dgm_lower_ops.py | bkumaran/TAF | 27f39eb913fa89b55cdd88ee1c7ef0bb8c094407 | [
"Apache-2.0"
] | null | null | null | pytests/bucket_collections/collection_ops_specs/volume_test_load_1_percent_dgm_lower_ops.py | bkumaran/TAF | 27f39eb913fa89b55cdd88ee1c7ef0bb8c094407 | [
"Apache-2.0"
] | 1 | 2019-05-22T09:10:44.000Z | 2019-05-22T09:10:44.000Z | from collections_helper.collections_spec_constants import MetaCrudParams
spec = {
# Scope/Collection ops params
MetaCrudParams.COLLECTIONS_TO_FLUSH: 0,
MetaCrudParams.COLLECTIONS_TO_DROP: 5,
MetaCrudParams.SCOPES_TO_DROP: 3,
MetaCrudParams.SCOPES_TO_ADD_PER_BUCKET: 0,
MetaCrudParams.COLLECTIONS_TO_ADD_FOR_NEW_SCOPES: 0,
MetaCrudParams.COLLECTIONS_TO_ADD_PER_BUCKET: 0,
MetaCrudParams.BUCKET_CONSIDERED_FOR_OPS: "all",
MetaCrudParams.SCOPES_CONSIDERED_FOR_OPS: "all",
MetaCrudParams.COLLECTIONS_CONSIDERED_FOR_OPS: "all",
# Doc loading params
"doc_crud": {
MetaCrudParams.DocCrud.COMMON_DOC_KEY: "test_collections",
MetaCrudParams.DocCrud.CREATE_PERCENTAGE_PER_COLLECTION: 1,
MetaCrudParams.DocCrud.READ_PERCENTAGE_PER_COLLECTION: 1,
MetaCrudParams.DocCrud.UPDATE_PERCENTAGE_PER_COLLECTION: 1,
MetaCrudParams.DocCrud.REPLACE_PERCENTAGE_PER_COLLECTION: 0,
MetaCrudParams.DocCrud.DELETE_PERCENTAGE_PER_COLLECTION: 0,
},
"subdoc_crud": {
MetaCrudParams.SubDocCrud.XATTR_TEST: False,
MetaCrudParams.SubDocCrud.INSERT_PER_COLLECTION: 0,
MetaCrudParams.SubDocCrud.UPSERT_PER_COLLECTION: 0,
MetaCrudParams.SubDocCrud.REMOVE_PER_COLLECTION: 0,
MetaCrudParams.SubDocCrud.LOOKUP_PER_COLLECTION: 0,
},
# Doc_loading task options
MetaCrudParams.DOC_TTL: 0,
MetaCrudParams.DURABILITY_LEVEL: "",
MetaCrudParams.SDK_TIMEOUT: 120, # Default is 60
MetaCrudParams.SDK_TIMEOUT_UNIT: "seconds",
MetaCrudParams.TARGET_VBUCKETS: "all",
MetaCrudParams.SKIP_READ_ON_ERROR: True,
MetaCrudParams.SUPPRESS_ERROR_TABLE: True,
# The below is to skip populating success dictionary for reads
MetaCrudParams.SKIP_READ_SUCCESS_RESULTS: True, # Default is False
MetaCrudParams.RETRY_EXCEPTIONS: [],
MetaCrudParams.IGNORE_EXCEPTIONS: [],
MetaCrudParams.COLLECTIONS_CONSIDERED_FOR_CRUD: "all",
MetaCrudParams.SCOPES_CONSIDERED_FOR_CRUD: "all",
MetaCrudParams.BUCKETS_CONSIDERED_FOR_CRUD: "all"
} | 39.188679 | 72 | 0.767453 |
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