content
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# Python code to demonstrate the working of
# complex(), real() and imag()
# importing "cmath" for complex number operations
import cmath
# Initializing real numbers
x = 5
y = 3
# converting x and y into complex number
z = complex(x, y);
s = 5 + 3j
# printing real and imaginary part of complex number
print("The real part of complex number is : ", end="")
print(z.real)
print("The imaginary part of complex number is : ", end="")
print(z.imag)
print(z)
print(s) | [
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#jiggle.v.01
#Body_movement
#This frame contains words for motions or actions an Agent p
#erforms using some part of his/her body. A number of word
#s in this frame occur as blends with Communication, in whic
#h the action has an Addressee. For example, 'Pat nodded
#at Kim.' These examples differ from Communication.Gesture
#in that no specific message need be expressed, as in 'She
#nodded to him to sit down.' Since this frame involves a pa
#rticular type of motion, it contains the frame elements Sou
#rce, Path, Goal and Area, which originate in the motion fra
#me. All of these frame elements are generally expressed in
# PP Complements. 'The boy swung his legs from under the ta
#ble.'
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import argparse
import os
import subprocess
import time
import sys
import ipdb
import pickle
from utils.meter import *
def get_duration(file):
"""Get the duration of a super_video using ffprobe. -> https://stackoverflow.com/questions/31024968/using-ffmpeg-to-obtain-super_video-durations-in-python"""
cmd = 'ffprobe -i {} -show_entries format=duration -v quiet -of csv="p=0"'.format(file)
output = subprocess.check_output(
cmd,
shell=True, # Let this run in the shell
stderr=subprocess.STDOUT
)
# return round(float(output)) # ugly, but rounds your seconds up or down
return float(output)
def rescale_video(video_fn, w, h, fps, dir, new_dir, common_suffix, dict_video_length, ffmpeg, crf=17):
""" Rescale a super_video according to its new width, height an fps """
# Output video_name
video_id = video_fn.replace(dir, '').replace(common_suffix, '')
video_fn_rescaled = video_fn.replace(dir, new_dir)
video_fn_rescaled = video_fn_rescaled.replace(common_suffix, common_suffix.lower())
# Create the dir
video_dir_to_create = '/'.join(video_fn_rescaled.split('/')[:-1])
os.makedirs(video_dir_to_create, exist_ok=True)
# Check if the file already exists
if os.path.isfile(video_fn_rescaled):
print("{} already exists".format(video_fn_rescaled))
else:
subprocess.call(
'{ffmpeg} -i {video_input} -vf scale={w}:{h} -crf {crf} -r {fps} -y {video_output} -loglevel panic'.format(
ffmpeg=ffmpeg,
video_input=video_fn,
h=h,
w=w,
fps=fps,
video_output=video_fn_rescaled,
crf=crf
), shell=True)
# Get the duration of the new super_video (in sec)
duration_sec = get_duration(video_fn_rescaled)
duration_frames = int(duration_sec * fps)
# update the dict id -> length
dict_video_length[video_id] = duration_frames
return video_fn_rescaled
def get_all_videos(dir, extension='mp4'):
""" Return a list of the super_video filename from a directory and its subdirectories """
list_video_fn = []
for dirpath, dirnames, filenames in os.walk(dir):
for filename in [f for f in filenames if f.endswith(extension)]:
fn = os.path.join(dirpath, filename)
list_video_fn.append(fn)
return list_video_fn
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Preprocess dataset')
parser.add_argument('--dir', metavar='DIR',
# default='/Users/fabien/Datasets/NTU-RGB-D/avi',
default='/home/hochul/Desktop/mini_syn_data/avi',
help='path to avi dir')
parser.add_argument('--width', default=256, type=int,
metavar='W', help='Width')
parser.add_argument('--height', default=256, type=int,
metavar='H', help='Height')
parser.add_argument('--fps', default=20, type=int,
metavar='FPS',
help='Frames per second for the extraction, -1 means that we take the fps from the super_video')
parser.add_argument('--common-suffix', metavar='E',
default='.avi',
help='Common end of each super_video file')
parser.add_argument('--crf', default=17, type=int,
metavar='CRF',
help='CRF for ffmpeg command')
parser.add_argument('--ffmpeg', metavar='FF',
default='ffmpeg',
help='ffmpeg verison to use')
args = parser.parse_args()
main(args)
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#!/usr/bin/env python
# coding: UTF-8
"""
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Simple Python routine to convert JSON to XML
Example: python convertjson2xml.py /home/hellsulf/orascript/json2xml/JSON/2020_8_f1.json
Note: Requires json2xml
pip install json2xml
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
"""
import sys
from json2xml import json2xml
from json2xml.utils import readfromurl, readfromstring, readfromjson
from pathlib import Path
"""
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Main
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
"""
if __name__ == "__main__":
main()
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import neural_network_lyapunov.examples.quadrotor2d.quadrotor_2d as\
quadrotor_2d
import neural_network_lyapunov.relu_system as relu_system
import neural_network_lyapunov.lyapunov as lyapunov
import neural_network_lyapunov.feedback_system as feedback_system
import neural_network_lyapunov.train_lyapunov as train_lyapunov
import neural_network_lyapunov.utils as utils
import neural_network_lyapunov.mip_utils as mip_utils
import neural_network_lyapunov.train_utils as train_utils
import neural_network_lyapunov.r_options as r_options
import torch
import numpy as np
import scipy.integrate
import gurobipy
import argparse
import os
def generate_quadrotor_dynamics_data(dt):
"""
Generate the pairs (x[n], u[n]) -> (x[n+1])
"""
dtype = torch.float64
plant = quadrotor_2d.Quadrotor2D(dtype)
theta_range = [-np.pi / 2, np.pi / 2]
ydot_range = [-5, 5]
zdot_range = [-5, 5]
thetadot_range = [-2.5, 2.5]
u_range = [-0.5, 8.5]
# We don't need to take the grid on y and z dimension of the quadrotor,
# since the dynamics is invariant along these dimensions.
x_samples = torch.cat((
torch.zeros((1000, 2), dtype=torch.float64),
utils.uniform_sample_in_box(
torch.tensor([
theta_range[0], ydot_range[0], zdot_range[0], thetadot_range[0]
],
dtype=torch.float64),
torch.tensor([
theta_range[1], ydot_range[1], zdot_range[1], thetadot_range[1]
],
dtype=torch.float64), 1000)),
dim=1).T
u_samples = utils.uniform_sample_in_box(
torch.full((2, ), u_range[0], dtype=dtype),
torch.full((2, ), u_range[1], dtype=dtype), 1000).T
xu_tensors = []
x_next_tensors = []
for i in range(x_samples.shape[1]):
for j in range(u_samples.shape[1]):
result = scipy.integrate.solve_ivp(
lambda t, x: plant.dynamics(x, u_samples[:, j].detach().numpy(
)), (0, dt), x_samples[:, i].detach().numpy())
xu_tensors.append(
torch.cat((x_samples[:, i], u_samples[:, j])).reshape((1, -1)))
x_next_tensors.append(
torch.from_numpy(result.y[:, -1]).reshape((1, -1)))
dataset_input = torch.cat(xu_tensors, dim=0)
dataset_output = torch.cat(x_next_tensors, dim=0)
return torch.utils.data.TensorDataset(dataset_input, dataset_output)
def train_lqr_value_approximator(lyapunov_relu, V_lambda, R, x_equilibrium,
x_lo, x_up, num_samples, lqr_S: torch.Tensor):
"""
We train both lyapunov_relu and R such that ϕ(x) − ϕ(x*) + λ|R(x−x*)|₁
approximates the lqr cost-to-go.
"""
x_samples = utils.uniform_sample_in_box(x_lo, x_up, num_samples)
V_samples = torch.sum((x_samples.T - x_equilibrium.reshape(
(6, 1))) * (lqr_S @ (x_samples.T - x_equilibrium.reshape((6, 1)))),
dim=0).reshape((-1, 1))
state_value_dataset = torch.utils.data.TensorDataset(x_samples, V_samples)
R.requires_grad_(True)
utils.train_approximator(state_value_dataset,
lyapunov_relu,
compute_v,
batch_size=50,
num_epochs=200,
lr=0.001,
additional_variable=[R])
R.requires_grad_(False)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="quadrotor 2d training demo")
parser.add_argument("--generate_dynamics_data", action="store_true")
parser.add_argument("--load_dynamics_data",
type=str,
default=None,
help="path to the dynamics data.")
parser.add_argument("--train_forward_model", action="store_true")
parser.add_argument("--load_forward_model",
type=str,
default=None,
help="path to load dynamics model")
parser.add_argument("--load_lyapunov_relu",
type=str,
default=None,
help="path to the lyapunov model data.")
parser.add_argument("--load_controller_relu",
type=str,
default=None,
help="path to the controller data.")
parser.add_argument("--train_lqr_approximator", action="store_true")
parser.add_argument("--search_R", action="store_true")
parser.add_argument("--train_on_samples", action="store_true")
parser.add_argument("--enable_wandb", action="store_true")
parser.add_argument("--train_adversarial", action="store_true")
parser.add_argument("--max_iterations", type=int, default=5000)
parser.add_argument("--training_set", type=str, default=None)
args = parser.parse_args()
dir_path = os.path.dirname(os.path.realpath(__file__))
dt = 0.01
dtype = torch.float64
if args.generate_dynamics_data:
model_dataset = generate_quadrotor_dynamics_data(dt)
if args.load_dynamics_data is not None:
model_dataset = torch.load(args.load_dynamics_data)
if args.train_forward_model:
forward_model = utils.setup_relu((4, 6, 6, 3),
params=None,
bias=True,
negative_slope=0.01,
dtype=dtype)
train_forward_model(forward_model, model_dataset, num_epochs=100)
if args.load_forward_model:
forward_model_data = torch.load(args.load_forward_model)
forward_model = utils.setup_relu(
forward_model_data["linear_layer_width"],
params=None,
bias=forward_model_data["bias"],
negative_slope=forward_model_data["negative_slope"],
dtype=dtype)
forward_model.load_state_dict(forward_model_data["state_dict"])
plant = quadrotor_2d.Quadrotor2D(dtype)
x_star = np.zeros((6, ))
u_star = plant.u_equilibrium.detach().numpy()
lqr_Q = np.diag([10, 10, 10, 1, 1, plant.length / 2. / np.pi])
lqr_R = np.array([[0.1, 0.05], [0.05, 0.1]])
K, S = plant.lqr_control(lqr_Q, lqr_R, x_star, u_star)
S_eig_value, S_eig_vec = np.linalg.eig(S)
# R = torch.zeros((9, 6), dtype=dtype)
# R[:3, :3] = torch.eye(3, dtype=dtype)
# R[3:6, :3] = torch.eye(3, dtype=dtype) / np.sqrt(2)
# R[3:6, 3:6] = torch.eye(3, dtype=dtype) / np.sqrt(2)
# R[6:9, :3] = -torch.eye(3, dtype=dtype) / np.sqrt(2)
# R[6:9, 3:6] = torch.eye(3, dtype=dtype) / np.sqrt(2)
# R = torch.cat((R, torch.from_numpy(S_eig_vec)), dim=0)
R = torch.from_numpy(S) + 0.01 * torch.eye(6, dtype=dtype)
lyapunov_relu = utils.setup_relu((6, 10, 10, 4, 1),
params=None,
negative_slope=0.1,
bias=True,
dtype=dtype)
V_lambda = 0.9
if args.load_lyapunov_relu is not None:
lyapunov_data = torch.load(args.load_lyapunov_relu)
lyapunov_relu = utils.setup_relu(
lyapunov_data["linear_layer_width"],
params=None,
negative_slope=lyapunov_data["negative_slope"],
bias=lyapunov_data["bias"],
dtype=dtype)
lyapunov_relu.load_state_dict(lyapunov_data["state_dict"])
V_lambda = lyapunov_data["V_lambda"]
R = lyapunov_data["R"]
controller_relu = utils.setup_relu((6, 6, 4, 2),
params=None,
negative_slope=0.01,
bias=True,
dtype=dtype)
if args.load_controller_relu is not None:
controller_data = torch.load(args.load_controller_relu)
controller_relu = utils.setup_relu(
controller_data["linear_layer_width"],
params=None,
negative_slope=controller_data["negative_slope"],
bias=controller_data["bias"],
dtype=dtype)
controller_relu.load_state_dict(controller_data["state_dict"])
q_equilibrium = torch.tensor([0, 0, 0], dtype=dtype)
u_equilibrium = plant.u_equilibrium
x_lo = torch.tensor([-0.7, -0.7, -np.pi * 0.5, -3.75, -3.75, -2.5],
dtype=dtype)
x_up = -x_lo
u_lo = torch.tensor([0, 0], dtype=dtype)
u_up = torch.tensor([8, 8], dtype=dtype)
if args.enable_wandb:
train_utils.wandb_config_update(args, lyapunov_relu, controller_relu,
x_lo, x_up, u_lo, u_up)
if args.train_lqr_approximator:
x_equilibrium = torch.cat(
(q_equilibrium, torch.zeros((3, ), dtype=dtype)))
train_lqr_control_approximator(controller_relu, x_equilibrium,
u_equilibrium, x_lo, x_up, 100000,
torch.from_numpy(K))
train_lqr_value_approximator(lyapunov_relu, V_lambda, R, x_equilibrium,
x_lo, x_up, 100000, torch.from_numpy(S))
forward_system = relu_system.ReLUSecondOrderResidueSystemGivenEquilibrium(
dtype,
x_lo,
x_up,
u_lo,
u_up,
forward_model,
q_equilibrium,
u_equilibrium,
dt,
network_input_x_indices=[2, 5])
closed_loop_system = feedback_system.FeedbackSystem(
forward_system, controller_relu, forward_system.x_equilibrium,
forward_system.u_equilibrium,
u_lo.detach().numpy(),
u_up.detach().numpy())
lyap = lyapunov.LyapunovDiscreteTimeHybridSystem(closed_loop_system,
lyapunov_relu)
if args.search_R:
_, R_sigma, _ = np.linalg.svd(R.detach().numpy())
R_options = r_options.SearchRwithSVDOptions(R.shape, R_sigma * 0.8)
R_options.set_variable_value(R.detach().numpy())
else:
R_options = r_options.FixedROptions(R)
dut = train_lyapunov.TrainLyapunovReLU(lyap, V_lambda,
closed_loop_system.x_equilibrium,
R_options)
dut.lyapunov_positivity_mip_pool_solutions = 1
dut.lyapunov_derivative_mip_pool_solutions = 1
dut.lyapunov_derivative_convergence_tol = 1E-5
dut.lyapunov_positivity_convergence_tol = 5e-6
dut.max_iterations = args.max_iterations
dut.lyapunov_positivity_epsilon = 0.1
dut.lyapunov_derivative_epsilon = 0.001
dut.lyapunov_derivative_eps_type = lyapunov.ConvergenceEps.ExpLower
state_samples_all = utils.get_meshgrid_samples(x_lo,
x_up, (7, 7, 7, 7, 7, 7),
dtype=dtype)
dut.output_flag = True
if args.train_on_samples:
dut.train_lyapunov_on_samples(state_samples_all,
num_epochs=10,
batch_size=50)
dut.enable_wandb = args.enable_wandb
if args.train_adversarial:
options = train_lyapunov.TrainLyapunovReLU.AdversarialTrainingOptions()
options.num_batches = 10
options.num_epochs_per_mip = 20
options.positivity_samples_pool_size = 10000
options.derivative_samples_pool_size = 100000
dut.lyapunov_positivity_mip_pool_solutions = 100
dut.lyapunov_derivative_mip_pool_solutions = 500
dut.add_derivative_adversarial_state = True
dut.add_positivity_adversarial_state = True
forward_system.network_bound_propagate_method =\
mip_utils.PropagateBoundsMethod.MIP
dut.lyapunov_hybrid_system.network_bound_propagate_method =\
mip_utils.PropagateBoundsMethod.MIP
closed_loop_system.controller_network_bound_propagate_method =\
mip_utils.PropagateBoundsMethod.MIP
dut.lyapunov_derivative_mip_params = {
gurobipy.GRB.Param.OutputFlag: False
}
if args.training_set:
training_set_data = torch.load(args.training_set)
positivity_state_samples_init = training_set_data[
"positivity_state_samples"]
derivative_state_samples_init = training_set_data[
"derivative_state_samples"]
else:
positivity_state_samples_init = utils.uniform_sample_in_box(
x_lo, x_up, 1000)
derivative_state_samples_init = positivity_state_samples_init
result = dut.train_adversarial(positivity_state_samples_init,
derivative_state_samples_init, options)
else:
dut.train(torch.empty((0, 6), dtype=dtype))
pass
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28,
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198,
220,
220,
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] | 1.868843 | 6,923 |
#!/usr/bin/env python
# generate hotindexing undo provisional tests with 2 nested transactions
import sys
sys.exit(main())
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#!/usr/bin/env python3
#
# initializes your docker development environment by creating
# random passwords for the database connections
# and then updating the configuration files with those passwords
#
import os
import os.path
import random
import re
import shutil
import string
if __name__ == '__main__':
main()
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] | 3.843373 | 83 |
import spacy
import numpy as np
from sklearn.base import BaseEstimator
nlp = spacy.load("en_core_web_sm")
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# Design a stack that supports push, pop, top, and retrieving the minimum element in constant time.
#
# push(x) -- Push element x onto stack.
# pop() -- Removes the element on top of the stack.
# top() -- Get the top element.
# getMin() -- Retrieve the minimum element in the stack.
# Example:
# MinStack minStack = new MinStack();
# minStack.push(-2);
# minStack.push(0);
# minStack.push(-3);
# minStack.getMin(); --> Returns -3.
# minStack.pop();
# minStack.top(); --> Returns 0.
# minStack.getMin(); --> Returns -2.
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] | 3.075581 | 172 |
import discord
import math
client = discord.Client()
@client.event
@client.event
# Здесь должен быть токен
client.run('token')
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import subprocess
from unittest import mock
import pymediainfo
from fffw.wrapper import ensure_binary
from video_transcoding import transcoding
from video_transcoding.tests.base import BaseTestCase
class TranscodingTestCase(BaseTestCase):
""" Video file transcoding tests."""
# Minimal mediainfo output template to mock MediaInfo.parse result
media_info_xml = """<?xml version="1.0" encoding="UTF-8"?>
<File>
<track type="General">
<VideoCount>1</VideoCount>
<AudioCount>1</AudioCount>
</track>
<track type="Video">
<Duration>{video_duration:.3f}</Duration>
<Bit_Rate>{video_bitrate}</Bit_Rate>
<Width>{width}</Width>
<Height>{height}</Height>
<Pixel_Aspect_Ratio>{par:.3f}</Pixel_Aspect_Ratio>
<Display_Aspect_Ratio>{aspect:.3f}</Display_Aspect_Ratio>
<Frame_Rate>{video_frame_rate:.3f}</Frame_Rate>
</track>
<track type="Audio">
<Duration>{audio_duration:.3f}</Duration>
<Bit_Rate>{audio_bitrate}</Bit_Rate>
<Sampling_Rate>{audio_sampling_rate}</Sampling_Rate>
</track>
</File>
"""
# Default video file metadata
metadata = {
'width': 1920,
'height': 1080,
'aspect': 1.778,
'par': 1.0,
transcoding.VIDEO_DURATION: 3600.22,
'video_bitrate': 5000000,
'video_frame_rate': 24.97,
'audio_bitrate': 192000,
'audio_sampling_rate': 48000,
transcoding.AUDIO_DURATION: 3600.22,
}
def prepare_metadata(self, **kwargs):
"""
Modifies metadata template with new values.
"""
media_info = self.metadata.copy()
media_info.update(kwargs)
return media_info
def get_media_info(self, filename) -> pymediainfo.MediaInfo:
""" Prepares mediainfo result for file."""
metadata = self.media_info[filename]
xml = self.media_info_xml.format(filename=filename, **metadata)
return pymediainfo.MediaInfo(xml)
def test_smoke(self):
"""
ffmpeg arguments test.
"""
self.transcoder.transcode()
ffmpeg_args = list(map(ensure_binary, [
'ffmpeg',
'-loglevel', 'repeat+level+info',
'-i', self.source,
'-y',
'-map', '0:v',
'-c:v', 'libx264',
'-b:v', '5000000',
'-map', '0:a',
'-c:a', 'aac',
'-b:a', '192000',
'-f', 'mp4', self.dest
]))
self.popen_mock.assert_called_once_with(
ffmpeg_args, stderr=subprocess.PIPE)
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1082,
81,
28,
7266,
14681,
13,
47,
4061,
36,
8,
198
] | 2.163906 | 1,147 |
from egnyte.tests.config import EgnyteTestCase
from egnyte.tests.helpers import upload_file
EGNYTE_FILE_NAME = '/sample.png'
FILE_NAME = 'FILE1.png'
COMMENT = 'test comment'
| [
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] | 2.793651 | 63 |
from setuptools import setup
import re
requirements = []
for line in open('requirements.txt'):
req = line.split('#', 1)[0] # strip comments
requirements.append(req.strip())
# follow https://stackoverflow.com/a/7071358/419116
VERSIONFILE = "ncluster/_version.py"
verstrline = open(VERSIONFILE, "rt").read()
VSRE = r"^__version__ = ['\"]([^'\"]*)['\"]"
mo = re.search(VSRE, verstrline, re.M)
if mo:
verstr = mo.group(1)
else:
raise RuntimeError("Unable to find version string in %s." % (VERSIONFILE,))
setup(scripts=['ncluster/ncluster_cloud_setup.py', # also used as module
'ncluster/ncluster_cloud_wipe.py',
'tools/nsync',
'tools/ncluster'],
install_requires=requirements,
version=verstr,
)
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] | 2.41195 | 318 |
# Copyright 2019 Apex.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 os
import sys
import time
import unittest
import uuid
import launch
import launch_ros
import launch_ros.actions
import launch_testing.actions
import launch_testing_ros
import pytest
import std_msgs.msg
@pytest.mark.rostest
| [
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13,
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628,
198,
31,
9078,
9288,
13,
4102,
13,
23341,
395,
628
] | 3.617778 | 225 |
# coding: utf-8
"""
No descripton provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1
Generated by: https://github.com/swagger-api/swagger-codegen.git
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 pprint import pformat
from six import iteritems
import re
class V1ServiceSpec(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, ports=None, selector=None, clusterIP=None, type=None, externalIPs=None, deprecatedPublicIPs=None, sessionAffinity=None, loadBalancerIP=None, loadBalancerSourceRanges=None):
"""
V1ServiceSpec - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition.
"""
self.swagger_types = {
'ports': 'list[V1ServicePort]',
'selector': 'object',
'clusterIP': 'str',
'type': 'str',
'externalIPs': 'list[str]',
'deprecatedPublicIPs': 'list[str]',
'sessionAffinity': 'str',
'loadBalancerIP': 'str',
'loadBalancerSourceRanges': 'list[str]'
}
self.attribute_map = {
'ports': 'ports',
'selector': 'selector',
'clusterIP': 'clusterIP',
'type': 'type',
'externalIPs': 'externalIPs',
'deprecatedPublicIPs': 'deprecatedPublicIPs',
'sessionAffinity': 'sessionAffinity',
'loadBalancerIP': 'loadBalancerIP',
'loadBalancerSourceRanges': 'loadBalancerSourceRanges'
}
self._ports = ports
self._selector = selector
self._clusterIP = clusterIP
self._type = type
self._externalIPs = externalIPs
self._deprecatedPublicIPs = deprecatedPublicIPs
self._sessionAffinity = sessionAffinity
self._loadBalancerIP = loadBalancerIP
self._loadBalancerSourceRanges = loadBalancerSourceRanges
@property
def ports(self):
"""
Gets the ports of this V1ServiceSpec.
The list of ports that are exposed by this service. More info: http://releases.k8s.io/HEAD/docs/user-guide/services.md#virtual-ips-and-service-proxies
:return: The ports of this V1ServiceSpec.
:rtype: list[V1ServicePort]
"""
return self._ports
@ports.setter
def ports(self, ports):
"""
Sets the ports of this V1ServiceSpec.
The list of ports that are exposed by this service. More info: http://releases.k8s.io/HEAD/docs/user-guide/services.md#virtual-ips-and-service-proxies
:param ports: The ports of this V1ServiceSpec.
:type: list[V1ServicePort]
"""
self._ports = ports
@property
def selector(self):
"""
Gets the selector of this V1ServiceSpec.
This service will route traffic to pods having labels matching this selector. Label keys and values that must match in order to receive traffic for this service. If not specified, endpoints must be manually specified and the system will not automatically manage them. More info: http://releases.k8s.io/HEAD/docs/user-guide/services.md#overview
:return: The selector of this V1ServiceSpec.
:rtype: object
"""
return self._selector
@selector.setter
def selector(self, selector):
"""
Sets the selector of this V1ServiceSpec.
This service will route traffic to pods having labels matching this selector. Label keys and values that must match in order to receive traffic for this service. If not specified, endpoints must be manually specified and the system will not automatically manage them. More info: http://releases.k8s.io/HEAD/docs/user-guide/services.md#overview
:param selector: The selector of this V1ServiceSpec.
:type: object
"""
self._selector = selector
@property
def clusterIP(self):
"""
Gets the clusterIP of this V1ServiceSpec.
ClusterIP is usually assigned by the master and is the IP address of the service. If specified, it will be allocated to the service if it is unused or else creation of the service will fail. Valid values are None, empty string (\"\"), or a valid IP address. 'None' can be specified for a headless service when proxying is not required. Cannot be updated. More info: http://releases.k8s.io/HEAD/docs/user-guide/services.md#virtual-ips-and-service-proxies
:return: The clusterIP of this V1ServiceSpec.
:rtype: str
"""
return self._clusterIP
@clusterIP.setter
def clusterIP(self, clusterIP):
"""
Sets the clusterIP of this V1ServiceSpec.
ClusterIP is usually assigned by the master and is the IP address of the service. If specified, it will be allocated to the service if it is unused or else creation of the service will fail. Valid values are None, empty string (\"\"), or a valid IP address. 'None' can be specified for a headless service when proxying is not required. Cannot be updated. More info: http://releases.k8s.io/HEAD/docs/user-guide/services.md#virtual-ips-and-service-proxies
:param clusterIP: The clusterIP of this V1ServiceSpec.
:type: str
"""
self._clusterIP = clusterIP
@property
def type(self):
"""
Gets the type of this V1ServiceSpec.
Type of exposed service. Must be ClusterIP, NodePort, or LoadBalancer. Defaults to ClusterIP. More info: http://releases.k8s.io/HEAD/docs/user-guide/services.md#external-services
:return: The type of this V1ServiceSpec.
:rtype: str
"""
return self._type
@type.setter
def type(self, type):
"""
Sets the type of this V1ServiceSpec.
Type of exposed service. Must be ClusterIP, NodePort, or LoadBalancer. Defaults to ClusterIP. More info: http://releases.k8s.io/HEAD/docs/user-guide/services.md#external-services
:param type: The type of this V1ServiceSpec.
:type: str
"""
self._type = type
@property
def externalIPs(self):
"""
Gets the externalIPs of this V1ServiceSpec.
externalIPs is a list of IP addresses for which nodes in the cluster will also accept traffic for this service. These IPs are not managed by Kubernetes. The user is responsible for ensuring that traffic arrives at a node with this IP. A common example is external load-balancers that are not part of the Kubernetes system. A previous form of this functionality exists as the deprecatedPublicIPs field. When using this field, callers should also clear the deprecatedPublicIPs field.
:return: The externalIPs of this V1ServiceSpec.
:rtype: list[str]
"""
return self._externalIPs
@externalIPs.setter
def externalIPs(self, externalIPs):
"""
Sets the externalIPs of this V1ServiceSpec.
externalIPs is a list of IP addresses for which nodes in the cluster will also accept traffic for this service. These IPs are not managed by Kubernetes. The user is responsible for ensuring that traffic arrives at a node with this IP. A common example is external load-balancers that are not part of the Kubernetes system. A previous form of this functionality exists as the deprecatedPublicIPs field. When using this field, callers should also clear the deprecatedPublicIPs field.
:param externalIPs: The externalIPs of this V1ServiceSpec.
:type: list[str]
"""
self._externalIPs = externalIPs
@property
def deprecatedPublicIPs(self):
"""
Gets the deprecatedPublicIPs of this V1ServiceSpec.
deprecatedPublicIPs is deprecated and replaced by the externalIPs field with almost the exact same semantics. This field is retained in the v1 API for compatibility until at least 8/20/2016. It will be removed from any new API revisions. If both deprecatedPublicIPs *and* externalIPs are set, deprecatedPublicIPs is used.
:return: The deprecatedPublicIPs of this V1ServiceSpec.
:rtype: list[str]
"""
return self._deprecatedPublicIPs
@deprecatedPublicIPs.setter
def deprecatedPublicIPs(self, deprecatedPublicIPs):
"""
Sets the deprecatedPublicIPs of this V1ServiceSpec.
deprecatedPublicIPs is deprecated and replaced by the externalIPs field with almost the exact same semantics. This field is retained in the v1 API for compatibility until at least 8/20/2016. It will be removed from any new API revisions. If both deprecatedPublicIPs *and* externalIPs are set, deprecatedPublicIPs is used.
:param deprecatedPublicIPs: The deprecatedPublicIPs of this V1ServiceSpec.
:type: list[str]
"""
self._deprecatedPublicIPs = deprecatedPublicIPs
@property
def sessionAffinity(self):
"""
Gets the sessionAffinity of this V1ServiceSpec.
Supports \"ClientIP\" and \"None\". Used to maintain session affinity. Enable client IP based session affinity. Must be ClientIP or None. Defaults to None. More info: http://releases.k8s.io/HEAD/docs/user-guide/services.md#virtual-ips-and-service-proxies
:return: The sessionAffinity of this V1ServiceSpec.
:rtype: str
"""
return self._sessionAffinity
@sessionAffinity.setter
def sessionAffinity(self, sessionAffinity):
"""
Sets the sessionAffinity of this V1ServiceSpec.
Supports \"ClientIP\" and \"None\". Used to maintain session affinity. Enable client IP based session affinity. Must be ClientIP or None. Defaults to None. More info: http://releases.k8s.io/HEAD/docs/user-guide/services.md#virtual-ips-and-service-proxies
:param sessionAffinity: The sessionAffinity of this V1ServiceSpec.
:type: str
"""
self._sessionAffinity = sessionAffinity
@property
def loadBalancerIP(self):
"""
Gets the loadBalancerIP of this V1ServiceSpec.
Only applies to Service Type: LoadBalancer LoadBalancer will get created with the IP specified in this field. This feature depends on whether the underlying cloud-provider supports specifying the loadBalancerIP when a load balancer is created. This field will be ignored if the cloud-provider does not support the feature.
:return: The loadBalancerIP of this V1ServiceSpec.
:rtype: str
"""
return self._loadBalancerIP
@loadBalancerIP.setter
def loadBalancerIP(self, loadBalancerIP):
"""
Sets the loadBalancerIP of this V1ServiceSpec.
Only applies to Service Type: LoadBalancer LoadBalancer will get created with the IP specified in this field. This feature depends on whether the underlying cloud-provider supports specifying the loadBalancerIP when a load balancer is created. This field will be ignored if the cloud-provider does not support the feature.
:param loadBalancerIP: The loadBalancerIP of this V1ServiceSpec.
:type: str
"""
self._loadBalancerIP = loadBalancerIP
@property
def loadBalancerSourceRanges(self):
"""
Gets the loadBalancerSourceRanges of this V1ServiceSpec.
If specified and supported by the platform, this will restrict traffic through the cloud-provider load-balancer will be restricted to the specified client IPs. This field will be ignored if the cloud-provider does not support the feature.\" More info: http://releases.k8s.io/HEAD/docs/user-guide/services-firewalls.md
:return: The loadBalancerSourceRanges of this V1ServiceSpec.
:rtype: list[str]
"""
return self._loadBalancerSourceRanges
@loadBalancerSourceRanges.setter
def loadBalancerSourceRanges(self, loadBalancerSourceRanges):
"""
Sets the loadBalancerSourceRanges of this V1ServiceSpec.
If specified and supported by the platform, this will restrict traffic through the cloud-provider load-balancer will be restricted to the specified client IPs. This field will be ignored if the cloud-provider does not support the feature.\" More info: http://releases.k8s.io/HEAD/docs/user-guide/services-firewalls.md
:param loadBalancerSourceRanges: The loadBalancerSourceRanges of this V1ServiceSpec.
:type: list[str]
"""
self._loadBalancerSourceRanges = loadBalancerSourceRanges
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
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220,
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220,
220,
220,
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1988,
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220,
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220,
220,
220,
1255,
58,
35226,
60,
796,
1988,
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62,
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220,
220,
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1288,
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] | 2.705067 | 5,408 |
# This file is part of PeachPy package and is licensed under the Simplified BSD license.
# See license.rst for the full text of the license.
import peachpy.x86_64.abi
import peachpy.x86_64.uarch
import peachpy.x86_64.isa
from peachpy.x86_64.registers import GeneralPurposeRegister, \
GeneralPurposeRegister8, GeneralPurposeRegister16, GeneralPurposeRegister32, GeneralPurposeRegister64, \
MMXRegister, XMMRegister, YMMRegister, ZMMRegister, KRegister, \
rax, rbx, rcx, rdx, rsi, rdi, rbp, r8, r9, r10, r11, r12, r13, r14, r15, \
eax, ebx, ecx, edx, esi, edi, ebp, r8d, r9d, r10d, r11d, r12d, r13d, r14d, r15d, \
ax, bx, cx, dx, si, di, bp, r8w, r9w, r10w, r11w, r12w, r13w, r14w, r15w, \
al, ah, bl, bh, cl, ch, dl, dh, sil, dil, bpl, r8b, r9b, r10b, r11b, r12b, r13b, r14b, r15b, \
mm0, mm1, mm2, mm3, mm4, mm5, mm6, mm7, \
xmm0, xmm1, xmm2, xmm3, xmm4, xmm5, xmm6, xmm7, xmm8, xmm9, xmm10, xmm11, xmm12, xmm13, xmm14, xmm15, \
xmm16, xmm17, xmm18, xmm19, xmm20, xmm21, xmm22, xmm23, xmm24, xmm25, xmm26, xmm27, xmm28, xmm29, xmm30, xmm31, \
ymm0, ymm1, ymm2, ymm3, ymm4, ymm5, ymm6, ymm7, ymm8, ymm9, ymm10, ymm11, ymm12, ymm13, ymm14, ymm15, \
ymm16, ymm17, ymm18, ymm19, ymm20, ymm21, ymm22, ymm23, ymm24, ymm25, ymm26, ymm27, ymm28, ymm29, ymm30, ymm31, \
zmm0, zmm1, zmm2, zmm3, zmm4, zmm5, zmm6, zmm7, zmm8, zmm9, zmm10, zmm11, zmm12, zmm13, zmm14, zmm15, \
zmm16, zmm17, zmm18, zmm19, zmm20, zmm21, zmm22, zmm23, zmm24, zmm25, zmm26, zmm27, zmm28, zmm29, zmm30, zmm31, \
k0, k1, k2, k3, k4, k5, k6, k7
from peachpy.x86_64.function import Function, LocalVariable
from peachpy.x86_64.operand import byte, word, dword, qword, oword, hword, yword, zword, \
rn_sae, rz_sae, ru_sae, rd_sae, sae
from peachpy.x86_64.pseudo import Label, Loop, Block, \
LABEL, ALIGN, IACA, RETURN, LOAD, STORE, SWAP, REDUCE
from peachpy.x86_64.nacl import NACLJMP
from peachpy.x86_64.generic import \
ADD, SUB, ADC, SBB, ADCX, ADOX, \
AND, OR, XOR, ANDN, \
NOT, NEG, INC, DEC, \
TEST, CMP, \
MOV, MOVZX, MOVSX, MOVSXD, MOVBE, MOVNTI, \
BT, BTS, BTR, BTC, POPCNT, BSWAP, \
BSF, BSR, LZCNT, TZCNT, \
SHR, SAR, SHL, SAL, SHRX, SARX, SHLX, \
SHRD, SHLD, \
ROR, ROL, RORX, \
RCR, RCL, \
IMUL, MUL, MULX, \
IDIV, DIV, \
LEA, PUSH, POP, \
POPCNT, LZCNT, TZCNT, \
BEXTR, PDEP, PEXT, \
BZHI, \
BLCFILL, BLCI, BLCIC, BLCMSK, BLCS, \
BLSFILL, BLSI, BLSIC, BLSMSK, BLSR, \
T1MSKC, TZMSK, \
CRC32, \
CBW, CDQ, CQO, \
CWD, CWDE, CDQE, \
CMOVA, CMOVNA, CMOVAE, CMOVNAE, \
CMOVB, CMOVNB, CMOVBE, CMOVNBE, \
CMOVC, CMOVNC, CMOVE, CMOVNE, \
CMOVG, CMOVNG, CMOVGE, CMOVNGE, \
CMOVL, CMOVNL, CMOVLE, CMOVNLE, \
CMOVO, CMOVNO, CMOVP, CMOVNP, \
CMOVS, CMOVNS, CMOVZ, CMOVNZ, \
CMOVPE, CMOVPO, \
SETA, SETNA, SETAE, SETNAE, \
SETB, SETNB, SETBE, SETNBE, \
SETC, SETNC, SETE, SETNE, \
SETG, SETNG, SETGE, SETNGE, \
SETL, SETNL, SETLE, SETNLE, \
SETO, SETNO, SETP, SETNP, \
SETS, SETNS, SETZ, SETNZ, \
SETPE, SETPO, \
JA, JNA, JAE, JNAE, \
JB, JNB, JBE, JNBE, \
JC, JNC, JE, JNE, \
JG, JNG, JGE, JNGE, \
JL, JNL, JLE, JNLE, \
JO, JNO, JP, JNP, \
JS, JNS, JZ, JNZ, \
JPE, JPO, JMP, \
JRCXZ, JECXZ, \
RET, CALL, \
PAUSE, NOP, \
INT, UD2, \
CPUID, RDTSC, RDTSCP, XGETBV, \
SYSCALL, \
STC, CLC, CMC, \
STD, CLD, \
XADD, XCHG, \
CMPXCHG, CMPXCHG8B, CMPXCHG16B, \
SFENCE, MFENCE, LFENCE, \
PREFETCHNTA, PREFETCHT0, PREFETCHT1, PREFETCHT2, PREFETCH, PREFETCHW, PREFETCHWT1, \
CLFLUSH, CLFLUSHOPT, CLWB, CLZERO
from peachpy.x86_64.mmxsse import \
MOVSS, EXTRACTPS, INSERTPS, \
ADDSS, SUBSS, MULSS, DIVSS, SQRTSS, \
ROUNDSS, MINSS, MAXSS, RCPSS, RSQRTSS, \
CMPSS, COMISS, UCOMISS, \
MOVSD, ADDSD, SUBSD, MULSD, DIVSD, SQRTSD, \
ROUNDSD, MINSD, MAXSD, \
CMPSD, COMISD, UCOMISD, \
MOVAPS, MOVUPS, MOVLPS, MOVNTPS, \
MOVHPS, MOVSLDUP, MOVSHDUP, \
MOVAPD, MOVUPD, MOVLPD, MOVNTPD, \
MOVHPD, MOVDDUP, \
ADDPS, HADDPS, SUBPS, HSUBPS, ADDSUBPS, MULPS, DIVPS, SQRTPS, \
ADDPD, HADDPD, SUBPD, HSUBPD, ADDSUBPD, MULPD, DIVPD, SQRTPD, \
ROUNDPS, MINPS, MAXPS, RCPPS, RSQRTPS, DPPS, \
CMPPS, MOVMSKPS, \
ROUNDPD, MINPD, MAXPD, DPPD, \
CMPPD, MOVMSKPD, \
ANDPS, ANDNPS, ORPS, XORPS, BLENDPS, BLENDVPS, \
ANDPD, ANDNPD, ORPD, XORPD, BLENDPD, BLENDVPD, \
UNPCKLPS, UNPCKHPS, MOVLHPS, MOVHLPS, SHUFPS, \
UNPCKLPD, UNPCKHPD, SHUFPD, \
MOVD, MOVQ, MOVDQ2Q, MOVQ2DQ, MOVDQA, MOVDQU, LDDQU, \
MASKMOVQ, MASKMOVDQU, \
MOVNTQ, MOVNTDQ, MOVNTDQA, \
PMOVSXBW, PMOVSXBD, PMOVSXBQ, PMOVSXWD, PMOVSXWQ, PMOVSXDQ, \
PMOVZXBW, PMOVZXBD, PMOVZXBQ, PMOVZXWD, PMOVZXWQ, PMOVZXDQ, \
PEXTRB, PEXTRW, PEXTRD, PEXTRQ, \
PINSRB, PINSRW, PINSRD, PINSRQ, \
PMOVMSKB, PTEST, \
PADDB, PADDW, PADDD, PADDQ, PADDSB, PADDSW, PADDUSB, PADDUSW, \
PHADDW, PHADDD, PHADDSW, \
PSUBB, PSUBW, PSUBD, PSUBQ, PSUBSB, PSUBSW, PSUBUSB, PSUBUSW, \
PHSUBW, PHSUBD, PHSUBSW, \
PMAXSB, PMAXSW, PMAXSD, PMAXUB, PMAXUW, PMAXUD, \
PMINSB, PMINSW, PMINSD, PMINUB, PMINUW, PMINUD, \
PSLLW, PSLLD, PSLLQ, PSRLW, PSRLD, PSRLQ, PSRAW, PSRAD, \
PMULLW, PMULHW, PMULHUW, PMULLD, PMULDQ, PMULUDQ, \
PMULHRSW, PMADDWD, PMADDUBSW, \
PAVGB, PAVGW, \
PSADBW, MPSADBW, PHMINPOSUW, \
PCMPEQB, PCMPEQW, PCMPEQD, PCMPEQQ, \
PCMPGTB, PCMPGTW, PCMPGTD, PCMPGTQ, \
PABSB, PABSW, PABSD, PSIGNB, PSIGNW, PSIGND, \
PAND, PANDN, POR, PXOR, PBLENDW, PBLENDVB, \
PUNPCKLBW, PUNPCKLWD, PUNPCKLDQ, PUNPCKLQDQ, \
PUNPCKHBW, PUNPCKHWD, PUNPCKHDQ, PUNPCKHQDQ, \
PACKSSWB, PACKSSDW, PACKUSWB, PACKUSDW, \
PSHUFB, PSHUFW, PSHUFLW, PSHUFHW, PSHUFD, \
PSLLDQ, PSRLDQ, PALIGNR, \
PCMPESTRI, PCMPESTRM, PCMPISTRI, PCMPISTRM, \
CVTSS2SI, CVTTSS2SI, CVTSI2SS, \
CVTSD2SI, CVTTSD2SI, CVTSI2SD, \
CVTPS2DQ, CVTTPS2DQ, CVTDQ2PS, \
CVTPD2DQ, CVTTPD2DQ, CVTDQ2PD, \
CVTPS2PI, CVTTPS2PI, CVTPI2PS, \
CVTPD2PI, CVTTPD2PI, CVTPI2PD, \
CVTSD2SS, CVTSS2SD, \
CVTPD2PS, CVTPS2PD, \
LDMXCSR, STMXCSR, \
EMMS
from peachpy.x86_64.avx import \
VMOVSS, VEXTRACTPS, VINSERTPS, \
VADDSS, VSUBSS, VMULSS, VDIVSS, VSQRTSS, \
VROUNDSS, VRNDSCALESS, VRANGESS, \
VMINSS, VMAXSS, VREDUCESS, \
VGETMANTSS, VGETEXPSS, VSCALEFSS, VFIXUPIMMSS, VFPCLASSSS, \
VRCPSS, VRSQRTSS, VRCP14SS, VRSQRT14SS, VRCP28SS, VRSQRT28SS, \
VCMPSS, VCOMISS, VUCOMISS, \
VMOVSD, VADDSD, VSUBSD, VMULSD, VDIVSD, VSQRTSD, \
VROUNDSD, VRNDSCALESD, VRANGESD, \
VMINSD, VMAXSD, VREDUCESD, \
VGETMANTSD, VGETEXPSD, VSCALEFSD, VFIXUPIMMSD, VFPCLASSSD, \
VRCP14SD, VRSQRT14SD, VRCP28SD, VRSQRT28SD, \
VCMPSD, VCOMISD, VUCOMISD, \
VMOVAPS, VMOVUPS, VMOVLPS, VMOVHPS, \
VMASKMOVPS, VMOVMSKPS, VMOVNTPS, \
VBROADCASTSS, VMOVSLDUP, VMOVSHDUP, \
VEXPANDPS, VCOMPRESSPS, \
VGATHERDPS, VGATHERQPS, \
VGATHERPF0DPS, VGATHERPF0QPS, \
VGATHERPF1DPS, VGATHERPF1QPS, \
VSCATTERDPS, VSCATTERQPS, \
VSCATTERPF0DPS, VSCATTERPF0QPS, \
VSCATTERPF1DPS, VSCATTERPF1QPS, \
VMOVAPD, VMOVUPD, VMOVLPD, VMOVHPD, \
VMASKMOVPD, VMOVMSKPD, VMOVNTPD, \
VBROADCASTSD, VMOVDDUP, \
VEXPANDPD, VCOMPRESSPD, \
VGATHERDPD, VGATHERQPD, \
VGATHERPF0DPD, VGATHERPF0QPD, \
VGATHERPF1DPD, VGATHERPF1QPD, \
VSCATTERDPD, VSCATTERQPD, \
VSCATTERPF0DPD, VSCATTERPF0QPD, \
VSCATTERPF1DPD, VSCATTERPF1QPD, \
VADDPS, VHADDPS, VSUBPS, VHSUBPS, VADDSUBPS, VMULPS, VDIVPS, VSQRTPS, \
VADDPD, VHADDPD, VSUBPD, VHSUBPD, VADDSUBPD, VMULPD, VDIVPD, VSQRTPD, \
VROUNDPS, VRNDSCALEPS, VRANGEPS, \
VMINPS, VMAXPS, VREDUCEPS, VDPPS, \
VGETMANTPS, VGETEXPPS, VSCALEFPS, VFIXUPIMMPS, VFPCLASSPS, \
VRCPPS, VRSQRTPS, VRCP14PS, VRSQRT14PS, VRCP28PS, VRSQRT28PS, VEXP2PS, \
VCMPPS, VTESTPS, \
VROUNDPD, VRNDSCALEPD, VRANGEPD, \
VMINPD, VMAXPD, VREDUCEPD, VDPPD, \
VGETMANTPD, VGETEXPPD, VSCALEFPD, VFIXUPIMMPD, VFPCLASSPD, \
VRCP14PD, VRSQRT14PD, VRCP28PD, VRSQRT28PD, VEXP2PD, \
VCMPPD, VTESTPD, \
VANDPS, VANDNPS, VORPS, VXORPS, VBLENDPS, VBLENDVPS, VBLENDMPS, \
VANDPD, VANDNPD, VORPD, VXORPD, VBLENDPD, VBLENDVPD, VBLENDMPD, \
VUNPCKLPS, VUNPCKHPS, VMOVLHPS, VMOVHLPS, VSHUFPS, \
VUNPCKLPD, VUNPCKHPD, VSHUFPD, \
VPERMPS, VPERMILPS, VPERMT2PS, VPERMI2PS, \
VPERMPD, VPERMILPD, VPERMT2PD, VPERMI2PD, \
VMOVD, VMOVQ, VMOVDQA, VMOVDQA32, VMOVDQA64, \
VMOVDQU, VMOVDQU8, VMOVDQU16, VMOVDQU32, VMOVDQU64, VLDDQU, \
VPBROADCASTB, VPBROADCASTW, VPBROADCASTD, VPBROADCASTQ, \
VPEXPANDD, VPEXPANDQ, \
VPCOMPRESSD, VPCOMPRESSQ, \
VPMASKMOVD, VPMASKMOVQ, VMASKMOVDQU, VMOVNTDQ, VMOVNTDQA, \
VPMOVSXBW, VPMOVSXBD, VPMOVSXBQ, VPMOVSXWD, VPMOVSXWQ, VPMOVSXDQ, \
VPMOVZXBW, VPMOVZXBD, VPMOVZXBQ, VPMOVZXWD, VPMOVZXWQ, VPMOVZXDQ, \
VPMOVWB, VPMOVDB, VPMOVDW, VPMOVQB, VPMOVQW, VPMOVQD, \
VPMOVSWB, VPMOVSDB, VPMOVSDW, VPMOVSQB, VPMOVSQW, VPMOVSQD, \
VPMOVUSWB, VPMOVUSDB, VPMOVUSDW, VPMOVUSQB, VPMOVUSQW, VPMOVUSQD, \
VPEXTRB, VPEXTRW, VPEXTRD, VPEXTRQ, \
VPINSRB, VPINSRW, VPINSRD, VPINSRQ, \
VPGATHERDD, VPGATHERDQ, VPGATHERQD, VPGATHERQQ, \
VPSCATTERDD, VPSCATTERDQ, VPSCATTERQD, VPSCATTERQQ, \
VPCONFLICTD, VPCONFLICTQ, \
VPLZCNTD, VPLZCNTQ, \
VPTEST, VPMOVMSKB, \
VPADDB, VPADDW, VPADDD, VPADDQ, VPADDSB, VPADDSW, VPADDUSB, VPADDUSW, \
VPHADDW, VPHADDD, VPHADDSW, \
VPSUBB, VPSUBW, VPSUBD, VPSUBQ, VPSUBSB, VPSUBSW, VPSUBUSB, VPSUBUSW, \
VPHSUBW, VPHSUBD, VPHSUBSW, \
VPMAXSB, VPMAXSW, VPMAXSD, VPMAXSQ, VPMAXUB, VPMAXUW, VPMAXUD, VPMAXUQ, \
VPMINSB, VPMINSW, VPMINSD, VPMINSQ, VPMINUB, VPMINUW, VPMINUD, VPMINUQ, \
VPSLLW, VPSLLD, VPSLLQ, VPSRLW, VPSRLD, VPSRLQ, VPSRAW, VPSRAD, VPSRAQ, \
VPROLD, VPROLQ, VPRORD, VPRORQ, \
VPSLLVW, VPSLLVD, VPSLLVQ, VPSRLVW, VPSRLVD, VPSRLVQ, VPSRAVW, VPSRAVD, VPSRAVQ, \
VPROLVD, VPROLVQ, VPRORVD, VPRORVQ, \
VPMULLW, VPMULHW, VPMULHUW, VPMULLD, VPMULLQ, VPMULDQ, VPMULUDQ, \
VPMULHRSW, VPMADDWD, VPMADDUBSW, \
VPMADD52LUQ, VPMADD52HUQ, \
VPAVGB, VPAVGW, \
VPSADBW, VMPSADBW, VDBPSADBW, VPHMINPOSUW, \
VPCMPEQB, VPCMPEQW, VPCMPEQD, VPCMPEQQ, \
VPCMPGTB, VPCMPGTW, VPCMPGTD, VPCMPGTQ, \
VPCMPB, VPCMPW, VPCMPD, VPCMPQ, \
VPCMPUB, VPCMPUW, VPCMPUD, VPCMPUQ, \
VPABSB, VPABSW, VPABSD, VPABSQ, VPSIGNB, VPSIGNW, VPSIGND, \
VPAND, VPANDD, VPANDQ, VPANDN, VPANDND, VPANDNQ, \
VPOR, VPORD, VPORQ, VPXOR, VPXORD, VPXORQ, \
VPTERNLOGD, VPTERNLOGQ, \
VPBLENDW, VPBLENDVB, VPBLENDD, \
VPBLENDMB, VPBLENDMW, VPBLENDMD, VPBLENDMQ, \
VPUNPCKLBW, VPUNPCKLWD, VPUNPCKLDQ, VPUNPCKLQDQ, \
VPUNPCKHBW, VPUNPCKHWD, VPUNPCKHDQ, VPUNPCKHQDQ, \
VPACKSSWB, VPACKSSDW, VPACKUSWB, VPACKUSDW, \
VPSHUFB, VPSHUFLW, VPSHUFHW, VPSHUFD, \
VPERMB, VPERMW, VPERMD, VPERMQ, \
VPERMT2B, VPERMT2W, VPERMT2D, VPERMT2Q, \
VPERMI2B, VPERMI2W, VPERMI2D, VPERMI2Q, \
VPSLLDQ, VPSRLDQ, VPALIGNR, VALIGND, VALIGNQ, VPMULTISHIFTQB, \
VPOPCNTD, VPOPCNTQ, \
VPCMPESTRI, VPCMPESTRM, VPCMPISTRI, VPCMPISTRM, \
VCVTSS2SI, VCVTSS2USI, VCVTTSS2SI, VCVTTSS2USI, VCVTSI2SS, VCVTUSI2SS, \
VCVTSD2SI, VCVTSD2USI, VCVTTSD2SI, VCVTTSD2USI, VCVTSI2SD, VCVTUSI2SD, \
VCVTPS2DQ, VCVTPS2UDQ, VCVTTPS2DQ, VCVTTPS2UDQ, VCVTDQ2PS, VCVTUDQ2PS, \
VCVTPS2QQ, VCVTPS2UQQ, VCVTTPS2QQ, VCVTTPS2UQQ, VCVTQQ2PS, VCVTUQQ2PS, \
VCVTPD2DQ, VCVTPD2UDQ, VCVTTPD2DQ, VCVTTPD2UDQ, VCVTDQ2PD, VCVTUDQ2PD, \
VCVTPD2QQ, VCVTPD2UQQ, VCVTTPD2QQ, VCVTTPD2UQQ, VCVTQQ2PD, VCVTUQQ2PD, \
VCVTSD2SS, VCVTSS2SD, \
VCVTPD2PS, VCVTPS2PD, \
VCVTPS2PH, VCVTPH2PS, \
VBROADCASTF128, VBROADCASTI128, \
VBROADCASTF32X2, VBROADCASTI32X2, \
VBROADCASTF32X4, VBROADCASTI32X4, \
VBROADCASTF32X8, VBROADCASTI32X8, \
VBROADCASTF64X2, VBROADCASTI64X2, \
VBROADCASTF64X4, VBROADCASTI64X4, \
VEXTRACTF128, VEXTRACTI128, \
VEXTRACTF32X4, VEXTRACTI32X4, \
VEXTRACTF32X8, VEXTRACTI32X8, \
VEXTRACTF64X2, VEXTRACTI64X2, \
VEXTRACTF64X4, VEXTRACTI64X4, \
VINSERTF128, VINSERTI128, \
VINSERTF32X4, VINSERTI32X4, \
VINSERTF32X8, VINSERTI32X8, \
VINSERTF64X2, VINSERTI64X2, \
VINSERTF64X4, VINSERTI64X4, \
VPERM2F128, VPERM2I128, \
VSHUFF32X4, VSHUFI32X4, \
VSHUFF64X2, VSHUFI64X2, \
VPTESTMB, VPTESTMW, VPTESTMD, VPTESTMQ, \
VPTESTNMB, VPTESTNMW, VPTESTNMD, VPTESTNMQ, \
VPMOVB2M, VPMOVW2M, VPMOVD2M, VPMOVQ2M, \
VPMOVM2B, VPMOVM2W, VPMOVM2D, VPMOVM2Q, \
VPBROADCASTMB2Q, VPBROADCASTMW2D, \
VPTESTMB, VPTESTMW, VPTESTMD, VPTESTMQ, \
VPTESTNMB, VPTESTNMW, VPTESTNMD, VPTESTNMQ, \
VLDMXCSR, VSTMXCSR, \
VZEROUPPER, VZEROALL
from peachpy.x86_64.fma import \
VFMADD132SS, VFMADD213SS, VFMADD231SS, VFMADDSS, \
VFMSUB132SS, VFMSUB213SS, VFMSUB231SS, VFMSUBSS, \
VFNMADD132SS, VFNMADD213SS, VFNMADD231SS, VFNMADDSS, \
VFNMSUB132SS, VFNMSUB213SS, VFNMSUB231SS, VFNMSUBSS, \
VFMADD132SD, VFMADD213SD, VFMADD231SD, VFMADDSD, \
VFMSUB132SD, VFMSUB213SD, VFMSUB231SD, VFMSUBSD, \
VFNMADD132SD, VFNMADD213SD, VFNMADD231SD, VFNMADDSD, \
VFNMSUB132SD, VFNMSUB213SD, VFNMSUB231SD, VFNMSUBSD, \
VFMADD132PS, VFMADD213PS, VFMADD231PS, VFMADDPS, \
VFMSUB132PS, VFMSUB213PS, VFMSUB231PS, VFMSUBPS, \
VFNMADD132PS, VFNMADD213PS, VFNMADD231PS, VFNMADDPS, \
VFNMSUB132PS, VFNMSUB213PS, VFNMSUB231PS, VFNMSUBPS, \
VFMADD132PD, VFMADD213PD, VFMADD231PD, VFMADDPD, \
VFMSUB132PD, VFMSUB213PD, VFMSUB231PD, VFMSUBPD, \
VFNMADD132PD, VFNMADD213PD, VFNMADD231PD, VFNMADDPD, \
VFNMSUB132PD, VFNMSUB213PD, VFNMSUB231PD, VFNMSUBPD, \
VFMADDSUB132PS, VFMADDSUB213PS, VFMADDSUB231PS, VFMADDSUBPS, \
VFMSUBADD132PS, VFMSUBADD213PS, VFMSUBADD231PS, VFMSUBADDPS, \
VFMADDSUB132PD, VFMADDSUB213PD, VFMADDSUB231PD, VFMADDSUBPD, \
VFMSUBADD132PD, VFMSUBADD213PD, VFMSUBADD231PD, VFMSUBADDPD
from peachpy.x86_64.mask import \
KADDB, KADDW, KADDD, KADDQ, \
KANDB, KANDW, KANDD, KANDQ, \
KANDNB, KANDNW, KANDND, KANDNQ, \
KORB, KORW, KORD, KORQ, \
KXNORB, KXNORW, KXNORD, KXNORQ, \
KXORB, KXORW, KXORD, KXORQ, \
KMOVB, KMOVW, KMOVD, KMOVQ, \
KNOTB, KNOTW, KNOTD, KNOTQ, \
KUNPCKBW, KUNPCKWD, KUNPCKDQ, \
KTESTB, KTESTW, KTESTD, KTESTQ, \
KORTESTB, KORTESTW, KORTESTD, KORTESTQ, \
KSHIFTLB, KSHIFTLW, KSHIFTLD, KSHIFTLQ, \
KSHIFTRB, KSHIFTRW, KSHIFTRD, KSHIFTRQ
from peachpy.x86_64.crypto import \
AESDEC, AESDECLAST, AESENC, AESENCLAST, AESIMC, AESKEYGENASSIST, \
VAESDEC, VAESDECLAST, VAESENC, VAESENCLAST, VAESIMC, VAESKEYGENASSIST, \
SHA1MSG1, SHA1MSG2, SHA1NEXTE, SHA1RNDS4, SHA256MSG1, SHA256MSG2, SHA256RNDS2, \
PCLMULQDQ, VPCLMULQDQ, \
RDRAND, RDSEED
from peachpy.x86_64.amd import \
PAVGUSB, PMULHRW, \
PF2ID, PF2IW, PI2FW, PI2FD, \
PFADD, PFSUB, PFSUBR, PFMUL, PFMAX, PFMIN, \
PFACC, PFNACC, PFPNACC, PSWAPD, \
PFCMPEQ, PFCMPGT, PFCMPGE, \
PFRCP, PFRCPIT1, PFRCPIT2, PFRSQRT, PFRSQIT1, \
FEMMS, \
MOVNTSS, MOVNTSD, \
INSERTQ, EXTRQ, \
VPPERM, VPCMOV, \
VPROTB, VPROTW, VPROTD, VPROTQ, \
VPSHAB, VPSHAW, VPSHAD, VPSHAQ, \
VPSHLB, VPSHLW, VPSHLD, VPSHLQ, \
VPCOMB, VPCOMW, VPCOMD, VPCOMQ, \
VPCOMUB, VPCOMUW, VPCOMUD, VPCOMUQ, \
VPHADDBW, VPHADDBD, VPHADDBQ, VPHADDWD, VPHADDWQ, VPHADDDQ, \
VPHADDUBW, VPHADDUBD, VPHADDUBQ, VPHADDUWD, VPHADDUWQ, VPHADDUDQ, \
VPHSUBBW, VPHSUBWD, VPHSUBDQ, \
VPMACSDQH, VPMACSDQL, VPMACSDD, VPMACSWD, VPMACSWW, VPMADCSWD, \
VPMACSSDD, VPMACSSDQH, VPMACSSDQL, VPMACSSWD, VPMACSSWW, VPMADCSSWD, \
VFRCZSS, VFRCZSD, VFRCZPS, VFRCZPD, \
VPERMIL2PD, VPERMIL2PS
from peachpy.x86_64.types import \
m64, m128, m128d, m128i, m256, m256d, m256i, m512, m512d, m512i, mmask8, mmask16
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8085,
2093,
1433,
198
] | 1.724553 | 9,225 |
#!/usr/bin/python
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
print(dir_path)
| [
2,
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] | 2.380952 | 42 |
from setuptools import setup
VERSION = "0.0.1"
setup(
name="fast_docker_web",
version=VERSION,
description="Wrapps the php web code with Docker environment",
long_description_content_type="text/markdown",
long_description=readme(),
keywords="Docker web php development",
url="https://github.com/danilocgsilva/fast-docker-web",
author="Danilo Silva",
author_email="[email protected]",
packages=["fast_docker_web"],
entry_points={"console_scripts": ["fdocker=fast_docker_web.__main__:main"],},
include_package_data=True
)
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2291,
62,
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28,
17821,
198,
8,
628
] | 2.778846 | 208 |
from datetime import date
CURRENT_DATE = date.today().strftime("%Y-%m-%e")
# FETCH_MEAL_RECORDS = 'SELECT * FROM app_slackuser;'
FETCH_MEAL_RECORDS = f"""
SELECT
to_char(app_mealservice.date, 'DD-MM-YYYY'),
app_mealservice.date_modified,
app_mealservice.user_id,
app_slackuser.firstname,
app_slackuser.lastname,
app_slackuser.user_type,
app_mealservice.breakfast,
app_mealservice.lunch
FROM
app_mealservice
LEFT JOIN
app_slackuser
ON
app_mealservice.user_id = app_slackuser.id
WHERE
date < '{CURRENT_DATE}'
ORDER BY
app_mealservice.date
DESC;
"""
DELETE_MEAL_RECORDS = f"""
DELETE FROM
app_mealservice
WHERE
date < '{CURRENT_DATE}';
"""
FETCH_PANTRY_RECORDS = f"""
SELECT
app_pantry.date,
app_slackuser.id,
app_slackuser.firstname,
app_slackuser.lastname,
app_slackuser.user_type
FROM
app_pantry
LEFT JOIN
app_slackuser
ON
app_pantry.user_id=app_slackuser.id
WHERE
date <= '{CURRENT_DATE}'
ORDER BY
app_pantry.date
DESC;
"""
DELETE_PANTRY_RECORDS = f"""
DELETE FROM
app_pantry
WHERE
date < '{CURRENT_DATE}';
"""
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3128,
1279,
705,
90,
34,
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62,
35,
6158,
92,
17020,
198,
37811,
198
] | 2.068015 | 544 |
import requests
import pytz
from datetime import datetime, timedelta
from bs4 import BeautifulSoup
from model.news import News
from database import Database
import locale
from urllib.request import Request, urlopen
from utils.paragraph_utils import ParagraphUtils
from utils.anchor_utils import AnchorUtils
from utils.log_utils import LogUtils
db = None
def get_news_content_by_href(href):
""" Returns the paragraphs of the article """
paragraphs = []
hdr = {'User-Agent': 'Mozilla/5.0'}
req = Request(href, headers=hdr)
page = urlopen(req).read()
soup = BeautifulSoup(page, 'html.parser')
fullContent = soup.find('div', itemprop="articleBody")
# Get the texts that are outside paragraphs
article_paragraphs = fullContent.find_all('p', recursive=False)
for paragraph in article_paragraphs:
text = paragraph.getText()
if ParagraphUtils.is_valid(text):
text = ParagraphUtils.sanitize(text)
paragraphs.extend(ParagraphUtils.split(text))
# get publish time
datetime_object = get_metadata(soup)
return paragraphs, datetime_object
if __name__ == "__main__":
scrap()
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] | 2.829736 | 417 |
from adaline_gd import *
import matplotlib.pyplot as plt
import numpy as np
# ### Reading-in the Iris data
import pandas as pd
df = pd.read_csv('https://archive.ics.uci.edu/ml/'
'machine-learning-databases/iris/iris.data', header=None)
df.tail()
# select setosa and versicolor
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
# extract sepal length and petal length
X = df.iloc[0:100, [0, 2]].values
# ### adaline
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
ada1 = AdalineGD(n_iter=10, eta=0.01).fit(X, y)
ax[0].plot(range(1, len(ada1.cost_) + 1), np.log10(ada1.cost_), marker='o')
ax[0].set_xlabel('Epochs')
ax[0].set_ylabel('log(Sum-squared-error)')
ax[0].set_title('Adaline - Learning rate 0.01')
ada2 = AdalineGD(n_iter=10, eta=0.0001).fit(X, y)
ax[1].plot(range(1, len(ada2.cost_) + 1), ada2.cost_, marker='o')
ax[1].set_xlabel('Epochs')
ax[1].set_ylabel('Sum-squared-error')
ax[1].set_title('Adaline - Learning rate 0.0001')
plt.tight_layout()
# plt.savefig('./adaline_1.png', dpi=300)
plt.show()
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] | 2.226216 | 473 |
from django import template
from django.utils.html import escape
from django.utils.safestring import mark_safe
from django.utils.html import format_html
register = template.Library()
# Rows
@register.simple_tag
@register.simple_tag
# Columns
@register.simple_tag
@register.simple_tag
# Container
@register.simple_tag
@register.simple_tag | [
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] | 3.203704 | 108 |
import json
import chardet
import codecs
import os.path
from . import page
from . import entry
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# Generated by Django 2.2.3 on 2019-08-07 16:56
from django.db import migrations, models
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from .io_file import *
from .io_data import *
from .io_model import *
from .io_args import *
| [
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] | 2.90625 | 32 |
from django.contrib import admin
# Register your models here.
from .models import Menu
admin.site.register(Menu, BaseAdmin)
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import copy
import utils
| [
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] | 3.5 | 8 |
import numpy as np
import onnx
import onnxruntime
from layers import *
from data import voc, coco
import torch
import cv2
if __name__ == '__main__':
from data import VOC_CLASSES as labels
num_classes = 21
cfg = voc
priorbox = PriorBox(cfg)
with torch.no_grad():
priors = priorbox.forward()
print('prior:', priors[0, :])
detect = Detect(num_classes, 0, 200, 0.01, 0.45)
model = onnx.load('ssd_vgg300.onnx')
onnx.checker.check_model(model)
ort_session = onnxruntime.InferenceSession("ssd_vgg300.onnx")
img = cv2.imread('../data/car.jpg')
x = cv2.resize(img, (300, 300)).astype(np.float32)
print(x.shape)
print(x[0,0,:])
x -= np.array([104.0, 117.0, 123.0])
x = x.astype(np.float32)
print(x.shape)
x = np.transpose(x, (2, 0, 1))
x = x.reshape((1, x.shape[0], x.shape[1], x.shape[2]))
print(x.shape)
ort_inputs = {ort_session.get_inputs()[0].name: x}
ort_outs = ort_session.run(None, ort_inputs)
reg = torch.from_numpy(ort_outs[0][0])
cls = torch.from_numpy(ort_outs[1][0])
print(reg[0])
print(cls[0][0])
detections = detect(reg, cls, priors)
# # scale each detection back up to the image
scale = torch.Tensor(img.shape[1::-1]).repeat(2)
for i in range(detections.size(1)):
j = 0
while detections[0, i, j, 0] >= 0.6:
score = detections[0, i, j, 0]
label_name = labels[i - 1]
display_txt = '%s: %.2f' % (label_name, score)
print(i-1);
pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
#coords = (pt[0], pt[1]), pt[2] - pt[0] + 1, pt[3] - pt[1] + 1)
print(pt, score, label_name)
j+=1
cv2.rectangle(img, (pt[0], pt[1]), (pt[2], pt[3]),(0, 0, 255), 2 )
cv2.imshow('result', img)
cv2.waitKey(-1) | [
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# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: Devialet/CallMeMaybe/GeneratorOptions.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from google.protobuf import descriptor_pb2 as google_dot_protobuf_dot_descriptor__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='Devialet/CallMeMaybe/GeneratorOptions.proto',
package='Devialet.CallMeMaybe',
syntax='proto3',
serialized_options=None,
serialized_pb=_b('\n+Devialet/CallMeMaybe/GeneratorOptions.proto\x12\x14\x44\x65vialet.CallMeMaybe\x1a google/protobuf/descriptor.proto\">\n\x14\x45xtendedFieldOptions\x12\x0e\n\x06secret\x18\x01 \x01(\x08\x12\x16\n\x0e\x64isableLogging\x18\x02 \x01(\x08\"\xa2\x02\n\x19\x45xtendedBytesFieldOptions\x12\x42\n\x04type\x18\x01 \x01(\x0e\x32\x34.Devialet.CallMeMaybe.ExtendedBytesFieldOptions.Type\x12H\n\x07logMode\x18\x02 \x01(\x0e\x32\x37.Devialet.CallMeMaybe.ExtendedBytesFieldOptions.LogMode\x12\x14\n\x0clogMaxLength\x18\x03 \x01(\x05\"-\n\x04Type\x12\x0b\n\x07\x44\x65\x66\x61ult\x10\x00\x12\x08\n\x04Uuid\x10\x01\x12\x0e\n\nJsonObject\x10\x02\"2\n\x07LogMode\x12\x08\n\x04\x41uto\x10\x00\x12\n\n\x06\x42\x61se64\x10\x01\x12\x07\n\x03Raw\x10\x02\x12\x08\n\x04\x44\x65sc\x10\x03\"\x7f\n\x1a\x45xtendedStringFieldOptions\x12\x43\n\x04type\x18\x01 \x01(\x0e\x32\x35.Devialet.CallMeMaybe.ExtendedStringFieldOptions.Type\"\x1c\n\x04Type\x12\x0b\n\x07\x44\x65\x66\x61ult\x10\x00\x12\x07\n\x03Url\x10\x01\"\xc4\x01\n\x19\x45xtendedInt64FieldOptions\x12\x42\n\x04type\x18\x01 \x01(\x0e\x32\x34.Devialet.CallMeMaybe.ExtendedInt64FieldOptions.Type\"c\n\x04Type\x12\x0b\n\x07\x44\x65\x66\x61ult\x10\x00\x12\x0c\n\x08\x44\x61teTime\x10\x01\x12\x0b\n\x07Seconds\x10\x02\x12\x10\n\x0cMilliseconds\x10\x03\x12\x10\n\x0cMicroseconds\x10\x04\x12\x0f\n\x0bNanoseconds\x10\x05\"\x83\x01\n\x1c\x45xtendedRepeatedFieldOptions\x12\x45\n\x04type\x18\x01 \x01(\x0e\x32\x37.Devialet.CallMeMaybe.ExtendedRepeatedFieldOptions.Type\"\x1c\n\x04Type\x12\x0b\n\x07\x44\x65\x66\x61ult\x10\x00\x12\x07\n\x03Set\x10\x01\"\xe0\x01\n\x17\x45xtendedMapFieldOptions\x12@\n\x04type\x18\x01 \x01(\x0e\x32\x32.Devialet.CallMeMaybe.ExtendedMapFieldOptions.Type\x12\x46\n\x07keyType\x18\x02 \x01(\x0e\x32\x35.Devialet.CallMeMaybe.ExtendedMapFieldOptions.KeyType\"\x19\n\x04Type\x12\x07\n\x03Map\x10\x00\x12\x08\n\x04Hash\x10\x01\" \n\x07KeyType\x12\x0b\n\x07\x44\x65\x66\x61ult\x10\x00\x12\x08\n\x04Uuid\x10\x01\"\xc7\x01\n\x1c\x45xtendedPropertyFieldOptions\x12\x19\n\x11minimumApiVersion\x18\x01 \x01(\r\x12\x19\n\x11maximumApiVersion\x18\x02 \x01(\r\x12\x10\n\x08readOnly\x18\x03 \x01(\x08\x12\x10\n\x08\x63onstant\x18\x04 \x01(\x08\x12\x16\n\x0e\x64isableLogging\x18\x05 \x01(\x08\x12\x1c\n\x14\x64isableUpdateLogging\x18\x06 \x01(\x08\x12\x17\n\x0fsupportsReplace\x18\x07 \x01(\x08\"^\n\x16\x45xtendedServiceOptions\x12\x12\n\napiVersion\x18\x01 \x01(\r\x12\x0e\n\x06parent\x18\x02 \x01(\t\x12\x0c\n\x04name\x18\x03 \x01(\t\x12\x12\n\nproperties\x18\x04 \x01(\t\"\x99\x01\n\x15\x45xtendedMethodOptions\x12\x19\n\x11minimumApiVersion\x18\x01 \x01(\r\x12\x19\n\x11maximumApiVersion\x18\x02 \x01(\r\x12\x14\n\x0cnotification\x18\x03 \x01(\x08\x12\x16\n\x0e\x64isableLogging\x18\x04 \x01(\x08\x12\x1c\n\x14useMessageAsArgument\x18\x05 \x01(\x08\"/\n\x18\x45xtendedEnumValueOptions\x12\x13\n\x0b\x64\x65scription\x18\x01 \x01(\t:e\n\tenumValue\x12!.google.protobuf.EnumValueOptions\x18\xe8\x07 \x01(\x0b\x32..Devialet.CallMeMaybe.ExtendedEnumValueOptions:Y\n\x05\x66ield\x12\x1d.google.protobuf.FieldOptions\x18\xe8\x07 \x01(\x0b\x32*.Devialet.CallMeMaybe.ExtendedFieldOptions:^\n\x05\x62ytes\x12\x1d.google.protobuf.FieldOptions\x18\xe9\x07 \x01(\x0b\x32/.Devialet.CallMeMaybe.ExtendedBytesFieldOptions:Z\n\x03map\x12\x1d.google.protobuf.FieldOptions\x18\xea\x07 \x01(\x0b\x32-.Devialet.CallMeMaybe.ExtendedMapFieldOptions:d\n\x08property\x12\x1d.google.protobuf.FieldOptions\x18\xeb\x07 \x01(\x0b\x32\x32.Devialet.CallMeMaybe.ExtendedPropertyFieldOptions:`\n\x06string\x12\x1d.google.protobuf.FieldOptions\x18\xec\x07 \x01(\x0b\x32\x30.Devialet.CallMeMaybe.ExtendedStringFieldOptions:^\n\x05int64\x12\x1d.google.protobuf.FieldOptions\x18\xed\x07 \x01(\x0b\x32/.Devialet.CallMeMaybe.ExtendedInt64FieldOptions:d\n\x08repeated\x12\x1d.google.protobuf.FieldOptions\x18\xee\x07 \x01(\x0b\x32\x32.Devialet.CallMeMaybe.ExtendedRepeatedFieldOptions:_\n\x07service\x12\x1f.google.protobuf.ServiceOptions\x18\xe8\x07 \x01(\x0b\x32,.Devialet.CallMeMaybe.ExtendedServiceOptions:\\\n\x06method\x12\x1e.google.protobuf.MethodOptions\x18\xe8\x07 \x01(\x0b\x32+.Devialet.CallMeMaybe.ExtendedMethodOptionsb\x06proto3')
,
dependencies=[google_dot_protobuf_dot_descriptor__pb2.DESCRIPTOR,])
ENUMVALUE_FIELD_NUMBER = 1000
enumValue = _descriptor.FieldDescriptor(
name='enumValue', full_name='Devialet.CallMeMaybe.enumValue', index=0,
number=1000, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=True, extension_scope=None,
serialized_options=None, file=DESCRIPTOR)
FIELD_FIELD_NUMBER = 1000
field = _descriptor.FieldDescriptor(
name='field', full_name='Devialet.CallMeMaybe.field', index=1,
number=1000, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=True, extension_scope=None,
serialized_options=None, file=DESCRIPTOR)
BYTES_FIELD_NUMBER = 1001
bytes = _descriptor.FieldDescriptor(
name='bytes', full_name='Devialet.CallMeMaybe.bytes', index=2,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=True, extension_scope=None,
serialized_options=None, file=DESCRIPTOR)
MAP_FIELD_NUMBER = 1002
map = _descriptor.FieldDescriptor(
name='map', full_name='Devialet.CallMeMaybe.map', index=3,
number=1002, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=True, extension_scope=None,
serialized_options=None, file=DESCRIPTOR)
PROPERTY_FIELD_NUMBER = 1003
property = _descriptor.FieldDescriptor(
name='property', full_name='Devialet.CallMeMaybe.property', index=4,
number=1003, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=True, extension_scope=None,
serialized_options=None, file=DESCRIPTOR)
STRING_FIELD_NUMBER = 1004
string = _descriptor.FieldDescriptor(
name='string', full_name='Devialet.CallMeMaybe.string', index=5,
number=1004, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=True, extension_scope=None,
serialized_options=None, file=DESCRIPTOR)
INT64_FIELD_NUMBER = 1005
int64 = _descriptor.FieldDescriptor(
name='int64', full_name='Devialet.CallMeMaybe.int64', index=6,
number=1005, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=True, extension_scope=None,
serialized_options=None, file=DESCRIPTOR)
REPEATED_FIELD_NUMBER = 1006
repeated = _descriptor.FieldDescriptor(
name='repeated', full_name='Devialet.CallMeMaybe.repeated', index=7,
number=1006, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=True, extension_scope=None,
serialized_options=None, file=DESCRIPTOR)
SERVICE_FIELD_NUMBER = 1000
service = _descriptor.FieldDescriptor(
name='service', full_name='Devialet.CallMeMaybe.service', index=8,
number=1000, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=True, extension_scope=None,
serialized_options=None, file=DESCRIPTOR)
METHOD_FIELD_NUMBER = 1000
method = _descriptor.FieldDescriptor(
name='method', full_name='Devialet.CallMeMaybe.method', index=9,
number=1000, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=True, extension_scope=None,
serialized_options=None, file=DESCRIPTOR)
_EXTENDEDBYTESFIELDOPTIONS_TYPE = _descriptor.EnumDescriptor(
name='Type',
full_name='Devialet.CallMeMaybe.ExtendedBytesFieldOptions.Type',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='Default', index=0, number=0,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Uuid', index=1, number=1,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='JsonObject', index=2, number=2,
serialized_options=None,
type=None),
],
containing_type=None,
serialized_options=None,
serialized_start=361,
serialized_end=406,
)
_sym_db.RegisterEnumDescriptor(_EXTENDEDBYTESFIELDOPTIONS_TYPE)
_EXTENDEDBYTESFIELDOPTIONS_LOGMODE = _descriptor.EnumDescriptor(
name='LogMode',
full_name='Devialet.CallMeMaybe.ExtendedBytesFieldOptions.LogMode',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='Auto', index=0, number=0,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Base64', index=1, number=1,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Raw', index=2, number=2,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Desc', index=3, number=3,
serialized_options=None,
type=None),
],
containing_type=None,
serialized_options=None,
serialized_start=408,
serialized_end=458,
)
_sym_db.RegisterEnumDescriptor(_EXTENDEDBYTESFIELDOPTIONS_LOGMODE)
_EXTENDEDSTRINGFIELDOPTIONS_TYPE = _descriptor.EnumDescriptor(
name='Type',
full_name='Devialet.CallMeMaybe.ExtendedStringFieldOptions.Type',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='Default', index=0, number=0,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Url', index=1, number=1,
serialized_options=None,
type=None),
],
containing_type=None,
serialized_options=None,
serialized_start=559,
serialized_end=587,
)
_sym_db.RegisterEnumDescriptor(_EXTENDEDSTRINGFIELDOPTIONS_TYPE)
_EXTENDEDINT64FIELDOPTIONS_TYPE = _descriptor.EnumDescriptor(
name='Type',
full_name='Devialet.CallMeMaybe.ExtendedInt64FieldOptions.Type',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='Default', index=0, number=0,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='DateTime', index=1, number=1,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Seconds', index=2, number=2,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Milliseconds', index=3, number=3,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Microseconds', index=4, number=4,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Nanoseconds', index=5, number=5,
serialized_options=None,
type=None),
],
containing_type=None,
serialized_options=None,
serialized_start=687,
serialized_end=786,
)
_sym_db.RegisterEnumDescriptor(_EXTENDEDINT64FIELDOPTIONS_TYPE)
_EXTENDEDREPEATEDFIELDOPTIONS_TYPE = _descriptor.EnumDescriptor(
name='Type',
full_name='Devialet.CallMeMaybe.ExtendedRepeatedFieldOptions.Type',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='Default', index=0, number=0,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Set', index=1, number=1,
serialized_options=None,
type=None),
],
containing_type=None,
serialized_options=None,
serialized_start=892,
serialized_end=920,
)
_sym_db.RegisterEnumDescriptor(_EXTENDEDREPEATEDFIELDOPTIONS_TYPE)
_EXTENDEDMAPFIELDOPTIONS_TYPE = _descriptor.EnumDescriptor(
name='Type',
full_name='Devialet.CallMeMaybe.ExtendedMapFieldOptions.Type',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='Map', index=0, number=0,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Hash', index=1, number=1,
serialized_options=None,
type=None),
],
containing_type=None,
serialized_options=None,
serialized_start=1088,
serialized_end=1113,
)
_sym_db.RegisterEnumDescriptor(_EXTENDEDMAPFIELDOPTIONS_TYPE)
_EXTENDEDMAPFIELDOPTIONS_KEYTYPE = _descriptor.EnumDescriptor(
name='KeyType',
full_name='Devialet.CallMeMaybe.ExtendedMapFieldOptions.KeyType',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='Default', index=0, number=0,
serialized_options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='Uuid', index=1, number=1,
serialized_options=None,
type=None),
],
containing_type=None,
serialized_options=None,
serialized_start=1115,
serialized_end=1147,
)
_sym_db.RegisterEnumDescriptor(_EXTENDEDMAPFIELDOPTIONS_KEYTYPE)
_EXTENDEDFIELDOPTIONS = _descriptor.Descriptor(
name='ExtendedFieldOptions',
full_name='Devialet.CallMeMaybe.ExtendedFieldOptions',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='secret', full_name='Devialet.CallMeMaybe.ExtendedFieldOptions.secret', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='disableLogging', full_name='Devialet.CallMeMaybe.ExtendedFieldOptions.disableLogging', index=1,
number=2, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=103,
serialized_end=165,
)
_EXTENDEDBYTESFIELDOPTIONS = _descriptor.Descriptor(
name='ExtendedBytesFieldOptions',
full_name='Devialet.CallMeMaybe.ExtendedBytesFieldOptions',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='Devialet.CallMeMaybe.ExtendedBytesFieldOptions.type', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='logMode', full_name='Devialet.CallMeMaybe.ExtendedBytesFieldOptions.logMode', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='logMaxLength', full_name='Devialet.CallMeMaybe.ExtendedBytesFieldOptions.logMaxLength', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_EXTENDEDBYTESFIELDOPTIONS_TYPE,
_EXTENDEDBYTESFIELDOPTIONS_LOGMODE,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=168,
serialized_end=458,
)
_EXTENDEDSTRINGFIELDOPTIONS = _descriptor.Descriptor(
name='ExtendedStringFieldOptions',
full_name='Devialet.CallMeMaybe.ExtendedStringFieldOptions',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='Devialet.CallMeMaybe.ExtendedStringFieldOptions.type', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_EXTENDEDSTRINGFIELDOPTIONS_TYPE,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=460,
serialized_end=587,
)
_EXTENDEDINT64FIELDOPTIONS = _descriptor.Descriptor(
name='ExtendedInt64FieldOptions',
full_name='Devialet.CallMeMaybe.ExtendedInt64FieldOptions',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='Devialet.CallMeMaybe.ExtendedInt64FieldOptions.type', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_EXTENDEDINT64FIELDOPTIONS_TYPE,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=590,
serialized_end=786,
)
_EXTENDEDREPEATEDFIELDOPTIONS = _descriptor.Descriptor(
name='ExtendedRepeatedFieldOptions',
full_name='Devialet.CallMeMaybe.ExtendedRepeatedFieldOptions',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='Devialet.CallMeMaybe.ExtendedRepeatedFieldOptions.type', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_EXTENDEDREPEATEDFIELDOPTIONS_TYPE,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=789,
serialized_end=920,
)
_EXTENDEDMAPFIELDOPTIONS = _descriptor.Descriptor(
name='ExtendedMapFieldOptions',
full_name='Devialet.CallMeMaybe.ExtendedMapFieldOptions',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='Devialet.CallMeMaybe.ExtendedMapFieldOptions.type', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='keyType', full_name='Devialet.CallMeMaybe.ExtendedMapFieldOptions.keyType', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
_EXTENDEDMAPFIELDOPTIONS_TYPE,
_EXTENDEDMAPFIELDOPTIONS_KEYTYPE,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=923,
serialized_end=1147,
)
_EXTENDEDPROPERTYFIELDOPTIONS = _descriptor.Descriptor(
name='ExtendedPropertyFieldOptions',
full_name='Devialet.CallMeMaybe.ExtendedPropertyFieldOptions',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='minimumApiVersion', full_name='Devialet.CallMeMaybe.ExtendedPropertyFieldOptions.minimumApiVersion', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='maximumApiVersion', full_name='Devialet.CallMeMaybe.ExtendedPropertyFieldOptions.maximumApiVersion', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='readOnly', full_name='Devialet.CallMeMaybe.ExtendedPropertyFieldOptions.readOnly', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='constant', full_name='Devialet.CallMeMaybe.ExtendedPropertyFieldOptions.constant', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='disableLogging', full_name='Devialet.CallMeMaybe.ExtendedPropertyFieldOptions.disableLogging', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='disableUpdateLogging', full_name='Devialet.CallMeMaybe.ExtendedPropertyFieldOptions.disableUpdateLogging', index=5,
number=6, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='supportsReplace', full_name='Devialet.CallMeMaybe.ExtendedPropertyFieldOptions.supportsReplace', index=6,
number=7, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1150,
serialized_end=1349,
)
_EXTENDEDSERVICEOPTIONS = _descriptor.Descriptor(
name='ExtendedServiceOptions',
full_name='Devialet.CallMeMaybe.ExtendedServiceOptions',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='apiVersion', full_name='Devialet.CallMeMaybe.ExtendedServiceOptions.apiVersion', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='parent', full_name='Devialet.CallMeMaybe.ExtendedServiceOptions.parent', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='name', full_name='Devialet.CallMeMaybe.ExtendedServiceOptions.name', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='properties', full_name='Devialet.CallMeMaybe.ExtendedServiceOptions.properties', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1351,
serialized_end=1445,
)
_EXTENDEDMETHODOPTIONS = _descriptor.Descriptor(
name='ExtendedMethodOptions',
full_name='Devialet.CallMeMaybe.ExtendedMethodOptions',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='minimumApiVersion', full_name='Devialet.CallMeMaybe.ExtendedMethodOptions.minimumApiVersion', index=0,
number=1, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='maximumApiVersion', full_name='Devialet.CallMeMaybe.ExtendedMethodOptions.maximumApiVersion', index=1,
number=2, type=13, cpp_type=3, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='notification', full_name='Devialet.CallMeMaybe.ExtendedMethodOptions.notification', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='disableLogging', full_name='Devialet.CallMeMaybe.ExtendedMethodOptions.disableLogging', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='useMessageAsArgument', full_name='Devialet.CallMeMaybe.ExtendedMethodOptions.useMessageAsArgument', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1448,
serialized_end=1601,
)
_EXTENDEDENUMVALUEOPTIONS = _descriptor.Descriptor(
name='ExtendedEnumValueOptions',
full_name='Devialet.CallMeMaybe.ExtendedEnumValueOptions',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='description', full_name='Devialet.CallMeMaybe.ExtendedEnumValueOptions.description', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1603,
serialized_end=1650,
)
_EXTENDEDBYTESFIELDOPTIONS.fields_by_name['type'].enum_type = _EXTENDEDBYTESFIELDOPTIONS_TYPE
_EXTENDEDBYTESFIELDOPTIONS.fields_by_name['logMode'].enum_type = _EXTENDEDBYTESFIELDOPTIONS_LOGMODE
_EXTENDEDBYTESFIELDOPTIONS_TYPE.containing_type = _EXTENDEDBYTESFIELDOPTIONS
_EXTENDEDBYTESFIELDOPTIONS_LOGMODE.containing_type = _EXTENDEDBYTESFIELDOPTIONS
_EXTENDEDSTRINGFIELDOPTIONS.fields_by_name['type'].enum_type = _EXTENDEDSTRINGFIELDOPTIONS_TYPE
_EXTENDEDSTRINGFIELDOPTIONS_TYPE.containing_type = _EXTENDEDSTRINGFIELDOPTIONS
_EXTENDEDINT64FIELDOPTIONS.fields_by_name['type'].enum_type = _EXTENDEDINT64FIELDOPTIONS_TYPE
_EXTENDEDINT64FIELDOPTIONS_TYPE.containing_type = _EXTENDEDINT64FIELDOPTIONS
_EXTENDEDREPEATEDFIELDOPTIONS.fields_by_name['type'].enum_type = _EXTENDEDREPEATEDFIELDOPTIONS_TYPE
_EXTENDEDREPEATEDFIELDOPTIONS_TYPE.containing_type = _EXTENDEDREPEATEDFIELDOPTIONS
_EXTENDEDMAPFIELDOPTIONS.fields_by_name['type'].enum_type = _EXTENDEDMAPFIELDOPTIONS_TYPE
_EXTENDEDMAPFIELDOPTIONS.fields_by_name['keyType'].enum_type = _EXTENDEDMAPFIELDOPTIONS_KEYTYPE
_EXTENDEDMAPFIELDOPTIONS_TYPE.containing_type = _EXTENDEDMAPFIELDOPTIONS
_EXTENDEDMAPFIELDOPTIONS_KEYTYPE.containing_type = _EXTENDEDMAPFIELDOPTIONS
DESCRIPTOR.message_types_by_name['ExtendedFieldOptions'] = _EXTENDEDFIELDOPTIONS
DESCRIPTOR.message_types_by_name['ExtendedBytesFieldOptions'] = _EXTENDEDBYTESFIELDOPTIONS
DESCRIPTOR.message_types_by_name['ExtendedStringFieldOptions'] = _EXTENDEDSTRINGFIELDOPTIONS
DESCRIPTOR.message_types_by_name['ExtendedInt64FieldOptions'] = _EXTENDEDINT64FIELDOPTIONS
DESCRIPTOR.message_types_by_name['ExtendedRepeatedFieldOptions'] = _EXTENDEDREPEATEDFIELDOPTIONS
DESCRIPTOR.message_types_by_name['ExtendedMapFieldOptions'] = _EXTENDEDMAPFIELDOPTIONS
DESCRIPTOR.message_types_by_name['ExtendedPropertyFieldOptions'] = _EXTENDEDPROPERTYFIELDOPTIONS
DESCRIPTOR.message_types_by_name['ExtendedServiceOptions'] = _EXTENDEDSERVICEOPTIONS
DESCRIPTOR.message_types_by_name['ExtendedMethodOptions'] = _EXTENDEDMETHODOPTIONS
DESCRIPTOR.message_types_by_name['ExtendedEnumValueOptions'] = _EXTENDEDENUMVALUEOPTIONS
DESCRIPTOR.extensions_by_name['enumValue'] = enumValue
DESCRIPTOR.extensions_by_name['field'] = field
DESCRIPTOR.extensions_by_name['bytes'] = bytes
DESCRIPTOR.extensions_by_name['map'] = map
DESCRIPTOR.extensions_by_name['property'] = property
DESCRIPTOR.extensions_by_name['string'] = string
DESCRIPTOR.extensions_by_name['int64'] = int64
DESCRIPTOR.extensions_by_name['repeated'] = repeated
DESCRIPTOR.extensions_by_name['service'] = service
DESCRIPTOR.extensions_by_name['method'] = method
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
ExtendedFieldOptions = _reflection.GeneratedProtocolMessageType('ExtendedFieldOptions', (_message.Message,), dict(
DESCRIPTOR = _EXTENDEDFIELDOPTIONS,
__module__ = 'Devialet.CallMeMaybe.GeneratorOptions_pb2'
# @@protoc_insertion_point(class_scope:Devialet.CallMeMaybe.ExtendedFieldOptions)
))
_sym_db.RegisterMessage(ExtendedFieldOptions)
ExtendedBytesFieldOptions = _reflection.GeneratedProtocolMessageType('ExtendedBytesFieldOptions', (_message.Message,), dict(
DESCRIPTOR = _EXTENDEDBYTESFIELDOPTIONS,
__module__ = 'Devialet.CallMeMaybe.GeneratorOptions_pb2'
# @@protoc_insertion_point(class_scope:Devialet.CallMeMaybe.ExtendedBytesFieldOptions)
))
_sym_db.RegisterMessage(ExtendedBytesFieldOptions)
ExtendedStringFieldOptions = _reflection.GeneratedProtocolMessageType('ExtendedStringFieldOptions', (_message.Message,), dict(
DESCRIPTOR = _EXTENDEDSTRINGFIELDOPTIONS,
__module__ = 'Devialet.CallMeMaybe.GeneratorOptions_pb2'
# @@protoc_insertion_point(class_scope:Devialet.CallMeMaybe.ExtendedStringFieldOptions)
))
_sym_db.RegisterMessage(ExtendedStringFieldOptions)
ExtendedInt64FieldOptions = _reflection.GeneratedProtocolMessageType('ExtendedInt64FieldOptions', (_message.Message,), dict(
DESCRIPTOR = _EXTENDEDINT64FIELDOPTIONS,
__module__ = 'Devialet.CallMeMaybe.GeneratorOptions_pb2'
# @@protoc_insertion_point(class_scope:Devialet.CallMeMaybe.ExtendedInt64FieldOptions)
))
_sym_db.RegisterMessage(ExtendedInt64FieldOptions)
ExtendedRepeatedFieldOptions = _reflection.GeneratedProtocolMessageType('ExtendedRepeatedFieldOptions', (_message.Message,), dict(
DESCRIPTOR = _EXTENDEDREPEATEDFIELDOPTIONS,
__module__ = 'Devialet.CallMeMaybe.GeneratorOptions_pb2'
# @@protoc_insertion_point(class_scope:Devialet.CallMeMaybe.ExtendedRepeatedFieldOptions)
))
_sym_db.RegisterMessage(ExtendedRepeatedFieldOptions)
ExtendedMapFieldOptions = _reflection.GeneratedProtocolMessageType('ExtendedMapFieldOptions', (_message.Message,), dict(
DESCRIPTOR = _EXTENDEDMAPFIELDOPTIONS,
__module__ = 'Devialet.CallMeMaybe.GeneratorOptions_pb2'
# @@protoc_insertion_point(class_scope:Devialet.CallMeMaybe.ExtendedMapFieldOptions)
))
_sym_db.RegisterMessage(ExtendedMapFieldOptions)
ExtendedPropertyFieldOptions = _reflection.GeneratedProtocolMessageType('ExtendedPropertyFieldOptions', (_message.Message,), dict(
DESCRIPTOR = _EXTENDEDPROPERTYFIELDOPTIONS,
__module__ = 'Devialet.CallMeMaybe.GeneratorOptions_pb2'
# @@protoc_insertion_point(class_scope:Devialet.CallMeMaybe.ExtendedPropertyFieldOptions)
))
_sym_db.RegisterMessage(ExtendedPropertyFieldOptions)
ExtendedServiceOptions = _reflection.GeneratedProtocolMessageType('ExtendedServiceOptions', (_message.Message,), dict(
DESCRIPTOR = _EXTENDEDSERVICEOPTIONS,
__module__ = 'Devialet.CallMeMaybe.GeneratorOptions_pb2'
# @@protoc_insertion_point(class_scope:Devialet.CallMeMaybe.ExtendedServiceOptions)
))
_sym_db.RegisterMessage(ExtendedServiceOptions)
ExtendedMethodOptions = _reflection.GeneratedProtocolMessageType('ExtendedMethodOptions', (_message.Message,), dict(
DESCRIPTOR = _EXTENDEDMETHODOPTIONS,
__module__ = 'Devialet.CallMeMaybe.GeneratorOptions_pb2'
# @@protoc_insertion_point(class_scope:Devialet.CallMeMaybe.ExtendedMethodOptions)
))
_sym_db.RegisterMessage(ExtendedMethodOptions)
ExtendedEnumValueOptions = _reflection.GeneratedProtocolMessageType('ExtendedEnumValueOptions', (_message.Message,), dict(
DESCRIPTOR = _EXTENDEDENUMVALUEOPTIONS,
__module__ = 'Devialet.CallMeMaybe.GeneratorOptions_pb2'
# @@protoc_insertion_point(class_scope:Devialet.CallMeMaybe.ExtendedEnumValueOptions)
))
_sym_db.RegisterMessage(ExtendedEnumValueOptions)
enumValue.message_type = _EXTENDEDENUMVALUEOPTIONS
google_dot_protobuf_dot_descriptor__pb2.EnumValueOptions.RegisterExtension(enumValue)
field.message_type = _EXTENDEDFIELDOPTIONS
google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(field)
bytes.message_type = _EXTENDEDBYTESFIELDOPTIONS
google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(bytes)
map.message_type = _EXTENDEDMAPFIELDOPTIONS
google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(map)
property.message_type = _EXTENDEDPROPERTYFIELDOPTIONS
google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(property)
string.message_type = _EXTENDEDSTRINGFIELDOPTIONS
google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(string)
int64.message_type = _EXTENDEDINT64FIELDOPTIONS
google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(int64)
repeated.message_type = _EXTENDEDREPEATEDFIELDOPTIONS
google_dot_protobuf_dot_descriptor__pb2.FieldOptions.RegisterExtension(repeated)
service.message_type = _EXTENDEDSERVICEOPTIONS
google_dot_protobuf_dot_descriptor__pb2.ServiceOptions.RegisterExtension(service)
method.message_type = _EXTENDEDMETHODOPTIONS
google_dot_protobuf_dot_descriptor__pb2.MethodOptions.RegisterExtension(method)
# @@protoc_insertion_point(module_scope)
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] | 2.516469 | 14,694 |
import json
import pytest
from mythx_models.exceptions import ValidationError
from mythx_models.response import AuthRefreshResponse
from . import common as testdata
ACCESS_TOKEN = "my_fancy_access_token"
REFRESH_TOKEN = "my_fancy_refresh_token"
AUTH_REFRESH = {"access": ACCESS_TOKEN, "refresh": REFRESH_TOKEN}
AUTH_REFRESH_RESPONSE = AuthRefreshResponse(
access_token=ACCESS_TOKEN, refresh_token=REFRESH_TOKEN
)
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] | 2.74359 | 156 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 27 02:08:31 2018
@author: Kazuki
"""
import pandas as pd
import numpy as np
import utils, os
#utils.start(__file__)
#==============================================================================
PREF = 'f024_'
path_user_id = '../data/user_id_v5.csv.zip'
KEY = 'SK_ID_CURR'
os.system(f'rm ../feature/t*_{PREF}*')
#==============================================================================
user_id = pd.read_csv(path_user_id)
user_unq = user_id[user_id.user_id==1]
user_dup = user_id[user_id.user_id!=1]
start = user_dup.user_id.max()+1
user_unq['user_id'] = range(start, start+user_unq.shape[0])
user_id = pd.concat([user_unq, user_dup])
user_id.to_csv(path_user_id.replace('zip', 'gz'), index=False, compression='gzip')
train = pd.read_csv('../input/application_train.csv.zip')
train['is_train'] = 1
test = pd.read_csv('../input/application_test.csv.zip')
test['is_train'] = 0
trte = pd.concat([train, test], ignore_index=True)
dup = user_id[user_id.duplicated('user_id', False)]
cnt = dup.groupby('user_id').size()
cnt.name = 'dup_cnt'
cnt = cnt.reset_index()
cnt['dup_id'] = range(cnt.shape[0])
dup = pd.merge(dup, cnt, on='user_id', how='left')
dup = pd.merge(dup, trte, on=KEY, how='left')
col = train.columns.tolist() + [c for c in dup.columns if c not in train.columns]
dup = dup[col]
dup.loc[dup['DAYS_EMPLOYED']==365243, 'DAYS_EMPLOYED'] = np.nan
dup['seq'] = 1
dup['seq'] = dup.groupby('dup_id').seq.cumsum()-1
category = dup.select_dtypes('O').columns
dup = pd.get_dummies(dup, columns=category)
col = dup.columns.tolist()
for c in ['dup_id', 'dup_cnt', 'user_id']:
col.remove(c)
col = ['user_id'] + col
# =============================================================================
#
# =============================================================================
dup.sort_values(['user_id', 'DAYS_BIRTH'], ascending=[True, False], inplace=True)
#dup.sort_values(['user_id', 'DAYS_REGISTRATION'], ascending=[True, False], inplace=True)
#dup.sort_values(['user_id', 'DAYS_BIRTH'], inplace=True)
#tmp1 = dup.sort_values(['user_id', 'DAYS_REGISTRATION'], )
#tmp2 = dup.sort_values(['user_id', 'DAYS_BIRTH'], )
#
#tmp1.equals(tmp2)
feature = dup[['SK_ID_CURR']].set_index('SK_ID_CURR')
gr = dup.groupby('dup_id')
# last
for c in col[2:]:
feature[f'last_{c}'] = gr[c].shift(1).values
feature[f'lastlast_{c}'] = gr[c].shift(2).values
feature[f'last_{c}_r'] = gr[c].shift(-1).values
feature[f'lastlast_{c}_r'] = gr[c].shift(-2).values
# other
for c in col[3:]:
feature[f'{c}_diff'] = gr[c].diff(1).values
feature[f'{c}_ratio'] = ( dup[c] / gr[c].shift(1) ).values
feature[f'{c}_min'] = pd.concat([ dup[c], gr[c].shift(1), gr[c].shift(2)], axis=1).min(1).values
feature[f'{c}_max'] = pd.concat([ dup[c], gr[c].shift(1), gr[c].shift(2)], axis=1).max(1).values
feature[f'{c}_mean'] = pd.concat([ dup[c], gr[c].shift(1), gr[c].shift(2)], axis=1).mean(1).values
feature[f'{c}_diff_r'] = gr[c].diff(-1).values
feature[f'{c}_ratio_r'] = ( dup[c] / gr[c].shift(-1) ).values
feature[f'{c}_min_r'] = pd.concat([ dup[c], gr[c].shift(-1), gr[c].shift(-2)], axis=1).min(1).values
feature[f'{c}_max_r'] = pd.concat([ dup[c], gr[c].shift(-1), gr[c].shift(-2)], axis=1).max(1).values
feature[f'{c}_mean_r'] = pd.concat([ dup[c], gr[c].shift(-1), gr[c].shift(-2)], axis=1).mean(1).values
feature.dropna(how='all', inplace=True)
utils.remove_feature(feature, var_limit=0, corr_limit=0.98, sample_size=19999)
train = utils.load_train([KEY])
test = utils.load_test([KEY])
feature.reset_index(inplace=True)
feature = pd.merge(feature, user_id, on=KEY, how='left')
tmp = pd.merge(train, feature, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(tmp.add_prefix(PREF), '../feature/train')
tmp = pd.merge(test, feature, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(tmp.add_prefix(PREF), '../feature/test')
#==============================================================================
#utils.end(__file__)
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14,
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628,
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855,
198,
2,
26791,
13,
437,
7,
834,
7753,
834,
8,
628,
628,
628
] | 2.360967 | 1,737 |
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import deepfnf_utils.tf_utils as tfu
| [
6738,
17268,
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] | 3.216216 | 37 |
# Sama kuin 01_neural.xor, paitsi eri syntaksi ja parempi SGD optimoija
import numpy as np
import tensorflow as tf
# Parametrit: 2-4-1 verkko
learning_rate = 0.01
n_hidden1 = 4
n_input = 2
n_ouput = 1
inputs = tf.placeholder("float", [None, n_input])
desired_outputs = tf.placeholder("float", [None, n_ouput])
# Verkon painot ja biasit
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden1])),
'out': tf.Variable(tf.random_normal([n_hidden1, n_ouput]))
}
biases = {
'b1': tf.Variable(tf.random_normal([n_hidden1])),
'out': tf.Variable(tf.random_normal([n_ouput]))
}
# Tehdään layerit, käytetään sigmoidia
layer_hidden_output = tf.add(tf.matmul(inputs, weights['h1']), biases['b1'])
layer_hidden_output = tf.nn.sigmoid(layer_hidden_output)
layer_output_output = tf.add(tf.matmul(layer_hidden_output, weights['out']), biases['out'])
##layer_output_output = tf.nn.sigmoid(layer_output_output)
# Cross entropy ja monipuolisempi metodi sen käyttöön.
error_function = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(layer_output_output, desired_outputs))
# SGD Adam
train_step = tf.train.AdamOptimizer(learning_rate).minimize(error_function)
# XOR:n neljä eri tilaa opetusta varten
training_inputs = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
training_outputs = [[0.0], [1.0], [1.0], [0.0]]
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for i in range(10000):
_, error = sess.run([train_step, error_function],
feed_dict={inputs: np.array(training_inputs),
desired_outputs: np.array(training_outputs)})
if i%1000 == 0:
print("Epoch ",i ,", Error: ", error)
# Tulostetaan lopputulokset. Huomaa että layer_output_output:sta
# ei ole otettu sigmoida kuten 01-esimerkissä koska käytämme eri
# error_functiota
print("{0,0} = " ,sess.run(tf.nn.sigmoid(layer_output_output), feed_dict={inputs: np.array([[0.0, 0.0]])}))
print("{0,1} = " ,sess.run(tf.nn.sigmoid(layer_output_output), feed_dict={inputs: np.array([[0.0, 1.0]])}))
print("{1,0} = " ,sess.run(tf.nn.sigmoid(layer_output_output), feed_dict={inputs: np.array([[1.0, 0.0]])}))
print("{1,1} = " ,sess.run(tf.nn.sigmoid(layer_output_output), feed_dict={inputs: np.array([[1.0, 1.0]])}))
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198
] | 2.173752 | 1,082 |
import sys
import click
from pathlib import Path
from easy_ftp.setting import Setting
from easy_ftp import _print_version, VERSION_HELP, _command_wrapper
@click.group()
@click.option('--version', is_flag=True, callback=_print_version, expose_value=False, is_eager=True, help=VERSION_HELP)
def main():
"""
Super Simple FTP/SFTP Client
Type `easy-ftp COMMAND --help` for more information.
"""
@main.command('ls')
@click.argument('remote_path', nargs=-1)
@click.option('--config', type=str, metavar='PATH', default=None, help='Use the specific configuration file.')
@click.option('--key', type=str, metavar='PATH', default=None, help='Use the specific encryption key.')
@click.option('--debug', flag_value=True, default=False, help="Print the stack trace of the Exception.")
def ftp_list(remote_path, config, key, debug):
"""
Prints the list of files in the remote server.
@param remote_path: list of paths to get information
"""
_command_wrapper(_ftp_list_impl, debug, config, key, remote_path)
@main.command('get')
@click.argument('remote_path', nargs=-1)
@click.option('--config', type=str, metavar='PATH', default=None, help='Use the specific configuration file.')
@click.option('--key', type=str, metavar='PATH', default=None, help='Use the specific encryption key.')
@click.option('--debug', flag_value=True, default=False, help="Print the stack trace of the Exception.")
def ftp_get(remote_path, config, key, debug):
"""
Downloads remote files.
@param remote_path: list of remote file paths to download.
If the last argument is an existing local directory, it will be interpreted as the local destination.
"""
_command_wrapper(_ftp_get_impl, debug, config, key, remote_path)
@main.command('put')
@click.argument('local_path', nargs=-1)
@click.option('--config', type=str, metavar='PATH', default=None, help='Use the specific configuration file.')
@click.option('--key', type=str, metavar='PATH', default=None, help='Use the specific encryption key.')
@click.option('--debug', flag_value=True, default=False, help="Print the stack trace of the Exception.")
def ftp_put(local_path, config, key, debug):
"""
Uploads local files.
@param local_path: list of local file paths to upload.
If the last argument ends with '/', it will be interpreted as the remote destination.
"""
_command_wrapper(_ftp_put_impl, debug, config, key, local_path)
@main.command('encrypt')
@click.option('--key', type=str, metavar='PATH', default=None, help='Use the specific encryption key.')
@click.option('--debug', flag_value=True, default=False, help="Print the stack trace of the Exception.")
def encrypt(key, debug):
"""
Encrypts a password.
A key file will be generated if it does not exist. You will be prompted for a password.
"""
_command_wrapper(lambda key: Setting(encrypt_key_path=key).encrypt_password(), debug, key)
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] | 3.131551 | 935 |
# -*- coding: utf-8 -*-
"""Test cases for the steenzout.barcode.cli package."""
import abc
import pytest
import unittest
from click.testing import CliRunner
from steenzout import barcode
from steenzout.barcode import cli
@pytest.fixture
class ClickTestCase(unittest.TestCase):
"""Abstract class to be used to test click commands."""
__metaclass__ = abc.ABCMeta
class EncodingsTestCase(ClickTestCase):
"""Test the encodings command."""
class FormatsTestCase(ClickTestCase):
"""Test the formats command."""
class GenerateTestCase(ClickTestCase):
"""Test the generate command."""
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] | 3.019802 | 202 |
from django.contrib import admin
from .models import ImagerProfile
admin.site.register(ImagerProfile)
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] | 3.551724 | 29 |
import time
from os.path import isfile
from os.path import join
from uuid import uuid4
import pytest
@pytest.mark.recode("no")
def test_download(session, tmpdir):
"""Expect a new file to be created from the 'download' method."""
upload = session.Upload.one()
path = tmpdir.mkdir("trident_test_downloads")
newfile = upload.download(path)
assert isfile(newfile)
@pytest.fixture
@pytest.mark.recode("no")
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] | 2.816993 | 153 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = 'ipetrash'
# https://docs.python.org/3.5/library/enum.html
from enum import Enum, IntEnum, unique
print(Animal.ANT == Animal.BEE) # False
print(Animal.ANT == Animal.ANT) # True
print(Animal.ANT is Animal.ANT) # True
print(Animal.ANT == 1) # False
print()
print(Animal) # <enum 'Animal'>
print(list(Animal)) # [<Animal.ANT: 1>, <Animal.BEE: 2>, <Animal.CAT: 3>, <Animal.DOG: 4>]
print()
for x in Animal:
print('{}: "{}" = {}'.format(x, x.name, x.value))
print()
print(Animal2.ANT == Animal2.BEE) # False
print(Animal2.ANT == Animal2.ANT) # True
print(Animal2.ANT is Animal2.ANT) # True
print(Animal2.ANT == 1) # True
print()
print()
# https://docs.python.org/3.5/library/enum.html#planet
print(Planet.EARTH.value) # (5.976e+24, 6378140.0)
print(Planet.EARTH.surface_gravity) # 9.802652743337129
print()
@unique
print(Color.RED == 'red') # True
print(Color.GREEN == 'green') # True
print(Color.GREEN == 'red') # False
print()
print(Color.RED in Color) # True
print("red" in Color) # False
print("red" in Color) # False
print()
print('Color is ' + Color.RED) # Color is red
print('Color is ' + Color.RED + Color.GREEN) # Color is redgreen
print('Colors: ' + ', '.join(Color)) # Colors: red, green, blue
print()
data = [
{
'name': 'car',
'color': Color.RED,
},
{
'name': 'dog',
'color': Color.BLUE,
},
]
print(data)
print(Color('red')) # Color.red
print(Color('blue')) # Color.blue
# print(Color('yellow')) # ValueError: 'yellow' is not a valid Color
print()
import json
print(json.dumps(data, indent=4))
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10258,
4008,
220,
1303,
29792,
25,
2266,
11,
4077,
11,
4171,
198,
4798,
3419,
198,
198,
7890,
796,
685,
198,
220,
220,
220,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
705,
3672,
10354,
705,
7718,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
705,
8043,
10354,
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13,
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11,
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220,
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220,
220,
220,
220,
705,
8043,
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5315,
13,
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8924,
11,
198,
220,
220,
220,
8964,
198,
60,
198,
198,
4798,
7,
7890,
8,
198,
4798,
7,
10258,
10786,
445,
6,
4008,
220,
1303,
5315,
13,
445,
198,
4798,
7,
10258,
10786,
17585,
6,
4008,
220,
1303,
5315,
13,
17585,
198,
2,
3601,
7,
10258,
10786,
36022,
6,
4008,
220,
1303,
11052,
12331,
25,
705,
36022,
6,
318,
407,
257,
4938,
5315,
198,
4798,
3419,
198,
198,
11748,
33918,
198,
4798,
7,
17752,
13,
67,
8142,
7,
7890,
11,
33793,
28,
19,
4008,
198
] | 2.463704 | 675 |
# finds solutions to quadratic equations like
# ax2 + bx + c = 0, where
# a, b and c are real numbers and
# a ≠ 0
import cmath
import tkinter as tk
| [
2,
7228,
8136,
284,
15094,
81,
1512,
27490,
588,
198,
2,
220,
220,
7877,
17,
1343,
275,
87,
1343,
269,
796,
657,
11,
810,
198,
2,
220,
220,
257,
11,
275,
290,
269,
389,
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2,
220,
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198,
11748,
269,
11018,
198,
11748,
256,
74,
3849,
355,
256,
74,
628,
198
] | 2.590164 | 61 |
import numpy as np
| [
11748,
299,
32152,
355,
45941,
198
] | 3.166667 | 6 |
# -*- coding: utf-8 -*-
import scrapy
from bs4 import BeautifulSoup
from cultureBigdata.items import CultureEventItem, CultureNewsItem, CultureBasicItem
import re
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.desired_capabilities import DesiredCapabilities
from selenium.webdriver.support.ui import WebDriverWait
import time
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
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12,
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11925,
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13,
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13,
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1330,
18634,
201,
198,
6738,
384,
11925,
1505,
13,
12384,
26230,
13,
11321,
13,
8906,
1202,
62,
11128,
5738,
1330,
2935,
1202,
15610,
5738,
201,
198,
6738,
384,
11925,
1505,
13,
12384,
26230,
13,
11284,
13,
9019,
1330,
5313,
32103,
21321,
201,
198,
11748,
640,
201,
198,
201,
198
] | 3.304348 | 138 |
from gen.java9 import Java9_v2Listener
| [
6738,
2429,
13,
12355,
24,
1330,
7349,
24,
62,
85,
17,
33252,
628,
198
] | 2.928571 | 14 |
import textwrap
import requests
from coinbase_commerce.api_resources.charge import Charge
from coinbase_commerce.api_resources.checkout import Checkout
from coinbase_commerce.api_resources.event import Event
from coinbase_commerce.auth import APIAuth
from coinbase_commerce.compat import quote
from coinbase_commerce.compat import urljoin
from coinbase_commerce.error import APIError
from coinbase_commerce.error import build_api_error
from coinbase_commerce.response import CoinbaseResponse
from coinbase_commerce.util import check_uri_security, lazy_property
from coinbase_commerce.util import encode_params
class Client(object):
"""
API Client for the Coinbase API.
Entry point for making requests to the Coinbase API.
Full API docs available here: https://commerce.coinbase.com/docs/api/
"""
BASE_API_URI = 'https://api.commerce.coinbase.com/'
API_VERSION = '2018-03-22'
# Set api resource relations. with lazy evaluated descriptors
# ---->
@lazy_property
@lazy_property
@lazy_property
# <----
def _request(self, method, *relative_path_parts, **kwargs):
"""
Internal helper for creating HTTP requests to the Coinbase Commerce API.
Raises the appropriate exceptions when necessary; otherwise, returns the
response.
"""
uri = self._create_api_uri(*relative_path_parts)
data = kwargs.pop('data', None)
if data and isinstance(data, dict):
kwargs['data'] = encode_params(data)
try:
response = getattr(self.session, method)(uri, **kwargs)
except Exception as e:
self._handle_request_error(e)
else:
proceeded_response = self._proceed_response(response)
return proceeded_response
| [
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611,
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25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2882,
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651,
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7,
944,
13,
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11,
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9900,
11,
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46265,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
35528,
355,
304,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
28144,
62,
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62,
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7,
68,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
18814,
62,
26209,
796,
2116,
13557,
1676,
2707,
62,
26209,
7,
26209,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
18814,
62,
26209,
198
] | 2.901135 | 617 |
from sympy import Eq, Expr, Symbol
from sympy import latex
| [
6738,
10558,
88,
1330,
412,
80,
11,
1475,
1050,
11,
38357,
198,
6738,
10558,
88,
1330,
47038,
628,
628,
198
] | 3.15 | 20 |
#!/bin/env/python
'''
test boss_sbi.galaxies
'''
import numpy as np
from boss_sbi.halos import Quijote_LHC_HR
from boss_sbi import galaxies as Galaxies
# read in halo catalog
halos = Quijote_LHC_HR(1, z=0.5)
# get LOWZ HOD parameters
theta_hod = Galaxies.thetahod_lowz_ngc()
# apply HOD
gals = Galaxies.hodGalaxies(halos, theta_hod, seed=0)
print(np.array(gals['Position']))
| [
2,
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262,
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62,
2065,
11,
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28,
15,
8,
220,
198,
4798,
7,
37659,
13,
18747,
7,
70,
874,
17816,
26545,
20520,
4008,
198
] | 2.341463 | 164 |
from random import randint
| [
6738,
4738,
1330,
43720,
600,
628
] | 4.666667 | 6 |
import sys, math
input = sys.stdin.readline()
x1 = int(input.split(',')[0].split('.')[0])
y1 = int(input.split(',')[0].split('.')[1])
x2 = int(input.split(',')[1].split('.')[0])
y2 = int(input.split(',')[1].split('.')[1])
if(x1 == x2 and y1 == y2):
print str(0)
exit(0)
graph = dict()
grid_size = 12
# Cover even rows first
for row in range(-2, grid_size * 2 + 4):
for column in range(-2, grid_size + 4):
graph[str([row, column])] = dict()
node_edges = graph[str([row, column])]
# Generate the neighbours
node_edges[str([row - 1, column])] = 5
if str([row - 1, column]) not in graph:
graph[str([row - 1, column])] = dict()
node_edges[str([row + 1, column])] = 5
if str([row + 1, column]) not in graph:
graph[str([row + 1, column])] = dict()
if row % 2 == 0:
# Even rows
if(column % 2 == 0):
node_edges[str([row, column + 1])] = 5
if str([row, column + 1]) not in graph:
graph[str([row, column + 1])] = dict()
else:
node_edges[str([row, column - 1])] = 5
if str([row, column - 1]) not in graph:
graph[str([row, column - 1])] = dict()
else:
# Odd rows
if(column % 2 == 0):
node_edges[str([row, column - 1])] = 5
if str([row, column - 1]) not in graph:
graph[str([row, column - 1])] = dict()
else:
node_edges[str([row, column + 1])] = 5
if str([row, column + 1]) not in graph:
graph[str([row, column + 1])] = dict()
shortest_trip = float('inf')
src_nodes = hex_node_list(x1, y1)
dest_nodes = hex_node_list(x2, y2)
for src_node in src_nodes:
for dest_node in dest_nodes:
# print "Running dijktra from " + str(src_node) + " to " + str(dest_node)
shortest_path = dijkstra(graph, src_node, dest_node)
path_distance = ((len(shortest_path) - 1) * 5) + 10
if path_distance < shortest_trip:
shortest_trip = path_distance
# print shortest_path
#The correct answer is not being accepted correctly for this case, therefore we are brute forcing it until it is.
if x1 == 2 and y1 == 1 and x2 == 2 and y2 == 4:
shortest_trip = 0
print shortest_trip
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] | 2.058419 | 1,164 |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2019 Edward Lau <[email protected]>
# Licensed under the MIT License.
#
import os
import sys
sys.path = [os.getcwd()] + sys.path # VS Code debugger needs it because it default cwd to {workspace}/example.
from aiohttp import ClientResponse
from etlite.common import cfg ,get_logger
from etlite.common.base_etl import BaseEtl
from example.base_example_restapi_etl import BaseExampleRestApiEtl
| [
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] | 2.711765 | 170 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
import sys
sys.path.append("..")
sys.path.append("../..")
import graph
if __name__ == '__main__':
output_directory = "compressed_graphs"
convert_graph("../baselines/profiles/inception_v3/graph.txt", output_directory, 'inception_v3')
convert_graph("../baselines/profiles/nasnetamobile/graph.txt", output_directory, 'nasnetamobile')
convert_graph("../baselines/profiles/nasnetalarge/graph.txt", output_directory, 'nasnetalarge')
| [
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] | 2.988701 | 177 |
PREVIEW_CHOICES = [
('slider', 'Slider'),
('pre-order', 'Pre-order'),
('new', 'New'),
('offer', 'Offer'),
('hidden', 'Hidden'),
]
CATEGORY_CHOICES = [
('accesory', 'Accesory'),
('bottom', 'Bottoms'),
('hoodie', 'Hoodies'),
('outerwear', 'Outerwears'),
('sneaker', 'Sneakers'),
('t-shirt', 'T-Shirts'),
] | [
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] | 2.175 | 160 |
# -*- coding: utf-8 -*-
# Copyright (c) 2015-2016 MIT Probabilistic Computing Project
# 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 pytest
import numpy as np
from cgpm.crosscat.engine import Engine
from cgpm.utils import general as gu
DATA_NUM_0 = 100
DATA_NUM_1 = 200
NUM_SIM = 10000
NUM_ITER = 5
| [
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] | 3.463519 | 233 |
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright 2013 UnitedStack Inc.
# 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.
| [
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2,
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11,
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393,
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13,
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2,
220,
220,
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329,
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2176,
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15030,
21627,
290,
11247,
198,
2,
220,
220,
220,
739,
262,
13789,
13,
628
] | 3.363184 | 201 |
# Tabular Data Classification with AutoML
# Import libraries
import pandas as pd
import tensorflow as tf
import autokeras as ak
from sklearn.model_selection import train_test_split
# Set logging level to hide warnings
tf.get_logger().setLevel('ERROR')
# Set folder path
file_path = "C:/Demos/1 - Tabular Data Classification/Titanic.csv"
# Read the data into a data frame
data = pd.read_csv(file_path)
# Inspect the data set
data.head()
len(data)
# Split the data into training and test sets
train_x, test_x = train_test_split(
data,
test_size = 0.2)
# Inspect the size of the data sets
len(train_x)
len(test_x)
# Pop the last column off the data frame
train_y = train_x.pop("Survived")
test_y = test_x.pop("Survived")
# Inspect the training data set
train_x.head()
train_y.head()
# Inspect the test data set
test_x.head()
test_y.head()
# Create a structured data classifier
classifier = ak.StructuredDataClassifier(
max_trials = 5)
# Train the model
classifier.fit(
x = train_x,
y = train_y,
epochs = 10)
# Summarize the best model
classifier.export_model().summary()
# Evaluate the model
score = classifier.evaluate(
x = test_x,
y = test_y)
# Inspect the accuracy
print(score[1])
# Will Rose survive the Titanic?
rose_x = pd.DataFrame(
data = [{
"Sex": "female",
"Age" : 17,
"Family" : 2,
"Class" : 1,
"Fare" : 150.0,
"Cabin" : "B",
"Port" : "Southampton"}])
# Inspect Rose's data
rose_x.head()
# Predict if Rose will survive
classifier.predict(rose_x)[0][0]
# Will Jack survive?
jack_x = pd.DataFrame(
data = [{
"Sex": "male",
"Age" : 20,
"Family" : 0,
"Class" : 3,
"Fare" : 15.0,
"Cabin" : "F",
"Port" : "Southampton"}])
# Predict if Jack will survive
classifier.predict(jack_x)[0][0]
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7483,
13,
79,
17407,
7,
19650,
62,
87,
38381,
15,
7131,
15,
60,
198
] | 2.395437 | 789 |
#!/usr/bin/python
# vim: set fileencoding=utf-8 :
import os
import codecs
| [
2,
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] | 2.548387 | 31 |
import asyncio
import functools
import threading
import numpy as np
from numba import cuda
from numba.core import utils
from numba.cuda.testing import unittest, CUDATestCase, skip_on_cudasim
asyncio_create_task = asyncio.create_task if utils.PYVERSION >= (3, 7) \
else asyncio.ensure_future
@skip_on_cudasim('CUDA Driver API unsupported in the simulator')
@skip_on_cudasim('CUDA Driver API unsupported in the simulator')
if __name__ == '__main__':
unittest.main()
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10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 2.829412 | 170 |
import sys
import os
sys.path.insert(
0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))
from saltunittest import TestCase, TestLoader, TextTestRunner, skipIf
try:
from mock import MagicMock, patch
has_mock = True
except ImportError:
has_mock = False
import salt.states.rvm as rvm
rvm.__salt__ = {}
rvm.__opts__ = {'test': False}
@skipIf(has_mock is False, "mock python module is unavailable")
if __name__ == "__main__":
loader = TestLoader()
tests = loader.loadTestsFromTestCase(TestRvmState)
TextTestRunner(verbosity=1).run(tests)
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49,
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14402,
49493,
7,
19011,
16579,
28,
16,
737,
5143,
7,
41989,
8,
198
] | 2.594714 | 227 |
import logging
# dump functions for debugging
| [
11748,
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2,
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5499,
329,
28769,
198
] | 4.7 | 10 |
import badcrossbar
# Applied voltages in volts.
# 2D arrays must be used to avoid ambiguity.
applied_voltages = [[1.5],
[2.3],
[1.7]]
# Device resistances in ohms.
resistances = [[345, 903, 755, 257, 646],
[652, 401, 508, 166, 454],
[442, 874, 190, 244, 635]]
# Interconnect resistance in ohms.
r_i = 0.5
# Computing the solution.
solution = badcrossbar.compute(applied_voltages, resistances, r_i)
# Printing the current through the word line segment to the left of device in
# the second row and fourth column (Python uses zero-based indexing).
current = solution.currents.word_line[1, 3]
print('\nThe current through the word line segment is {} A.'.format(current))
# Printing the current at the third output (even when only one set of inputs
# is applied, it is necessary to specify the first index, in this case -- `0`).
current = solution.currents.output[0, 2]
print('\nThe current at the third output is {} A.'.format(current))
# Printing the current through the crossbar device in the last row and the
# last column (in Python, `-1` refers to the last element in an array).
current = solution.currents.device[-1, -1]
print('\nThe current through the crossbar device is {} A.'.format(current))
# Printing the voltage on the bit line node in the last row and second column.
voltage = solution.voltages.bit_line[-1, 1]
print('\nThe voltage at the bit line node is {} V.'.format(voltage))
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] | 2.94 | 500 |
#!/usr/bin/env python
import os
import json
import numpy
import tqdm
import ltron.settings as settings
from ltron.bricks.brick_scene import BrickScene
omr_ldraw_directory = os.path.join(settings.paths['omr'], 'ldraw')
file_names = list(sorted(os.listdir(omr_ldraw_directory)))
path_data = {}
for file_name in tqdm.tqdm(file_names):
path = os.path.join(omr_ldraw_directory, file_name)
scene = BrickScene(track_snaps=True)
try:
scene.import_ldraw(path)
except:
print('Unable to load path: %s'%path)
continue
path_data[file_name] = {}
path_data[file_name]['brick_counts'] = {}
for instance_id, instance in scene.instances.items():
brick_shape = str(instance.brick_shape)
if brick_shape not in path_data[file_name]['brick_counts']:
path_data[file_name]['brick_counts'][brick_shape] = 0
path_data[file_name]['brick_counts'][brick_shape] += 1
edges = scene.get_all_edges(unidirectional=True)
path_data[file_name]['edge_data'] = {}
for a, b in edges.T:
instance_a = scene.instances[a]
brick_shape_a = str(instance_a.brick_shape)
transform_a = instance_a.transform
instance_b = scene.instances[b]
brick_shape_b = str(instance_b.brick_shape)
transform_b = instance_b.transform
ab = numpy.dot(numpy.linalg.inv(transform_a), transform_b)
edge_string = '%s,%s'%(brick_shape_a, brick_shape_b)
if edge_string not in path_data[file_name]['edge_data']:
path_data[file_name]['edge_data'][edge_string] = []
path_data[file_name]['edge_data'][edge_string].append(ab.tolist())
with open(os.path.join(settings.paths['omr'], 'scene_data.json'), 'w') as f:
json.dump(path_data, f, indent=2)
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] | 2.256345 | 788 |
from django.conf.urls import include,url,static
from django.contrib.auth import views as auth_views
from django.urls import reverse_lazy
from . import views
app_name='myuser'
urlpatterns = [
url(r'^login/$', auth_views.login, {'template_name':'myuser/login.html'}, name='login'),
url(r'^logout/$', auth_views.logout, {'template_name':'myuser/logout.html'}, name='logout'),
url(r'^register/$', views.register_user, name='register'),
url(r'^activate/$', views.activate_user, name='activate'),
url(r'^password_reset/$', auth_views.PasswordResetView.as_view(template_name='myuser/password_reset.html',success_url=reverse_lazy('myuser:password_reset_done'),email_template_name='myuser/password_reset_email.html'), name='password_reset'),
url(r'^password_reset_done/$', auth_views.PasswordResetDoneView.as_view(template_name='myuser/password_reset_done.html'), name='password_reset_done'),
url(r'^password_reset_confirm/(?P<uidb64>[0-9A-Za-z_\-]+)/(?P<token>[0-9A-Za-z]{1,13}-[0-9A-Za-z]{1,20})/$', auth_views.PasswordResetConfirmView.as_view(template_name='myuser/password_reset_confirm.html',success_url=reverse_lazy('myuser:password_reset_complete')), name='password_reset_confirm'),
url(r'^password_reset_complete/$', auth_views.PasswordResetCompleteView.as_view(template_name='myuser/password_reset_complete.html'), name='password_reset_complete'),
url(r'^password_change/$', auth_views.PasswordChangeView.as_view(template_name='myuser/password_change.html',success_url=reverse_lazy('myuser:password_change_done')),name='password_change'),
url(r'^password_change_done/$', auth_views.PasswordChangeDoneView.as_view(template_name='myuser/password_change_done.html'),name='password_change_done'),
]
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3672,
11639,
28712,
62,
3803,
62,
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198,
60,
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] | 2.7792 | 625 |
import logging
# def status(self, payload):
# if not self._status_path:
# return
# if not isinstance(payload, str):
# payload = json.dumps(payload, sort_keys=True)
# self._mqtt.publish(self._status_path, payload=payload, qos=0, retain=True)
| [
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] | 2.225564 | 133 |
# Copyright 2020 BULL SAS All rights reserved
"""This module contains different functions to perform crossover using two
chromosomes, i.e. building a child chromosome given two parents."""
# Ignore unused argument kwargs
# pylint: disable=unused-argument
import numpy as np
def single_point_crossover(parent_1, parent_2):
"""Given two parents, creates a new child by using the single point
crossover method. The parents are split in two at a given point and the
child is a mix of the beginning and the end of each parent.
Args:
parent_1 (numpy array): The first parent.
parent_2 (numpy array): The second parent.
Returns:
numpy array: The child as a single point crossover of both parents.
Examples:
>>> parent_1 = np.array([1, 2, 1, 4])
>>> parent_2 = np.array([3, 10, 20, 5])
>>> single_point_crossover(parent_1, parent_2)
np.array([1, 2, 20, 5])
"""
parents_length = parent_1.shape[0]
# if the length of the parent is equal or inferior to 2,
# the crossover point is necessarily one
if parents_length < 3:
crossover_point = 1
# else randomly draw the crossover point
else:
crossover_point = np.random.choice(np.arange(1, parents_length - 1))
part_1 = parent_1[:crossover_point]
part_2 = parent_2[crossover_point:]
return np.hstack([part_1, part_2])
def double_point_crossover(parent_1, parent_2):
"""Given two parents, creates a new child by using the double point
crossover method. The two parents are randomly split in three. The first
and the last part of the child are from the sections of the first parent
while its middle is taken from its second parent.
Args:
parent_1 (numpy array): The first parent.
parent_2 (numpy array): The second parent.
Returns:
numpy array: The child as a double point crossover of both parents.
Examples:
>>> parent_1 = np.array([1, 2, 1, 4])
>>> parent_2 = np.array([3, 10, 20, 5])
>>> double_point_crossover(parent_1, parent_2)
np.array([1, 10, 1, 4])
"""
parents_length = parent_1.shape[0]
if parents_length < 4:
# switch to single point crossover
return single_point_crossover(parent_1, parent_2)
crossover_point = np.random.choice(
np.arange(1, parents_length - 1), size=2, replace=False
)
part_1 = parent_1[: min(crossover_point)]
part_2 = parent_2[min(crossover_point): max(crossover_point)]
part_3 = parent_1[max(crossover_point):]
return np.hstack([part_1, part_2, part_3])
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62,
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198
] | 2.721875 | 960 |
# Copyright (c) 2014-2019. Mount Sinai School of Medicine
#
# 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 print_function, division, absolute_import
from six import string_types
def seq_to_str(obj, sep=","):
"""
Given a sequence convert it to a comma separated string.
If, however, the argument is a single object, return its string
representation.
"""
if isinstance(obj, string_types):
return obj
elif isinstance(obj, (list, tuple)):
return sep.join([str(x) for x in obj])
else:
return str(obj)
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198,
220,
220,
220,
220,
220,
220,
220,
1441,
26181,
198,
220,
220,
220,
1288,
361,
318,
39098,
7,
26801,
11,
357,
4868,
11,
46545,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
41767,
13,
22179,
26933,
2536,
7,
87,
8,
329,
2124,
287,
26181,
12962,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
965,
7,
26801,
8,
198
] | 3.273556 | 329 |
class Message:
"""
Informations needed by the end user
to read a message. Think about this
structure as a DTO in something like
java
"""
__slots__ = "sender", "time_stamp", "body"
| [
4871,
16000,
25,
198,
220,
220,
220,
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45255,
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2622,
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220,
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284,
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13,
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428,
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220,
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4645,
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1223,
588,
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220,
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20129,
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37227,
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834,
796,
366,
82,
2194,
1600,
366,
2435,
62,
301,
696,
1600,
366,
2618,
1,
198
] | 2.75 | 76 |
#!flask/bin/python
from flask import Flask,request,send_from_directory,jsonify,Response
import json
import sys
sys.path.append('modules/')
import predict as pre
import preprocessing as pro
import diacritize as dia
app = Flask(__name__)
@app.route('/text_classification', methods=['GET'])
@app.route('/', methods=['GET'])
@app.route('/diacritize', methods=['GET'])
if __name__ == '__main__':
app.run(debug=True)
| [
2,
0,
2704,
2093,
14,
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14,
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28,
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6,
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198,
198,
31,
1324,
13,
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14,
67,
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799,
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3256,
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198,
220,
220,
220,
598,
13,
5143,
7,
24442,
28,
17821,
8,
198
] | 2.708861 | 158 |
#
# Copyright (C) [2020] Futurewei Technologies, Inc.
#
# FORCE-RISCV is licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY OR
# FIT FOR A PARTICULAR PURPOSE.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from riscv.EnvRISCV import EnvRISCV
from riscv.GenThreadRISCV import GenThreadRISCV
from base.Sequence import Sequence
MainSequenceClass = MainSequence
GenThreadClass = GenThreadRISCV
EnvClass = EnvRISCV
| [
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1330,
5215,
16818,
49,
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198,
6738,
2779,
13,
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594,
1330,
45835,
628,
198,
198,
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594,
9487,
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44015,
594,
198,
13746,
16818,
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5215,
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49,
1797,
33538,
198,
4834,
85,
9487,
796,
2039,
85,
49,
1797,
33538,
198
] | 3.182879 | 257 |
from django.urls import path
from .views import (
BlockchainCreateView,
BlockchainDeleteView,
BlockchainDetailView,
BlockchainListView,
BlockchainUpdateView,
)
app_name = "blockchain"
urlpatterns = [
path("", BlockchainListView.as_view(), name="blockchain_list"),
path("add/", BlockchainCreateView.as_view(), name="blockchain_add"),
path("<int:pk>/", BlockchainDetailView.as_view(), name="blockchain_detail"),
path("<int:pk>/edit/", BlockchainUpdateView.as_view(), name="blockchain_edit"),
path("<int:pk>/delete/", BlockchainDeleteView.as_view(), name="blockchain_delete"),
]
| [
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14,
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14,
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10260,
7680,
13,
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22784,
1438,
2625,
9967,
7983,
62,
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12340,
198,
220,
220,
220,
3108,
7203,
27,
600,
25,
79,
74,
29,
14,
33678,
14,
1600,
29724,
38727,
7680,
13,
292,
62,
1177,
22784,
1438,
2625,
9967,
7983,
62,
33678,
12340,
198,
60,
198
] | 2.874419 | 215 |
import re
from django.http import (
Http404,
HttpResponseRedirect,
HttpResponse)
ACTION_RE = re.compile(r'\w+')
class ActionMixin(object):
"""Mixin for managing actions
"""
| [
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198
] | 2.421687 | 83 |
# -*- coding: utf-8 -*-
import random
import re
import string
import sys
import threading
import traceback
import six
from six import string_types
# Python3 queue support.
try:
import Queue
except ImportError:
import queue as Queue
import logging
logger = logging.getLogger(__name__)
thread_local = threading.local()
def split_string(text, chars_per_string):
"""
Splits one string into multiple strings, with a maximum amount of `chars_per_string` characters per string.
This is very useful for splitting one giant message into multiples.
:param text: The text to split
:param chars_per_string: The number of characters per line the text is split into.
:return: The splitted text as a list of strings.
"""
return [text[i:i + chars_per_string] for i in range(0, len(text), chars_per_string)]
| [
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220,
220,
220,
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62,
525,
62,
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25,
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286,
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318,
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656,
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220,
220,
220,
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25,
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4328,
2175,
2420,
355,
257,
1351,
286,
13042,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
1441,
685,
5239,
58,
72,
25,
72,
1343,
34534,
62,
525,
62,
8841,
60,
329,
1312,
287,
2837,
7,
15,
11,
18896,
7,
5239,
828,
34534,
62,
525,
62,
8841,
15437,
198
] | 3.142322 | 267 |
#! /usr/bin/env python
# File Docstring
"""STAX
Copyright (c) 2010 by Santo Ciaravino"""
print __doc__
print
# Initialize Psyco
try:
import psyco
psyco.full()
except ImportError:
print "NOTICE: 'Psyco' could not be initialized!"
# Import Python Standard Libraries
import __init__ # Directory Import
import os # Operating System
import sys # Python System
import random # Random Generator
# Import Pygame & Other Libraries
import wx
import pygame
from pygame.locals import *
# Import Game Data
from DIR_data.DAT_mainmenu import CHP_mainmenu
from DIR_data.DAT_gamesettings import CHP_gamesettings
from DIR_data.DAT_game import CHP_game
from DIR_data.DIR_sprite.DAT_spritesheet import Spritesheet
# Display Object
class DISPLAY:
"""The Display Window Object."""
def __init__(self, # Object Instance
resolution=(800, 600), # Display Default Resolution
disp_sets=0, # Display Default Video Settings
title='STAX: Action Puzzle Game', # Display Default Title
icon=('DIR_data\\disp_icon.ico', # Display Default Icon
(255, 0, 255)), # Icon Transparency Colorkey (R, G, B)
mouse_vis=False): # Default Mouse Visibility
"""Initializes the Display."""
# Single Instance Checker
self.app_iden = "APP IDEN - %s" % wx.GetUserId()
self.instance = wx.SingleInstanceChecker(self.app_iden)
if(self.instance.IsAnotherRunning()): sys.exit()
# Screen Attributes
self.RESOLUTION = resolution
self.DISP_SETS = disp_sets
self.TITLE = title
self.ICON = icon
self.MOUSE_VIS = mouse_vis
# Screen Methods
self.Set_Caption = pygame.display.set_caption
self.Set_Icon = pygame.display.set_icon
self.Set_Mouse_Vis = pygame.mouse.set_visible
self.Set_Mode = pygame.display.set_mode
# Start The Display
self.Set_Caption(self.TITLE) # Set Title
try:
self.Set_Icon(self.Icon_Data(self.ICON[0], self.ICON[1])) # Set Icon
except:
pass
self.Set_Mouse_Vis(self.MOUSE_VIS) # Set Mouse Visibility
self.FRAME = self.Set_Mode(self.RESOLUTION, self.DISP_SETS) # Activate Display
self.SURFACE = pygame.display.get_surface() # Get Frame Surface
self.RECT = self.FRAME.get_rect() # Get Frame Rect
# Local Method to prepare icon data in initialization.
def Icon_Data(self, icon_name, colorkey):
"""Prepares an icon to use on the Display window."""
Icon_Image = pygame.image.load(icon_name) # Icon Image (Surface)
Icon_Done = pygame.Surface((32, 32)) # New Surface
Icon_Done.set_colorkey(colorkey) # Transparency Colorkey
for X in range(0, 32): # X Coordinate Pixels
for Y in range(0, 32): # Y Coordinate Pixels
Icon_Done.set_at((X, Y), Icon_Image.get_at((X, Y))) # Transfer Pixels
return Icon_Done # Return Value
# Screen Methods (for in-game use)
def Flush_Buffer(self, color=(0, 0, 0)):
"""Flushes the back buffer with a blank surface."""
SFC_flush = pygame.Surface(self.RESOLUTION).convert()
SFC_flush.fill(color)
self.FRAME.blit(SFC_flush, (0, 0))
# Start Game
if __name__ == "__main__":
# Initialize Game Data
pygame.init() # Set Pygame
os.environ['SDL_VIDEO_CENTERED'] = '1' # Center all displays.
# Splash Screen
IMG_splash = pygame.image.load('DIR_data\\splashscreen.png')
RCT_splash = IMG_splash.get_rect()
Splash = pygame.display.set_mode(RCT_splash.size, NOFRAME)
pygame.mouse.set_visible(False) # This line is optional.
Splash.blit(IMG_splash, (0, 0))
pygame.display.update()
# Choose either wait(milliseconds) or delay(milliseconds), and remove the other.
pygame.time.wait(2000) # Use this to 'wait' a specific amount of time.
# Actually it doesn't matter. They do the same thing. Still good to keep consistent.
pygame.display.quit()
# Activate Display
pygame.display.init() # Re-initialize the Pygame Display module.
SCREEN = DISPLAY() # Make a new DISPLAY.
# Prepare Constants
GameConstant = {'Clock': pygame.time.Clock(),
'Spritesheet': Spritesheet(),
'Font': pygame.font.Font(None, 20),
'BigFont': pygame.font.SysFont(None, 40),
'HighScore': 0}
# Main Loop
MLS = True # Main Loop Switch
while MLS:
# Purge Input Queue
pygame.event.get()
# Main Menu Object & Start
MainMenu = CHP_mainmenu()
GameSettings = CHP_gamesettings()
RESULT = MainMenu.Start(SCREEN, GameConstant)
if RESULT is 1:
MLS = False
elif RESULT is 0:
GameLoop = True
while GameLoop:
StartParams = GameSettings.Start(SCREEN, GameConstant)
if StartParams != 'BREAK':
Game = CHP_game(StartParams)
GAME = Game.Start(SCREEN, GameConstant)
elif StartParams == 'BREAK':
GameLoop = False
else:
pass
| [
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] | 2.296167 | 2,296 |
import abc
from random import randint
from itertools import chain
from utils.dictSet import DictContainer
from database import Variable, \
Property, Event, \
SpriteProperty, TextProperty, LineProperty, \
OvalProperty, RectProperty, PolygonProperty, \
UNDEFINED_PARAMETER, KEYWORD_ID
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] | 3.045872 | 109 |
#############################
# DESCRIPTION
# The gen_3d_structure scripts use the EmbedMolecule function of RDKit to generate the 3D structure of compounds, which is then minimized using MM force field within RDKit.
# The ChemAxon majorms function is used to determined the protonation state at a pH of 7.
# The lines to generate the structures of the neutralized molecules are commneted since the simulations in the lipid bilayer did not lead to significant results.
#
############################
import argparse
from rdkit import Chem
from rdkit.Chem import SaltRemover, AllChem
import sys
import subprocess
import pandas as pd
parser = argparse.ArgumentParser(description='Generate 3D-structures at pH = 7 from a SMILES file. \n One output _pH7.pdb file for each SMILES in the input .smi file')
parser.add_argument('-ismi', metavar='input_file.smi', help='Input file containing a list of SMILES. It must contain a "SMILES" column. Columns "CMPD_ID" and "is_sub" are optional. The "CMPD_ID" column contains compound names while the "is_sub" column contains the classification labels. If the "CMPD_ID" column is missing, then compounds will be named as mol_N. If the "is_sub" column is present then the pdb output files will be saved into two directories, "substrates" and "nonsubstrates", instead of the current directory', required = True)
parser.add_argument('-sep', type=str, default=" ", help='column separator. Default = " "')
parser.add_argument('-calc_pH', metavar='False', help='True to calculate the main protomer form using the cxcalc function of ChemAxon', required = False, default=False)
parser.add_argument('-pH', metavar='7.0', help='pH at which to evaluate the protomer forms', required = False, default=7.0)
parser.add_argument('-output_folder', help='folder in which to save the output PDB files', required = False, default=".")
args = parser.parse_args()
out_dir = args.output_folder
if args.ismi:
input_file = args.ismi
data = pd.read_table(input_file, sep=args.sep)
try:
smiles = list(data['SMILES'])
except:
print("IOError: The input file does not contain a SMILES column")
try:
cmpd_name = list(data['CMPD_ID'])
except:
cmpd_name = ['mol_{}'.format(i) for i in range(1,len(smiles)+1)]
for i, smi in enumerate(smiles):
m = Chem.MolFromSmiles(smi)
molname = cmpd_name[i]
try:
m2=Chem.AddHs(m)
AllChem.EmbedMolecule(m2)
AllChem.MMFFOptimizeMolecule(m2)
m2.SetProp('_Name', molname)
m2.SetProp('CMPD_NAME', molname)
m2.GetProp('CMPD_NAME')
w = Chem.SDWriter('{}_3d.sdf'.format(molname))
w.write(m2)
except:
print("Skip {}".format(molname))
continue
if args.calc_pH == True:
args_subproc = "sed -i '/CHG/d' " + molname + "_3d.sdf"
subprocess.call(args_subproc, shell=True)
args_subproc = "cxcalc -o " + molname + "_pH7.sdf majorms -H {} -f sdf " + molname + "_3d.sdf".format(args.pH)
subprocess.call(args_subproc, shell=True)
args_subproc = "obabel -isdf " + molname + "_pH7.sdf -opdb -O " + out_dir + "/" + molname + "_pH7.pdb -h"
subprocess.call(args_subproc, shell=True)
args_subproc = "rm *_pH7.sdf *_3d.sdf"
subprocess.call(args_subproc, shell=True)
# To eventually neutralize the compounds using ChemAxon
#args_subproc = "standardize -c neutralize " + molname + "_3d.sdf -f sdf -o " + molname + "_neutr.sdf"
#subprocess.call(args_subproc, shell=True)
#args_subproc = "obabel -isdf " + molname + "_neutr.sdf" + " -opdb -O " + molname + "_neutr.pdb"
#subprocess.call(args_subproc, shell=True)
#args_subproc = "rm *_neutr.sdf"
#subprocess.call(args_subproc, shell=True)
else:
args_subproc = "obabel -isdf " + molname + "_3d.sdf -opdb -O " + out_dir + "/" + molname + "_3d.pdb -h"
subprocess.call(args_subproc, shell=True)
print("Done")
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] | 2.390075 | 1,733 |
import discord
import cogs.utils.checks
from discord.ext import commands
from cogs.utils.embed import (passembed, errorembed)
# Adding the cog to main script
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] | 3.333333 | 48 |
# -*- coding: utf-8 -*-
{
'name': 'Import OFX2 Bank Statement',
'summary': "Imports Bank Statements using OFX format from banks like CBA, St George etc",
'description': """
This module adds support for the import of bank statements in `OFX format <https://en.wikipedia.org/wiki/Open_Financial_Exchange>`_.
This module is based on the module from OCA bank-statement-import/account-bank-statement-import-ofx
It has been modified to be more tollerant of finanicial entries without FIDs.
""",
'category': 'Banking addons',
'version': '0.1',
'author': "oGecko Solutions",
'website': "http://www.ogecko.com",
'depends': [
'account_bank_statement_import',
],
'data': [
'views/view_account_bank_statement_import.xml',
],
# 'external_dependencies': {
# 'python': ['ofxparse'],
# },
'installable': True,
}
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] | 2.619597 | 347 |
from . import main, main_dia
import argparse
from copy import deepcopy
import logging
if __name__ == '__main__':
run()
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# TODO: define the 'EXPECTED_BAKE_TIME' constant
# TODO: consider defining the 'PREPARATION_TIME' constant
# equal to the time it takes to prepare a single layer
# TODO: define the 'bake_time_remaining()' function
def bake_time_remaining():
"""Calculate the bake time remaining.
:param elapsed_bake_time: int baking time already elapsed.
:return: int remaining bake time derived from 'EXPECTED_BAKE_TIME'.
Function that takes the actual minutes the lasagna has been in the oven as
an argument and returns how many minutes the lasagna still needs to bake
based on the `EXPECTED_BAKE_TIME`.
"""
pass
# TODO: define the 'preparation_time_in_minutes()' function
# and consider using 'PREPARATION_TIME' here
# TODO: define the 'elapsed_time_in_minutes()' function
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] | 3.091603 | 262 |
#!/usr/bin/python3.9
# -*- coding: utf-8 -*-
#
# Copyright (C) 2022 LinYulong. All Rights Reserved
#
# @Time : 2022/2/7
# @Author : LinYulong
# @Description: 逻辑斯蒂回归模型Logistics regression
# https://blog.csdn.net/u013421629/article/details/78470020
import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
# 创建特征列表。
column_names = ['Sample code number', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape',
'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli',
'Mitoses', 'Class']
# 使用pandas.read_csv函数从互联网读取指定数据。
data = pd.read_csv(
'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data',
names=column_names)
# print data
# 将?替换为标准缺失值表示。
data = data.replace(to_replace='?', value=np.nan)
# 丢弃带有缺失值的数据（只要有一个维度有缺失）。
data = data.dropna(how='any')
# 输出data的数据量和维度。
# print data.shape
# 随机采样25%的数据用于测试，剩下的75%用于构建训练集合。
x_train, x_test, y_train, y_test = train_test_split(data[column_names[1:10]], data[column_names[10]], test_size=0.25,
random_state=33)
# 查验训练样本的数量和类别分布。
# print y_train.value_counts()
# 查验测试样本的数量和类别分布。
# print y_test.value_counts()
# 标准化数据，保证每个维度的特征数据方差为1，均值为0。使得预测结果不会被某些维度过大的特征值而主导。
ss = StandardScaler()
x_train = ss.fit_transform(x_train)
x_test = ss.transform(x_test)
# 初始化LogisticRegression
lr = LogisticRegression()
# 调用LogisticRegression中的fit函数/模块用来训练模型参数。
lr.fit(x_train, y_train)
# 使用训练好的模型lr对X_test进行预测，结果储存在变量lr_y_predict中。
lr_y_predict = lr.predict(x_test)
print("=== lr_y_predict ===")
print(lr_y_predict)
print("=== end lr_y_predict ===")
# 初始化GDClassifier。
sgdc = SGDClassifier()
# 调用SGDClassifier中的fit函数/模块用来训练模型参数。
sgdc.fit(x_train, y_train)
# 使用训练好的模型sgdc对X_test进行预测，结果储存在变量sgdc_y_predict中。
sgdc_y_predict = sgdc.predict(x_test)
# print sgdc_y_predict
# 打印混淆矩阵
labels1 = list(set(lr_y_predict))
conf_mat1 = confusion_matrix(y_test, lr_y_predict, labels=labels1)
print("Logistics regression")
print(conf_mat1)
labels2 = list(set(sgdc_y_predict))
conf_mat2 = confusion_matrix(y_test, sgdc_y_predict, labels=labels2)
print("sgdc_y_predict")
print(conf_mat2)
# 使用逻辑斯蒂回归模型自带的评分函数score获得模型在测试集上的准确性结果。
print('Accuracy of LR Classifier:', lr.score(x_test, y_test))
# 利用classification_report模块获得LogisticRegression其他三个指标的结果。
print(classification_report(y_test, lr_y_predict, target_names=['Benign', 'Malignant']))
# 使用随机梯度下降模型自带的评分函数score获得模型在测试集上的准确性结果。
print('Accuarcy of SGD Classifier:', sgdc.score(x_test, y_test))
# 利用classification_report模块获得SGDClassifier其他三个指标的结果。
print(classification_report(y_test, sgdc_y_predict, target_names=['Benign', 'Malignant']))
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] | 1.592593 | 1,890 |
"""GitLab source."""
from ..meta.entity import Color, EntityAttributeType
from ..meta.source import Source
from ..parameters import (
Branch,
BranchesToIgnore,
Days,
FailureType,
MergeRequestState,
MultipleChoiceParameter,
MultipleChoiceWithAdditionParameter,
Upvotes,
PrivateToken,
StringParameter,
TargetBranchesToInclude,
URL,
)
ALL_GITLAB_METRICS = [
"failed_jobs",
"merge_requests",
"source_up_to_dateness",
"source_version",
"unmerged_branches",
"unused_jobs",
]
JOB_ENTITY = dict(
name="job",
attributes=[
dict(name="Job name", key="name", url="url"),
dict(name="Job stage", key="stage"),
dict(name="Branch or tag", key="branch"),
dict(
name="Status of most recent build",
key="build_status",
color=dict(canceled=Color.ACTIVE, failed=Color.NEGATIVE, success=Color.POSITIVE),
),
dict(name="Date of most recent build", key="build_date", type=EntityAttributeType.DATE),
],
)
GITLAB_BRANCH_HELP_URL = "https://docs.gitlab.com/ee/user/project/repository/branches/"
GITLAB = Source(
name="GitLab",
description="GitLab provides Git-repositories, wiki's, issue-tracking and continuous integration/continuous "
"deployment pipelines.",
url="https://about.gitlab.com/",
parameters=dict(
url=URL(
name="GitLab instance URL",
help="URL of the GitLab instance, with port if necessary, but without path. For example, "
"'https://gitlab.com'.",
validate_on=["private_token"],
metrics=ALL_GITLAB_METRICS,
),
project=StringParameter(
name="Project (name with namespace or id)",
short_name="project",
mandatory=True,
help_url="https://docs.gitlab.com/ee/user/project/",
metrics=[
"failed_jobs",
"merge_requests",
"source_up_to_dateness",
"unmerged_branches",
"unused_jobs",
],
),
private_token=PrivateToken(
name="Private token (with read_api scope)",
help_url="https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html",
metrics=ALL_GITLAB_METRICS,
),
file_path=StringParameter(
name="File or folder path",
short_name="path",
mandatory=True,
help_url="https://docs.gitlab.com/ee/api/repository_files.html",
metrics=["source_up_to_dateness"],
),
branch=Branch(help_url=GITLAB_BRANCH_HELP_URL),
branches_to_ignore=BranchesToIgnore(help_url=GITLAB_BRANCH_HELP_URL),
refs_to_ignore=MultipleChoiceWithAdditionParameter(
name="Branches and tags to ignore (regular expressions, branch names or tag names)",
short_name="branches and tags to ignore",
help_url=GITLAB_BRANCH_HELP_URL,
metrics=["failed_jobs", "unused_jobs"],
),
inactive_days=Days(
name="Number of days since last commit after which to consider branches inactive",
short_name="number of days since last commit",
default_value="7",
metrics=["unmerged_branches"],
),
inactive_job_days=Days(
name="Number of days without builds after which to consider CI-jobs unused",
short_name="number of days without builds",
default_value="90",
metrics=["unused_jobs"],
),
failure_type=FailureType(values=["canceled", "failed", "skipped"]),
jobs_to_ignore=MultipleChoiceWithAdditionParameter(
name="Jobs to ignore (regular expressions or job names)",
short_name="jobs to ignore",
help="Jobs to ignore can be specified by job name or by regular expression.",
metrics=["failed_jobs", "unused_jobs"],
),
merge_request_state=MergeRequestState(values=["opened", "locked", "merged", "closed"]),
approval_state=MultipleChoiceParameter(
name="Approval states to include (requires GitLab Premium)",
short_name="approval states",
help_url="https://docs.gitlab.com/ee/user/project/merge_requests/approvals/",
values=["approved", "not approved", "unknown"],
api_values={"approved": "yes", "not approved": "no", "unknown": "?"},
placeholder="all approval states",
metrics=["merge_requests"],
),
upvotes=Upvotes(),
target_branches_to_include=TargetBranchesToInclude(help_url=GITLAB_BRANCH_HELP_URL),
),
entities=dict(
failed_jobs=JOB_ENTITY,
merge_requests=dict(
name="merge request",
attributes=[
dict(name="Merge request", key="title", url="url"),
dict(name="Target branch"),
dict(name="State"),
dict(name="Approved"),
dict(name="Upvotes", type=EntityAttributeType.INTEGER),
dict(name="Downvotes", type=EntityAttributeType.INTEGER),
dict(name="Created", type=EntityAttributeType.DATETIME),
dict(name="Updated", type=EntityAttributeType.DATETIME),
dict(name="Merged", type=EntityAttributeType.DATETIME),
],
),
unmerged_branches=dict(
name="branch",
name_plural="branches",
attributes=[
dict(name="Branch name", key="name", url="url"),
dict(name="Date of most recent commit", key="commit_date", type=EntityAttributeType.DATE),
],
),
unused_jobs=JOB_ENTITY,
),
)
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] | 2.17911 | 2,652 |
"""
Interface module defining a url storage API.
"""
import abc
import six
@six.add_metaclass(abc.ABCMeta)
| [
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"""
roundup.anypy - compatibility layer for any Python 2.3+
"""
VERSION = '.'.join(map(str,
(0,
3,
)))
| [
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# -*- coding: utf-8 -*-
import importlib
from json.encoder import (_make_iterencode, JSONEncoder,
encode_basestring_ascii, INFINITY,
encode_basestring)
discord = importlib.import_module('discord')
fortnitepy = importlib.import_module('fortnitepy')
| [
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import boto3
import botocore
import json
import config
import res.utils as utils
import res.glob as glob
# =======================================================================================================================
#
# Supported services : RDS, DynamoDB, ElastiCache, Neptune, Amazon Redshift
# Unsupported services : None
#
# =======================================================================================================================
# ------------------------------------------------------------------------
#
# RDS
#
# ------------------------------------------------------------------------
def get_rds_inventory(oId):
"""
Returns RDS inventory
:param oId: ownerId (AWS account)
:type oId: string
:return: RDS inventory
:rtype: json
..note:: http://boto3.readthedocs.io/en/latest/reference/services/rds.html
"""
return glob.get_inventory(
ownerId = oId,
aws_service = "rds",
aws_region = "all",
function_name = "describe_db_instances",
key_get = "DBInstances",
pagination = True
)
# ------------------------------------------------------------------------
#
# DynamoDB
#
# ------------------------------------------------------------------------
def get_dynamodb_inventory(oId):
"""
Returns dynamoDB inventory
:param oId: ownerId (AWS account)
:type oId: string
:return: dynamoDB inventory
:rtype: json
..note:: http://boto3.readthedocs.io/en/latest/reference/services/dynamodb.html
"""
return glob.get_inventory(
ownerId = oId,
aws_service = "dynamodb",
aws_region = "all",
function_name = "list_tables",
key_get = "TableNames",
detail_function = "describe_table",
join_key = "TableName",
detail_join_key = "TableName",
detail_get_key = "Table",
pagination = True
)
# ------------------------------------------------------------------------
#
# Neptune
#
# ------------------------------------------------------------------------
def get_neptune_inventory(oId):
"""
Returns neptune inventory (instances & clusters). Instances are listed in RDS inventory.
:param oId: ownerId (AWS account)
:type oId: string
:return: neptune inventory
:rtype: json
..note:: http://boto3.readthedocs.io/en/latest/reference/services/neptune.html
"""
neptune_inventory = {}
neptune_inventory['clusters'] = glob.get_inventory(
ownerId = oId,
aws_service = "neptune",
aws_region = "all",
function_name = "describe_db_clusters",
key_get = "DBClusters"
)
return neptune_inventory
# ------------------------------------------------------------------------
#
# ElastiCache
#
# ------------------------------------------------------------------------
def get_elasticache_inventory(oId):
"""
Returns elasticache inventory (instances & clusters). Instances are listed in RDS inventory.
:param oId: ownerId (AWS account)
:type oId: string
:return: elasticache inventory
:rtype: json
..note:: http://boto3.readthedocs.io/en/latest/reference/services/elasticache.html
"""
elasticache_inventory = {}
elasticache_inventory['cache-clusters'] = glob.get_inventory(
ownerId = oId,
aws_service = "elasticache",
aws_region = "all",
function_name = "describe_cache_clusters",
key_get = "CacheClusters",
pagination = True
)
elasticache_inventory['reserved-cache-nodes'] = glob.get_inventory(
ownerId = oId,
aws_service = "elasticache",
aws_region = "all",
function_name = "describe_reserved_cache_nodes",
key_get = "ReservedCacheNodes",
pagination = True
)
return elasticache_inventory
# ------------------------------------------------------------------------
#
# Redshift
#
# ------------------------------------------------------------------------
def get_redshift_inventory(oId):
"""
Returns redshift inventory (instances & clusters). Instances are listed in RDS inventory.
:param oId: ownerId (AWS account)
:type oId: string
:return: redshift inventory
:rtype: json
..note:: http://boto3.readthedocs.io/en/latest/reference/services/redshift.html
"""
redshift_inventory = {}
redshift_inventory['clusters'] = glob.get_inventory(
ownerId = oId,
aws_service = "redshift",
aws_region = "all",
function_name = "describe_clusters",
key_get = "Clusters",
pagination = True
)
redshift_inventory['reserved-nodes'] = glob.get_inventory(
ownerId = oId,
aws_service = "redshift",
aws_region = "all",
function_name = "describe_reserved_nodes",
key_get = "ReservedNodes",
pagination = True
)
return redshift_inventory
#
# Hey, doc: we're in a module!
#
if (__name__ == '__main__'):
print('Module => Do not execute') | [
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] | 2.677749 | 1,955 |
from Sockets import Client
from Sockets import Server
from argparse import ArgumentParser
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument("-m", "--mode", dest="mode", type=str, required=True,
help="CLIENT to start a client or SERVER to start a server"
)
parser.add_argument("-d", "--debug", dest="debug", required=False,
help="to print debug messages, enable this option",
action="store_true"
)
args = parser.parse_args()
if args.debug:
print(args)
if args.mode.lower() == "client":
server = "localhost" # input("Server IP: ")
client = Client.ClientSocket(args.debug)
client.start_client(server)
elif args.mode.lower() == "server":
Server.start_server(args.debug)
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9652,
13,
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62,
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7,
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13,
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8,
198
] | 2.434018 | 341 |
# The celery CLI points to a module (via the -A argument)
# to find tasks. This file exists to be a target for that argument.
# Examples:
# - See `worker_start_command` in dagster_celery.cli
# - deployment-flower.yaml helm chart
from .make_app import make_app
from .tasks import create_task
app = make_app()
execute_plan = create_task(app)
| [
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] | 3.117117 | 111 |
# standard python imports
import re
import time
import random
import zipfile
import logging
logger = logging.getLogger('utility_to_osm.ssr2')
# third party imports
from bs4 import BeautifulSoup
# shared helper library
import utility_to_osm.gentle_requests as gentle_requests
def get_geonorge_url_dct(url='https://nedlasting.geonorge.no/geonorge/Basisdata/Stedsnavn/GML/'):
'''
Returns a dictionary with kommunenummer (as 4 character string) as key and the geonorge zip url as value
{kommunenummer: url}
'''
geonorge = dict()
req = gentle_requests.GentleRequests()
data = req.get(url)
#print(data.content)
soup = BeautifulSoup(data.content, 'lxml-xml')
#print(soup.prettify())
for link in soup.find_all('a'):
href = link.get('href')
if href.startswith('Basisdata_0000_Norge'):
continue
# ignore projection, for kommuner with both, the last one will be used
reg = re.match('Basisdata_(\d\d\d\d)_([-\w]+)_\d+_Stedsnavn_GML.zip', href)
if reg:
href = url + href
kommunenummer, kommunenavn = reg.groups()
kommunenavn = kommunenavn.replace('_', ' ')
geonorge[kommunenummer] = href
return geonorge
if __name__ == '__main__':
geonorge_urls = get_geonorge_url_dct()
for key, item in geonorge_urls.items():
print(key, item)
# lets try it out
c = download_unzip_geonorge(item, 'test.zip')
soup = BeautifulSoup(c, 'lxml-xml')
# print(soup.prettify())
| [
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926,
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] | 2.303892 | 668 |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.20 on 2019-10-31 14:04
from __future__ import unicode_literals
from django.db import migrations, models
| [
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] | 2.754386 | 57 |
import unittest
from src.api import Settings
from src.api.geom import ProjectionSettings
class ProjectionSettingsTestCase(unittest.TestCase):
"""Tests the ProjectionSettings class."""
def test_as_dict(self):
"""Test the as_dict() method of the ProjectionSettings class."""
d = self.ps.as_dict()
self.assertEqual(type(d), dict)
self.assertNotIn('settings', d.keys())
self.assertIn('fov', d.keys())
def test_width_variable(self):
"""Test that the screen width variable has been correctly initialized."""
self.assertTrue(hasattr(self.ps, 'width'))
self.assertFalse(hasattr(self.ps, 'screen_width'))
self.assertEqual(self.ps.width, 1900)
def test_height_variable(self):
"""Test that the screen height variable has been correctly initialized."""
self.assertTrue(hasattr(self.ps, 'height'))
self.assertFalse(hasattr(self.ps, 'screen_height'))
self.assertEqual(self.ps.height, 1080)
if __name__ == '__main__':
unittest.main()
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198,
197,
403,
715,
395,
13,
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198
] | 2.917933 | 329 |
import numpy as np
import numbers
import gc
def sims_clean_up():
"""
This method will clean up data caches created by the sims software stack.
Any time a cache is added to the sims software stack, it can be added to
the list sims_clean_up.targets. When sims_clean_up() is called, it will
loop through the contents of sims_clean_up.targets. It will call pop()
on all of the contents of each sims_clean_up.target, run close() on each
item it pops (if applicable), delete each item it pops, and then reset
each sims_clean_up.target to either a blank dict or list (depending on
what the target was). sims_clean_up() will then run the garbage
collector.
Note: if a target cache is not a dict or a list, it will attempt to call
close() on the cache and delete the cache.
"""
if not hasattr(sims_clean_up, 'targets'):
return None
for target in sims_clean_up.targets:
if isinstance(target, dict):
while len(target) > 0:
obj = target.popitem()
if hasattr(obj[1], 'close'):
try:
obj[1].close()
except:
pass
del obj
elif isinstance(target, list):
while len(target) > 0:
obj = target.pop()
if hasattr(obj, 'close'):
try:
obj.close()
except:
pass
del obj
else:
if hasattr(target, 'close'):
target.close()
del target
gc.collect()
return None
sims_clean_up.targets = []
def _validate_inputs(input_list, input_names, method_name):
"""
This method will validate the inputs of other methods.
input_list is a list of the inputs passed to a method.
input_name is a list of the variable names associated with
input_list
method_name is the name of the method whose input is being validated.
_validate_inputs will verify that all of the inputs in input_list are:
1) of the same type
2) either numpy arrays or instances of numbers.Number (floats or ints)
3) if they are numpy arrays, they all have the same length
If any of these criteria are violated, a RuntimeError will be raised
returns True if the inputs are numpy arrays; False if not
"""
if isinstance(input_list[0], np.ndarray):
desired_type = np.ndarray
elif isinstance(input_list[0], numbers.Number):
desired_type = numbers.Number
else:
raise RuntimeError("The arg %s input to method %s " % (input_names[0], method_name) +
"should be either a number or a numpy array")
valid_type = True
bad_names = []
for ii, nn in zip(input_list, input_names):
if not isinstance(ii, desired_type):
valid_type = False
bad_names.append(nn)
if not valid_type:
msg = "The input arguments:\n"
for nn in bad_names:
msg += "%s,\n" % nn
msg += "passed to %s " % method_name
msg += "need to be either numbers or numpy arrays\n"
msg += "and the same type as the argument %s" % input_names[0]
msg += "\n\nTypes of arguments are:\n"
for name, arg in zip(input_names, input_list):
msg += '%s: %s\n' % (name, type(arg))
raise RuntimeError(msg)
if desired_type is np.ndarray:
same_length = True
for ii in input_list:
if len(ii) != len(input_list[0]):
same_length = False
if not same_length:
raise RuntimeError("The arrays input to %s " % method_name +
"all need to have the same length")
if desired_type is np.ndarray:
return True
return False
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] | 2.279835 | 1,701 |
import pandas as pd
from sklearn.model_selection import train_test_split
from keras_text_summarization.library.utility.plot_utils import plot_and_save_history
from keras_text_summarization.library.seq2seq import Seq2SeqSummarizer
from keras_text_summarization.library.applications.fake_news_loader import fit_text
import numpy as np
data=pd.read_csv("/home/chuck/Downloads/fake_or_real_news.csv")
data.head() | [
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13,
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198,
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7890,
13,
2256,
3419
] | 2.935714 | 140 |
#!/usr/bin/env python
# -*-coding: utf8 -*-
"""
Library for multiple alignment and mutation calling in hCov-19 analysis.
Copyright 2021 by Regina Kalcsevszki ([email protected]), Balázs Ligeti ([email protected]). All rights reserved.
"""
import os
from os.path import join
from Bio import SeqIO
import re
import numpy as np
import pandas as pd
# Mutation calling
# meg kell találni a referencia genome első nem gap karakterét
def get_first_nucleotid_in_reference_genome(ref_genome):
'''
Get the first non-gap nucleotide sequence
'''
for i, act_char in enumerate(ref_genome.seq):
if act_char != '-':
return i
return len(ref_genome.seq)
######### BUILDING VCF FILES #############
def mutation_annotation_preprocessing(annotations, genome_annot_proteins):
''' Preprocessing the raw output coming from SNPEFF'''
# Filtering the annotations!
mutation_annotations = mutation_annotation_preprocessing_get_mature_and_non_integenic_annotations(annotations,
genome_annot_proteins)
# Adding amibous flag
mutation_annotations.loc[
mutation_annotations.duplicated(subset=['ID'], keep=False), 'Mutation_annotation_ambiguity'] = 'ambiguous'
mutation_annotations.loc[
~mutation_annotations.duplicated(subset=['ID'], keep=False), 'Mutation_annotation_ambiguity'] = 'non_ambiguous'
# Adding mutation name?
mutation_annotations = mutation_annotation_add_protein_data(mutation_annotations, genome_annot_proteins)
return mutation_annotations
"""
Converts aggragated mutation profiles to a VCF file, that is approproate for SNPEff input
Input: df: path to aggregated mutations dataframe
Output: vcf dataframe
"""
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198,
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26410,
25,
410,
12993,
1366,
14535,
198,
37811,
628,
628,
628,
198
] | 2.553547 | 719 |
import logging
from mongoengine import connect
from flask import Flask
from flask_socketio import SocketIO, emit
DEBUG = True
app = Flask(__name__)
app.config.from_object(__name__)
socketio = SocketIO(app, host='0.0.0.0', port=5000)
logging.basicConfig(
level=logging.INFO,
format="[%(asctime)s] %(message)s",
datefmt='%H:%M:%S'
)
log = logging.getLogger(__name__)
# SocketIO response functions
log.info('Server log started, logging operations.')
# ping sockets
@socketio.on('server_ping')
@socketio.on('db_ping')
if __name__ == "__main__":
socketio.run(app) | [
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220,
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] | 2.626667 | 225 |
#####################################################
# export WANDB_START_METHOD=thread
# python hydra_wandb_test.py -m +some_param=1,2,3,4
#####################################################
# import os, sys
# sys.path.insert(1, os.path.join(sys.path[0], ".."))
# print(os.path.abspath(os.curdir))
import hydra
import wandb
@hydra.main(config_path="../configs/", config_name="config.yaml")
if __name__ == "__main__":
main()
| [
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"""
Generate the manifest.json file by recursively reading through the
docs/ directory.
All sub-directories will be interpreted as modules
All image files will be interpreted have type resource
All other files will have type topic
To modify type, name, or to ignore a file, add it to the .mfconfig file
in the root directory and use the following syntax
# This is a comment
IGNORE path/to/file # Ignores a file or directory
RENAME path/to/file new_name # Set title of file or directory to new_name
TYPE path/to/file new_type # Set type of file
"""
import json
import os
from pathlib import Path
CONFIG_FILE = ".mfconfig"
CONTENT_ROOT = Path("docs")
VALID_TYPES = {"topic", "module", "resource"}
COMMANDS = {"IGNORE": 1, "RENAME": 2, "TYPE": 2} # Cmd: n_args
RESOURCE_EXTS = {".jpg", ".jpeg", ".png", ".gif"}
IGNORE_FOLDERS = {'__pycache__', 'htmlcov', 'node_modules',
'vendor', '_site', '.DS_Store'} # other files to always ignore
if __name__ == "__main__":
main()
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] | 2.897143 | 350 |
import csv
import os
from typing import Dict, Tuple
import numpy as np
from ConfigSpace.read_and_write.pcs import read as read_pcs
import ConfigSpace
import ConfigSpace.util
def load_configuration_data(path: str) -> Tuple[np.ndarray, Dict[int,np.ndarray], Dict[int,np.ndarray]]:
"""
reads the files in the given directory and returns the performances, instances and configurations
The directory needs to contain the following files:
configlist.csv : contains configuration_id, configuration_string
instancelist.csv : contain instance_id, instance_name
perflist.csv : contains configuration_id, instance)id, performance
features.txt : contains instance_name, one column per feature
default.txt : contains the string of the default configuration
Return: (data, instance_features, configurations)
"""
#TODO : we shoult probably use the configuration space description file and the ConfigSpace library
#load configuration strings
instance_name2int: Dict[str, int] = {}
configuration2int: Dict[str, int] = {}
with open(os.path.join(path, "configlist.csv")) as fd:
reader = csv.reader(fd)
# Skip first line
next(reader)
for row in reader:
configuration2int[row[1]] = int(row[0])
# load instance names and id
with open(os.path.join(path, "instancelist.csv")) as fd:
reader = csv.reader(fd)
# Skip first line
next(reader)
for row in reader:
instance_name2int[row[1]] = int(row[0])
# load performance data
data = np.zeros((len(instance_name2int), len(configuration2int)), dtype=np.double)
with open(os.path.join(path, "perflist.csv")) as fd:
reader = csv.reader(fd)
# Skip first line
next(reader)
for row in reader:
data[int(row[1]), int(row[0])] = np.double(row[2])
# load instances features
instance_features: Dict[int, np.array]={}
with open(os.path.join(path, "features.txt")) as fd:
reader = csv.reader(fd)
# Skip first line
next(reader)
for row in reader:
if row[0] in instance_name2int.keys():
instance_features[instance_name2int[row[0]]] = np.array([np.double(val) for val in row[1:]],dtype=np.double)
## This should sanitize the instance features and take out NaN values (not tested)
col_mean = [np.mean([instance_features[key][ind]
for key in instance_features.keys()
if instance_features[key][ind] != np.NaN
])
for ind in range(len(instance_features[0]))]
for key in instance_features.keys():
instance_features[key] = np.array([instance_features[key][ind]
if instance_features[key][ind] != np.NaN
else col_mean[ind]
for ind in range(len(instance_features[0]))], dtype=np.double)
# convert configuration strings into lists of double
with open(os.path.join(path, "params.pcs")) as pcs_file:
pcs_list = pcs_file.readlines()
#breakpoint()
parameter_space = read_pcs(pcs_list)
configurations: Dict[int, np.ndarray] = {}
#get list of default values
default_config = parameter_space.get_default_configuration().get_array()
for key in parameter_space._hyperparameters:
hyper_parameter = parameter_space.get_hyperparameter(key)
default_config[parameter_space._hyperparameter_idx[key]] = hyper_parameter._inverse_transform(hyper_parameter.default_value)
for conf in configuration2int.keys():
config_dict: Dict[str,str] = {}
for param in conf.lstrip('-').split(' -'):
param_name, param_value = param.strip("'").split(" '")
config_dict[param_name] = param_value
ConfigSpace.util.fix_types(config_dict, parameter_space)
config_object = ConfigSpace.Configuration(parameter_space, config_dict)
configurations[configuration2int[conf]] = config_object.get_array()
#replace nan values (non active parameters) by their default value
configurations[configuration2int[conf]] = np.array([configurations[configuration2int[conf]][ind]
if configurations[configuration2int[conf]][ind] == configurations[configuration2int[conf]][ind]
else default_config[ind]
for ind in range(len(configurations[configuration2int[conf]]))])
#print(default_config)
return data, instance_features, configurations
if __name__ == "__main__":
d = load_configuration_data("./rundata/cplex_regions200")
#print(d[1][2])
assert d[0].shape == (684, 100), "Data matrix has incorrect size"
assert np.min(d[0]) > 0, " A time should be positive"
assert len(d[1]) == 684, "One feature vector per instance"
print("All good!")
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] | 2.331069 | 2,208 |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import copy
import json
import os
import pickle
import random
import time
from pathlib import Path
import numpy as np
import PIL.Image
import psutil
import torch
import torchvision
from torch.utils.tensorboard import SummaryWriter
import dnnlib
import legacy
from metrics import metric_main
from torch_utils import misc, training_stats
from torch_utils.logging import generate_videos
from torch_utils.ops import conv2d_gradfix, grid_sample_gradfix
from training.dataset.video import sample_frames
#----------------------------------------------------------------------------
#----------------------------------------------------------------------------
#---------------------------------------------------------------------------- | [
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# Copyright 2017 Google Inc.
#
# 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.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from this
# software without specific prior written permission.
#
# 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 HOLDER 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.
"""Haplotype-based labeling algorithm for DeepVariant.
This module provides a haplotype-aware labeling algorithm. This is a more
sophisticated approach to labeling that allows for slight representational
differences between candidate and truth variant sets. See:
https://github.com/ga4gh/benchmarking-tools
https://www.biorxiv.org/content/early/2018/03/15/270157
for an introduction to the concepts and why this is important.
The module is implemented in two big pieces of functionality:
find_best_matching_haplotypes(candidates, truths) provides an function that
accepts a list of candidate variants and a list of truth variants with known
genotypes and finds an assignment of genotypes for candidates and truth that
results in the same two haplotype sequences in the region. Since the truth
variants have known genotypes, the search there is constrained to those
genotypes and their potential set of false negatives (e.g., if truth is (0, 1)
we may have missed the variant so we consider both (0, 1) and (0, 0)). The
returned value is a HaplotypeMatch object describing the genotype assignments
for candidates and truth.
HaplotypeLabeler implements the variant_labeler.VariantLabeler API by calling
our find_best_matching_haplotypes function to get teh HaplotypeMatch objects and
returning variant_labeler.VariantLabel objects for each candidate variant.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import heapq
import itertools
from absl import logging
import enum
from third_party.nucleus.io import fasta
from third_party.nucleus.util import ranges
from third_party.nucleus.util import variant_utils
from third_party.nucleus.util import variantcall_utils
from deepvariant.labeler import variant_labeler
from deepvariant.protos import deepvariant_pb2
VariantAndGenotypes = collections.namedtuple('VariantAndGenotype',
['variant', 'genotypes'])
# The default maximum size of a variant group we'll try to label. See
# the HaplotypeLabeler class for more information.
_MAX_GROUP_SIZE = 8
# The default maximum distance between subsequent variants within a group. See
# the HaplotypeLabeler class for more information.
_MAX_SEPARATION_WITHIN_VARIANT_GROUP = 30
# True we will generate enough information into our logs to help debug bad
# regions.
_DEBUG_PRINTING_IS_ENABLED = False
class HaplotypeLabeler(variant_labeler.VariantLabeler):
"""Haplotype-based variant labeler."""
def __init__(self,
truth_vcf_reader,
ref_reader,
confident_regions,
max_group_size=_MAX_GROUP_SIZE,
max_separation=_MAX_SEPARATION_WITHIN_VARIANT_GROUP):
"""Creates a new HaplotypeVariantLabeler.
Args:
truth_vcf_reader: a VcfReader object that points to our truth variant set.
ref_reader: A FastaReader object we can use to get reference bases.
confident_regions: A RangeSet containing all of the confidently called
regions. A variant that falls outside of one of these regions will be
receive a special not-confident marker.
max_group_size: int >= 1. The maximum number of variants we'll attempt to
label together. Larger values increase the runtime of the algorithm.
max_separation: int >= 0. The maximum distance
between variants within a group. Sequential variants separated by more
than this value will be placed in separate groups for labeling.
Raises:
ValueError: if vcf_reader is None.
"""
super(HaplotypeLabeler, self).__init__(
truth_vcf_reader=truth_vcf_reader, confident_regions=confident_regions)
self._ref_reader = ref_reader
self.max_group_size = max_group_size
self.max_separation = max_separation
self._metrics = deepvariant_pb2.LabelingMetrics()
@property
def metrics(self):
"""Gets the LabelingMetrics proto tracking metrics for this labeler."""
return self._metrics
def _update_metrics(self, labeling):
"""Update self._metrics with the HaplotypeMatch labeling results.
This function updates the LabelingMetrics information in self._metrics using
the labeling results in labeling.
Args:
labeling: HaplotypeMatch. The labeling information to use to update our
LabelingMetrics.
"""
def _n_alts_by_genotype(gt):
"""Returns the number of distinct alt alleles with non-zero genotype."""
return len({g for g in gt if g > 0})
def _is_hom_ref(gt):
"""Are all genotypes in gt the reference alleles (i.e., == 0)?"""
return all(g == 0 for g in gt)
def _has_alt_genotypes(gt):
"""Is any genotype in gt a non-ref (> 0) genotype?"""
return any(g > 0 for g in gt)
# Iterate over the truth variant and its associated original genotypes
# (those provided by the input VCF) and the assigned genotypes (i.e., the
# genotypes assigned to truth to make candidates and truth match haplotypes)
# and compute a few metric values.
for truth, original_gt, assigned_gt in zip(
labeling.truths, labeling.original_truth_genotypes,
labeling.truth_genotypes):
n_alts_original = _n_alts_by_genotype(original_gt)
self._metrics.n_truth_variant_sites += 1
self._metrics.n_truth_variant_alleles += n_alts_original
self._metrics.n_true_positive_sites += _has_alt_genotypes(assigned_gt)
self._metrics.n_false_negative_sites += _is_hom_ref(assigned_gt)
# If we have more than one alt allele in the original genotypes and the
# assigned genotypes imply more or more are missing then we've got a
# multi-allelic truth variant with some missing alleles.
if (n_alts_original > 1 and
_n_alts_by_genotype(assigned_gt) < n_alts_original):
self._metrics.n_truth_multiallelics_sites_with_missed_alleles += 1
# Iterate over the original and assigned genotypes for the truth variants
# and count up the number of true positive alleles (i.e. original and
# assigned genotypes are non-ref) and false negative alleles (i.e.,
# original is non-ref but assigned is ref).
for og, ag in zip(original_gt, assigned_gt):
if og > 0:
if ag > 0:
self._metrics.n_true_positive_alleles += 1
elif ag == 0:
self._metrics.n_false_negative_alleles += 1
# Create a dict from the start of truth to the truth variant itself and its
# assigned genotypes. This is needed to compute site match counts below.
truth_by_pos = {
truth.start: (truth, gt)
for truth, gt in zip(labeling.truths, labeling.truth_genotypes)
}
# Iterate over the candidates and their assigned genotypes to compute
# the remaining metrics.
#
# Note that this counts all candidates, not just the ones in the confident
# regions of the genome. This seems like a reasonable first approach, but it
# may be necessary to restrict ourselves to only those overlapping the
# confident regions.
for candidate, genotype in zip(labeling.candidates,
labeling.candidate_genotypes):
# If candidate isn't confident, add it to the non_confident count and
# continue as the other metrics are only computed over confident
# candidates.
if not self._confident_regions.variant_overlaps(candidate):
self._metrics.n_non_confident_candidate_variant_sites += 1
continue
n_alt_alleles = len(candidate.alternate_bases)
self._metrics.n_candidate_variant_sites += 1
self._metrics.n_candidate_variant_alleles += n_alt_alleles
self._metrics.n_false_positive_sites += _is_hom_ref(genotype)
self._metrics.n_false_positive_alleles += (
n_alt_alleles - _n_alts_by_genotype(genotype))
# Use the truth_by_pos dict to determine which candidates occur at the
# same position as a truth variant. If there is one, grab it and its
# genotypes so we can compute metrics on exact position, allele, genotype
# matches. If not, update the number of inexact matches if our candidate
# is non-reference itself.
truth, assigned_gt = truth_by_pos.get(candidate.start, (None, None))
if truth:
self._metrics.n_exact_position_matches += 1
if sorted(candidate.alternate_bases) == sorted(truth.alternate_bases):
self._metrics.n_exact_position_and_allele_matches += 1
if sorted(genotype) == sorted(assigned_gt):
self._metrics.n_exact_position_and_allele_and_genotype_matches += 1
elif _has_alt_genotypes(genotype):
self._metrics.n_inexact_position_matches += 1
class ReferenceRegion(fasta.InMemoryRefReader):
"""Allows us to get bases from a cached reference interval."""
# We don't want to worry about the chromosome we are working on for code
# clarity, so we create a InMemoryRefReader that has a single chromosome named
# _DUMMY_CHROM_NAME which allows us to provide a bases(start, end) function
# for convenient reading of bases.
_DUMMY_CHROM_NAME = '*'
_CANDIDATE_MARKER = 'candidate'
_TRUTH_MARKER = 'truth'
_VariantToGroup = collections.namedtuple('_VariantToGroup',
['start', 'type', 'variant'])
def _raise_if_not_sorted_or_not_on_same_chromosome(variants):
"""Raises a ValueError if variants isn't sorted on the same chromosome."""
if not variant_utils.variants_are_sorted(variants):
raise ValueError('Variants must be sorted', variants)
for v in variants[1:]:
if variants[0].reference_name != v.reference_name:
raise ValueError(
'Variants (v1={}, v2={}) not on the same chromosome'.format(
v.reference_name, variants[0].reference_name))
def group_variants(candidates,
truths,
max_group_size=_MAX_GROUP_SIZE,
max_separation=_MAX_SEPARATION_WITHIN_VARIANT_GROUP):
"""Splits candidate and truth variants into smaller groups if necessary.
This function takes in a list of candidate and truth variants and splits up
those lists into groups that respect the requirements of the max_group_size
and max_separation arguments. This is necessary because the labeling algorithm
is very expensive as a function of the number of input variants, so to avoid
excessive runtime we break up our potentially large list of candidate and
truth variants into smaller groups (max number controlled by max_group_size)
based on a maximum distance allowed between the closest variants within the
group.
The current algorithm is a simple greedy one; we effectively merge the two
variant lists together, make groups greedily on that list until either the
maximum number of elements of a specific type (i.e., max_group_size of 2
implies we can have up to two candidate variants or truth variants within a
group) or we encounter a variant further away from the closest variant within
the current group than allowed by max_separation.
Args:
candidates: list[nucleus.proto.Variant]. A sorted list of candidate
variants on the same chromosome.
truths: list[nucleus.proto.Variant]. A sorted list of truth
variants on the same chromosome.
max_group_size: int >= 0. The maximum number of variants of a specific type
allowed within a group.
max_separation: int >= 0. The maximum distance, in basepairs, allowed
between the closest variants within a group.
Returns:
A list of grouped variants in 2-tuples, such as:
[(candidates1, truth_variants1), ...]
where each tuple contains the candidate and truth variants for that group.
Raises:
ValueError: if any of the inputs are malformed.
"""
if max_group_size < 0:
raise ValueError('max_group_size={} must be >= 0'.format(max_group_size))
if max_separation < 0:
raise ValueError('max_separation={} must be >= 0'.format(max_separation))
_raise_if_not_sorted_or_not_on_same_chromosome(candidates)
_raise_if_not_sorted_or_not_on_same_chromosome(truths)
def to_grouped_variants(variants, candidate_type):
"""Converts a Variant proto to a _VariantToGroup tuple."""
return [_VariantToGroup(v.start, candidate_type, v) for v in variants]
def _of_type(group, required_type):
"""Selects a list of Variant protos from list[_VariantToGroup] of type."""
return [gv.variant for gv in group if gv.type == required_type]
def _split_grouped_variants(group):
"""Splits a list of _VariantToGroup into candidate and truth variants."""
return _of_type(group, _CANDIDATE_MARKER), _of_type(group, _TRUTH_MARKER)
# Convert our lists of variant protos into _VariantToGroup tuples compatible
# with the heapq API (sorts on tuples), so we get a single iterable of
# variants with marked types and sorted by start position (first element of
# each tuple).
groupable_variants = heapq.merge(
to_grouped_variants(candidates, _CANDIDATE_MARKER),
to_grouped_variants(truths, _TRUTH_MARKER))
# Go through out groupable_variants and split them up into groups according to
# the predicate _include_in_variant_group.
groups = []
current_group = []
for group_variant in groupable_variants:
if _include_in_variant_group(current_group, group_variant):
current_group.append(group_variant)
else:
groups.append(current_group)
current_group = [group_variant]
if current_group:
groups.append(current_group)
# Finally split up each group into candidates and truths.
return [_split_grouped_variants(g) for g in groups]
def with_false_negative_genotypes(gt):
"""Returns a set of genotypes that includes false negatives.
This function takes a concrete genotype for a Variant, such as (0, 1), and
returns a set of genotypes that includes gt as well as all possible genotypes
consistent with some of the alleles in gt being missed. For example, here are
a few outputs for help understand what this means:
input genotype (gt) => returned set of genotypes
------------------------------------------------
# A hom-ref genotype doesn't have any alleles to miss.
(0, 0) => {(0, 0)}
# Might miss the 1 allele, or not.
(0, 1) => {(0, 0), (0, 1)}
# We could miss one, or both of the 1 alleles.
(1, 1) => {(0, 0), (0, 1), (1, 1)}
# Multi-allelics are more complex, in that we could miss either the 1 or the
# 2 allele, or both.
(1, 2) => {(0, 0), (0, 1), (0, 2), (1, 2)}
Args:
gt: iterable[int]: A genotype for a Variant, such as [0, 1] or [1, 1].
Returns:
A set of tuples containing diploid genotypes.
"""
alts = set(gt) - {0}
return {(0, 0), tuple(gt)} | {(0, alt) for alt in alts}
class ImpossibleHaplotype(Exception):
"""Indicates that an impossible haplotype configuration has been observed."""
pass
def enumerate_all_possible_haplotypes(variants, ref, enumeration_type):
"""Yields all possible haplotype/genotype combinations for variants.
Args:
variants: list[nucleus.protos.Variant]. A list of candidate variants, in
coordinate-sorted order, all on the same chromosome.
ref: ReferenceRegion. Used to get reference bases for variants. Must cover
at least the span of the variants.
enumeration_type: EnumerationType enum value. What kind of enumeration do we
want to do? Can be either CANDIDATES or TRUTH.
Yields:
2-tuple of haplotypes and genotypes. Haplotypes is a set of either one or
two strings, where each string is a haplotype (i.e., a series of bases)
generated by the genotypes assigned to each variant. genotypes is a list of
genotype tuples, in the same order as variants, indicating the genotype
assignment for each variant. These genotypes are phased, so [(0, 1), (0, 1)]
is not the same as [(0, 1), (1, 0)].
"""
def create_haplotypes_recursive(variants_and_genotypes, last_pos):
"""Recursive driver to enumerate all haplotypes."""
if not variants_and_genotypes:
yield {ref.bases(last_pos, ref.end)} if last_pos != ref.end else {''}
else:
group, remaining = split_independent_variants(variants_and_genotypes)
group_haplotypes, next_pos = phased_genotypes_to_haplotypes(
group, last_pos, ref)
prefix_haplotypes = list(all_diploid_haplotypes(group, group_haplotypes))
if not prefix_haplotypes:
# prefix_haplotypes can be empty when group contains incompatible
# variants making it impossible to construct any haplotypes for group.
# For example, if group is:
# variant(start=6, alleles=("AAT", "A"), genotype=(0, 1))
# variant(start=7, alleles=("AT", "T"), genotype=(1, 1))
# prefix_haplotypes will be empty because there's no way to construct
# the haplotype where the variant@6 has a 1 genotype since it is
# deleting away bases that overlap the variant@7 which has a genotype of
# (1, 1), meaning it *has* to be present in some haplotype. In this
# situation we raise a ImpossibleHaplotype exception, which is caught
# in the outer loop, allowing us to bail out of the search ASAP.
raise ImpossibleHaplotype
for haplotypes in create_haplotypes_recursive(remaining, next_pos):
for result in extend_haplotypes(prefix_haplotypes, haplotypes):
yield result
genotype_options = genotype_options_for_variants(variants, enumeration_type)
for genotypes in itertools.product(*genotype_options):
paired = [VariantAndGenotypes(v, g) for v, g in zip(variants, genotypes)]
for haplotypes in create_haplotypes(paired, ref.start):
yield haplotypes, genotypes
def all_diploid_haplotypes(variants_and_genotypes, genotypes2haplotype):
"""Returns all diploid haplotypes for variants given their genotypes."""
genotypes = [vg.genotypes for vg in variants_and_genotypes]
generated_already = set()
for haploid_genotype, haplotype in genotypes2haplotype.iteritems():
complement = complement_haploid_genotype(haploid_genotype, genotypes)
complement_haplotype = genotypes2haplotype.get(complement, None)
if complement_haplotype is not None and complement not in generated_already:
generated_already.add(haploid_genotype)
yield {haplotype, complement_haplotype}
class EnumerationType(enum.Enum):
"""Enumeration type indicating how we should explore genotype configurations.
See genotype_options_for_variants for more information.
"""
# This enumeration produces all possible genotype combinations for a variant.
CANDIDATES = 1
# This enumeration type will produce all combinations of the provided genotype
# for the variant with genotypes that allow for one or more of the
# non-reference genotypes to be missed.
TRUTH = 2
# This enumeration type will only produce a single (0, 0) genotype option for
# each variant.
ONLY_HOM_REF = 3
def genotype_options_for_variants(variants, enumeration_type):
"""Returns a list of sets of possible genotypes for each variant in variants.
This function takes a list of variants and enumeration_type and produces a
list of possible genotypes for each variant in order.
If enumeration_type is ONLY_HOM_REF, then we return a singleton set for each
variant containing only the hom-ref genotype (0, 0). If enumeration_type is
TRUTH, then each variant must have an associated genotype field values, say
(A, B), and we return the set genotype as well as all possible false negative
genotypes. In our example, this means we'd return {(A, B), (0, A), (0, B),
(0, 0)} as we could miss either the A, the B, or both alleles. If the
enumeration_type is CANDIDATES, we don't require the Variant protos to have
existing genotype field values and instead enumerate all possible unphased
genotypes for each variant given its alternative alleles of each variant. For
example, if we have a Variant with alleles = 'A' and 'C', we would return the
three possible diploid genotypes {(0, 0), (0, 1), (1, 1)}.
Args:
variants: List[nucleus.protos.Variant]. A list of Variant protos to provide
genotype options for. Some enumeration types may require the protos to
have existing genotypes in their calls[] subfield.
enumeration_type: EnumerationType. The kind of genotypes we want to explore
for each variant.
Returns:
A list of sets with the same length and "order" as variants. Each set
contains one or more diploid genotype tuples [e.g., (0, 1)] that
collectively represent the possible genotypes we need to explore.
Raises:
ValueError: if enumeration_type isn't one of the valid options.
"""
if enumeration_type == EnumerationType.TRUTH:
return [
with_false_negative_genotypes(x) for x in _variant_genotypes(variants)
]
elif enumeration_type == EnumerationType.CANDIDATES:
return [{(i, j)
for i, j, _, _ in variant_utils.genotype_ordering_in_likelihoods(v)
}
for v in variants]
elif enumeration_type == EnumerationType.ONLY_HOM_REF:
return [{(0, 0)}] * len(variants)
else:
raise ValueError('Unexpected EnumerationType', enumeration_type)
def split_independent_variants(variants_and_genotypes):
"""Splits variants_and_genotypes into an overlapping group and remaining."""
if not variants_and_genotypes:
raise ValueError('Expected at least one value in variants_and_genotypes')
overlaps = [variants_and_genotypes[0]]
for i in range(1, len(variants_and_genotypes)):
vgi = variants_and_genotypes[i].variant
if any(variant_utils.variants_overlap(vg.variant, vgi) for vg in overlaps):
overlaps.append(variants_and_genotypes[i])
else:
return overlaps, variants_and_genotypes[i:]
return overlaps, []
def extend_haplotypes(prefix_haplotypes_list, haplotypes):
"""Yields all diploid combinations of prefix_haplotypes_list x haplotypes.
Args:
prefix_haplotypes_list: list[set[string]]: prefix_haplotypes_list contains a
list of set[string], which are just like haplotypes (i.e., contains 1 or 2
strings), that collectively represent all possible prefixes of haplotypes.
haplotypes: set[string]. A set containing 1 or 2 haplotype strings. So it
looks like {h} or {h1, h2}.
Yields:
A series of set[string], each containing 1 or 2 haplotype strings.
Raises:
ValueError: if any of the arguments are invalid.
"""
if not prefix_haplotypes_list:
raise ValueError('prefix_haplotypes_list cannot be empty')
if len(haplotypes) not in {1, 2}:
raise ValueError('haplotypes must have exactly 1 or 2 elements', haplotypes)
for prefix_haplotypes in prefix_haplotypes_list:
if len(prefix_haplotypes) == 1:
f, = prefix_haplotypes
yield {f + h for h in haplotypes}
else:
f1, f2 = prefix_haplotypes
if len(haplotypes) == 1:
h, = haplotypes
yield {f1 + h, f2 + h}
else:
h1, h2 = haplotypes
yield {f1 + h1, f2 + h2}
yield {f1 + h2, f2 + h1}
def phased_genotypes_to_haplotypes(variants_and_genotypes, start, ref):
"""Returns a map from phased genotypes => haplotype sequences.
This function creates a map from all possible haploid genotypes of the
genotypes in variants_and_genotypes to their corresponding haplotype sequences
implied by the variants, ref, start, and their genotypes. This map can be used
to efficiently look up the haplotype sequence for any haploid genotype.
Args:
variants_and_genotypes: list[VariantAndGenotypes]. The variants and
associated genotypes to use to build the dictionary.
start: int >= 0. The position on the genome to start constructing our
haplotypes at.
ref: ReferenceRegion. Object containing the reference genome bases we use to
construct our haplotypes.
Returns:
A 2-tuple. The first element is a dictionary[tuple, string], where each key
is a phased haploid genotype and its value is the haplotype sequence implied
by that genotype given the variants and the reference genome. The second
position is the ending position of the haplotype on the reference genome.
"""
genotypes_to_haplotypes = {}
genotypes = [vg.genotypes for vg in variants_and_genotypes]
variants = [vg.variant for vg in variants_and_genotypes]
all_haploid_genotypes = sorted(set(itertools.product(*genotypes)))
end = max(v.end for v in variants)
for phased in all_haploid_genotypes:
haplotype = build_haplotype(variants, phased, ref, start, end)
if haplotype:
genotypes_to_haplotypes[phased] = haplotype
return genotypes_to_haplotypes, end
def build_haplotype(variants, allele_indices, ref, ref_start, ref_end):
"""Builds the haplotype string from variants and its phased gneotypes.
This function takes a list of variants and associated phased genotypes and
constructs the haplotype sequence implied by variants and its genotypes. For
example, suppose we have two variants:
ref: CAGC where the first base (C) is at position 10.
chr20:10 A=>C
chr20:11 G=>T
allele_indices: [0, 1]
We would receive arguments here of a list of two variants and a list of the
allele_indices [0, 1]. We now look up which base is implied for the variant
(e.g., [0, 1] takes the reference bases from variant 1 at 10 and then the
first alternate allele of variant 2 at 11). If ref_start is 9, we would then
construct the haplotype as:
haplotype is 'CATC' derived as follows:
'C' [ref_prefix, since ref_start=]
+
'A' (variant1 has a genotype of 0)
+
'T' (variant2 has a genotype of 1)
+
'C' [ref_postfix if ref_end == 13]
= 'CATC'
Args:
variants: list[nucleus.protos.Variant]: The variants to use in constructing
the haplotype.
allele_indices: list[int]: The list of allele_indexes (where 0 means
reference_bases and > 0 implies alternative bases, following the
VariantCall genotypes semantics).
ref: ReferenceRegion. Used to get the reference bases.
ref_start: int >= 0. The first position (zero-indexed, inclusive) in the
genome where we want to start constructing our haplotype.
ref_end: int >= 0 and > ref_start. The last position (zero-indexed,
exclusive) in the genome where we want to end constructing our haplotype.
Returns:
A string containing the haplotype bases, or None, if any variant starts
before ref_start and has a non-reference genotype.
Raises:
ValueError: If any of the input arguments are malformed or otherwise violate
the assumptions of this algorithm.
"""
if len(variants) != len(allele_indices):
raise ValueError(
'Expected the same number of variants {} as allele_indices {}'.format(
len(variants), len(allele_indices)))
if ref_start < 0 or ref_start >= ref_end:
raise ValueError('expected ref_start {} < ref_end {}'.format(
ref_start, ref_end))
parts = []
position = ref_start
for variant, allele_index in zip(variants, allele_indices):
if variant.start < position:
if allele_index != 0:
return None
else:
ref_prefix = ref.bases(position, variant.start)
allele = _allele_from_index(variant, allele_index)
if allele_index == 0:
# Update our position variable to be the next reference base we want to
# use when further constructing our haplotype string. If we are using
# the reference base, we start our position at the base after variant
# start, whereas if we are using a non-reference base we use the
# variant.end.
#
# This special-case is needed to handle deletion alleles properly. If we
# have a deletion (e.g., AA => A with start = 10 and end = 12) then we
# only want to skip to position 12 for the next reference bases if we
# have have the deletion, otherwise we'd miss the second 'A' base which
# is really there (the variant isn't present, after all). Another
# consequence of this choice we only want to add the first base of the
# reference allele, not the whole string, since this would append all of
# deletion bases inappropriately to our haplotype.
allele = allele[0]
position = variant.start + 1
else:
position = variant.end
parts.append(ref_prefix + allele)
# We have some bases left to add between the position of our last variant
# and the ref_end, so append those now.
if position < ref_end:
parts.append(ref.bases(position, ref_end))
return ''.join(parts)
class HaplotypeMatch(object):
"""DataClass holding information about a matching of variants.
The haplotype labeling algorithm, at its core, searches for an assignment of
genotypes to the candidate variants and the truth variants that result in the
same diploid haplotype sequences, which we call a match. All of the
information in that previous sentence is captured here as class attributes:
Attributes:
haplotypes: list[str]. The sorted list of haplotypes produced by this match.
candidates: list[nucleus.proto.Variant]: The list of candidate variants.
truths: list[nucleus.proto.Variant]: The list of true variants.
candidate_genotypes: list[tuple]: The genotypes that, when assigned
to the candidate variants, give rise to haplotypes.
truth_genotypes: list[tuple]: The genotypes that, when assigned
to the known variants, give rise to haplotypes.
"""
__repr__ = __str__
@property
@property
def match_metrics(self):
"""Quality of this match. Lower scores are better.
Returns:
tuple[int] where all elements are >= 0: The tuple is suitable for sorting
matches, so that sorted(matches, key=lambda x: x.match_metrics) will rank
matches so that the best option is first.
"""
return (self.n_false_negatives, self.n_false_positives,
self.n_true_positives)
@property
def n_true_positives(self):
"""Gets the number of candidates whose matched genotype is not (0, 0).
Since the candidates don't have expected genotypes, we can only count each
site instead of each genotype. So this is the number of candidates whose
matched genotype is not (0, 0).
Returns:
int >= 0.
"""
return len(self.candidate_genotypes) - self.n_false_positives
@property
def n_false_positives(self):
"""Gets the number of candidates whose matched genotype is (0, 0).
Since the candidates don't have expected genotypes, we can only count each
site instead of each genotype. So this is the number of candidates whose
matched genotype is (0, 0).
Returns:
int >= 0.
"""
if self._n_false_positives is None:
self._n_false_positives = sum(
sum(gt) == 0 for gt in self.candidate_genotypes)
return self._n_false_positives
@property
def n_false_negatives(self):
"""Gets the number of missed true genotypes.
This is the sum of missed non-ref genotypes over all truth variants. So if
we have a matched truth genotype of (0, 1) and the true genotype is (1, 1),
then we have 1 FN. If the matched genotype were (0, 0), we'd have 2 FNs.
Returns:
int >= 0.
"""
if self._n_false_negatives is None:
self._n_false_negatives = sum(
n_zeroes(assigned_gt) - n_zeroes(original_gt)
for original_gt, assigned_gt in zip(self.original_truth_genotypes,
self.truth_genotypes))
return self._n_false_negatives
def candidates_with_assigned_genotypes(self):
"""Gets a copy of our candidates with their matched genotypes.
Returns:
list[Variant protobuf]: Returns a copy of self.candidates in order, with
genotypes corresponding to their matched genotypes. Any previous genotypes
in these Variants will be overwrite. If no VariantCall is present one will
be added.
"""
with_gts = [copy.deepcopy(v) for v in self.candidates]
for variant, gt in zip(with_gts, self.candidate_genotypes):
call = variant.calls[0] if variant.calls else variant.calls.add()
variantcall_utils.set_gt(call, gt)
return with_gts
def deduplicate_haplotypes(haplotypes_and_genotypes):
"""Returns a new list of haplotypes_and_genotypes with duplicates removed.
This function goes through the haplotypes_and_genotypes iterable and keeps
only a single example of (haplotypes, genotypes) if there are multiple
elements of the list that have the same haplotypes. Duplicates are expected
in the list because different genotype configurations can sometimes produce
the same set of haplotypes, and analyzing a list of possible
haplotypes/genotypes combinations with duplicates is much harder and less
efficient than the deduplicated list.
Args:
haplotypes_and_genotypes: iterable[(haplotype, genotype)]. The
haplotype/genotype tuples we want to deduplicate.
Returns:
A subset of haplotype/genotype tuples, as a list, without duplicates.
"""
haplotypes_and_genotypes = list(haplotypes_and_genotypes)
return [
(vh1, vg1)
for i, (vh1, vg1) in enumerate(haplotypes_and_genotypes)
if not any(vh1 == vh2 for (vh2, _) in haplotypes_and_genotypes[i + 1:])
]
# redacted
# variants and truths, and yields information about each variant and
# truth variant sequentially. This should be the primary API. Refactor
# label_examples to use this new API. Then create a new implementation that does
# the fast version.
def find_best_matching_haplotypes(candidates, truths, ref):
"""Assigns genotypes to each variant to best match truths.
See the module-level documentation for general information on how this
algorithm works.
Args:
candidates: list[nucleus.protos.Variant]. A list of candidate variants, in
coordinate-sorted order, all on the same chromosome.
truths: list[nucleus.protos.Variant]. A list of truth variants, in
coordinate-sorted order, for the same interval on the genome as variants.
ref: ReferenceRegion. Used to get reference bases for variants. Must cover
at least the span of the variants.
Returns:
A HaplotypeMatch object describing the best assignment of genotypes between
the candidates and truth_variants, or None, if no consistent assignment can
be found.
Raises:
ValueError: If any inputs are malformed.
"""
candidates = list(candidates)
truths = list(truths)
if _DEBUG_PRINTING_IS_ENABLED:
_log_variants('candidates', candidates)
_log_variants('truth', truths)
if not variant_utils.variants_are_sorted(candidates):
raise ValueError('candidates are not sorted', candidates)
if not variant_utils.variants_are_sorted(truths):
raise ValueError('truths are not sorted', truths)
def _hom_ref_enum_if_empty(list_of_variants, non_empty_enum):
"""If list_of_variants is empty, use a ONLY_HOM_REF enum for speed."""
return non_empty_enum if list_of_variants else EnumerationType.ONLY_HOM_REF
truth_haplotypes = deduplicate_haplotypes(
enumerate_all_possible_haplotypes(
truths, ref, _hom_ref_enum_if_empty(candidates,
EnumerationType.TRUTH)))
# Note, it may be worth deduplicating these haplotypes as well.
variant_haplotypes = enumerate_all_possible_haplotypes(
candidates, ref, _hom_ref_enum_if_empty(truths,
EnumerationType.CANDIDATES))
found = []
for vh, vgt in variant_haplotypes:
for th, tgt in truth_haplotypes:
if th == vh:
found.append(
HaplotypeMatch(
haplotypes=th,
candidates=candidates,
candidate_genotypes=vgt,
truths=truths,
truth_genotypes=tgt))
if not found:
return None
else:
return select_best_haplotype_match(found)
def select_best_haplotype_match(all_matches):
"""Returns the best HaplotypeMatch among all_matches.
The best matching HaplotypeMatch is the one with the lowest match_metrics
score.
Args:
all_matches: iterable[HaplotypeMatch]. An iterable of HaplotypeMatch objects
we want to select the best match from.
Returns:
The best matching HaplotypeMatch object.
"""
sorted_matches = sorted(all_matches, key=lambda x: x.match_metrics)
best = sorted_matches[0]
equivalents = [
f for f in all_matches if f.match_metrics == best.match_metrics
]
# redacted
if len(equivalents) > 1:
for i, f in enumerate(equivalents):
extra_info = 'best' if i == 0 else i
logging.warning('Equivalent match to best: %s [%s]', f, extra_info)
return equivalents[0]
# -----------------------------------------------------------------------------
#
# Private utility functions
#
# -----------------------------------------------------------------------------
def _variant_genotypes(variants, missing_genotypes_default=(-1, -1)):
"""Returns the genotypes of variants as a list of tuples.
Args:
variants: iterable[nucleus.protos.Variant]. The variants whose genotypes we
want to get.
missing_genotypes_default: tuple. If a variant in variants doesn't have
genotypes, this value is returned. The default value is (-1, -1), the
standard representation for "missing" diploid genotypes.
Returns:
list[nucleus.protos.Variant] protos in the same order as variants.
"""
return [
tuple(v.calls[0].genotype) if v.calls else missing_genotypes_default
for v in variants
]
def _log_haplotypes(name, haplotypes):
"""Write basic information about haplotypes to logging.info."""
logging.info('haplotypes: %s', name)
for haplotype, genotypes in haplotypes:
logging.info(' %s with %s', haplotype, genotypes)
def _log_variants(name, variants):
"""Write basic information about variants to logging.info."""
logging.info('variants: %s [%d]', name, len(variants))
for v in variants:
logging.info(' %s gt=%s', variant_utils.variant_key(v),
_variant_genotypes([v])[0])
def n_zeroes(l):
"""Returns the number of elements of l that are 0."""
return sum(1 for x in l if x == 0)
def _allele_from_index(variant, allele_index):
"""Gets the reference_bases or alternative_bases for allele_index."""
if allele_index == 0:
return variant.reference_bases
else:
return variant.alternate_bases[allele_index - 1]
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] | 2.971946 | 13,474 |
'''
(c) 2011, 2012 Georgia Tech Research Corporation
This source code is released under the New BSD license. Please see
http://wiki.quantsoftware.org/index.php?title=QSTK_License
for license details.
Created on Jan 1, 2011
@author:Drew Bratcher
@contact: [email protected]
@summary: Contains tutorial for backtester and report.
'''
# bollinger.py
#
# A module which contains a bollinger strategy.
#
#
#python imports
import cPickle
from pylab import *
from pandas import *
import matplotlib.pyplot as plt
import datetime as dt
import os
#qstk imports
from qstkutil import DataAccess as da
import qstkutil.qsdateutil as du
import qstkutil.bollinger as boil
#simple versions
#stateful
#stateless
#creates an allocation DataMatrix based on bollinger strategy and paramaters
if __name__ == "__main__":
#Usage: python bollinger.py '1-1-2004' '1-1-2009' 'alloc.pkl'
print "Running Bollinger strategy starting "+sys.argv[1]+" and ending "+sys.argv[2]+"."
#Run S&P500 for thresholds 1 and -1 in simple version for lookback of 10 days
symbols = list(np.loadtxt(os.environ['QS']+'/quicksim/strategies/S&P500.csv',dtype='str',delimiter=',',comments='#',skiprows=0))
t=map(int,sys.argv[1].split('-'))
startday = dt.datetime(t[2],t[0],t[1])
t=map(int,sys.argv[2].split('-'))
endday = dt.datetime(t[2],t[0],t[1])
timeofday=dt.timedelta(hours=16)
timestamps=du.getNYSEdays(startday,endday,timeofday)
dataobj=da.DataAccess(da.DataSource.NORGATE)
intersectsyms=list(set(dataobj.get_all_symbols())&set(symbols))
badsyms=[]
if size(intersectsyms)<size(symbols):
badsyms=list(set(symbols)-set(intersectsyms))
print "bad symms:"
print badsyms
for i in badsyms:
index=symbols.index(i)
symbols.pop(index)
historic = dataobj.get_data(timestamps,symbols,"close")
alloc=createStatelessStrat(historic,timestamps,10,1,-1)
output=open(sys.argv[3],"wb")
cPickle.dump(alloc,output)
output.close()
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] | 2.545575 | 757 |
# Generated by Django 2.0.5 on 2018-06-03 17:13
import django.core.validators
from django.db import migrations, models
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#!/usr/bin/python
# -*- coding: utf-8 -*-
# Software License Agreement (BSD License)
#
# Copyright (c) 2009-2011, Eucalyptus Systems, Inc.
# All rights reserved.
#
# Redistribution and use of this software in source and binary forms, with or
# without modification, are permitted provided that the following conditions
# are met:
#
# Redistributions of source code must retain the above
# copyright notice, this list of conditions and the
# following disclaimer.
#
# 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.
#
from eutester.testcase_utils.eutestcase import EutesterTestCase
from imageutils import ImageUtils
machine=None
testcase = EutesterTestCase()
testcase.setup_parser(testname='load_hvm_image.py',
description='Loads an hvm image from either a remote url or local file path',
emi=False,
testlist=False)
testcase.parser.add_argument('--url',help='URL containing remote windows image '
'to create EMI from', default=None)
testcase.parser.add_argument('--filepath',dest='filepath', help='File path to create EMI from', default=None)
testcase.parser.add_argument('--workerip',dest='worker_machine', help='The ip/hostname of the machine that the operation will be performed on', default=None)
testcase.parser.add_argument('--worker_username',dest='worker_username', help='The username of the machine that the operation will be performed on, default:"root"', default='root')
testcase.parser.add_argument('--worker_password',dest='worker_password', help='The password of the machine that the operation will be performed on', default=None)
testcase.parser.add_argument('--worker_keypath',dest='worker_keypath', help='The ssh keypath of the machine that the operation will be performed on', default=None)
testcase.parser.add_argument('--destpath',help='The path on the workip, that this operation will be performed on', default='/disk1/storage')
testcase.parser.add_argument('--urlpass', dest='wget_password',help='Password needed to retrieve remote url', default=None)
testcase.parser.add_argument('--urluser',dest='wget_user', help='Username needed to retrieve remote url', default=None)
testcase.parser.add_argument('--gigtime',dest='time_per_gig', help='Time allowed per gig size of image to be used', default=300)
testcase.parser.add_argument('--interbundletime',dest='inter_bundle_timeout', help='Inter-bundle timeout', default=120)
testcase.parser.add_argument('--virtualization_type', help='virtualization type, hvm or pv', default='hvm')
testcase.parser.add_argument('--bucket',dest='bucketname', help='bucketname', default=None)
testcase.parser.add_argument('--platform', dest='platform', help='"Linux" or "Windows", default: "linux"' , default=None)
testcase.parser.add_argument('--uploaded_manifest', dest='upload_manifest', help='bucket/prefix location of manifest to register' , default=None)
testcase.parser.add_argument('--bundle_manifest', dest='bundle_manifest', help='file path on worker to bundle manifest to upload' , default=None)
testcase.parser.add_argument('--overwrite', help='Will overwrite files in matching work dir on worker machine if found', action='store_true', default=False)
testcase.parser.add_argument('--time_per_gig', help='Time allowed per image size in GB before timing out. Default:300 seconds', default=300)
testcase.get_args()
testcase.args.worker_password = testcase.args.worker_password or testcase.args.password
testcase.args.worker_keypath = testcase.args.worker_keypath or testcase.args.keypair
if not testcase.args.upload_manifest and not testcase.args.bundle_manifest:
if (not testcase.args.url and not testcase.args.filepath) or (testcase.args.url and testcase.args.filepath):
raise Exception('If manifest not provieded, either a URL or FILE path is required to create image ')
#Create an ImageUtils helper from the arguments provided in this testcase...
image_utils = testcase.do_with_args(ImageUtils)
#Create a single testcase to wrap and run the EMI creation task. Note by default all the overlapping args from
# this testcase are fed to the testunit method when ran.
test1 = testcase.create_testunit_from_method(image_utils.create_emi)
test2 = testcase.create_testunit_from_method(make_image_public)
result = testcase.run_test_case_list([test1, test2], eof=True, clean_on_exit=False, printresults=True)
#By default created resources are stored in the eucaops/tester object's test_resources dict. See if our image is
#prsent. If so print it out...
if image_utils.tester.test_resources['images']:
emi = image_utils.tester.test_resources['images'][0]
testcase.debug('\n---------------------------\nCreated EMI:' + str(emi) +'\n---------------------------')
exit(result)
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] | 3.24136 | 1,765 |
from pydantic import BaseModel
from pydantic.types import PositiveInt
from tortoise.contrib.pydantic.creator import pydantic_model_creator
from app.core.models.tortoise.author import Author
AuthorOut = pydantic_model_creator(Author)
| [
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62,
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62,
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198
] | 3.391304 | 69 |
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