text string | size int64 | token_count int64 |
|---|---|---|
from machine import Pin
import neopixel
import time
class NeoMatrix:
def __init__(self, x, y):
self.colors = []
for i in range(x):
for j in range(x):
colors[i][j] = Color(0,0,0)
self.np = neopixel.NeoPixel(Pin(4, Pin.OUT),x*y)
self.np.write()
def set_pixel(self,x,y,r=0,g=0,b=0):
self.colors[x][y] = Color(r,g,b)
self.np.write()
class Color:
def __init__(self,r,g,b):
self.r = r
self.g = g
self.b = b
# def clear(np):
# set(np)
# def cycle(np,r=0,g=0,b=0,delay=25,cycles=1,invert=False):
# for i in range(cycles):
# bounce(np,r,g,b,delay,1,invert)
# def bounce(np,r=0,g=0,b=0,delay=25,cycles=2,invert=False):
# for i in range(cycles * np.n):
# if invert:
# for j in range(np.n):
# np[j] = (r, g, b)
# if (i // np.n) % 2 == 0:
# np[i % np.n] = (0, 0, 0)
# else:
# np[np.n - 1 - (i % np.n)] = (0, 0, 0)
# else:
# for j in range(np.n):
# np[j] = (0, 0, 0)
# if (i // np.n) % 2 == 0:
# np[i % np.n] = (r, g, b)
# else:
# np[np.n - 1 - (i % np.n)] = (r, g, b)
# np.write()
# time.sleep_ms(delay)
# clear(np)
# def fade_in(np,r=0,g=0,b=0,delay=25,cycles=1):
# for i in range(0,256,8):
# rn = my_map(r,in_max=i,out_max=r)
# gn = my_map(g,in_max=i,out_max=g)
# bn = my_map(b,in_max=i,out_max=b)
# set(np,rn,gn,bn)
# print(rn)
# def my_map(val,in_min=0,in_max=255,out_min=0,out_max=255):
# try:
# return int((val - in_min) * (out_max - out_min) / (in_max - in_min) + out_min)
# except ZeroDivisionError:
# return 0
# def demo(np):
# n = np.n
# # fade in/out
# for i in range(0, 4 * 256, 8):
# for j in range(n):
# if (i // 256) % 2 == 0:
# val = i & 0xff
# else:
# val = 255 - (i & 0xff)
# np[j] = (val, 0, 0)
# np.write()
# # clear
# clear(np)
| 1,782 | 1,070 |
import unittest
from platron.request.request_builders.recurring_get_schedule_builder import RecurringGetScheduleBuilder
class RecurringGetScheduleBuilderTest(unittest.TestCase):
def test_get_params(self):
builder = RecurringGetScheduleBuilder('12345')
params = builder.get_params()
self.assertEquals('12345', params.get('pg_recurring_profile'))
| 377 | 120 |
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: malleus/api/domain/protos/timings.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from malleus.api.domain.protos import timing_pb2 as malleus_dot_api_dot_domain_dot_protos_dot_timing__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='malleus/api/domain/protos/timings.proto',
package='malleus.api.domain',
syntax='proto3',
serialized_pb=_b('\n\'malleus/api/domain/protos/timings.proto\x12\x12malleus.api.domain\x1a&malleus/api/domain/protos/timing.proto\"6\n\x07Timings\x12+\n\x07timings\x18\x01 \x03(\x0b\x32\x1a.malleus.api.domain.Timingb\x06proto3')
,
dependencies=[malleus_dot_api_dot_domain_dot_protos_dot_timing__pb2.DESCRIPTOR,])
_TIMINGS = _descriptor.Descriptor(
name='Timings',
full_name='malleus.api.domain.Timings',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='timings', full_name='malleus.api.domain.Timings.timings', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=103,
serialized_end=157,
)
_TIMINGS.fields_by_name['timings'].message_type = malleus_dot_api_dot_domain_dot_protos_dot_timing__pb2._TIMING
DESCRIPTOR.message_types_by_name['Timings'] = _TIMINGS
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
Timings = _reflection.GeneratedProtocolMessageType('Timings', (_message.Message,), dict(
DESCRIPTOR = _TIMINGS,
__module__ = 'malleus.api.domain.protos.timings_pb2'
# @@protoc_insertion_point(class_scope:malleus.api.domain.Timings)
))
_sym_db.RegisterMessage(Timings)
# @@protoc_insertion_point(module_scope)
| 2,372 | 926 |
from __future__ import unicode_literals
import os.path
from django.core.files.storage import default_storage as storage
from django.core.files.uploadedfile import SimpleUploadedFile
from django.utils.six import StringIO
from django.utils.encoding import smart_text
try:
import Image
except ImportError:
try:
from PIL import Image
except ImportError:
raise ImportError('Cannot import Python Image Library')
class ImageManipulator():
def __init__(self, format="JPEG", extension="jpg", quality=75):
self.format = format
self.extension = extension
self.quality = quality
# define the path for the resized image
def resized_path(self, path, size, method):
directory, name = os.path.split(path)
image_name, ext = name.rsplit('.', 1)
return os.path.join(directory,
smart_text('{}_{}_{}.{}').format(image_name,
method,
size,
self.extension))
# take an image, create a copy and scale the copied image
def scale(self, image_field, size):
image_path = self.resized_path(image_field.name, size, 'scale')
image_dir, image_filename = os.path.split(image_path)
if not storage.exists(image_path):
f = storage.open(image_field.name, 'r')
image = Image.open(f)
if image.mode != 'RGB':
image = image.convert('RGB')
width, height = [int(i) for i in size.split('x')]
image.thumbnail((width, height), Image.ANTIALIAS)
f_scale = StringIO()
image.save(f_scale, self.format, quality=self.quality)
f_scale.seek(0)
suf = SimpleUploadedFile(os.path.split(image_path)[-1].split('.')[0],
f_scale.read(),
content_type='image/{}'.format(
self.format.lower()))
return image_filename, suf
return image_filename, None
| 2,191 | 577 |
from flask import Flask, render_template, Response
import cam
import os
import cv2
app = Flask(__name__,template_folder='templates')
overlay_image=[]
header_img = "Images"
header_img_list = os.listdir(header_img)
for i in header_img_list:
image = cv2.imread(f'{header_img}/{i}')
overlay_image.append(image)
@app.route('/')
def index():
return render_template('index.html')
def gen():
cam1 = cam.VideoCamera(overlay_image= overlay_image)
while True:
frame = cam1.get_frame(overlay_image=overlay_image)
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
@app.route('/video_feed')
def video_feed():
return Response(gen(),
mimetype='multipart/x-mixed-replace; boundary=frame')
if __name__ == '__main__':
app.run(host='0.0.0.0', debug=False)
#192.168.0.105 | 872 | 324 |
import os
import re
from pathlib import Path
from urllib.parse import urlparse
from github import Github
class CommitParser(object):
github_access_token: str
repository: str
jira_project: str
jira_server: str
def __init__(self, repository, jira_project, jira_server, github_access_token):
self.repository = repository
self.jira_project = jira_project
self.jira_server = jira_server
self.github_access_token = github_access_token
@property
def jira_regex_format(self) -> str:
return f"({self.jira_project}-[0-9]*)"
def jira_tickets(self, start_tag, end_tag) -> [str]:
commits = self._get_commits(start_tag, end_tag)
jira_tickets, github_prs = self._process_commits(commits, self.jira_regex_format)
jira_tickets += self._get_jira_tickets_from_github(github_prs, self.jira_regex_format)
return jira_tickets
def _get_commits(self, start, end) -> [str]:
return os.popen("git log --pretty=%s " + start + "..." + end)
def _process_commits(self, commits: [str], regex_format: str) -> ([str], [str]):
jira_ticket_regex = re.compile(regex_format)
# Github adds pull request number (#XXXX) at the end of its title.
github_pr_regex = re.compile("(\\(#[0-9]*\\))")
jira_tickets: [str] = []
github_prs: [str] = []
for commit in commits:
jira_search = jira_ticket_regex.search(commit)
if jira_search is not None:
jira_tickets.append(jira_search.group())
elif github_pr_regex.findall(commit):
pr_number_text = github_pr_regex.findall(commit)[-1]
# Keep only the PR number and remove (#).
pr_number = pr_number_text.translate({ord(i): None for i in "()#"})
github_prs.append(pr_number)
return (jira_tickets, github_prs)
def _get_jira_tickets_from_github(self, github_prs: [str], regex_format: str):
github = Github(self.github_access_token)
repo = github.get_repo(self.repository)
# Include the serve in the url.
server_netloc = urlparse(self.jira_server).netloc
url_regex = re.compile(f"https?:\\/\\/{server_netloc}\\b([-a-zA-Z0-9@:%_\\+.~#?&//=]*{regex_format})")
jira_ticket_regex = re.compile(regex_format)
jira_tickets = []
for pr_number in github_prs:
pr = repo.get_pull(int(pr_number))
url_match = url_regex.search(pr.body)
if url_match is None:
# If no url is found the PR will be skipped.
continue
jira_ticket_match = jira_ticket_regex.search(url_match.group())
url_path = Path(urlparse(url_match.group()).path)
# In case the ticket ends with 1XXXX, the regex match will not contain the XXXX.
# The match will be PROJECT-1, which is wrong.
# This check is to exclude this results.
if jira_ticket_match is not None and jira_ticket_match.group() == url_path.name:
jira_tickets.append(jira_ticket_match.group())
return jira_tickets
| 3,159 | 1,023 |
# 短信签名
SMS_SIGN = 'demo'
# 短信验证码模板ID
SMS_VERIFICATION_CODE_TEMPLATE_ID = 'SMS_151231777'
| 95 | 78 |
import calculos as c
import os
import matplotlib.pyplot as plt
import numpy as np
movie="Men in black"
m="moviesdata"
g="actordata"
s='/'
apidata=os.listdir(m+s+movie)
actordata=os.listdir(g+s+movie)
print(movie)
print(apidata)
print(actordata)
DM=0.0 # 0.0 distancia minima posible
AM=0 # 1.0 angle maxim posible
x=[]
y=[]
while DM<2:
DM=DM+0.01
# DM=1.2
x.append(DM)
for unkownactor in apidata:
act=c.extraerSublistaArchivo(m+s+movie+s+unkownactor)
# print("\n")
# print('{} subcaras para el actor {}'.format(len(act),unkownactor.strip('.txt')))
for subactor in actordata:
currentactor=c.extraerSublistaArchivo(g+s+movie+s+subactor)
count=0
for subcara in act:
for caratest in currentactor:
if(c.similitudCoseno(caratest,subcara)>AM):
if(c.distanciaEuclidea(caratest,subcara)<DM):
count=count+1
if(count>0):
print('{} true per {}, {} vegades amb DM: {}'.format(subactor.strip('.txt'),unkownactor.strip('.txt'),count,DM))
y.append(count)
else:
y.append(0)
# pass
# print("no hi han cares valides per els coeficients AM : {} i DM: {} per l'actor {}".format(AM,DM,subactor.strip('.txt')))
# DM=50
# plt.style.use('bmh')
plt.plot(x,y)
plt.title(actordata[0].strip(".txt"))
plt.xlabel("Coeficiente DM")
plt.ylabel("Verdaderos positivos")
plt.grid()
plt.show()
| 1,548 | 562 |
#!/usr/bin/env python
import getopt, sys, datetime, os
from treqs import mUSProcessor, mSysReqProcessor, mTCProcessor
def main(argv):
#Default paths for respective files (user stories, test cases, system requirements). Can be provided as function arguments.
usDir = 'requirements'
tcDir = 'tests'
sysReqDir = 'requirements'
#Default patterns for respective filenames (user stories, test cases, system requirements). Can be provided as function arguments.
usPattern = 'US_.*?\.md'
tcPattern = 'TC_.*?(\.py|\.md)$'
sysReqPattern = 'SR_.*?\.md'
recursive = False
quiet = False
# argument options for this script.
try:
opts, args = getopt.getopt(argv[1:], "hu:s:t:r:q", ["help","usdir=","sysreqdir=","tcdir=","uspattern=","srpattern=","tcpattern="])
except getopt.GetoptError:
print('Usage: ' + argv[0] + ' [-u <user story directory>] [-s <system requirements directory>] [-t <test case directory>] [-r] [-q]')
sys.exit(2)
for opt, arg in opts:
if opt in ("-u", "--usdir"):
usDir = os.path.normpath(arg)
elif opt in ("-s", "--sysreqdir"):
sysReqDir = os.path.normpath(arg)
elif opt in ("-t", "--tcdir"):
tcDir = os.path.normpath(arg)
elif opt in ("--uspattern"):
usPattern = os.path.normpath(arg)
elif opt in ("--srpattern"):
sysReqPattern = os.path.normpath(arg)
elif opt in ("--tcpattern"):
tcPattern = os.path.normpath(arg)
elif opt in ("-r"):
recursive = True
elif opt in ("-q"):
quiet = True
elif opt in ("-h", "--help"):
print('Usage: ' + argv[0] + ' -u <user story directory> -s <system requirements directory> -t <test case directory> [-r]')
sys.exit(2)
try:
os.makedirs('logs')
except OSError:
if not os.path.isdir('logs'):
raise
#Do all the data processing here
reqProcessor = mSysReqProcessor.SysReqsProcessor()
success = reqProcessor.processAllLines(sysReqDir, recursive, sysReqPattern)
usProcessor = mUSProcessor.USProcessor()
success = usProcessor.processAllUS(usDir, recursive, usPattern) and success
tcProcessor = mTCProcessor.TCProcessor()
success = tcProcessor.processAllTC(tcDir, recursive, tcPattern) and success
#Get all the user stories and traces to them
existingStories = usProcessor.storySet
tracedStoriesFromReqs = reqProcessor.storySet
tracedStoriesFromTests = tcProcessor.storySet
#Calculate which story IDs are not traced to
diffUSReq = existingStories.difference(tracedStoriesFromReqs)
diffUSTests = existingStories.difference(tracedStoriesFromTests)
#Calculate whether there are any traces to non-existing stories
nonExistingUSReqs = tracedStoriesFromReqs.difference(existingStories)
nonExistingUSTests = tracedStoriesFromTests.difference(existingStories)
#Get all the requirements and traces to them
existingReqs = reqProcessor.reqIDSet
tracedReqsFromTests = tcProcessor.reqSet
#Calculate which Req IDs are not traced to
diffReqTests = existingReqs.difference(tracedReqsFromTests)
#Calculate which Req IDs are traced to but do not exist
nonExistingReqTC = tracedReqsFromTests.difference(existingReqs)
#Get all duplicate IDs
duplicateUS = usProcessor.duplicateStorySet
duplicateTC = tcProcessor.duplicateIDSet
duplicateReq = reqProcessor.duplicateIDSet
#Get all IDs of artifacts missing traces
reqsWithoutUSTraces = reqProcessor.noUSTracingSet
testsWithoutUSTraces = tcProcessor.noUSTracingSet
testsWithoutReqTraces = tcProcessor.noReqTracingSet
if success and (len(nonExistingUSReqs)>0 or len(nonExistingUSTests)>0 or len(nonExistingReqTC)>0):
success = False
filename = 'logs/Summary_log_'+datetime.datetime.now().strftime("%Y%m%d%H%M%S")+'.md'
log = open(filename,"w")
log.write('# T-Reqs commit report #\n\n')
log.write('### Duplicate IDs ###\n')
log.write('The following duplicate User Story IDs exist:\n\n')
for currentID in duplicateUS:
log.write('* User Story ' + currentID + '\n')
log.write('\n')
log.write('The following duplicate System Requirement IDs exist:\n\n')
for currentID in duplicateReq:
log.write('* System Requirement ' + currentID + '\n')
log.write('\n')
log.write('The following duplicate Test Case IDs exist:\n\n')
for currentID in duplicateTC:
log.write('* Test Case ' + currentID + '\n')
log.write('\n')
log.write('### Items without traces ###\n')
log.write('The following System Requirements lack traces to user stories:\n\n')
for currentID in reqsWithoutUSTraces:
log.write('* System Requirement ' + currentID + '\n')
log.write('\n')
log.write('The following Test Cases lack traces to user stories:\n\n')
for currentID in testsWithoutUSTraces:
log.write('* Test Case ' + currentID + '\n')
log.write('\n')
log.write('The following Test Cases lack traces to system requirements:\n\n')
for currentID in testsWithoutReqTraces:
log.write('* Test Case ' + currentID + '\n')
log.write('\n')
log.write('### Missing traces ###\n')
log.write('The following user stories are not referenced by any system requirement:\n\n')
for currentID in diffUSReq:
log.write('* User Story ' + currentID + '\n')
log.write('\n')
log.write('The following user stories are not referenced by any test case:\n\n')
for currentID in diffUSTests:
log.write('* User Story ' + currentID + '\n')
log.write('\n')
log.write('The following system requirements are not referenced by any test case:\n\n')
for currentID in diffReqTests:
log.write('* System Requirement ' + currentID + '\n')
log.write('\n')
log.write('### Missing items ###\n')
log.write('The following user stories are referenced by requirements, but do not exist:\n\n')
for currentID in nonExistingUSReqs:
log.write('* User Story ' + currentID + '\n')
log.write('\n')
log.write('The following user stories are referenced by test cases, but do not exist:\n\n')
for currentID in nonExistingUSTests:
log.write('* User Story ' + currentID + '\n')
log.write('\n')
log.write('The following requirements are referenced by test cases, but do not exist:\n\n')
for currentID in nonExistingReqTC:
log.write('* Requirement ' + currentID + '\n')
log.close()
#Return -1 if the validation has failed, and print the log to the console
if not success and not quiet:
print('Validation failed with the following output:\n')
with open(filename, 'r') as f:
print(f.read())
return -1
if __name__ == '__main__':
main(sys.argv)
| 6,972 | 2,163 |
import numpy as np
from sklearn.base import clone
from nudging.model.base import BaseModel
from nudging.model.biregressor import BiRegressor
class XRegressor(BaseModel):
"""Class for X-learner regression
See https://www.pnas.org/cgi/doi/10.1073/pnas.1804597116. It trains
two BiRegressors (T-learners) and then uses a crossover mechanism.
"""
def __init__(self, model, predictors=None, **kwargs):
super().__init__(model, predictors, **kwargs)
self.biregressor = BiRegressor(model, predictors=predictors)
self.nudge_control_model = clone(model)
self.control_nudge_model = clone(model)
def _fit(self, X, nudge, outcome):
self.biregressor._fit(X, nudge, outcome)
nudge_idx = np.where(nudge == 1)[0]
control_idx = np.where(nudge == 0)[0]
self.nudge_propensity = len(nudge_idx)/(len(nudge_idx) + len(control_idx))
imputed_treatment_control = outcome[nudge_idx] - self.biregressor._predict(
X[nudge_idx], 1-nudge[nudge_idx])
imputed_treatment_nudge = self.biregressor._predict(
X[control_idx], 1-nudge[control_idx]) - outcome[control_idx]
self.nudge_control_model.fit(X[nudge_idx], imputed_treatment_control)
self.control_nudge_model.fit(X[control_idx], imputed_treatment_nudge)
def train(self, data):
self._fit(*self._X_nudge_outcome(data))
def _predict(self, X, nudge):
control_cate = self.control_nudge_model.predict(X)
nudge_cate = self.nudge_control_model.predict(X)
cate = (1-self.nudge_propensity)*control_cate + self.nudge_propensity*nudge_cate
base_pred = self.biregressor._predict(X, np.zeros_like(nudge))
return base_pred + cate*nudge
def predict_outcome(self, data):
return self._predict(*self._X_nudge(data))
| 1,836 | 697 |
from manabi.test_helpers import ManabiTestCase
| 47 | 15 |
#!/usr/bin/env python3
# One-time use script to convert previous log file format into JSON
import json
out = {}
for line in open("old-airq").readlines():
fields = line.split()
if 'PM 1.0' in line:
date = " ".join(fields[0:3])
out['pm1.0'] = fields[6]
if 'PM 2.5' in line:
out['pm2.5'] = fields[6]
if 'PM 10.0' in line:
out['pm10'] = fields[6]
print(f"{date} {json.dumps(out)}")
| 437 | 170 |
import numpy as np
from numpy import linalg as LA
import pickle
from collections import Counter
import csv
class Vocabulary(object):
def __init__(self, vocab_file, emb_file='', dim_emb=0):
with open(vocab_file, 'rb') as f:
self.size, self.word2id, self.id2word = pickle.load(f)
self.dim_emb = dim_emb
self.embedding = np.random.random_sample(
(self.size, self.dim_emb)) - 0.5
if emb_file:
with open(emb_file) as f:
for line in f:
parts = line.split()
word = parts[0]
vec = np.array([float(x) for x in parts[1:]])
if word in self.word2id:
self.embedding[self.word2id[word]] = vec
for i in range(self.size):
self.embedding[i] /= LA.norm(self.embedding[i])
def build_vocab(data, vocab_path, vocab_metadata_path, min_occur=5):
word2id = {'<pad>':0, '<go>':1, '<eos>':2, '<unk>':3}
id2word = ['<pad>', '<go>', '<eos>', '<unk>']
words = [word for sent in data for word in sent]
cnt = Counter(words)
for word in cnt:
if cnt[word] >= min_occur:
word2id[word] = len(word2id)
id2word.append(word)
vocab_size = len(word2id)
with open(vocab_path, 'wb') as f:
pickle.dump((vocab_size, word2id, id2word), f, pickle.HIGHEST_PROTOCOL)
"""Writes metadata file for Tensorboard word embedding visualizer as described here:
https://www.tensorflow.org/get_started/embedding_viz
"""
print("Writing word embedding metadata file to %s" % (vocab_metadata_path))
with open(vocab_metadata_path, "w") as f:
fieldnames = ['word']
writer = csv.DictWriter(f, delimiter="\t", fieldnames=fieldnames)
for w in id2word:
writer.writerow({"word": w}) | 1,796 | 639 |
def required_model(name: object, kwargs: object) -> object:
required_fields = kwargs['required_fields'] if 'required_fields' in kwargs else []
task_f = kwargs['required_task_fields'] if 'required_task_fields' in kwargs else []
proc_f = kwargs['required_proc_fields'] if 'required_proc_fields' in kwargs else []
display_name = kwargs['display_name'] if 'display_name' in kwargs else name
def f(klass):
return klass
return f | 432 | 142 |
import pytest
import asyncio
from starkware.starknet.testing.starknet import Starknet
from utils import Signer, str_to_felt, assert_revert
signer = Signer(123456789987654321)
# bools (for readability)
false = 0
true = 1
# random uint256 tokenIDs
first_token_id = (5042, 0)
second_token_id = (7921, 1)
third_token_id = (0, 13)
# random data (mimicking bytes in Solidity)
data = [str_to_felt('0x42'), str_to_felt('0x89'), str_to_felt('0x55')]
@pytest.fixture(scope='module')
def event_loop():
return asyncio.new_event_loop()
@pytest.fixture(scope='function')
async def erc721_factory():
starknet = await Starknet.empty()
owner = await starknet.deploy(
"contracts/Account.cairo",
constructor_calldata=[signer.public_key]
)
other = await starknet.deploy(
"contracts/Account.cairo",
constructor_calldata=[signer.public_key]
)
erc721 = await starknet.deploy(
"contracts/token/ERC721_Pausable.cairo",
constructor_calldata=[
str_to_felt("Non Fungible Token"), # name
str_to_felt("NFT"), # ticker
owner.contract_address # owner
]
)
erc721_holder = await starknet.deploy("contracts/token/utils/ERC721_Holder.cairo")
# mint tokens to owner
tokens = [first_token_id, second_token_id]
for token in tokens:
await signer.send_transaction(
owner, erc721.contract_address, 'mint', [
owner.contract_address, *token]
)
return starknet, erc721, owner, other, erc721_holder
@pytest.mark.asyncio
async def test_pause(erc721_factory):
_, erc721, owner, other, erc721_holder = erc721_factory
# pause
await signer.send_transaction(owner, erc721.contract_address, 'pause', [])
execution_info = await erc721.paused().call()
assert execution_info.result.paused == 1
await assert_revert(signer.send_transaction(
owner, erc721.contract_address, 'approve', [
other.contract_address,
*first_token_id
])
)
await assert_revert(signer.send_transaction(
owner, erc721.contract_address, 'setApprovalForAll', [
other.contract_address,
true
])
)
await assert_revert(signer.send_transaction(
owner, erc721.contract_address, 'transferFrom', [
owner.contract_address,
other.contract_address,
*first_token_id
])
)
await assert_revert(signer.send_transaction(
owner, erc721.contract_address, 'safeTransferFrom', [
owner.contract_address,
erc721_holder.contract_address,
*first_token_id,
len(data),
*data
])
)
await assert_revert(signer.send_transaction(
owner, erc721.contract_address, 'mint', [
other.contract_address,
*third_token_id
])
)
@pytest.mark.asyncio
async def test_unpause(erc721_factory):
_, erc721, owner, other, erc721_holder = erc721_factory
# pause
await signer.send_transaction(owner, erc721.contract_address, 'pause', [])
# unpause
await signer.send_transaction(owner, erc721.contract_address, 'unpause', [])
execution_info = await erc721.paused().call()
assert execution_info.result.paused == 0
await signer.send_transaction(
owner, erc721.contract_address, 'approve', [
other.contract_address,
*first_token_id
]
)
await signer.send_transaction(
owner, erc721.contract_address, 'setApprovalForAll', [
other.contract_address,
true
]
)
await signer.send_transaction(
owner, erc721.contract_address, 'transferFrom', [
owner.contract_address,
other.contract_address,
*first_token_id
]
)
await signer.send_transaction(
other, erc721.contract_address, 'safeTransferFrom', [
owner.contract_address,
erc721_holder.contract_address,
*second_token_id,
len(data),
*data
]
)
await signer.send_transaction(
owner, erc721.contract_address, 'mint', [
other.contract_address,
*third_token_id
]
)
@pytest.mark.asyncio
async def test_only_owner(erc721_factory):
_, erc721, owner, other, _ = erc721_factory
# not-owner pause should revert
await assert_revert(signer.send_transaction(
other, erc721.contract_address, 'pause', []))
# owner pause
await signer.send_transaction(owner, erc721.contract_address, 'pause', [])
# not-owner unpause should revert
await assert_revert(signer.send_transaction(
other, erc721.contract_address, 'unpause', []))
# owner unpause
await signer.send_transaction(owner, erc721.contract_address, 'unpause', [])
| 4,929 | 1,732 |
from datetime import datetime
import pytz
if __name__ == '__main__':
places_tz = ['Asia/Tokyo', 'Europe/Madrid', 'America/Argentina/Buenos_Aires', 'US/eastern', 'US/Pacific', 'UTC']
cities_name = ['Tokyo', 'Madrid', 'Buenos Aires', 'New York', 'California', 'UTC']
for place_tz, city_name in zip(places_tz, cities_name):
city_time = datetime.now(pytz.timezone(place_tz))
print(f'Fecha en {city_name} - {city_time}')
| 445 | 170 |
from algebra_utilities.objects.baseobjects import *
from algebra_utilities.structures.baseobjects import Printable
from algebra_utilities.utils.errors import UnexpectedTypeError
from algebra_utilities.utils.errors import NonAssociativeSetError
from algebra_utilities.utils.errors import ElementsOverflow
class SemiGroup(Printable):
"""
Esta clase representa un semigrupo, un semigrupo es un conjunto no vacio
S con una operacion binaria multiplicacion (*): S x S -> S que es
asociativa
Atributos
---------
generators: lista con los elementos generadores del semigrupo
name: nombre del semigrupo
"""
def __init__(self, generators, name='G'):
super(SemiGroup, self).__init__()
# nombre del semigrupo
self.name = name
# todos los generadores deben heredar de SemiAlgebraicObject o de
# AlgebraicObject, para asegurar la definicion de operaciones basicas
for generator in generators:
if not isinstance(generator, SemiAlgebraicObject) and \
not isinstance(generator, AlgebraicObject):
raise UnexpectedTypeError('The objects has an invalid type in SemiGroup initialization')
# lista de los generadores del semigrupo, esta podria coincidir con la
# lista de todos los elementos del semigrupo
self.generators = generators
# TODO: discutir si se guardan los elementos en un "list" o en un "set"
self.elements = self.generate_elements(generators)
# un semigrupo es un conjunto con una operacion binaria que ademas es
# asociativa
if not self.check_associativity():
raise NonAssociativeSetError('The operation defined is not associative')
def __len__(self):
return len(self.elements)
def generate_orbit(self, element):
"""
Este metodo genera (de forma iterativa) todas las potencias (bajo la
operacion) de un elemento dado hasta llegar a la identidad
"""
orbit = []
pow_element = element
while pow_element not in orbit:
orbit.append(pow_element)
# potencias
pow_element *= element
return orbit
def remove_repeating_elements(self, elements):
"""
Este metodo elimina elementos repetidos en la lista pasada como
argumento
"""
dummy = []
for element in elements:
if element not in dummy:
dummy.append(element)
return dummy
def all_posible_multiplication(self, elements, limit=-1):
"""
Este metodo realiza todas las posibles multiplicaciones entre los
elementos pasados como argumentos y aquellos que se van generando
"""
old_length = -1
current_length = len(elements)
# la longitud de la lista cambia siempre que aparezcan nuevos elementos
while old_length != current_length:
# TODO: this is a bottle and must be reimplemented
for i in range(current_length):
for j in range(current_length):
# no siempre el producto es conmutativo
left_multiplication = elements[i] * elements[j]
right_multiplication = elements[j] * elements[i]
if left_multiplication not in elements:
elements.append(left_multiplication)
if right_multiplication not in elements:
elements.append(right_multiplication)
# se actualiza el valor de la longitud
current_length, old_length = len(elements), current_length
if limit > 0 and current_length > limit:
raise ElementsOverflow('Limit of allowed elements exceeded in the generation of elements')
return elements
def generate_elements(self, generators):
"""
Este metodo genera todos los elementos del grupo
"""
elements = []
# se generan las orbitas de cada generador
for generator in generators:
elements.extend(self.generate_orbit(generator))
# se eliminan elementos repetidos y se realizan todas las posibles
# multiplicaciones
elements = self.remove_repeating_elements(elements)
elements = self.all_posible_multiplication(elements)
return elements
def add_element(self, element):
"""
Este metodo agrega un nuevo generador al grupo y genera los nuevos
elementos
"""
# chequeo de tipo
if not isinstance(generator, SemiAlgebraicObject) and \
not isinstance(generator, AlgebraicObject):
raise UnexpectedTypeError('The objects has an invalid type in element aggregation')
if element not in self.elements:
self.elements.extend(self.generate_orbit(element))
self.elements = self.all_posible_multiplication(self.elements)
def check_associativity(self):
# TODO: buscar un algoritmo de orden menor a n^3
for a in self.elements:
for b in self.elements:
for c in self.elements:
if a * (b * c) != (a * b) * c:
return False
return True
def get_cayley_table(self, a=None, b=None):
"""
Este metodo muestra todas las posibles multiplicaciones
"""
if a is None:
a = self.elements
if b is None:
b = self.elements
for i in range(len(a)):
for j in range(len(b)):
c = a[i] * b[j]
print('%s * %s = %s' % (a[i], b[j], c))
| 5,871 | 1,640 |
from output.models.nist_data.atomic.duration.schema_instance.nistschema_sv_iv_atomic_duration_pattern_1_xsd.nistschema_sv_iv_atomic_duration_pattern_1 import NistschemaSvIvAtomicDurationPattern1
__all__ = [
"NistschemaSvIvAtomicDurationPattern1",
]
| 254 | 91 |
# -*- coding: utf-8 -*-
"""
Created on 2018/9/20
@author: gaoan
"""
ORDER_NO = "order_no"
PREVIEW_ORDER = "preview_order"
PLACE_ORDER = "place_order"
CANCEL_ORDER = "cancel_order"
MODIFY_ORDER = "modify_order"
"""
账户/资产
"""
ACCOUNTS = "accounts"
ASSETS = "assets"
POSITIONS = "positions"
ORDERS = "orders"
ACTIVE_ORDERS = "active_orders" # 待成交订单
INACTIVE_ORDERS = "inactive_orders" # 已撤销订单
FILLED_ORDERS = "filled_orders" # 已成交订单
"""
合约
"""
CONTRACT = "contract"
CONTRACTS = "contracts"
"""
行情
"""
MARKET_STATE = "market_state"
ALL_SYMBOLS = "all_symbols"
ALL_SYMBOL_NAMES = "all_symbol_names"
BRIEF = "brief"
STOCK_DETAIL = "stock_detail"
TIMELINE = "timeline"
KLINE = "kline"
TRADE_TICK = "trade_tick"
QUOTE_REAL_TIME = "quote_real_time"
QUOTE_SHORTABLE_STOCKS = "quote_shortable_stocks"
QUOTE_STOCK_TRADE = "quote_stock_trade"
# 期权行情
OPTION_EXPIRATION = "option_expiration"
OPTION_CHAIN = "option_chain"
OPTION_BRIEF = "option_brief"
OPTION_KLINE = "option_kline"
OPTION_TRADE_TICK = "option_trade_tick"
# 期货行情
FUTURE_EXCHANGE = "future_exchange"
FUTURE_CONTRACT_BY_CONTRACT_CODE = "future_contract_by_contract_code"
FUTURE_CONTRACT_BY_EXCHANGE_CODE = "future_contract_by_exchange_code"
FUTURE_CONTINUOUS_CONTRACTS = "future_continuous_contracts"
FUTURE_CURRENT_CONTRACT = "future_current_contract"
FUTURE_KLINE = "future_kline"
FUTURE_REAL_TIME_QUOTE = "future_real_time_quote"
FUTURE_TICK = "future_tick"
FUTURE_TRADING_DATE = "future_trading_date"
# 公司行动, 财务数据
FINANCIAL_DAILY = 'financial_daily'
FINANCIAL_REPORT = 'financial_report'
CORPORATE_ACTION = 'corporate_action'
| 1,590 | 792 |
#!/usr/bin/python
from distutils.core import setup
setup(
name = 'payment_processor',
version = '0.2.0',
description = 'A simple payment gateway api wrapper',
author = 'Ian Halpern',
author_email = 'ian@ian-halpern.com',
url = 'https://launchpad.net/python-payment',
download_url = 'https://launchpad.net/python-payment/+download',
packages = (
'payment_processor',
'payment_processor.gateways',
'payment_processor.methods',
'payment_processor.exceptions',
'payment_processor.utils'
)
)
| 540 | 183 |
from pathlib import Path
from typing import Any, Tuple
import abc
import h5py
from torch.utils.data import Dataset
# inputs vary for each model, hence we use Any here
Input = Any
PairwiseTrainingInput = Tuple[Input, Input]
PointwiseTrainingInput = Tuple[Input, int]
ValTestInput = Tuple[int, int, Input, int]
class PointwiseTrainDatasetBase(Dataset, abc.ABC):
"""Abstract base class for pointwise training datasets. Methods to be implemented:
* get_single_input
* collate_fn (optional)
Args:
data_file (Path): Data file containing queries and documents
train_file (Path): Trainingset file
"""
def __init__(self, data_file: Path, train_file: Path):
self.data_file = data_file
self.train_file = train_file
with h5py.File(train_file, "r") as fp:
self.length = len(fp["q_ids"])
@abc.abstractmethod
def get_single_input(self, query: str, doc: str) -> Input:
"""Create a single model input from a query and a document.
Args:
query (str): The query
doc (str): The document
Returns:
Input: The model input
"""
pass
def __getitem__(self, index: int) -> PointwiseTrainingInput:
"""Return inputs and label for pointwise training.
Args:
index (int): Item index
Returns:
PointwiseTrainingInput: Inputs and label for pointwise training
"""
with h5py.File(self.train_file, "r") as fp:
q_id = fp["q_ids"][index]
doc_id = fp["doc_ids"][index]
label = fp["labels"][index]
with h5py.File(self.data_file, "r") as fp:
query = fp["queries"].asstr()[q_id]
doc = fp["docs"].asstr()[doc_id]
return self.get_single_input(query, doc), label
def __len__(self) -> int:
"""Number of training instances.
Returns:
int: The dataset length
"""
return self.length
class PairwiseTrainDatasetBase(Dataset, abc.ABC):
"""Abstract base class for pairwise training datasets. Methods to be implemented:
* get_single_input
* collate_fn (optional)
Args:
data_file (Path): Data file containing queries and documents
train_file (Path): Trainingset file
"""
def __init__(self, data_file: Path, train_file: Path):
self.data_file = data_file
self.train_file = train_file
with h5py.File(train_file, "r") as fp:
self.length = len(fp["q_ids"])
@abc.abstractmethod
def get_single_input(self, query: str, doc: str) -> Input:
"""Create a single model input from a query and a document.
Args:
query (str): The query
doc (str): The document
Returns:
Input: The model input
"""
pass
def __getitem__(self, index: int) -> PairwiseTrainingInput:
"""Return a pair of positive and negative inputs for pairwise training.
Args:
index (int): Item index
Returns:
PairwiseTrainingInput: Positive and negative inputs for pairwise training
"""
with h5py.File(self.train_file, "r") as fp:
q_id = fp["q_ids"][index]
pos_doc_id = fp["pos_doc_ids"][index]
neg_doc_id = fp["neg_doc_ids"][index]
with h5py.File(self.data_file, "r") as fp:
query = fp["queries"].asstr()[q_id]
pos_doc = fp["docs"].asstr()[pos_doc_id]
neg_doc = fp["docs"].asstr()[neg_doc_id]
return (
self.get_single_input(query, pos_doc),
self.get_single_input(query, neg_doc),
)
def __len__(self) -> int:
"""Number of training instances.
Returns:
int: The dataset length
"""
return self.length
class ValTestDatasetBase(Dataset, abc.ABC):
"""Abstract base class for validation/testing datasets. Methods to be implemented:
* get_single_input
* collate_fn (optional)
The datasets yields internal integer IDs that can be held by tensors.
The original IDs can be recovered using `get_original_query_id` and `get_original_document_id`.
Args:
data_file (Path): Data file containing queries and documents
val_test_file (Path): Validation-/testset file
"""
def __init__(self, data_file: Path, val_test_file: Path):
self.data_file = data_file
self.val_test_file = val_test_file
with h5py.File(val_test_file, "r") as fp:
self.length = len(fp["q_ids"])
def get_original_query_id(self, q_id: int) -> str:
"""Return the original (string) query ID for a given internal ID.
Args:
q_id (int): Internal query ID
Returns:
str: Original query ID
"""
with h5py.File(self.data_file, "r") as fp:
return fp["orig_q_ids"].asstr()[q_id]
def get_original_document_id(self, doc_id: int) -> str:
"""Return the original (string) document ID for a given internal ID.
Args:
doc_id (int): Internal document ID
Returns:
str: Original document ID
"""
with h5py.File(self.data_file, "r") as fp:
return fp["orig_doc_ids"].asstr()[doc_id]
@abc.abstractmethod
def get_single_input(self, query: str, doc: str) -> Input:
"""Create a single model input from a query and a document.
Args:
query (str): The query
doc (str): The document
Returns:
Input: The model input
"""
pass
def __getitem__(self, index: int) -> ValTestInput:
"""Return an item.
Args:
index (int): Item index
Returns:
ValTestInput: Query ID, input and label
"""
with h5py.File(self.val_test_file, "r") as fp:
q_id = fp["q_ids"][index]
doc_id = fp["doc_ids"][index]
label = fp["labels"][index]
with h5py.File(self.data_file, "r") as fp:
query = fp["queries"].asstr()[q_id]
doc = fp["docs"].asstr()[doc_id]
# return the internal query and document IDs here
return q_id, doc_id, self.get_single_input(query, doc), label
def __len__(self) -> int:
"""Number of validation/testing instances.
Returns:
int: The dataset length
"""
return self.length
| 6,513 | 1,980 |
import json
from unittest import TestCase
from mock import Mock, patch, call
from nose_parameterized import parameterized
from samcli.lib.logs.formatter import LogsFormatter, LambdaLogMsgFormatters, KeywordHighlighter, JSONMsgFormatter
from samcli.lib.logs.event import LogEvent
class TestLogsFormatter_pretty_print_event(TestCase):
def setUp(self):
self.colored_mock = Mock()
self.group_name = "group name"
self.stream_name = "stream name"
self.message = "message"
self.event_dict = {"timestamp": 1, "message": self.message, "logStreamName": self.stream_name}
def test_must_serialize_event(self):
colored_timestamp = "colored timestamp"
colored_stream_name = "colored stream name"
self.colored_mock.yellow.return_value = colored_timestamp
self.colored_mock.cyan.return_value = colored_stream_name
event = LogEvent(self.group_name, self.event_dict)
expected = " ".join([colored_stream_name, colored_timestamp, self.message])
result = LogsFormatter._pretty_print_event(event, self.colored_mock)
self.assertEquals(expected, result)
self.colored_mock.yellow.has_calls()
self.colored_mock.cyan.assert_called_with(self.stream_name)
def _passthru_formatter(event, colored):
return event
class TestLogsFormatter_do_format(TestCase):
def setUp(self):
self.colored_mock = Mock()
# Set formatter chain method to return the input unaltered.
self.chain_method1 = Mock(wraps=_passthru_formatter)
self.chain_method2 = Mock(wraps=_passthru_formatter)
self.chain_method3 = Mock(wraps=_passthru_formatter)
self.formatter_chain = [self.chain_method1, self.chain_method2, self.chain_method3]
@patch.object(LogsFormatter, "_pretty_print_event", wraps=_passthru_formatter)
def test_must_map_formatters_sequentially(self, pretty_print_mock):
events_iterable = [1, 2, 3]
expected_result = [1, 2, 3]
expected_call_order = [
call(1, colored=self.colored_mock),
call(2, colored=self.colored_mock),
call(3, colored=self.colored_mock),
]
formatter = LogsFormatter(self.colored_mock, self.formatter_chain)
result_iterable = formatter.do_format(events_iterable)
self.assertEquals(list(result_iterable), expected_result)
self.chain_method1.assert_has_calls(expected_call_order)
self.chain_method2.assert_has_calls(expected_call_order)
self.chain_method3.assert_has_calls(expected_call_order)
pretty_print_mock.assert_has_calls(expected_call_order) # Pretty Printer must always be called
@patch.object(LogsFormatter, "_pretty_print_event", wraps=_passthru_formatter)
def test_must_work_without_formatter_chain(self, pretty_print_mock):
events_iterable = [1, 2, 3]
expected_result = [1, 2, 3]
expected_call_order = [
call(1, colored=self.colored_mock),
call(2, colored=self.colored_mock),
call(3, colored=self.colored_mock),
]
# No formatter chain.
formatter = LogsFormatter(self.colored_mock)
result_iterable = formatter.do_format(events_iterable)
self.assertEquals(list(result_iterable), expected_result)
# Pretty Print is always called, even if there are no other formatters in the chain.
pretty_print_mock.assert_has_calls(expected_call_order)
self.chain_method1.assert_not_called()
self.chain_method2.assert_not_called()
self.chain_method3.assert_not_called()
class TestLambdaLogMsgFormatters_colorize_crashes(TestCase):
@parameterized.expand(
[
"Task timed out",
"Something happened. Task timed out. Something else happend",
"Process exited before completing request",
]
)
def test_must_color_crash_messages(self, input_msg):
color_result = "colored messaage"
colored = Mock()
colored.red.return_value = color_result
event = LogEvent("group_name", {"message": input_msg})
result = LambdaLogMsgFormatters.colorize_errors(event, colored)
self.assertEquals(result.message, color_result)
colored.red.assert_called_with(input_msg)
def test_must_ignore_other_messages(self):
colored = Mock()
event = LogEvent("group_name", {"message": "some msg"})
result = LambdaLogMsgFormatters.colorize_errors(event, colored)
self.assertEquals(result.message, "some msg")
colored.red.assert_not_called()
class TestKeywordHighlight_highlight_keyword(TestCase):
def test_must_highlight_all_keywords(self):
input_msg = "this keyword some keyword other keyword"
keyword = "keyword"
color_result = "colored"
expected_msg = "this colored some colored other colored"
colored = Mock()
colored.underline.return_value = color_result
event = LogEvent("group_name", {"message": input_msg})
result = KeywordHighlighter(keyword).highlight_keywords(event, colored)
self.assertEquals(result.message, expected_msg)
colored.underline.assert_called_with(keyword)
def test_must_ignore_if_keyword_is_absent(self):
colored = Mock()
input_msg = "this keyword some keyword other keyword"
event = LogEvent("group_name", {"message": input_msg})
result = KeywordHighlighter().highlight_keywords(event, colored)
self.assertEquals(result.message, input_msg)
colored.underline.assert_not_called()
class TestJSONMsgFormatter_format_json(TestCase):
def test_must_pretty_print_json(self):
data = {"a": "b"}
input_msg = '{"a": "b"}'
expected_msg = json.dumps(data, indent=2)
event = LogEvent("group_name", {"message": input_msg})
result = JSONMsgFormatter.format_json(event, None)
self.assertEquals(result.message, expected_msg)
@parameterized.expand(["this is not json", '{"not a valid json"}'])
def test_ignore_non_json(self, input_msg):
event = LogEvent("group_name", {"message": input_msg})
result = JSONMsgFormatter.format_json(event, None)
self.assertEquals(result.message, input_msg)
| 6,301 | 1,928 |
#!/usr/bin/env python3
import argparse, pexpect
from getpass import getpass
from time import sleep
# Set up argument parser
parser = argparse.ArgumentParser(prog='openconnect-cli', description='Automate logins to the OpenConnect SSL VPN client')
# Type of VPN to initiate
parser_type = parser.add_mutually_exclusive_group(required=False)
parser_type.add_argument('--anyconnect', action='store_true', default=False, help='Cisco AnyConnect SSL VPN')
parser_type.add_argument('--fortinet', action='store_true', default=False, help='Fortinet FortiClient SSL VPN')
parser_type.add_argument('--pulsesecure', action='store_true', default=False, help='Juniper Network Connect / Pulse Secure SSL VPN')
parser_type.add_argument('--paloalto', action='store_true', default=False, help='Palo Alto Networks (PAN) GlobalProtect SSL VPN')
# VPN server details
parser_dst = parser.add_argument_group('VPN Server Details', 'Any missing fields will be prompted on launch')
parser_dst.add_argument('--host', type=str, default=False, help='DNS hostname of SSL VPN server')
parser_dst.add_argument('--user', type=str, default=False, help='Username for SSL VPN account')
parser_dst.add_argument('--pw', type=str, default=False, help='Password for SSL VPN account')
# Import options, output help if none provided
args = vars(parser.parse_args())
#args = vars(parser.parse_args(args=None if sys.argv[1:] else ['--help']))
def vpnTypePrompt():
try:
print('Please enter one of the following and press enter:')
print('1 for Cisco AnyConnect')
print('2 for Fortinet FortiClient')
print('3 for Pulse Secure or Juniper Network Connect')
print('4 for Palo Alto Networks GlobalProtect')
protocol = int(input('SSL VPN Type: '))
if protocol == 1:
return 'anyconnect'
elif protocol == 2:
return 'fortinet'
elif protocol == 3:
return 'nc'
elif protocol == 4:
return 'gp'
else:
return False
except:
return False
if 'anyconnect' in args and args['anyconnect']:
args['protocol'] = 'anyconnect'
elif 'fortinet' in args and args['fortinet']:
args['protocol'] = 'fortinet'
elif 'pulsesecure' in args and args['pulsesecure']:
args['protocol'] = 'nc'
elif 'paloalto' in args and args['paloalto']:
args['protocol'] = 'gp'
else:
args['protocol'] = False
while args['protocol'] == False:
args['protocol'] = vpnTypePrompt()
# Fields to prompt for when False
prompt_for = {
'host': 'DNS hostname of SSL VPN server: ',
'user': 'Username for SSL VPN account: ',
'pw': 'Password for SSL VPN account: '
}
# Interate through fields and prompt for missing ones
if 'help' not in args:
for field,prompt in prompt_for.items():
if str(field) not in args or not args[field]:
while args[field] == False:
try:
if field == 'pw' and args['protocol'] != 'gp':
args[field] = 'N/A'
elif field == 'pw':
args[field] = getpass(prompt)
else:
args[field] = input(prompt)
except:
pass
# Collate arguments for command
command = [
'sudo openconnect',
'--interface=vpn0',
'--script=/usr/share/vpnc-scripts/vpnc-script',
'--protocol="' + args['protocol'] + '"',
'--user="' + args['user'] + '"',
args['host']
]
# Compile command
command = ' '.join(command)
# Start process
process = pexpect.spawnu('/bin/bash', ['-c', command])
# Automate login process for Palo Alto GlobalProtect
if args['protocol'] == 'gp':
process.expect('Password: ')
process.sendline(args['pw'])
process.expect('GATEWAY: ')
process.sendline('Primary GP')
process.expect('anything else to view:')
process.sendline('yes')
process.expect('Password: ')
process.sendline(args['pw'])
# Clear remaining private data
args = None
command = None
# Hand over input to user, wait for process to end if interactive mode ends
process.interact()
while process.isalive():
sleep(5)
| 3,921 | 1,256 |
import unittest
#4.定义测试类，父类为unittest.TestCase。
#可继承unittest.TestCase的方法，如setUp和tearDown方法，不过此方法可以在子类重写，覆盖父类方法。
#可继承unittest.TestCase的各种断言方法。
class Test(unittest.TestCase):
#5.定义setUp()方法用于测试用例执行前的初始化工作。
#注意，所有类中方法的入参为self，定义方法的变量也要“self.变量”
def setUp(self):
print("开始。。。。。。。。")
self.number = 10
#6.定义测试用例，以“test_”开头命名的方法
#注意，方法的入参为self
#可使用unittest.TestCase类下面的各种断言方法用于对测试结果的判断
#可定义多个测试用例
#最重要的就是该部分
def test_case1(self):
self.assertEqual(10,10)
def test_case2(self):
# self.number为期望值，20为实际值
self.assertEqual(self.number,20,msg="your input is not 20")
@unittest.skip('暂时跳过用例3的测试')
def test_case3(self):
self.assertEqual(self.number,30,msg='Your input is not 30')
#7.定义tearDown()方法用于测试用例执行之后的善后工作。
#注意，方法的入参为self
def tearDown(self):
print("结束。。。。。。。。")
#8如果直接运行该文件(__name__值为__main__),则执行以下语句，常用于测试脚本是否能够正常运行
if __name__=='__main__':
#8.1执行测试用例方案一如下：
#unittest.main()方法会搜索该模块下所有以test开头的测试用例方法，并自动执行它们。
#执行顺序是命名顺序：先执行test_case1，再执行test_case2
unittest.main() | 1,047 | 679 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
class Unit:
def __init__(self, x, y, color, name):
self.x = x
self.y = y
self.color = color
self.name = name
self.taken = False
class OpUnit(Unit):
def __init__(self, x, y, color, name):
super().__init__(x, y, color, name)
self.blue = 0.0 | 351 | 128 |
"""Remote host configuration."""
from os import getenv, path
from dotenv import load_dotenv
from .log import LOGGER
import pudb
# Load environment variables from .env
# Originally set to the "installation" directory of the app...
BASE_DIR = path.abspath(path.dirname(__file__))
# But using /tmp might just be easier.
BASE_DIR = '/tmp'
load_dotenv(path.join(BASE_DIR, ".env"))
# SSH Connection Variables
ENVIRONMENT = getenv("ENVIRONMENT")
SSH_REMOTE_HOST = getenv("SSH_REMOTE_HOST")
SSH_USERNAME = getenv("SSH_USERNAME")
SSH_PASSWORD = getenv("SSH_PASSWORD")
SSH_KEY_FILEPATH = getenv("SSH_KEY_FILEPATH")
SCP_DESTINATION_FOLDER = getenv("SCP_DESTINATION_FOLDER")
SSH_CONFIG_VALUES = [
{"host": SSH_REMOTE_HOST},
{"user": SSH_USERNAME},
{"password": SSH_PASSWORD},
{"ssh": SSH_KEY_FILEPATH},
{"path": SCP_DESTINATION_FOLDER},
]
# Kerberos
KERBEROS_USER = getenv("KERBEROS_USER")
# Database config
DATABASE_HOSTS = [
{"hdprod": getenv("DATABASE_HDPROD_URI")},
{"sdprod": getenv("DATABASE_SDPROD_URI")},
{"gamedata": getenv("DATABASE_GAMEDATA_URI")},
{"gameentry": getenv("DATABASE_GAMEENTRY_URI")},
{"boxfile": getenv("DATABASE_BOXFILE_URI")},
]
# EC2 instances in a devstack
DEVSTACK_BOXES = ["web", "api", "app", "state", ""]
# Verify all config values are present
for config in SSH_CONFIG_VALUES + SSH_CONFIG_VALUES:
if None in config.values():
LOGGER.warning(f"Config value not set: {config.popitem()}")
raise Exception("Please set your environment variables via a `.env` file.")
# Local file directory (no trailing slashes)
LOCAL_FILE_DIRECTORY = f"{BASE_DIR}"
| 1,752 | 613 |
# Copyright 2020 DeepMind Technologies Limited.
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests of the human_players levels."""
import collections
from unittest import mock
from absl.testing import absltest
from absl.testing import parameterized
from dm_env import specs
import numpy as np
import pygame
import dmlab2d
from meltingpot.python.configs.substrates import (
allelopathic_harvest as mp_allelopathic_harvest,
)
from meltingpot.python.configs.substrates import (
arena_running_with_scissors_in_the_matrix as mp_arena_running_with_scissors_itm,
)
from meltingpot.python.configs.substrates import (
bach_or_stravinsky_in_the_matrix as mp_bach_or_stravinsky_itm,
)
from meltingpot.python.configs.substrates import capture_the_flag as mp_capture_the_flag
from meltingpot.python.configs.substrates import (
chemistry_metabolic_cycles as mp_chemistry_metabolic_cycles,
)
from meltingpot.python.configs.substrates import chicken_in_the_matrix as mp_chicken_itm
from meltingpot.python.configs.substrates import clean_up as mp_clean_up
from meltingpot.python.configs.substrates import (
collaborative_cooking_passable as mp_collaborative_cooking_passable,
)
from meltingpot.python.configs.substrates import (
commons_harvest_closed as mp_commons_harvest_closed,
)
from meltingpot.python.configs.substrates import king_of_the_hill as mp_king_of_the_hill
from meltingpot.python.configs.substrates import (
prisoners_dilemma_in_the_matrix as mp_prisoners_dilemma_itm,
)
from meltingpot.python.configs.substrates import (
pure_coordination_in_the_matrix as mp_pure_coordination_itm,
)
from meltingpot.python.configs.substrates import (
rationalizable_coordination_in_the_matrix as mp_rationalizable_coordination_itm,
)
from meltingpot.python.configs.substrates import (
running_with_scissors_in_the_matrix as mp_running_with_scissors_itm,
)
from meltingpot.python.configs.substrates import (
stag_hunt_in_the_matrix as mp_stag_hunt_itm,
)
from meltingpot.python.configs.substrates import territory_rooms as mp_territory_rooms
from meltingpot.python.human_players import level_playing_utils
from meltingpot.python.human_players import play_allelopathic_harvest
from meltingpot.python.human_players import play_any_paintball_game
from meltingpot.python.human_players import play_anything_in_the_matrix
from meltingpot.python.human_players import play_clean_up
from meltingpot.python.human_players import play_collaborative_cooking
from meltingpot.python.human_players import play_commons_harvest
from meltingpot.python.human_players import play_grid_land
from meltingpot.python.human_players import play_territory
class HumanActionReaderTest(parameterized.TestCase):
@parameterized.parameters(
(
{ # Capture the following key events,
"move": level_playing_utils.get_direction_pressed,
}, # given this action name, key pressed, for this player index; and
pygame.K_w,
0,
# Expecting this action list out.
{"1.move": 1, "2.move": 0, "3.move": 0},
),
(
{ # Capture the following key events,
"move": level_playing_utils.get_direction_pressed,
}, # given this action name, key pressed, for this player index; and
pygame.K_s,
2,
# Expecting this action list out.
{"1.move": 0, "2.move": 0, "3.move": 3},
),
(
{ # Capture the following key events,
"move": level_playing_utils.get_direction_pressed,
}, # given this action name, key pressed, for this player index; and
pygame.K_s,
0,
# Expecting this action list out.
{"1.move": 3, "2.move": 0, "3.move": 0},
),
(
{ # Capture the following key events,
"move": level_playing_utils.get_direction_pressed,
}, # given action name, irrelevant key pressed, for player 0; and
pygame.K_x,
0,
# Expecting this action list out.
{"1.move": 0, "2.move": 0, "3.move": 0},
),
(
{ # Capture the following key events (don't need to make sense),
"move": level_playing_utils.get_space_key_pressed,
}, # given action name, irrelevant key pressed, for player 0; and
pygame.K_SPACE,
0,
# Expecting this action list out.
{"1.move": 1, "2.move": 0, "3.move": 0},
),
)
@mock.patch.object(pygame, "key")
def test_human_action(
self, action_map, key_pressed, player_index, expected_action, mock_key
):
retval = collections.defaultdict(bool)
retval[key_pressed] = True
mock_key.get_pressed.return_value = retval
move_array = specs.BoundedArray(
shape=tuple(), dtype=np.intc, minimum=0, maximum=4, name="move"
)
action_spec = {
"1.move": move_array,
"2.move": move_array,
"3.move": move_array,
}
with mock.patch.object(dmlab2d, "Lab2d") as env:
env.action_spec.return_value = action_spec
har = level_playing_utils.ActionReader(env, action_map)
np.testing.assert_array_equal(har.step(player_index), expected_action)
class PlayLevelTest(parameterized.TestCase):
@parameterized.parameters(
(mp_allelopathic_harvest, play_allelopathic_harvest),
(mp_arena_running_with_scissors_itm, play_anything_in_the_matrix),
(mp_bach_or_stravinsky_itm, play_anything_in_the_matrix),
(mp_capture_the_flag, play_any_paintball_game),
(mp_chemistry_metabolic_cycles, play_grid_land),
(mp_chicken_itm, play_anything_in_the_matrix),
(mp_clean_up, play_clean_up),
(mp_collaborative_cooking_passable, play_collaborative_cooking),
(mp_commons_harvest_closed, play_commons_harvest),
(mp_king_of_the_hill, play_any_paintball_game),
(mp_prisoners_dilemma_itm, play_anything_in_the_matrix),
(mp_pure_coordination_itm, play_anything_in_the_matrix),
(mp_rationalizable_coordination_itm, play_anything_in_the_matrix),
(mp_running_with_scissors_itm, play_anything_in_the_matrix),
(mp_stag_hunt_itm, play_anything_in_the_matrix),
(mp_territory_rooms, play_territory),
)
@mock.patch.object(pygame, "key")
@mock.patch.object(pygame, "display")
@mock.patch.object(pygame, "event")
@mock.patch.object(pygame, "time")
def test_run_level(
self, config_module, play_module, unused_k, unused_d, unused_e, unused_t
):
full_config = config_module.get_config()
full_config["lab2d_settings"]["episodeLengthFrames"] = 10
level_playing_utils.run_episode("RGB", {}, play_module._ACTION_MAP, full_config)
if __name__ == "__main__":
absltest.main()
| 7,468 | 2,435 |
from pyNastran.bdf.bdf import BDF
model = BDF()
model.is_nx = True
section = 5
#filename = r'D:\SNC IAS\01 - FAST Program\05 - Modified Sections\01 - AeroComBAT Files\section_{}.dat'.format(section)
filename = r'C:\Users\benna\Desktop\Work Temp\SNC\FAST\SIMPLE_SECTIONS\CTRIA6_1_100.dat'
model.read_bdf(filename, xref=True)
#Create Export File
f = open(filename[:-4]+'_AeroComBAT.dat','w')
f.write('$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\n')
f.write('$$$$$$$$$$$$$ AEROCOMBAT INPUT FILE $$$$$$$$$$$$\n')
f.write('$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$\n')
for NID, node in model.nodes.items():
node_pos = node.get_position()
#Write node line
f.write('XNODE,{},{},{}\n'.format(NID,node_pos[1],node_pos[2]))
# TEMP LINE FOR MAT PROPERTY
f.write('MAT_ISO,1,Lower Upper Aeroshell,8781151.,0.232915,0.0004144,0.0083\n')
f.write('MAT_ISO,2,Ring Frame Flange,7627582.,0.201668,0.0004144,0.0083\n')
f.write('MAT_ISO,3,Cabin Skin,8473671.,0.259765,0.0004144,0.0083\n')
f.write('MAT_ISO,4,Hat Stiffeners,9283126.,0.206558,0.0004144,0.0083\n')
f.write('MAT_ISO,5,Lower Outer Aeroshell,6544552.,0.428299,0.0004144,0.0083\n')
f.write('MAT_ISO,6,Upper Cabin,8196235.,0.284012,0.0004144,0.0083\n')
f.write('MAT_ISO,7,Titanium,16000000.,0.31,0.0004144,0.0083\n')
f.write('MAT_ISO,8,Quasi Iso,7944519.,0.306626,0.000144,0.0083\n')
f.write('MAT_ISO,9,Outer Aeroshell,7505270,0.344368,0.000144,0.0083\n')
f.write('MAT_ISO,10,Aluminum,10300000.,0.33,0.0002615,0.0083\n')
EIDs = []
for EID, elem in model.elements.items():
if elem.pid==7000003:
tmp_MID=1
elif elem.pid == 7000004:
tmp_MID=2
elif elem.pid == 7000005:
tmp_MID=3
elif elem.pid == 7000006:
tmp_MID=4
elif elem.pid == 7000007:
tmp_MID=5
elif elem.pid == 7000008:
tmp_MID=6
elif elem.pid == 7000000:
tmp_MID=7
elif elem.pid == 7000001:
tmp_MID=8
elif elem.pid == 7000002:
tmp_MID=9
elif elem.pid == 7000009:
tmp_MID=10
else:
raise ValueError('Encountered an unexpected Material Prop {}',elem.pid)
EIDs += [EID]
node_ids = elem.node_ids
if elem.type=='CQUAD8':
n1 = node_ids[0]
n2 = node_ids[4]
n3 = node_ids[1]
n4 = node_ids[5]
n5 = node_ids[2]
n6 = node_ids[6]
n7 = node_ids[3]
n8 = node_ids[7]
f.write('XQUAD8,{},{},{},{},{},{},{},{},{},{}\n'.format(EID,\
n1,n2,n3,n4,n5,n6,n7,n8,tmp_MID))
elif elem.type=='CQUAD4':
n1 = node_ids[0]
n2 = node_ids[1]
n3 = node_ids[2]
n4 = node_ids[3]
f.write('XQUAD4,{},{},{},{},{},{}\n'.format(EID,\
n1,n2,n3,n4,tmp_MID))
elif elem.type=='CTRIA3':
n1 = node_ids[0]
n2 = node_ids[1]
n3 = node_ids[2]
f.write('XTRIA3,{},{},{},{},{}\n'.format(EID,\
n1,n2,n3,tmp_MID))
elif elem.type=='CTRIA6':
n1 = node_ids[0]
n2 = node_ids[1]
n3 = node_ids[2]
n4 = node_ids[3]
n5 = node_ids[4]
n6 = node_ids[5]
f.write('XTRIA6,{},{},{},{},{},{},{},{}\n'.format(EID,\
n1,n2,n3,n4,n5,n6,tmp_MID))
f.write('SECTIONG,{},{}\n'.format(section,section))
#EIDs = list(model.elements.keys())
f.write('LIST,{},INT,'.format(section)+str(EIDs)[1:-1]+'\n')
f.close() | 3,436 | 1,749 |
#!/usr/bin/env python
def main():
f = open('stage1_solution.csv')
ones = 0
zeros = 0
total = 0
for line in f:
if line[:3] == 'id,':
continue
line = line.strip().split(',')
label = int(line[1])
if label == 1:
ones += 1
total += 1
zeros = total-ones
print float(zeros)/total
f.close()
if __name__ == '__main__':
main() | 343 | 157 |
#!/usr/bin/env python
#coding: utf-8
class Node:
def __init__(self, elem=None, next=None):
self.elem = elem
self.next = next
if __name__ == "__main__":
n1 = Node(1, None)
n2 = Node(2, None)
n1.next = n2
| 242 | 100 |
from PyQt5.Qt import QObject
from PyQt5.Qt import QDateTime
from PyQt5.Qt import QUrl
from PyQt5.Qt import pyqtSignal
from mc.app.Settings import Settings
from calendar import month_name
from .HistoryModel import HistoryModel
from mc.common.models import HistoryDbModel
from mc.common.models import IconsDbModel
from mc.common.globalvars import gVar
class History(QObject):
class HistoryEntry:
def __init__(self):
self.id = 0
self.count = 0
self.date = QDateTime()
self.url = QUrl()
self.urlString = ''
self.title = ''
def fillDbobj(self, dbobj):
for field in ('id', 'count', 'urlString', 'title'):
setattr(dbobj, field, getattr(self, field))
dbobj.date = self.date.toMSecsSinceEpoch()
dbobj.url = self.url.toString()
@classmethod
def CreateFromDbobj(cls, dbobj):
entry = cls()
entry.id = dbobj.id
entry.count = dbobj.id
entry.date = QDateTime.fromMSecsSinceEpoch(dbobj.date)
entry.url = QUrl(dbobj.url)
entry.urlString = entry.url.toEncoded().data().decode()
entry.title = dbobj.title
return entry
def __init__(self, parent):
super().__init__(parent)
self._isSaving = False
self._model = None # HistoryModel
self.loadSettings()
@classmethod
def titleCaseLocalizedMonth(cls, month):
index = month - 1
if index < len(month_name):
return month_name[index]
else:
print('warning: Month number(%s) out of range!' % month)
return ''
def model(self):
'''
@return: HistoryModel
'''
if not self._model:
self._model = HistoryModel(self)
return self._model
def addHistoryEntryByView(self, view):
'''
@param: view WebView
'''
if not self._isSaving:
return
url = view.url()
title = view.title()
self.addHistoryEntryByUrlAndTitle(url, title)
def addHistoryEntryByUrlAndTitle(self, url, title):
'''
@param: url QUrl
@param: title QString
'''
if not self._isSaving:
return
schemes = ['http', 'https', 'ftp', 'file']
if url.scheme() not in schemes:
return
if not title:
title = _('Empty Page')
def addEntryFunc():
dbobj = HistoryDbModel.select().where(HistoryDbModel.url == url.toString()).first()
if dbobj:
# update
before = self.HistoryEntry()
before.id = dbobj.id
before.count = dbobj.count
before.date = QDateTime.fromMSecsSinceEpoch(dbobj.date)
before.url = url
before.urlString = before.url.toEncoded().data().decode()
before.title = dbobj.title
after = self.HistoryEntry()
after.id = dbobj.id
after.count = dbobj.count + 1
after.date = QDateTime.currentDateTime()
after.url = url
after.urlString = after.url.toEncoded().data().decode()
after.title = title
after.fillDbobj(dbobj)
dbobj.save()
self.historyEntryEdited.emit(before, after)
else:
# insert
dbobj = HistoryDbModel.create(**{
'count': 1,
'date': QDateTime.currentMSecsSinceEpoch(),
'url': url.toString(),
'title': title,
})
entry = self.HistoryEntry.CreateFromDbobj(dbobj)
self.historyEntryAdded.emit(entry)
gVar.executor.submit(addEntryFunc)
def deleteHistoryEntry(self, indexOrList):
'''
@param: indexOrList int/list/tuple
'''
if not isinstance(indexOrList, (list, tuple)):
list_ = []
list_.append(indexOrList)
else:
list_ = indexOrList
self._deleteHistoryEntryByIndexList(list_)
def _deleteHistoryEntryByIndexList(self, list_):
'''
@param: list_ QList<int>
'''
dbobjs = list(HistoryDbModel.select().where(HistoryDbModel.id.in_(list_)))
HistoryDbModel.delete().where(HistoryDbModel.id.in_(list_)).execute()
urls = [ QUrl(obj.url).toEncoded(QUrl.RemoveFragment) for obj in dbobjs ]
IconsDbModel.delete().where(IconsDbModel.url.in_(urls)).execute()
for dbobj in dbobjs:
entry = self.HistoryEntry.CreateFromDbobj(dbobj)
self.historyEntryDeleted.emit(entry)
def deleteHistoryEntryByUrl(self, url):
'''
@param: url QUrl
'''
items = HistoryDbModel.select(columns=['id']).where(HistoryDbModel.url == url).dicts()
import ipdb; ipdb.set_trace()
ids = [ item['id'] for item in dicts ]
self._deleteHistoryEntryByIndexList(ids)
def deleteHistoryEntryByUrlAndTitle(self, url, title):
'''
@param: url QUrl
@param: title QString
'''
items = HistoryDbModel.select(columns=['id']).where(HistoryDbModel.url == url,
HistoryDbModel.title == title).dicts()
import ipdb; ipdb.set_trace()
ids = [ item['id'] for item in dicts ]
self._deleteHistoryEntryByIndexList(ids)
def indexesFromTimeRange(self, start, end):
'''
@param: start qint64
@param: end qint64
@return: QList<int>
'''
list_ = [] # QList<int>
if start < 0 or end < 0:
return list_
ids = HistoryDbModel.select(HistoryDbModel.id, ).where(HistoryDbModel.date.between(end, start))
list_ = [ item for item in ids ]
return list_
def mostVisited(self, count):
'''
@param: count int
@return: QVector<HistoryEntry>
'''
result = []
for dbobj in HistoryDbModel.select().order_by(HistoryDbModel.count.desc()).limit(count):
entry = self.HistoryEntry()
entry.count = dbobj.count
entry.date = QDateTime.fromMSecsSinceEpoch(dbobj.date)
entry.id = dbobj.id
entry.title = dbobj.title
entry.url = QUrl(dbobj.url)
result.append(entry)
return result
def clearHistory(self):
HistoryDbModel.delete().execute()
HistoryDbModel.raw('VACUUM').execute()
gVar.app.webProfile().clearAllVisitedLinks()
self.resetHistory.emit()
def isSaving(self):
'''
@return: bool
'''
return self._isSaving
def setSaving(self, state):
'''
@param: state bool
'''
self._isSaving = state
def loadSettings(self):
settings = Settings()
settings.beginGroup('Web-Browser-Settings')
self._isSaving = settings.value('allowHistory', True, type=bool)
settings.endGroup()
def searchHistoryEntry(self, text):
'''
@param: text QString
'''
list_ = [] # QList<HistoryEntry>
qs = HistoryDbModel.select().where(HistoryDbModel.title.contains(text) |
HistoryDbModel.url.contains(text)).limit(50)
for item in qs:
entry = self.HistoryEntry()
entry.count = item.count
entry.date = QDateTime.fromMSecsSinceEpoch(item.date)
entry.id = item.id
entry.title = item.title
entry.url = QUrl(item.url)
list_.append(entry)
return list_
def getHistoryEntry(self, text):
'''
@param: text QString
'''
dbobj = HistoryDbModel.select().where(HistoryDbModel.url == text).first()
entry = self.HistoryEntry()
if dbobj:
entry.count = dbobj.count
entry.date = QDateTime.fromMSecsSinceEpoch(entry.date)
entry.id = dbobj.id
entry.title = dbobj.title
entry.url = QUrl(dbobj.url)
return entry
# Q_SIGNALS
historyEntryAdded = pyqtSignal(HistoryEntry) # entry
historyEntryDeleted = pyqtSignal(HistoryEntry) # entry
historyEntryEdited = pyqtSignal(HistoryEntry, HistoryEntry) # before, after
resetHistory = pyqtSignal()
HistoryEntry = History.HistoryEntry
| 8,493 | 2,429 |
from flask_socketio import send, emit
from pkg.system.servlog import srvlog
#----------------------------------------------------------------------------------------
# External calls
# introduced u7
# The livelog functions allows other functions which are not registered with
# socketio to emit a message.
# Generalization (u8) 31/05/2019
# Now this can be called anywhere. just create listener sockets on the namespaces
# for it to work!
#
# @author ToraNova
# @mailto chia_jason96@live.com
#----------------------------------------------------------------------------------------
def sockemit( enamespace, ename, emsg, eroom=None):
from pkg.source import out as socketio # use carefully to prevent circular imports
try:
#live logins - update7
if(eroom is None):
socketio.emit( ename, emsg, namespace= enamespace)
else:
socketio.emit( ename, emsg, room = eroom, namespace= enamespace)
# emit may also contain namespaces to emit to other classes
except Exception as e:
print("[EX]",__name__," : ","Exception has occurred",str(e))
srvlog["oper"].info("Exception ocurred in sockemit :"+str(e))
| 1,184 | 325 |
# coding: utf-8
import argparse
import logging
import os
import sys
from util.func import (
data_from_json,
)
if 'SUMO_HOME' in os.environ:
sys.path.append(os.path.join(os.environ['SUMO_HOME'], 'tools'))
import traci
else:
sys.exit("Please declare environment variable 'SUMO_HOME'")
# ----- GLOBAL_VARS -----
CTRL_C_PRESSED_MESSAGE = "ctrl-c is pressed."
# ----- Class -----
class TracisSyncronizer:
def __init__(self, main_sumo_host_port, other_sumo_host_ports, order):
self.main_traci = traci.connect(host=main_sumo_host_port.split(":")[0], port=int(main_sumo_host_port.split(":")[1]))
self.other_tracis = [traci.connect(host=o_host_port.split(":")[0], port=int(o_host_port.split(":")[1])) for o_host_port in other_sumo_host_ports]
# ----- set order -----
self.tracis = [self.main_traci] + self.other_tracis
for tmp_traci in self.tracis:
tmp_traci.setOrder(order)
def start(self):
while self.check_sumo_finish is not False:
self.main_traci.simulationStep()
for o_traci in self.other_tracis:
self.sync_tracis(self.main_traci, o_traci)
o_traci.simulationStep()
def check_sumo_finish(self):
try:
for tmp_traci in self.tracis:
if traci.simulation.getMinExpectedNumber() <= 0:
return True
else:
continue
return False
except Exception as e:
logging.log(e)
return True
def close_tracis(self):
try:
if 0 < len(self.tracis):
self.tracis[0].close()
self.tracis.pop(0)
return self.close_tracis()
return self.tracis
except Exception as e:
logging.error(e)
logging.error(f"{len(self.tracis)} tracis are remained.")
return self.tracis
def simulationStep(self):
for tmp_traci in self.tracis:
tmp_traci.simulationStep()
def sync_tracis(self, m_traci, o_traci):
# ----- add departed vehicle -----
diff_add_vehicle_ids = set(m_traci.vehicle.getIDList()) - set(o_traci.vehicle.getIDList())
for dav_id in diff_add_vehicle_ids:
new_route_id = str(m_traci.vehicle.getIDCount() + 1)
try:
o_traci.route.add(
routeID=new_route_id,
edges=m_traci.vehicle.getRoute(dav_id)
)
o_traci.vehicle.add(
vehID=dav_id,
routeID=new_route_id
)
except Exception as e:
logging.error(e)
continue
# ----- remove vehicles -----
diff_remove_vehicle_ids = set(o_traci.vehicle.getIDList()) - set(m_traci.vehicle.getIDList())
for drv_id in diff_remove_vehicle_ids:
try:
o_traci.vehicle.remove(drv_id)
except Exception as e:
logging.error(e)
continue
# ----- sync vehicle positions -----
for m_veh_id in m_traci.vehicle.getIDList():
try:
o_traci.vehicle.moveToXY(
vehID=m_veh_id,
edgeID=m_traci.lane.getEdgeID(m_traci.vehicle.getLaneID(m_veh_id)),
lane=int(str(m_traci.vehicle.getLaneID(m_veh_id)).split('_')[1]),
x=m_traci.vehicle.getPosition(m_veh_id)[0],
y=m_traci.vehicle.getPosition(m_veh_id)[1],
angle=m_traci.vehicle.getAngle(m_veh_id)
)
except Exception as e:
logging.error(e)
continue
# ----- function -----
def start_tracis_syncronizer(main_sumo_host_port, other_sumo_host_ports, order):
tracis_syncronizer = TracisSyncronizer(main_sumo_host_port, other_sumo_host_ports, order)
try:
tracis_syncronizer.start()
tracis_syncronizer.close_tracis()
except KeyboardInterrupt:
logging.info(CTRL_C_PRESSED_MESSAGE)
tracis_syncronizer.close_tracis()
except Exception as e:
logging.error(e)
tracis_syncronizer.close_tracis()
# ----- main -----
if __name__ == "__main__":
env = data_from_json("./env.json")
# ----- get args -----
parser = argparse.ArgumentParser(description='This script is a middleware for tracis synchronization.')
parser.add_argument('--main_sumo_host_port', default=f"127.0.0.1:{env['carla_sumo_port']}")
parser.add_argument('--other_sumo_host_ports', nargs='*', default=f"{env['vagrant_ip']}:{env['veins_sumo_port']}")
parser.add_argument('--sumo_order', type=int, default=1)
parser.add_argument('--log_file_path', default="./log/tracis_logger.log")
args = parser.parse_args()
# ----- set logging -----
logging.basicConfig(
handlers=[logging.FileHandler(filename=args.log_file_path), logging.StreamHandler(sys.stdout)],
format='[%(asctime)s] {%(filename)s:%(lineno)d} %(levelname)s: %(message)s',
level=logging.DEBUG
)
start_tracis_syncronizer(
main_sumo_host_port=args.main_sumo_host_port,
other_sumo_host_ports=args.other_sumo_host_ports,
order=args.sumo_order
)
| 5,322 | 1,835 |
## @file
## @defgroup flask flask
## @brief minimized Flask-based backend
## @ingroup web
import config
from core.env import *
from core.io import Dir
from .web import Web
from core.meta import Module
from core.time import *
from gen.js import jsFile
from gen.s import S
from web.html import htmlFile
import os, re
import flask
from flask_socketio import SocketIO, emit
env['static'] = Dir('static')
env['templates'] = Dir('templates')
## web application
## @ingroup flask
class App(Web, Module):
## @param[in] V string | File in form of `web.App(__file__)``
def __init__(self, V):
Module.__init__(self, V)
env << self
env >> self
#
self['static'] = Dir(self)
env.static // self.static
self['js'] = jsFile(self)
self.static // self['js']
#
self['templates'] = Dir(self)
env.templates // self.templates
self['html'] = htmlFile(self)
self.templates // self['html']
#
self.flask = flask
self.app = flask.Flask(self.value)
self.app.config['SECRET_KEY'] = config.SECRET_KEY
self.watch()
self.router()
#
self.sio = SocketIO(self.app)
self.socketio()
## configure SocketIO event processors
def socketio(self):
@self.sio.on('connect')
def connect(): self.sio.emit('localtime', LocalTime().json())
@self.sio.on('localtime')
def localtime(): self.sio.emit('localtime', LocalTime().json())
## put application name in page/window title
def title(self): return self.head(test=True)
## configure file watch list
def watch(self):
self.extra_files = [
'config.py', f'{self.value}.py',
'web/flask.py', 'core/object.py']
## lookup in global `env`
## @param[in] path slashed path to the needed element
def lookup(self, path):
assert isinstance(path, str)
ret = env
if not path:
return ret
for i in path.split('/'):
if re.match(r'^\d+$', i):
i = int(i)
ret = ret[i]
return ret
## configure routes statically
def router(self):
@self.app.route('/')
def index():
return flask.redirect(f'/{self.value}')
@self.app.route('/dump/<path:path>')
@self.app.route('/dump/')
@self.app.route('/dump')
def dump(path=''):
item = self.lookup(path)
return flask.render_template('dump.html', env=env, app=self, item=item)
@self.app.route(f'/{self.value}')
@self.app.route('/app')
def app():
return flask.render_template(f'{self.value}/index.html', env=env, app=self)
## run application as web backend
def run(self):
print(env)
self.sio.run(self.app,
host=config.HOST, port=config.PORT, debug=True,
extra_files=self.extra_files)
| 2,977 | 918 |
#!/usr/bin/env python
from distutils.core import setup, Extension
from distutils.util import get_platform
import os
festival_include = os.environ.get("FESTIVAL_INCLUDE", '/usr/include/festival')
speech_tools_include = os.environ.get("SPECCH_INCLUDE", '/usr/include/speech_tools')
festival_lib = os.environ.get("FESTIVAL_LIB", '/usr/lib')
festival_classifiers = [
"Programming Language :: Python :: 2",
"Programming Language :: Python :: 3",
"Intended Audience :: Developers",
"License :: OSI Approved :: BSD License",
"Topic :: Software Development :: Libraries",
"Topic :: Utilities",
"Topic :: Multimedia :: Sound/Audio :: Sound Synthesis",
"Topic :: Multimedia :: Sound/Audio :: Speech"
]
long_description = """A Python wrapper around the Festival Speech Synthesis System:
http://www.cstr.ed.ac.uk/projects/festival/
pyfestival creates a C module built on top of the festival source code, making it a self-contained Python library
(there is no need to run festival-server alongside).
pyfestival supports (and is tested on) Python 2.7+ including Python 3
"""
libraries = ['Festival', 'estools', 'estbase', 'eststring']
if get_platform().startswith('macosx-'):
macos_frameworks = ['CoreAudio', 'AudioToolbox', 'Carbon']
libraries.append('ncurses')
else:
macos_frameworks = []
setup(
name='pyfestival',
description='Python Festival module',
long_description=long_description,
url="https://github.com/techiaith/pyfestival",
author="Patrick Robertson",
author_email="techiaith@bangor.ac.uk",
license="BSD",
py_modules=['festival'],
ext_modules=[
Extension(
'_festival',
['_festival.cpp'],
include_dirs=[festival_include, speech_tools_include],
library_dirs=[festival_lib],
libraries=libraries,
extra_link_args=[x for name in macos_frameworks for x in ('-framework', name)],
),
],
platforms=["*nix"],
bugtrack_url="https://github.com/techiaith/pyfestival/issues",
version="0.5",
)
| 2,038 | 646 |
from parse_manual.parser import parse as parse_manual
from parse_pyparsing.parser import parse as parse_pyparsing
from parse_yaml.parser import parse as parse_yaml
from parse_xml.parser import parse as parse_xml
from timer.PerformanceTimer import PerformanceTimer
# Manual Parsing
manual_timer = PerformanceTimer('Manual Parsing')
manual_timer.measure_function(parse_manual, 'sample.gen')
manual_timer.print()
# Pyparsing
pyparsing_timer = PerformanceTimer('Pyparsing')
pyparsing_timer.measure_function(parse_pyparsing, 'sample.gen')
pyparsing_timer.print()
#YAML
yaml_timer = PerformanceTimer('YAML Parsing')
yaml_timer.measure_function(parse_yaml, './parse_yaml/sample.yaml')
yaml_timer.print()
#XML
xml_timer = PerformanceTimer('XML Parsing', 10)
xml_timer.measure_function(parse_xml, './parse_xml/sample.xml')
xml_timer.print()
| 838 | 262 |
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import unittest
from apache.aurora.client import base
from gen.apache.aurora.api.ttypes import (
PopulateJobResult,
Response,
ResponseCode,
ResponseDetail,
Result,
TaskConfig
)
class TestBase(unittest.TestCase):
def test_format_response_with_message(self):
resp = Response(responseCode=ResponseCode.ERROR, details=[ResponseDetail(message='Error')])
formatted = base.format_response(resp)
assert formatted == 'Response from scheduler: ERROR (message: Error)'
def test_format_response_with_details(self):
resp = Response(responseCode=ResponseCode.ERROR, details=[ResponseDetail(message='Error')])
formatted = base.format_response(resp)
assert formatted == 'Response from scheduler: ERROR (message: Error)'
def test_combine_messages(self):
resp = Response(responseCode=ResponseCode.ERROR)
assert base.combine_messages(resp) == ''
resp = Response(responseCode=ResponseCode.ERROR, details=[])
assert base.combine_messages(resp) == ''
resp = Response(responseCode=ResponseCode.ERROR, details=[ResponseDetail(message='Error')])
assert base.combine_messages(resp) == 'Error'
resp = Response(responseCode=ResponseCode.ERROR, details=[ResponseDetail()])
assert base.combine_messages(resp) == 'Unknown error'
resp = Response(
responseCode=ResponseCode.ERROR,
details=[ResponseDetail(message='Error1'), ResponseDetail(message='Error2')])
assert base.combine_messages(resp) == 'Error1, Error2'
def test_get_populated_task_config_set(self):
config = TaskConfig()
resp = Response(responseCode=ResponseCode.OK, result=Result(populateJobResult=PopulateJobResult(
taskConfig=config)))
assert config == resp.result.populateJobResult.taskConfig
def test_synthesize_url(self):
base_url = 'http://example.com'
role = 'some-role'
environment = 'some-environment'
job = 'some-job'
update_id = 'some-update-id'
assert (('%s/scheduler/%s/%s/%s/update/%s' % (base_url, role, environment, job, update_id)) ==
base.synthesize_url(base_url, role, environment, job, update_id=update_id))
assert (('%s/scheduler/%s/%s/%s' % (base_url, role, environment, job)) ==
base.synthesize_url(base_url, role, environment, job))
assert (('%s/scheduler/%s/%s' % (base_url, role, environment)) ==
base.synthesize_url(base_url, role, environment))
assert (('%s/scheduler/%s' % (base_url, role)) ==
base.synthesize_url(base_url, role))
assert (('%s/scheduler/%s' % (base_url, role)) ==
base.synthesize_url(base_url, role))
| 3,152 | 962 |
import os
import glob
import warnings
import logging
from collections import deque
from six import string_types as basestring
from lxml import etree
try:
log = logging.getLogger(os.path.basename(__file__))
except Exception:
log = None
from collections import OrderedDict, defaultdict
from weakref import WeakValueDictionary
try:
WindowsError
except NameError:
raise ImportError("Platform Not Supported")
try:
import comtypes
from comtypes.client import GetModule, CreateObject
except (ImportError, NameError) as e:
raise ImportError("Could not import comtypes")
import numpy as np
from ms_deisotope.data_source.common import (
ScanDataSource,
RandomAccessScanSource,
Scan,
ScanBunch,
PrecursorInformation,
ActivationInformation,
IsolationWindow,
InstrumentInformation,
ComponentGroup,
component,
FileInformation,
SourceFile,
ScanAcquisitionInformation,
ScanEventInformation,
ScanWindow)
try:
# Load previously built COM wrapper
from comtypes.gen import (
MassSpecDataReader,
BaseCommon,
BaseDataAccess)
DLL_IS_LOADED = True
except (ImportError, TypeError):
DLL_IS_LOADED = False
_default_paths = []
def _register_dll_dir(search_paths=None):
from ms_deisotope.config import get_config
if search_paths is None:
search_paths = []
global DLL_IS_LOADED
if DLL_IS_LOADED:
return True
search_paths = list(search_paths)
search_paths.extend(_default_paths)
search_paths.extend(get_config().get('vendor_readers', {}).get('agilent-com', []))
for dll_dir in search_paths:
try:
GetModule(os.path.join(dll_dir, 'MassSpecDataReader.tlb'))
GetModule(os.path.join(dll_dir, 'BaseCommon.tlb'))
GetModule(os.path.join(dll_dir, 'BaseDataAccess.tlb'))
DLL_IS_LOADED = True
return True
except Exception:
continue
else:
return False
def register_dll_dir(search_paths=None):
if search_paths is None:
search_paths = []
if isinstance(search_paths, basestring):
search_paths = [search_paths]
loaded = _register_dll_dir(search_paths)
if not loaded:
log.debug("Could not resolve Agilent-related DLL")
search_paths.extend(_default_paths)
msg = '''
1) The MassSpecDataReader, BaseCommon, BaseDataAccess DLLs/TLBs may not be installed and
therefore not registered to the COM server.
2) The MassSpecDataReader, BaseCommon, BaseDataAccess DLLs/TLBs may not be on these paths:
%s
''' % ('\n'.join(search_paths))
raise ImportError(msg)
class CaseInsensitiveDict(dict):
def __init__(self, template=None):
if isinstance(template, dict):
template = {k.lower(): v for k, v in template.items()}
dict.__init__(self, template)
def __getitem__(self, key):
key = key.lower()
return dict.__getitem__(self, key)
def __delitem__(self, key):
return super(CaseInsensitiveDict, self).__delitem__(key.lower())
def __setitem__(self, key, value):
key = key.lower()
return dict.__setitem__(self, key, value)
def __contains__(self, key):
return super(CaseInsensitiveDict, self).__contains__(key.lower())
device_to_component_group_map = CaseInsensitiveDict({
"QTOF": [
ComponentGroup("analyzer", [component("quadrupole")], 2),
ComponentGroup("analyzer", [component("quadrupole")], 3),
ComponentGroup("analyzer", [component("time-of-flight")], 4)
],
"Quadrupole": [
ComponentGroup("analyzer", [component("quadrupole")], 2),
],
"TandemQuadrupole": [
ComponentGroup("analyzer", [component("quadrupole")], 2),
ComponentGroup("analyzer", [component("quadrupole")], 3),
ComponentGroup("analyzer", [component("quadrupole")], 4)
],
"IonTrap": [
ComponentGroup("analyzer", [component("iontrap")], 2)
],
"TOF": [
ComponentGroup("analyzer", [component("time-of-flight")], 2)
]
})
polarity_map = {
1: -1,
0: 1,
3: 0,
2: None
}
ion_mode_map = {
0: 'Unspecified',
1: 'Mixed',
2: 'EI',
4: 'CI',
8: 'Maldi',
16: 'Appi',
32: 'Apci',
64: 'ESI',
128: 'NanoEsi',
512: 'MsChip',
1024: 'ICP',
2048: 'Jetstream'
}
ionization_map = CaseInsensitiveDict({
"EI": component("electron ionization"),
"CI": component("chemical ionization"),
"ESI": component("electrospray ionization"),
"NanoEsi": component("nanoelectrospray"),
"Appi": component('atmospheric pressure photoionization'),
"Apci": component("atmospheric pressure chemical ionization"),
"Maldi": component("matrix assisted laser desorption ionization"),
"MsChip": component("nanoelectrospray"),
"ICP": component("plasma desorption ionization"),
"Jetstream": component("nanoelectrospray")
})
inlet_map = CaseInsensitiveDict({
"EI": component("direct inlet"),
"CI": component("direct inlet"),
"Maldi": component("particle beam"),
"Appi": component("direct inlet"),
"Apci": component("direct inlet"),
"Esi": component("electrospray inlet"),
"NanoEsi": component("nanospray inlet"),
"MsChip": component("nanospray inlet"),
"ICP": component("component(inductively coupled plasma"),
"JetStream": component("nanospray inlet"),
})
peak_mode_map = {
'profile': 0,
'centroid': 1,
'profilepreferred': 2,
'centroidpreferred': 3
}
device_type_map = {
0: 'Unknown',
1: 'Mixed',
2: 'Quadrupole',
3: 'IsocraticPump',
4: 'TOF',
5: 'TandemQuadrupole',
6: 'QTOF',
10: 'FlourescenceDetector',
11: 'ThermalConductivityDetector',
12: 'RefractiveIndexDetector',
13: 'MultiWavelengthDetector',
14: 'ElectronCaptureDetector',
15: 'VariableWavelengthDetector',
16: 'AnalogDigitalConverter',
17: 'EvaporativeLightScatteringDetector',
18: 'GCDetector',
19: 'FlameIonizationDetector',
20: 'ALS',
21: 'WellPlateSampler',
22: 'MicroWellPlateSampler',
23: 'DiodeArrayDetector',
31: 'CANValves',
32: 'QuaternaryPump',
33: 'ChipCube',
34: 'Nanopump',
40: 'ThermostattedColumnCompartment',
41: 'CTC',
42: 'CapillaryPump',
50: 'IonTrap'
}
scan_type_map = CaseInsensitiveDict({
"Unspecified": 0,
"All": 7951,
"AllMS": 15,
"AllMSN": 7936,
"Scan": 1,
"SelectedIon": 2,
"HighResolutionScan": 4,
"TotalIon": 8,
"MultipleReaction": 256,
"ProductIon": 512,
"PrecursorIon": 1024,
"NeutralLoss": 2048,
"NeutralGain": 4096
})
PEAK_MODE = 0
def make_scan_id_string(scan_id):
return "scanId=%s" % (scan_id,)
class AgilentDScanPtr(object):
def __init__(self, index):
self.index = index
def __repr__(self):
return "AgilentDScanPtr(%d)" % (self.index,)
class AgilentDDataInterface(ScanDataSource):
def _get_spectrum_obj(self, scan, peak_mode=PEAK_MODE):
index = scan.index
spectrum = self.source.GetSpectrum_8(rowNumber=index, storageType=peak_mode)
return spectrum
def _get_scan_record(self, scan):
index = scan.index
record = self.source.GetScanRecord(index)
return record
def _scan_index(self, scan):
return scan.index
def _scan_id(self, scan):
record = self._get_scan_record(scan)
return make_scan_id_string(record.ScanId)
def _scan_title(self, scan):
return self._scan_id(scan)
def _scan_arrays(self, scan):
spectrum = self._get_spectrum_obj(scan)
return (np.array(spectrum.XArray, dtype=float),
np.array(spectrum.YArray, dtype=float))
def _polarity(self, scan):
record = self._get_scan_record(scan)
polarity_enum = record.IonPolarity
polarity = polarity_map.get(polarity_enum)
if polarity in (0, None):
warnings.warn("Unknown Scan Polarity: %r" % (polarity,))
return polarity
def _scan_time(self, scan):
record = self._get_scan_record(scan)
return record.retentionTime
def _is_profile(self, scan):
spectrum_obj = self._get_spectrum_obj(scan)
mode = spectrum_obj.MSStorageMode
return mode in (0, 2, 3)
def _ms_level(self, scan):
record = self._get_scan_record(scan)
return record.MSLevel
def _precursor_information(self, scan):
if self._ms_level(scan) < 2:
return None
spectrum_obj = self._get_spectrum_obj(scan)
precursor_scan_id = make_scan_id_string(spectrum_obj.ParentScanId)
n, ions = spectrum_obj.GetPrecursorIon()
if n < 1:
return None
mz = ions[0]
charge, _ = spectrum_obj.GetPrecursorCharge()
intensity, _ = spectrum_obj.GetPrecursorIntensity()
return PrecursorInformation(mz, intensity, charge, precursor_scan_id, self)
def _acquisition_information(self, scan):
spectrum_obj = self._get_spectrum_obj(scan)
try:
low = spectrum_obj.MeasuredMassRange.Start
high = spectrum_obj.MeasuredMassRange.End
except Exception:
arrays = self._scan_arrays(scan)
mz_array = arrays[0]
if len(mz_array) != 0:
low = mz_array.min()
high = mz_array.max()
else:
low = high = 0
window = ScanWindow(low, high)
event = ScanEventInformation(
self._scan_time(scan),
window_list=[window])
return ScanAcquisitionInformation("no combination", [event])
def _activation(self, scan):
record = self._get_scan_record(scan)
return ActivationInformation('cid', record.CollisionEnergy)
def _isolation_window(self, scan):
if self._ms_level(scan) < 2:
return None
spectrum_obj = self._get_spectrum_obj(scan)
n, ions = spectrum_obj.GetPrecursorIon()
if n < 1:
return None
return IsolationWindow(0, ions[0], 0)
def _instrument_configuration(self, scan):
return self._instrument_config[1]
class _AgilentDDirectory(object):
@staticmethod
def create_com_object():
if not DLL_IS_LOADED:
raise WindowsError("Could not locate Agilent DLLs")
reader = CreateObject('Agilent.MassSpectrometry.DataAnalysis.MassSpecDataReader')
return reader
@staticmethod
def create_com_object_filter():
if not DLL_IS_LOADED:
raise WindowsError("Could not locate Agilent DLLs")
no_filter = CreateObject('Agilent.MassSpectrometry.DataAnalysis.MsdrPeakFilter')
return no_filter
@staticmethod
def is_valid(path):
if os.path.exists(path):
if os.path.isdir(path):
return os.path.exists(os.path.join(path, "AcqData", "Contents.xml"))
return False
class _AgilentMethod(object):
def __init__(self, method_parameters):
self.parameters = list(method_parameters)
def __getitem__(self, i):
return self.parameters[i]
def __len__(self):
return len(self.parameters)
def __iter__(self):
return iter(self.parameters)
def __repr__(self):
return "_AgilentMethod(%d)" % (len(self),)
def search_by_name(self, name):
for param in self:
try:
if param['Name'].lower() == name.lower():
return param
except (AttributeError, KeyError):
continue
class _AgilentDMetadataLoader(object):
def _has_ms1_scans(self):
return bool(self._scan_types_flags & scan_type_map['Scan'])
def _has_msn_scans(self):
return bool(self._scan_types_flags & scan_type_map['ProductIon'])
def has_msn_scans(self):
return self._has_msn_scans()
def has_ms1_scans(self):
return self._has_ms1_scans()
def file_description(self):
fi = FileInformation(contents={}, source_files=[])
if self._has_ms1_scans():
fi.add_content("MS1 spectrum")
if self._has_msn_scans():
fi.add_content("MSn spectrum")
basename = os.path.basename
dirname = os.path.dirname
file_queue = deque()
file_queue.extend(glob.glob(os.path.join(self.dirpath, "AcqData", "*")))
# for source_file in file_queue:
while file_queue:
source_file = file_queue.popleft()
if os.path.isdir(source_file):
file_queue.extendleft(glob.glob(os.path.join(source_file, "*")))
else:
sf = SourceFile(
basename(source_file), dirname(source_file),
None, *("Agilent MassHunter nativeID format", "Agilent MassHunter format"))
sf.add_checksum("sha1")
fi.add_file(sf, check=False)
return fi
def _get_instrument_info(self):
ion_modes_flags = self.source.MSScanFileInformation.IonModes
ionization = []
for bit, label in ion_mode_map.items():
if ion_modes_flags & bit:
ionization.append(label)
configs = []
i = 1
for ionizer in ionization:
groups = [ComponentGroup("source", [ionization_map[ionizer], inlet_map[ionizer]], 1)]
groups.extend(device_to_component_group_map[self.device])
config = InstrumentInformation(i, groups)
i += 1
configs.append(config)
self._instrument_config = {
c.id: c for c in configs
}
return configs
def instrument_configuration(self):
return sorted(self._instrument_config.values(), key=lambda x: x.id)
def data_processing(self):
return []
def _acquisition_method_xml_path(self):
return os.path.join(self.dirpath, "AcqData", "AcqMethod.xml")
def _parse_method_xml(self):
try:
path = self._acquisition_method_xml_path()
tree = etree.parse(path)
nsmap = {"ns": "http://tempuri.org/DataFileReport.xsd"}
elt = tree.find(".//ns:SCICDevicesXml", namespaces=nsmap)
method_xml = etree.fromstring(elt.text)
except (IOError, OSError, ValueError, TypeError) as e:
print(e)
self._method = []
return self._method
method = list()
for section in method_xml.iterfind(".//SectionInfo"):
section_dict = {}
for child in section:
name = child.tag
value = child.text
section_dict[name] = value
method.append(section_dict)
method = _AgilentMethod(method)
self._method = method
return method
_ADM = _AgilentDMetadataLoader
_ADD = _AgilentDDirectory
class AgilentDLoader(AgilentDDataInterface, _ADD, RandomAccessScanSource, _ADM):
def __init__(self, dirpath, **kwargs):
self.dirpath = dirpath
self.dirpath = os.path.abspath(self.dirpath)
self.dirpath = os.path.normpath(self.dirpath)
self.source = self.create_com_object()
self.filter = self.create_com_object_filter()
try:
self.source.OpenDataFile(self.dirpath)
except comtypes.COMError as err:
raise IOError(str(err))
self._TIC = self.source.GetTIC()
self.device = self._TIC.DeviceName
self._n_spectra = self._TIC.TotalDataPoints
self._scan_types_flags = self.source.MSScanFileInformation.ScanTypes
self._producer = self._scan_group_iterator()
self.initialize_scan_cache()
self._index = self._pack_index()
self._get_instrument_info()
def __reduce__(self):
return self.__class__, (self.dirpath,)
@property
def index(self):
return self._index
def __len__(self):
return len(self.index)
def __repr__(self):
return "AgilentDLoader(%r)" % (self.dirpath)
def reset(self):
self.make_iterator(None)
self.initialize_scan_cache()
def close(self):
# seems to make attempting to re-open the same datafile cause a segfault
# self.source.CloseDataFile()
self._dispose()
def _pack_index(self):
index = OrderedDict()
for sn in range(self._n_spectra):
rec = self._get_scan_record(AgilentDScanPtr(sn))
index[make_scan_id_string(rec.ScanId)] = sn
return index
def _make_pointer_iterator(self, start_index=None, start_time=None):
iterator = self._make_scan_index_producer(start_index, start_time)
for i in iterator:
yield AgilentDScanPtr(i)
def _make_default_iterator(self):
return self._make_pointer_iterator()
def _make_scan_index_producer(self, start_index=None, start_time=None):
if start_index is not None:
return range(start_index, self._n_spectra)
elif start_time is not None:
start_index = self._source.ScanNumFromRT(start_time)
while start_index != 0:
scan = self.get_scan_by_index(start_index)
if scan.ms_level > 1:
start_index -= 1
else:
break
return range(start_index, self._n_spectra)
else:
return range(0, self._n_spectra)
def get_scan_by_id(self, scan_id):
"""Retrieve the scan object for the specified scan id.
If the scan object is still bound and in memory somewhere,
a reference to that same object will be returned. Otherwise,
a new object will be created.
Parameters
----------
scan_id : str
The unique scan id value to be retrieved
Returns
-------
Scan
"""
index = self._index[scan_id]
return self.get_scan_by_index(index)
def get_scan_by_index(self, index):
"""Retrieve the scan object for the specified scan index.
This internally calls :meth:`get_scan_by_id` which will
use its cache.
Parameters
----------
index: int
The index to get the scan for
Returns
-------
Scan
"""
scan_number = int(index)
try:
return self._scan_cache[scan_number]
except KeyError:
package = AgilentDScanPtr(scan_number)
scan = Scan(package, self)
self._cache_scan(scan)
return scan
def get_scan_by_time(self, time):
time_array = self._TIC.XArray
lo = 0
hi = self._n_spectra
if time == float('inf'):
return self.get_scan_by_index(len(self) - 1)
best_match = None
best_error = float('inf')
while hi != lo:
mid = (hi + lo) // 2
scan_time = time_array[mid]
err = abs(scan_time - time)
if err < best_error:
best_error = err
best_match = mid
if scan_time == time:
return self.get_scan_by_index(mid)
elif (hi - lo) == 1:
return self.get_scan_by_index(best_match)
elif scan_time > time:
hi = mid
else:
lo = mid
def start_from_scan(self, scan_id=None, rt=None, index=None, require_ms1=True, grouped=True):
'''Reconstruct an iterator which will start from the scan matching one of ``scan_id``,
``rt``, or ``index``. Only one may be provided.
After invoking this method, the iterator this object wraps will be changed to begin
yielding scan bunchs (or single scans if ``grouped`` is ``False``).
Arguments
---------
scan_id: str, optional
Start from the scan with the specified id.
rt: float, optional
Start from the scan nearest to specified time (in minutes) in the run. If no
exact match is found, the nearest scan time will be found, rounded up.
index: int, optional
Start from the scan with the specified index.
require_ms1: bool, optional
Whether the iterator must start from an MS1 scan. True by default.
grouped: bool, optional
whether the iterator should yield scan bunches or single scans. True by default.
'''
if scan_id is not None:
scan_number = self.get_scan_by_id(scan_id).index
elif index is not None:
scan_number = int(index)
elif rt is not None:
scan_number = self.get_scan_by_time(rt).index
if require_ms1:
start_index = scan_number
while start_index != 0:
scan = self.get_scan_by_index(start_index)
if scan.ms_level > 1:
start_index -= 1
else:
break
scan_number = start_index
iterator = self._make_scan_index_producer(start_index=scan_number)
if grouped:
self._producer = self._scan_group_iterator(iterator)
else:
self._producer = self._single_scan_iterator(iterator)
return self
def _make_cache_key(self, scan):
return scan._data.index
def _single_scan_iterator(self, iterator=None, mode=None):
if iterator is None:
iterator = self._make_scan_index_producer()
for ix in iterator:
packed = self.get_scan_by_index(ix)
self._cache_scan(packed)
yield packed
def _scan_group_iterator(self, iterator=None, mode=None):
if iterator is None:
iterator = self._make_scan_index_producer()
precursor_scan = None
product_scans = []
current_level = 1
for ix in iterator:
packed = self.get_scan_by_index(ix)
self._cache_scan(packed)
if packed.ms_level > 1:
# inceasing ms level
if current_level < packed.ms_level:
current_level = packed.ms_level
# decreasing ms level
elif current_level > packed.ms_level:
current_level = packed.ms_level
product_scans.append(packed)
elif packed.ms_level == 1:
if current_level > 1 and precursor_scan is not None:
precursor_scan.product_scans = list(product_scans)
yield ScanBunch(precursor_scan, product_scans)
else:
if precursor_scan is not None:
precursor_scan.product_scans = list(product_scans)
yield ScanBunch(precursor_scan, product_scans)
precursor_scan = packed
product_scans = []
else:
raise Exception("This object is not able to handle MS levels higher than 2")
if precursor_scan is not None:
yield ScanBunch(precursor_scan, product_scans)
def next(self):
return next(self._producer)
| 23,204 | 7,202 |
import logging
import sys
from datetime import datetime
import requests
from tqdm import tqdm
import log
from datamodel.advisory import AdvisoryRecord
from datamodel.commit import Commit
from filtering.filter import filter_commits
from git.git import GIT_CACHE, Git
from git.version_to_tag import get_tag_for_version
from log.util import init_local_logger
# from processing.commit.feature_extractor import extract_features
from processing.commit.preprocessor import preprocess_commit
from ranking.rank import rank
from ranking.rules import apply_rules
# from util.profile import profile
from stats.execution import Counter, ExecutionTimer, execution_statistics
_logger = init_local_logger()
SECS_PER_DAY = 86400
TIME_LIMIT_BEFORE = 3 * 365 * SECS_PER_DAY
TIME_LIMIT_AFTER = 180 * SECS_PER_DAY
MAX_CANDIDATES = 1000
core_statistics = execution_statistics.sub_collection("core")
# @profile
def prospector( # noqa: C901
vulnerability_id: str,
repository_url: str,
publication_date: str = "",
vuln_descr: str = "",
tag_interval: str = "",
version_interval: str = "",
modified_files: "list[str]" = [],
code_tokens: "list[str]" = [],
time_limit_before: int = TIME_LIMIT_BEFORE,
time_limit_after: int = TIME_LIMIT_AFTER,
use_nvd: bool = False,
nvd_rest_endpoint: str = "",
backend_address: str = "",
git_cache: str = GIT_CACHE,
limit_candidates: int = MAX_CANDIDATES,
active_rules: "list[str]" = ["ALL"],
model_name: str = "",
) -> "list[Commit]":
_logger.info("begin main commit and CVE processing")
# -------------------------------------------------------------------------
# advisory record extraction
# -------------------------------------------------------------------------
advisory_record = AdvisoryRecord(
vulnerability_id=vulnerability_id,
repository_url=repository_url,
description=vuln_descr,
from_nvd=use_nvd,
nvd_rest_endpoint=nvd_rest_endpoint,
)
_logger.pretty_log(advisory_record)
advisory_record.analyze(use_nvd=use_nvd)
_logger.info(f"{advisory_record.code_tokens=}")
if publication_date != "":
advisory_record.published_timestamp = int(
datetime.strptime(publication_date, r"%Y-%m-%dT%H:%M%z").timestamp()
)
if len(code_tokens) > 0:
advisory_record.code_tokens += tuple(code_tokens)
# drop duplicates
advisory_record.code_tokens = list(set(advisory_record.code_tokens))
# FIXME this should be handled better (or '' should not end up in the modified_files in
# the first place)
if modified_files != [""]:
advisory_record.paths += modified_files
_logger.info(f"{advisory_record.code_tokens=}")
# print(advisory_record.paths)
# -------------------------------------------------------------------------
# retrieval of commit candidates
# -------------------------------------------------------------------------
with ExecutionTimer(
core_statistics.sub_collection(name="retrieval of commit candidates")
):
_logger.info(
"Downloading repository {} in {}..".format(repository_url, git_cache)
)
repository = Git(repository_url, git_cache)
repository.clone()
tags = repository.get_tags()
_logger.debug(f"Found tags: {tags}")
_logger.info("Done retrieving %s" % repository_url)
prev_tag = None
following_tag = None
if tag_interval != "":
prev_tag, following_tag = tag_interval.split(":")
elif version_interval != "":
vuln_version, fixed_version = version_interval.split(":")
prev_tag = get_tag_for_version(tags, vuln_version)[0]
following_tag = get_tag_for_version(tags, fixed_version)[0]
since = None
until = None
if advisory_record.published_timestamp:
since = advisory_record.published_timestamp - time_limit_before
until = advisory_record.published_timestamp + time_limit_after
candidates = repository.get_commits(
since=since,
until=until,
ancestors_of=following_tag,
exclude_ancestors_of=prev_tag,
filter_files="*.java",
)
_logger.info("Found %d candidates" % len(candidates))
# if some code_tokens were found in the advisory text, require
# that candidate commits touch some file whose path contains those tokens
# NOTE: this works quite well for Java, not sure how general this criterion is
# -------------------------------------------------------------------------
# commit filtering
#
# Here we apply additional criteria to discard commits from the initial
# set extracted from the repository
# # -------------------------------------------------------------------------
# if advisory_record.code_tokens != []:
# _logger.info(
# "Detected tokens in advisory text, searching for files whose path contains those tokens"
# )
# _logger.info(advisory_record.code_tokens)
# if modified_files == [""]:
# modified_files = advisory_record.code_tokens
# else:
# modified_files.extend(advisory_record.code_tokens)
# candidates = filter_by_changed_files(candidates, modified_files, repository)
with ExecutionTimer(core_statistics.sub_collection(name="commit filtering")):
candidates = filter_commits(candidates)
_logger.debug(f"Collected {len(candidates)} candidates")
if len(candidates) > limit_candidates:
_logger.error(
"Number of candidates exceeds %d, aborting." % limit_candidates
)
_logger.error(
"Possible cause: the backend might be unreachable or otherwise unable to provide details about the advisory."
)
sys.exit(-1)
# -------------------------------------------------------------------------
# commit preprocessing
# -------------------------------------------------------------------------
with ExecutionTimer(
core_statistics.sub_collection(name="commit preprocessing")
) as timer:
raw_commit_data = dict()
missing = []
try:
# Exploit the preprocessed commits already stored in the backend
# and only process those that are missing. Note: the endpoint
# does not exist (yet)
r = requests.get(
backend_address
+ "/commits/"
+ repository_url
+ "?commit_id="
+ ",".join(candidates)
)
_logger.info("The backend returned status '%d'" % r.status_code)
if r.status_code != 200:
_logger.error("This is weird...Continuing anyway.")
missing = candidates
else:
raw_commit_data = r.json()
_logger.info(
"Found {} preprocessed commits".format(len(raw_commit_data))
)
except requests.exceptions.ConnectionError:
_logger.error(
"Could not reach backend, is it running? The result of commit pre-processing will not be saved.",
exc_info=log.config.level < logging.WARNING,
)
missing = candidates
preprocessed_commits: "list[Commit]" = []
for idx, commit in enumerate(raw_commit_data):
if (
commit
): # None results are not in the DB, collect them to missing list, they need local preprocessing
preprocessed_commits.append(Commit.parse_obj(commit))
else:
missing.append(candidates[idx])
_logger.info("Preprocessing commits...")
first_missing = len(preprocessed_commits)
pbar = tqdm(missing)
with Counter(
timer.collection.sub_collection(name="commit preprocessing")
) as counter:
counter.initialize("preprocessed commits", unit="commit")
for commit_id in pbar:
counter.increment("preprocessed commits")
preprocessed_commits.append(
preprocess_commit(repository.get_commit(commit_id))
)
_logger.pretty_log(advisory_record)
_logger.debug(f"preprocessed {len(preprocessed_commits)} commits")
payload = [c.__dict__ for c in preprocessed_commits[first_missing:]]
# -------------------------------------------------------------------------
# save preprocessed commits to backend
# -------------------------------------------------------------------------
with ExecutionTimer(
core_statistics.sub_collection(name="save preprocessed commits to backend")
):
_logger.info("Sending preprocessing commits to backend...")
try:
r = requests.post(backend_address + "/commits/", json=payload)
_logger.info(
"Saving to backend completed (status code: %d)" % r.status_code
)
except requests.exceptions.ConnectionError:
_logger.error(
"Could not reach backend, is it running?"
"The result of commit pre-processing will not be saved."
"Continuing anyway.....",
exc_info=log.config.level < logging.WARNING,
)
# TODO compute actual rank
# This can be done by a POST request that creates a "search" job
# whose inputs are the AdvisoryRecord, and the repository URL
# The API returns immediately indicating a job id. From this
# id, a URL can be constructed to poll the results asynchronously.
# ranked_results = [repository.get_commit(c) for c in preprocessed_commits]
# -------------------------------------------------------------------------
# analyze candidates by applying rules and ML predictor
# -------------------------------------------------------------------------
with ExecutionTimer(
core_statistics.sub_collection(name="analyze candidates")
) as timer:
_logger.info("Extracting features from commits...")
# annotated_candidates = []
# with Counter(timer.collection.sub_collection("commit analysing")) as counter:
# counter.initialize("analyzed commits", unit="commit")
# # TODO remove "proactive" invocation of feature extraction
# for commit in tqdm(preprocessed_commits):
# counter.increment("analyzed commits")
# annotated_candidates.append(extract_features(commit, advisory_record))
annotated_candidates = apply_rules(
preprocessed_commits, advisory_record, active_rules=active_rules
)
annotated_candidates = rank(annotated_candidates, model_name=model_name)
return annotated_candidates, advisory_record
# def filter_by_changed_files(
# candidates: "list[str]", modified_files: "list[str]", git_repository: Git
# ) -> list:
# """
# Takes a list of commit ids in input and returns in output the list
# of ids of the commits that modify at least one path that contains one of the strings
# in "modified_files"
# """
# modified_files = [f.lower() for f in modified_files if f != ""]
# if len(modified_files) == 0:
# return candidates
# filtered_candidates = []
# if len(modified_files) != 0:
# for commit_id in candidates:
# commit_obj = git_repository.get_commit(commit_id)
# commit_changed_files = commit_obj.get_changed_files()
# for ccf in commit_changed_files:
# for f in modified_files:
# ccf = ccf.lower()
# if f in ccf:
# # if f in [e.lower() for e in ccf]:
# # print(f, commit_obj.get_id())
# filtered_candidates.append(commit_obj.get_id())
# return list(set(filtered_candidates))
| 12,080 | 3,288 |
print("This is the master branch") | 34 | 9 |
import os
import uuid
import StringIO
import ConfigParser
from pyethereum.utils import data_dir
from pyethereum.packeter import Packeter
from pyethereum.utils import sha3
def default_data_dir():
data_dir._set_default()
return data_dir.path
def default_config_path():
return os.path.join(default_data_dir(), 'config.txt')
def default_client_version():
return Packeter.CLIENT_VERSION # FIXME
def default_node_id():
return sha3(str(uuid.uuid1())).encode('hex')
config_template = \
"""
# NETWORK OPTIONS ###########
[network]
# Connect to remote host/port
# poc-6.ethdev.com:30300
remote_host = 207.12.89.101
remote_port = 30303
# Listen on the given host/port for incoming connections
listen_host = 0.0.0.0
listen_port = 30303
# Number of peer to connections to establish
num_peers = 10
# unique id of this node
node_id = {0}
# API OPTIONS ###########
[api]
# Serve the restful json api on the given host/port
listen_host = 0.0.0.0
listen_port = 30203
# path to server the api at
api_path = /api/v02a
# MISC OIPTIONS #########
[misc]
# Load database from path
data_dir = {1}
# percent cpu devoted to mining 0=off
mining = 30
# how verbose should the client be (1-3)
verbosity = 3
# set log level and filters (WARM, INFO, DEBUG)
# examples:
# get every log message from every module
# :DEBUG
# get every warning from every module
# :WARN
# get every message from module chainmanager and all warnings
# pyethereum.chainmanager:DEBUG,:WARN
logging = pyethereum.chainmanager:DEBUG,pyethereum.synchronizer:DEBUG,:INFO
# WALLET OPTIONS ##################
[wallet]
# Set the coinbase (mining payout) address
coinbase = 6c386a4b26f73c802f34673f7248bb118f97424a
""".format(default_node_id(), default_data_dir())
def get_default_config():
f = StringIO.StringIO()
f.write(config_template)
f.seek(0)
config = ConfigParser.ConfigParser()
config.readfp(f)
config.set('network', 'client_version', default_client_version())
return config
def read_config(cfg_path = default_config_path()):
# create default if not existent
if not os.path.exists(cfg_path):
open(cfg_path, 'w').write(config_template)
# extend on the default config
config = get_default_config()
config.read(cfg_path)
return config
def dump_config(config):
r = ['']
for section in config.sections():
for a,v in config.items(section):
r.append('[%s] %s = %r' %( section, a, v))
return '\n'.join(r)
| 2,507 | 913 |
from sqlmodel import SQLModel
from sb_backend.app.service.base.base_service import ServiceBase
from sb_backend.app.crud.setup.crud_noseriesline import CRUDBase, noseriesline
class ServiceBase(ServiceBase[CRUDBase, SQLModel, SQLModel]):
pass
noseriesline_s = ServiceBase(noseriesline)
| 290 | 93 |
# coding=utf-8
from selenium import webdriver
import time
driver = webdriver.PhantomJS(executable_path=r'E:\Documents\Apps\phantomjs-2.1.1-windows\bin\phantomjs.exe')
driver.get("https://movie.douban.com/typerank?type_name=剧情&type=11&interval_id=100:90&action=")
# 向下滚动10000像素
js = "document.body.scrollTop=10000"
#js="var q=document.documentElement.scrollTop=10000"
time.sleep(3)
#查看页面快照
driver.save_screenshot("29_js2a.png")
# 执行JS语句
driver.execute_script(js)
time.sleep(10)
#查看页面快照
driver.save_screenshot("29_js2b.png")
driver.quit() | 543 | 267 |
from sys import version_info
if version_info[:2] >= (3, 6):
from pony.orm.tests.py36_test_f_strings import * | 113 | 44 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
try:
from setuptools import setup, find_packages
except ImportError:
from distutils.core import setup
with open('README.md') as readme_file:
readme = readme_file.read()
install_requireent = []
setup_requires = [
'pandas',
'config2',
'notion'
]
install_requires = [
'pandas',
'config2',
'notion'
]
setup(
name='notion-as-db',
author='Junsang Park',
author_email='publichey@gmail.com',
url='https://github.com/hoosiki/notion-as-db.git',
version='0.0.1',
long_description=readme,
long_description_content_type="text/markdown",
description='Package for convenient Notion usage. Once you set default.yaml file, You don\'t need to find v2token.',
packages=find_packages(),
license='BSD',
include_package_date=False,
setup_requires=setup_requires,
install_requires=install_requires,
download_url='https://github.com/hoosiki/notion-as-db/blob/master/dist/notion-as-db-0.0.1.tar.gz'
)
| 1,042 | 374 |
import sys
from collections import defaultdict
sys.stdin.readline()
my_results = defaultdict(int)
def add_contact(contact):
for index, _ in enumerate(contact):
my_contact = contact[0:index]
my_results[my_contact] +=1
for line in sys.stdin.readlines():
operation, contact = line.strip().split(' ')
if operation == 'add':
map(add_contact, contact)
else:
print(my_results[contact])
| 435 | 137 |
import copy
from troposphere import (
Ref, FindInMap, Not, Equals, And, Condition, Join, ec2, autoscaling,
If, GetAtt, Output
)
from troposphere import elasticloadbalancing as elb
from troposphere.autoscaling import Tag as ASTag
from troposphere.route53 import RecordSetType
from stacker.blueprints.base import Blueprint
from stacker.blueprints.variables.types import TroposphereType
from stacker.blueprints.variables.types import (
CFNCommaDelimitedList,
CFNNumber,
CFNString,
EC2VPCId,
EC2KeyPairKeyName,
EC2SecurityGroupId,
EC2SubnetIdList,
)
CLUSTER_SG_NAME = "%sSG"
ELB_SG_NAME = "%sElbSG"
ELB_NAME = "%sLoadBalancer"
class AutoscalingGroup(Blueprint):
VARIABLES = {
'VpcId': {'type': EC2VPCId, 'description': 'Vpc Id'},
'DefaultSG': {'type': EC2SecurityGroupId,
'description': 'Top level security group.'},
'BaseDomain': {
'type': CFNString,
'default': '',
'description': 'Base domain for the stack.'},
'PrivateSubnets': {'type': EC2SubnetIdList,
'description': 'Subnets to deploy private '
'instances in.'},
'PublicSubnets': {'type': EC2SubnetIdList,
'description': 'Subnets to deploy public (elb) '
'instances in.'},
'AvailabilityZones': {'type': CFNCommaDelimitedList,
'description': 'Availability Zones to deploy '
'instances in.'},
'InstanceType': {'type': CFNString,
'description': 'EC2 Instance Type',
'default': 'm3.medium'},
'MinSize': {'type': CFNNumber,
'description': 'Minimum # of instances.',
'default': '1'},
'MaxSize': {'type': CFNNumber,
'description': 'Maximum # of instances.',
'default': '5'},
'SshKeyName': {'type': EC2KeyPairKeyName},
'ImageName': {
'type': CFNString,
'description': 'The image name to use from the AMIMap (usually '
'found in the config file.)'},
'ELBHostName': {
'type': CFNString,
'description': 'A hostname to give to the ELB. If not given '
'no ELB will be created.',
'default': ''},
'ELBCertName': {
'type': CFNString,
'description': 'The SSL certificate name to use on the ELB.',
'default': ''},
'ELBCertType': {
'type': CFNString,
'description': 'The SSL certificate type to use on the ELB.',
'default': ''},
}
def create_conditions(self):
self.template.add_condition(
"CreateELB",
Not(Equals(Ref("ELBHostName"), "")))
self.template.add_condition(
"SetupDNS",
Not(Equals(Ref("BaseDomain"), "")))
self.template.add_condition(
"UseSSL",
Not(Equals(Ref("ELBCertName"), "")))
self.template.add_condition(
"CreateSSLELB",
And(Condition("CreateELB"), Condition("UseSSL")))
self.template.add_condition(
"SetupELBDNS",
And(Condition("CreateELB"), Condition("SetupDNS")))
self.template.add_condition(
"UseIAMCert",
Not(Equals(Ref("ELBCertType"), "acm")))
def create_security_groups(self):
t = self.template
asg_sg = CLUSTER_SG_NAME % self.name
elb_sg = ELB_SG_NAME % self.name
t.add_resource(ec2.SecurityGroup(
asg_sg,
GroupDescription=asg_sg,
VpcId=Ref("VpcId")))
# ELB Security group, if ELB is used
t.add_resource(
ec2.SecurityGroup(
elb_sg,
GroupDescription=elb_sg,
VpcId=Ref("VpcId"),
Condition="CreateELB"))
# Add SG rules here
# Allow ELB to connect to ASG on port 80
t.add_resource(ec2.SecurityGroupIngress(
"%sElbToASGPort80" % self.name,
IpProtocol="tcp", FromPort="80", ToPort="80",
SourceSecurityGroupId=Ref(elb_sg),
GroupId=Ref(asg_sg),
Condition="CreateELB"))
# Allow Internet to connect to ELB on port 80
t.add_resource(ec2.SecurityGroupIngress(
"InternetTo%sElbPort80" % self.name,
IpProtocol="tcp", FromPort="80", ToPort="80",
CidrIp="0.0.0.0/0",
GroupId=Ref(elb_sg),
Condition="CreateELB"))
t.add_resource(ec2.SecurityGroupIngress(
"InternetTo%sElbPort443" % self.name,
IpProtocol="tcp", FromPort="443", ToPort="443",
CidrIp="0.0.0.0/0",
GroupId=Ref(elb_sg),
Condition="CreateSSLELB"))
def setup_listeners(self):
no_ssl = [elb.Listener(
LoadBalancerPort=80,
Protocol='HTTP',
InstancePort=80,
InstanceProtocol='HTTP'
)]
# Choose proper certificate source
acm_cert = Join("", [
"arn:aws:acm:", Ref("AWS::Region"), ":", Ref("AWS::AccountId"),
":certificate/", Ref("ELBCertName")])
iam_cert = Join("", [
"arn:aws:iam::", Ref("AWS::AccountId"), ":server-certificate/",
Ref("ELBCertName")])
cert_id = If("UseIAMCert", iam_cert, acm_cert)
with_ssl = copy.deepcopy(no_ssl)
with_ssl.append(elb.Listener(
LoadBalancerPort=443,
InstancePort=80,
Protocol='HTTPS',
InstanceProtocol="HTTP",
SSLCertificateId=cert_id))
listeners = If("UseSSL", with_ssl, no_ssl)
return listeners
def create_load_balancer(self):
t = self.template
elb_name = ELB_NAME % self.name
elb_sg = ELB_SG_NAME % self.name
t.add_resource(elb.LoadBalancer(
elb_name,
HealthCheck=elb.HealthCheck(
Target='HTTP:80/',
HealthyThreshold=3,
UnhealthyThreshold=3,
Interval=5,
Timeout=3),
Listeners=self.setup_listeners(),
SecurityGroups=[Ref(elb_sg), ],
Subnets=Ref("PublicSubnets"),
Condition="CreateELB"))
# Setup ELB DNS
t.add_resource(
RecordSetType(
'%sDnsRecord' % elb_name,
# Appends a '.' to the end of the domain
HostedZoneName=Join("", [Ref("BaseDomain"), "."]),
Comment='Router ELB DNS',
Name=Join('.', [Ref("ELBHostName"), Ref("BaseDomain")]),
Type='CNAME',
TTL='120',
ResourceRecords=[
GetAtt(elb_name, 'DNSName')],
Condition="SetupELBDNS"))
def get_launch_configuration_parameters(self):
return {
'ImageId': FindInMap('AmiMap', Ref("AWS::Region"),
Ref('ImageName')),
'InstanceType': Ref("InstanceType"),
'KeyName': Ref("SshKeyName"),
'SecurityGroups': self.get_launch_configuration_security_groups(),
}
def get_autoscaling_group_parameters(self, launch_config_name, elb_name):
return {
'AvailabilityZones': Ref("AvailabilityZones"),
'LaunchConfigurationName': Ref(launch_config_name),
'MinSize': Ref("MinSize"),
'MaxSize': Ref("MaxSize"),
'VPCZoneIdentifier': Ref("PrivateSubnets"),
'LoadBalancerNames': If("CreateELB", [Ref(elb_name), ], []),
'Tags': [ASTag('Name', self.name, True)],
}
def get_launch_configuration_security_groups(self):
sg_name = CLUSTER_SG_NAME % self.name
return [Ref("DefaultSG"), Ref(sg_name)]
def create_autoscaling_group(self):
name = "%sASG" % self.name
launch_config = "%sLaunchConfig" % name
elb_name = ELB_NAME % self.name
t = self.template
t.add_resource(autoscaling.LaunchConfiguration(
launch_config,
**self.get_launch_configuration_parameters()
))
t.add_resource(autoscaling.AutoScalingGroup(
name,
**self.get_autoscaling_group_parameters(launch_config, elb_name)
))
def create_template(self):
self.create_conditions()
self.create_security_groups()
self.create_load_balancer()
self.create_autoscaling_group()
class FlexibleAutoScalingGroup(Blueprint):
""" A more flexible AutoscalingGroup Blueprint.
Uses TroposphereTypes to make creating AutoscalingGroups and their
associated LaunchConfiguration more flexible. This comes at the price of
doing less for you.
"""
VARIABLES = {
"LaunchConfiguration": {
"type": TroposphereType(autoscaling.LaunchConfiguration),
"description": "The LaunchConfiguration for the autoscaling "
"group.",
},
"AutoScalingGroup": {
"type": TroposphereType(autoscaling.AutoScalingGroup),
"description": "The Autoscaling definition. Do not provide a "
"LaunchConfiguration parameter, that will be "
"automatically added from the LaunchConfiguration "
"Variable.",
},
}
def create_launch_configuration(self):
t = self.template
variables = self.get_variables()
self.launch_config = t.add_resource(variables["LaunchConfiguration"])
t.add_output(
Output("LaunchConfiguration", Value=self.launch_config.Ref())
)
def add_launch_config_variable(self, asg):
if getattr(asg, "LaunchConfigurationName", False):
raise ValueError("Do not provide a LaunchConfigurationName "
"variable for the AutoScalingGroup config.")
asg.LaunchConfigurationName = self.launch_config.Ref()
return asg
def create_autoscaling_group(self):
t = self.template
variables = self.get_variables()
asg = variables["AutoScalingGroup"]
asg = self.add_launch_config_variable(asg)
t.add_resource(asg)
t.add_output(Output("AutoScalingGroup", Value=asg.Ref()))
def create_template(self):
self.create_launch_configuration()
self.create_autoscaling_group()
| 10,658 | 3,119 |
# Generated by Django 2.1.1 on 2019-05-24 00:55
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('input', '0015_auto_20190524_0052'),
]
operations = [
migrations.AlterField(
model_name='post',
name='ctime',
field=models.DateTimeField(auto_now=True),
),
]
| 403 | 158 |
# Copyright (c) 2017 Wladimir J. van der Laan
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
import opcodes
####### Basic blocks ########
class BasicBlock:
'''Basic block of instructions'''
def __init__(self, addr):
self.addr = addr # Starting address
self.instructions = [] # Instructions in basic block
self.pred = [] # Predecessors
self.succ = [] # Successors
# Filled in by dataflow analysis:
# self.ins
# self.outs
def __repr__(self):
return 'bb@0x%04x' % self.addr
class BasicBlocks:
'''Container for basic blocks'''
def __init__(self, root, blocks):
self.root = root # Entry block
self.blocks = blocks # Array of blocks, in code order
class BBPassInfo:
'''
Metadata for pass 1
'''
def __init__(self):
self.incoming = [] # jump target: first of basic block
self.bbend = False # last instruction in basic block
self.bb = None # back-reference to bb
def find_basic_blocks(proc, dseg, proclist, debug=False):
'''
Perform control flow analysis on a procedure to find basic blocks.
'''
if proc.is_native: # Cannot do native code
return
seg = dseg.info
# create mapping from address to index
inst_by_addr = {inst.addr:inst for inst in proc.instructions}
# Add temporary information to instructions
for inst in proc.instructions:
inst.data = BBPassInfo()
# pass 1: mark jump targets and ends of basic blocks
for inst in proc.instructions:
op = opcodes.OPCODES[inst.opcode]
if inst.opcode == opcodes.RPU:
inst.data.bbend = True
break # full stop
elif op[2] & opcodes.CFLOW:
for tgt in inst.get_flow_targets(dseg):
inst_by_addr[tgt].data.incoming.append(inst)
inst.data.bbend = True
# create list of basic blocks
blocks = []
block = None
prev = None
for inst in proc.instructions:
if block is None or inst.data.incoming or (prev is not None and prev.data.bbend):
if prev is not None and not prev.data.bbend:
# Last instruction was not a bb-ending instruction, add an incoming edge
# from previous instruciton.
inst.data.incoming.append(prev)
# Start new basic block
block = BasicBlock(inst.addr)
blocks.append(block)
block.instructions.append(inst)
prev = inst
# pass 2: cross-reference basic blocks
for bb in blocks:
for inst in bb.instructions:
inst.data.bb = bb
# set successors and predecessors
for bb in blocks:
for src in bb.instructions[0].data.incoming:
bb.pred.append(src.data.bb)
src.data.bb.succ.append(bb)
# clean up
for inst in proc.instructions:
del inst.data
return BasicBlocks(blocks[0], blocks)
| 3,038 | 907 |
import hail as hl
def prepare_exac_regional_missense_constraint(path):
ds = hl.import_table(
path,
missing="",
types={
"transcript": hl.tstr,
"gene": hl.tstr,
"chr": hl.tstr,
"amino_acids": hl.tstr,
"genomic_start": hl.tint,
"genomic_end": hl.tint,
"obs_mis": hl.tfloat,
"exp_mis": hl.tfloat,
"obs_exp": hl.tfloat,
"chisq_diff_null": hl.tfloat,
"region_name": hl.tstr,
},
)
ds = ds.annotate(obs_mis=hl.int(ds.obs_mis))
ds = ds.annotate(start=hl.min(ds.genomic_start, ds.genomic_end), stop=hl.max(ds.genomic_start, ds.genomic_end))
ds = ds.drop("amino_acids", "chr", "gene", "genomic_start", "genomic_end", "region_name")
ds = ds.transmute(transcript_id=ds.transcript.split("\\.")[0])
ds = ds.group_by("transcript_id").aggregate(regions=hl.agg.collect(ds.row_value))
ds = ds.annotate(regions=hl.sorted(ds.regions, lambda region: region.start))
ds = ds.select(exac_regional_missense_constraint_regions=ds.regions)
return ds
| 1,136 | 441 |
import os
import logging
import requests
import ambari.api as api
from utils.utils import logmethodcall
class RangerRequestError(Exception):
pass
class Ranger:
def __init__(self, request_timeout=10):
self.timeout = request_timeout
self.ranger_schema = os.environ.get('RANGER_SCHEMA', 'http')
self.ranger_host = os.environ.get('RANGER_HOST', 'sandbox.hortonworks.com')
self.ranger_port = os.environ.get('RANGER_PORT', 6080)
logging.basicConfig(level=logging.DEBUG,
format='{asctime} ({levelname}) {funcName}(): {message}',
style="{",
filename='ranger.log')
self.logger = logging.getLogger(__name__)
@logmethodcall
def get_ranger_url(self):
return '{0}://{1}:{2}/'.format(self.ranger_schema, self.ranger_host, self.ranger_port)
@logmethodcall
def is_ranger_online(self):
try:
requests.get(self.get_ranger_url(), timeout=self.timeout)
return True
except:
return False
@logmethodcall
def stop_ranger_admin(self):
ambari = api.Api(logger=self.logger)
ranger_admin_ambari_info = ambari.get_component_info('RANGER', 'RANGER_ADMIN')
rnd_ranger_host, rnd_ranger_component = ambari.get_random_host_and_component_path(ranger_admin_ambari_info)
self.logger.info("Selected random Ranger admin host for stopping: {0}, {1}"
.format(rnd_ranger_host, rnd_ranger_component))
ambari.change_host_component_state_and_wait(rnd_ranger_component, state='INSTALLED')
@logmethodcall
def check_ranger_status(self):
ranger_url = '{0}://{1}:{2}/'.format(self.ranger_schema, self.ranger_host, self.ranger_port)
self.logger.debug(ranger_url)
response = requests.get(ranger_url, timeout=self.timeout)
self.verify_ranger_response(response)
@logmethodcall
def verify_ranger_response(self, response):
if response.status_code != 200:
self.logger.error(
"RangerResponse returned with error status [{0}], response was: {1}".format(response.status_code,
response.text))
raise RangerRequestError("RangerResponse returned with error status [{0}]".format(response.status_code))
| 2,426 | 740 |
import unittest
from dominion_game_engine.card_util import *
from dominion_game_engine.cards import *
from dominion_game_engine.player_state import PlayerState
class TestPlayerState(unittest.TestCase):
def setUp(self):
card_types = get_card_types().values()
piles = setup_piles(card_types, 4)
self.player_state = PlayerState('tester1', piles)
def test_initialize_draw_deck(self):
"""
√ Create new player state
√ Check if the player has exactly 7 coppers and 3 estates in their Draw Deck
"""
self.player_state.draw_deck.cards += self.player_state.hand
self.player_state.hand = []
num_coppers = sum(1 if type(c) == Copper else 0 for c in self.player_state.draw_deck.cards)
num_estates = sum(1 if type(c) == Estate else 0 for c in self.player_state.draw_deck.cards)
self.assertEqual(num_coppers, 7)
self.assertEqual(num_estates, 3)
def test_draw_cards(self):
"""
draw 0 cards
draw 1 card with no cards in deck or discard
draw multiple cards with no cards in deck or discard
draw 1 card with no cards in deck and 1 card in discard
draw multiple cards with no cards in deck and 1 card in discard
draw 1 card with 1 card in deck and multiple cards in discard
draw multiple cards with 1 card in deck and multiple cards in discard
draw 1 card with multiple cards in deck
draw multiple cards with multiple cards in deck
"""
# draw 0 cards
copper = Copper()
silver = Silver()
gold = Gold()
estate = Estate()
self.player_state.hand = [copper]
self.player_state.draw_deck.cards = [silver]
self.player_state.discard_pile.cards = []
self.player_state.draw_cards(0)
expected = [copper]
self.assertEqual(self.player_state.hand, expected)
self.assertEqual(self.player_state.draw_deck.cards, [silver])
# draw 1 card with no cards in deck or discard
self.player_state.hand = [copper]
self.player_state.draw_deck.cards = []
expected = [copper]
self.player_state.draw_cards(1)
self.assertEqual(self.player_state.hand, expected)
expected = [copper]
self.player_state.draw_cards(3)
self.assertEqual(self.player_state.hand, expected)
# draw 1 card with no cards in deck and 1 card in discard
self.player_state.hand = [copper]
self.player_state.draw_deck.cards = []
self.player_state.discard_pile.cards = [silver]
self.player_state.draw_cards(1)
expected = [copper, silver]
self.assertEqual(self.player_state.hand, expected)
# draw multiple cards with no cards in deck and 1 card in discard
self.player_state.hand = [copper]
self.player_state.draw_deck.cards = []
self.player_state.discard_pile.cards = [silver]
expected = [copper, silver]
self.player_state.draw_cards(3)
self.assertEqual(self.player_state.hand, expected)
# draw 1 card with 1 card in deck and multiple cards in discard
self.player_state.hand = [copper]
self.player_state.draw_deck.cards = [silver]
self.player_state.discard_pile.cards = [gold, estate]
self.player_state.draw_cards(1)
expected = [copper, silver]
self.assertEqual(self.player_state.hand, expected)
# draw multiple cards with 1 card in deck and multiple cards in discard
self.player_state.hand = [copper]
self.player_state.draw_deck.cards = [silver]
self.player_state.draw_cards(3)
expected = as_dict([copper, silver, gold, estate])
self.assertEqual(as_dict(self.player_state.hand), expected)
# draw 1 card with multiple cards in deck
self.player_state.hand = [copper]
self.player_state.draw_deck.cards = [silver, gold, estate]
self.player_state.discard_pile.cards = []
self.player_state.draw_cards(1)
expected = [copper, silver]
self.assertEqual(self.player_state.hand, expected)
# draw multiple cards with multiple cards in deck
self.player_state.hand = [copper]
self.player_state.draw_deck.cards = [silver, gold, estate]
self.player_state.discard_pile.cards = []
self.player_state.draw_cards(3)
expected = [copper, silver, gold, estate]
self.assertEqual(self.player_state.hand, expected)
if __name__ == '__main__':
unittest.main()
| 4,567 | 1,394 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup
setup(name='mercury',
version='1.12.9',
description='Python Language pack for Mercury',
author='Eric Law',
author_email='eric.law@accenture.com',
url='https://github.com/Accenture/mercury-python',
project_urls={
'Parent': 'https://github.com/Accenture/mercury'
},
packages=['mercury', 'mercury.system', 'mercury.resources'],
license='Apache 2.0',
python_requires='>=3.6.7',
install_requires=['aiohttp', 'msgpack-python'],
classifiers=[
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Intended Audience :: Developers',
'License :: OSI Approved :: Apache Software License'
]
)
| 923 | 297 |
# -*- coding: utf-8 -*-
# Copyright 2013 Mirantis, 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.
from nailgun import objects
def notify(topic, message, cluster_id=None, node_id=None, task_uuid=None):
objects.Notification.create({
"topic": topic,
"message": message,
"cluster_id": cluster_id,
"node_id": node_id,
"task_uuid": task_uuid
})
| 926 | 287 |
import numpy as np
score_stack = np.array([
[ 0, 0, 0, 0, 0, 0, 0, 0, ],
[ 0, -240, -330, -210, -140, -210, -210, -140, ],
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[ 0, -140, -150, 130, 30, -60, -90, 130, ],
], dtype=np.float32) / -100.
score_mismatch_hairpin = np.array([
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[
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],
], dtype=np.float32) / -100.
score_mismatch_internal = np.array([
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[
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[
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[
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[
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[ 70, -30, 70, -30, 70, ],
[ 70, 70, 70, 70, 10, ],
],
], dtype=np.float32) / -100.
score_mismatch_internal_1n = np.array([
[
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[
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[
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],
[
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],
[
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[ 70, 70, 70, 70, 70, ],
[ 70, 70, 70, 70, 70, ],
],
], dtype=np.float32) / -100.
score_mismatch_internal_23 = np.array([
[
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[
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],
[
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],
[
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],
[
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],
[
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[ 70, 70, 70, 70, 70, ],
[ 70, -40, 70, 0, 70, ],
[ 70, 70, 70, 70, 40, ],
],
], dtype=np.float32) / -100.
score_mismatch_multi = np.array([
[
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[ 0, 0, 0, 0, 0, ],
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[ 0, 0, 0, 0, 0, ],
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],
[
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],
[
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[ -110, -150, -110, -150, -140, ],
[ -100, -140, -100, -160, -100, ],
[ -80, -150, -80, -150, -120, ],
],
[
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[ -60, -80, -60, -80, -60, ],
[ -70, -110, -70, -80, -70, ],
[ -50, -80, -50, -80, -50, ],
],
[
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[ -60, -80, -60, -80, -60, ],
[ -60, -100, -70, -100, -60, ],
],
[
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],
[
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[ -70, -100, -70, -100, -80, ],
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],
[
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[ -60, -80, -60, -80, -60, ],
[ -50, -80, -50, -80, -50, ],
],
], dtype=np.float32) / -100.
score_mismatch_external = np.array([
[
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[ -50, -150, -50, -150, -70, ],
],
[
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[ -100, -140, -100, -160, -100, ],
[ -80, -150, -80, -150, -120, ],
],
[
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[ -60, -80, -60, -80, -60, ],
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[ -50, -80, -50, -80, -50, ],
],
[
[ -30, -30, -60, -60, -60, ],
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[ -70, -100, -70, -100, -80, ],
[ -60, -80, -60, -80, -60, ],
[ -60, -100, -70, -100, -60, ],
],
[
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[ -60, -80, -60, -80, -60, ],
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[ -50, -80, -50, -80, -50, ],
],
[
[ -60, -80, -60, -80, -60, ],
[ -60, -80, -60, -80, -60, ],
[ -70, -100, -70, -100, -80, ],
[ -60, -80, -60, -80, -60, ],
[ -70, -100, -70, -100, -80, ],
],
[
[ -30, -30, -50, -50, -50, ],
[ -30, -30, -60, -50, -60, ],
[ -60, -80, -60, -80, -60, ],
[ -60, -80, -60, -80, -60, ],
[ -50, -80, -50, -80, -50, ],
],
], dtype=np.float32) / -100.
score_dangle5 = np.array([
[0, 0, 0, 0, 0, ],
[-10, -50, -30, -20, -10, ],
[0, -20, -30, 0, 0, ],
[-20, -30, -30, -40, -20, ],
[-10, -30, -10, -20, -20, ],
[-20, -30, -30, -40, -20, ],
[-10, -30, -10, -20, -20, ],
[0, -20, -10, 0, 0, ],
], dtype=np.float32) / -100.
score_dangle3 = np.array([
[0, 0, 0, 0, 0, ],
[-40, -110, -40, -130, -60, ],
[-80, -170, -80, -170, -120, ],
[-10, -70, -10, -70, -10, ],
[-50, -80, -50, -80, -60, ],
[-10, -70, -10, -70, -10, ],
[-50, -80, -50, -80, -60, ],
[-10, -70, -10, -70, -10, ],
], dtype=np.float32) / -100.
score_int11 = np.array([
[
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], dtype=np.float32) / -100.
score_hairpin = np.array([np.inf, np.inf, np.inf, 540, 560, 570, 540, 600, 550, 640, 650, 660, 670, 680, 690, 690, 700, 710, 710, 720, 720, 730, 730, 740, 740, 750, 750, 750, 760, 760, 770, ], dtype=np.float32) / -100.
score_bulge = np.array([np.inf, 380, 280, 320, 360, 400, 440, 460, 470, 480, 490, 500, 510, 520, 530, 540, 540, 550, 550, 560, 570, 570, 580, 580, 580, 590, 590, 600, 600, 600, 610, ], dtype=np.float32) / -100.
score_internal = np.array([np.inf, np.inf, 100, 100, 110, 200, 200, 210, 230, 240, 250, 260, 270, 280, 290, 290, 300, 310, 310, 320, 330, 330, 340, 340, 350, 350, 350, 360, 360, 370, 370, ], dtype=np.float32) / -100.
score_ml_base = np.array([0], dtype=np.float32) / -100.
score_ml_closing = np.array([930], dtype=np.float32) / -100.
score_ml_intern = np.array([-90], dtype=np.float32) / -100.
score_ninio = np.array([60], dtype=np.float32) / -100.
score_max_ninio = np.array([300], dtype=np.float32) / -100.
score_duplex_init = np.array([410], dtype=np.float32) / -100.
score_terminalAU = np.array([50], dtype=np.float32) / -100.
score_lxc = np.array([107.856], dtype=np.float32) / -100.
| 316,484 | 203,431 |
from __future__ import unicode_literals
import io
from ansible.module_utils import six
try:
from ansible.plugins import callback_loader
except ImportError:
from ansible.plugins.loader import callback_loader
def printi(tio, obj, key=None, indent=0):
def write(s, *args):
if args:
s %= args
tio.write(' ' * indent)
if key is not None:
tio.write('%s: ' % (key,))
tio.write(s)
tio.write('\n')
if isinstance(obj, (list, tuple)):
write('[')
for i, obj2 in enumerate(obj):
printi(tio, obj2, key=i, indent=indent+1)
key = None
write(']')
elif isinstance(obj, dict):
write('{')
for key2, obj2 in sorted(six.iteritems(obj)):
if not (key2.startswith('_ansible_') or
key2.endswith('_lines')):
printi(tio, obj2, key=key2, indent=indent+1)
key = None
write('}')
elif isinstance(obj, six.text_type):
for line in obj.splitlines():
write('%s', line.rstrip('\r\n'))
elif isinstance(obj, six.binary_type):
obj = obj.decode('utf-8', 'replace')
for line in obj.splitlines():
write('%s', line.rstrip('\r\n'))
else:
write('%r', obj)
DefaultModule = callback_loader.get('default', class_only=True)
class CallbackModule(DefaultModule):
def _dump_results(self, result, *args, **kwargs):
try:
tio = io.StringIO()
printi(tio, result)
return tio.getvalue() #.encode('ascii', 'replace')
except:
import traceback
traceback.print_exc()
raise
| 1,687 | 536 |
import os
import numpy as np
import csv
import matplotlib.pyplot as plt
from moviepy.editor import *
from matplotlib.image import imsave
import matplotlib
matplotlib.use('Agg')
# import tensorflow as tf
# from stable_baselines.common.callbacks import BaseCallback, EvalCallback
# from stable_baselines.common.vec_env import DummyVecEnv
class MonitorCallback(EvalCallback):
"""
Callback for saving a model (the check is done every ``check_freq`` steps)
based on the training reward (in practice, we recommend using ``EvalCallback``).
:param check_freq: (int)
:param log_dir: (str) Path to the folder where the model will be saved.
It must contains the file created by the ``Monitor`` wrapper.
:param verbose: (int)
"""
def __init__(self, eval_env, check_freq: int, save_example_freq: int, log_dir: str,sacred=None, n_eval_episodes=5, render=False, verbose=1):
super(MonitorCallback, self).__init__(verbose=verbose,
eval_env=eval_env,
best_model_save_path=log_dir,
log_path=log_dir,
eval_freq=check_freq,
n_eval_episodes=n_eval_episodes,
deterministic=False,
render=render)
self.render = render
self.verbose = verbose
self.env = eval_env
self.check_freq = check_freq
self.save_example_freq = save_example_freq
self.log_dir = log_dir
self.save_path = os.path.join(log_dir, 'best_model')
self.best_mean_reward = -np.inf
self.sacred = sacred
self.sequence = False
if self.env.__class__.__name__ in ['DarSeqEnv','DummyVecEnv'] :
self.sequence = True
self.statistics = {
'step_reward': [],
'reward': [],
'std_reward': [],
'duration': [],
'GAP': [],
'GAP*': [],
'fit_solution': [],
'delivered': []
# 'policy_loss': [],
# 'value_loss': [],
# 'policy_entropy': []
}
def _init_callback(self) -> None:
# Create folder if needed
if self.log_dir is not None:
os.makedirs(self.log_dir, exist_ok=True)
def _on_training_start(self) -> None:
"""
This method is called before the first rollout starts.
"""
pass
def _on_training_end(self) -> None:
"""
This event is triggered before exiting the `learn()` method.
"""
pass
def plot_statistics(self, show=False):
# Print them
if self.verbose:
print('\t ->[Epoch %d]<- mean episodic reward: %.3f' % (self.num_timesteps + 1, self.statistics['reward'][-1]))
print('\t * Mean duration : %0.3f' % (self.statistics['duration'][-1]))
print('\t * Mean std_reward : %0.3f' % (self.statistics['std_reward'][-1]))
print('\t * Mean step_reward : %0.3f' % (self.statistics['step_reward'][-1]))
# print('\t ** policy_loss : %0.3f' % (self.statistics['policy_loss'][-1]))
# print('\t ** value_loss : %0.3f' % (self.statistics['value_loss'][-1]))
# print('\t ** policy_entropy : %0.3f' % (self.statistics['policy_entropy'][-1]))
# Create plot of the statiscs, saved in folder
colors = [plt.cm.tab20(0),plt.cm.tab20(1),plt.cm.tab20c(2),
plt.cm.tab20c(3), plt.cm.tab20c(4),
plt.cm.tab20c(5),plt.cm.tab20c(6),plt.cm.tab20c(7)]
fig, (axis) = plt.subplots(1, len(self.statistics), figsize=(20, 10))
fig.suptitle(' - PPO Training: ' + self.log_dir)
for i, key in enumerate(self.statistics):
# Sacred (The one thing to keep here)
if self.sacred :
self.sacred.get_logger().report_scalar(title='Train stats',
series=key, value=self.statistics[key][-1], iteration=self.num_timesteps)
# self.sacred.log_scalar(key, self.statistics[key][-1], len(self.statistics[key]))
axis[i].plot(self.statistics[key], color=colors[i])
axis[i].set_title(' Plot of ' + key)
if show :
fig.show()
fig.savefig(self.log_dir + '/result_figure.jpg')
fig.clf()
plt.close(fig)
# Save the statistics as CSV file
if not self.sacred:
try:
with open(self.log_dir + '/statistics.csv', 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=self.statistics.keys())
writer.writeheader()
# for key in statistics
writer.writerow(self.statistics)
except IOError:
print("I/O error")
def save_image_batch(self, images, rewards, txt='test'):
''' Saving some examples of input -> output to see how the model behave '''
print(' - Saving some examples - ')
number_i = min(len(images), 50)
plt.figure()
fig, axis = plt.subplots(number_i, 2, figsize=(10, 50)) #2 rows for input, output
fig.tight_layout()
fig.suptitle(' - examples of network - ')
for i in range(min(self.batch_size, number_i)):
input_map = indices2image(data[0][i], self.image_size)
axis[i, 0].imshow(input_map)
im = indice_map2image(outputs[i], self.image_size).cpu().numpy()
normalized = (im - im.min() ) / (im.max() - im.min())
axis[i, 1].imshow(normalized)
img_name = self.path_name + '/example_' + str(self.num_timesteps) + '.png'
plt.savefig(img_name)
plt.close()
if self.sacred :
self.sacred.add_artifact(img_name, content_type='image')
def save_example(self, observations, rewards, number):
noms = []
dir = self.log_dir + '/example/' + str(self.num_timesteps) + '/ex_number' + str(number)
if dir is not None:
os.makedirs(dir, exist_ok=True)
for i, obs in enumerate(observations):
save_name = dir + '/' + str(i) + '_r=' + str(rewards[i]) + '.png' #[np.array(img) for i, img in enumerate(images)
if self.env.__class__.__name__ == 'DummyVecEnv':
image = self.norm_image(obs[0], scale=1)
else :
image = self.norm_image(obs, scale=1)
# print('SHae after image', np.shape(image))
imsave(save_name, image)
noms.append(save_name)
# Save the imges as video
video_name = dir + 'r=' + str(np.sum(rewards)) + '.mp4'
clips = [ImageClip(m).set_duration(0.2)
for m in noms]
concat_clip = concatenate_videoclips(clips, method="compose")
concat_clip.write_videofile(video_name, fps=24, verbose=None, logger=None)
if self.sacred :
self.sacred.get_logger().report_media('video', 'Res_' + str(number) + '_Rwd=' + str(np.sum(rewards)),
iteration=self.num_timesteps // self.check_freq,
local_path=video_name)
del concat_clip
del clips
def norm_image(self, image, type=None, scale=10):
image = np.kron(image, np.ones((scale, scale)))
if type=='rgb':
ret = np.empty((image.shape[0], image.shape[0], 3), dtype=np.uint8)
ret[:, :, 0] = image.copy()
ret[:, :, 1] = image.copy()
ret[:, :, 2] = image.copy()
image = ret.copy()
return (255 * (image - np.min(image)) / (np.max(image) - np.min(image))).astype(np.uint8)
def save_gif(self, observations, rewards):
# print(observations)
# print(rewards)
# length = min(len(observations), 10)
# observations = 255 * ((np.array(observations) + 1) / (np.max(observations) + 1)).astype(np.uint8)
save_name = self.log_dir + '/example' + str(self.num_timesteps) + '.gif'
images = [self.norm_image(observations[i]) for i in range(len(observations)) if rewards[i] >= 0] #[np.array(img) for i, img in enumerate(images)]
# imageio.mimsave(save_name, images, fps=1)
if self.sacred :
self.sacred.get_logger().report_media('GIF', 'isgif', iteration=self.num_timesteps, local_path=save_name)
def save_video(self, observations, rewards):
save_name = self.log_dir + '/example' + str(self.num_timesteps) + '.mp4'
images = [self.norm_image(observations[i], type='rgb') for i in range(len(observations)) if rewards[i] >= 0] #[np.array(img) for i, img in enumerate(images)
clips = [ImageClip(m).set_duration(2)
for m in images]
concat_clip = concatenate_videoclips(clips, method="compose").resize(100)
concat_clip.write_videofile(save_name, fps=24, verbose=False)
if self.sacred :
self.sacred.get_logger().report_media('video', 'results', iteration=self.num_timesteps, local_path=save_name)
def _on_step(self) -> bool:
"""
In addition to EvalCallback we needs
Examples of eviroonment elements -> Save them as gif for exemple
Statistics to save -> save as plot and in database
-> reward, length, loss, additional metrics (accuraccy, best move ?)
"""
# super(MonitorCallback, self)._on_step()
if self.num_timesteps % self.check_freq == 0 :
episode_rewards, episode_lengths = [], []
gap, fit_solution, delivered = [], [], []
wrapped_env = DummyVecEnv([lambda: self.env])
for i in range(self.n_eval_episodes):
obs = wrapped_env.reset()
done, state = False, None
last_time = 0
if self.sequence :
if self.env.__class__.__name__ == 'DummyVecEnv':
observations = [self.env.env_method('get_image_representation')]
else :
observations = [self.env.get_image_representation()]
else :
observations = [obs.copy()]
episode_reward = [0.0]
episode_lengths.append(0)
while not done:
# Run of simulation
action, state = self.model.predict(obs, state=state, deterministic=False)
new_obs, reward, done, info = wrapped_env.step(action)
obs = new_obs
# Save observation only if time step evolved
if self.sequence:
if self.env.__class__.__name__ == 'DummyVecEnv':
if self.env.get_attr('time_step')[0] > last_time :
last_time = self.env.get_attr('time_step')[0]
observations.append(self.env.env_method('get_image_representation'))
else :
if self.env.time_step > last_time :
last_time = self.env.time_step
observations.append(self.env.get_image_representation())
else :
observations.append(obs.copy())
episode_reward.append(reward)
episode_lengths[-1] += 1
if self.render:
self.env.render()
info = info[0]
gap.append(info['GAP'])
delivered.append(info['delivered'])
fit_solution.append(info['fit_solution'])
episode_rewards.append(np.sum(episode_reward))
# self.save_gif(observations, episode_reward)
if self.num_timesteps % self.save_example_freq == 0 :
self.save_example(observations, episode_reward,number=i)
del observations
self.statistics['GAP'].append(np.mean(gap))
self.statistics['GAP*'].append(np.min(gap))
self.statistics['fit_solution'].append(np.mean(fit_solution))
self.statistics['delivered'].append(np.mean(delivered))
self.statistics['reward'].append(np.mean(episode_rewards))
self.statistics['std_reward'].append(np.std(episode_rewards))
self.statistics['step_reward'].append(np.mean([episode_rewards[i]/episode_lengths[i] for i in range(len(episode_lengths))]))
self.statistics['duration'].append(np.mean(episode_lengths))
# self.statistics['policy_loss'].append(self.model.pg_loss.numpy())
# self.statistics['value_loss'].append(self.model.vf_loss.numpy())
# self.statistics['policy_entropy'].append(self.model.entropy.numpy())
self.plot_statistics()
# Save best model
if self.statistics['reward'][-1] == np.max(self.statistics['reward']):
save_path = self.log_dir + '/best_model'
if self.verbose > 0:
print("Saving new best model to {}".format(save_path))
self.model.save(save_path)
return True
| 13,386 | 4,092 |
import json
import torch
from sqlnet.utils import *
from sqlnet.model.seq2sql import Seq2SQL
from sqlnet.model.sqlnet import SQLNet
import numpy as np
import datetime
#import mxnet as mx
#from bert_embedding import BertEmbedding
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--toy', action='store_true',
help='If set, use small data; used for fast debugging.')
parser.add_argument('--suffix', type=str, default='',
help='The suffix at the end of saved model name.')
parser.add_argument('--ca', action='store_true',
help='Use conditional attention.')
parser.add_argument('--dataset', type=int, default=0,
help='0: original dataset, 1: re-split dataset')
parser.add_argument('--rl', action='store_true',
help='Use RL for Seq2SQL(requires pretrained model).')
parser.add_argument('--baseline', action='store_true',
help='If set, then train Seq2SQL model; default is SQLNet model.')
parser.add_argument('--train_emb', action='store_true',
help='Train word embedding for SQLNet(requires pretrained model).')
args = parser.parse_args()
N_word=300
B_word=42
if args.toy:
USE_SMALL=True
GPU=False
BATCH_SIZE=15
else:
USE_SMALL=False
GPU=False
BATCH_SIZE=64
TRAIN_ENTRY=(True, True, True) # (AGG, SEL, COND)
TRAIN_AGG, TRAIN_SEL, TRAIN_COND = TRAIN_ENTRY
learning_rate = 1e-4 if args.rl else 1e-3
sql_data, table_data, val_sql_data, val_table_data, \
test_sql_data, test_table_data, \
TRAIN_DB, DEV_DB, TEST_DB = load_dataset(
args.dataset, use_small=USE_SMALL)
word_emb = load_word_emb('glove/glove.%dB.%dd.txt'%(B_word,N_word), \
load_used=args.train_emb, use_small=USE_SMALL)
if args.baseline:
model = Seq2SQL(word_emb, N_word=N_word, gpu=GPU,
trainable_emb = args.train_emb)
assert not args.train_emb, "Seq2SQL can\'t train embedding."
else:
model = SQLNet(word_emb, N_word=N_word, use_ca=args.ca,
gpu=GPU, trainable_emb = args.train_emb)
assert not args.rl, "SQLNet can\'t do reinforcement learning."
optimizer = torch.optim.Adam(model.parameters(),
lr=learning_rate, weight_decay = 0)
if args.train_emb:
agg_m, sel_m, cond_m, agg_e, sel_e, cond_e = best_model_name(args)
else:
agg_m, sel_m, cond_m = best_model_name(args)
if args.rl or args.train_emb: # Load pretrained model.
agg_lm, sel_lm, cond_lm = best_model_name(args, for_load=True)
print("Loading from {}".format(agg_lm))
model.agg_pred.load_state_dict(torch.load(agg_lm))
print("Loading from {}".format(sel_lm))
model.sel_pred.load_state_dict(torch.load(sel_lm))
print("Loading from {}".format(cond_lm))
model.cond_pred.load_state_dict(torch.load(cond_lm))
if args.rl:
best_acc = 0.0
best_idx = -1
print("Init dev acc_qm: \n breakdown on (agg, sel, where): {}".format( \
epoch_acc(model, BATCH_SIZE, val_sql_data,\
val_table_data, TRAIN_ENTRY)))
print("Init dev acc_ex: {}".formatepoch_exec_acc(
model, BATCH_SIZE, val_sql_data, val_table_data, DEV_DB))
torch.save(model.cond_pred.state_dict(), cond_m)
for i in range(100):
print('Epoch {} @ {}'.format((i+1, datetime.datetime.now())))
print(' Avg reward = {}'.format(epoch_reinforce_train( \
model, optimizer, BATCH_SIZE, sql_data, table_data, TRAIN_DB)))
print(' dev acc_qm: \n breakdown result: {}'.format(epoch_acc(\
model, BATCH_SIZE, val_sql_data, val_table_data, TRAIN_ENTRY)))
exec_acc = epoch_exec_acc(
model, BATCH_SIZE, val_sql_data, val_table_data, DEV_DB)
print(' dev acc_ex: {}'.format(exec_acc))
if exec_acc[0] > best_acc:
best_acc = exec_acc[0]
best_idx = i+1
torch.save(model.cond_pred.state_dict(),
'saved_model/epoch%d.cond_model%s'%(i+1, args.suffix))
torch.save(model.cond_pred.state_dict(), cond_m)
print(' Best exec acc = {}, on epoch {}'.format((best_acc, best_idx)))
else:
init_acc = epoch_acc(model, BATCH_SIZE,
val_sql_data, val_table_data, TRAIN_ENTRY)
best_agg_acc = init_acc[1][0]
best_agg_idx = 0
best_sel_acc = init_acc[1][1]
best_sel_idx = 0
best_cond_acc = init_acc[1][2]
best_cond_idx = 0
#print('Init dev acc_qm: {}\n breakdown on (agg, sel, where): {}'.format(\
# init_acc))
if TRAIN_AGG:
torch.save(model.agg_pred.state_dict(), agg_m)
if args.train_emb:
torch.save(model.agg_embed_layer.state_dict(), agg_e)
if TRAIN_SEL:
torch.save(model.sel_pred.state_dict(), sel_m)
if args.train_emb:
torch.save(model.sel_embed_layer.state_dict(), sel_e)
if TRAIN_COND:
torch.save(model.cond_pred.state_dict(), cond_m)
if args.train_emb:
torch.save(model.cond_embed_layer.state_dict(), cond_e)
for i in range(100):
print('Epoch {} @ {}'.format(i+1, datetime.datetime.now()))
print('Loss = {}'.format(epoch_train( \
model, optimizer, BATCH_SIZE,
sql_data, table_data, TRAIN_ENTRY)))
print(' Train acc_qm: \n breakdown result: {}'.format(epoch_acc( model, BATCH_SIZE, sql_data, table_data, TRAIN_ENTRY)))
#val_acc = epoch_token_acc(model, BATCH_SIZE, val_sql_data, val_table_data, TRAIN_ENTRY)
val_acc = epoch_acc(model,
BATCH_SIZE, val_sql_data, val_table_data, TRAIN_ENTRY)
print(' Dev acc_qm: \n breakdown result: {}'.format(val_acc))
if TRAIN_AGG:
if val_acc[1][0] > best_agg_acc:
best_agg_acc = val_acc[1][0]
best_agg_idx = i+1
torch.save(model.agg_pred.state_dict(),
'saved_model/epoch%d.agg_model%s'%(i+1, args.suffix))
torch.save(model.agg_pred.state_dict(), agg_m)
if args.train_emb:
torch.save(model.agg_embed_layer.state_dict(),
'saved_model/epoch%d.agg_embed%s'%(i+1, args.suffix))
torch.save(model.agg_embed_layer.state_dict(), agg_e)
if TRAIN_SEL:
if val_acc[1][1] > best_sel_acc:
best_sel_acc = val_acc[1][1]
best_sel_idx = i+1
torch.save(model.sel_pred.state_dict(),
'saved_model/epoch%d.sel_model%s'%(i+1, args.suffix))
torch.save(model.sel_pred.state_dict(), sel_m)
if args.train_emb:
torch.save(model.sel_embed_layer.state_dict(),
'saved_model/epoch%d.sel_embed%s'%(i+1, args.suffix))
torch.save(model.sel_embed_layer.state_dict(), sel_e)
if TRAIN_COND:
if val_acc[1][2] > best_cond_acc:
best_cond_acc = val_acc[1][2]
best_cond_idx = i+1
torch.save(model.cond_pred.state_dict(),
'saved_model/epoch%d.cond_model%s'%(i+1, args.suffix))
torch.save(model.cond_pred.state_dict(), cond_m)
if args.train_emb:
torch.save(model.cond_embed_layer.state_dict(),
'saved_model/epoch%d.cond_embed%s'%(i+1, args.suffix))
torch.save(model.cond_embed_layer.state_dict(), cond_e)
print(' Best val acc = {}, on epoch {} individually'.format(
(best_agg_acc, best_sel_acc, best_cond_acc),
(best_agg_idx, best_sel_idx, best_cond_idx)))
| 8,196 | 2,828 |
# Solution of;
# Project Euler Problem 685: Inverse Digit Sum II
# https://projecteuler.net/problem=685
#
# Writing down the numbers which have a digit sum of 10 in ascending order, we
# get:$19, 28, 37, 46,55,64,73,82,91,109, 118,\dots$Let $f(n,m)$ be the
# $m^{\text{th}}$ occurrence of the digit sum $n$. For example, $f(10,1)=19$,
# $f(10,10)=109$ and $f(10,100)=1423$. Let $\displaystyle S(k)=\sum_{n=1}^k
# f(n^3,n^4)$. For example $S(3)=7128$ and $S(10)\equiv 32287064 \mod
# 1\,000\,000\,007$. Find $S(10\,000)$ modulo $1\,000\,000\,007$.
#
# by lcsm29 http://github.com/lcsm29/project-euler
import timed
def dummy(n):
pass
if __name__ == '__main__':
n = 1000
i = 10000
prob_id = 685
timed.caller(dummy, n, i, prob_id)
| 758 | 389 |
from django.db import models
class Role(models.IntegerChoices):
ADMIN = 0, 'Admin'
GENERAL = 1, 'General'
GUEST = 2, 'Guest'
ACCOUNTING = 3, 'Accounting'
IT = 4, 'IT'
| 189 | 81 |
#!/usr/bin/env python
import socket
import sys
import json
#HOST="localhost"
HOST="172.20.1.11"
PORT=37568
def send_and_receive_msg(msg):
# Create a socket (SOCK_STREAM means a TCP socket)
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# Connect host:port
sock.connect((HOST, PORT))
# Connect to server and send data
sock.send(msg)
# Receive data from the server and shut down
recv_message = []
time = 100
while time > 0:
time = time - 1
d = sock.recv(1024)
if d:
recv_message.append(d)
else:
break
sock.close()
print "\n"
print "Sent: {}".format(msg)
print "Received: {}".format(recv_message)
json_recv = json.loads(recv_message[0])
for key, value in json_recv.items():
if cmp(json_recv[key], "succeed") == 0 :
return True
elif cmp(json_recv[key], "failed") == 0:
return False
if __name__ == '__main__':
#test get cases:right input, case 0
cmd = {}
cmd["cmd-set-parameters"] = {}
cmd["cmd-set-parameters"]["evt-version"] = "evt-version-2.0"
cmd["cmd-set-parameters"]["camera-name"] = "remote-camera-avm-synthesis"
cmd_string = json.dumps(cmd)
result = send_and_receive_msg(cmd_string)
if result == False:
print "case 0 failed!"
exit()
print "case 0 succeed!"
#test get cases:right input, case 1
cmd = {}
cmd["cmd-get-parameters"] = {}
cmd["cmd-get-parameters"]["camera-name"]="remote-camera-avm-synthesis"
cmd_string = json.dumps(cmd)
result = send_and_receive_msg(cmd_string)
if result == False:
print "case 1 failed!"
exit()
print "case 1 succeed!"
#test get cases:right input, case 2
cmd = {}
cmd["cmd-set-parameters"]={}
cmd["cmd-set-parameters"]["preview-format"]="preview-format-h264"
cmd["cmd-set-parameters"]["preview-size"]="1280x800"
cmd["cmd-set-parameters"]["preview-fps-values"]="30"
cmd_string = json.dumps(cmd)
result = send_and_receive_msg(cmd_string)
if result == False:
print "case 2 failed!"
exit()
print "case 2 succeed!"
| 2,196 | 793 |
def test_check_sanity(client):
resp = client.get('/sanity')
assert resp.status_code == 200
assert 'Sanity check passed.' == resp.data.decode()
# 'list collections' tests
def test_get_api_root(client):
resp = client.get('/', content_type='application/json')
assert resp.status_code == 200
resp_data = resp.get_json()
assert 'keys_url' in resp_data
assert len(resp_data) == 1
assert resp_data['keys_url'][-5:] == '/keys'
def test_delete_api_root_not_allowed(client):
resp = client.delete('/', content_type='application/json')
assert resp.status_code == 405
# 'list keys' tests
def test_get_empty_keys_list(client):
resp = client.get('/keys', content_type='application/json')
assert resp.status_code == 200
resp_data = resp.get_json()
assert len(resp_data) == 0
def test_get_nonempty_keys_list(client, keys, add_to_keys):
add_to_keys({'key': 'babboon', 'value': 'Larry'})
add_to_keys({'key': 'bees', 'value': ['Ann', 'Joe', 'Dee']})
resp = client.get('/keys', content_type='application/json')
assert resp.status_code == 200
resp_data = resp.get_json()
assert isinstance(resp_data, list)
assert len(resp_data) == 2
for doc_idx in (0, 1):
for k in ('key', 'http_url'):
assert k in resp_data[doc_idx]
if resp_data[doc_idx]['key'] == 'babboon':
assert resp_data[doc_idx]['http_url'][-13:] == '/keys/babboon'
else:
assert resp_data[doc_idx]['http_url'][-10:] == '/keys/bees'
def test_delete_on_keys_not_allowed(client):
resp = client.delete('/keys', content_type='application/json')
assert resp.status_code == 405
# 'get a key' tests
def test_get_existing_key(client, keys, add_to_keys):
add_to_keys({'key': 'babboon', 'value': 'Larry'})
add_to_keys({'key': 'bees', 'value': ['Ann', 'Joe', 'Dee']})
resp = client.get('/keys/bees', content_type='application/json')
assert resp.status_code == 200
resp_data = resp.get_json()
assert isinstance(resp_data, dict)
for k in ('key', 'http_url', 'value'):
assert k in resp_data
assert resp_data['key'] == 'bees'
assert resp_data['http_url'][-10:] == '/keys/bees'
assert resp_data['value'] == ['Ann', 'Joe', 'Dee']
def test_get_nonexisting_key(client, keys):
resp = client.get('/keys/bees', content_type='application/json')
assert resp.status_code == 404
def test_post_on_a_key_not_allowed(client):
resp = client.post('/keys/bees', content_type='application/json')
assert resp.status_code == 405
# 'create a key' tests
def test_create_new_key(client, keys):
new_doc = {'key': 'oscillator', 'value': 'Colpitts'}
resp = client.post(
'/keys',
json=new_doc,
content_type='application/json'
)
assert resp.status_code == 201
resp_data = resp.get_json()
assert isinstance(resp_data, dict)
for k in ('key', 'http_url', 'value'):
assert k in resp_data
assert resp_data['key'] == new_doc['key']
assert resp_data['value'] == new_doc['value']
assert resp_data['http_url'][-16:] == '/keys/oscillator'
def test_create_duplicate_key(client, keys, add_to_keys):
new_doc = {'key': 'oscillator', 'value': 'Colpitts'}
add_to_keys(new_doc.copy())
resp = client.post(
'/keys',
json=new_doc,
content_type='application/json'
)
assert resp.status_code == 400
resp_data = resp.get_json()
assert 'error' in resp_data
assert resp_data['error'] == "Can't create duplicate key (oscillator)."
def test_create_new_key_missing_key(client, keys):
new_doc = {'value': 'Colpitts'}
resp = client.post(
'/keys',
json=new_doc,
content_type='application/json'
)
assert resp.status_code == 400
resp_data = resp.get_json()
assert 'error' in resp_data
assert resp_data['error'] == 'Please provide the missing "key" parameter!'
def test_create_new_key_missing_value(client, keys):
new_doc = {'key': 'oscillator'}
resp = client.post(
'/keys',
json=new_doc,
content_type='application/json'
)
assert resp.status_code == 400
resp_data = resp.get_json()
assert 'error' in resp_data
assert resp_data['error'] == 'Please provide the missing "value" ' \
'parameter!'
# 'update a key' tests
def test_update_a_key_existing(client, keys, add_to_keys):
add_to_keys({'key': 'oscillator', 'value': 'Colpitts'})
update_value = {'value': ['Pierce', 'Hartley']}
resp = client.put(
'/keys/oscillator',
json=update_value,
content_type='application/json'
)
assert resp.status_code == 204
def test_update_a_key_nonexisting(client, keys, add_to_keys):
add_to_keys({'key': 'oscillator', 'value': 'Colpitts'})
update_value = {'value': ['Pierce', 'Hartley']}
resp = client.put(
'/keys/gadget',
json=update_value,
content_type='application/json'
)
assert resp.status_code == 400
resp_data = resp.get_json()
assert 'error' in resp_data
assert resp_data['error'] == 'Update failed.'
# 'delete a key' tests
def test_delete_a_key_existing(client, keys, add_to_keys):
add_to_keys({'key': 'oscillator', 'value': 'Colpitts'})
resp = client.delete(
'/keys/oscillator',
content_type='application/json'
)
assert resp.status_code == 204
def test_delete_a_key_nonexisting(client, keys):
resp = client.delete(
'/keys/oscillator',
content_type='application/json'
)
assert resp.status_code == 404
| 5,628 | 1,988 |
p = touch.autograd.Variable()
| 31 | 14 |
import logging
import signal
import gevent
import msgpack
from zerorpc import Publisher, Puller, Pusher, Server
import numpy as np
import jsonpickle
from .store import store
from .data import Data
from .operations.operation import Operation
from .utils.singleton import Singleton
__all__ = ['ServerAPI']
class ServerAPI(Server, metaclass=Singleton):
"""
RPC server class.
"""
def __init__(self, publisher=None, *args, **kwargs):
super(ServerAPI, self).__init__(*args, **kwargs)
self.publisher = publisher
def undo(self):
"""
Undo an operation popping from the stack and calling its `undo` method.
"""
Operation.pop().undo()
def redo(self):
"""
Call the `redo` method on the latest operation to be added to stack.
"""
Operation.redo()
def register(self, msg):
pass
# self.publisher.testing("This is a test on client.")
def load_data(self, path, format):
"""
Load a data file given path and format.
"""
import astropy.units as u
# data = Data.read(path, format=format)
data = Data(np.random.sample(100) * u.Jy, spectral_axis=np.linspace(1100, 1200, 100) * u.AA)
self.publisher.data_loaded(data.identifier)
def create_data(self, *args, **kwargs):
data = Data(*args, **kwargs)
self.publisher.data_created(data.identifier)
return data.identifier
def query_loader_formats(self):
"""
Returns a list of available data loader formats.
"""
from specutils import Spectrum1D
from astropy.io import registry as io_registry
all_formats = io_registry.get_formats(Spectrum1D)['Format']
return all_formats
def query_data(self, identifier):
data = store[identifier]
data_dict = {
'name': data.name,
'identifier': data.identifier,
'spectral_axis': data.spectral_axis.value.tolist(),
'spectral_axis_unit': data.spectral_axis.unit.to_string(),
'flux': data.flux.value.tolist(),
'unit': data.flux.unit.to_string()
}
return data_dict
def query_data_attribute(self, identifier, name):
data = store[identifier]
data_attr = getattr(data, name)
packed_data_attr = data.encode(data_attr)
return packed_data_attr
def launch(server_address=None, publisher_address=None, block=True):
server_address = server_address or "tcp://127.0.0.1:4242"
publisher_address = publisher_address or "tcp://127.0.0.1:4243"
# Establish the publisher service. This will send events to any
# subscribed services along the designated address.
publisher = Publisher()
publisher.connect(publisher_address)
# Setup the server service. This will be the api that clients
# will send events to.
server = ServerAPI(publisher)
server.bind(server_address)
logging.info(
"Server is now listening on %s and sending on %s.",
server_address, publisher_address)
# Allow for stopping the server via ctrl-c
gevent.signal(signal.SIGINT, server.stop)
server.run() if block else gevent.spawn(server.run) | 3,251 | 995 |
# -*- coding: utf-8 -*-
# Author: t0pep0
# e-mail: t0pep0.gentoo@gmail.com
# Jabber: t0pep0@jabber.ru
# BTC : 1ipEA2fcVyjiUnBqUx7PVy5efktz2hucb
# donate free =)
# Forked and modified by Codarren Velvindron
# Compatible Python3
import hmac
import hashlib
import time
import urllib.request, urllib.parse, urllib.error
import json
class Api(object):
__username = ''
__api_key = ''
__api_secret = ''
__nonce_v = ''
# Init class
def __init__(self, username, api_key, api_secret):
self.__username = username
self.__api_key = api_key
self.__api_secret = api_secret
# get timestamp as nonce
def __nonce(self):
self.__nonce_v = '{:.10f}'.format(time.time() * 1000).split('.')[0]
# generate signature
def __signature(self):
byte_secret = bytes(self.__api_secret, "ascii")
string = self.__nonce_v + self.__username + self.__api_key # create string
encode_string = string.encode ('utf-8')
signature = hmac.new(byte_secret, encode_string, digestmod=hashlib.sha256).hexdigest().upper() # create signature
return signature
def __post(self, url, param): # Post Request (Low Level API call)
post_url = url
header = { 'User-agent': 'bot-cex.io-' }
params = urllib.parse.urlencode(param)
post_data = params.encode( "ascii")
req = urllib.request.Request(url = post_url, data = post_data, headers = header)
page = urllib.request.urlopen(req).read()
return page
def api_call(self, method, param={}, private=0, couple=''): # api call (Middle level)
url = 'https://cex.io/api/' + method + '/' # generate url
if couple != '':
url = url + couple + '/' # set couple if needed
if private == 1: # add auth-data if needed
self.__nonce()
param.update({
'key': self.__api_key,
'signature': self.__signature(),
'nonce': self.__nonce_v})
answer = self.__post(url, param) # Post Request
a = answer.decode("utf-8")
#return json.loads(answer) # generate dict and return
return a # generates a valid json output
def ticker(self, couple='MHC/BTC'):
return self.api_call('ticker', {}, 0, couple)
def tickers(self, couple='USD'):
return self.api_call('tickers', {}, 0, couple)
def ohlcv(self, end_date, couple='BTC/USD'):
return self.api_call('ohlcv/hd/'+str(end_date), {}, 0, couple)
def order_book(self, couple='MHC/BTC'):
return self.api_call('order_book', {}, 0, couple)
def trade_history(self, since=1, couple='MHC/BTC'):
return self.api_call('trade_history', {"since": str(since)}, 0, couple)
def balance(self):
return self.api_call('balance', {}, 1)
def current_orders(self, couple='MHC/BTC'):
return self.api_call('open_orders', {}, 1, couple)
def cancel_order(self, order_id):
return self.api_call('cancel_order', {"id": order_id}, 1)
def place_order(self, ptype='buy', amount=1, price=1, couple='MHC/BTC'):
return self.api_call('place_order', {"type": ptype, "amount": str(amount), "price": str(price)}, 1, couple)
def archived_orders(self, couple='XLM/USD'):
return self.api_call('archived_orders', {}, 1, couple)
def price_stats(self, last_hours, max_resp_arr_size, couple='MHC/BTC'):
return self.api_call(
'price_stats',
{"lastHours": last_hours, "maxRespArrSize": max_resp_arr_size},
0, couple)
| 3,612 | 1,224 |
import numpy as np
from GeneralUtils import list_to_sum
class Fourier:
def __init__(self,amp=[1],freq=[1],ph=[0]):
self.amp = amp
self.freq = freq
self.ph = ph
def __str__(self):
out = []
for i in range(len(self.amp)):
if self.amp[i] != 1:
a = f"{self.amp[i]}*"
else:
a = ""
if self.freq[i] != 1:
f = f"*{self.freq[i]}"
else:
f = ""
if self.ph[i] != 0:
p = f"+{self.ph[i]}"
else:
p = ""
out.append(f"{a}sin(x{f}{p})")
return list_to_sum(out)
def __add__(self,other):
a = self.amp + other.amp
f = self.freq + other.freq
p = self.ph + other.ph
return Fourier(a,f,p)
def evaluate_series(F,x):
out = np.zeros_like(x)
for i in range(len(F.amp)):
a = F.amp[i]
f = F.freq[i]
p = F.ph[i]
out += a*np.sin(x*f+p)
return out
| 1,125 | 394 |
# Elastic search mapping definition for the Molecule entity
from glados.es.ws2es.es_util import DefaultMappings
# Shards size - can be overridden from the default calculated value here
# shards = 3,
replicas = 1
analysis = DefaultMappings.COMMON_ANALYSIS
mappings = \
{
'properties':
{
'_metadata':
{
'properties':
{
'es_completion': 'TEXT',
# EXAMPLES:
# '{'weight': 100, 'input': 'Retina/plasma'}' , '{'weight': 10, 'input': 'CHEMBL3987832'}' , '{'weig
# ht': 10, 'input': 'UBERON:0001066'}' , '{'weight': 100, 'input': 'Intraorbital lacrimal gland'}' ,
# '{'weight': 10, 'input': 'CHEMBL3833873'}' , '{'weight': 10, 'input': 'CHEMBL3987959'}' , '{'weig
# ht': 10, 'input': 'CHEMBL3988202'}' , '{'weight': 10, 'input': 'BTO:0001442'}' , '{'weight': 10, '
# input': 'UBERON:0000200'}' , '{'weight': 100, 'input': 'Aortic valve'}'
'organism_taxonomy':
{
'properties':
{
'l1': 'TEXT',
# EXAMPLES:
# 'Eukaryotes' , 'Eukaryotes' , 'Eukaryotes' , 'Eukaryotes' , 'Eukaryotes' , 'Eukaryotes' ,
# 'Eukaryotes' , 'Eukaryotes' , 'Bacteria' , 'Bacteria'
'l2': 'TEXT',
# EXAMPLES:
# 'Mammalia' , 'Mammalia' , 'Mammalia' , 'Mammalia' , 'Mammalia' , 'Mammalia' , 'Mammalia' ,
# 'Mammalia' , 'Gram-Positive' , 'Gram-Positive'
'l3': 'TEXT',
# EXAMPLES:
# 'Rodentia' , 'Primates' , 'Rodentia' , 'Rodentia' , 'Primates' , 'Lagomorpha' , 'Rodentia'
# , 'Rodentia' , 'Streptococcus' , 'Staphylococcus'
'oc_id': 'NUMERIC',
# EXAMPLES:
# '42' , '7' , '42' , '42' , '60' , '69' , '42' , '42' , '590' , '561'
'tax_id': 'NUMERIC',
# EXAMPLES:
# '10116' , '9606' , '10116' , '10116' , '9544' , '9986' , '10116' , '10116' , '1313' , '128
# 0'
}
},
'related_activities':
{
'properties':
{
'all_chembl_ids': 'TEXT',
# EXAMPLES:
# '' , '' , '' , '' , '' , '' , '' , '' , '' , ''
'count': 'NUMERIC',
# EXAMPLES:
# '5' , '1' , '4' , '1' , '63' , '15' , '1' , '2' , '110' , '31'
}
},
'related_assays':
{
'properties':
{
'all_chembl_ids': 'TEXT',
# EXAMPLES:
# 'CHEMBL3271354 CHEMBL3271351 CHEMBL3271352 CHEMBL3271353 CHEMBL3749611' , 'CHEMBL3266981'
# , 'CHEMBL3231741 CHEMBL3232032 CHEMBL3232142 CHEMBL3232050' , 'CHEMBL2212114' , 'CHEMBL102
# 2344 CHEMBL1275020 CHEMBL1274863 CHEMBL1274891 CHEMBL1274030 CHEMBL1022341 CHEMBL1011798 C
# HEMBL1019674 CHEMBL1274912 CHEMBL1274662 CHEMBL1017034 CHEMBL1274842 CHEMBL1274933 CHEMBL1
# 275069 CHEMBL1274558 CHEMBL1274898 CHEMBL1017033 CHEMBL1274849 CHEMBL1274565 CHEMBL1274593
# CHEMBL1274683 CHEMBL4000834 CHEMBL1011797 CHEMBL1274856 CHEMBL1274572 CHEMBL964747 CHEMBL
# 1275027 CHEMBL1274037 CHEMBL1274551 CHEMBL964745 CHEMBL1274926 CHEMBL1274919 CHEMBL1274690
# CHEMBL1275034 CHEMBL1274877 CHEMBL1274669 CHEMBL1275048 CHEMBL1274884 CHEMBL1017010 CHEMB
# L1017032 CHEMBL1022342 CHEMBL1022346 CHEMBL1017035 CHEMBL1275076 CHEMBL1275090 CHEMBL10170
# 09 CHEMBL1275062 CHEMBL1274579 CHEMBL1274905 CHEMBL1274676 CHEMBL1019675 CHEMBL1274586 CHE
# MBL964744 CHEMBL1274655 CHEMBL1022345 CHEMBL1275055 CHEMBL1011799 CHEMBL1275041 CHEMBL1275
# 083 CHEMBL1022343 CHEMBL964746 CHEMBL1274870 CHEMBL1274544' , 'CHEMBL3862853 CHEMBL3862825
# CHEMBL3862826' , 'CHEMBL3373694' , 'CHEMBL4054773 CHEMBL4054770' , 'CHEMBL1827722 CHEMBL3
# 583725 CHEMBL3389745 CHEMBL935349 CHEMBL3091242 CHEMBL3583724 CHEMBL3091255 CHEMBL3583723
# CHEMBL940282 CHEMBL3738486 CHEMBL1926063 CHEMBL1055389 CHEMBL1924493 CHEMBL3736923 CHEMBL3
# 736913 CHEMBL3583729 CHEMBL1924492 CHEMBL3738482 CHEMBL3737062 CHEMBL1924491 CHEMBL3389206
# CHEMBL1260643 CHEMBL935348 CHEMBL3389742 CHEMBL3389743 CHEMBL936043 CHEMBL3583728 CHEMBL1
# 805837 CHEMBL3606187 CHEMBL3389744 CHEMBL3736767 CHEMBL948103 CHEMBL1924490 CHEMBL940281 C
# HEMBL935351 CHEMBL3362796 CHEMBL935350 CHEMBL3606186 CHEMBL3389205 CHEMBL3583726 CHEMBL373
# 6765 CHEMBL1273661 CHEMBL1055388 CHEMBL1273660 CHEMBL1067241 CHEMBL1924489 CHEMBL935352 CH
# EMBL2214928 CHEMBL1067242' , 'CHEMBL994370 CHEMBL1218348 CHEMBL994358 CHEMBL994366 CHEMBL9
# 94362 CHEMBL994369 CHEMBL1218345 CHEMBL994359 CHEMBL1657220 CHEMBL1657221 CHEMBL1653373 CH
# EMBL1655162 CHEMBL994372 CHEMBL1218462 CHEMBL1654323 CHEMBL994371 CHEMBL1654028 CHEMBL1654
# 324 CHEMBL994368 CHEMBL1218349 CHEMBL1218341 CHEMBL994367 CHEMBL994363 CHEMBL1654322 CHEMB
# L994365 CHEMBL1654030 CHEMBL1654029'
'count': 'NUMERIC',
# EXAMPLES:
# '5' , '1' , '4' , '1' , '63' , '3' , '1' , '2' , '49' , '27'
}
},
'related_cell_lines':
{
'properties':
{
'all_chembl_ids': 'TEXT',
# EXAMPLES:
# 'CHEMBL3833683' , 'CHEMBL3307768' , 'CHEMBL3307627' , 'CHEMBL3307355' , 'CHEMBL3307965' ,
# 'CHEMBL3307651' , 'CHEMBL3307762' , 'CHEMBL3307627' , 'CHEMBL3308019 CHEMBL3307570 CHEMBL3
# 307965 CHEMBL3307564' , 'CHEMBL3307383'
'count': 'NUMERIC',
# EXAMPLES:
# '1' , '1' , '1' , '1' , '1' , '1' , '1' , '1' , '4' , '1'
}
},
'related_compounds':
{
'properties':
{
'all_chembl_ids': 'TEXT',
# EXAMPLES:
# 'CHEMBL2420629 CHEMBL3746776 CHEMBL3260358' , 'CHEMBL3260771' , 'CHEMBL3229240 CHEMBL32292
# 38' , 'CHEMBL2203701' , 'CHEMBL395998 CHEMBL2017983 CHEMBL1270517' , 'CHEMBL2403888 CHEMBL
# 3922006 CHEMBL3895075 CHEMBL3948952 CHEMBL3905199 CHEMBL3933212 CHEMBL3906900 CHEMBL210573
# 5 CHEMBL3921126' , 'CHEMBL3358920' , 'CHEMBL4074669' , 'CHEMBL3604813 CHEMBL188635 CHEMBL1
# 922318 CHEMBL1922327 CHEMBL2441068 CHEMBL1922489 CHEMBL510944 CHEMBL1922481 CHEMBL2206420
# CHEMBL3086523 CHEMBL1922499 CHEMBL1922323 CHEMBL1922328 CHEMBL1923420 CHEMBL1922486 CHEMBL
# 1922494 CHEMBL3580908 CHEMBL3580926 CHEMBL1922490 CHEMBL1922336 CHEMBL3735824 CHEMBL497 CH
# EMBL1922480 CHEMBL1922337 CHEMBL1922326 CHEMBL1922482 CHEMBL1258462 CHEMBL581906 CHEMBL192
# 2333 CHEMBL1922315 CHEMBL1922496 CHEMBL1922477 CHEMBL1272278 CHEMBL1922321 CHEMBL1922316 C
# HEMBL1822871 CHEMBL1922484 CHEMBL1922487 CHEMBL1272227 CHEMBL1922332 CHEMBL1922330 CHEMBL4
# 5 CHEMBL286615 CHEMBL1922479 CHEMBL411440 CHEMBL1922478 CHEMBL1922322 CHEMBL161 CHEMBL3876
# 75 CHEMBL1922500 CHEMBL1922335 CHEMBL1922324 CHEMBL1922497 CHEMBL551359 CHEMBL3580919 CHEM
# BL1922491 CHEMBL1922317 CHEMBL1922325 CHEMBL1922493 CHEMBL1922331 CHEMBL1922319 CHEMBL1922
# 483 CHEMBL75267 CHEMBL465372 CHEMBL1922488 CHEMBL1922498 CHEMBL1922329 CHEMBL1922334 CHEMB
# L1800922 CHEMBL3580916 CHEMBL502 CHEMBL1922492 CHEMBL1922320 CHEMBL1922495 CHEMBL1922485 C
# HEMBL2206412' , 'CHEMBL262777 CHEMBL520642 CHEMBL501122 CHEMBL32 CHEMBL126 CHEMBL387675'
'count': 'NUMERIC',
# EXAMPLES:
# '3' , '1' , '2' , '1' , '3' , '9' , '1' , '1' , '76' , '6'
}
},
'related_documents':
{
'properties':
{
'all_chembl_ids': 'TEXT',
# EXAMPLES:
# 'CHEMBL3745705 CHEMBL3259558' , 'CHEMBL3259671' , 'CHEMBL3227952' , 'CHEMBL2203285' , 'CHE
# MBL1151757 CHEMBL1268908 CHEMBL4000173' , 'CHEMBL3861981' , 'CHEMBL3352115' , 'CHEMBL40526
# 43' , 'CHEMBL3603820 CHEMBL1921774 CHEMBL3734674 CHEMBL1143818 CHEMBL3085641 CHEMBL1156916
# CHEMBL3351484 CHEMBL1151477 CHEMBL1255186 CHEMBL3352025 CHEMBL1149049 CHEMBL1821588 CHEMB
# L3580567 CHEMBL1142351 CHEMBL2203249 CHEMBL1921784 CHEMBL1800034 CHEMBL1269010' , 'CHEMBL1
# 155768 CHEMBL1649142 CHEMBL1649273 CHEMBL1212779'
'count': 'NUMERIC',
# EXAMPLES:
# '2' , '1' , '1' , '1' , '3' , '1' , '1' , '1' , '18' , '4'
}
},
'related_targets':
{
'properties':
{
'all_chembl_ids': 'TEXT',
# EXAMPLES:
# 'CHEMBL612558 CHEMBL345' , 'CHEMBL1836' , 'CHEMBL612558' , 'CHEMBL612558' , 'CHEMBL612545
# CHEMBL612558' , 'CHEMBL612546' , 'CHEMBL376' , 'CHEMBL612545' , 'CHEMBL2574 CHEMBL375 CHEM
# BL612545 CHEMBL612546 CHEMBL612670 CHEMBL376 CHEMBL612558 CHEMBL613631 CHEMBL347' , 'CHEMB
# L352 CHEMBL374 CHEMBL362'
'count': 'NUMERIC',
# EXAMPLES:
# '2' , '1' , '1' , '1' , '2' , '1' , '1' , '1' , '9' , '3'
}
}
}
},
'bto_id': 'TEXT',
# EXAMPLES:
# 'BTO:0001442' , 'BTO:0001279' , 'BTO:0000156' , 'BTO:0001388' , 'BTO:0000928' , 'BTO:0000573' , 'BTO:00010
# 67' , 'BTO:0000493' , 'BTO:0004345' , 'BTO:0001063'
'caloha_id': 'TEXT',
# EXAMPLES:
# 'TS-0953' , 'TS-0099' , 'TS-1060' , 'TS-1307' , 'TS-0054' , 'TS-0394' , 'TS-0309' , 'TS-0813' , 'TS-1047'
# , 'TS-0469'
'efo_id': 'TEXT',
# EXAMPLES:
# 'EFO:0001914' , 'UBERON:0002240' , 'UBERON:0001348' , 'UBERON:0003126' , 'UBERON:0001637' , 'UBERON:000211
# 0' , 'UBERON:0002728' , 'UBERON:0000970' , 'UBERON:0001851' , 'UBERON:0000988'
'pref_name': 'TEXT',
# EXAMPLES:
# 'Retina/plasma' , 'Meningeal artery' , 'Intervertebral disk' , 'Intraorbital lacrimal gland' , 'Occipital
# lobe' , 'Sinoatrial node' , 'Ankle/Knee' , 'Brain ventricle' , 'Gyrus' , 'Aortic valve'
'tissue_chembl_id': 'TEXT',
# EXAMPLES:
# 'CHEMBL4296362' , 'CHEMBL3987832' , 'CHEMBL3987785' , 'CHEMBL3987787' , 'CHEMBL3833873' , 'CHEMBL3987959'
# , 'CHEMBL3988202' , 'CHEMBL4296347' , 'CHEMBL3987758' , 'CHEMBL3987638'
'uberon_id': 'TEXT',
# EXAMPLES:
# 'UBERON:0003474' , 'UBERON:0001066' , 'UBERON:0019324' , 'UBERON:0002021' , 'UBERON:0002351' , 'UBERON:000
# 4086' , 'UBERON:0000200' , 'UBERON:0002137' , 'UBERON:0001881' , 'UBERON:0002240'
}
}
| 13,194 | 6,397 |
from rest_framework.decorators import api_view
from rest_framework.views import APIView
from rest_framework import status
from rest_framework.response import Response
from .models import Book
from .serializers import BookSerializer
# Create your views here.
class GetAllData(APIView):
def get(self, request):
query = Book.objects.all().order_by('-create_at')
serializers = BookSerializer(query , many=True)
return Response(serializers.data, status=status.HTTP_200_OK)
@api_view(['GET'])
def allApi(request):
if request.method == 'GET':
query = Book.objects.all().order_by('-create_at')
serializer = BookSerializer(query, many=True)
return Response(serializer.data, status=status.HTTP_200_OK)
@api_view(['POST'])
def SetData(request):
if request.method == 'POST':
serializer = BookSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data, status=status.HTTP_201_CREATE)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class GetFavData(APIView):
def get(self,request):
query = Book.objects.filter(fav=True)
serializer = BookSerializer(query, many=True)
return Response(serializer.data, status=status.HTTP_200_OK)
class UpdateFavData(APIView):
def get(self, request, pk):
query = Book.objects.get(pk=pk)
serializer = BookSerializer(query)
return Response(serializer.data, status=status.HTTP_200_OK)
def put(self, request, pk):
query = Book.objects.get(pk=pk)
serializer = BookSerializer(query, data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data, status=status.HTTP_201_CREATED)
else:
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class PostModelData(APIView):
def post(self, request):
serializer = BookSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class SearchData(APIView):
def get(self, request):
search = request.GET['name']
query = Book.objects.filter(store_name__contains=search)
serializer= BookSerializer(query, many=True)
return Response(serializer.data, status=status.HTTP_200_OK)
class DeleteData(APIView):
def delete(self, request, pk):
query = Book.objects.get(pk=pk)
query.delete()
return Response(status=status.HTTP_204_NO_CONTENT) | 2,733 | 819 |
from django.conf.urls import include, url
from django.contrib import admin
from django.contrib.staticfiles.urls import staticfiles_urlpatterns
urlpatterns = [
# Examples:
# url(r'^$', 'mysite.views.home', name='home'),
# url(r'^blog/', include('blog.urls')),
url(r'^$', 'polls.views.home', name='home'),
url(r'^admin/', include(admin.site.urls)),
# user auth urls
url(r'^accounts/login/$', 'polls.views.login'),
url(r'^accounts/auth/$', 'polls.views.auth_view'),
url(r'^accounts/logout/$', 'polls.views.logout'),
url(r'^accounts/loggedin/$', 'polls.views.loggedin'),
url(r'^accounts/invalid/$', 'polls.views.invalid_login'),
url(r'^accounts/register/$', 'polls.views.register_user'),
url(r'^accounts/register_success/$', 'polls.views.register_success'),
# report
url(r'^reports/new/$', 'polls.views.new_report'),
url(r'^reports/list/$', 'polls.views.user_report'),
url(r'^reports/detail/(?P<id>\d+)/$', 'polls.views.report_details'),
url(r'^reports/delete/(?P<id>\d+)/$','polls.views.delete'),
url(r'^reports/all/$','polls.views.report_all'),
url(r'^reports/edit/(?P<id>\d+)/$', 'polls.views.edit_report'),
# folder
url(r'^folder/new/$', 'polls.views.new_folder'),
#search
url(r'^search-form/$', 'polls.views.search_form'),
url(r'^search/$', 'polls.views.search'),
]
| 1,463 | 570 |
'''
Module camera provides the VideoStream class which
offers a threaded interface to multiple types of cameras.
'''
from threading import Thread
import io
import os
import platform
import numpy as np # pylint: disable=import-error
class VideoStream:
'''
Instantiate the VideoStream class.
Use the method read() to get the frame.
'''
def __init__(self, camera_type="video0", path_to_camera="/dev/video0", width="1920", height="1080"):
''' Constructor. Chooses a camera to read from. '''
print("VideoStream: {}, {}, {}, {}".format(camera_type, path_to_camera, width, height))
self.camera_type = camera_type
self.path_to_camera = path_to_camera
self.width = width
self.height = height
if self.camera_type == "Darwin":
print("VideoStream: Opening webcam")
self.path_to_camera = "Webcam"
import cv2 # pylint: disable=import-error
self.stream = cv2.VideoCapture(0)
self.stream.set(3, self.width)
self.stream.set(4, self.height)
elif self.camera_type == "video0":
print("VideoStream: Opening {}".format(self.path_to_camera))
import cv2 # pylint: disable=import-error
self.stream = cv2.VideoCapture(self.path_to_camera)
print("VideoStream: Stream opened = {}".format(self.stream.isOpened()))
elif self.camera_type == "awscam":
print("VideoStream: Opening awscam")
import awscam # pylint: disable=import-error
self.stream = awscam
self.stream.read = self.stream.getLastFrame
print("VideoStream: awscam opened")
elif self.camera_type == "picamera":
print("VideoStream: Opening picamera")
import picamera # pylint: disable=import-error
def piCameraCapture(self):
_stream = io.BytesIO()
# time.sleep(2)
PICAMERA.capture(_stream, format='jpeg')
# Construct a numpy array from the _stream
data = np.fromstring(_stream.getvalue(), dtype=np.uint8)
# "Decode" the image from the array, preserving colour
return True, cv2.imdecode(data, 1)
picamera.PiCamera.read = piCameraCapture
PICAMERA = self.stream = picamera.PiCamera()
self.stream.resolution = (self.width, self.height)
self.stream.start_preview()
print("VideoStream: picamera opened")
else:
self.path_to_camera = "GStreamer"
HD_2K = False
if HD_2K:
self.width = 2592 # 648
self.height = 1944 # 486
else:
self.width = 1296 # 324
self.height = 972 # 243
gst_str = ("nvcamerasrc ! "
"video/x-raw(memory:NVMM), width=(int)2592, height=(int)1944,"
"format=(string)I420, framerate=(fraction)30/1 ! "
"nvvidconv ! video/x-raw, width=(int){}, height=(int){}, "
"format=(string)BGRx ! videoconvert ! appsink").format(self.width, self.height)
self.stream = cv2.VideoCapture(gst_str, cv2.CAP_GSTREAMER)
self.stopped = False
self.ret, self.frame = self.stream.read()
print("Videostream init done.")
def get_height(self):
return self.height
def get_width(self):
return self.width
def get_white_frame(self):
return 255*np.ones([self.width, self.height, 3])
def start(self):
'''start() starts the thread'''
thread = Thread(target=self.update, args=())
thread.daemon = True
thread.start()
return self
def update(self):
'''update() constantly read the camera stream'''
print("VideoStream: udpate: starting the camera reads")
while not self.stopped:
self.ret, self.frame = self.stream.read()
def read(self):
'''read() return the last frame captured'''
return self.ret, self.frame
# def read(self):
# '''read() return the last frame captured'''
# return self.stream.read()
def stop(self):
'''stop() set a flag to stop the update loop'''
self.stopped = True
| 4,338 | 1,299 |
from functools import partial
from matplotlib.pyplot import xcorr
import numpy as np
import jax
import jax.numpy as jnp
import flax
from flax import linen as nn
import distrax
from .jax_utils import batch_to_jax, extend_and_repeat, next_rng
def update_target_network(main_params, target_params, tau):
return jax.tree_multimap(
lambda x, y: tau * x + (1.0 - tau) * y,
main_params, target_params
)
# def multiple_action_q_function(forward):
# # Forward the q function with multiple actions on each state, to be used as a decorator
# def wrapped(self, observations, actions, **kwargs):
# multiple_actions = False
# batch_size = observations.shape[0]
# if actions.ndim == 3 and observations.ndim == 2:
# multiple_actions = True
# observations = extend_and_repeat(observations, 1, actions.shape[1]).reshape(-1, observations.shape[-1])
# actions = actions.reshape(-1, actions.shape[-1])
# q_values = forward(self, observations, actions, **kwargs)
# if multiple_actions:
# q_values = q_values.reshape(batch_size, -1)
# return q_values
# return wrapped
def multiple_action_q_function(forward):
# Forward the q function with multiple actions on each state, to be used as a decorator
def wrapped(self, observations, actions, **kwargs):
multiple_actions = False
batch_size = observations.shape[0]
if actions.ndim == 3 and observations.ndim == 2:
multiple_actions = True
observations = extend_and_repeat(observations, 1, actions.shape[1]).reshape(-1, observations.shape[-1])
actions = actions.reshape(-1, actions.shape[-1])
q_last_layer, q_values = forward(self, observations, actions, **kwargs)
if multiple_actions:
q_last_layer = q_last_layer.reshape(batch_size, -1)
q_values = q_values.reshape(batch_size, -1)
return q_last_layer, q_values
return wrapped
def multiple_action_encode_function(forward):
# Forward the rep function with multiple actions on each state, to be used as a decorator
def wrapped(self, rng, observations, actions, **kwargs):
multiple_actions = False
batch_size = observations.shape[0]
if actions.ndim == 3 and observations.ndim == 2:
repeat = actions.shape[1]
multiple_actions = True
observations = extend_and_repeat(observations, 1, actions.shape[1]).reshape(-1, observations.shape[-1])
actions = actions.reshape(-1, actions.shape[-1])
samples, log_probs = forward(self, rng, observations, actions, **kwargs)
if multiple_actions:
samples = samples.reshape(batch_size, repeat, -1)
log_probs = log_probs.reshape(batch_size, repeat, -1)
return samples, log_probs
return wrapped
def multiple_action_decode_function(forward):
# Forward the rep function with multiple actions on each state, to be used as a decorator
def wrapped(self, observations, actions, **kwargs):
multiple_actions = False
batch_size = observations.shape[0]
if actions.ndim == 3 and observations.ndim == 2:
repeat = actions.shape[1]
multiple_actions = True
observations = extend_and_repeat(observations, 1, actions.shape[1]).reshape(-1, observations.shape[-1])
actions = actions.reshape(-1, actions.shape[-1])
q_values = forward(self, observations, actions, **kwargs)
if multiple_actions:
q_values = q_values.reshape(batch_size, repeat, -1)
return q_values
return wrapped
class Scalar(nn.Module):
init_value: float
def setup(self):
self.value = self.param('value', lambda x:self.init_value)
def __call__(self):
return self.value
class FullyConnectedNetwork(nn.Module):
output_dim: int
arch: str = '256-256'
orthogonal_init: bool = False
# batch_norm: bool = False
@nn.compact
def __call__(self, input_tensor):
x = input_tensor
hidden_sizes = [int(h) for h in self.arch.split('-')]
for h in hidden_sizes:
if self.orthogonal_init:
x = nn.Dense(
h,
kernel_init=jax.nn.initializers.orthogonal(jnp.sqrt(2.0)),
bias_init=jax.nn.initializers.zeros
)(x)
else:
x = nn.Dense(h)(x)
# if self.batch_norm:
# x = nn.BatchNorm(use_running_average=not train_mode, momentum=0.9,
# epsilon=1e-5,
# dtype=jnp.float32)(x)
x = nn.relu(x)
if self.orthogonal_init:
output = nn.Dense(
self.output_dim,
kernel_init=jax.nn.initializers.orthogonal(1e-2),
bias_init=jax.nn.initializers.zeros
)(x)
else:
output = nn.Dense(
self.output_dim,
kernel_init=jax.nn.initializers.variance_scaling(
1e-2, 'fan_in', 'uniform'
),
bias_init=jax.nn.initializers.zeros
)(x)
return output
class FullyConnectedNetworkWithLastLayer(nn.Module):
output_dim: int
arch: str = '256-256'
orthogonal_init: bool = False
@nn.compact
def __call__(self, input_tensor):
x = input_tensor
batch, _ = jnp.shape(x)
hidden_sizes = [int(h) for h in self.arch.split('-')]
for h in hidden_sizes:
if self.orthogonal_init:
x = nn.Dense(
h,
kernel_init=jax.nn.initializers.orthogonal(jnp.sqrt(2.0)),
bias_init=jax.nn.initializers.zeros
)(x)
else:
x = nn.Dense(h)(x)
# x = nn.LayerNorm()(x)
x = nn.relu(x)
normalized = jnp.reshape(jnp.sqrt(jnp.sum(x**2, axis=-1) + 1e-6), (batch,1))
x = x / normalized
# x = x / (jnp.sqrt(jnp.sum(x**2, axis=-1) + 1e-6))
if self.orthogonal_init:
output = nn.Dense(
self.output_dim,
kernel_init=jax.nn.initializers.orthogonal(1e-2),
bias_init=jax.nn.initializers.zeros
)(x)
else:
output = nn.Dense(
self.output_dim,
kernel_init=jax.nn.initializers.variance_scaling(
1e-2, 'fan_in', 'uniform'
),
bias_init=jax.nn.initializers.zeros
)(x)
return x, output
class FullyConnectedQFunction(nn.Module):
observation_dim: int
action_dim: int
arch: str = '256-256'
orthogonal_init: bool = False
@nn.compact
@multiple_action_q_function
def __call__(self, observations, actions):
x = jnp.concatenate([observations, actions], axis=-1)
# x = FullyConnectedNetwork(output_dim=1, arch=self.arch, orthogonal_init=self.orthogonal_init)(x)
last_layer_x, x = FullyConnectedNetworkWithLastLayer(output_dim=1, arch=self.arch, orthogonal_init=self.orthogonal_init)(x)
return last_layer_x, jnp.squeeze(x, -1)
class FullyConnectedActionQFunction(nn.Module):
observation_dim: int
action_dim: int
output_dim: int = 1
arch: str = '256-256'
orthogonal_init: bool = False
normalize: bool = False
@nn.compact
@multiple_action_q_function
def __call__(self, observations, actions):
x = jnp.concatenate([observations, actions], axis=-1)
batch, _ = jnp.shape(x)
hidden_sizes = [int(h) for h in self.arch.split('-')]
for h in hidden_sizes:
if self.orthogonal_init:
x = nn.Dense(
h,
kernel_init=jax.nn.initializers.orthogonal(jnp.sqrt(2.0)),
bias_init=jax.nn.initializers.zeros
)(x)
else:
x = nn.Dense(h)(jnp.concatenate([x, actions], axis=-1))
x = nn.relu(x)
if self.normalize:
normalized = jnp.reshape(jnp.sqrt(jnp.sum(x**2, axis=-1) + 1e-6), (batch,1))
x = x / normalized
if self.orthogonal_init:
output = nn.Dense(
self.output_dim,
kernel_init=jax.nn.initializers.orthogonal(1e-2),
bias_init=jax.nn.initializers.zeros
)(jnp.concatenate([x, actions], axis=-1))
else:
output = nn.Dense(
self.output_dim,
kernel_init=jax.nn.initializers.variance_scaling(
1e-2, 'fan_in', 'uniform'
),
bias_init=jax.nn.initializers.zeros
)(jnp.concatenate([x, actions], axis=-1))
return x, jnp.squeeze(output, -1)
class TanhGaussianPolicy(nn.Module):
observation_dim: int
action_dim: int
arch: str = '256-256'
orthogonal_init: bool = False
log_std_multiplier: float = 1.0
log_std_offset: float = -1.0
action_scale: float = 1.0
def setup(self):
self.base_network = FullyConnectedNetwork(
output_dim=2 * self.action_dim, arch=self.arch, orthogonal_init=self.orthogonal_init
)
self.log_std_multiplier_module = Scalar(self.log_std_multiplier)
self.log_std_offset_module = Scalar(self.log_std_offset)
def log_prob(self, observations, actions):
if actions.ndim == 3:
observations = extend_and_repeat(observations, 1, actions.shape[1])
base_network_output = self.base_network(observations)
mean, log_std = jnp.split(base_network_output, 2, axis=-1)
log_std = self.log_std_multiplier_module() * log_std + self.log_std_offset_module()
log_std = jnp.clip(log_std, -20.0, 2.0)
action_distribution = distrax.Transformed(
distrax.MultivariateNormalDiag(mean, jnp.exp(log_std)),
distrax.Block(distrax.Tanh(), ndims=1)
)
return action_distribution.log_prob(actions / self.action_scale)
def __call__(self, rng, observations, deterministic=False, repeat=None):
if repeat is not None:
observations = extend_and_repeat(observations, 1, repeat)
base_network_output = self.base_network(observations)
mean, log_std = jnp.split(base_network_output, 2, axis=-1)
log_std = self.log_std_multiplier_module() * log_std + self.log_std_offset_module()
log_std = jnp.clip(log_std, -20.0, 2.0)
action_distribution = distrax.Transformed(
distrax.MultivariateNormalDiag(mean, jnp.exp(log_std)),
distrax.Block(distrax.Tanh(), ndims=1)
)
if deterministic:
mean = jnp.clip(mean, -6, 6)
samples = jnp.tanh(mean)
log_prob = action_distribution.log_prob(samples)
else:
samples, log_prob = action_distribution.sample_and_log_prob(seed=rng)
samples = samples * self.action_scale
return samples, log_prob
class ActionRepresentationPolicy(nn.Module):
observation_dim: int
action_dim: int
latent_action_dim: int
arch: str = '256-256'
orthogonal_init: bool = False
no_tanh: bool = False
log_std_multiplier: float = 1.0
log_std_offset: float = -1.0
# batch_norm: bool = True
def setup(self):
self.base_network = FullyConnectedNetwork(
output_dim=2 * self.latent_action_dim, arch=self.arch, orthogonal_init=self.orthogonal_init
)
self.log_std_multiplier_module = Scalar(self.log_std_multiplier)
self.log_std_offset_module = Scalar(self.log_std_offset)
def log_prob(self, observations, actions, latent_actions):
if actions.ndim == 3:
observations = extend_and_repeat(observations, 1, actions.shape[1])
x = jnp.concatenate([observations, actions], axis=-1)
base_network_output = self.base_network(x)
mean, log_std = jnp.split(base_network_output, 2, axis=-1)
log_std = self.log_std_multiplier_module() * log_std + self.log_std_offset_module()
log_std = jnp.clip(log_std, -20.0, 2.0)
if self.no_tanh:
action_distribution = distrax.MultivariateNormalDiag(mean, jnp.exp(log_std))
else:
action_distribution = distrax.Transformed(
distrax.MultivariateNormalDiag(mean, jnp.exp(log_std)),
distrax.Block(distrax.Tanh(), ndims=1)
)
return action_distribution.log_prob(latent_actions)
@nn.compact
@multiple_action_encode_function
def __call__(self, rng, observations, actions, deterministic=False, repeat=None):
if repeat is not None:
observations = extend_and_repeat(observations, 1, repeat)
x = jnp.concatenate([observations, actions], axis=-1)
base_network_output = self.base_network(x)
mean, log_std = jnp.split(base_network_output, 2, axis=-1)
log_std = self.log_std_multiplier_module() * log_std + self.log_std_offset_module()
log_std = jnp.clip(log_std, -20.0, 2.0)
if self.no_tanh:
action_distribution = distrax.MultivariateNormalDiag(mean, jnp.exp(log_std))
else:
action_distribution = distrax.Transformed(
distrax.MultivariateNormalDiag(mean, jnp.exp(log_std)),
distrax.Block(distrax.Tanh(), ndims=1)
)
if deterministic:
samples = jnp.tanh(mean)
log_prob = action_distribution.log_prob(samples)
else:
samples, log_prob = action_distribution.sample_and_log_prob(seed=rng)
return samples, log_prob
def get_statistics(self, rng, observations, actions, deterministic=False, repeat=None):
if repeat is not None:
observations = extend_and_repeat(observations, 1, repeat)
x = jnp.concatenate([observations, actions], axis=-1)
base_network_output = self.base_network(x)
mean, log_std = jnp.split(base_network_output, 2, axis=-1)
log_std = self.log_std_multiplier_module() * log_std + self.log_std_offset_module()
log_std = jnp.clip(log_std, -20.0, 2.0)
if self.no_tanh:
action_distribution = distrax.MultivariateNormalDiag(mean, jnp.exp(log_std))
else:
action_distribution = distrax.Transformed(
distrax.MultivariateNormalDiag(mean, jnp.exp(log_std)),
distrax.Block(distrax.Tanh(), ndims=1)
)
if deterministic:
samples = jnp.tanh(mean)
log_prob = action_distribution.log_prob(samples)
else:
samples, log_prob = action_distribution.sample_and_log_prob(seed=rng)
return samples, mean, log_std
class ActionOnlyRepresentationPolicy(nn.Module):
action_dim: int
latent_action_dim: int
arch: str = '256-256'
orthogonal_init: bool = False
no_tanh: bool = False
log_std_multiplier: float = 1.0
log_std_offset: float = -1.0
def setup(self):
self.base_network = FullyConnectedNetwork(
output_dim=2 * self.latent_action_dim, arch=self.arch, orthogonal_init=self.orthogonal_init
)
self.log_std_multiplier_module = Scalar(self.log_std_multiplier)
self.log_std_offset_module = Scalar(self.log_std_offset)
def log_prob(self, actions, latent_actions):
if actions.ndim == 3:
observations = extend_and_repeat(observations, 1, actions.shape[1])
base_network_output = self.base_network(actions)
mean, log_std = jnp.split(base_network_output, 2, axis=-1)
log_std = self.log_std_multiplier_module() * log_std + self.log_std_offset_module()
log_std = jnp.clip(log_std, -20.0, 2.0)
if self.no_tanh:
action_distribution = distrax.MultivariateNormalDiag(mean, jnp.exp(log_std))
else:
action_distribution = distrax.Transformed(
distrax.MultivariateNormalDiag(mean, jnp.exp(log_std)),
distrax.Block(distrax.Tanh(), ndims=1)
)
return action_distribution.log_prob(latent_actions)
@nn.compact
def __call__(self, rng, actions, deterministic=False, repeat=None):
if repeat is not None:
observations = extend_and_repeat(observations, 1, repeat)
base_network_output = self.base_network(actions)
mean, log_std = jnp.split(base_network_output, 2, axis=-1)
log_std = self.log_std_multiplier_module() * log_std + self.log_std_offset_module()
log_std = jnp.clip(log_std, -20.0, 2.0)
if self.no_tanh:
action_distribution = distrax.MultivariateNormalDiag(mean, jnp.exp(log_std))
else:
action_distribution = distrax.Transformed(
distrax.MultivariateNormalDiag(mean, jnp.exp(log_std)),
distrax.Block(distrax.Tanh(), ndims=1)
)
if deterministic:
samples = jnp.tanh(mean)
log_prob = action_distribution.log_prob(samples)
else:
samples, log_prob = action_distribution.sample_and_log_prob(seed=rng)
return samples, log_prob
def get_statistics(self, rng, actions, deterministic=False, repeat=None):
if repeat is not None:
observations = extend_and_repeat(observations, 1, repeat)
base_network_output = self.base_network(actions)
mean, log_std = jnp.split(base_network_output, 2, axis=-1)
log_std = self.log_std_multiplier_module() * log_std + self.log_std_offset_module()
log_std = jnp.clip(log_std, -20.0, 2.0)
if self.no_tanh:
action_distribution = distrax.MultivariateNormalDiag(mean, jnp.exp(log_std))
else:
action_distribution = distrax.Transformed(
distrax.MultivariateNormalDiag(mean, jnp.exp(log_std)),
distrax.Block(distrax.Tanh(), ndims=1)
)
if deterministic:
samples = jnp.tanh(mean)
log_prob = action_distribution.log_prob(samples)
else:
samples, log_prob = action_distribution.sample_and_log_prob(seed=rng)
return samples, mean, log_std
class ActionDecoder(nn.Module):
observation_dim: int
latent_action_dim: int
action_dim: int
arch: str = '256-256'
orthogonal_init: bool = False
@nn.compact
@multiple_action_decode_function
def __call__(self, observations, latent_actions):
x = jnp.concatenate([observations, latent_actions], axis=-1)
hidden_sizes = [int(h) for h in self.arch.split('-')]
for h in hidden_sizes:
if self.orthogonal_init:
x = nn.Dense(
h,
kernel_init=jax.nn.initializers.orthogonal(jnp.sqrt(2.0)),
bias_init=jax.nn.initializers.zeros
)(x)
else:
x = nn.Dense(h)(x)
x = nn.relu(x)
if self.orthogonal_init:
x = nn.Dense(
self.action_dim,
kernel_init=jax.nn.initializers.orthogonal(1e-2),
bias_init=jax.nn.initializers.zeros
)(x)
else:
x = nn.Dense(
self.action_dim,
kernel_init=jax.nn.initializers.variance_scaling(
1e-2, 'fan_in', 'uniform'
),
bias_init=jax.nn.initializers.zeros
)(x)
output = nn.tanh(x)
return output
class ActionSeperatedDecoder(nn.Module):
observation_dim: int
latent_action_dim: int
action_dim: int
arch: str = '256-256'
orthogonal_init: bool = False
# batch_norm: bool = True
@nn.compact
@multiple_action_decode_function
def __call__(self, observations, latent_actions):
x = observations
hidden_sizes = [int(h) for h in self.arch.split('-')]
for h in hidden_sizes:
if self.orthogonal_init:
x = nn.Dense(
h,
kernel_init=jax.nn.initializers.orthogonal(jnp.sqrt(2.0)),
bias_init=jax.nn.initializers.zeros
)(jnp.concatenate([x, latent_actions], axis=-1))
else:
x = nn.Dense(h)(jnp.concatenate([x, latent_actions], axis=-1))
# if self.batch_norm:
# x = nn.BatchNorm(use_running_average=not train_mode, momentum=0.9,
# epsilon=1e-5,
# dtype=jnp.float32)(x)
x = nn.relu(x)
if self.orthogonal_init:
x = nn.Dense(
self.action_dim,
kernel_init=jax.nn.initializers.orthogonal(1e-2),
bias_init=jax.nn.initializers.zeros
)(jnp.concatenate([x, latent_actions], axis=-1))
else:
x = nn.Dense(
self.action_dim,
kernel_init=jax.nn.initializers.variance_scaling(
1e-2, 'fan_in', 'uniform'
),
bias_init=jax.nn.initializers.zeros
)(jnp.concatenate([x, latent_actions], axis=-1))
output = nn.tanh(x)
return output
class Discriminator(nn.Module):
observation_dim: int
latent_action_dim: int
arch: str = '512-256'
dropout: bool = True
@nn.compact
def __call__(self, observations, latent_actions, train=False):
x = jnp.concatenate([observations, latent_actions], axis=-1)
hidden_sizes = [int(h) for h in self.arch.split('-')]
for h in hidden_sizes:
x = nn.Dense(h)(x)
# dropout
# layer norm
x = nn.leaky_relu(x, 0.2)
if self.dropout:
x = nn.Dropout(0.1)(x, deterministic=not train)
output = nn.Dense(1)(x)
return output
class SamplerPolicy(object):
def __init__(self, policy, params):
self.policy = policy
self.params = params
def update_params(self, params):
self.params = params
return self
@partial(jax.jit, static_argnames=('self', 'deterministic'))
def act(self, params, rng, observations, deterministic):
return self.policy.apply(params, rng, observations, deterministic, repeat=None)
def __call__(self, observations, deterministic=False):
actions, _ = self.act(self.params, next_rng(), observations, deterministic=deterministic)
assert jnp.all(jnp.isfinite(actions))
return jax.device_get(actions)
class SamplerDecoder(object):
def __init__(self, decoder, params):
self.decoder = decoder
self.params = params
def update_params(self, params):
self.params = params
return self
@partial(jax.jit, static_argnames=('self'))
def act(self, params, observations, actions_rep):
return self.decoder.apply(params, observations, actions_rep)
def __call__(self, observations, actions_rep):
actions = self.act(self.params, observations, actions_rep)
assert jnp.all(jnp.isfinite(actions))
return jax.device_get(actions)
class SamplerEncoder(object):
def __init__(self, encoder, params):
self.encoder = encoder
self.params = params
def update_params(self, params):
self.params = params
return self
@partial(jax.jit, static_argnames=('self'))
def act(self, params, rng, observations, actions):
return self.encoder.apply(params, rng, observations, actions)[0]
def __call__(self, rng, observations, actions):
actions = self.act(self.params, rng, observations, actions)
assert jnp.all(jnp.isfinite(actions))
return jax.device_get(actions) | 23,838 | 7,650 |
#!/usr/bin/env python
f = open("repair.log", "r");
lines = f.readlines();
cnt = 0;
for line in lines:
tokens = line.strip().split();
if (len(tokens) > 3):
if (tokens[0] == "Total") and (tokens[1] == "return"):
cnt += int(tokens[3]);
if (tokens[0] == "Total") and (tokens[2] == "different") and (tokens[3] == "repair"):
cnt += int(tokens[1]);
print "Total size: " + str(cnt);
| 423 | 164 |
"""
Copyright (c) 2019 Intel Corporation
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 logging
import numpy as np
from ..utils.blob import to_numpy
from ..utils.profile import PerformanceCounters
class OpenVINONet(object):
def __init__(self, xml_file_path, bin_file_path, device='CPU',
plugin_dir=None, cpu_extension_lib_path=None, collect_perf_counters=False):
from openvino.inference_engine import IENetwork, IEPlugin
logging.info('Creating {} plugin...'.format(device))
self.plugin = IEPlugin(device=device, plugin_dirs=plugin_dir)
if cpu_extension_lib_path and 'CPU' in device:
logging.info('Adding CPU extensions...')
self.plugin.add_cpu_extension(cpu_extension_lib_path)
# Read IR
logging.info('Reading network from IR...')
self.net = IENetwork(model=xml_file_path, weights=bin_file_path)
if self.plugin.device == 'CPU':
logging.info('Check that all layers are supported...')
supported_layers = self.plugin.get_supported_layers(self.net)
not_supported_layers = [l for l in self.net.layers.keys() if l not in supported_layers]
if len(not_supported_layers) != 0:
unsupported_info = '\n\t'.join('{} ({} with params {})'.format(layer_id,
self.net.layers[layer_id].type,
str(self.net.layers[layer_id].params))
for layer_id in not_supported_layers)
logging.warning('Following layers are not supported '
'by the plugin for specified device {}:'
'\n\t{}'.format(self.plugin.device, unsupported_info))
logging.warning('Please try to specify cpu extensions library path.')
raise ValueError('Some of the layers are not supported.')
logging.info('Loading network to plugin...')
self.exec_net = self.plugin.load(network=self.net, num_requests=1)
self.perf_counters = None
if collect_perf_counters:
self.perf_counters = PerformanceCounters()
def __call__(self, inputs):
outputs = self.exec_net.infer(inputs)
if self.perf_counters:
perf_counters = self.exec_net.requests[0].get_perf_counts()
self.perf_counters.update(perf_counters)
return outputs
def print_performance_counters(self):
if self.perf_counters:
self.perf_counters.print()
def __del__(self):
del self.net
del self.exec_net
del self.plugin
class MaskRCNNOpenVINO(OpenVINONet):
def __init__(self, *args, **kwargs):
super(MaskRCNNOpenVINO, self).__init__(*args, **kwargs)
required_input_keys = {'im_data', 'im_info'}
assert required_input_keys == set(self.net.inputs.keys())
required_output_keys = {'boxes', 'scores', 'classes', 'raw_masks'}
assert required_output_keys.issubset(self.net.outputs.keys())
self.n, self.c, self.h, self.w = self.net.inputs['im_data'].shape
assert self.n == 1, 'Only batch 1 is supported.'
def __call__(self, im_data, im_info, **kwargs):
im_data = to_numpy(im_data[0])
im_info = to_numpy(im_info[0])
if (self.h - im_data.shape[1] < 0) or (self.w - im_data.shape[2] < 0):
raise ValueError('Input image should resolution of {}x{} or less, '
'got {}x{}.'.format(self.w, self.h, im_data.shape[2], im_data.shape[1]))
im_data = np.pad(im_data, ((0, 0),
(0, self.h - im_data.shape[1]),
(0, self.w - im_data.shape[2])),
mode='constant', constant_values=0).reshape(1, self.c, self.h, self.w)
im_info = im_info.reshape(1, *im_info.shape)
output = super().__call__(dict(im_data=im_data, im_info=im_info))
classes = output['classes']
valid_detections_mask = classes > 0
classes = classes[valid_detections_mask]
boxes = output['boxes'][valid_detections_mask]
scores = output['scores'][valid_detections_mask]
masks = output['raw_masks'][valid_detections_mask]
return boxes, classes, scores, np.full(len(classes), 0, dtype=np.int32), masks
| 4,968 | 1,501 |
from django.apps import AppConfig
class CohortsConfig(AppConfig):
name = 'scuole.cohorts'
| 96 | 36 |
"""Place fixtures in this file for use across all test files"""
import pytest
@pytest.fixture(scope="function")
def logger(caplog):
caplog.set_level("DEBUG")
return caplog
@pytest.fixture
def log_and_exit_mock(mocker):
return mocker.patch("scripts.generate_pipeline.log_and_exit")
| 297 | 102 |
import numpy as np
import moch
import soch
import os
import sys
import scipy.io
import thorns
def main(parseID):
parseIn = parseID + 'In.mat'
parseOut = parseID + 'Out.mat'
parse = scipy.io.loadmat(parseIn)
os.remove(parseIn)
lagSpace = 1. * parse['lagSpace'] / 1000
parsStruct = parse['pars'][0, 0]
# Parametres
est = {'duration' : 1. * parsStruct['est'][0,0]['dur'][0][0] / 1000,
'loudness' : 1. * parsStruct['est'][0,0]['loud'][0][0],
'intv' : 1. * parsStruct['est'][0,0]['interval'][0] / 1000,
'onset' : 1. * parsStruct['est'][0,0]['onset' ][0][0] / 1000,
'tail' : 1. * parsStruct['est'][0,0]['tail'][0][0] / 1000,
'maskN' : parsStruct['est'][0,0]['maskNoise'][0][0],
'filename' : parsStruct['est'][0,0]['filename'][0],
'bandpass' : parsStruct['est'][0,0]['bandpass'][0],
'save' : parsStruct['est'][0,0]['save'][0]
}
if est['filename'] == -1:
est['type'] = parsStruct['est'][0,0]['type'][0]
est['freq'] = parsStruct['est'][0,0]['f'][0][0]
est['harms'] = parsStruct['est'][0,0]['harms'][0]
est['harmFact'] = parsStruct['est'][0,0]['harmFact'][0][0]
est['shift'] = parsStruct['est'][0,0]['shift'][0][0]
est['nOfIts'] = parsStruct['est'][0,0]['nOfIts'][0][0]
est['notes'] = parsStruct['est'][0,0]['notes'][0]
est['tuning'] = parsStruct['est'][0,0]['tuning'][0]
est['noiseOff'] = 1. * parsStruct['est'][0,0]['noiseOff'][0][0] / 1000
else:
est['type'] = 'external'
par = {'periphFs' : 100000,
'cochChanns' : (125, 10000, 30),
'SACFTau' : 1. * parsStruct['tauSACF'][0,0] / 1000,
'subCortTau' : 1. * parsStruct['tauSubthal'][0,0] / 1000,
'solvOnset' : 1. * parsStruct['solvOnset'][0] / 1000,
'subCortFs' : 100000,
'subCortAff' : parsStruct['subCortAff'][0,0],
'regularise' : parsStruct['regularise'][0,0],
'mu0' : parsStruct['mu0'][0,0],
'SACFGround' : parsStruct['SACFGround'][0,0],
'cortFs' : parsStruct['cortFs'][0,0],
'subDelay' : 1. * parsStruct['subDelay'][0,0] / 1000,
'subDelayDy' : 1. * parsStruct['subDelayDy'][0,0] / 1000,
}
if ('chord' in est['type']) and (est['notes'][0] != est['notes'][1]):
est['onset'] += par['subDelayDy']
par['mu0'] = 2 * par['mu0']
else:
est['onset'] += par['subDelay']
[A, n, b] = thalamicInput(lagSpace, par, est)
duration = 1.* len(A) / par['cortFs']
dti = 1./par['cortFs']
timeSpace = np.arange(start = dti, stop = duration + dti, step = dti)
if 'off' in est.keys():
timeSpace = timeSpace - est['off']
scipy.io.savemat(parseOut, {'A':A, 'n':n, 'b':b, 'timeSpace': timeSpace})
def thalamicInput(lagSpace, par, est, raster = False):
fs = par['periphFs']
# Subcortical processing
sound = soch.createStimulus(est, par['periphFs'])
prob = moch.peripheral(sound, par)
[A, n, b] = moch.subcortical(prob, lagSpace, par)
for i in range(1, par['subCortAff']):
sound = soch.createStimulus(est, par['periphFs'])
prob = moch.peripheral(sound, par)
[A0, n0, b0] = moch.subcortical(prob, lagSpace, par)
A = A + A0
n = n + n0
b = b + b0
A = (1. / par['subCortAff']) * A
n = (1. / par['subCortAff']) * n
b = (1. / par['subCortAff']) * b
if raster:
anfTrains = moch.peripheralSpikes(sound, par, fs = -1)
thorns.plot_raster(anfTrains)
thorns.show()
return [A, n, b]
main(sys.argv[1])
| 3,763 | 1,633 |
# (C) British Crown Copyright 2011 - 2018, Met Office
#
# This file is part of cartopy.
#
# cartopy is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# cartopy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with cartopy. If not, see <https://www.gnu.org/licenses/>.
from __future__ import (absolute_import, division, print_function)
import types
import numpy as np
from numpy.testing import assert_array_almost_equal as assert_arr_almost
import pytest
import shapely.geometry as sgeom
import cartopy.crs as ccrs
import cartopy.io.img_tiles as cimgt
#: Maps Google tile coordinates to native mercator coordinates as defined
#: by https://goo.gl/pgJi.
KNOWN_EXTENTS = {(0, 0, 0): (-20037508.342789244, 20037508.342789244,
-20037508.342789244, 20037508.342789244),
(2, 0, 2): (0., 10018754.17139462,
10018754.17139462, 20037508.342789244),
(0, 2, 2): (-20037508.342789244, -10018754.171394622,
-10018754.171394622, 0),
(2, 2, 2): (0, 10018754.17139462,
-10018754.171394622, 0),
(8, 9, 4): (0, 2504688.542848654,
-5009377.085697312, -2504688.542848654),
}
if ccrs.PROJ4_VERSION == (5, 0, 0):
KNOWN_EXTENTS = {
(0, 0, 0): (-20037508.342789244, 20037508.342789244,
-19994827.892149, 19994827.892149),
(2, 0, 2): (0, 10018754.171395,
9997413.946075, 19994827.892149),
(0, 2, 2): (-20037508.342789244, -10018754.171394622,
-9997413.946075, 0),
(2, 2, 2): (0, 10018754.171395,
-9997413.946075, 0),
(8, 9, 4): (0, 2504688.542849,
-4998706.973037, -2499353.486519),
}
def GOOGLE_IMAGE_URL_REPLACEMENT(self, tile):
url = ('https://chart.googleapis.com/chart?chst=d_text_outline&'
'chs=256x256&chf=bg,s,00000055&chld=FFFFFF|16|h|000000|b||||'
'Google:%20%20(' + str(tile[0]) + ',' + str(tile[1]) + ')'
'|Zoom%20' + str(tile[2]) + '||||||______________________'
'______')
return url
def test_google_tile_styles():
"""
Tests that setting the Google Maps tile style works as expected.
This is essentially just assures information is properly propagated through
the class structure.
"""
reference_url = ("https://mts0.google.com/vt/lyrs={style}@177000000&hl=en"
"&src=api&x=1&y=2&z=3&s=G")
tile = ["1", "2", "3"]
# Default is street.
gt = cimgt.GoogleTiles()
url = gt._image_url(tile)
assert reference_url.format(style="m") == url
# Street
gt = cimgt.GoogleTiles(style="street")
url = gt._image_url(tile)
assert reference_url.format(style="m") == url
# Satellite
gt = cimgt.GoogleTiles(style="satellite")
url = gt._image_url(tile)
assert reference_url.format(style="s") == url
# Terrain
gt = cimgt.GoogleTiles(style="terrain")
url = gt._image_url(tile)
assert reference_url.format(style="t") == url
# Streets only
gt = cimgt.GoogleTiles(style="only_streets")
url = gt._image_url(tile)
assert reference_url.format(style="h") == url
# Exception is raised if unknown style is passed.
with pytest.raises(ValueError):
cimgt.GoogleTiles(style="random_style")
def test_google_wts():
gt = cimgt.GoogleTiles()
ll_target_domain = sgeom.box(-15, 50, 0, 60)
multi_poly = gt.crs.project_geometry(ll_target_domain, ccrs.PlateCarree())
target_domain = multi_poly.geoms[0]
with pytest.raises(AssertionError):
list(gt.find_images(target_domain, -1))
assert (tuple(gt.find_images(target_domain, 0)) ==
((0, 0, 0),))
assert (tuple(gt.find_images(target_domain, 2)) ==
((1, 1, 2), (2, 1, 2)))
assert (list(gt.subtiles((0, 0, 0))) ==
[(0, 0, 1), (0, 1, 1), (1, 0, 1), (1, 1, 1)])
assert (list(gt.subtiles((1, 0, 1))) ==
[(2, 0, 2), (2, 1, 2), (3, 0, 2), (3, 1, 2)])
with pytest.raises(AssertionError):
gt.tileextent((0, 1, 0))
assert_arr_almost(gt.tileextent((0, 0, 0)), KNOWN_EXTENTS[(0, 0, 0)])
assert_arr_almost(gt.tileextent((2, 0, 2)), KNOWN_EXTENTS[(2, 0, 2)])
assert_arr_almost(gt.tileextent((0, 2, 2)), KNOWN_EXTENTS[(0, 2, 2)])
assert_arr_almost(gt.tileextent((2, 2, 2)), KNOWN_EXTENTS[(2, 2, 2)])
assert_arr_almost(gt.tileextent((8, 9, 4)), KNOWN_EXTENTS[(8, 9, 4)])
def test_tile_bbox_y0_at_south_pole():
tms = cimgt.MapQuestOpenAerial()
# Check the y0_at_north_pole keywords returns the appropriate bounds.
assert_arr_almost(tms.tile_bbox(8, 6, 4, y0_at_north_pole=False),
np.array(KNOWN_EXTENTS[(8, 9, 4)]).reshape([2, 2]))
def test_tile_find_images():
gt = cimgt.GoogleTiles()
# Test the find_images method on a GoogleTiles instance.
ll_target_domain = sgeom.box(-10, 50, 10, 60)
multi_poly = gt.crs.project_geometry(ll_target_domain, ccrs.PlateCarree())
target_domain = multi_poly.geoms[0]
assert (list(gt.find_images(target_domain, 4)) ==
[(7, 4, 4), (7, 5, 4), (8, 4, 4), (8, 5, 4)])
@pytest.mark.network
def test_image_for_domain():
gt = cimgt.GoogleTiles()
gt._image_url = types.MethodType(GOOGLE_IMAGE_URL_REPLACEMENT, gt)
ll_target_domain = sgeom.box(-10, 50, 10, 60)
multi_poly = gt.crs.project_geometry(ll_target_domain, ccrs.PlateCarree())
target_domain = multi_poly.geoms[0]
_, extent, _ = gt.image_for_domain(target_domain, 6)
ll_extent = ccrs.Geodetic().transform_points(gt.crs,
np.array(extent[:2]),
np.array(extent[2:]))
if ccrs.PROJ4_VERSION == (5, 0, 0):
assert_arr_almost(ll_extent[:, :2],
[[-11.25, 49.033955],
[11.25, 61.687101]])
else:
assert_arr_almost(ll_extent[:, :2],
[[-11.25, 48.92249926],
[11.25, 61.60639637]])
def test_quadtree_wts():
qt = cimgt.QuadtreeTiles()
ll_target_domain = sgeom.box(-15, 50, 0, 60)
multi_poly = qt.crs.project_geometry(ll_target_domain, ccrs.PlateCarree())
target_domain = multi_poly.geoms[0]
with pytest.raises(ValueError):
list(qt.find_images(target_domain, 0))
assert qt.tms_to_quadkey((1, 1, 1)) == '1'
assert qt.quadkey_to_tms('1') == (1, 1, 1)
assert qt.tms_to_quadkey((8, 9, 4)) == '1220'
assert qt.quadkey_to_tms('1220') == (8, 9, 4)
assert tuple(qt.find_images(target_domain, 1)) == ('0', '1')
assert tuple(qt.find_images(target_domain, 2)) == ('03', '12')
assert list(qt.subtiles('0')) == ['00', '01', '02', '03']
assert list(qt.subtiles('11')) == ['110', '111', '112', '113']
with pytest.raises(ValueError):
qt.tileextent('4')
assert_arr_almost(qt.tileextent(''), KNOWN_EXTENTS[(0, 0, 0)])
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((2, 0, 2), google=True)),
KNOWN_EXTENTS[(2, 0, 2)])
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((0, 2, 2), google=True)),
KNOWN_EXTENTS[(0, 2, 2)])
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((2, 0, 2), google=True)),
KNOWN_EXTENTS[(2, 0, 2)])
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((2, 2, 2), google=True)),
KNOWN_EXTENTS[(2, 2, 2)])
assert_arr_almost(qt.tileextent(qt.tms_to_quadkey((8, 9, 4), google=True)),
KNOWN_EXTENTS[(8, 9, 4)])
def test_mapbox_tiles_api_url():
token = 'foo'
map_name = 'bar'
tile = [0, 1, 2]
exp_url = ('https://api.mapbox.com/v4/mapbox.bar'
'/2/0/1.png?access_token=foo')
mapbox_sample = cimgt.MapboxTiles(token, map_name)
url_str = mapbox_sample._image_url(tile)
assert url_str == exp_url
def test_mapbox_style_tiles_api_url():
token = 'foo'
username = 'baz'
map_id = 'bar'
tile = [0, 1, 2]
exp_url = ('https://api.mapbox.com/styles/v1/'
'baz/bar/tiles/256/2/0/1'
'?access_token=foo')
mapbox_sample = cimgt.MapboxStyleTiles(token, username, map_id)
url_str = mapbox_sample._image_url(tile)
assert url_str == exp_url
| 8,911 | 3,960 |
import os
import pdb
import h5py
import pickle
import numpy as np
from scipy.io import loadmat
import cv2
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
import csv
import bisect
import matplotlib as mpl
import matplotlib.cm as cm
import tensorflow as tf
# from bilateral_filter import bilateral_filter
from tools import *
def resave_imu_data():
dataset = '/freezer/nyudepthV2_raw'
seqs = os.listdir(dataset)
for seq in seqs:
seq_dir = dataset + '/' + seq
for data in os.listdir(seq_dir):
if data[0] == 'a':
imu_data_path = seq_dir + '/' + data
resave_imu_data_path = seq_dir + '/' + data[:-4] + '.txt'
call_resave_imu(imu_data_path, resave_imu_data_path)
def call_resave_imu(orig_path, resave_path):
command = './resave_imu ' + orig_path + ' ' + resave_path
os.system(command)
def collect_acc_data(folder):
data_list = []
for file in os.listdir(folder):
if file[0] == 'a' and file[-1] == 't':
data_list.append(folder + '/' + file)
return sorted(data_list)
def get_acc_timestamp(path):
return float(path.split('-')[1])
def read_acc_data(file_path):
timestamp = get_acc_timestamp(file_path)
file = open(file_path, 'r')
data = file.read().split(',')
for i in range(len(data)):
data[i] = float(data[i])
data.insert(0, timestamp)
return data
def plot_acc_data(folder):
acc_path = collect_acc_data(folder)
x = []
y = []
z = []
for path in acc_path:
data = read_acc_data(path)
x.append(data[1])
y.append(data[2])
z.append(data[3])
plt.plot(x)
plt.plot(y)
plt.plot(z)
plt.show()
def compute_acc_vel_pos(acc_data_1, acc_data_2, v_1, p_1):
t1 = acc_data_1[0]
t2 = acc_data_2[0]
t_delta = t2 - t1
acc_xyz_1 = np.array(acc_data_1[1:4])
acc_xyz_2 = np.array(acc_data_2[1:4])
a_avg = (acc_xyz_1 + acc_xyz_2) / 2.
v_2 = v_1 + a_avg * t_delta
p_2 = p_1 + v_1 * t_delta + a_avg * t_delta * t_delta / 2.
# pdb.set_trace()
return v_2, p_2
def plot_imu_traj(folder):
acc_path = collect_acc_data(folder)
p_x = []
p_y = []
p_z = []
v_cur = np.array([0., 0., 0.])
p_cur = np.array([0., 0., 0.])
N = len(acc_path)
for idx in range(N-1):
p_x.append(p_cur[0])
p_y.append(p_cur[1])
p_z.append(p_cur[2])
acc_1 = read_acc_data(acc_path[idx])
acc_2 = read_acc_data(acc_path[idx + 1])
v_cur, p_cur = compute_acc_vel_pos(acc_1, acc_2, v_cur, p_cur)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#ax.scatter(p_x[:], p_y[:], p_z[:0])
ax.plot(p_x[:-1], p_y[:-1], p_z[:-1])
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()
# plt.plot(p_x)
# plt.plot(p_y)
# plt.plot(p_z)
# plt.show()
def plot_trajectory(data_file_name):
data = open(data_file_name,"rb")
poses_log = pickle.load(data)
poses_mat_log = []
import torch
for i in range(len(poses_log.keys())):
pose = poses_log[i]
pose = np.expand_dims(pose, axis=0)
pose = np.expand_dims(pose, axis=0)
pose_mat = transformation_from_parameters(torch.tensor(pose[:, :, :3]).float(), torch.tensor(pose[:, :, 3:]).float(), False)
poses_mat_log.append(pose_mat.numpy())
xyzs = np.array(dump_xyz(poses_mat_log))
xs = []
ys = []
zs = []
for i in range(xyzs.shape[0]):
xs.append(xyzs[i][0])
ys.append(xyzs[i][1])
zs.append(xyzs[i][2])
plt.plot(xs, ys)
plt.savefig(
'/home/jiatian/dataset/rs_eval/pose/' + str(i).zfill(6) + '.jpg')
def delete_folder(folder, num):
dirlist = sorted(os.listdir(folder))
num_total = len(dirlist)
for idx in range(num):
datapath = folder + '/' + dirlist[idx]
os.remove(datapath)
for idx in range(num_total - num, num_total):
datapath = folder + '/' + dirlist[idx]
os.remove(datapath)
def vis_rgb_pose_image(folder_1, folder_2):
dirlist_1 = sorted(os.listdir(folder_1))
dirlist_2 = sorted(os.listdir(folder_2))
for idx in range(len(dirlist_1)):
data_1 = Image.open(folder_1 + '/' + dirlist_1[idx]).convert('RGB')
data_2 = Image.open(folder_2 + '/' + dirlist_2[idx]).convert('RGB')
image_show = np.hstack((np.array(data_1), np.array(data_2)))
img = Image.fromarray(image_show)
img.save('/home/jiatian/dataset/rs_eval/comp/' + dirlist_2[idx])
def read_csv(path, folder):
dict_gt = {}
ts = []
with open(path, mode='r') as csv_file:
csv_reader = csv.DictReader(csv_file)
line_count = 0
for row in csv_reader:
if line_count == 0:
print(f'Column names are {", ".join(row)}')
line_count += 1
line_count += 1
dict_gt[row['#timestamp']] = row
ts.append(row['#timestamp'])
print(f'Processed {line_count} lines.')
dict_img = {}
dirlist = sorted(os.listdir(folder))
match = 0
unmatch = 0
ts_valid = []
for img_path in dirlist:
key = img_path[:-4]
ts_valid.append(key)
try:
print(dict_gt[key])
match += 1
dict_img[key] = dict_gt[key]
except:
unmatch += 1
idx = bisect.bisect_left(ts, key)
if idx == len(ts):
idx -= 1
dict_img[key] = dict_gt[ts[idx]]
print('ERROR', key)
print('UNMATCH', unmatch)
# plot_xyz(ts_valid, dict_img)
calAxisAngle(ts_valid, dict_img)
def calAxisAngle(ts_valid, dict_img):
nums = len(ts_valid)
dict_pose = {}
for i in range(nums-2):
ts_cur = ts_valid[i]
ts_next = ts_valid[i+2]
data_cur = dict_img[ts_cur]
data_next = dict_img[ts_next]
diff_tx = float(data_next[' p_RS_R_x [m]']) - float(data_cur[' p_RS_R_x [m]'])
diff_ty = float(data_next[' p_RS_R_y [m]']) - float(data_cur[' p_RS_R_y [m]'])
diff_tz = float(data_next[' p_RS_R_z [m]']) - float(data_cur[' p_RS_R_z [m]'])
diff_qw = float(data_next[' q_RS_w []']) - float(data_cur[' q_RS_w []'])
diff_qx = float(data_next[' q_RS_x []']) - float(data_cur[' q_RS_x []'])
diff_qy = float(data_next[' q_RS_y []']) - float(data_cur[' q_RS_y []'])
diff_qz = float(data_next[' q_RS_z []']) - float(data_cur[' q_RS_z []'])
diff_norm = np.linalg.norm([diff_qw, diff_qx, diff_qy, diff_qz])
diff_qw = diff_qw / (diff_norm + 1e-7)
diff_qx = diff_qx / (diff_norm + 1e-7)
diff_qy = diff_qy / (diff_norm + 1e-7)
diff_qz = diff_qz / (diff_norm + 1e-7)
angle = 2 * np.arccos(diff_qw)
rx = diff_qx * angle / np.sqrt(1 - diff_qw * diff_qw)
ry = diff_qy * angle / np.sqrt(1 - diff_qw * diff_qw)
rz = diff_qz * angle / np.sqrt(1 - diff_qw * diff_qw)
dict_pose[ts_cur] = [rx, ry, rz, diff_tx, diff_ty, diff_tz]
dict_file_name = '/home/jiatian/dataset/euroc/V1_01_easy/mav0/cam0_pose.pkl'
f = open(dict_file_name, "wb")
pickle.dump(dict_pose, f)
f.close()
def plot_xyz(ts, dict):
xs = []
ys = []
zs = []
nums = len(ts)
for i in range(nums):
time = ts[i]
xs.append(float(dict[time][' p_RS_R_x [m]']))
ys.append(float(dict[time][' p_RS_R_y [m]']))
zs.append(float(dict[time][' p_RS_R_z [m]']))
# ' q_RS_w []' ' q_RS_x []' ' q_RS_y []' ' q_RS_z []'
plt.plot(xs, ys)
plt.savefig(
'/home/jiatian/dataset/euroc/V1_01_easy/mav0/cam0/traj' + str(i).zfill(6) + '.jpg')
if __name__ == "__main__":
read_csv('/home/jiatian/dataset/euroc/V1_01_easy/mav0/state_groundtruth_estimate0/data.csv', '/home/jiatian/dataset/euroc/V1_01_easy/mav0/cam0/data')
# vis_rgb_pose_image('/home/jiatian/dataset/recordvi/402-000-undistort', '/home/jiatian/dataset/rs_eval/pose')
# plot_trajectory('/home/jiatian/dataset/rs_eval/pose/poses_log.pickle')
# delete_folder('/home/jiatian/dataset/realsense/recordvi-4-02-00/402-004-undistort', 300) | 8,310 | 3,392 |
from ..gene import Gene
from ..scalar_gene_property import ScalarGeneProperty
from ..color_gene_property import ColorGeneProperty
from ...visible_objects.sphere import Sphere
class SphereGene(Gene):
def __init__(self):
self._size_property = ScalarGeneProperty(min=0.1, max=20, value=2)
self._color_property = ColorGeneProperty(hsv=[0.0, 0.0, 1.0])
def all_properties(self):
return [
self._size_property,
self._color_property
]
def express(self, genome_expression):
location = [0, 0, 0] # TODO genome_expression stack position
size = self._size_property.value
hsv = self._color_property.value
sphere = Sphere(location=location, size=size, hsv=hsv)
genome_expression.add_visible_object(sphere)
| 808 | 261 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 argparse
import doctest
import os
from subprocess import check_output
from typing import Dict, List, TextIO
THRIFT_HEADER = """
# This file is generated by `fbcode/thrift/test/testset:generator`
# {'@' + 'generated'}
namespace cpp2 apache.thrift.test.testset
"""
FIELD_COUNT = 2 # Number of fields per structs
def format_dict(
d: Dict[str, str], key_format: str, value_format: str
) -> Dict[str, str]:
"""Format key/value of dict
>>> result = format_dict({"foo_k": "foo_v", "bar_k": "bar_v"}, 'prefix_{}', "{}_suffix")
>>> result == {'prefix_foo_k': 'foo_v_suffix', 'prefix_bar_k': 'bar_v_suffix'}
True
"""
return {key_format.format(k): value_format.format(d[k]) for k in d}
PRIMITIVE_TYPES = [
"bool",
"byte",
"i16",
"i32",
"i64",
"float",
"double",
"binary",
"string",
]
def generate_union_names_to_types() -> Dict[str, str]:
""" Generate display name to thrift type mapping in union. Display name will be used in file name, rule name, etc """
ret = {t: t for t in PRIMITIVE_TYPES}
ret.update(format_dict(ret, "set_{}", "set<{}>"))
ret.update(format_dict(ret, "map_string_{}", "map<string, {}>"))
ret.update(format_dict(ret, "{}_cpp_ref", "{} (cpp.ref = 'true')"))
return ret
def generate_struct_names_to_types() -> Dict[str, str]:
""" Similar to thrift types in union. Difference is that unions cannot contain qualified fields. """
ret = generate_union_names_to_types()
ret.update(
**format_dict(ret, "optional_{}", "optional {}"),
**format_dict(ret, "required_{}", "required {}"),
)
return ret
def generate_class(class_type: str, name: str, types: List[str]) -> str:
"""Generate thrift struct from types
>>> print(generate_class("struct", "Foo", ["i64", "optional string", "set<i32> (cpp.ref = 'true')"]))
struct Foo {
1: i64 field_1;
2: optional string field_2;
3: set<i32> (cpp.ref = 'true') field_3;
}
"""
lines = [f"{class_type} {name} {{"]
for i, t in enumerate(types):
lines.append(" {0}: {1} field_{0};".format(i + 1, t))
lines.append(f'}} (any_type.name="facebook.com/thrift/test/testset/{name}")')
return "\n".join(lines)
def print_thrift_class(
file: TextIO, class_type: str, names_to_types: Dict[str, str]
) -> None:
name = "empty_" + class_type
print(generate_class(class_type, name, []), file=file)
classes = [name]
for display_name, type in names_to_types.items():
class_name = class_type + "_" + display_name
classes.append(class_name)
print(generate_class(class_type, class_name, [type] * FIELD_COUNT), file=file)
# Thrift class that contains all other generated classes with same-type
print(generate_class(class_type, class_type + "_all", classes), file=file)
def gen_struct_all(path: str) -> None:
with open(path, "w") as file:
print(THRIFT_HEADER, file=file)
print_thrift_class(file, "struct", generate_struct_names_to_types())
print_thrift_class(file, "union", generate_union_names_to_types())
def main() -> None:
doctest.testmod()
os.chdir(check_output(["buck", "root"]).strip())
parser = argparse.ArgumentParser()
parser.add_argument("--install_dir", required=True)
parser.add_argument("--filename", required=True)
args = parser.parse_args()
gen_struct_all(os.path.join(args.install_dir, args.filename))
if __name__ == "__main__":
main()
| 4,092 | 1,408 |
from tornado.testing import AsyncHTTPTestCase
import unittest
def run_tests(application):
BaseAsyncTest.application = application
BaseAsyncTest.database_name = application.settings['database_name']
BaseAsyncTest.conn = application.settings['conn']
testsuite = unittest.TestLoader().discover('test')
return unittest.TextTestRunner(verbosity=2).run(testsuite)
class BaseAsyncTest(AsyncHTTPTestCase):
application = None
conn = None
database_name = ''
def get_app(self):
return self.application
| 540 | 150 |
from django.shortcuts import render
# Create your views here.
def landing(request):
title = "Home"
context = {"title": title}
return render(request, 'landing.html', context)
| 189 | 57 |
'''def operations(a,b,c):
if(c=='+'):
return a+b
elif(c=='-'):
return a-b
elif(c=='*'):
return a*b
elif(c=='/'):
return a/b
elif(c=='%'):
return a%b
elif(c=='**'):
return a**b
elif(c=='//'):
return a//b
else:
print("Non specfied soperation")
print(operations(10,5,'+'))
print(operations(10,5,'-'))
print(operations(10,5,'*'))
print(operations(10,5,'/'))
print(operations(10,5,'%'))
print(operations(10,2,'**'))
print(operations(10,3,'//'))
print(operations((int(input("Enter a "))),int(input("Enter b ")),input("Enter c ")))'''
def evaluate():
print(eval(input("Enter an arithmetic expression: ")))
evaluate() | 736 | 300 |
import os
import csv
import itertools
from datetime import datetime
class DataLoader:
def __init__(self):
self.file_name = os.path.join(os.getcwd(), 'data', 'covid_19_data.csv')
self.data_set_full = []
self.data_set_grouped = []
def prepare_data_set_full(self):
"""
prepare the full dataset function
:return:
"""
reader = csv.DictReader(open(self.file_name, 'r', newline='\n'))
for line in reader:
_line = {k: v for k, v in line.items()}
_line['ObservationDate'] = datetime.strptime(line['ObservationDate'], '%m/%d/%Y').strftime('%m-%d-%Y')
self.data_set_full.append(_line)
self.data_set_full = sorted(self.data_set_full, key=lambda x: x['ObservationDate'])
return self.data_set_full
def prepare_data_set_grouped(self):
"""
prepare the grouped by date dataset function
:return:
"""
for key, group in itertools.groupby(self.data_set_full, key=lambda x: x['ObservationDate']):
_group = list(group)
self.data_set_grouped.append((key,
sum([float(r.get('Confirmed')) for r in _group]),
sum([float(r.get('Deaths')) for r in _group]),
sum([float(r.get('Recovered')) for r in _group])
))
return self.data_set_grouped
| 1,491 | 444 |
"""
ddupdate plugin updating data on dnspark.com.
See: ddupdate(8)
See: https://dnspark.zendesk.com/hc/en-us/articles/
216322723-Dynamic-DNS-API-Documentation
"""
from ddupdate.ddplugin import ServicePlugin, ServiceError
from ddupdate.ddplugin import http_basic_auth_setup, get_response
class DnsparkPlugin(ServicePlugin):
"""
Update a dns entry on dnspark.com.
Supports most address plugins including default-web-ip, default-if and
ip-disabled. ipv6 is not supported.
You need to own a domain and delegate it to dnspark to use the service,
nothing like myhost.dnspark.net is supported.
Note that the dynamic dns user and password is a separate set of
credentials created in the web interface.
netrc: Use a line like
machine control.dnspark.com login <username> password <password>
Options:
none
"""
_name = 'dnspark.com'
_oneliner = 'Updates on https://dnspark.com/'
_url = "https://control.dnspark.com/api/dynamic/update.php?hostname={0}"
def register(self, log, hostname, ip, options):
"""Implement ServicePlugin.register()."""
url = self._url.format(hostname)
if ip and ip.v4:
url += "&ip=" + ip.v4
http_basic_auth_setup(url)
reply = get_response(log, url).strip()
if reply not in ['ok', 'nochange']:
raise ServiceError("Unexpected update reply: " + reply)
| 1,430 | 453 |
# C O D E A Z/ Samil
from userbot import BOT_USERNAME
from userbot.events import register
# ██████ LANGUAGE CONSTANTS ██████ #
from userbot.language import get_value
LANG = get_value("__helpme")
# ████████████████████████████████ #
@register(outgoing=True, pattern="^.yard[iı]m|^.help")
async def yardim(event):
tgbotusername = BOT_USERNAME
if tgbotusername is not None:
results = await event.client.inline_query(
tgbotusername,
"@Codeaz"
)
await results[0].click(
event.chat_id,
reply_to=event.reply_to_msg_id,
hide_via=True
)
await event.delete()
else:
await event.edit(LANG["NO_BOT"])
| 714 | 272 |
dias = int(input("quantos dias voce deseja alugar o carro? "))
km = float(input("Quantos kilometros você andou? "))
pago = dias * 60 + (km * 0.15)
print("o valor do aluguel do carro foi de R$ {:.2f}" .format(pago))
| 216 | 92 |
from functools import lru_cache
from typing import Sequence, Dict, Union, Tuple, List, Optional, cast, Iterable
import numpy as np
import quimb
import quimb.tensor as qtn
import cirq
@lru_cache()
def _qpos_tag(qubits: Union[cirq.LineQubit, Tuple[cirq.LineQubit]]):
"""Given a qubit or qubits, return a "position tag" (used for drawing).
For multiple qubits, the tag is for the first qubit.
"""
if isinstance(qubits, cirq.LineQubit):
return _qpos_tag((qubits,))
x = min(q.x for q in qubits)
return f'q{x}'
@lru_cache()
def _qpos_y(qubits: Union[cirq.LineQubit, Tuple[cirq.LineQubit]],
tot_n_qubits: int):
"""Given a qubit or qubits, return the position y value (used for drawing).
For multiple qubits, the position is the mean of the qubit indices.
This "flips" the coordinate so qubit 0 is at the maximal y position.
Args:
qubits: The qubits involved in the tensor.
tot_n_qubits: The total number of qubits in the circuit, allowing us
to position the zero'th qubit at the top.
"""
if isinstance(qubits, cirq.LineQubit):
return _qpos_y((qubits,), tot_n_qubits)
x = np.mean([q.x for q in qubits]).item()
return tot_n_qubits - x - 1
def _add_to_positions(positions: Dict[Tuple[str, str], Tuple[float, float]],
mi: int,
qubits: Union[cirq.LineQubit, Tuple[cirq.LineQubit]], *,
tot_n_qubits: int, x_scale, y_scale, x_nudge, yb_offset):
"""Helper function to update the `positions` dictionary.
Args:
positions: The dictionary to update. Quimb will consume this for drawing
mi: Moment index (used for x-positioning)
qubits: The qubits (used for y-positioning)
tot_n_qubits: The total number of qubits in the circuit, allowing us
to position the zero'th qubit at the top.
x_scale: Stretch coordinates in the x direction
y_scale: Stretch coordinates in the y direction
x_nudge: Kraus operators will have vertical lines connecting the
"forward" and "backward" circuits, so the x position of each
tensor is nudged (according to its y position) to help see all
the lines.
yb_offset: Offset the "backwards" circuit by this much.
"""
qy = _qpos_y(qubits, tot_n_qubits)
positions[(f'i{mi}f', _qpos_tag(qubits))] = \
(mi * x_scale + qy * x_nudge, y_scale * qy)
positions[(f'i{mi}b', _qpos_tag(qubits))] = \
(mi * x_scale, y_scale * qy + yb_offset)
def circuit_to_density_matrix_tensors(
circuit: cirq.Circuit, qubits: Optional[Sequence[cirq.LineQubit]] = None
) -> Tuple[List[qtn.Tensor], Dict['cirq.Qid', int],
Dict[Tuple[str, str], Tuple[float, float]]]:
"""Given a circuit with mixtures or channels, construct a tensor network
representation of the density matrix.
This assumes you start in the |0..0><0..0| state. Indices are named
"nf{i}_q{x}" and "nb{i}_q{x}" where i is a time index and x is a
qubit index. nf- and nb- refer to the "forwards" and "backwards"
copies of the circuit. Kraus indices are named "k{j}" where j is an
independent "kraus" internal index which you probably never need to access.
Args:
circuit: The circuit containing operations that support the
cirq.unitary() or cirq.channel() protocols.
qubits: The qubits in the circuit.
Returns:
tensors: A list of Quimb Tensor objects
qubit_frontier: A mapping from qubit to time index at the end of
the circuit. This can be used to deduce the names of the free
tensor indices.
positions: A positions dictionary suitable for passing to tn.graph()'s
`fix` argument to draw the resulting tensor network similar to a
quantum circuit.
"""
if qubits is None:
# coverage: ignore
qubits = sorted(cast(Iterable[cirq.LineQubit], circuit.all_qubits()))
qubit_frontier: Dict[cirq.Qid, int] = {q: 0 for q in qubits}
kraus_frontier = 0
positions: Dict[Tuple[str, str], Tuple[float, float]] = {}
tensors: List[qtn.Tensor] = []
x_scale = 2
y_scale = 3
x_nudge = 0.3
n_qubits = len(qubits)
yb_offset = (n_qubits + 0.5) * y_scale
def _positions(mi, qubits):
return _add_to_positions(positions,
mi,
qubits,
tot_n_qubits=n_qubits,
x_scale=x_scale,
y_scale=y_scale,
x_nudge=x_nudge,
yb_offset=yb_offset)
# Initialize forwards and backwards qubits into the 0 state, i.e. prepare
# rho_0 = |0><0|.
for q in qubits:
tensors += [
qtn.Tensor(data=quimb.up().squeeze(),
inds=(f'nf0_q{q.x}',),
tags={'Q0', 'i0f', _qpos_tag(q)}),
qtn.Tensor(data=quimb.up().squeeze(),
inds=(f'nb0_q{q.x}',),
tags={'Q0', 'i0b', _qpos_tag(q)})
]
_positions(0, q)
for mi, moment in enumerate(circuit.moments):
for op in moment.operations:
start_inds_f = [f'nf{qubit_frontier[q]}_q{q.x}' for q in op.qubits]
start_inds_b = [f'nb{qubit_frontier[q]}_q{q.x}' for q in op.qubits]
for q in op.qubits:
qubit_frontier[q] += 1
end_inds_f = [f'nf{qubit_frontier[q]}_q{q.x}' for q in op.qubits]
end_inds_b = [f'nb{qubit_frontier[q]}_q{q.x}' for q in op.qubits]
if cirq.has_unitary(op):
U = cirq.unitary(op).reshape(
(2,) * 2 * len(op.qubits)).astype(np.complex128)
tensors.append(
qtn.Tensor(data=U,
inds=end_inds_f + start_inds_f,
tags={
f'Q{len(op.qubits)}', f'i{mi + 1}f',
_qpos_tag(op.qubits)
}))
tensors.append(
qtn.Tensor(data=np.conj(U),
inds=end_inds_b + start_inds_b,
tags={
f'Q{len(op.qubits)}', f'i{mi + 1}b',
_qpos_tag(op.qubits)
}))
elif cirq.has_channel(op):
K = np.asarray(cirq.channel(op), dtype=np.complex128)
kraus_inds = [f'k{kraus_frontier}']
tensors.append(
qtn.Tensor(data=K,
inds=kraus_inds + end_inds_f + start_inds_f,
tags={
f'kQ{len(op.qubits)}', f'i{mi + 1}f',
_qpos_tag(op.qubits)
}))
tensors.append(
qtn.Tensor(data=np.conj(K),
inds=kraus_inds + end_inds_b + start_inds_b,
tags={
f'kQ{len(op.qubits)}', f'i{mi + 1}b',
_qpos_tag(op.qubits)
}))
kraus_frontier += 1
else:
raise ValueError(repr(op)) # coverage: ignore
_positions(mi + 1, op.qubits)
return tensors, qubit_frontier, positions
def tensor_density_matrix(circuit: cirq.Circuit,
qubits: Optional[List[cirq.LineQubit]] = None
) -> np.ndarray:
"""Given a circuit with mixtures or channels, contract a tensor network
representing the resultant density matrix.
Note: If the circuit contains 6 qubits or fewer, we use a bespoke
contraction ordering that corresponds to the "normal" in-time contraction
ordering. Otherwise, the contraction order determination could take
longer than doing the contraction. Your mileage may vary and benchmarking
is encouraged for your particular problem if performance is important.
"""
if qubits is None:
qubits = sorted(cast(Iterable[cirq.LineQubit], circuit.all_qubits()))
tensors, qubit_frontier, _ = circuit_to_density_matrix_tensors(
circuit=circuit, qubits=qubits)
tn = qtn.TensorNetwork(tensors)
f_inds = tuple(f'nf{qubit_frontier[q]}_q{q.x}' for q in qubits)
b_inds = tuple(f'nb{qubit_frontier[q]}_q{q.x}' for q in qubits)
if len(qubits) <= 6:
# Heuristic: don't try to determine best order for low qubit number
# Just contract in time.
tags_seq = [(f'i{i}b', f'i{i}f') for i in range(len(circuit) + 1)]
tn.contract_cumulative(tags_seq, inplace=True)
else:
tn.contract(inplace=True)
return tn.to_dense(f_inds, b_inds)
| 9,010 | 2,926 |
from dataclasses import dataclass
import struct
from typing import Tuple
from plugins.mgba_bridge.script_disassembler.utils import barray_to_u16_hex, u16_to_hex
from plugins.mgba_bridge.script_disassembler.definitions import get_pointer, commands, parameters, get_script_label, used_labels
# Disassembler for tmc scripts
# Input 'macros' to generate the macros for the script commands
# Input the script bytes as hex to disassemble the script
# Build macros: echo "macros" | python script_disassembler.py > ~/git/tmc/github/asm/macros/scripts.inc
@dataclass
class Context:
ptr: int
data: bytes
script_addr: int
# Remove the ScriptCommand_ prefix for the asm macros
def build_script_command(name: str):
name = name.replace("ScriptCommand_", "")
if name[0].isdigit(): # asm macros cannot start with an _
return f'_{name}'
return name
def print_rest_bytes(ctx):
print('\n'.join(['.byte ' + hex(x) for x in ctx.data[ctx.ptr:]]))
@dataclass
class Instruction:
addr: int
text: str
def disassemble_command(ctx: Context, add_all_annotations=False) -> Tuple[int, Instruction]:
global used_labels
# if (add_all_annotations or ctx.script_addr + ctx.ptr in used_labels) and ctx.ptr != 0:
# print offsets to debug when manually inserting labels
#print(f'{get_script_label(ctx.script_addr + ctx.ptr)}:')
cmd = struct.unpack('H', ctx.data[ctx.ptr:ctx.ptr + 2])[0]
if cmd == 0:
# this does not need to be the end of the script
#print('\t.2byte 0x0000')
ctx.ptr += 2
return (1, Instruction(ctx.ptr-2, '0000'))
if cmd == 0xffff:
ctx.ptr += 2
# print('SCRIPT_END')
if ctx.ptr >= len(ctx.data) - 1:
# This is already the end
return (2, Instruction(ctx.ptr-2, 'SCRIPT_END'))
cmd = struct.unpack('H', ctx.data[ctx.ptr:ctx.ptr + 2])[0]
if cmd == 0x0000:
# This is actually the end of the script
#print('\t.2byte 0x0000')
ctx.ptr += 2
return (2, Instruction(ctx.ptr-4, 'SCRIPT_END'))
# There is a SCRIPT_END without 0x0000 afterwards, but still split into a new file, please
return (3, Instruction(ctx.ptr-2, 'SCRIPT_END'))
commandStartAddress = ctx.ptr
commandSize = cmd >> 0xA
if commandSize == 0:
raise Exception(f'Zero commandSize not allowed')
commandId = cmd & 0x3FF
if commandId >= len(commands):
raise Exception(
f'Invalid commandId {commandId} / {len(commands)} {cmd}')
command = commands[commandId]
param_length = commandSize - 1
if commandSize > 1:
if ctx.ptr + 2 * commandSize > len(ctx.data):
raise Exception(f'Not enough data to fetch {commandSize-1} params')
# Handle parameters
if not 'params' in command:
raise Exception(
f'Parameters not defined for {command["fun"]}. Should be of length {str(param_length)}')
params = None
suffix = ''
# When there are multiple variants of parameters, choose the one with the correct count for this
if isinstance(command['params'], list):
for i, param in enumerate(command['params']):
if not param in parameters:
raise Exception(f'Parameter configuration {param} not defined')
candidate = parameters[param]
if candidate['length'] == commandSize - 1:
params = candidate
if i != 0:
# We need to add a suffix to distinguish the correct parameter variant
suffix = f'_{params["length"]}'
break
if params is None:
raise Exception(
f'No suitable parameter configuration with length {commandSize-1} found for {command["fun"]}')
else:
if not command['params'] in parameters:
raise Exception(
f'Parameter configuration {command["params"]} not defined')
params = parameters[command['params']]
command_name = f'{command["fun"]}{suffix}'
if params['length'] == -1: # variable parameter length
# print(f'\t.2byte {u16_to_hex(cmd)} @ {build_script_command(command_name)} with {commandSize-1} parameters')
# if commandSize > 1:
# print('\n'.join(['\t.2byte ' + x for x in barray_to_u16_hex(ctx.data[ctx.ptr + 2:ctx.ptr + commandSize * 2])]))
# print(f'@ End of parameters')
ctx.ptr += commandSize * 2
return (1, Instruction(commandStartAddress, 'TODO'))
elif params['length'] == -2: # point and var
# print(f'\t.2byte {u16_to_hex(cmd)} @ {build_script_command(command_name)} with {commandSize-3} parameters')
# print('\t.4byte ' + get_pointer(ctx.data[ctx.ptr + 2:ctx.ptr + 6]))
# if commandSize > 3:
# print('\n'.join(['\t.2byte ' + x for x in barray_to_u16_hex(ctx.data[ctx.ptr + 6:ctx.ptr + commandSize * 2])]))
# print(f'@ End of parameters')
ctx.ptr += commandSize * 2
return (1, Instruction(commandStartAddress, 'TODO'))
if commandSize-1 != params['length']:
raise Exception(
f'Call {command_name} with {commandSize-1} length, while length of {params["length"]} defined')
# print(f'\t{build_script_command(command_name)} {params["read"](ctx)}')
# Execute script
ctx.ptr += commandSize * 2
return (1, Instruction(commandStartAddress, 'TODO'))
def disassemble_script(input_bytes, script_addr, add_all_annotations=False) -> Tuple[int, list[Instruction]]:
ctx = Context(0, input_bytes, script_addr)
foundEnd = False
instructions: list[Instruction] = []
while True:
# End of file (there need to be at least two bytes remaining for the next operation id)
if ctx.ptr >= len(ctx.data) - 1:
break
#print('remaining', len(ctx.data)-ctx.ptr)
(res, instruction) = disassemble_command(ctx, add_all_annotations)
# print(instruction.addr)
instructions.append(instruction)
if res == 0:
break
elif res == 2:
foundEnd = True
break
elif res == 3:
# End in the middle of the script, please create a new file
return (ctx.ptr, instructions)
# Print rest (did not manage to get there)
if ctx.ptr < len(ctx.data):
if (len(ctx.data) - ctx.ptr) % 2 != 0:
print_rest_bytes(ctx)
raise Exception(
f'There is extra data at the end {ctx.ptr} / {len(ctx.data)}')
print(
'\n'.join(['.2byte ' + x for x in barray_to_u16_hex(ctx.data[ctx.ptr:])]))
raise Exception(
f'There is extra data at the end {ctx.ptr} / {len(ctx.data)}')
if not foundEnd:
# Sadly, there are script files without and end?
return (0, instructions)
#print('\033[93mNo end found\033[0m')
return (0, instructions)
def generate_macros():
print('@ All the macro functions for scripts')
print('@ Generated by disassemble_script.py')
print('.macro SCRIPT_START name')
print(' .globl \\name')
print(' .section .text')
print('\\name:')
print('.endm')
print('.macro SCRIPT_END')
print(' .2byte 0xffff')
print('.endm')
print('')
for num, command in enumerate(commands):
if not 'params' in command:
raise Exception(f'Parameters not defined for {command["fun"]}')
def emit_macro(command_name, id, params):
print(f'.macro {command_name} {params["param"]}')
print(f' .2byte {u16_to_hex(id)}')
if params['expr'] != '':
print(params['expr'])
print('.endm')
print('')
if isinstance(command['params'], list):
# emit macros for all variants
for i, variant in enumerate(command['params']):
if not variant in parameters:
raise Exception(
f'Parameter configuration {variant} not defined')
params = parameters[variant]
id = ((params['length'] + 1) << 0xA) + num
suffix = ''
if i != 0:
suffix = f'_{params["length"]}'
emit_macro(
f'{build_script_command(command["fun"])}{suffix}', id, params)
else:
if not command['params'] in parameters:
raise Exception(
f'Parameter configuration {command["params"]} not defined')
params = parameters[command['params']]
id = ((params['length'] + 1) << 0xA) + num
if params['length'] < 0: # Don't emit anything for variable parameters
continue
emit_macro(build_script_command(command['fun']), id, params)
print('')
def main():
# Read input
input_data = input()
if input_data.strip() == 'macros':
generate_macros()
return
disassemble_script(bytearray.fromhex(input_data))
if __name__ == '__main__':
main()
| 9,150 | 2,765 |
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community
Edition) available.
Copyright (C) 2017-2020 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://opensource.org/licenses/MIT
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 ujson as json
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from django.views.decorators.http import require_POST
from blueapps.account.decorators import login_exempt
from gcloud import err_code
from gcloud.apigw.decorators import api_verify_proj_perms
from gcloud.apigw.decorators import mark_request_whether_is_trust
from gcloud.apigw.decorators import project_inject
from gcloud.contrib.analysis.analyse_items import task_flow_instance
from gcloud.core.permissions import project_resource
from gcloud.apigw.views.utils import logger
try:
from bkoauth.decorators import apigw_required
except ImportError:
from packages.bkoauth.decorators import apigw_required
@login_exempt
@csrf_exempt
@require_POST
@apigw_required
@mark_request_whether_is_trust
@project_inject
@api_verify_proj_perms([project_resource.actions.view])
def query_task_count(request, project_id):
"""
@summary: 按照不同维度统计业务任务总数
@param request:
@param project_id:
@return:
"""
try:
params = json.loads(request.body)
except Exception:
return JsonResponse(
{
"result": False,
"message": "invalid json format",
"code": err_code.REQUEST_PARAM_INVALID.code,
}
)
project = request.project
conditions = params.get("conditions", {})
group_by = params.get("group_by")
if not isinstance(conditions, dict):
message = (
"[API] query_task_list params conditions[%s] are invalid dict data"
% conditions
)
logger.error(message)
return JsonResponse(
{
"result": False,
"message": message,
"code": err_code.REQUEST_PARAM_INVALID.code,
}
)
if group_by not in ["category", "create_method", "flow_type", "status"]:
message = "[API] query_task_list params group_by[%s] is invalid" % group_by
logger.error(message)
return JsonResponse(
{
"result": False,
"message": message,
"code": err_code.REQUEST_PARAM_INVALID.code,
}
)
filters = {"project_id": project.id, "is_deleted": False}
filters.update(conditions)
success, content = task_flow_instance.dispatch(group_by, filters)
if not success:
return JsonResponse(
{"result": False, "message": content, "code": err_code.UNKNOWN_ERROR.code}
)
return JsonResponse(
{"result": True, "data": content, "code": err_code.SUCCESS.code}
)
| 3,366 | 1,009 |
# Import Python libraries
import numpy as np
# Import distributed framework
from exaqute import *
try:
init()
except:
pass
try:
computing_units_auxiliar_utilities = int(os.environ["computing_units_auxiliar_utilities"])
except:
computing_units_auxiliar_utilities = 1
"""
auxiliary function of UpdateOnePassCentralMoments of the StatisticalVariable class
input: sample: new value that will update the statistics
old_mean : old mean
old_central_moment_1 : old first central moment
compute_M1 : boolean setting if computation is needed
old_central_moment_2 : old second central moment
compute_M2 : boolean setting if computation is needed
old_central_moment_3 : old third central moment
compute_M3 : boolean setting if computation is needed
old_central_moment_1 : old fourth central moment
compute_M4 : boolean settings if computation is needed
nsamples : old number of samples computed, starts from 1
output: new_mean : updated mean
new_sample_variance : updated sample variance
new_central_moment_1 : updated central_moment_1
new_central_moment_2 : updated central_moment_2
new_central_moment_3 : updated central_moment_3
new_central_moment_4 : updated central_moment_4
nsamples : updated number of samples
"""
@constraint(computing_units=computing_units_auxiliar_utilities)
@task(keep=True,returns=7,priority=True)
def UpdateOnePassCentralMomentsAux_Task(sample,old_mean,old_central_moment_1,compute_M1,old_central_moment_2,compute_M2,old_central_moment_3,compute_M3,old_central_moment_4,compute_M4,nsamples):
old_M1 = old_central_moment_1 * nsamples
old_M2 = old_central_moment_2 * nsamples
old_M3 = old_central_moment_3 * nsamples
old_M4 = old_central_moment_4 * nsamples
nsamples = nsamples + 1
if nsamples == 1:
new_mean = sample
new_M1 = 0.0
new_M2 = 0.0
new_sample_variance = 0.0
new_M3 = 0.0
new_M4 = 0.0
else:
delta = np.subtract(sample,old_mean)
new_mean = old_mean + np.divide(delta,nsamples)
if (compute_M1):
new_M1 = old_M1 # we are not updating, first central moment = 0.0
else:
new_M1 = old_M1 # we are not updating, first central moment = 0.0
if (compute_M2):
new_M2 = old_M2 + delta*np.subtract(sample,new_mean)
else:
raise Exception ("Not computing StatisticalVariable.central_moment_2, set StatisticalVariable.central_moment_2_to_compute to True")
new_sample_variance = np.divide(new_M2,np.subtract(nsamples,1))
if (compute_M3):
new_M3 = old_M3 - 3.0*old_M2*np.divide(delta,nsamples) + np.divide(np.multiply((nsamples-1)*(nsamples-2),(delta**3)),(nsamples**2))
else:
new_M3 = old_M3 # we are not updating
if (compute_M4):
new_M4 = old_M4 - 4.0*old_M3*np.divide(delta,nsamples) + 6.0*old_M2*np.divide(delta,nsamples)**2 + np.multiply((nsamples-1)*(nsamples**2-3*nsamples+3),np.divide(delta**4,nsamples**3))
else:
new_M4 = old_M4 # we are not updating
new_central_moment_1 = new_M1 / nsamples
new_central_moment_2 = new_M2 / nsamples
new_central_moment_3 = new_M3 / nsamples
new_central_moment_4 = new_M4 / nsamples
return new_mean,new_sample_variance,new_central_moment_1,new_central_moment_2,new_central_moment_3,new_central_moment_4,nsamples
"""
auxiliary function of UpdateOnePassPowerSums of the StatisticalVariable class
input: sample : new value that will update the statistics
old_S1 : old first power sum
old_S2 : old second power sum
old_S3 : old third power sum
old_S4 : old fourth power sum
nsamples : number of samples, it has already been updated in UpdateOnePassCentralMomentsAux_Task
output: new_S1 : updated first power sum
new_s2 : updated second power sum
new_S3 : updated third power sum
new_S4 : updated fourth power sum
"""
@constraint(computing_units=computing_units_auxiliar_utilities)
@task(keep=True,returns=5,priority=True)
def UpdateOnePassPowerSumsAux_Task(sample,old_S1,old_S2,old_S3,old_S4,nsamples):
nsamples = nsamples + 1
if nsamples == 1:
new_S1 = sample
new_S2 = sample**2
new_S3 = sample**3
new_S4 = sample**4
else:
new_S1 = old_S1 + sample
new_S2 = old_S2 + sample**2
new_S3 = old_S3 + sample**3
new_S4 = old_S4 + sample**4
return new_S1,new_S2,new_S3,new_S4,nsamples
"""
auxiliary function of UpdateGlobalPowerSums of the StatisticalVariable class
input: old_S1 : old first power sum
old_S2 : old second power sum
old_S3 : old third power sum
old_S4 : old fourth power sum
number_samples_level : number of samples, it has already been updated in UpdateOnePassCentralMomentsAux_Task
add_S1 : power sum order one to add
add_S2 : power sum order two to add
add_S3 : power sum order three to add
add_S4 : power sum order four to add
add_number_samples_level : number of samples to add
output: new_S1 : updated first power sum
new_s2 : updated second power sum
new_S3 : updated third power sum
new_S4 : updated fourth power sum
number_samples_level : number of samples of current level
"""
@constraint(computing_units=computing_units_auxiliar_utilities)
@task(keep=True,returns=5,priority=True)
def UpdateGlobalPowerSumsAux_Task(old_S1,old_S2,old_S3,old_S4,number_samples_level,add_S1,add_S2,add_S3,add_S4,add_number_samples_level):
new_S1 = old_S1 + add_S1
new_S2 = old_S2 + add_S2
new_S3 = old_S3 + add_S3
new_S4 = old_S4 + add_S4
number_samples_level = number_samples_level + add_number_samples_level
return new_S1,new_S2,new_S3,new_S4,number_samples_level
"""
function unfolding values from a list, needed by PyCOMPSs for list of lists
input: sample : the list of lists
output: sample[*] : list position * of the list of lists
"""
@constraint(computing_units=computing_units_auxiliar_utilities)
@task(keep=True,returns=4, priority=True)
def UnfoldValuesAux_Task(sample):
return sample[0], sample[1], sample[2], sample[3]
"""
auxiliary function of UpdateBatchesPassPowerSums
input: samples : list of samples
output: return the sum, done in mini_batch_size batches, of the samples components
"""
@constraint(computing_units=computing_units_auxiliar_utilities)
@task(keep=True,returns=1,priority=True)
def UpdateBatchesPassPowerSumsAux_Task(*samples):
samples_list = np.array(list(samples))
return np.sum(samples_list, axis = 0)
"""
if nsamples == 0:
new_S1 = samples[0]
new_S2 = samples[0]**2
new_S3 = samples[0]**3
new_S4 = samples[0]**4
old_S1 = new_S1
old_S2 = new_S2
old_S3 = new_S3
old_S4 = new_S4
nsamples = 1
samples=samples[1:]
for sample in samples:
nsamples = nsamples + 1
new_S1 = old_S1 + sample
new_S2 = old_S2 + sample**2
new_S3 = old_S3 + sample**3
new_S4 = old_S4 + sample**4
old_S1 = new_S1
old_S2 = new_S2
old_S3 = new_S3
old_S4 = new_S4
return new_S1,new_S2,new_S3,new_S4,nsamples
"""
"""
auxiliary function of UpdateHStatistics of the StatisticalVariable class
input: S1_level : first power sum at defined level
S2_level : second power sum at defined level
S3_level : third power sum at defined level
S4_level : fourth power sum at defined level
number_samples_level : number of samples (already update) for defined level
output: h1_level : first h statistics for defined level
h2_level : second h statistics for defined level
h3_level : third h statistics for defined level
h4_level : fourth h statistics for defined level
"""
@constraint(computing_units=computing_units_auxiliar_utilities)
@task(keep=True,returns=4,priority=True)
def ComputeHStatisticsAux_Task(S1_level,S2_level,S3_level,S4_level,number_samples_level):
h1_level = S1_level / number_samples_level
h2_level = (number_samples_level*S2_level-S1_level**2) / ((number_samples_level-1)*number_samples_level)
h3_level = (number_samples_level**2*S3_level-3*number_samples_level*S2_level*S1_level+2*S1_level**3) / \
((number_samples_level-2)*(number_samples_level-1)*number_samples_level)
h4_level = ((-4*number_samples_level**2+8*number_samples_level-12)*S3_level*S1_level+ \
(number_samples_level**3-2*number_samples_level**2+3*number_samples_level)*S4_level+ \
6*number_samples_level*S2_level*S1_level**2+(9-6*number_samples_level)*S2_level**2-3*S1_level**4) / \
((number_samples_level-3)*(number_samples_level-2)*(number_samples_level-1)*number_samples_level)
return h1_level,h2_level,h3_level,h4_level
"""
auxiliary function of ComputeSkewnessKurtosis of the StatisticalVariable class
input: h2_level : second h statistics for defined level
h3_level : third h statistics for defined level
h4_level : fourth h statistics for defined level
output: skewness_level : skewness for defined level
kurtosis_level : kurtosis for defined level
"""
@constraint(computing_units=computing_units_auxiliar_utilities)
@task(keep=True,returns=2,priority=True)
def ComputeSkewnessKurtosisAux_Task(h2_level,h3_level,h4_level):
skewness_level = h3_level / (np.sqrt(h2_level**3))
kurtosis_level = h4_level / (h2_level**2)
return skewness_level,kurtosis_level
"""
auxiliary function of ComputeSampleCentralMomentsFromScratch of the StatisticalVariable class
input: sample: new value that will update the statistics
number_samples_level : number of samples for defined level
central_moment_from_scratch_1_to_compute : boolean setting if computation is needed
central_moment_from_scratch_2_to_compute : boolean setting if computation is needed
central_moment_from_scratch_3_to_compute : boolean setting if computation is needed
central_moment_from_scratch_3_absolute_to_compute : boolean setting if computation is needed
central_moment_from_scratch_4_to_compute : boolean setting if computation is needed
central_moment_from_scratch_1 : old first central moment
central_moment_from_scratch_2 : old second central moment
central_moment_from_scratch_3 : old third central moment
central_moment_from_scratch_3_absolute : old third central moment absolute value
central_moment_from_scratch_4 : old fourth central moment
output: central_moment_from_scratch_1 : updated first central moment
central_moment_from_scratch_2 : updated second central moment
central_moment_from_scratch_3 : updated third central moment
central_moment_from_scratch_3_absolute : updated third central moment absolute value
central_moment_from_scratch_4 : update fourth central moment
"""
@constraint(computing_units=computing_units_auxiliar_utilities)
@task(keep=True,returns=5,priority=True)
def ComputeSampleCentralMomentsFromScratchAux_Task(number_samples_level,central_moment_from_scratch_1_to_compute,central_moment_from_scratch_2_to_compute, \
central_moment_from_scratch_3_to_compute,central_moment_from_scratch_3_absolute_to_compute,central_moment_from_scratch_4_to_compute, \
central_moment_from_scratch_1,central_moment_from_scratch_2,central_moment_from_scratch_3,central_moment_from_scratch_3_absolute,central_moment_from_scratch_4, \
samples):
# generate a single list from a list of lists
samples = [item for sublist in samples for item in sublist]
# compute the mean
auxiliary_mean = 0.0
for sample in samples:
auxiliary_mean = auxiliary_mean + sample
curr_mean = auxiliary_mean / number_samples_level
for sample in samples:
if (central_moment_from_scratch_1_to_compute):
central_moment_from_scratch_1 = central_moment_from_scratch_1 + ((sample - curr_mean)**1) / number_samples_level
if (central_moment_from_scratch_2_to_compute):
central_moment_from_scratch_2 = central_moment_from_scratch_2 + ((sample - curr_mean)**2) / number_samples_level
if (central_moment_from_scratch_3_to_compute):
central_moment_from_scratch_3 = central_moment_from_scratch_3 + ((sample - curr_mean)**3) / number_samples_level
if (central_moment_from_scratch_3_absolute_to_compute):
central_moment_from_scratch_3_absolute = central_moment_from_scratch_3_absolute + (np.abs(sample - curr_mean)**3) / number_samples_level
if (central_moment_from_scratch_4_to_compute):
central_moment_from_scratch_4 = central_moment_from_scratch_4 + ((sample - curr_mean)**4) / number_samples_level
return central_moment_from_scratch_1,central_moment_from_scratch_2,central_moment_from_scratch_3,central_moment_from_scratch_3_absolute,central_moment_from_scratch_4
class StatisticalVariable(object):
"""The base class for statistical variables"""
def __init__(self):
"""constructor of the class
Keyword arguments:
self : an instance of a class
"""
# values of the variable, organized per level
self.values = []
# mean of the variable per each level
self.raw_moment_1 = []
# sample variance of the variable per each level
self.unbiased_central_moment_2 = []
# moments of the variable per each level M_p = n * mu_p
# mu_p = p-th central moment
# n = number of values
self.central_moment_1 = []
self.central_moment_2 = []
self.central_moment_3 = []
self.central_moment_4 = []
# set which central moments will be computed (moment_2 is mandatory to be computed because it is exploited in the mean evaluation)
self.central_moment_1_to_compute = True
self.central_moment_2_to_compute = True
self.central_moment_3_to_compute = True
self.central_moment_4_to_compute = True
# bias error of the variable
self.bias_error = None
# statistical error of the variable
self.statistical_error = None
# type of variable: scalar or field
self.type = None
# number of samples of the variable
self.number_samples = None
self.batches_number_samples = []
# global power sums
# S_p = \sum_{i=1}^{n} Q(sample_i)**p, organized per level
self.power_sum_1 = []
self.power_sum_2 = []
self.power_sum_3 = []
self.power_sum_4 = []
# power sums batches
self.power_sum_batches_1 = []
self.power_sum_batches_2 = []
self.power_sum_batches_3 = []
self.power_sum_batches_4 = []
# sample central moments \mu_p = \sum_{i=1}^{n} (Q(sample_i)-mean_n)**p / n, organized per level
self.central_moment_from_scratch_1 = []
self.central_moment_from_scratch_2 = []
self.central_moment_from_scratch_3 = []
self.central_moment_from_scratch_3_absolute = [] # \mu_p = \sum_{i=1}^{n} abs((Q(sample_i)-mean_n)**p) / n
self.central_moment_from_scratch_4 = []
self.central_moment_from_scratch_1_to_compute = False
self.central_moment_from_scratch_2_to_compute = False
self.central_moment_from_scratch_3_to_compute = False
self.central_moment_from_scratch_3_absolute_to_compute = False
self.central_moment_from_scratch_4_to_compute = False
# h-statistics h_p, the unbiased central moment estimator with minimal variance, organized per level
self.h_statistics_1 = []
self.h_statistics_2 = []
self.h_statistics_3 = []
self.h_statistics_4 = []
self.h_statistics_computed = False
# skewness of the variable per each level
self.skewness = []
# kurtosis of the variable per each level
self.kurtosis = []
# convergence criteria of the algorithm
self.convergence_criteria = None
"""
function initializing variables of the Statistical Variable class in lists given number of levels
input: self : an instance of the class
number_levels : number of levels considered
number_initial_batches : number of batches of iteration zero
"""
def InitializeLists(self,number_levels,number_initial_batches):
self.number_samples = [0 for _ in range (number_levels)]
self.values = [[[] for _ in range (number_levels)] for _ in range (number_initial_batches)]
self.raw_moment_1 = [[] for _ in range (number_levels)]
self.central_moment_1 = [[] for _ in range (number_levels)]
self.central_moment_2 = [[] for _ in range (number_levels)]
self.central_moment_3 = [[] for _ in range (number_levels)]
self.central_moment_4 = [[] for _ in range (number_levels)]
self.unbiased_central_moment_2 = [[] for _ in range (number_levels)]
self.power_sum_1 = [0 for _ in range (number_levels)]
self.power_sum_2 = [0 for _ in range (number_levels)]
self.power_sum_3 = [0 for _ in range (number_levels)]
self.power_sum_4 = [0 for _ in range (number_levels)]
self.power_sum_batches_1 = [[[] for _ in range (number_levels)] for _ in range (number_initial_batches)]
self.power_sum_batches_2 = [[[] for _ in range (number_levels)] for _ in range (number_initial_batches)]
self.power_sum_batches_3 = [[[] for _ in range (number_levels)] for _ in range (number_initial_batches)]
self.power_sum_batches_4 = [[[] for _ in range (number_levels)] for _ in range (number_initial_batches)]
self.h_statistics_1 = [[] for _ in range (number_levels)]
self.h_statistics_2 = [[] for _ in range (number_levels)]
self.h_statistics_3 = [[] for _ in range (number_levels)]
self.h_statistics_4 = [[] for _ in range (number_levels)]
self.skewness = [[] for _ in range (number_levels)]
self.kurtosis = [[] for _ in range (number_levels)]
self.central_moment_from_scratch_1 = [[] for _ in range (number_levels)]
self.central_moment_from_scratch_2 = [[] for _ in range (number_levels)]
self.central_moment_from_scratch_3 = [[] for _ in range (number_levels)]
self.central_moment_from_scratch_3_absolute = [[] for _ in range (number_levels)]
self.central_moment_from_scratch_4 = [[] for _ in range (number_levels)]
self.batches_number_samples = [[0 for _ in range (number_levels)] for _ in range (number_initial_batches)]
"""
function updating statistic moments and number of samples
input: self : an instance of the class
level : defined level
i_sample : defined sample in level
"""
def UpdateOnePassCentralMoments(self,level,i_sample):
number_samples_level = self.number_samples[level]
sample = self.values[level][i_sample]
old_mean = self.raw_moment_1[level]
# old_M1 = self.central_moment_1[level] * number_samples_level
old_central_moment_1 = self.central_moment_1[level]
compute_M1 = self.central_moment_1_to_compute
# old_M2 = self.central_moment_2[level] * number_samples_level
old_central_moment_2 = self.central_moment_2[level]
compute_M2 = self.central_moment_2_to_compute
# old_M3 = self.central_moment_3[level] * number_samples_level
old_central_moment_3 = self.central_moment_3[level]
compute_M3 = self.central_moment_3_to_compute
# old_M4 = self.central_moment_4[level] * number_samples_level
old_central_moment_4 = self.central_moment_4[level]
compute_M4 = self.central_moment_4_to_compute
new_mean,new_sample_variance,new_central_moment_1,new_central_moment_2,new_central_moment_3,new_central_moment_4,number_samples_level \
= UpdateOnePassCentralMomentsAux_Task(sample,old_mean,old_central_moment_1,compute_M1,old_central_moment_2,compute_M2,old_central_moment_3,compute_M3,old_central_moment_4,compute_M4,number_samples_level)
self.raw_moment_1[level] = new_mean
self.unbiased_central_moment_2[level] = new_sample_variance
self.central_moment_1[level] = new_central_moment_1
self.central_moment_2[level] = new_central_moment_2
self.central_moment_3[level] = new_central_moment_3
self.central_moment_4[level] = new_central_moment_4
self.number_samples[level] = number_samples_level
"""
function updating the power sums S_p
input: self : an instance of the class
level : defined level
i_sample : defined sample in level
"""
def UpdateOnePassPowerSums(self,level,i_sample):
sample = self.values[level][i_sample]
old_S1 = self.power_sum_1[level]
old_S2 = self.power_sum_2[level]
old_S3 = self.power_sum_3[level]
old_S4 = self.power_sum_4[level]
number_samples_level = self.number_samples[level]
new_S1,new_S2,new_S3,new_S4,number_samples_level = UpdateOnePassPowerSumsAux_Task(sample,old_S1,old_S2,old_S3,old_S4,number_samples_level)
self.power_sum_1[level] = new_S1
self.power_sum_2[level] = new_S2
self.power_sum_3[level] = new_S3
self.power_sum_4[level] = new_S4
self.number_samples[level] = number_samples_level
"""
function updating the global power sums
input: self : an instance of the class
level : current level
batch_counter : current batch
"""
def UpdateGlobalPowerSums(self,level,batch_counter):
old_S1 = self.power_sum_1[level]
old_S2 = self.power_sum_2[level]
old_S3 = self.power_sum_3[level]
old_S4 = self.power_sum_4[level]
number_samples_level = self.number_samples[level]
add_S1 = self.power_sum_batches_1[batch_counter][level]
add_S2 = self.power_sum_batches_2[batch_counter][level]
add_S3 = self.power_sum_batches_3[batch_counter][level]
add_S4 = self.power_sum_batches_4[batch_counter][level]
add_number_samples_level = self.batches_number_samples[batch_counter][level]
new_S1,new_S2,new_S3,new_S4,number_samples_level = UpdateGlobalPowerSumsAux_Task(old_S1,old_S2,old_S3,old_S4,number_samples_level,add_S1,add_S2,add_S3,add_S4,add_number_samples_level)
self.power_sum_1[level] = new_S1
self.power_sum_2[level] = new_S2
self.power_sum_3[level] = new_S3
self.power_sum_4[level] = new_S4
self.number_samples[level] = number_samples_level
"""
function updating the in-batch power sums
input: self : an instance of the class
level : current level
batch_counter : current batch
mini_batch : size such that we update the in-batch power sums with mini_batch samples
"""
def UpdateBatchesPassPowerSum(self,level,batch_counter,mini_batch=50):
samples = self.values[batch_counter][level]
#for mini_batch in range (0,len(samples)):
while len(samples) > 1:
mini_batches_samples = samples[:mini_batch]
samples = samples[mini_batch:]
new_power_sums = UpdateBatchesPassPowerSumsAux_Task(*mini_batches_samples)
samples.append(new_power_sums)
new_S1, new_S2, new_S3, new_S4 = UnfoldValuesAux_Task(samples[0])
self.power_sum_batches_1[batch_counter][level] = new_S1
self.power_sum_batches_2[batch_counter][level] = new_S2
self.power_sum_batches_3[batch_counter][level] = new_S3
self.power_sum_batches_4[batch_counter][level] = new_S4
"""
function computing the h statistics h_p from the power sums
input: self : an instance of the class
level : defined level
"""
def ComputeHStatistics(self,level):
number_samples_level = self.number_samples[level]
S1_level = self.power_sum_1[level]
S2_level = self.power_sum_2[level]
S3_level = self.power_sum_3[level]
S4_level = self.power_sum_4[level]
self.h_statistics_computed = True
h1_level,h2_level,h3_level,h4_level = ComputeHStatisticsAux_Task(S1_level,S2_level,S3_level,S4_level,number_samples_level)
self.h_statistics_1[level] = h1_level
self.h_statistics_2[level] = h2_level
self.h_statistics_3[level] = h3_level
self.h_statistics_4[level] = h4_level
"""
function computing from scratch the central moments and the absolute third central moment
input: self : an instance of the class
level : defined level
"""
def ComputeSampleCentralMomentsFromScratch(self,level,number_samples_level):
# initialize central moments
central_moment_from_scratch_1 = 0.0
central_moment_from_scratch_2 = 0.0
central_moment_from_scratch_3 = 0.0
central_moment_from_scratch_3_absolute = 0.0
central_moment_from_scratch_4 = 0.0
central_moment_from_scratch_1_to_compute = self.central_moment_from_scratch_1_to_compute
central_moment_from_scratch_2_to_compute = self.central_moment_from_scratch_2_to_compute
central_moment_from_scratch_3_to_compute = self.central_moment_from_scratch_3_to_compute
central_moment_from_scratch_3_absolute_to_compute = self.central_moment_from_scratch_3_absolute_to_compute
central_moment_from_scratch_4_to_compute = self.central_moment_from_scratch_4_to_compute
samples = []
for batch in range (len(self.values)):
for mini_batch_samples in self.values[batch][level]:
samples.append(mini_batch_samples)
central_moment_from_scratch_1,central_moment_from_scratch_2,central_moment_from_scratch_3,central_moment_from_scratch_3_absolute,central_moment_from_scratch_4 = \
ComputeSampleCentralMomentsFromScratchAux_Task(number_samples_level,central_moment_from_scratch_1_to_compute, \
central_moment_from_scratch_2_to_compute,central_moment_from_scratch_3_to_compute,central_moment_from_scratch_3_absolute_to_compute,central_moment_from_scratch_4_to_compute, \
central_moment_from_scratch_1,central_moment_from_scratch_2,central_moment_from_scratch_3,central_moment_from_scratch_3_absolute,central_moment_from_scratch_4, samples)
self.central_moment_from_scratch_1[level] = central_moment_from_scratch_1
self.central_moment_from_scratch_2[level] = central_moment_from_scratch_2
self.central_moment_from_scratch_3[level] = central_moment_from_scratch_3
self.central_moment_from_scratch_3_absolute[level] = central_moment_from_scratch_3_absolute
self.central_moment_from_scratch_4[level] = central_moment_from_scratch_4
"""
function computing the skewness and the kurtosis from the h statistics
skewness = \mu_3 / \sqrt(\mu_2^3)
kurtosis = \mu_4 / \mu_2^2
input: self : an instance of the class
level : defined level
"""
def ComputeSkewnessKurtosis(self,level):
if (self.h_statistics_computed):
h2_level = self.h_statistics_2[level]
h3_level = self.h_statistics_3[level]
h4_level = self.h_statistics_4[level]
skewness_level,kurtosis_level =ComputeSkewnessKurtosisAux_Task(h2_level,h3_level,h4_level)
self.skewness[level] = skewness_level
self.kurtosis[level] = kurtosis_level
| 28,222 | 9,284 |
solution = MaximumAverageSubarrayI()
assert X == solution.findMaxAverage( ) | 75 | 23 |
# -*- coding: utf-8 -
#
# This file is part of gaffer. See the NOTICE for more information.
import os
import time
import pytest
import pyuv
from gaffer import __version__
from gaffer.manager import Manager
from gaffer.http_handler import HttpEndpoint, HttpHandler
from gaffer.httpclient import (Server, Process, ProcessId,
GafferNotFound, GafferConflict)
from test_manager import dummy_cmd
TEST_HOST = '127.0.0.1'
TEST_PORT = (os.getpid() % 31000) + 1024
TEST_PORT2 = (os.getpid() % 31000) + 1023
def start_manager():
http_endpoint = HttpEndpoint(uri="%s:%s" % (TEST_HOST, TEST_PORT))
http_handler = HttpHandler(endpoints=[http_endpoint])
m = Manager()
m.start(apps=[http_handler])
time.sleep(0.2)
return m
def get_server(loop):
return Server("http://%s:%s" % (TEST_HOST, TEST_PORT), loop=loop)
def init():
m = start_manager()
s = get_server(m.loop)
return (m, s)
def test_basic():
m = start_manager()
s = get_server(m.loop)
assert s.version == __version__
m.stop()
m.run()
def test_multiple_handers():
http_endpoint = HttpEndpoint(uri="%s:%s" % (TEST_HOST, TEST_PORT))
http_endpoint2 = HttpEndpoint(uri="%s:%s" % (TEST_HOST, TEST_PORT2))
http_handler = HttpHandler(endpoints=[http_endpoint, http_endpoint2])
m = Manager()
m.start(apps=[http_handler])
time.sleep(0.2)
s = Server("http://%s:%s" % (TEST_HOST, TEST_PORT), loop=m.loop)
s2 = Server("http://%s:%s" % (TEST_HOST, TEST_PORT2), loop=m.loop)
assert TEST_PORT != TEST_PORT2
assert s.version == __version__
assert s2.version == __version__
m.stop()
m.run()
def test_processes():
m, s = init()
assert s.processes() == []
testfile, cmd, args, wdir = dummy_cmd()
m.add_process("dummy", cmd, args=args, cwd=wdir, start=False)
time.sleep(0.2)
assert len(m.processes) == 1
assert len(s.processes()) == 1
assert s.processes()[0] == "dummy"
m.stop()
m.run()
def test_process_create():
m, s = init()
testfile, cmd, args, wdir = dummy_cmd()
s.add_process("dummy", cmd, args=args, cwd=wdir, start=False)
time.sleep(0.2)
assert len(m.processes) == 1
assert len(s.processes()) == 1
assert s.processes()[0] == "dummy"
assert "dummy" in m.processes
assert len(m.running) == 0
with pytest.raises(GafferConflict):
s.add_process("dummy", cmd, args=args, cwd=wdir, start=False)
p = s.get_process("dummy")
assert isinstance(p, Process)
m.stop()
m.run()
def test_process_remove():
m, s = init()
testfile, cmd, args, wdir = dummy_cmd()
s.add_process("dummy", cmd, args=args, cwd=wdir, start=False)
assert s.processes()[0] == "dummy"
s.remove_process("dummy")
assert len(s.processes()) == 0
assert len(m.processes) == 0
m.stop()
m.run()
def test_notfound():
m, s = init()
with pytest.raises(GafferNotFound):
s.get_process("dummy")
m.stop()
m.run()
def test_process_start_stop():
m, s = init()
testfile, cmd, args, wdir = dummy_cmd()
p = s.add_process("dummy", cmd, args=args, cwd=wdir, start=False)
assert isinstance(p, Process)
p.start()
time.sleep(0.2)
assert len(m.running) == 1
status = p.status()
assert status['running'] == 1
assert status['active'] == True
assert status['max_processes'] == 1
p.stop()
time.sleep(0.2)
assert len(m.running) == 0
assert p.active == False
s.remove_process("dummy")
assert len(s.processes()) == 0
p = s.add_process("dummy", cmd, args=args, cwd=wdir, start=True)
time.sleep(0.2)
assert len(m.running) == 1
assert p.active == True
p.restart()
time.sleep(0.4)
assert len(m.running) == 1
assert p.active == True
m.stop()
m.run()
def test_process_add_sub():
m, s = init()
testfile, cmd, args, wdir = dummy_cmd()
p = s.add_process("dummy", cmd, args=args, cwd=wdir)
time.sleep(0.2)
assert isinstance(p, Process)
assert p.active == True
assert p.numprocesses == 1
p.add(3)
time.sleep(0.2)
assert p.numprocesses == 4
assert p.running == 4
p.sub(3)
time.sleep(0.2)
assert p.numprocesses == 1
assert p.running == 1
m.stop()
m.run()
def test_running():
m, s = init()
testfile, cmd, args, wdir = dummy_cmd()
s.add_process("dummy", cmd, args=args, cwd=wdir)
time.sleep(0.2)
assert len(m.running) == 1
assert len(s.running()) == 1
assert 1 in m.running
assert s.running()[0] == 1
m.stop()
m.run()
def test_pids():
m, s = init()
testfile, cmd, args, wdir = dummy_cmd()
p = s.add_process("dummy", cmd, args=args, cwd=wdir)
time.sleep(0.2)
p = s.get_process("dummy")
assert isinstance(p, Process) == True
pid = s.get_process(1)
assert isinstance(pid, ProcessId) == True
assert pid.pid == 1
assert pid.process.get('name') == "dummy"
assert p.pids == [1]
pid.stop()
assert 1 not in m.running
time.sleep(0.2)
assert p.pids == [2]
m.stop()
m.run()
def test_groups():
m, s = init()
started = []
stopped = []
def cb(evtype, info):
if evtype == "start":
started.append(info['name'])
elif evtype == "stop":
stopped.append(info['name'])
m.subscribe('start', cb)
m.subscribe('stop', cb)
testfile, cmd, args, wdir = dummy_cmd()
m.add_process("ga:a", cmd, args=args, cwd=wdir, start=False)
m.add_process("ga:b", cmd, args=args, cwd=wdir, start=False)
m.add_process("gb:a", cmd, args=args, cwd=wdir, start=False)
groups = sorted(s.groups())
ga1 = s.get_group('ga')
gb1 = s.get_group('gb')
s.start_group("ga")
s.stop_group("ga")
time.sleep(0.2)
m.remove_process("ga:a")
time.sleep(0.2)
ga2 = s.get_group('ga')
m.stop_group("gb")
def stop(handle):
m.unsubscribe("start", cb)
m.unsubscribe("stop", cb)
m.stop()
t = pyuv.Timer(m.loop)
t.start(stop, 0.4, 0.0)
m.run()
assert groups == ['ga', 'gb']
assert ga1 == ['ga:a', 'ga:b']
assert gb1 == ['gb:a']
assert started == ['ga:a', 'ga:b']
assert stopped == ['ga:a', 'ga:b', 'gb:a']
assert ga2 == ['ga:b']
| 6,310 | 2,472 |
# -*- coding: utf-8 -*-
from __future__ import print_function
from enum import unique
import sys
from clu.enums import AliasingEnum, alias
from clu.exporting import Exporter
exporter = Exporter(path=__file__)
export = exporter.decorator()
@unique
class Status(AliasingEnum):
DISCARD = 200
REPLACE_TEXT = 201
REPLACE_DOCUMENT = 202
INSERT_TEXT = 203
INSERT_SNIPPET = 204
SHOW_HTML = 205
SHOW_TOOLTIP = 206
NEW_DOCUMENT = 207
INSERT_SNIPPET_NOINDENT = 208
NOP = alias(DISCARD)
CREATE_NEW_DOCUMENT = alias(NEW_DOCUMENT)
@property
def code(self):
return int(self.value)
def exit(self, output):
sys.stdout.write(output)
sys.stdout.flush()
sys.exit(self.code)
# Assign the modules’ `__all__` and `__dir__` using the exporter:
__all__, __dir__ = exporter.all_and_dir()
def test():
# from clu.testing.utils import inline
# @inline
def test_one():
pass # INSERT TESTING CODE HERE, pt. I
#@inline
def test_two():
pass # INSERT TESTING CODE HERE, pt. II
#@inline.diagnostic
def show_me_some_values():
pass # INSERT DIAGNOSTIC CODE HERE
# return inline.test(100)
if __name__ == '__main__':
sys.exit(test())
| 1,366 | 498 |
# Create list baseball
import numpy as np
baseball = [180, 215, 210, 210, 188, 176, 209, 200]
# Import the numpy package as np
# Create a numpy array from baseball: np_baseball
np_baseball = np.array(baseball)
# Print out type of np_baseball
print(type(np_baseball))
# height is available as a regular list
# Import numpy
# Create a numpy array from height_in: np_height_in
np_height_in = np.array(height_in)
# Print out np_height_in
print(np_height_in)
# Convert np_height_in to m: np_height_m
np_height_m = np_height_in * 0.0254
# Print np_height_m
print(np_height_m)
# height and weight are available as regular lists
# Import numpy
# Create array from height_in with metric units: np_height_m
np_height_m = np.array(height_in) * 0.0254
# Create array from weight_lb with metric units: np_weight_kg
np_weight_kg = np.array(weight_lb)*0.453592
# Calculate the BMI: bmi
bmi = np_weight_kg/(np_height_m**2)
# height and weight are available as a regular lists
# Import numpy
# Calculate the BMI: bmi
np_height_m = np.array(height_in) * 0.0254
np_weight_kg = np.array(weight_lb) * 0.453592
bmi = np_weight_kg / np_height_m ** 2
# Create the light array
light = bmi < 21
# Print out light
print(light)
# height and weight are available as a regular lists
# Import numpy
# Store weight and height lists as numpy arrays
np_weight_lb = np.array(weight_lb)
np_height_in = np.array(height_in)
# Print out the weight at index 50
print(np_weight_lb[50])
# Print out sub-array of np_height_in: index 100 up to and including index 110
print(np_height_in[100:111])
# Create baseball, a list of lists
baseball = [[180, 78.4],
[215, 102.7],
[210, 98.5],
[188, 75.2]]
# Import numpy
# Create a 2D numpy array from baseball: np_baseball
np_baseball = np.array(baseball)
# Print out the type of np_baseball
print(type(np_baseball))
# baseball is available as a regular list of lists
# Import numpy package
# Create a 2D numpy array from baseball: np_baseball
np_baseball = np.array(baseball)
# Print out the shape of np_baseball
print(np_baseball.shape)
# baseball is available as a regular list of lists
# Import numpy package
# Create np_baseball (2 cols)
np_baseball = np.array(baseball)
# Print out the 50th row of np_baseball
print(np_baseball[49, :])
# Select the entire second column of np_baseball: np_weight_lb
np_weight_lb = np_baseball[:, 1]
# Print out height of 124th player
# baseball is available as a regular list of lists
# updated is available as 2D numpy array
# Import numpy package
# Create np_baseball (3 cols)
np_baseball = np.array(baseball)
# Print out addition of np_baseball and updated
print(np_baseball+updated)
# Create numpy array: conversion
conversion = np.array([0.0254, 0.453592, 1])
# np_baseball is available
# Import numpy
# Create np_height_in from np_baseball
np_height_in = np_baseball[:, 0]
# Print out the mean of np_height_in
print(np_height_in.mean())
# Print out the median of np_height_in
print(np.median(np_height_in))
# np_baseball is available
# Import numpy
# Print mean height (first column)
avg = np.mean(np_baseball[:, 0])
print("Average: " + str(avg))
# Print median height. Replace 'None'
med = np.median(np_baseball[:, 0])
print("Median: " + str(med))
# Print out the standard deviation on height. Replace 'None'
stddev = np.std(np_baseball[:, 0])
print("Standard Deviation: " + str(stddev))
# Print out correlation between first and second column. Replace 'None'
corr = np.corrcoef(np_baseball[:, 0], np_baseball[:, 1])
print("Correlation: " + str(corr))
# heights and positions are available as lists
# Import numpy
# Convert positions and heights to numpy arrays: np_positions, np_heights
np_positions = np.array(positions)
np_heights = np.array(heights)
# Heights of the goalkeepers: gk_heights
gk_heights = np_heights[np_positions == 'GK']
# Heights of the other players: other_heights
other_heights = np_heights[np_positions != 'GK']
# Print out the median height of goalkeepers. Replace 'None'
print("Median height of goalkeepers: " + str(np.median(gk_heights)))
# Print out the median height of other players. Replace 'None'
print("Median height of other players: " + str(np.median(other_heights)))
| 4,256 | 1,553 |
'''
Sets up the SCF data for use in the SolvingMicroDSOPs estimation.
'''
from __future__ import division # Use new division function
from __future__ import print_function
from __future__ import absolute_import
from builtins import zip
from builtins import str
from builtins import range
import os, sys
# Find pathname to this file:
my_file_path = os.path.dirname(os.path.abspath(__file__))
# Pathnames to the other files:
calibration_dir = os.path.join(my_file_path, "../Calibration/") # Relative directory for primitive parameter files
tables_dir = os.path.join(my_file_path, "../Tables/") # Relative directory for primitive parameter files
figures_dir = os.path.join(my_file_path, "../Figures/") # Relative directory for primitive parameter files
code_dir = os.path.join(my_file_path, "../Code/") # Relative directory for primitive parameter files
# Need to rely on the manual insertion of pathnames to all files in do_all.py
# NOTE sys.path.insert(0, os.path.abspath(tables_dir)), etc. may need to be
# copied from do_all.py to here
# Import files first:
from EstimationParameters import initial_age, empirical_cohort_age_groups
# The following libraries are part of the standard python distribution
import numpy as np # Numerical Python
import csv # Comma-separated variable reader
# Set the path to the empirical data:
scf_data_path = data_location = os.path.dirname(os.path.abspath(__file__)) # os.path.abspath('./') #'./'
# Open the file handle and create a reader object and a csv header
infile = open(scf_data_path + '/SCFdata.csv', 'r')
csv_reader = csv.reader(infile)
data_csv_header = next(csv_reader)
# Pull the column index from the data_csv_header
data_column_index = data_csv_header.index('wealth_income_ratio') # scf_w_col
age_group_column_index = data_csv_header.index('age_group') # scf_ages_col
data_weight_column_index = data_csv_header.index('weight') # scf_weights_col
# Initialize empty lists for the data
w_to_y_data = [] # Ratio of wealth to permanent income
empirical_weights = [] # Weighting for this observation
empirical_groups = [] # Which age group this observation belongs to (1-7)
# Read in the data from the datafile by looping over each record (row) in the file.
for record in csv_reader:
w_to_y_data.append(np.float64(record[data_column_index]))
empirical_groups.append(np.float64(record[age_group_column_index]))
empirical_weights.append(np.float64(record[data_weight_column_index]))
# Generate a single array of SCF data, useful for resampling for bootstrap
scf_data_array = np.array([w_to_y_data,empirical_groups,empirical_weights]).T
# Convert SCF data to numpy's array format for easier math
w_to_y_data = np.array(w_to_y_data)
empirical_groups = np.array(empirical_groups)
empirical_weights = np.array(empirical_weights)
# Close the data file
infile.close()
# Generate a mapping between the real ages in the groups and the indices of simulated data
simulation_map_cohorts_to_age_indices = []
for ages in empirical_cohort_age_groups:
simulation_map_cohorts_to_age_indices.append(np.array(ages) - initial_age)
if __name__ == '__main__':
print("Sorry, SetupSCFdata doesn't actually do anything on its own.")
print("This module is imported by StructEstimation, providing data for")
print("the example estimation. Please see that module if you want more")
print("interesting output.")
| 3,469 | 1,088 |
from app import app
from flask import render_template, redirect, session, request, send_from_directory
from app import models, db, reqs
from flask_socketio import SocketIO, emit
import json
from xhtml2pdf import pisa
import os
from datetime import datetime
socketio = SocketIO(app)
if __name__ == '__main__':
socketio.run(app)
usernames = {}
class Inside_date:
def __init__(self, d, m, y):
months = ['января', 'февраля', 'марта', 'апреля', 'мая', 'июня', 'июля', 'августа',
'сентября', 'октября', 'ноября', 'декабря']
self.d = d
self.m = months[m - 1]
self.y = y
@socketio.on('connection')
def user_connected():
print("user connect")
@socketio.on('add user')
def add_user(data):
global usernames
print("add user")
print(data)
session['username'] = data
usernames[data] = session['username']
emit('user joined', {'username': session['username']}, broadcast= True)
def sendTasks():
tasks = []
Tasks = models.Tasks.query.all()
if models.User.query.filter_by(login=session['username']).first():
user = models.User.query.filter_by(login=session['username']).first()
else:
user = models.User.query.filter_by(email=session['username']).first()
for i in Tasks:
try:
if 'all' in json.loads(i.Visibility):
tasks.append(json.loads(table_to_json([i]))[0])
elif str(user.id) in json.loads(i.Visibility):
tasks.append(json.loads(table_to_json([i]))[0])
except Exception as er:
print(er)
emit('showTasks', json.dumps(tasks))
@socketio.on('addTask')
def addTask(message):
task = models.Tasks()
task.Visibility = json.dumps(message['data']['task_whom'])
task.User_id = int(message['data']['task_who'])
task.Type = message['data']['task_type']
task.Date = message['data']['task_date']
task.Time = message['data']['task_time']
task.Comment = message['data']['task_comment']
db.session.add(task)
db.session.commit()
emit('refreshTasks', broadcast=True)
@socketio.on('showTasks')
def showTasks():
sendTasks()
def table_to_json(query):
result = []
for i in query:
subres = i.__dict__
if '_sa_instance_state' in subres:
subres.pop('_sa_instance_state', None)
if 'Date' in subres:
if subres['Date'] != None:
try:
subres['Date'] = subres['Date'].strftime("%d.%m.%Y")
except Exception:
print(subres['Date'])
result.append(subres)
return json.dumps(result)
def to_PDF(owner, name, delivery):
document = models.Document()
if name == "Договор":
name = "Dogovor"
document.Type = 'Dogovor'
else:
name = "Zayavka"
document.Type = 'Zayavka'
f = open(os.path.dirname(__file__) + '/upload/{}.pdf'.format(owner.__tablename__ + str(owner.id)), "w+b")
info = reqs.getINNinfo(owner.UHH)['suggestions'][0]
date = Inside_date(d=str(datetime.now().day), m=int(datetime.now().month), y=str(datetime.now().year))
document.Client_name = info['value']
document.UHH = owner.UHH
document.Date = str(datetime.now().month) + '/' + str(datetime.now().year)
document.Client_contact_name = info['data']['management']['name']
document.Bik = owner.Bik
document.KPP = info['data']['kpp']
document.rc = owner.rc
document.kc = owner.kc
document.Owner_id = owner.id
document.MonthNum = document.getMonthNum()
document.OGRN = info['data']['ogrn']
if delivery != '':
Client = models.Client.query.filter_by(Name=delivery.Client).first()
Delivery_client_info = {'Name': Client.Name, 'Address': Client.Adress}
else:
Delivery_client_info = ''
db.session.add(document)
db.session.commit()
html = render_template('{}.html'.format(name), document=document, date=date,
owner=owner, path=os.path.dirname(__file__), delivery=delivery,
Delivery_client_info=Delivery_client_info)
pisa.CreatePDF(html, dest=f, encoding='utf-8')
f.close()
dir_u = os.path.abspath(os.path.dirname(__file__) + '/upload')
return send_from_directory(directory=dir_u, filename='{}.pdf'.format(owner.__tablename__ + str(owner.id)))
@app.route('/')
@app.route('/index')
def index():
try:
print(session['username'])
except Exception:
print("Not logged in")
if 'username' in session:
return render_template('index.html')
else:
return render_template('login.html')
@app.route('/getAllTasks')
def getAllTasks():
return table_to_json(models.Tasks.query.all())
@app.route('/auth', methods=['GET'])
def auth():
if 'login' in request.args:
login = request.args['login']
else:
return 'ERROR 400 BAD REQUEST'
if 'password' in request.args:
password = request.args['password']
else:
return 'ERROR 400 BAD REQUEST'
if models.User.query.filter_by(login=login).first():
user = models.User.query.filter_by(login=login).first()
if user.password == password:
session['username'] = login
# join_room('all')
return redirect('/', code=302)
return json.dumps({'message': 'Неверный пароль', 'success': False})
elif models.User.query.filter_by(email=login).first():
user = models.User.query.filter_by(email=login).first()
if user.password == password:
session['username'] = login
return redirect('/', code=302)
return json.dumps({'message': 'Неверный пароль', 'success': False})
return json.dumps({'message': 'Неверный логин/email', 'success': False})
@app.route('/logout', methods=['GET'])
def logout():
if 'username' in session:
session.pop('username', None)
return redirect('/', code=302)
@app.route('/addAccountPaymentHistory', methods=['GET'])
def addAccountPaymentHistory():
table = models.Account.query.filter_by(id=request.args['account_id']).first()
table.Payment_history = request.args['account_payment_history']
db.session.commit()
return 'OK'
@app.route('/getTemplates', methods=['GET'])
def getTemplates():
if 'username' in session:
return table_to_json(models.Template.query.all())
else:
return redirect('/', code=302)
@app.route('/downloadDoc', methods=['GET'])
def downloadDoc():
if request.args['name'] == 'Заявка':
delivery = models.Delivery.query.filter_by(id=request.args['delivery_id'])
else:
delivery = ''
if 'username' in session:
if request.args['category'] == 'client':
owner = models.Client.query.filter_by(id=request.args['card_id']).first()
elif request.args['category'] == 'provider':
owner = models.Provider.query.filter_by(id=request.args['card_id']).first()
elif request.args['category'] == 'carrier':
owner = models.Carrier.query.filter_by(id=request.args['card_id']).first()
else:
return 'Error 400'
return to_PDF(owner, request.args['name'], delivery)
else:
return redirect('/', code=302)
@app.route('/getClients', methods=['GET'])
def getClients():
if 'username' in session:
return table_to_json(models.Client.query.all())
else:
return redirect('/', code=302)
@app.route('/deleteMember', methods=['GET'])
def deleteMember():
user = models.User.query.filter_by(id=request.args['id']).first()
db.session.delete(user)
db.session.commit()
return 'OK'
@app.route('/stockTransit', methods=['GET'])
def stockTransit():
Item = models.Item.query.filter_by(Item_id=request.args['id_product']).first()
Stock = models.Stock.query.filter_by(Name=request.args['stock_select']).first()
for i in Stock.Items:
if i.Name == Item.Name:
Item.Volume = str(int(Item.Volume) - int(request.args['product_volume']))
i.Volume = str(int(i.Volume) + int(request.args['product_volume']))
db.session.commit()
return 'OK'
item = models.Item()
item.Weight = Item.Weight
item.Packing = Item.Packing
item.Fraction = Item.Fraction
item.Creator = Item.Creator
item.Name = Item.Name
item.Cost = Item.Cost
Item.Volume = str(int(Item.Volume) - int(request.args['product_volume']))
item.Volume = str(request.args['product_volume'])
item.NDS = Item.NDS
item.Group_id = Item.Group_id
item.Prefix = Item.Prefix
item.Group_name = Item.Group_name
Stock.Items.append(item)
db.session.commit()
return 'OK'
@app.route('/findContacts', methods=['GET'])
def findContacts():
if 'username' in session:
result = []
data = request.args['data']
Contacts = models.Contacts.query.all()
Deliveryies = models.Delivery.query.all()
Users = models.User.query.all()
for i in Deliveryies:
if i['Contact_End'] == data or i['Contact_Number'] == data:
result.append(json.loads(table_to_json([i]))[0])
for i in Contacts:
if i.Number == data or i.Email == data:
result.append(json.loads(table_to_json([i]))[0])
for i in Users:
if i.email == data:
subres = json.loads(table_to_json([i]))[0]
subres.pop('password', None)
result.append(subres)
return json.dumps(result)
else:
return redirect('/', code=302)
@app.route('/getMessages', methods=['GET'])
def getMessages():
if 'username' in session:
if request.args['category'] == 'client':
client = request.args['id']
Client = models.Client.query.filter_by(id=client).first()
return table_to_json(models.Notes.query.filter_by(Author=Client).all())
elif request.args['category'] == 'provider':
provider = request.args['id']
Provider = models.Provider.query.filter_by(id=provider).first()
return table_to_json(models.Notes.query.filter_by(Provider=Provider).all())
elif request.args['category'] == 'carrier':
carrier = request.args['id']
Carrier = models.Provider.query.filter_by(id=carrier).first()
return table_to_json(models.Notes.query.filter_by(Carrier=Carrier).all())
else:
return 'ERROR 400 BAD REQUEST'
else:
return redirect('/', code=302)
@app.route('/addMessages', methods=['GET'])
def addMessages():
if 'username' in session:
if request.args['category'] == 'client':
Owner = models.Client.query.filter_by(id=request.args['id']).first()
elif request.args['category'] == 'provider':
Owner = models.Provider.query.filter_by(id=request.args['id']).first()
else:
Owner = models.Carrier.query.filter_by(id=request.args['id']).first()
i = json.loads(request.args['comments'])
Message = models.Notes()
Message.Date = i['comment_date']
Message.Manager = i['comment_role']
Message.Note = i['comment_content']
if request.args['category'] == 'client':
Message.Client_id = request.args['id']
elif request.args['category'] == 'provider':
Message.Provider_id = request.args['id']
elif request.args['category'] == 'carrier':
Message.Carrier_id = request.args['id']
Owner.Notes.append(Message)
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/getDeliveries', methods=['GET'])
def getDeliveries():
if 'username' in session:
deliveries = models.Delivery.query.all()
result = []
carriers = models.Carrier.query.all()
for delivery in deliveries:
if delivery.Carrier_id:
print(len(carriers))
carrier = carriers[delivery.Carrier_id - 1]
result.append({'carrier': json.loads(table_to_json([carrier]))[0],
'delivery': json.loads(table_to_json([delivery]))[0]})
else:
result.append({'carrier': None, 'delivery': json.loads(table_to_json([delivery]))[0]})
return json.dumps(result)
else:
return redirect('/', code=302)
@app.route('/addDelivery', methods=['GET'])
def addDelivery():
if 'username' in session:
data = request.args
print(data['delivery_id'])
if data['delivery_id'] == 'new':
table = models.Delivery()
else:
table = models.Delivery.query.filter_by(id=data['delivery_id']).first()
table.Name = data['delivery_name']
table.Date = data['delivery_date']
table.Price = data['delivery_price']
table.Contact_Number = data['delivery_contact_number']
table.Contact_Name = data['delivery_contact_name']
if data['delivery_carrier_id']:
table.Carrier_id = data['delivery_carrier_id']
if data['delivery_account_id']:
table.Account_id = data['delivery_account_id']
table.Comment = data['delivery_comment']
table.Client = data['delivery_client']
table.NDS = data['delivery_vat']
table.Contact_End = data['delivery_contact_end']
table.Customer = data['delivery_customer']
table.End_date = data['delivery_end_date']
table.Load_type = data['delivery_load_type']
table.Payment_date = data['delivery_payment_date']
table.Prefix = data['delivery_prefix']
table.Start_date = data['delivery_start_date']
table.Stock = data['delivery_stock']
table.Type = data['delivery_type']
table.Item_ids = data['delivery_item_ids']
table.Amounts = data['delivery_amounts']
table.Auto = data['delivery_car']
table.Passport_data = data['delivery_passport']
if 'payment_list' in data:
table.Payment_list = data['payment_list']
else:
table.Payment_list = None
if data['delivery_id'] == 'new':
db.session.add(table)
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/getContacts', methods=['GET'])
def getContacts():
if 'username' in session:
if request.args['category'] == 'client':
client = request.args['id']
Client = models.Client.query.filter_by(id=client).first()
return table_to_json(models.Contacts.query.filter_by(Owner=Client).all())
elif request.args['category'] == 'provider':
provider = request.args['id']
Provider = models.Provider.query.filter_by(id=provider).first()
return table_to_json(models.Contacts.query.filter_by(Provider=Provider).all())
elif request.args['category'] == 'carrier':
carrier = request.args['id']
Carrier = models.Provider.query.filter_by(id=carrier).first()
return table_to_json(models.Contacts.query.filter_by(Carrier=Carrier).all())
else:
return 'ERROR 400 BAD REQUEST'
else:
return redirect('/', code=302)
@app.route('/addStock', methods=['GET'])
def addStock():
if 'username' in session:
name = request.args['stock_name']
stock = models.Stock()
stock.Name = name
db.session.add(stock)
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/getStockTable', methods=['GET'])
def getStockTable():
if 'username' in session:
result = []
Stocks = models.Stock.query.all()
for stock in Stocks:
subres = {'items': json.loads(table_to_json(stock.Items))}
subres['stock_address'] = stock.Name
result.append(subres)
return json.dumps(result)
else:
return redirect('/', code=302)
@app.route('/getStockItems', methods=['GET'])
def getStockItems():
if 'username' in session:
data = request.args
Stocks = models.Stock.query.filter_by(id=data['stock_id']).all()
if len(Stocks):
Stock = Stocks[0]
else:
return 'Bad Stock'
return table_to_json(Stock.Items)
else:
return redirect('/', code=302)
@app.route('/addItemGroup', methods=['GET'])
def addItemGroup():
if 'username' in session:
group = models.Item_groups()
group.Group = request.args['group_name']
db.session.add(group)
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/getItemGroup', methods=['GET'])
def getItemGroup():
if 'username' in session:
return table_to_json(models.Item_groups.query.all())
else:
return redirect('/', code=302)
@app.route('/getAllItems', methods=['GET'])
def getAllItems():
if 'username' in session:
return table_to_json(models.Item.query.all())
else:
return redirect('/', code=302)
@app.route('/getAccounts', methods=['GET'])
def getAccounts():
if 'username' in session:
result = []
Items = models.Item.query.all()
for i in models.Account.query.all():
items = []
for j in json.loads(i.Item_ids):
print(j)
item = Items[int(j['id']) - 1]
subres = json.loads(table_to_json([item]))[0]
subres['Transferred_volume'] = j['volume']
items.append(subres)
account = json.loads(table_to_json([i]))[0]
subres = {'items': items, 'account': account}
result.append(subres)
return json.dumps(result)
else:
return redirect('/', code=302)
@app.route('/addUser', methods=['GET'])
def addUser():
if 'username' in session:
data = request.args
if data['id'] == 'new':
user = models.User()
else:
user = models.User.query.filter_by(id=data['id']).first()
user.login = data['create_login']
user.email = data['create_email']
user.second_name = data['create_last_name']
user.name = data['create_first_name']
user.third_name = data['create_patronymic']
user.role = data['create_role']
user.password = data['create_password']
if data['id'] == 'new':
db.session.add(user)
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/addAccount', methods=['GET'])
def addAccount():
if 'username' in session:
data = request.args
table = models.Account()
table.Name = data['name']
table.Status = data['status']
table.Date = data['date']
table.Hello = data['hello']
table.Sale = data['sale']
table.Shipping = data['shipping']
table.Sum = data['sum']
table.Item_ids = data['item_ids']
db.session.add(table)
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/addItemToStock', methods=['GET'])
def addItemToStock():
if 'username' in session:
data = request.args
Stocks = models.Stock.query.filter_by(id=data['stock_id']).all()
if len(Stocks):
Stock = Stocks[0]
else:
return 'Bad Stock'
item = models.Item()
item.Weight = data['item_weight']
item.Packing = data['item_packing']
item.Fraction = data['item_fraction']
item.Creator = data['item_creator']
item.Name = data['item_product']
item.Cost = data['item_price']
item.Volume = data['item_volume']
item.NDS = data['item_vat']
item.Group_id = data['group_id']
item.Prefix = data['item_prefix']
item.Group_name = models.Item_groups.query.filter_by(id=data['group_id']).first().Group
Stock.Items.append(item)
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/getStocks', methods=['GET'])
def getStocks():
if 'username' in session:
return table_to_json(models.Stock.query.all())
else:
return redirect('/', code=302)
@app.route('/addContacts', methods=['GET'])
def addContacts():
if 'username' in session:
if request.args['category'] == 'client':
Owner = models.Client.query.filter_by(id=request.args['id']).first()
elif request.args['category'] == 'provider':
Owner = models.Provider.query.filter_by(id=request.args['id']).first()
else:
Owner = models.Carrier.query.filter_by(id=request.args['id']).first()
Contacts = []
args = json.loads(request.args['contacts'])
for i in args:
Contact = models.Contacts()
Contact.Name = i['first_name']
Contact.Last_name = i['last_name']
Contact.Number = i['phone']
Contact.Email = i['email']
Contact.Position = i['role']
Contact.Visible = i['visible']
if request.args['category'] == 'client':
Contact.Client_id = request.args['id']
elif request.args['category'] == 'provider':
Contact.Provider_id = request.args['id']
elif request.args['category'] == 'carrier':
Contact.Carrier_id = request.args['id']
Contacts.append(Contact)
Owner.Contacts = Contacts
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/addManagerToCard', methods=['GET'])
def addManagerToCard():
if 'username' in session:
if request.args['category'] == 'client':
Owner = models.Client.query.filter_by(id=request.args['card_id']).first()
elif request.args['category'] == 'provider':
Owner = models.Provider.query.filter_by(id=request.args['card_id']).first()
else:
return '400 BAD REQUEST'
Owner.Manager_active = True
Owner.Manager_id = request.args['manager_id']
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/deleteManagerFromCard', methods=['GET'])
def deleteManagerFromCard():
if 'username' in session:
if request.args['category'] == 'client':
Owner = models.Client.query.filter_by(id=request.args['card_id']).first()
elif request.args['category'] == 'provider':
Owner = models.Provider.query.filter_by(id=request.args['card_id']).first()
else:
return '400 BAD REQUEST'
Owner.Manager_active = False
Owner.Manager_date = request.args['date']
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/getThisUser', methods=['GET'])
def getThisUser():
if 'username' in session:
if models.User.query.filter_by(login=session['username']).first():
user = models.User.query.filter_by(login=session['username']).first()
else:
user = models.User.query.filter_by(email=session['username']).first()
result = json.loads(table_to_json([user]))[0]
result.pop('password', None)
return json.dumps(result)
else:
return redirect('/', code=302)
@app.route('/addItems', methods=['GET'])
def addItems():
if 'username' in session:
if request.args['category'] == 'client':
isClient = True
Owner = models.Client.query.filter_by(id=request.args['id']).first()
elif request.args['category'] == 'provider':
isClient = False
Owner = models.Provider.query.filter_by(id=request.args['id']).first()
else:
return '400 BAD REQUEST'
Items = []
args = json.loads(request.args['item'])
for i in args:
if i['item_product']:
Item = models.Junk_item()
if isClient:
Item.Volume = i['item_volume']
Item.Creator = i['item_creator']
Item.Client_id = request.args['id']
else:
Item.NDS = i['item_vat']
Item.Fraction = i['item_fraction']
Item.Packing = i['item_packing']
Item.Weight = i['item_weight']
Item.Provider_id = request.args['id']
Item.Name = i['item_product']
Item.Cost = i['item_price']
Items.append(Item)
Owner.Junk_items = Items
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/getItems', methods=['GET'])
def getItems():
if 'username' in session:
if request.args['category'] == 'client':
client = request.args['id']
Client = models.Client.query.filter_by(id=client).first()
return table_to_json(models.Junk_item.query.filter_by(Client=Client).all())
elif request.args['category'] == 'provider':
provider = request.args['id']
Provider = models.Provider.query.filter_by(id=provider).first()
return table_to_json(models.Junk_item.query.filter_by(Provider=Provider).all())
elif request.args['category'] == 'carrier':
carrier = request.args['id']
Carrier = models.Provider.query.filter_by(id=carrier).first()
return table_to_json(models.Junk_item.query.filter_by(Carrier=Carrier).all())
else:
return 'ERROR 400 BAD REQUEST'
else:
return redirect('/', code=302)
@app.route('/getProviders', methods=['GET'])
def getProviders():
if 'username' in session:
return table_to_json(models.Provider.query.all())
else:
return redirect('/', code=302)
@app.route('/getTasks', methods=['GET'])
def getTasks(login):
if 'username' in session:
user = models.User.query.filter_by(login=login).first()
return user.get_task_by_login()
else:
return redirect('/', code=302)
@app.route('/getUsers', methods=['GET'])
def getUsers():
if 'username' in session:
return table_to_json(models.User.query.all())
else:
return redirect('/', code=302)
@app.route('/getCarriers', methods=['GET'])
def getCarriers():
if 'username' in session:
return table_to_json(models.Carrier.query.all())
else:
return redirect('/', code=302)
@app.route('/addProvider', methods=['GET'])
def addProvider():
if 'username' in session:
data = request.args
if data['provider_data'] != 'new':
new = False
else:
new = True
if not new:
Provider = models.Provider.query.filter_by(id=data['provider_data']).first()
else:
Provider = models.Provider()
Provider.Name = data['provider_name']
Provider.Rayon = data['provider_area']
Provider.Category = data['provider_category']
Provider.Distance = data['provider_distance']
Provider.UHH = data['provider_inn']
Provider.Price = data['provider_price']
Provider.Oblast = data['provider_region']
Provider.Train = data['provider_station']
Provider.Tag = data['provider_tag']
Provider.Adress = data['provider_address']
Provider.NDS = data['provider_vat']
Provider.Merc = data['provider_merc']
Provider.Volume = data['provider_volume']
Provider.Holding = data['provider_holding']
if new:
db.session.add(Provider)
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/addComment', methods=['GET'])
def addComment():
if 'username' in session:
if request.args['category'] == 'client':
Owner = models.Client.query.filter_by(id=request.args['id']).first()
elif request.args['category'] == 'provider':
Owner = models.Provider.query.filter_by(id=request.args['id']).first()
elif request.args['category'] == 'carrier':
Owner = models.Carrier.query.filter_by(id=request.args['id']).first()
else:
return '400 BAD REQUEST'
Owner.Comment = request['comment']
db.session.commit()
else:
return redirect('/', code=302)
@app.route('/getComment', methods=['GET'])
def getComment():
if 'username' in session:
if request.args['category'] == 'client':
Owner = models.Client.query.filter_by(id=request.args['id']).first()
elif request.args['category'] == 'provider':
Owner = models.Provider.query.filter_by(id=request.args['id']).first()
elif request.args['category'] == 'carrier':
Owner = models.Carrier.query.filter_by(id=request.args['id']).first()
else:
return '400 BAD REQUEST'
return Owner.Comment
else:
return redirect('/', code=302)
@app.route('/addCarrier', methods=['GET'])
def addCarier():
if 'username' in session:
data = request.args
if data['carrier_data'] != 'new':
new = False
else:
new = True
if not new:
Carrier = models.Carrier.query.filter_by(id=data['carrier_data']).first()
else:
Carrier = models.Carrier()
Carrier.Name = data['carrier_name']
Carrier.Address = data['carrier_address']
Carrier.Area = data['carrier_area']
Carrier.Capacity = data['carrier_capacity']
Carrier.UHH = data['carrier_inn']
Carrier.Region = data['carrier_region']
Carrier.View = data['carrier_view']
if new:
db.session.add(Carrier)
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
@app.route('/addClient', methods=['GET'])
def addClient():
if 'username' in session:
data = request.args
if data['client_data'] != 'new':
new = False
else:
new = True
if not new:
Client = models.Client.query.filter_by(id=data['client_data']).first()
else:
Client = models.Client()
Client.Name = data['client_name']
Client.Rayon = data['client_area']
Client.Category = data['client_category']
Client.Distance = data['client_distance']
Client.Segment = data['client_industry']
Client.UHH = data['client_inn']
Client.Price = data['client_price']
Client.Oblast = data['client_region']
Client.Station = data['client_station']
Client.Tag = data['client_tag']
Client.Adress = data['client_address']
Client.Holding = data['client_holding']
Client.Site = data['client_site']
Client.Demand_item = data['demand_product']
Client.Demand_volume = data['demand_volume']
Client.Livestock_all = data['livestock_general']
Client.Livestock_milking = data['livestock_milking']
Client.Livestock_milkyield = data['livestock_milkyield']
if new:
db.session.add(Client)
db.session.commit()
return 'OK'
else:
return redirect('/', code=302)
| 31,436 | 9,651 |
import numpy as np
x = np.array([0,1])
w = np.array([0.5,0.5])
b = -0.7
print(w*x)
print(np.sum(w*x))
print(np.sum(w*x)+b)
def AND(x1,x2):
x = np.array([x1,x2])
w = np.array([0.5,0.5])
b = -0.7
tmp = np.sum(w*x)+b
if tmp <= 0:
return 0
else:
return 1
def NAND(x1,x2):
x = np.array([x1,x2])
w = np.array([-0.5,-0.5])
b = 0.7
tmp = np.sum(w * x) + b
if tmp <= 0:
return 0
else:
return 1
def OR(x1,x2):
x = np.array([x1,x2])
w = np.array([0.5,0.5])
b = -0.2
tmp = np.sum(w*x)+b
if tmp <= 0:
return 0
else:
return 1
def XOR(x1,x2):
s1 = NAND(x1,x2)
s2 = OR(x1,x2)
y = AND(s1,s2)
return y
print(XOR(0,0))
print(XOR(1,0))
print(XOR(0,1))
print(XOR(1,1))
| 790 | 422 |
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU()
def forward(self, x):
out = self.relu(self.bn1(self.conv1(x)))
out = self.relu(self.bn2(self.conv2(out)))
return out
class ResNet(nn.Module):
def __init__(self, block, num_classes=10):
super(ResNet, self).__init__()
self.prep_layer = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.layer1 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.MaxPool2d(2,2),
nn.BatchNorm2d(128),
nn.ReLU()
)
self.resblock1 = block(128, 128, stride=1)
self.layer2 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=False),
nn.MaxPool2d(2,2),
nn.BatchNorm2d(256),
nn.ReLU()
)
self.layer3 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1, bias=False),
nn.MaxPool2d(2,2),
nn.BatchNorm2d(512),
nn.ReLU()
)
self.resblock2 = block(512, 512, stride=1)
self.pool = nn.MaxPool2d(4, 4)
self.linear = nn.Linear(512, 10,bias=False)
def forward(self, x):
# Prep Layer
out = self.prep_layer(x)
out = self.layer1(out)
res1 = self.resblock1(out)
out = out + res1
out = self.layer2(out)
out = self.layer3(out)
res2 = self.resblock2(out)
out = out + res2
out = self.pool(out)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def CustomResNet():
return ResNet(BasicBlock)
| 2,413 | 933 |
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
"""
Detectron2 training script with a plain training loop.
This script reads a given config file and runs the training or evaluation.
It is an entry point that is able to train standard models in detectron2.
In order to let one script support training of many models,
this script contains logic that are specific to these built-in models and therefore
may not be suitable for your own project.
For example, your research project perhaps only needs a single "evaluator".
Therefore, we recommend you to use detectron2 as a library and take
this file as an example of how to use the library.
You may want to write your own script with your datasets and other customizations.
Compared to "train_net.py", this script supports fewer default features.
It also includes fewer abstraction, therefore is easier to add custom logic.
"""
import logging
import os
import time
import datetime
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
import detectron2.utils.comm as comm
from detectron2.utils.comm import get_world_size
from detectron2.utils.logger import log_every_n_seconds
from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer
from detectron2.config import get_cfg
from detectron2.data import (
MetadataCatalog,
build_detection_test_loader,
build_detection_train_loader,
)
from detectron2.data.common import DatasetFromList
from detectron2.data.datasets import register_coco_instances
from detectron2.engine import default_argument_parser, default_setup, launch
from detectron2.evaluation import (
CityscapesInstanceEvaluator,
CityscapesSemSegEvaluator,
COCOEvaluator,
COCOPanopticEvaluator,
DatasetEvaluators,
LVISEvaluator,
PascalVOCDetectionEvaluator,
SemSegEvaluator,
# inference_on_dataset,
print_csv_format,
inference_context,
)
from detectron2.modeling import build_model
from detectron2.layers import route_func
from detectron2.solver import build_lr_scheduler, build_optimizer
from detectron2.utils.events import (
CommonMetricPrinter,
EventStorage,
JSONWriter,
TensorboardXWriter,
)
from network import MyNetwork
logger = logging.getLogger("detectron2")
def get_evaluator(cfg, dataset_name, output_folder=None):
"""
Create evaluator(s) for a given dataset.
This uses the special metadata "evaluator_type" associated with each builtin dataset.
For your own dataset, you can simply create an evaluator manually in your
script and do not have to worry about the hacky if-else logic here.
"""
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
evaluator_list = []
evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
if evaluator_type in ["sem_seg", "coco_panoptic_seg"]:
evaluator_list.append(
SemSegEvaluator(
dataset_name,
distributed=True,
output_dir=output_folder,
)
)
if evaluator_type in ["coco", "coco_panoptic_seg"]:
evaluator_list.append(COCOEvaluator(dataset_name, output_dir=output_folder))
if evaluator_type == "coco_panoptic_seg":
evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
if evaluator_type == "cityscapes_instance":
assert (
torch.cuda.device_count() >= comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesInstanceEvaluator(dataset_name)
if evaluator_type == "cityscapes_sem_seg":
assert (
torch.cuda.device_count() >= comm.get_rank()
), "CityscapesEvaluator currently do not work with multiple machines."
return CityscapesSemSegEvaluator(dataset_name)
if evaluator_type == "pascal_voc":
return PascalVOCDetectionEvaluator(dataset_name)
if evaluator_type == "lvis":
return LVISEvaluator(dataset_name, cfg, True, output_folder)
if len(evaluator_list) == 0:
raise NotImplementedError(
"no Evaluator for the dataset {} with the type {}".format(dataset_name, evaluator_type)
)
if len(evaluator_list) == 1:
return evaluator_list[0]
return DatasetEvaluators(evaluator_list)
def inference_on_dataset(model, data_loader, evaluator):
"""
Run model on the data_loader and evaluate the metrics with evaluator.
Also benchmark the inference speed of `model.forward` accurately.
The model will be used in eval mode.
Args:
model (nn.Module): a module which accepts an object from
`data_loader` and returns some outputs. It will be temporarily set to `eval` mode.
If you wish to evaluate a model in `training` mode instead, you can
wrap the given model and override its behavior of `.eval()` and `.train()`.
data_loader: an iterable object with a length.
The elements it generates will be the inputs to the model.
evaluator (DatasetEvaluator): the evaluator to run. Use `None` if you only want
to benchmark, but don't want to do any evaluation.
Returns:
The return value of `evaluator.evaluate()`
"""
num_devices = get_world_size()
logger = logging.getLogger(__name__)
logger.info("Start inference on {} images".format(len(data_loader)))
total = len(data_loader) # inference data loader must have a fixed length
if evaluator is None:
# create a no-op evaluator
evaluator = DatasetEvaluators([])
evaluator.reset()
num_warmup = min(5, total - 1)
start_time = time.perf_counter()
total_compute_time = 0
routing_weights = torch.load("routing_weights.pth")
with inference_context(model), torch.no_grad():
for idx, inputs in enumerate(data_loader):
if idx == num_warmup:
start_time = time.perf_counter()
total_compute_time = 0
start_compute_time = time.perf_counter()
routing_weight = routing_weights[idx].unsqueeze(0)
routing_weight = [routing_weight[:, :8],
routing_weight[:, 8:16],
routing_weight[:, 16:]]
outputs = model(inputs, routing_weight)
if torch.cuda.is_available():
torch.cuda.synchronize()
total_compute_time += time.perf_counter() - start_compute_time
evaluator.process(inputs, outputs)
iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)
seconds_per_img = total_compute_time / iters_after_start
if idx >= num_warmup * 2 or seconds_per_img > 5:
total_seconds_per_img = (time.perf_counter() - start_time) / iters_after_start
eta = datetime.timedelta(seconds=int(total_seconds_per_img * (total - idx - 1)))
log_every_n_seconds(
logging.INFO,
"Inference done {}/{}. {:.4f} s / img. ETA={}".format(
idx + 1, total, seconds_per_img, str(eta)
),
n=5,
)
# Measure the time only for this worker (before the synchronization barrier)
total_time = time.perf_counter() - start_time
total_time_str = str(datetime.timedelta(seconds=total_time))
# NOTE this format is parsed by grep
logger.info(
"Total inference time: {} ({:.6f} s / img per device, on {} devices)".format(
total_time_str, total_time / (total - num_warmup), num_devices
)
)
total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time)))
logger.info(
"Total inference pure compute time: {} ({:.6f} s / img per device, on {} devices)".format(
total_compute_time_str, total_compute_time / (total - num_warmup), num_devices
)
)
results = evaluator.evaluate()
# An evaluator may return None when not in main process.
# Replace it by an empty dict instead to make it easier for downstream code to handle
if results is None:
results = {}
return results
def do_test(cfg, model):
results = OrderedDict()
for dataset_name in cfg.DATASETS.TEST:
data_loader = build_detection_test_loader(cfg, dataset_name)
evaluator = get_evaluator(
cfg, dataset_name, os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
)
results_i = inference_on_dataset(model, data_loader, evaluator)
results[dataset_name] = results_i
if comm.is_main_process():
logger.info("Evaluation results for {} in csv format:".format(dataset_name))
print_csv_format(results_i)
if len(results) == 1:
results = list(results.values())[0]
return results
def do_train(cfg, model, resume=False):
model.train()
model_weights = torch.load(cfg.MODEL.WEIGHTS)
if "model" in model_weights:
model_weights = model_weights["model"]
model.load_state_dict(model_weights, strict=False) # should better set True for once to see if it's loaded right
assert cfg.SOLVER.IMS_PER_BATCH == 1, f"should set batchsize=1"
sampler = torch.utils.data.sampler.SequentialSampler(range(1725))
data_loader = build_detection_train_loader(cfg, sampler=sampler, aspect_ratio_grouping=False)
num_images = 1725
params = []
for m in model.modules():
if isinstance(m, route_func):
print("found")
params = params + list(m.parameters())
optimizer = torch.optim.SGD(params, lr=cfg.SOLVER.BASE_LR,
momentum=cfg.SOLVER.MOMENTUM, weight_decay=0)
logger.info("Starting solving optimized routing weights")
all_routing_weights = []
with EventStorage(start_iter=0) as storage:
for data, iteration in zip(data_loader, range(num_images)):
storage.iter = iteration
print(iteration)
for _ in range(1):
loss_dict, routing_weights = model(data)
losses = sum(loss_dict.values())
assert torch.isfinite(losses).all(), loss_dict
loss_dict_reduced = {k: v.item() for k, v in comm.reduce_dict(loss_dict).items()}
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
if comm.is_main_process():
storage.put_scalars(total_loss=losses_reduced, **loss_dict_reduced)
optimizer.zero_grad()
losses.backward()
# optimizer.step()
print(losses.item())
all_routing_weights.append(routing_weights)
print(routing_weights.shape)
routing_weights = torch.cat(all_routing_weights).cpu()
torch.save(routing_weights, "routing_weights.pth")
return routing_weights
def setup(args):
"""
Create configs and perform basic setups.
"""
register_coco_instances("domain", {}, "domain/annotations.json", "domain")
register_coco_instances("domain_train", {}, "domain/train_annotations.json", "domain")
register_coco_instances("domain_test", {}, "domain/test_annotations.json", "domain")
register_coco_instances("routine_train", {}, "domain/train_routine_5fc766.json", "domain")
register_coco_instances("routine_test", {}, "domain/test_routine_5fc877.json", "domain")
cfg = get_cfg()
assert args.config_file == "", f"This code automatically uses the config file in this directory"
args.config_file = "configs.yaml"
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(
cfg, args
) # if you don't like any of the default setup, write your own setup code
return cfg
def main(args):
cfg = setup(args)
model = MyNetwork(cfg)
model.to(torch.device(cfg.MODEL.DEVICE))
logger.info("Model:\n{}".format(model))
if args.eval_only:
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume
)
return do_test(cfg, model)
distributed = comm.get_world_size() > 1
if distributed:
model = DistributedDataParallel(
model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
)
return do_train(cfg, model, resume=args.resume)
if __name__ == "__main__":
args = default_argument_parser().parse_args()
print("Command Line Args:", args)
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args,),
)
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