content stringlengths 1 1.04M | input_ids listlengths 1 774k | ratio_char_token float64 0.38 22.9 | token_count int64 1 774k |
|---|---|---|---|
import importlib
from django import forms
from s3file.apps import S3FileConfig
from s3file.forms import S3FileInputMixin
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from django.contrib.sites.models import Site
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"""These methods are copied from https://github.com/Kyubyong/dc_tts/"""
import os
import copy
import librosa
import scipy.io.wavfile
import numpy as np
from tqdm import tqdm
from scipy import signal
from hparams import HParams as hp
def spectrogram2wav(mag):
'''# Generate wave file from linear magnitude spectrogram
Args:
mag: A numpy array of (T, 1+n_fft//2)
Returns:
wav: A 1-D numpy array.
'''
# transpose
mag = mag.T
# de-noramlize
mag = (np.clip(mag, 0, 1) * hp.max_db) - hp.max_db + hp.ref_db
# to amplitude
mag = np.power(10.0, mag * 0.05)
# wav reconstruction
wav = griffin_lim(mag ** hp.power)
# de-preemphasis
wav = signal.lfilter([1], [1, -hp.preemphasis], wav)
# trim
wav, _ = librosa.effects.trim(wav)
return wav.astype(np.float32)
def griffin_lim(spectrogram):
'''Applies Griffin-Lim's raw.'''
X_best = copy.deepcopy(spectrogram)
for i in range(hp.n_iter):
X_t = invert_spectrogram(X_best)
est = librosa.stft(X_t, hp.n_fft, hp.hop_length, win_length=hp.win_length)
phase = est / np.maximum(1e-8, np.abs(est))
X_best = spectrogram * phase
X_t = invert_spectrogram(X_best)
y = np.real(X_t)
return y
def invert_spectrogram(spectrogram):
'''Applies inverse fft.
Args:
spectrogram: [1+n_fft//2, t]
'''
return librosa.istft(spectrogram, hp.hop_length, win_length=hp.win_length, window="hann")
def get_spectrograms(fpath):
'''Parse the wave file in `fpath` and
Returns normalized melspectrogram and linear spectrogram.
Args:
fpath: A string. The full path of a sound file.
Returns:
mel: A 2d array of shape (T, n_mels) and dtype of float32.
mag: A 2d array of shape (T, 1+n_fft/2) and dtype of float32.
'''
# Loading sound file
y, sr = librosa.load(fpath, sr=hp.sr)
# Trimming
y, _ = librosa.effects.trim(y)
# Preemphasis
y = np.append(y[0], y[1:] - hp.preemphasis * y[:-1])
# stft
linear = librosa.stft(y=y,
n_fft=hp.n_fft,
hop_length=hp.hop_length,
win_length=hp.win_length)
# magnitude spectrogram
mag = np.abs(linear) # (1+n_fft//2, T)
# mel spectrogram
mel_basis = librosa.filters.mel(hp.sr, hp.n_fft, hp.n_mels) # (n_mels, 1+n_fft//2)
mel = np.dot(mel_basis, mag) # (n_mels, t)
# to decibel
mel = 20 * np.log10(np.maximum(1e-5, mel))
mag = 20 * np.log10(np.maximum(1e-5, mag))
# normalize
mel = np.clip((mel - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1)
mag = np.clip((mag - hp.ref_db + hp.max_db) / hp.max_db, 1e-8, 1)
# Transpose
mel = mel.T.astype(np.float32) # (T, n_mels)
mag = mag.T.astype(np.float32) # (T, 1+n_fft//2)
return mel, mag
def save_to_wav(mag, filename):
"""Generate and save an audio file from the given linear spectrogram using Griffin-Lim."""
wav = spectrogram2wav(mag)
scipy.io.wavfile.write(filename, hp.sr, wav)
def preprocess(dataset_path, speech_dataset):
"""Preprocess the given dataset."""
wavs_path = os.path.join(dataset_path, 'wavs')
mels_path = os.path.join(dataset_path, 'mels')
if not os.path.isdir(mels_path):
os.mkdir(mels_path)
mags_path = os.path.join(dataset_path, 'mags')
if not os.path.isdir(mags_path):
os.mkdir(mags_path)
for fname in tqdm(speech_dataset.fnames):
mel, mag = get_spectrograms(os.path.join(wavs_path, '%s.wav' % fname))
t = mel.shape[0]
# Marginal padding for reduction shape sync.
num_paddings = hp.reduction_rate - (t % hp.reduction_rate) if t % hp.reduction_rate != 0 else 0
mel = np.pad(mel, [[0, num_paddings], [0, 0]], mode="constant")
mag = np.pad(mag, [[0, num_paddings], [0, 0]], mode="constant")
# Reduction
mel = mel[::hp.reduction_rate, :]
np.save(os.path.join(mels_path, '%s.npy' % fname), mel)
np.save(os.path.join(mags_path, '%s.npy' % fname), mag)
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13,... | 2.111859 | 1,931 |
# Copyright (c) DEV Corporation. All rights reserved.
# Licensed under MIT License.
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2,
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"""
Django settings for medical project.
Generated by 'django-admin startproject' using Django 3.0.8.
For more information on this file, see
https://docs.djangoproject.com/en/3.0/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.0/ref/settings/
"""
import os
from .dev import *
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = os.environ.get('MEDSTORE_SECRET_KEY', 'g+!ccv7(cweo2*#8^6+%7(x4$na09w-0*+&)18nkt8)=um_+p(')
ACCOUNT_FORMS = {
"signup": "home.forms.UserSignUp"
}
ACCOUNT_EMAIL_VERIFICATION = 'none'
CRISPY_TEMPLATE_PACK = 'bootstrap4'
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'django.contrib.sites',
'django_filters',
'django.contrib.sitemaps', # For SiteMap
# extensions
'crispy_forms',
# apps
'medicines.apps.MedicinesConfig',
'home.apps.HomeConfig',
'cart',
'checkout',
'accounts',
# For Authentication
'allauth',
'allauth.account',
'allauth.socialaccount',
# To whitelist frontend
'corsheaders',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
'corsheaders.middleware.CorsMiddleware',
]
ROOT_URLCONF = 'medical.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'medical.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.0/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
AUTHENTICATION_BACKENDS = [
'django.contrib.auth.backends.ModelBackend',
'allauth.account.auth_backends.AuthenticationBackend'
]
# Password validation
# https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.0/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'Asia/Kolkata'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.0/howto/static-files/
STATIC_URL = '/static/'
SITE_ID = 1
LOGIN_REDIRECT_URL = "/"
LOGIN_URL = "/login/"
try:
from . import stripe_conf
STR_PUB = stripe_conf.publishable_key
STR_SEC = stripe_conf.secret_key
STRIPE_ENDPOINT_KEY = stripe_conf.end_key
except (ModuleNotFoundError, ImportError):
STR_PUB = ''
STR_SEC = ''
STRIPE_ENDPOINT_KEY = ''
# For SMTP Email
EMAIL_USE_TLS = True
EMAIL_HOST = 'smtp.gmail.com'
EMAIL_PORT = 587
# Include production settings
try:
# Check if there's a production environment file
from .prod import *
except (ModuleNotFoundError, ImportError):
pass
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1378... | 2.365615 | 1,838 |
import dataclasses
import enum
from typing import Any
@dataclasses.dataclass
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from deepsegment import DeepSegment
# The default language is 'en'
segmenter = DeepSegment('en')
segmenter.segment('I am Batman i live in gotham')
# ['I am Batman', 'i live in gotham']
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30... | 2.983871 | 62 |
# -*- coding: utf-8 -*-
from django.core.urlresolvers import reverse
from django.db import models
from django.utils.translation import ugettext_lazy as _
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33... | 2.961538 | 52 |
import sys; sys.path.append("..")
from policies import max_min_fairness, max_min_fairness_strategy_proof
import random
import time
import numpy as np
np.set_printoptions(precision=3, suppress=True)
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... | 3.076923 | 65 |
import os
import os.path
from print_function import *
# Exemple
# main.py server start
# main.py server start /home/user/kafka/config/server.propersties
# main.py server stop
# main.py server restart
# main.py server restart /home/user/kafka/config/server.propersties
KAFKA_HOME = str(os.getenv("KAFKA_HOME"))
LOG_DIR = "server.log"
BACKGROUND = " > "+LOG_DIR+" 2>&1 &"
DEFAULT_CONF = KAFKA_HOME+"config/server.properties"
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7... | 2.511364 | 176 |
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
# Generated file, DO NOT EDIT
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------------------------
from msrest.serialization import Model
class ContributedFeatureState(Model):
"""ContributedFeatureState.
:param feature_id: The full contribution id of the feature
:type feature_id: str
:param overridden: True if the effective state was set by an override rule (indicating that the state cannot be managed by the end user)
:type overridden: bool
:param reason: Reason that the state was set (by a plugin/rule).
:type reason: str
:param scope: The scope at which this state applies
:type scope: :class:`ContributedFeatureSettingScope <feature-management.v4_1.models.ContributedFeatureSettingScope>`
:param state: The current state of this feature
:type state: object
"""
_attribute_map = {
'feature_id': {'key': 'featureId', 'type': 'str'},
'overridden': {'key': 'overridden', 'type': 'bool'},
'reason': {'key': 'reason', 'type': 'str'},
'scope': {'key': 'scope', 'type': 'ContributedFeatureSettingScope'},
'state': {'key': 'state', 'type': 'object'}
}
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... | 3.481953 | 471 |
"""
Gathers up all of the expenses and breaks down the values by month.
"""
import time
import math
from operator import itemgetter
from gnucash_reports.collate.bucket import PeriodCollate, CategoryCollate, AccountCollate
from gnucash_reports.collate.bucket_generation import decimal_generator
from gnucash_reports.collate.store import split_summation
from gnucash_reports.periods import PeriodStart, PeriodEnd, PeriodSize
from gnucash_reports.wrapper import get_splits, account_walker, parse_walker_parameters
def expenses_period(expenses=None, start=PeriodStart.this_month_year_ago, end=PeriodEnd.this_month,
period_size=PeriodSize.month):
"""
Calculate the amount of money that when into an expense account over the given period.
:param expenses: account walker parameters for accounts to calculate
:param start: the start of the time frame for the report
:param end: the end of the time frame for the report
:param period_size: the size of the buckets for the report
:return: dictionary containing:
expenses: sorted dictionary containing keys for date and value.
"""
expenses = expenses or []
accounts = parse_walker_parameters(expenses)
start_period = PeriodStart(start)
end_period = PeriodEnd(end)
period_size = PeriodSize(period_size)
bucket = PeriodCollate(start_period.date, end_period.date, decimal_generator, split_summation,
frequency=period_size.frequency, interval=period_size.interval)
for account in account_walker(**accounts):
for split in get_splits(account, start_period.date, end_period.date):
bucket.store_value(split)
sorted_results = []
for key, value in bucket.container.iteritems():
sorted_results.append((time.mktime(key.timetuple()), value))
data_set = {
'expenses': sorted(sorted_results, key=itemgetter(0))
}
return data_set
def expenses_box(expenses=None, start=PeriodStart.this_month_year_ago, end=PeriodEnd.this_month,
period_size=PeriodSize.month):
"""
Calculate the amount of money that when into an expense account over the given period.
:param expenses: account walker parameters for accounts to calculate
:param start: the start of the time frame for the report
:param end: the end of the time frame for the report
:param period_size: the size of the buckets for the report
:return: dictionary containing:
expenses: dictionary containing the following keys:
low - lowest amount spent
high - highest amount spent
q1 - first quartile value
q2 - second quartile value
q3 - third quartile value
"""
expenses = expenses or []
accounts = parse_walker_parameters(expenses)
start_period = PeriodStart(start)
end_period = PeriodEnd(end)
period_size = PeriodSize(period_size)
bucket = PeriodCollate(start_period.date, end_period.date, decimal_generator, split_summation,
frequency=period_size.frequency, interval=period_size.interval)
for account in account_walker(**accounts):
for split in get_splits(account, start_period.date, end_period.date):
bucket.store_value(split)
results = []
for key, value in bucket.container.iteritems():
results.append(float(value))
results = sorted(results)
return {'low': results[0], 'high': results[-1], 'q1': get_median(get_lower_half(results)),
'q2': get_median(results), 'q3': get_median(get_upper_half(results))}
def expenses_categories(expenses=None, start=PeriodStart.this_month, end=PeriodEnd.this_month):
"""
Walk through the accounts defined in expenses base and collate the spending in the period into the categories
defined in the configuration object.
:param expenses: account walker definition of the accounts to grab expenses for.
:param start: when the report should start collecting data from
:param end: when the report should stop collecting data
:return: dictionary containing:
categories - list of tuples (category name, value) containing the results sorted by category name
"""
expenses = expenses or []
accounts = parse_walker_parameters(expenses)
start_period = PeriodStart(start)
end_period = PeriodEnd(end)
bucket = CategoryCollate(decimal_generator, split_summation)
for account in account_walker(**accounts):
for split in get_splits(account, start_period.date, end_period.date):
bucket.store_value(split)
return {'categories': sorted([[key, value] for key, value in bucket.container.iteritems()], key=itemgetter(0))}
def expense_accounts(expenses=None, start=PeriodStart.this_month_year_ago, end=PeriodEnd.this_month):
"""
Walk through the accounts defined in expenses base and collate the spending into categories that are named after the
leaf account name.
:param expenses: account walker definition of the accounts to grab expenses for.
:param start: when the report should start collecting data from
:param end: when the report should stop collecting data
:return: dictionary containing:
categories - list of tuples (category name, value) containing the results sorted by category name
"""
expenses = expenses or []
accounts = parse_walker_parameters(expenses)
start_period = PeriodStart(start)
end_period = PeriodEnd(end)
bucket = AccountCollate(decimal_generator, split_summation)
for account in account_walker(**accounts):
for split in get_splits(account, start_period.date, end_period.date):
bucket.store_value(split)
return {'categories': sorted([[key, value] for key, value in bucket.container.iteritems()], key=itemgetter(0))}
# Calculating the quartiles based on:
# https://en.wikipedia.org/wiki/Quartile Method 1
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... | 3.036617 | 1,939 |
import pandas as pd
import numpy as np
import sklearn
import joblib
from flask import Flask, render_template, request, url_for
app = Flask(__name__)
@app.route('/')
@app.route('/predict', methods=['GET', 'POST'])
if __name__ == '__main__':
app.run(debug=True)
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201... | 2.469565 | 115 |
from typing import Tuple
from math import prod
with open('input', 'r') as fd:
bus_lines_raw = fd.readlines()[1].strip().split(',')
bus_lines = list(map(int, filter(lambda bl: bl != 'x', bus_lines_raw)))
bl_offsets = [i for i, n in enumerate(bus_lines_raw) if n != 'x']
# For an explanation of how this works, look up the Chinese Remainder Theorem, e.g.:
# https://en.wikipedia.org/wiki/Chinese_remainder_theorem#Existence_(direct_construction)
N = prod(bus_lines)
result = 0
for i in range(len(bus_lines)):
a_i = bus_lines[i] - bl_offsets[i]
n_i = bus_lines[i]
N_i = N // n_i # Using a float value here with N / n_i results in overflow errors for high numbers
M_i, _ = bezout_coeff(N_i, n_i)
result += a_i * M_i * N_i
# This calculation finds _a_ solution - other solutions are obtained by adding multiples of N.
# We are interested in the smallest positive solution.
result %= N
print(result)
| [
6738,
19720,
1330,
309,
29291,
198,
6738,
10688,
1330,
40426,
198,
198,
4480,
1280,
10786,
15414,
3256,
705,
81,
11537,
355,
277,
67,
25,
198,
220,
220,
220,
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62,
6615,
62,
1831,
796,
277,
67,
13,
961,
6615,
3419,
58,
16,
4083,... | 2.715543 | 341 |
"""
Concerned with storing and returning books from a list.
"""
books = []
# def delete_book(name): # This is considered as a Bad Practice.
# for book in books:
# if book['name'] == name:
# books.remove(book)
"""
SCOPE - as in <line 26> : global books
states that books in local scope = (is equal to the) books in the outer scope
"""
| [
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198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
... | 2.705036 | 139 |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Fit and illustrate example spectral data in XSPEC format.
Run `xspec_fake.py` first to generate the example input file.
"""
| [
2,
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... | 3.555556 | 54 |
# -*- coding: utf-8 -*-
# @Author : William
# @Project : TextGAN-william
# @FileName : text_process.py
# @Time : Created at 2019-05-14
# @Blog : http://zhiweil.ml/
# @Description :
# Copyrights (C) 2018. All Rights Reserved.
import nltk
import os
import torch
import config as cfg
def get_tokenlized(file):
"""tokenlize the file"""
tokenlized = list()
with open(file) as raw:
for text in raw:
text = nltk.word_tokenize(text.lower())
tokenlized.append(text)
return tokenlized
def get_word_list(tokens):
"""get word set"""
word_set = list()
for sentence in tokens:
for word in sentence:
word_set.append(word)
return list(set(word_set))
def get_dict(word_set):
"""get word_index_dict and index_word_dict"""
word_index_dict = dict()
index_word_dict = dict()
index = 2
word_index_dict[cfg.padding_token] = str(cfg.padding_idx)
index_word_dict[str(cfg.padding_idx)] = cfg.padding_token
word_index_dict[cfg.start_token] = str(cfg.start_letter)
index_word_dict[str(cfg.start_letter)] = cfg.start_token
for word in word_set:
word_index_dict[word] = str(index)
index_word_dict[str(index)] = word
index += 1
return word_index_dict, index_word_dict
def text_process(train_text_loc, test_text_loc=None):
"""get sequence length and dict size"""
train_tokens = get_tokenlized(train_text_loc)
if test_text_loc is None:
test_tokens = list()
else:
test_tokens = get_tokenlized(test_text_loc)
word_set = get_word_list(train_tokens + test_tokens)
word_index_dict, index_word_dict = get_dict(word_set)
if test_text_loc is None:
sequence_len = len(max(train_tokens, key=len))
else:
sequence_len = max(len(max(train_tokens, key=len)), len(max(test_tokens, key=len)))
return sequence_len, len(word_index_dict)
# ========================================================================
def init_dict(dataset):
"""
Initialize dictionaries of dataset, please note that '0': padding_idx, '1': start_letter.
Finally save dictionary files locally.
"""
tokens = get_tokenlized('dataset/{}.txt'.format(dataset))
# tokens.extend(get_tokenlized('dataset/testdata/{}_test.txt'.format(dataset))) # !!! no test data
word_set = get_word_list(tokens)
word_index_dict, index_word_dict = get_dict(word_set)
with open('dataset/{}_wi_dict.txt'.format(dataset), 'w') as dictout:
dictout.write(str(word_index_dict))
with open('dataset/{}_iw_dict.txt'.format(dataset), 'w') as dictout:
dictout.write(str(index_word_dict))
print('total tokens: ', len(word_index_dict))
def load_dict(dataset):
"""Load dictionary from local files"""
iw_path = 'dataset/{}_iw_dict.txt'.format(dataset)
wi_path = 'dataset/{}_wi_dict.txt'.format(dataset)
if not os.path.exists(iw_path) or not os.path.exists(iw_path): # initialize dictionaries
init_dict(dataset)
with open(iw_path, 'r') as dictin:
index_word_dict = eval(dictin.read().strip())
with open(wi_path, 'r') as dictin:
word_index_dict = eval(dictin.read().strip())
return word_index_dict, index_word_dict
def tensor_to_tokens(tensor, dictionary):
"""transform Tensor to word tokens"""
tokens = []
for sent in tensor:
sent_token = []
for word in sent.tolist():
if word == cfg.padding_idx:
break
sent_token.append(dictionary[str(word)])
tokens.append(sent_token)
return tokens
def tokens_to_tensor(tokens, dictionary):
"""transform word tokens to Tensor"""
tensor = []
for sent in tokens:
sent_ten = []
for i, word in enumerate(sent):
if word == cfg.padding_token:
break
sent_ten.append(int(dictionary[str(word)]))
while i < cfg.max_seq_len - 1:
sent_ten.append(cfg.padding_idx)
i += 1
tensor.append(sent_ten[:cfg.max_seq_len])
return torch.LongTensor(tensor)
def padding_token(tokens):
"""pad sentences with padding_token"""
pad_tokens = []
for sent in tokens:
sent_token = []
for i, word in enumerate(sent):
if word == cfg.padding_token:
break
sent_token.append(word)
while i < cfg.max_seq_len - 1:
sent_token.append(cfg.padding_token)
i += 1
pad_tokens.append(sent_token)
return pad_tokens
def write_tokens(filename, tokens):
"""Write word tokens to a local file (For Real data)"""
with open(filename, 'w') as fout:
for sent in tokens:
fout.write(' '.join(sent))
fout.write('\n')
def write_tensor(filename, tensor):
"""Write Tensor to a local file (For Oracle data)"""
with open(filename, 'w') as fout:
for sent in tensor:
fout.write(' '.join([str(i) for i in sent.tolist()]))
fout.write('\n')
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
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220,
220,
220,
220,
220,
220,
1058,
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198,
2,
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220,
220,
220,
220,
220,
1058,
8255,
45028,
12,
10594,
1789,
198,
2,
2488,
8979,
5376,
... | 2.274237 | 2,228 |
import json
from os.path import expanduser, exists
from os import makedirs
conoha_home = expanduser('~/.conoha')
config_path = f'{conoha_home}/config.json'
credential_path = f'{conoha_home}/credential.json'
token_path = f'{conoha_home}/token.json'
if not exists(conoha_home):
makedirs(conoha_home)
| [
11748,
33918,
198,
6738,
28686,
13,
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1330,
4292,
7220,
11,
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198,
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28083,
62,
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796,
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7220,
10786,
93,
11757,
1102,
28083,
11537,
198,
11250,
62,
6978,
796,
277,
6,
... | 2.596639 | 119 |
#!/usr/bin/env python
#
# Copyright (c) 2013 Juniper Networks, Inc. All rights reserved.
#
#
# sandesh_msg_test
#
import unittest
import sys
import os
import socket
import test_utils
import time
import uuid
from itertools import chain
sys.path.insert(1, sys.path[0]+'/../../../python')
from pysandesh.sandesh_base import *
from pysandesh.sandesh_client import *
from pysandesh.sandesh_session import *
from gen_py.msg_test.ttypes import *
# end __init__
# end write
# end SandeshSessionTestHelper
# end class SandeshMsgTest
if __name__ == '__main__':
unittest.main(verbosity=2, catchbreak=True)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
198,
2,
198,
2,
15069,
357,
66,
8,
2211,
7653,
9346,
27862,
11,
3457,
13,
1439,
2489,
10395,
13,
198,
2,
198,
198,
2,
198,
2,
6450,
5069,
62,
19662,
62,
9288,
198,
2,
198,
198,
... | 2.795455 | 220 |
from turtle import *
side = 50
COUNT = 8
checker(side)
| [
6738,
28699,
1330,
1635,
198,
1589,
796,
2026,
198,
34,
28270,
796,
807,
628,
198,
9122,
263,
7,
1589,
8,
198
] | 2.714286 | 21 |
# flake8: noqa
from .base import *
from .distillation import *
from .metrics import *
from .regularizations import *
from .unsupervised import *
from .losses import *
| [
2,
781,
539,
23,
25,
645,
20402,
198,
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764,
8692,
1330,
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198,
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764,
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1330,
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198,
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764,
4164,
10466,
1330,
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198,
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764,
16338,
4582,
1330,
1635,
198,
6738,
764,
403,
16668,
16149,
1330,
16... | 3.34 | 50 |
#!/usr/bin/env python
"""
Fluentd queue check for v3
"""
import argparse
import time
import subprocess
import math
from dateutil import parser
from datetime import datetime
from openshift_tools.monitoring.ocutil import OCUtil
from openshift_tools.monitoring.metric_sender import MetricSender
import logging
logging.basicConfig(
format='%(asctime)s - %(relativeCreated)6d - %(levelname)-8s - %(message)s',
)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
ocutil = OCUtil()
if __name__ == '__main__':
OFQC = OpenshiftFluentdQueueCheck()
OFQC.run()
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
37811,
198,
220,
34070,
298,
67,
16834,
2198,
329,
410,
18,
198,
37811,
198,
198,
11748,
1822,
29572,
198,
11748,
640,
198,
11748,
850,
14681,
198,
11748,
10688,
198,
6738,
3128,
22602,
... | 2.78744 | 207 |
import discord
import os
import inspect
from discord.ext import commands
class Community():
"""Commands for The Bad Server community"""
@property
@commands.command()
async def source(self, ctx, *, command: str = None):
"""Displays the Bad Bot's source"""
if command is None:
return await ctx.send(self.source_url)
object = self.bot.get_command(command.replace('.', ' '))
if object is None:
return await ctx.send('Command not found')
src = object.callback.__code__
lines, firstlineno = inspect.getsourcelines(src)
if not object.callback.__module__.startswith('discord'):
location = os.path.relpath(src.co_filename).replace('\\', '/')
else:
location = object.callback.__module__.replace('.', '/') + '.py'
await ctx.send(f'<{self.source_url}/blob/master/{location}#L{firstlineno}-L{firstlineno + len(lines) - 1}>')
| [
11748,
36446,
198,
11748,
28686,
198,
11748,
10104,
198,
6738,
36446,
13,
2302,
1330,
9729,
198,
198,
4871,
8108,
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198,
220,
220,
220,
37227,
6935,
1746,
329,
383,
7772,
9652,
2055,
37811,
198,
220,
220,
220,
220,
198,
220,
220,
... | 2.423559 | 399 |
from machine_learning_library.model_discriminant_analysis import Classification
import numpy as np
| [
6738,
4572,
62,
40684,
62,
32016,
13,
19849,
62,
15410,
3036,
42483,
62,
20930,
1330,
40984,
198,
11748,
299,
32152,
355,
45941,
628
] | 4.347826 | 23 |
# Generated by Django 3.0.7 on 2020-06-12 08:38
from django.db import migrations, models
import django.db.models.deletion
| [
2,
2980,
515,
416,
37770,
513,
13,
15,
13,
22,
319,
12131,
12,
3312,
12,
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25,
2548,
198,
198,
6738,
42625,
14208,
13,
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1330,
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602,
11,
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198,
11748,
42625,
14208,
13,
9945,
13,
27530,
13,
2934,
1616,
295,
... | 2.818182 | 44 |
import ast
import functools
from typing import Iterable
from typing import List
from typing import Optional
from typing import Tuple
from tokenize_rt import Offset
from tokenize_rt import Token
from pyupgrade._ast_helpers import ast_to_offset
from pyupgrade._data import register
from pyupgrade._data import State
from pyupgrade._data import TokenFunc
from pyupgrade._token_helpers import find_token
MOCK_MODULES = frozenset(('mock', 'mock.mock'))
@register(ast.ImportFrom)
@register(ast.Import)
@register(ast.Attribute)
| [
11748,
6468,
198,
11748,
1257,
310,
10141,
198,
6738,
19720,
1330,
40806,
540,
198,
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1330,
7343,
198,
6738,
19720,
1330,
32233,
198,
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19720,
1330,
309,
29291,
198,
198,
6738,
11241,
1096,
62,
17034,
1330,
3242,
2617,
198,... | 3.268293 | 164 |
import itertools
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly.express as px
from scipy.optimize import minimize, LinearConstraint, NonlinearConstraint
from tqdm import tqdm
from utils import constraint_cost, calc_time, calc_cost, ConstraintTime, f_ey, f_conv, f_ideal, \
calc_ideal, calc_conv, fit_ey
def main_spb(method):
"""
Проведение расчетов по предложенной методике для аэропорта Пулково.
:param method: Метод решения однокритериальной задачи.
"""
mu1 = [1 / 87, 1 / 30, 1 / 70]
n_max1 = np.array([88, 26, 24])
lam1 = 28659.2 / (24 * 60 * 60)
p1 = [0.999, 0.999]
s_max = 120
s = np.ones(len(mu1))
t_max = 4 * 60
optf = OptimalFinder(n_max1, s_max, t_max, s, [mu1, lam1, p1])
optf.find_optimal_time(method)
optf.find_optimal_cost(method)
optf.find_optimal_conv(method, [0.8, 0.2])
for metric in ['2-norm', 'inf']:
optf.find_optimal_ideal(method, [0.8, 0.2], metric)
df = pd.json_normalize(optf.experiments, sep=' узел ').sort_values(['Критерий', 'Метод'], ignore_index=True)
# df.index += 1
# df.index.name = 'Номер эксперимента'
df = df.round(2)
print(df.iloc[:, 2:].to_string(index=False, decimal=','))
def main_svo(method):
"""
Проведение расчетов по предложенной методике для терминала С аэропорта Шереметьево.
:param method: Метод решения однокритериальной задачи.
"""
mu1 = [1 / 75, 1 / 50, 1 / 70]
n_max1 = np.array([84, 60, 20])
lam1 = 67307.5 * 0.12 / (24 * 60 * 60)
p1 = [0.999, 0.999]
s_max = 40
s = np.ones(len(mu1))
t_max = 2 * 60
optf = OptimalFinder(n_max1, s_max, t_max, s, [mu1, lam1, p1])
optf.find_optimal_time(method)
optf.find_optimal_cost(method)
optf.find_optimal_conv(method, [0.8, 0.2])
for metric in ['2-norm', 'inf']:
optf.find_optimal_ideal(method, [0.8, 0.2], metric)
df = pd.json_normalize(optf.experiments, sep=' узел ').sort_values(['Критерий', 'Метод'], ignore_index=True)
# df.index += 1
# df.index.name = 'Номер эксперимента'
df = df.round(2)
print(df.iloc[:, 2:].to_string(index=False, decimal=','))
def main_spb_brute_force():
"""
Проведение расчетов с помощью метода полного перебора для аэропорта Пулково.
"""
mu1 = [1 / 87, 1 / 30, 1 / 70]
n_max1 = np.array([88, 26, 24])
lam1 = 28659.2 / (24 * 60 * 60)
p1 = [0.999, 0.999]
s_max = 120
s = np.ones(len(mu1))
t_max = 4 * 60
optf = OptimalFinder(n_max1, s_max, t_max, s, [mu1, lam1, p1])
n_df = optf.brute_force([0.8, 0.2], [0.8, 0.2])
n_df = n_df.sort_values(['Критерий'], ignore_index=True)
n_df = n_df.round(2)
print(n_df.iloc[:, 1:].to_string(index=False, decimal=','))
def main_svo_brute_force():
"""
Проведение расчетов с помощью метода полного перебора для терминала С аэропорта Шереметьево.
"""
mu1 = [1 / 75, 1 / 50, 1 / 70]
n_max1 = np.array([84, 60, 20])
lam1 = 67307.5 * 0.12 / (24 * 60 * 60)
p1 = [0.999, 0.999]
s_max = 40
s = np.ones(len(mu1))
t_max = 2 * 60
optf = OptimalFinder(n_max1, s_max, t_max, s, [mu1, lam1, p1])
n_df = optf.brute_force([0.8, 0.2], [0.8, 0.2])
n_df = n_df.sort_values(['Критерий'], ignore_index=True)
n_df = n_df.round(2)
print(n_df.iloc[:, 1:].to_string(index=False, decimal=','))
def main_synthetic(method, size):
"""
Проведение расчетов по предложенной методике для синтетического примера.
:param method: Метод решения однокритериальной задачи.
:param size: Размер сети аэропорта.
"""
mu1 = [1 / 70] * size
n_max1 = np.array([24] * size)
lam1 = 7307.5 * 0.12 / (24 * 60 * 60)
p1 = [0.999] * (size - 1)
s_max = 40 * size
s = np.ones(len(mu1))
t_max = 1.5 * 60 * size
optf = OptimalFinder(n_max1, s_max, t_max, s, [mu1, lam1, p1])
optf.find_optimal_time(method)
optf.find_optimal_cost(method)
optf.find_optimal_conv(method, [0.8, 0.2])
for metric in ['2-norm', 'inf']:
optf.find_optimal_ideal(method, [0.8, 0.2], metric)
df = pd.json_normalize(optf.experiments, sep=' узел ').sort_values(['Критерий', 'Метод'], ignore_index=True)
# df.index += 1
# df.index.name = 'Номер эксперимента'
df = df.round(2)
print(df.iloc[:, 2:].to_string(index=False, decimal=','))
def main_synthetic_brute_force(size):
"""
Проведение расчетов методом полного перебора для синтетического примера.
:param size: Размер сети аэропорта.
"""
mu1 = [1 / 70] * size
n_max1 = np.array([24] * size)
lam1 = 7307.5 * 0.12 / (24 * 60 * 60)
p1 = [0.999] * (size - 1)
s_max = 40 * size
s = np.ones(len(mu1))
t_max = 1.5 * 60 * size
optf = OptimalFinder(n_max1, s_max, t_max, s, [mu1, lam1, p1])
n_df = optf.brute_force([0.8, 0.2], [0.8, 0.2])
n_df = n_df.sort_values(['Критерий'], ignore_index=True)
n_df = n_df.round(2)
print(n_df.iloc[:, 1:].to_string(index=False, decimal=','))
| [
11748,
340,
861,
10141,
201,
198,
201,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
201,
198,
11748,
299,
32152,
355,
45941,
201,
198,
11748,
19798,
292,
355,
279,
67,
201,
198,
11748,
7110,
306,
13,
42712,
355,
279,
... | 1.649712 | 3,126 |
from __future__ import print_function
import argparse
import random
import torch
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable
import numpy as np
import os
import utils
from torch_geometric.data import Data, Dataset,DataLoader
from torch_scatter import scatter_mean
import torch_geometric.transforms as GT
import math
import json
from model2 import TbNet
from dataset2 import ScitsrDataset
parser = argparse.ArgumentParser()
parser.add_argument('--workers', type=int, help='number of data loading workers', default=8)
parser.add_argument('--batchSize', type=int, default=32, help='input batch size')
parser.add_argument('--imgH', type=int, default=32, help='the height of the input image to network')
parser.add_argument('--imgW', type=int, default=32, help='the width of the input image to network')
parser.add_argument('--nh', type=int, default=256, help='size of the lstm hidden state')
parser.add_argument('--niter', type=int, default=10, help='number of epochs to train for')
parser.add_argument('--lr', type=float, default=0.001, help='learning rate for Critic, default=0.00005')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
parser.add_argument('--cuda', action='store_true', help='enables cuda')
parser.add_argument('--ngpu', type=int, default=0, help='number of GPUs to use')
parser.add_argument('--crnn', default='', help="path to crnn (to continue training)")
parser.add_argument('--alphabet', type=str, default='0123456789abcdefghijklmnopqrstuvwxyz\'')
parser.add_argument('--experiment', default=None, help='Where to store samples and models')
parser.add_argument('--displayInterval', type=int, default=20, help='Interval to be displayed')
parser.add_argument('--n_test_disp', type=int, default=100, help='Number of samples to display when test')
parser.add_argument('--valInterval', type=int, default=1, help='Interval to be displayed')
parser.add_argument('--saveInterval', type=int, default=10, help='Interval to be displayed')
parser.add_argument('--adam', action='store_true', help='Whether to use adam (default is rmsprop)')
parser.add_argument('--adadelta', action='store_true', help='Whether to use adadelta (default is rmsprop)')
parser.add_argument('--keep_ratio', action='store_true', help='whether to keep ratio for image resize')
parser.add_argument('--random_sample', action='store_true', help='whether to sample the dataset with random sampler')
opt = parser.parse_args()
#print(opt)
if opt.experiment is None:
opt.experiment = 'expr'
os.system('mkdir {0}'.format(opt.experiment))
opt.manualSeed = random.randint(1, 10000) # fix seed
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
np.random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
#cudnn.benchmark = True
#if torch.cuda.is_available() and not opt.cuda:
# print("WARNING: You have a CUDA device, so you should probably run with --cuda")
root_path = ''
train_dataset = ScitsrDataset(root_path)
root_path = ''
test_dataset = ScitsrDataset(root_path)
root_path = '/home/deepvision/lyr/out'
eval_dataset = ScitsrDataset(root_path)
print("samples:",len(train_dataset),len(eval_dataset)) ,len(test_dataset)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
#test_loader = DataLoader(ds_test, batch_size=32)
#vob=open("./data/arti-images/vocab_fapiao.txt",'r')
#opt.alphabet=vob.readline()
#converter = utils.strLabelConverter(opt.alphabet)
#criterion = CTCLoss() # pytorch 0.4
#criterion = torch.nn.CTCLoss # pytorch 1.0.0
nclass = 2
input_num = 8
vocab_size = 39
num_text_features = 64
print('num of classes:',nclass)
# custom weights initialization called on crnn
device = torch.device("cpu" )
model = TbNet(input_num,vocab_size,num_text_features,nclass)
model.cuda()
#for k,v in crnn.state_dict().items():
# print(k)
model.apply(weights_init)
criterion = torch.nn.NLLLoss()
if opt.cuda:
model.cuda()
criterion = criterion.cuda()
# loss averager
loss_avg = utils.averager()
# setup optimizer
if opt.adam:
optimizer = optim.Adam(model.parameters(), lr=opt.lr,
betas=(opt.beta1, 0.999))
elif opt.adadelta:
optimizer = optim.Adadelta(model.parameters(), lr=opt.lr)
else:
optimizer = optim.RMSprop(model.parameters(), lr=opt.lr)
if opt.crnn != '':
print('loading pretrained model from %s' % opt.crnn)
crnn.load_state_dict(torch.load(opt.crnn),strict=False)
# 直接val一下。
print("On SciTSR Test:")
val(model, test_dataset, criterion)
print("On Eval:")
val(model, eval_dataset, criterion)
for epoch in range(opt.niter):
train_iter = iter(train_loader)
i = 0
print('epoch',epoch, ' dataset size:', len(train_loader))
while i < len(train_loader):
for p in model.parameters():
p.requires_grad = True
model.train()
cost = trainBatch(train_iter, model, criterion, optimizer)
loss_avg.add(cost)
i += 1
#print(loss_avg)
if i % opt.displayInterval == 0:
print('[%d/%d][%d/%d] Loss: %f' %
(epoch, opt.niter, i, len(train_loader), loss_avg.val()))
loss_avg.reset()
if epoch % opt.valInterval == 0:
print("On SciTSR Test:")
val(model, test_dataset, criterion)
print("On Eval:")
val(model, eval_dataset, criterion)
# do checkpointing
if epoch % opt.saveInterval == 0 :
torch.save(model.state_dict(), '{0}/net_{1}_{2}.pth'.format(opt.experiment, epoch, i))
#for k,v in crnn.state_dict().items():
# print(k)
| [
6738,
11593,
37443,
834,
1330,
3601,
62,
8818,
198,
11748,
1822,
29572,
198,
11748,
4738,
198,
11748,
28034,
198,
11748,
28034,
13,
40085,
355,
6436,
198,
11748,
28034,
13,
26791,
13,
7890,
198,
6738,
28034,
13,
2306,
519,
6335,
1330,
3... | 2.600092 | 2,163 |
import sixdegrees
import numpy as np
import scipy.sparse as sprs
from scipy.special import binom
import matplotlib.pyplot as pl
N_meas = 1000
betas = np.logspace(-3,0,10)
k = 8.
N = 100
for beta in betas:
hist = np.zeros((N,))
for meas in range(N_meas):
_, row, col = sixdegrees.modified_small_world_network_coord_lists(
N,
k,
beta,
use_giant_component = False,
)
A = sprs.csr_matrix((np.ones_like(row),(row,col)), shape=(N,N))
degree = np.asarray(A.sum(axis=1)).reshape((N,))
for k_ in degree:
hist[k_] += 1.0
hist /= hist.sum()
kmax = (np.where(hist>0)[0]).max()
degrees = np.arange(kmax+1,dtype=int)
this_plot, = pl.step(degrees,hist[:kmax+1],where='mid')
pl.step(degrees,P_theory(N,k,beta,kmax),c=this_plot.get_color(),lw=3, alpha=0.4,linestyle = '--',where='mid')
pl.show()
| [
11748,
2237,
13500,
6037,
220,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
629,
541,
88,
13,
82,
29572,
355,
599,
3808,
198,
6738,
629,
541,
88,
13,
20887,
1330,
9874,
296,
220,
198,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,... | 1.964876 | 484 |
import pygame, copy
from pygame.locals import *
##########################################################################
## Character ##
## -------------------------------------------------------------------- ##
## Class that defines an instance of an on-screen character sprite. Has ##
## various attributes that help optimize drawing routines. ##
##########################################################################
#################
## Constructor ##
#################
######################################
## Method to draw to target surface ##
######################################
| [
11748,
12972,
6057,
11,
4866,
198,
6738,
12972,
6057,
13,
17946,
874,
1330,
1635,
198,
198,
29113,
29113,
7804,
2235,
198,
2235,
15684,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2... | 3.797814 | 183 |
default_json_pool = {
"previousState": "Closing",
"stateTransitionTime": "2021-03-18T14:34:51Z",
"previousStateTransitionTime": "2021-03-18T14:34:39Z",
"lastModified": "2021-03-18T14:34:51Z",
"elasticProperty": {
"isElastic": False,
"minTotalSlots": 1,
"maxTotalSlots": 1,
"minIdleSlots": 0,
"resizePeriod": 180,
"rampResizeFactor": 0.8,
"minIdleTimeSeconds": 30
},
"preparationTask": None,
"constants": [
],
"tags": [
],
"errors": [
{
"code": "GHX0782I",
"message": "The task was cancelled: The task was cancelled",
"debug": None
}
],
"resourceBuckets": [
],
"advancedResourceBuckets": None,
"status": {
"timestamp": "0001-01-01T00:00:00Z",
"lastUpdateTimestamp": "0001-01-01T00:00:00Z",
"downloadProgress": 0,
"executionProgress": 100,
"uploadProgress": 0,
"instanceCount": 0,
"downloadTime": "00:00:00",
"downloadTimeSec": 0,
"environmentTime": "00:00:00",
"environmentTimeSec": 0,
"executionTime": "00:00:00",
"executionTimeSec": 0,
"executionTimeByCpuModel": [
],
"executionTimeGhzByCpuModel": [
],
"uploadTime": "00:00:00",
"uploadTimeSec": 0,
"wallTime": "00:00:15",
"wallTimeSec": 15,
"succeededRange": "",
"executedRange": "0",
"failedRange": "0",
"startedOnceRange": "",
"runningInstancesInfo": {
"perRunningInstanceInfo": [
],
"snapshotResults": [
],
"timestamp": "0001-01-01T00:00:00Z",
"averageFrequencyGHz": 0,
"maxFrequencyGHz": 0,
"minFrequencyGHz": 0,
"averageMaxFrequencyGHz": 0,
"averageCpuUsage": 0,
"clusterPowerIndicator": 1,
"averageMemoryUsage": 0,
"averageNetworkInKbps": 0,
"averageNetworkOutKbps": 0,
"totalNetworkInKbps": 0,
"totalNetworkOutKbps": 0,
"runningCoreCountByCpuModel": [
]
}
},
"autoDeleteOnCompletion": False,
"completionTimeToLive": "00:00:00",
"uuid": "6dce9bff-20f0-4909-9ba5-4abe2326a07c",
"name": "hello",
"shortname": "6dce9bff-20f0-4909-9ba5-4abe2326a07c",
"profile": "docker-batch",
"state": "Closed",
"instanceCount": 1,
"creationDate": "2021-03-18T14:34:34Z",
"endDate": "2021-03-18T14:34:51Z",
"runningInstanceCount": 0,
"runningCoreCount": 0,
"executionTime": "00:00:00",
"wallTime": "00:00:15",
"taskDefaultWaitForPoolResourcesSynchronization": None,
"credits": 0.01
}
| [
198,
12286,
62,
17752,
62,
7742,
796,
1391,
198,
220,
220,
220,
366,
3866,
1442,
9012,
1298,
220,
366,
2601,
2752,
1600,
198,
220,
220,
220,
366,
5219,
8291,
653,
7575,
1298,
220,
366,
1238,
2481,
12,
3070,
12,
1507,
51,
1415,
25,
... | 1.869622 | 1,534 |
import unittest
from gocdapi.go import Go
from gocdapi.pipeline import Pipeline
from gocdapi.stage import Stage
if __name__ == '__main__':
unittest.main()
| [
11748,
555,
715,
395,
198,
198,
6738,
308,
420,
67,
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13,
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308,
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67,
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13,
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1330,
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198,
6738,
308,
420,
67,
15042,
13,
14247,
1330,
15371,
628,
198,
198,
361,
11593,
3672... | 2.672131 | 61 |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 4 16:39:41 2021
This will be used to query an FTP server to download csv data from the sensors of interest.
@author: sHristov
"""
import os
import time
import datetime
import logging
import gzip
import typing
import urllib.error
import urllib.request
import requests
import bs4
import numpy as np
import pandas as pd
def daterange(start_date: datetime.datetime, end_date: datetime.datetime) -> typing.Iterable[datetime.datetime]:
"""
Function to create a range given start and end dates.
Acknowledgment:
https://stackoverflow.com/questions/1060279/iterating-through-a-range-of-dates-in-python
"""
for n in range(int((end_date - start_date).days)):
yield start_date + datetime.timedelta(n)
def download_data(sensor_name: str, sensor_id: str, start_date = datetime.date(2020, 4, 1)) -> None:
"""
Function to download data from the sensor.community server and puts it in a data folder.
Args:
sensor_name - name of sensor
sensor_id - id of sensor
Returns:
None
"""
folder_main = os.getcwd()
data_folder = folder_main + os.sep + 'data' + os.sep
try:
os.mkdir(data_folder)
except FileExistsError:
pass
# Get data until yesterday - the server is only updated at 00:00 GMT
end_date = datetime.date.today() - datetime.timedelta(days=1)
for single_date in daterange(start_date, end_date):
print('Downloading: ' + str(single_date))
filename = "%s_%s_sensor_%s.csv" % (str(single_date), sensor_name, sensor_id)
save_name = os.path.join(data_folder, filename)
if os.path.exists(save_name):
logging.debug(f"{save_name} exists, skipping file")
else:
logging.debug("Trying to download...")
time.sleep(1)
url = 'https://archive.sensor.community/%s/%s' % (str(single_date), filename)
req = requests.get(url)
url_content = req.content
with open(save_name, 'wb') as csv_file:
csv_file.write(url_content)
csv_file.close()
if __name__ == '__main__':
# code below is used for testing purposes
sensor_name = 'sds011'
sensor_id = '6127'
""" raise HTTPError (urllib.error.HTTPError) if data does not exist"""
download_data(sensor_name=sensor_name, sensor_id=sensor_id)
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
201,
198,
37811,
201,
198,
41972,
319,
2892,
2365,
220,
604,
1467,
25,
2670,
25,
3901,
33448,
201,
198,
201,
198,
1212,
481,
307,
973,
284,
12405,
281,
45854,
4382,
284,
43... | 2.288058 | 1,097 |
#!/bin/env python3
import time
import gex
with gex.Client(gex.TrxRawUSB()) as client:
ow = gex.OneWire(client, 'ow')
# print("Presence: ", ow.test_presence())
print("Devices:", ow.search())
while True:
(a, b) = meas2(6558392391241695016, 1802309978572980008)
# a = meas(6558392391241695016)
# b = meas(1802309978572980008)
print("in: %.2f °C, out: %f °C" % (a, b))
# # search the bus for alarm
# if False:
# ow = gex.OneWire(client, 'ow')
# print("Presence: ", ow.test_presence())
# print("Devices w alarm:", ow.search(alarm=True))
#
# # simple 1w check
# if False:
# ow = gex.OneWire(client, 'ow')
# print("Presence: ", ow.test_presence())
# print("ROM: 0x%016x" % ow.read_address())
# print("Scratch:", ow.query([0xBE], rcount=9, addr=0x7100080104c77610, as_array=True))
#
# # testing ds1820 temp meas without polling
# if False:
# ow = gex.OneWire(client, 'ow')
# print("Presence: ", ow.test_presence())
# print("Starting measure...")
# ow.write([0x44])
# time.sleep(1)
# print("Scratch:", ow.query([0xBE], 9))
#
# # testing ds1820 temp meas with polling
# if False:
# ow = gex.OneWire(client, 'ow')
# print("Presence: ", ow.test_presence())
# print("Starting measure...")
# ow.write([0x44])
# ow.wait_ready()
# data = ow.query([0xBE], 9)
#
# pp = gex.PayloadParser(data)
#
# temp = pp.i16()/2.0
# th = pp.i8()
# tl = pp.i8()
# reserved = pp.i16()
# remain = float(pp.u8())
# perc = float(pp.u8())
#
# realtemp = temp - 0.25+(perc-remain)/perc
# print("Temperature = %f °C (th %d, tl %d)" % (realtemp, th, tl))
| [
2,
48443,
8800,
14,
24330,
21015,
18,
198,
11748,
640,
198,
198,
11748,
308,
1069,
198,
198,
4480,
308,
1069,
13,
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7,
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13,
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355,
5456,
25,
198,
220,
220,
220,
12334,
796,
308,
1069,
13,
3198... | 2.006515 | 921 |
#coding:utf-8
#
# id: bugs.core_5018
# title: Regression: Non-indexed predicates may not be applied immediately after retrieval when tables are being joined
# decription:
# tracker_id: CORE-5018
# min_versions: ['3.0']
# versions: 3.0
# qmid: None
import pytest
from firebird.qa import db_factory, isql_act, Action
# version: 3.0
# resources: None
substitutions_1 = []
init_script_1 = """"""
db_1 = db_factory(from_backup='mon-stat-gathering-3_0.fbk', init=init_script_1)
test_script_1 = """
recreate table zf (
id int primary key,
kont_id int not null
);
recreate table u (
id int primary key,
kont_id int not null
);
recreate table k (
id int primary key
);
commit;
insert into zf (id, kont_id) values ('1', '1');
insert into zf (id, kont_id) values ('2', '7');
insert into zf (id, kont_id) values ('3', '3');
insert into zf (id, kont_id) values ('4', '5');
insert into zf (id, kont_id) values ('5', '5');
insert into zf (id, kont_id) values ('6', '1');
insert into zf (id, kont_id) values ('7', '4');
insert into zf (id, kont_id) values ('8', '2');
insert into zf (id, kont_id) values ('9', '9');
insert into zf (id, kont_id) values ('10', '1');
insert into k (id) values ('1');
insert into k (id) values ('2');
insert into k (id) values ('3');
insert into k (id) values ('4');
insert into k (id) values ('5');
insert into k (id) values ('6');
insert into k (id) values ('7');
insert into k (id) values ('8');
insert into k (id) values ('9');
insert into k (id) values ('10');
insert into u (id, kont_id) values ('1', '4');
insert into u (id, kont_id) values ('2', '6');
insert into u (id, kont_id) values ('3', '3');
insert into u (id, kont_id) values ('4', '2');
insert into u (id, kont_id) values ('5', '5');
insert into u (id, kont_id) values ('6', '2');
insert into u (id, kont_id) values ('7', '9');
insert into u (id, kont_id) values ('8', '2');
insert into u (id, kont_id) values ('9', '10');
insert into u (id, kont_id) values ('10', '1');
commit;
execute procedure sp_truncate_stat;
commit;
execute procedure sp_gather_stat;
commit;
set term ^;
execute block as
declare c int;
begin
select count(*)
from zf
inner join u on zf.id=u.id
left join k kzf on zf.kont_id=kzf.id
left join k kum on u.kont_id=kum.id
where zf.kont_id<>u.kont_id
into c;
if ( rdb$get_context('SYSTEM','ENGINE_VERSION') starting with '4.0' ) then
rdb$set_context('USER_SESSION', 'MAX_IDX_READS', '45'); -- 27.07.2016
else
rdb$set_context('USER_SESSION', 'MAX_IDX_READS', '30');
-- ^
-- |
-- ### T H R E S H O L D ###-------+
end
^
set term ;^
execute procedure sp_gather_stat;
commit;
set list on;
select iif( indexed_reads <= c_max_idx_reads,
'ACCEPTABLE',
'FAILED, TOO BIG: ' || indexed_reads || ' > ' || c_max_idx_reads
) as idx_reads_estimation
from (
select indexed_reads, cast(rdb$get_context('USER_SESSION', 'MAX_IDX_READS') as int) as c_max_idx_reads
from v_agg_stat_main
);
-- WI-V2.5.5.26952 IR=22
-- WI-V3.0.0.32140 IR=33
-- WI-V3.0.0.32179 IR=25
-- WI-T4.0.0.313: IR=39
"""
act_1 = isql_act('db_1', test_script_1, substitutions=substitutions_1)
expected_stdout_1 = """
IDX_READS_ESTIMATION ACCEPTABLE
"""
@pytest.mark.version('>=3.0')
| [
2,
66,
7656,
25,
40477,
12,
23,
198,
2,
198,
2,
4686,
25,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11316,
13,
7295,
62,
20,
29159,
198,
2,
3670,
25,
220,
220,
220,
220,
220,
220,
220,
3310,
2234,
25,
8504,
12,
9630,
... | 2.090508 | 1,812 |
from torch import random
import torch
from torch.nn.modules import linear
import torch.optim as optim
import torch.nn.functional as F
from torch.nn.utils import clip_grad_norm_
import logging
import numpy as np
import random
from value_network import ValueNetwork, ConvValueNetwork
| [
6738,
28034,
1330,
4738,
198,
11748,
28034,
198,
6738,
28034,
13,
20471,
13,
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1330,
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198,
11748,
28034,
13,
40085,
355,
6436,
198,
11748,
28034,
13,
20471,
13,
45124,
355,
376,
198,
6738,
28034,
13,
20471,
13,
26791,
1330,
1... | 3.944444 | 72 |
from sky_iot.utils import UtilsTool
_init()
read_config() | [
6738,
6766,
62,
5151,
13,
26791,
1330,
7273,
4487,
25391,
198,
198,
62,
15003,
3419,
198,
198,
961,
62,
11250,
3419
] | 2.809524 | 21 |
#!/usr/bin/python
import sys,subprocess
#user and group id
id=171
if len(sys.argv) == 2:
if sys.argv[1] == "create-glint-user":
create_group()
add_user()
elif sys.argv[1] == "remove-glint-user":
remove_user()
#remove_group()
else:
show_usage()
else:
show_usage()
| [
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... | 2.018868 | 159 |
from django.apps import apps
from django.test import TestCase
from model_bakery import baker
from rest_framework import serializers
from api.serializers.common import MappedSerializerMixin
from api.serializers.registration import UserSerializer
from common.constants import models
Profile = apps.get_model(models.PROFILE_MODEL)
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1... | 3.761364 | 88 |
import os
import pickle
def var_to_pickle(var, filename):
'''
Writes the given variable to a pickle file
Args:
var (any): variable to be written to pickle file
filename (str): path and filename of pickle file
Returns:
None
'''
try:
with open(filename, 'wb') as f:
pickle.dump(var, f)
except:
print(f'Failed to save pickle to \'{filename}\'')
return
def read_pickle(filename):
'''
Reads the given pickle file
Args:
filename (str): path and filename of pickle file
Returns:
any: contents of pickle file if it exists, None if not
'''
output = None
if os.path.exists(filename):
try:
with open(filename, 'rb') as f:
output = pickle.load(f)
except:
print(f'Failed to load pickle from \'{filename}\'')
return output
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220,... | 2.254364 | 401 |
from dbgen import Model
from . import schema # noqa: F401
from .generators import add_generators
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] | 3.535714 | 28 |
import bisect
import collections
import functools
import math
import operator
try:
import graphviz
GRAPHVIZ_INSTALLED = True
except ImportError:
GRAPHVIZ_INSTALLED = False
from .. import base
from .. import utils
from . import enum
def find_best_split(class_counts, feature_counts, split_enum):
"""
>>> class_counts = {'slow': 2, 'fast': 2}
>>> feature_counts = {
... 'grade': {
... 'steep': collections.Counter({'slow': 2, 'fast': 1}),
... 'flat': collections.Counter({'fast': 1})
... },
... 'bumpiness': {
... 'bumpy': collections.Counter({'slow': 1, 'fast': 1}),
... 'smooth': collections.Counter({'slow': 1, 'fast': 1})
... },
... 'speed_limit': {
... 'yes': collections.Counter({'slow': 2}),
... 'no': collections.Counter({'fast': 2})
... }
... }
>>> split_enum = enum.UnaryEnumerator()
>>> find_best_split(class_counts, feature_counts, split_enum)
(1.0, 0.311278..., 'speed_limit', ['no'])
"""
best_gain = -math.inf
second_best_gain = -math.inf
best_feature = None
best_values = None
current_entropy = utils.entropy(class_counts)
for feature, counts in feature_counts.items():
for left, right in split_enum(sorted(counts.keys())):
left_counts = sum_counters(counts[v] for v in left)
right_counts = sum_counters(counts[v] for v in right)
left_total = sum(left_counts.values())
right_total = sum(right_counts.values())
entropy = left_total * utils.entropy(left_counts) + \
right_total * utils.entropy(right_counts)
entropy /= (left_total + right_total)
gain = current_entropy - entropy
if gain > best_gain:
best_gain, second_best_gain = gain, best_gain
best_feature = feature
best_values = left
elif gain > second_best_gain and gain != best_gain:
second_best_gain = gain
return best_gain, second_best_gain, best_feature, best_values
class DecisionTreeClassifier(base.MultiClassifier):
"""
Parameters:
max_bins (int): Maximum number of bins used for discretizing continuous values when using
`utils.Histogram`.
Attributes:
histograms (collections.defaultdict)
"""
def to_dot(self):
"""Returns a GraphViz representation of the decision tree."""
if not GRAPHVIZ_INSTALLED:
raise RuntimeError('graphviz is not installed')
dot = graphviz.Digraph()
add_node(self.root, '0')
return dot
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... | 2.298723 | 1,175 |
from influxdb import InfluxDBClient
import json
import config
import blegateway
influxCONFIG = config.get_config('influx')
ids = config.get_config('identifiers') | [
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... | 3.446809 | 47 |
"""The data models used in ARL:
.. note::
There are two visibility formats:
:class:`BlockVisibility` is conceived as an ingest and calibration format. The visibility
data are kept in a block of shape (number antennas, number antennas, number channels,
number polarisation). One block is kept per integration. The other columns are time and uvw.
The sampling in time is therefore the same for all baselines.
:class:`Visibility` is designed to hold coalesced data where the integration time and
channel width can vary with baseline length. The visibility data are kept in a visibility
vector of length equal to the number of polarisations. Everything else is a separate
column: time, frequency, uvw, channel_bandwidth, integration time.
"""
import sys
import logging
from copy import deepcopy
from typing import Union
import numpy
from astropy import units as u
from astropy.coordinates import SkyCoord
import warnings
from astropy.wcs import FITSFixedWarning
warnings.simplefilter('ignore', FITSFixedWarning)
from data_models.polarisation import PolarisationFrame, ReceptorFrame
log = logging.getLogger(__name__)
class Configuration:
""" Describe a Configuration as locations in x,y,z, mount type, diameter, names, and
overall location
"""
def __init__(self, name='', data=None, location=None,
names="%s", xyz=None, mount="alt-az", frame=None,
receptor_frame=ReceptorFrame("linear"),
diameter=None):
"""Configuration object describing data for processing
:param name:
:param data:
:param location:
:param names:
:param xyz:
:param mount:
:param frame:
:param receptor_frame:
:param diameter:
"""
if data is None and xyz is not None:
desc = [('names', '>U6'),
('xyz', '>f8', (3,)),
('diameter', '>f8'),
('mount', '>U5')]
nants = xyz.shape[0]
if isinstance(names, str):
names = [names % ant for ant in range(nants)]
if isinstance(mount, str):
mount = numpy.repeat(mount, nants)
data = numpy.zeros(shape=[nants], dtype=desc)
data['names'] = names
data['xyz'] = xyz
data['mount'] = mount
data['diameter'] = diameter
self.name = name
self.data = data
self.location = location
self.frame = frame
self.receptor_frame = receptor_frame
def __str__(self):
"""Default printer for Skycomponent
"""
s = "Configuration:\n"
s += "\nName: %s\n" % self.name
s += "\tNumber of antennas/stations: %s\n" % len(self.names)
s += "\tNames: %s\n" % self.names
s += "\tDiameter: %s\n" % self.diameter
s += "\tMount: %s\n" % self.mount
s += "\tXYZ: %s\n" % self.xyz
return s
def size(self):
""" Return size in GB
"""
size = 0
size += self.data.size * sys.getsizeof(self.data)
return size / 1024.0 / 1024.0 / 1024.0
@property
def names(self):
""" Names of the antennas/stations"""
return self.data['names']
@property
def diameter(self):
""" diameter of antennas/stations
"""
return self.data['diameter']
@property
def xyz(self):
""" XYZ locations of antennas/stations
"""
return self.data['xyz']
@property
def mount(self):
""" Mount type
"""
return self.data['mount']
class GainTable:
""" Gain table with data_models: time, antenna, gain[:, chan, rec, rec], weight columns
The weight is usually that output from gain solvers.
"""
def __init__(self, data=None, gain: numpy.array = None, time: numpy.array = None, interval=None,
weight: numpy.array = None, residual: numpy.array = None, frequency: numpy.array = None,
receptor_frame: ReceptorFrame = ReceptorFrame("linear")):
""" Create a gaintable from arrays
The definition of gain is:
Vobs = g_i g_j^* Vmodel
:param interval:
:param data:
:param gain: [:, nchan, nrec, nrec]
:param time: Centroid of solution
:param interval: Interval of validity
:param weight:
:param residual:
:param frequency:
:param receptor_frame:
:return: Gaintable
"""
if data is None and gain is not None:
nrec = receptor_frame.nrec
nrows = gain.shape[0]
nants = gain.shape[1]
nchan = gain.shape[2]
assert len(frequency) == nchan, "Discrepancy in frequency channels"
desc = [('gain', '>c16', (nants, nchan, nrec, nrec)),
('weight', '>f8', (nants, nchan, nrec, nrec)),
('residual', '>f8', (nchan, nrec, nrec)),
('time', '>f8'),
('interval', '>f8')]
data = numpy.zeros(shape=[nrows], dtype=desc)
data['gain'] = gain
data['weight'] = weight
data['time'] = time
data['interval'] = interval
data['residual'] = residual
self.data = data
self.frequency = frequency
self.receptor_frame = receptor_frame
def size(self):
""" Return size in GB
"""
size = 0
size += self.data.size * sys.getsizeof(self.data)
return size / 1024.0 / 1024.0 / 1024.0
@property
@property
@property
@property
@property
@property
@property
@property
@property
def __str__(self):
"""Default printer for GainTable
"""
s = "GainTable:\n"
s += "\tTimes: %s\n" % str(self.ntimes)
s += "\tData shape: %s\n" % str(self.data.shape)
s += "\tReceptor frame: %s\n" % str(self.receptor_frame.type)
return s
class Image:
"""Image class with Image data (as a numpy.array) and the AstroPy `implementation of
a World Coodinate System <http://docs.astropy.org/en/stable/wcs>`_
Many operations can be done conveniently using numpy processing_library on Image.data_models.
Most of the imaging processing_library require an image in canonical format:
- 4 axes: RA, DEC, POL, FREQ
The conventions for indexing in WCS and numpy are opposite.
- In astropy.wcs, the order is (longitude, latitude, polarisation, frequency)
- in numpy, the order is (frequency, polarisation, latitude, longitude)
.. warning::
The polarisation_frame is kept in two places, the WCS and the polarisation_frame
variable. The latter should be considered definitive.
"""
def __init__(self):
""" Empty image
"""
self.data = None
self.wcs = None
self.polarisation_frame = None
def size(self):
""" Return size in GB
"""
size = 0
size += self.data.nbytes
return size / 1024.0 / 1024.0 / 1024.0
# noinspection PyArgumentList
@property
@property
@property
@property
@property
@property
@property
def __str__(self):
"""Default printer for Image
"""
s = "Image:\n"
s += "\tShape: %s\n" % str(self.data.shape)
s += "\tWCS: %s\n" % self.wcs
s += "\tPolarisation frame: %s\n" % str(self.polarisation_frame.type)
return s
class GridData:
"""Class to hold Gridded data for Fourier processing
- Has four or more coordinates: [chan, pol, z, y, x] where x can be u, l; y can be v, m; z can be w, n
The conventions for indexing in WCS and numpy are opposite.
- In astropy.wcs, the order is (longitude, latitude, polarisation, frequency)
- in numpy, the order is (frequency, polarisation, depth, latitude, longitude)
.. warning::
The polarisation_frame is kept in two places, the WCS and the polarisation_frame
variable. The latter should be considered definitive.
"""
def __init__(self):
""" Empty image
"""
self.data = None
self.grid_wcs = None
self.projection_wcs = None
self.polarisation_frame = None
def size(self):
""" Return size in GB
"""
size = 0
size += self.data.nbytes
return size / 1024.0 / 1024.0 / 1024.0
# noinspection PyArgumentList
@property
@property
@property
@property
@property
@property
@property
@property
def __str__(self):
"""Default printer for GriddedData
"""
s = "Gridded data:\n"
s += "\tShape: %s\n" % str(self.data.shape)
s += "\tGrid WCS: %s\n" % self.grid_wcs
s += "\tProjection WCS: %s\n" % self.projection_wcs
s += "\tPolarisation frame: %s\n" % str(self.polarisation_frame.type)
return s
class ConvolutionFunction:
"""Class to hold Gridded data for Fourier processing
- Has four or more coordinates: [chan, pol, z, y, x] where x can be u, l; y can be v, m; z can be w, n
The conventions for indexing in WCS and numpy are opposite.
- In astropy.wcs, the order is (longitude, latitude, polarisation, frequency)
- in numpy, the order is (frequency, polarisation, depth, latitude, longitude)
.. warning::
The polarisation_frame is kept in two places, the WCS and the polarisation_frame
variable. The latter should be considered definitive.
"""
def __init__(self):
""" Empty image
"""
self.data = None
self.grid_wcs = None
self.projection_wcs = None
self.polarisation_frame = None
def size(self):
""" Return size in GB
"""
size = 0
size += self.data.nbytes
return size / 1024.0 / 1024.0 / 1024.0
# noinspection PyArgumentList
@property
@property
@property
@property
@property
@property
@property
@property
def __str__(self):
"""Default printer for GriddedData
"""
s = "Convolution function:\n"
s += "\tShape: %s\n" % str(self.data.shape)
s += "\tGrid WCS: %s\n" % self.grid_wcs
s += "\tProjection WCS: %s\n" % self.projection_wcs
s += "\tPolarisation frame: %s\n" % str(self.polarisation_frame.type)
return s
class Skycomponent:
"""Skycomponents are used to represent compact sources on the sky. They possess direction,
flux as a function of frequency and polarisation, shape (with params), and polarisation frame.
For example, the following creates and predicts the visibility from a collection of point sources
drawn from the GLEAM catalog::
sc = create_low_test_skycomponents_from_gleam(flux_limit=1.0,
polarisation_frame=PolarisationFrame("stokesIQUV"),
frequency=frequency, kind='cubic',
phasecentre=phasecentre,
radius=0.1)
model = create_image_from_visibility(vis, cellsize=0.001, npixel=512, frequency=frequency,
polarisation_frame=PolarisationFrame('stokesIQUV'))
bm = create_low_test_beam(model=model)
sc = apply_beam_to_skycomponent(sc, bm)
vis = predict_skycomponent_visibility(vis, sc)
"""
def __init__(self,
direction=None, frequency=None, name=None, flux=None, shape='Point',
polarisation_frame=PolarisationFrame('stokesIQUV'), params=None):
""" Define the required structure
:param direction: SkyCoord
:param frequency: numpy.array [nchan]
:param name: user friendly name
:param flux: numpy.array [nchan, npol]
:param shape: str e.g. 'Point' 'Gaussian'
:param params: numpy.array shape dependent parameters
:param polarisation_frame: Polarisation_frame
"""
self.direction = direction
self.frequency = numpy.array(frequency)
self.name = name
self.flux = numpy.array(flux)
self.shape = shape
if params is None:
params = {}
self.params = params
self.polarisation_frame = polarisation_frame
assert len(self.frequency.shape) == 1, frequency
assert len(self.flux.shape) == 2, flux
assert self.frequency.shape[0] == self.flux.shape[0], \
"Frequency shape %s, flux shape %s" % (self.frequency.shape, self.flux.shape)
assert polarisation_frame.npol == self.flux.shape[1], \
"Polarisation is %s, flux shape %s" % (polarisation_frame.type, self.flux.shape)
@property
@property
def __str__(self):
"""Default printer for Skycomponent
"""
s = "Skycomponent:\n"
s += "\tName: %s\n" % self.name
s += "\tFlux: %s\n" % self.flux
s += "\tFrequency: %s\n" % self.frequency
s += "\tDirection: %s\n" % self.direction
s += "\tShape: %s\n" % self.shape
s += "\tParams: %s\n" % self.params
s += "\tPolarisation frame: %s\n" % str(self.polarisation_frame.type)
return s
class SkyModel:
""" A model for the sky
"""
def __init__(self, images=None, components=None, fixed=False):
""" A model of the sky as a list of images and a list of components
Use copy_skymodel to make a proper copy of skymodel
"""
if images is None:
images = []
if components is None:
components = []
self.images = [im for im in images]
self.components = [sc for sc in components]
self.fixed = fixed
def __str__(self):
"""Default printer for SkyModel
"""
s = "SkyModel: fixed: %s\n" % self.fixed
for i, sc in enumerate(self.components):
s += str(sc)
s += "\n"
for i, im in enumerate(self.images):
s += str(im)
s += "\n"
return s
class Visibility:
""" Visibility table class
Visibility with uvw, time, integration_time, frequency, channel_bandwidth, a1, a2, vis, weight
as separate columns in a numpy structured array, The fundemental unit is a complex vector of polarisation.
Visibility is defined to hold an observation with one direction.
Polarisation frame is the same for the entire data set and can be stokes, circular, linear
The configuration is also an attribute
The phasecentre is the direct of delay tracking i.e. n=0. If uvw are rotated then this
should be updated with the new delay tracking centre. This is important for wstack and wproject
algorithms.
If a visibility is created by coalescence then the cindex column is filled with a pointer to the
row in the original block visibility that this row has a value for. The original blockvisibility
is also preserves as n attribute so that decoalescence is expedited. If you don't need that then
the storage can be released by setting self.blockvis to None
"""
def __init__(self,
data=None, frequency=None, channel_bandwidth=None,
phasecentre=None, configuration=None, uvw=None,
time=None, antenna1=None, antenna2=None, vis=None,
weight=None, imaging_weight=None, integration_time=None,
polarisation_frame=PolarisationFrame('stokesI'), cindex=None,
blockvis=None):
"""Visibility
:param data:
:param frequency:
:param channel_bandwidth:
:param phasecentre:
:param configuration:
:param uvw:
:param time:
:param antenna1:
:param antenna2:
:param vis:
:param weight:
:param imaging_weight:
:param integration_time:
:param polarisation_frame:
:param cindex:
:param blockvis:
"""
if data is None and vis is not None:
if imaging_weight is None:
imaging_weight = weight
nvis = vis.shape[0]
assert len(time) == nvis
assert len(frequency) == nvis
assert len(channel_bandwidth) == nvis
assert len(antenna1) == nvis
assert len(antenna2) == nvis
npol = polarisation_frame.npol
desc = [('index', '>i8'),
('uvw', '>f8', (3,)),
('time', '>f8'),
('frequency', '>f8'),
('channel_bandwidth', '>f8'),
('integration_time', '>f8'),
('antenna1', '>i8'),
('antenna2', '>i8'),
('vis', '>c16', (npol,)),
('weight', '>f8', (npol,)),
('imaging_weight', '>f8', (npol,))]
data = numpy.zeros(shape=[nvis], dtype=desc)
data['index'] = list(range(nvis))
data['uvw'] = uvw
data['time'] = time
data['frequency'] = frequency
data['channel_bandwidth'] = channel_bandwidth
data['integration_time'] = integration_time
data['antenna1'] = antenna1
data['antenna2'] = antenna2
data['vis'] = vis
data['weight'] = weight
data['imaging_weight'] = imaging_weight
self.data = data # numpy structured array
self.cindex = cindex
self.blockvis = blockvis
self.phasecentre = phasecentre # Phase centre of observation
self.configuration = configuration # Antenna/station configuration
self.polarisation_frame = polarisation_frame
self.frequency_map = None
def __str__(self):
"""Default printer for Skycomponent
"""
ufrequency = numpy.unique(self.frequency)
s = "Visibility:\n"
s += "\tNumber of visibilities: %s\n" % self.nvis
s += "\tNumber of channels: %d\n" % len(ufrequency)
s += "\tFrequency: %s\n" % ufrequency
s += "\tNumber of polarisations: %s\n" % self.npol
s += "\tVisibility shape: %s\n" % str(self.vis.shape)
s += "\tPolarisation Frame: %s\n" % self.polarisation_frame.type
s += "\tPhasecentre: %s\n" % self.phasecentre
s += "\tConfiguration: %s\n" % self.configuration.name
return s
def size(self):
""" Return size in GB
"""
size = 0
for col in self.data.dtype.fields.keys():
size += self.data[col].nbytes
return size / 1024.0 / 1024.0 / 1024.0
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
class BlockVisibility:
""" Block Visibility table class
BlockVisibility with uvw, time, integration_time, frequency, channel_bandwidth, pol,
a1, a2, vis, weight Columns in a numpy structured array.
BlockVisibility is defined to hold an observation with one direction.
The phasecentre is the direct of delay tracking i.e. n=0. If uvw are rotated then this
should be updated with the new delay tracking centre. This is important for wstack and wproject
algorithms.
Polarisation frame is the same for the entire data set and can be stokesI, circular, linear
The configuration is also an attribute
"""
def __init__(self,
data=None, frequency=None, channel_bandwidth=None,
phasecentre=None, configuration=None, uvw=None,
time=None, vis=None, weight=None, integration_time=None,
polarisation_frame=PolarisationFrame('stokesI'),
imaging_weight=None):
"""BlockVisibility
:param data:
:param frequency:
:param channel_bandwidth:
:param phasecentre:
:param configuration:
:param uvw:
:param time:
:param vis:
:param weight:
:param integration_time:
:param polarisation_frame:
"""
if data is None and vis is not None:
ntimes, nants, _, nchan, npol = vis.shape
assert vis.shape == weight.shape
assert len(frequency) == nchan
assert len(channel_bandwidth) == nchan
desc = [('index', '>i8'),
('uvw', '>f8', (nants, nants, 3)),
('time', '>f8'),
('integration_time', '>f8'),
('vis', '>c16', (nants, nants, nchan, npol)),
('weight', '>f8', (nants, nants, nchan, npol)),
('imaging_weight', '>f8', (nants, nants, nchan, npol))]
data = numpy.zeros(shape=[ntimes], dtype=desc)
data['index'] = list(range(ntimes))
data['uvw'] = uvw
data['time'] = time
data['integration_time'] = integration_time
data['vis'] = vis
data['weight'] = weight
data['imaging_weight'] = imaging_weight
self.data = data # numpy structured array
self.frequency = frequency
self.channel_bandwidth = channel_bandwidth
self.phasecentre = phasecentre # Phase centre of observation
self.configuration = configuration # Antenna/station configuration
self.polarisation_frame = polarisation_frame
def __str__(self):
"""Default printer for BlockVisibility
"""
s = "BlockVisibility:\n"
s += "\tNumber of visibilities: %s\n" % self.nvis
s += "\tNumber of integrations: %s\n" % len(self.time)
s += "\tVisibility shape: %s\n" % str(self.vis.shape)
s += "\tNumber of channels: %d\n" % len(self.frequency)
s += "\tFrequency: %s\n" % self.frequency
s += "\tNumber of polarisations: %s\n" % self.npol
s += "\tPolarisation Frame: %s\n" % self.polarisation_frame.type
s += "\tPhasecentre: %s\n" % self.phasecentre
s += "\tConfiguration: %s\n" % self.configuration.name
return s
def size(self):
""" Return size in GB
"""
size = 0
for col in self.data.dtype.fields.keys():
size += self.data[col].nbytes
return size / 1024.0 / 1024.0 / 1024.0
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
@property
class QA:
""" Quality assessment
"""
def __init__(self, origin=None, data=None, context=None):
"""QA
:param origin:
:param data:
:param context:
"""
self.origin = origin # Name of function originating QA assessment
self.data = data # Dictionary containing standard fields
self.context = context # Context string
def __str__(self):
"""Default printer for QA
"""
s = "Quality assessment:\n"
s += "\tOrigin: %s\n" % self.origin
s += "\tContext: %s\n" % self.context
s += "\tData:\n"
for dataname in self.data.keys():
s += "\t\t%s: %r\n" % (dataname, str(self.data[dataname]))
return s
class ScienceDataModel:
""" Science Data Model"""
def __str__(self):
""" Deflaut printer for Science Data Model
:return:
"""
return ""
def assert_same_chan_pol(o1, o2):
"""
Assert that two entities indexed over channels and polarisations
have the same number of them.
"""
assert o1.npol == o2.npol, \
"%s and %s have different number of polarisations: %d != %d" % \
(type(o1).__name__, type(o2).__name__, o1.npol, o2.npol)
if isinstance(o1, BlockVisibility) and isinstance(o2, BlockVisibility):
assert o1.nchan == o2.nchan, \
"%s and %s have different number of channels: %d != %d" % \
(type(o1).__name__, type(o2).__name__, o1.nchan, o2.nchan)
def assert_vis_gt_compatible(vis: Union[Visibility, BlockVisibility], gt: GainTable):
""" Check if visibility and gaintable are compatible
:param vis:
:param gt:
:return:
"""
assert vis.nchan == gt.nchan
assert vis.npol == gt.nrec * gt.nrec
| [
37811,
464,
1366,
4981,
973,
287,
5923,
43,
25,
198,
198,
492,
3465,
3712,
198,
220,
220,
220,
1318,
389,
734,
20742,
17519,
25,
628,
220,
220,
220,
1058,
4871,
25,
63,
12235,
15854,
2247,
63,
318,
21581,
355,
281,
26151,
290,
36537... | 2.200369 | 11,394 |
#tk13.pyw
import tkinter as tk
root = tk.Tk()
root.geometry('300x200')
lb = tk.Label(text='This is a Label,This is a label,This is a Label')
ms = tk.Label(text='This is a Message.This is a a Message.This is a Message.This is a Message')
[widget.pack()for widget in (lb,ms)]
root.mainloop() | [
2,
30488,
1485,
13,
9078,
86,
198,
11748,
256,
74,
3849,
355,
256,
74,
198,
198,
15763,
796,
256,
74,
13,
51,
74,
3419,
198,
15763,
13,
469,
15748,
10786,
6200,
87,
2167,
11537,
198,
23160,
796,
256,
74,
13,
33986,
7,
5239,
11639,... | 2.621622 | 111 |
from itertools import izip_longest
outpath = "redd-test/"
dir = "redd-test/house_1/"
with open(dir+'channel_5_6.dat') as f3,\
open(dir+'channel_6_7.dat') as f4, open(dir+'channel_7_5.dat') as f5, open(dir+'channel_8_5.dat') as f6,\
open(dir+'channel_9_3.dat') as f7, open(dir+'channel_11_9.dat') as f8, open(dir+'channel_12_10.dat') as f9,\
open(dir+'channel_15_5.dat') as f12, open(dir+'channel_17_3.dat') as f10, open(dir+'channel_18_3.dat') as f11,\
open(outpath+'redd-combined-1.dat', 'wb') as res: # open(dir+'channel_2.dat') as f12,
for l1,l2,l3,l4,l5,l6,l7,l8,l9,l10 in izip_longest(f3,f4,f5,f6,f7,f8,f9,f10,f11,f12, fillvalue=""):
res.write("{},{},{},{},{},{},{},{},{},{}\n".\
format(l1.rstrip(), l2.rstrip(),l3.rstrip(), l4.rstrip(),\
l5.rstrip(), l6.rstrip(),l7.rstrip(), l8.rstrip(),\
l9.rstrip(), l10.rstrip()))
dir2 = "redd-test/house_2/"
with open(dir2+'channel_3_5.dat') as f3,\
open(dir2+'channel_4_3.dat') as f4, open(dir2+'channel_5_1.dat') as f5, open(dir2+'channel_6_9.dat') as f6,\
open(dir2+'channel_7_4.dat') as f7, open(dir2+'channel_8_5.dat') as f8, open(dir2+'channel_9_6.dat') as f9,\
open(outpath+'redd-combined-2.dat', 'wb') as res:
for l1,l2,l3,l4,l5,l6,l7 in izip_longest(f3,f4,f5,f6,f7,f8,f9, fillvalue=""):
res.write("{},{},{},{},{},{},{}\n".\
format(l1.rstrip(), l2.rstrip(),l3.rstrip(), l4.rstrip(),\
l5.rstrip(), l6.rstrip(),l7.rstrip()))
dir3 = "redd-test/house_3/"
with open(dir3+'channel_5_3.dat') as f3,\
open(dir3+'channel_7_6.dat') as f4, open(dir3+'channel_9_7.dat') as f5, open(dir3+'channel_10_8.dat') as f6,\
open(dir3+'channel_11_3.dat') as f7, open(dir3+'channel_15_3.dat') as f8, open(dir3+'channel_16_9.dat') as f9,\
open(dir3+'channel_17_3.dat') as f10, open(dir3+'channel_19_3.dat') as f11,\
open(outpath+'redd-combined-3.dat', 'wb') as res:
for l1,l2,l3,l4,l5,l6,l7,l8,l9 in izip_longest(f3,f4,f5,f6,f7,f8,f9,f10,f11, fillvalue=""):
res.write("{},{},{},{},{},{},{},{},{}\n".\
format(l1.rstrip(), l2.rstrip(),l3.rstrip(), l4.rstrip(),\
l5.rstrip(), l6.rstrip(),l7.rstrip(), l8.rstrip(),\
l9.rstrip()))
dir4 = "redd-test/house_4/"
with open(dir4+'channel_3_3.dat') as f3,\
open(dir4+'channel_4_8.dat') as f4, open(dir4+'channel_5_5.dat') as f5, open(dir4+'channel_7_4.dat') as f6,\
open(dir4+'channel_8_1.dat') as f7, open(dir4+'channel_13_3.dat') as f8, open(dir4+'channel_14_5.dat') as f9,\
open(dir4+'channel_18_3.dat') as f10, open(dir4+'channel_19_3.dat') as f11,\
open(outpath+'redd-combined-4.dat', 'wb') as res:
for l1,l2,l3,l4,l5,l6,l7,l8,l9 in izip_longest(f3,f4,f5,f6,f7,f8,f9,f10,f11, fillvalue=""):
res.write("{},{},{},{},{},{},{},{},{}\n".\
format(l1.rstrip(), l2.rstrip(),l3.rstrip(), l4.rstrip(),\
l5.rstrip(), l6.rstrip(),l7.rstrip(), l8.rstrip(),\
l9.rstrip()))
dir5 = "redd-test/house_5/"
with open(dir5+'channel_3_9.dat') as f3,\
open(dir5+'channel_6_8.dat') as f4, open(dir5+'channel_14_3.dat') as f5, open(dir5+'channel_16_10.dat') as f6,\
open(dir5+'channel_18_6.dat') as f7, open(dir5+'channel_19_3.dat') as f8, open(dir5+'channel_20_7.dat') as f9,\
open(dir5+'channel_23_3.dat') as f10,\
open(outpath+'redd-combined-5.dat', 'wb') as res:
for l1,l2,l3,l4,l5,l6,l7,l8 in izip_longest(f3,f4,f5,f6,f7,f8,f9,f10, fillvalue=""):
res.write("{},{},{},{},{},{},{},{}\n".\
format(l1.rstrip(), l2.rstrip(),l3.rstrip(), l4.rstrip(),\
l5.rstrip(), l6.rstrip(),l7.rstrip(), l8.rstrip()))
dir6 = "redd-test/house_6/"
with open(dir6+'channel_3_5.dat') as f3,\
open(dir6+'channel_4_4.dat') as f4, open(dir6+'channel_5_1.dat') as f5, open(dir6+'channel_7_10.dat') as f6,\
open(dir6+'channel_8_6.dat') as f7, open(dir6+'channel_12_2.dat') as f8, open(dir6+'channel_13_2.dat') as f9,\
open(dir6+'channel_14_3.dat') as f10, open(dir6+'channel_15_2.dat') as f11,\
open(outpath+'redd-combined-6.dat', 'wb') as res:
for l1,l2,l3,l4,l5,l6,l7,l8,l9 in izip_longest(f3,f4,f5,f6,f7,f8,f9,f10,f11, fillvalue=""):
res.write("{},{},{},{},{},{},{},{},{}\n".\
format(l1.rstrip(), l2.rstrip(),l3.rstrip(), l4.rstrip(),\
l5.rstrip(), l6.rstrip(),l7.rstrip(), l8.rstrip(),\
l9.rstrip()))
| [
6738,
340,
861,
10141,
1330,
220,
528,
541,
62,
6511,
395,
198,
198,
448,
6978,
796,
366,
26504,
12,
9288,
30487,
198,
198,
15908,
796,
366,
26504,
12,
9288,
14,
4803,
62,
16,
30487,
198,
198,
4480,
1280,
7,
15908,
10,
6,
17620,
6... | 1.823653 | 2,376 |
import argparse
import subprocess
from typing import Iterable, Optional, Tuple
from attr import attrib, attrs
__version__ = '0.15.0'
Command = Tuple[str, ...]
@attrs(frozen=True)
TOOLS = [
CommandTool('flake8', default_files=()),
CommandTool(
'isort', run_params=('-c',), fix_params=(), default_files=('.',)
),
CommandTool('mypy'),
CommandTool(
'black',
run_params=('--check',),
fix_params=(),
default_files=('.',),
),
]
if __name__ == '__main__':
main()
| [
11748,
1822,
29572,
198,
11748,
850,
14681,
198,
6738,
19720,
1330,
40806,
540,
11,
32233,
11,
309,
29291,
198,
198,
6738,
708,
81,
1330,
708,
822,
11,
708,
3808,
198,
198,
834,
9641,
834,
796,
705,
15,
13,
1314,
13,
15,
6,
628,
1... | 2.294872 | 234 |
import numpy as np
import math
import matplotlib.pyplot as plt
from matplotlib import cm
# Create random input and output data
x = np.linspace(-math.pi, math.pi, 2000)
y = np.sin(x)
# Randomly initialize weights
a = np.random.randn()
b = np.random.randn()
c = np.random.randn()
d = np.random.randn()
learning_rate = 1e-6
for t in range(2000):
# Forward pass: compute predicted y
# y = a + b x + c x^2 + d x^3
y_pred = fn_3poly(x, a, b, c, d)
# Compute and print loss
loss = np.square(y_pred - y).sum()
if t % 100 == 99:
print(t, loss)
# Backprop to compute gradients of a, b, c, d with respect to loss
grad_y_pred = 2.0 * (y_pred - y)
grad_a = grad_y_pred.sum()
grad_b = (grad_y_pred * x).sum()
grad_c = (grad_y_pred * x ** 2).sum()
grad_d = (grad_y_pred * x ** 3).sum()
# Update weights
a -= learning_rate * grad_a
b -= learning_rate * grad_b
c -= learning_rate * grad_c
d -= learning_rate * grad_d
print(f'Result: y = {a} + {b} x + {c} x^2 + {d} x^3')
x_plot = np.arange(-4, 4, 0.02)
fig, ax = plt.subplots(1)
ax.plot(x_plot, np.sin(x_plot))
ax.plot(x_plot, fn_3poly(x_plot, a, b, c, d))
ax.legend([r'$f(x)=\sin(x)$', r'$a + bx + cx^2 + dx^3$'])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
fig.tight_layout()
fig.show()
# Saliency map
def fn_3poly_dr(x):
"""direvative of fn_3poly"""
return b + 2 * c * x + 3 * d * x **2
saliency = fn_3poly_dr(x_plot)
fig, ax = plt.subplots(1)
ax.plot(x_plot, np.sin(x_plot))
ax.plot(x_plot, fn_3poly(x_plot, a, b, c, d))
ax.scatter(
x_plot, fn_3poly(x_plot, a, b, c, d),
c=np.array(cm.tab10.colors[1]).reshape(1, -1), marker='.',
s=20 * (saliency - saliency.min())
)
ax.legend([r'$f(x)=\sin(x)$', '$a + bx + cx^2 + dx^3$\nthickness represents saliency'])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
fig.tight_layout()
fig.show()
fig.savefig('images/saliency.png')
| [
11748,
299,
32152,
355,
45941,
198,
11748,
10688,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
198,
6738,
2603,
29487,
8019,
1330,
12067,
198,
198,
2,
13610,
4738,
5128,
290,
5072,
1366,
198,
87,
796,
45941,
13,
21602,
... | 2.159737 | 914 |
# Copyright (c) 2015 Microsoft Corporation
from z3 import *
set_option(auto_config=True)
x, y = Ints('x y')
print eq(x + y, x + y)
print eq(x + y, y + x)
n = x + y
print eq(n, x + y)
# x2 is eq to x
x2 = Int('x')
print eq(x, x2)
# the integer variable x is not equal to
# the real variable x
print eq(Int('x'), Real('x'))
| [
198,
2,
15069,
357,
66,
8,
1853,
5413,
10501,
198,
6738,
1976,
18,
1330,
1635,
198,
2617,
62,
18076,
7,
23736,
62,
11250,
28,
17821,
8,
198,
198,
87,
11,
331,
796,
2558,
82,
10786,
87,
331,
11537,
198,
4798,
37430,
7,
87,
1343,
... | 2.414815 | 135 |
# This is a script that turns a KaTrain AI into a sort-of GTP compatible bot
import json
import random
import sys
import time
import os
import json
import traceback
import math
os.environ["KCFG_KIVY_LOG_LEVEL"] = os.environ.get("KCFG_KIVY_LOG_LEVEL", "warning")
from katrain.core.ai import generate_ai_move
from katrain.core.base_katrain import KaTrainBase
from katrain.core.constants import OUTPUT_ERROR, OUTPUT_INFO
from katrain.core.engine import EngineDiedException, KataGoEngine
from katrain.core.game import Game
from katrain.core.sgf_parser import Move
from settings import DEFAULT_PORT, bot_strategies, Logger
bot = sys.argv[1].strip()
port = int(sys.argv[2]) if len(sys.argv) > 2 else DEFAULT_PORT
REPORT_SCORE_THRESHOLD = 1.5
MAX_WAIT_ANALYSIS = 10
MAX_PASS = 3 # after opponent passes this many times, we always pass
logger = Logger()
ENGINE_SETTINGS = {
"katago": "", # actual engine settings in engine_server.py
"model": "",
"config": "",
"threads": "",
"max_visits": 5,
"max_time": 5.0,
"_enable_ownership": False,
}
engine = KataGoEngine(logger, ENGINE_SETTINGS, override_command=f"python engine_connector.py {port}")
with open("config.json") as f:
settings = json.load(f)
all_ai_settings = settings["ai"]
sgf_dir = "sgf_ogs/"
ai_strategy, x_ai_settings, x_engine_settings = bot_strategies[bot]
ai_settings = {**all_ai_settings[ai_strategy], **x_ai_settings}
ENGINE_SETTINGS.update(x_engine_settings)
print(f"starting bot {bot} using server port {port}", file=sys.stderr)
print("setup: ", ai_strategy, ai_settings, engine.override_settings, file=sys.stderr)
print(ENGINE_SETTINGS, file=sys.stderr)
print(ai_strategy, ai_settings, file=sys.stderr)
game = Game(Logger(), engine, game_properties={"SZ": 19, "PW": "OGS", "PB": "OGS"})
while True:
line = input().strip()
logger.log(f"GOT INPUT {line}", OUTPUT_ERROR)
if line.startswith("boardsize"):
_, *size = line.strip().split(" ")
if len(size) > 1:
size = f"{size[0]}:{size[1]}"
else:
size = int(size[0])
game = Game(Logger(), engine, game_properties={"SZ": size, "PW": "OGS", "PB": "OGS"})
logger.log(f"Init game {game.root.properties}", OUTPUT_ERROR)
elif line.startswith("komi"):
_, komi = line.split(" ")
game.root.set_property("KM", komi.strip())
game.root.set_property("RU", "chinese")
logger.log(f"Setting komi {game.root.properties}", OUTPUT_ERROR)
elif line.startswith("place_free_handicap"):
_, n = line.split(" ")
n = int(n)
game.place_handicap_stones(n)
handicaps = set(game.root.get_list_property("AB"))
bx, by = game.board_size
while len(handicaps) < min(n, bx * by): # really obscure cases
handicaps.add(
Move((random.randint(0, bx - 1), random.randint(0, by - 1)), player="B").sgf(board_size=game.board_size)
)
game.root.set_property("AB", list(handicaps))
game._calculate_groups()
gtp = [Move.from_sgf(m, game.board_size, "B").gtp() for m in handicaps]
logger.log(f"Chose handicap placements as {gtp}", OUTPUT_ERROR)
print(f"= {' '.join(gtp)}\n")
sys.stdout.flush()
game.analyze_all_nodes() # re-evaluate root
while engine.queries: # and make sure this gets processed
time.sleep(0.001)
continue
elif line.startswith("set_free_handicap"):
_, *stones = line.split(" ")
game.root.set_property("AB", [Move.from_gtp(move.upper()).sgf(game.board_size) for move in stones])
game._calculate_groups()
game.analyze_all_nodes() # re-evaluate root
while engine.queries: # and make sure this gets processed
time.sleep(0.001)
logger.log(f"Set handicap placements to {game.root.get_list_property('AB')}", OUTPUT_ERROR)
elif line.startswith("genmove"):
_, player = line.strip().split(" ")
if player[0].upper() != game.current_node.next_player:
logger.log(
f"ERROR generating move: UNEXPECTED PLAYER {player} != {game.current_node.next_player}.", OUTPUT_ERROR
)
print(f"= ??\n")
sys.stdout.flush()
continue
logger.log(f"{ai_strategy} generating move", OUTPUT_ERROR)
game.current_node.analyze(engine)
malkovich_analysis(game.current_node)
game.root.properties[f"P{game.current_node.next_player}"] = [f"KaTrain {ai_strategy}"]
num_passes = sum(
[int(n.is_pass or False) for n in game.current_node.nodes_from_root[::-1][0 : 2 * MAX_PASS : 2]]
)
bx, by = game.board_size
if num_passes >= MAX_PASS and game.current_node.depth - 2 * MAX_PASS >= bx + by:
logger.log(f"Forced pass as opponent is passing {MAX_PASS} times", OUTPUT_ERROR)
pol = game.current_node.policy
if not pol:
pol = ["??"]
print(
f"DISCUSSION:OK, since you passed {MAX_PASS} times after the {bx+by}th move, I will pass as well [policy {pol[-1]:.3%}].",
file=sys.stderr,
)
move = game.play(Move(None, player=game.current_node.next_player)).move
else:
move, node = generate_ai_move(game, ai_strategy, ai_settings)
logger.log(f"Generated move {move}", OUTPUT_ERROR)
print(f"= {move.gtp()}\n")
sys.stdout.flush()
malkovich_analysis(game.current_node)
continue
elif line.startswith("play"):
_, player, move = line.split(" ")
node = game.play(Move.from_gtp(move.upper(), player=player[0].upper()), analyze=False)
logger.log(f"played {player} {move}", OUTPUT_ERROR)
elif line.startswith("final_score"):
logger.log("line=" + line, OUTPUT_ERROR)
if "{" in line:
gamedata_str = line[12:]
game.root.set_property("C", f"AI {ai_strategy} {ai_settings}\nOGS Gamedata: {gamedata_str}")
try:
gamedata = json.loads(gamedata_str)
game.root.set_property(
"PW",
f"{gamedata['players']['white']['username']} ({rank_to_string(gamedata['players']['white']['rank'])})",
)
game.root.set_property(
"PB",
f"{gamedata['players']['black']['username']} ({rank_to_string(gamedata['players']['black']['rank'])})",
)
if any(gamedata["players"][p]["username"] == "katrain-dev-beta" for p in ["white", "black"]):
sgf_dir = "sgf_ogs_beta/"
except Exception as e:
_, _, tb = sys.exc_info()
logger.log(f"error while processing gamedata: {e}\n{traceback.format_tb(tb)}", OUTPUT_ERROR)
score = game.current_node.format_score()
game.game_id += f"_{score}"
sgf = game.write_sgf(
sgf_dir, trainer_config={"eval_show_ai": True, "save_feedback": [True], "eval_thresholds": []}
)
logger.log(f"Game ended. Score was {score} -> saved sgf to {sgf}", OUTPUT_ERROR)
sys.stderr.flush()
sys.stdout.flush()
time.sleep(0.1) # ensure our logging gets handled
print(f"= {score}\n")
sys.stdout.flush()
continue
elif line.startswith("quit"):
print(f"= \n")
break
print(f"= \n")
sys.stdout.flush()
sys.stderr.flush()
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12... | 2.127159 | 3,531 |
'''
The follwing code runs a test lstm network on the CIFAR dataset
I will explicitly write the networks here for ease of understanding
One convlstm layer right after the first cnn
cnn_dropout = 0.4 rnn_dropout = 0.2 , WITH cnivlstm_dropout samples = 10, h = 256, epochs = 50, convlstm activation = relu - out.373233 (Based on best results from cnn - gru)
################# convlstm_cnn_mix_v0_True Validation Accuracy = [0.3555999994277954, 0.37619999051094055, 0.4235999882221222, 0.4275999963283539, 0.46779999136924744, 0.4819999933242798, 0.48980000615119934, 0.4968000054359436, 0.5194000005722046, 0.5131999850273132, 0.5242000222206116, 0.5284000039100647, 0.5144000053405762, 0.5365999937057495, 0.5465999841690063, 0.5496000051498413, 0.5407999753952026, 0.5626000165939331, 0.5573999881744385, 0.5558000206947327, 0.5586000084877014, 0.5645999908447266, 0.5717999935150146, 0.569599986076355, 0.5727999806404114, 0.5630000233650208, 0.571399986743927, 0.5580000281333923, 0.5852000117301941, 0.5753999948501587, 0.5738000273704529, 0.579200029373169, 0.5726000070571899, 0.5789999961853027, 0.5648000240325928, 0.5776000022888184, 0.5763999819755554, 0.5799999833106995, 0.58160001039505, 0.5839999914169312, 0.5860000252723694, 0.5834000110626221, 0.5825999975204468, 0.5860000252723694, 0.5807999968528748, 0.5831999778747559, 0.5789999961853027, 0.5758000016212463, 0.58160001039505, 0.5807999968528748]
################# convlstm_cnn_mix_v0_True Training Accuracy = [0.25715556740760803, 0.36764445900917053, 0.4077777862548828, 0.4264444410800934, 0.4399999976158142, 0.4568444490432739, 0.46862220764160156, 0.4764222204685211, 0.4854666590690613, 0.4959777891635895, 0.5049999952316284, 0.5096889138221741, 0.5180666446685791, 0.5244666934013367, 0.5313777923583984, 0.536133348941803, 0.5363110899925232, 0.5413333177566528, 0.549311101436615, 0.5522222518920898, 0.557022213935852, 0.5595999956130981, 0.563955545425415, 0.5663999915122986, 0.5641111135482788, 0.5719777941703796, 0.5754444599151611, 0.5798222422599792, 0.580644428730011, 0.5836222171783447, 0.5849555730819702, 0.586222231388092, 0.5915555357933044, 0.594355583190918, 0.5944888591766357, 0.5996444225311279, 0.6037111282348633, 0.6028888821601868, 0.6078444719314575, 0.6094889044761658, 0.6093555688858032, 0.6102889180183411, 0.6134666800498962, 0.6152889132499695, 0.6180889010429382, 0.6205999851226807, 0.6215111017227173, 0.6247333288192749, 0.6240666508674622, 0.6295999884605408]
cnn_dropout = 0.4 rnn_dropout = 0.2 , WITH cnivlstm_dropout samples = 10, h = 256, epochs = 150, convlstm activation = relu - out.373440 (Based on best results from cnn - gru)
################# convlstm_cnn_mix_v01_True Validation Accuracy = [0.31619998812675476, 0.4059999883174896, 0.4047999978065491, 0.436599999666214, 0.45899999141693115, 0.4731999933719635, 0.4819999933242798, 0.487199991941452, 0.4936000108718872, 0.4991999864578247, 0.5113999843597412, 0.5221999883651733, 0.5235999822616577, 0.5278000235557556, 0.5368000268936157, 0.5289999842643738, 0.5253999829292297, 0.5428000092506409, 0.5382000207901001, 0.5414000153541565, 0.5450000166893005, 0.5511999726295471, 0.5523999929428101, 0.5672000050544739, 0.5468000173568726, 0.5619999766349792, 0.5522000193595886, 0.5722000002861023, 0.5673999786376953, 0.5720000267028809, 0.5690000057220459, 0.5726000070571899, 0.5763999819755554, 0.571399986743927, 0.5673999786376953, 0.5722000002861023, 0.5691999793052673, 0.5781999826431274, 0.578000009059906, 0.5795999765396118, 0.5748000144958496, 0.5888000130653381, 0.5809999704360962, 0.5821999907493591, 0.5759999752044678, 0.5771999955177307, 0.5705999732017517, 0.5825999975204468, 0.5770000219345093, 0.5738000273704529, 0.5756000280380249, 0.5752000212669373, 0.5802000164985657, 0.5781999826431274, 0.5709999799728394, 0.5717999935150146, 0.5781999826431274, 0.5831999778747559, 0.5812000036239624, 0.5849999785423279, 0.5789999961853027, 0.5756000280380249, 0.5758000016212463, 0.5771999955177307, 0.5856000185012817, 0.5785999894142151, 0.5720000267028809, 0.5734000205993652, 0.5741999745368958, 0.5824000239372253, 0.5776000022888184, 0.5756000280380249, 0.5687999725341797, 0.5676000118255615, 0.5738000273704529, 0.5857999920845032, 0.5799999833106995, 0.5766000151634216, 0.5788000226020813, 0.5831999778747559, 0.5753999948501587, 0.5734000205993652, 0.5631999969482422, 0.5687999725341797, 0.5788000226020813, 0.578000009059906, 0.5756000280380249, 0.5708000063896179, 0.5618000030517578, 0.5708000063896179, 0.5741999745368958, 0.5753999948501587, 0.5720000267028809, 0.5722000002861023, 0.5637999773025513, 0.574999988079071, 0.5690000057220459, 0.58160001039505, 0.5694000124931335, 0.5676000118255615, 0.5738000273704529, 0.5651999711990356, 0.574999988079071, 0.5703999996185303, 0.569599986076355, 0.5690000057220459, 0.5672000050544739, 0.5655999779701233, 0.5637999773025513, 0.5691999793052673, 0.5702000260353088, 0.5680000185966492, 0.5613999962806702, 0.5662000179290771, 0.5623999834060669, 0.567799985408783, 0.5662000179290771, 0.5712000131607056, 0.5673999786376953, 0.5630000233650208, 0.5594000220298767, 0.5608000159263611, 0.5681999921798706, 0.5659999847412109, 0.5630000233650208, 0.5619999766349792, 0.5626000165939331, 0.5654000043869019, 0.5654000043869019, 0.5631999969482422, 0.5680000185966492, 0.5601999759674072, 0.5586000084877014, 0.5590000152587891, 0.5605999827384949, 0.5558000206947327, 0.5636000037193298, 0.555400013923645, 0.5644000172615051, 0.5595999956130981, 0.5608000159263611, 0.5685999989509583, 0.5626000165939331, 0.5590000152587891, 0.5623999834060669, 0.5541999936103821, 0.5523999929428101, 0.5519999861717224, 0.5582000017166138, 0.5558000206947327]
################# convlstm_cnn_mix_v01_True Training Accuracy = [0.249466672539711, 0.358822226524353, 0.39959999918937683, 0.41804444789886475, 0.43524444103240967, 0.4512222111225128, 0.4600444436073303, 0.46995556354522705, 0.47760000824928284, 0.48697778582572937, 0.4950222074985504, 0.49779999256134033, 0.5047777891159058, 0.5094444155693054, 0.5156221985816956, 0.5198000073432922, 0.5297999978065491, 0.5285778045654297, 0.5348444581031799, 0.5395777821540833, 0.5428000092506409, 0.5471110939979553, 0.5546444654464722, 0.5557777881622314, 0.5594000220298767, 0.5629777908325195, 0.5652666687965393, 0.5679110884666443, 0.5738222002983093, 0.5741999745368958, 0.5807777643203735, 0.5786888599395752, 0.5838666558265686, 0.5865111351013184, 0.5898444652557373, 0.592199981212616, 0.5955111384391785, 0.5958889126777649, 0.6001777648925781, 0.6027555465698242, 0.6022666692733765, 0.606844425201416, 0.6050666570663452, 0.6121333241462708, 0.6107555627822876, 0.6132222414016724, 0.6163111329078674, 0.6169777512550354, 0.6183333396911621, 0.622511088848114, 0.624822199344635, 0.6251555681228638, 0.6260666847229004, 0.6275110840797424, 0.6320444345474243, 0.6323999762535095, 0.6347333192825317, 0.6363333463668823, 0.6373777985572815, 0.6391111016273499, 0.6382444500923157, 0.63919997215271, 0.6450222134590149, 0.6434000134468079, 0.6443555355072021, 0.6475555300712585, 0.6497777700424194, 0.6504666805267334, 0.6514222025871277, 0.6546000242233276, 0.6522889137268066, 0.6551111340522766, 0.6570000052452087, 0.6564444303512573, 0.658466637134552, 0.6592222452163696, 0.662066638469696, 0.6632444262504578, 0.6638222336769104, 0.6681333184242249, 0.6650221943855286, 0.6649555563926697, 0.6700000166893005, 0.6726666688919067, 0.6718888878822327, 0.6709111332893372, 0.6744444370269775, 0.6745333075523376, 0.6758221983909607, 0.6766666769981384, 0.6747111082077026, 0.6774666905403137, 0.6812666654586792, 0.6800888776779175, 0.6768222451210022, 0.6801333427429199, 0.6830888986587524, 0.6844000220298767, 0.6855999827384949, 0.6861777901649475, 0.686822235584259, 0.6895111203193665, 0.6921555399894714, 0.6860666871070862, 0.6852666735649109, 0.6915333271026611, 0.6921333074569702, 0.6931777596473694, 0.6941555738449097, 0.6948666572570801, 0.6968888640403748, 0.6943777799606323, 0.6979555487632751, 0.6966888904571533, 0.6968888640403748, 0.6993555426597595, 0.6975555419921875, 0.7030444741249084, 0.6989777684211731, 0.7029555439949036, 0.7020221948623657, 0.7024222016334534, 0.7059333324432373, 0.7076444625854492, 0.7047333121299744, 0.7077111005783081, 0.7068444490432739, 0.7082666754722595, 0.7079333066940308, 0.7075555324554443, 0.7076888680458069, 0.7130222320556641, 0.71224445104599, 0.7111555337905884, 0.7123333215713501, 0.7143333554267883, 0.7105333209037781, 0.718155562877655, 0.7120888829231262, 0.7151333093643188, 0.7185778021812439, 0.7164000272750854, 0.7191110849380493, 0.7173110842704773, 0.7188666462898254, 0.7190889120101929, 0.7207777500152588, 0.7199777960777283, 0.7187111377716064, 0.722000002861023]
################# convlstm_cnn_mix_v0_True Validation Accuracy = [0.3555999994277954, 0.37619999051094055, 0.4235999882221222, 0.4275999963283539, 0.46779999136924744, 0.4819999933242798, 0.48980000615119934, 0.4968000054359436, 0.5194000005722046, 0.5131999850273132, 0.5242000222206116, 0.5284000039100647, 0.5144000053405762, 0.5365999937057495, 0.5465999841690063, 0.5496000051498413, 0.5407999753952026, 0.5626000165939331, 0.5573999881744385, 0.5558000206947327, 0.5586000084877014, 0.5645999908447266, 0.5717999935150146, 0.569599986076355, 0.5727999806404114, 0.5630000233650208, 0.571399986743927, 0.5580000281333923, 0.5852000117301941, 0.5753999948501587, 0.5738000273704529, 0.579200029373169, 0.5726000070571899, 0.5789999961853027, 0.5648000240325928, 0.5776000022888184, 0.5763999819755554, 0.5799999833106995, 0.58160001039505, 0.5839999914169312, 0.5860000252723694, 0.5834000110626221, 0.5825999975204468, 0.5860000252723694, 0.5807999968528748, 0.5831999778747559, 0.5789999961853027, 0.5758000016212463, 0.58160001039505, 0.5807999968528748]
################# convlstm_cnn_mix_v0_True Training Accuracy = [0.25715556740760803, 0.36764445900917053, 0.4077777862548828, 0.4264444410800934, 0.4399999976158142, 0.4568444490432739, 0.46862220764160156, 0.4764222204685211, 0.4854666590690613, 0.4959777891635895, 0.5049999952316284, 0.5096889138221741, 0.5180666446685791, 0.5244666934013367, 0.5313777923583984, 0.536133348941803, 0.5363110899925232, 0.5413333177566528, 0.549311101436615, 0.5522222518920898, 0.557022213935852, 0.5595999956130981, 0.563955545425415, 0.5663999915122986, 0.5641111135482788, 0.5719777941703796, 0.5754444599151611, 0.5798222422599792, 0.580644428730011, 0.5836222171783447, 0.5849555730819702, 0.586222231388092, 0.5915555357933044, 0.594355583190918, 0.5944888591766357, 0.5996444225311279, 0.6037111282348633, 0.6028888821601868, 0.6078444719314575, 0.6094889044761658, 0.6093555688858032, 0.6102889180183411, 0.6134666800498962, 0.6152889132499695, 0.6180889010429382, 0.6205999851226807, 0.6215111017227173, 0.6247333288192749, 0.6240666508674622, 0.6295999884605408]
with 20 samples and epochs = 50, h=128, out.980236
with 20 samples and epochs = 50, h=256, out.980276
'''
from __future__ import division, print_function, absolute_import
print('Starting..................................')
import sys
sys.path.insert(1, '/home/labs/ahissarlab/orra/imagewalker')
import numpy as np
import cv2
import misc
import pandas as pd
import matplotlib.pyplot as plt
import pickle
from keras_utils import *
from misc import *
import tensorflow.keras as keras
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
from tensorflow.keras.datasets import cifar10
# load dataset
(trainX, trainy), (testX, testy) = cifar10.load_data()
images, labels = trainX, trainy
#Define function for low resolution lens on syclop
kernel_regularizer_list = [None, keras.regularizers.l1(),keras.regularizers.l2(),keras.regularizers.l1_l2()]
optimizer_list = [tf.keras.optimizers.Adam, tf.keras.optimizers.Nadam, tf.keras.optimizers.RMSprop]
if len(sys.argv) > 1:
paramaters = {
'epochs' : int(sys.argv[1]),
'sample' : int(sys.argv[2]),
'res' : int(sys.argv[3]),
'hidden_size' : int(sys.argv[4]),
'concat' : int(sys.argv[5]),
'regularizer' : keras.regularizers.l1(),#kernel_regularizer_list[int(sys.argv[6])],
'optimizer' : optimizer_list[int(sys.argv[7])],
'cnn_dropout' : 0.4,
'rnn_dropout' : 0.2,
'lr' : 5e-4,
'run_id' : np.random.randint(1000,9000)
}
else:
paramaters = {
'epochs' : 1,
'sample' : 5,
'res' : 8,
'hidden_size' : 128,
'concat' : 1,
'regularizer' : None,
'optimizer' : optimizer_list[0],
'cnn_dropout' : 0.4,
'rnn_dropout' : 0.2,
'lr' : 5e-4,
'run_id' : np.random.randint(1000,9000)
}
print(paramaters)
for key,val in paramaters.items():
exec(key + '=val')
epochs = epochs
sample = sample
res = res
hidden_size =hidden_size
concat = concat
regularizer = regularizer
optimizer = optimizer
cnn_dropout = cnn_dropout
rnn_dropout = rnn_dropout
lr = lr
run_id = run_id
n_timesteps = sample
def convlstm(n_timesteps = 5, hidden_size = 128,input_size = 32, concat = True):
'''
CNN RNN combination that extends the CNN to a network that achieves
~80% accuracy on full res cifar.
Parameters
----------
n_timesteps : TYPE, optional
DESCRIPTION. The default is 5.
img_dim : TYPE, optional
DESCRIPTION. The default is 32.
hidden_size : TYPE, optional
DESCRIPTION. The default is 128.
input_size : TYPE, optional
DESCRIPTION. The default is 32.
Returns
-------
model : TYPE
DESCRIPTION.
'''
inputA = keras.layers.Input(shape=(n_timesteps,input_size,input_size,3))
inputB = keras.layers.Input(shape=(n_timesteps,2))
# define CNN model
x1=keras.layers.TimeDistributed(keras.layers.Conv2D(32,(3,3), activation='relu',padding = 'same'))(inputA)
x1=keras.layers.ConvLSTM2D(32,(3,3), padding = 'same', dropout = cnn_dropout, recurrent_dropout=rnn_dropout,return_sequences=True)(x1)
x1=keras.layers.TimeDistributed(keras.layers.MaxPooling2D(pool_size=(2, 2)))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Dropout(cnn_dropout))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Conv2D(64,(3,3),activation='relu', padding = 'same'))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Conv2D(64,(3,3),activation='relu', padding = 'same'))(x1)
x1=keras.layers.TimeDistributed(keras.layers.MaxPooling2D(pool_size=(2, 2)))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Dropout(cnn_dropout))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Conv2D(128,(3,3),activation='relu', padding = 'same'))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Conv2D(128,(3,3),activation='relu', padding = 'same'))(x1)
x1=keras.layers.TimeDistributed(keras.layers.MaxPooling2D(pool_size=(2, 2)))(x1)
x1=keras.layers.TimeDistributed(keras.layers.Dropout(cnn_dropout))(x1)
print(x1.shape)
x1=keras.layers.TimeDistributed(keras.layers.Flatten())(x1)
print(x1.shape)
if concat:
x = keras.layers.Concatenate()([x1,inputB])
else:
x = x1
print(x.shape)
# define LSTM model
x = keras.layers.GRU(hidden_size,input_shape=(n_timesteps, None),return_sequences=True,recurrent_dropout=rnn_dropout)(x)
x = keras.layers.Flatten()(x)
x = keras.layers.Dense(10,activation="softmax")(x)
model = keras.models.Model(inputs=[inputA,inputB],outputs=x, name = 'convlstm_cnn_mix_v01_{}'.format(concat))
opt=tf.keras.optimizers.Adam(lr=lr)
model.compile(
optimizer=opt,
loss="sparse_categorical_crossentropy",
metrics=["sparse_categorical_accuracy"],
)
return model
rnn_net = convlstm(n_timesteps = sample, hidden_size = hidden_size,input_size = res, concat = True)
#keras.utils.plot_model(rnn_net, expand_nested=True, to_file='{}.png'.format(rnn_net.name))
#cnn_net = cnn_net = extended_cnn_one_img(n_timesteps = sample, input_size = res, dropout = cnn_dropout)
# hp = HP()
# hp.save_path = 'saved_runs'
# hp.description = "syclop cifar net search runs"
# hp.this_run_name = 'syclop_{}'.format(rnn_net.name)
# deploy_logs()
train_dataset, test_dataset = create_cifar_dataset(images, labels,res = res,
sample = sample, return_datasets=True,
mixed_state = False, add_seed = 0,
)
#bad_res_func = bad_res101, up_sample = True)
train_dataset_x, train_dataset_y = split_dataset_xy(train_dataset)
test_dataset_x, test_dataset_y = split_dataset_xy(test_dataset)
print("##################### Fit {} and trajectories model on training data res = {} ##################".format(rnn_net.name,res))
rnn_history = rnn_net.fit(
train_dataset_x,
train_dataset_y,
batch_size=64,
epochs=epochs,
# We pass some validation for
# monitoring validation loss and metrics
# at the end of each epoch
validation_data=(test_dataset_x, test_dataset_y),
verbose = 0)
# print('################# {} Validation Accuracy = '.format(cnn_net.name),cnn_history.history['val_sparse_categorical_accuracy'])
# print('################# {} Training Accuracy = '.format(cnn_net.name),rnn_history.history['sparse_categorical_accuracy'])
print('################# {} Validation Accuracy = '.format(rnn_net.name),rnn_history.history['val_sparse_categorical_accuracy'])
print('################# {} Training Accuracy = '.format(rnn_net.name),rnn_history.history['sparse_categorical_accuracy'])
plt.figure()
plt.plot(rnn_history.history['sparse_categorical_accuracy'], label = 'train')
plt.plot(rnn_history.history['val_sparse_categorical_accuracy'], label = 'val')
# plt.plot(cnn_history.history['sparse_categorical_accuracy'], label = 'cnn train')
# plt.plot(cnn_history.history['val_sparse_categorical_accuracy'], label = 'cnn val')
plt.legend()
plt.title('{} on cifar res = {} hs = {} dropout = {}, num samples = {}'.format(rnn_net.name, res, hidden_size,cnn_dropout,sample))
plt.savefig('{} on Cifar res = {}, no upsample, val accur = {} hs = {} dropout = {}.png'.format(rnn_net.name,res,rnn_history.history['val_sparse_categorical_accuracy'][-1], hidden_size,cnn_dropout))
with open('/home/labs/ahissarlab/orra/imagewalker/cifar_net_search/{}HistoryDict{}_{}'.format(rnn_net.name, hidden_size,cnn_dropout), 'wb') as file_pi:
pickle.dump(rnn_history.history, file_pi)
# with open('/home/labs/ahissarlab/orra/imagewalker/cifar_net_search/{}HistoryDict'.format(cnn_net.name), 'wb') as file_pi:
# pickle.dump(cnn_history.history, file_pi)
dataset_update(rnn_history, rnn_net,paramaters)
write_to_file(rnn_history, rnn_net,paramaters)
| [
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6,
198,
464,
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257,
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... | 2.174104 | 8,673 |
import pytest
""" Test Dependency Installation
The purpose is to check if core dependencies are installed properly.
Typically, failure to these tests indicate an incorrection installation
or wrong activation of the virtual environment (i.e. conda, venv, etc.).
"""
| [
11748,
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5254,
7603,
281,
5970,
8243,
9988,
220,
198,
273,
2642,
14916,
... | 4.33871 | 62 |
from treys import Evaluator, Deck
from treys.card import pretty
d = Deck.fresh()
print(d)
print(pretty(d))
| [
6738,
2054,
893,
1330,
26439,
84,
1352,
11,
20961,
198,
6738,
2054,
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198,
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13,
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198,
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7,
67,
8,
198,
4798,
7,
37784,
7,
67,
4008,
628,
198
] | 2.820513 | 39 |
#!/usr/bin/env python
r"""
This module provides command execution functions such as cmd_fnc and cmd_fnc_u.
"""
import sys
import subprocess
robot_env = 1
try:
from robot.libraries.BuiltIn import BuiltIn
except ImportError:
robot_env = 0
import gen_print as gp
import gen_valid as gv
import gen_misc as gm
if robot_env:
import gen_robot_print as grp
###############################################################################
def cmd_fnc(cmd_buf,
quiet=None,
test_mode=None,
debug=0,
print_output=1,
show_err=1):
r"""
Run the given command in a shell and return the shell return code.
Description of arguments:
cmd_buf The command string to be run in a shell.
quiet Indicates whether this function should run
the pissuing()
function prints an "Issuing: <cmd string>" to stdout.
test_mode If test_mode is set, this function will
not actually run
the command.
debug If debug is set, this function will print
extra debug info.
print_output If this is set, this function will print
the stdout/stderr
generated by the shell command.
show_err If show_err is set, this function will
print a standardized
error report if the shell command returns non-zero.
"""
quiet = int(gm.global_default(quiet, 0))
test_mode = int(gm.global_default(test_mode, 0))
if debug:
gp.print_vars(cmd_buf, quiet, test_mode, debug)
err_msg = gv.svalid_value(cmd_buf)
if err_msg != "":
raise ValueError(err_msg)
if not quiet:
gp.pissuing(cmd_buf, test_mode)
if test_mode:
return 0, ""
sub_proc = subprocess.Popen(cmd_buf,
bufsize=1,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
out_buf = ""
for line in sub_proc.stdout:
out_buf += line
if not print_output:
continue
if robot_env:
grp.rprint(line)
else:
sys.stdout.write(line)
if print_output and not robot_env:
sys.stdout.flush()
sub_proc.communicate()
shell_rc = sub_proc.returncode
if shell_rc != 0 and show_err:
if robot_env:
grp.rprint_error_report("The prior command failed.\n" +
gp.sprint_var(shell_rc, 1))
else:
gp.print_error_report("The prior command failed.\n" +
gp.sprint_var(shell_rc, 1))
return shell_rc, out_buf
###############################################################################
###############################################################################
def cmd_fnc_u(cmd_buf,
quiet=None,
debug=None,
print_output=1,
show_err=1):
r"""
Call cmd_fnc with test_mode=0. See cmd_fnc (above) for details.
Note the "u" in "cmd_fnc_u" stands for "unconditional".
"""
return cmd_fnc(cmd_buf, test_mode=0, quiet=quiet, debug=debug,
print_output=print_output, show_err=show_err)
###############################################################################
| [
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850,
1... | 2.048904 | 1,779 |
import turtle as trt
distance = 70
angle = 90
for compteur in range(4):
if compteur == 0:
trt.color("blue")
elif compteur == 1:
trt.color("red")
elif compteur == 2:
trt.color("green")
else :
trt.color("orange")
trt.write(compteur)
trt.forward(distance)
trt.left(angle)
trt.done()
| [
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... | 2.214815 | 135 |
#!/usr/bin/python
#
# Author: Jashua R. Cloutier (contact via sourceforge username:senexcanis)
#
# Copyright (C) 2010, Jashua R. Cloutier
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in
# the documentation and/or other materials provided with the
# distribution.
#
# * Neither the name of Jashua R. Cloutier nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
#
# The CppHeaderParser.py script is written in Python 2.4 and released to
# the open source community for continuous improvements under the BSD
# 2.0 new license, which can be found at:
#
# http://www.opensource.org/licenses/bsd-license.php
#
"""
CppHeaderParser2.0: April 2011 - August 2011
by HartsAntler
http://pyppet.blogspot.com
Quick Start - User API:
h = CppHeaderParser.CppHeader("someheader.h")
for name in h.classes:
c = h.classes[name]
for method in c['methods']['public']:
print method['name']
print dir(method) # view the rest of the API here.
... TODO document more ...
New Features by Hart:
should be able to parse all c++ files, not just headers
parsing global typedefs with resolution
parsing global structs
fixes nested struct in class changes accessor type
parsing if class is abstract
parsing more info about variables
save ordering of classes, structs, and typedefs
handle forward decl of class in a class
handle mutable, static, and other variable types
handle 1D arrays
handle throw keyword and function prefix __attribute__((__const__))
handle nameless parameters "void method(void);"
handle simple templates, and global functions.
Internal Developer Notes:
1. double name stacks:
. the main stack is self.nameStack, this stack is simpler and easy to get hints from
. the secondary stack is self.stack is the full name stack, required for parsing somethings
. each stack maybe cleared at different points, since they are used to detect different things
. it looks ugly but it works :)
2. Had to make the __repr__ methods simple because some of these dicts are interlinked.
For nice printing, call something.show()
"""
import ply.lex as lex
import os
import sys
import re
import inspect
def lineno():
"""Returns the current line number in our program."""
return inspect.currentframe().f_back.f_lineno
version = __version__ = "1.9.9o"
tokens = [
'NUMBER',
'NAME',
'OPEN_PAREN',
'CLOSE_PAREN',
'OPEN_BRACE',
'CLOSE_BRACE',
'COLON',
'SEMI_COLON',
'COMMA',
'COMMENT_SINGLELINE',
'COMMENT_MULTILINE',
'PRECOMP_MACRO',
'PRECOMP_MACRO_CONT',
'ASTERISK',
'AMPERSTAND',
'EQUALS',
'MINUS',
'PLUS',
'DIVIDE',
'CHAR_LITERAL',
'STRING_LITERAL',
'OPERATOR_DIVIDE_OVERLOAD',
'NEW_LINE',
'OPEN_BRACKET',
'CLOSE_BRACKET',
]
t_OPEN_BRACKET = r'\['
t_CLOSE_BRACKET = r'\]'
#t_ignore = " \t\r[].|!?%@" # (cppheaderparser 1.9x)
#t_ignore = " \t\r[].|!?%@'^\\"
t_ignore = " \t\r.|!?%@'^\\"
t_NUMBER = r'[0-9][0-9XxA-Fa-f]*'
t_NAME = r'[<>A-Za-z_~][A-Za-z0-9_]*'
t_OPERATOR_DIVIDE_OVERLOAD = r'/='
t_OPEN_PAREN = r'\('
t_CLOSE_PAREN = r'\)'
t_OPEN_BRACE = r'{'
t_CLOSE_BRACE = r'}'
t_SEMI_COLON = r';'
t_COLON = r':'
t_COMMA = r','
t_PRECOMP_MACRO = r'\#.*'
t_PRECOMP_MACRO_CONT = r'.*\\\n'
def t_COMMENT_SINGLELINE(t):
r'\/\/.*\n'
global doxygenCommentCache
if t.value.startswith("///") or t.value.startswith("//!"):
if doxygenCommentCache:
doxygenCommentCache += "\n"
if t.value.endswith("\n"):
doxygenCommentCache += t.value[:-1]
else:
doxygenCommentCache += t.value
t_ASTERISK = r'\*'
t_MINUS = r'\-'
t_PLUS = r'\+'
t_DIVIDE = r'/[^/]' # fails to catch "/(" - method operator that overloads divide
t_AMPERSTAND = r'&'
t_EQUALS = r'='
t_CHAR_LITERAL = "'.'"
#found at http://wordaligned.org/articles/string-literals-and-regular-expressions
#TODO: This does not work with the string "bla \" bla"
t_STRING_LITERAL = r'"([^"\\]|\\.)*"'
#Found at http://ostermiller.org/findcomment.html
def t_COMMENT_MULTILINE(t):
r'/\*([^*]|[\r\n]|(\*+([^*/]|[\r\n])))*\*+/'
global doxygenCommentCache
if t.value.startswith("/**") or t.value.startswith("/*!"):
#not sure why, but get double new lines
v = t.value.replace("\n\n", "\n")
#strip prefixing whitespace
v = re.sub("\n[\s]+\*", "\n*", v)
doxygenCommentCache += v
def t_NEWLINE(t):
r'\n+'
t.lexer.lineno += len(t.value)
lex.lex()
debug = 0
debug_trace = 0
supportedAccessSpecifier = [
'public',
'protected',
'private'
]
enumMaintianValueFormat = False
doxygenCommentCache = ""
def is_namespace(nameStack):
"""Determines if a namespace is being specified"""
if len(nameStack) == 0:
return False
if nameStack[0] == "namespace":
return True
return False
def is_enum_namestack(nameStack):
"""Determines if a namestack is an enum namestack"""
if len(nameStack) == 0:
return False
if nameStack[0] == "enum":
return True
if len(nameStack) > 1 and nameStack[0] == "typedef" and nameStack[1] == "enum":
return True
return False
class CppClass( _CppClass ):
"""Takes a name stack and turns it into a class
Contains the following Keys:
self['name'] - Name of the class
self['doxygen'] - Doxygen comments associated with the class if they exist
self['inherits'] - List of Classes that this one inherits where the values
are of the form {"access": Anything in supportedAccessSpecifier
"class": Name of the class
self['methods'] - Dictionary where keys are from supportedAccessSpecifier
and values are a lists of CppMethod's
self['properties'] - Dictionary where keys are from supportedAccessSpecifier
and values are lists of CppVariable's
self['enums'] - Dictionary where keys are from supportedAccessSpecifier and
values are lists of CppEnum's
self['structs'] - Dictionary where keys are from supportedAccessSpecifier and
values are lists of nested Struct's
An example of how this could look is as follows:
#self =
{
'name': ""
'inherits':[]
'methods':
{
'public':[],
'protected':[],
'private':[]
},
'properties':
{
'public':[],
'protected':[],
'private':[]
},
'enums':
{
'public':[],
'protected':[],
'private':[]
}
}
"""
def show(self):
"""Convert class to a string"""
namespace_prefix = ""
if self["namespace"]: namespace_prefix = self["namespace"] + "::"
rtn = "class %s"%(namespace_prefix + self["name"])
if self['abstract']: rtn += ' (abstract)\n'
else: rtn += '\n'
if 'doxygen' in self.keys(): rtn += self["doxygen"] + '\n'
if 'parent' in self.keys() and self['parent']: rtn += 'parent class:' + self['parent'] + '\n'
if "inherits" in self.keys():
rtn += " Inherits: "
for inheritClass in self["inherits"]:
rtn += "%s %s, "%(inheritClass["access"], inheritClass["class"])
rtn += "\n"
rtn += " {\n"
for accessSpecifier in supportedAccessSpecifier:
rtn += " %s\n"%(accessSpecifier)
#Enums
if (len(self["enums"][accessSpecifier])):
rtn += " <Enums>\n"
for enum in self["enums"][accessSpecifier]:
rtn += " %s\n"%(repr(enum))
#Properties
if (len(self["properties"][accessSpecifier])):
rtn += " <Properties>\n"
for property in self["properties"][accessSpecifier]:
rtn += " %s\n"%(repr(property))
#Methods
if (len(self["methods"][accessSpecifier])):
rtn += " <Methods>\n"
for method in self["methods"][accessSpecifier]:
rtn += "\t\t" + method.show() + '\n'
rtn += " }\n"
print( rtn )
class CppMethod( _CppMethod ):
"""Takes a name stack and turns it into a method
Contains the following Keys:
self['returns'] - Return type of the method (ex. "int")
self['name'] - Name of the method (ex. "getSize")
self['doxygen'] - Doxygen comments associated with the method if they exist
self['parameters'] - List of CppVariables
"""
class CppVariable( _CppVariable ):
"""Takes a name stack and turns it into a method
Contains the following Keys:
self['type'] - Type for the variable (ex. "const string &")
self['raw_type'] - Type of variable without pointers or other markup (ex. "string")
self['name'] - Name of the variable (ex. "numItems")
self['namespace'] - Namespace containing the enum
self['desc'] - Description of the variable if part of a method (optional)
self['doxygen'] - Doxygen comments associated with the method if they exist
self['defalt'] - Default value of the variable, this key will only exist if there is a default value
"""
Vars = []
class CppEnum(_CppEnum):
"""Takes a name stack and turns it into an Enum
Contains the following Keys:
self['name'] - Name of the enum (ex. "ItemState")
self['namespace'] - Namespace containing the enum
self['values'] - List of values where the values are a dictionary of the
form {"name": name of the key (ex. "PARSING_HEADER"),
"value": Specified value of the enum, this key will only exist
if a value for a given enum value was defined
}
"""
C99_NONSTANDARD = {
'int8' : 'signed char',
'int16' : 'short int',
'int32' : 'int',
'int64' : 'int64_t', # this can be: long int (64bit), or long long int (32bit)
'uint' : 'unsigned int',
'uint8' : 'unsigned char',
'uint16' : 'unsigned short int',
'uint32' : 'unsigned int',
'uint64' : 'uint64_t', # depends on host bits
}
class CppHeader( _CppHeader ):
"""Parsed C++ class header
Variables produced:
self.classes - Dictionary of classes found in a given header file where the
key is the name of the class
"""
IGNORE_NAMES = '__extension__'.split()
def evaluate_enum_stack(self):
"""Create an Enum out of the name stack"""
newEnum = CppEnum(self.nameStack)
if len(newEnum.keys()):
if len(self.curClass):
newEnum["namespace"] = self.cur_namespace(False)
klass = self.classes[self.curClass]
klass["enums"][self.curAccessSpecifier].append(newEnum)
if self.curAccessSpecifier == 'public':
if 'name' in newEnum and newEnum['name']:
klass._public_enums[ newEnum['name'] ] = newEnum
else:
newEnum["namespace"] = self.cur_namespace(True)
self.enums.append(newEnum)
if 'name' in newEnum and newEnum['name']: self.global_enums[ newEnum['name'] ] = newEnum
#This enum has instances, turn them into properties
if newEnum.has_key("instances"):
instanceType = "enum"
if newEnum.has_key("name"):
instanceType = newEnum["name"]
for instance in newEnum["instances"]:
self.nameStack = [instanceType, instance]
self.evaluate_property_stack()
del newEnum["instances"]
def __init__(self, headerFileName, argType="file", **kwargs):
"""Create the parsed C++ header file parse tree
headerFileName - Name of the file to parse OR actual file contents (depends on argType)
argType - Indicates how to interpret headerFileName as a file string or file name
kwargs - Supports the following keywords
"enumMaintianValueFormat" - Set to true for enum values to maintain the original format ('j' will not convert to 106)
"""
## reset global state ##
global doxygenCommentCache
doxygenCommentCache = ""
CppVariable.Vars = []
CppStruct.Structs = []
if (argType == "file"):
self.headerFileName = os.path.expandvars(headerFileName)
self.mainClass = os.path.split(self.headerFileName)[1][:-2]
headerFileStr = ""
elif argType == "string":
self.headerFileName = ""
self.mainClass = "???"
headerFileStr = headerFileName
else:
raise Exception("Arg type must be either file or string")
self.curClass = ""
global enumMaintianValueFormat
if kwargs.has_key("enumMaintianValueFormat"):
enumMaintianValueFormat = kwargs["enumMaintianValueFormat"]
else:
enumMaintianValueFormat = False
# nested classes have parent::nested, but no extra namespace,
# this keeps the API compatible, TODO proper namespace for everything.
Resolver.CLASSES = {}
self.classes = Resolver.CLASSES
self.enums = []
self.global_enums = {}
self.nameStack = []
self.nameSpaces = []
self.curAccessSpecifier = 'private' # private is default
self._current_access = []
self.initextra() # harts hack
if (len(self.headerFileName)):
headerFileStr = "\n".join(open(self.headerFileName).readlines())
self.braceDepth = 0
lex.input(headerFileStr)
curLine = 0
curChar = 0
if 1: #try:
while True:
tok = lex.token()
if not tok: break
if tok.type == 'NAME' and tok.value in self.IGNORE_NAMES: continue
if tok.type not in ('PRECOMP_MACRO', 'PRECOMP_MACRO_CONT'): self.stack.append( tok.value )
curLine = tok.lineno
curChar = tok.lexpos
if tok.type in ('OPEN_BRACKET', 'CLOSE_BRACKET'): self.nameStack.append( tok.value )
elif (tok.type == 'OPEN_BRACE'):
_brace = True
if len(self.nameStack)>=2 and self.nameStack[0]=='extern' and self.nameStack[1]=='"C"':
_brace = False; print( 'extern C')
elif len(self.nameStack)>=2 and self.nameStack[0]=='extern' and self.nameStack[1]=='"C++"':
_brace = False; print( 'extern C++' )
if _brace: self.braceDepth += 1
if len(self.nameStack) >= 2 and is_namespace(self.nameStack): # namespace {} with no name used in boost, this sets default?
self.nameSpaces.append(self.nameStack[1])
ns = self.cur_namespace(); self.stack = []
if ns not in self.namespaces: self.namespaces.append( ns )
if len(self.nameStack) and not is_enum_namestack(self.nameStack):
self.evaluate_stack()
else:
self.nameStack.append(tok.value)
if self.stack and self.stack[0] == 'class': self.stack = []
#if _brace: self.braceDepth += 1
elif (tok.type == 'CLOSE_BRACE'):
if self.braceDepth == 0:
continue
if (self.braceDepth == len(self.nameSpaces)):
tmp = self.nameSpaces.pop()
self.stack = [] # clear stack when namespace ends?
if len(self.nameStack) and is_enum_namestack(self.nameStack):
self.nameStack.append(tok.value)
elif self.braceDepth < 10:
self.evaluate_stack()
else:
self.nameStack = []
self.braceDepth -= 1
if self.braceDepth < 0:
print('---------- END OF EXTERN -----------')
self.braceDepth = 0
if self.curClass and debug: print( 'CURBD', self._classes_brace_level[ self.curClass ] )
if (self.braceDepth == 0) or (self.curClass and self._classes_brace_level[self.curClass] > self.braceDepth):
if self.curClass: print( '------------END OF CLASS DEF-------------', 'braceDepth:', self.braceDepth )
if self._current_access: self._current_access.pop()
if self.curClass and self.classes[ self.curClass ]['parent']:
self.curClass = self.classes[ self.curClass ]['parent']
if self._current_access: self.curAccessSpecifier = self._current_access[-1]
else:
self.curClass = ""
self.stack = []
#if self.curStruct: self.curStruct = None
if self.braceDepth==0 or (self.curStruct and not self.curStruct['type']) or (self.curStruct and self._structs_brace_level[self.curStruct['type']] > self.braceDepth):
if self.curStruct: print( '---------END OF STRUCT DEF-------------' )
if self.curStruct and not self.curStruct['type']: self._struct_needs_name = self.curStruct
self.curStruct = None
if self._method_body and self.braceDepth < self._method_body:
self._method_body = None; self.stack = []; self.nameStack = []; print( 'FORCE CLEAR METHBODY' )
if (tok.type == 'OPEN_PAREN'):
self.nameStack.append(tok.value)
elif (tok.type == 'CLOSE_PAREN'):
self.nameStack.append(tok.value)
elif (tok.type == 'EQUALS'):
self.nameStack.append(tok.value)
elif (tok.type == 'COMMA'):
self.nameStack.append(tok.value)
elif (tok.type == 'NUMBER'):
self.nameStack.append(tok.value)
elif (tok.type == 'MINUS'):
self.nameStack.append(tok.value)
elif (tok.type == 'PLUS'):
self.nameStack.append(tok.value)
elif (tok.type == 'STRING_LITERAL'):
self.nameStack.append(tok.value)
elif (tok.type == 'NAME' or tok.type == 'AMPERSTAND' or tok.type == 'ASTERISK'):
self.nameStack.append(tok.value)
elif (tok.type == 'COLON'):
#Dont want colon to be first in stack
if len(self.nameStack) == 0:
continue
if self.nameStack and self.nameStack[-1] in supportedAccessSpecifier:
if self.curClass or self.curStruct:
cas = self.nameStack[-1]
self.curAccessSpecifier = cas; print('CURACCESS-set', cas)
if self.curClass:
if self._current_access: self._current_access[-1] = cas
else: self._current_access.append( cas )
else: print('warning - "public ::namespace"', ' '.join(self.nameStack))
self.stack = []; self.nameStack = [] # need to clear nameStack to so that nested structs can be found
else:
self.nameStack.append(tok.value)
elif (tok.type == 'SEMI_COLON'):
if (self.braceDepth < 10): self.evaluate_stack( tok.type )
if not self.stack: continue
if self.stack[0]=='typedef' and ( '{' not in self.stack or '}' in self.stack ): self.stack = []; trace_print( "REAL CLEAR")
elif self.stack[0] != 'typedef': self.stack = []; trace_print('CLEAR STACK')
#except:
# raise CppParseError("Not able to parse %s on line %d evaluating \"%s\"\nError around: %s"
# % (self.headerFileName, tok.lineno, tok.value, " ".join(self.nameStack)))
self.finalize()
def evaluate_stack(self, token=None):
"""Evaluates the current name stack"""
global doxygenCommentCache
print( "Evaluating stack %s\nBraceDepth: %s" %(self.nameStack,self.braceDepth))
print( "Evaluating stack %s\nBraceDepth: %s" %(self.stack,self.braceDepth))
if (len(self.curClass)):
if (debug): print( "%s (%s) "%(self.curClass, self.curAccessSpecifier))
#if 'typedef' in self.nameStack: self.evaluate_typedef() # allows nested typedefs, probably a bad idea
if not self.curClass and 'typedef' in self.nameStack:
print('HIT TYPEDEF', self.stack)
if token == 'SEMI_COLON' and ('{' not in self.stack or '}' in self.stack): self.evaluate_typedef()
else: return
elif (len(self.nameStack) == 0):
if (debug): print( "line ",lineno() )
if (debug): print( "(Empty Stack)" )
return
elif (self.nameStack[0] == "namespace"):
#Taken care of outside of here
pass
elif len(self.nameStack) >= 2 and self.nameStack[0] == 'using' and self.nameStack[1] == 'namespace': pass # TODO
elif is_enum_namestack(self.nameStack):
if (debug): print( "line ",lineno() )
self.evaluate_enum_stack()
elif self._method_body and self.braceDepth >= self._method_body:
#print( 'INSIDE METHOD DEF', self.nameStack )
self.stack = []
#elif is_method_namestack(self.stack) and '(' in self.nameStack: # this fails on "operator /(..."
elif ')' in self.nameStack and is_method_namestack(self.stack):
#print( 'eval method', self.nameStack )
self.evaluate_method_stack()
self.stack = []
elif len(self.nameStack) >= 2 and (self.nameStack[0]=='friend' and self.nameStack[1]=='class'): pass
elif ('class' in self.nameStack or 'struct' in self.nameStack) and self.stack[-1] == ';': self.evaluate_forward_decl()
elif (self.nameStack[0] == "class") or (self.nameStack[0]=='template' and 'class' in self.nameStack):
#print('^^^^^^^^^^^^^^^^^^^^')
self.evaluate_class_stack()
elif (self.nameStack[0] == "struct") or (len(self.nameStack)>3 and self.stack[-1]=='{' and self.nameStack[-3]=='struct'):
print( '------------new struct-----------' )
self.evaluate_struct_stack()
self.stack = []
elif self.nameStack[0]=='template' and self.stack[-1]=='{' and 'struct' in self.nameStack:
print( '------------new struct - unsafe?' )
self.evaluate_struct_stack()
self.stack = []
elif '(' not in self.nameStack and ')' not in self.nameStack and self.stack[-1] == ';': # catching all props?
self.evaluate_property_stack()
elif not self.curClass:
if (debug): print( "line ",lineno() )
if is_enum_namestack(self.nameStack): self.evaluate_enum_stack()
elif self.curStruct and self.stack[-1] == ';': self.evaluate_property_stack() # this catches fields of global structs
self.nameStack = []
doxygenCommentCache = ""
return
elif (self.braceDepth < 1):
if (debug): print( "line ",lineno() )
#Ignore global stuff for now
if (debug): print( "Global stuff: ", self.nameStack )
self.nameStack = []
self._method_body = None
doxygenCommentCache = ""
return
elif (self.braceDepth > len(self.nameSpaces) + 1):
if (debug): print( "line ",lineno() )
self.nameStack = []
doxygenCommentCache = ""
return
self.nameStack = [] # some if/else above return and others not, so this may or may not be reset
doxygenCommentCache = ""
| [
2,
48443,
14629,
14,
8800,
14,
29412,
198,
2,
198,
2,
6434,
25,
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1077,
6413,
371,
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1012,
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959,
357,
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2884,
2723,
30293,
20579,
25,
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8,
198,
2,
198,
2,
15069,
357,
34,
8,
3050,
11,
449,
1077... | 2.164682 | 11,926 |
from csv_cti.blueprints.web_api import web_api
from flask import request,current_app,render_template
from csv_cti.blueprints.op.md5_token import encrypt_md5
from csv_cti.blueprints.op.tiers import Tiers_op
#tiers
@web_api.route('/tiers-add/',methods=['POST'])
@web_api.route('/tiers-rm/',methods=['POST'])
@web_api.route('/tiers-list/',methods=['POST'])
@web_api.route('/tiers-test/',methods=['GET']) | [
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17585,
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13,
404,
13... | 2.404762 | 168 |
x = lst(1,2,3,4,5,6,7,8,9,10,11,12)
print x
x.move_even_to_end()
print x
x.reverse()
print x
| [
220,
220,
220,
220,
220,
220,
220,
220,
198,
87,
796,
300,
301,
7,
16,
11,
17,
11,
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11,
19,
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20,
11,
21,
11,
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11,
1065,
8,
198,
4798,
2124,
198,
87,
13,
21084,
62,
10197,
62,
1... | 1.59375 | 64 |
import argparse
import pandas as pd
import numpy as np
import os
# from os.path import join
import sys
import logging
# import joblib
from sklearn.externals import joblib
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor
from sklearn import metrics
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler(sys.stdout))
if 'SAGEMAKER_METRICS_DIRECTORY' in os.environ:
log_file_handler = logging.FileHandler(os.path.join(os.environ['SAGEMAKER_METRICS_DIRECTORY'],
"metrics.json"))
log_file_handler.setFormatter(
"{'time':'%(asctime)s', 'name': '%(name)s', \
'level': '%(levelname)s', 'message': '%(message)s'}"
)
logger.addHandler(log_file_handler)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Hyperparameters are described here. In this simple example we are just including one hyperparameter.
parser.add_argument('--model_name', type=str, default='decision_tree')
parser.add_argument('--n_estimators', type=int, default=10)
parser.add_argument('--max_features', type=float, default=0.5)
parser.add_argument('--max_depth', type=int, default=4)
parser.add_argument('--criterion', type=str, default='mse')
# Sagemaker specific arguments. Defaults are set in the environment variables.
parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
parser.add_argument('--validation', type=str, default=os.environ.get('SM_CHANNEL_VALIDATION'))
args = parser.parse_args()
logger.info("Get train data loader")
train_data = pd.read_csv('{}/final_train.csv'.format(args.train), engine="python")
logger.info("Get valdation data loader")
validation_data = pd.read_csv('{}/final_validate.csv'.format(args.validation), engine="python")
train_x = train_data.drop('unit_sales', axis=1)
train_y = train_data['unit_sales']
model_name = args.model_name
n_estimators = args.n_estimators
max_features = args.max_features
max_depth = args.max_depth
criterion = args.criterion
if (model_name == 'random_forest'):
clf = RandomForestRegressor(random_state=None, n_estimators=n_estimators, max_features=max_features)
elif (model_name == 'adaboost'):
clf = AdaBoostRegressor(random_state=None, n_estimators=n_estimators)
elif (model_name == 'gradient_boosting'):
clf = GradientBoostingRegressor(random_state=None, n_estimators=n_estimators, max_depth=max_depth)
elif (model_name == 'decision_tree'):
clf = DecisionTreeRegressor(random_state=None, criterion=criterion)
else:
logger.debug("Invalid model name")
logger.debug("Training starts")
clf = clf.fit(train_x, train_y)
logger.debug("Training done")
# Save the model
joblib.dump(clf, os.path.join(args.model_dir, "model.joblib"))
logger.debug("Model written in model_dir")
logger.debug("Making prediction on validation data")
validation_predictions = make_predictions(clf, validation_data)
logger.info('nwrmsle: {:.4f};\n'.format(eval_nwrmsle(validation_predictions,
validation_data['unit_sales'].values,
validation_data['perishable'].values)))
logger.info('r2_score: {:.4f};\n'.format(metrics.r2_score(y_true=validation_data['unit_sales'].values,
y_pred=validation_predictions))) | [
11748,
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2,
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6738,
... | 2.389841 | 1,634 |
from discretize.utils import (
exampleLrmGrid, meshTensor, closestPoints, ExtractCoreMesh
)
| [
6738,
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1096,
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11,
29677,
14055,
37031,
198,
8,
198
] | 3.2 | 30 |
import asyncio
import pytest
from typing import List
from tests.setup_nodes import setup_full_system
from src.util.ints import uint32
from src.types.full_block import FullBlock
from src.util.make_test_constants import make_test_constants_with_genesis
from tests.time_out_assert import time_out_assert, time_out_assert_custom_interval
test_constants, bt = make_test_constants_with_genesis(
{
"DIFFICULTY_STARTING": 1000,
"MIN_ITERS_STARTING": 100000,
"NUMBER_ZERO_BITS_CHALLENGE_SIG": 1,
}
)
@pytest.fixture(scope="module")
| [
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19199... | 2.585253 | 217 |
from __future__ import annotations
from typing import Type, Union
from enum import Enum
import inspect
from ..base_schema import Schema
from ..dom import DOMElement
from ..base_schema import SchemaPrimitive, SchemaEnum
def resolve_arg_to_schema(arg: Union[Type, Schema]) -> Schema:
"""
Resolve an argument of heterogeneous type to a `Schema` instance.
:param arg: Argument to resolve to a Schema. Must be one of:
A primitive Python type (str, int, bool, float)
A subclass of `UserObject`
An instance of `Schema`.
:return: A `Schema` instance corresponding to the supplied argument.
"""
if inspect.isclass(arg):
if issubclass(arg, DOMElement):
return arg.__json_schema__()
if issubclass(arg, Enum):
return SchemaEnum(arg)
else:
return SchemaPrimitive(arg)
elif isinstance(arg, Schema):
return arg
else:
raise TypeError(f"Unexpected object type: {type(arg)}")
| [
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... | 2.491484 | 411 |
##############################
# support query serve for front web system
# filename:query.py
# author: liwei
# StuID: 1711350
# date: 2019.12.1
##############################
#查询构建
from whoosh import highlight
from whoosh import qparser
from whoosh import index
from flask import Flask
from flask import request
from flask import jsonify,render_template,abort, redirect, url_for,session, escape,Markup
from flask_cors import *
import re
import logging
from numpy import std
from data import xy_dict
from data import get_html,get_teacher_info,pagerank
# from audio import *
app = Flask(__name__)
CORS(app,supports_credentials=True) # 解决跨域请求无响应问题
app.secret_key=b'\xfa\n\x08\xb9\x84I\xe5xRdE\xea\x9f\xba\xce\x81'
mysession =dict() # 自定义的session用来传输数据
url_dict,scores = pagerank(get_teacher_info()) # 获取pageranke计算结果，返回链接映射和排名得分
# 定义日志记录文件的配置
LOG_FORMAT = "%(asctime)s - %(levelname)s - %(message)s"
DATE_FORMAT = "%m/%d/%Y %H:%M:%S %p"
logging.basicConfig(filename='my.log', level=logging.DEBUG, format=LOG_FORMAT, datefmt=DATE_FORMAT)
ix = index.open_dir("index") #打开该目录一遍存储索引文件
# 网页快照路由
@app.route('/snapshots/<xueyuan>/<filename>',methods=["GET"])
# 主页路由
@app.route('/',methods=["GET"])
# 结果展示页面路由
@app.route('/display/',methods=["GET","POST"])
# 结果展示get请求页面响应
@app.route('/display/<count>&<query>')
# # 实现语音输入查询
# @app.route('/audio',methods=['GET','POST'])
# def audio_query():
# assert request.path == '/audio'
# # 通过语音识别API获取查询输入
# get_audio(in_path)
# # 测试代码
# filename = "./speechs/input.wav"
# signal = open(filename, "rb").read()
# rate = 16000
# token = get_token()
# msg = recognize(signal, rate, token)
# query_sentence = " "
# if "err_no" in dict(msg).keys():
# logging.warning("%d,没有获取有效语音输入！错误消息%s 错误代码%d" %( 404,msg["err_msg"],msg["err_no"]))
# return "%d,没有获取有效语音输入！错误消息%s 错误代码%d" %( 404,msg["err_msg"],msg["err_no"]), 404
# else:
# query_sentence = msg['result']
# # 记录日志
# logging.info("Audio Query sentence: %s" % query_sentence)
# res = []
# with ix.searcher() as searcher:
# # 对输入的查询文本进行解析，如果存在按域查询的需求则区分按域查询，默认采用多属性查询模式
# # mark 表示是否需要高亮学院查询区域，默认情况下需要
# highlight_xy = True
# # 默认的多域查询
# query = qparser.MultifieldParser(["content", "title", "mtext", "xueyuan"], ix.schema)
# if query_sentence.endswith("$姓名$"):
# # 按名字查询
# query = qparser.SimpleParser("title", ix.schema)
# query_sentence = query_sentence.strip('$姓名$')
# elif query_sentence.endswith("$学院$"):
# # 按学院查询
# query = qparser.SimpleParser("xueyuan", ix.schema)
# query_sentence = query_sentence.strip('$学院$')
#
# elif query_sentence.endswith("$网页$"):
# # 按网页内容查询
# query = qparser.SimpleParser("content", ix.schema)
# query_sentence = query_sentence.strip('$网页$')
#
# # print(query_sentence)
# # 引入查询解析器插件
# query.add_plugin(qparser.WildcardPlugin)
#
# # query.remove_plugin_class(qparser.WildcardPlugin)
# query.add_plugin(qparser.PrefixPlugin())
# query.add_plugin(qparser.OperatorsPlugin)
# query.add_plugin(qparser.RegexPlugin)
# query.add_plugin(qparser.PhrasePlugin)
#
# # 解析得到查询器
# q = query.parse(query_sentence)
# logging.info("Query parse result: %s" % str(q))
# print(q)
# # 获取查询结果
# result = searcher.search(q, limit=20)
# # print(result)
# # 设置碎片的属性
# # Allow larger fragments
# my_cf = highlight.ContextFragmenter(maxchars=200, surround=30)
# hf = highlight.HtmlFormatter(tagname='em', classname='match', termclass='term')
#
# hi = highlight.Highlighter(fragmenter=my_cf, formatter=hf)
# for hit in result:
# print(hit["picpath"])
# print(hit["title"])
# print(escape(hi.highlight_hit(hit, "content")))
# if hit['picpath'] == '#':
# if highlight_xy:
# res.append({"title": hit['title'],
# "xueyuan": Markup(hi.highlight_hit(hit, "xueyuan")),
# "url": hit["url"],
# 'shotpath': hit['shotpath'],
# "content": Markup(hi.highlight_hit(hit, "content")),
# "parenturl": hit["parenturl"],
# "picpath": '#',
# "pagerank": scores[url_dict[hit["url"]]]
# })
# else:
# res.append({"title": hit['title'],
# "xueyuan": hit["xueyuan"],
# "url": hit["url"],
# 'shotpath': hit['shotpath'],
# "content": Markup(hi.highlight_hit(hit, "content")),
# "parenturl": hit["parenturl"],
# "picpath": '#',
# "pagerank": scores[url_dict[hit["url"]]]
# })
# else:
# if highlight_xy:
# res.append({"title": hit['title'],
# "xueyuan": Markup(hi.highlight_hit(hit, "xueyuan")),
# "url": hit["url"],
# 'shotpath': hit['shotpath'],
# "content": Markup(hi.highlight_hit(hit, "content")),
# "parenturl": hit["parenturl"],
# "picpath": "images/%s/%s" % (
# hit['picpath'].split('/')[-3], hit['picpath'].split('/')[-1]),
# "pagerank": scores[url_dict[hit["url"]]]
# })
# else:
# res.append({"title": hit['title'],
# "xueyuan": hit["xueyuan"],
# "url": hit["url"],
# 'shotpath': hit['shotpath'],
# "content": Markup(hi.highlight_hit(hit, "content")),
# "parenturl": hit["parenturl"],
# "picpath": "images/%s/%s" % (
# hit['picpath'].split('/')[-3], hit['picpath'].split('/')[-1]),
# "pagerank": scores[url_dict[hit["url"]]]
# })
# print(len(result))
# print(res)
# count = len(result)
#
# if count == 0:
# logging.warning("%d,没有查询到相关内容！" % 404)
# return "没有查询到相关内容！", 404
# else:
# # 记录查询日志
# log = "Response: "
# for item in res:
# log = log + " (name:%s,url:%s) " % (item["title"], item["url"])
# logging.info(log)
#
# # # 基于page rank 对链接进行排序
# # res.sort(key=lambda k:(k.get("pagerank",0)),reverse = True)
# # print(res)
#
# mysession["data"] = res # 使用会话session传递参数
# return jsonify({"url": "/display/%d&%s" % (count, query_sentence)})
# 基本查询函数，实现前缀、通配、正则匹配，短语、关系运算查询功能
# 基于whoosh的highlighter实现返回高亮查询词块
@app.route('/index',methods=['GET','POST'])
if __name__ == '__main__':
app.run(debug=False,use_reloader=False)
| [
14468,
7804,
4242,
2235,
198,
2,
1104,
12405,
4691,
329,
2166,
3992,
1080,
198,
2,
29472,
25,
22766,
13,
9078,
198,
2,
1772,
25,
220,
7649,
42990,
198,
2,
520,
84,
2389,
25,
220,
220,
1596,
1157,
14877,
198,
2,
3128,
25,
220,
220,... | 1.531307 | 4,903 |
"""
Make time series plots.
"""
import galene as ga
import datetime
data_id_list = [
'cmems-nrt',
'run001',
'run002',
]
var_list = ['slev', 'temp']
start_time = datetime.datetime(2016, 6, 1)
end_time = datetime.datetime(2018, 7, 1)
for var in var_list:
dataset_list = []
for data_id in data_id_list:
d = ga.read_dataset(data_id, 'timeseries', var)
dataset_list.append(d)
# find pairs
pairs = ga.find_station_pairs(*dataset_list)
for key in pairs:
try:
cube_list = []
for data_id in data_id_list:
if data_id in pairs[key]:
cube = pairs[key][data_id]
cube_list.append(cube)
data_id_str = '-'.join(data_id_list)
datatype = 'timeseries'
outdir = os.path.join('plots', data_id_str, datatype, var)
ga.save_timeseries_figure(
cube_list, output_dir=outdir, alpha=0.7, start_time=start_time,
end_time=end_time, time_extent='intersection'
)
except Exception:
pass
| [
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12,
77,
17034,
3256,
198,
220,
220,
... | 1.948944 | 568 |
import numpy as np
import copy
from . import cv
#TODO need to refactaring | [
11748,
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355,
45941,
198,
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4866,
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764,
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198,
2,
51,
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] | 2.884615 | 26 |
"""
three-layers logistic regression model for not-MNIST dataset
Got ~ 87% accuracy.
not-MNIST: http://yaroslavvb. blogspot. it/2011/09/notmnist-dataset.html
author: ANDY (andy929910266@gmail.com)
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import numpy as np
import tensorflow as tf
import time
import utils
# Define paramaters for the model
learning_rate = 0.01
batch_size = 128
n_epochs = 50
n_train = 60000
n_test = 10000
# Step 1: Read in data
not_mnist_folder = "../../examples/data/not-mnist"
# I download the data manually and save into respective non-mnist_folder
# use utils.download_mnist(not_mnist_folder)
train, val, test = utils.read_mnist(not_mnist_folder, flatten=True)
# Step 2: Create datasets and iterator
# create training Dataset and batch it
train_data = tf.data.Dataset.from_tensor_slices(train)
train_data = train_data.shuffle(10000) # if you want to shuffle your data
train_data = train_data.batch(batch_size)
# create testing Dataset and batch it
test_data = tf.data.Dataset.from_tensor_slices(test)
test_data = test_data.batch(batch_size)
# create one iterator and initialize it with different datasets
iterator = tf.data.Iterator.from_structure(train_data.output_types,
train_data.output_shapes)
img, label = iterator.get_next()
train_init = iterator.make_initializer(train_data) # initializer for train_data
test_init = iterator.make_initializer(test_data) # initializer for train_data
# Step 3: create weights and bias
# weights are initialized to random variables with mean of 0, stddev of 0.01
# biases are initialized to 0
# shape of w1 --> (784,256)
# shape of b1 --> (1,256)
# shape of w2 --> (256,128)
# shape of b2 --> (1,128)
# shape of w3 --> (128,10)
# shape of b3 --> (1,10)
w1 = tf.get_variable(name="weight1", shape=[784, 256], initializer=tf.random_normal_initializer())
b1 = tf.get_variable(name="bias1", shape=[1, 256], initializer=tf.zeros_initializer())
w2 = tf.get_variable(name="weight2", shape=[256, 128], initializer=tf.random_normal_initializer())
b2 = tf.get_variable(name="bias2", shape=[1, 128], initializer=tf.zeros_initializer())
w3 = tf.get_variable(name="weight3", shape=[128, 10], initializer=tf.random_normal_initializer())
b3 = tf.get_variable(name="bias3", shape=[1, 10], initializer=tf.zeros_initializer())
# Step 4: build model
# the model that returns the logits.
# this logits will be later passed through softmax layer
hidden_layer1 = tf.matmul(img, w1) + b1
hidden_layer2 = tf.matmul(hidden_layer1, w2) + b2
logits = tf.matmul(hidden_layer2, w3) + b3
# Step 5: define loss function
# use cross entropy of softmax of logits as the loss function
entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=label, logits=logits, name="entopy")
loss = tf.reduce_mean(entropy, name="loss")
# Step 6: define optimizer
# using Adamn Optimizer with pre-defined learning rate to minimize loss
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
# Step 7: calculate accuracy with test set
preds = tf.nn.softmax(logits)
correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(label, 1))
accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32))
writer = tf.summary.FileWriter('../../examples/graphs/not-mnist', tf.get_default_graph())
with tf.Session() as sess:
start_time = time.time()
sess.run(tf.global_variables_initializer())
# train the model n_epochs times
for i in range(n_epochs):
sess.run(train_init) # drawing samples from train_data
total_loss = 0
n_batches = 0
try:
while True:
_, l = sess.run([optimizer, loss])
total_loss += l
n_batches += 1
except tf.errors.OutOfRangeError:
pass
print('Average loss epoch {0}: {1}'.format(i, total_loss / n_batches))
print('Total time: {0} seconds'.format(time.time() - start_time))
# test the model
sess.run(test_init) # drawing samples from test_data
total_correct_preds = 0
try:
while True:
accuracy_batch = sess.run(accuracy)
total_correct_preds += accuracy_batch
except tf.errors.OutOfRangeError:
pass
print('Accuracy {0}'.format(total_correct_preds / n_test))
writer.close()
| [
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13... | 2.751333 | 1,500 |
import click
from collections import defaultdict
import importlib
import logging
import redis
import structlog
from .redis_scripts import RedisScripts
from ._internal import *
from .exceptions import *
from .retry import *
from .schedule import *
from .task import Task
from .worker import Worker
__all__ = ['TaskTiger', 'Worker', 'Task',
# Exceptions
'JobTimeoutException', 'RetryException', 'StopRetry',
'TaskImportError', 'TaskNotFound',
# Retry methods
'fixed', 'linear', 'exponential',
# Schedules
'periodic',
]
"""
Redis keys:
Set of all queues that contain items in the given state.
SET <prefix>:queued
SET <prefix>:active
SET <prefix>:error
SET <prefix>:scheduled
Serialized task for the given task ID.
STRING <prefix>:task:<task_id>
List of (failed) task executions
LIST <prefix>:task:<task_id>:executions
Task IDs waiting in the given queue to be processed, scored by the time the
task was queued.
ZSET <prefix>:queued:<queue>
Task IDs being processed in the specific queue, scored by the time processing
started.
ZSET <prefix>:active:<queue>
Task IDs that failed, scored by the time processing failed.
ZSET <prefix>:error:<queue>
Task IDs that are scheduled to be executed at a specific time, scored by the
time they should be executed.
ZSET <prefix>:scheduled:<queue>
Channel that receives the queue name as a message whenever a task is queued.
CHANNEL <prefix>:activity
Task locks
STRING <prefix>:lock:<lock_hash>
Queue periodic tasks lock
STRING <prefix>:queue_periodic_tasks_lock
"""
@click.command()
@click.option('-q', '--queues', help='If specified, only the given queue(s) '
'are processed. Multiple queues can be '
'separated by comma.')
@click.option('-m', '--module', help="Module(s) to import when launching the "
"worker. This improves task performance "
"since the module doesn't have to be "
"reimported every time a task is forked. "
"Multiple modules can be separated by "
"comma.")
@click.option('-e', '--exclude-queues', help='If specified, exclude the given '
'queue(s) from processing. '
'Multiple queues can be '
'separated by comma.')
@click.option('-h', '--host', help='Redis server hostname')
@click.option('-p', '--port', help='Redis server port')
@click.option('-a', '--password', help='Redis password')
@click.option('-n', '--db', help='Redis database number')
@click.pass_context
if __name__ == '__main__':
run_worker()
| [
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271,
62,
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1330,
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271,
7391,
82,
198,
198,
6738,
47540,
3253... | 2.405542 | 1,191 |
# auxiliary
from MDAOfabric.accessories import *
# solvers
from MDAOfabric.solvers import *
| [
2,
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# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'AnalysisPlotWidgetTemplate.ui'
#
# Created: Mon Aug 16 15:31:49 2010
# by: PyQt4 UI code generator 4.5.4
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore, QtGui
from acq4.pyqtgraph.PlotWidget import PlotWidget
| [
2,
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25,
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69,
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23,
532,
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2,
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7560,
422,
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72,
2393,
705,
32750,
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30800,
13,
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6,
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2,
198,
2,
15622,
25,
2892,
2447,
1467,
1315,
25,
3132,
25,
29... | 2.836207 | 116 |
r"""
Balanced Incomplete Block Designs (BIBD)
This module gathers everything related to Balanced Incomplete Block Designs. One can build a
BIBD (or check that it can be built) with :func:`balanced_incomplete_block_design`::
sage: BIBD = designs.balanced_incomplete_block_design(7,3)
In particular, Sage can build a `(v,k,1)`-BIBD when one exists for all `k\leq
5`. The following functions are available:
.. csv-table::
:class: contentstable
:widths: 30, 70
:delim: |
:func:`balanced_incomplete_block_design` | Return a BIBD of parameters `v,k`.
:func:`BIBD_from_TD` | Return a BIBD through TD-based constructions.
:func:`BIBD_from_difference_family` | Return the BIBD associated to the difference family ``D`` on the group ``G``.
:func:`BIBD_from_PBD` | Return a `(v,k,1)`-BIBD from a `(r,K)`-PBD where `r=(v-1)/(k-1)`.
:func:`PBD_from_TD` | Return a `(kt,\{k,t\})`-PBD if `u=0` and a `(kt+u,\{k,k+1,t,u\})`-PBD otherwise.
:func:`steiner_triple_system` | Return a Steiner Triple System.
:func:`v_5_1_BIBD` | Return a `(v,5,1)`-BIBD.
:func:`v_4_1_BIBD` | Return a `(v,4,1)`-BIBD.
:func:`PBD_4_5_8_9_12` | Return a `(v,\{4,5,8,9,12\})`-PBD on `v` elements.
:func:`BIBD_5q_5_for_q_prime_power` | Return a `(5q,5,1)`-BIBD with `q\equiv 1\pmod 4` a prime power.
**Construction of BIBD when** `k=4`
Decompositions of `K_v` into `K_4` (i.e. `(v,4,1)`-BIBD) are built following
Douglas Stinson's construction as presented in [Stinson2004]_ page 167. It is
based upon the construction of `(v\{4,5,8,9,12\})`-PBD (see the doc of
:func:`PBD_4_5_8_9_12`), knowing that a `(v\{4,5,8,9,12\})`-PBD on `v` points
can always be transformed into a `((k-1)v+1,4,1)`-BIBD, which covers all
possible cases of `(v,4,1)`-BIBD.
**Construction of BIBD when** `k=5`
Decompositions of `K_v` into `K_4` (i.e. `(v,4,1)`-BIBD) are built following
Clayton Smith's construction [ClaytonSmith]_.
.. [ClaytonSmith] On the existence of `(v,5,1)`-BIBD.
http://www.argilo.net/files/bibd.pdf
Clayton Smith
Functions
---------
"""
from sage.categories.sets_cat import EmptySetError
from sage.misc.unknown import Unknown
from design_catalog import transversal_design
from block_design import BlockDesign
from sage.rings.arith import binomial, is_prime_power
from group_divisible_designs import GroupDivisibleDesign
from designs_pyx import is_pairwise_balanced_design
def balanced_incomplete_block_design(v, k, existence=False, use_LJCR=False):
r"""
Return a BIBD of parameters `v,k`.
A Balanced Incomplete Block Design of parameters `v,k` is a collection
`\mathcal C` of `k`-subsets of `V=\{0,\dots,v-1\}` such that for any two
distinct elements `x,y\in V` there is a unique element `S\in \mathcal C`
such that `x,y\in S`.
More general definitions sometimes involve a `\lambda` parameter, and we
assume here that `\lambda=1`.
For more information on BIBD, see the
:wikipedia:`corresponding Wikipedia entry <Block_design#Definition_of_a_BIBD_.28or_2-design.29>`.
INPUT:
- ``v,k`` (integers)
- ``existence`` (boolean) -- instead of building the design, return:
- ``True`` -- meaning that Sage knows how to build the design
- ``Unknown`` -- meaning that Sage does not know how to build the
design, but that the design may exist (see :mod:`sage.misc.unknown`).
- ``False`` -- meaning that the design does not exist.
- ``use_LJCR`` (boolean) -- whether to query the La Jolla Covering
Repository for the design when Sage does not know how to build it (see
:func:`~sage.combinat.designs.covering_design.best_known_covering_design_www`). This
requires internet.
.. SEEALSO::
* :func:`steiner_triple_system`
* :func:`v_4_1_BIBD`
* :func:`v_5_1_BIBD`
TODO:
* Implement other constructions from the Handbook of Combinatorial
Designs.
EXAMPLES::
sage: designs.balanced_incomplete_block_design(7, 3).blocks()
[[0, 1, 3], [0, 2, 4], [0, 5, 6], [1, 2, 6], [1, 4, 5], [2, 3, 5], [3, 4, 6]]
sage: B = designs.balanced_incomplete_block_design(66, 6, use_LJCR=True) # optional - internet
sage: B # optional - internet
Incidence structure with 66 points and 143 blocks
sage: B.blocks() # optional - internet
[[0, 1, 2, 3, 4, 65], [0, 5, 24, 25, 39, 57], [0, 6, 27, 38, 44, 55], ...
sage: designs.balanced_incomplete_block_design(66, 6, use_LJCR=True) # optional - internet
Incidence structure with 66 points and 143 blocks
sage: designs.balanced_incomplete_block_design(216, 6)
Traceback (most recent call last):
...
NotImplementedError: I don't know how to build a (216,6,1)-BIBD!
TESTS::
sage: designs.balanced_incomplete_block_design(85,5,existence=True)
True
sage: _ = designs.balanced_incomplete_block_design(85,5)
A BIBD from a Finite Projective Plane::
sage: _ = designs.balanced_incomplete_block_design(21,5)
Some trivial BIBD::
sage: designs.balanced_incomplete_block_design(10,10)
(10,10,1)-Balanced Incomplete Block Design
sage: designs.balanced_incomplete_block_design(1,10)
(1,0,1)-Balanced Incomplete Block Design
Existence of BIBD with `k=3,4,5`::
sage: [v for v in xrange(50) if designs.balanced_incomplete_block_design(v,3,existence=True)]
[1, 3, 7, 9, 13, 15, 19, 21, 25, 27, 31, 33, 37, 39, 43, 45, 49]
sage: [v for v in xrange(100) if designs.balanced_incomplete_block_design(v,4,existence=True)]
[1, 4, 13, 16, 25, 28, 37, 40, 49, 52, 61, 64, 73, 76, 85, 88, 97]
sage: [v for v in xrange(150) if designs.balanced_incomplete_block_design(v,5,existence=True)]
[1, 5, 21, 25, 41, 45, 61, 65, 81, 85, 101, 105, 121, 125, 141, 145]
For `k > 5` there are currently very few constructions::
sage: [v for v in xrange(300) if designs.balanced_incomplete_block_design(v,6,existence=True) is True]
[1, 6, 31, 66, 76, 91, 96, 106, 111, 121, 126, 136, 141, 151, 156, 171, 181, 186, 196, 201, 211, 241, 271]
sage: [v for v in xrange(300) if designs.balanced_incomplete_block_design(v,6,existence=True) is Unknown]
[51, 61, 81, 166, 216, 226, 231, 246, 256, 261, 276, 286, 291]
Here are some constructions with `k \geq 7` and `v` a prime power::
sage: designs.balanced_incomplete_block_design(169,7)
(169,7,1)-Balanced Incomplete Block Design
sage: designs.balanced_incomplete_block_design(617,8)
(617,8,1)-Balanced Incomplete Block Design
sage: designs.balanced_incomplete_block_design(433,9)
(433,9,1)-Balanced Incomplete Block Design
sage: designs.balanced_incomplete_block_design(1171,10)
(1171,10,1)-Balanced Incomplete Block Design
And we know some inexistence results::
sage: designs.balanced_incomplete_block_design(21,6,existence=True)
False
"""
lmbd = 1
# Trivial BIBD
if v == 1:
if existence:
return True
return BalancedIncompleteBlockDesign(v, [], check=False)
if k == v:
if existence:
return True
return BalancedIncompleteBlockDesign(v, [range(v)], check=False, copy=False)
# Non-existence of BIBD
if (v < k or
k < 2 or
(v-1) % (k-1) != 0 or
(v*(v-1)) % (k*(k-1)) != 0 or
# From the Handbook of combinatorial designs:
#
# With lambda>1 other exceptions are
# (15,5,2),(21,6,2),(22,7,2),(22,8,4).
(k==6 and v in [36,46]) or
(k==7 and v == 43) or
# Fisher's inequality
(v*(v-1))/(k*(k-1)) < v):
if existence:
return False
raise EmptySetError("There exists no ({},{},{})-BIBD".format(v,k,lmbd))
if k == 2:
if existence:
return True
from itertools import combinations
return BalancedIncompleteBlockDesign(v, combinations(range(v),2), check=False, copy=True)
if k == 3:
if existence:
return v%6 == 1 or v%6 == 3
return steiner_triple_system(v)
if k == 4:
if existence:
return v%12 == 1 or v%12 == 4
return BalancedIncompleteBlockDesign(v, v_4_1_BIBD(v), copy=False)
if k == 5:
if existence:
return v%20 == 1 or v%20 == 5
return BalancedIncompleteBlockDesign(v, v_5_1_BIBD(v), copy=False)
from difference_family import difference_family
from database import BIBD_constructions
if (v,k,1) in BIBD_constructions:
if existence:
return True
return BlockDesign(v,BIBD_constructions[(v,k,1)](), copy=False)
if BIBD_from_arc_in_desarguesian_projective_plane(v,k,existence=True):
if existence:
return True
B = BIBD_from_arc_in_desarguesian_projective_plane(v,k)
return BalancedIncompleteBlockDesign(v, B, copy=False)
if BIBD_from_TD(v,k,existence=True):
if existence:
return True
return BalancedIncompleteBlockDesign(v, BIBD_from_TD(v,k), copy=False)
if v == (k-1)**2+k and is_prime_power(k-1):
if existence:
return True
from block_design import projective_plane
return BalancedIncompleteBlockDesign(v, projective_plane(k-1),copy=False)
if difference_family(v,k,existence=True):
if existence:
return True
G,D = difference_family(v,k)
return BalancedIncompleteBlockDesign(v, BIBD_from_difference_family(G,D,check=False), copy=False)
if use_LJCR:
from covering_design import best_known_covering_design_www
B = best_known_covering_design_www(v,k,2)
# Is it a BIBD or just a good covering ?
expected_n_of_blocks = binomial(v,2)/binomial(k,2)
if B.low_bd() > expected_n_of_blocks:
if existence:
return False
raise EmptySetError("There exists no ({},{},{})-BIBD".format(v,k,lmbd))
B = B.incidence_structure()
if B.num_blocks() == expected_n_of_blocks:
if existence:
return True
else:
return B
if existence:
return Unknown
else:
raise NotImplementedError("I don't know how to build a ({},{},1)-BIBD!".format(v,k))
def steiner_triple_system(n):
r"""
Return a Steiner Triple System
A Steiner Triple System (STS) of a set `\{0,...,n-1\}`
is a family `S` of 3-sets such that for any `i \not = j`
there exists exactly one set of `S` in which they are
both contained.
It can alternatively be thought of as a factorization of
the complete graph `K_n` with triangles.
A Steiner Triple System of a `n`-set exists if and only if
`n \equiv 1 \pmod 6` or `n \equiv 3 \pmod 6`, in which case
one can be found through Bose's and Skolem's constructions,
respectively [AndHonk97]_.
INPUT:
- ``n`` return a Steiner Triple System of `\{0,...,n-1\}`
EXAMPLE:
A Steiner Triple System on `9` elements ::
sage: sts = designs.steiner_triple_system(9)
sage: sts
(9,3,1)-Balanced Incomplete Block Design
sage: list(sts)
[[0, 1, 5], [0, 2, 4], [0, 3, 6], [0, 7, 8], [1, 2, 3],
[1, 4, 7], [1, 6, 8], [2, 5, 8], [2, 6, 7], [3, 4, 8],
[3, 5, 7], [4, 5, 6]]
As any pair of vertices is covered once, its parameters are ::
sage: sts.is_t_design(return_parameters=True)
(True, (2, 9, 3, 1))
An exception is raised for invalid values of ``n`` ::
sage: designs.steiner_triple_system(10)
Traceback (most recent call last):
...
EmptySetError: Steiner triple systems only exist for n = 1 mod 6 or n = 3 mod 6
REFERENCE:
.. [AndHonk97] A short course in Combinatorial Designs,
Ian Anderson, Iiro Honkala,
Internet Editions, Spring 1997,
http://www.utu.fi/~honkala/designs.ps
"""
name = "Steiner Triple System on "+str(n)+" elements"
if n%6 == 3:
t = (n-3) // 6
Z = range(2*t+1)
T = lambda x_y : x_y[0] + (2*t+1)*x_y[1]
sts = [[(i,0),(i,1),(i,2)] for i in Z] + \
[[(i,k),(j,k),(((t+1)*(i+j)) % (2*t+1),(k+1)%3)] for k in range(3) for i in Z for j in Z if i != j]
elif n%6 == 1:
t = (n-1) // 6
N = range(2*t)
T = lambda x_y : x_y[0]+x_y[1]*t*2 if x_y != (-1,-1) else n-1
L1 = lambda i,j : (i+j) % ((n-1)//3)
L = lambda i,j : L1(i,j)//2 if L1(i,j)%2 == 0 else t+(L1(i,j)-1)//2
sts = [[(i,0),(i,1),(i,2)] for i in range(t)] + \
[[(-1,-1),(i,k),(i-t,(k+1) % 3)] for i in range(t,2*t) for k in [0,1,2]] + \
[[(i,k),(j,k),(L(i,j),(k+1) % 3)] for k in [0,1,2] for i in N for j in N if i < j]
else:
raise EmptySetError("Steiner triple systems only exist for n = 1 mod 6 or n = 3 mod 6")
# apply T and remove duplicates
sts = set(frozenset(T(xx) for xx in x) for x in sts)
return BalancedIncompleteBlockDesign(n, sts, name=name,check=False)
def BIBD_from_TD(v,k,existence=False):
r"""
Return a BIBD through TD-based constructions.
INPUT:
- ``v,k`` (integers) -- computes a `(v,k,1)`-BIBD.
- ``existence`` (boolean) -- instead of building the design, return:
- ``True`` -- meaning that Sage knows how to build the design
- ``Unknown`` -- meaning that Sage does not know how to build the
design, but that the design may exist (see :mod:`sage.misc.unknown`).
- ``False`` -- meaning that the design does not exist.
This method implements three constructions:
- If there exists a `TD(k,v)` and a `(v,k,1)`-BIBD then there exists a
`(kv,k,1)`-BIBD.
The BIBD is obtained from all blocks of the `TD`, and from the blocks of
the `(v,k,1)`-BIBDs defined over the `k` groups of the `TD`.
- If there exists a `TD(k,v)` and a `(v+1,k,1)`-BIBD then there exists a
`(kv+1,k,1)`-BIBD.
The BIBD is obtained from all blocks of the `TD`, and from the blocks of
the `(v+1,k,1)`-BIBDs defined over the sets `V_1\cup \infty,\dots,V_k\cup
\infty` where the `V_1,\dots,V_k` are the groups of the TD.
- If there exists a `TD(k,v)` and a `(v+k,k,1)`-BIBD then there exists a
`(kv+k,k,1)`-BIBD.
The BIBD is obtained from all blocks of the `TD`, and from the blocks of
the `(v+k,k,1)`-BIBDs defined over the sets `V_1\cup
\{\infty_1,\dots,\infty_k\},\dots,V_k\cup \{\infty_1,\dots,\infty_k\}`
where the `V_1,\dots,V_k` are the groups of the TD. By making sure that
all copies of the `(v+k,k,1)`-BIBD contain the block
`\{\infty_1,\dots,\infty_k\}`, the result is also a BIBD.
These constructions can be found in
`<http://www.argilo.net/files/bibd.pdf>`_.
EXAMPLES:
First construction::
sage: from sage.combinat.designs.bibd import BIBD_from_TD
sage: BIBD_from_TD(25,5,existence=True)
True
sage: _ = BlockDesign(25,BIBD_from_TD(25,5))
Second construction::
sage: from sage.combinat.designs.bibd import BIBD_from_TD
sage: BIBD_from_TD(21,5,existence=True)
True
sage: _ = BlockDesign(21,BIBD_from_TD(21,5))
Third construction::
sage: from sage.combinat.designs.bibd import BIBD_from_TD
sage: BIBD_from_TD(85,5,existence=True)
True
sage: _ = BlockDesign(85,BIBD_from_TD(85,5))
No idea::
sage: from sage.combinat.designs.bibd import BIBD_from_TD
sage: BIBD_from_TD(20,5,existence=True)
Unknown
sage: BIBD_from_TD(20,5)
Traceback (most recent call last):
...
NotImplementedError: I do not know how to build a (20,5,1)-BIBD!
"""
# First construction
if (v%k == 0 and
balanced_incomplete_block_design(v//k,k,existence=True) and
transversal_design(k,v//k,existence=True)):
if existence:
return True
v = v//k
BIBDvk = balanced_incomplete_block_design(v,k)._blocks
TDkv = transversal_design(k,v,check=False)
BIBD = TDkv._blocks
for i in range(k):
BIBD.extend([[x+i*v for x in B] for B in BIBDvk])
# Second construction
elif ((v-1)%k == 0 and
balanced_incomplete_block_design((v-1)//k+1,k,existence=True) and
transversal_design(k,(v-1)//k,existence=True)):
if existence:
return True
v = (v-1)//k
BIBDv1k = balanced_incomplete_block_design(v+1,k)._blocks
TDkv = transversal_design(k,v,check=False)._blocks
inf = v*k
BIBD = TDkv
for i in range(k):
BIBD.extend([[inf if x == v else x+i*v for x in B] for B in BIBDv1k])
# Third construction
elif ((v-k)%k == 0 and
balanced_incomplete_block_design((v-k)//k+k,k,existence=True) and
transversal_design(k,(v-k)//k,existence=True)):
if existence:
return True
v = (v-k)//k
BIBDvpkk = balanced_incomplete_block_design(v+k,k)
TDkv = transversal_design(k,v,check=False)._blocks
inf = v*k
BIBD = TDkv
# makes sure that [v,...,v+k-1] is a block of BIBDvpkk. Then, we remove it.
BIBDvpkk = _relabel_bibd(BIBDvpkk,v+k)
BIBDvpkk = [B for B in BIBDvpkk if min(B) < v]
for i in range(k):
BIBD.extend([[(x-v)+inf if x >= v else x+i*v for x in B] for B in BIBDvpkk])
BIBD.append(range(k*v,v*k+k))
# No idea ...
else:
if existence:
return Unknown
else:
raise NotImplementedError("I do not know how to build a ({},{},1)-BIBD!".format(v,k))
return BIBD
def BIBD_from_difference_family(G, D, lambd=None, check=True):
r"""
Return the BIBD associated to the difference family ``D`` on the group ``G``.
Let `G` be a group. A `(G,k,\lambda)`-*difference family* is a family `B =
\{B_1,B_2,\ldots,B_b\}` of `k`-subsets of `G` such that for each element of
`G \backslash \{0\}` there exists exactly `\lambda` pairs of elements
`(x,y)`, `x` and `y` belonging to the same block, such that `x - y = g` (or
x y^{-1} = g` in multiplicative notation).
If `\{B_1, B_2, \ldots, B_b\}` is a `(G,k,\lambda)`-difference family then
its set of translates `\{B_i \cdot g; i \in \{1,\ldots,b\}, g \in G\}` is a
`(v,k,\lambda)`-BIBD where `v` is the cardinality of `G`.
INPUT:
- ``G`` - a finite additive Abelian group
- ``D`` - a difference family on ``G`` (short blocks are allowed).
- ``lambd`` - the `\lambda` parameter (optional, only used if ``check`` is
``True``)
- ``check`` - whether or not we check the output (default: ``True``)
EXAMPLES::
sage: G = Zmod(21)
sage: D = [[0,1,4,14,16]]
sage: print sorted(G(x-y) for x in D[0] for y in D[0] if x != y)
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
sage: from sage.combinat.designs.bibd import BIBD_from_difference_family
sage: BIBD_from_difference_family(G, D)
[[0, 1, 4, 14, 16],
[1, 2, 5, 15, 17],
[2, 3, 6, 16, 18],
[3, 4, 7, 17, 19],
[4, 5, 8, 18, 20],
[5, 6, 9, 19, 0],
[6, 7, 10, 20, 1],
[7, 8, 11, 0, 2],
[8, 9, 12, 1, 3],
[9, 10, 13, 2, 4],
[10, 11, 14, 3, 5],
[11, 12, 15, 4, 6],
[12, 13, 16, 5, 7],
[13, 14, 17, 6, 8],
[14, 15, 18, 7, 9],
[15, 16, 19, 8, 10],
[16, 17, 20, 9, 11],
[17, 18, 0, 10, 12],
[18, 19, 1, 11, 13],
[19, 20, 2, 12, 14],
[20, 0, 3, 13, 15]]
"""
from difference_family import group_law, block_stabilizer
identity, mul, inv = group_law(G)
bibd = []
Gset = set(G)
p_to_i = {g:i for i,g in enumerate(Gset)}
for b in D:
b = [G(_) for _ in b]
S = block_stabilizer(G,b)
GG = Gset.copy()
while GG:
g = GG.pop()
if S: GG.difference_update(mul(s,g) for s in S)
bibd.append([p_to_i[mul(i,g)] for i in b])
if check:
if lambd is None:
k = len(bibd[0])
v = G.cardinality()
lambd = (len(bibd) * k * (k-1)) // (v * (v-1))
assert is_pairwise_balanced_design(bibd, G.cardinality(), [len(D[0])], lambd=lambd)
return bibd
################
# (v,4,1)-BIBD #
################
def v_4_1_BIBD(v, check=True):
r"""
Return a `(v,4,1)`-BIBD.
A `(v,4,1)`-BIBD is an edge-decomposition of the complete graph `K_v` into
copies of `K_4`. For more information, see
:func:`balanced_incomplete_block_design`. It exists if and only if `v\equiv 1,4
\pmod {12}`.
See page 167 of [Stinson2004]_ for the construction details.
.. SEEALSO::
* :func:`balanced_incomplete_block_design`
INPUT:
- ``v`` (integer) -- number of points.
- ``check`` (boolean) -- whether to check that output is correct before
returning it. As this is expected to be useless (but we are cautious
guys), you may want to disable it whenever you want speed. Set to ``True``
by default.
EXAMPLES::
sage: from sage.combinat.designs.bibd import v_4_1_BIBD # long time
sage: for n in range(13,100): # long time
....: if n%12 in [1,4]: # long time
....: _ = v_4_1_BIBD(n, check = True) # long time
TESTS:
Check that the `(25,4)` and `(37,4)`-difference family are available::
sage: assert designs.difference_family(25,4,existence=True)
sage: _ = designs.difference_family(25,4)
sage: assert designs.difference_family(37,4,existence=True)
sage: _ = designs.difference_family(37,4)
Check some larger `(v,4,1)`-BIBD (see :trac:`17557`)::
sage: for v in range(400): # long time
....: if v%12 in [1,4]: # long time
....: _ = designs.balanced_incomplete_block_design(v,4) # long time
"""
k = 4
if v == 0:
return []
if v <= 12 or v%12 not in [1,4]:
raise EmptySetError("A K_4-decomposition of K_v exists iif v=2,4 mod 12, v>12 or v==0")
# Step 1. Base cases.
if v == 13:
# note: this construction can also be obtained from difference_family
from block_design import projective_plane
return projective_plane(3)._blocks
if v == 16:
from block_design import AffineGeometryDesign
from sage.rings.finite_rings.constructor import FiniteField
return AffineGeometryDesign(2,1,FiniteField(4,'x'))._blocks
if v == 25 or v == 37:
from difference_family import difference_family
G,D = difference_family(v,4)
return BIBD_from_difference_family(G,D,check=False)
if v == 28:
return [[0, 1, 23, 26], [0, 2, 10, 11], [0, 3, 16, 18], [0, 4, 15, 20],
[0, 5, 8, 9], [0, 6, 22, 25], [0, 7, 14, 21], [0, 12, 17, 27],
[0, 13, 19, 24], [1, 2, 24, 27], [1, 3, 11, 12], [1, 4, 17, 19],
[1, 5, 14, 16], [1, 6, 9, 10], [1, 7, 20, 25], [1, 8, 15, 22],
[1, 13, 18, 21], [2, 3, 21, 25], [2, 4, 12, 13], [2, 5, 18, 20],
[2, 6, 15, 17], [2, 7, 19, 22], [2, 8, 14, 26], [2, 9, 16, 23],
[3, 4, 22, 26], [3, 5, 7, 13], [3, 6, 14, 19], [3, 8, 20, 23],
[3, 9, 15, 27], [3, 10, 17, 24], [4, 5, 23, 27], [4, 6, 7, 8],
[4, 9, 14, 24], [4, 10, 16, 21], [4, 11, 18, 25], [5, 6, 21, 24],
[5, 10, 15, 25], [5, 11, 17, 22], [5, 12, 19, 26], [6, 11, 16, 26],
[6, 12, 18, 23], [6, 13, 20, 27], [7, 9, 17, 18], [7, 10, 26, 27],
[7, 11, 23, 24], [7, 12, 15, 16], [8, 10, 18, 19], [8, 11, 21, 27],
[8, 12, 24, 25], [8, 13, 16, 17], [9, 11, 19, 20], [9, 12, 21, 22],
[9, 13, 25, 26], [10, 12, 14, 20], [10, 13, 22, 23], [11, 13, 14, 15],
[14, 17, 23, 25], [14, 18, 22, 27], [15, 18, 24, 26], [15, 19, 21, 23],
[16, 19, 25, 27], [16, 20, 22, 24], [17, 20, 21, 26]]
# Step 2 : this is function PBD_4_5_8_9_12
PBD = PBD_4_5_8_9_12((v-1)/(k-1),check=False)
# Step 3 : Theorem 7.20
bibd = BIBD_from_PBD(PBD,v,k,check=False)
if check:
assert is_pairwise_balanced_design(bibd,v,[k])
return bibd
def BIBD_from_PBD(PBD,v,k,check=True,base_cases={}):
r"""
Return a `(v,k,1)`-BIBD from a `(r,K)`-PBD where `r=(v-1)/(k-1)`.
This is Theorem 7.20 from [Stinson2004]_.
INPUT:
- ``v,k`` -- integers.
- ``PBD`` -- A PBD on `r=(v-1)/(k-1)` points, such that for any block of
``PBD`` of size `s` there must exist a `((k-1)s+1,k,1)`-BIBD.
- ``check`` (boolean) -- whether to check that output is correct before
returning it. As this is expected to be useless (but we are cautious
guys), you may want to disable it whenever you want speed. Set to ``True``
by default.
- ``base_cases`` -- caching system, for internal use.
EXAMPLES::
sage: from sage.combinat.designs.bibd import PBD_4_5_8_9_12
sage: from sage.combinat.designs.bibd import BIBD_from_PBD
sage: from sage.combinat.designs.bibd import is_pairwise_balanced_design
sage: PBD = PBD_4_5_8_9_12(17)
sage: bibd = is_pairwise_balanced_design(BIBD_from_PBD(PBD,52,4),52,[4])
"""
r = (v-1) // (k-1)
bibd = []
for X in PBD:
n = len(X)
N = (k-1)*n+1
if not (n,k) in base_cases:
base_cases[n,k] = _relabel_bibd(balanced_incomplete_block_design(N,k), N)
for XX in base_cases[n,k]:
if N-1 in XX:
continue
bibd.append([X[x//(k-1)] + (x%(k-1))*r for x in XX])
for x in range(r):
bibd.append([x+i*r for i in range(k-1)]+[v-1])
if check:
assert is_pairwise_balanced_design(bibd,v,[k])
return bibd
def _relabel_bibd(B,n,p=None):
r"""
Relabels the BIBD on `n` points and blocks of size k such that
`\{0,...,k-2,n-1\},\{k-1,...,2k-3,n-1\},...,\{n-k,...,n-2,n-1\}` are blocks
of the BIBD.
INPUT:
- ``B`` -- a list of blocks.
- ``n`` (integer) -- number of points.
- ``p`` (optional) -- the point that will be labeled with n-1.
EXAMPLE::
sage: designs.balanced_incomplete_block_design(40,4).blocks() # indirect doctest
[[0, 1, 2, 12], [0, 3, 6, 9], [0, 4, 8, 10],
[0, 5, 7, 11], [0, 13, 26, 39], [0, 14, 25, 28],
[0, 15, 27, 38], [0, 16, 22, 32], [0, 17, 23, 34],
...
"""
if p is None:
p = n-1
found = 0
last = n-1
d = {}
for X in B:
if last in X:
for x in X:
if x == last:
continue
d[x] = found
found += 1
if found == n-1:
break
d[p] = n-1
return [[d[x] for x in X] for X in B]
def PBD_4_5_8_9_12(v, check=True):
"""
Return a `(v,\{4,5,8,9,12\})`-PBD on `v` elements.
A `(v,\{4,5,8,9,12\})`-PBD exists if and only if `v\equiv 0,1 \pmod 4`. The
construction implemented here appears page 168 in [Stinson2004]_.
INPUT:
- ``v`` -- an integer congruent to `0` or `1` modulo `4`.
- ``check`` (boolean) -- whether to check that output is correct before
returning it. As this is expected to be useless (but we are cautious
guys), you may want to disable it whenever you want speed. Set to ``True``
by default.
EXAMPLES::
sage: designs.balanced_incomplete_block_design(40,4).blocks() # indirect doctest
[[0, 1, 2, 12], [0, 3, 6, 9], [0, 4, 8, 10],
[0, 5, 7, 11], [0, 13, 26, 39], [0, 14, 25, 28],
[0, 15, 27, 38], [0, 16, 22, 32], [0, 17, 23, 34],
...
Check that :trac:`16476` is fixed::
sage: from sage.combinat.designs.bibd import PBD_4_5_8_9_12
sage: for v in (0,1,4,5,8,9,12,13,16,17,20,21,24,25):
....: _ = PBD_4_5_8_9_12(v)
"""
if not v%4 in [0,1]:
raise ValueError
if v <= 1:
PBD = []
elif v <= 12:
PBD = [range(v)]
elif v == 13 or v == 28:
PBD = v_4_1_BIBD(v, check=False)
elif v == 29:
TD47 = transversal_design(4,7)._blocks
four_more_sets = [[28]+[i*7+j for j in range(7)] for i in range(4)]
PBD = TD47 + four_more_sets
elif v == 41:
TD59 = transversal_design(5,9)
PBD = ([[x for x in X if x<41] for X in TD59]
+[[i*9+j for j in range(9)] for i in range(4)]
+[[36,37,38,39,40]])
elif v == 44:
TD59 = transversal_design(5,9)
PBD = ([[x for x in X if x<44] for X in TD59]
+[[i*9+j for j in range(9)] for i in range(4)]
+[[36,37,38,39,40,41,42,43]])
elif v == 45:
TD59 = transversal_design(5,9)._blocks
PBD = (TD59+[[i*9+j for j in range(9)] for i in range(5)])
elif v == 48:
TD4_12 = transversal_design(4,12)._blocks
PBD = (TD4_12+[[i*12+j for j in range(12)] for i in range(4)])
elif v == 49:
# Lemma 7.16 : A (49,{4,13})-PBD
TD4_12 = transversal_design(4,12)._blocks
# Replacing the block of size 13 with a BIBD
BIBD_13_4 = v_4_1_BIBD(13)
for i in range(4):
for B in BIBD_13_4:
TD4_12.append([i*12+x if x != 12 else 48
for x in B])
PBD = TD4_12
else:
t,u = _get_t_u(v)
TD = transversal_design(5,t)
TD = [[x for x in X if x<4*t+u] for X in TD]
for B in [range(t*i,t*(i+1)) for i in range(4)]:
TD.extend(_PBD_4_5_8_9_12_closure([B]))
if u > 1:
TD.extend(_PBD_4_5_8_9_12_closure([range(4*t,4*t+u)]))
PBD = TD
if check:
assert is_pairwise_balanced_design(PBD,v,[4,5,8,9,12])
return PBD
def _PBD_4_5_8_9_12_closure(B):
r"""
Makes sure all blocks of `B` have size in `\{4,5,8,9,12\}`.
This is a helper function for :func:`PBD_4_5_8_9_12`. Given that
`\{4,5,8,9,12\}` is PBD-closed, any block of size not in `\{4,5,8,9,12\}`
can be decomposed further.
EXAMPLES::
sage: designs.balanced_incomplete_block_design(40,4).blocks() # indirect doctest
[[0, 1, 2, 12], [0, 3, 6, 9], [0, 4, 8, 10],
[0, 5, 7, 11], [0, 13, 26, 39], [0, 14, 25, 28],
[0, 15, 27, 38], [0, 16, 22, 32], [0, 17, 23, 34],
...
"""
BB = []
for X in B:
if len(X) not in [4,5,8,9,12]:
PBD = PBD_4_5_8_9_12(len(X), check = False)
X = [[X[i] for i in XX] for XX in PBD]
BB.extend(X)
else:
BB.append(X)
return BB
table_7_1 = {
0:{'t':-4,'u':16,'s':2},
1:{'t':-4,'u':17,'s':2},
4:{'t':1,'u':0,'s':1},
5:{'t':1,'u':1,'s':1},
8:{'t':1,'u':4,'s':1},
9:{'t':1,'u':5,'s':1},
12:{'t':1,'u':8,'s':1},
13:{'t':1,'u':9,'s':1},
16:{'t':4,'u':0,'s':0},
17:{'t':4,'u':1,'s':0},
20:{'t':5,'u':0,'s':0},
21:{'t':5,'u':1,'s':0},
24:{'t':5,'u':4,'s':0},
25:{'t':5,'u':5,'s':0},
28:{'t':5,'u':8,'s':1},
29:{'t':5,'u':9,'s':1},
32:{'t':8,'u':0,'s':0},
33:{'t':8,'u':1,'s':0},
36:{'t':8,'u':4,'s':0},
37:{'t':8,'u':5,'s':0},
40:{'t':8,'u':8,'s':0},
41:{'t':8,'u':9,'s':1},
44:{'t':8,'u':12,'s':1},
45:{'t':8,'u':13,'s':1},
}
def _get_t_u(v):
r"""
Return the parameters of table 7.1 from [Stinson2004]_.
INPUT:
- ``v`` (integer)
EXAMPLE::
sage: from sage.combinat.designs.bibd import _get_t_u
sage: _get_t_u(20)
(5, 0)
"""
# Table 7.1
v = int(v)
global table_7_1
d = table_7_1[v%48]
s = v//48
if s < d['s']:
raise RuntimeError("This should not have happened.")
t = 12*s+d['t']
u = d['u']
return t,u
################
# (v,5,1)-BIBD #
################
def v_5_1_BIBD(v, check=True):
r"""
Return a `(v,5,1)`-BIBD.
This method follows the constuction from [ClaytonSmith]_.
INPUT:
- ``v`` (integer)
.. SEEALSO::
* :func:`balanced_incomplete_block_design`
EXAMPLES::
sage: from sage.combinat.designs.bibd import v_5_1_BIBD
sage: i = 0
sage: while i<200:
....: i += 20
....: _ = v_5_1_BIBD(i+1)
....: _ = v_5_1_BIBD(i+5)
TESTS:
Check that the needed difference families are there::
sage: for v in [21,41,61,81,141,161,281]:
....: assert designs.difference_family(v,5,existence=True)
....: _ = designs.difference_family(v,5)
"""
v = int(v)
assert (v > 1)
assert (v%20 == 5 or v%20 == 1) # note: equivalent to (v-1)%4 == 0 and (v*(v-1))%20 == 0
# Lemma 27
if v%5 == 0 and (v//5)%4 == 1 and is_prime_power(v//5):
bibd = BIBD_5q_5_for_q_prime_power(v//5)
# Lemma 28
elif v in [21,41,61,81,141,161,281]:
from difference_family import difference_family
G,D = difference_family(v,5)
bibd = BIBD_from_difference_family(G, D, check=False)
# Lemma 29
elif v == 165:
bibd = BIBD_from_PBD(v_5_1_BIBD(41,check=False),165,5,check=False)
elif v == 181:
bibd = BIBD_from_PBD(v_5_1_BIBD(45,check=False),181,5,check=False)
elif v in (201,285,301,401,421,425):
# Call directly the BIBD_from_TD function
# note: there are (201,5,1) and (421,5)-difference families that can be
# obtained from the general constructor
bibd = BIBD_from_TD(v,5)
# Theorem 31.2
elif (v-1)//4 in [80, 81, 85, 86, 90, 91, 95, 96, 110, 111, 115, 116, 120, 121, 250, 251, 255, 256, 260, 261, 265, 266, 270, 271]:
r = (v-1)//4
if r <= 96:
k,t,u = 5, 16, r-80
elif r <= 121:
k,t,u = 10, 11, r-110
else:
k,t,u = 10, 25, r-250
bibd = BIBD_from_PBD(PBD_from_TD(k,t,u),v,5,check=False)
else:
r,s,t,u = _get_r_s_t_u(v)
bibd = BIBD_from_PBD(PBD_from_TD(5,t,u),v,5,check=False)
if check:
assert is_pairwise_balanced_design(bibd,v,[5])
return bibd
def _get_r_s_t_u(v):
r"""
Implements the table from [ClaytonSmith]_
Return the parameters ``r,s,t,u`` associated with an integer ``v``.
INPUT:
- ``v`` (integer)
EXAMPLES::
sage: from sage.combinat.designs.bibd import _get_r_s_t_u
sage: _get_r_s_t_u(25)
(6, 0, 1, 1)
"""
r = int((v-1)/4)
s = r//150
x = r%150
if x == 0: t,u = 30*s-5, 25
elif x == 1: t,u = 30*s-5, 26
elif x <= 21: t,u = 30*s+1, x-5
elif x == 25: t,u = 30*s+5, 0
elif x == 26: t,u = 30*s+5, 1
elif x == 30: t,u = 30*s+5, 5
elif x <= 51: t,u = 30*s+5, x-25
elif x <= 66: t,u = 30*s+11, x-55
elif x <= 96: t,u = 30*s+11, x-55
elif x <= 121: t,u = 30*s+11, x-55
elif x <= 146: t,u = 30*s+25, x-125
return r,s,t,u
def PBD_from_TD(k,t,u):
r"""
Return a `(kt,\{k,t\})`-PBD if `u=0` and a `(kt+u,\{k,k+1,t,u\})`-PBD otherwise.
This is theorem 23 from [ClaytonSmith]_. The PBD is obtained from the blocks
a truncated `TD(k+1,t)`, to which are added the blocks corresponding to the
groups of the TD. When `u=0`, a `TD(k,t)` is used instead.
INPUT:
- ``k,t,u`` -- integers such that `0\leq u \leq t`.
EXAMPLES::
sage: from sage.combinat.designs.bibd import PBD_from_TD
sage: from sage.combinat.designs.bibd import is_pairwise_balanced_design
sage: PBD = PBD_from_TD(2,2,1); PBD
[[0, 2, 4], [0, 3], [1, 2], [1, 3, 4], [0, 1], [2, 3]]
sage: is_pairwise_balanced_design(PBD,2*2+1,[2,3])
True
"""
from orthogonal_arrays import transversal_design
TD = transversal_design(k+bool(u),t, check=False)
TD = [[x for x in X if x<k*t+u] for X in TD]
for i in range(k):
TD.append(range(t*i,t*i+t))
if u>=2:
TD.append(range(k*t,k*t+u))
return TD
def BIBD_5q_5_for_q_prime_power(q):
r"""
Return a `(5q,5,1)`-BIBD with `q\equiv 1\pmod 4` a prime power.
See Theorem 24 [ClaytonSmith]_.
INPUT:
- ``q`` (integer) -- a prime power such that `q\equiv 1\pmod 4`.
EXAMPLES::
sage: from sage.combinat.designs.bibd import BIBD_5q_5_for_q_prime_power
sage: for q in [25, 45, 65, 85, 125, 145, 185, 205, 305, 405, 605]: # long time
....: _ = BIBD_5q_5_for_q_prime_power(q/5) # long time
"""
from sage.rings.finite_rings.constructor import FiniteField
if q%4 != 1 or not is_prime_power(q):
raise ValueError("q is not a prime power or q%4!=1.")
d = (q-1)/4
B = []
F = FiniteField(q,'x')
a = F.primitive_element()
L = {b:i for i,b in enumerate(F)}
for b in L:
B.append([i*q + L[b] for i in range(5)])
for i in range(5):
for j in range(d):
B.append([ i*q + L[b ],
((i+1)%5)*q + L[ a**j+b ],
((i+1)%5)*q + L[-a**j+b ],
((i+4)%5)*q + L[ a**(j+d)+b],
((i+4)%5)*q + L[-a**(j+d)+b],
])
return B
def BIBD_from_arc_in_desarguesian_projective_plane(n,k,existence=False):
r"""
Returns a `(n,k,1)`-BIBD from a maximal arc in a projective plane.
This function implements a construction from Denniston [Denniston69]_, who
describes a maximal :meth:`arc
<sage.combinat.designs.bibd.BalancedIncompleteBlockDesign.arc>` in a
:func:`Desarguesian Projective Plane
<sage.combinat.designs.block_design.DesarguesianProjectivePlaneDesign>` of
order `2^k`. From two powers of two `n,q` with `n<q`, it produces a
`((n-1)(q+1)+1,n,1)`-BIBD.
INPUT:
- ``n,k`` (integers) -- must be powers of two (among other restrictions).
- ``existence`` (boolean) -- whether to return the BIBD obtained through
this construction (default), or to merely indicate with a boolean return
value whether this method *can* build the requested BIBD.
EXAMPLES:
A `(232,8,1)`-BIBD::
sage: from sage.combinat.designs.bibd import BIBD_from_arc_in_desarguesian_projective_plane
sage: from sage.combinat.designs.bibd import BalancedIncompleteBlockDesign
sage: D = BIBD_from_arc_in_desarguesian_projective_plane(232,8)
sage: BalancedIncompleteBlockDesign(232,D)
(232,8,1)-Balanced Incomplete Block Design
A `(120,8,1)`-BIBD::
sage: D = BIBD_from_arc_in_desarguesian_projective_plane(120,8)
sage: BalancedIncompleteBlockDesign(120,D)
(120,8,1)-Balanced Incomplete Block Design
Other parameters::
sage: all(BIBD_from_arc_in_desarguesian_projective_plane(n,k,existence=True)
....: for n,k in
....: [(120, 8), (232, 8), (456, 8), (904, 8), (496, 16),
....: (976, 16), (1936, 16), (2016, 32), (4000, 32), (8128, 64)])
True
Of course, not all can be built this way::
sage: BIBD_from_arc_in_desarguesian_projective_plane(7,3,existence=True)
False
sage: BIBD_from_arc_in_desarguesian_projective_plane(7,3)
Traceback (most recent call last):
...
ValueError: This function cannot produce a (7,3,1)-BIBD
REFERENCE:
.. [Denniston69] R. H. F. Denniston,
Some maximal arcs in finite projective planes.
Journal of Combinatorial Theory 6, no. 3 (1969): 317-319.
http://dx.doi.org/10.1016/S0021-9800(69)80095-5
"""
q = (n-1)//(k-1)-1
if (k % 2 or
q % 2 or
q <= k or
n != (k-1)*(q+1)+1 or
not is_prime_power(k) or
not is_prime_power(q)):
if existence:
return False
raise ValueError("This function cannot produce a ({},{},1)-BIBD".format(n,k))
if existence:
return True
n = k
# From now on, the code assumes the notations of [Denniston69] for n,q, so
# that the BIBD returned by the method will have the requested parameters.
from sage.rings.finite_rings.constructor import FiniteField as GF
from sage.libs.gap.libgap import libgap
from sage.matrix.constructor import Matrix
K = GF(q,'a')
one = K.one()
# An irreducible quadratic form over K[X,Y]
GO = libgap.GeneralOrthogonalGroup(-1,2,q)
M = libgap.InvariantQuadraticForm(GO)['matrix']
M = Matrix(M)
M = M.change_ring(K)
Q = lambda xx,yy : M[0,0]*xx**2+(M[0,1]+M[1,0])*xx*yy+M[1,1]*yy**2
# Here, the additive subgroup H (of order n) of K mentioned in
# [Denniston69] is the set of all elements of K of degree < log_n
# (seeing elements of K as polynomials in 'a')
K_iter = list(K) # faster iterations
log_n = is_prime_power(n,get_data=True)[1]
C = [(x,y,one)
for x in K_iter
for y in K_iter
if Q(x,y).polynomial().degree() < log_n]
from sage.combinat.designs.block_design import DesarguesianProjectivePlaneDesign
return DesarguesianProjectivePlaneDesign(q).trace(C)._blocks
class PairwiseBalancedDesign(GroupDivisibleDesign):
r"""
Pairwise Balanced Design (PBD)
A Pairwise Balanced Design, or `(v,K,\lambda)`-PBD, is a collection
`\mathcal B` of blocks defined on a set `X` of size `v`, such that any block
pair of points `p_1,p_2\in X` occurs in exactly `\lambda` blocks of
`\mathcal B`. Besides, for every block `B\in \mathcal B` we must have
`|B|\in K`.
INPUT:
- ``points`` -- the underlying set. If ``points`` is an integer `v`, then
the set is considered to be `\{0, ..., v-1\}`.
- ``blocks`` -- collection of blocks
- ``K`` -- list of integers of which the sizes of the blocks must be
elements. Set to ``None`` (automatic guess) by default.
- ``lambd`` (integer) -- value of `\lambda`, set to `1` by default.
- ``check`` (boolean) -- whether to check that the design is a `PBD` with
the right parameters.
- ``copy`` -- (use with caution) if set to ``False`` then ``blocks`` must be
a list of lists of integers. The list will not be copied but will be
modified in place (each block is sorted, and the whole list is
sorted). Your ``blocks`` object will become the instance's internal data.
"""
def __init__(self, points, blocks, K=None, lambd=1, check=True, copy=True,**kwds):
r"""
Constructor
EXAMPLE::
sage: designs.balanced_incomplete_block_design(13,3) # indirect doctest
(13,3,1)-Balanced Incomplete Block Design
"""
try:
i = int(points)
except TypeError:
pass
else:
points = range(i)
GroupDivisibleDesign.__init__(self,
points,
[[x] for x in points],
blocks,
K=K,
lambd=lambd,
check=check,
copy=copy,
**kwds)
def __repr__(self):
r"""
Returns a string describing the PBD
EXAMPLES::
sage: designs.balanced_incomplete_block_design(13,3) # indirect doctest
(13,3,1)-Balanced Incomplete Block Design
"""
return "Pairwise Balanced Design on {} points with sets of sizes in {}".format(self.num_points(),set(self.block_sizes()))
class BalancedIncompleteBlockDesign(PairwiseBalancedDesign):
r""""
Balanced Incomplete Block Design (BIBD)
INPUT:
- ``points`` -- the underlying set. If ``points`` is an integer `v`, then
the set is considered to be `\{0, ..., v-1\}`.
- ``blocks`` -- collection of blocks
- ``k`` (integer) -- size of the blocks. Set to ``None`` (automatic guess)
by default.
- ``lambd`` (integer) -- value of `\lambda`, set to `1` by default.
- ``check`` (boolean) -- whether to check that the design is a `PBD` with
the right parameters.
- ``copy`` -- (use with caution) if set to ``False`` then ``blocks`` must be
a list of lists of integers. The list will not be copied but will be
modified in place (each block is sorted, and the whole list is
sorted). Your ``blocks`` object will become the instance's internal data.
EXAMPLES::
sage: b=designs.balanced_incomplete_block_design(9,3); b
(9,3,1)-Balanced Incomplete Block Design
"""
def __init__(self, points, blocks, k=None, lambd=1, check=True, copy=True,**kwds):
r"""
Constructor
EXAMPLE::
sage: b=designs.balanced_incomplete_block_design(9,3); b
(9,3,1)-Balanced Incomplete Block Design
"""
PairwiseBalancedDesign.__init__(self,
points,
blocks,
K=[k] if k is not None else None,
lambd=lambd,
check=check,
copy=copy,
**kwds)
def __repr__(self):
r"""
A string to describe self
EXAMPLE::
sage: b=designs.balanced_incomplete_block_design(9,3); b
(9,3,1)-Balanced Incomplete Block Design
"""
v = self.num_points()
k = len(self._blocks[0]) if self._blocks else 0
l = self._lambd
return "({},{},{})-Balanced Incomplete Block Design".format(v,k,l)
def arc(self, s=2, solver=None, verbose=0):
r"""
Return the ``s``-arc with maximum cardinality.
A `s`-arc is a subset of points in a BIBD that intersects each block on
at most `s` points. It is one possible generalization of independent set
for graphs.
A simple counting shows that the cardinality of a `s`-arc is at most
`(s-1) * r + 1` where `r` is the number of blocks incident to any point.
A `s`-arc in a BIBD with cardinality `(s-1) * r + 1` is called maximal
and is characterized by the following property: it is not empty and each
block either contains `0` or `s` points of this arc. Equivalently, the
trace of the BIBD on these points is again a BIBD (with block size `s`).
For more informations, see :wikipedia:`Arc_(projective_geometry)`.
INPUT:
- ``s`` - (default to ``2``) the maximum number of points from the arc
in each block
- ``solver`` -- (default: ``None``) Specify a Linear Program (LP)
solver to be used. If set to ``None``, the default one is used. For
more information on LP solvers and which default solver is used, see
the method
:meth:`solve <sage.numerical.mip.MixedIntegerLinearProgram.solve>`
of the class
:class:`MixedIntegerLinearProgram <sage.numerical.mip.MixedIntegerLinearProgram>`.
- ``verbose`` -- integer (default: ``0``). Sets the level of
verbosity. Set to 0 by default, which means quiet.
EXAMPLES::
sage: B = designs.balanced_incomplete_block_design(21, 5)
sage: a2 = B.arc()
sage: a2 # random
[5, 9, 10, 12, 15, 20]
sage: len(a2)
6
sage: a4 = B.arc(4)
sage: a4 # random
[0, 1, 2, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20]
sage: len(a4)
16
The `2`-arc and `4`-arc above are maximal. One can check that they
intersect the blocks in either 0 or `s` points. Or equivalently that the
traces are again BIBD::
sage: r = (21-1)/(5-1)
sage: 1 + r*1
6
sage: 1 + r*3
16
sage: B.trace(a2).is_t_design(2, return_parameters=True)
(True, (2, 6, 2, 1))
sage: B.trace(a4).is_t_design(2, return_parameters=True)
(True, (2, 16, 4, 1))
Some other examples which are not maximal::
sage: B = designs.balanced_incomplete_block_design(25, 4)
sage: a2 = B.arc(2)
sage: r = (25-1)/(4-1)
sage: print len(a2), 1 + r
8 9
sage: sa2 = set(a2)
sage: set(len(sa2.intersection(b)) for b in B.blocks())
{0, 1, 2}
sage: B.trace(a2).is_t_design(2)
False
sage: a3 = B.arc(3)
sage: print len(a3), 1 + 2*r
15 17
sage: sa3 = set(a3)
sage: set(len(sa3.intersection(b)) for b in B.blocks()) == set([0,3])
False
sage: B.trace(a3).is_t_design(3)
False
TESTS:
Test consistency with relabeling::
sage: b = designs.balanced_incomplete_block_design(7,3)
sage: b.relabel(list("abcdefg"))
sage: set(b.arc()).issubset(b.ground_set())
True
"""
s = int(s)
# trivial cases
if s <= 0:
return []
elif s >= max(self.block_sizes()):
return self._points[:]
# linear program
from sage.numerical.mip import MixedIntegerLinearProgram
p = MixedIntegerLinearProgram(solver=solver)
b = p.new_variable(binary=True)
p.set_objective(p.sum(b[i] for i in range(len(self._points))))
for i in self._blocks:
p.add_constraint(p.sum(b[k] for k in i) <= s)
p.solve(log=verbose)
return [self._points[i] for (i,j) in p.get_values(b).items() if j == 1]
| [
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8,
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257,
198,
3483,
14529,
357,
273,
2198,
326,
340,
460,
307,
3... | 2.030018 | 24,785 |
import numpy as np
from collections import OrderedDict
from GenPlayground import GenPlayground
if __name__ == '__main__':
np.set_printoptions(linewidth=200)
IP, IP_cycle, IP_pe = GenPlayground()
# print(IP_cycle)
# print(IP_pe)
Gen_CTRL(IP, IP_cycle, IP_pe)
| [
11748,
299,
32152,
355,
45941,
198,
6738,
17268,
1330,
14230,
1068,
35,
713,
198,
198,
6738,
5215,
11002,
2833,
1330,
5215,
11002,
2833,
628,
198,
361,
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3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
45941,
13,
... | 2.517857 | 112 |
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
from knack.help_files import helps
# region VirtualHub
helps['network vhub'] = """
type: group
short-summary: Manage virtual hubs.
"""
helps['network vhub create'] = """
type: command
short-summary: Create a virtual hub.
"""
helps['network vhub list'] = """
type: command
short-summary: List virtual hubs.
"""
helps['network vhub show'] = """
type: command
short-summary: Get the details of a virtual hub.
"""
helps['network vhub update'] = """
type: command
short-summary: Update settings of a virtual hub.
"""
helps['network vhub delete'] = """
type: command
short-summary: Delete a virtual hub.
"""
helps['network vhub connection'] = """
type: group
short-summary: Manage virtual hub VNet connections.
"""
helps['network vhub connection create'] = """
type: command
short-summary: Create a virtual hub VNet connection.
"""
helps['network vhub connection list'] = """
type: command
short-summary: List virtual hub VNet connections.
"""
helps['network vhub connection show'] = """
type: command
short-summary: Get the details of a virtual hub VNet connection.
"""
helps['network vhub connection delete'] = """
type: command
short-summary: Delete a virtual hub VNet connection.
"""
helps['network vhub route'] = """
type: group
short-summary: Manage entries in the virtual hub route table.
"""
helps['network vhub route add'] = """
type: command
short-summary: Add a route to the virtual hub route table.
"""
helps['network vhub route list'] = """
type: command
short-summary: List routes in the virtual hub route table.
"""
helps['network vhub route remove'] = """
type: command
short-summary: Remove a route from the virtual hub route table.
"""
# endregion
# region VirtualWAN
helps['network vwan'] = """
type: group
short-summary: Manage virtual WANs.
"""
helps['network vwan create'] = """
type: command
short-summary: Create a virtual WAN.
"""
helps['network vwan list'] = """
type: command
short-summary: List virtual WANs.
"""
helps['network vwan show'] = """
type: command
short-summary: Get the details of a virtual WAN.
"""
helps['network vwan update'] = """
type: command
short-summary: Update settings of a virtual WAN.
"""
helps['network vwan delete'] = """
type: command
short-summary: Delete a virtual WAN.
"""
# endregion
# region VpnGateway
helps['network vpn-gateway'] = """
type: group
short-summary: Manage VPN gateways.
"""
helps['network vpn-gateway create'] = """
type: command
short-summary: Create a VPN gateway.
"""
helps['network vpn-gateway list'] = """
type: command
short-summary: List VPN gateways.
"""
helps['network vpn-gateway show'] = """
type: command
short-summary: Get the details of a VPN gateway.
"""
helps['network vpn-gateway update'] = """
type: command
short-summary: Update settings of a VPN gateway.
"""
helps['network vpn-gateway delete'] = """
type: command
short-summary: Delete a VPN gateway.
"""
helps['network vpn-gateway connection'] = """
type: group
short-summary: Manage VPN gateway connections.
"""
helps['network vpn-gateway connection create'] = """
type: command
short-summary: Create a VPN gateway connection.
"""
helps['network vpn-gateway connection list'] = """
type: command
short-summary: List VPN gateway connections.
"""
helps['network vpn-gateway connection show'] = """
type: command
short-summary: Get the details of a VPN gateway connection.
"""
helps['network vpn-gateway connection delete'] = """
type: command
short-summary: Delete a VPN gateway connection.
"""
helps['network vpn-gateway connection ipsec-policy'] = """
type: group
short-summary: Manage VPN gateway connection IPSec policies.
"""
helps['network vpn-gateway connection ipsec-policy add'] = """
type: command
short-summary: Add an IPSec policy to a VPN gateway connection.
"""
helps['network vpn-gateway connection ipsec-policy list'] = """
type: command
short-summary: List VPN gateway connection IPSec policies.
"""
helps['network vpn-gateway connection ipsec-policy remove'] = """
type: command
short-summary: Remove an IPSec policy from a VPN gateway connection.
"""
# endregion
# region VpnSite
helps['network vpn-site'] = """
type: group
short-summary: Manage VPN site configurations.
"""
helps['network vpn-site create'] = """
type: command
short-summary: Create a VPN site configuration.
"""
helps['network vpn-site list'] = """
type: command
short-summary: List VPN site configurations.
"""
helps['network vpn-site show'] = """
type: command
short-summary: Get the details of a VPN site configuration.
"""
helps['network vpn-site update'] = """
type: command
short-summary: Update settings of a VPN site configuration.
"""
helps['network vpn-site delete'] = """
type: command
short-summary: Delete a VPN site configuration.
"""
helps['network vpn-site download'] = """
type: command
short-summary: Provide a SAS-URL to download the configuration for a VPN site.
"""
# endregion
| [
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2,
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66,
8,
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13,
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2,
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13,
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13,
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287,
262,
1628,
6808,
329,
5964,
1321,
13,
198,
2,
16529,
1783,
10541,
198,... | 3.272353 | 1,700 |
from django.apps import AppConfig
| [
6738,
42625,
14208,
13,
18211,
1330,
2034,
16934,
628
] | 3.888889 | 9 |
import signal
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters
from telegram import Sticker, InlineKeyboardButton, InlineKeyboardMarkup
import logging
from Utils import telegram_util, twitch_util, config_util
if __name__ == '__main__':
config = config_util.get_config()
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
if config_util.token_key in config and config[config_util.token_key] is not '':
bot = TwitchStickersBot(token=config[config_util.token_key])
bot.start_bot()
else:
logging.log(logging.ERROR, f"{config_util.token_key} not in {config_util.config_path}!")
| [
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9218,
3526,
21864,
11,
554,
1370,
9218,
3526,
9704,
929,
198... | 2.610909 | 275 |
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
import math
import matplotlib.colors as colors
from matplotlib import cm
from matplotlib import rc
__author__ = 'ernesto'
# if use latex or mathtext
rc('text', usetex=False)
rc('mathtext', fontset='cm')
# auxiliar function for plot ticks of equal length in x and y axis despite its scales.
#####################################
# PARAMETERS - This can be modified #
#####################################
# normal pdf standard deviation
sigma1 = 1
sigma2 = sigma1 / 10
# normal pdf mean
h1 = 3
h2 = h1 / 2
# maximum deviation from the mean where to plot each gaussian
max_mean_dev = 3 * sigma1
#####################
# END OF PARAMETERS #
#####################
# abscissa values
xmin = h2 - max_mean_dev
xmax = h1 + max_mean_dev
x = np.linspace(xmin, xmax, 300)
# normal distribution and density values in x
pdf_h1 = norm.pdf(x, h1, sigma1)
pdf_h1_avg = norm.pdf(x, h1, math.sqrt(sigma2))
pdf_h2 = norm.pdf(x, h2, sigma1)
pdf_h2_avg = norm.pdf(x, h2, math.sqrt(sigma2))
# axis parameters
dx = xmax / 20
xmin_ax = xmin - dx
xmax_ax = xmax + dx
ym = np.amax(pdf_h1_avg)
ymax_ax = ym + ym / 10
ymin_ax = -ym / 10
# length of the ticks for all subplot (6 pixels)
display_length = 6 # in pixels
# x ticks labels margin
xtm = -0.03
# font size
fontsize = 14
# colors from coolwarm
cNorm = colors.Normalize(vmin=0, vmax=1)
scalarMap = cm.ScalarMappable(norm=cNorm, cmap=cm.coolwarm)
col10 = scalarMap.to_rgba(0)
col20 = scalarMap.to_rgba(1)
fig = plt.figure(0, figsize=(10, 3), frameon=False)
# PLOT OF F(x | x < a)
ax = plt.subplot2grid((1, 8), (0, 0), rowspan=1, colspan=4)
plt.xlim(xmin_ax, xmax_ax)
plt.ylim(ymin_ax, ymax_ax)
# horizontal and vertical ticks length
xtl, ytl = convert_display_to_data_coordinates(ax.transData, length=display_length)
# axis arrows
plt.annotate("", xytext=(xmin_ax, 0), xycoords='data', xy=(xmax_ax, 0), textcoords='data',
arrowprops=dict(width=0.1, headwidth=6, headlength=8, facecolor='black', shrink=0.002))
plt.annotate("", xytext=(0, ymin_ax), xycoords='data', xy=(0, ymax_ax), textcoords='data',
arrowprops=dict(width=0.1, headwidth=6, headlength=8, facecolor='black', shrink=0.002))
plt.plot(x, pdf_h1, color=col10, linewidth=2)
plt.plot(x, pdf_h1_avg, color=col20, linewidth=2)
# xlabels and xtickslabels
plt.plot([h1, h1], [0, xtl], 'k')
plt.text(h1, xtm, '$h$', fontsize=fontsize, ha='center', va='top')
plt.text(xmin_ax, ymax_ax-0.1, '$\\alpha=1$', fontsize=fontsize, ha='left', va='baseline')
plt.axis('off')
# PLOT OF F(x | x < a)
ax = plt.subplot2grid((1, 8), (0, 4), rowspan=1, colspan=4)
plt.xlim(xmin_ax, xmax_ax)
plt.ylim(ymin_ax, ymax_ax)
# axis arrows
plt.annotate("", xytext=(xmin_ax, 0), xycoords='data', xy=(xmax_ax, 0), textcoords='data',
arrowprops=dict(width=0.1, headwidth=6, headlength=8, facecolor='black', shrink=0.002))
plt.annotate("", xytext=(0, ymin_ax), xycoords='data', xy=(0, ymax_ax), textcoords='data',
arrowprops=dict(width=0.1, headwidth=6, headlength=8, facecolor='black', shrink=0.002))
plt.plot(x, pdf_h2, color=col10, linewidth=2)
plt.plot(x, pdf_h2_avg, color=col20, linewidth=2)
# xlabels and xtickslabels
plt.plot([h1, h1], [0, xtl], 'k')
plt.text(h1, xtm, '$h$', fontsize=fontsize, ha='center', va='top')
plt.plot([h2, h2], [0, xtl], 'k')
plt.text(h2, xtm, '$\\dfrac{h}{2}$', fontsize=fontsize, ha='center', va='top')
plt.text(xmin_ax, ymax_ax-0.1, '$\\alpha=\\dfrac{1}{2}$', fontsize=fontsize, ha='left', va='baseline')
# legend
leg = plt.legend(['$p(\hat{h}_i)$', '$p(\hat{h})$'], loc=1, fontsize=fontsize)
leg.get_frame().set_facecolor(0.97*np.ones((3,)))
leg.get_frame().set_edgecolor(0.97*np.ones((3,)))
plt.axis('off')
# save as pdf image
plt.savefig('problem_2_4.pdf', bbox_inches='tight')
plt.show()
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2... | 2.276432 | 1,693 |
"""Generate a matplotlib canvas and add it to a QWidget contained in a QMainWindow. This will provide the
display and interactions for the PyCCD plots."""
from lcmap_tap.logger import log, exc_handler
from lcmap_tap.Plotting import POINTS, LINES
import sys
import datetime as dt
import numpy as np
import pkg_resources
from PyQt5 import QtWidgets, QtCore
from PyQt5.QtGui import QIcon, QPixmap
import matplotlib
matplotlib.use("Qt5Agg")
from matplotlib.collections import PathCollection
from matplotlib.lines import Line2D
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
sys.excepthook = exc_handler
class MplCanvas(FigureCanvas):
"""
TODO: Add summary line
"""
def __init__(self, fig):
"""
TODO: Add Summary
Args:
fig:
"""
self.fig = fig
FigureCanvas.__init__(self, self.fig)
if len(fig.axes) >= 3:
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Ignored, QtWidgets.QSizePolicy.Minimum)
else:
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Ignored, QtWidgets.QSizePolicy.Ignored)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
FigureCanvas.setSizePolicy(self, sizePolicy)
FigureCanvas.updateGeometry(self)
| [
37811,
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257,
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526,
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198,
198,
67... | 2.501748 | 572 |
"""
Miscellaneous functions for working with files
"""
import os
def canonicalize_path(path: str):
"""Converts a path string to its canonical form (easier for comparisons)"""
return os.path.abspath(os.path.realpath(os.path.expanduser(path)))
| [
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4299,
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62,
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7,
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198,
220,
220,
220,
37227,
3103,
24040,
257,
3108,
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284,
663,
4009... | 3.135802 | 81 |
import os
import _winreg
def get_python_executables():
"""
Find the Maya installation paths using _winreg. The path to the python
executable is extended from the installation path. The dictionary is made
up of keys that are made up of the Maya versions found installed and a
path to the executable of that version of Maya as a value.
:return: Windows maya python executables
:rtype: dict
"""
# variables
maya_pythons = {}
registry = _winreg.HKEY_LOCAL_MACHINE
registry_maya_path = r"SOFTWARE\Autodesk\Maya"
# get maya key
maya_key_data = [
registry,
registry_maya_path,
0,
_winreg.KEY_READ
]
with _winreg.OpenKey(*maya_key_data) as maya_key:
# loop keys
for i in xrange(0, _winreg.QueryInfoKey(maya_key)[0]):
# get version
maya_version = _winreg.EnumKey(maya_key, i)
# validate version
if not maya_version.split(".")[0].isdigit():
continue
# get install path
registry_maya_install_path = os.path.join(
registry_maya_path, maya_version, "Setup", "InstallPath"
)
# get install key
maya_install_key_data = [
registry,
registry_maya_install_path,
0,
_winreg.KEY_READ
]
with _winreg.OpenKey(*maya_install_key_data) as maya_install_key:
# get path
maya_location_data = [maya_install_key, "MAYA_INSTALL_LOCATION"]
maya_location = _winreg.QueryValueEx(*maya_location_data)[0]
# set data
maya_py = os.path.join(maya_location, "bin", "mayapy.exe")
maya_pythons[maya_version] = maya_py
return maya_pythons
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21... | 1.950455 | 989 |
"""A rule that provides file(s) specific via DefaultInfo from a given target's DefaultInfo or OutputGroupInfo
"""
load(
"//lib/private:output_files.bzl",
_make_output_files = "make_output_files",
_output_files = "output_files",
)
output_files = _output_files
make_output_files = _make_output_files
| [
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1... | 2.971429 | 105 |
"""This script converts a collection of MIDI files to multitrack pianorolls.
"""
import os
import json
import argparse
import warnings
import pretty_midi
from pypianoroll import Multitrack
from utils import make_sure_path_exists, change_prefix, findall_endswith
from config import CONFIG
if CONFIG['multicore'] > 1:
import joblib
warnings.filterwarnings('ignore')
def parse_args():
"""Return the parsed command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument('src', help="root path to the source dataset")
parser.add_argument('dst', help="root path to the destination dataset")
parser.add_argument('--midi-info-path', dest='midi_info_path',
help="path to save the MIDI info dictionary")
args = parser.parse_args()
return args.src, args.dst, args.midi_info_path
def get_midi_info(pm):
"""Return useful information from a MIDI object."""
if pm.time_signature_changes:
pm.time_signature_changes.sort(key=lambda x: x.time)
first_beat_time = pm.time_signature_changes[0].time
else:
first_beat_time = pm.estimate_beat_start()
tc_times, tempi = pm.get_tempo_changes()
if len(pm.time_signature_changes) == 1:
time_sign = '{}/{}'.format(pm.time_signature_changes[0].numerator,
pm.time_signature_changes[0].denominator)
else:
time_sign = None
midi_info = {
'first_beat_time': first_beat_time,
'num_time_signature_change': len(pm.time_signature_changes),
'constant_time_signature': time_sign,
'constant_tempo': tempi[0] if len(tc_times) == 1 else None
}
return midi_info
def converter(filepath, src, dst):
"""Convert a MIDI file to a multi-track piano-roll and save the
resulting multi-track piano-roll to the destination directory. Return a
tuple of `midi_md5` and useful information extracted from the MIDI file.
"""
try:
midi_md5 = os.path.splitext(os.path.basename(filepath))[0]
multitrack = Multitrack(beat_resolution=CONFIG['beat_resolution'],
name=midi_md5)
pm = pretty_midi.PrettyMIDI(filepath)
multitrack.parse_pretty_midi(pm)
midi_info = get_midi_info(pm)
result_dir = change_prefix(os.path.dirname(filepath), src, dst)
make_sure_path_exists(result_dir)
multitrack.save(os.path.join(result_dir, midi_md5 + '.npz'))
return (midi_md5, midi_info)
except:
return None
def main():
"""Main function."""
src, dst, midi_info_path = parse_args()
make_sure_path_exists(dst)
midi_info = {}
if CONFIG['multicore'] > 1:
kv_pairs = joblib.Parallel(n_jobs=CONFIG['multicore'], verbose=5)(
joblib.delayed(converter)(midi_path, src, dst)
for midi_path in findall_endswith('.mid', src))
for kv_pair in kv_pairs:
if kv_pair is not None:
midi_info[kv_pair[0]] = kv_pair[1]
else:
for midi_path in findall_endswith('.mid', src):
kv_pair = converter(midi_path, src, dst)
if kv_pair is not None:
midi_info[kv_pair[0]] = kv_pair[1]
if midi_info_path is not None:
with open(midi_info_path, 'w') as f:
json.dump(midi_info, f)
print("{} files have been successfully converted".format(len(midi_info)))
if __name__ == "__main__":
main()
| [
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2495,... | 2.191004 | 1,623 |
from flask import request, current_app
import jwt
from app.models.auth import User
from app.api import api
# required_token decorator
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198
] | 3.605263 | 38 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Fetches audit log data from the Mimecast API and saves to a folder for Sumo Logic data collection"""
import base64
import hashlib
import hmac
import json
import logging
import os
import pickle
import uuid
import time
import datetime
import sys
from os.path import dirname, abspath
import requests
# Program Start
with open(os.path.join(os.path.join(dirname(dirname(abspath(__file__))),
'checkpoint', 'config.txt')), 'rb') as f:
config = pickle.load(f)
log_dir = os.path.join(os.path.join(dirname(dirname(abspath(__file__))), 'log'))
log_name = 'audit_' + datetime.datetime.utcnow().strftime('%d%m%Y') + '.log'
logging.basicConfig(filename=os.path.join(log_dir, log_name), level=logging.INFO,
format='%(levelname)s|%(asctime)s|%(message)s')
account_code = config['account_code']
if len(account_code) < 0:
logging.error('Log collection aborted. Account code not found, exiting.')
sys.exit()
logging.info('***** Mimecast Data Collector for Sumo Logic v1.0 *****')
logging.info('Starting audit log collection for ' + account_code)
data_dir = config['data_dir']
if len(data_dir) < 0:
logging.error('Data directory not set, exiting.')
sys.exit()
logging.info('Using data directory: ' + data_dir)
access_key = config['access_key']
if len(access_key) < 0:
logging.error('Access Key not set, exiting.')
sys.exit()
secret_key = config['secret_key']
if len(secret_key) < 0:
logging.error('Secret Key not set, exiting.')
sys.exit()
api_base_url = config['api_base_url']
if len(api_base_url) < 0:
logging.error('API base URL not set, exiting.')
sys.exit()
if os.path.exists(os.path.join(dirname(dirname(abspath(__file__))), 'checkpoint', 'checkpoint_audit_start')):
with open(os.path.join(dirname(dirname(abspath(__file__))), 'checkpoint', 'checkpoint_audit_start')) as csd:
start = csd.read()
else:
start = get_iso_time(60)
end = get_iso_time(0)
while get_audit_logs(start=start, end=end) is True:
logging.info('Collecting Audit logs')
#Clean up data files
remove_files(os.path.join(dirname(dirname(abspath(__file__))), 'log'))
#Clean up log files
remove_files(os.path.join(data_dir, 'audit'))
logging.info('Audit log collection complete')
sys.exit()
| [
2,
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14,
8800,
14,
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198,
2,
532,
9,
12,
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25,
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69,
12,
23,
532,
9,
12,
198,
198,
37811,
37,
316,
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422,
262,
337,
524,
2701,
7824,
290,
16031,
284,
257,
9483,
329,
5060,
... | 2.570166 | 905 |
import unittest
from core.poly_parser import *
if __name__ == '__main__':
unittest.main()
| [
11748,
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10354,
198,
220,
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555,
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3419,
198
] | 2.621622 | 37 |
from typing import List, Optional
from dataclasses import dataclass
from botbuilder.schema import ActivityTypes, Activity
from botbuilder.core import MessageFactory
from botbuilder.dialogs import (
WaterfallDialog,
WaterfallStepContext,
DialogTurnResult,
PromptOptions,
Choice,
ChoicePrompt,
)
from botbuilder.dialogs.prompts import OAuthPrompt, OAuthPromptSettings
from cards import get_azure_vms_card, AZURE_VMS_CARD_MAX_VMS
from dialogs import LogoutDialog
from cloud_clients import AzureClient
from cloud_models.azure import Subscription, Vm
@dataclass
| [
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382... | 3.202186 | 183 |
# Theory: Introduction to Python
# Computer science has been around for a while, and
# programming languages are one of its main tools. These are
# designed to help us implement software to run on a computer.
# Just as natural languages for people, they serve as a
# communication tool, only between people and machines. In this
# topic, we will learn about the one exponentially gaining
# popularity among developers recently. Behold, this is an
# introduction to Python!
# 1. What is Python?
# Python is a modern general-purpose programming language
# initially developed by a Dutch programmer named Guido van
# Rossum in the late 1980s. The name comes from the popular
# Monty Python show, not the snake, as you might think. This
# language has a clean, uniform, and well-readable syntax and is
# designed to be easy to learn and use in practice.
# Nowadays, Python is one of the most popular programming
# languages worldwide, according to the TIOBE index. The
# number of programmers who use it is growing every day!
# The language has a huge community of developers around the
# world. If you have a problem, you can always ask other
# programmers for help or find a suitable answer on a site like
# Stack Overflow.
# Developing software with Python is easy and fun!
# Python has a wide range of possible applications, especially in:
# - Web development;
# - Data science (including machine learning);
# - Scripting (task automation, such as text processing or simulation of typical user actions).
# It is also used in desktop development, though less commonly.
# 2. Python is data science
# Python's huge popularity in recent years is mostly due to its use
# in data science. What makes it better than other languages for
# this purpose. Well, there's a number of reasons:
# - Its simple syntax allows people from non-programming
# backgrounds to use it for data processing and model
# training without spending much time learning a new
# language.
# - Python supports a very large number of third-party
# libraries for machine learning, neural networks, statistics,
# numeric calculations, which makes your job much
# easier.
# - With Python, it is possible to collect, clean, and explore
# data, as well as train models and visualize the results - all
# in one setting!
# - Python ML developers' community is very large, so you
# can always find support for your tasks.
# As you can see, Python doest have a lot to offer for data science
# enthusiats.
# 3. A short history of Python
# Like other programming languages, Python has gone through
# a number of versions. Python 1.0 was released in 1994 and laid
# the basic principles of the language with emphasis on simplicity.
#
# Python 2.0 was released in 2000. This version has become very
# popular among programmers. Different 2.x subversions (2.6,
# 2.7) are still used in various projects and libraries. The symbol x
# in 2.x means any subversion of Python 2.
#
# Python 3.0 was the next major version released in 2008. It
# broke backward compatibility with its predecessors in order to
# rid the language of historic clutter and make Python more
# readable and consistent.
#
# Today, two similar but incompatible versions of Python are
# commonly used.
# Throughout this course, we will be working with Python
# 3.x.
# 4. First program example
# Here is a single line of Python that prints Learn Python to
# be great!.
print("Learn Python to be great!")
# For now, you do not need to understand how this code works:
# just appreciate its beautiful syntax that isn't too far from normal
# English.
# 5. Summary
# Let's summarize what we've learned in this topic:
# - what Python is;
# - it applications;
# - the reasons why Python is so popular in the field of data
# science;
# - the history of the language;
# - how to write simple code in Python.
# Now, when you know the most basic things about Python, you're
# all setteld to continue your journey into it!
| [
2,
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284,
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198,
2,
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587,
1088,
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198,
2,
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3562,
284,
1037,
514,
3494,
3788,
284,
1057,
319,
257,
3644,
... | 4.026342 | 987 |
from __future__ import print_function
import pytest
import requests
from jenkinsapi.jenkins import Requester
from jenkinsapi.custom_exceptions import JenkinsAPIException
from mock import patch
@patch('jenkinsapi.jenkins.Requester.AUTH_COOKIE', 'FAKE')
@patch('jenkinsapi.jenkins.Requester.AUTH_COOKIE', 'FAKE')
| [
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62,
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198,
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12972,
9288,
198,
11748,
7007,
198,
6738,
474,
268,
5331,
15042,
13,
48796,
5331,
1330,
9394,
7834,
198,
6738,
474,
268,
5331,
15042,
13,
23144,
62,
1069,
11755,
1330,
21835,
17... | 3.008772 | 114 |
from flask import Blueprint, flash, redirect, render_template, request, url_for
from flask_login import login_required
from app.dao.rent import get_all_rentcodes
from app.dao.money import get_acc_descs, get_moneydets, get_money_item, toggle_cleared
from app.main.money import collect_search_filter, mget_money_acc, mget_money_items, mget_moneydict, \
mpost_money_acc, \
mpost_money_item, mpost_transfer
from app.main.dashboard import mget_recent_money_searches, mpost_recent_money, mpost_search
from app import db
money_bp = Blueprint('money_bp', __name__)
@money_bp.route('/money', methods=['GET', 'POST'])
@money_bp.route('/money_acc/<int:acc_id>', methods=['GET', 'POST'])
@login_required
@money_bp.route('/money_item/<int:money_item_id>', methods=['GET', 'POST'])
@money_bp.route('/money_items/<int:acc_id>', methods=["GET", "POST"])
@login_required
@money_bp.route('/money_save_search', methods=['GET', 'POST'])
@money_bp.route('/money_transfer/<int:acc_id>', methods=["GET", "POST"])
@login_required
@money_bp.route('/toggle/<int:money_item_id>', methods=['GET', 'POST'])
| [
6738,
42903,
1330,
39932,
11,
7644,
11,
18941,
11,
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62,
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11,
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2581,
11,
19016,
62,
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6738,
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62,
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598,
13,
67,
5488,
13,
1156,
1330,
651,
62,
439,
62,
1156,
40148... | 2.70098 | 408 |
from synonym_dict import SynonymDict, SynonymSet
class QuantitySynonyms(SynonymSet):
"""
QuantitySynonyms are string terms that all refer to the same quantity of measure. They must all have the same
unit, because they are used to define the unit of measure of flowables. To repeat: quantity instances that have
the same dimensionality but different units (e.g. kWh and MJ) are NOT SYNONYMS but distinct quantities. The
LciaEngine should be able to handle conversions between these kinds of quantities.
"""
@classmethod
@property
@quantity.setter
def _save_synonyms(self, other):
"""
adds to other's synonym set-
:param other:
:return:
"""
for k in other.terms:
self._quantity.add_synonym(k)
@property
@property
| [
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220,
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198,
220,
220,
220,
39789,
29934,
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389,
4731,
... | 2.885417 | 288 |
import os
import sys
from algorithms import dijkstra, floyd
from utils import *
TASK1_START = 0
print '-'*35 + '\ntask1 - shortest ways from ({})\n'.format(TASK1_START + 1) + '-'*35
g_list, g_mat = read_graph_list('task1.in')
dist, prev, table = dijkstra(g_list, TASK1_START)
user_interaction(prev, dist, g_mat)
write_debug_table('task1.out.md', table)
print '\n' + '-'*35 + '\ntask2\n' + '-'*35
os.remove('task2.out.md')
g_mat = read_graph_matrix('task2.in')
dist, path = floyd(g_mat)
| [
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7... | 2.288372 | 215 |
bashCommand = "rostopic pub -1 /spray_onoff std_msgs/Float32 1.0"
import subprocess
process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
output, error = process.communicate() | [
41757,
21575,
796,
366,
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532,
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850,
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7,
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21575,
13,... | 2.969231 | 65 |
from fastapi import FastAPI
from config import ServiceConfig
from apps.libs import init_app
| [
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] | 3.916667 | 24 |
import os
from office365.sharepoint.client_context import ClientContext
SITE_URL = os.getenv('SITE_URL')
USER = os.getenv('USER')
PASS = os.getenv('PASS')
DESTINATION = os.getenv('DESTINATION')
FILE = os.getenv('FILE')
FILE_ON_SERVER = os.getenv('FILE_ON_SERVER')
if __name__ == "__main__":
main()
| [
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1... | 2.571429 | 119 |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torch.nn as nn
import torch.nn.functional as F
from crlapi.core import CLModel
from crlapi.sl.clmodels.finetune import Finetune
import time
import copy
import numpy as np
from pydoc import locate
class IndexDataset(torch.utils.data.Dataset):
""" Wrapper that additionally returns the index for each sample """
class BoostingSampler(torch.utils.data.Sampler):
""" Upsample points based on sample weight """
| [
2,
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66,
8,
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11,
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663,
29116,
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2,
38559,
24290,
2393,
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6808,
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286,
42... | 3.42246 | 187 |
from ibm_db_sa import requirements
Requirements = requirements.Requirements
| [
6738,
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62,
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62,
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1330,
5359,
198,
198,
42249,
796,
5359,
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42249,
198
] | 4.529412 | 17 |
from datetime import datetime, time, timedelta
from PyShift.test.base_test import BaseTest
from PyShift.workschedule.work_schedule import WorkSchedule
| [
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5950,
... | 2.009174 | 109 |
# -*- coding: utf-8 -*-
"""
ppmeasurements.util
~~~~~~~~~~~~~~
This module contains utility functions for parsing information,
checking for non-global IP address, convert traceroutes to machine-readable
structures, etc.
:author: Muzammil Abdul Rehman
:copyright: Northeastern University © 2018.
:license: Custom BSD, see LICENSE for more details.
:email: passport@ccs.neu.edu
"""
import json
import trparse
import socket
import datetime
import time
###remove-me-later-muz###import ormsettings as DJANGO_SETTINGS
from ppstore.models import Hints_country_codes
import os
import csv
import pputils
import ipaddress
"""
Sample JSON:
{"source": "127.0.1.1", "destName": "8.8.8.8", "destIP": "8.8.8.8", "resolvers": ["8.8.8.8"],
"type": "Input_DNS_Resolver", "startTime": 1472651763386, "timestamp": 1472651763632, "entries": [
{"rtt": [0.231, 0.213, 0.242], "router": ["129.10.113.1", null, null], "routerName": ["unknown", null, null],
"numRouters": 1},
{"rtt": [2.21, 2.389, 2.733], "router": ["129.10.110.2", null, null], "routerName": ["unknown", null, null],
"numRouters": 1},
{"rtt": [0.963, 0.951, 0.951], "router": ["10.2.29.52", null, null], "routerName": ["unknown", null, null],
"numRouters": 1},
{"rtt": [1.465, 1.518, 1.505], "router": ["10.2.29.33", null, null], "routerName": ["unknown", null, null],
"numRouters": 1},
{"rtt": [1.554, 1.544, 1.489], "router": ["10.2.29.230", null, null], "routerName": ["unknown", null, null],
"numRouters": 1}, {"rtt": [4.289, 4.469, 4.513], "router": ["207.210.142.101", null, null],
"routerName": ["nox1sumgw1-neu-cps.nox.org.", null, null], "numRouters": 1},
{"rtt": [31.826, 31.246, 31.229], "router": ["198.71.47.61", null, null],
"routerName": ["et-10-0-0.122.rtr.eqch.net.internet2.edu.", null, null], "numRouters": 1},
{"rtt": [31.204, 30.928, 31.072], "router": ["74.125.49.146", null, null],
"routerName": ["unknown", null, null], "numRouters": 1},
{"rtt": [31.263, 31.251, 31.791], "router": ["209.85.143.154", "209.85.242.133", "209.85.254.120"],
"routerName": ["unknown", "unknown", "unknown"], "numRouters": 3},
{"rtt": [31.787, 31.628, 31.447], "router": ["209.85.243.163", "209.85.241.47", null],
"routerName": ["unknown", "unknown", null], "numRouters": 2},
{"rtt": [40.979, 41.171, 40.825], "router": ["209.85.247.4", "216.239.47.121", "209.85.247.4"],
"routerName": ["unknown", "unknown", "unknown"], "numRouters": 3},
{"rtt": [40.97, 45.834, 45.785], "router": ["72.14.234.81", "216.239.62.13", "209.85.248.89"],
"routerName": ["unknown", "unknown", "unknown"], "numRouters": 3},
{"rtt": [-1.0, -1.0, -1.0], "router": ["unknown", "unknown", "unknown"],
"routerName": ["unknown", "unknown", "unknown"], "numRouters": 3},
{"rtt": [40.757, 41.006, 40.924], "router": ["8.8.8.8", null, null],
"routerName": ["google-public-dns-a.google.com.", null, null], "numRouters": 1}]}
"""
if __name__ == "__main__":
#test_from_traceroute_to_server_json()
pass
| [
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628,
220,
220,
220,
770,
8265,
4909,
10361,
5499,
329,
32096,
... | 2.181818 | 1,485 |
# Copyright (c) 2019-2021, Jonas Eschle, Jim Pivarski, Eduardo Rodrigues, and Henry Schreiner.
#
# Distributed under the 3-clause BSD license, see accompanying file LICENSE
# or https://github.com/scikit-hep/vector for details.
import pytest
import vector
| [
2,
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565,
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34... | 3.158537 | 82 |
# vim:set ts=8 sw=2 sts=2 et:
"""Store signals."""
import itertools
import struct
import wave
SAMPLE_SIZE = 16 # In bits
class WaveStream(object):
"""A stream of PCM audio data."""
def __init__(self, channels, sample_rate, sample_size):
"""Initialize a WaveStream with integer samples.
Args:
channels: An iterable of the audio channels in the stream. Each item is
also an iterable containing the samples of that audio channel. All audio
channels must have the same number of samples. The samples are signed
integers that fit in the sample size.
sample_rate: The number of samples per second in the audio stream, in Hz.
sample_size: The number of bits used to store each sample. Must be 16.
"""
self.channels = channels
self.sample_size = sample_size
self.sample_rate = sample_rate
self.num_channels = len(channels)
self.num_samples = len(channels[0])
# TODO(serban): It's convenient to use the struct module to encode the
# samples as 16-bit little endian signed integers. To support other sample
# sizes, like 20 bits or 24 bits per sample, I would need to use another
# utility to encode the samples. For now, only 16-bit samples are supported.
assert sample_size == 16, 'Sorry, only 16-bit samples are supported'
@classmethod
def from_floating_point(cls, channels, sample_rate):
"""Initialize a WaveStream with floating point samples.
Args:
channels: An iterable of the audio channels in the stream. Each item is
also an iterable containing the samples of that audio channel. All audio
channels must have the same number of samples. The samples are floats
between -1.0 and 1.0.
sample_rate: The number of samples per second in the audio stream, in Hz.
Returns:
A WaveStream
"""
sample_max = 2**(SAMPLE_SIZE-1) - 1
int_channels = [
[int(sample * sample_max) for sample in channel]
for channel in channels]
return cls(int_channels, sample_rate, SAMPLE_SIZE)
def get_interleaved_samples(self):
"""Interleave the samples in the channels into a single bytestring.
Returns:
A bytestring of little endian signed integers
"""
num_interleaved_samples = self.num_channels * self.num_samples
interleaved_samples = itertools.chain.from_iterable(zip(*self.channels))
struct_format = '<{}h'.format(num_interleaved_samples)
return struct.pack(struct_format, *interleaved_samples)
def write_wave_file(self, output_path):
"""Write a WAVE file of the stream contents.
Args:
output_path: The path to the resulting WAVE file.
"""
# TODO(serban): As of January 2014, Python 3.4 is still in beta. wave.open()
# supports the context manager protocol in Python 3.4, but I'll wait until
# it becomes stable before using a context manager here. See
# http://docs.python.org/dev/whatsnew/3.4.html#wave for more information.
output_file = wave.open(output_path, 'wb')
output_file.setsampwidth(self.sample_size // 8)
output_file.setframerate(self.sample_rate)
output_file.setnchannels(self.num_channels)
output_file.setnframes(self.num_samples)
output_file.setcomptype('NONE', 'not compressed')
output_file.writeframes(self.get_interleaved_samples())
output_file.close()
| [
2,
43907,
25,
2617,
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28,
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28,
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28,
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628,
198,
49302,
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62,
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796,
1467,
... | 2.937118 | 1,145 |
# encoding=utf-8
import logging
logger = logging.getLogger(__name__)
| [
2,
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12,
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11187,
1362,
7,
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3672,
834,
8,
198
] | 2.8 | 25 |
import xml.etree.ElementTree as ET
from pprint import pprint as pp
import re
# from OMDB
xmlstring = '''<?xml version="1.0" encoding="UTF-8"?>
<root response="True">
<movie title="The Prestige" year="2006" rated="PG-13" released="20 Oct 2006" runtime="130 min" genre="Drama, Mystery, Sci-Fi" director="Christopher Nolan" />
<movie title="The Dark Knight" year="2008" rated="PG-13" released="18 Jul 2008" runtime="152 min" genre="Action, Crime, Drama" director="Christopher Nolan" />
<movie title="The Dark Knight Rises" year="2012" rated="PG-13" released="20 Jul 2012" runtime="164 min" genre="Action, Thriller" director="Christopher Nolan" />
<movie title="Dunkirk" year="2017" rated="PG-13" released="21 Jul 2017" runtime="106 min" genre="Action, Drama, History" director="Christopher Nolan" />
<movie title="Interstellar" year="2014" rated="PG-13" released="07 Nov 2014" runtime="169 min" genre="Adventure, Drama, Sci-Fi" director="Christopher Nolan"/>
</root>''' # noqa E501
def get_tree():
"""You probably want to use ET.fromstring"""
root = ET.fromstring(xmlstring)
return ET.ElementTree(root)
def get_movies():
"""Call get_tree and retrieve all movie titles, return a list or generator"""
tree = get_tree()
root = tree.getroot()
for movie in root:
yield movie.attrib['title']
def _get_runtime(movie):
'''internal help function for receiving runtime of movie'''
return int(re.search(r'(\d+) min', movie.attrib['runtime']).group(1))
def get_movie_longest_runtime():
"""Call get_tree again and return the movie with the longest runtime in minutes,
for latter consider adding a _get_runtime helper"""
tree = get_tree()
root = tree.getroot()
max_runtime = 0
max_runtime_movie = ''
for movie in root:
if _get_runtime(movie) > max_runtime:
max_runtime_movie = movie.attrib['title']
return max_runtime_movie
| [
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27660,
355,
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201,
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279,
4798,
1330,
279,
4798,
355,
9788,
201,
198,
11748,
302,
201,
198,
201,
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201,
198,
2,
422,
32468,
11012,
201,
198,
19875,
8841,
796,
705,
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47934,
... | 2.874818 | 687 |