content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
"""
Abstract types
"""
from abc import ABC, abstractmethod
import typing
from hacenada.const import STR_DICT
class SessionStorage(ABC):
"""
Provide access to the session's underlying storage through any mechanism
"""
answer: typing.Any
meta: typing.Any
@property
def script_path(self):
"""
The path to the script associated with this storage
Concrete method, implementing this is optional
"""
@script_path.setter
def script_path(self, value):
"""
Set the path to the script associated with this storage
Concrete method, implementing this is optional
"""
@property # type: ignore
@abstractmethod
def description(self):
"""
A description of this hacenada session
"""
@description.setter # type: ignore
@abstractmethod
def description(self, val):
"""
Set the description
"""
@abstractmethod
def save_answer(self, answer: STR_DICT):
"""
Save a single answer
"""
@abstractmethod
def update_meta(self, **kw):
"""
Update meta properties based on keywords (e.g. description="hello world")
"""
@abstractmethod
def get_answer(self, label: str):
"""
Look up a single answer by str
"""
def drop(self):
"""
Delete the storage
Concrete method, implementing this is optional
"""
class Render(ABC):
"""
Rendering operations for question types
"""
@abstractmethod
def render(self, step, context) -> STR_DICT:
"""
Output a question to a device, should return a 0-item label:value dict
"""
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] | 2.557353 | 680 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
from django.db import models
from django.contrib.auth.models import User
from logging import root, basicConfig
import openbabel
import sys
import re
import tempfile
import os
import codecs
import md5
import compounddb.sdfiterator
import string
import random
cur_dir = os.path.dirname(__file__)
from compounddb.models import *
basicConfig()
inchiconv = openbabel.OBConversion()
#########################
# sdf-related processings
#########################
def get_sdf_tags(sdf):
"""parse the sdf tags"""
tag_pattern = re.compile(""">\s+<([^>]+)>[^
]*
([^>$]+)""")
tags = tag_pattern.findall(sdf)
tagdict = dict()
# process each tag
for (name, value) in tags:
tagdict[name.strip()] = value.strip()
return tagdict
def parse_annotation(sdf, namekey):
""" parse annotation from SDF file """
# parse the sdf tags
moldata = get_sdf_tags(sdf)
# --- inchi
inchiconv.SetInAndOutFormats('sdf', 'Inchi')
mol = openbabel.OBMol()
res = inchiconv.ReadString(mol, codecs.encode(sdf, 'utf-8'))
if mol.Empty():
root.warning(' --> ERROR on sdf')
raise Exception
# standard data generated
# --- inchi/formula/weight
moldata['inchi'] = inchiconv.WriteString(mol).strip()
moldata['formula'] = mol.GetFormula()
moldata['id'] = mol.GetTitle()
if moldata['id'] == '':
moldata['id'] = 'unspecified_' \
+ ''.join(random.sample(string.digits, 6))
mol.AddHydrogens()
moldata['weight'] = str(mol.GetMolWt())
# if the name is not in sdf:
if not moldata.has_key(namekey):
moldata[namekey] = ''
# smiles
inchiconv.SetInAndOutFormats('sdf', 'smi')
mol = openbabel.OBMol()
res = inchiconv.ReadString(mol, codecs.encode(sdf, 'utf-8'))
if mol.Empty():
root.warning(' --> ERROR on sdf')
raise Exception
moldata['smiles'] = inchiconv.WriteString(mol).strip()
return moldata
############################
# single compound operations
############################
def insert_single_compound(
moldata,
sdf,
namekey,
idkey,
user,
):
""" insert single compound into database """
cid = moldata[idkey]
name = moldata[namekey]
if '\n' in name:
name = name.split('\n')[0]
# compound
c = Compound(
cid=cid,
name=name,
formula=moldata['formula'],
weight=moldata['weight'],
inchi=moldata['inchi'],
smiles=moldata['smiles'],
user=user,
)
# sdf_file=s)
c.save()
c_id = c.id
root.warning(' -->new compound inserted: c_id=%s, cid=%s' % (c_id,
cid))
# sdf file
s = SDFFile(sdffile=sdf, compound=c)
s.save()
sdfid = s.id
return c.id
#####################################
# Physical Chemical Property - JOELib
#####################################
def gen_joelib_property(sdf):
"""run and parse the property output """
# save the input in FS
t = tempfile.NamedTemporaryFile(suffix='.sdf')
t.write(codecs.encode(sdf, 'utf-8'))
t.flush()
# prepare the output file
(f, out) = tempfile.mkstemp(suffix='.sdf')
os.close(f)
# convert
cmd = \
"""JAVA_HOME=/opt/jre/ JOELIB2=/opt/JOELib2-alpha-20070303/ /opt/JOELib2-alpha-20070303/moleculeConversion.sh +d +h -iSDF -osdf "%s" "%s" > /dev/null""" \
% (t.name, out)
root.warning(' --> running:%s' % cmd)
if os.system(cmd) != 0:
os.unlink(out)
raise 'cannot run JOELib'
# read and parse
f = file(out)
tags = get_sdf_tags(codecs.decode(f.read(), 'utf-8'))
f.close()
# clean
os.unlink(out)
return tags
######
# MISC
######
def update_mw(
lib_name,
lib_ver,
input,
rev=False,
):
"""goal: to update MW value with hydrogen added
.... when calculating JOELib
.... 'rev': in ChemMineV2, some libraries got compound ID and compound name switched, like 'Aurora'"""
import datetime
begin = datetime.datetime.now()
print 'starts at: %s' % begin
library = get_library(lib_name, lib_ver)
mw = PropertyField.objects.get(name='MW')
fp = file(input)
line1 = fp.readline()
count = 1
for line in fp:
(cid, weight) = line.strip().split('\t')
try:
if rev:
c = Compound.objects.get(library=library, name=cid)
else:
c = Compound.objects.get(library=library, cid=cid)
except Compound.DoesNotExist:
print 'not found: line %s, cid=%s' % (count, cid)
pass
try:
p = Property.objects.get(compound=c, field=mw)
p.value = weight
p.save()
except Property.DoesNotExist:
p = Property(field=mw, compound=c, value=weight)
p.save()
print 'new p for %s, line %s' % (cid, count)
except:
print '----->line %s, cid=%s' % (count, cid)
pass
count += 1
# print "%s: %s -> %s", (cid, old, weight)
fp.close()
end = datetime.datetime.now()
print 'ends at: %s' % end
return
def del_duplicate_mw(lib_name, lib_ver):
"""some libraries has 2 mw """
library = get_library(lib_name, lib_ver)
mw = PropertyField.objects.get(name='MW')
for c in library.compound_set.all():
if c.property_set.filter(field=mw).count() == 2:
c.property_set.filter(field=mw)[1].delete()
return
def fix_kegg_cid():
"""some cid in KEGG still has '(noMol)', fix them"""
library = get_library('KEGG', 0)
count = 0
for c in library.compound_set.all():
if '(noMol)' in c.cid:
old = c.cid
print old
c.cid = old.strip('(noMol)')
c.save()
count += 1
print '%s compounds updated with new cid' % count
return
def format_sdf_for_qsar(sdffile, output, ID_tag):
"""Cerius2 uses 1st line in SDF as ID tag
.... some sdf has blank 1st line, so we need to format SDF
.... by filling cid to 1st line in SDF"""
fp = file(output, 'w')
for sdf in sdfiterator.sdf_iter(sdffile):
tagdict = get_sdf_tags(sdf)
cid = tagdict[ID_tag]
fp.write('%s\n' % cid)
fp.write(sdf.split('\n', 1)[1].split('M END')[0])
fp.write('M END\n')
fp.write('''> <%s>
%s
''' % (ID_tag, cid))
fp.write('$$$$\n')
fp.close()
return
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] | 2.182121 | 2,998 |
# Generated by Django 3.1.14 on 2021-12-13 11:06
import django.db.models.deletion
from django.db import migrations, models
import reservation_units.models
| [
2,
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] | 3.038462 | 52 |
"""xtapi"""
from fastapi import (
Query,
Path,
Body,
Cookie,
Header,
Form,
File,
UploadFile,
Request,
Response,
status,
Depends,
APIRouter,
HTTPException,
BackgroundTasks
)
from .main import MainApp
from .templates import Templates
__all__ = [
'Query',
'Path',
'Body',
'Cookie',
'Header',
'Form',
'File',
'UploadFile',
'status',
'Request',
'Response',
'Depends',
'APIRouter',
'HTTPException',
'BackgroundTasks',
'MainApp',
'Templates'
]
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3256,
198,
220,
220,
220,
705,
12966,
17041,
6,
198,
60,
198
] | 2.163498 | 263 |
from abc import ABC, ABCMeta
from typing import Any
from . import registry
from tensortrade.core.context import TradingContext, Context
from tensortrade.core.base import Identifiable
class InitContextMeta(ABCMeta):
"""Metaclass that executes `__init__` of instance in its core.
This class works with the `TradingContext` class to ensure the correct
data is being given to the instance created by a concrete class that has
subclassed `Component`.
"""
def __call__(cls, *args, **kwargs) -> 'InitContextMeta':
"""
Parameters
----------
args :
positional arguments to give constructor of subclass of `Component`
kwargs :
keyword arguments to give constructor of subclass of `Component`
Returns
-------
`Component`
An instance of a concrete class the subclasses `Component`
"""
context = TradingContext.get_context()
registered_name = registry.registry()[cls]
data = context.data.get(registered_name, {})
config = {**context.shared, **data}
instance = cls.__new__(cls, *args, **kwargs)
setattr(instance, 'context', Context(**config))
instance.__init__(*args, **kwargs)
return instance
class ContextualizedMixin(object):
"""A mixin that is to be mixed with any class that must function in a
contextual setting.
"""
@property
def context(self) -> Context:
"""Gets the `Context` the object is under.
Returns
-------
`Context`
The context the object is under.
"""
return self._context
@context.setter
def context(self, context: Context) -> None:
"""Sets the context for the object.
Parameters
----------
context : `Context`
The context to set for the object.
"""
self._context = context
class Component(ABC, ContextualizedMixin, Identifiable, metaclass=InitContextMeta):
"""The main class for setting up components to be used in the `TradingEnv`.
This class if responsible for providing a common way in which different
components of the library can be created. Specifically, it enables the
creation of components from a `TradingContext`. Therefore making the creation
of complex environments simpler where there are only a few things that
need to be changed from case to case.
Attributes
----------
registered_name : str
The name under which constructor arguments are to be given in a dictionary
and passed to a `TradingContext`.
"""
registered_name = None
def __init_subclass__(cls, **kwargs) -> None:
"""Constructs the concrete subclass of `Component`.
In constructing the subclass, the concrete subclass is also registered
into the project level registry.
Parameters
----------
kwargs : keyword arguments
The keyword arguments to be provided to the concrete subclass of `Component`
to create an instance.
"""
super().__init_subclass__(**kwargs)
if cls not in registry.registry():
registry.register(cls, cls.registered_name)
def default(self, key: str, value: Any, kwargs: dict = None) -> Any:
"""Resolves which defaults value to use for construction.
A concrete subclass will use this method to resolve which default value
it should use when creating an instance. The default value should go to
the value specified for the variable within the `TradingContext`. If that
one is not provided it will resolve to `value`.
Parameters
----------
key : str
The name of the attribute to be resolved for the class.
value : any
The `value` the attribute should be set to if not provided in the
`TradingContext`.
kwargs : dict, optional
The dictionary to search through for the value associated with `key`.
"""
if not kwargs:
return self.context.get(key, None) or value
return self.context.get(key, None) or kwargs.get(key, value)
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] | 2.787599 | 1,516 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from math import sqrt
from math import sin
# 函数作为参数传入
# 使用可变参数
print(same(3, abs, sqrt, sin))
print(do_fun([1, 2, 4, 9], abs, sqrt, sin)) | [
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] | 1.780952 | 105 |
from selenium import webdriver
import time
try:
# link = "http://suninjuly.github.io/registration1.html"
link = "http://suninjuly.github.io/registration2.html"
browser = webdriver.Chrome()
browser.get(link)
# Ваш код, который заполняет обязательные поля
input_first_name = browser.find_element_by_tag_name("input")
input_first_name.send_keys("Ivan")
input_last_name = browser.find_element_by_css_selector('input[placeholder="Input your last name"]')
input_last_name.send_keys("Petrov")
input_email = browser.find_element_by_css_selector("[placeholder='Input your email']")
input_email.send_keys("[email protected]")
# Отправляем заполненную форму
button = browser.find_element_by_css_selector("button.btn")
button.click()
# Проверяем, что смогли зарегистрироваться
# ждем загрузки страницы
time.sleep(3)
# находим элемент, содержащий текст
welcome_text_elt = browser.find_element_by_tag_name("h1")
# записываем в переменную welcome_text текст из элемента welcome_text_elt
welcome_text = welcome_text_elt.text
# с помощью assert проверяем, что ожидаемый текст совпадает с текстом на странице сайта
assert "Congratulations! You have successfully registered!" == welcome_text
print("Тест успешно завершен. 10 сек на закрытие браузера...")
finally:
# ожидание чтобы визуально оценить результаты прохождения скрипта
time.sleep(10)
# закрываем браузер после всех манипуляций
browser.close()
time.sleep(2)
browser.quit() | [
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16843,
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12466,
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640,
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7,
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220,
220,
220,
6444,
13,
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3419
] | 1.668838 | 921 |
import numpy as np
# https://gist.github.com/bwhite/3726239
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628,
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] | 2.538462 | 26 |
from kfp.v2.dsl import (
component,
Input,
Output,
Dataset,
Artifact,
HTML,
)
@component(
packages_to_install=[
"dask[dataframe]==2021.12.0",
"gcsfs==2021.11.1"]
)
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import logging
GROUP_SEPARATOR = f"{'-' * 10}"
N_TEAMS = 42
N_MEMBERS = 3
N_TEAMS_MAX = 50
N_MEMBERS_MAX = 15
BODY_TEAMS_KEY = 'teams'
BODY_ERRORS_KEY = 'errors'
ERROR_TAG = 'Error'
ERROR_MAX_MSG = f"User input Error. Maximum {N_TEAMS_MAX} teams and {N_MEMBERS_MAX} members for team. " \
f"Values must be numbers!"
ERROR_NOT_ENOUGH_MSG = 'Not enough Characters to generate this team'
CALC_TEAM_MEMBER_MAX_TRIES = 100
ERROR_MAX_TRIES_MSG = f"Max tries exceeded while choosing a team member: {CALC_TEAM_MEMBER_MAX_TRIES}. Name: %s"
LOGGER_FORMAT = '%(asctime)s %(levelname)s %(name)s: %(message)s'
logging.basicConfig(format=LOGGER_FORMAT)
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
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] | 2.339806 | 309 |
import matplotlib as mpl
import matplotlib.pyplot as plt
import multiprocessing as multi
import numpy as np
import os
import pandas as pd
import pmagpy
import pmagpy.ipmag as ipmag
import pmagpy.pmag as pmag
import pmagpy.pmagplotlib as pmagplotlib
import re
import scipy.integrate as integrate
import scipy.stats as stats
import seaborn as sns
import SPD.lib.leastsq_jacobian as lib_k
import sys
from datetime import datetime as dt
from importlib import reload
from multiprocessing import Pool
from scipy.stats import linregress
def find_best_API_portion_r(combinedRegs1,minFrac,minR,minSlopeT,maxSlopeT):
"""
Finds the best portion for NRM-TRM1* and TRM1-TRM2* plots by r criteria of Yamamoto+2003
(1) calculate API statistics for all possible coercivity intervals
(2) discard the statistics not satisfying the usual selection criteria (when applicable)
omitted - (3) sort the statistics by dAPI (rel. departure from the expected API),
and select the best 10 statistics
(4) sort the statistics by frac_n, and select the best one
Curvature (k) calculation is made by the code for Arai plot by Lisa.
This is done for inverterd-X (e.g. -TRM1, -ARM1, ..) and original-Y (e.g. NRM, ARM0, ..).
The inverted-X is offset (positive) to zero as a minimum.
revised 2021/09/06
__________
combinedRegs1 : combined API parameters
minFrac,minR,minSlopeT,maxSlopeT : thresholds for the r criteria
Returns
______
trm1_star_min
trm1_star_max
trm2_star_min
trm2_star_max
"""
print('[criteria, 2nd heating]')
#
screened=combinedRegs1[combinedRegs1.frac_t>=minFrac]
if (len(screened)>0):
print(' Frac_t >=', minFrac, ': ', len(screened),'step-combinations')
else:
print(' Frac_t >=', minFrac, ': no step-combinations satisfied')
screened=combinedRegs1
#
screened2=screened[screened.r_t>=minR]
if (len(screened2)>0):
print(' r_t >=', minR, ': ', len(screened2),'step-combinations')
screened=screened2
else:
print(' r_t >=', minR, ': no step-combinations satisfied')
#
screened3=screened[(screened.slope_t>=minSlopeT)\
&(screened.slope_t<=maxSlopeT)]
if (len(screened3)>0):
print(' ', minSlopeT, '<= slope_t <=', maxSlopeT, \
': ', len(screened3),'step-combinations')
screened=screened3
else:
print(' ', minSlopeT, '<= slope_t <=', maxSlopeT, \
': no step-combinations satisfied')
#
print('[criteria, 1st heating]')
#
screened4=screened[screened.frac_n>=minFrac]
if (len(screened4)>0):
print(' Frac_n >=', minFrac, ': ', len(screened4),'step-combinations')
screened=screened4
else:
print(' Frac_n >=', minFrac, ': no step-combinations satisfied')
#
screened5=screened[screened.r_n>=minR]
if (len(screened5)>0):
print(' r_n >=', minR, ': ', len(screened5),'step-combinations')
screened=screened5
else:
print(' r_n >=', minR, ': no step-combinations satisfied')
## sort by dAPI, then select top 10
#print('[sort by dAPI and select the top 10 data]')
#screened=screened.sort_values('dAPI')
#screened=screened.iloc[:10]
#
# sort by frac_n, then select the best
print('[sort by frac_n and select the best step-combination]')
screened=screened.sort_values('frac_n', ascending=False)
screened_best_fn=screened.iloc[:1]
#print(screened)
trm2_star_min=screened_best_fn['step_min_t'].iloc[0]
trm2_star_max=screened_best_fn['step_max'].iloc[0]
trm1_star_min=screened_best_fn['step_min_n'].iloc[0]
trm1_star_max=screened_best_fn['step_max'].iloc[0]
#
return trm1_star_min, trm1_star_max, trm2_star_min, trm2_star_max, screened
def find_best_API_portion_k(combinedRegs1,maxBeta,maxFresid,maxKrv):
"""
Finds the best portion for NRM-TRM1* and TRM1-TRM2* plots by k' criteria of Lloyd+2021
(1) calculate API statistics for all possible coercivity intervals
(2) discard the statistics not satisfying the Beta criterion (0.1) and the k' criterion (0.2)
omitted - (3) sort the statistics by dAPI (rel. departure from the expected API),
and select the best 10 statistics
(4) sort the statistics by frac_n, and select the best one
__________
combinedRegs1 : combined API parameters
minFrac,minR,minSlopeT,maxSlopeT : thresholds for the r criteria
Returns
______
trm1_star_min
trm1_star_max
trm2_star_min
trm2_star_max
"""
print('[criteria, 2nd heating]')
screened=combinedRegs1
#
#screened=combinedRegs1[combinedRegs1.frac_t>=minFrac]
#if (len(screened)>0):
# print(' Frac_t >=', minFrac, ': ', len(screened),'step-combinations')
#else:
# print(' Frac_t >=', minFrac, ': no step-combinations satisfied')
# screened=combinedRegs1
##
#screened2=screened[screened.krvd_t<=maxKrv]
#if (len(screened2)>0):
# print(' k\' <=', maxKrv, ': ', len(screened2),'step-combinations')
# screened=screened2
#else:
# print(' k\' <=', maxKrv, ': no step-combinations satisfied')
##
#screened3=screened[(screened.slope_t>=minSlopeT)\
# &(screened.slope_t<=maxSlopeT)]
#if (len(screened3)>0):
# print(' ', minSlopeT, '<= slope_t <=', maxSlopeT, \
# ': ', len(screened3),'step-combinations')
# screened=screened3
#else:
# print(' ', minSlopeT, '<= slope_t <=', maxSlopeT, \
# ': no step-combinations satisfied')
##
print('[criteria, 1st heating]')
#
#screened4=screened[screened.frac_n>=minFrac]
#if (len(screened4)>0):
# print(' Frac_n >=', minFrac, ': ', len(screened4),'step-combinations')
# screened=screened4
#else:
# print(' Frac_n >=', minFrac, ': no step-combinations satisfied')
#
screened5=screened[screened.beta_n<=maxBeta]
if (len(screened5)>0):
print(' beta <=', maxBeta, ': ', len(screened5),'step-combinations')
screened=screened5
else:
print(' beta <=', maxBeta, ': no step-combinations satisfied')
#
screened6=screened[screened.f_resid_n<=maxFresid]
if (len(screened6)>0):
print(' f_resid <=', maxBeta, ': ', len(screened6),'step-combinations')
screened=screened6
else:
print(' f_resid <=', maxBeta, ': no step-combinations satisfied')
#
screened7=screened[abs(screened.krvd_n)<=maxKrv]
if (len(screened7)>0):
print(' abs_k\' <=', maxKrv, ': ', len(screened7),'step-combinations')
screened=screened7
else:
print(' abs_k\' <=', maxKrv, ': no step-combinations satisfied')
## sort by dAPI, then select top 10
#print('[sort by dAPI and select the top 10 data]')
#screened=screened.sort_values('dAPI')
#screened=screened.iloc[:10]
# sort by frac_n, then select the best
print('[sort by frac_n and select the best step-combination]')
screened=screened.sort_values('frac_n', ascending=False)
screened_fn=screened.iloc[:1]
#print(screened)
trm2_star_min=screened_fn['step_min_t'].iloc[0]
trm2_star_max=screened_fn['step_max'].iloc[0]
trm1_star_min=screened_fn['step_min_n'].iloc[0]
trm1_star_max=screened_fn['step_max'].iloc[0]
#
return trm1_star_min, trm1_star_max, trm2_star_min, trm2_star_max, screened
def find_mdf(df):
"""
Finds the median destructive field for AF demag data
Parameters
__________
df : dataframe of measurements
Returns
______
mdf : median destructive field
"""
mdf_df=df[df.meas_norm<=0.5]
mdf_high=mdf_df.treat_ac_field_mT.values[0]
mdf_df=df[df.meas_norm>=0.5]
mdf_low=mdf_df.treat_ac_field_mT.values[-1]
mdf=int(0.5*(mdf_high+mdf_low))
return mdf
def set_ARM_data(df,rem_type):
""" choose and calculate ARM data (except pre-LTD 0 data) from the inpud data
Paramters
_________
df : dataframe of measurement data
rem_type : remanence type
Returns
________
afxrm : XRM data with "meas_norm" column
df3 : with base-vector-subtracted data
"""
XRM0 = str(rem_type) + '0'
df2=subtract_base_vector(df,rem_type)
df3=df2[df2.description.str.contains(rem_type)]
afxrm=df3
if (len(afxrm)>0):
meas0=afxrm.magn_mass_diff.tolist()[0]
afxrm['meas_norm']=afxrm['magn_mass_diff']/meas0
afxrm=afxrm.loc[afxrm.method_codes.str.contains('LT-LT-Z')==False]
afxrm=df2[df2.description.str.contains(rem_type)]
afxrm=afxrm[afxrm.description.str.contains(XRM0)==False]
meas0=afxrm.magn_mass_diff.tolist()[0]
afxrm['meas_norm']=afxrm['magn_mass_diff']/meas0
return afxrm,df3
def set_NTRM_data(df,rem_type):
""" choose and calculate NTRM data from the inpud data
Paramters
_________
df : dataframe of measurement data
rem_type : remanence type
Returns
________
afxrm : XRM data with "meas_norm" column
df3 : with base-vector-subtracted data
"""
XRM0 = str(rem_type) + '0'
df2=subtract_base_vector(df,rem_type)
df3=df2[df2.description==rem_type]
df4=df2[df2.description.str.contains(XRM0)==True]
df5=pd.concat([df3,df4])
#df5.to_csv('_temp.csv',index=True)
afxrm=df3
if (len(afxrm)>0):
afxrm=afxrm[afxrm.description.str.contains(XRM0)==False]
meas0=afxrm.magn_mass.tolist()[0] # get first measurement (after LTD)
afxrm['meas_norm']=afxrm['magn_mass']/meas0 # normalized by first measurement
return afxrm,df5
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] | 2.197628 | 4,468 |
import datetime
from sqlalchemy import Boolean, Column, Integer, String, ForeignKey, Date
from sqlalchemy.orm import relationship
from app.db.base_class import Base
class User(Base):
"""用户表"""
id = Column(Integer, primary_key=True, index=True)
username = Column(String(32), unique=True, index=True, nullable=False, doc="编码")
nickname = Column(String(32), doc="姓名")
sex = Column(String(8), doc="性别")
identity_card = Column(String(32), doc="身份证")
phone = Column(String(32), doc="手机号")
address = Column(String(32), doc="地址")
work_start = Column(Date, doc="入职日期", default=datetime.datetime.today())
hashed_password = Column(String(128), nullable=False, doc="密码")
avatar = Column(String(128), doc="头像",
default="https://wpimg.wallstcn.com/f778738c-e4f8-4870-b634-56703b4acafe.gif?imageView2/1/w/80/h/80")
introduction = Column(String(256), doc="自我介绍")
status = Column(String(32), nullable=False, doc="状态")
is_active = Column(Boolean(), default=True, doc="是否活跃")
is_superuser = Column(Boolean(), default=False, doc="是否超级管理员")
user_role = relationship("UserRole", backref="user")
user_department = relationship("UserDepartment", backref="user")
user_dict = relationship("UserDict", backref="user")
class UserRole(Base):
"""用户-权限组-中间表"""
id = Column(Integer, primary_key=True, index=True)
user_id = Column(Integer, ForeignKey("user.id", ondelete='CASCADE'))
role_id = Column(Integer, ForeignKey("role.id"))
role = relationship("Role")
class UserDepartment(Base):
"""用户-部门-中间表"""
id = Column(Integer, primary_key=True, index=True)
user_id = Column(Integer, ForeignKey("user.id", ondelete='CASCADE'))
department_id = Column(Integer, ForeignKey("department.id"))
department = relationship("Department")
class UserDict(Base):
"""用户-字典-中间表"""
id = Column(Integer, primary_key=True, index=True)
user_id = Column(Integer, ForeignKey("user.id", ondelete='CASCADE'))
dict_id = Column(Integer, ForeignKey("dict_data.id", ondelete='CASCADE'))
dict_data = relationship("DictData", backref="user_dict")
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] | 2.416198 | 889 |
from .model import HighResNet3D
| [
6738,
764,
19849,
1330,
3334,
4965,
7934,
18,
35,
198
] | 3.2 | 10 |
# Copyright 2021 Hathor Labs
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from enum import Enum
from typing import List, NamedTuple
| [
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] | 3.843373 | 166 |
import sys
from libya_elections.settings.base import * # noqa
DEBUG = True
SECRET_KEY = 'dummy secret key for testing only'
INTERNAL_IPS = ('127.0.0.1', )
EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend'
CELERY_TASK_ALWAYS_EAGER = False
INSTALLED_BACKENDS = {
HTTPTESTER_BACKEND: {
"ENGINE": "rapidsms.backends.database.DatabaseBackend",
},
"vumi-fake-smsc": {
"ENGINE": "rapidsms.backends.vumi.VumiBackend",
# Default to localhost, but allow override
"sendsms_url": os.getenv("vumi_fake_smsc_sendsms_url", "http://127.0.0.1:9000/send/"),
},
"vumi-http": {
"ENGINE": "rapidsms.backends.vumi.VumiBackend",
# Default to localhost, but allow override
"sendsms_url": os.getenv("VUMI_HTTP_SENDSMS_URL", "http://127.0.0.1:9000/send/"),
},
}
CACHES = {
'default': {
# Use same backend as in production
'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache',
# Assume memcached is local
'LOCATION': '127.0.0.1:11211',
'TIMEOUT': 60 * 60, # one hour
}
}
# Special test settings
if 'test' in sys.argv:
CELERY_TASK_ALWAYS_EAGER = True
CELERY_TASK_EAGER_PROPAGATES = True
PASSWORD_HASHERS = (
'django.contrib.auth.hashers.SHA1PasswordHasher',
'django.contrib.auth.hashers.MD5PasswordHasher',
)
CAPTCHA_TEST_MODE = True
REPORTING_REDIS_KEY_PREFIX = 'os_reporting_api_ut_'
# use default storage for tests, since we don't run collectstatic for tests
STATICFILES_STORAGE = 'django.contrib.staticfiles.storage.StaticFilesStorage'
else:
# Enable all tools for local development, but not when running tests.
ENABLE_ALL_TOOLS = True
# Enable django-debug-toolbar if not running tests
INSTALLED_APPS[-1:-1] = (
"debug_toolbar",
)
DEBUG_TOOLBAR_PATCH_SETTINGS = False
MIDDLEWARE += (
'debug_toolbar.middleware.DebugToolbarMiddleware',
)
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] | 2.244898 | 882 |
# -*- coding: utf-8 -*-
import pytest
from django.core.urlresolvers import reverse
from pontoon.administration.forms import (
ProjectForm,
)
from pontoon.administration.views import _create_or_update_translated_resources
from pontoon.base.models import (
Entity,
Locale,
Project,
ProjectLocale,
Resource,
TranslatedResource,
)
from pontoon.test.factories import (
EntityFactory,
LocaleFactory,
ProjectFactory,
ResourceFactory,
TranslationFactory,
UserFactory,
)
@pytest.mark.django_db
@pytest.mark.django_db
@pytest.mark.django_db
@pytest.mark.django_db
@pytest.mark.django_db
def test_manage_project_strings_translated_resource(client_superuser):
"""Test that adding new strings to a project enables translation of that
project on all enabled locales.
"""
locales = [
LocaleFactory.create(code='kl', name='Klingon'),
LocaleFactory.create(code='gs', name='Geonosian'),
]
project = ProjectFactory.create(
data_source='database',
locales=locales,
repositories=[]
)
locales_count = len(locales)
_create_or_update_translated_resources(project, locales)
url = reverse('pontoon.admin.project.strings', args=(project.slug,))
new_strings = """
Morty, do you know what "Wubba lubba dub dub" means?
Oh that's just Rick's stupid non-sense catch phrase.
It's not.
In my people's tongue, it means "I am in great pain, please help me".
"""
strings_count = 4
response = client_superuser.post(url, {'new_strings': new_strings})
assert response.status_code == 200
# Verify no strings have been created as entities.
entities = list(Entity.objects.filter(resource__project=project))
assert len(entities) == strings_count
# Verify the resource has the right stats.
resources = Resource.objects.filter(project=project)
assert len(resources) == 1
resource = resources[0]
assert resource.total_strings == strings_count
# Verify the correct TranslatedResource objects have been created.
translated_resources = TranslatedResource.objects.filter(resource__project=project)
assert len(translated_resources) == locales_count
# Verify stats have been correctly updated on locale, project and resource.
for tr in translated_resources:
assert tr.total_strings == strings_count
project = Project.objects.get(id=project.id)
assert project.total_strings == strings_count * locales_count
for l in locales:
locale = Locale.objects.get(id=l.id)
assert locale.total_strings == strings_count
@pytest.mark.django_db
def test_manage_project_strings_new_all_empty(client_superuser):
"""Test that sending empty data doesn't create empty strings in the database.
"""
project = ProjectFactory.create(data_source='database', repositories=[])
url = reverse('pontoon.admin.project.strings', args=(project.slug,))
# Test sending an empty batch of strings.
new_strings = " \n \n\n"
response = client_superuser.post(url, {'new_strings': new_strings})
assert response.status_code == 200
# Verify no strings have been created as entities.
entities = list(Entity.objects.filter(resource__project=project))
assert len(entities) == 0
@pytest.mark.django_db
@pytest.mark.django_db
@pytest.mark.django_db
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31,
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9288,
13,
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13,
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62,
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628,
198,
31,
9078,
9288,
13,
4102,
13,
28241,
14208,
62,
9945,
628,
198,
31,
9078,
9288,
13,
4102,
13,
28241,
14208,
62,
9945,
198
] | 2.881255 | 1,179 |
# -*- coding: utf-8 -*-
# Copyright 2018-2022 the orix developers
#
# This file is part of orix.
#
# orix 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 3 of the License, or
# (at your option) any later version.
#
# orix 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 orix. If not, see <http://www.gnu.org/licenses/>.
"""Four-dimensional objects.
In a simplified sense, quaternions are an extension of the concept of complex
numbers, represented by :math:`a + bi + cj + dk` where :math:`i`, :math:`j`, and
:math:`k` are quaternion units and :math:`i^2 = j^2 = k^2 = ijk = -1`. For
further reference see
`the Wikipedia article <https://en.wikipedia.org/wiki/Quaternion>`_.
Unit quaternions are efficient objects for representing rotations, and hence
orientations.
"""
from orix.quaternion.quaternion import Quaternion, check_quaternion # isort: skip
from orix.quaternion.orientation import Misorientation, Orientation
from orix.quaternion.orientation_region import OrientationRegion, get_proper_groups
from orix.quaternion.rotation import Rotation, von_mises
from orix.quaternion.symmetry import Symmetry, get_distinguished_points, get_point_group
# Lists what will be imported when calling "from orix.quaternion import *"
__all__ = [
"check_quaternion",
"Quaternion",
"Rotation",
"von_mises",
"Misorientation",
"Orientation",
"get_proper_groups",
"OrientationRegion",
"get_distinguished_points",
"get_point_group",
"Symmetry",
]
| [
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198,
220,
220,
220,
366,
13940,
3020,
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1600,
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60,
198
] | 3.096026 | 604 |
import numpy as np
| [
11748,
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355,
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628,
628,
628,
628
] | 2.888889 | 9 |
import random
import matplotlib.pyplot as plt
import numpy as np
from prettytable import PrettyTable
from auction import Auction
if __name__ == '__main__':
buyers = 10
strategy = [1 for n in range(buyers)]
# strategy[0] = 4
auctioneer = Auctioneer(0.1,
bidding_factor_strategy=strategy,
M_types=3,
K_sellers=4,
N_buyers=buyers,
R_rounds=100,
level_comm_flag=False,
use_seller=False,
debug=True)
auctioneer.start_auction()
auctioneer.plot_statistics()
print("\nBidding factors when the simulation is finished")
auctioneer.print_alphas()
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] | 1.86215 | 428 |
import argparse
from utils.utils import *
from utils.line import Line
from tqdm import trange
import torch
import torch.optim as optim
import sys
import pickle
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-g", "--graph_path", type=str)
parser.add_argument("-save", "--save_path", type=str)
parser.add_argument("-lossdata", "--lossdata_path", type=str)
# Hyperparams.
parser.add_argument("-order", "--order", type=int, default=2)
parser.add_argument("-neg", "--negsamplesize", type=int, default=5)
parser.add_argument("-dim", "--dimension", type=int, default=128)
parser.add_argument("-batchsize", "--batchsize", type=int, default=5)
parser.add_argument("-epochs", "--epochs", type=int, default=1)
parser.add_argument("-lr", "--learning_rate", type=float,
default=0.025) # As starting value in paper
parser.add_argument("-negpow", "--negativepower", type=float, default=0.75)
args = parser.parse_args()
# Create dict of distribution when opening file
edgedistdict, nodedistdict, weights, nodedegrees, maxindex = makeDist(
args.graph_path, args.negativepower)
edgesaliassampler = VoseAlias(edgedistdict)
nodesaliassampler = VoseAlias(nodedistdict)
batchrange = int(len(edgedistdict) / args.batchsize)
print(maxindex)
line = Line(maxindex + 1, embed_dim=args.dimension, order=args.order)
opt = optim.SGD(line.parameters(), lr=args.learning_rate,
momentum=0.9, nesterov=True)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
lossdata = {"it": [], "loss": []}
it = 0
print("\nTraining on {}...\n".format(device))
for epoch in range(args.epochs):
print("Epoch {}".format(epoch))
for b in trange(batchrange):
samplededges = edgesaliassampler.sample_n(args.batchsize)
batch = list(makeData(samplededges, args.negsamplesize, weights, nodedegrees,
nodesaliassampler))
batch = torch.LongTensor(batch)
v_i = batch[:, 0]
v_j = batch[:, 1]
negsamples = batch[:, 2:]
line.zero_grad()
loss = line(v_i, v_j, negsamples, device)
loss.backward()
opt.step()
lossdata["loss"].append(loss.item())
lossdata["it"].append(it)
it += 1
print("\nDone training, saving model to {}".format(args.save_path))
torch.save(line, "{}".format(args.save_path))
print("Saving loss data at {}".format(args.lossdata_path))
with open(args.lossdata_path, "wb") as ldata:
pickle.dump(lossdata, ldata)
sys.exit()
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] | 2.341902 | 1,167 |
# SDT4Parser.py
#
# Callback target class for the ElementTree parser to parse a SDT4
from .SDT4Classes import *
#
# Hanlder for each of the element types
#
#
# Assignment of element types and (handlerFunction, (tuple of allowed parents))
#
handlers = {
SDT4Parser.actionTag : (handleAction, (SDT4ModuleClass,)),
SDT4Parser.argTag : (handleArg, (SDT4Action,)),
SDT4Parser.arrayTypeTag : (handleArrayType, (SDT4DataType,)),
SDT4Parser.bTag : (handleB, (SDT4Doc, SDT4DocP)),
SDT4Parser.constraintTag : (handleConstraint, (SDT4DataType,)),
SDT4Parser.dataPointTag : (handleDataPoint, (SDT4Event, SDT4ModuleClass)),
SDT4Parser.dataTypeTag : (handleDataType, (SDT4Action, SDT4DataPoint, SDT4Event, SDT4Arg, SDT4StructType, SDT4ArrayType, SDT4Domain)),
SDT4Parser.deviceClassTag : (handleDeviceClass, (SDT4Domain,)),
SDT4Parser.docTag : (handleDoc, (SDT4Domain, SDT4ProductClass, SDT4DeviceClass, SDT4SubDevice, SDT4DataType, SDT4ModuleClass, SDT4Action, SDT4DataPoint, SDT4Event, SDT4EnumValue, SDT4Arg, SDT4Constraint, SDT4Property)),
SDT4Parser.domainTag : (handleDomain, None),
SDT4Parser.emTag : (handleEM, (SDT4Doc, SDT4DocP)),
SDT4Parser.enumTypeTag : (handleEnumType, (SDT4DataType,)),
SDT4Parser.enumValueTag : (handleEnumValue, (SDT4EnumType,)),
SDT4Parser.eventTag : (handleEvent, (SDT4ModuleClass,)),
SDT4Parser.excludeTag : (handleExtendExclude, (SDT4Extend,)),
SDT4Parser.extendTag : (handleExtend, (SDT4ModuleClass, SDT4DataType, SDT4ProductClass, SDT4SubDevice)),
SDT4Parser.imgTag : (handleImg, (SDT4Doc, SDT4DocP)),
SDT4Parser.imgCaptionTag : (handleImgCaption, (SDT4DocIMG,)),
SDT4Parser.includeTag : (handleInclude, (SDT4Domain, SDT4Extend)),
SDT4Parser.moduleClassTag : (handleModuleClass, (SDT4Domain, SDT4ProductClass, SDT4DeviceClass, SDT4SubDevice, SDT4ProductClass)),
SDT4Parser.pTag : (handleP, (SDT4Doc, SDT4DocP)),
SDT4Parser.productClassTag : (handleProductClass, (SDT4Domain,)),
SDT4Parser.propertyTag : (handleProperty, (SDT4ProductClass, SDT4DeviceClass, SDT4SubDevice, SDT4ModuleClass)),
SDT4Parser.simpleTypeTag : (handleSimpleType, (SDT4DataType, SDT4Property)),
SDT4Parser.structTypeTag : (handleStructType, (SDT4DataType,)),
SDT4Parser.subDeviceTag : (handleSubDevice, (SDT4DeviceClass, SDT4ProductClass, SDT4Domain)),
SDT4Parser.ttTag : (handleTT, (SDT4Doc, SDT4DocP))
}
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11,
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51,
19,
6601,
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51,
19,
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13,
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25,
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7004,
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11,
357,
10305,
51,
19,
24728,
9487,
11,
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51,
19,
15667,
9487,
11,
9834,
51,
19,
43961,
36911,
198,
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25,
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10305,
51,
19,
23579,
11,
9834,
51,
19,
23579,
47,
4008,
198,
92,
198
] | 2.408184 | 1,002 |
from data import dex
import re
def validate_team(team):
'''
team is an array of six pokemon sets
'''
if len(team) > 6:
raise InValidSetError("more than 6 pokemon")
pokemon_names = set()
for pokemon in team:
# check if the pokemon is an actual pokemon
species = re.sub(r'\W+', '', pokemon['species'].lower())
pokemon_names.add(species)
if species not in dex.pokedex:
raise InValidSetError(species + " is not a real pokemon species")
if len(pokemon['moves']) > 4:
raise InValidSetError("more than 4 moves")
for move in pokemon['moves']:
if move not in dex.simple_learnsets[species]:
raise InValidSetError(species + " can't learn the move " + move)
if pokemon['ability'] not in [re.sub(r'\W+', '', ability.lower()) for ability in list(filter(None.__ne__, list(dex.pokedex[species].abilities)))]:
raise InValidSetError(species + " cant have the ability, " + pokemon['ability'])
for i in range(6):
if pokemon['evs'][i] > 255 or pokemon['evs'][i] < 0:
raise InVaidSetError("ev value is out of range: " + str(pokemon['evs'][i]))
if pokemon['ivs'][i] > 31 or pokemon['ivs'][i] < 0:
raise InVaidSetError("iv value is out of range: " + str(pokemon['ivs'][i]))
if sum(pokemon['evs']) > 510:
raise InValidSetError("sum of evs is over 510")
if len(team) != len(pokemon_names):
raise InValidSetError("cannot have multiple of the same pokemon")
return True
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5298,
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423,
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286,
262,
976,
43962,
4943,
628,
220,
220,
220,
1441,
6407,
628
] | 2.361891 | 677 |
from collections import namedtuple as Struct
from sklearn.model_selection import GroupShuffleSplit, ShuffleSplit
DataSplitConfig = Struct('DataSplitConfig', ['validation_size', 'test_size', 'random_seed'])
DEFAULT_SPLIT_CONFIG = DataSplitConfig(0.2, 0.2, 1337)
| [
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13,
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11,
657,
13,
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11,
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2718,
8,
198
] | 3.207317 | 82 |
import csv
file = open("sex-ratio.csv")
csvreader = csv.reader(file)
header = next(csvreader)
mapped = {}
for row in csvreader:
if(row[0] not in mapped):
mapped[row[0]]={}
mapped[row[0]][row[2]] = row[3]
# f = open("converted.csv",'w')
rows=[]
for c in mapped:
row = [c]
for y in mapped[c]:
row.append(mapped[c][y])
rows.append(row)
header =['country']
for i in range(1950,2018):
header.append(str(i))
with open('converted.csv', 'w', encoding='UTF8') as f:
writer = csv.writer(f)
# write the header
writer.writerow(header)
# write the data
for row in rows:
writer.writerow(row)
| [
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7,
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198,
220,
220,
220,
329,
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220,
220,
220,
220,
220,
220,
6260,
13,
16002,
322,
7,
808,
8,
628,
198
] | 2.254296 | 291 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals, absolute_import
# Markdown is optional
try:
import markdown
def apply_markdown(text):
"""
Simple wrapper around :func:`markdown.markdown` to set the base level
of '#' style headers to <h2>.
"""
extensions = ['headerid(level=2)']
safe_mode = False
md = markdown.Markdown(extensions=extensions, safe_mode=safe_mode)
return md.convert(text)
except ImportError:
apply_markdown = None
| [
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8,
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220,
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220,
220,
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220,
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13,
1102,
1851,
7,
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8,
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17267,
12331,
25,
198,
220,
220,
220,
4174,
62,
4102,
2902,
796,
6045,
198
] | 2.44186 | 215 |
"""Finite Difference Methods
"""
import numpy as np
def FDMWeights(M, x0, alpha):
"""Calculate the weights in finite difference formulas
for any order of derivative and to any order of accuracy
on onedimensional grids with arbitrary spacing.
Args:
M (int): Order of derivative
x0 (float): Approximations at this point
alpha (np.array): x-cordinates. length must be N
Attributes:
N (int): Order of accuracy, which is equivalent to len(alpha)-1.
Returns:
np.array: Weights
References:
Bengt Fornberg, "Generation of Finite Difference Formulas on Arbitrarily Spaced Grids", 1988.
"""
N = len(alpha) - 1
delta = np.zeros([M+1,N+1,N+1])
delta[0,0,0] = 1.
c1 = 1.
for n in range(1, N+1):
c2 = 1.
for nu in range(n):
c3 = alpha[n] - alpha[nu]
c2 *= c3
for m in range(min(n, M)+1):
delta[m,n,nu] = ((alpha[n]-x0)*delta[m,n-1,nu] - m*delta[m-1,n-1,nu]) / c3
for m in range(min(n, M)+1):
delta[m,n,n] = c1/c2 * (m*delta[m-1,n-1,n-1] - (alpha[n-1]-x0)*delta[m,n-1,n-1])
c1 = c2
return delta
class centralFDM(object):
"""Central Finite Difference Method
Args:
order (int, optional): The order of the accuracy. Defaults to 2.
highestDerivative (int, optional): The order of the highest derivative. Defaults to 1.
"""
def __call__(self, f, axis=-1, derivative=1, h=1.):
"""Calculate the derivative.
Args:
f (np.array): An array containing samples.
axis (int, optional): The derivative is calculated only along the given axis. Defaults to -1.
derivative (int, optional): The order of the derivative. Defaults to 1.
h (float, optional): The space of the uniform grid. Defaults to 1..
Returns:
np.array: The derivative.
"""
df = np.zeros_like(f)
weight_ = self.weight[derivative]
alpha_ = self.alpha[weight_!=0]
weight_ = weight_[weight_!=0]
for i, alpha_i in enumerate(alpha_):
df += np.roll(f, shift=-int(alpha_i), axis=axis) * weight_[i]
return df / h**derivative
class upwindFDM(object):
"""Upwind Finite Difference Method
Args:
order (int, optional): The order of the accuracy. Defaults to 1.
highestDerivative (int, optional): The order of the highest derivative. Defaults to 1.
"""
def __call__(self, f, axis=-1, derivative=1, h=1., c=None):
"""Calculate the derivative.
Args:
f (np.array): An array containing samples.
axis (int, optional): The derivative is calculated only along the given axis. Defaults to -1.
derivative (int, optional): The order of the derivative. Defaults to 1.
h (float, optional): The space of the uniform grid. Defaults to 1..
c (float or np.array, optional): The advection speed. Defaults to None.
Returns:
np.array: The derivative.
"""
df = np.zeros_like(f)
df2 = np.zeros_like(f)
for i, alpha_i in enumerate(self.alpha):
df += np.roll(f, shift=-int(alpha_i), axis=axis) * self.weight[derivative,i]
df2 += np.roll(f, shift=int(alpha_i), axis=axis) * self.weight2[derivative,i]
if c == None:
c = f
df = np.where(c>=0, df, df2)
return df / h**derivative
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] | 2.21195 | 1,590 |
#!/usr/bin/env python3
import os
import time
import datetime
from bluepy import btle
from bluepy.btle import Scanner, DefaultDelegate, Peripheral, Characteristic, ScanEntry, Service, UUID
import curses
import curses.textpad
from carcontrol import CarControl
# Scan timeout in seconds
SCAN_TIMEOUT = 10
## screen parts
LINE_HEADING = 0
LINE_OPTIONS = 1
LINE_STATUS = 5
LINE_ERROR = 6
COL_START = 0
HEIGHT_TOP = 8
HEIGHT_BOT = 3
LOOP_DURATION = 0.05
DISPLAY_COUNT = 100
LINE_RECT = 30
RECT_HEIGHT = 12
RECT_WIDTH = 40
MSG_WELCOME = "Welcome to Carmageddon - in real life!\n"
MSG_OPTIONS = " [S] - start scanning...\t\t\t\t[Q] - Exit\n"
MSG_OPTIONS = MSG_OPTIONS + " [1...9] - Direct connect to device by number\t\t[D] - Disconnect \n"
MSG_DRIVE_HELP = "Use [Arrows] to drive, [SPACE] to Fire"
if __name__ == '__main__':
try:
screen = MainScreen()
except KeyboardInterrupt:
os.sys.exit(0)
# finally:
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] | 2.373171 | 410 |
import math
import torch
import torch.nn as nn
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198,
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] | 3.1875 | 16 |
import json
from app.models import BaseModel
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] | 3.916667 | 12 |
"""
This module contains all the functions from the ``operator`` module (bar some
functions that dind't feel like they belonged here) transformed into a
spice so it can be used more confortable.
:Example:
Consider adding ``2`` to a list of numbers::
map(add(2), [1,2,3,4,5])
"""
import operator
from spycy import spice
__all__ = [ 'add', 'and_', 'contains', 'concat', 'countOf', 'eq', 'floordiv'
, 'ge', 'getitem', 'gt', 'indexOf', 'is_', 'is_not', 'le', 'lshift'
, 'lt', 'matmul', 'mod', 'mul', 'ne', 'or_', 'pos', 'pow', 'rshift'
, 'sub', 'truediv', 'xor', 'neg', 'not_', 'index', 'itemgetter'
, 'methodcaller', 'attrgetter', 'truth']
add = spice(lambda x,y: operator.__add__(x,y), name='add', doc=operator.add.__doc__)
__add__ = spice(lambda x,y: operator.__add__(x,y), name='__add__', doc=operator.add.__doc__)
and_ = spice(lambda x,y: operator.and_(x,y), name='and_', doc=operator.and_.__doc__)
__and__ = spice(lambda x,y: operator.__and__(x,y), name='__and__', doc=operator.and_.__doc__)
__contains__ = spice(lambda x,y: operator.__contains__(x,y), name='__contains__', doc=operator.contains.__doc__)
contains = spice(lambda x,y: operator.contains(x,y), name='contains', doc=operator.contains.__doc__)
concat = spice(lambda x,y: operator.concat(x,y), name='concat', doc=operator.concat.__doc__)
countOf = spice(lambda x,y: operator.countOf(x,y), name='countOf', doc=operator.countOf.__doc__)
eq = spice(lambda x,y: operator.eq(x,y), name='eq', doc=operator.eq.__doc__)
__eq__ = spice(lambda x,y: operator.__eq__(x,y), name='__eq__', doc=operator.eq.__doc__)
floordiv = spice(lambda x,y: operator.floordiv(x,y), name='floordiv', doc=operator.floordiv.__doc__)
__floordiv__ = spice(lambda x,y: operator.__floordiv__(x,y), name='__floordiv__', doc=operator.floordiv.__doc__)
# reversed
ge = spice(lambda x,y: operator.ge(y,x), name='ge')
__ge__ = spice(lambda x,y: operator.__ge__(y,x), name='__ge__')
getitem = spice(lambda x,y: operator.getitem(x,y), name='getitem', doc=operator.getitem.__doc__)
__getitem__ = spice(lambda x,y: operator.__getitem__(x,y), name='__getitem__', doc=operator.getitem.__doc__)
# reversed
gt = spice(lambda x,y: operator.gt(y,x), name='gt')
__gt__ = spice(lambda x,y: operator.__gt__(y,x))
indexOf = spice(lambda x,y: operator.indexOf(x,y), name='indexOf', doc=operator.indexOf.__doc__)
is_ = spice(lambda x,y: operator.is_(x,y), name='is_', doc=operator.is_.__doc__)
is_not = spice(lambda x,y: operator.is_not(x,y), name='is_not', doc=operator.is_not.__doc__)
# reversed
le = spice(lambda x,y: operator.le(y,x), name='le')
__le__ = spice(lambda x,y: operator.__le__(y,x), name='__le__')
# reversed
lshift = spice(lambda x,y: operator.lshift(y,x), name='lshift')
__lshift__ = spice(lambda x,y: operator.__lshift__(y,x), name='__lshift__')
# reversed
lt = spice(lambda x,y: operator.lt(y,x), name='lt')
__lt__ = spice(lambda x,y: operator.__lt__(y,x), name='__lt__')
# reversed
matmul = spice(lambda x,y: operator.matmul(y,x), name='matmul')
__matmul__ = spice(lambda x,y: operator.__matmul__(y,x), name='__matmul__')
# reversed
mod = spice(lambda x,y: operator.mod(y,x), name='mod')
__mod__ = spice(lambda x,y: operator.__mod__(y,x), name='__mod__')
mul = spice(lambda x,y: operator.mul(x,y), name='mul', doc=operator.mul.__doc__)
__mul__ = spice(lambda x,y: operator.__mul__(x,y), name='__mul__', doc=operator.mul.__doc__)
ne = spice(lambda x,y: operator.ne(x,y), name='ne', doc=operator.ne.__doc__)
__ne__ = spice(lambda x,y: operator.__ne__(x,y), name='__ne__', doc=operator.ne.__doc__)
or_ = spice(lambda x,y: operator.or_(x,y), name='or_', doc=operator.or_.__doc__)
__or__ = spice(lambda x,y: operator.__or__(x,y), name='__or__', doc=operator.or_.__doc__)
pos = spice(lambda x,y: operator.pos(x,y), name='pos', doc=operator.pos.__doc__)
#reversed
pow = spice(lambda x,y: operator.pow(y,x), name='pow')
__pow__ = spice(lambda x,y: operator.__pow__(y,x), name='__pow__')
# reversed
rshift = spice(lambda x,y: operator.rshift(y,x), name='rshift')
__rshift__ = spice(lambda x,y: operator.__rshift__(y,x), name='__rshift__')
# reversed
sub = spice(lambda x,y: operator.sub(y,x), name='sub')
__sub__ = spice(lambda x,y: operator.__sub__(y,x), name='__sub__')
# reversed
truediv = spice(lambda x,y: operator.truediv(y,x), name='truediv')
__truediv__ = spice(lambda x,y: operator.__truediv__(y,x), name='__truediv__')
xor = spice(lambda x,y: operator.xor(x,y), name='xor', doc=operator.xor.__doc__)
__xor__ = spice(lambda x,y: operator.__xor__(x,y), name='__xor__', doc=operator.xor.__doc__)
#################################################
neg = spice(lambda x: operator.neg(x), name='neg', doc=operator.neg.__doc__)
__neg__ = spice(lambda x: operator.__neg__(x), name='__neg__', doc=operator.neg.__doc__)
not_ = spice(lambda x: operator.not_(x), name='not_', doc=operator.not_.__doc__)
__not__ = spice(lambda x: operator.__not__(x), name='__not__', doc=operator.not_.__doc__)
index = spice(lambda x: operator.index(x), name='index', doc=operator.index.__doc__)
__index__ = spice(lambda x: operator.__index__(x), name='__index__', doc=operator.index.__doc__)
itemgetter = spice(lambda x: operator.itemgetter(x), name='itemgetter', doc=operator.itemgetter.__doc__)
methodcaller = spice(lambda x: operator.methodcaller(x), name='methodcaller', doc=operator.methodcaller.__doc__)
attrgetter = spice(lambda x: operator.attrgetter(x), name='attrgetter', doc=operator.attrgetter.__doc__)
truth = spice(lambda x: operator.truth(x), name='truth', doc=operator.truth.__doc__)
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] | 2.504488 | 2,228 |
# Copyright 2021 curoky([email protected]).
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
config = {
"name": "com_github_google_brotli",
"type": "git_repository",
# "remote": "https://github.com/google/brotli",
"remote": "https://github.com/pefoley2/brotli",
"used_version": "heads/master",
"versions": {
"heads/master": {},
"tags/v1.0.9": {},
},
}
# Note:
# 1: after v1.0.9, brotli use vla-parameter, which gcc-11 throw error by default
# fix pr: https://github.com/google/brotli/pull/904
# external/com_github_google_brotli/c/dec/decode.c:2036:41: error: argument 2 of type 'const uint8_t *' {aka 'const unsigned char *'} declared as a pointer [-Werror=vla-parameter]
# 2036 | size_t encoded_size, const uint8_t* encoded_buffer, size_t* decoded_size,
# | ~~~~~~~~~~~~~~~^~~~~~~~~~~~~~
# In file included from external/com_github_google_brotli/c/dec/decode.c:7:
# bazel-out/k8-dbg/bin/external/com_github_google_brotli/_virtual_includes/brotli_inc/brotli/decode.h:204:19: note: previously declared as a variable length array 'const uint8_t[*decoded_size]' {aka 'const unsigned char[*decoded_size]'}
# 204 | const uint8_t encoded_buffer[BROTLI_ARRAY_PARAM(encoded_size)],
# | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# external/com_github_google_brotli/c/dec/decode.c:2037:14: error: argument 4 of type 'uint8_t *' {aka 'unsigned char *'} declared as a pointer [-Werror=vla-parameter]
# 2037 | uint8_t* decoded_buffer) {
# | ~~~~~~~~~^~~~~~~~~~~~~~
# In file included from external/com_github_google_brotli/c/dec/decode.c:7:
# bazel-out/k8-dbg/bin/external/com_github_google_brotli/_virtual_includes/brotli_inc/brotli/decode.h:206:13: note: previously declared as a variable length array 'uint8_t[encoded_size]' {aka 'unsigned char[encoded_size]'}
# 206 | uint8_t decoded_buffer[BROTLI_ARRAY_PARAM(*decoded_size)]);
# | ~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# cc1: all warnings being treated as errors
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] | 2.681582 | 961 |
# -*- coding: utf-8 -*-
from django.contrib.contenttypes.models import ContentType
from rest_framework import serializers
from vvphotos.models import Album
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# coding: utf-8
"""
DocuSign REST API
The DocuSign REST API provides you with a powerful, convenient, and simple Web services API for interacting with DocuSign.
OpenAPI spec version: v2.1
Contact: [email protected]
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from pprint import pformat
from six import iteritems
import re
class CurrencyFeatureSetPrice(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, currency_code=None, currency_symbol=None, envelope_fee=None, fixed_fee=None, seat_fee=None):
"""
CurrencyFeatureSetPrice - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition.
"""
self.swagger_types = {
'currency_code': 'str',
'currency_symbol': 'str',
'envelope_fee': 'str',
'fixed_fee': 'str',
'seat_fee': 'str'
}
self.attribute_map = {
'currency_code': 'currencyCode',
'currency_symbol': 'currencySymbol',
'envelope_fee': 'envelopeFee',
'fixed_fee': 'fixedFee',
'seat_fee': 'seatFee'
}
self._currency_code = currency_code
self._currency_symbol = currency_symbol
self._envelope_fee = envelope_fee
self._fixed_fee = fixed_fee
self._seat_fee = seat_fee
@property
def currency_code(self):
"""
Gets the currency_code of this CurrencyFeatureSetPrice.
Specifies the alternate ISO currency code for the account.
:return: The currency_code of this CurrencyFeatureSetPrice.
:rtype: str
"""
return self._currency_code
@currency_code.setter
def currency_code(self, currency_code):
"""
Sets the currency_code of this CurrencyFeatureSetPrice.
Specifies the alternate ISO currency code for the account.
:param currency_code: The currency_code of this CurrencyFeatureSetPrice.
:type: str
"""
self._currency_code = currency_code
@property
def currency_symbol(self):
"""
Gets the currency_symbol of this CurrencyFeatureSetPrice.
Specifies the alternate currency symbol for the account.
:return: The currency_symbol of this CurrencyFeatureSetPrice.
:rtype: str
"""
return self._currency_symbol
@currency_symbol.setter
def currency_symbol(self, currency_symbol):
"""
Sets the currency_symbol of this CurrencyFeatureSetPrice.
Specifies the alternate currency symbol for the account.
:param currency_symbol: The currency_symbol of this CurrencyFeatureSetPrice.
:type: str
"""
self._currency_symbol = currency_symbol
@property
def envelope_fee(self):
"""
Gets the envelope_fee of this CurrencyFeatureSetPrice.
An incremental envelope cost for plans with envelope overages (when `isEnabled` is set to **true**.)
:return: The envelope_fee of this CurrencyFeatureSetPrice.
:rtype: str
"""
return self._envelope_fee
@envelope_fee.setter
def envelope_fee(self, envelope_fee):
"""
Sets the envelope_fee of this CurrencyFeatureSetPrice.
An incremental envelope cost for plans with envelope overages (when `isEnabled` is set to **true**.)
:param envelope_fee: The envelope_fee of this CurrencyFeatureSetPrice.
:type: str
"""
self._envelope_fee = envelope_fee
@property
def fixed_fee(self):
"""
Gets the fixed_fee of this CurrencyFeatureSetPrice.
Specifies a one-time fee associated with the plan (when `isEnabled` is set to **true**.)
:return: The fixed_fee of this CurrencyFeatureSetPrice.
:rtype: str
"""
return self._fixed_fee
@fixed_fee.setter
def fixed_fee(self, fixed_fee):
"""
Sets the fixed_fee of this CurrencyFeatureSetPrice.
Specifies a one-time fee associated with the plan (when `isEnabled` is set to **true**.)
:param fixed_fee: The fixed_fee of this CurrencyFeatureSetPrice.
:type: str
"""
self._fixed_fee = fixed_fee
@property
def seat_fee(self):
"""
Gets the seat_fee of this CurrencyFeatureSetPrice.
Specifies an incremental seat cost for seat-based plans (when `isEnabled` is set to **true**.)
:return: The seat_fee of this CurrencyFeatureSetPrice.
:rtype: str
"""
return self._seat_fee
@seat_fee.setter
def seat_fee(self, seat_fee):
"""
Sets the seat_fee of this CurrencyFeatureSetPrice.
Specifies an incremental seat cost for seat-based plans (when `isEnabled` is set to **true**.)
:param seat_fee: The seat_fee of this CurrencyFeatureSetPrice.
:type: str
"""
self._seat_fee = seat_fee
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
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63,
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284,
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25,
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13,
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7,
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62,
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220,
220,
220,
220,
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220,
220,
220,
220,
220,
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21394,
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62,
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63,
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220,
1058,
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25,
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220,
220,
220,
220,
220,
220,
220,
37227,
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220,
220,
220,
220,
220,
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2116,
13557,
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62,
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796,
5852,
62,
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628,
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220,
220,
825,
284,
62,
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7,
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220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
262,
2746,
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355,
257,
8633,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1255,
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23884,
628,
220,
220,
220,
220,
220,
220,
220,
329,
708,
81,
11,
4808,
287,
11629,
23814,
7,
944,
13,
2032,
7928,
62,
19199,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
651,
35226,
7,
944,
11,
708,
81,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
8367,
11,
1351,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1255,
58,
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60,
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1351,
7,
8899,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37456,
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25,
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13,
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62,
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3419,
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35226,
7,
87,
11,
366,
1462,
62,
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4943,
2073,
2124,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15306,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
468,
35226,
7,
8367,
11,
366,
1462,
62,
11600,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1255,
58,
35226,
60,
796,
1988,
13,
1462,
62,
11600,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
318,
39098,
7,
8367,
11,
8633,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1255,
58,
35226,
60,
796,
8633,
7,
8899,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37456,
2378,
25,
357,
9186,
58,
15,
4357,
2378,
58,
16,
4083,
1462,
62,
11600,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
468,
35226,
7,
9186,
58,
16,
4357,
366,
1462,
62,
11600,
4943,
2073,
2378,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
13,
23814,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15306,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1255,
58,
35226,
60,
796,
1988,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
1255,
628,
220,
220,
220,
825,
284,
62,
2536,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
262,
4731,
10552,
286,
262,
2746,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
279,
18982,
7,
944,
13,
1462,
62,
11600,
28955,
628,
220,
220,
220,
825,
11593,
260,
1050,
834,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1114,
4600,
4798,
63,
290,
4600,
381,
22272,
63,
198,
220,
220,
220,
220,
220,
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1441,
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584,
198
] | 2.327616 | 2,857 |
from flask.helpers import url_for
from pyTrendsExtensions import GetTrendingOverTime
from flask import Flask, redirect
# from flask_restful import Api, Resource, reqparse, abort, fields, marshal_with
# from flask_sqlalchemy import SQLAlchemy
app = Flask(__name__)
# api = Api(app)
@app.route("/")
@app.route("/<keyword>") | [
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'''
TCP server interface for console.
The TCP server will be automatically built.
- @interface: The function for user interface, and keep the server running.
'''
from . import logger
from .defines import TCPServer
server = TCPServer()
server.start()
| [
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from django.shortcuts import render
from django.views.generic import ListView
from .models import Video,Audio,Image,Note
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from worldtools import *
from enum import Enum
from math import sin, cos, pi
from random import uniform
class Animal:
"""Class representing Animal in the world"""
def __init__(self, world, pos: (float, float), speed: float):
"""
Initializes the Animal
Args:
world (World): The world
pos ( (float, float) ): Starting position
speed (float): Animal speed
"""
self.speed = speed
self.pos = pos
self.world = world
# Movement variables
self.target = None
self.movement_angle = uniform(0, pi*2)
# Food variables
self.hunger = 100
self.eat_count = 0
self._food_checkpoint = 0
# Set state
self.state = State.ROAM
def move(self) -> Exception:
"""
Moves an animal based on state
Raises:
NotImplementedError: Should be overwritten in a derived class
Returns:
Exception: Will always raise NotImplementedError if called from Animal class
"""
raise NotImplementedError()
def draw(self, screen) -> Exception:
"""
Draws an animal to the screen
Args:
screen (pygame.screen): pygame screen
Raises:
NotImplementedError: Should be overwritten in a derived class
Returns:
Exception: Will always raise NotImplementedError if called from Animal class
"""
raise NotImplementedError()
def sight_entities(self) -> (["Food"], ["Rabbit"], ["Wolf"]):
"""
Returns all entites in vision of the Animal
Args:
self (Animal): self
Returns:
([Food], [Rabbit], [Wolf]): Returns a 3-tuple with Food, Rabbit, Wolf in vision
"""
# Get foods around self
foodlist = []
for food in self.world.food:
if self != food and self._in_sight(food): foodlist.append(food)
# Get rabbits around self
rabbitlist = []
for rabbit in self.world.rabbits:
if self != rabbit and self._in_sight(rabbit): rabbitlist.append(rabbit)
# Get wolves around self
wolflist = []
for wolf in self.world.wolves:
if self != wolf and self._in_sight(wolf): wolflist.append(wolf)
# Sort by distance to self
foodlist.sort(key=lambda x: distance(self.pos, x.pos))
rabbitlist.sort(key=lambda x: distance(self.pos, x.pos))
wolflist.sort(key=lambda x: distance(self.pos, x.pos))
return (foodlist, rabbitlist, wolflist)
def eat(self, inc: float) -> None:
"""
Increments eating food source and adds to hunger
Args:
inc (int): Amount to increase hunger
"""
# Increment eat count
self.eat_count += 1
# Limit to 100
if self.hunger + inc >= 100:
self.hunger = 100
else:
self.hunger += inc
def roam_move(self) -> None:
"""
Moves Animal in the direction they are facing and slightly changes movement angle
"""
# Proposed move
new_x = self.pos[0] + (self.speed * cos(self.movement_angle))
new_y = self.pos[1] + (self.speed * sin(self.movement_angle))
# Check if valid move
while not self.world.in_bounds((new_x, new_y)):
# Reset move
self.movement_angle += pi/2
new_x = self.pos[0] + (self.speed * cos(self.movement_angle))
new_y = self.pos[1] + (self.speed * sin(self.movement_angle))
# Confirm move
self.pos = (
new_x,
new_y
)
# Adjust movement angle
self.movement_angle += uniform(-pi*2 / 36, pi*2 / 36)
def _in_sight(self, entity) -> bool:
"""
Returns if an entity (which has a pos) is in sight of the Animal
Args:
entity (Animal or Food): Entity to check
Returns:
bool: True if entity is in sight, False otherwise
"""
return distance(self.pos, entity.pos) <= self.sight | [
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1930,
11,
9312,
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1930,
8,
19841,
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13,
18627
] | 2.709148 | 1,279 |
import graphgallery.nn.models.dgl as models
from graphgallery.data.sequence import FullBatchSequence
from graphgallery import functional as gf
from graphgallery.gallery.nodeclas import NodeClasTrainer
from graphgallery.gallery.nodeclas import DGL
@DGL.register()
class APPNP(NodeClasTrainer):
"""Implementation of approximated personalized propagation of neural
predictions (APPNP).
`Predict then Propagate: Graph Neural Networks meet Personalized
PageRank" <https://arxiv.org/abs/1810.05997>`
Tensorflow 1.x implementation: <https://github.com/klicperajo/ppnp>
Pytorch implementation: <https://github.com/klicperajo/ppnp>
"""
| [
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] | 2.817814 | 247 |
#!/usr/bin/env python3
""" This module contains all class to manage variable choice Heuristic """
class VariableChoiceHeuristic:
""" Super class to handle variable choice heuristic """
def __init__(self, vars):
"""
Args:
vars (set): variables used in all clauses.
"""
#: set: All variables of a set of clauses program must be analyzed
self.vars = vars
def getVariabeTriplet(self, S):
"""Method to get variable
Args:
S: assignment set
Returns:
a triplet (X, v, v') such as X is variable, v is value of X and v' is alternative value of X
"""
if len(S) == 0:
return (min(self.vars), 1, -1)
s = set(list(zip(*S))[0])
return (min(self.vars-s), 1, -1)
class SimpleVariableChoiceHeuristic(VariableChoiceHeuristic):
""" First approach to choose variable, it is simple. we choose the first variable wich is not yet in assignment set (S) """
def getVariableTriplet(self, S):
"""Method to get variable
Args:
S: assignment set
Returns:
a triplet (X, v, v') such as X is variable, v is value of X and v' is alternative value of X
"""
return super().getVariabeTriplet(S)
class LevelTwoVariableChoiceHeuristic(VariableChoiceHeuristic):
""" This approach to choose variable is better than SimpleVariableChoiceHeuristic because it considers unitary clause"""
def getVariableTriplet(self, S):
"""Method to get variable
Args:
S(list): assignment set
Returns:
a set of tuple, i.e a triplet (X, v, v') such as X is variable, v is value of X and v' is alternative value of X
"""
if len(self.unitClauseLitteral)!=0:
return self.unitClauseLitteral
return super().getVariabeTriplet(S) | [
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7,
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13,
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43,
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28,
15,
25,
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220,
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2601,
682,
43,
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282,
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220,
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1441,
2208,
22446,
1136,
23907,
11231,
14824,
37069,
7,
50,
8
] | 2.35006 | 837 |
from mayan.apps.views.forms import FileDisplayForm
| [
6738,
743,
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13,
18211,
13,
33571,
13,
23914,
1330,
9220,
23114,
8479,
201,
198,
201,
198
] | 3.176471 | 17 |
from matplotlib import pyplot as plt
import math, sigfig, warnings # module "sigfig" requires "pip install sigfig" at command line
import numpy as np
# TRUSS INNER CLASSES END HERE
# MAIN FUNCTIONS END HERE
build_truss(815, True)
| [
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62,
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7,
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11,
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8,
628
] | 2.962963 | 81 |
import rospy
import numpy as np
from std_msgs.msg import Float64
from gazebo_msgs.srv import *
from geometry_msgs.msg import *
import sys, select, os
import roslib
if os.name == 'nt':
import msvcrt
else:
import tty, termios
roslib.load_manifest('dual_gazebo')
if __name__ == '__main__':
try:
rospy.init_node('mecanum_key')
if os.name != 'nt':
settings = termios.tcgetattr(sys.stdin)
linear = [0, 0, 0]
angular = [0, 0, 0]
plant_x = 0
while(1):
key = getKey()
if key == 'w' :
linear[0] += 1
linear, angular[2] = move_mecanum([linear,angular])
elif key == 'x' :
linear[0] -= 1
linear, angular[2] = move_mecanum([linear,angular])
elif key == 'a' :
angular[2] += 0.5
linear, angular[2] = move_mecanum([linear,angular])
elif key == 'd' :
angular[2] -= 0.5
linear, angular[2] = move_mecanum([linear,angular])
elif key == 'q' :
plant_x += 0.01
move_chassis(plant_x)
elif key == 'e' :
plant_x -= 0.01
move_chassis(plant_x)
elif key == 's' :
linear = [0, 0, 0]
angular = [0, 0, 0]
linear, angular[2] = move_mecanum([linear,angular])
if (key == '\x03'):
linear = [0, 0, 0]
angular = [0, 0, 0]
linear, angular[2] = move_mecanum([linear,angular])
break
except rospy.ROSInt:
pass
| [
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] | 1.738555 | 983 |
# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 noet:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
from os.path import exists
from requests.exceptions import InvalidURL
from ....utils import chpwd
from ....dochelpers import exc_str
from ....tests.utils import assert_true, assert_raises, assert_false
from ....tests.utils import SkipTest
from ....tests.utils import with_tempfile, skip_if_no_network, use_cassette
from ....tests.utils import skip_if_url_is_not_available
from datalad.crawler.pipelines.tests.utils import _test_smoke_pipelines
from datalad.crawler.pipelines.fcptable import *
from datalad.crawler.pipeline import run_pipeline
import logging
from logging import getLogger
lgr = getLogger('datalad.crawl.tests')
from ..fcptable import pipeline, superdataset_pipeline
TOPURL = "http://fcon_1000.projects.nitrc.org/fcpClassic/FcpTable.html"
@use_cassette('test_fcptable_dataset')
@skip_if_no_network
@with_tempfile(mkdir=True)
| [
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] | 2.978365 | 416 |
# Water Jug problem
print("Solution for Water Jug problem!")
x = int(input("Enter the capacity of jug1 : "))
y = int(input("Entert the capacity of jug2 : "))
target = int(input("Enter the target volume : "))
start = [0, 0]
if target % gcd(x,y) == 0:
print(bfs(start, target, x, y))
else:
print("No solution")
| [
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] | 2.315789 | 152 |
'''
author: cxn
version: 0.1.0
read camera calibration from mat
'''
import numpy as np
import cv2
from scipy.io import loadmat
import matplotlib.pyplot as plt
#双目相机参数
# 畸变校正和立体校正
def rectifyImage(image1, image2, map1x, map1y, map2x, map2y):
"""
cv2.remap重映射,就是把一幅图像中某位置的像素放置到另一个图片指定位置的过程
"""
rectifyed_img1 = cv2.remap(image1, map1x, map1y, cv2.INTER_AREA)
rectifyed_img2 = cv2.remap(image2, map2x, map2y, cv2.INTER_AREA)
return rectifyed_img1, rectifyed_img2
#视差计算
#计算三维坐标,并删除错误点
# 立体校正检验----画线
imgL = cv2.imread("D:/cxn_project/Strain-gauges-recognition/cali_img/left/l6.bmp")
imgR = cv2.imread("D:/cxn_project/Strain-gauges-recognition/cali_img/right/r6.bmp")
height, width = imgL.shape[0:2]
# 读取相机内参和外参
config = stereoCameral()
map1x, map1y, map2x, map2y, Q = getRectifyTransform(height, width, config)
iml_rectified, imr_rectified = rectifyImage(imgL, imgR, map1x,
map1y, map2x, map2y)
disp = sgbm(iml_rectified, imr_rectified)
plt.imshow(disp)
target_point = threeD(disp, Q) # 计算目标点的3D坐标（左相机坐标系下）
print(target_point)
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Put your models here
from sqlalchemy import Column, BigInteger, Integer, String, SmallInteger, Float, Boolean, DECIMAL, Text, DateTime, Date, \
Index, UniqueConstraint
from sqlalchemy.dialects.mysql import MEDIUMTEXT, LONGTEXT, BIGINT, INTEGER, SMALLINT, TINYINT, TIMESTAMP
from sqlalchemy.ext.declarative import declarative_base
from decimal import Decimal
from sqlalchemy.schema import Sequence
from lib.model.base import Base, BaseModel
"""
建表规范
1.之后建表 请继承BaseModel
2.表字段主键自增强制取名 不允许是id
3.comment备注强制每个字段都要
4.建表之后如果如果关联其他表字段时候 名字别乱取 要统一
5.字段取名 出现下划线警示时候请自行注意单词拼写
"""
if __name__ == '__main__':
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker, scoped_session
from sqlalchemy import create_engine
from setting import MYSQL
engine = create_engine(MYSQL)
DBSession = scoped_session(sessionmaker(bind=engine))
Base.metadata.create_all(engine)
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# -*- coding: utf-8 -*-
"""
Author: Zhao Xinlu
School: BUPT
Date: 2018-01-15
Function: Some different searching algorithms and its performance
"""
def Simple_search(lists, key):
'''
Simple_search: 数据不排序的线性查找，遍历数据元素;
性能：
时间复杂度：O(n)
:param lists: search list
:param key: the value of key
:return: the key's location in the list
'''
length = len(lists)
for i in range(0, length):
if lists[i] == key:
return i
return False
def Binary_search(lists, key):
'''
Binary search(二分查找):在查找表中不断取中间元素与查找值进行比较，以二分之一的倍率进行表范围的缩小。
性能：
时间复杂度：O(logn)
:param lists: search list
:param key: the value of key
:return: the key's location in the list
'''
length = len(lists)
low = 0
high = length - 1
while low < high:
mid = int((low + high) / 2)
# mid = low + 1/2 * (high - low)
if lists[mid] > key:
high = mid - 1
elif lists[mid] < key:
low = mid + 1
else:
return mid
return False
def Binary_search2(lists, key, low, high):
'''
Binary search 2(二分查找的递归实现)
:param lists: search list
:param key: the value of key
:param low:
:param high:
:return: the key's location in the list
'''
mid = int((low + high) / 2)
if lists[mid] == key:
return mid
elif lists[mid] < key:
return Binary_search2(lists, key, mid+1, high)
else:
return Binary_search2(lists, key, low, mid-1)
def Binary_search_plus(lists, key):
'''
Binary search plus(插值查找):二分查找的优化
对半过滤还不够狠，要是每次都排除十分之九的数据岂不是更好？选择这个值就是关键问题
:param lists: search list
:param key: the value of key
:return: the key's location in the list
'''
length = len(lists)
low = 0
high = length - 1
while low < high:
mid = low + int((high - low) * (key - lists[low]) / (lists[high] - lists[low]))
# 插值的核心公式： value = (key - list[low])/(list[high] - list[low])
if lists[mid] > key:
high = mid - 1
elif lists[mid] < key:
low = mid + 1
else:
return mid
return False
def Fibonacci_search(lists, key):
'''
Fibonacci search(斐波那契查找):利用斐波那契数列的性质，黄金分割的原理来确定mid的位置.
性能：
时间复杂的：O(logn)
:param lists: search list
:param key: the value of search key
:return: the key's location in the list
'''
# 需要一个现成的斐波那契列表, 其最大元素的值必须超过查找表中元素个数的数值。
FibonacciList = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144,
233, 377, 610, 987, 1597, 2584, 4181, 6765,
10946, 17711, 28657, 46368]
length = len(lists)
low = 0
high = length - 1
# 为了使得查找表满足斐波那契特性，在表的最后添加几个同样的值
# 这个值是原查找表的最后那个元素的值
# 添加的个数由F[k]-1-high决定
k = 0
while high > FibonacciList[k] - 1:
k += 1
print k
i = high
while FibonacciList[k] - 1 > i:
lists.append(lists[high])
i += 1
print lists
# 算法主逻辑
while low <= high:
if k < 2:
mid = low
else:
mid = low + FibonacciList[k] - 1
# 利用斐波那契数列来找寻下一个要比较的关键字的位置
if key < lists[mid]:
high = mid - 1
k -= 1
elif key > lists[mid]:
low = mid + 1
k -= 2
else:
if mid <= high:
return mid
else:
return high
return False
if __name__ == '__main__':
key = 7
TestList1 = [3, 6, 5, 9, 7, 1, 8, 2, 4]
TestList2 = [1, 2, 3, 4, 5, 6, 7, 8, 9]
TestList3 = [1, 5, 7, 8, 22, 54, 99, 123, 200, 222, 444]
# result = Simple_search(TestList1, key)
# result = Binary_search(TestList2, key)
# result = Binary_search2(TestList2, key, 0, len(TestList2))
# result = Binary_search_plus(TestList2, key)
result = Fibonacci_search(TestList3, key=444)
print "Key's location of the list is : lists[", result, "]" | [
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] | 1.626442 | 2,428 |
"""
resources.oauth_ropc
~~~~~~~~~~~~~~~~~~~~
OAuth2 Resource Owner Password Credentials Grant resource
object with responders.
This resource should be used to accept access_token requests
according to RFC 6749 section 4.3:
tools.ietf.org/html/rfc6749#section-4.3
The resource requires a callable to be passed in as the
auth_creds property which will be given a username &
password. The callable should return a token.
Returning a string will be interpreted as an error &
a RFC 6749 compliant error response will be sent with
the error message as the error_description field in
the response.
"""
import falcon
import goldman
from goldman.exceptions import AuthRejected
from ..resources.base import Resource as BaseResource
class Resource(BaseResource):
""" OAuth2 Resource Owner Password Credentials Grant resource """
DESERIALIZERS = [
goldman.FormUrlEncodedDeserializer,
]
SERIALIZERS = [
goldman.JsonSerializer,
]
@property
def _realm(self):
""" Return a string representation of the authentication realm """
return 'Bearer realm="%s"' % goldman.config.AUTH_REALM
def on_post(self, req, resp):
""" Validate the access token request for spec compliance
The spec also dictates the JSON based error response
on failure & is handled in this responder.
"""
grant_type = req.get_param('grant_type')
password = req.get_param('password')
username = req.get_param('username')
# errors or not, disable client caching along the way
# per the spec
resp.disable_caching()
if not grant_type or not password or not username:
resp.status = falcon.HTTP_400
resp.serialize({
'error': 'invalid_request',
'error_description': 'A grant_type, username, & password '
'parameters are all required when '
'requesting an OAuth access_token',
'error_uri': 'tools.ietf.org/html/rfc6749#section-4.3.2',
})
elif grant_type != 'password':
resp.status = falcon.HTTP_400
resp.serialize({
'error': 'unsupported_grant_type',
'error_description': 'The grant_type parameter MUST be set '
'to "password" not "%s"' % grant_type,
'error_uri': 'tools.ietf.org/html/rfc6749#section-4.3.2',
})
else:
try:
token = self.auth_creds(username, password)
resp.serialize({
'access_token': token,
'token_type': 'Bearer',
})
except AuthRejected as exc:
resp.status = falcon.HTTP_401
resp.set_header('WWW-Authenticate', self._realm)
resp.serialize({
'error': 'invalid_client',
'error_description': exc.detail,
})
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] | 2.254176 | 1,377 |
import os
from flask import Flask, request
from fbmessenger import BaseMessenger
from fbmessenger import quick_replies
from fbmessenger.elements import Text
from fbmessenger.thread_settings import GreetingText, GetStartedButton, MessengerProfile
from fbmessenger import elements
from fbmessenger import templates
ACCESS_TOKEN = "Baisiai slaptas"
VERIFY_TOKEN = "Dar slaptesnis"
app = Flask(__name__)
app.debug = True
messenger = Messenger(ACCESS_TOKEN)
@app.route("/", methods=["GET", "POST"])
if __name__ == "__main__":
app.run(host="0.0.0.0") | [
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] | 2.952128 | 188 |
# -*- coding: utf-8 -*-
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from model.contact import ContactBaseData
import re
| [
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# ################################################################################################
# ------------------------------------------------------------------------------------------------
# File: text_recognition_tesseract_engine.py
# Author: Luis Monteiro
#
# Created on nov 17, 2019, 22:00 PM
# ------------------------------------------------------------------------------------------------
# ################################################################################################
# external
from pytesseract import image_to_string
# ################################################################################################
# ------------------------------------------------------------------------------------------------
# TextRecognitionTesseract
# ------------------------------------------------------------------------------------------------
# ################################################################################################
#
# -------------------------------------------------------------------------
# initialization
# -------------------------------------------------------------------------
#
#
# -------------------------------------------------------------------------
# process
# -------------------------------------------------------------------------
#
# ################################################################################################
# ------------------------------------------------------------------------------------------------
# End
# ------------------------------------------------------------------------------------------------
# ################################################################################################
| [
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3880,
198,
2,
1303,
29113,
29113,
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7804,
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198
] | 8.147465 | 217 |
import OSC, time
#import rtmidi_python as rtmidi
#midi_out = rtmidi.MidiOut()
#midi_out.open_port(0)
if __name__ == "__main__":
s = OSC.OSCServer(('10.100.7.151', 57120)) # listen on localhost, port 57120
s.addMsgHandler('/startup', handler) # call handler() for OSC messages received with the /startup address
s.serve_forever()
| [
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] | 2.423611 | 144 |
# -*- coding: utf-8 -*-
# @Author: Manuel Rodriguez <valle>
# @Date: 28-Aug-2017
# @Email: [email protected]
# @Filename: views.py
# @Last modified by: valle
# @Last modified time: 02-Mar-2018
# @License: Apache license vesion 2.0
from django.forms.models import model_to_dict
from django.db.models import Q
from django.conf import settings
from django.shortcuts import render, redirect
try:
from django.core.urlresolvers import reverse
except ImportError:
from django.urls import reverse
from django.contrib.auth.decorators import login_required, permission_required
from django.template.loader import render_to_string
from django.http import HttpResponse
#from django.template import Context
from django.template.loader import get_template
from adminshop.utility import get_documento_compra, get_documento_testeo
from adminshop.forms import (CPClientesForm, CPProductosForm, ProductosForm, MODProductosForm,
FinTratoForm, ValidarCompra, VistaValidarForm, ModelosForm)
from adminshop.models import (Modelos, Clientes, Testeo, ConfigSite, Historial, Firmas,
Productos, Compras, Tipos, Direcciones, DocumentoTesteo, ListaTesteo)
from adminshop.utility import save_historial, save_doc_firmas, save_doc_testeo
from . import (validoDNI, get_first_direccion, set_first_direccion)
from tokenapi.http import JsonResponse
import threading
import base64
import json
import trml2pdf
import os
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
@login_required(login_url='login_tk')
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] | 2.751765 | 850 |
from __future__ import (
absolute_import,
unicode_literals,
)
import unittest
from pysoa.common.errors import Error
from pysoa.test.plan.grammar import assertions
from pysoa.test.plan.grammar.data_types import AnyValue
# noinspection PyTypeChecker
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] | 2.954545 | 88 |
import asyncio
import socket
import time
import logging
from unittest.mock import Mock
from torba.testcase import IntegrationTestCase, Conductor
import lbry.wallet
from lbry.schema.claim import Claim
from lbry.wallet.transaction import Transaction, Output
from lbry.wallet.dewies import dewies_to_lbc as d2l, lbc_to_dewies as l2d
log = logging.getLogger(__name__)
class TestSessionBloat(IntegrationTestCase):
"""
ERROR:asyncio:Fatal read error on socket transport
protocol: <lbrynet.wallet.server.session.LBRYElectrumX object at 0x7f7e3bfcaf60>
transport: <_SelectorSocketTransport fd=3236 read=polling write=<idle, bufsize=0>>
Traceback (most recent call last):
File "/usr/lib/python3.7/asyncio/selector_events.py", line 801, in _read_ready__data_received
data = self._sock.recv(self.max_size)
TimeoutError: [Errno 110] Connection timed out
"""
LEDGER = lbry.wallet
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62,
47844,
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220,
220,
220,
220,
220,
220,
220,
1366,
796,
2116,
13557,
82,
735,
13,
8344,
85,
7,
944,
13,
9806,
62,
7857,
8,
198,
220,
220,
220,
3862,
448,
12331,
25,
685,
9139,
81,
3919,
9796,
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] | 2.76506 | 332 |
import sqlite3
conn = sqlite3.connect('northwind_small.sqlite3')
curs = conn.cursor()
query = '''SELECT ProductName FROM Product
ORDER BY UnitPrice DESC
LIMIT 10'''
curs.execute(query)
results = curs.fetchall()
print('Ten most expensive items (per unit price):')
for result in results:
print(result[0])
query = '''SELECT avg(HireDate - BirthDate)
FROM Employee'''
curs.execute(query)
print('Average age of an employee at the time of their hiring:', curs.fetchall()[0][0])
query = '''SELECT City, avg(HireDate - BirthDate) as Age
FROM Employee
GROUP BY City'''
curs.execute(query)
print('Average age of an employee at the time of their hiring by city:')
results = curs.fetchall()
for result in results:
print(result[0], result[1])
query = '''SELECT ProductName, CompanyName FROM Product
INNER JOIN Supplier
ON Product.SupplierId = Supplier.Id
ORDER BY UnitPrice DESC
LIMIT 10'''
curs.execute(query)
results = curs.fetchall()
print('Ten most expensive items (per unit price) and their suppliers:')
print('Product', 'Supplier', sep='\t\t\t')
for result in results:
if len(result[0]) > 15:
sep = '\t'
else:
sep = '\t\t'
print(result[0], result[1], sep=sep)
query = '''SELECT CategoryName, count(Product.Id) as ProductCount FROM Category
INNER JOIN Product
ON Category.Id = Product.CategoryId
GROUP BY CategoryId
ORDER BY ProductCount DESC
LIMIT 1'''
curs.execute(query)
print('Largest category (by number of products in it):', curs.fetchall()[0][0])
query = '''SELECT LastName, FirstName, count(Territory.TerritoryDescription) as TerritoryCount
FROM Employee, Territory
JOIN EmployeeTerritory
ON Employee.Id = EmployeeTerritory.EmployeeId
GROUP BY Employee.Id
ORDER BY TerritoryCount DESC
LIMIT 1'''
curs.execute(query)
results = curs.fetchall()
print('Employee with the most territories, and number of territories they have:',
results[0][1], results[0][0] + ';', results[0][2])
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"""Module for functional data manipulation in a basis system.
Defines functional data object in a basis function system representation and
the corresponding basis classes.
"""
import copy
import warnings
from abc import ABC, abstractmethod
from typing import Tuple
import numpy as np
from ..._utils import _domain_range, _reshape_eval_points, _same_domain
from . import _fdatabasis
class Basis(ABC):
"""Defines the structure of a basis function system.
Attributes:
domain_range (tuple): a tuple of length 2 containing the initial and
end values of the interval over which the basis can be evaluated.
n_basis (int): number of functions in the basis.
"""
def __init__(self, *, domain_range=None, n_basis: int = 1):
"""Basis constructor.
Args:
domain_range (tuple or list of tuples, optional): Definition of the
interval where the basis defines a space. Defaults to (0,1).
n_basis: Number of functions that form the basis. Defaults to 1.
"""
if domain_range is not None:
domain_range = _domain_range(domain_range)
# Some checks
_check_domain(domain_range)
if n_basis < 1:
raise ValueError(
"The number of basis has to be strictly positive.",
)
self._domain_range = domain_range
self._n_basis = n_basis
super().__init__()
def __call__(self, *args, **kwargs) -> np.ndarray:
"""Evaluate the basis using :meth:`evaluate`."""
return self.evaluate(*args, **kwargs)
@property
@property
@property
@property
@abstractmethod
def _evaluate(self, eval_points) -> np.ndarray:
"""Subclasses must override this to provide basis evaluation."""
pass
def evaluate(self, eval_points, *, derivative: int = 0) -> np.ndarray:
"""Evaluate Basis objects and its derivatives.
Evaluates the basis function system or its derivatives at a list of
given values.
Args:
eval_points (array_like): List of points where the basis is
evaluated.
Returns:
Matrix whose rows are the values of the each
basis function or its derivatives at the values specified in
eval_points.
"""
if derivative < 0:
raise ValueError("derivative only takes non-negative values.")
elif derivative != 0:
warnings.warn("Parameter derivative is deprecated. Use the "
"derivative function instead.", DeprecationWarning)
return self.derivative(order=derivative)(eval_points)
eval_points = _reshape_eval_points(eval_points,
aligned=True,
n_samples=self.n_basis,
dim_domain=self.dim_domain)
return self._evaluate(eval_points).reshape(
(self.n_basis, len(eval_points), self.dim_codomain))
def derivative(self, *, order: int = 1) -> '_fdatabasis.FDataBasis':
"""Construct a FDataBasis object containing the derivative.
Args:
order: Order of the derivative. Defaults to 1.
Returns:
Derivative object.
"""
return self.to_basis().derivative(order=order)
def _derivative_basis_and_coefs(self, coefs: np.ndarray, order: int = 1):
"""
Subclasses can override this to provide derivative construction.
A basis can provide derivative evaluation at given points
without providing a basis representation for its derivatives,
although is recommended to provide both if possible.
"""
raise NotImplementedError(f"{type(self)} basis does not support "
"the construction of a basis of the "
"derivatives.")
def plot(self, chart=None, **kwargs):
"""Plot the basis object or its derivatives.
Args:
chart (figure object, axe or list of axes, optional): figure over
with the graphs are plotted or axis over where the graphs are
plotted.
**kwargs: keyword arguments to be passed to the
fdata.plot function.
Returns:
fig (figure): figure object in which the graphs are plotted.
"""
self.to_basis().plot(chart=chart, **kwargs)
def _coordinate_nonfull(self, fdatabasis, key):
"""
Returns a fdatagrid for the coordinate functions indexed by key.
Subclasses can override this to provide coordinate indexing.
The key parameter has been already validated and is an integer or
slice in the range [0, self.dim_codomain.
"""
raise NotImplementedError("Coordinate indexing not implemented")
def _coordinate(self, fdatabasis, key):
"""Returns a fdatagrid for the coordinate functions indexed by key."""
# Raises error if not in range and normalize key
r_key = range(self.dim_codomain)[key]
if isinstance(r_key, range) and len(r_key) == 0:
raise IndexError("Empty number of coordinates selected")
# Full fdatabasis case
if (self.dim_codomain == 1 and r_key == 0) or (
isinstance(r_key, range) and len(r_key) == self.dim_codomain):
return fdatabasis.copy()
else:
return self._coordinate_nonfull(fdatabasis=fdatabasis, key=r_key)
def rescale(self, domain_range=None):
r"""Return a copy of the basis with a new :term:`domain` range, with
the corresponding values rescaled to the new bounds.
Args:
domain_range (tuple, optional): Definition of the interval
where the basis defines a space. Defaults uses the same as
the original basis.
"""
return self.copy(domain_range=domain_range)
def copy(self, domain_range=None):
"""Basis copy"""
new_copy = copy.deepcopy(self)
if domain_range is not None:
domain_range = _domain_range(domain_range)
# Some checks
_check_domain(domain_range)
new_copy._domain_range = domain_range
return new_copy
def to_basis(self) -> '_fdatabasis.FDataBasis':
"""Convert the Basis to FDatabasis.
Returns:
FDataBasis with this basis as its basis, and all basis functions
as observations.
"""
from . import FDataBasis
return FDataBasis(self.copy(), np.identity(self.n_basis))
def inner_product_matrix(self, other: 'Basis' = None) -> np.array:
r"""Return the Inner Product Matrix of a pair of basis.
The Inner Product Matrix is defined as
.. math::
IP_{ij} = \langle\phi_i, \theta_j\rangle
where :math:`\phi_i` is the ith element of the basi and
:math:`\theta_j` is the jth element of the second basis.
This matrix helps on the calculation of the inner product
between objects on two basis and for the change of basis.
Args:
other: Basis to compute the inner product
matrix. If not basis is given, it computes the matrix with
itself returning the Gram Matrix
Returns:
Inner Product Matrix of two basis
"""
from ...misc import inner_product_matrix
if other is None or self == other:
return self.gram_matrix()
return inner_product_matrix(self, other)
def _gram_matrix_numerical(self) -> np.array:
"""
Compute the Gram matrix numerically.
"""
from ...misc import inner_product_matrix
return inner_product_matrix(self, force_numerical=True)
def _gram_matrix(self) -> np.array:
"""
Compute the Gram matrix.
Subclasses may override this method for improving computation
of the Gram matrix.
"""
return self._gram_matrix_numerical()
def gram_matrix(self) -> np.array:
r"""Return the Gram Matrix of a basis
The Gram Matrix is defined as
.. math::
G_{ij} = \langle\phi_i, \phi_j\rangle
where :math:`\phi_i` is the ith element of the basis. This is a
symmetric matrix and positive-semidefinite.
Returns:
Gram Matrix of the basis.
"""
gram = getattr(self, "_gram_matrix_cached", None)
if gram is None:
gram = self._gram_matrix()
self._gram_matrix_cached = gram
return gram
def __repr__(self) -> str:
"""Representation of a Basis object."""
return (f"{self.__class__.__name__}(domain_range={self.domain_range}, "
f"n_basis={self.n_basis})")
def __eq__(self, other) -> bool:
"""Equality of Basis"""
return (type(self) == type(other)
and _same_domain(self, other)
and self.n_basis == other.n_basis)
def __hash__(self) -> int:
"""Hash of Basis"""
return hash((self.domain_range, self.n_basis))
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220,
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286,
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468,
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1267,
628,
220,
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27830,
62,
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796,
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220,
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77,
62,
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271,
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299,
62,
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271,
628,
220,
220,
220,
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22446,
834,
15003,
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3419,
628,
220,
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220,
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11593,
13345,
834,
7,
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11,
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11,
12429,
46265,
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8,
4613,
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13,
358,
18747,
25,
198,
220,
220,
220,
220,
220,
220,
220,
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36,
2100,
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262,
4308,
1262,
1058,
76,
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25,
63,
49786,
63,
526,
15931,
198,
220,
220,
220,
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2116,
13,
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46491,
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11,
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46265,
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8,
628,
220,
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220,
2488,
26745,
628,
220,
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26745,
628,
220,
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26745,
628,
220,
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26745,
628,
220,
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397,
8709,
24396,
198,
220,
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220,
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4808,
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7,
944,
11,
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62,
13033,
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45941,
13,
358,
18747,
25,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
7004,
37724,
1276,
20957,
428,
284,
2148,
4308,
12660,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
1208,
628,
220,
220,
220,
825,
13446,
7,
944,
11,
5418,
62,
13033,
11,
1635,
11,
27255,
25,
493,
796,
657,
8,
4613,
45941,
13,
358,
18747,
25,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
36,
2100,
4985,
6455,
271,
5563,
290,
663,
28486,
13,
628,
220,
220,
220,
220,
220,
220,
220,
26439,
12632,
262,
4308,
2163,
1080,
393,
663,
28486,
379,
257,
1351,
286,
198,
220,
220,
220,
220,
220,
220,
220,
1813,
3815,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5418,
62,
13033,
357,
18747,
62,
2339,
2599,
7343,
286,
2173,
810,
262,
4308,
318,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16726,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24936,
3025,
15274,
389,
262,
3815,
286,
262,
1123,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4308,
2163,
393,
663,
28486,
379,
262,
3815,
7368,
287,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5418,
62,
13033,
13,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
27255,
1279,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7203,
1082,
452,
876,
691,
2753,
1729,
12,
31591,
3815,
19570,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
27255,
14512,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14601,
13,
40539,
7203,
36301,
27255,
318,
39224,
13,
5765,
262,
366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
1082,
452,
876,
2163,
2427,
33283,
2129,
8344,
341,
20361,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
1082,
452,
876,
7,
2875,
28,
1082,
452,
876,
5769,
18206,
62,
13033,
8,
628,
220,
220,
220,
220,
220,
220,
220,
5418,
62,
13033,
796,
4808,
3447,
1758,
62,
18206,
62,
13033,
7,
18206,
62,
13033,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19874,
28,
17821,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
299,
62,
82,
12629,
28,
944,
13,
77,
62,
12093,
271,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5391,
62,
27830,
28,
944,
13,
27740,
62,
27830,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
49786,
7,
18206,
62,
13033,
737,
3447,
1758,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
944,
13,
77,
62,
12093,
271,
11,
18896,
7,
18206,
62,
13033,
828,
2116,
13,
27740,
62,
19815,
296,
391,
4008,
628,
220,
220,
220,
825,
27255,
7,
944,
11,
1635,
11,
1502,
25,
493,
796,
352,
8,
4613,
705,
62,
16344,
265,
397,
17765,
13,
37,
6601,
15522,
271,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
42316,
257,
376,
6601,
15522,
271,
2134,
7268,
262,
27255,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1502,
25,
8284,
286,
262,
27255,
13,
2896,
13185,
284,
352,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9626,
452,
876,
2134,
13,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
1462,
62,
12093,
271,
22446,
1082,
452,
876,
7,
2875,
28,
2875,
8,
628,
220,
220,
220,
825,
4808,
1082,
452,
876,
62,
12093,
271,
62,
392,
62,
1073,
891,
82,
7,
944,
11,
763,
891,
82,
25,
45941,
13,
358,
18747,
11,
1502,
25,
493,
796,
352,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3834,
37724,
460,
20957,
428,
284,
2148,
27255,
5103,
13,
628,
220,
220,
220,
220,
220,
220,
220,
317,
4308,
460,
2148,
27255,
12660,
379,
1813,
2173,
198,
220,
220,
220,
220,
220,
220,
220,
1231,
4955,
257,
4308,
10552,
329,
663,
28486,
11,
198,
220,
220,
220,
220,
220,
220,
220,
3584,
318,
7151,
284,
2148,
1111,
611,
1744,
13,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
1892,
3546,
1154,
12061,
12331,
7,
69,
1,
90,
4906,
7,
944,
38165,
4308,
857,
407,
1104,
366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
1169,
5103,
286,
257,
4308,
286,
262,
366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
1082,
452,
2929,
19570,
628,
220,
220,
220,
825,
7110,
7,
944,
11,
8262,
28,
14202,
11,
12429,
46265,
22046,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
43328,
262,
4308,
2134,
393,
663,
28486,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8262,
357,
26875,
2134,
11,
23272,
393,
1351,
286,
34197,
11,
11902,
2599,
3785,
625,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
351,
262,
28770,
389,
37515,
393,
16488,
625,
810,
262,
28770,
389,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37515,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
46265,
22046,
25,
21179,
7159,
284,
307,
3804,
284,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
277,
7890,
13,
29487,
2163,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2336,
357,
26875,
2599,
3785,
2134,
287,
543,
262,
28770,
389,
37515,
13,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
1462,
62,
12093,
271,
22446,
29487,
7,
40926,
28,
40926,
11,
12429,
46265,
22046,
8,
628,
220,
220,
220,
825,
4808,
37652,
4559,
62,
13159,
12853,
7,
944,
11,
277,
19608,
397,
17765,
11,
1994,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
16409,
257,
277,
19608,
363,
6058,
329,
262,
20435,
5499,
41497,
416,
1994,
13,
628,
220,
220,
220,
220,
220,
220,
220,
3834,
37724,
460,
20957,
428,
284,
2148,
20435,
6376,
278,
13,
628,
220,
220,
220,
220,
220,
220,
220,
383,
1994,
11507,
468,
587,
1541,
31031,
290,
318,
281,
18253,
393,
198,
220,
220,
220,
220,
220,
220,
220,
16416,
287,
262,
2837,
685,
15,
11,
2116,
13,
27740,
62,
19815,
296,
391,
13,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
1892,
3546,
1154,
12061,
12331,
7203,
7222,
45480,
6376,
278,
407,
9177,
4943,
628,
220,
220,
220,
825,
4808,
37652,
4559,
7,
944,
11,
277,
19608,
397,
17765,
11,
1994,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
35561,
257,
277,
19608,
363,
6058,
329,
262,
20435,
5499,
41497,
416,
1994,
526,
15931,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
7567,
2696,
4049,
611,
407,
287,
2837,
290,
3487,
1096,
1994,
198,
220,
220,
220,
220,
220,
220,
220,
374,
62,
2539,
796,
2837,
7,
944,
13,
27740,
62,
19815,
296,
391,
38381,
2539,
60,
628,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
81,
62,
2539,
11,
2837,
8,
290,
18896,
7,
81,
62,
2539,
8,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
12901,
12331,
7203,
40613,
1271,
286,
22715,
6163,
4943,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
6462,
277,
19608,
397,
17765,
1339,
198,
220,
220,
220,
220,
220,
220,
220,
611,
357,
944,
13,
27740,
62,
19815,
296,
391,
6624,
352,
290,
374,
62,
2539,
6624,
657,
8,
393,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
318,
39098,
7,
81,
62,
2539,
11,
2837,
8,
290,
18896,
7,
81,
62,
2539,
8,
6624,
2116,
13,
27740,
62,
19815,
296,
391,
2599,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
277,
19608,
397,
17765,
13,
30073,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13557,
37652,
4559,
62,
13159,
12853,
7,
16344,
265,
397,
17765,
28,
16344,
265,
397,
17765,
11,
1994,
28,
81,
62,
2539,
8,
628,
220,
220,
220,
825,
6811,
1000,
7,
944,
11,
7386,
62,
9521,
28,
14202,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
374,
37811,
13615,
257,
4866,
286,
262,
4308,
351,
257,
649,
1058,
4354,
25,
63,
27830,
63,
2837,
11,
351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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] | 2.346202 | 3,963 |
from collections import defaultdict
import math
import time
import random
import tensorflow as tf
import numpy as np
# The length of the n-gram
N = 2
# Functions to read in the corpus
# NOTE: We are using data from the Penn Treebank, which is already converted
# into an easy-to-use format with "<unk>" symbols. If we were using other
# data we would have to do pre-processing and consider how to choose
# unknown words, etc.
w2i = defaultdict(lambda: len(w2i))
S = w2i["<s>"]
UNK = w2i["<unk>"]
# Read in the data
train = list(read_dataset("../data/ptb/train.txt"))
w2i = defaultdict(lambda: UNK, w2i)
dev = list(read_dataset("../data/ptb/valid.txt"))
i2w = {v: k for k, v in w2i.items()}
nwords = len(w2i)
x1 = tf.placeholder(shape=(1,), dtype=tf.int32)
x2 = tf.placeholder(shape=(1,), dtype=tf.int32)
y = tf.placeholder(shape=(1,None), dtype=tf.int32)
embedding1 = tf.get_variable(name="embedding1", shape=(nwords, nwords), initializer=tf.glorot_normal_initializer())
embedding2 = tf.get_variable(name="embedding2",shape=(nwords, nwords), initializer=tf.glorot_normal_initializer())
bias = tf.get_variable(name="bias", shape=(nwords), initializer=tf.glorot_normal_initializer())
embed1 = tf.nn.embedding_lookup(embedding1, x1)
embed2 = tf.nn.embedding_lookup(embedding2, x2)
score = embed1 + embed2 + bias
loss = tf.nn.softmax_cross_entropy_with_logits(logits=score, labels=y)
optimizer = tf.train.AdamOptimizer().minimize(loss)
session = tf.Session()
session.run(tf.global_variables_initializer())
for i in range(10):
random.shuffle(train)
total_loss = 0
train_words = 0
for id, sentence in enumerate(train):
history = [S] * N
sentence_loss = 0
for i in sentence + [S]:
y_one_hot = np.zeros(shape=(1, nwords))
y_one_hot[0][i] = 1
input1, input2 = history
history = history[1:] + [nwords]
feed_train = {x1: [input1],
x2: [input2],
y: y_one_hot}
char_loss, _ = session.run(fetches=[loss, optimizer], feed_dict=feed_train)
sentence_loss += char_loss
total_loss += sentence_loss
train_words += len(sentence)
if (id + 1) % 5000 == 0:
print("--finished %r sentences, %.4f" % (id + 1, (total_loss / train_words)))
print("iter %r: train loss/word=%.4f, ppl=%.4f" % (
i, total_loss / train_words, math.exp(total_loss / train_words)))
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11201,
7,
23350,
62,
22462,
1220,
4512,
62,
10879,
22305,
198
] | 2.345317 | 1,057 |
# Generated by Django 2.2.12 on 2020-08-02 14:03
from django.db import migrations
| [
2,
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1330,
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] | 2.8 | 30 |
t.left(90)
t.color("green")
t.speed(1)
tree(90)
| [
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7,
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8,
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] | 1.821429 | 28 |
# genetic algorithm search of the one max optimization problem
from numpy.random import randint
from numpy.random import rand
import numpy as np
import json
# objective function
# tournament selection
# crossover two parents to create two children
# mutation operator
# genetic algorithm
if False:
# define the total iterations
n_iter = 100
# bits
n_bits = 500 #20
# define the population size
n_pop = n_bits * 5 #100
# crossover rate
r_cross = 0.9
# mutation rate
r_mut = 1.0 / float(n_bits)
# perform the genetic algorithm search
best, score = genetic_algorithm(onemax, n_bits, n_iter, n_pop, r_cross, r_mut)
print('Done!')
print('f(%s) = %f' % (best, score))
| [
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0,
11537,
198,
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4,
82,
8,
796,
4064,
69,
6,
4064,
357,
13466,
11,
4776,
4008,
198
] | 2.869565 | 253 |
from django import forms
from joblistings.models import Job
from accounts.models import Employer
from ace.constants import CATEGORY_CHOICES, MAX_LENGTH_TITLE, MAX_LENGTH_DESCRIPTION, MAX_LENGTH_RESPONSABILITIES, MAX_LENGTH_REQUIREMENTS, MAX_LENGTH_STANDARDFIELDS, LOCATION_CHOICES
from tinymce.widgets import TinyMCE
from companies.models import Company
from joblistings.models import Job, JobPDFDescription
from django.shortcuts import get_object_or_404
from accounts.models import Employer
| [
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198
] | 3.346939 | 147 |
from .cluster_dw import ClusterWrapper
from .graphsage_dw import GraphSAGEDataWrapper
from .m3s_dw import M3SDataWrapper
from .network_embedding_dw import NetworkEmbeddingDataWrapper
from .node_classification_dw import FullBatchNodeClfDataWrapper
from .pprgo_dw import PPRGoDataWrapper
from .sagn_dw import SAGNDataWrapper
| [
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] | 2.833333 | 114 |
#!/usr/bin/env python3
import io
import sys
import generator
from generator.cmdline import *
if __name__ == '__main__':
if len(sys.argv) == 1:
run_cli()
else:
cmds = []
line_buf = []
for arg in sys.argv[1:]:
if arg == '--':
cmds.append(' '.join(line_buf))
line_buf = []
else:
line_buf.append(arg)
cmds.append(' '.join(line_buf))
run_cmds(io.StringIO('\n'.join(cmds)))
| [
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] | 1.865672 | 268 |
import os
import pathlib
import click
import parse
from fishtools.config import Config
@click.command()
@click.argument('config_fpath')
if __name__ == "__main__":
main()
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] | 3 | 61 |
#!/usr/bin/env python
#
# Copyright 2015 Martin Cochran
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from game_model import Game
from scores_messages import AgeBracket
from scores_messages import Division
from scores_messages import League
class ListIdBiMap:
"""Encapsulates mappings to and from list id and structured properties."""
# List ID definitions corresponding to lists defined in the twitter account of
# @martin_cochran.
USAU_COLLEGE_OPEN_LIST_ID = '186814318'
USAU_COLLEGE_WOMENS_LIST_ID = '186814882'
USAU_CLUB_OPEN_LIST_ID = '186732484'
USAU_CLUB_WOMENS_LIST_ID = '186732631'
USAU_CLUB_MIXED_LIST_ID = '186815046'
AUDL_LIST_ID = '186926608'
MLU_LIST_ID = '186926651'
ALL_LISTS = [
USAU_COLLEGE_OPEN_LIST_ID,
USAU_COLLEGE_WOMENS_LIST_ID,
USAU_CLUB_OPEN_LIST_ID,
USAU_CLUB_WOMENS_LIST_ID,
USAU_CLUB_MIXED_LIST_ID,
AUDL_LIST_ID,
MLU_LIST_ID
]
# Simple data structure to lookup lists if the league, division, and age
# bracket were specified in the request.
LIST_ID_MAP = {
League.USAU: {
Division.OPEN: {
AgeBracket.COLLEGE: USAU_COLLEGE_OPEN_LIST_ID,
AgeBracket.NO_RESTRICTION: USAU_CLUB_OPEN_LIST_ID,
},
Division.WOMENS: {
AgeBracket.COLLEGE: USAU_COLLEGE_WOMENS_LIST_ID,
AgeBracket.NO_RESTRICTION: USAU_CLUB_WOMENS_LIST_ID,
},
Division.MIXED: {
AgeBracket.NO_RESTRICTION: USAU_CLUB_MIXED_LIST_ID,
},
},
League.AUDL: {
Division.OPEN: {
AgeBracket.NO_RESTRICTION: AUDL_LIST_ID,
},
},
League.MLU: {
Division.OPEN: {
AgeBracket.NO_RESTRICTION: MLU_LIST_ID,
},
},
}
LIST_ID_TO_DIVISION = {
USAU_COLLEGE_OPEN_LIST_ID: Division.OPEN,
USAU_COLLEGE_WOMENS_LIST_ID: Division.WOMENS,
USAU_CLUB_OPEN_LIST_ID: Division.OPEN,
USAU_CLUB_WOMENS_LIST_ID: Division.WOMENS,
USAU_CLUB_MIXED_LIST_ID: Division.MIXED,
AUDL_LIST_ID: Division.OPEN,
MLU_LIST_ID: Division.OPEN,
}
LIST_ID_TO_AGE_BRACKET = {
USAU_COLLEGE_OPEN_LIST_ID: AgeBracket.COLLEGE,
USAU_COLLEGE_WOMENS_LIST_ID: AgeBracket.COLLEGE,
USAU_CLUB_OPEN_LIST_ID: AgeBracket.NO_RESTRICTION,
USAU_CLUB_WOMENS_LIST_ID: AgeBracket.NO_RESTRICTION,
USAU_CLUB_MIXED_LIST_ID: AgeBracket.NO_RESTRICTION,
AUDL_LIST_ID: AgeBracket.NO_RESTRICTION,
MLU_LIST_ID: AgeBracket.NO_RESTRICTION,
}
LIST_ID_TO_LEAGUE = {
USAU_COLLEGE_OPEN_LIST_ID: League.USAU,
USAU_COLLEGE_WOMENS_LIST_ID: League.USAU,
USAU_CLUB_OPEN_LIST_ID: League.USAU,
USAU_CLUB_WOMENS_LIST_ID: League.USAU,
USAU_CLUB_MIXED_LIST_ID: League.USAU,
AUDL_LIST_ID: League.AUDL,
MLU_LIST_ID: League.MLU,
}
@staticmethod
def GetListId(division, age_bracket, league):
"""Looks up the list_id which corresponds to the given division and league.
Args:
division: Division of interest
age_bracket: AgeBracket of interest
league: League of interest
Returns:
The list id corresponding to that league and division, or '' if no such
list exists.
"""
d = ListIdBiMap.LIST_ID_MAP.get(league, {})
if not d:
return ''
d = d.get(division, {})
if not d:
return ''
return d.get(age_bracket, '')
@staticmethod
def GetStructuredPropertiesForList(list_id):
"""Returns the division, age_bracket, and league for the given list id.
Defaults to Division.OPEN, AgeBracket.NO_RESTRICTION, and League.USAU,
if the division, age_bracket, or leauge, respectively, does not exist in
the map for the given list_id.
Args:
list_id: ID of list for which to retrieve properties.
Returns:
(division, age_bracket, league) tuple for the given list ID.
"""
division = ListIdBiMap.LIST_ID_TO_DIVISION.get(list_id, Division.OPEN)
age_bracket = ListIdBiMap.LIST_ID_TO_AGE_BRACKET.get(list_id, AgeBracket.NO_RESTRICTION)
league = ListIdBiMap.LIST_ID_TO_LEAGUE.get(list_id, League.USAU)
return (division, age_bracket, league)
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8,
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] | 2.278214 | 2,038 |
from owslib.wms import WebMapService
import pyproj
from PIL import Image
from typing import Tuple, List, Dict, Any
import os.path
from pathlib import Path
FORMAT_ENDINGS = {"image/jpeg": "jpg"}
| [
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from __future__ import print_function
import os.path
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
import time
from email.mime.text import MIMEText
from .models import Email
import base64
import email
import json
import datetime
import pytz
import re
# If modifying these scopes, delete the file token.json.
SCOPES = ['https://www.googleapis.com/auth/gmail.modify']
creds = None
# The file token.json stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists('token.json'):
creds = Credentials.from_authorized_user_file('token.json', SCOPES)
# If there are no (valid) credentials available, let the user log in.
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(
'credentials.json', SCOPES)
creds = flow.run_local_server(port=0)
# Save the credentials for the next run
with open('token.json', 'w') as token:
token.write(creds.to_json())
service = build('gmail', 'v1', credentials=creds)
| [
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] | 2.953229 | 449 |
# This module is automatically generated by autogen.sh. DO NOT EDIT.
from . import _IBM
# Aliases
| [
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_base_='../swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py'
dataset_type='CocoDataset'
prefix='../coco-annotator/datasets/test/'
classes=('plasticbottle','alu can','box')
# classes=('',)
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=3),
mask_head=dict(num_classes=3)))
# train_pipeline = [
# dict(type='LoadImageFromFile'),
# dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
# dict(type='Resize', img_scale=(128,128), keep_ratio=True),
# dict(type='RandomFlip', flip_ratio=0.5),
# dict(type='Normalize', **img_norm_cfg),
# dict(type='Pad', size_divisor=32),
# dict(type='DefaultFormatBundle'),
# dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
# # ]
# train1=dict(
# type=dataset_type,
# classes=classes,
# ann_file=['data/own/test-1.json'],
# img_prefix=prefix,
# pipeline=train_pipeline
# )
# train2=dict(
# type=dataset_type,
# classes=classes,
# ann_file=['data/own/ann_map_to_1.json'],
# img_prefix=prefix,
# pipeline=train_pipeline
# )
data=dict(
train=dict(
type=dataset_type,
classes=classes,
ann_file=['data/own/test-1.json','data/own/ann_map_to_1.json'],
img_prefix=prefix
),
# train=[train1,train2],
val=dict(
type=dataset_type,
classes=classes,
ann_file='data/own/ann_map_to_1.json',
img_prefix=prefix
),
test=dict(
type=dataset_type,
classes=classes,
ann_file='data/own/ann_map_to_1.json',
img_prefix=prefix
)
) | [
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] | 1.97343 | 828 |
"""
Handle MySQL I/O via sqlalchemy engine and ORM
"""
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from flickipedia.config import schema
from flickipedia.config import log, settings
class DataIOMySQL(object):
""" Class implementing data IO for MySQL. Utilizes sqlalchemy [1].
Database and table schemas will be stored in schema. Modifications
to this schema will be persisted with sync
[1] http://docs.sqlalchemy.org
"""
DEFAULTS = {
'dialect': 'mysql',
'driver': '',
'host': 'localhost',
'port': 3306,
'db': settings.__mysql_db__,
'user': settings.__mysql_user__,
'pwrd': settings.__mysql_pass__,
}
def connect(self, log=False):
""" dialect+driver://username:password@host:port/database """
if self.driver:
connect_str = '{0}+{1}://{2}:{3}@{4}/{5}'.format(
self.dialect,
self.driver,
self.user,
self.pwrd,
self.host,
self.db,
)
else:
connect_str = '{0}://{1}:{2}@{3}/{4}'.format(
self.dialect,
self.user,
self.pwrd,
self.host,
self.db,
)
if log:
log.info('Establishing connection to "%s://%s@%s/%s"' % (
self.dialect,
self.user,
self.host,
self.db
))
self.engine = create_engine(connect_str)
self.make_session()
def connect_lite(self):
""" Use an in-memory db """
self.engine = create_engine('sqlite://')
self.make_session()
def make_session(self):
""" Create a session """
Session = sessionmaker()
Session.configure(bind=self.engine)
self.sess = Session()
@property
def create_table(self, obj_name):
"""
Method for table creation
:param name: schema object name
:return: boolean indicating status
"""
if hasattr(schema, obj_name):
getattr(schema, obj_name).__table__.create(bind=self.engine)
return True
else:
log.error('Schema object not found for "%s"' % obj_name)
return False
def drop_table(self, obj_name):
"""
Method to drop creation
:param name: schema object name
:return: boolean indicating status
"""
if hasattr(schema, obj_name):
getattr(schema, obj_name).__table__.drop(bind=self.engine)
return True
else:
return False
def fetch_all_rows(self, obj_name):
"""
Method to extract all rows from database.
:param name: object to persist
:return: row list from table
"""
obj = getattr(schema, obj_name)
return self.session.query(obj, obj.name).all()
def fetch_row(self, tbl, col, value):
"""
Fetch a row by id
:param tbl: str, table name
:param col: str, column name
:param value: *, value on whih to filter
"""
schema_obj = getattr(schema, tbl)
try:
return self.session.query(schema_obj).filter(
getattr(schema_obj, col) == value)
except Exception as e:
log.error('Couldn\'t filter row: "%s"' % e.message)
return []
def insert(self, obj_name, **kwargs):
"""
Method to insert rows in database
:param name: object to persist
:param **kwargs: field values
:return: boolean indicating status of action
"""
if not self.session:
log.error('No session')
return False
try:
log.info('Attempting to insert row in schema "%s": "%s"' % (
obj_name, str([key + ':' + str(kwargs[key])[:100] for key in kwargs])))
self.session.add(getattr(schema, obj_name)(**kwargs))
self.session.commit()
return True
except Exception as e:
log.error('Failed to insert row: "%s"' % e.message)
return False
def delete(self, qry_obj):
"""
Method to delete rows from database
:param qry_obj: object to delete
:return: boolean indicating status of action
"""
if not self.session:
log.error('No session')
return False
try:
self.session.delete(qry_obj)
self.session.commit()
return True
except Exception as e:
log.error('Failed to delete row "%s": "%s"' % (str(qry_obj), e.message()))
return False
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] | 2.028834 | 2,393 |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.5 on 2017-10-21 23:08
from __future__ import unicode_literals
from django.db import migrations
| [
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"""Tests for the Patient model."""
| [
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15931,
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] | 3.6 | 10 |
# Python 3.7.9
# pip install clipboard
# pip install pywin32
# pip install pyautogui
# pip install pynput
# Google chrome Keyboard Shortcuts for Google Translate https://chrome.google.com/webstore/detail/keyboard-shortcuts-for-go/akjhnbnjanndggbcegmdggfjjclohjpo
# alt+j listen google translate
# Google chrome Dark Reader https://chrome.google.com/webstore/detail/dark-reader/eimadpbcbfnmbkopoojfekhnkhdbieeh
# Microsoft edge 110% zoom - https://www.phrasereader.com/
# Google chrome 125% zoom - https://translate.google.com/
from clipboard import copy, paste
from win32api import SetCursorPos, mouse_event
from win32con import MOUSEEVENTF_LEFTDOWN, MOUSEEVENTF_LEFTUP
from time import sleep
from pyautogui import hotkey
from pynput.keyboard import Listener, Key
next_x = 612
next_y = 562
prev_x = 359
prev_y = 562
translate_text_x = 1356
translate_text_y = 352
translate_blank_x = 1392
translate_blank_y = 222
text = ""
x = []
hasbeencaptured = False
last_key = 0
was_pressed_next = False
was_pressed_prev = False
was_pressed_one = False
was_pressed_two = False
was_pressed_three = False
was_pressed_four = False
was_pressed_allwords = False
with Listener(on_press=on_press, on_release=on_release) as listener:
listener.join()
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] | 2.851936 | 439 |
# -*- coding: UTF-8 -*-
#
# Given a linked list, swap every two adjacent nodes and return its head.
#
# For example,
# Given 1->2->3->4, you should return the list as 2->1->4->3.
#
# Your algorithm should use only constant space. You may not modify the values in the list, only nodes itself can be changed.
#
# Python, Python3 all accepted.
| [
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] | 3.266667 | 105 |
from unittest.case import TestCase
from probability.discrete import Discrete, Conditional
| [
6738,
555,
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] | 4 | 23 |
"""Adversarial Variational Bayes (AVB).
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative
Adversarial Networks
http://arxiv.org/abs/1701.04722
Ref)
https://github.com/gdikov/adversarial-variational-bayes
http://seiya-kumada.blogspot.com/2018/07/adversarial-variational-bayes.html
https://github.com/LMescheder/AdversarialVariationalBayes
https://nbviewer.jupyter.org/github/hayashiyus/Thermal-VAE/blob/master/adversarial%20variational%20bayes%20toy%20example-cyclical-annealing-MNIST-898-4000.ipynb
"""
from typing import Dict, Iterator, Optional, Tuple
import torch
from torch import Tensor, nn
from .base import BaseVAE, nll_bernoulli
class Encoder(nn.Module):
"""Encoder q(z|x, e).
Args:
in_channels (int): Channel size of inputs.
z_dim (int): Dimension size of latents.
e_dim (int): Dimension size of noises.
"""
def forward(self, x: Tensor, e: Tensor) -> Tensor:
"""Encodes z given x, e.
Args:
x (torch.Tensor): Observations, size `(b, c, h, w)`.
e (torch.Tensor): Noises, size `(b, e)`.
Returns:
z (torch.Tensor): Encoded latents, size `(b, z)`.
"""
h_x = self.conv(x)
h_x = h_x.view(-1, 1024)
h_x = self.fc_x(h_x)
h_e = self.fc_e(e)
z = self.fc(torch.cat([h_x, h_e], dim=1))
return z
class Decoder(nn.Module):
"""Decoder p(x|z).
Args:
in_channels (int): Channel size of inputs.
z_dim (int): Dimension size of latents.
"""
def forward(self, z: Tensor) -> Tensor:
"""Encodes z given x.
Args:
z (torch.Tensor): Latents, size `(b, z)`.
Returns:
probs (torch.Tensor): Decoded observations, size `(b, c, h, w)`.
"""
h = self.fc(z)
h = h.view(-1, 64, 4, 4)
probs = self.deconv(h)
return probs
class Discriminator(nn.Module):
"""Discriminator T(x, z).
Args:
in_channels (int): Channel size of inputs.
z_dim (int): Dimension size of latents.
"""
def forward(self, x: Tensor, z: Tensor) -> Tensor:
"""Discriminate p(x)p(z) from p(x)q(z|x).
Args:
x (torch.Tensor): Observations, size `(b, c, h, w)`.
z (torch.Tensor): Latents, size `(b, z)`.
Returns:
logits (torch.Tensor): Logits, size `(b, 1)`.
"""
h_x = self.disc_x(x)
h_x = self.fc_x(h_x.view(-1, 1024))
h_z = self.disc_z(z)
logits = self.fc(torch.cat([h_x, h_z], dim=1))
return logits
class AVB(BaseVAE):
"""Adversarial Variational Bayes.
Args:
in_channels (int, optional): Channel size of inputs.
z_dim (int, optional): Dimension size of latents.
e_dim (int, optional): Dimension size of noises.
"""
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] | 2.088857 | 1,373 |
from pytest_httpx import HTTPXMock
import httpx_auth
from tests.auth_helper import get_header
| [
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#!/usr/bin/env python3
import argparse
import sys
import os
# This key table has to match the one in bootloader
keyTbl = [0xDEADBEEF, 0xAAAAAAAA, 0x11111111, 0x00000000, 0xFFFFFFFF, 0x55555555, 0xA5A5A5A5, 0x66666666]
#******************************************************************************
#
# Main function
#
#******************************************************************************
#******************************************************************************
#
# Turn a 32-bit number into a series of bytes for transmission.
#
# This command will split a 32-bit integer into an array of bytes, ordered
# LSB-first for transmission over the UART.
#
#******************************************************************************
#******************************************************************************
#
# Extract a word from a byte array
#
#******************************************************************************
#******************************************************************************
#
# CRC function that matches the CRC used by the Apollo bootloader.
#
#******************************************************************************
poly32 = 0x1EDC6F41
#******************************************************************************
#
# Main program flow
#
#******************************************************************************
if __name__ == '__main__':
parser = argparse.ArgumentParser(description =
'Secure Image generation utility for Apollo or Apollo2')
parser.add_argument('binfile',
help = 'Binary file to program into the target device')
parser.add_argument('keyidxVal', default=0, type=int, help = 'encryption key index')
parser.add_argument('protectionVal', default=0, help = 'Image Protection Value (hex)')
parser.add_argument('encimagefile', help = 'Destination file for Encrypted image')
parser.add_argument('sectrailerfile', help = 'Destination file for security trailer')
args = parser.parse_args()
main()
| [
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796,
30751,
13,
29572,
62,
22046,
3419,
628,
220,
220,
220,
1388,
3419,
198
] | 4.005769 | 520 |
from django.views.generic import TemplateView
from django.views.decorators.cache import never_cache
from django.db.models import Count, Sum
from django.db.models.functions import Coalesce
from backend.api.models import Profile, ProfileDisplayFields, PostAggregateFields
from django.http import JsonResponse
from django.http import HttpRequest
# Serve Vue Application
index_view = never_cache(TemplateView.as_view(template_name="index.html"))
def profiles(request: HttpRequest) -> JsonResponse:
"""
Data about profiles and their posts
:param request: Request from the client
:return: JsonResponse containing a list of dictionaries that
represent profiles and their posts.
EX:
[
{
"name": "lifeoftanyamarie",
"thumbnail": "thumbnail.com",
"followers": 90900,
"post_count": 2,
"likes": 4310
},...
]
"""
fields = [
display.value for display in [*ProfileDisplayFields, *PostAggregateFields]
]
profiles_qs = (
Profile.objects.all()
.annotate(
post_count=Coalesce(Count("post"), 0),
likes=Coalesce(Sum("post__likes"), 0),
)
.values(*fields)
)
return JsonResponse(list(profiles_qs), safe=False)
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220,
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7,
5577,
2915,
62,
48382,
828,
3338,
28,
25101,
8,
198
] | 2.477186 | 526 |
#
# Created on Wed Nov 18 2020
#
# Copyright (c) 2020 - Simon Prast
#
import os
import uuid
from django.conf import settings
from django.db import models
from user.models import User
| [
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] | 3.183333 | 60 |
p1 = People("Maria", 1999)
print(p1.name)
print(p1.birthYear)
print(p1.age)
p1.pillar = "Architecture and Sustainable Design (ASD)"
print(f"{p1.name} is {p1.age} years old, and she is majored in {p1.pillar}")
| [
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198
] | 2.366667 | 90 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Perform a functional test of the list command."""
import os
import orion.core.cli
def test_no_exp(monkeypatch, clean_db, capsys):
"""Test that nothing is printed when there are no experiments."""
monkeypatch.chdir(os.path.dirname(os.path.abspath(__file__)))
orion.core.cli.main(['list'])
captured = capsys.readouterr().out
assert captured == ""
def test_single_exp(clean_db, one_experiment, capsys):
"""Test that the name of the experiment is printed when there is one experiment."""
orion.core.cli.main(['list'])
captured = capsys.readouterr().out
assert captured == " test_single_exp-v1\n"
def test_no_version_backward_compatible(clean_db, one_experiment_no_version, capsys):
"""Test status with no experiments."""
orion.core.cli.main(['list'])
captured = capsys.readouterr().out
assert captured == " test_single_exp-no-version-v1\n"
def test_broken_refers(clean_db, broken_refers, capsys):
"""Test that experiment without refers dict can be handled properly."""
orion.core.cli.main(['list'])
captured = capsys.readouterr().out
assert captured == " test_single_exp-v1\n"
def test_two_exp(capsys, clean_db, two_experiments):
"""Test that experiment and child are printed."""
orion.core.cli.main(['list'])
captured = capsys.readouterr().out
assert captured == """\
test_double_exp-v1┐
└test_double_exp_child-v1
"""
def test_three_exp(capsys, clean_db, three_experiments):
"""Test that experiment, child and grand-child are printed."""
orion.core.cli.main(['list'])
captured = capsys.readouterr().out
assert captured == """\
test_double_exp-v1┐
└test_double_exp_child-v1
test_single_exp-v1
"""
def test_no_exp_name(clean_db, three_experiments, monkeypatch, capsys):
"""Test that nothing is printed when there are no experiments with a given name."""
monkeypatch.chdir(os.path.dirname(os.path.abspath(__file__)))
orion.core.cli.main(['list', '--name', 'I don\'t exist'])
captured = capsys.readouterr().out
assert captured == ""
def test_exp_name(clean_db, three_experiments, monkeypatch, capsys):
"""Test that only the specified experiment is printed."""
monkeypatch.chdir(os.path.dirname(os.path.abspath(__file__)))
orion.core.cli.main(['list', '--name', 'test_single_exp'])
captured = capsys.readouterr().out
assert captured == " test_single_exp-v1\n"
def test_exp_name_with_child(clean_db, three_experiments, monkeypatch, capsys):
"""Test that only the specified experiment is printed, and with its child."""
monkeypatch.chdir(os.path.dirname(os.path.abspath(__file__)))
orion.core.cli.main(['list', '--name', 'test_double_exp'])
captured = capsys.readouterr().out
assert captured == """\
test_double_exp-v1┐
└test_double_exp_child-v1
"""
def test_exp_name_child(clean_db, three_experiments, monkeypatch, capsys):
"""Test that only the specified child experiment is printed."""
monkeypatch.chdir(os.path.dirname(os.path.abspath(__file__)))
orion.core.cli.main(['list', '--name', 'test_double_exp_child'])
captured = capsys.readouterr().out
assert captured == " test_double_exp_child-v1\n"
def test_exp_same_name(clean_db, two_experiments_same_name, monkeypatch, capsys):
"""Test that two experiments with the same name and different versions are correctly printed."""
monkeypatch.chdir(os.path.dirname(os.path.abspath(__file__)))
orion.core.cli.main(['list'])
captured = capsys.readouterr().out
assert captured == """\
test_single_exp-v1┐
└test_single_exp-v2
"""
def test_exp_family_same_name(clean_db, three_experiments_family_same_name, monkeypatch, capsys):
"""Test that two experiments with the same name and different versions are correctly printed
even when one of them has a child.
"""
monkeypatch.chdir(os.path.dirname(os.path.abspath(__file__)))
orion.core.cli.main(['list'])
captured = capsys.readouterr().out
assert captured == """\
┌test_single_exp-v2
test_single_exp-v1┤
└test_single_exp_child-v1
"""
def test_exp_family_branch_same_name(clean_db, three_experiments_branch_same_name,
monkeypatch, capsys):
"""Test that two experiments with the same name and different versions are correctly printed
even when last one has a child.
"""
monkeypatch.chdir(os.path.dirname(os.path.abspath(__file__)))
orion.core.cli.main(['list'])
captured = capsys.readouterr().out
assert captured == """\
test_single_exp-v1┐
└test_single_exp-v2┐
└test_single_exp_child-v1
"""
| [
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] | 2.559429 | 1,893 |
from rest_framework import serializers
from .models import Teacher,Timetable,Klass,Pupil,Cabinet,Subject, Grade
class TeacherSerializer(serializers.ModelSerializer):
"""Список учителей"""
class TeacherAddSerializer(serializers.ModelSerializer):
"""Добавление учителя"""
class PupilSerializer(serializers.ModelSerializer):
"""Список учеников"""
class GradeCreateSerializer(serializers.ModelSerializer):
"""Добавление оценки"""
class GradeSerializer(serializers.ModelSerializer):
"""Вывод оценок"""
subject = serializers.SlugRelatedField(slug_field="subject", read_only=True)
class PupilDetailSerializer(serializers.ModelSerializer):
"""Досье ученика"""
klass = serializers.SlugRelatedField(slug_field = "number", read_only=True)
grades = GradeSerializer(many=True)
class PupilAddSerializer(serializers.ModelSerializer):
"""Добавление ученика"""
class TimetableAddSerializer(serializers.ModelSerializer):
"""Добавление расписания"""
class TimetableSerializer(serializers.ModelSerializer):
"""Вывод расписания"""
subject_name = serializers.SlugRelatedField(slug_field="subject", read_only=True)
cabinet_number = serializers.SlugRelatedField(slug_field="number", read_only=True)
teacher_name = serializers.SlugRelatedField(slug_field="last_name", read_only=True)
klass_name = serializers.SlugRelatedField(slug_field="number", read_only=True)
class KlassSerializer(serializers.ModelSerializer):
"""Список классов"""
teacher = serializers.SlugRelatedField(slug_field="last_name", read_only=True)
class KlassAddSerializer(serializers.ModelSerializer):
"""Добавление класса"""
class KlassDetailSerializer(serializers.ModelSerializer):
"""Описание класса"""
teacher = serializers.SlugRelatedField(slug_field="last_name", read_only=True)
pupils = PupilSerializer(many=True)
timetable = TimetableSerializer(many=True)
class SubjectSerializer(serializers.ModelSerializer):
"""Список предметов"""
class CabinetSerializer(serializers.ModelSerializer):
"""Список кабинетов"""
teacher = serializers.SlugRelatedField(slug_field="last_name", read_only=True)
class TeacherDetailSerializer(serializers.ModelSerializer):
"""Досье учителя"""
subject = serializers.SlugRelatedField(slug_field="subject", read_only=True)
klass = KlassSerializer(many=True)
cabinet = CabinetSerializer(many=True)
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] | 2.307763 | 1,082 |
import argparse
import os
import numpy as np
from tqdm import tqdm
from mypath import Path
from dataloaders import make_data_loader
from modeling.sync_batchnorm.replicate import patch_replication_callback
from modeling.erfnet_road import *
from utils.loss import SegmentationLosses
from utils.calculate_weights import calculate_weigths_labels
from utils.lr_scheduler import LR_Scheduler
from utils.saver import Saver
from utils.summaries import TensorboardSummary
from utils.metrics import Evaluator
from utils.LossWithUncertainty import LossWithUncertainty
from dataloaders.utils import decode_segmap
if __name__ == "__main__":
main()
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] | 3.205 | 200 |
name0_1_0_0_0_3_0 = None
name0_1_0_0_0_3_1 = None
name0_1_0_0_0_3_2 = None
name0_1_0_0_0_3_3 = None
name0_1_0_0_0_3_4 = None | [
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] | 1.454545 | 88 |
import csv
import config as C
import pandas as pd
from sklearn import preprocessing
import numpy as np
if __name__ == '__main__':
df = pd.read_csv('./JsonToCSV/data0126.csv')
ecgList = []
recordLen = 10000
for i in range(len(df.ECG)):
ecgList.append(changeToList(df.ECG[i].split(" ")))
for j in range(len(ecgList)):
if recordLen > len(ecgList[j]):
recordLen = len(ecgList[j])
numOfRow = []
for k in range(recordLen - 1):
numOfRow.append(k)
with open('try0126.csv', 'w', newline='') as csvFile:
writer = csv.writer(csvFile)
writer.writerow(numOfRow)
for j in range(len(ecgList)):
# 標準化處理
# Min_Max_Scaler = preprocessing.MinMaxScaler(feature_range=(-5, 5)) # 設定縮放的區間上下限
# MinMax_Data = Min_Max_Scaler.fit_transform(ecgList[j]) # Data 為原始資料
# # npa = np.asarray(ecgList[j], dtype=np.float32)
# # norm = np.linalg.norm(npa)
# # normal_array = npa / norm
X = preprocessing.scale(ecgList[j])
final = np.round(X, 4)
writer.writerow(final[0:(recordLen - 1)])
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220,
220,
220,
220,
220,
220,
220,
997,
5189,
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13,
33295,
7,
74,
8,
628,
220,
220,
220,
351,
1280,
10786,
28311,
486,
2075,
13,
40664,
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705,
86,
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649,
1370,
28,
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220,
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220,
329,
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1395,
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220,
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220,
220,
220,
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2457,
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744,
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11,
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628,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6260,
13,
16002,
322,
7,
20311,
58,
15,
37498,
22105,
30659,
532,
352,
8,
12962,
198
] | 1.911184 | 608 |
import sys
import piLock.configuration as conf
import classErrorLog as errorLog
| [
11748,
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31028,
25392,
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11250,
3924,
355,
1013,
198,
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1398,
12331,
11187,
355,
4049,
11187,
198
] | 4 | 20 |
# Github : https://github.com/adarsh2104
# HR-Profile: https://www.hackerrank.com/adarsh_2104
# Challenge : https://www.hackerrank.com/challenges/s10-quartiles
# Max Score : 30
n = input()
input_array = sorted([int(x) for x in input().split()])
print(find_median(input_array[:len(input_array)//2]))
print(find_median(input_array))
print(find_median(input_array[len(input_array) // 2 + len(input_array) % 2:]))
| [
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18896,
7,
15414,
62,
18747,
8,
4064,
362,
47715,
4008,
198
] | 2.588957 | 163 |
import attr
from couchexport.export import export_raw
from couchexport.models import Format
TITLE_ROW = [
'Source Field',
'Field',
'Map Via',
'Data Source',
'Filter Name',
'Filter Value',
'Table Name',
'Format Via',
]
@attr.s
@attr.s
@attr.s
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] | 2.369748 | 119 |
""" Handles visit long trends (scaling factors) applied to the observation. The
classic cases are the `hook' and long term ramp
"""
import abc
import numpy as np
class BaseVisitTrend(object):
""" Visit trends take input the visit planner output and generate a
scaling factor that will be multiplied per exposure.
They must implement the method `_gen_scaling_factors` which outputs
a list of scaling factors, one per exposure
"""
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def get_scale_factor(self, exp_num):
""" Returns the scale factor for the exposure number `exp_num`."""
return self.scale_factors[exp_num]
def gen_orbit_start_times_per_exp(time_array, obs_start_index):
"""Generates t0, the time of an orbit for each orbit so it can vectorised
i.e for each element time_array there will be a matching element in t_0 giving the
orbit start time.
"""
obs_index = obs_start_index[:]
obs_index.append(len(time_array))
t_0 = np.zeros(len(time_array))
for i in xrange(len(obs_index) - 1):
t_0[obs_index[i]:obs_index[i + 1]] = time_array[obs_start_index[i]]
return t_0
| [
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220,
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62,
9630,
58,
72,
11907,
628,
220,
220,
220,
1441,
256,
62,
15,
198
] | 2.873171 | 410 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import datetime
from django.db import migrations
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
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4818,
8079,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
628,
198
] | 2.9 | 40 |
#
# Author : Manuel Bernal Llinares
# Project : trackhub-creator
# Timestamp : 07-09-2017 11:24
# ---
# © 2017 Manuel Bernal Llinares <[email protected]>
# All rights reserved.
#
"""
This pipeline creates a trackhub for a PRIDE project, based on the information provided via a JSON formatted file, as it
can be seen on this sample:
{
"trackHubName" : "PXD000625",
"trackHubShortLabel" : "<a href=\"http://www.ebi.ac.uk/pride/archive/projects/PXD000625\">PXD000625</a> - Hepatoc...",
"trackHubLongLabel" : "Experimental design For the label-free ...",
"trackHubType" : "PROTEOMICS",
"trackHubEmail" : "[email protected]",
"trackHubInternalAbsolutePath" : "...",
"trackhubCreationReportFilePath": "...",
"trackMaps" : [ {
"trackName" : "PXD000625_10090_Original",
"trackShortLabel" : "<a href=\"http://www.ebi.ac.uk/pride/archive/projects/PXD000625\">PXD000625</a> - Mus musc...",
"trackLongLabel" : "Experimental design For the label-free proteome analysis 17 mice were used composed of 5 ...",
"trackSpecies" : "10090",
"pogoFile" : "..."
} ]
}
"""
import os
import json
import time
# App imports
import config_manager
import ensembl.service
import ensembl.data_downloader
import trackhub.models as trackhubs
import toolbox.general as general_toolbox
from parallel.models import ParallelRunnerManagerFactory
from parallel.exceptions import NoMoreAliveRunnersException
from pogo.models import PogoRunnerFactory
from pipelines.template_pipeline import TrackhubCreationPogoBasedDirector, DirectorConfigurationManager
# Globals
__configuration_file = None
__pipeline_arguments = None
__pipeline_director = None
# Pipeline properties access
# Models for dealing with the data file that describes the project
class ProjectTrackDescriptor:
"""
This class models the tracks that are defined in the given project under the "trackMaps" section
"""
# Project Data File keys relative to every TrackMap object
_PROJECT_DATA_FILE_KEY_TRACK_NAME = 'trackName'
_PROJECT_DATA_FILE_KEY_TRACK_SHORT_LABEL = 'trackShortLabel'
_PROJECT_DATA_FILE_KEY_TRACK_LONG_LABEL = 'trackLongLabel'
_PROJECT_DATA_FILE_KEY_TRACK_SPECIES = 'trackSpecies'
_PROJECT_DATA_FILE_KEY_TRACK_POGO_FILE_PATH = 'pogoFile'
class ProjectTrackhubDescriptor:
"""
This class models the trackhub as described by the given project description data, see sample project description
information at the top of this module
"""
# Project Data File keys
_PROJECT_DATA_FILE_KEY_TRACKHUB_NAME = 'trackHubName'
_PROJECT_DATA_FILE_KEY_TRACKHUB_SHORT_LABEL = 'trackHubShortLabel'
_PROJECT_DATA_FILE_KEY_TRACKHUB_LONG_LABEL = 'trackHubLongLabel'
_PROJECT_DATA_FILE_KEY_TRACKHUB_HUB_TYPE = 'trackHubType'
_PROJECT_DATA_FILE_KEY_TRACKHUB_EMAIL = 'trackHubEmail'
_PROJECT_DATA_FILE_KEY_TRACKHUB_INTERNAL_ABSOLUTE_PATH = 'trackHubInternalAbsolutePath'
_PROJECT_DATA_FILE_KEY_TRACKHUB_REPORT_FILE = 'trackhubCreationReportFilePath'
_PROJECT_DATA_FILE_KEY_TRACKHUB_SECTION_TRACKMAPS = 'trackMaps'
class PipelineResult:
"""
This class models the pipeline report that will be made available at the end of the pipeline execution
"""
_VALUE_STATUS_SUCCESS = 'SUCCESS'
_VALUE_STATUS_ERROR = 'ERROR'
_VALUE_STATUS_WARNING = 'WARNING'
def add_error_message(self, error_message):
"""
Adds an error message to the pipeline report. As this report is the final word on how the pipeline performed,
the first error message that is set will set the status of the pipeline as 'failed'
:param error_message: error message
:return: no return value
"""
# This is the report on the final result from running the pipeline
self.set_status_error()
self.error_messages.append(error_message)
def add_success_message(self, success_message):
"""
This will add messages to the pipeline report, but it doesn't change its status.
:param success_message: message to add
:return: no return value
"""
self.success_messages.append(success_message)
def add_warning_message(self, warning_message):
"""
This will add warning messages to the pipeline report, setting the status to 'WARNING' if it wasn't in 'ERROR'
status.
:param warning_message: warning message to add
:return: no return value
"""
self.warning_messages.append(warning_message)
if self.status != self._VALUE_STATUS_ERROR:
self.status = self._VALUE_STATUS_WARNING
def add_log_files(self, log_files):
"""
Add all the log files produce by the pipeline to its final report
:param log_files: a list of log files to add
:return: no return value
"""
self.file_path_log_files.extend(log_files)
class TrackhubCreatorForProject(TrackhubCreationPogoBasedDirector):
"""
Given a project description file that contains the information specified at the beginning of this module, this
pipeline creates a trackhub for all the project defined tracks
"""
def __get_valid_project_tracks(self):
"""
This helper creates a list of valid trackhub tracks from the given project, i.e. tracks that meet this cirteria:
- Its taxonomy ID is available on Ensembl
The list of valid tracks is cached, so it won't change between multiple calls
:return: a list of valid trackhub tracks for the given project
"""
if not self.__valid_project_tracks:
self.__valid_project_tracks = []
ensembl_service = ensembl.service.get_service()
for project_track_descriptor in self.__project_trackhub_descriptor.get_trackhub_project_defined_tracks():
if ensembl_service.get_species_data_service().get_species_entry_for_taxonomy_id(
project_track_descriptor.get_track_species()):
self.__valid_project_tracks.append(project_track_descriptor)
else:
self.__pipeline_result_object \
.add_warning_message("MISSING Taxonomy #{} on Ensembl"
.format(project_track_descriptor.get_track_species()))
return self.__valid_project_tracks
def __get_index_project_track_for_taxonomy_id(self):
"""
Get the project tracks indexed by taxonomy id
:return: map (taxonomy_id, project_track)
"""
if not self.__indexed_project_tracks_by_taxonomy_id:
self.__indexed_project_tracks_by_taxonomy_id = {}
self._get_logger().debug("Indexing #{} valid project tracks".format(len(self.__get_valid_project_tracks())))
for project_track in self.__get_valid_project_tracks():
if project_track.get_track_species() in self.__indexed_project_tracks_by_taxonomy_id:
self._get_logger() \
.error("ERROR DUPLICATED TAXONOMY indexing project track '{}', "
"another project track, '{}' is in the index - SKIP -"
.format(project_track.get_track_name(),
self.__indexed_project_tracks_by_taxonomy_id[
project_track.get_track_species()].get_track_name()))
continue
self.__indexed_project_tracks_by_taxonomy_id[project_track.get_track_species()] = project_track
self._get_logger().debug("Project track '{}' indexed with taxonomy ID '{}'"
.format(project_track.get_track_name(),
project_track.get_track_species()))
return self.__indexed_project_tracks_by_taxonomy_id
# Helpers
# Override
# Override
# Override
# Override
# Override
def _after(self):
"""
Dump to a file the pipeline report
:return: no return value
"""
if not self.is_pipeline_status_ok():
self._get_logger().warning("This Pipeline is finishing with NON-OK status.")
report_files = [self.__config_manager.get_file_path_trackhub_creation_report()]
if self.__project_trackhub_descriptor \
and self.__project_trackhub_descriptor.get_trackhub_report_file_path():
report_files.append(self.__project_trackhub_descriptor.get_trackhub_report_file_path())
for report_file in report_files:
self._get_logger().info("Dumping Pipeline Report to '{}'".format(report_file))
with open(report_file, 'w') as f:
f.write(str(self.__pipeline_result_object))
return True
if __name__ == '__main__':
print("ERROR: This script is part of a pipeline collection and it is not meant to be run in stand alone mode")
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] | 2.497216 | 3,592 |
import pytest
from pytest_mock import mocker
import pandas as pd
from kipoiseq.transforms.functional import translate, rc_dna
from kipoiseq.dataclasses import Interval, Variant
from kipoiseq.extractors.protein import cut_transcript_seq, gtf_row2interval, \
CDSFetcher, TranscriptSeqExtractor, ProteinSeqExtractor, \
ProteinVCFSeqExtractor, SingleSeqProteinVCFSeqExtractor, \
SingleVariantProteinVCFSeqExtractor
gtf_file = 'tests/data/sample_1_protein.gtf'
fasta_file = 'tests/data/demo_dna_seq.fa'
transcript_id = 'enst_test1'
vcf_file = 'tests/data/singleVar_vcf_enst_test2.vcf.gz'
intervals = [
Interval('22', 580, 596, strand='+', attrs={'tag': 'cds_end_NF'}),
Interval('22', 597, 610, strand='+', attrs={'tag': 'cds_end_NF'})
]
@pytest.fixture
@pytest.fixture
@pytest.fixture
# TODO: write test for with sample_id
@pytest.fixture
@pytest.fixture
@pytest.fixture
# TODO: add for all proteins.pep.all.fa
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] | 2.4225 | 400 |
from PIL import Image
import numpy as np
# Works when launched from terminal
# noinspection PyUnresolvedReferences
from k_means import k_means
input_image_file = 'lena.jpg'
output_image_prefix = 'out_lena'
n_clusters = [2, 3, 5]
max_iterations = 100
launch_count = 3
main()
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] | 2.818182 | 99 |
# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Find expression by Monte Carlo Tree Search guided by neural networks."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from neural_guided_symbolic_regression.mcts import policies
from neural_guided_symbolic_regression.mcts import rewards
from neural_guided_symbolic_regression.mcts import states
from neural_guided_symbolic_regression.models import metrics
from neural_guided_symbolic_regression.models import partial_sequence_model_generator
class NeuralProductionRuleAppendPolicy(policies.PolicyBase):
"""Appends a valid production rule on existing list of production rules.
The probabilities of the actions will be determined by the partial sequence
model.
"""
def __init__(self, sess, grammar, max_length, symbolic_properties_dict):
"""Initializer.
Args:
sess: tf.Session, the session contains the trained model to predict next
production rule from input partial sequence. If None, each step will
be selected randomly.
grammar: arithmetic_grammar.Grammar object.
max_length: Integer, the max length of production rule sequence.
symbolic_properties_dict: Dict, the keys are the symbolic properties used
as conditions. Values are the corresponding desired values of the
symbolic properties.
"""
self._sess = sess
self._grammar = grammar
self._max_length = max_length
conditions = {}
if symbolic_properties_dict is not None:
conditions.update({
key: np.array([value], dtype=np.float32)
for key, value in symbolic_properties_dict.iteritems()
})
self._conditions = conditions
def get_new_states_probs(self, state):
"""Gets new state from current state by appending a valid production rule.
Args:
state: A mcts.states.ProductionRulesState object. Contains a list of
nltk.grammar.Production objects in attribute
production_rules_sequence.
Returns:
new_states: A list of next states. Each state is a result from apply an
action in the instance attribute actions to the input state.
action_probs: A float numpy array with shape [num_actions,]. The
probability of each action in the class attribute actions.
Raises:
TypeError: If input state is not states.ProductionRulesState object.
"""
if not isinstance(state, states.ProductionRulesState):
raise TypeError('Input state shoud be an instance of '
'states.ProductionRulesState but got %s' % type(state))
production_rules_sequence = state.production_rules_sequence
if len(production_rules_sequence) > self._max_length:
# Do not allow the length of production rules sequence exceed _max_length.
# All nan probabilities will stop the rollout in MCTS.
masked_probabilities = [np.nan] * self._grammar.num_production_rules
else:
masked_probabilities = (
partial_sequence_model_generator.get_masked_probabilities_from_model(
sess=self._sess,
max_length=self._max_length,
partial_sequence=[self._grammar.prod_rule_to_index[str(prod_rule)]
for prod_rule in production_rules_sequence],
next_production_rule_mask=self._grammar.masks[
self._grammar.lhs_to_index[state.stack_peek()]],
conditions=self._conditions))
new_states = []
action_probs = []
for probability, production_rule in zip(
masked_probabilities, self._grammar.prod_rules):
if state.is_valid_to_append(production_rule):
new_state = state.copy()
new_state.append_production_rule(production_rule)
new_states.append(new_state)
action_probs.append(probability)
else:
new_states.append(None)
action_probs.append(np.nan)
action_probs = np.asarray(action_probs)
action_probs /= np.nansum(action_probs)
return new_states, action_probs
class LeadingPowers(rewards.RewardBase):
"""Computes reward for univariate expression only on leading powers.
This reward measures a univariate expression by whether this expression
satisfies the desired leading powers at 0 and infinity.
reward = -abs(leading power difference at 0)
- abs(leading power difference at infinity))
"""
def __init__(
self,
leading_at_0,
leading_at_inf,
variable_symbol='x',
post_transformer=None,
allow_nonterminal=False,
default_value=None):
"""Initializer.
Args:
leading_at_0: Float, desired leading power at 0.
leading_at_inf: Float, desired leading power at inf.
variable_symbol: String, the symbol of variable in function expression.
post_transformer: Callable. This function takes one float number and
output a float number as the transformed value of input. It is used
to post-transformation the reward evaluated on a state. Default None,
no post-transformation will be applied.
allow_nonterminal: Boolean, if False, ValueError will be raised when
list of symbols to evaluate contains non-terminal symbol and
default_value is None. Default False.
default_value: Float, if allow_nonterminal is False and non-terminal
symbol exists, instead of raising a ValueError, return default_value
as the reward value.
"""
super(LeadingPowers, self).__init__(
post_transformer=post_transformer,
allow_nonterminal=allow_nonterminal,
default_value=default_value)
self._leading_at_0 = leading_at_0
self._leading_at_inf = leading_at_inf
self._variable_symbol = variable_symbol
def get_leading_power_error(self, state):
"""Gets the leading power error.
The leading power error is defined as
abs(leading power difference at 0) + abs(leading power difference at inf).
Args:
state: mcts.states.StateBase object. Records all the information of
expression.
Returns:
Float.
"""
true_leading_at_0, true_leading_at_inf = (
metrics.evaluate_leading_powers_at_0_inf(
expression_string=state.get_expression(),
symbol=self._variable_symbol))
return (abs(true_leading_at_0 - self._leading_at_0)
+ abs(true_leading_at_inf - self._leading_at_inf))
def _evaluate(self, state):
"""Evaluates the reward from input state.
Args:
state: mcts.states.StateBase object. Records all the information of
expression.
Returns:
Float, the reward of the current state.
"""
leading_power_error = self.get_leading_power_error(state)
if np.isfinite(leading_power_error):
return -float(leading_power_error)
else:
return self._default_value
class NumericalPointsAndLeadingPowers(LeadingPowers):
"""Computes reward for univariate expression with leading powers and values.
This reward measures an univariate expression in two aspects:
1. The mean square error of numerical values defined by input_values and
output_values.
2. Whether this expression satisfies the desired leading powers at 0 and
infinity.
hard_penalty_default_value decides whether to use soft or hard penalty when
the expression does not match the desired leading powers.
Soft penalty
reward = (
-(root mean square error)
- abs(leading power difference at 0)
- abs(leading power difference at infinity))
Hard penalty
If leading power at 0 and infinity are both correct
reward = -(root mean square error)
Otherwise reward = hard_penalty_default_value
If include_leading_powers is False, the reward is just
-(root mean square error).
"""
def __init__(
self,
input_values,
output_values,
leading_at_0,
leading_at_inf,
hard_penalty_default_value=None,
variable_symbol='x',
include_leading_powers=True,
post_transformer=None,
allow_nonterminal=False,
default_value=None):
"""Initializer.
Args:
input_values: Numpy array with shape [num_input_values]. List of input
values to univariate function.
output_values: Numpy array with shape [num_output_values]. List of output
values from the univariate function.
leading_at_0: Float, desired leading power at 0.
leading_at_inf: Float, desired leading power at inf.
hard_penalty_default_value: Float, the default value for hard penalty.
Default None, the reward will be computed by soft penalty instead of
hard penalty.
variable_symbol: String, the symbol of variable in function expression.
include_leading_powers: Boolean, whether to include leading powers in
reward.
post_transformer: Callable. This function takes one float number and
output a float number as the transformed value of input. It is used
to post-transformation the reward evaluated on a state. Default None,
no post-transformation will be applied.
allow_nonterminal: Boolean, if False, ValueError will be raised when
list of symbols to evaluate contains non-terminal symbol and
default_value is None. Default False.
default_value: Float, if allow_nonterminal is False and non-terminal
symbol exists, instead of raising a ValueError, return default_value
as the reward value.
"""
super(NumericalPointsAndLeadingPowers, self).__init__(
leading_at_0=leading_at_0,
leading_at_inf=leading_at_inf,
variable_symbol=variable_symbol,
post_transformer=post_transformer,
allow_nonterminal=allow_nonterminal,
default_value=default_value)
self._input_values = input_values
self._output_values = output_values
self._include_leading_powers = include_leading_powers
self._hard_penalty_default_value = hard_penalty_default_value
def get_input_values_rmse(self, state):
"""Evaluates root mean square error on input_values.
Args:
state: mcts.states.StateBase object. Records all the information of
expression.
Returns:
Float.
"""
expression_output_values = metrics.evaluate_expression(
expression_string=state.get_expression(),
grids=self._input_values,
symbol=self._variable_symbol)
return np.sqrt(
np.mean((expression_output_values - self._output_values) ** 2))
def _evaluate(self, state):
"""Evaluates the reward from input state.
Args:
state: mcts.states.StateBase object. Records all the information of
expression.
Returns:
Float, the reward of the current state.
"""
input_values_rmse = self.get_input_values_rmse(state)
if not self._include_leading_powers:
if np.isfinite(input_values_rmse):
return -input_values_rmse
else:
return self._default_value
# NOTE(leeley): If computing the leading power fails
# (timeout or sympy ValueError) or functions in symbolic_properties return
# nan (for example, 1 / (x - x)).
leading_power_error = self.get_leading_power_error(state)
if self._hard_penalty_default_value is None:
# Soft penalty.
if np.isfinite(leading_power_error):
return -input_values_rmse - leading_power_error
else:
return self._default_value
else:
# Hard penalty.
if (np.isfinite(leading_power_error)
and np.isclose(leading_power_error, 0)):
return -input_values_rmse
else:
return self._hard_penalty_default_value
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] | 2.817932 | 4,361 |
from .pytransform import pyarmor_runtime
pyarmor_runtime()
__pyarmor__(__name__, __file__, b'\x50\x59\x41\x52\x4d\x4f\x52\x00\x00\x03\x09\x00\x61\x0d\x0d\x0a\x08\x2d\xa0\x01\x00\x00\x00\x00\x01\x00\x00\x00\x40\x00\x00\x00\xed\x00\x00\x00\x00\x00\x00\x18\x3d\x71\xc5\x03\x9e\x68\x9a\xa0\x37\x72\x21\xef\xad\x8a\xf4\x10\x00\x00\x00\x00\x00\x00\x00\x00\xb4\x8c\x82\x42\x16\x77\xe5\x90\x93\xcb\xad\x1f\x2f\x25\x62\x6c\xf5\x02\xd8\xd5\xa2\x5e\x70\x77\xac\xd7\x78\x2f\xbe\x60\x40\x8f\x2b\x57\x02\x4f\xa0\x4f\xb9\x5f\x3f\x67\x56\x7c\x8c\x15\x95\x26\xdf\xaf\x5d\x30\xf2\xbc\x4b\x06\x6d\x66\x77\x1d\xf1\xd6\x67\x18\x5f\xe5\x7f\x4a\x8d\x4e\x82\x97\x42\x19\xfa\xff\x42\xe3\x1b\xe7\xa1\x36\x46\x2b\x63\x0b\x2b\x4a\x53\x6e\x1b\x06\xf1\x8d\xc9\xf5\x16\x5c\xcd\xd0\xc8\xd3\xaf\x08\x86\x5e\x20\xc7\xad\x33\x4a\x8c\x06\x71\x4d\x9a\x1e\xbe\xa7\xe8\x08\x3f\xf1\x6b\x6e\x54\x4e\x6f\x4b\xe3\x3b\x98\x9a\x2a\x3a\x01\xfa\x52\xc3\xf6\x64\x3c\xeb\xa6\xbf\x4c\xb6\x5e\xf4\x59\x40\xd3\xb9\x02\x01\x63\x0f\xa8\x5a\x9f\x60\x26\xc4\xdc\xa6\xb6\xe6\xf8\xac\xea\xaa\x04\xa4\x23\x1a\x50\xb2\x67\x91\xf9\xee\xed\xbc\x35\x18\xff\x1f\x5a\xab\x0b\xbe\x95\xc6\x72\x12\x2d\x31\xf9\x4a\x52\x60\x1f\x42\x0f\x5d\xcc\xf1\x4c\xa0\xed\xc5\x2b\x49\x68\x71\xa4\x0f\x7b\x76\x16\x50\xe6\xdb\x83\xd7\x2f\xc4\x57\xc7\x12\x02\x30\xc8\xef\xe8\x38\xf6', 2) | [
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] | 1.285291 | 1,013 |
# -*- coding: utf-8 -*-
'''
:codeauthor: Rupesh Tare <[email protected]>
'''
# Import Python Libs
from __future__ import absolute_import
# Import Salt Testing Libs
from tests.support.mixins import LoaderModuleMockMixin
from tests.support.unit import TestCase, skipIf
from tests.support.mock import (
patch,
NO_MOCK,
NO_MOCK_REASON
)
# Import Salt Libs
import salt.modules.mine as mine
@skipIf(NO_MOCK, NO_MOCK_REASON)
class MineTestCase(TestCase, LoaderModuleMockMixin):
'''
Test cases for salt.modules.mine
'''
def test_get_docker(self):
'''
Test for Get all mine data for 'docker.ps' and run an
aggregation.
'''
ps_response = {
'localhost': {
'host': {
'interfaces': {
'docker0': {
'hwaddr': '88:99:00:00:99:99',
'inet': [{'address': '172.17.42.1',
'broadcast': None,
'label': 'docker0',
'netmask': '255.255.0.0'}],
'inet6': [{'address': 'ffff::eeee:aaaa:bbbb:8888',
'prefixlen': '64'}],
'up': True},
'eth0': {'hwaddr': '88:99:00:99:99:99',
'inet': [{'address': '192.168.0.1',
'broadcast': '192.168.0.255',
'label': 'eth0',
'netmask': '255.255.255.0'}],
'inet6': [{'address':
'ffff::aaaa:aaaa:bbbb:8888',
'prefixlen': '64'}],
'up': True},
}},
'abcdefhjhi1234567899': { # container Id
'Ports': [{'IP': '0.0.0.0', # we bind on every interfaces
'PrivatePort': 80,
'PublicPort': 80,
'Type': 'tcp'}],
'Image': 'image:latest',
'Info': {'Id': 'abcdefhjhi1234567899'},
},
}}
with patch.object(mine, 'get', return_value=ps_response):
ret = mine.get_docker()
# Sort ifaces since that will change between py2 and py3
ret['image:latest']['ipv4'][80] = sorted(ret['image:latest']['ipv4'][80])
self.assertEqual(ret,
{'image:latest': {
'ipv4': {80: sorted([
'172.17.42.1:80',
'192.168.0.1:80',
])}}})
def test_get_docker_with_container_id(self):
'''
Test for Get all mine data for 'docker.ps' and run an
aggregation.
'''
ps_response = {
'localhost': {
'host': {
'interfaces': {
'docker0': {
'hwaddr': '88:99:00:00:99:99',
'inet': [{'address': '172.17.42.1',
'broadcast': None,
'label': 'docker0',
'netmask': '255.255.0.0'}],
'inet6': [{'address': 'ffff::eeee:aaaa:bbbb:8888',
'prefixlen': '64'}],
'up': True},
'eth0': {'hwaddr': '88:99:00:99:99:99',
'inet': [{'address': '192.168.0.1',
'broadcast': '192.168.0.255',
'label': 'eth0',
'netmask': '255.255.255.0'}],
'inet6': [{'address':
'ffff::aaaa:aaaa:bbbb:8888',
'prefixlen': '64'}],
'up': True},
}},
'abcdefhjhi1234567899': { # container Id
'Ports': [{'IP': '0.0.0.0', # we bind on every interfaces
'PrivatePort': 80,
'PublicPort': 80,
'Type': 'tcp'}],
'Image': 'image:latest',
'Info': {'Id': 'abcdefhjhi1234567899'},
},
}}
with patch.object(mine, 'get', return_value=ps_response):
ret = mine.get_docker(with_container_id=True)
# Sort ifaces since that will change between py2 and py3
ret['image:latest']['ipv4'][80] = sorted(ret['image:latest']['ipv4'][80])
self.assertEqual(ret,
{'image:latest': {
'ipv4': {80: sorted([
('172.17.42.1:80', 'abcdefhjhi1234567899'),
('192.168.0.1:80', 'abcdefhjhi1234567899'),
])}}})
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] | 1.489651 | 3,672 |
import torch
from torch import nn
import torch.nn.functional as F
from metrics.ssim import ssim
from metrics.tv_loss import TVLoss
#import models.networks as networks
from metrics.my_ssim import ssim_loss
# class CSSIM(nn.Module): # Complementary SSIM
# def __init__(self, default_range=1, filter_size=11, k1=0.01, k2=0.03, sigma=1.5, reduction='mean'):
# super().__init__()
# self.max_val = default_range
# self.filter_size = filter_size
# self.k1 = k1
# self.k2 = k2
# self.sigma = sigma
# self.reduction = reduction
# def forward(self, input, target, max_val=None):
# max_val = self.max_val if max_val is None else max_val
# return 1 - ssim(input, target, max_val=max_val, filter_size=self.filter_size,
# sigma=self.sigma, reduction=self.reduction)
# class CSSIM(nn.Module): # Replace this with a system of summing losses in Model Trainer later on.
# def __init__(self, default_range=1, filter_size=11, k1=0.01, k2=0.03, sigma=1.5, reduction='mean'):
# super().__init__()
# self.max_val = default_range
# self.filter_size = filter_size
# self.k1 = k1
# self.k2 = k2
# self.sigma = sigma
# self.reduction = reduction
# def forward(self, input, target, max_val=None):
# max_val = self.max_val if max_val is None else max_val
# input = input.unsqueeze(1)
# target = target.unsqueeze(1)
# ssim_value = ssim(input, target, max_val=max_val, filter_size=self.filter_size, sigma=self.sigma, reduction=self.reduction)
# return ssim_value #+ self.l1_weight * l1_loss
## Combination loss for SRRaGAN
class CharbonnierLoss(nn.Module):
"""Charbonnier Loss (L1)"""
# Define GAN loss: [vanilla | lsgan | wgan-gp]
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] | 2.207637 | 838 |
"""
Test the Google Cloud Storage Client and associated helper functions
"""
# Python stl imports
import os
import unittest
# Project imports
from gcloud.gcs import StorageClient
# Third-party imports
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